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Notch signaling in pancreatic endocrine cell and diabetes

Recent studies have improved our understanding of the physiological function of Notch signaling pathway and now there is compelling evidence demonstrating that Notch is a key regulator of embryonic development and tissue homeostasis. Although further extensive studies are necessary to illustrate the molecular mechanisms, new insights into the role of Notch signaling in pancreas development and diabetes have been achieved. Importantly, the ability to regulate Notch signaling intensity both positively and negatively may have therapeutic relevance for diabetes. Thus, this paper reviews the current knowledge of the roles of Notch signaling in the pancreatic endocrine cell system.

notch_signaling_in_pancreatic_endocrine_cell_and_diabetes

Overview of pancreatic endocrine cell<!>Notch signaling pathway<!>Notch receptors<!>Notch ligands<!>Hes1<!>Rbp-Jk<!>Ngn3<!>Lateral inhibition by Notch signaling<!>Suppressive maintenance by Notch signaling; Mesenchyme, FGF10, and Notch signaling<!>Implication for Notch signaling and diabetes<!>Conclusion and perspective

<p>The adult pancreas is a heterogeneous organ with three main functions: (1) the exocrine acinar tissue produces digestive enzymes that facilitate nutrient digestion and absorption in the gut, (2) the small ducts, or ductules, collect digestive enzymes produced from exocrine cells, and (3) islets of Langerhans that include α, β, δ, PP and ε cells, which produce glucagon, insulin, somatostatin, pancreatic polypeptide (PP) and ghrelin, respectively. The islets of Langerhans are scattered throughout the exocrine tissue and comprise about 1% of the total pancreatic tissue [1]. Endocrine cells are derived from a common pancreatic progenitor cells located within the early gut endoderm (Fig. 1). The earliest endocrine cells detected in the pancreatic bud of the foregut contain glucagon, and a subset of these cells co-expresses insulin and sometimes peptide YY (PYY) [2]. At ~e14.5, there is a secondary transition, and cells divide into distinct lineages that express glucagon and insulin; it seems that this second wave, based on lineage tracing, does not arise from the early primitive endocrine cells of the e9.5 stage [3]. Within the next 24h, the first somatostatin-expressing cells arise [4]. Finally, at e18, shortly before birth, PP cells appear while endocrine cells begin to organize into functional islets of Langerhans, embedded within the exocrine matrix [2]. In rodent islets, β cells are most prominent (60-80% of total) and they are concentrated in the islet core, with other cell types arranged closer to the mantle: however, human islets do not show quite the same anatomical subdivisions or stereotypy and α, δ, and PP cells are scattered among the β cells. α cells comprise 15-20% of the islet, with a few percent remaining for the other three cell types [1]. In type 1 diabetic patients, whose β cells are destroyed, α cells comprise approximately 75% of the total cell number [5,6]. However, in type 2 diabetes, α cell hyperplasia occurs and the β cell mass is probably reduced, at least in long-standing type 2 diabetes, as a result of increased apoptosis [5,7].</p><!><p>Notch signaling is intimately involved in embryonic development and, in adults, in maintenance of homeostasis of multicellular organisms, such as cell differentiation, survival/apoptosis, and the cell cycle under both physiologic and pathologic contexts. In mammals, loss of Notch signaling leads to severe defects in embryonic development and tissue homeostasis and its dysregulation is linked to multiple developmental and physiological disorders [8-11]. Furthermore, mutations that lead to Notch malfunction have been associated with a variety of diseases [11]. The last few years have witnessed the elucidation of new functions for Notch signaling during pancreas organogenesis and adulthood and it is clear that normal, uninterrupted Notch signaling pathways are required for normal pancreatic development. Enforced activation of Notch signaling in pancreatic progenitors impairs their differentiation into the various pancreatic cell lineages, whereas inactivation of Notch signaling leads to premature differentiation of endocrine pancreas [8-10]. Not surprisingly, the Notch pathway has also been implicated in diabetes.</p><p>Four Notch receptors (Notch1-Notch4) have been described in mammals and their intracellular portion conveys the signal to the nucleus. The Notch ligands are type I transmembrane proteins and there are two major classes of ligands: Delta or Delta-like (Dll) and Serrate (Jagged in mammals). Mammals possess five ligands (Delta-1, -3 and -4 and Jagged-1 and Jagged-2). In addition to Delta and Jagged, the neural adhesion molecule F3/contactin, the related NB-3 protein, the EGF repeat protein DNER, and a diffusible protein in C. elegans have all been identified as possible Notch ligands.</p><p>Notch signaling is activated by ligand-receptor interaction between two neighboring cells. The interaction leads to successive proteolytic cleavages by a metalloprotease of the ADAM/TACE/Kuzbanian family and then γ-secretase that ultimately release the Notch intracellular domain (NICD). NICD then translocates to the nucleus, where it assembles into a ternary complex with members of the CSL (for CBF1 in mammals, Suppressor of Hairless in Drosophola, Lag-1 in C. elegans) family of transcription factors. Several targets of Notch signaling have been identified. One well-known target in Drosophila and mammals is the HES (hairy/enhancer of split) family of the basic helix-loop-helix (bHLH)-type transcriptional repressors [12], which negatively regulate the expression of genes by recruiting a set of co-repressors or by sequestering transcriptional activators. In addition, NICD also directly stimulates expression of cell cycle regulators (e.g., p21 and Cyclin D1), transcription factors (e.g., c-Myc and NF-kB2), and growth factor receptors (e.g., ErbB2). This complexity of interactions explains why Notch signaling is involved in a variety of cellular events.</p><!><p>Specific Notch pathway elements and intracellular effectors necessary for normal pancreatic development are expressed in the developing pancreas [8,9,13]. Loss-of-function mutation of various Notch signaling pathway genes (Rbp-Jk, Delta1, and Hes1) display up-regulated expression of the basic helix-loop-helix Neurogenin 3 (Ngn3) gene and consequent accelerated and increased pancreatic endocrine development, leading to depletion of precursor cells followed by pancreatic hypoplasia [8,9]. Conversely, expression of constitutively active NICD traps pancreatic precursor cells in an undifferentiated state and prevents both endocrine and exocrine pancreas development [10,14,15]. Mis-expression of activated Notch1 in developing pancreas using Pdx1 promoter prevents both endocrine and exocrine development and appears to trap both early and late progenitors in an undifferentiated state [10,14], whereas expression in fully differentiated endocrine cells is without effect [10]. These studies suggest that cells undergoing endocrine differentiation lose responsiveness to Notch signaling. More recent study indicates that Notch signaling is active within a committed exocrine progenitor pool in developing mouse pancreas, and that Notch signaling blocks terminal acinar cell differentiation, but not initial commitment to the exocrine lineage [15].</p><p>In contrast to NICDs of Notch1, Notch2 and Notch4, NICD of Notch3 represses Notch1-mediated up-regulation of Hes1 expression through competition with NICD of Notch1 for a common co-activator present in limiting amounts, and for access to Rbp-Jk [16]. One recent report assessing knockout and transgenic mice for components of the Notch signaling pathway suggests that NICD expression of Notch3 in the developing pancreas using Ipf1/Pdx1 promoter impairs pancreatic epithelial proliferation, morphogenesis, and exocrine, but not endocrine, cell differentiation [9]. In this mouse, the pancreatic epithelium is poorly branched and the pancreatic buds are decreased in size. Consistent with the function of Notch3 as a repressor [16], Hes1 is expressed at a low level, and Ngn3-expressing cells are found throughout the epithelium, meaning that differentiated endocrine cells are dispersed throughout the immature pancreatic epithelia. A similar phenotype is observed in mice deficient for Dll1, Rbp-Jk, and Hes1 as well as in mouse over-expressing Ngn3 [8,9].</p><!><p>Mice deficient for Dll1 show that epithelial IPF1/PDX1+ progenitor cells within the pancreatic bud differentiate prematurely into endocrine cells, causing a lack of expansion of cells within the pancreatic buds [9]. In addition to Dll1, the developing pancreatic epithelium expresses Notch ligands of the Serrate/Jagged family, and this ligand family is involved in endocrine development [17]. In knockdown studies in zebrafish embryos, loss of specific Jagged ligands causes ectopic islet-cell differentiation [18]. A similar phenotype has been observed in mice overexpressing Ngn3 or the intracellular form of Notch3 as discussed above.</p><!><p>The Hes1 gene, which is transcriptionally activated by Notch signaling, encodes for bHLH transcriptional repressors. Hes1, although expressed in the pancreatic buds, and later by the pancreatic epithelial precursors together with Notch1 and 2 [8,9,13,19], is present in exocrine cells, but not in endocrine cells, while expression of Ngn3 is restricted to endocrine cells (Fig. 1 and Fig. 2). Hes1 prevents expression of Ngn3 by binding to several silencer sites located near the transcription initiation site, and therefore suppresses endocrine precursor patterning through the Notch signaling pathway [20]. Hes1-deficient mice show pancreatic hypoplasia because of depletion of pancreatic epithelial precursors resulting from accelerated differentiation of pancreatic endocrine cells [8], as stated above.</p><p>Recently, the Hes1-mediated Notch pathway was shown to control proper regional specification of pancreas in the developing foregut endoderm through Ptf1a regulation [21]. Loss of Hes1 leads to mis-expression of Ptf1a in localized regions of the primitive stomach and duodenum as well as throughout the common bile duct. Lineage tracing shows that all the ectopic Ptf1a-expressing cells were reprogrammed to multipotent pancreatic progenitors that then differentiated into mature pancreatic exocrine, endocrine, and duct cells [21]. Similar to this study, inactivation of Hes1 induced the conversion of biliary epithelium to pancreatic tissue. Biliary epithelium in Hes1-deficient mice ectopically expressed Ngn3, differentiated into endocrine and exocrine cells and formed acini and islet-like structures in the mutant bile duct [22]. In addition, Hes1 likely regulates the binary decision choice of pancreatic progenitor to either exit the cell cycle or self-renew through transcriptional repression of p57, the cyclin kinase inhibitor [23]. Inactivation of Hes1 caused upregulation of p57 expression in progenitors, leading to cell cycle arrest, early differentiation and depletion of the progenitor pool.</p><!><p>DNA-binding protein Rbp-Jk, which is ubiquitously expressed and associates with all four types of Notch receptors, is a down-stream partner in Notch signaling. NICD interacts with Rbp-Jk to activate expression of Hes genes. Rbp-Jk mutant embryos showed accelerated differentiation of pancreatic endocrine cells [9,24]. The loss of Rbp-Jk at the initial stage of pancreatic development leads to accelerated α and PP cell differentiation and a concomitant decrease in the number of Ngn3-positive cells. This mice exhibited insulin-deficient diabetes because of endocrine hypoplasia and exocrine pancreatic hypoplasia was also present [24]. In contrast, the loss of Rbp-Jk specifically in β cells did not affect β cell number and function. Taken together, these studies demonstrate that Notch/Rbp-Jk signaling prevents premature differentiation of pancreatic progenitor cells into endocrine and ductal cells during early development and its presence in mature β cells in young animals is not required. However, no experiments with these mice were carried out over the natural life span of the animals so we do not know if they have an aging phenotype.</p><!><p>As is evident from the above literature, the most important transcription factor for driving pancreatic precursors towards an endocrine cell fate is the Ngn3. Sommer and colleagues were the first group to outline the expression of Ngn3 in the developing pancreas [25]. It is expressed exclusively in endocrine precursor cells before they differentiate and it is thought not to be present in differentiated endocrine cells [9,19,26]. Its expression starts at e9.5, peaks at e15.5 during the major wave of endocrine cell genesis and then decreases at birth, with almost undetectable levels in adult pancreas [20]. Lineage tracing analysis demonstrates that Ngn3-expressing cells are indeed the endocrine cell precursors [27]. Mice lacking Ngn3 function failed to develop any endocrine cells and died postnatally because of elevated blood glucose levels [28]. Conversely, over-expression of Ngn3 in the early pancreas induced massive premature differentiation of the entire pancreas into endocrine cells similar to those in Hes1 deficient mice [9,26]. Moreover, mis-expression of Ngn3 induced endocrine differentiation throughout the gut epithelium [29] and adenovirus-mediated expression of Ngn3 in adult human pancreatic ductal cells induced an endocrine phenotype [30]. These results suggest that Notch signaling also operates in adult duct cells, driving them into an endocrine phenotype and formation of insulin-expressing cells.</p><p>The differentiating activity of Ngn3 is under control of Notch signaling, because studies with the Ngn3 promoter have identified that it contains multiple-binding sites for the Hes-1 and reduced Notch signaling leads to increased expression of Ngn3. Indeed, null mutant mice for Dll-1 and Rbp-jk suffered from accelerated differentiation of pancreatic epithelial cells expressing Ngn3 and Hes1-deficient mice also showed premature endocrine differentiation and exocrine cell defects similar to those broadly mis-expressing Ngn3 [8,9]. In addition, the expression of NICD in Ngn3-positive endocrine progenitors was shown to inhibit their differentiation [10], but the ultimate fate of these cells could not be analyzed due to embryonal death, presumably because of Notch mis-expression in the Ngn3+ domain outside of the pancreas. In summary, in the developing pancreas, Notch signaling controls the choice between differentiated endocrine and progenitor cell fates through the regulation of Ngn3 expression such that when Notch pathway signaling is defective, the resultant high Ngn3 levels drives the epithelial cells to an endocrine fate. In contrast, cells with over-active Notch signaling result in an up-regulation of Hes1 expression and down-regulation of Ngn3 and the epithelial cells remain in an undifferentiated progenitor state (Fig. 2a).</p><!><p>Several studies have shown that, analogous to the generation of neurons during neurogenesis, the endocrine and exocrine cells of the pancreas via Notch signaling are constructed by lateral specification within ductal epithelium [20,31]. Ngn3 induces expression of Notch ligands such as Delta and Jagged that then activate Notch receptors on neighboring cells [30]. In concert with Rbp-Jk, NICD promotes expression of Hes1 in the adjacent cells. Hes1 or/and Hes1-modified genes repress expression of Ngn3 and other target genes, thereby preventing premature endocrine differentiation in the adjacent cells or at a stage that would not allow sufficient proliferation of endocrine precursor cells. Additionally, Hes1 activation in pancreatic progenitors suppresses the expression of p57 to prevent exiting from the cell cycle and subsequent premature differentiation [23]. Therefore, cells with active Notch signaling resulting in up-regulation of Hes1 expression maintain their proliferative capacity, while cells lacking Notch signaling express Ngn3, exit the cell cycle, and differentiate into endocrine cells [23]. As a consequence, Notch signaling helps to specify endocrine-cell differentiation and the lateral specification model provides a mechanism by which cells destined to progress along the endocrine lineage (ngn3 positive) inhibit endocrine differentiation of their neighboring cells, forcing them to retain a non-endocrine fate (Fig. 2a). This lateral specification model in developing pancreas has been supported by several transgenic studies. Loss of various Notch signaling pathway genes (Hes1, Dll1, Rbp-Jk) during pancreatic development led to up-regulation of Ngn3 and consequently accelerated differentiation of pancreatic endocrine cells paralleled by a depletion of the pool of pancreatic precursor cells [8,9], and a similar phenotype was observed in mice overexpressing Ngn3 and overexpressing the intracellular form of Notch3 [9]. Similarly, the increased expression of Dll-1 and Dll-3 in Hes1 mutants [8] and the activation of Dll-1 and Dll-4 in pancreatic duct cells transfected with adenovirus-Ngn3 [30] also support the lateral specification model in pancreas development.</p><!><p>The earlier and highly sophisticated studies on pancreas development have shown that signals originating in the mesenchyme play an essential role in the proliferation of pancreatic epithelial cells, precursor pool maintenance and the ratio of endocrine- to exocrine-cell differentiation [31]. In the absence of mesenchyme, embryonal pancreatic epithelium gives rise to endocrine cells, but not acinar structures, and the same experiments provided the first evidence that islets derive directly from early pancreatic precursor cells and not from ducts or early ductular structures [31]. A member of the fibroblast growth factor (FGF) family, FGF10, which is produced and secreted by pancreatic mesenchyme at stages that coincide with the rapid growth of epithelial buds, is also an important player for normal, fully developed pancreas. Loss of FGF10 results in pancreatic hypoplasia and absence of endocrine cells because there is a dramatic reduction in the proliferation of the epithelial progenitor cells marked by the production of Pdx1 [32]. In contrast, two recent studies have shown that persistent expression of FGF10 in the embryonic pancreas resulted in a large pancreas due to enhanced and prolonged proliferation of pancreatic precursor cells and a block in exocrine, ductal, and endocrine cell differentiation [17,33]. In these mice and in contrast to the wild-type situation [8,9], the pancreatic precursor cells remain strongly positive for Notch1 and Notch2 as well as Hes1, whereas Ngn3 expression is repressed [17,33]. Thus, both studies provide evidence that ectopic FGF10 signaling maintains Notch signaling in an active state throughout the developing pancreatic epithelium, which results in impaired expression of Ngn3 within the pancreatic epithelium and prevention of its differentiation [17,31,33]. Furthermore, Notch ligands Jagged-1 and Jagged-2 are expressed in the normal pancreatic epithelial cells, thereby overlapping with Notch1 and Notch2 [9,13,17], and this pattern was maintained in the presence of ectopic FGF10 [17]. These observations have led to the suggestion that "suppressive maintenance", another mechanism different from lateral specification, is defined by Notch-mediated Hes1 activation throughout the precursor cell population with the outcome that cell differentiation cues are suppressed and the progenitor state is maintained [17,31] (Fig. 2b).</p><!><p>Pancreatic development follows three well-defined steps: endoderm formation, pancreatic morphogenesis, and, finally, differentiation of exocrine and endocrine cells. In this process, nowhere is it obvious why β cells only, as happens in type 1 diabetes, should be subject to later autoimmune destruction. In islets of type 1 diabetic patients, β cells only are destroyed by the immune system and so far no one has figured out a method in human of differentiating or trans-differentiating existing pancreatic cells, in vivo, to become β cells in order to replenish an effective pool of cells. Initially it had been thought that Sel1L, a Notch repressor, might be involved in type 1 diabetes, but on subsequent genetic studies, it was found not to be linked to type I diabetes in a panel of 20 Danish patients [34,35]. Isolating islets from cadavers and using them for transplantation is simply not feasible on any large scale. First, there is a paucity of donors. Second, islets from more than one donor are required for exogenous insulin-free living, and, third, the transplanted islets eventually become non-functioning [36]. The non-functionality may be due to lack of cell turnover within the transplanted islets, in addition to the use of immunosuppressors. Lack of cell turnover within islets may be overcome with use of specific Notch receptor agonists, once greater knowledge is gained on the transplantation and embedding process that occur in the liver. It is possible that as the islets become vascularized in their new site in the liver, that Notch would become activated, and in a window-of-opportunity, be capable of being activated and allow cell proliferation. However, in the long run, alternate strategies of cell-based transplanted material besides islets are required. One possibility that has been frequently touted is the use of embryonic stem (ES) cells that might be guided to a β-cell-like phenotype. So far, this has not been accomplished in any meaningful or useful way [37,38]. Continuously sustained Notch activation with exogenous ligands might lead to continued proliferation of ES cells that could then be differentiated as needed. One possibility is the use of γ-secretase inhibitors that may be able to rapidly prevent or suppress Notch activation and allow differentiation to occur by favoring Ngn3 expression. It has already been shown that Notch signaling (using Delta-4 and Jagged-1 analogs) markedly reduced fetal neural stem cell death [39]. Additionally, the cells retained the capacity to become neurons, astrocytes and oligodendrocytes. Pancreatic precursors from mouse e13.5 were also maintained in an undifferentiated state by Dl-4 treatment and continued to proliferate.</p><p>It is intriguing that in type 2 diabetes, β-cell apoptosis appears to be a consistent feature [7]. Normalization of blood glucose may prevent the apoptosis, though this cannot really be tested in individual humans. Because Notch ligands can prevent apoptosis of ES cells the development of Notch agonists may also find a use in treating type 2 diabetes.</p><!><p>We have come a long way to understanding the routes taken by the embryo during development from formation of primitive gut through to a mature pancreas, complete with all its diverse functions. So far, we have not been successful in turning his knowledge into useful treatments for type 1 and type 2 diabetes. As we study Notch pathways beyond the developmental stage and into the mature pancreas, we may uncover ways of activating it within existing β cells and be able to modify the rate on cell turnover. Additionally, the knowledge may be useful in tissue engineering for replenishing β cells in type 1 diabetes.</p>

PubMed Author Manuscript

Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control

We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features (i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) data-driven regression models for fault magnitude estimation via the random forest algorithm, and (iv) a parametric optimization and control (PAROC) framework for the design of the explicit/multiparametric model predictive controller. The resulting explicit control strategies correspond to affine functions of the system states and the magnitude of the detected fault. A semibatch process, an example for penicillin production, is presented to demonstrate how the proposed framework ensures smart operation for which rapid switches between a priori computed explicit control action strategies are enabled by continuous process monitoring information.

integrated_data-driven_process_monitoring_and_explicit_fault-tolerant_multiparametric_control

INTRODUCTION<!>BENCHMARK SEMIBATCH PROCESS: PENICILLIN PRODUCTION<!>PARAMETRIC FAULT-TOLERANT CONTROL FRAMEWORK<!>Offline Fault-Tolerant mp-MPC Design via PAROC Framework.<!>High Fidelity Dynamic Modeling.<!>Model Approximation.<!><!>Model Approximation.<!>Designing the mp-MPC.<!>Remark 1.<!>Closed-Loop Validation.<!>Offline Fault Detection and Reconstruction Mechanism Development.<!>Data Preprocessing.<!>Targeted Data Collection.<!>Unfolding 3D Batch Process Data into 2D.<!>Extracting Additional Features.<!>Data Normalization and Reduction.<!>Parameter Tuning.<!>Parameter Tuning for C-SVM Models.<!>Parameter Tuning for Random Forest Regression Models.<!>Model Building.<!>Training C-SVM Models.<!>Step-1. Feature Ranking via C-SVM Modeling.<!>Step-2. Developing C-SVM Models for each Feature Subset.<!>Step-3. Choosing the C-SVM Model with Optimal Feature Subset.<!>Training Random Forest Models.<!>Closed-Loop Validation and Online Implementation.<!>RESULTS<!>CONCLUSIONS

<p>Achieving high process reliability and availability is of utmost importance and one of the major growing demands in process systems engineering.1 As automation increases in industry with initiatives such as Smart Manufacturing and Industrie 4.0, process systems become more vulnerable to faults.2 Deficiencies in sensors, actuators, controllers or disturbances in a process may cause fault, which can be amplified within a closed-loop control system and lead to a serious failure unless faulty operation is detected, recovered, and returned back to its normal condition.3 Subsequently, rapid detection and diagnosis of faults play a key role in defining the necessary corrective actions in order to prevent fault propagation and further development of simple faults into failure. One way to approach this challenging problem is to build "fault-aware" control systems, which would understand the existence of faults in a process and adjust the controller actions accordingly, and rapidly to guarantee stability and satisfactory performance. Such control systems are referred as fault-tolerant control (FTC) systems in the literature and have been studied extensively for the last 40 years.</p><p>FTC has become an emerging research field in automatic control in the late 70s in order to overcome the limitations posed by conventional feedback control, in cases in which conventional feedback control design may end up performing poorly and lead to instabilities in the event of actuator, sensor, or another system component malfunctions.4 The motivation in designing fault-tolerant systems has been driven by problems observed in aircraft control systems, for which particular automatic fault accommodation strategies are needed to guide pilots, and prevent the development of simple faults into severe failures which may lead to accidents.5,6 FTC has been studied extensively in the literature;7–10 however, interest spiked especially in the late 90s and early 2000s,3,11–13 with applications starting to become prevalent especially in safety-critical systems with the increase in computational power and advancements in sensor technology.14,15 Today, fault-tolerant systems are widely used in numerous fields including aircrafts,6,16,17 mechatronics,18 power plants,19,20 spacecrafts,21–24 and industrial plants producing hazardous materials such as nuclear plants.25,26 The number of application areas is further increasing as the demand for higher process availability and profitability grows, and tolerance for process failures decreases in industries under smart manufacturing initiatives.10</p><p>The objective of constructing FTC systems is to increase process resilience by building a tolerance for unexpected events causing faults. FTC enables recovery to the original system performance by using the same control objective of the controlled system.4,27 There are two different approaches in building a FTC system: one with active and the other one with passive fault-tolerance strategies (Figure 1). Passive approaches use robust control techniques to protect the system from instabilities and ensure the closed-loop control system remains insensitive to certain faults by using the existing controller parameters. On the other hand, active fault-tolerant strategies do not necessarily use existing controller parameters. They use online fault detection and identification (FDI) mechanisms to monitor the process and collect information on faults when they occur for further accommodation. Active approaches are further grouped under two categories: (i) projection-based and (ii) online reconfiguration/adaptation. For projection-based FTC approaches, in addition to an accurate and robust FDI mechanism for getting online fault information, a priori knowledge on expected fault types is required to design controllers prior to possible faults that can be observed in the process. Hence, this technique necessitates offline calculation and storage of control laws. Once the information is received on a certain fault from an online FDI system, the corresponding projected controller actions are activated via one of three approaches: (i) model switching or blending, (ii) scheduling, and (iii) prediction.2 Online reconfiguration/adaptation approaches benefit from adaptive control and reconfiguration/restructuring of the control signal distribution (i.e control allocation). Regardless of the active FTC category, reliability of online FDI mechanisms play a significant role in determining their effectiveness and robustness.</p><p>In this work, we integrate multiparametric model predictive control (mp-MPC) with a data-driven process monitoring framework28,29 to introduce a novel parametric fault-tolerant control (FTC) design framework. The developed framework can replace the conventional approach, online controller parameter retuning, and be used as a novel corrective maintenance strategy that significantly minimizes the process downtime spent under faulty conditions by storing precalculated control laws. By using multiparametric programming,30,31 we are able to establish the control actions for the faulty state explicitly and generate a priori, offline, maps of approximate control actions to be implemented in the online phase. This is an active fault-tolerant control strategy, specifically model-switching-based active FTC, where we need to use an online fault detection and identification (FDI) mechanism to monitor the process and get information on faults for further fault accommodation. Although switching-based active fault-tolerant control strategies that use multiparametric programming have been introduced in the literature,32–34 the major challenge has remained to have a reliable and robust FDI system which can provide accurate fault information and minimize the number of false alarms. Thus, we build data-driven fault detection and diagnosis models via the support vector machine (SVM)-based feature selection algorithm28,35,36 and develop data-driven models for fault magnitude estimation via the random forest algorithm. The developed control strategies are affine functions of the system states and the magnitude of the detected fault which are transferred to the controller via the built machine learning-based fault detection and identification (i.e., magnitude estimation) mechanism. The premise of the presented framework is to increase process resilience and minimize process downtime while maintaining a safe and profitable operation by enabling rapid switches between a priori mapped control action strategies. The results are presented through a semibatch process for penicillin production. The paper is organized as follows: Section 1 introduces the adopted benchmark semibatch process. Section 2 describes the details of the parametric fault-tolerant control design framework. Section 3 reports the application of the framework on two distinct fault types. Finally Section 4 provides the conclusion of the presented work.</p><!><p>We adopt a fed-batch penicillin production process based on the PenSim benchmark model37 (Figure 2). The process operates in two modes. First, it starts in batch mode with high substrate (glucose) concentrations stimulating biomass growth. After the initial glucose level is depleted in the bioreactor, the process switches to fed-batch mode where low but nonzero glucose concentration is provided. Then, under these stressful conditions penicillin production is triggered via biomass.37,38 In this work, we simulate process data for fed-batch penicillin production by using the RAYMOND simulation package.39 We produce 25 simulations for each fault magnitude and onset combinations in addition to the 200 simulations of normal operating condition (NOC) by using the RAYMOND software. Of note, fault direction is defined as measured value – real value within the RAYMOND simulator. In this work, a nominal feed rate of 0.06 L/h is chosen for the fed-batch phase of the simulations. A batch is terminated after a total of 30 L of substrate has been added. This corresponds to a total batch duration of approximately 549 h. The initial fermenter volume V0, biomass concentration Cx,0, and substrate concentration Cs,0 are all independently sampled from normal distributions with mean μ and standard deviation σ. Values are limited to μ ± 2.5σ in order to avoid outliers in the initial conditions. Measurements are collected from 20 process variables, where white noise is included into each of them (Table 1). Sensors are sampled every 0.2 h which has generated an average of 2745 sample points per batch. Please note that only a subset of these 20 process variables can be readily measured online in real-life and these are marked with an asterisk in Table 1. In fact, biomass, penicillin, and substrate concentrations are indicated to be measured only offline usually every 8–10 h.37,38 We have utilized all 20 process variables in this work in order to demonstrate the capability of our FTC framework in handling large set of process variables.</p><p>In this work, we control the reactor temperature via fault-tolerant mp-MPC by manipulating water flow rate. We have studied two distinct fault types: (i) sensor fault, which introduces a bias in reactor temperature measurements, and (ii) actuator fault, which creates bias in water flow rate.</p><!><p>To develop a parametric fault-tolerant control system, we design a fault-tolerant mp-MPC where we achieve offline optimal control strategies to be implemented for online control of the process, and build a mechanism for fault detection and magnitude estimation in the offline phase. The perspective is to obtain information on fault existence as well as magnitude of the detected fault in order to inform the mp-MPC with the corrected measurements. Fault is defined as the unpermitted deviation in at least one observed variable or computed parameter of the system where controllers cannot reverse it.28 In this work, we assume that once a fault arises in the system, it does not fade out, therefore we need to inform the controller about the deviation in order to ensure smooth control actions.</p><p>The common first step is data acquisition. Data can be achieved via either historical operation data or process data simulations based on the dynamic model of the system, which is often readily available in industrial applications. For the offline design of the fault-tolerant mp-MPC, we use normal operation data. We collect both normal and faulty operation data for building fault detection, and diagnosis and magnitude estimation models.</p><p>For fault detection, we develop two-class classification models by following simultaneous fault detection and diagnosis (s-FDD) framework.28,36 The major advantage of the s-FDD framework compared to the other fault detection and diagnosis (FDD) frameworks in the literature is the increased process monitoring efficiency by having one model that can detect and diagnose a fault. This creates a significant advantage during online process monitoring in terms of time efficiency where both detection and diagnosis can be achieved simultaneously with a unique function evaluation. In other words, s-FDD framework yields a classification model which recognizes process abnormalities, and marks them as process faults while it lists the major contributing process variables causing the detected fault, thus providing diagnosis. Furthermore, fault magnitude estimation models are achieved by developing regression models.</p><!><p>We build a fault-tolerant mp-MPC by using the parametric optimization and control (PAROC) framework30 (Figure 3), which provides a systematic methodology to design advanced model-based controllers via multiparametric programming. The PAROC framework presents an extensive environment for designing chemical processes, building controllers, and performing parameter estimation based on high-fidelity models while benefiting from the most recent advances in the field of multiparametric programming. Numerous applications of the PAROC framework are demonstrated in the literature for the integration of (i) process design and control,40–42 (ii) process scheduling and control,43 and (iii) process design, control, and scheduling.44</p><p>The initial step of the PAROC framework is high fidelity modeling and analysis in order to acquire a mathematical model that can describe the system of interest accurately. However, oftentimes the derived mathematical models are highly dimensional with a large number of variables and/or complex in nature posing a significant challenge during their optimization in terms of computational expense. This further hinders the direct use of these models for the development of model-based strategies and necessitates model approximation or reduction steps prior to the controller design. The reduced model is then used to build a model predictive control (MPC) scheme, and solved via multiparametric programming to obtain mp-MPC, which produces the offline map of optimal control actions under both normal and faulty operations. Here, the fault-tolerant control scheme is achieved by introducing the mismatch (fault) information as an additional dimension during the mp-MPC design. The final step is "closed-loop validation", where we implement the extracted offline map of fault-tolerant control actions to the original mathematical model of the process to observe the closed-loop behavior of the system. The PAROC framework has been applied to numerous fields successfully.40–44 The details of each step are provided below.</p><!><p>A detailed and accurate representation of the system dynamics based on first principle dynamics and empirical correlations is used to simulate the open loop characteristics of the fed-batch penicillin production process. In this work, we employ the differential algebraic model (DAE) model presented by Birol et al.37 as generalized below. (1)x˙=f(x,u) where x is the states of the system, u is the manipulated variables given in Table 1, and f is a generic function.</p><!><p>The detailed model represented by eq 1 features complex and highly nonlinear dynamics among the manipulated variables and the observed outputs, rendering it inappropriate to develop advanced parametric controllers. Therefore, we develop an affine approximate model that accurately represents the high fidelity dynamics by model reduction or subspace identification techniques. In this work, we use the MATLAB System Identification Toolbox to capture the dynamics of eq 1 by the discrete time state space model, given by the equation below. (2)xt+1=Axt+But+Cdty^t=Dxt+Eut+Fdt where subscript t denotes the discretized time step, and y^ is the output prediction. The state space matrices are developed based on the simulated process outputs y under randomized input profiles for u and d. The state space model used in this study is provided in eq 3. Note that C, E, and F are zero matrices since there are no measured disturbances or zeroth order inputs in the system.</p><p>Note the following remarks:</p><!><p>The identified states x do not represent the real system states.</p><p>The input-output data are generated without any sensor or actuator faults. The two fault types are accounted for analytically in the mp-MPC design phase, described in section 2.1.3.</p><p>The process faults directly affect the process dynamics, hence they are considered as added disturbances in the dt term. However, we only present the sensor and actuator faults in this study.</p><!><p>Acquiring satisfactory closed-loop performance relies heavily on developing accurate approximate models. Katz et al.45 investigated the effects of approximating the high fidelity models by simpler models in the context of multiparametric programming, and introduced novel error metrics to evaluate open and closed-loop performances. In this work, we use the open and closed loop metrics introduced in Katz et al. to increase the confidence of the developed approximate models. Specifically, we evaluate the performance of the candidate approximate models in open loop via the standard integral time absolute error (ITAE) technique that shows the cumulative error against the high fidelity model, as well as the decision space volume comparison introduced by Katz et al.45 After building the controller based on the developed approximate model, we further use ITAE to evaluate the tracking capacity of the developed controller for a given set point. These open loop and closed loop metrics provide relative criteria to assess the reliability of the developed approximate models. The details regarding the candidate approximate models and their evaluation are omitted here for brevity and to focus on the fault-tolerant explicit control scheme.</p><!><p>The offline control strategy is designed to (i) track the output set points determined prior to the operation, and (ii) acquire smooth control actions to maintain the longevity of the processing equipment. Therefore, the objective function of the control problem is given by the following equation. (4)∑t=1N‖yt−ytsp‖QR2+∑t=0M‖Δut−θa‖R12 where N is the prediction horizon, M is the control horizon, θa is the magnitude of the fault acting on the corresponding actuator, ‖·‖ψ denotes a weighted vector norm with a weight matrix ψ, QR and R1 are the corresponding weight matrices, and the superscript sp denotes the set point. Hence, the quadratic objective function is minimized only if the process outputs track the designated set points ysp, and the consecutive control actions are smooth in the existence of faulty actuators θa.</p><p>The developed objective function is subjected to the approximated process model, given by eq 2. However, using an approximate model to achieve closed-loop control creates a mismatch between the real process outputs, y, and the predicted output values, y^. We address this mismatch by including eq 5 in the mp-MPC formulation. (5)e=yt−y^t,    t=0 where the error term e denotes the mismatch magnitude between the real and predicted output values at the time of measurement, t = 0. The error term is carried over the entire prediction horizon, as given by the equation below.</p><p>Note that apart from the mismatch term, we also incorporate a sensor bias term θs to account for the sensor faults in the mp-MPC. The path constraints are formulated as box constraints for the process variables to maintain certain product specifications, as presented by the equation shown below.</p><p>Lastly, we define the set of parameters in the control problem as following: (8)θ:=[xt=0T,ut=−1T,yt=0T,(ytsp)T,dt=0T,θa,θs]T where θ is the vector of parameters. Therefore, we postulate the explicit control strategy described by eq 9.</p><p>Note that the control strategy formulated by eq 9 is a multiparametric optimization problem with a quadratic objective function and a set of linear constraints. This class of problems can be solved exactly by using the Parametric Optimization (POP) toolbox,46 and the solution to these problems are expressed as a single piecewise affine function of the parameters. Therefore, the explicit control law is derived as given by the equation below. (10)ut(θ)=Kiθ+ri,∀t∈{1,2,…,M},   ∀θ∈CRiCRi:={θ∈Θ|CRiAθ≤CRib} where CR denotes a polyhedral partition of the feasible parameter space, and Θ is a closed and bounded set.</p><!><p>Equation 10 explicitly maps the exact optimal control actions for any parameter realization in Θ, if a feasible solution exists. Therefore, inclusion of the monitored faults as parameters in the explicit control law identifies the range of recovery in the existence of faulty sensors and/or actuators prior to the operation.</p><!><p>The proposed control problem is developed based on an approximate model. Therefore, the closed-loop strategy should be validated against the high-fidelity model by observing the set point tracking performance and path constraint violation by exhaustive simulations under numerous uncertainty scenarios. Note that due to the explicit nature of the closed-loop strategy, the control law can be embedded in the high-fidelity model exactly. Therefore, the closed-loop profiles can be simulated without the necessity of solving any online optimization problems.</p><p>In the case of insufficient or poor closed-loop performance, one can (i) adjust the weight matrices QR and R1 in the objective function given by eq 4, (ii) develop a new approximate model using a different technique, or (iii) develop multiple discrete time state space models that are used to govern different operating regions.</p><!><p>The fault detection and reconstruction mechanism is responsible for two main tasks: (i) precise and rapid fault detection and diagnosis, and (ii) accurate fault direction and magnitude estimation (a.k.a. fault reconstruction). We follow the main steps of the s-FDD framework to build fault and time-specific classification models for fault detection and diagnosis. Additionally, in order to predict the magnitude of the detected fault, we develop regression models by adopting the random forest algorithm.47 Specifically, we regress the water flow rate measurements for the actuator fault, and reactor temperature measurements for the sensor fault. The modeling procedure for both analyses is summarized in three main steps. The initial step is data preprocessing which includes targeted data collection, unfolding of 3-dimensional (3D) batch process data into 2D, extracting additional process descriptors when necessary, and data scaling, respectively. This is followed by parameter tuning and model building steps.</p><!><p>Data preprocessing steps are necessary prior to model building in order to prevent bias and improve the performance of the model. Generally, data preprocessing comprises data formatting, scaling, and cleaning steps, where data cleaning includes both outlier removal and missing data handling. Below, we describe these three main pillars of data preprocessing in four steps: Data formatting, where we collect targeted process data; unfolding the 3D data into 2D; extracting further features when necessary to enrich the data set; and data scaling and cleaning.</p><!><p>We are building (i) fault and time specific two-class C-SVM classification models for fault detection and diagnosis, and (ii) regression models for fault magnitude estimation after fault onset time. Therefore, we need to gather process data around the fault onset time for both models. In this work, we have selected four different fault onset times, 100, 200, 300, and 400 h, where we introduce two different faults in various magnitudes. The details on the fault types and their magnitude are provided in section 2. In each batch, we consider the time periods that encompass the fault onset time and 10 h (50 sensor samples) afterward, where the sensor sampling frequncency is every 0.2 h.</p><p>During fault detection classifier building, we extract process data by following a sliding window approach in which we receive five samples per hour (sensor sampling frequency of every 0.2 h). At each sensor sample, we collect historical data in 10 h blocks. For instance, to build a classifier around 100 h, we consider the time period of 100–110 h of a batch. Next, starting from the fault onset time 100 h until the 110th h, we obtain process data in 10 h blocks: At hour 100, we collect data from the 90th to the 100th h. Similarly at the next sensor sample time, 100.2 h, we collect data from 90.2 to 100.2 h. We obtain the process data iteratively until the end of the considered time period, 110 h. The schematic representation of the targeted data collection is presented in Figure 4, wherein the gray boxes mark the fault onset time of the classification models being built. The blue line indicates the first and the red line indicates the last 10 h data block extracted from the 90–110 h time period for the 100 h fault detection classifier. Each data collection from the selected window adds a new instance in the data set. This approach yields a 3-dimensional (3D) data set with a size of 2500 × 20 × 50. The first dimension of the data set is obtained with 50 sliding window iterations in 50 batches (25 faulty and 25 normal operating). Furthermore, we observe 20 process variables that include both state and manipulated variables 1 in 50 sensor sample periods (i.e., 100–110 h for 100 h classifier building). The data set size is consistent for each fault and time-specific fault detection model building.</p><p>On the other hand, during fault magnitude regression development, we consider solely the process variables and do not extract any further process descriptors. Here, we collect a 10 h block for actuator fault, and 1 h for sensor fault magnitude estimation model development. We also combine all faulty operation data with varying fault magnitudes. Specifically, we have simulated six distinct fault magnitudes for sensor fault and eight for actuator faults. For each magnitude, we have simulated 25 batches. This yields 150 faulty batches with sensor fault and 200 faulty batches with actuator fault. Next, we include equal amounts of normal operating batches to our data sets. Thus, the size of the obtained data set is 300 × 20 × 5 for regression model development for sensor fault magnitude estimation, whereas the data set size for regression model development for actuator fault magnitude estimation becomes 400 × 20 × 50. Here, the first dimension belongs to the total number of batches (with equal number of faulty and normal operating batches), the second dimension is the 20 process variable measurements, and the last dimension indicates the 10 h (50 sensor sample) block, and 1 h (5 sensor sample) block examined after the fault onset time of actuator and sensor faults, respectively.</p><!><p>The collected 3D data needs to be unfolded into 2D prior to model building steps. The 3D data set can be unfolded in three ways by placing one out of three dimensions as rows, and grouping the other two dimensions as columns. In this work, we perform batch-wise unfolding for classification and measurement-wise unfolding for regression analysis.48 In batch-wise unfolding, batches are the instances which are provided in the rows of the 2D data set, whereas in measurement-wise unfolding, we keep the process variable measurements as the features and place them to the columns of the 2D data set for regression analysis. The features that constitute the columns of the 2D data sets are time-specific process variable measurements for classification and time-specific-batch ID for regression models. After the unfolding step, the data set size becomes 2500 × 1000 for classification analysis. On the other hand, the unfolded data set size becomes 20000 × 20 for actuator fault and 1500 × 20 for sensor fault magnitude estimation.</p><!><p>This step is optional. We apply this step only during classification analysis. The aim of this step is to enrich the data set by including additional process descriptors to capture the process nonlinearity, which can then improve classification model performances. To do this, we calculate the (i) mean, (ii) standard deviation, and (iii) slope of 20 process measurements within each sliding time window and incorporate them into the unfolded data set. This increases the classification data set sizes to 2500 × 1060.</p><!><p>The final data-preprocessing step is scaling of the reconfigured data set and a priori dimensionality reduction to remove redundant features. This procedure is common to both classification and regression analysis. Each column of the 2D data set is scaled by a z-score calculation, in which the mean of the column is extracted from each value and then divided into the standard deviation of the column. Redundant features, where the standard deviation is less than 10−8, are removed in order to decrease the computational cost during the offline model building phase.</p><!><p>We are training (i) C-SVM (two-class) classification models by using the Gaussian radial basis function (RBF) as the nonlinear kernel function for fault detection and diagnosis, and (ii) regression models via random forest algorithm for fault magnitude estimation after fault detection. Note that any regression model can be used for fault magnitude estimation, yet nonlinear regression techniques are expected to be superior than linear techniques in terms of providing more accurate fault magnitude estimations due to the nonlinear relationship between the process variables. In this work, we investigated two advanced regression techniques, namely random forest regression and C-parametrized support vector regression (C-SVR). Specifically, we have trained C-SVR models by using the introduced feature selection algorithm in Onel et al.28,36 The results from the C-SVR models are tabulated in Table S1 for actuator fault and Table S2 for sensor fault in the Supporting Information. The results provided in Tables S1 and S2 show that dimensionality reduction does not necessarily improve the model R2 values. This is mainly due to there being a low ratio of the number of features to the number of instances in the process data set. Therefore, we use the entire process variables that remain after the data preprocessing step during regressor training. In this work, we prefer random forest algorithm over C-SVR due to the added benefit of the bagging technique of the random forest algorithm, which allows us to train more accurate regressors with the entire process variables for fault magnitude estimation. Regardless of the analysis, the initial step is parameter tuning which is required to achieve the optimal model performance.</p><!><p>Here, we have two parameters to tune: (i) C (cost) parameter of C-SVM, and (ii) γ parameter of the Gaussian radial basis kernel function. The first parameter acts as a regularization parameter that controls the trade-off between low training error and low test error. In other words, this parameter regulates the balance between model complexity and model generalization. When the training error is lower, the model complexity is higher and the model generalizability is lower. On the other hand, when the testing error is lower, the model complexity is lower and the model generalizability is higher but with a higher training error. Finding an optimal balance is crucial to the development of an accurate classifier. Furthermore, the γ parameter determines the complexity of the Gaussian RBF kernel and affects the radius of influence of the samples selected as support vectors by the model.</p><p>In LIBSVM, the default value for the RBF kernel parameter, γ, is 1/n, where n is the number of features. Thus, we tune parameter γ^ where (11)γ=2γ^n Moreover, we tune parameter C^, where the relation between C^ and C is (12)C=2C^ According to the described iterative feature selection algorithm in our previous papers,28,36 γ^ can be retuned after each feature elimination step with the available set of features: (13)γ=2γ^zT1 We have performed the parameter tuning via a grid search and 10-fold cross-validation. In particular, we have used the values between −1:1 for C^, and −10:10 for γ^. We have performed the parameter tuning once in the beginning where we have the entire features in the data set. Although repeating the grid search for parameters tuning after each feature elimination would be ideal, we avoid the computational cost since the attained model performance has been observed to be satisfactory. If one obtains a poor-performing model, tuning can be repeated with each available feature subsets. Finally, the parameters that produce the highest average testing accuracy are chosen for the next steps. The optimal parameters for the fault-and-time specific C-SVM models are provided in Table 2.</p><!><p>In regression analysis, we have one parameter to tune, which is the number of features that can be used in the training of each decision tree of the random forest model, mtry. This is performed via a grid search among the total number of features until 1 while training random forest models via 10-fold cross-validation. The optimal mtry parameters are obtained by using the "trainControl" function of the "caret" package of the R statistical software. The optimal mtry values for each time-specific regressor are provided in Table 3.</p><!><p>Here, we address the model building steps separately for classification and regression analysis. We follow the s-FDD framework28,36 to build the C-SVM classifiers for fault detection and diagnosis. The application of the framework and data-specific details are provided below. Furthermore, we describe the model building steps for regression analysis via random forest algorithm.</p><!><p>The overall procedure for fault detection model development is summarized in Figure 5.</p><!><p>The tuned parameters are incorporated into a simultaneous model-informed feature selection and classification algorithm via C-SVMs.28,35,36 C-SVM binary classification models with the Gaussian radial basis function (RBF) kernel are trained iteratively with each feature subset as features being eliminated one by one. Features are eliminated based on the Lagrangian sensitivity of the dual objective function of the built C-SVM model with respect to the feature subset size at each iteration. This iterative process is performed with each of the 10 train-test data set pairs which produces 10 separate feature ranking lists. Next, we create an average feature rank list based on the statistical distribution of the feature ranks among the 10 ranking lists.</p><!><p>Here, we rebuild C-SVM models by using the optimal parameters and 10-fold cross validation, where we use the average feature rank list to guide the iterative feature elimination process. We start with the whole set of features and eliminate them one by one based on this final ranking list. This process produces 10 C-SVM classifiers for each feature subset due to the 10-fold cross-validation. The performance of each model is assessed via accuracy, area under the curve (AUC), fault detection rate, and false alarm rate. We average the performance of 10 classifiers and obtain one C-SVM model performance per feature subset. At the end of this step, we tabulate the performance of C-SVM models with each feature subset. Specifically, in this work, we have generated 1060 C-SVM models.</p><!><p>This step determines the final C-SVM models to be implemented in the online phase for process monitoring. Here, we select the classifier that has provided the highest model accuracy and area under the curve with minimum number of features among the 1060 C-SVM models produced in Step 2. The selected feature subset is used in analyzing the root-cause of the detected fault. Therefore, selecting the minimum number of features is significant in order to facilitate the interpretation of the fault diagnosis. The performance of the selected fault-and-time specific C-SVM models are tabulated in Table 4.</p><!><p>By using the optimal mtry parameters, we train random forest models with 500 decision trees. Training is performed via the "randomForest" function of the "randomForest" package of R statistical software. The performance of the fault-and-time specific random forest models are tabulated in Table 5.</p><!><p>Prior to the online implementation, we have implemented the developed fault-tolerant mp-MPC, and fault detection and reconstruction mechanism to the RAYMOND simulator separately in order to validate their individual performances. The performance of fault-tolerant mp-MPC is assessed by providing the fault onset time and magnitude information to the controller. We have observed that the controller adapts to the faulty condition once it is provided with accurate information on the fault type, onset time, and magnitude. The accuracy of the fault detection and reconstruction mechanism is also tested and validated separately, where we have simulated a process with known fault onset and magnitude without incorporating any fault-tolerant control actions during the simulation. Finally, we implement the fault detection and reconstruction mechanism with the fault-tolerant mp-MPC in the RAYMOND simulator. During the online phase, the received signals on process variables are (i) initially collected and processed to detect the existence of any sensor or actuator faults, (ii) then reconstructed to determine the magnitude of the fault, and (iii) finally passed on to the controller for the optimal control action in the existence/absence of fault. The online procedure is illustrated in Figure 6.</p><!><p>In this work, we control reactor temperature by manipulating the water flow rate during penicillin production. We build a fault-tolerant control scheme that can tolerate both actuator and sensor fault. We introduce sensor bias in water flow rate measurements for actuator fault, whereas we introduce sensor bias in reactor temperature measurements to induce sensor fault. Numerous fault magnitudes and onset time are simulated for each fault type. Particularly, we select −2.5, −2.0, −1.5, +1.5, +2.0, +2.5, and −2.0, −1.5, −1.0, −0.5, +0.5, +1.0, +1.5, +2.0 for actuator and sensor fault magnitudes during the simulations, respectively. We have developed highly accurate fault and time-specific fault detection models and regression models for fault magnitude estimation (Tables 4 and 5) and implemented them for the fault detection and reconstruction mechanism of the established parametric fault-tolerant control system.</p><p>Figure 7 provides a comparison of the open and closed (via fault-tolerant mp-MPC) loop simulation, which signifies the importance of having accurate control actions on the reactor temperature by manipulating the water flow rate. The mp-MPC yields an offline, a priori, map of optimal control actions for the process. Figure 8 deliniates the distinct control laws for various magnitudes of sensor and actuator faults at the fixed parameters. The major advantage of the built fault-tolerant system is to gain a priori knowledge on the control actions for different fault magnitudes of actuator and sensor fault separately, as well as for different combinations of the two distinct fault types simultaneously. This map further draws the limits of the fault tolerance for each fault types. These limits indicate specific fault magnitudes for each fault type until the point at which the the designed fault-tolerant mp-MPC can recover the process back to the normal condition.</p><p>From the beginning, we monitor the process with the fault tolerant mp-MPC and acquire information on the existence of any fault within the system from the fault detection classifier models. In this work, the adopted alarm policy is the generation of three consecutive alarms. In other words, we conclude on the fault existence when we obtain three consecutive positive responses from the fault detection classifiers. Once the fault is detected, we initiate to regress the magnitude and direction of the fault. The random forest models use the online process variable measurements to estimate the amount of deviation from the reactor temperature of the normal operating condition. Here, early detection of the faults is crucial to initiate the fault estimation process. If the fault detection latency is high, that is when fault is detected late during the operation, the controller may not be able to return the process back to the normal condition. The reason can be 2-fold: (i) the validity of the regressor may expire, thus accuracy of the fault estimation deteriorates, and (ii) there may be significant damage on the process which is irreparable. Table 6 presents the average fault detection latency of each fault and time specific classifier among the entire simulations with varying fault magnitudes.</p><p>Achieving low latency with the fault and time-specific C-SVM models indicates early fault detection. When we compare the two different fault types, the average latency is lower for the actuator fault models. The main reason for this can be the fact that changes in water flow rate may affect the other process variables in a more definite way. This may lead to sudden changes not only in one but numerous process variables, thus facilitating the fault detection. Furthermore, the process nonlinearity affects the detection latency in distinct ways for different fault types. Specifically, we observe that we detect the actuator fault more rapidly in the later stages of the batch process, namely 300 and 400 h models. On the other hand, the separation in the average latency is not that clear among the sensor fault detection models. Here, the high latency can be linked to the low number of process variables used in the fault magnitude estimator models, which may not be adequate to capture the process behavior in the specific process time.</p><p>We are building fault and time-specific regression models for fault magnitude estimation. Therefore, it is crucial to assess the accuracy of the fault reconstruction performance after the fault onset time. During the online operation, we use the regressors that are trained around the simulated fault onset time. As the operation progresses after the fault onset time, where the process is kept under normal condition thanks to the fault-tolerant mp-MPC, the regressor model continues to use the online process data at every new sampling point. However, as the sampling time moves away from the fault onset time, the process data characteristics can significantly change, which renders the regressor inaccurate for fault estimation. Fault estimation may not be performed as accurate as it is done near the fault onset time, which hinders the controller's learning about the process condition. This, in turn, may lead to insufficient control actions to recover the process back to the normal condition. Note that the extended validity of the regressor accuracy heavily depends on the amount of deviation of the process data characteristics. As a result, it is significant and necessary to assess the time-sensitivity of the fault estimators and identify when we need new models for accurate fault reconstruction. Furthermore, the limit of each regressor determines the targeted data collection location for the next regression model training.</p><p>Tables 7 and 8 tabulate the extent of the validity of the time-specific fault detection classifiers and magnitude estimation regressors for two sets of thresholds around the reactor temperature set point being ±0.5 and ±0.75 K. The complete set of reactor temperature and water flow rate profiles with ±0.5 K threshold on the set point for each time-specific model is provided in the Supporting Information. In particular, we assess the extent of the validity of each time-specific model until the next time-specific model territory (i.e., the 100th h models are tested until the 200th h, etc.). The results for the actuator fault case show that the models that are built at the 200th and 300th h have successfully provided necessary control actions until the target process time is the 300th and 400th h, respectively. Similarly, models built at the 400th h have enabled satisfactory control actions until the end of the operation. The results for the models built for the 100th h show that the models are valid on average for the next 73.5 and 75.4 h for ±0.5 and 0.75 K thresholds around the reacture temperature set point, respectively. This highlights that we need to have additional models for accurate fault detection and magnitude estimation between the 100th and 200th h of the batch operation.</p><p>On the other hand, for the sensor fault case, we note that the models built for the 200th and 400th h are not valid for an extended process time when negative fault magnitudes are observed. On average, the models are valid for another 1.3 and 1.5 h after the fault is introduced in the 200th and 400th h, respectively, when the reactor temperature deviation threshold is set to 0.5 K. When we increase the threshold to 0.75 K around the set point, we observe that the models built at 200th h can maintain a smooth operation for the entire targeted operation range, which is the next 100 h, because the latency in fault detection has caused a deviation that is higher than 0.5 but lower than 0.75 K. However, this does not apply to the models for the 400th h. The threshold increase does not extend the validity of the 400th h models since the maximum deviation observed is as high as 2.1 K (Figure S52). The limited model validity for the two time-specific models at 200 and 400 h is due to the high fault detection latency. In other words, by the time we detect the fault occurring at the 200th and 400th h, the deviation from the reactor temperature set point already exceeds the predetermined thresholds (Figure S34–S37 for the 200th models and Figure S52–S55 for the 400th models). Therefore, required control actions are not provided by the controller as it has not been notified of the existence and magnitude of the fault. To overcome this problem, fault detection latency must be improved. This can be achieved by increasing the frequency of the fault detection classifiers between 200 and 400 h of the batch operation.</p><p>To provide a comparison between the two fault types, we provide the reactor temperature and water flow rate profiles for 100 h models. Particularly, we display the profiles of the simulations in which we introduce actuator faults with −2.5 and +2.5 fault magnitude in Figures 9 and 10, respectively. Additionally, Figures 11 and 12 demonstrate the profiles of the simulations in which we introduce sensor faults with −2 and +2 fault magnitude. The profiles with actuator fault show that once the regressor model validity expires with the altering dynamics of the batch process, the correction in the faulty water flow rate disrupts and deteriorates. This leads to a significant increase in the reactor temperature that leads to a possible system failure. On the other hand, early capture of the sensor fault leads to rapid and necessary changes in the water flow rate which enable a fast process recovery back to the normal condition. Of note, in order to ensure smooth control actions, one needs to switch to the next valid model once the validity of the previous model expires. This is necessary in order to capture dynamic process characteristics and detect any possible faults. The presented simulation profiles with actuator and sensor faults prove that the designed fault-tolerant mp-MPC provides smooth control actions successfully.</p><p>Finally, we perform a sensitivity analysis with the time-specific fault detection and magnitude estimation models built at 100th and 200th h in order to determine the perimeter of the model effectiveness. To this end, we use the time-specific models for ±30 h perimeter of their corresponding process time. Particularly, the C-SVM model for fault detection and random forest model for the fault magnitude estimation are utilized for every 5 h fault onsets between 70th and 130th h with the models built at 100th h and between 170th and 230th h with the models built at 200th h (Figure 13). We adopt the ±0.5 K threshold around the reactor temperature set point and only utilize the extreme negative and positive fault magnitudes simulated in this work (−2.5 and +2.5 for actuator and −2 and +2 for sensor fault) for the analysis. The results reveal that actuator fault models have more limited range compared to sensor fault models. In particular, the models built at 100th have successfully been used between the 90th and 100th h of the batch operation. The validity range for the models built at the 200th reaches to 15 and 20 h for negative and positive fault magnitudes, respectively. On the other hand, the analysis yields that the models built at the 100th and 200th h for the sensor fault were able to perform the required control actions for the analyzed 30 h perimeter except for the analysis performed with a negative fault magnitude with models built at the 200th h. This is again due to the fact that by the time the fault is detected the raise in the reactor temperature exceeds the allowed region (Figure S34). When the deviation threshold is raised to ±0.75 K, the time-specific models are shown to be valid for the entire analyzed 30 h perimeter (Figure 14). This analysis elucidates the effectiveness limit of the time-specific models which is required to determine the model switching frequency during online monitoring. Overall, the results demonstrate the need for additional models during 100–200 h of the operation if a strict deviation threshold (i.e., 0.5 K) is preferred during the operation. Yet for a 0.75 K deviation threshold, the presented time-specific models have successfully provided satisfactory control actions under faulty conditions.</p><!><p>As the effect of smart manufacturing revolution propagates and influences the vision of numerous industrial operations, the development of a fault-tolerant control system becomes one of the major factors in achieving high process resilience. Traditional corrective maintenance strategies include controller retuning which leads to longer process downtime that may adverse the end-product quality and cause higher operation cost. This work proposes a novel parametric fault-tolerant control framework that enables rapid and accurate switches within the offline map of control actions to eliminate process downtime and maximize process reliability. This further enables attaining higher product quality which leads to higher profit from the operation.</p><p>In this work, we present a novel active fault-tolerant strategy and corrective maintenance strategy which benefits from multiparametric programming and machine learning-based process monitoring. Particularly, we have designed a multi-parametric model predictive controller by following the PAROC framework30 and the s-FDD framework. The s-FDD framework is used to formulate the fault detection and reconstruction mechanism of the fault-tolerant system, where the built classifiers provide the information on fault existence and regressors yield the fault magnitude and direction estimation. The trained C-SVM models with the optimal feature subset further enable the rapid diagnosis of the detected fault. The average accuracy of the classifiers is 98.44%, and 97.70% for the actuator and sensor faults, respectively. Moreover, the average R2 of the trained regressors is 0.999 and 0.958 for the actuator and sensor faults, respectively. The presented approach formulates as a novel active fault-tolerant strategy in which an accurate and robust fault detection and reconstruction mechanism is ensured via the s-FDD framework and multi-parametric MPC enables rapid switches between fault-tolerant control actions. Please note that the presented fault-tolerant strategy is agnostic to any fault types, thus it can be extended to process faults by treating them as measured disturbances. Finally, we note that the design of the fault-tolerant mp-MPC can further enable the handling of simultaneous faults as it includes the deviation in both process variables (i.e., reactor temperature and water flow rate) as additional parameters.</p>

PubMed Author Manuscript

Engineering the metamorphic chemokine Lymphotactin/XCL1 into the GAG-binding, HIV-inhibitory dimer conformation

Unlike other chemokines, XCL1 undergoes a distinct metamorphic interconversion between a canonical monomeric chemokine fold and a unique \xce\xb2-sandwich dimer. The monomeric conformation binds and activates the receptor XCR1, while the dimer binds extracellular matrix glycosaminoglycans and has been associated with anti-human immunodeficiency virus (HIV) activity. Functional studies of WT-XCL1 are complex as both conformations are populated in solution. To overcome this limitation, we engineered a stabilized dimeric variant of XCL1 designated CC5. This variant features a neo-disulfide bond (A46C-A49C) that prevents structural interconversion by locking the chemokine into the \xce\xb2-sandwich dimeric conformation, as demonstrated by NMR structural analysis and hydrogen-deuterium exchange experiments. Functional studies analyzing glycosaminoglycan binding demonstrate that CC5 binds with high affinity to heparin. In addition, CC5 exhibits potent inhibition of HIV-1 activity in primary peripheral blood mononuclear cells (PBMCs), demonstrating the importance of the dimer in blocking viral infection. Conformational variants like CC5 are valuable tools for elucidating the biological relevance of the XCL1 native-state interconversion and will assist in future anti-viral and functional studies.

engineering_the_metamorphic_chemokine_lymphotactin/xcl1_into_the_gag-binding,_hiv-inhibitory_dimer_c

Introduction<!>Protein Engineering<!>XCL1 CC5 is folded and non-metamorphic<!>CC5 adopts the XCL1dim fold<!>H/D exchange NMR reveals increased amide backbone protection for CC5<!>Disulfide-locked XCL1 dimer binds heparin with high affinity<!>CC5 is a potent HIV-1 inhibitor<!>Discussion<!>Mutagenesis, protein expression and purification<!>NMR spectroscopy<!>Structure Calculation<!>HD Exchange<!>Surface Plasmon Resonance<!>HIV-1 infection assays

<p>Chemokines are a family of ~50 secreted signaling proteins that induce chemotactic cellular migration in a variety of physiological functions such as immune response, wound healing, and tissue maintenance 1. Chemokines stimulate chemotaxis by activating specific G-protein coupled receptors (GPCRs) 1, and adhering to extracellular matrix glycosaminoglycans (GAGs) 2, 3. Chemokines are grouped into subfamilies based on the configuration of two conserved disulfide bonds found within the N-terminus 4 (i.e. CXC, CC, CX3C, C). All chemokines share a canonical tertiary fold comprised of a three-stranded β-sheet and C-terminal α-helix, and most of them self-associate to form dimers, higher-order oligomers 5, or polymers 6. It is generally understood that cell surface GPCR activation is associated with the chemokine monomer, while interactions with GAGs tend to promote oligomerization that aids in the formation of chemotactic gradients 7.</p><p>Lymphotactin (Ltn, XCL1), the defining member of the C-class subfamily, is unique in that it contains only one of the two conserved disulfide bonds typically associated with chemokines. Additionally, whereas most chemokines activate GPCRs and bind GAGs within a framework of the canonical fold, XCL1 exhibits reversible conformational heterogeneity and reversibly interconverts between two distinct structural states 8. In one state, termed XCL1mon (previously known as Ltn10, Table 1) the protein adopts a monomeric canonical fold that is capable of activating its cognate GPCR, XCR1 9. However XCL1 can also access another unique conformational state, termed XCL1dim (previously known as Ltn40, Table 1), comprised of a dimeric 4-stranded β-sheet structure capable of high affinity GAG interactions. This conformational switching involves unfolding of the protein and a complete restructuring of hydrogen bonding networks 8, 10. The structural equilibrium can be shifted by changes in temperature and ionic strength, with high salt and low temperature favoring the monomeric chemokine fold (XCL1mon), while low salt and high temperature favoring the alternative all-β sheet dimer (XCL1dim). Under near-physiological conditions (37°C, 150 mM NaCl), the two conformational states are equally abundant and interconvert at a rate of ~1 s−1 11, 12. Thus, XCL1 samples at least three distinct conformational states in solution: folded XCL1mon, folded XCL1dim, and unfolded.</p><p>Our goal is to resolve structure-activity relationships encoded within each conformational state. This includes the characteristic interactions with XCR1 and GAGs required for XCL1 to function as a chemoattractant in vivo. Like many chemokines and other cationic peptides XCL1 exhibits bactericidal activity 13, and it was recently shown to be a broad-spectrum inhibitor of HIV-1 14. Consequently, the scope of our XCL1 structure-function studies is expanding to include multiple types of antimicrobial activity. Although the relative populations of the two folded states in the wild-type protein can be manipulated to a degree, it is difficult to interpret in vitro or in vivo functional studies unless each conformation can be analyzed in isolation. In previous studies, we have used structure-guided protein engineering to generate conformationally selective XCL1 variants, summarized in Table 1. An XCL1 double mutant (V21C V59C; CC3) containing a second disulfide bond restricts the protein to the monomeric chemokine fold (XCL1mon) and preserves full XCR1 agonist activity 9. By selectively destabilizing the XCL1mon chemokine conformation, the XCL1 W55D variant (W55D) enriches the XCL1dim population and binds GAGs with high affinity but no longer activates XCR1 8. Upon comparing the conformationally restricted XCL1 variants, Guzzo, et al. found that WT-XCL1 and W55D exhibited equally potent anti-HIV activity, while the CC3 variant was inactive 14. While these results implicated XCL1dim as the inhibitory state, a role for the unfolded state, which is equally accessible in the wild-type and W55D proteins but not CC3, could not be ruled out.</p><p>To enable XCL1 structure-function studies that fully isolate the XCL1mon, XCL1dim, and unfolded states, we engineered an XCL1 variant that is restricted to the dimeric 4-stranded β-sheet fold (CC5, A36C-A49C) through introduction of a novel disulfide cross-link. NMR structural analysis indicates that the CC5 variant forms a more stable XCL1dim than WT-XCL1 or W55D as determined by hydrogen/deuterium (H/D) exchange. CC5 binds heparin with a 15-fold higher affinity than CC3 and potently inhibits HIV-1, whereas an unfolded XCL1 variant is inactive. These results define a new structure-function relationship for the metamorphic native state of XCL1 and establish the CC5 locked XCL1dim as a useful tool for future investigations.</p><!><p>A new XCL1dim-stabilizing variant was empirically designed by exploiting the unusual structural changes that occur upon XCL1mon-XCL1dim interconversion. Metamorphic rearrangement of the conserved chemokine fold (XCL1mon) into the XCL1dim involves a register shift and inversion of adjacent β-strands hydrogen bonding networks 8. We reasoned that a disulfide joining adjacent β-strands would restrict structural interconversion, thus making the dimeric fold the only accessible conformation. For example, inspection of the high temperature XCL1dim structure revealed two alanine residues (A36 and A49) separated by an α-carbon distance of ~2.5 Å (~6 Å separation between side chain β-carbons) and located on the adjacent β2 and β3 strands (Fig. 1A). Conversion to the XCL1mon conformation repositions the A36 and A49 side chains on opposite faces of the β-sheet (Fig. 1B). This change in orientation and proximity revealed the potential for conformation-specific disulfide bond formation in the dimer. Cysteine mutations were introduced at these sites (i.e. A36C and A49C) and recombinant [U-15N, 13C]-XCL1 CC5 was expressed in E. coli, refolded and purified to homogeneity for structural and functional characterization.</p><!><p>A 1H -15N heteronuclear single quantum coherence (HSQC) spectrum for CC5 was acquired in 20 mM NaHPO4, pH 6 at 40° C. The initial spectrum presented well-resolved cross peaks with uniform intensities accounting for 88% of expected backbone resonances and indicative of an ordered 3D structure. Comparison of CC5 to a WT-XCL1 HSQC spectrum acquired under identical conditions revealed numerous differences in NH backbone resonances that did not allow direct transfer of all chemical shift assignments (Fig. 1C, 40°C). The HSQC spectrum can be considered a diagnostic fingerprint of the protein backbone, sensitive to slight perturbations in electronic environments, and observation of chemical shift differences between CC5 and WT-XCL1 was not surprising considering that the mutations were designed to restrict conformational accessibility through introduction of a disulfide cross-link. Spectra were also collected and compared for both proteins at 25°C (0 M NaCl). Under these conditions, WT-XCL1 is known to be in equilibrium between both conformational states that are observable by NMR 15. Interestingly at 25°C, CC5 showed no evidence of dual state behavior while WT-XCL1 presented clear indications of structural heterogeneity (highlighted boxes in Fig. 1C, 25°C).</p><p>To further assess the conformational stability of CC5, we compared its HSQC spectrum with XCL1 W55D over a range of temperatures. The W55D variant was designed to restrict conformational equilibrium and select for XCL1dim by destabilizing the hydrophobic core of XCL1mon. Even though W55D does not access XCL1mon, it is able to undergo an on-pathway unfolding event that is part of the WT conformational exchange. This is evident in the highlighted areas of the HSQC spectra shown in Fig. 1D. At higher temperatures the spectra for these variants show peak dispersion indicative of XCL1dim, while spectra collected at lower temperatures exhibit weaker or absent signals that result from protein unfolding. This is consistent with measurements of urea-induced equilibrium unfolding of XCL1 W55D showing maximum thermostability at ~40 °C and significantly lower stability at 10 °C (RC Tyler and BF Volkman, unpublished results).</p><p>To confirm that the absence of dual-state behavior was caused by the introduction of a novel disulfide link between C36-C49, we analyzed 3D NMR spectra (i.e. 13C, 15N, 1H) in order to determine 13Cα and 13Cβ chemical shift values, which indicate the oxidation state of cysteine residues in proteins. Specifically, if the Cβ chemical shift is less than 32.0 ppm or greater than 35.0 ppm, the redox state is assigned to reduced or oxidized, respectively 16. Analysis of NMR spectra indicated that all four cysteine residues of CC5 are in the oxidized state (13Cβ > 35 ppm), consistent with the presence of both the original XCL1 disulfide bond and the engineered C36-C49 disulfide (data not shown).</p><p>The assigned HSQC spectrum for CC5 is displayed in Supplemental Figure 1. All backbone and side chain chemical shifts for CC5 were verified or assigned from analysis of standard triple resonance experiments described in material and methods. The complete set of NMR data was deposited into the Biological Magnetic Resonance Bank (BMRB) under accession number 25693 and the RCSB Protein Data Bank (PDB: 2n54).</p><!><p>The structure of CC5 was determined by NMR spectroscopy at 40°C. All distance restraints used in the calculation were derived from 15N-edited and 13C-edited NOESY spectra. Resulting NOE cross peaks were assigned using an established protocol described in material and methods. Intermolecular NOEs between dimer subunits were identified from 13C-12C filtered NOESY spectra allowing determination of quaternary structure (Supplemental Figure 2). After final analysis a total of 1456 NOE distance restraints were determined and used in the calculation of the CC5 structure. A stereo view of the final ensemble of 20 conformers (PDB: 2n54) is shown in Fig. 2A, with a close up view of the engineered disulfide bond between C36-C49 shown in Fig. 2B. A summary of all experimental restraints and structural statistics generated for the ensemble is presented in Table 2.</p><p>The mutant structure displayed the expected 4 β-strands per subunit with the dimer displaying a two-fold axis of symmetry approximately perpendicular to the β-sheet. Comparison of the overall geometries comprising the mutant and WT-XCL1dim folds revealed very similar regions of secondary structure (Fig. 2C) producing a Cα backbone alignment RMSD of 1.9 Å (residues 2-55). It is noted that the engineered C36-C49 disulfide bond linking the β2/β3 strands is buried within the hydrophobic core of the protein (Fig. 2B). This is consistent with the location of the analogous alanine methyl groups found within the wild-type dimer fold and signifies that the engineered disulfide introduced minimal distortion of structural elements relative to the XCL1dim structure.</p><!><p>The conformational dynamics of CC5 was probed by H/D exchange NMR. This experiment relies on chemical exchange between amide protons and NMR silent deuterons giving an indication of solvent accessibility of the protein backbone. Previous experiments with XCL1 are consistent with extremely rapid H/D exchange regardless of pH, temperature, or salt concentration indicating little protection of backbone amide protons within the dead time of the experiment (~ 5 min) 11. The lack of observable signal can be rationalized given the presence of metamorphic interconversion that likely requires global unfolding 10. This unfolding allows for exposure of backbone amide hydrogen to solvent and results in the rapid exchange of 1H-15N signals. The CC3 variant was designed to lock XCL1 into the XCL1mon conformation with the addition of a disulfide that prevents unfolding and interconversion 9. As expected, H/D exchange analysis of CC3 showed significant protection from amide exchange as evidenced by the persistence of 1H signal ~ 20 minutes after deuterium addition (Fig. 3A). HSQC analysis comparing CC3 in both 10% D2O and 100% D2O solutions revealed 13 protected residues (Fig. 3B & C). Peak intensities for protected residues were measured from HSQCs collected over a time frame of 24 hours and used to calculate exchange rates (kex), protection factors (P), and free energy values (ΔG) (Supplemental Table 1). Residues that displayed the highest logP values (Fig. 3D) were localized in or near the hydrophobic pocket within the α-helix and the 1st and 2nd β-strands (Fig. 3E).</p><p>The W55D variant was originally designed to bias the XCL1 conformational equilibrium toward the XCL1dim by disrupting the XCL1mon hydrophobic core 8. Attempts to measure H/D exchange rates for W55D after addition of 100% D2O yielded no observable 1H and HSQC signals after addition of 100% D2O within the dead time of experiment (~ 5 min) (Fig. 3 F-H). This result suggests that, although the W55D mutation favors the XCL1dim conformation (Fig. 3G & I), the XCL1dim-unfolded transition leads to rapid solvent exchange as previously observed for WT-XCL1. In contrast, H/D exchange measurements on CC5 yielded significant 1H protection (Fig. 3J) with HSQC analysis identifying 9 protected residues (C36, V37, I38, F39, I40, T41, K46, V47, and C48) upon 20 minutes of 100% D2O exposure (Fig. 3K & L and Supplemental Table 1). These residues are localized to the internal strands (β2 and β3) of the dimer (Fig. 3M & N) and suggest that global unfolding of CC5 is significantly slowed relative to WT-XCL1 or W55D, by the engineered intramolecular disulfide bond.</p><!><p>As the ability of chemokines to bind extracellular GAGs is largely dependent on surface exposed arginine, lysine, and histidine residues, the electrostatic surfaces comprising the structured regions of the XCL1dim and the CC5 variant are displayed in Fig. 2D. Based on the structure it was apparent that the charge distribution of the disulfide mutant was similar to WT-XCL1dim, suggesting that the mutant construct would also be capable of high affinity GAG binding. Reported heparin binding affinities (Kd) for XCL1 derived from surface plasmon resonance (SPR) measurements range from about ~20 to 90 nM 17, 18 Therefore the ability of CC5 to bind to heparin was also investigated by SPR in order to determine binding affinity. In these experiments several concentrations of CC5 were applied to a heparin-coated SPR chip and resulting sensorgrams were analyzed. Initial measurements revealed robust responses of CC5 toward the heparin-coated SPR chip indicating significant interaction (RU > 10) (Fig. 4A). However, the response curves appeared to display multiphasic kinetic behavior indicative of complex binding, which precluded analysis using a simple bimolecular interaction model. This same phenomenon had been observed previously in similar SPR investigations involving XCL1/GAG interactions 18. As a result, an apparent Kd value (Kd') was calculated from nonlinear fitting of the dose-dependent steady-state response of the interaction (Rmax) (Fig. 4C). This equilibrium binding analysis yielded an affinity for heparin of approximately 59 nM, consistent with previous measurement of XCL1-GAG binding and confirming that CC5 preserves the high-affinity GAG binding conformation. For comparison with a disulfide stabilized version of the XCL1 chemokine fold, we measured heparin binding to the CC3 monomer under identical solution conditions used to investigate CC5 (Fig. 4B). Despite the fact that CC3 retains the same overall charge and number of Arg, Lys, and His residues as WT-XCL1, SPR measurements yielded a Kd' ~ 1 μM, approximately 15 times weaker than dimeric CC5 (Fig. 4C).</p><p>We also noted a disparity in Rmax at equivalent concentrations of CC5 and CC3. The recent work of Salanga and co-workers investigated the role of chemokine oligomerization in GAG binding and observed a similar reduction in SPR response units when a non-oligomerizing variant of CCL2 (P8A) was applied to a heparin surface 19. This reduced SPR signal associated with P8A-CCL2 was accompanied by a 10-fold reduction in heparin binding relative to WT-CCL2, illustrating the role of chemokine self-association in modulating GAG binding affinity 19. We conclude that the dramatically lower Rmax of CC3 derives from the lack of XCL1 self-association, whereas CC5 can form a more extensive chemokine-GAG interface and may interact with multiple heparin molecules. Taken together, SPR analysis of heparin binding indicates that the alternative XCL1 dimer is the dominant GAG binding structure and that oligomerization of the chemokine is critical for high-affinity XCL1-GAG interactions.</p><!><p>In a recent publication Guzzo, et al. demonstrated that both WT XCL1 and the W55D dimer were potent inhibitors of HIV-1 infection in human peripheral blood mononuclear cells (PBMCs) and in an engineered target cell line 14. Analysis of the locked monomer (CC3) revealed that the canonical chemokine fold of XCL1 displayed no anti-HIV inhibition 14. However, due to the relative instability of the W55D variant, it remained to be determined whether the HIV-1 inhibition is specifically associated with the dimeric conformation or results from access to an unfolded state. To resolve this question, HIV-1 inhibition assays were performed by comparing the effects of CC5 with those of WT, W55D, CC3, and an unfolded variant (C11A-C48; CC0) of XCL1. The CCR5 chemokine ligand CCL5 and the CXCR4 ligand CXCL12 were included as positive and negative controls, respectively, for competitive inhibition of chemokine receptor binding by the BaL (CCR5-tropic) HIV-1 isolate. CC5 as well as the variants that have access to the XCL1dim exhibited HIV-1 inhibition, indicating that anti-viral activity is associated with the dimeric all-β structure of XCL1 (Fig. 4D). Both CC0 and CC3 were ineffective at HIV-1 inhibition. These results show that the anti-HIV activity is associated exclusively with the alternative dimer conformation and not with the canonical chemokine monomer or the unfolded state of XCL1.</p><!><p>XCL1 defines a new category of metamorphic proteins, polypeptides that interconvert between unrelated structures in the native state 8, 20. Since these structures carry out distinct functions, protein metamorphosis represents a new and potentially widespread regulatory mechanism. However, because conventional structural methods tend to exclude or obscure metamorphic proteins, the sequence characteristics that define them remain to be uncovered. In parallel with our efforts to discover the origin of metamorphic XCL1 folding, we are systematically defining structure-function relationships for each accessible conformation.</p><p>Because XCL1 unfolds and interconverts readily between its metamorphic states, stabilized variants for the monomer and dimer are needed for functional studies of each conformation. XCL1 CC3 preserves XCR1 binding and activation, while W55D has been used to represent the XCL1dim state and study XCL1-GAG interactions and anti-HIV activity. W55D is distinct from the disulfide-locked CC3 variant because it accesses an unfolded intermediate state that is more analogous to WT-XCL1 interconversion. In this work, we developed a new disulfide-locked variant (CC5, A36C-A49C) that adopts the XCL1 dimer structure with a longer lifetime than the W55D single mutant, as gauged by H/D exchange, and retains high affinity GAG binding. At lower temperatures, both W55D and CC5 display reduced stability (Fig. 1D), suggesting that these variants retain essential thermodynamic characteristics of the wild-type XCL1dim conformation. With the advent of CC5, we now have a toolkit of variants designed to isolate different subsets of the XCL1 conformational states sampled by the native metamorphic protein (Table 1).</p><p>Recent studies have shown that XCL1 is important for facilitating inflammatory interactions between XCR1+ dendritic cells and CD8+ positive T cells as well as mediating a homeostatic role in the maintenance of self-tolerance through the development of T regulatory (Treg) cells in the thymus 21. While the specific role remains to be determined, we speculate that metamorphic XCL1 interconversion is necessary for its roles in antigen presentation and immune system development. Conformationally restricted XCL1 variants, like CC5, are valuable tools for elucidating the biological relevance of the XCL1 native-state interconversion.</p><p>Besides the chemoattractant and GAG-binding functions of XCL1, we are beginning to understand the role of metamorphic equilibrium in the context of viral infection. XCL1 variants that access the dimeric β-sheet fold (XCL1dim) are potent inhibitors of HIV-1 infection, while the canonical chemokine fold (XCL1mon) is ineffective. XCL1 is able to block HIV-1 infection via an unconventional mechanism which is not mediated by engagement of viral co-receptors 22, but rather by direct binding to the external viral envelope glycoprotein, gp120 14. Taken together, this work further established unique roles for the different structural states of XCL1 in anti-HIV activity. In addition to HIV, other viruses such as herpesviruses have been shown to disrupt/exploit XCL1-XCR1 signaling through the production of XCL1 and XCR1 mimics 18, 23, 24. It is reasonable to speculate that the conformational states of XCL1 may be implicated in other pathogenic pathways. Perhaps microbial evolutionary pressures have selected for the unique conformational equilibrium of XCL1 and access to the XCL1dim state to combat invading microbes. Our future goal is to use CC5 and other XCL1 variants to better understand XCL1 biology in the context of its direct antimicrobial activity and XCR1-mediated immune functions.</p><!><p>Site directed mutagenesis was performed on a pQE30 vector containing WT-XCL1 using pairs of complementary primers and the QuickChange kit to incorporate cysteine to alanine mutations at positions 36 and 49 relative to the protein primary sequence. The resulting plasmid was transformed into SG13009 (pREP4) and grown on M9 minimal medium containing 15N-ammonium chloride and 13C-glucose as the sole nitrogen and carbon sources. Cultures were grown to OD ~1 and protein expression induced by addition of 1mM isopropyl β-D-1-thiogalactopyranodise at 37°C. Subsequent purification and refolding of CC5 as well as the other XCL1 variants was accomplished following established methods that have been previously described17.</p><!><p>NMR experiments were performed at 40°C on a Bruker DRX600 equipped with a triple-resonance cryogenic probe. Samples were prepared in 20 mM NaHPO4, pH 6.0 at ~1 mM protein concentration. Complete resonance assignments were derived from the following experiments: HNCA, HNCO, HNCACB, HNCOCA, HNCACO, CCONH, HBHACONH, HCCONH, HCCH-TOCSY. Data processing and analysis were performed using NMRPipe 25 and XEASY software packages 26.</p><!><p>Structures of CC5 were calculated using NOE distance restraints obtained from 3D 15N-edited NOESY-HSQC, 13C(aliphatic)-edited NOESY-HSQC, 13C(aromatic)-edited NOESY-HSQC, and 13C-12C-filtered NOESY-HSQC (Supplemental Figure 2). Additional backbone dihedral angle restraints (ϕ and ψ angles) were derived from 1Hα, 13Cα, 13Cβ, 13CO, 15N chemical shift data using TALOS 27. Structures were generated in an automated manner using the NOEASSIGN module of the torsion angle dynamics program CYANA 2.1 28. This method produced an ensemble of high precision structures that required minimal manual refinement. The 20 CYANA conformers with lowest target function were further refined by a molecular dynamics protocol in explicit solvent 29 using XPLOR-NIH 30.</p><!><p>Purified, recombinant proteins (XCL1 W55D, CC5, and CC3) were analyzed for structural stability by hydrogen/deuterium (H/D) exchange NMR. Lyophilized protein were initially suspended in 20 mM NaHPO4 (pH 6.0) with 10 % D2O to a final concentration of 1 mM. 1D 1H and 2D 1H-15N HSQC were collected at 25 °C on a Bruker Avance 500 MHz spectrometer to verify the protein structure. These samples were dried on a speed vac in the buffer solution and resuspended in 100 % D2O. A series of 2D 1H-15N HSQCs were collected over time (~24 hours) at 25 °C to monitor the persistence of amide hydrogen signals. Peaks that demonstrated prolonged protection from deuterium exchange were identified based on previous NMR assignments and analyzed by non-linear fitting of an exponential decay function to calculate H/D exchange rates (kex) using ProFit software (QuantumSoft). Protection factors (Log(P)) and free energy values (ΔG) were calculated using FBMME HD exchange spreadsheets (http://hx2.med.upenn.edu).</p><!><p>Analysis of XCL1/heparin interactions was done using a BIAcore 3000 instrument equipped with a C1 sensor chip. The C1 sensor chip was prepared by activation with a 1:1 mixture of NHS and EDC (300 μL at 20 μL/min), followed by immobilization of Neutravidin (Invitrogen) at 20 μL/min in 10 mM NaOAc, pH 6, then deactivation of excess groups with ethanolamine and finally washing of the surface with 10 mM NaOAc, pH 5.5 buffer prior to heparin addition. Biotinylated heparin (Sigma) was applied to the activated C1 chip to saturation based on response units. For analysis, varying concentrations of protein (50, 100, 200, 250, 400, 500, 750, and 1000 nM) was applied to the chip for 5 min at 40 μL/min in SPR running buffer (10 mM HEPES, 150 mM NaCl, 3 mM EDTA, 0.05% Tween-20, pH 7.4), followed by 5 min of dissociation. Surfaces were regenerated after each injection using 0.1 M glycine, 1 M NaCl and 0.1% Tween-20, pH 9.5 (31). All K'd values were determined from steady state analysis using an average of maximum response value generated for each protein concentration, and fit with the BIAevaluation software (BIAcore) using a 1:1 association model.</p><!><p>The BaL (R5-tropic) laboratory strain of HIV-1 was used to infect human PBMCs as previously described 14. Briefly, PBMCs were isolated from individuals and activated with PHA and IL-2 for 72 hrs. The cells were resuspended in RPMI + 10% FBS + 20 U/mL of IL-2 and plated at 2 × 105 cells/well in a round-bottom 96-well plate in duplicate. HIV-1 infection was initiated by addition of 100 pg of p24 Gag antigen to each well. Infected cells were incubated with and without XCL1 proteins in concentrations ranging from 0.12 – 1 μM. HIV replication was assayed by measuring the extracellular release of p24 Gag protein in supernatants harvested from cell cultures 4 days post-infection. The Alpha (Amplified Luminescent Proximity Homogenous Assay) technology immunoassay (AlphaLISA HIV p24 Research Immunoassay Kit, PerkinElmer) was used to measure p24 levels. Data were normalized to control cultures and values are represented as mean and standard deviation of three replicates.</p>

PubMed Author Manuscript

Boronic acid–DMAPO cooperative catalysis for dehydrative condensation between carboxylic acids and amines

Arylboronic acid and 4-(N,N-dimethylamino)pyridine N-oxide (DMAPO) cooperatively catalyse the dehydrative condensation reaction between carboxylic acids and amines to give the corresponding amides under azeotropic reflux conditions. This cooperative use is much more effective than their individual use as catalysts, and chemoselectively promotes the amide condensation of (poly)conjugated carboxylic acids. The present method is practical and scalable, and has been applied to the synthesis of sitagliptin and a drug candidate.

boronic_acid–dmapo_cooperative_catalysis_for_dehydrative_condensation_between_carboxylic_acids_and_a

Introduction<!>Results and discussion<!>Conclusions

<p>The catalytic dehydrative condensation reaction between carboxylic acids and amines is one of the most ideal methods for synthesizing the corresponding amides. 1 In 1996, Yamamoto et al. reported the rst example of the dehydrative amide condensation reaction catalysed by meta-or para-electron-decient group-substituted phenylboronic acids such as 3,4,5-tri-uorophenylboronic acid (1) (pK a ¼ 6.8) 2a and 3,5bis(triuoromethyl)phenylboronic acid (2) (pK a ¼ 7.2) 2b under azeotropic reux conditions (Scheme 1). 3 These boronic acids are more acidic than phenylboronic acid (pK a ¼ 8.8, 8.9). 2 In 2006, Whiting et al. reported that ortho-Brønsted basesubstituted phenylboronic acids such as 2-(N,N-diisopropylaminomethyl)phenylboronic acid (3) were effective catalysts for the amide condensation of aromatic carboxylic acids under the same conditions as above. 1a,4 In 2008 and 2012, Hall et al. reported that 2-iodophenylboronic acid (4a) and 2-iodo-5methoxyphenylboronic acid (4b) were also effective catalysts for the amide condensation in the presence of drying agents (activated 4 Å molecular sieves) at lower temperature. 5 The o-iodo group of 4a and 4b assists the catalysis of amide condensation as a weak base. 6 In addition to these boronic acids, boric acid, 7a,c benzo[1,3,2]dioxaborol-2-ol, 7b methylboronic acid, 7d and some o-Brønsted base-substituted boronic acids 8 have been reported to be useful as amidation catalysts. However, the substrate scope is still quite limited. For example, harsh conditions (higher temperature, prolonged reaction time, excess amounts of substrates, increased amounts of catalysts, etc.) are required for sterically hindered a-branched carboxylic acids and conjugated carboxylic acids. In 2013, Whiting et al. discovered an interesting synergistic catalytic effect between o-tolylboronic acid (50 mol%) and o-nitrophenylboronic acid (50 mol%) in dipeptide synthesis. 3f To the best of our knowledge, this was the rst example of two cooperative promoters for direct amidation. 3f,9 In the process catalysed by arylboronic acid, a mixed anhydride intermediate 5 is generated from the carboxylic acid and arylboronic acid under azeotropic reux conditions or in the presence of drying agents in the rst stage (Schemes 1 and 2). This is the rst activation of the carboxylic acid. a tetrahedral intermediate 6, the amide condensation may proceed more rapidly. However, if Nu preferentially coordinates as a Lewis base to the boron atom of 5, a less active species 8 is generated and the amide condensation may be suppressed.</p><p>Here we report that arylboronic acids and N,N-dimethylaminopyridine N-oxide (DMAPO) cooperatively promote the dehydrative condensation between various carboxylic acids and amines.</p><!><p>First, the amide condensation reaction between 2-phenylbutyric acid and benzylamine was examined in the presence of 5 mol% each of boronic acid 2 and a nucleophilic additive under azeotropic reux conditions in uorobenzene (bp. 85 C) 3f for 17 h (Table 1). Boronic acid 2 did not promote the reaction in the absence of additive under these conditions (entry 1). Tertiary amines such as N,N-diisopropylethylamine and 4-(N,N-dimethylamino)pyridine (DMAP) 11 were not effective as additives (entries 2 and 3). 4-Methoxypyridine N-oxide (MPO) was also less active (entry 4). In contrast, a more nucleophilic but weak base, DMAPO, 12 was quite effective for the amide condensation (entry 5). However, a more nucleophilic additive such as 4-(pyrrolidin-1-yl)pyridine N-oxide (PPYO) was less effective than DMAPO (entry 6), perhaps because the strong nucleophilicity of PPYO might reduce the activity of 7.</p><p>Next, the cooperative effects of several boronic acids (5 mol%) were compared in the condensation reaction between 2-phenylbutyric acid or benzoic acid and benzylamine in the presence of DMAPO (5 mol%) (Table 2). These less reactive carboxylic acids were not activated by the individual use of boronic acids under the same conditions. As expected, 2-DMAPO and 4b-DMAPO efficiently activated 2-phenylbutyric acid (entries 1 and 3). Phenylboronic acid and 3 were almost inert, even in the presence of DMAPO (entries 2 and 4). Interestingly, 2-DMAPO was more effective than 4b-DMAPO for the amide condensation of benzoic acid (entries 1 and 3). While Whiting's catalyst 3 was quite effective for the amide condensation of benzoic acid, the catalytic activity of 3 was suppressed in the presence of DMAPO (entry 4). 13 To explore the substrate scope using the cooperative catalysts, 2-DMAPO, the amide condensation reactions of several less reactive a-branched carboxylic acids and arenecarboxylic acids were examined under azeotropic reux conditions in uorobenzene (bp. 85 C) or toluene (bp. 110 C). As shown in Table 3, in each example, the cooperative catalysts were much more effective than 2 alone, the results for which are shown in brackets. Notably, not only aliphatic primary amines but also sterically hindered aliphatic secondary amines, less nucleophilic anilines and alkoxyamines reacted with these carboxylic acids. In particular, 2-DMAPO was effective in the amidation of arenecarboxylic acids with sterically hindered amines, in comparison with 3 and 4b (entries 9-14). This cooperative method is scalable to practical volumes: the catalytic loading of 2-DMAPO could be reduced to 2.5 mol% for the dehydrative condensation on an 80 mmol scale (entry 4). a A solution of 2-phenylbutyric acid (0.5 mmol) and benzylamine (0.5 mmol) in uorobenzene was heated in the presence of 2 (5 mol%) and additive (0 or 5 mol%) under azeotropic reux conditions. b Isolated yield. The boronic acid-catalysed condensation of relatively more reactive a-nonbranched carboxylic acids with sterically hindered secondary amines and less nucleophilic anilines proceeded even in the absence of DMAPO, as shown in brackets in Table 4.</p><p>Nevertheless, the addition of DMAPO was also quite effective for these reactions. Interestingly, 4b and phenylboronic acid were slightly more reactive than 2 in the presence of DMAPO. In particular, the utility of inexpensive phenylboronic acid is industrially signicant. This catalytic method is readily scalable. 2.5 g of N-Boc protected sitagliptin, 14 an anti-diabetic drug, was obtained by carrying out the condensation on a 5 mmol scale (entry 4).</p><p>The results in Tables 1-4 suggest that both the nucleophilicity of the additive and the Lewis acidity and steric effect of the boronic acid are important in the cooperative catalysis with an ArB(OH) 2 -nucleophilic base (Table 5). The reactivity from highest to lowest followed the order arenecarboxylic acids, a-branched carboxylic acids, a-nonbranched carboxylic acids. As a result, 2 was more effective for arenecarboxylic acids and a-branched carboxylic acids. On the other hand, 4b and phenylboronic acid were more effective for a-nonbranched carboxylic acids.</p><p>The amide condensation reaction should occur through the active intermediate 6 (Scheme 2). However, not only 6 but also a Unless noted otherwise, 0.5 mmol of carboxylic acid and 0.5 mmol of amine were used in the presence of 5 mol% of 2 and 0 or 5 mol% of DMAPO. b The results when both catalysts were used are shown. For comparison, the results without DMAPO are shown in brackets. c 10 mol% of each of the catalysts was used. d 2.5 mol% of each of 2 and DMAPO was used on an 80 mmol scale in 70 mL of toluene. e 15 mol% of each of the catalysts was used. f 99% ee. g 3 was used. h 4b was used. a Unless noted otherwise, 0.55 mmol of carboxylic acid and 0.50 mmol of amine were used in the presence of 5 mol% of ArB(OH) 2 and 0 or 5 mol% of DMAPO. b The results when both catalysts were used are shown. For comparison, the results without DMAPO are shown in brackets. c 10 mol% of each of the catalysts was used. d The reaction was carried out at a 5 mmol scale.</p><p>Table 5 Relationship between the cooperative effects of boronic acid-DMAPO and the reactivity of carboxylic acids</p><p>the undesired complex 8 would be generated in an equilibrium mixture. Complex 8 might be converted to the more stable complex 9, which is inert to the amide condensation. In fact, the generation of inert complex 9 was ascertained by 11 B and 1 H NMR analysis in the amidation of less-hindered carboxylic acids. 15 Also, the chemical structure of the cyclic complex prepared from 2, phthalic acid, and DMAPO was determined to be that of 9z by X-ray diffraction analysis (Fig. 1). 16 For sterically hindered carboxylic acids such as arenecarboxylic acids and a-branched carboxylic acids, the desired intermediate 7 was preferentially generated. Thus, o-nonsubstituted and m-or p-electron-decient group-substituted phenylboronic acids such as 1 and 2 were more suitable. In contrast, for less sterically hindered a-nonbranched carboxylic acids, the undesired complex 9 was generated more easily. In addition, the strong Lewis acidity of 2 helped to stabilize 9 by the tight coordination of DMAPO to the boron centre. This is why 4b and phenylboronic acid were slightly more effective than 2 for the condensation of a-nonbranched carboxylic acids. Not only Lewis acidity, but also the bulkiness of the o-substituent of the boronic acid might suppress the generation and stability of 9. It is noted that the effect of DMAPO was not striking at ambient temperature. Heating was required to accelerate the equilibrium between 6 and 8.</p><p>The utility of the cooperative catalysts was also demonstrated for the selective amide condensation of b-substituted acrylic acids to give the corresponding amides 10 (Table 6). The production of Michael adducts 11 was fairly minimal. In contrast, when boronic acids were used in the absence of DMAPO, the yield and selectivity of the reaction for 10 were moderate. Control experiments ascertained that 10 (n ¼ 1) was selectively obtained from 13, 17 and 11 (n ¼ 1) was not generated from 10 (n ¼ 1) but 14. Amide 10c is known to be a potential antimitotic agent, especially for brain cancers (entry 6). 18 The cooperative catalysts were effective for the selective amide condensation of not only b-substituted acrylic acids, but also polyconjugated carboxylic acids and but-2-ynoic acid (entries 12-17).</p><!><p>In conclusion, this new cooperative catalytic system is quite effective for the amidation reaction of less reactive carboxylic acids, such as sterically hindered a-branched carboxylic acids and arenecarboxylic acids, and the chemoselective amidation reaction of conjugated carboxylic acids. Based on the NMR spectra and X-ray diffraction analysis of inert species 9, a preliminary mechanism was proposed. Further mechanistic studies are in progress. We believe that these ndings will trigger the further development of high-performance amidation catalysts.</p>

Royal Society of Chemistry (RSC)

Identifying Therapeutic Compounds Targeting RNA-Dependent-RNA-Polymerase of Sars-Cov-2

COVID-19 has emerged as the biggest threat of this century for mankind. This contagious disease was initially transmitted from animals (probably bats or pangolins) to humans and later it spread across the globe through human to human transmission. Scientists rushed to understand the structure and mechanism of the virus so that antiviral drugs or vaccines to control this disease can be developed. A key to stop the progression of the disease is to inhibit the replication mechanism of Sars-Cov-2. RNAdependent-RNA polymerase protein also called RdRp protein is the engine of Sars-Cov-2 that replicates the virus using viral RNA when it gains entry into the human cell. The replication of the virus is the main process that acts as a catalyst in the progression of disease. RdRp is the main target of researchers working to develop antiviral drugs to inhibit the mechanism of the virus. Numerous drugs proposed for the treatment of COVID-19 such as Camostat Mesylate, Remdesivir, Famotidine, Hesperidin, etc. are under trial to analyze the aftermath of their medicinal use. Nature is enriched with compounds that have antiviral activities and can potentially play a pivotal role to inhibit this virus. This study focuses on the phytochemicals that have the potential to exhibit antiviral activities. A large number of compounds were screened and a cohort of most suitable ones are suggested via in-silico evidence that can inhibit the functionality of RdRp and hence the replication of Sars-Cov-2.

identifying_therapeutic_compounds_targeting_rna-dependent-rna-polymerase_of_sars-cov-2

Introduction:<!>Structure of SARS-COV-2:<!>Subdomain<!>Related Work:<!>Phytochemicals Preparation:<!>Receptor Protein Preparation:<!>Virtual Screening:<!>Molecular Docking:<!>Universal force field (UFF) Optimization:<!>Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method:<!>Visualization:<!>Molecular Dynamic Simulation:<!>Electrostatic Potential Calculation:<!>Results:<!>Naringin (PubChem ID: 442428):<!>Desacetylnimbinolide (PubChem ID: 102285346)<!>Sennaglucosides (PubChem ID: 5199)<!>4-({3,4-dihydroxy-5-[(3,4,5-trihydroxybenzoyl)oxy]benzoyl}oxy)-1-hydroxy-3,5-bis[(3,4,5trihydroxybenzoyl)oxy]cyclohexane-1-carboxylic acid (PubChem ID: 442676)<!>1) Naringin & Sennaglucosides:<!>Figure 12 Combined Interaction of Sennaglucosides and Naringin<!>Figure 13 Combined Interaction of Sennaglucosides and Desacetylnimbinolide<!>3) Desacetylnimbinolide and Naringin:<!>5) Sennaglucosides, Cyclohexane-1-carboxylic acid and 8-difluoro-7-hydroxy chromen-4one:<!>Electrostatic Potential Distribution:<!>Molecular Dynamic Simulation Analysis:<!>Suggested Combination:<!>Conclusion:<!>Future Work:

<p>Coronavirus is a vast family of viruses. 7 known coronaviruses can enter into human cells. The first case of coronavirus in humans was reported in 1965, which had mild symptoms of flu and fever. Coronaviruses are significant pathogens for both humans and animals. These are medium-sized but can have a very large RNA genome. They can bind with the host cells and mutate when they transfer from one species to another. Subsequent mutation can lead to its transmission into humans. They can bind themselves to the respiratory tract causing an infection. The symptoms of coronavirus infection are: illness, flu, mild fever, diarrhea, and difficulty in breathing. Severe Acute Respiratory Syndrome named as Sars is an infectious disease caused by Sars-CoV that spreads swiftly and causes illness and flu at the initial stage. Sars-Cov-2 is just like Sars-Cov in its working and structure but more dangerous in terms of severity. It spreads from person to person through coughing or sneezing droplets and physical contact. In 2019, Sars-CoV-2 emerged from Wuhan, China, and took the world by storm. The world was not prepared for it and as a result, both humans and the world economy have suffered very adversely. At this point, over 9.5 million infected cases have been reported and the death toll has reached over 480,000. The onset of COVID-19 has led to a drastic reduction in social and economic activities throughout the world. At this point, doctors and researchers from every country are trying hard to devise an effective strategy for controlling the disease. To propose an effective and long-lasting solution, understanding of the structure of the virus and its action is very important. Recent studies have been able to develop an understanding of the mechanism and structure of the virus through 3D modeling.</p><!><p>To get an insight into the action of Sars-Cov-2 viruses and discover suitable antiviral compounds, it is very important to elucidate the proteomic buildup of Sars-CoV-2. Its proteomic data encompasses different proteins that form its makeup such as the Spike and RNA dependent RNA polymerase (RdRp) proteins. The entire Genome RNA structure inside the coronavirus is nearly 30000 bases long. As a whole, it contains 4 proteins that form the viral envelope which are the Spike protein, E protein, Hemagglutinin (M) protein, and N protein. It is very important to understand how Sars-Cov-2 gains entry into the human cells. Angiotensinogen is a hormone found inside the liver which is also found in kidneys and different segments of the brain. This hormone is responsible for managing blood pressure. Angiotensinogen is converted into Angiotensin 1 also named AT-I, by an enzyme produced by a kidney called Renin. In the next step, this AT-I is converted into Angiotensin 2 which is named AT-II by an enzyme called ACE which is produced in the lungs. AT-II is a vasoconstrictor that means it narrows the blood vessels, as a result, aldosterone is produced which causes an increase in blood pressure. AT-II creates two states in the body, one is a low state in which ACE2 binds with Angiotensin Receptor I also named ATR-1 on the surface of the membrane. As a result of this ACE2-ATR-I binding, ACE2 creates Angiotensin 17 (AT-17) which is responsible for vasodilation and decreases inflammation which is good for the human body.</p><p>The second state is called high state in which due to the high level of AT-II, it does not allow to bind ATR-I with the sites of ACE2 resulting in a gap on the site of ACE2. Because of this gap, Spike protein at the surface of Sars-Cov-2 finds sites to attack, it binds with the sites of ACE2 where ATR-I did not bind and Sars-Cov-2 anchors itself to an entry point into the human cell. It is worth mentioning here that Spike binds with Human Ace with an affinity of -21 kcal/mol, if the spike is to be targeted, ligand must have a binding affinity of more than -21 kcal/mol, which makes it almost impossible to find such a ligand which could bind with Spike protein with a higher binding affinity [1]. This leads to the conclusion that finding ligands that target Spike proteins may not prove fruitful. After entry, the virus needs to replicate itself so that it can propagate itself within its host cells. The RdRp protein plays a pivotal role during this replication process. RdRp is the most significant gene in the virus genome which is encoded inside the RNA of the virus, it speeds up the process of RNA replication from the RNA template and provides safe passage to the virus that is just entered into human cells.</p><p>Endoplasmic reticulum is a system of membrane that performs multiple functions i.e. Modification, folding, and transfer of proteins. After entering into the human cell, the virus contacts this system and persuades the development of a double-membrane vesicle by developing a complex with it. It generates a copy of genomic RNA. Further, it converts this Negative RNA to positive RNA which makes it mRNA. But this mRNA cannot replicate by itself and translate into a protein. The virus exploits the ribosome machinery of the human cell. The ribosome is tricked into working for the virus and translates the mRNA, creating viral proteins in thousands in each replication cycle. These viral proteins are received by the Golgi apparatus which pack them into vesicles and later send to different destinations. In this way, the whole protein creation apparatus of a human cell is used by the virus for its multiplication. Below figure 2 is the illustration of virus attachment and replication mechanism. As depicted in figure 3 below, RdRp which is also Non-Structural Protein 12 (nsp12) illustrated in the complex with two small proteins nsp7 and nsp8 and has right-hand cup structure with palm subdomain, thumb subdomain, and fingers subdomain. Table 1 shows the range of residues that cover palm, fingers, and thumb subdomains in the structure of RNA-Dependent-RNA-Polymerase.</p><!><p>Residue Range Palm T582-P620 and T680-Q815 Fingers L366-A581 and K621 Thumb H816-E920</p><p>The Amino acid sequence of the Sars-Cov-2 genome in many respects resembles Sars-Cov that caused the SARS outbreak in 2002-2003. One strategy to inhibit the progression of the disease is to find ligands that target the RdRp protein. Antiviral drugs that can considerably compromise the function of RdRp protein will be able to suppress the viral multiplication and hence disease progression. Researchers are working to discover an antiviral drug that targets its key residues by splitting the strands of RNA that cause replications for the virus. In this way, its replication and connection with the virus could be inhibited compromising its proper functioning. Table 2 shows different Motifs and the residues that cover each motif along with the type of residues. [3]. A drug therapy that targets these residues of RdRp protein will be able to produce an antiviral effect by inhibiting its function. Moreover, clinically proven drugs like Remdesivir binds to THR 680, SER 682, and VAL 557, pp-sofosbuvir binds to ASN 691, ARG 555, and ASP 623 [3]. Binding details of both drugs will give an insight to discover potential compounds that can cover these binding residues as well as remaining residues that are not covered by Remdesivir and pp-sofosbuvir.</p><p>Phytochemicals are naturally occurring substances that can contain antiviral and antibiotic properties proving effective for the treatment of diseases. Several plants have therapeutic compounds for example compounds of Artemisia can inhibit tumor growth inside the body and can be used as anticancer substances. Compounds of Azadirachta are used as an antibacterial and for the treatment of skin diseases, stomach upsets, diabetes, fever, and different eye diseases [4]. Compounds of aconitum heterophyllum are antibacterial, antiviral, and anti-inflammatory, these are used for fever, flu, cough, upper tract respiratory diseases, and malaria. Many natural plants and herbs contain substances that have been used as antiviral, antioxidant, and antibacterial purposes for centuries. A large set of plants containing flavonoids, alkaloids, Vitamin C, Sennosides etc. have antiviral properties that can be effective for the treatment of disease. There are thousands of phytochemicals and natural substances whose structures are openly accessible in databases like PubChem, RCSB, chEMBL, and ChemSpider. Medicinal trials on these substances for a specific ailment can consume huge effort in terms of time and money and still required results may not be achieved. In-silico simulation techniques can considerably narrow down on the number of relevant substances through very accurate and meticulous modeling. These methods are capable of providing an insight into the compound structure, analyze physical and chemical properties, and predict the suitability of compounds against target receptors.</p><!><p>Different in silico methods have been used recently to simulate interactions and to evaluate the suitability of drugs for a specific disease. ModeBase was used to create the 3D model of Spike Protein to exhibit the binding of Angiotensin-Converting-Enzyme and Spike Protein. Docking was done by using different virtual screening methods through software named Schrodinger. Grid Generator tool was used to create a grid [5]. To analyze correlation, Claudia Cava et al. performed an analysis between Human Ace2 and other proteins by TCGA-LUAD to get all the possible interactions while path enrichment analysis is performed using a Fisher's test [6]. T. Joshi et al. used virtual screening to screen 318 phytochemicals to get a suitable compound to analyze the interaction with Human Ace2. PyMol is used to remove ions and water molecules. Open babel is used to convert the SDF format ligand file to PDB format. Rigid docking method is performed to get different conformations of ligand at different binding sites and in the results observed in Lig-Plot+ software [7]. Ammar D. et al. used Computer-Aided Design (CADD) to show the interactions of ligands and receptors. The study also showed that molecular docking is done to evaluate the interactions between Human enzymes and potential ligands. Molecular docking study and ADMET profiling is used to analyze the inhibitors. Homology modeling is used to develop a structure of a protein by using its sequence and then to perform structure-based virtual screening from a large number of chemical compounds AutoDock Vina is used. For binding residues and pockets, AutoDock Vina 4.2 is used [8]. Manoj Kumar et al. used Molecular Dynamic Simulation to study the structure of protein. Further, the DrugMint server is used to prepare drugs like ligands for screening, CASTp is used to calculate the pockets in the protein. Subsequently, autodock is used for docking ligands with receptors and analyzing binding affinity of every compound to set the threshold. Additionally, the comparative analysis of sequences was performed by Multalin [9]. Several researchers have also applied machine learning and artificial intelligence-based models to study the genomic properties [10]- [24].</p><p>In this study, a method is proposed to carefully examine Sars-Cov-2 specific antiviral properties of substances. Irrelevant or ineffective chemicals are screened out. The selected compounds are further scrutinized by different docking and interaction techniques. Based on these results the most suitable compounds are proposed for the treatment of COVID-19 that can be the potential therapeutic candidates for the treatment of COVID-19 and open broad-spectrum treatment for other RNA viruses.</p><!><p>The selection is performed by analyzing the properties of numerous plants. Then 3D chemical structure of 4596 phytochemicals obtained from natural herbs was extracted from databases like PubChem [25], ChemSpider [26], chEMBL [27] and IMMPAT [28]. Compounds converted from SDF format to PDB format using Open Babel. Subsequently, the phytochemical library is prepared for further processing.</p><!><p>The recent crystal structure of RdRp protein is retrieved from Protein Data Bank (PDB ID: 6M71). Molecule SARS-Cov-2 NSP 12 has one chain with 942 amino acids. Water molecules and hydrogen atoms were removed from the Receptor by using the MGL tools of Autodock Vina.</p><!><p>Virtual screening of phytochemical compounds is performed by the RPBS webserver to narrow down the potential structures that are likely to bind with the receptor. This server uses the AutoDock Vina package which is accurate and yields good screening results [30]. Grid Center Coordinates were set to: X=-2.3, Y=45.7, Z=28.6. The search space was set to: X=55, Y=55, Z=55. Listed compounds were uploaded on the server for virtual screening. Results with a binding energy of a vast number of compounds are analyzed and all ligands which had binding affinity numerically greater than -7 were discarded.</p><!><p>Suitable compounds that were selected from the results of virtual screening were further docked with the target Protein using AutoDock Vina. Grid box parameters were set to: X=-3.27, Y=44.29, Z=-28.65 and Dimensions were set to: X=35, Y=35, Z=35 (Angstrom). Universal Force Field (UFF) method was used for minimization which is more effective in finding the minimized energy than any other method. A webbased tool named admetSAR was used for profiling and finding drug similarity.</p><!><p>After loading ligands in Autodock Vina, UFF optimization is used to carry out the optimization of molecular geometry with the help of molecular mechanics. Method of energy minimization is used before the process of docking. This ensures that ligand's length, structure, and angles of bonds are precise before performing the docking process. This method provides good results with organic and inorganic compounds.</p><!><p>Broyden-Fletcher-Goldfarb-Shanno (BFGS) is used in autodock Vina for local optimization. This method helps to generate different conformers of ligands. Just like other optimization methods, BFGS also uses gradients with scoring function i.e. the derivative of functions with its arguments. In this situation, arguments contain position, orientation of ligands, and torsion values for effective bonds. This gradient is used to decide the direction of local optima. Before calculating the second derivative which may prove costly, BFGS estimates using top-level updates provided by gradient assessment. In the end, an optimized structure is chosen for selection and by using the Metropolis basis, the next iteration will start from this structure and if this structure scores better than the best available solution then this will be again optimized and will be used as the current best solution [31]. This search process continues until the limit of iterations is reached.</p><!><p>After the completion of the above process, the most suitable results and interaction of every Ligand are further analyzed to visualize the ligand-receptor binding sites in PyMol. PyMol is a very efficient visualization software that supports 2D and 3D structure of the complex and binding interactions as well as distance measurement with sequence information of protein and ligand.</p><!><p>Molecular dynamic (MD) simulation is widely used to evaluate the structural behavior and stability of the protein. In this study, Nanoscale Molecular Dynamic (NAMD) software is used to perform simulation on RdRp protein and proposed compounds [32]. The temperature of 310K was set for simulation. Configuration files were generated using the CHARMM website [33]. Parameter files were obtained using the CHARMM General Force Field (CGenFF) tool. Protein with complex was solvated using water molecules. The energy of the system was minimized for 2000 steps and dielectric was set to 1.0.</p><!><p>Electrostatic potential simulation was performed by PyMol which uses the Poisson-Boltzmann method based on cubic spline charge discretization. Solute dielectric and solvent dielectric were set to 2.0 and 78.0 respectively. Temperature was set to 310K whereas the solvent probe radius was 1.400 Å. Table 3 shows grid values that were used for the calculation of electrostatic charge of RdRp. Figure 4 shows the flow chart of the above-described methodology and a brief overview of the screening and selection process of phytochemicals that are extracted from different databases.</p><!><p>Table 3 shows the names of selected ligands along with the binding affinity value of each ligand. For each ligand, the residue sites of RdRp with which it binds to are also listed. All screened compounds have at least -7 binding affinity. Mentioned compounds are mostly naturally occurring substances and they have shown promising antiviral activities and very good binding affinity values with RdRp during the docking process. 4 shows a comparison of compounds selected under this criteria with some of those which are currently under trial. Ligands' names along with the binding details, distance of interaction from each binding site, and estimated inhibition constant is also shown. The comparison shows that the proposed compounds have very good binding affinity and bind to key residues to compromise the replication of RdRp. Below compounds (highlighted in Green) have better binding affinity and binding interaction with RdRp key residues than the compounds that are currently under trial i.e. Camostat Mesylate [34], Hesperidin [35] and Remdesivir [36].</p><!><p>Naringin is a bioflavonoid and it belongs to the family of flavonoids, it is found in citrus fruit and has exhibited antiviral, anti-inflammatory, and possesses antioxidant properties. It is used in the treatment of diabetes, hypertension, and metabolic syndrome. It has also shown anticancer effects as it behaves as suppressing or blocking agents in the treatment of cancer. It induces cell apoptosis and impedes cell proliferation in tumor cells of Bladder cancer, Breast cancer, and cervical cancer [37]. Figure 5 shows the chemical structure of Naringin.</p><!><p>This naturally occurring substance is extracted from plant Azadirachta Indica which is commonly known as Neem in the Indian subcontinent. It has been used in Chinese and Unani medicines for many years. It is enriched with antioxidants. It plays a vital role in anticancer management [38]. It is very safe for medicinal purposes and used in the treatment of diabetes, fever, and skin disease. Figure 6 depicts the structure of Desacetylnimbinolide.</p><!><p>This is the most effective substance found in this research. This is also a naturally occurring compound that is extracted from a plant called Alexandria Senna. Its leaves are used for medicinal purposes. It is used to treat constipation and has strong laxative effects. It is also used to empty the stomach before surgery and its medication is taken by mouth. It prevents the reabsorption of water and electrolytes which results in increment of fluids in the intestine. It is safe and well-tolerated. Figure 7 is showing the structure of Sennaglucosides.</p><p>Figure 7 Structure of Sennaglucosides [25] Famotidine (PubChem ID: 5702160)</p><p>Famotidine is used to decrease acids produced in the stomach and intestine. It is a histamine-2 inhibitor and used to treat ulcers in the stomach. It is also used for ZES (Zollinger-Ellison-Syndrome) in which the stomach produces excessive acids. It also prevents ulcers from coming back. It is available in the market with the name of Pepcid and its medication is taken by mouth. Figure 8 is showing the structure of famotidine.</p><p>Figure 8 Chemical Structure of Famotidine [25] Famotidone (PubChem ID: 129849878)</p><p>Famotidone can be used for hayfever, skin allergies, and itchy nose. It can also be used for the treatment of skin rashes for adults and children over 6 years. Figure 9 is showing the structure of famotidone.</p><!><p>This compound which is also named Quinic acid (multiplied acylated with galloyl moieties) is extracted from Eucalyptus bark. It is antiseptic and anti-inflammatory and used for the treatment of asthma. It contains a substance that kills bacteria. It is also used for skin diseases like skin ulcers and Gout. This compound is extracted from Rutaceae which belongs to the rue family of flowering plants. This is also found in citrus fruits like orange and lemon. It is used in many diseases like asthma, constipation, fever, and diarrhea. Proposed compounds can be used in combinations of 2 or 3 to inhibit the working of RdRp most effectively. Now we shall visualize results in combinations.</p><!><p>Figure 12, shows that a combination of two promising compounds (Sennaglucosides and Naringin) binds to 5 key binding sites of RdRp. Magenta and Green Color represent Sennaglucosides and Naringin respectively. Yellow dots illustrate binding interactions between the combination of compounds and RdRp.</p><!><p>To further understand the results, table 5 shows that Sennaglucosides and Naringin have inhibition constants of 685 nM and 488 nM respectively, and interact with 5 key sites (both compounds bind with ASP 623 simultaneously, that's why this binding site is neglected for Naringin) of RdRp protein. It is also shown in the table that the combination of Sennaglucosides and Naringin interacts with the key binding residues of RdRp with a good binding affinity of over -8.3. 2) Sennaglucosides and Desacetylnimbinolide:</p><p>Figure 13 shows the conformations of Sennaglucosides (Magenta) and Desacetylnimbinolide (Green) in the combination which best fit the key residues and cover over 10 binding sites but our main focus is key residues. This combination covers 6 binding residues and binding interactions are shown in yellow dotted lines.</p><!><p>Table 6 shows that Sennaglucosides binds to 4 key residues with an inhibition constant of 685 nM and Desacetylnimbinolide binds to 2 key residues with an inhibition constant of 0.146 nM and combination of both compounds can cover 6 key binding sites that are very important to inhibit the function of RdRp. This interaction with the key binding residues can halt the exponential growth of Sars-Cov-2 in human cells by compromising the function of RdRp.</p><!><p>In figure 14, the interaction of Desacetylnimbinolide (represented in Green) and Naringin (represented in Magenta) has been illustrated with key sites of RdRp. Binding interactions are shown in yellow dots. Table 7 shows that the combination of these two compounds covers 3 key binding sites (ARG-555 is common in both compounds' interaction, ARG 555 from Naringin is not included in Figure 13 and neglected in Table 8). The details of binding residues that each compound cover along with the binding affinity and inhibition constant is also shown. These compounds can be very effective for the treatment of RNA-related and antiviral diseases.</p><!><p>In figure 16, a combination of Cyclohexane-1-carboxylic acid which is also known as Quinic Acid (represented in Blue), Sennaglucosides (represented in Magenta), and 8-difluoro-7-hydroxy chromen-4one (represented in Orange) yields the best results in terms of binding to key residues. Table 9 shows that Cyclohexane-1-carboxylic acid (Quinic Acid) binds to 3 key residues, Sennaglucosides binds to 4 key residues, and 8-difluoro-7-hydroxy chromen-4-one binds to 1 key residue, making it an effective combination that binds to 8 key residues of RdRp collectively including ASN 691 and THR 680 which are very important and there are very rare compounds that bind to these two (ASN 691, THR 680) key residues. Binding affinity and inhibition constant of each compound is also mentioned.</p><!><p>Electrostatic potential is an effective way to understand the structural properties and characteristics of protein and ligands which bind to it. Electrostatic potential charges are mapped on the surface of the RdRp protein of Sars-Cov-2, to show the distribution of positive and negative charges and the intensity on the surface of the protein. Distribution of Positive potential charges (Blue) covers the inner cavity of binding pockets of RdRp protein and the remaining surface is covered by the negative charges (Red). Above Figure 17 shows the location of combinations of ligands in the inner cavity of binding pockets of protein which is a clear indication of the fact that proposed compounds bind to key binding sites (Cavity of Binding pockets is shown in the blue) which are the main cause of replication and progression of Virus in the host cells. Thus covering these sites will inhibit the working of RdRp protein.</p><!><p>In this study, Root Mean Square Deviation (RMSD) is measured to evaluate the distance between backbone atoms of superimposed molecules. As shown in Figure 18, RMSD of RdRp protein remained stable between 16ns to 25 ns timescale at 1.581 Å, then showed a slight upward deviation until 34ns and at 35ns it persisted at 1.581 Å till the end. The RMSD of RdRp_ Quinic Acid, Sennaglucosides, and 8difluoro-7-hydroxy chromen-4-one showed rise until 20ns at 1.7 Å and after slight fluctuation it gained stability at 25ns at 1.76 Å. The RMSD of RdRp_Sennaglucosides and Desacetylnimbinolide showed stability at 15ns timescale at 1.55 Å and after slight upward fluctuation it system was balanced at 28na timescale at 1.77 Å. The RMSD of RdRp_Naringin and Sennaglucosides increased up to 15ns timescale at 1.62 Å and then fluctuated downward on timescale at 1.57 Å and the system was balanced at 34ns timescale at 1.66 Å. The RMSD of RdRp_Desacetylnimbinolide and Naringin gained stability at 21ns timescale at 1.61 Å. The RMSD of Famotidine and Famotidone ascended until 11ns and then the system was stable until 22ns timescale at 1.53 Å. Figure 18 shows the RMSD plots of protein with all suggested compounds. A brief analysis has shown the Root Mean Square Fluctuation (RMSF) of residues of RdRp protein with its complexes. In Figure 19, RdRp and its binding compounds have shown the fluctuations between 1.2 Å and 1.8 Å. This depicts that proposed compounds have maintained close binding contact with the binding residues during Molecular Dynamic simulation.</p><!><p>By analyzing the above results, we can predict the most suitable combination according to the number of key residues that it covers. In table 11, combinations of the proposed compounds are listed according to the most suitable first. The table also shows the names of binding residues that these compounds cover along with the number of compounds in each combination. Combinations of compounds are selected according to the ability to cover the maximum key residues as well as binding affinity with RdRp.</p><!><p>COVID-19 is a viral disease that has caused a pandemic in the modern era. Not only has it affected social life but it has imparted an impeding effect on world economies. People having an underlying health condition are at great risk. The only way to undo this threat is either by finding a vaccine or a potent antiviral therapy against the virus. Researchers all over the world have proposed numerous drug therapies for the disease. This study covers in-silico identification of phytochemicals that can prove effective in inhibiting the function of RdRp proteins of Sars-Cov-2. The study proposes 7 compounds that can prove effective as per in-silico evidence when used in combinations or individually. These compounds have shown promising signs towards the development of antiviral medications for the COVID-19. Most of them are naturally occurring substances with low toxicity, very few side effects, proven anti-pathogenic effects, and most importantly are easily available. They bind to the key sites of RdRp protein to inhibit its functioning and stop the replication of coronavirus. All the results have been carefully analyzed through the use of in silico methods and machine learning models. Their binding affinities and binding sites are thoroughly observed for result compilation. The most promising observation from the simulation is that a therapy based on the combination of Cyclohexane-1-carboxylic acid (Quinic Acid), Sennaglucosides, and 8-difluoro-7-hydroxy chromen-4-one can bind to eight out of nine key residue sites of RdRp protein of Sars-Cov-2. This is a strong indication that the combination of these compounds can significantly compromise the replication cycle of Sars-Cov-2 and hence alleviate the severity of the disease.</p><!><p>All the results shown in the study are obtained from in silico methods. The proper clinical trial and medical observation will reveal more crucial information about their effectiveness. If the proposed compounds make an impact in the development of the vaccine of COVID-19 then these compounds can also be used in further research of RNA-related viral and other contagious diseases.</p>

ChemRxiv

Validation methods for low-resolution fitting of atomic structures to electron microscopy data

Fitting of atomic-resolution structures into reconstructions from electron cryo-microscopy is routinely used to understand the structure and function of macromolecular machines. Despite the fact that a plethora of fitting methods has been developed over recent years, standard protocols for quality assessment and validation of these fits have not been established. Here, we present the general concepts underlying current validation ideas as they relate to fitting of atomic-resolution models into electron cryo-microscopy reconstructions, with an emphasis on reconstructions with resolutions below the sub-nanometer range.

validation_methods_for_low-resolution_fitting_of_atomic_structures_to_electron_microscopy_data

Introduction<!>Accuracy versus precision<!>Sources of errors<!>Crossvalidation<!>Crossvalidation in cryo-EM fitting<!>Confidence intervals<!>Conclusions

<p>Due to dramatic improvements in experimental methods and computational techniques, electron cryo-microscopy (cryo-EM) has matured into a powerful collection of methods that allow the high-resolution visualization of the structure and the dynamics of an extraordinary range of biological assemblies in their native aqueous environment. Recent hardware and software developments have revolutionized the field [1]. The increased signal-to-noise ratio of a new generation of cameras that detect electrons directly [2] in combination with their ability to correct for beam-induced movements, have allowed the field to obtain structural information even for particles with low or no symmetry at resolutions around 3 Å [3–8], sufficient to build de novo structural models [9].</p><p>However, the majority of reconstructions obtained by cryo-EM are of insufficient resolution for such direct structure determination. In fact, currently over 70% of the reconstructions deposited in the electron microscopy data bank [10] do not reach a resolution of better than 10 Å. While at resolutions between 5 and 10 Å secondary structural elements are often visible as rods (α-helices) and sheets (β-sheets), at resolutions below the 10-Å mark, internal features of the reconstructions are not straightforward to interpret (Figure 1).</p><p>As cryo-EM methodology continues to improve, atomic-resolution reconstructions are likely to become more common. These reconstructions will likely be of highly rigid molecules. At the same time, the advances in cryo-EM technology will also open the door to structure determination of complexes that were previously too small, too heterogeneous, too flexible, or otherwise challenging, albeit at lower resolution. In addition, electron cryo-tomography has become a powerful alternative for structure determination of samples that are not amenable to single-particle approaches. New software developments and careful experimental design [11] enable the determination of structures from cryo tomograms at around 8 Å, but resolutions below 20 Å are more common. As a net-effect, the majority of cryo-EM reconstructions are likely to remain at the resolution range worse than 10 Å for the foreseeable future.</p><p>Fitting of atomic components into cryo-EM density maps insufficient for direct de-novo model building is routinely used to understand the structure and function of these macromolecular machines. Many fitting methods have been developed, but standard protocols for successful fitting remain to be established. Broadly, fitting methods can be divided into two major groups, rigid-body and flexible fitting methods. In rigid-body fitting approaches the atomic structures of components are fitted as single units. These units can be composed of entire proteins, domains, or even smaller groups of structural element. In flexible fitting approaches the entire atomic structures are allowed to distort in some way to improve the fit with the reconstruction, subject to constraints such as molecular dynamics force-fields or normal modes to counter-balance fitting of spurious noise. Comprehensive reviews of the various fitting approaches that are available were provided in several recent articles [12–14].</p><p>Despite the plethora of available fitting techniques, generally accepted criteria for assessing the accuracy and quality of the fitted models have not been established yet [15]. In this review, our aim is to present the general concepts underlying current validation ideas as they relate to fitting of atomic models into cryo-EM reconstructions, with an emphasis on reconstructions with resolutions that do not reach the sub-nanometer range.</p><!><p>In the context of fitting, it is important to emphasize the difference between accuracy and precision (Figure 2). In short, a fit is precise if similar fits are obtained with repeated runs. In contrast, a fit is accurate if it is close to the true structure (or ensemble of structures) underlying the data. Consensus approaches that compare results from different fitting methods [16,17] or from multiple scoring functions [18] using a single data set, for example, can only inform on the precision of the fit. While it has been claimed that precision gives a lower bound for the accuracy, this is not necessarily true (Figure 2). In fact, in the context of fitting atomic structures into cryo-EM reconstructions, the fitting that appears less precise can actually be more accurate than a fit with the same center position and a narrower spread. The reason is that cryo-EM reconstructions, unlike crystal structures, often represent an ensemble of conformers that coexisted in the sample at the time of freezing, reflecting the structural dynamics of the complex in solution. This can also lead to anisotropy of the resolution in the reconstructions, further complicating the issue.</p><p>It is not immediately clear how precision can be quantified within the context of fitting atomic models. This issue is of major importance especially at low resolution, where ambiguities may arise from the geometry of the reconstruction alone [19] and an objective criterion to allow favoring one solution over another is needed. One approach towards this goal is the use of statistical methods to define confidence intervals (see below), which will then allow to define an objective precision estimate. However, the real quantity that is of interest is the accuracy or how close the obtained fit is to the true structure. Without knowledge of the true structure accuracy cannot be directly assessed.</p><!><p>Every cryo-EM reconstruction has some uncertainty due to the presence of noise and its generally limited resolution. If only such random errors exist, a quantified precision measure may actually be a reasonable estimate for the accuracy of a fit. More troublesome in this context are potential systematic errors such as those originating in the electron microscopy data-collection and reconstruction procedures. These include misestimations of the magnification, incomplete corrections of the microscope's contrast transfer function, uneven distribution of projection images in Euler space, and misestimation of the resolution. All of these can introduce bias and throw off the fits from the true solution in a non-random fashion. Other factors that can bias fitting results in significant ways include incompleteness of the structure to be fitted in relation to the target reconstruction and the possible presence of conformational mixtures. Generally, rigid-body techniques are less susceptible to generating artifacts in the presence of errors simply because the underlying structure is kept intact and only six parameters per rigid body, three for the center-of-mass position and three for the orientation, need to be determined.</p><p>Another source of systematic errors is the fact that, at low resolution, the amount of structural information that can be obtained is limited. Care must be taken that the number of refinable parameters used during fitting does not exceed the number of independent observations in the reconstruction. Otherwise, overfitting will inevitably ensue. Even fitting of a single rigid body using only the six rotational and translational degrees of freedom can lead to ambiguities in the resulting models at intermediate resolution [19]. Generally, all available experimental restrains that are independent of the fitting process should be employed for validation purposes. However, inclusion of such restrains into the fitting process can sometimes be helpful to resolve ambiguities.</p><p>Recently developed methods for incorporating data from other sources such as proteomics [20], Förster resonance energy transfer [21], or sparse distance restraints [22] into the fitting process have shown some very encouraging potential [23,24], but they also have to be handled with care. An example is a discrepancy in fitting-based interpretation of the ryanodine receptor [25,26]. In the earlier study, a homology model was fitted into the region of a reconstruction that was indicated by antibody labeling. In the other study, fitting with an atomic structure of a fragment was performed followed by an evaluation of ambiguities using confidence intervals. The differences in fitting protocols completely altered the final fit. More recent fits based on high-resolution reconstructions [27,28] confirmed the statistics based fit [26] to be correct indicating that the labeling constraint [25] threw off the fitting rather than improved the fit (Figure 3).</p><!><p>One technique that can help overcoming overfitting caused by an inadequate ratio of refinable parameters to observables is crossvalidation [29]. In crossvalidation, the data is split into a "training set" and a "test set". Calculations are then performed on the training set while evaluation is done using the test set, so that an adequate set of parameters can be determined (Figure 4). While the fit to the training set will continue to improve due to overfitting, the test set fit will at some point worsen, thus giving optimal values for the fitting parameters involved. This scheme corresponds to a 2-fold crossvalidation also known as holdout method. This is the simplest form of k-fold crossvalidation and has the disadvantage that it usually needs to set apart a sizable portion of the data into the test set that is not accessible for use in the fitting and that an "unfortunate" split can lead to severe misestimations of the parameters. k-fold crossvalidation overcomes this problem by repeating the calculations with k different splits that cover the whole data set and thus are less amenable to artifacts caused by a specific split and also allows a larger fraction of the data to participate in the fitting calculations.</p><p>In structural biology, the most common form of crossvalidation is a 2-fold crossvalidation scheme implemented in Fourier space for X-ray crystallography in the form of the "free R-factor" [30]. A crucial prerequisite for crossvalidation to work is that the information in the test set is independent from that in the work set. For the Fourier terms in X-ray crystallography this assumption is usually justified. In electron microscopy the Fourier terms are strongly correlated so that the free R-factor it is not applicable in a direct analogy to crystallography. Several factors introduce correlations between Fourier terms in cryo-EM reconstructions. If the particle is of limited size in real space, placed in an empty box, then there are automatically correlations between neighboring Fourier components. Equally, low-pass filtering, a common operation in cryo-EM, has the same effect. The alignment of images during the reconstruction process also tends to introduce correlation in the noise of these images [31].</p><!><p>In the current context of fitting atomic structures into cryo-EM reconstructions, two conceptually different approaches have been proposed [32,33]. The first approach [32] relies on splitting the data according to frequency ranges where low-resolution frequency data with significant signal-to-noise ratio is used as a training set for model building and a shell of high-frequency data is used as a test set. As expected, significant correlations between the training and test sets can be detected. The authors propose a work-around where they compare the signal in the test set with a signal from a "perfectly overfitted" bead model, which is used as a baseline. Results using test cases with resolutions between 5 and 10 Å are promising but whether the method works at lower resolutions remains to be seen, especially because correlations between shells will be a more serious problem for lower resolution (the Fourier components with a spatial frequency corresponding to 8 and 9 Å are closer in Fourier space than the 3 and 4 Å components).</p><p>In the second crossvalidation approach [33] the data is split randomly into two independent sets and reconstructions are built independently from each set. One of these reconstructions is used as a training set, the second is used as test set. This type of methodology can be used to select sensible weighting terms between the density and all-atom energy contributions using the program Rosetta [34]. However, the authors find that at resolution below 12 Å significant correlation between the two independent maps occur, outweighing the usefulness of the approach at resolution ranges below 12 Å [33].</p><!><p>A confidence interval is a range of values that is believed, with some stated level of confidence, to contain the true value of interest. In general, high levels of confidence can be achieved with wider intervals, while narrower, more precise intervals carry less confidence. Thus, there is a trade off between precision and confidence and, in fact, any statement of precision without a corresponding confidence level is incomplete. The advantage of providing a range of values for the estimate is that it will be more likely to include the correct one. Generally, the width of a confidence interval can be interpreted as a measure of precision while the confidence level of an interval is a measure of accuracy. However, this statement is only true if all error sources are accounted for correctly. If undetected systematic errors are present, this relationship is broken.</p><p>The use of confidence intervals was shown to be a powerful tool in rigid-body fitting approaches for interpreting cryo-EM reconstructions [35]. In this approach, a global search is followed by a global statistical analysis of the score distribution resulting in the definition of confidence intervals. All fits that have scores within that confidence interval satisfy the data within the error margin defined by the errors in the data and the chosen confidence level. If all errors are accounted for, this will give excellent estimates for precision and accuracy. To account for systematic errors, it is an advantage to use as many independent data sets under varying conditions as possible. Crossvalidation by splitting data into random halves is also an option [35].</p><p>Structural parameters of interest can be evaluated as properties of the sets satisfying the confidence interval criterion. For example, the uncertainty of each atom position of the fitted structure can be approximated by calculating the root-mean-square deviation for each atom using all members of the set. The statistical nature of the approach allows the use of standard statistical tests, such as Student's t-test, to evaluate the significance of differences between models in different orientations [19] or functional states [23] and to help model the corresponding conformational changes in a robust and reliable way.</p><!><p>Fitting of atomic structures into low-resolution reconstructions from cryo-EM need de facto standards and tools for assessing the quality and estimating the accuracy of the resulting fits. It is clear that rigorous and objective evaluation criteria are still needed to validate conclusions drawn from fitting of high-resolution structures into lower-resolution reconstructions from electron microscopy. Currently, several promising approaches based on cross-validation and statistical tools to obtain confidence intervals are in development but generally accepted standards are lacking. Thus, better validation criteria are likely to continue to be the subject of intense development in the near future.</p>

PubMed Author Manuscript

Synthesis and antibacterial and antifungal activities of N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones of substituted 4-formylsydnones

BackgroundSydnone is a heterocycle that exhibits remarkable pharmacological activities, including antimicrobial, anti-inflammatory, analgesic, antipyretic and antioxidant activities. Thiosemicarbazones are of compounds that contain the –NHCSNHN=C< linkage group and are considerable interest because they exhibit important chemical properties and potentially beneficial biological activities. Similarly, thiosemicarbazones having carbohydrate moieties also exhibit various significant biological activities.ResultsThe compounds of 3-formyl-4-phenylsydnones were obtained by Vilsmeyer-Haack’s formylation reaction and were transformed into thiosemicarbazones by condensation reaction with N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazide. Reaction were performed in the presence glacial acetic acid as catalyst using microwave-assisted heating method. Reaction yields were 43‒85 %. The antimicrobial activities of these thiosemicarbazones were screened in vitro by using agar well diffusion and MIC methods. Among these thiosemicarbazones, compounds 4k, 4l, 4m and 4n were more active against all tested bacterial strains, especially against S. epidermidis, B. subtilis and E. coli. The MIC values in these cases are 0.156, 0.156 and 0.313 μg/mL, respectively. All compounds showed weak to moderate antifungal activity against C. albicans and A. niger than nystatin (MIC = 0.156‒0.625 μg/mL vs. MIC = 0.078 μg/mL of nystatin), and thiosemicarbazones 4l, 4m and 4n exhibited significant activity with MIC = 0.156 μg/mL. These compounds also had good antifungal activity against F. oxysporum similarly to nystatin (MIC = 0.156 μg/mL). Among the tested compounds having halogen group 4k, 4l, 4m and 4n showed highest activity against three strains of fungal organisms.ConclusionsIn summary, we have developed a clean and efficient methodology for the synthesis of novel thiosemicarbazone derivatives bearing sydnone ring and d-glucose moiety; the heterocyclic and monosaccharide system being connected via ‒NH‒C(=S)NH‒N=C< linker using molecular modification approach. The methodology could be further extended and used for the synthesis of other thiosemicarbazones of biological importance. 4-Formyl-3-arylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones have been synthesized under microwave-assisted heating conditions. Almost all obtained compounds showed remarkable activities against the tested microorganisms. Among the tested compounds having halogen group 4k, 4l, 4m and 4n showed highest activity against all tested strains of bacterial and fungal organisms.Graphical abstract:Synthesis and antibacterial and antifungal activities of N-(tetra-O-acetyl-β-D-glucopyranosyl)thiosemicarbazones of substituted 4-formylsydnones

synthesis_and_antibacterial_and_antifungal_activities_of_n-(tetra-o-acetyl-β-d-glucopyranosyl)thiose

<!>Background<!><!>Chemistry<!><!>Chemistry<!><!>Antibacterial activities<!><!>Antifungal activities<!><!>Conclusions<!>General methods<!>Synthesis of N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazide (3)<!>General procedure for synthesis of 3-aryl-4-formylsydnone N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones (4a-o)<!>3-Phenyl-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4a)<!>3-(2-Methylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4b)<!>3-(3-Methylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4c)<!>3-(4-Methylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4d)<!>3-(2,3-Dimethylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4e)<!>3-(2,4-Dimethylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4f)<!>3-(4-Ethylphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4g)<!>3-(3-Methoxyphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4h)<!>3-(4-Methoxyphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4i)<!>3-(4-Ethoxyphenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazone (4j)<!>3-(4-Fluorophenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl -β-d-glucopyranosyl)thiosemicarbazon (4k)<!>3-(4-Bromophenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl thiosemicarbazon (4l)<!>3-(4-Iodophenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl -β-d-glucopyranosyl)thiosemicarbazon (4m)<!>3-(2-Methyl-5-chlorophenyl)-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazon (4n)<!>3-Cyclohexyl-4-formylsydnone N-(2′,3′,4′, 6′-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazon (4o)<!>Antibacterial activity<!>Antifungal activity<!>

<p>Synthesis and antibacterial and antifungal activities of N-(tetra-O-acetyl-β-D-glucopyranosyl)thiosemicarbazones of substituted 4-formylsydnones</p><!><p>Sydnone is a mesoionic aromatic system, which could be described with some polar resonance structures [1]. Several compounds containing a sydnone ring exhibit remarkable pharmacological activities, including antimicrobial, anti-inflammatory, analgesic, antipyretic and antioxidant activities [2–5].</p><p>Thiosemicarbazones are compounds that contain the –NHCSNHN=C< linkage group. This class of compounds is of considerable interest because thiosemicarbazones exhibit the important chemical properties and potentially beneficial biological activities [6–9]. Some thiosemicarbazones of 3-aryl-4-formylsydnones were synthesized in good yields by the reactions of 3-aryl-4-formylsydnones with 4′-phenylthiosemicarbazide and thiosemicarbazide, respectively [3, 4]. On the other hand, some monosaccharide thiosemicarbazides are of interested because these derivatives could be used as versatile intermediates for synthesis of various derivatives (especially heterocycles [10]) as well as be used for making complex formations of metallic ions [11, 12].</p><p>Thiosemicarbazones having carbohydrate moieties also exhibit various significant biological activities. In recent times, a number of thiosemicarbazones derivatives containing monosaccharide moiety have not yet been synthesized more. In general, thiosemicarbazones derivatives containing monosaccharide moiety have showed remarkable anti-microorganism and antioxidant activity both in vivo and in vitro [13–15]. Some articles have been reported about the synthesis of substituted aromatic aldehyde/ketone N-(per-O-acetylated glycopyranosyl)thiosemicarbazones in the past [10, 13–15]. These compounds have been synthesized by reaction of N-(per-O-acetylglycosyl)thiosemicarbazides with the corresponding carbonyl compounds [10, 13, 16–24], but the thiosemicarbazones containing both monosaccharide and sydnone moieties have not been reported yet. Continuing the previous studies on the synthesis and the reactivity of N-(per-O-acetyl-d-glycopyranosyl)thiosemicarbazides [15, 24], we report in the present paper a study on the synthesis, spectral characterization, antibacterial and antifungal activity of a series of N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones having sydnone moiety by using microwave-assisted heating method [25].</p><!><p>Synthetic pathway for 3-aryl-4-formylsydnones 2a-n and 3-cyclohexyl-4-formylsydnone 2o</p><p>Synthetic pathway for 3-aryl- and 3-cyclohexyl-4-formylsydnone 4-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones 4a-o</p><p>Different microwave powers used for synthesis of 4a from 2a and 3 in absolute ethanol</p><p>aCatalyst: glacial acetic acid (2 mmol %) in absolute ethanol for 25 min</p><p>bIsolated yields</p><!><p>In the process of synthesizing the compounds of 3-aryl-4-formylsydnone N-(2,3,4,6-tetra-O-β-d-glucopyranosyl)thiosemicarbazones 4a–o, the reaction times were monitored by the thin-layer chromatography with eluent system ethyl acetate-toluene (2:1 v/v). In the case of conventional heating method, product was obtained in yield of 50 % for 120 min under refluxing, while in the case of microwave-assisted heating method, this reaction afforded the yield of 71 % in only 25-min irradiation (The reaction time of 25 min was fixed in order to investigate the microwave power). We found that, initially, the pulses of 1 min of microwave irradiation at maximum power (800 W) were applied, but the yields were not reproducible, and it was difficult to maintain the heating of the reaction mixture. On the other hand, the pulses of 1 min allow to monitor when the reaction is complete by TLC, especially, in cases of the compound 4n which reaction time was 45 min.</p><!><p>Synthesis of 3-aryl- and 3-cyclohexyl-4-formylsydnone N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones (4a–o) under conventional and μ-wave heating</p><p>aCyclohexyl group is attached directly to sydnone ring at position 4</p><!><p>We found that, in general, the electronic nature of the substituents R on the benzene ring of 4-arylsydnones does not affect significantly the reaction yields. However, the strong electron-withdrawing substituents such as NO2, Cl, Br, I slow down the reaction and prolong reaction time more than the electron-donating groups such as CH3, C2H5, OCH3, OC2H5 (Table 2). The yields of obtained thiosemicarbazones is quite high, from 63 to 85 %, except the compound 4o, in this case the yield reached only 43 % after 45 min irradiation. As the result, compounds of 3-aryl-4-formylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones (4a–o) have been synthesized with yields of 43‒85 %. Meanwhile, the conventional heating method only gave the yields of 50‒60 % during prolonged reaction time from 100 min to 150 min.</p><p>IR spectra show the characteristic absorption bands for two molecular components: sydnone and monosaccharide. IR spectral regions are 3476‒3343 and 3334‒3164 cm‒1 (νNH thiosemicarbazone), 1777‒1746 cm−1 (νC=O ester), 1624‒1599 cm‒1 (νCH=N), 1228–1222 and 1056–1043 cm−1 (νCOC ester), 1092‒1090 cm‒1 (νC=S), some bands at 1549–1505 cm−1 (νC=C aromatic). The absorbance of carbonyl-lactone group of the sydnone ring was sometimes superposed partially by carbonyl-ester group in the range 1777‒1746 cm‒1. The presence of the characteristic spectral regions for two moieties, 3-arylsydnone and monosaccharide, and characteristic absorbance band in the range 1624‒1600 cm‒1 belong to azomethine bond in IR spectra indicated that the reaction of 3-aryl-4-formylsydnones and N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazide was occurred.</p><!><p>COSY spectrum of thiosemicarbazone 4i</p><p>HMBC spectrum of thiosemicarbazone 4i</p><!><p>Bacterium Staphylococcus epidermidis an cause a range of illnesses, from minor skin infections, such as pimples, impetigo, boils (furuncles), cellulitis folliculitis, carbuncles, scalded skin syndrome, and abscesses, to life-threatening diseases such as pneumonia, meningitis, osteomyelitis, endocarditis, toxic shock syndrome (TSS), bacteremia,… It is not a known human pathogen or disease causing agent. Bacillus subtilis produces the enzyme subtilisin, which has been reported to cause dermal allergic or hypersensitivity reactions in individuals repeatedly exposed to this enzyme. The bacteria Salmonella is commonly associated with food poisoning in countries all over the world, and the species that most people refer to when they talk about Salmonella is S. enterica. Salmonella infections can originate from household pets containing the bacteria, particularly reptiles, improperly prepared meats and seafood, or the surfaces of raw eggs, fruits, or vegetables that have not been adequately disinfected. As their name suggests Salmonella enterica are involved in causing diseases of the intestines (enteric means pertaining to the intestine). The three main serovars of Salmonella enterica are Typhimurium, Enteritidis, and Typhi.</p><!><p>Antibacterial activity (paper disc diffusion method) of thiosemicarbazones 4a–o</p><p>Zone diameter of growth inhibition (mm) after 24 h: 50 μL of stock solution was applied in each hole of each paper disk, i.e. 25 μg/hole. Ciprofloxacin is used as a standard antibacterial reference. Control sample is 10 % DMSO solution in water</p><p>Antibacterial activity (minimum inhibitory concentration, μg/mL) of thiosemicarbazones 4a–o</p><!><p>There are over 20 species of Candida yeasts that can cause infection in humans, the most common of which is Candida albicans. Candida yeasts normally live on the skin and mucous membranes without causing infection; however, overgrowth of these organisms can cause symptoms to develop. Symptoms of candidiasis vary depending on the area of the body that is infected. Fungus Fusarium oxysporum plays the role of a silent assassin—the pathogenic strains of this fungus can be dormant for 30 years before resuming virulence and infecting a plant. F. oxysporum is infamous for causing a condition called Fusarium wilt. Furthermore, F. oxysporum can be harmful to both humans and animals, with its mycotoxins causing the diseases fungal keratitis, Onychomycosis, and Hyalohyphomycosis. Aspergillus niger is a fungus and one of the most common species of the genus Aspergillus. It causes a disease called black mould on certain fruits and vegetables such as grapes, apricots, onions, and peanuts, and is a common contaminant of food, but may also infect humans through inhalation of fungal spores.</p><!><p>Antifungal activity (paper disc diffusion method) of thiosemicarbazones 4a–o</p><p>Zone diameter of growth inhibition (mm) after 24 h: 50 μL of stock solution was applied in each hole of each paper disk, i.e. 25 μg/hole. Nystatin is used as a standard antifungal reference. Control sample is 10 % DMSO solution in water</p><p>Antifungal activity (minimum inhibitory concentration, μg/mL) of thiosemicarbazones 4a–o</p><!><p>The authors have developed an effective method for synthesis of 4-formyl-3-arylsydnone N-(2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazones under microwave-assisted conditions. These thiosemicarbazones have been obtained in good to excellent yields, except compound 4o, and fully characterized on the basis of their detailed spectral studies. Among the tested compounds having halogen group 4k, 4l, 4m and 4n showed highest activity against all tested strains of bacterial and fungal organisms. This heating method is advantageous in having a smaller solvent volume and a shorter reaction time. We also believe that the procedural simplicity, the efficiency and the easy accessibility of the reaction components give access to a wide array of heterocyclic frameworks bearing monosaccharide moiety. Almost all synthesized compounds had their antibacterial and antifungal activities evaluated and showed remarkable results. In summary, we have developed a clean and efficient methodology for the synthesis of novel thiosemicarbazone derivatives bearing sydnone ring and d-glucose moiety; the heterocyclic and monosaccharide system being connected via ‒NH‒C(=S)NH‒N=C< linker using molecular modification approach. The methodology could be further extended and used for the synthesis of other thiosemicarbazones of biological importance.</p><!><p>All chemicals used for the synthesis of the desired compounds were obtained from Merck chemicals. All other commercial reagents were used as received without additional purification. Melting points were measured on STUART SMP3 (BIBBY STERILIN, UK). The FTIS-spectra was recorded on Impact 410 FT-IR Spectrometer (Nicolet, USA), as KBr discs. The 1H NMR and 13C NMR spectra were recorded on an Avance Spectrometer AV500 (Bruker, Germany) at 500.13 and 125.77 MHz, respectively, using DMSO-d6 as solvent and TMS as an internal standard. Mass spectra were recorded on mass spectrometer LC–MS LTQ Orbitrap XL (ThermoScientific, USA) or Agilent 6310 Ion Trap (Agilent Technologies, USA) in methanol, using ESI method. Thin-layer chromatography was performed on silica gel plates 60F254 No. 5715 (Merck, Germany) with toluene: ethyl acetate = 1:2 (by volume) as solvent system, and spots were visualized with UV light or iodine vapour. N-(Tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazide was synthesized using the method which described in Ref. [24] from corresponding isothiocyanate. Tetra-O-acetyl-β-glucopyranosyl isothiocyanate were prepared by the reaction of tetra-O-acetyl-β-glucopyranosyl bromide with dry ammonium thiocyanate in absolute acetonitrile using tetrabutylammonium bromide as transfer catalyst (modifying the Tashpulatov's method [19, 20]). This bromide derivative was prepared from d-glucose using Lemieux's procedure [31]. The obtained thiosemicarbazones were yellow or orange solids, insoluble in water, but easily soluble in ethanol, methanol, benzene, dichloromethane, chloroform, ethyl acetate.</p><!><p>To a solution of 2,3,4,6-tetra-O-acetyl-β-d-glucopyranosyl isothiocyanate (3.89 g, 10 mmol) in 25 mL of absolute ethanol, a solution of 85 % hydrazine hydrate (10 mmol, 1.2 ml) in 10 mL of absolute ethanol was added dropwise slowly with stirring in 30 min so that the reaction temperature is below 10 °C. The white precipitate appears immediately when several drops of hydrazine are added due to low solubility of this thiosemicarbazide in ethanol. The temperature of solution was maintained between 10 and 12 °C. The mixture was continuously stirred at 20 °C for 30 min. The solid product then was isolated by filtering with suction. The crude product was crystallized from 96 % ethanol to yield 3.75 g of white product 3. Yield 85 %, mp 156–158 °C; Ref. [19]: 169‒171 °C. IR (KBr, cm‒1): ν 3322, 3129 (νNH), 1752 (νC=O ester), 1355 (νC=S), 1242, 1043 (νCOC ester); 1H NMR (DMSO-d6) δ (ppm): 12.77 (s, 1H, NHb), 9.23 (s, 1H, NH), 8.17 (s, 1H, NH), 4.58 (s, 2H, NH2), 5.80 (m, 1H, H-1), 5.07 (t, J = 9.5 Hz, 1H, H-2), 5.34 (t, J = 9.75 Hz, 1H, H-3), 4.91 (t, J = 9.75 Hz, 1H, H-4), 4.14 (dd, J = 12.25, 4.75 Hz, 1H, H-6a), 3.98‒3.93 (m, 2H, H-5 & H-6b), 1.98–1.94 (s, 12H, 4 × CH3CO); 13C NMR (DMSO-d6) δ (ppm): 182.1 (C=S), 169.9–169.2 (4 × COCH3), 81.0 (C-1), 70.5 (C-2), 72.5 (C-3), 68.1 (C-4), 72.1 (C-5), 61.8 (C-6), 20.4–20.2 (4 × CH3 CO); MS (+ESI): m/z (%) = 422.42 (45) [M+H]+, 462.28 (100) [M+K]+; calcd. for C15H23N3O9S = 421.12 Da.</p><!><p>To a solution of N-(tetra-O-acetyl-β-d-glucopyranosyl)thiosemicarbazide 3 (2 mmol) in absolute ethanol (5 mL) was added substituted 3-aryl-4-formylsydnone 2a–o (2 mmol). Glacial acetic acid (2 mmol%) as catalyst was added dropwise with stirring. The obtained mixture was then irradiated in microwave oven for 25‒45 min (Tables 1, 2), cooled to room temperature, the separated precipitate was filtered and recrystallized from 96 % ethanol to afford 4a–o.</p><!><p>Pale yellow crystals, mp 137‒138 °C (from 96 % ethanol), Rf = 0.57; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +44.0 (c = 0.21, CHCl3); FTIR (KBr): ν/cm‒1 3343, 3122 (νNH), 1750 (νC=O ester and sydnone), 1600 (νCH=N), 1541 (νC=C), 1080 (νC=S), 1235, 1037 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.96 (s, 1H, NH-2), 7.83‒7.74 (m, 5H, H-2‴, H-3‴, H-4‴, H-5‴, H-6‴), 7.79 (s, 1H, CH=N), 7.05 (d, 1H, J = 9.5 Hz, NH-4), 5.88 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.40 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.02 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.81 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.23 (dd, 1H, J = 4.5, 12.25 Hz, H-6ʹa), 4.09 (ddd, 1H, J = 1.75, 3.75, 9.75 Hz, H-5ʹ), 3.99 (dd, 1H, J = 1.0, 12.25 Hz, H-6ʹb), 2.06‒1.90 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.7 (C=S), 170.5‒169.8 (4 × CH3CO), 165.6 (C-5ʹʹ), 134.4 (C-1‴), 132.8 (C-3‴, C-4‴, C-5‴), 130.1 (CH = N), 126.0 (C-2‴, C-6‴), 105.6 (C-4ʹʹ), 81.3 (C-1ʹ), 72.9 (C-3ʹ), 72.7 (C-5ʹ), 71.3 (C-2ʹ), 68.3 (C-4ʹ), 61.2 (C-6ʹ), 21.0‒20.6 (4 × CH3CO); ESI–MS (+MS): m/z (%) 594.01 (M + H, 67), 407.12 (25), 390.21 (10), 348.17 (20), 331.28 (8), 218.28 (5), 190.37 (8), 176.39 (60), 132,56 (7), 117.41 (100), 102.78 (60), 76.75 (10), 74.59 (33), 59.47 (55); calc. for C24H27N5O11S = 593.14 Da.</p><!><p>Pale yellow crystals, mp 119‒121 °C (from 96 % ethanol), Rf = 0.60; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +47.0 (c = 0.22, CHCl3); FTIR (KBr): ν/cm‒1 3343 (νNH), 1749 (νC=O ester and sydnone), 1600 (νCH=N), 1521 (νC=C), 1051 (νC=S), 1222, 1056 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.0 (s, 1H, NH-2), 7.72 (s, 1H, CH = N), 7.71–7.68 (m, 2H, NH-4, H-3‴), 7.65‒7.60 (m, 1H, H-5‴), 7.60‒7.50 (m, 1H, H-4‴), 6.50‒6.40 (m, 1H, H-6‴), 5.85 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.40 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.05 (t, 1H, J = 10.0 Hz, H-4ʹ), 4.75 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.26 (dd, 1H, J = 4.5, 12.0 Hz, H-6ʹa), 4.10 (ddd, 1H, J = 2.0, 4.0, 10.0 Hz, H-5ʹ), 3.99 (d, 1H, J = 12.0 Hz, H-6ʹb), 2.21 (s, 3H, 2‴-CH3), 2.09‒1.90 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 176.9 (C=S), 170.0‒169.3 (4 × CH3CO), 165.5 (C-5ʹʹ), 133.6 (C-1‴), 132.3 (C-3‴), 131.6 (C-5‴), 128.8 (C-4‴),128.6 (CH=N), 127.7 (C-6‴), 126.2 (C-2‴), 105.0 (C-4ʹʹ), 80.7 (C-1ʹ), 72.4 (C-5ʹ), 72.2 (C-3ʹ), 70.9 (C-2ʹ), 67.6 (C-4ʹ), 61.7 (C-6ʹ), 20.5‒20.2 (4 × CH3CO), 20.1 (2‴-CH3); ESI–MS (‒MS): m/z (%) 606.0 (M‒H, 100); calc. for C25H29N5O11S = 607.16 Da.</p><!><p>Yellow crystals, mp 148‒150 °C (from 96 % ethanol), Rf = 0.58; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +59.1 (c = 0.27, CHCl3); FTIR (KBr): ν/cm‒1 3525, 3164 (νNH), 1756 (νC=O ester and sydnone), 1624 (νCH=N), 1532 (νC=C), 1084 (νC=S), 1237, 1041 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 11.98 (s, 1H, NH-2), 7.78 (s, 1H, CH=N), 7.63‒7.60 (m, 4H, H-2‴, H-4‴, H-5ʹʹ, H-6‴), 7.00 (d, 1H, J = 10.0 Hz, NH-4), 5.87 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.41 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.01 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.72 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.24 (dd, 1H, J = 4.5, 12.5 Hz, H-6ʹa), 4.10 (ddd, 1H, J = 2.0, 4.5, 10.0 Hz, H-5ʹ), 3.98 (dd, 1H, J = 1.5, 12.0 Hz, H-6ʹb), 2.46 (s, 3H, 3‴-CH3), 2.05‒1.90 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.2 (C=S), 170.0‒169.3 (4 × CH3CO), 129.5 (CH=N), 80.7 (C-1ʹ), 70.9 (C-2ʹ), 72.2 (C-3ʹ), 67.8 (C-4ʹ), 72.3 (C-5ʹ), 61.7 (C-6ʹ), 104.9 (C-4ʹʹ), 165.1 (C-5ʹʹ), 140.2 (C-1‴), 122.6 (C-2‴), 133.9 (C-3‴), 129.9 (C-4‴), 132.9 (C-5‴), 125.6 (C-6‴), 20.7‒20.16 (4 × CH3CO), 20.7 (3‴-CH3); ESI–MS (‒MS): m/z (%) 606.1 (M‒H, 100); calc. for C25H29N5O11S = 607.16 Da.</p><!><p>Yellow crystals, mp 149‒151 °C (from 96 % ethanol), Rf = 0.58; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +52.3 (c = 0.25, CHCl3); FTIR (KBr): ν/cm‒1 3329, 3215 (νNH), 1747 (νC=O ester and sydnone), 1601 (νCH=N), 1510, 1537 (νC=C), 1083 (νC=S), 1226, 1043 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.04 (s, 1H, NH-2), 7.70 (s, 1H, CH = N), 7.75 (d, 2H, J = 9.0 Hz, H-3‴, H-5‴), 7.27 (d, 2H, J = 9.0 Hz, H-2‴, H-6‴), 6.73 (d, 1H, J = 10.0 Hz, NH-4), 5.85 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.41 (t, 1H, J = 9.75 Hz, H-3ʹ), 5.12 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.54 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.27 (dd, 1H, J = 4.5, 12.5 Hz, H-6ʹa), 4.11 (ddd, 1H, J = 2.0, 4.5, 10.0 Hz, H-5ʹ), 3.99 (d, 1H, J = 12.5 Hz, H-6ʹb), 3.97 (s, 3H, 4‴–CH3), 2.06‒1.87 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.2 (C = S), 170.1‒169.2 (4 × CH3CO), 165.9 (C-5ʹʹ), 161.5 (C-4‴), 129.9 (CH=N), 126.9 (C-3‴, C-5‴), 126.8 (C-1‴), 115.1 (C-2‴, C-6‴), 104.6 (C-4ʹʹ), 80.4 (C-1ʹ), 72.3 (C-5ʹ), 72.1 (C-3ʹ), 70.8 (C-2ʹ), 67.5 (C-4ʹ), 61.6 (C-6ʹ), 55.8 (4‴-CH3), 20.5‒20.1 (4 × CH3CO); ESI–MS (+MS): m/z (%) 608.00 (M+H, 55), 536.00 (10), 412.11 (14), 407.15 (20), 390.19 (7), 348.13 (10), 321.36 (25), 290.19 (8), 218.32 (5), 204, 138.30 (55), 139.18 (37), 117.32 (95), 102.45 (100), 81.37 (18), 74.58 (35), 59.45 (55)calc. for C25H29N5O11S = 607.16 Da.</p><!><p>Pale yellow crystals, mp 138‒140 °C (from 96 % ethanol), Rf = 0.53; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +47.0 (c = 0.23, CHCl3); FTIR (KBr): ν/cm‒1 1750 (νC=O ester and sydnone), 3338, 3124 (νNH), 1610 (νCH=N), 1490, 1450 (νC=C), 1085 (νC=S), 1039, 1229 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 11.97 (s, 1H, NH-2), 7.70 (s, 1 H, CH = N), 7.39 (t, 2H, J = 7.0), H-4‴, H-5‴), 7.61 (s, 1H, H-6‴), 6.33 (dd, 1H, J = 9.5 Hz, NH-4), 5.81 (m, 1H, H-1ʹ), 5.36 (t, 2H, J = 9.5 Hz, H-3ʹ, H-4ʹ), 4.77 (m, 1H, H-2ʹ), 4.33 (t, 1H, J = 11.5 Hz, H-5ʹ), 4.09 (d, 1H, J = 9.0 Hz, H-6ʹa, H-6ʹb), 2.45‒2.39 (s, 3H, 2‴-CH3), 2.39‒2.09 (s, 12H, 4 × CH3CO), 1.89 (s, 3H, 3‴-CH3); 13C NMR (125 MHz, DMSO-d6): δ 177.1 (C=S), 170‒169.3 (4 × CH3CO), 165.6 (C-5ʹʹ), 139.0 (C-1‴), 133.7 (C-2‴), 133.6 (C-3‴), 132.5 (C-4‴), 128.5 (CH=N), 127.1 (C-6‴), 123.7 (C-5‴), 105.1 (C-4ʹʹ), 80.6 (C-1ʹ), 72.1 (C-5ʹ), 71.7 (C-3ʹ), 71.4 (C-2ʹ), 67.6 (C-4ʹ), 61.6 (C-6ʹ), 20.5‒20.1 (4 × CH3CO), 13.2 (2‴-CH3), 19.7 (3‴-CH3); ESI–MS (+MS): m/z (%) 622.03 (M+H, 87), 600.44 (5), 590.29 (10), 556.47 (8), 473.51 (10), 407.29 (10), 390.41 (6), 348.25 (12), 331.40 (6), 218.39 (12), 202.42 (40), 132.44 (8), 122.33 (10), 117.36 (100), 102.59 (38), 74.43 (25), 59.18 (53); calc. for C26H31N5O11S = 621.17 Da.</p><!><p>Pale yellow crystals, mp 119‒121 °C (from 96 % ethanol), Rf = 0.55; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +46.0 (c = 0.22, CHCl3); FTIR (KBr): ν/cm‒1 1753 (νC=O ester and sydnone), 3334, 3256 (νNH), 1600 (νCH=N), 1530, 1450 (νC=C), 1080 (νC=S), 1039, 1224 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.04 (s, 1H, NH-2), 7.74 s, 1H, CH=N), 7.57 (t, 1H, J = 8.0 Hz, H-3‴), 7.42 (s, 1H, H-6‴), 7.35 (t, 1H, J = 8.0 Hz, H-5‴), 6.57 d; 1H, J = 10.0 Hz, NH-4), 5.89 (m, 1H, H-1ʹ), 5.42 (m, 1H, H-3ʹ), 5.05 (s, 1H, H-4ʹ), 4.62 (s, 1H, H-2ʹ), 4.21 (m, 1H, H-5ʹ), 4.15 d; 1H, J = 10.0 Hz, H-6ʹa), 3.99 d; 1H, J = 5.75 Hz, H-6ʹb), 2.01‒1.90 (s, 12 H, 4 × CH3CO), 2.52 (s, 3H (2‴–CH3), 2.12 (s, 3H (4‴–CH3); 13C NMR (125 MHz, DMSO-d6): δ 177.2 (C=S), 169.9‒169.2 (4 × CH3CO), 165.6 (C-5ʹʹ), 142.0 (C-1‴), 133.4 (C-4‴), 131.9 (C-2‴), 131.2 (C-5‴), 129.1 (CH=N), 127.9 (C-3‴), 126.0 (C-6‴), 104.9 (C-4ʹʹ), 80.6 (C-1ʹ), 72.5 (C-5ʹ), 70.9 (C-3ʹ), 67.8 (C-2ʹ), 65.0 (C-6ʹ), 61.6 (C-4ʹ), 20.7‒20.1 (4 × CH3CO), 21.0 (4‴-CH3), 16.1 (2‴-CH3); ESI–MS (+MS): m/z (%) 622.07 (M + H, 100), 607.11 (10), 331.29 (6), 315.32 (20), 277.08 (5), 247.60 (50), 219.29 (13), 189.51 (14), 161.50 (6), 132.50 (15), 117.25 (85), 102.56 (10), 74.29 (6), 58.12 (47); calc. for C26H31N5O11S = 621.17 Da.</p><!><p>Pale yellow crystals, mp 138‒140 °C (from 96 % ethanol), Rf = 0.58; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +59.0 (c = 0.27, CHCl3); FTIR (KBr): ν/cm‒1 3310, 3228 (νNH), 1777 (νC=O ester and sydnone), 1600 (νCH=N), 1551, 1518 (νC=C), 1084 (νC=S), 1228, 1043 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.01 (s, 1H, NH-2), 7.81 (s, 1H, CH = N), 7.74 (d, 2H, J = 8.25 Hz, H-3‴, H-5‴), 7.58 (d, 2H, J = 8.25 Hz, H-2‴, H-6‴), 7.08 (d, 1H, J = 10.0 Hz, NH-4), 5.90 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.44 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.00 (t, 1H, J = 9.5 Hz, H-4ʹ), 4.73 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.19 (dd, 1H, J = 4.5, 12.5 Hz, H-6ʹa), 4.10 (ddd, 1H, J = 2.0, 4.5, 10.0 Hz, H-5ʹ), 3.99 (dd, 1H, J = 1.5, 12.5 Hz, H-6ʹb), 2.85 (q, 2H, J = 7.5 Hz, 4‴-CH2CH3), 2.04‒1.91 (s, 12H, 4 × CH3CO), 1.30 (t, 3H, J = 7.5 Hz, 4‴-CH2CH3); 13C NMR (125 MHz, DMSO-d6): δ 177.3 (C=S), 170.0‒169.3 (4 × CH3CO), 165.2 (C-5ʹʹ), 148.5 (C-1‴), 131.6 (C-4‴), 129.9 (CH=N), 129.1 (C-3‴, C-5‴), 125.4 (C-2‴, C-6‴), 104.8 (C-4ʹʹ), 80.7 (C-1ʹ), 72.3 (C-5ʹ), 72.1 (C-3ʹ), 70.9 (C-2ʹ), 67.7 (C-4ʹ), 61.4 (C-6ʹ), 28.0 (4‴-CH2CH3), 20.6‒20.2 (4 × CH3CO), 15.0 (4‴-CH2CH3); ESI–MS (‒MS): m/z (%) 620.3 (M‒H, 100); calc. for C26H31N5O11S = 621.17 Da.</p><!><p>Yellow crystals, mp 139‒141 °C (from 96 % ethanol), Rf = 0.60; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +53.2 (c = 0.24, CHCl3); FTIR (KBr): ν/cm‒1 3476, 3334 (νNH), 1756 (νC=O ester and sydnone), 1609 (νCH=N), 1528 (νC=C), 1093 (νC=S), 1228, 1040 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 11.97 (s, 1H, NH-2), 7.81 (s, 1H, CH=N), 7.64 (t, 1H, J = 7.5 Hz, H-5‴), 7.47 (t, 1H, J = 2.0 Hz, H-2‴), 7.38 (dd, 1H, J = 1.0, 7.5 Hz, H-4‴), 7.34 (dd, 1H, J = 2.0, 7.5 Hz, H-6‴), 7.18 (d, 1H, J = 9.5 Hz, NH-4), 5.88 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.42 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.00 (t, 1H, J = 9.5 Hz, H-4ʹ), 4.80 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.21 (dd, 1H, J = 5.0, 12.25 Hz, H-6ʹa), 4.10 (ddd, 1H, J = 2.0, 4.5, 10.0 Hz, H-5ʹ), 3.99 (dd, 1H, J = 1.5, 12.25 Hz, H-6ʹb), 3.86 (s, 3H, 3‴-OCH3), 2.05‒1.90 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.2 (C=S), 170.1‒169.3 (4 × CH3CO), 164.8 (C-5ʹʹ), 160.0 (C-3‴), 134.8 (C-1‴), 131.0 (C-5‴), 129.7 (CH = N), 118.4 (C-6‴), 117.5 (C-4‴), 111.0 (C-2‴), 105.1 (C-4ʹʹ), 80.8 (C-1ʹ), 72.3 (C-5ʹ), 72.2 (C-3ʹ), 71.0 (C-2ʹ), 67.9 (C-4ʹ), 61.8 (C-6ʹ), 55.8 (3‴-OCH3), 20.5‒20.2 (4 × CH3CO); ESI–MS (‒MS): m/z (%) 622.3 (M‒H, 100); calc. for C25H29N5O12S = 623.15 Da.</p><!><p>Light yellow crystals, mp 160‒162 °C (from 96 % ethanol), Rf = 0.58; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +65.0 (c = 0.26, CHCl3); FTIR (KBr): ν/cm‒1 3344, 3260 (νNH), 1746 (νC=O ester and sydnone), 1599 (νCH=N), 1549, 1505 (νC=C), 1093 (νC=S), 1223, 1043 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.02 (s, 1H, NH-2), 7.77 (s, 1H, CH=N), 7.74 (d, 2H, J = 8.75 Hz, H-3‴, H-5‴), 7.27 (d, 2H, J = 8.75 Hz, H-2‴, H-6‴), 6.75 (d, 1H, J = 10.0 Hz, NH-4), 5.86 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.41 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.12 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.55 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.27 (dd, 1H, J = 4.0, 12.25 Hz, H-6ʹa), 4.12‒4.10 (m, 1H, H-5ʹ), 4.00 (d, 1H, J = 12.25 Hz, H-6ʹb), 3.97 (s, 3H, 4‴-OCH3), 2.06‒1.78 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.2 (C=S), 170.1‒169.3 (4 × CH3CO), 165.9 (C-5ʹʹ), 161.5 (C-4‴), 129.2 (CH=N), 126.9 (C-1‴), 127.0 (C-3‴, C-5‴), 115.1 (C-2‴, C-6‴), 104.6 (C-4ʹʹ), 80.4 (C-1ʹ), 72.3 (C-5ʹ), 72.1 (C-3ʹ), 70.9 (C-2ʹ), 67.5 (C-4ʹ), 61.6 (C-6ʹ), 55.8 (4‴-OCH3), 20.5‒20.1 (4 × CH3CO); ESI–MS (+MS): m/z(%) 624.01 (M + H, 100), 556.02 (7), 407.11 (15), 391.21 (5), 348.17 (8), 331.25 (5), 204.21 (75), 124.22 (8), 117.15 (80), 102.25 (95), 84.25 (12), 74.18 (50), 59.08 (67); calc. for C25H29N5O12S = 623.15 Da.</p><!><p>Light yellow crystals, mp 159–161 °C (from 96 % ethanol), Rf = 0.60; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +54.0 (c = 0.22, CHCl3); FTIR (KBr): ν/cm‒1 3324, 3202 (νNH), 1737 (νC=O ester), 1601 (νC=N), 1548, 1490 (νC=C), 1085 (νC=S), 1234, 1042 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.04 (s, 1H, NH-2), 7.78 (s, 1H, CH=N), 7.73 (d, 2H, J = 8.75 Hz, H-3‴, H-5‴), 7.24 (d, 2H, J = 8.75 Hz, H-2‴, H-6‴), 6.75 (d, 1H, J = 10.0 Hz, NH-4), 5.88 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.42 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.06 (t, 1H, J = 9.5 Hz, H-4ʹ), 4.60 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.26‒4.18 (m, 1H, H-6ʹa), 4.22 (q, 2H, J = 7.5 Hz, 4‴-OCH2CH3), 4.10‒4.07 (m, 1H, H-5ʹ), 3.99 (d, 1H, J = 12.5 Hz, H-6ʹb), 3.97 (t, 3H, J = 7.5 Hz, 4‴-OCH2CH3), 2.07‒1.87 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.3 (C=S), 170.1‒169.2 (4 × CH3CO), 165.9 (C-5ʹʹ), 161.5 (C-4‴), 129.3 (CH = N), 126.9 (C-3‴, C-5‴), 126.6 (C-1‴), 115.4 (C-2‴, C-6‴), 104.6 (C-4ʹʹ), 80.5 (C-1ʹ), 72.4 (C-3ʹ), 72.2 (C-5ʹ), 70.7 (C-2ʹ), 67.7 (C-4ʹ), 64.1 (4‴-OCH2CH3), 61.6 (C-6ʹ), 20.5‒20.2 (4 × CH3CO), 14.2 (4‴-OCH2CH3); ESI–MS (+MS): m/z(%) 638.00 (M + H, 60), 432.13 (7), 390.19 (8), 348.11 (10), 331.20 (6), 234.30 (5), 218.29 (45), 190.29 (5), 138.29 (10), 117.27 (100), 102.45 (62), 76.57 (13), 74.45 (23), 59.30 (43); calc. for C26H31N5O12S = 637.17 Da.</p><!><p>Light yellow crystals, mp 176‒178 °C (from 96 % ethanol), Rf = 0.55; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +47.2 (c = 0.24, CHCl3); FTIR (KBr): ν/cm‒1 1744 (νC=O ester and sydnone), 3329, 3186 (νNH), 1597 (νCH=N), 1518, 1550 (νC=C), 1090 (νC=S), 1056, 1229 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.00 (s, 1H, NH-2), 7.94‒7.91 (m, 2H, H-3‴,H-5‴), 7.77 (s, 1H, CH=N), 7.58 (t, 2H, J = 8.75 Hz, H-2‴, H-6‴), 6.74 (d, 1H, J = 10.0 Hz, NH-4), 5.87 (t, 1H, J = 9.75 Hz, H-1ʹ), 5.44 (t, 1H, J = 9.75 Hz, H-3ʹ), 5.01 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.69 (t, 1H, J = 9.75 Hz, H-2ʹ), 4.22 (dd, 1H, J = 9.0;9.0 Hz, H-5ʹ), 4.10 (m, 1H, H-6ʹa), 4.07‒4.00 (m, 1H, H-6ʹb), 2.05‒1.89 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.0 (C=S), 170.7‒169.4 (4 × CH3CO), 167.2 (C-5ʹʹ), 165.9 (C-4‴), 163.8 (CH=N), 144.1 (C-1‴), 129.9 (C-2‴), 127.6 (C-6‴), 121.8 (C-3‴), 117.0 (C-5‴), 101.3 (C-4ʹʹ), 84.0 (C-1ʹ), 83.9 (C-2ʹ), 73.8 (C-5ʹ), 72.5 (C-3ʹ), 70.4 (C-4ʹ), 61.4 (C-6ʹ), 20.6‒20.5 (4 × CH3CO); ESI–MS (+MS): m/z (%) 612.00 (M + H, 100), 580.18 (14), 503.97 (6), 452.18 (5), 391.57 (35), 353.79 (8), 331.25 (8), 296.06 (12), 287.06 (20), 272.29 (25), 246.83 (30), 229.10 (10), 202.44 (25), 189.21 (27), 173.56 (45), 164.51 (14), 144.43 (10), 117.24 (82), 102.27 (53), 84.29 (10), 74.32 (17), 59.20 (53); calc. for C24H26FN5O11S = 611.4 Da.</p><!><p>Dark yellow crystals, mp 157‒159 °C (from 96 % ethanol), Rf = 0.53; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +57.3 (c = 0.26, CHCl3); FTIR (KBr): ν/cm‒1 1746 (νC=O ester and sydnone), 3083, 3289 (νNH), 1610 (νCH=N), 1478, 1520 (νC=C), 1041 (νC=S), 1036, 1222 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 11.98 (s, 1H, NH-2), 8.05 (d, 2H, J = 9.0 Hz, H-3‴, H-45ʹʹ), 7.96 (s, 1H, CH = N), 7.90 (d, 2H, J = 8.5 Hz, H-2‴, H-6‴), 6.75 (d, 1H, J = 10.0 Hz, NH-4), 5.88 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.48 (t, 1H, J = 9.5 Hz, H-3ʹ), 5.26 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.68 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.23 (dd, 1H, J = 9.5;8.0 Hz, H-5ʹ), 4.10 (d, 1H, J = 10.0 Hz, H-6ʹa), 4.01 (d, 1H, J = 12.0 Hz, H-6ʹb), 2.08‒1.89 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.4 (C=S), 170.5‒169.8 (4 × CH3CO), 156.2 (C-5ʹʹ), 136.4 (C-1‴), 133.0 (C-3‴, C-5‴), 128.5 (CH = N), 123.3 (C-2‴,C-6‴), 121.7 (C-4‴), 104.5 (C-4ʹʹ), 81.1 (C-1ʹ), 71.3 (C-2ʹ), 72.9 (C-5ʹ), 72.3 (C-3ʹ), 68.3 (C-4ʹ), 62.2 (C-6ʹ), 21.1‒20.6 (4 × CH3CO); ESI–MS (+MS): calc. for C24H2679BrN5O11S/C24H2681BrN5O11S = 671.05/673.05 Da; m/z (%) 671.13 (100)/673.15 (90) (M+), 642.01 (5), 586.32(5), 331.23 (4), 298.36 (5).</p><!><p>Dark yellow crystals, mp 128‒130 °C (from 96 % ethanol), Rf = 0.51; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +55.0 (c = 0.20, CHCl3); FTIR (KBr): ν/cm‒1 1750 (νC=O ester and sydnone), 2944, 3355 (νNH), 1521 (νCH=N), 1456, 1521 (νC=C), 1045 (νC=S), 1045, 1226 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 11.99 (s, 1H, NH-2), 8.12 (d, 2H, J = 9.0 Hz, H-3‴, H-5‴), 7.80 (s, 1H, CH = N), 7.64 (d, 2H, J = 8.5 Hz, H-2‴, H-6‴), 7.06 (d, 1H, J = 10.0 Hz, NH-4), 5.91 (t, 1H, 9.5 Hz, H-1ʹ), 5.46 (t, 1H, J = 9.75 Hz, H-3ʹ), 5.21 (t, 1H, J = 9.75 Hz, H-4ʹ), 4.81 (t, 1H, J = 9.5 Hz, H-2ʹ), 4.20 (dd, 1H, J = 9.5;9.0 Hz, H-5ʹ), 4.11‒4.07 (m, 1H, H-6ʹa), 4.00 (dd, 1H J = 4.0, 3.0 Hz, H-6ʹb), 2.06‒1.90 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.3 (C=S), 170.0‒169.2 (4 × CH3CO), 165.1 (C-5ʹʹ), 138.8 (C-1‴), 132.5 (C-3‴, C-5‴), 129.8 (CH=N), 127.4 (C-2‴, C-6‴), 119.3 (C-4‴), 104.9 (C-4ʹʹ), 80.7 (C-1ʹ), 72.5 (C-5ʹ), 72.0 (C-3ʹ), 70.7 (C-2ʹ), 68.0 (C-4ʹ), 61.7 (C-6ʹ), 20.6‒20.1 (4 × CH3CO); ESI–MS (‒MS): m/z (%) 717.7 (M‒2H, 100); calc. for C24H26IN5O11S = 719.04 Da.</p><!><p>Dark yellow crystals, mp 122‒123 °C (from 96 % ethanol), Rf = 0.53; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +43.2 (c = 0.22, CHCl3); FTIR (KBr): ν/cm‒1 1754 (νC=O ester and sydnone), 3341, 3249 (νNH), 1600 (νCH=N), 1526, 1450 (νC=C), 1080 (νC=S), 1040, 1227 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.20 (s, 1H, Hz, NH-2), 8.03 (d, 1H, J = 9.0 Hz, NH-4), 7.56 (s, 1H, CH = N), 7.70‒7.47 (m, 3H, H-3‴, H-4‴, H-6‴), 7.70‒7.47 (m, 2H, H-5‴, H-6‴), 5.97‒5.90 (m, 1H, H-1ʹ), 5.29 (t, 1H, J = 9.75 Hz, H-3ʹ), 5.12 (t, 1H, J = 9.75 Hz, H-4ʹ), 5.08‒5.02 (m, 1H, H-2ʹ), 4.30 (dd, 1H, J = 12.5, 4.5 Hz, H-5ʹ), 4.10-4.07 (m, 1H, H-6ʹb), 3.87 (s, 3H, 2‴-CH3), 3.84‒3.80 (m, 1H, H-6ʹa), 2.21–1.96 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 179.6 (C = S), 170.9‒169.6 (4 × CH3CO), 166.4 (C-5ʹʹ), 139.8 (C-1‴), 131.9 (C-2‴), 132.4 (C-3‴), 126.4 (C-4‴), 132.9 (C-5‴), 129.9 (CH = N), 127.3 (C-6‴), 104.3 (C-4ʹʹ), 82.1 (C-1ʹ), 82.0 (C-2ʹ), 74.0 (C-5ʹ), 70.0 (C-3ʹ), 68.5 (C-4ʹ), 62.0 (C-6ʹ), 20.8‒20.4 (4 × CH3CO), 16.6 (2ʹʹ-CH3); ESI–MS (+MS): m/z (%) 642.02/644.03 (M + H/M + H+2, 65/25), 619.15 (14), 605.51 (6), 550.78 (10), 5232.91 (15), 474.38 (10), 462.39 (20), 448.45 (10), 430.52 (14), 414.45 (10), 374.37 (6), 335.48 (12), 296.77 (10), 267.57 (40), 240.37 (10), 139.54 (35), 117.58 (100), 102.52 (87), 81.39 (17), 54.25 (47); calc. for C25H28535ClN5O11S/C25H2837ClN5O11S = 641.12/643.11 Da.</p><!><p>Dark yellow crystals, mp 126‒128 °C (from 96 % ethanol), Rf = 0.61; \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$[lpha ]_{ ext{D}}^{25}$$\end{document}[α]D25 +44.0 (c = 0.21, CHCl3); FTIR (KBr): ν/cm‒1 1756 (νC=O ester and sydnone), 3271, 2950 (νNH), 1596 (νCH=N), 1530–1378 (νC=C), 1043 (νC=S), 1043, 1223 (νCOC ester); 1H NMR (500 MHz, DMSO-d6): δ 12.07 (s, 1H Hz, NH-2), 8.21 (d, 1H, J = 9.5 Hz, NH-4), 7.86 (s, 1H, CH=N), 5.97 (t, 1H, J = 9.5 Hz, H-1ʹ), 5.44 (t, 1H, J = 9.75 Hz, H-3ʹ), 5.29 (t, 1H, J = 10.5 Hz, H-1‴), 5.10 (t, 1H, J = 9.5 Hz, H-4ʹ), 4.93 (t, 1H, J = 9.75 HzH-2ʹ), 4.19 (dd, 1H, J = 2.0; 12.5 Hz, H-5ʹ), 4.11 (dd, 1H, J = 4.5, 12.5 Hz, H-6ʹa), 3.97 (d, 1H, J = 12.0 Hz, H-6ʹb), 2.20‒2.18 (m, 2H, 2 × H-3‴), 1.81‒1.74 (m, 2H, 2 × H-4‴), 1.71‒1.63 (m, 2H, 2 × H-5‴), 1.54‒1.52 (m, 2H, 2 × H-6‴), 1.29‒1.23 (m, 2H, 2 × H-2‴), 2.00‒1.95 (s, 12H, 4 × CH3CO); 13C NMR (125 MHz, DMSO-d6): δ 177.8 (C=S), 169.9‒169.3 (4 × CH3CO), 166.6 (C-5ʹʹ), 130.8 (CH=N), 101.5 (C-4ʹʹ), 81.2 (C-1ʹ), 72.5 (C-5ʹ), 72.3 (C-3ʹ), 70.8 (C-2ʹ), 67.8 (C-4ʹ), 63.6 (C-1‴), 61.7 (C-6ʹ), 30.6 (C-2‴), 30.0 (C-6‴), 24.5 (C-4‴), 24.1 (C-3‴), 24.0 (C-5‴), 20.4‒20.3 (4 × CH3CO); ESI–MS (‒MS): m/z (%) 598.3 (M‒H, 15), 559.1 (5), 459.2 (100), 431.4 (12); calc. for C24H33N5O11S = 599.19 Da.</p><!><p>The synthesized compounds 4a–o were screened in vitro for their antibacterial activities against bacteria namely Staphylococcus epidermidis (ATCC 12228) and Bacillus subtilis (ATCC 6633) as Gram positive bacteria, Escherichia coli (ATCC 25922) and Salmonella enterica (ATCC 15442) as Gram negative bacteria, were tested by using agar well diffusion (cup-plate) method [32]. The sterilized nutrient agar medium was distributed 100 mL each and allowed to cool to room temperature. The 24 h old Mueller–Hinton broth cultures of test bacteria were swabbed on sterile Mueller–Hinton agar plates in sterilized Petri dishes using sterile cotton swab followed by punching wells of 6 mm with the help of sterile cork borer. The standard drug (ciprofloxacin, 1 mg/mL of sterile distilled water), compounds 4a–o (500 μg/mL in 10 % DMSO, prepared by dissolving 2.5 mg of substance in 5 mL of 10 % DMSO solution in water), and control sample (a 10 % solution of DMSO in water) were added to the respectively labelled 6 mm diameter wells. The plates were allowed to stand for 30 min and then incubated at 37 °C for 72 h in upright position. When growth inhibition zones were developed surrounding each cup, their diameter in mm was measured and compared with that of ciprofloxacin (Table 3).</p><p>The antibacterial activities against above bacteria of all the synthesized derivatives also were evaluated in vitro by serial tube dilution method [33]. The compounds and standard drug ciprofloxacin were dissolved in DMSO to give a concentration of 5 μg/mL (stock solution). A set of test tubes of capacity 5 mL was washed, cleaned and dried completely. Double strength nutrient broth was used as a growth/culture media for all bacteria. The culture media was made by dissolving 15 g of nutrient broth No. 2 in 1 L of distilled water. Approximately 1 mL of this culture media was prepared and transferred to each test tube by micropipette and capped with non-adsorbent cotton plugs. A set of test tubes containing 1 mL culture media was sterilized in an autoclave at 15 psi pressure at 121 °C for 20 min. Sub-culturing of bacteria was done by transferring a loopful of particular bacterial strain from standard bacterial agar slant to 10 mL sterilized nutrient broth aseptically in a laminar air flow cabinet. It was then incubated for a period of 24 h at 37 °C in a incubator. After 24 h incubation the bacterial stain suspension was prepared by aseptically inoculating 0.2 mL of revived bacterial colony into 100 mL of 0.9 % m/v saline. The study involved a series of five assay tubes for each compound against each strain. A stock solution of each test compound at concentration 5 μg/mL was serially diluted in series of 5 assay test tubes (containing 1 mL nutrient broth) to give concentration of 2.5, 1.25, 0.625, 0.313 and 0.156 μg/mL. Then, 0.1 mL of normal saline suspension of revived bacteria was added to each test tube. The inoculated tubes were incubated at 37 °C for 24 h. The MIC (minimum inhibitory concentration) values were determined by subsequently checking for the absence of visual turbidity (Table 4).</p><p>Experiments were repeated three times, and the results were expressed as average values.</p><!><p>The synthesized compounds 4a–o were screened for their antifungal activity against three fungal strains [34], namely Aspergillus niger 439, Candida albicans ATCC 7754, Fusarium oxysporum M42, at the concentration levels of 500 μg/mL (Table 4) by agar well diffusion (cup-plate) method, using nystatin as the standard and control sample is a 10 % solution of DMSO in water. The sterilized potato dextrose agar medium incubated at 30 °C for 48 h, then the subculture of fungus were added, and shaken thoroughly to ensure uniform distribution. After that, this was poured into previously sterilized and labelled Petri dishes and allowed to solidify. Two cups were filled with 0.1 mL of two test dilutions and the other two cups with respective concentrations of standard dilutions. The plates were left as it is for 2–3 h for diffusion and then they were kept for 24 h at 37 °C for incubation. Then the diameter of the zones of growth inhibition was measured and compared with that of standard (nystatin).</p><p>Similarly, the antifungal activities against above fungi of all thiosemicarbazone derivatives also were evaluated in vitro by serial tube dilution method [33, 34]. Experiments were repeated three times, and the results were expressed as average values.</p><!><p>acetyl</p><p>N,N-dimethylformamide</p><p>dimethyl sulfoxide</p><p>dimethyl</p><p>Fourier-transformed infrared spectroscopy</p><p>mass spectrometry</p><p>nuclear magnetic resonance spectroscopy</p><p>electron-spray ionization</p>

PubMed Open Access

Reappraising the appropriate calculation of a common meteorological quantity: Potential Temperature

The potential temperature is a widely used quantity in atmospheric science since it is conserved for dry air\xe2\x80\x99s adiabatic changes of state. Its definition involves the specific heat capacity of dry air, which is traditionally assumed as constant. However, the literature provides different values of this allegedly constant parameter, which are reviewed and discussed in this study. Furthermore, we derive the potential temperature for a temperature-dependent parameterisation of the specific heat capacity of dry air, thus providing a new reference potential temperature with a more rigorous basis. This new reference shows different values and vertical gradients, in particular in the stratosphere and above, compared to the potential temperature that assumes constant heat capacity. The application of the new reference potential temperature is discussed for computations of the Brunt-V\xc3\xa4is\xc3\xa4l\xc3\xa4 frequency, Ertel\xe2\x80\x99s potential vorticity, diabatic heating rates, and for the vertical sorting of observational data.

reappraising_the_appropriate_calculation_of_a_common_meteorological_quantity:_potential_temperature

Introduction<!><!>Introduction<!>Derivation of the potential temperature for an ideal gas<!><!>Derivation of the potential temperature for an ideal gas<!>Examining the assumption of constant cp for dry air<!>Accounting for the temperature dependence of air\xe2\x80\x99s specific heat capacity<!><!>The temperature dependence of the ideal-gas specific heat capacity<!><!>NIST\xe2\x80\x99s parameterisation of cp0T<!>The parameterisation of cp0T from an engineer\xe2\x80\x99s perspective<!>The \xce\xb8cpT from the temperature-dependent specific heat capacity of air<!>Derivation of \xce\xb8cpT based on the temperature-dependent specific heat capacity of dry air<!><!>Approximations of the reference potential temperature<!>Implementation aspects<!>The potential temperature for air as a real gas<!>Implications on the use of the potential temperature<!>The Brunt-V\xc3\xa4is\xc3\xa4l\xc3\xa4 frequency<!>The Potential Vorticity<!>Vertical sorting of data<!>Diabatic heating rates<!>Summary and Conclusions

<p>According to the book Thermodynamics of the Atmosphere by Alfred Wegener (1911), the first published use of the expression potential temperature in meteorology is credited to Wladimir Köppen (1888)1 and Wilhelm von Bezold (1888), both following the conclusions of Hermann von Helmholtz (1888) (see also Kutzbach, 2016). Even prior to the introduction of the entropy, Poisson (1833) and Thomson (1862) used the "adiabatic equation", the basis of what is understood today as "potential temperature"2, to describe adiabatic processes, e.g., the coincident variation of temperature and pressure on the movement of air, which is "independent of the effects produced by the radiation or conduction of heat" (Thomson, 1862)3. Approximately 26 years later, von Helmholtz perceived that within the atmosphere the heat exchange between air masses of different temperatures, which are relatively moved, is insufficiently explained by heat transfer due only to radiation and convection. He argued that wind phenomena (e.g., the trade winds), storm events, and the atmospheric circulation were more intense, of larger extent, and more persistent than observed if the air's heat exchange within the discontinuity region (the friction surface of the different air masses) was not mainly due to eddy-driven mixing. On his way to analytically describe the heat exchange of different air masses within the atmosphere, in May of 1880, von Helmholtz introduced the air's immanent heat while its absolute temperature changes with changing pressure (von Helmholtz, 1888). In essence, von Helmholtz concluded that the temperature gained by a volume of dry air due to its adiabatic descent from a certain initial pressure level (p) to ground pressure (p0) corresponds to the air's immanent heat. In November of the same year, in agreement with von Helmholtz and probably inspired by a presentation that was given in June by Köppen (1888), this property was renamed and reintroduced as the air's potential temperature (θ in the following) by vonBezold (1888) with the following definition for strictly adiabatic changes of state: (1)θ=Tp0pγ−1γ, where T and p are the absolute temperature and pressure, respectively, of an air parcel at a certain initial (pressure-) altitude level. The quantities θ and p0 are corresponding values of the same air parcel's absolute temperature and pressure if the air was exposed to conditions at ground level. The dimensionless coefficient γ, nowadays called the isentropic exponent, was specified as 1.41 (von Bezold, 1888).</p><p>Moreover, in the same publication, von Bezold concluded that for moist air's adiabatic changes of state, its potential temperature remains unchanged as long as the change of state occurs within dry-adiabatic limits; and further, if there is condensation and precipitation, the potential temperature changes by a magnitude that is determined by the amount of water that falls out of the air parcel. From a modern perspective, it is clear that the air parcel is an isolated thermodynamic system, and adiabatic processes correspond to processes with conserved entropy (i.e., isentropic processes). The description of the immanent heat is then equivalent to the thermodynamic state function entropy, which corresponds to potential temperature of dry air in a one-to-one relationship.</p><p>In general, the potential temperature has the benefit of providing a practicable vertical coordinate (equivalent to the pressure level or the altitude above, e.g., sea level) to visualise and analyse the vertical distribution and variability of (measured) data related to any type of atmospheric parameter. Admittedly, the use of the potential temperature as a vertical coordinate is initially less intuitive than applying altitude or pressure coordinates. Indeed, the potential temperature bears a certain abstractness to describe an air parcel's state at a certain altitude level by its imaginary dry-adiabatic descent to ground conditions. However, one major advantage of using the potential temperature as a vertical coordinate is that the (measured) data are sortable with respect to the entropy state at which the atmospheric samples were taken. Thus, comparing repeated measurements of an atmospheric parameter on an isentropic surface or layer excludes any diabatic change of the probed air mass.</p><p>Apart from characterising the isentropes, the vertical profiles of the potential temperature (θ as a function of height z) are used as the reference for evaluating the atmosphere's actual vertical temperature gradient, which allows characterising its static stability. Notably, von Bezold (1888) already proposed the potential temperature as an atmospheric stability criterion. In its basic formulation, the potential temperature exclusively refers to the state of dry air, and thus the potential temperature characterises the atmosphere's static stability with respect to vertical displacements of a dry air parcel. In meteorology, the static stability parameter is expressed in terms of the (squared) Brunt-Väisälä frequency N, often written in the form (2)N2=gθ∂θ∂z, where g = 9.81ms−2 is the gravitational acceleration. The potential temperature twice enters the formulation of the stability parameter, as the denominator (θ−1) and as the vertical gradient ∂θ∂z. In the research field of dynamical meteorology, the potential vorticity (PV) is often used (Ertel, 1942; Hoskins et al., 1985; Schubert et al., 2004). The PV is proportional to the scalar product of the atmosphere's vorticity (the air's local spinning motion) and its stratification (the air's tendency to spread in layers of diminished exchange). More concretely, the PV is the scalar product of the absolute vorticity vector and the three-dimensional gradient of θ, i.e., not only the potential temperature's vertical gradient but also its partial derivatives on the horizontal plane add to the resulting PV, although, particularly at stratospheric altitudes, the vertical gradient constitutes the dominant contribution. For the analytical description of a fluid's motion within a rotational system, as is the atmosphere, the PV provides a quantity that varies exclusively due to diabatic processes. Frequently, the PV is used to define the tropopause height (usually at 2 PV units, see, e.g., Gettelman et al., 2011) or the edge of a large-scale cyclone such as the polar winter vortex on specific θ levels (cf. Curtius et al., 2005).</p><p>While for a dry atmosphere (i.e., with little or no water vapour) the potential temperature is the correct conserved quantity (corresponding to entropy) for reversible processes, for an atmosphere containing water in two or more phases (vapour, liquid, and/or solid phases) energy transfers due to phase changes play a major role. Thus, the formulation of the potential temperature has to be extended since entropy is still the right quantity for reversible processes, including phase changes. Starting from the equation for the moist specific entropy, as derived from the first law of thermodynamics and the Gibbs equation, further extensions of the dry air potential temperature have been developed (Hauf and Höller, 1987; Emanuel, 1994; Marquet, 2011; Marquet and Geleyn, 2015) to account for phase changes and deviations from thermodynamic equilibrium, e.g., by irreversible processes. By assuming only reversible processes (i.e., conserved entropy), approximate formulas can be derived (e.g., Emanuel, 1994). However, in the case of large hydrometeors, liquid or solid particles are removed due to gravitational acceleration, leading to an irreversible process, hence the formulas based on the assumption of a reversible process are no longer applicable. Sometimes for this situation a so-called pseudo adiabatic potential temperature is defined, assuming instantaneous removal of hydrometeors from the air parcel; usually, meaningful approximations to this quantity are given, since generally it cannot be derived from first principles. Equivalent potential temperature including phase changes for vapour and liquid water is often used for the determination of convective instabilities. The general formulation can be easily adapted for an ice equivalent potential temperature, i.e., for reversible processes in pure ice clouds (see, e.g., Spichtinger, 2014). Although the latent heat of sublimation is larger than the latent heat of vaporisation, the absolute mass content of water vapour decreases exponentially with decreasing temperature, leading to only small corrections due to phase changes in pure ice clouds.</p><p>At altitudes above the clouds' top, within the upper troposphere and across the tropopause, the air is substantially dried out compared to tropospheric in-cloud conditions. Therefore, above clouds and further aloft, e.g., within the stratosphere, the conventional dry-air potential temperature may suffice to provide a meaningful vertical coordinate. Moreover, the potential temperature or the virtual potential temperature, which includes water vapour, are commonly used as prognostic variables in numerical models for the formulations of the energy equation (e.g., Skamarock et al., 2005; Skamarock and Klemp, 2008; Zängl et al., 2015; Borchert et al., 2019). Thereby, very often both variants, the potential temperature as well as the equivalent potential temperature, are involved to account for dry air situations and cloud conditions.</p><!><p>θ should be based on a rigorous derivation to ensure its validity as a function of atmospheric altitude in order not to corrupt its character as a vertical coordinate that allows for appropriately comparing (measured) atmospheric parameters, and</p><p>θ should approximate to the greatest possible extent the true entropy state of a probed air mass and should preferably account for the implied dependencies on atmospheric variables, even under the assumption that air behaves as an ideal gas,</p><!><p>In principle, the concept of the potential temperature is transferable to all systems of thermally stratified fluids such as a planetary gas atmosphere or an ocean, to investigate heat fluxes (advection or diffusion) or the static stability of the fluid. In astrophysics, the potential temperature is used almost identically as in atmospheric sciences to describe dynamic processes and thermodynamic properties (e.g., static stability or vorticity) in the atmosphere of planets other than the Earth. Here, the same value p0 = 1000 hPa, as applied to the Earth's atmosphere, is frequently used as a reference pressure for the atmosphere of other planets (Catling, 2015, Table 4), whereby the formulations of the specific heat capacity require adaptations to account for the individual gas composition of the respective planetary atmosphere. In order to simulate the weather in the atmosphere of other planets, the Weather Research and Forecasting model (WRF) was extended to "planetWRF" (Richardson et al., 2007) and the governing equations considered within the WRF model include a prognostic equation for the potential temperature (Skamarock et al., 2005; Skamarock and Klemp, 2008). However, the temperature dependency of the isobaric heat capacity cp is not generally negligible, especially when taking "deep atmospheres, such as on Venus" (Catling, 2015, p. 436) into account or the temperature lapse rates on other planets (Li et al., 2018). The atmosphere of Saturn's moon Titan, the only known moon with a substantial atmosphere, was comprehensively studied with frequent application of the potential temperature based on profile measurement of temperature and pressure in Titan's atmosphere by the Huygens probe (Müller-Wodarg et al., 2014).</p><p>Moreover, the potential temperature is a frequently used quantity in oceanography (e.g., McDougall et al., 2003; Feistel, 2008), while here the consideration of sea water's salinity and its impact on the specific heat capacity of sea water implies additional complexity. In particular, McDougall et al. (2003) suggests a re-assessment of the potential temperature as applied in oceanography to approximate the adiabatic lapse rate, thus this study bears certain parallels to the present investigation aiming at the reappraisal of the potential temperature for atmosphere-related purposes. These studies from other disciplines motivate the need for a re-assessment of the potential temperature for the atmospheric sciences. Thus, the approach provided herein proposes a modified calculation of the widely used quantity of the potential temperature by additionally accounting for the current state of knowledge concerning air's properties.</p><p>The study is organised as follows: The derivation of the potential temperature for an ideal gas with constant specific heat capacity cp is recalled in Section 2. In Section 3 the assumption of a constant cp is discussed together with a synopsis of various cp values as provided in the literature. The temperature dependency of cp is examined in Section 4 and a parameterisation is given. Section 5 is devoted to the definition and computation of a new reference potential temperature θref based on the temperature-dependent specific heat capacity, while Section 6 focuses on the influence of real-gas effects on the resulting potential temperature. Section 7 presents some implications of the use of θref and concluding remarks are given in Section 8.</p><!><p>The Gibbs equation (see, e.g., Kondepudi and Prigogine, 1998) is a general thermodynamic relation to describe the state of a system with m components and reads as (3)TdS=dH−V dp−∑k=1mμkdMk, where T denotes the absolute temperature in K, S the entropy in JK−1, H the enthalpy in J, V the volume in m3, μk the chemical potential of component k in J kg−1, Mk the mass of component k in kg, and p the static pressure in Pa. Assuming no phase conversion or chemical reaction within the system, the mass of each component does not change, hence dMk = 0 for each component k.</p><!><p>The ideal gas law (5)pV=MaRaT can be applied with the specific gas constant Ra of dry air, which is (6)Ra=RMmol,a=8.31446261815324 J mol−1 K−10.0289586 kg mol−1±0.0000002 kg mol−1∈287.11350 J kg−1 K−1,287.11748 J kg−1 K−1, with the molar gas constant R in J mol−1 K−1 (Tiesinga et al., 2020; Newell et al., 2018) and Mmol,a the molar mass of dry air (Lemmon et al., 2000), composed of nitrogen N2, oxygen O2, and argon Ar.</p><p>The specific enthalpy is given by (7)dh=cpdT with cp the specific heat capacity of dry air.</p><!><p>Based on these assumptions, the change of the specific entropy (within the fluid dry air) is given by (8)ds=cpTdT−Radpp. For isentropic changes of state, i.e., ds = 0, equation (8) reduces to (9)cpTdT=Radpp.</p><p>Note that the assumption of dry air being an ideal gas does not imply that in (9) the specific heat capacity cp is constant. While statistical mechanics excludes any pressure dependence in the ideal-gas heat capacity, the general derivation (cf. Appendix A) permits a temperature dependence of cp. However, usually the temperature dependence is neglected in atmospheric physics and, instead, cp is assumed as constant (see, e.g., Ambaum, 2010, page 48/49, where vibrational modes of the air molecules are neglected). Immediately below and in Section 3, the treatment of cp as a temperature-independent constant is discussed. The introduction of the temperature dependence then follows in Section 4.</p><p>Treating cp as a constant, rearrangement of (9) leads to (10)dTT=Racpdpp. Integration of (10) over the range from ground-level pressure and temperature (p0, T0) to the pressure and temperature at a specific height (p, T) yields (11)lnTT0=∫T0TdT′T′=Racp∫p0pdp′p′=Racplnpp0, and, after another straightforward conversion, one arrives at (12)lnT0T=Racplnp0p. With the definition θcp=T0, equation (12) is transformed into the commonly used expression for determining the potential temperature (13)θcp=Tp0pRacp, for which the ground-level pressure p0 is arbitrary but usually set to p0 = 1000 hPa. This choice coincides with the definition of the World Meteorological Organisation (WMO, 1966) and the standard-state pressure (Tiesinga et al., 2020), but should not be confused with the standard atmosphere 101325Pa (Tiesinga et al., 2020). In the following, θcp denotes the potential temperature based on a constant cp and, when a specific value of cp is applied, the subscript cp in the potential temperature's notation is replaced by the corresponding cp value.</p><!><p>The general theory of thermodynamics, assuming dry air as an ideal gas, gives the expression (14)cp=1+f2Ra for the constant specific heat capacity, which is based on the results of statistical mechanics and the equipartition theorem (e.g., Huang, 1987). In (14), the parameter f = ftrans + frot + fvib is equal to the total number of degrees of freedom of the gas molecules of which dry air consists. The individual contributions to f comprise the degrees of freedom of translation ftrans, rotation frot, and vibration fvib. Assuming further that dry air exclusively consists of the linear molecules N2 and O2 (implying ftrans = 3 and frot = 2, while the contribution of Ar remains disregarded) and additionally neglecting the vibrational degrees of freedom (fvib = 0), the general relation (14) reduces to (15)cp=1+3+22Ra=72Ra. Although the neglect of vibrational excitation, particularly at very low temperatures, seems plausible and appropriate, errors are already introduced by this assumption for the temperature range relevant in the atmosphere.</p><p>In atmospheric sciences, for the majority of computations that require the specific heat capacity of dry air, a constant value of cp may be appropriate. According to the WMO (1966), the recommended value for cp of dry air is 1005 J kg−1 K−1 and, furthermore (ibid.), it is defined that γ=cpcv=75=1.4, cf. (1). This definition is consistent with the general thermodynamic theory together with all aforementioned additional assumptions and results in (15) as well.</p><p>Even assuming a universally valid constant cp, a single consistently used value of cp was not found. Instead, the specified values of cp vary among different textbooks and other sources. In Table 1, some of the available values of constant specific heat capacity for dry air are compiled, indicating a variability of cp that ranges from 994 J kg−1 K−1 to 1011 J kg−1 K−1. However, the extremes in Table 1 are from old references of historical interest only; to reflect recently stated values the narrower range 1000 J kg−1 K−1 to 1010 J kg−1 K−1 is considered.</p><p>These different values of constant cp scatter within a small range (below ±1.1%) around the WMO's recommendation 1005 J kg−1 K−1, which may seem negligible if cp contributes only as a linear coefficient within an equation (e.g., in the expression of a correction factor, cf. Weigel et al., 2016). However, in the formulation of the potential temperature θcp, cf. (13), the specific heat capacity cp does not contribute linearly but rather as the denominator in the exponent. Thus, the variety of different cp values, although scattering within a small range, impact the resulting θcp significantly. To illustrate this impact, a computation of θcp by using (13) was based on the values of static pressure (p, cf. Figure 1a) and absolute temperature (T, cf. Figure 1b) corresponding to the US Standard Atmosphere (United States Committee on Extension to the Standard Atmosphere, 1976). From the list of the different cp in Table 1, the extreme values were selected in order to initially illustrate the sensitivity of the resulting θcp to variations in cp in the range of ~ 1%, as seen in the literature. In Figure 2a, the individual profiles of θcp are shown for the extremes of the historic cp values (Table 1), while Figure 2b illustrates the absolute differences Δθcp=θ994−θ1011 (red curve), Δθcp=θ1000−θ1010 (blue curve), and Δθcp=θ1003.5−θ1006.5 (green curve). The absolute error exhibited with the blue curve in Figure 2b is based on the extremes of most recently referred cp values in the literature (Table 1). At an altitude of 8.5km, the difference Δθcp already exceeds 1 K (blue curve). The values of Δθcp reach approximately 1.2 K at 10km and rise further, above 4 K, with increasing altitude up to 20km. At 50km, approximately where the stratopause is located, which is the chosen upper height limit for this investigation, the computed Δθcp reaches 43 K. The green curve corresponds to the more realistic cp interval 1005 J kg−1 K−1 ± 1.5 J kg−1 K−1 as recommended by the WMO; the difference reaches approximately 13 K at the stratopause.</p><p>Figure 2 illustrates the possible spread of θcp based on a range of cp values from different literature references; hence, if one uses a different value for cp from the literature than that defined by WMO (1966), the difference θ1005−θcp might be significant. Since the cp values provided by some literature references are close to the value cp = 1005J kg−1 K−1 recommended by the WMO (1966), the subsequent comparisons will be made to θ1005. The θcp based on cp values other than 1005 J kg−1 K−1 are only used to illustrate respective deviations. Although the curves in Figure 2b depict extremes in the deviation of potential temperatures, as they are based on the extremes of cp values (cf. Table 1), they nevertheless illustrate the sensitive response of θcp to even small variations in cp, on the order of 1%. Further proof of this sensitivity from the mathematical perspective is provided in Appendix B. The impact of this sensitivity becomes important at altitudes of ~ 10km and above, thus, where the use of the potential temperature becomes increasingly meaningful. Here, and in particular above the cloud tops, the small-scale and comparatively fast tropospheric dynamics (causing vertical transport and implying diabatic processes) become diminished, while further above, towards the stratosphere, an increasingly layered vertical structure of the atmosphere is taking over.</p><p>As indicated above, the reason for this sensitivity to small variations of air's specific heat capacity is that it affects the exponent of the equation for θcp. The studies of Ooyama (1990, 2001) document an interesting attempt to formulate, e.g., the energy balance equations for the moist atmosphere, wherein entropy replaces the more common formulation using the potential temperature. This substitution avoids the use of the potential temperature, which "is merely an exponential transform of the entropy expressed in units of temperature" (Ooyama, 2001), thus, within this equation, air's specific heat capacity is implied exclusively as a linear coefficient. Consequently, a parameterisation for the temperature dependence of the specific heat capacity (cp(T), cf. Section 4) may be easily adopted. However, the crucial drawback of the entropy-based equations is that to gain a numerical model for, e.g., weather forecast purposes, the parameterisations of most of the physical processes within the atmosphere would require a reformulation.</p><p>It should be noted that not only do literature values of air's specific heat capacity cp vary, but also the values of the gas constant Ra vary slightly due to different historical approximations for the molar gas constant4 R and for the composition of dry air. The variation of values for Ra is typically only on the order of 0.1 J kg−1 K−1, whereas the variability in cp is on the order of a few J kg−1 K−1 (cf. Table 1). Therefore, within the exponent of the expression (13) for θcp, the variability of cp has by far a stronger impact on the resulting θcp value than the variability of Ra.</p><p>However, accepting for a moment the WMO's definition (15) of cp (WMO, 1966), the variability of air's cp should naturally be constrained to certain limits. With the specific gas constant Ra = 287.05 J kg−1 K−1 (WMO, 1966), the WMO's definition leads to cp = 1004.675 J kg−1 K−1. In contrast, taking into account the uncertainty introduced in Ra by the molar mass of dry air, cf. Equation (6), the resulting range for air's specific heat capacity is 1004.897 J kg−1 K−1 ≤ cp ≤ 1004.912 J kg−1 K−1. It may be surmised that the rounded value cp = 1005 J kg−1 K−1 as recommended by the WMO (1966) had the main goal to simplify certain calculations, which at the time may have been mostly done by hand.</p><!><p>Next, while retaining the ideal-gas assumption, we consider the dependence of air's cp on temperature, mainly over the atmospherically relevant range (180 K to 300 K). The temperature dependence of cp is, of course, not a new finding. Experimental approaches for determining the calorimetric properties of air and the temperature dependence of a fluid's specific heat capacity are described by Witkowski (1896), who investigated the change of the mean cp as a function of temperature intervals between room temperature (as a fixed reference) and various warmer and colder temperatures, for atmospheric pressures and slightly beyond. Despite the potentially high uncertainty of the experimental results from these times, Witkowski (1896) already indicated that with decreasing temperature the experimentally determined cp values initially decline, then pass a minimum, and subsequently increase again at lower temperatures (T < 170 K). The description of refined experiments and ascertainable data of air's cp(T) for temperatures below 293 K is summarised by Scheel and Heuse (1912), Jakob (1923), and Roebuck (1925, 1930), illustrating in comprehensive detail the experimental effort and providing the resulting data. The review by Awano (1936) compiled and compared the data of cp(T) of dry air ("air containing neither carbon-dioxide nor steam", Awano, 1936) and he attested—at that time—the previously mentioned studies to constitute "the most reliable experiments". During the decades following these experiments, further insights were gained and landmarks were reached which are summarised in the comprehensive survey by Lemmon et al. (2000) of the progress of modern formulations for the thermodynamic properties of air and about the experiments the previous formulations were based on.</p><p>Figure 3 illustrates the range of suggested constant values for the specific heat capacity as indicated in Table 1 (dashed curves) together with the measurements that were made to obtain air's behaviour as a function of temperature and pressure. Note, Figure 3 includes data at other atmospheric pressures, indicated by squares, diamonds, and triangles. In the same figure, calculated values of cp(T) of dry air are displayed resulting from the equation of state which was derived from experimental p, V, and T data by Vasserman et al. (1966), who provided an extensive review of previous experimental and theoretical works and of the state of knowledge at that time. In addition, Figure 3 exhibits two different parameterisations, by Lemmon et al. (2000) and by Dixon (2007, see page 376 in his book, the accuracy is "within 0.1% from 200 K to 450 K"), which account for the temperature dependence of the specific heat capacity cp(T). The parameterisation by Lemmon et al. (2000), to be discussed in detail in Section 4.2, is valid for dry air assumed as an ideal gas whereas this distinction is not explicitely made in Dixon (2007). Moreover, Figure 3 contains discrete values of dry air's cp(T) extracted from the database REFPROP (Reference Fluid Thermodynamic and Transport Properties Database by NIST, the National Institute of Standards and Technology, Lemmon et al., 2018), which is based on parameterisations resulting from thermodynamic considerations discussed later.</p><!><p>the measurements of cp(T) have a precision likely no better than 1% (in particular the historical measurements), and there could be systematic errors, especially at low temperatures;</p><p>the measured data reflect the true thermodynamic behaviour of the real gas, rather than that of an ideal gas.</p><!><p>As already indicated by the data depicted in Figure 3, the specific heat capacity cp depends on the gas temperature. With regard to measured values, the lack of constancy may be due to real-gas effects or to a dependence of the ideal-gas heat capacity on temperature. In this section, we focus on the latter effect, denoting the ideal-gas isobaric specific heat capacity by cp0T, where the superscript 0 indicates the underlying ideal-gas assumption. For an individual gas, there is always a contribution from the three translational degrees of freedom, cp,trans0=52Ri, where Ri is the specific gas constant of the gas. If the molecule is assumed to be a rigid rotor, there is also a rotational contribution given by (16)cp,rot0=Ri,for lineare.g., diatomic molecules,32Ri,for nonlinear molecules.</p><p>As mentioned previously, at finite temperatures molecules also have contributions to cp0T from intramolecular vibrations (and, at high temperatures, excited electronic states). To arrive at a temperature-dependent parameterisation for the ideal-gas specific heat capacity of dry air, the compounds' individual contributions, considering all degrees of freedom, need to be parameterised and then combined according to each compound's proportion in the mixture. For the following, dry air is considered a three-component mixture: the diatomic gases nitrogen (N2) and oxygen (O2) and the monatomic gas argon (Ar).</p><p>To determine the contribution of N2 to cp0T, both Bücker et al. (2002) and Lemmon et al. (2000) use the ideal-gas heat capacity from the reference equation of state of Span et al. (2000) that compares well with the findings from other studies within an uncertainty Δcp0 of less than 0.02%.</p><p>For the contribution of O2, Lemmon et al. (2000) use the formulation given by Schmidt and Wagner (1985). Alternatively, Bücker et al. (2002) provide a slightly different formulation from the International Union of Pure and Applied Chemistry (IU-PAC, Wagner and de Reuck, 1987), after refitting it to more recently obtained data, thereby achieving an overall uncertainty Δcp0 of less than ±0.015% for O2 (Bücker et al., 2002). However, the difference in the resulting specific heat capacity contribution by O2 between the two approaches (Lemmon et al. (2000) or Bücker et al. (2002)) is comparatively small. The recent work of Furtenbacher et al. (2019) leads to values of cp0 for O2 with even smaller uncertainties, but the differences from the values used here are negligible in our context.</p><p>For a monoatomic gas such as Ar, vibrational and rotational contributions to the heat capacity do not exist, and Bücker et al. (2002) consider that argon's excited electronic states are relevant only at temperatures above 3500 K. Hence, the contribution of Ar to the specific heat capacity of air reduces to cp0=52RAr.</p><p>The approach by Bücker et al. (2002) additionally considers the contribution of further constituents of air, such as water, carbon monoxide, carbon dioxide, and sulfur dioxide. These authors provide an analytical expression for specific heat capacity, accounting for this more complex but proportionally invariant air composition which is specified to deviate from the used reference by Δcp0≤±0.015% in the temperature range of 200 K ≤ T ≤ 3300 K. At atmospheric altitudes above the clouds' top, i.e., on average above ~ 11km, the air is assumed to have lost most of its water and is deemed as dry. Furthermore, for the following, trace gases that contribute to air's composition by molar fractions of less than that of Ar are neglected.</p><!><p>an empirical model-based equation of state for standard (dry) air considered as a pseudo-pure fluid, and</p><p>assembly of a mixture model from equations of state for each pure fluid.</p><!><p>Each approach allows calculating the thermodynamic properties, e.g., cp, of gas mixtures such as dry air, and both are real-gas models with the ideal-gas behaviour as a boundary condition. The major difference between the models is that the first approach considers air as a pseudo-pure fluid while the second, more rigorous approach treats air as a mixture composed of N2, O2, and Ar, in molar fractions of 0.7812, 0.2096, and 0.0092, respectively, following Lemmon et al. (2000, their table 3). This fractional composition of dry air is assumed to be constant from ground level up to 80km height (United States Committee on Extension to the Standard Atmosphere, 1976) and its fractional composition would have to be shifted significantly to cause a serious deviation of the resulting potential temperature. The contribution to the composition by carbon dioxide (CO2) and of any other trace species was assumed to be negligible. The validity of both approaches is specified for various states of dry air, from its solidification point (59.75 K) up to temperatures of 1000 K, and for pressures up to 100MPa and even much further beyond the pressure range that is relevant for atmospheric investigations. Both the pseudo-pure fluid model and the mixture model are implemented in NIST's REFPROP database (cf. https://www.nist.gov/srd/refprop) for various physical properties of fluids over a wide range of temperatures and pressures.</p><p>Both the pseudo-pure fluid model and the mixture model of Lemmon et al. (2000) use the same expression for the ideal-gas heat capacity, which is rigorously given as a sum of the pure-component contributions: (17)Cp0TR=xN2Cp0TRN2+xArCp0TRAr+xO2Cp0TRO2, where xi denotes the molar fraction of species i, and Cp0 as well as the molar gas constant R are given in units of J mol−1 K−1.</p><p>Like Bücker et al. (2002), Lemmon et al. (2000) use the expression of Span et al. (2000) for the contribution of N2 to the heat capacity and adopt Cp0=52R for Ar. Together with the contribution by O2 according to the formulation by Schmidt and Wagner (1985), the expression provided by Lemmon et al. (2000, equation 18 therein) for the ideal-gas heat capacity of dry air is (18)Cp0TR=N1+N2T+N3T2+N4T3+N5T−32+N6N92T2expN9TexpN9T−12+N7N102T2expN10TexpN10T−12+2N83N112T2exp−N11T23exp−N11T+12, with the scalar coefficients Ni for dry air (ibid.), (19)N1=3.490888032,N2=2.395525583⋅10−6,N3=7.172111248⋅10−9,N4=−3.115413101⋅10−13,N5=0.223806688,N6=0.791309509,N7=0.212236768,N8=0.197938904,N9=3364.011,N10=2242.45,N11=11580.4, which is specified as valid for temperatures from 2000 K. Because the underlying calculations are based on rigorous statistical mechanics and accurate spectroscopic data, Cp0TR should be accurate to within 0.01% throughout this range, as discussed by Span et al. (2000).</p><p>The parameterisation (18) provides the isobaric specific heat capacity of dry air, considered as a mixture of ideal gases. This represents a more rigorous and accurate behaviour than assuming it to be a constant.</p><!><p>The parameterisation from Dixon (2007) (20)cpT=1002.5+275⋅10−6⋅T−2002 for 200 K ≤ T ≤ 450 K is not explicitly described to be based on particular assumptions or data sets. The author indicates his suggested parameterisation to hold within 0.1% for temperatures between 200 K and 450 K. For elevated air temperatures, the deviation between the ideal-gas limit cp0T (Lemmon et al., 2000) and Dixon's parameterisation substantially increases. This is most likely due to the chosen type of polynomial approximation (Dixon, 2007), which increasingly departs from the reference cp0T for gas temperatures exceeding 450 K.</p><p>Concerning the thermophysical properties of humid air, the study by Tsilingiris (2008) provides further insight. Its purpose was to evaluate the transport properties as a function of different levels of the relative humidity and as a function of temperature (from 273 K to 373 K) for the gas mixture of air with water vapour at a constant pressure (1013 hPa). The atmospherically relevant pressure range below 1013 hPa and temperatures smaller than 273 K were not considered. Although this study focused on providing a comprehensive account of moisture within air, mainly for technical purposes and engineering calculations, the possible usefulness of these findings to atmospheric investigations is also apparent. However, the impact of water vapour on the resulting gas mixture's cp(T) is significantly larger (cf. Tsilingiris, 2008) than the uncertainty of dry air's cp(T) that is discussed in the present work. Furthermore, the consideration of water vapour as a component of air requires very individual and case-specific computations of cp(T) of moist air, as water vapour is among the most variable constituents of the atmosphere.</p><p>The effort required to produce an analytical formulation for gas properties which best reflects the true gas behaviour may indicate that for engineering purposes (pneumatic shock absorbers, engines' combustion efficiency, improvements of turbofan/-prop propulsion, aerodynamics, material sciences, etc.), especially where pressures exceed atmospheric, the assumption of ideal-gas behaviour introduces excessive uncertainty.</p><!><p>Previously introduced approaches for computing the specific heat capacity of dry air call for a brief discussion on how to use the obtained cp(T) to derive the potential temperature. In the following, θcpT denotes the derived potential temperature that accounts for the temperature dependence of dry air's specific heat capacity. Furthermore, it should be noted that simply substituting any cp(T) value into the conventionally used and defining equation (13) for θcp (WMO, 1966) may appear tempting but definitely leads to results inconsistent with θcpT that is based on the reference parameterisation of dry air's cp(T). Therefore, the thermodynamically consistent use of cp(T) in the derivation of θ is described in the following.</p><!><p>In the derivation of the potential temperature (cf. Section 2), we note that, until reaching the expression for isentropic changes of state (9), no assumption was made about the specific heat capacity. As soon as the temperature dependence of the specific heat capacity comes into play, the re-assessment of (9) leads to (21)cpTTdT=Radpp. Integration of (21) from the basic state (p0, θcpT) to any other state (p, T) yields (22)Ralnpp0=Ra∫p0pdp′p′=∫θcpTTcpT′T′dT′, where θcpT is the desired potential temperature.</p><p>The rearrangement of (22) makes evident that the desired potential temperature is a zero of the function F(x), given by (23)Fx=∫xTcpT′T′dT′−Ralnpp0.</p><p>To arrive at the desired potential temperature θcpT for any given temperature and pressure, the equation 0 = F(x) must be solved for the variable x, which is the desired θcpT. Equation (23) has at most only one real zero, since its integrand is strictly positive which means F(x) is strictly monotonic.</p><p>In the following, the ideal-gas reference potential temperature θref is introduced, based on the formulation of the ideal-gas limit of dry air's specific heat capacity cp0T in accordance with (18) as formulated by Lemmon et al. (2000). This reference potential temperature θref represents the zero of F(x) in (23), wherein cp(T′) is to be replaced by cp0T′, i.e. for given p, T the reference potential temperature θref solves the equation (24)0=Fθref=∫θrefTcpT′T′dT′−Ralnpp0. The parameterisation of cp0T′ is stated to give accurate values for temperatures from 60 K to 2000 K (cf. Section 4.2), thus values of θref should not exceed 2000 K, since otherwise cp0T′ within the integrand in (23) is evaluated outside of its range of validity. However, due to the division by T′, the value of the integrand cp0T′T′ may be expected to give nevertheless a good approximation even if the accuracy of cp0T′ is decreased, hence values θref > 2000 K should not be discarded.</p><p>It may be noted that further variants of a reference potential temperature are derivable by replacing cp(T′) in (23) by any other expression of the specific heat capacity of air which may appear sufficiently accurate. The steps to compute or approximate the zero of the function (23), described in this study, are independent of the chosen heat capacity formulation.</p><p>Unfortunately, for a straightforward solution of the integral (23), the suggested parameterisation of cp is too complex and an analytically insolvable nonlinear equation 0 = F(x) could result. Thus, an approximation of the equation's desired zero is required. Newton's method (cf., e.g., Deuflhard, 2011) provides a standard approach to numerically approximate the zero of a nonlinear equation. Proceeding from an initial guess x0, Newton's method constructs a sequence xkk∈ℕ defined by the recursion (25)xk+1=xk−FxkF′xk=xk−Fxk−cpxkxk=xkcpxkcpxk+Fxk=xkcpxkcpxk−Ralnpp0+∫xkTcpT′T′dT′. The constructed sequence xkk∈ℕ converges to the equation's desired zero. For the computations described here, the iteration is stopped as soon as the absolute difference |xk+1 − xk| of two consecutive iterations falls below 10−8 K.</p><p>For the reference of air's specific heat capacity, cp0T, the integral (23) turns out not to be explicitly solvable. Therefore, with each iteration, the solution of the integral ∫xkTcp0T′T′dT′ is approximated by subdividing the entire integration range, [xk, T], into intermediate intervals with respective size of at most 0.1 K, and by applying Simpson's rule on each subinterval.</p><p>As a first guess x0 for the Newton iteration, the conventional definition of θcp based on a constant specific heat capacity (WMO, 1966) is inserted: (26)x0=Tp0pRa1005 J kg−1K−1=θ1005. In the course of Newton's method, the sequence xkk∈ℕ will converge to the unique zero for any initial guess x0 due to the monotonicity of F(x). However, the right choice of the initial guess x0 substantially decreases the error of the first iteration x1, speeding up convergence to the desired zero of the function F(x). Therefore, it seems wise to use the conventional definition of θcp as the first guess for the Newton iteration (25).</p><p>Solving the previously described root-finding problem by Newton's method over the comprehensive range of iteration steps (until the set requirement, i.e., |xk+1 − xk| < 10−8 K, is fulfilled) finally leads to the reference potential temperature θref. This θref is based on the ideal-gas limit of dry air's specific heat capacity cp0T, which refers to the current thermodynamic state-of-knowledge and, thus, we use θref as our reference for the potential temperature in the following. For evaluating the results, the air temperature and pressure from the US Standard Atmosphere are used once more to set up the vertical profiles of the potential temperature. Figure 4a exhibits the resulting reference profile, i.e., θref (red curve). Additionally, for comparison with the reference, further potential temperature profiles θcp are shown based on the two (historical) extremes cp = 994 J kg−1 K−1 and cp = 1011 J kg−1 K−1 (dashed curves), and based on the range limits of more recent values cp = 1000 J kg−1 K−1 and cp = 1010 J kg−1 K−1 (solid green and magenta curves) of given constant values of air's specific heat capacity (cf. Table 1). Clearly, in particular at elevated altitudes, the courses of θ1000 and θ1010 significantly deviate from the reference. To quantitatively evaluate the match between the different profiles, the relative difference of the profiles based on a constant cp, with respect to the reference, i.e., Δθ/θref=θcp−θref/θref, is depicted in Figure 4b. The comparison demonstrates that the θcp profiles significantly depart from the reference by about ~ 300 K at 50km altitude, corresponding to a relative difference of about 16%. With both extremes of the recent constant values cp ∈ {1000 J kg−1 K−1, 1010 J kg−1 K−1}, the relative error level of 0.1% is exceeded at altitudes about 5km. While θ1000 continues to increasingly deviate from the reference, θ1010 re-enters and crosses the 0.1% relative error interval (grey-shaded area) at altitudes between ~ 19km and 21km, before it reaches similar errors to the other θcp profiles that are based on a constant cp. Although the extreme values cp ∈ {1000 J kg−1 K−1, 1010 J kg−1 K−1} appear in recent literature, these values may be considered unrealistic. For this reason, Figure 4b also shows the relative deviations for the values cp ∈ {1003.5 J kg−1 K−1, 1004 J kg−1 K−1, 1005 J kg−1 K−1, 1006.5 J kg−1 K−1}, which include the recommended value of the WMO (1966) and a more realistic range, i.e. cp = 1005 J kg−1 K−1 ± 1.5 J kg−1 K−1. Notably, up to an altitude of 15km, the reference potential temperature is comparably well matched by both the recommended θ1005 and θ1004 (based on the frequently used alternative cp = 1004 J kg−1 K−1, cf. Table 1). Until 15km altitude, both constant cp values lead to errors of calculated θcp which remain comparatively small within the 0.1% relative error interval. However, above ~ 17.5km, both θ1004 and θ1005 exceed the 0.1% relative error interval, and further aloft their relative error with respect to the reference θref increases rapidly.</p><p>In the context of numerical models of the atmosphere, the energy balance equation is occasionally formulated based on the potential temperature θ, thus θ constitutes a prognostic model variable. In such a case, the temperature T needs to be calculated from a given pair of values of pressure p and potential temperature θ. Using once more the defining equation (22), for given θ a zero of the function (27)0=−∫TθcpT′T′dT′−Ralnpp0 is to be computed. Since (27) corresponds to the function F defined in (23) with the exception of a negative sign, the identical approximation procedure as outlined above in this section for the calculation of (T, p) ↦ θ may be applied mutatis mutandis to calculate the transformation (θ, p) ↦ T.</p><p>In any case, a certain effort is required to implement the new formulation of the potential temperature in an atmospheric model, as this equation should be based on the implicit definition (22), and such a goal may be the subject of future endeavours.</p><!><p>the requirement to numerically solve the integral in the function F(x) and</p><p>the need to use Newton's method for an iteration sequence to approach the zero of F(x),</p><!><p>Proceeding from the definition (23) of the function F(x), the computation of the integral ∫xTcp0T′T′dT′ becomes the first obstacle to a practical approximation. Therefore, a plausible initial step is to replace the integral by an expression that is easier to treat. This expression may be proposed as f(T) − f(x), where the function f is defined as f(x) = b0+b1 ln(x−b2)+b3x+b4×2 and which is recognisable as an approximated primitive of cp0T′T′, see Appendix C1. The choice of the functional form of f is motivated by the exact primitive of the integral in the case of a constant cp.</p><p>As previously discussed (cf. Section 5.1), the formulation of a new expression for the potential temperature based on the temperature-dependent specific heat capacity cp(T) requires finding the zero of the equation 0 = F(x), where the function F(x) is defined in (23). Replacing the exact integral in (23) by the difference f(T) − f(x) means that F(x) is substituted by the function (28)F^x=fT−fx−Ralnpp0.</p><p>Consequently, the resulting approximated reference potential temperature, i.e., the respective zero of the function F^x, is denoted as θrefapprox.</p><p>The difference between the approximation result and the reference, i.e., (29)θref−θrefapprox, is then referred to as the basic error of the approximation. Note that the replacement of the function F by F^ only circumvents the integration in F; the root-finding problem 0=F^x for the approximated reference potential temperature θrefapprox remains analytically not solvable.</p><p>Therefore, the second move towards a practical approximation procedure is to construct approximations θ(k) to the zero of F^x by using Newton's method, see Appendix C2. Newton's method is an iterative procedure; the notation θ(k) refers to the k-th computed iterate. Hence, θ(k) constitutes an approximation to θrefapprox, and, in the limit k → ∞, the approximation error (30)θrefapprox−θk vanishes. Two formulations of Newton's method are distinguished in Appendix C2, i.e., the principal application of Newton's method, and its further derivative, called Householder's method. Both formulations require the stipulation of one of the iterates θ(k) as sufficient to obtain a result of appropriate accuracy. The higher the number of iterations, of course, the smaller is the error (30), whereas the basic error (29) remains unaffected by the number of iterations. Hence, in any case, the basic error (29) is to be accepted as at least implied in the final approximation, even though a well-chosen θ(k) could result in an approximation error θref − θ(k) that is smaller than the basic error.</p><p>The various errors implied in the proposed approximation procedure combining for the approximation's total error, as well as accompanying details, are discussed in Appendix D. In brief, Figure 5a illustrates the basic error (29) based on the pressure and temperature profiles of the US Standard Atmosphere, as these provide atmospherically meaningful averages of realistic temperature-pressure data pairs. Based on the parameters of the US Standard Atmosphere, the basic error inherent with the approximation remains below 1.25 K up to altitudes of 50 km. Thus, regarding the subsequent iteration process, a substantial improvement of the error compared to ~ 1.5 K is not to be expected for the total error of approximating the reference potential temperature.</p><p>An error analysis exclusively based on the US Standard Atmosphere is constrained to specific combinations of the air's pressure and temperature, potentially suppressing latent errors that may emerge if certain fluctuations of the real atmosphere's temperature and pressure profiles are considered. Thus, the error analysis is extended to an atmospheric pressure (p) and temperature (T) range, from 1000 hPa to 0.5 hPa and from 180 K to 300 K, such that the conditions within the entire troposphere and stratosphere, including the stratopause, are covered. Figure 5b illustrates the absolute basic error (29) for the extended ranges of pressure and temperature while Figure 5c illustrates the relative basic error θref−θref approx/θref. The contours in Figures 5b and 5c mainly highlight two regions: at ~ 100 hPa where Δθ never rises above 0.75 K which corresponds to a maximum relative basic error of 0.15%, and in a pressure range from ~ 5 hPa to 1 hPa where a Δθ of 1.25 K is never exceeded, corresponding to relative errors of at most 0.1%. Note that the entire Δθ scale ranges up to 3 K, which may only be reached at pressures below 0.8 hPa combined with temperatures above 280 K.</p><p>As previously discussed, the basic error is unavoidable and is to be accepted when applying the suggested substitution for the integral in the definition of the function F(x) in (23). However, as outlined in Appendix C2, the second iterate θ(2) of Newton's method (principal application), may thoroughly suffice for the final approximation to the reference potential temperature θref, as this iteration level already features an approximation error (30) which is negligibly small. Figure 6a illustrates the total relative error of the suggested approximation θ(2) with respect to the ultimate reference θref for the extended ranges of pressure and temperature. Indeed, the contour pattern in Figure 6a and the basic relative approximation error shown in Figure 5c are remarkably similar. Thus, the iteration process itself imparts only a minor contribution to the total error compared to the basic approximation error.</p><p>The total approximation error, which is (31)θref−θ2=θref−θrefapprox+θrefapprox−θ2, is dominated by the unavoidable basic error (first bracket) and augmented by a negligible error inherent to the iteration (second bracket), also supporting the conclusion that the second iterate of Newton's method is an appropriate approximation procedure. Figure 7 presents step-wise instructions for the computation of the second iterate approximation to the reference potential temperature, and may serve as a guide to follow the numerous equations and intermediate analytical steps described throughout the derivations in Appendix C.</p><p>For completeness, Figures 6b and 6c exhibit a final comparison by means of the logarithmic difference and the logarithmic relative difference between the reference potential temperature θref and the conventional definition θcp (WMO, 1966) based on a constant specific heat capacity cp = 1005 J kg−1 K−1. Notably, over a wide altitude range within the troposphere (i.e., for atmospheric pressures greater than ~ 100 hPa), the absolute error Δθ = |θ1005 − θref| remains below 1 K, cf. Figure 6b, corresponding to a relative error Δθ/θref of at most 0.1%. However, in the pressure range below ~ 100 hPa, deviations of the real atmospheric conditions from those of the US Standard Atmosphere could increase the absolute error Δθ from a few K to up to 10 K, corresponding to an increase of the relative error to 1%. Further critical pressure levels are at ~ 20 hPa and ~ 5 hPa, where the error's magnitude increases to several tens and several hundreds of K, respectively. At a pressure of 0.5 hPa, an absolute error Δθ of up to 500 K is reached, which corresponds to a relative error of 10% or even more.</p><!><p>The use of the new reference potential temperature θref in a numerical model requires additional computational effort to perform corresponding calculations. Hereafter, two aspects are briefly discussed: (i) the formulation of the model equations, which include θref and (ii) the calculation of θref.</p><p>Although it is beyond the scope of the present study to provide a general derivation of an appropriate energy equation based on θref for atmospheric models, a formulation of the total derivative of θref is given by (32)cpθrefdθrefθref=cpTdTT−Radpp, where the details of its derivation are given in Appendix E. The total derivative of θref may be useful, since the governing equations are commonly formulated as differential equations.</p><p>The calculation of both the reference potential temperature θref and its approximation θrefapprox on the basis of given values of pressure p and temperature T requires an iterative procedure. The additional computational effort inherent with these calculations depends on the number of iterations. If, however, the second iteration θ(2) already represents an appropriate approximation of θref (cf. Section 5.2), then the flowchart in Figure 7 immediately conveys the additional computational effort to be expected. The calculation of the starting value x0 is identical to computing θ1005. An additional effort results from the evaluation of the functions f (three times) and f′ (two times), respectively, and the combination (two times) of obtained values to determine x1 and x2. Since each of these evaluations causes additional numerical steps, the computational effort to obtain θ(2) is in total about seven times more than the calculation of the conventional θ1005, while the algorithmic complexity is constant.</p><!><p>To account for real-gas effects (that cause a behaviour other than that of an ideal gas cf. Section 4) on the potential temperature, we use the model embedded in REFPROP (Lemmon et al., 2018), a standard reference database from NIST. This model treats air as a mixture and employs state-of-the art reference equations of state for pure nitrogen (Span et al., 2000), oxygen (Schmidt and Wagner, 1985), and argon (Tegeler et al., 1999). The mixing rule and binary interaction parameters are taken from the GERG-2008 model (Kunz and Wagner, 2012). From its definition in terms of an isentropic process, the potential temperature θreal(T, p) is defined implicitly by (33)sθreal,p0=sT,p, where s is the specific entropy. Calculating θreal(T, p) is a two-step process. First, the specific entropy s is computed at temperature T and pressure p. Then, the temperature θreal is found that gives the same entropy s at the ground pressure p0. This is an iterative calculation, but it is accomplished automatically within the REFPROP software (Lemmon et al., 2018).</p><p>One caveat should be mentioned regarding the computed potential temperatures. The range of validity of the equations of state for the air components (Span et al., 2000; Schmidt and Wagner, 1985; Tegeler et al., 1999) extends only up to 2000 K. At very high altitudes, computed values of θreal exceed this limit. While all the equations extrapolate in a physically realistic way, their quantitative accuracy is less certain above 2000 K. This caveat also applies to the ideal-gas calculations; the correlations for cp0T for N2 and O2 are extrapolations beyond 2000 K. However, since the same ideal-gas values are used in the real-gas calculations, any inaccuracy in cp0T will cancel when evaluating the difference between ideal-gas and real-gas values of θ.</p><p>Figure 8 illustrates the comparison between the real-gas potential temperature θreal and the ideal-gas reference potential temperature θref. Figure 8a shows the difference θreal − θref along the p-T-profile of the US Standard Atmosphere and Figure 8b accounts for any p-T-combination of extended range but shows the relative difference instead. The difference between θreal and θref never exceeds 0.1 K for the absolute difference or 30 · 10−5 = 0.03% for the relative difference. As may be anticipated from the deviation of cp0 shown in Figure 3 at low temperatures both from the experimentally determined values (which may be inaccurate) as well as from the REFPROP data, the real-gas effect on the specific heat capacity of dry air tends to increase towards the coldest gas temperatures. However, the difference between the real- and ideal-gas approaches results in essentially no substantial difference between the resulting θ's, neither at ground conditions (for any temperature at ~ 1000 hPa) nor at very high altitudes (at pressures below ~ 1 hPa). While the negligible difference between θreal and θref near ground levels is less surprising, the diminished difference at higher altitudes reflects that in this region the potential temperature reaches such high values that the difference between the real-gas and the ideal-gas specific heat capacity becomes insignificant. Within the intermediate (stratospheric) region, the low pressures (and thus the low air densities) cause the ideal-gas assumption to be an accurate approximation even at low temperatures. In general, the degree to which a gas can be treated as ideal is primarily a function of the (molar) density. For an ideal gas, the density is proportional to the quotient pT; this is almost true also for real air. Hence, declining pressures together with rising temperatures both make the air's behaviour increasingly close to ideal.</p><!><p>As previously shown, the newly defined reference potential temperature θref deviates most from the WMO-defined potential temperature θ1005 at stratospheric altitudes and above (cf. Figure 6). More particularly, not only do the values from both θ definitions differ, but also their vertical derivatives, i.e., ∂θref∂z and ∂θ1005∂z. Whether such deviations have a significant effect on an application is very case-dependent and requires detailed examination and specific appraisal. Below, four typical applications of the potential temperature were selected and are examined regarding the quantitative effect on the results due to deviations of the introduced reference potential temperature compared to the conventional and commonly used θ1005. The purpose of this examination is to document the magnitude of errors to allow a well-founded, individual decision for each application of the potential temperature whether it is worth applying the more rigorous calculation in the particular context.</p><!><p>The formula for the (squared) Brunt-Väisälä frequency N2 is often given in the form of (2), i.e., a formula involving the potential temperature θ. The substitution of θ in equation (2) by the new reference potential temperature θref may be tempting, but it is erroneous and the resulting quantity is denoted as Nfalse 2. The Brunt-Väisälä frequency is not defined by equation (2), since this formula results from various simplifications in its derivation, e.g., by assuming hydrostatic conditions and a constant specific heat capacity. Consequently, the substitution of θref in equation (2) leads to a wrong formula for the Brunt-Väisälä frequency that does not correctly consider the temperature dependence of dry air's specific heat capacity.</p><p>The Brunt-Väisälä frequency is the oscillation frequency of an air parcel due to a local density perturbation (see, e.g., Durran and Klemp, 1982; Marquet and Geleyn, 2013; Wallace and Hobbs, 2006; Ambaum, 2010). Retaining the assumption of hydrostatic conditions, the defining formula yields (34)N2=gT∂T∂z+gcpT where the temperature-dependent specific heat capacity cp(T) was implied, and which quantifies the balance between the actual temperature stratification ∂T∂z and the dry adiabatic lapse rate −gcpT (e.g., Holton, 2004).</p><p>To illustrate the deviation of Nfalse2 from N2, vertical profiles of both variables were calculated based on the temperature profiles shown in Figure 9a. The temperature data are taken from the Upper Atmosphere Research Satellite Reference Atmosphere Project (URAP, see Swinbank and Ortland, 2003) data and are assumed as typical at mid-latitudes during June and December. The temperature profiles extend up to altitudes of 85km and thus cover the entire stratosphere and most of the mesosphere. The hydrostatic assumption allowed for computing pressure profiles along the URAP values for the vertical temperature distribution. Subsequently, the reference potential temperature θref and its vertical derivative were calculable. The resulting vertical profiles for Nfalse2 and the true Brunt-Väisälä frequency N2 are shown in Figure 9b. Evidently, the values of Nfalse2 (dashed lines) deviate significantly from N2 (solid lines) and increasingly so towards higher altitudes above 15km. However, the absolute deviation N2−N10052, using N10052 as calculated with θ1005 in accordance with Equation (2), does not exceed 1.6 · 10−6 s−2 (not shown), indicating that N10052 is a good representation of N2 along these temperature profiles.</p><p>For equations involving the potential temperature, however, it should be emphasised that the substitution of θ by θref rarely succeeds and that instead the entire derivation of the equations requires careful consideration of the assumptions, such as the constancy of cp, to avoid aberrations and erroneous conclusions.</p><!><p>Ertel's potential vorticity (e.g., Ertel, 1942; Hoskins et al., 1985; Schubert et al., 2004; Holton, 2004) may be defined as the potential vorticity of the dry air potential temperature by (35)PVθ=1ρ2Ω+∇×u⋅∇θ. In this definition, 2Ω+∇ × u is the absolute vorticity, Ω denotes Earth's angular velocity, u the three-dimensional wind vector, and ρ the air density (see, e.g., Hoskins et al., 1985; Cotton et al., 2011; Marquet, 2014). Since (35) represents the defining equation for Ertel's potential vorticity, the two potential vorticities (36)PVref=PVθref,PV1005=PVθ1005 based on the new reference potential temperature θref and θ1005, respectively, are considered. To provide a first comparison of these potential vorticities, u = 0 is assumed, i.e., an atmosphere at rest. Additionally, the potential temperature is assumed as horizontally constant. Consequently, (35) reduces to (37)PVθ=2sinϕρ2πtE∂θ∂z for a position on Earth with geographical latitude ϕ and tE = 24h, the duration of one rotation of the Earth.</p><p>Using the temperature profiles from Figure 9a together with the values of the potential temperatures θref and θ1005, the evaluation of the two potential vorticities (36) and (37) yields the potential vorticity profiles shown in Figure 10a while their relative deviations are shown in Figure 10b. Since the temperature profiles are representative for the north-hemispheric mid-latitudes, the geographical latitude ϕ in (37) was set to 52°N. At tropospheric altitudes, the relative deviation between θref and θ1005 is small and never exceeds ~ 1%, while it continuously increases towards higher altitudes. According to these profiles, the relative deviation exceeds 10% at 30km and reaches 100% at the highest altitudes.</p><p>It is noteworthy, however, that the computations of both N2 (cf. Section 7.1 and Figure 9b) and PV (Figure 10b) are based on the specific temperature profiles from URAP (cf. Section 7.1 and Figure 9a) and thus are not of general validity. The selection of these temperature profiles was entirely arbitrary and exclusively aimed at illustrating possible implications of the use of the developed reference potential temperature. The resulting and indicated deviations are ultimately subject to individual assessment on applying θref.</p><!><p>For atmospheric investigations, e.g., in the region of the upper troposphere and lower stratosphere (UT/LS), it is common practice to set vertical profiles of atmospheric parameters in relation to the potential temperature as vertical coordinate. This way, the increasingly isentropic stratification of the atmosphere above the UT is taken into account. The transport of an air mass along isentropic surfaces, i.e., surfaces of constant potential temperature and entropy, is to be regarded as adiabatic. Hence, the air's composition and properties within the same isentrope interval, regardless of the observation location, is better comparable than it would be if based on other isopleths (i.e., height or pressure coordinates). Investigations of air mass compositions over time and from different regions at the same θ-level largely exclude that, during its transport history, the air had experienced vertical displacement and/or diabatic processes (radiative heating, condensation/evaporation) which would result in energy conversion. The tropopause height is often used as a reference height in the θ coordinate system in connection with the vertical sorting of observational data, whereby the assignment of tropospheric and stratospheric processes is made, or exchanges across the tropopause are investigated (Holton et al., 1995; Stohl et al., 2003). Consequently, the tropopause height is also determined by the potential vorticity (e.g., Gettelman et al., 2011, and cf. Section 7.2), if the conventional tropopause definitions (cold point or lapse rate, WMO, 1957) do not allow for clearly determining the tropopause height, e.g., in the Asian Monsoon Anticyclone (cf. Höpfner et al., 2019) or in the polar winter vortex (Wilson et al., 1989; Weigel et al., 2014). The conventional definition of θ implies a systematic error in the vertical sorting of observational data in the θ coordinate system, independent of the measurement platform. Investigations with high-altitude research aircraft such as the G-550 HALO (e.g., Wendisch et al., 2016; Voigt et al., 2017), the NASA WB-57 or ER-2 (e.g., Murphy et al., 2007; Dessler, 2002), the M-55 Geophysica (Curtius et al., 2005; Borrmann et al., 2010; Frey, 2011), balloon-borne platforms (Lary et al., 1995; Vernier et al., 2018), or satellite-based vertical profiles (e.g., Davies et al., 2006; Spang et al., 2005), require consideration of the systematic error in θ if calculated as θcp in compliance with the definition by the WMO (1966). The possibly inconsistent use of a constant cp value of 1004 J kg−1 K−1 or 1005 J kg−1 K−1 (or any other) in different and compared data sets, which could be due to different literature references for this value (cf. Table 1), will not be explored here. At altitudes between 15 and 20km (ceiling of high-altitude research aircraft), an overestimation by about 0.1 – 0.5% is to be expected for the potential temperature according to the conventional definition, cf. Figure 4b. At altitudes of 30 – 35km, an overestimation by up to 2 – 5% results. Whether this error is significant or small compared to the uncertainty of ambient temperature and pressure measurement aboard the respective aircraft is left to individual judgement in the course of data processing. In the case of spacecraft-bound vertical soundings (e.g., from ASTROSPAS, SCIAMACHY, or ENVISAT), the error in the potential temperature determined by θcp exceeds 10% at altitudes above 40km, as shown in Figure 4b. Finally, we note that the specified errors apply exclusively along the vertical profile of the US standard atmosphere, and that deviations of the actual temperature profile from the US standard atmosphere, e.g., warmer temperatures, could lead to larger errors (cf. Figure 6).</p><!><p>Diabatic heating rates refer to the rate of energy dqdt supplied to a given air parcel, e.g., by radiative heating, and are given in units of J kg−1 s−1. This energy supply causes a temperature change of an air parcel at a rate which hereafter is referred to as the absolute heating rate, (38)AHRrefdqdt=dTdt=1cp0Tdqdt,AHR1005dqdt=dTdt=11005 J kg−1K−1dqdt. Again, the distinction was made between the temperature-dependent cp0T and the constant cp = 1005 J kg−1 K−1 specific heat capacity. From the defining equations (38), the relative difference between these absolute heating rates, where x designates an arbitrary diabatic heating rate, is (39)AHR1005x−AHRrefxAHRrefx=cp0T1005 J kg−1K−1−1.</p><p>Apart from the absolute heating rates for the change of absolute temperature, the change of potential temperature due to a diabatic heating rate dqdt is of interest. For example, it is the change of potential temperature that modifies the altitude of modelled trajectories in Lagrangian chemical transport models based on isentropic coordinates rather than the change in absolute temperature (e.g., the SLIMCAT (Chipperfield, 2006) or CLaMS model (Pommrich et al., 2014)).</p><p>Taking the relation T ds = dq for the specific entropy into account, Gibbs' equation (8) may be rewritten as (40)dqT=cpTTdT−Radpp. Comparing the right-hand side of this equation to the total derivative of the new reference potential temperature θref (see Appendix E for the detailed computation and Equation (E6) for the result) equation (40) amounts to (41)dqT=cpθrefdθrefθref. Consequently, the following two diabatic heating rates (42)dθref dt=θref cp0θref Tdqdt=HRref dqdt,dθ1005dt=θ10051005 J kg−1K−1⋅Tdqdt=HR1005dqdt for the potential temperatures θref and θ1005 may be defined. Denoting again by x an arbitrary diabatic heating rate, the relative difference between the heating rates (42) is (43)HR1005x−HRref xHRref x=θ1005θrefcp0θref1005 J kg−1K−1−1.</p><p>In order to judge the magnitudes of the relative differences (39) and (43), the monthly averaged temperature profiles from ERA-Interim (Dee et al., 2011) data for 52°N geographical latitude are used, see Figure 11a. The relative differences of the absolute heating rates (39) are shown in Figure 11b and the difference appears to be small. However, the relative differences of the heating rates (43) in Figure 11c are much larger, as relative deviations exceeding 50% are reached in the upper stratosphere and lower mesosphere (at pressures below 1 hPa). Additionally, the temperatures were computed that resulted after 24 h of heating with a constant heating rate dqdt as given in the (averaged) dataset, where a constant pressure is assumed for simplicity. As may be anticipated from the small deviations in Figure 11b, the difference in the final absolute temperatures by using the absolute heating rates AHR1005 or AHRref are smaller than 0.044 K. However, the differences in the potential temperatures θ1005*, θref*, computed with the heating rates HRref, HR1005, are much larger (Figure 11d), and amount to about 3% at 10 hPa and about 15% at 1 hPa. For transport calculations done in isentropic coordinates, these differences are of the same order of magnitude as the deviations resulting from the use of the temperature-dependent instead of the constant cp. It remains to be decided on individual application whether this additional effect in the calculation is significant.</p><p>A standard diagnostic for the speed of the stratospheric circulation is the time lag of the upward propagating seasonal signal in tropical stratospheric water vapour (the so-called tape recorder, Mote et al., 1996). Here, differences between calculations (done in isentropic coordinates) based on different current meteorological reanalysis data sets amount to about 10 – 30% for the signal's upward propagation below about 10 hPa (Tao et al., 2019), such that the additional deviation from using the temperature-dependent cp is comparably small. However, in cases of smaller inter-model differences the additional cp-related uncertainty needs to be assessed.</p><p>Note, the determination of absolute temperatures T1005*, Tref* which correspond to the resulting potential temperatures θ1005*, θref* after 24h differ by less than 0.014 K (not shown).</p><!><p>Under the assumption that dry air is an ideal gas, a re-assessment of computing the potential temperature was introduced that accounts for the hitherto unconsidered temperature dependence of air's specific heat capacity. The new reference potential temperature θref was introduced, which is thermodynamically consistent and based on a state-of-the-art parameterisation of the ideal-gas specific heat capacity of dry air from the National Institute of Standards and Technology (NIST). This reference potential temperature was compared to a potential temperature θreal wherein the real-gas behaviour of dry air is considered. In the range of temperatures from 180 K to 300 K and the range of pressures from 1000 hPa to 0.5 hPa, covering the atmospheric conditions of roughly the entire troposphere and stratosphere, the relative differences between θref and θreal are smaller than 0.03% and may be considered negligible. Consequently, θref even provides a reasonable approximation to the potential temperature of the real gas.</p><p>The difference between the newly derived reference potential temperature θref and the conventionally determined potential temperature θcp (with constant cp = 1005 J kg−1 K−1, as recommended by the World Meteorological Organisation, WMO, 1966) increases with altitude, e.g., Δθ ≥ 1 K at pressures p ≤ 60 hPa.</p><p>Derivation of a potential temperature that is consistent with thermodynamics and that accounts for the ideal-gas properties of dry air requires the integration of Gibbs' equation and the subsequent solution of the resulting nonlinear equation. With a constant cp, both analytical steps are straightforward, resulting in the conventional expression (13) as suggested by WMO (1966). However, if instead the temperature dependence of air's specific heat capacity cp(T) is considered, the integrals as well as the equations are not analytically solvable and, thus, the solution must be approximated. Both approximations were performed and described in detail. The integral was treated with the basic approximation and the solution of the nonlinear equation was approximated by the second iterate of Newton's method. As an alternative to Newton's classical method, a modified formulation of Householder's iteration method is provided, featuring accelerated convergence properties.</p><p>The suggested approximation steps to obtain a reference potential temperature have two main sources of error: the error θref−θrefapprox inherent in the integral's basic approximation and the error θrefapprox−θk of the k-th Newton iterate. The latter error approaches zero as k → ∞, whereas the error resulting from the basic approximation remains well below 0.1% (along the US Standard Atmosphere) for values of θref of up to ~ 2000 K, hence up to stratopause altitudes. To keep this low error level also for θref > 2000 K, the approximation may require an extension by means of a higher-order polynomial.</p><p>One of the foremost implications of the re-assessed potential temperature's definition concerns the use of θ as a vertical coordinate for the sorting, grouping, and comparison of (measured) data, e.g., along or across isentropes. Thereby, the re-assessed potential temperature constitutes a more accurate consideration of the air's actual properties. This particularly concerns, e.g., the specific heat capacity which is conventionally assumed as constant and for which various values are given depending on the textbook consulted (offering a range from 1000 J kg−1 K−1 to 1010 J kg−1 K−1, see Table 1).</p><p>Significant errors and biases may arise if, for instance, the conventional derivation of θ (WMO, 1966) is used together with values for air's specific gas constant (Ra) or air's specific heat capacity (cp) which better comply with the most recent state-of-knowledge. Moreover, the use of the standard pressure 1013.25 hPa instead of 1000 hPa as defined by WMO (1966) and consistently used herein as ground level pressure (p0) may cause an additional deviation of the resulting θ. Thus, the re-assessment of θ's definition could largely diminish such errors and biases and improve the comparability of data.</p><p>In addition to the vertical sorting of data, implications of the new reference potential temperature were discussed for several other applications in which the potential temperature is used. On the one hand, results may appear mostly unaffected by using θref instead of the convential θ1005, such as the values of the Brunt-Väisälä frequency or the temperature change of air parcels due to diabatic heating. On the other hand, it was illustrated that any formula which involves the potential temperature needs to be carefully reviewed to see if its derivation relies on the assumed constancy of the specific heat capacity. If this is the case, substituting θref for all occurrences of θ within the particular formula may lead to a wrong computation.</p><p>In contrast, examples were shown where the computation of Ertel's potential vorticity and the rate of change of potential temperature in response to diabatic heating yields different results by the use of θref instead of θ1005. The differences increased with altitude, hence they become more important for applications within the stratosphere and above.</p><p>It should be emphasised that all these examples were based on assuming particular profiles of temperature and pressure together with other assumptions. Moreover, only a limited number of examples could be investigated, while the applications of potential temperature are numerous. Consequently, a well-founded, individual decision is required for each application of the potential temperature as to whether it is worth applying the more rigorous calculation in the particular context.</p><p>On the one hand, such a re-assessment could take into account the current state of knowledge regarding the accuracy of thermodynamic variables and substance-related properties. On the other hand, this way, the conceptional abstractness already inherent in θ is not further complicated by a misleading selection of parameters or reputed constants. There is no doubt that the conventional method is suitable for the description of most processes occurring within the troposphere. However, at stratospheric or even mesospheric altitudes, the neglect of the temperature dependence of the ideal-gas heat capacity in the conventional definition increasingly distorts the resulting absolute values as well as the vertical course of the potential temperature. Ultimately, it seems obvious to profit from the computing capacities available today and from the known higher accuracy of physical variables and atmospheric parameters to carry out a reappraisal of the potential temperature, a useful (but not always consistently used) meteorological quantity.</p>

PubMed Author Manuscript

Advances in new psychoactive substances identification: the U.R.I.To.N. Consortium

AbstractIdentification of new psychoactive substances (NPS) in biological and non-biological samples represents a hard challenge for forensic toxicologists. Their great chemical variety and the speed with which new NPS are synthesised and spread make stringent the need of advanced tools for their detection based on multidisciplinary approaches. For this reason, in August 2016, the “Unit of Research and Innovation in Forensic Toxicology and Neuroscience of Addiction” (U.R.I.To.N.) was founded by the Forensic Toxicology Division of the University of Florence. In this Research Unit, various professionals (i.e. forensic toxicologists, chemists, physicians) collaborate to study all the aspects of drugs of abuse, especially NPS. Herein, we describe the multidisciplinary approach comprising liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS), gas chromatography hyphenated to mass spectrometry (GC–MS) and solution nuclear magnetic resonance analysis that allowed the identification of three NPS such as 1-(benzofuran-5-yl)-N-methylpropan-2-amine, 2-amino-1-(4-bromo-2,5-dimethoxyphenyl)ethan-1-one (bk-2C-B), and 3-(2-aminopropyl)indole (α-methyltryptamine) in seized materials.

advances_in_new_psychoactive_substances_identification:_the_u.r.i.to.n._consortium

Introduction<!><!>Introduction<!>Seized material<!>Chemicals and reagents<!>Sample preparation<!>GC-FID<!>GC–MS<!>LC–MS/MS<!>NMR<!>White powders<!><!>Light yellow powders<!><!>Grey/red powder<!><!>Discussion<!>GC-FID and GC–MS<!>LC–MS/MS<!>NMR<!><!>NMR<!><!>NMR<!>Conclusion<!>Disclosure statement

<p>Recent survey data (2016) show how the drug arena is getting far more complicated compared to years before. As presented in the last European Drug report1, not only we assist to the continuous development of "new drugs", but also to the upwards trending of the more established ones, with the addition of new production sites even in Europe. The term "new drugs", or "new psychoactive substances" (NPS), indicates a large number of synthetic molecules which belong to a vast array of chemical families. NPS are usually designed on the chemical scaffolds of classical drugs of current use for abuse and/or therapeutic purposes. Since the 1970, many NPS have been synthesised and described. Nowadays, by means of the introduction of known NPS within the illicit drug market together with new ones, there is a reappraisal of many of them2. The EU Early Warning System reported in 2014 that almost 5000 (i.e., 40 tonnes) seizures of NPS across European countries were accomplished, and among them NPS containing cathinones and synthetic cannabinoids accounted for about the 80% of the total3. Both the amount and diversity of NPS represent a hard challenge for the Law Enforcement Agencies and for the Forensic Laboratories appointed to detect and identify large quantities of such substances. As for the legal interventions, European members follow some useful and strategic planning tools such as: (i) EU policy cycle on organised and serious international crime, (ii) EU security strategies, and (iii) the EU drugs strategy 2013–2020 and its current action plan 2013–2016.</p><p>NPS legal status can differ from country to country since they are not under control of the International Drug Control Conventions4. That implies national legislations to be continuously updated on each new molecule and, in order to do so, the first step is to get them identified. Under the Forensic Toxicology perspective, the breadth of the challenge is warring as well. As reported into the UNODC Early Warning Advisory (EWA), 600 NPS were present on the market up to December 2015, and since then probably a larger number has become available5. Due to the incessant introduction in the market of new NPS as well as to the lack of reference standards and not yet established effective routine methods for their detection, there are challenges in the identification of some new chemical entities proposed as drugs. Thus, over the last years, several new analytical methods have been developed in order to improve the detection proficiency towards NPS6. Most of them require advanced technologies, such as liquid chromatography coupled to tandem (LC–MS/MS) or high resolution mass spectrometry (LC–HRMS). These are very performing tools but could be not enough when considering a totally unknown compound or a reference material is unavailable. In these cases, uncorrelated and more specific chemical analysis is surely beneficial7. The nuclear magnetic resonance (NMR) technique can provide detailed information when a structure identification of unknown molecules is required. Considering the urgency of a multidisciplinary approach and the previous NPS detection experiences8, our Forensic Toxicology Division established a "Unit of Research and Innovation in Forensic Toxicology and Neuroscience of Addiction" (U.R.I.To.N.) in August 2016. It is the first highly specialised unit in Italy, entirely focused on all aspects of drugs of abuse (NPS on top) by means of a multidisciplinary approach. In U.R.I.To.N., forensic and clinical toxicologists, neuroscientists, chemists, and physicians collaborate with the final intent to obtain a full comprehension of the various aspects related to NSP, which span from the analytical issues to the biological effects and harmful effects. In this paper, we describe the analytical procedure, adopted by our Research Unit on a seized material, which led to the identification of three NPS whose reference standards were not in our possession. 1-(Benzofuran-5-yl)-N-methylpropan-2-amine (5-MAPB), 2-amino-1-(4-bromo-2,5-dimethoxyphenyl)ethan-1-one (bk-2C-B), and 3-(2-aminopropyl)indole (α-methyltryptamine, AMT) were identified in three different coloured powders of the same seizure (Figures 1 and 2).</p><!><p>Chemical structures of 5-MAPB, bk-2C-B and AMT.</p><p>Picture of the seized material.</p><!><p>The analytical procedures included a first analysis by gas chromatography with flame ionisation detector (GC-FID) followed by GC–MS and LC–MS/MS detection, all of them performed in the laboratory of our Forensic Toxicology Division. In a later stage, a NMR characterisation was carried out at the Neurofarba Department of the University of Florence.</p><!><p>The seized material consisted in a small plastic bag containing: 19 folded slips of paper, 4 orange capsules, and 1 orange capsule coloured in blue. A white amorphous powder was found in all the slips of paper and in three orange capsules, a light yellow amorphous powder in the fourth orange capsule and a grey/red amorphous powder in the blue coloured capsule. Upon the plastic bag were listed the prices of each dose (Figure 2).</p><!><p>LC–MS CHROMASOLV® methanol (MeOH), LC–MS CHROMASOLV® acetonitrile (ACN), LC–MS CHROMASOLV® water (H2O), dimethyl sulfoxide-d6 (DMSO-d6) "100%", 99.96 atom %D, and heavy water (D2O) 99.9 atom % D were purchased by Sigma-Aldrich (St. Louis, MO).</p><!><p>GC-FID, GC–MS and LC–MS/MS: 5 mg of each sample was added with MeOH (10 mL) and then further diluted to 2 ng/µL.</p><p>NMR: 3 mg of each sample was introduced into a 5.0-mm diameter NMR-tube and dissolved with 0.5 mL of DMSO-d6.</p><!><p>The first qualitative analysis was carried out with an Agilent 7890B GC system (Agilent Technologies, Palo Alto, CA) equipped with a FID. The column was an Agilent HP-5 (30 m × 0.32 mm, 0.25 µm film thickness). The starting temperature was set at 100 °C for 1 min, programmed to 125 °C at 25 °C/min for 1 min and to 180 °C at 15 °C/min for 2 min. We routinely use this method to identify the presence of cocaine, heroin, morphine, Δ9-tetrahydrocannabinol, ketamine, amphetamine, methamphetamine, 3,4-methylenedioxy-methamphetamine (MDMA), 3,4-methylenedioxyamphetamine, 3,4-methylenedioxy-N-ethylamphetamine.</p><!><p>The GC–MS instrument consisted in an Agilent 7890 A GC system equipped with an Agilent 7683B series autosampler and interfaced to a single quadrupole Agilent 5975C mass spectrometer. The column was an Agilent HP-5MS (30 m × 0.25 mm, 0.25 µm film thickness). The gas carrier (He) flow was constant at 1 mL/min. The samples were analysed in full scan mode (adopted libraries: NIST08, WILEY27, SWGDRUG4). The oven temperature was initially set at 100 °C for 2.25 min, programmed to 180 °C at 40 °C/min and to 300 °C at 10 °C/min for 10 min. Injector and transfer line temperatures were always 300 and 230 °C, respectively. The injection volume was 1 mL in splitless mode. Data acquisition and elaboration were performed using the Agilent MassHunter Workstation software package.</p><!><p>Analysis was conducted using an HPLC Agilent 1290 Infinity system interfaced with an Agilent 6460 Triple Quad LC/MS (QQQ), equipped with an electrospray ion source (ESI) operating in positive mode. The ESI configuration was: gas temperature 325 °C; gas flow rate 10 L/min; nebuliser 20 psi; capillary 4000 V. Chromatographic separation was performed through a Zorbax Eclipse Plus C18 (2.1 mm × 50 mm, 1.8 m, Agilent Technologies). The mobile phase initially consisted of 5 mM aqueous formic acid (A) and ACN (B) 99:1. Gradient of elution was carried out by increasing the % ACN to 30% within 6 min; to 50% within 2 min; to 100% within 4 min and isocratic for 3 min. The flow rate was 0.4 mL/min until 8 min, then increase at 0.6 mL/min within 2 min. Analysis was carried out first in scan mode (50–500 m/z) in positive and negative ionisation and then the collision-induced dissociations (CIDs) were studied at different collision energies (CE, 10, 20, 30 and 40 eV). Agilent MassHunter Workstation software package was used for data acquisition and elaboration.</p><!><p>Nuclear magnetic resonance (1H NMR, 13C NMR) spectra were recorded using a Bruker Avance III 400 MHz spectrometer (Milan, Italy) in DMSO-d6. Chemical shifts are reported in parts per million (ppm) and the coupling constants (J) are expressed in Hertz (Hz). Splitting patterns are designated as follows: s, singlet; d, doublet; t, triplet; q, quadruplet; m, multiplet; brs, broad singlet; dd, double of doublets. The assignment of exchangeable protons (OH and NH) was confirmed by the addition of D2O.</p><!><p>Samples of each white powder were analysed following our protocol. GC-FID analysis excluded the presence of the most common drugs of abuse. GC–MS screening revealed 5-MAPB with a mean estimated concordance among all samples of 72% (peak at 6.793 vs. SWGDRUG 3.0 spectral library, Figure 3). Identification of 5-MAPB was then supported by LC–MS/MS findings (Figure 4) and definitely proved by NMR analysis.</p><!><p>GC–MS chromatogram of the white powder and mass spectrum of 5-MAPB.</p><p>LC–MS/MS chromatogram of the white powder and merged mass spectrum (CE: 10 and 20 eV) of 5-MAPB.</p><!><p>Bk-2C-B was identified in all the light-yellow powders. The synthetic cathinone was recognised by the GC–MS screening with a main match of 24% (peak at 7.794 vs. SWGDRUG 3.0, Figure 5). The study of the product ions in LC–MS/MS validated this finding (Figure 6) as the fragmentation profile was highly comparable with the ones reported in literature for bk-2C-B9. NMR confirmed this hypothesis.</p><!><p>GC–MS chromatogram of the yellow powder and mass spectrum of bk-2C-B.</p><p>LC–MS/MS chromatogram of the yellow powder and merged mass spectrum (CE: 10 and 20 eV) of bk-2C-B.</p><!><p>This powder was not pure, but consisted in a mixture of the previous substances and a third compound whose actual identification was possible by NMR analysis only. GC–MS screening (Figure 7) gave evidence of the sole presence of bk-2C-B (mean concordance between the spectra: 38%), and neither 5-MAPB nor AMT were identified (even though AMT mass spectrum is filed in the SWGDRUG 3.0 library). Thanks to the previous LC–MS/MS findings, we were able to recognise bk-2C-B and 5-MAPB with the same method, while the other compound stayed unknown. The attempt to understand and draw the structure of the latter (peak at 3.203 min, Figure 8), using data by LC–MS/MS only, was quite challenging. The main issue, after the detection of the [M + H]+, was the identification of the chemical formula. A couple of structures were hypothesised (mainly AMT and its isomers), and NMR was used to assess the presence of AMT among the others.</p><!><p>GC–MS chromatogram of the grey/red powder.</p><p>LC–MS/MS chromatogram of the grey/red powder and merged mass spectrum (CE: 10 and 20 eV) of AMT.</p><!><p>In this paper, we report the NPS multidisciplinary detection procedure adopted by the U.R.I.To.N. Research Unit. This procedure was based on a multi-analytical approach where each instrumentation provided specific findings that, once merged, lead to reliable and indubitable structure identification. This kind of strategy represents the state-of-the-art for non-targeted analysis and various technologies can be used and integrated each other. In recent years, LC–HRMS systems have become the first technological choice in structural investigation protocols as they allow to measure the exact mass (and chemical formula) of an unknown compound and to deeply investigate its fragment residues. Besides the usefulness, such a technique requires its data to be confirmed with uncorrelated methods, such as NMR. In our protocol, GC-FID, GC–MS and LC–MS/MS were used, together with NMR, to identify three NPS (5-MAPB, bk-2C-B and AMT) in the absence of their reference materials (Figure 1).</p><p>5-MAPB is a 2-aminopropyl-benzofurane analogue of MDMA, not subjected to control in Italy, and used for its stimulant and entactogenic effects10. Sahai et al.11 showed that 5-MAPB binds the dopamine transporters and slows down the reuptake of electrically evoked dopamine in the rat accumbens. The same authors also demonstrated that 5-MAPB, in analogy to amphetamine-like substances, is responsible of reverse transportation of dopamine induced euphoria, empathy and psychedelic effects12. The toxicity profile of subjects exposed to 5-MAPB is similar to the one resulting from the assumption of MDMA13. Similar effects were also observed for other benzofuran containing substances, such as the 5-APB (nor-5-MAPB)14.</p><p>The bk-2C-B is a compound structurally related to 2 C-B, a serotonergic hallucinogen phenethylamine described for the first time by Shulgin and Carter15. To date, little is known about the bk-2C-B induced physiological effects, and useful information of this kind can be only collected from forums on the web16. The presence of the 2-amino-1-phenyl-ethanone core does not allow bk-2C-B to be included among the analogues of 2-amino-1-phenyl-1-propanone containing compounds, which are under legal control in Italy.</p><p>AMT is a stimulant hallucinogen belonging to the chemical class of unsubstituted synthetic tryptamines17. This molecule presents a strong effect on the serotoninergic system due to the high affinity for serotonin transporter and receptors. AMT is also active on the dopaminergic and adrenergic receptors with a similar profile as the one of methamphetamine18. AMT induces adverse effects including increase of the blood pressure, tachycardia, nausea, impaired coordination, visual and auditory distortions19. There have been deaths related to AMT use, especially in co-consumption with other substances (cocaine, amphetamine, MDMA, and cannabinoids)20. AMT is still legal in Italy, whereas its isomer 5-(2-aminopropyl)indole (5-IT) is not.</p><!><p>The first step consisted in the GC-FID analysis aimed to exclude the presence of the main drugs of abuse, in this case amphetamines and cocaine. GC–MS plays a key role in our protocol as it can give an indication about the substances in the sample. For this reason, our mass spectra libraries are periodically updated. The high diagnostic skill of GC–MS screening is valuable for the analysis of the white and yellow powders. Identification of 5-MAPB and bk-2C-B, even if the concordances were not so high (especially for the phenethylamine), gave us a good starting point. The LC–MS/MS analyses were easier because mainly aimed to the confirmation of GC–MS results. Regarding the grey/red powder, the software recognised nothing but the bk-2C-B (38%) as in the yellow one. None of the other peaks were attributed to 5-MAPB and AMT, later detected by LC–MS/MS and NMR analysis. This may be mainly explained by their low amounts in the seized material. In this case, the GC–MS analysis suggested partially true findings underlining how important a multi-analytical strategy is.</p><!><p>LC–MS/MS was negatively affected by high in-source fragmentation of all compounds (at standard ESI conditions, see "Experimental" section) which makes identification of [M + H]+ hard. For 5-MAPB, this drawback was not so intense and the protonated molecule was easy to see at 190 m/z. Main fragments were the benzofuranyl propilium and the benzofuranyl methylium ions at 159 and 131 m/z. ESI fragmentation was more intense for bk-2C-B giving several product ions. Even though we were able to recognised the protonated molecule and the typical bromine isotopic pattern with the MH +0 (C10H1379BrNO3+, 274 m/z) and the MH +2 (C10H1381BrNO3+, 276 m/z) at similar intensities. The study of CID at different collision energies provided a fragmentation profile highly comparable with the ones described in literature for this compound9.</p><p>LC–MS/MS scan analysis of the grey/red powder revealed the presence of bk-2C-B (as indicated by the GC–MS), of 5-MAPB and of a third unidentified compound (peak at 3.203 min, main ion ≈ 158 m/z, Figure 8). No peaks were observed in negative ionisation mode. Identification of the unknown molecule was carried out through an ad hoc strategy consisting both in the study of its mass spectrum and research on compounds' databases. The first step was the measurement of the molecular weight (MW) reducing the fragmentor value (at 110, 90, 70 and 40 eV) in order to decrease the in-source fragmentation. We identified the [M + H]+ at 175 m/z with the highest abundance at 70 eV. Since the QQQ does not allow to measure the accurate mass, it was not possible to find the chemical formula. Studying the deconvoluted spectrum, we collected information about the elemental composition. In particular we excluded the presence of Cl, Br and S by the analysis of the isotopic pattern: 175 m/z (100%), MH +0; 176 m/z (9.8%), MH +1; 177 m/z (0.7%), M + 2. Product ion analysis at various collision energies provided several mass spectra comprising specific fragments useful for the structure drawing: the phenyl cation at 77 m/z (C6H5+), the tropylium cation at 91 m/z (C7H6+), and the phenylvinyl cation at 103 m/z (C8H7+). Another important fragment was the one at 158 m/z, originated from the protonated molecule by the loss of ammonia (NH3, −17 Da), typical of the primary amine (−NH2). It was possible to make some conclusions: (1) the compound had at least one phenyl ring equal to 4 unsaturation degrees; (2) C atoms were ≥8; (3) H atoms were ≥7; (4) I atom was not present because of its mass (MW: 129 Da); (5) since the unknown compound had an even MW, according to the empirical nitrogen rule, N atoms must be even. Resuming all these findings and considering only the most feasible atoms' ratios, the compound could be described by the generic chemical formula of C8-15H7-20N2-4O0-2F0-1. The mMass – Open Source Mass Spectrometry Tool software (downloadable at http://www.mmass.org/download/) provided us only four suitable chemical formulas: C10H7FN2, C9H10N4, C10H10N2O, C11H14N2. We typed them in various compounds' databases21, but the number of available structures was very huge. For this reason, we looked inside the NPS database of our Research Unit (available at http://allerta.dronetplus.eu/) and we found a correspondence for the formula C11H14N2 in AMT and its isomers. The fragmentation pattern was consistent with their structures and comparing our mass spectrum with the ones described in literature, we observed a high concordance, especially for AMT20a.</p><!><p>The 400 MHz 1H NMR spectrum of the 5-MAPB sample in DMSO-d6 (S1 in Supporting information), clearly revealed a doublet signal (d) at 1.15 ppm, with a typical vicinal coupling value of 6.8 Hz integrating for three protons, along with a singlet (s) at 2.16 ppm and integrating for the same value (three protons). These signals can be surely ascribed to 3′-methyl and to the N-methyl groups, respectively, placed at the 5-position of the benzofuranic scaffold. The one proton signal at 2′ position was easily identified with the multiplet centred at 3.22 ppm. The enantiotopic methylene protons at 1′ position gave two doublet of doublets (dd) both integrating for one proton and centred at 2.79 ppm (J 13.2 and 9.6 Hz) and 3.28 ppm (J 13.2 and 4.4 Hz) respectively. As expected each enantiotopic proton gave a geminal (J 13.2 Hz) and a vicinal coupling of 13.2 and 4.4 Hz, respectively. Such values are in good agreement with the Karplus equation on the correlation between vicinal coupling constants values and dihedral angles22. The presence of the benzofuranyl scaffold substituted at position 5, was assessed by means of: (i) two doublets at 6.95 and 7.97 ppm with a J coupling of 2.0 Hz, which can be ascribed to the protons in position 3 and 2 respectively. (ii) The proton in position 6 was identified as the doublet of doublets signal (dd; J 8.4 and 1.6 Hz) at 6.95 ppm. The typical vicinal aromatic coupling with the proton at position 7 (8.4 Hz) was easily ascribed, whereas the smaller coupling value of 1.6 Hz can be justified as the results of any rotameric effect given from the closed alkyl side chain and occurring within the NMR experimental acquisition time. (iii) As proof of such interpretation is worth mentioning that the proton at position 4 gave a doublet at 7.65 ppm with a J coupling value of 1.6 Hz. (iv) Partially superimposed to the proton signal at position 4, was the proton at 7 position which gave a doublet with a vicinal coupling value of 8.4 Hz. Finally a broad singlet at 8.95 ppm and integrating for two protons was also present. The addition of two drops of D2O into the DMSO-d6 solution of the sample (S2 in Supporting information) determined a total suppression of such a signal, thus giving evidence that the 5-MAPB is present as an ammonium salt which counter ion was not determined in the set of experiments herein reported (Figure 9).</p><!><p>Chemical structure and numbering of 1-(benzofuran-5-yl)-N-methylpropan-2-ammonium ion (5-MAPB).</p><!><p>The 13C NMR at 100 MHz afforded 12 signals, and its DEPT-135 deconvolution revealed the presence of one signal down (methylene) and eight signals up, thus in agreement with the 5-MAPB structure (S3 and S4 in Supporting information). A final proof of the NMR based structure elucidation of 5-MAPB was also given from the HSQC experiment which correlated in two dimensions the 1H and the 13C monodimensional spectra (S5 in Supporting information).</p><p>As for the bk-2C-B sample, the 400 MHz 1H NMR in DMSO-d6 (S6 in Supporting information) revealed: (i) two singlets at 3.90 and 3.90 ppm, each integrating for three protons which belong to the methoxy moieties present at position 2 and 5; (ii) a singlet at 4.35 ppm and integrating for two protons of the methylene moiety; (iii) two singlets at 7.47 and 7.63 ppm of the aromatic protons at position 3 and 6; (iv) a broad singlet at 8.32 which integrated for three protons. In analogy to the 5-MAPB, the addition of two drops of D2O into the DMSO-d6 solution of bk-2C-B, determined a total suppression of such a signal (S7 in Supporting information). Therefore, it is reasonable to assume that bk-2C-B is protonated at the nitrogen atom. Again no further experiments were run with the intent to determine which counter ion is associated (Figure 10).</p><!><p>Chemical structure and numbering of 2′-ammonium-1-(4-bromo-2,5-dimethoxyphenyl)ethan-1-one (bk-2C-B).</p><!><p>The structure of bk-2C-B was also in agreement with the obtained 1H decoupled–13C NMR data which afforded for 10 signals, and among them the peak at 192.5 ppm confirmed the presence of the carbonyl at 1′ position (S8 in Supporting information). The DEPT-135 experiment clearly reported four signals up, in agreement with two aromatic CHs and two methoxy groups, and a signal down as only one methylene group was present. Finally, the HSQC experiment determined a clear correlation between the protons and the carbons of the bk-2 C-B claimed structure (S9 and S10 in Supporting information).</p><p>The identification of the remaining compound 3-(2-aminopropyl)indole (α-methyltryptamine, AMT) by means of NMR techniques proved to be quite demanding and not feasible, as the substance was present in very small amounts. Attempts to collect a sufficient quantity of material to be used for NMR investigations were done by means of preparative silica gel thin layer chromatography separations, using 20% methanol in dichloromethane as mobile phase. The 400 MHz 1H NMR in DMSO-d6 of the collected material (not shown) revealed a very small amount of the α-methyltryptamine present. Among the key signals necessary to undoubtedly assign the structure only the peak at 8.00 ppm and 2.52 (overlapped with the residual DMSO) were identified 23.</p><!><p>The multidisciplinary approach, herein described, well represents an effective attempt to face the NPS detection challenge. The establishment of a highly specialised research unit, "U.R.I.To.N.", allowed to improve the identification skills of our Forensic Toxicology Division, as well as the exchange of knowledge and know-how among scientists with different expertise. In this paper, we applied this cooperation strategy to the identification of 5-MAPB, bk-2C-B and AMT in three different coloured powders from the same seizure. Their recognition was possible, even without the reference standards, by the combination of advanced analytical techniques (GC–MS, LC–MS/MS, and NMR) that provided peculiar information useful to puzzle up the molecular structures of the NPS present in the seized materials. The advantage offered by the cross interaction between the consortium members is also represented by the reduction of the times associated to the identification of the substances. Typically in a time frame of 4–6 h the identification of multiple NPS as rough substances in seized material is complete. This is an important example of how the collaboration and cooperation between different fields of knowledge is fundamental and desirable in order to be more effective and responsive to the NPS phenomenon and its continuously changing nature.</p><!><p>The authors declare no conflict of interest. The authors are solely responsible for the content and results presented in this paper.</p>

PubMed Open Access

Homochiral Self‐Sorted and Emissive IrIII Metallo‐Cryptophanes

AbstractThe racemic ligands (±)‐tris(isonicotinoyl)‐cyclotriguaiacylene (L1), or (±)‐tris(4‐pyridyl‐methyl)‐cyclotriguaiacylene (L2) assemble with racemic (Λ,Δ)‐[Ir(ppy)2(MeCN)2]+, in which ppy=2‐phenylpyridinato, to form [{Ir(ppy)2}3(L)2]3+ metallo‐cryptophane cages. The crystal structure of [{Ir(ppy)2}3(L1)2]⋅3BF4 has MM‐ΛΛΛ and PP‐ΔΔΔ isomers, and homochiral self‐sorting occurs in solution, a process accelerated by a chiral guest. Self‐recognition between L1 and L2 within cages does not occur, and cages show very slow ligand exchange. Both cages are phosphorescent, with [{Ir(ppy)2}3(L2)2]3+ having enhanced and blue‐shifted emission when compared with [{Ir(ppy)2}3(L1)2]3+.

homochiral_self‐sorted_and_emissive_iriii_metallo‐cryptophanes

<!>Conflict of interest<!>

<p>V. E. Pritchard, D. Rota Martir, S. Oldknow, S. Kai, S. Hiraoka, N. J. Cookson, E. Zysman-Colman, M. J. Hardie, Chem. Eur. J. 2017, 23, 6290.</p><p>Metallo‐cages are discrete 3D‐coordination assemblies with a hollow interior and have applications as hosts and nanoscale vessels.1 They form through the self‐assembly of multidentate ligands with metals, or with metal complexes with controlled available coordination sites ("metallo‐tectons"). Luminescent metallo‐cages are known,2, 3, 4, 5, 6 with most examples exhibiting fluorescence‐active ligands,2 alongside rarer examples of cages with pendant metal‐complex emissive groups.3 There are very few examples of metallo‐cages constructed from inherently phosphorescent structural components.4, 5, 6 Cyclometalated IrIII complexes bearing either two N‐donor ligands or one NN^ chelating ligand represent an important subclass of phosphorescent materials.7 Lusby and co‐workers reported the enantiopure IrIII metallo‐cage [{Ir(ppy)2}6(tcb)4]⋅(OTf)6 (tcb=1,3,5‐tricyanobenzene),4 which self‐assembles, despite the inertness of the d6 IrIII centre, as the C,C‐cis‐N,N‐trans arrangement of the ppy ligands has a trans‐labilising effect. The cage shows red‐shifted emission compared with a monomeric analogue, and enhanced photoluminescence quantum yields (Φ PL). To date, this is the only report of a 3D metallo‐cage that utilizes [Ir(ppy)2] as the sole metal centre, although mixed metal examples are known.5</p><p>Here, we report two metallo‐cages of the type [{Ir(ppy)2}3(L)2]3+, in which L is a chiral tripodal ligand related to the molecular host cyclotriveratrylene (CTV). [M(chelate)]3L2 cages with CTV‐type ligands are known as metallo‐cryptophanes, and most examples feature square planar metals.8 The [{Ir(ppy)2}3(L)2]3+ cages reported here show homochiral sorting on crystallization and in solution, and slow ligand exchange behaviour is observed.</p><p>Cages [{Ir(ppy)2}3(L1)2]3+ 1 and [{Ir(ppy)2}3(L2)2]3+ 2 are formed from nitromethane mixtures of (Λ,Δ)‐[Ir(ppy)2(MeCN)2]⋅X (X=PF6 −, BF4 −) and (±)‐L1 or (±)‐L2 in 3:2 stoichiometry (Scheme 1). Electrospray ionization mass spectrometry (ESI‐MS) gives a triply charged m/z peak at 983.1120 (cage 1) or at 955.2853 (cage 2), along with [{Ir(ppy)2}(L)]3+ and [{Ir(ppy)2}2(L)2]3+ fragment species (Figures S3 and S4 in the Supporting Information). Initial 1H NMR spectra of [Ir(ppy)2(NCMe)2]⋅X and L in [D3]‐MeNO2 show considerable broadening of the resonances and chemical shift changes, most saliently the ppy protons ortho to the coordinating N (HA′) and C (HH′) move upfield and downfield, respectively. For cage 2, the previously sharp CH2 bridge singlet of L2 at 5.19 ppm becomes a complex multiplet as free rotation is hindered (Figure S15). ROESY spectra of 1 and 2 give the expected couplings, including between HH′ on the ppy ligands and the ortho pyridyl protons of L (Figures S8 and S16). Diffusion ordered NMR spectroscopy in [D3]‐MeNO2 for 1⋅3PF6 (Figure S9) gave a hydrodynamic radius of 18.99 Å.</p><p>Synthesis of metallo‐cryptophane cage species.</p><p>The structure of 1⋅3BF4⋅n(MeNO2) was confirmed by crystallography (Figure 1).9 There are two independent cage 1 cations that show minor structural differences. Anions and additional solvent were not located due to significant disorder. Each cage has three pseudo‐octahedrally coordinated IrIII centres, each with two ppy ligands and the pyridyl groups from two L1 ligands are in a cis arrangement. The two L1 ligands bridge between three IrIII centres. The average torsion angle between cis‐pyridyl groups is 38.04°, typical for [Ir(ppy)2(pyridyl)2]‐type complexes10 with the bowl shape of CTV‐type ligands being able to accommodate these torsion angles within the cage structure.</p><p>A [{Ir(ppy)2}3(L1)2]3+ cage from the crystal structure of 1⋅3BF4⋅n(CH3NO2); L1 and ppy ligands shown in green and grey, respectively.</p><p>Both L1 ligands within each cage 1 are the same enantiomer, giving the chiral anti‐cryptophane isomer. Each [Ir(ppy)2] unit within a cage has the same chirality, such that only the enantiomeric MM‐ΛΛΛ and PP‐ΔΔΔ cage isomers are observed in the structure. Given that the Λ and Δ enantiomers of the [Ir(ppy)2]+ moieties and the M and P enantiomers of the l‐types ligands are present in the reaction mixture, there are twelve possible stereoisomers of the cage. The 1H NMR spectra of both cages 1 and 2 undergo significant sharpening upon standing (Figures S7 and S15 in the Supporting Information), and fully equilibrate after several months. The 1H NMR spectrum of cage 1⋅3PF6, collected after 3 months of standing, is virtually identical to that of the single crystals of 1⋅3BF4⋅n(CH3NO2) re‐dissolved in [D3]‐MeNO2 (Figure 2 a, b). (±)‐L1 was resolved into its constituent enantiomers by chiral HPLC,11 and each L1 enantiomer reacted with each of Λ‐[Ir(ppy)2(MeCN)2]⋅BF4 and Δ‐[Ir(ppy)2(MeCN)2]⋅BF4. As expected, the two combinations that were mis‐matched pairs of enantiomers gave poorly resolved 1H NMR spectra (Figures S10 and S11), whereas the two combinations that were matched pairs (presumably M‐Δ and P‐Λ) gave sharp spectra in short timeframes that were similar to the fully sorted cage mixture (Figures 2 d, S12, S13). ESI‐MS of matched and mis‐matched pairs are similar with all combinations showing cage formation (Figure S14). The observed 1H NMR spectral sharpening is therefore indicative of equilibration involving chiral self‐sorting of an initial mixture of cage stereoisomers; this was also seen in our previous studies of a [Pd6(L1)8]12+ cage but only the ligand was a chiral component.12 We could not resolve the sorted cages by analytical chiral HPLC.</p><p>1H NMR spectra in CD3NO2 of (a) re‐dissolved racemic single crystals of MM‐ΛΛΛ and PP‐ΔΔΔ cages of 1⋅3BF4; (b) (Λ,Δ)‐[Ir(ppy)2(MeCN)2]⋅PF6 and (±)‐L1 3 months after mixing; (c) (Λ,Δ)‐[Ir(ppy)2(MeCN)2]⋅PF6 and (±)‐L1 2 hrs after mixing; (d) matched pair of Δ‐[Ir(ppy)2(MeCN)2]+ and one L1 enantiomer after 2 hrs.</p><p>Homochiral metallo‐cages with tris‐chelate metal coordination are known both from achiral13a,13b and resolved chiral ligands.13c–13e Metallo‐cages that show homochiral self‐sorting from a racemic mixture of ligand enantiomers observed in solution are rare,14 although these include PdII metallo‐cryptophanes.8a The simultaneous chiral self‐sorting of both ligand and pre‐formed inert metallo‐tecton as reported here have not been previously reported. In a preliminary investigation of the influence of chiral guests on the self‐assembly of cage 1, globular additives were included in 3:2 mixtures of (Λ,Δ)‐[Ir(ppy)2(MeCN)2]⋅PF6 and (±)‐L1. Addition of chiral R‐camphor or S‐camphor led to noticeably faster sharpening of the 1H NMR spectra than in their absence, but this was not observed for the addition of achiral adamantane (Figures S15–S20 in the Supporting Information). Interestingly, addition of the related anionic species R‐(or S‐)‐10‐camphorsulfonic acid to the reaction mixture prevents cage formation presumably as carboxylate is a competing ligand for the iridium (Figures S21 and S22).</p><p>The cages do not show self‐recognition of l‐ligand species. ESI‐MS of a MeNO2 solution of L1, L2 and [Ir(ppy)2(MeCN)2]⋅BF4 shows a statistical mixture of 1:[{Ir(ppy)2}3(L1)(L2)]3+:2 cage species (Figure 3). Mixing 1⋅3BF4 and 2⋅3BF4 in MeNO2 results in very slow exchange between L1 and L2 with appreciable ligand exchange only observed after four weeks, and near‐statistical mixing reached after ten weeks (Figure S6 in the Supporting Information). Thus, these cages have a high degree of kinetic stability but are not completely inert. It is interesting to note that this speciation behaviour is in contrast with recently reported [Pd3L2]6+ metallo‐cryptophanes, which exclusively formed homocages from two different l‐type ligands, with no ligand exchange.8a</p><p>ESI‐MS of a 1:1:3 mixture of L1:L2: [Ir(ppy)2(MeCN)2]⋅BF4 in MeNO2 showing formation of a statistical mixture of homoleptic and heteroleptic cages.</p><p>The absorption spectra of 1 and 2 in dichloromethane (DCM) are similar to other [Ir(ppy)2(NN^ )]+ systems,7 and characterised by two intense ligand centred (1LC) transitions between 260 and 320 nm localised on the ppy and three lower intensity broad bands below 380 nm that consist of spin‐allowed and spin‐forbidden mixed metal‐to‐ligand and ligand‐to‐ligand charge transfer (1MLCT/1LLCT and 3MLCT/3LLCT, respectively) transitions (Figure S26 in the Supporting Information). The weak CT transition observed for 1 at 470 nm was not reported for the monomeric [Ir(ppy)2(4‐pyCO2Et)2]+ (4‐pyCO2Et=4‐ethyl isonicotinate),10c suggesting increased conjugation in 1 due to the CTV scaffold. For both 1 and 2, the excitation spectra in DCM match the absorption spectra and indicate a single photophysically active species.</p><p>Cages 1 and 2 are emissive in DCM solution and in the solid state. Upon photoexcitation of 1, a broad and unstructured emission is observed both in DCM and in the powder (Figure 4 a) due to emission from a mixed 3MLCT/3LLCT state.7 The photoluminescence spectrum in the powder is red‐shifted (λ max=648 nm) compared to that in DCM (λ max=604 nm); however, 1 possesses similarly low Φ PL of around 1 % and bi‐exponential decay kinetics in both media (Table 1). Due to the increased conjugation into the CTV scaffold, cage 1 shows red‐shifted emission and similar Φ PL compared to [Ir(ppy)2(4‐pyCO2Et)2]+ (λ max=560 nm; Φ PL=2 %).10c Lusby's [{Ir(ppy)2}6(tcb)4]6+ cage also showed a red‐shifted emission (λ max=575 nm) when compared with the corresponding [Ir(ppy)2(NCPh)2]OTf complex (λ max=525 nm); however, unlike for cage 1 and other Ir(ppy)2 discrete supramolecular systems,15 the Φ PL for the Lusby cage was enhanced compared with that of the mononuclear complex (Φ PL=4 % vs. Φ PL=<1 %).4</p><p>Normalised photoluminescence spectra of a) 1⋅3BF4 and b) 2⋅3BF4. Green lines are de‐aerated DCM solutions; blue lines are PMMA‐doped films with 5 wt % of cages spin‐coated on a quartz substrate; red lines are bulk powders.</p><p>Photophysical properties of complexes 1⋅3(BF4) and 2⋅3(BF4).</p><p>[a] Measurements in degassed DCM at 298 K. [b] Quinine sulfate employed as the external reference (Φ PL=54.6 % in 0.5 m H2SO4 at 298 K). [c] PMMA‐doped films (5 wt % of cage) formed by spin‐coating deposition on a quartz substrate. [d] Φ PL measurements were carried out under nitrogen (λ exc=360 nm). [e] Values obtained using an integrating sphere. [f] Principal emission peaks listed with values in parentheses indicating relative intensity. [g] λ exc=378 nm; values in parentheses are pre‐exponential weighting factors, in relative % intensity, of the emission decay kinetics.</p><p>To mitigate non‐radiative vibrational motion in the cage, we spin‐coated 5 wt % of 1 in polymethyl methacrylate (PMMA), which serves as an inert matrix. The emission in the thin film was blue‐shifted and more structured (λ max=514 nm) compared to both the powder and solution spectra. The Φ PL of 5.5 % was enhanced as a result of the rigidity conferred by the PMMA host and the emission lifetimes were significantly longer (τ e=634 and 2319 ns).</p><p>The photoluminescence spectrum of cage 2 in DCM is more structured and blue‐shifted (λ max=516 nm) compared to 1, indicating an emission that is more predominantly ligand‐centred (3LC; Figure 4 b). The blue‐shifted emission of 2 compared to 1 was expected considering the presence of the electron‐withdrawing ester moieties located on L1 in 1, which stabilise the LUMO.10c Cage 2 shows a significantly enhanced Φ PL and longer τ e compared to 1 in DCM (Φ PL=15 %, τ e=523, 887 ns).</p><p>Unlike 1, the emission of 2 as a powder is not significantly red‐shifted (λ max=519 nm), though the emission profile is less structured, showing less well‐resolved resolved vibrational bands as shoulders of the main emission peak. The emission profile for 2 in the PMMA‐doped thin film is likewise very similar to that in DCM. Although Φ PL values are low in the powder (Φ PL=1.6 %), in the doped film they are higher (Φ PL=10 %). Emission lifetimes are expectedly longer in dopedfilms than in powder (Table 1). Attempts to synthesize an analogous mononuclear complex of 4‐phenoxymethylpyridine for comparison were not successful due to ligand oligomerization.</p><p>In summary, phosphorescent [{Ir(ppy)2}3(L)2]3+ metallo‐cryptophanes can be synthesized in high yields, with the CTV‐type ligands being able to accommodate torsion angles typical of [Ir(ppy)2(L)2] complexes to form rare examples of 3D IrIII cyclometallated coordination cages. These cages undergo ligand exchange processes over months and show a remarkably high degree of homochiral self‐sorting of both ligand and metallo‐tecton, but not self‐recognition between similar l‐type ligands. Chiral sorting is enhanced by the presence of neutral chiral additives. For cage 1, chiral self‐sorting occurs relatively rapidly upon crystallisation through an induced seeding effect, but on a timescale of months in solution. Luminescence properties of the two cages are quite distinct, pointing to an ability to tune the photophysical properties of these systems. Cage 2 showed an enhanced and blue‐shifted emission compared to 1, reaching a Φ PL of 15 % in DCM solution and 10 % in doped film. These are promising systems for a variety of applications including semiochemical hosts, photoredox catalysts and in energy conversion materials.</p><!><p>The authors declare no conflict of interest.</p><!><p>As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.</p><p>Supplementary</p><p>Click here for additional data file.</p>

PubMed Open Access

Distinct biological effects of golgicide a derivatives on larval and adult mosquitoes

A collection of Golgicide A (GCA) analogs has been synthesized and evaluated in larval and adult mosquito assays. Commercially available GCA is a mixture of four compounds. One enantiomer (GCA-2) of the major diastereomer in this mixture was shown to be responsible for the unique activity of GCA. Structure\xe2\x80\x93activity studies (SAR) of the GCA architecture suggested that the pyridine ring was most easily manipulated without loss or gain in new activity. Eighteen GCA analogs were synthesized of which five displayed distinct behavior between larval and adult mosquitos, resulting in complete mortality of both Aedes aegypti and Anopheles stephensi larvae. Two analogs from the collection were shown to be distinct from the rest in displaying high selectivity and efficiency in killing An. stephensi larvae.

distinct_biological_effects_of_golgicide_a_derivatives_on_larval_and_adult_mosquitoes

<p>Aedes aegypti mosquitoes, the primary vector of dengue virus, have spread rapidly in the last decade into highly populated urban areas.1 This has led to a dramatic rise in the number of clinical cases of dengue fever in many parts of the world, with current estimates of 50 million infections/year resulting in 500,000 hospitalizations due to dengue hemorrhagic fever.2 On an even greater scale, Anopheline mosquitoes, primarily the biological vectors Anopheles gambiae and Anopheles stephensi, account for 250 million cases of malaria/year, leading to ~1 million deaths worldwide.3 Vector control has proven to be an effective strategy to combat mosquito-borne diseases, and there are many examples in which decreasing mosquito populations reduces disease incidence.4 However, because of increased mosquito-resistance to pyrethroid-based insecticides,5 we have been investigating the possibility that proteins required for efficient blood meal metabolism may be novel mosquito-selective targets. Three properties of mosquito blood meal metabolism make it an attractive biological target, (1) it is highly specialized and restricted to blood feeding arthropods, (2) it is a metabolically stressful process in that a ~2.0 mg female mosquito must digest/excrete ~0.5 mg of blood protein within 48 h, and (3) it is both time- and tissue-dependent, which is unlike homeostatic metabolism in non-blood feeding organisms. Taken together, control agents targeting mosquito blood meal metabolism could be more selective and chemically safer than the pyrethroid neurotoxins currently in use.</p><p>We have recently shown by RNA interference methods that blood fed mosquitoes are extremely sensitive to defects in COPI vesicle transport, displaying a ~90% mortality by 72 h post blood meal (PBM).6 The COPI vesicle transport system is known to function in retrograde vesicle transport between the Golgi and ER membrane compartments, as well as receptor targeting to the plasma membrane, and formation of lipid droplets.7–9 The COPI system consists of three functional components, (1) coatomer proteins (α,β,β',γ,δ,ε,ξ), which comprise the outer layer of newly formed vesicles, (2) Arf proteins, which are small G proteins that recruit coatomer proteins to the membrane and are activated by GTP–GDP exchange, and (3) the Arf regulatory proteins GBF1 and GAP1/2/3, which are prototypical guanine nucleotide exchange factors (GEFs) and GTPAse activating proteins (GAPs), respectively, that control Arf–GTP and Arf–GDP levels in the cell.10 A small molecule inhibitor of COPI vesicle transport, golgicide A (GCA, Scheme 1), has recently been shown to disrupt the interaction of GBF1 with Arf proteins, and thereby interfere with COPI-dependent protein targeting.11</p><p>Golgicide A represented an excellent small molecule starting point for evaluating our COPI vehicle transport inhibitory strategy for mosquitoes. Golgicide A is made in a single step using the Povarov reaction (Scheme 1),12 which involves imine formation followed by an acid catalyzed addition/cyclization cascade. This one step approach is not only an ideal way of providing rapid access to golgicide A, which is particularly attractive for long term practical applications, but it also lends itself well to being readily manipulated to allow access a vast number of golgicide A analogs. Since no follow up studies have been reported for golgicide A, it is not known how significant each of its five functional groups (two fluorines, piperidine and pyridine nitrogens, as well as the cyclopentene olefin) are in contributing to its biological activity. Moreover, no studies have been reported regarding the potential for GCA to inhibit mosquito metabolism. Furthermore, since golgicide A is now sold commercially,13 it is important to know which of the four compounds in the commercial preparation are the most active (Scheme 2). This racemic diastereomeric mixture, which results from the acid catalyzed Povarov reaction, is primarily composed of the cis-diastereomer (1 and 2) in favor of the trans-diastereomer (3 and 4). The first question we set out to answer was, are all four components of Golgicide A (GCA 1–4) equally potent with respect to their COPI vesicle inhibitory activity? It seemed unlikely to us that this would be the case as the fused rigid tricyclic architecture of GCA would present the pyridine substituent in a dramatically different non-flexible fashion to the target. Furthermore, if we were to assume that the major cis-components (1 and 2) of GCA were responsible for its biological response, it was still quite likely that the two enantiomeric rigid semi-bowl shaped structures would interact with different efficiency to their chiral target. We addressed this challenge by synthesizing GCA, separating the diastereomers (1 + 2 from 3 + 4) using a combination of chromatography and crystallization. The enantiomers were separated from each other using chiral HPLC-chromatography.14 These pure compounds (GCA-1, GCA-2 and GCA-3) were tested against GCA (mixture) using DMSO as a control in a mosquito ovarian development assay (Table 1). This mosquito biological assay was performed by micro-injecting each of the compounds into blood fed Ae. aegypti mosquitoes and measuring follicle lengths in the developing ovaries 48 h post blood meal. As we predicted, the individual components of the GCA mixture responded in a dramatically different fashion. The minor racemic trans-diastereomer (GCA-3) was shown to be close to inactive compared to GCA. When the two enantiomers (GCA-1 vs GCA-2) of the major cis-diastereomer were compared to each other the significance of chirality was shown to be strongly on display, with one enantiomer (GCA-1) performing poorly, while the other enantiomer (GCA-2) was superior to all four components as well as the commercial GCA mixture. With this new insight into golgicide-A (GCA), we set out to unambiguously determine the absolute configuration of the active component (GCA-2). We were fortunate to be able to grow X-ray quality crystals of GCA-2, which in turn allowed us to determine its absolute configuration shown in Scheme 2.15</p><p>GCA, which is not a rationally designed inhibitor, surfaced as a candidate from an activity screen of a known compound library.11 Having demonstrated the significance of substitution (trans vs cis) and chirality on inhibitory activity, we next set out to decipher the importance of each one of its substituents. The carbon skeleton of most drug architectures sets the conformational and three dimensional boundaries by which its substituents can interact with the biological target. With the functional groups being the most likely candidates responsible for target binding, we decided to evaluate how important the contribution of each one of GCA's five functional groups (two aryl fluorides, piperidine and pyridine nitrogen atoms and the cyclopentene olefin) was to the overall biological activity using the same in vivo biological assay (Table 1). The analogs we first synthesized are shown in Scheme 3. Each analog has one of the key GCA functional groups either replaced or substituted (piperidine nitrogen). These analogs were readily accessed by one of two approaches. GCA analogs 4–6 were made by reducing or substituting the major GCA-syn isomer (1 + 2), while analogs 7–9 took advantage of the flexibility of the Povarov reaction.16 When these new analogs were subjected to the same ovarian development assay as GCA's 1–3, it was revealed that all but one functional group contribute substantially to inhibitory activity. Removing the pyridine nitrogen (GCA-8) proved to be beneficial. This most active GCA derivative provided us with an opportunity for pursuing further analogs inspired by this new hit structure.</p><p>Inspired by GCA-8 we synthesized GCA analogs 10–17 (GCA-13, not shown, has the 4-fluoro group replaced with a trifluoromethyl group), wherein the 3-pyridine group has been replaced with heterocycles (10 and 12), alkyl- (11) or aryl (14–17) groups. Two analogs from this new set surfaced as noteworthy based on the in vivo mosquito reproduction assay. GCA-12, wherein the pyridine nitrogen is in the 2-position compared to the 3-position for GCA proved to be equipotent to GCA, but inferior to lead candidate GCA-8. Substituting the phenyl group of GCA-8 with a 4-trifluoromethyl group (GCA-17) proved to be a favorable substitution that resulted in a slight improvement in inhibitory activity.</p><p>Our analysis of this new data suggested that less polar aromatic replacements for the 3-pyridine group of GCA were performing better than other substitutions. With these hit structures, we decided to explore the potential of increasing the size of this substituent further in a flexible chainlike fashion with the hope of gaining useful secondary target interactions. Towards that end we decided to functionalize the amine group of GCA-16 with a diverse set of groups to increase our chance of identifying a new hit. For this purpose, a heterocycle (GCA-18), alkyl- (GCA-19) and aryl- (GCA-20 and 21) groups were chosen.</p><p>We were encouraged to find that all four analogs were superior to the starting structure (GCA-16). Three of these four analogs (GCA-18, 19 and 20) performed better than GCA (Table 1). This new data seems to support our assessment that more greasy compounds perform better. For example, substituting the polar benzylic nitrogen atom of GCA-16 with a greasy heptyl- (GCA-19) or napthyl- (GCA-20) groups affords analogs that are similar in potency to GCA-8 and GCA-17.</p><p>Of the eighteen GCA inspired analogs we synthesized, four performed better than the active enantiomer GCA-2 (GCA-8, GCA-18, GCA-19, GCA-20). However, only GCA-8 and GCA-17 displayed statistically significant improvement in inhibitory activity over GCA-2 (P <0.05). Equipped with these new insights about GCA and the collection of new analogs from our ovarian development assay in blood fed mosquitoes (Table 1), we next tested the performance of the compounds in a more sensitive cytotoxic assay in second instar Ae. aegypti and An. stephensi mosquito larvae (Table 2). In this larval assay, GCA derivatives were added directly to the culture media and larval mortality was measured 24 h later. The results from these studies are quite intriguing. GCA and its individual components (GCA 1–3) displayed minimal to no cytotoxicity in the Ae. aegypti assay, with the most interesting exception being GCA-2, which demonstrated strong cytotoxicity against An. stephensi mosquito larvae. Of the 21 compounds evaluated, four (GCA-5, GCA-9, GCA-12 and GCA-18) proved very effective in killing larvae from both mosquito species. GCA-16 followed closely behind in terms of general cytotoxicity. These five hits are quite diverse and represent all phases of our derivatization studies. Of these active analogs, two are from our initial functional group SAR editing campaign (Scheme 3), wherein the piperidine nitrogen has been methylated (GCA-5) and the 4-fluoro group of GCA removed (GCA-9). One (GCA-12) is from the second phase of our SAR studies in which we focused on replacing the GCA 3-pyridine group (Scheme 4) and the last two (GCA-16 and GCA-18) are members of our last compound collection (Scheme 5) whose goal was to achieve a second molecular target interaction. An equally interesting observation from the data presented in Table 2 is the high mosquito species selectivity for four of the GCA analogs (GCA-2, GCA-4, GCA-10 and GCA-11), which in all cases are about five times more selective in killing An. stephensi larvae compared to Ae. aegypti larvae. Despite being similar in terms of mosquito selectivity, their cytotoxicity varies significantly, ranging from 86.4% (GCA-11) to 37.6% (GCA-10). It is worth mentioning, that the most active component of the initial GCA mixture (GCA-2) again emerges as being unique.</p><p>When the data from Tables 1 and 2 are compared, several interesting observations emerge (Scheme 5). The most active compounds in the ovarian development assay (Table 1, GCA-8 and GCA-17), were not significantly better than DMSO in the larval mortality assay (Table 2), suggesting that the biological target may be different in larvae than in adults. Alternatively, it could be that the pharmacokinetics of these compounds are very different when injected into the mosquito hemolymph (ovarian development assay), compared to water (larvae cytotoxicity assay). It is remarkable that by simply methylating the piperidine nitrogen of GCA we are able to convert one of the most inactive compounds (GCA-5) presented in Table 1 into one of the most active one in the cytotoxic assay (Table 2). The 4-desfluoro GCA analog (GCA-9), displayed similarly drastic behavior. Closer inspection of the data from Tables 1 and 2 reveals that GCA-12 and GCA-18 are the only analogs that display strong effects in all three assays. In the Ae. aegypti ovarian development assay, GCA-12 and GCA-18 were shown to be equipotent with GCA (Table 1), while in the larvae cytotoxicity assay (Table 2), GCA-12 and GCA-18 were strongly cytotoxic against both An. stephensi and Ae. aegypti larvae and GCA is not. When comparing the structures of these two analogs to GCA, GCA-12 represents a miniscule structural change. In GCA-12 the pyridine nitrogen has simply been moved from the 3-position with respect to the piperidine ring in GCA to the 2-position. Although unknown at this time, this subtle structural change now provides both metal chelating and hydrogen bonding opportunities for the adjacent nitrogen atoms of GCA-12 which are not possible for GCA or any of the other analogs. GCA-18 on the other hand represents a significant structural change from GCA having the pyridine ring been replaced with a phenyl group containing a para-substituted benzylic secondary amine group attached to an additional piperidine functionality. It is noteworthy that GCA-18 had the lowest LC50 (0.05 mM) in both An. stephensi and Ae. aegypti larvae (Table 2). Four of the analogs shown in Scheme 6 represent minor structural changes compared to GCA-2, with three of these four having one of GCA-2 two nitrogen atoms either capped (GCA-5), deleted (GCA-8) or migrated (GCA-12). For the other four analogs, the 3-pyridine ring has been replaced by a hindered alkyl group (GCA-11) or para-substituted aryl group with a diverse group of substituents (GCA-16–18).</p><p>In conclusion, we have demonstrated that GCA-2 is the most active component of the commercially available GCA mixture. GCA-2 was superior to its three other components as shown by an ovarian development assay in blood fed Ae. aegypti mosquitoes and a more sensitive larvae cytotoxicity assay. In order to improve upon the performance of GCA-2, we synthesized 18 analogs, which were tested in both assays. The results from these studies showed that these analogs behaved distinctly differently in these assays, with GCA-12 and GCA-18 being the only two analogs that performed well in both assays. Five of the GCA analogs were shown to be effective in killing both Ae. aegypti and An. stephensi larvae, while two analogs (GCA-2 and GCA-11), displayed distinct and strongly selective cytotoxicity (>80%) towards Ae. stephensi larvae. Future studies are aimed at exploiting the unique biological insights gained from our GCA inspired collection toward making more active and selective compounds.</p>

PubMed Author Manuscript

Protein and Water Distribution Across Visual Axis in Mouse Lens: A Confocal Raman MicroSpectroscopic Study for Cold Cataract

Purpose: The aims of the study were to investigate cellular mechanisms of cold cataract in young lenses of wild-type C57BL/6J (B6WT) mice treated at different temperatures and to test a hypothesis that cold cataract formation is associated with the changes in lens protein and water distribution at different regions across lens fiber cells by Raman spectroscopy (RS). Methods: RS was utilized to scan the mouse lens at different regions with/without cold cataract. Three regions with various opacification along the equatorial axis in the anterior–posterior lens section were scanned. The intensity ratio of Raman bands at 2,935 and 3,390 cm−1 (Ip/Iw) were used to evaluate lens protein and water distribution. We further determined water molecular changes through Gaussian profiles of water Raman spectra. Results: Three specific regions 1, 2, and 3, located at 790–809, 515–534, and 415–434 μm away from the lens center, of postnatal day 14 B6WT lenses, were subjected to RS analysis. At 37°C, all three regions were transparent. At 25°C, only region 3 became opaque, while at 4°C, both regions 2 and 3 showed opacity. The sum of the difference between Ip/Iw and the value of linear fitting line from scattered-line at each scanning point was considered as fluctuation degree (FD) in each region. Among different temperatures, opaque regions showed relatively higher FD values (0.63 and 0.79 for regions 2 and 3, respectively, at 4°C, and 0.53 for region 3 at 25°C), while transparent regions provided lower FD values (less than 0.27). In addition, the decrease in Gaussian peak II and the rising of Gaussian peak III and IV from water Raman spectra indicated the instability of water molecule structure in the regions with cold cataract. Conclusion: Fluctuation degrees of RS data reveal new mechanistic information about cold cataract formation, which is associated with uneven distribution of lens proteins and water across lens fiber cells. It is possible that RS data partly reveals cold temperature-induced redistribution of lens proteins such as intermediate filaments in inner fiber cells. This lens protein redistribution might be related to unstable structure of water molecules according to Gaussian profiles of water RS.

protein_and_water_distribution_across_visual_axis_in_mouse_lens:_a_confocal_raman_microspectroscopic

Introduction<!>Animals and Lens Image<!>Lens Vibratome Section<!>Raman Spectroscopic Scanning Across Fiber Cells<!><!>Statistical Analysis<!>Lens Cold Cataract Formation in Microscopic Image<!><!>Lens Cold Cataract Formation in Microscopic Image<!>Raman Spectra Acquisition and Processing<!><!>Lens Protein and Water Quantitative Analyses<!><!>Lens Protein and Water Quantitative Analyses<!>Lens Water Molecular Level Analyses<!><!>Lens Water Molecular Level Analyses<!><!>Lens Water Molecular Level Analyses<!>Discussion<!>Conclusion

<p>Cataract is the main cause of blindness among various eye diseases, which has always been a global health issue (Huang and Chen, 2018). Cold cataract is a phenomenon that opacification occurs in young mammalian lenses when cooled, and the whole process is reversible when warmed (Zigman and Lerman, 1964; Zigman and Lerman, 1965; Lo, 1989). By controlling the temperature, cold cataract is a convenient and realizable model for studying cataract and related physicochemical changes in the laboratory (Lo, 1989; Sivak et al., 1992). The formation and components of cold cataract have been studied via various methods in the past decades, including laser scanning (Sivak et al., 1992), NMR (Lerman et al., 1982; Lerman et al., 1983), and protein analyses (Broide et al., 1991; Song et al., 2009).</p><p>It has been proven that beaded filament is significant to the lens optical transparency (Blankenship et al., 2001; Song et al., 2009). In biochemistry level, α-, β-, and γ-crystallin are related to the formation of cold cataract (Lo, 1989), and aggregation and phase separation of γ-crystallin play the most crucial role (Lerman et al., 1983; Broide et al., 1991). Therefore, γ-crystallin is considered to be the cryoprotein in cold cataract formation (Lerman et al., 1966; Siezen et al., 1985). Both intermediate filaments and crystallins are concerned with the formation of cold cataract. Furthermore, some other researches raised that supermolecular organization accounted for the formation of cold cataract rather than specific lens protein (Loewenstein and Bettelheim, 1979; Lerman et al., 1982). A further study on the mechanisms of cold cataract is still needed.</p><p>So far, there are various optical methods involved in interpreting cold cataract based on changes in optical signals. With the laser scanning system, the opacification of the lens can be evaluated for cold cataract by measuring the intensity of scattered light (Benedek et al., 1979; Petta et al., 2008), relative light transmittance (Banh and Sivak, 2004), as well as the equivalent focal length (Sivak et al., 1992). In addition, optical coherence tomography and optical coherence elastography were used to image the cold cataract model, providing structural information and biomechanical properties (Izatt et al., 1994; Zhang et al., 2018).</p><p>Raman spectroscopy (RS) is a non-invasive optical technique to determine the existence of certain molecules, which can be used in ophthalmology (Erckens et al., 2001; Lin et al., 2010). In eye lens study, Raman spectra from RS usually provide feature peaks, which are bound to vibrational modes of specific chemical bonds, such as CH2/CH3 vibration bond (Smeets et al., 1993) and disulfide bond (Ozaki et al., 1987). Furthermore, amino acid contents along the visual and equatorial axes were scanned in pig lens by Raman spectroscopy (Medina-Gutiérrez et al., 2004). For cold cataract study, Raman spectra for different species were acquired to test the changes in protein and water in lens with cold cataract (Ondruska and Hanson, 1983). Changes in the intensity ratio of tyrosine residues were found in the process of temperature alteration (Mizuno et al., 1984). To further analyze water and protein content in different regions of the lens based on acquired Raman spectra, different studies have used RS to scan lens and lens slices at different positions (Bot et al., 1989; Huizinga et al., 1989). However, among previous researches on cold cataract with RS, the scanning region was usually wide instead of focusing on a small range across lens fiber cells.</p><p>The lack of organelles is a feature of lens fiber cells. Lenticular water may play an important role in lens opacification. The state of lens water among different species was studied with NMR (Rácz et al., 1979). Free and bound water mass was evaluated in different lens regions with age-dependent and advanced nuclear cataract (Heys et al., 2008), whereas, molecular level of water distribution was rarely analyzed in the formation of cold cataract. RS is qualified to reveal molecular information of solid and liquid H2O (Carey and Korenowski, 1998). Raman spectra of water can be analyzed through four of five fitted Gaussian profiles (Crupi et al., 2008; Huang et al., 2009; Baschenko and Marchenko, 2011). These Gaussian peaks are able to provide intra-molecular vibrational distributions of water, and are considered to be various structural types of hydrogen bond in H2O (Crupi et al., 2008; Huang et al., 2009).</p><p>In order to study the cellular mechanisms of cold cataract formation at molecular level, we used RS to scan young mice lens sections across fiber cells in vibratome section in vitro for the first time. In this work, we aimed to test the hypothesis that cold cataract formation is associated with the changes in lens protein and water distribution at different regions. With microscopic imaging, the opacity occurred as temperature decreased from 37°C to 4°C. The opacity size varied under the treatment of different temperatures during the cold cataract formation. Raman spectra for three regions along the equatorial axis of anterior–posterior (A/P) lens section were obtained at different temperatures (4°C, 25°C, and 37°C). Protein and water content distribution was evaluated via related Raman vibrational band quantification. The distribution showed unevenness in opacification areas due to cold cataract. To further discover molecular changes in water, all the water spectra were analyzed in terms of Gaussian profiles.</p><!><p>For cold cataract occurring in young mammalian lenses, female C57bl/6J wild-type mice (B6WT) at the age of postnatal day 14 were used as experiment subjects. All the disposals during experiments with mice were according to the approval of the Animal Ethics Committee of Tsinghua Shenzhen International Graduate School. Mice were euthanized with a suitable amount of 4% chloral hydrate and sacrificed by injecting an overdose of anesthetic after surgery. Lenses were immediately isolated under a dissecting microscope (Leica MZ 95) and were immersed into phosphate-buffered saline (PBS). Three lenses were treated at temperatures 4°C, 25°C, and 37°C, separately. To observe cold cataract, lenses were imaged with the microscope at different temperatures. Lenses developed full cold cataract from transparency in 4°C PBS solution in about 2 min, and they were reversible to transparent in 37°C PBS solution in about 2 min. Images of lens samples were captured according to the software on the computer related to the microscope. The size of lenses and cold cataract regions were recorded.</p><!><p>According to previous studies, the fixation procedure shows no impact on water and protein content in the lenses (Bot et al., 1989; Huizinga et al., 1989; Siebinga et al., 1992). Before obtaining B6WT lens slices, fresh lenses should be fixed in 4% paraformaldehyde/PBS (PFA/PBS) solution at different temperatures. To examine 4°C cold cataract, fresh lenses were removed from eyeballs in 4°C PBS and fixed overnight in 4% PFA/PBS solution at 4°C. For lens samples treated at 25°C or 37°C, fresh lenses were dissected in PBS at room temperature, and then transferred to 4% PFA/PBS solution at 25°C or 37°C for 3–5 h. After fixation, lenses were washed with PBS three times and embedded in melting agarose gel on a plane block. The procedures of lens embedding and vibratome section are as follows: about 50 μl of melted agarose gel drop was added on the surface of the plane block. A wedge was cut on the edge of the solid agarose gel to hold the lens with the cutting direction of anterior–-posterior (A/P, along the optical axis) plane. Then to glue the lens and agarose gel on the block, about 0.5 μl of superglue drop was added to the wedge via pipette without touching the lens. The lens and gel drop were covered with more agarose gel as a whole. After solidification, the block was superglued on the cutting plate of the vibratome microscope (Leica VT 1200S). Then sections with a thickness of 100 μm were cut in a container of PBS. Lens sections around the equator were collected and kept in 4% PFA/PBS solution again for post-fixation for 15 min, then were washed with PBS three times. All the equatorial lens sections were scanned with Raman spectrometer at room temperature.</p><!><p>A confocal Raman microspectrometer (Horiba LabRAM HR800) was used for Raman spectra acquisition. Under the microscope of ×50, the spectrum grating was 600 lines, and the hole was 100 μm. The excitation laser wavelength was 532 nm with the power of 25 mW, and the spectral resolution was 1 cm−1. Through laser focus adjustment, the field of view was approximately 0.8 μm in diameter.</p><p>Equatorial lens sections were placed on glass slides and covered with cover glasses of 0.14-mm thickness. A drop of PBS solution was added between sliders and cover glasses to prevent lens sections from drying. To scan the lens section across the fiber cells, we set three scanning regions along the equatorial axis vertical to A/P axis (Figure 1) based on the diameter of the lenses and cataract regions under different temperatures. The scanning direction was from cortex to nucleus. For each region, 20 points were collected with the step length of 1 μm, focusing on 50 μm beneath the section surface to avoid uneven interference caused by cutting. In region 1, the lens stays transparent under all temperatures. In region 2, the lens turns opaque at 4°C, while it becomes transparent at 25°C. In region 3, the lens forms cold cataract at both 4°C and 25°C. Raman signal intensity as raw data were acquired for each wavenumber from 2,600 to 3,800 cm−1 at each scanning spot. The exposure time was 15 s, with three averaged measurements.</p><!><p>Schematic graph of lens section with different scanning regions. The left plot demonstrates the whole lens after dissection, and the right plot depicts the anterior–posterior (A/P) section of the lens cut as scanning sample. Index lines (a and b) show the orientation of the slice. 1, 2, and 3 in red represents scanning regions 1, 2, and 3 (20 μm in length for each).</p><!><p>Statistical significance was evaluated with one-way analysis of variance (ANOVA) with Tukey's test, using the software Origin (OriginLab, United States). Values pf p less than or equal to 0.05, 0.01, 0.001, and 0.0001 were considered statistically significant.</p><!><p>After being immersed in PBS at different temperatures for more than 2 min, lenses were imaged under the dissecting microscope. Three lenses were treated at each different temperature as one control group. As Figure 2A shows, from left to right, lenses were at 4°C, 25°C, and 37°C, separately. At 4°C and 25°C, obvious opacity due to cold cataract formation can be seen. However, the diameter of the opacity sphere at 4°C was much bigger than that at 25°C. At 37°C, no opacity was observed. When the temperature was raised from 4°C/25°C to 37°C, the opacity disappeared in about 2 min, which supported that the cold cataract phenomenon was entirely reversible. There was no significant difference in the formation of cold cataract between the different sexes of mice. Also, there was no noticeable difference between the two lenses from a single mouse.</p><!><p>Microscopic image of lens at different temperatures with cold cataract formation. (A) Anterior-view images of lens under dissecting microscope. From left to right, lenses were at 4°C, 25°C, and 37°C. Scale bar: 500 μm. (B) Plot profile of the equatorial line of the lenses at 4°C, 25°C, and 37°C.</p><!><p>To quantitatively measure the light scattering distribution due to cold cataract, lens images captured by the dissecting microscope were further analyzed via software ImageJ. The plot profile of the gray value in the equatorial section of the lens is illustrated in Figure 2B. The light scattering distribution of the lens apparently differed from each other at 4°C, 25°C, and 37°C, respectively. The sizes of all lenses of the different mice were similar with a diameter of about 1,780 ± 40 μm (n  9). The diameters of the cold cataracts at 4°C were 1,319 ± 28 μm, about 74% of the whole lens diameter. However, the diameter of the cold cataracts, at 25°C was 962 ± 21.0 μm, about 54% of the whole lens diameter.</p><!><p>Based on the different sizes of cold cataract at 4°C, 25°C, and 37°C, regions 1, 2, and 3 were located at about 790–809, 515–534, and 415–434 μm away from the lens center, respectively. To analyze protein distribution at each spot from three regions, three measurements were averaged during signal acquisition. High-wavenumber regions, 2,600–3,800 cm−1, were recorded as raw data in this work. Since the Raman intensities of 2,935 and 3,380 cm−1 bands represent C–H vibration mode and O–H vibration mode, lens protein and water content can be evaluated via these two bands, respectively.</p><p>Raman spectra were smoothed via Savitzky–Golay filter. Then background noises caused by fluorescence were subtracted through linear subtraction lines of the intensity of bands 2,600–2,800, 2,800–3,030, 3,030–3,100, and 3,100–3,800 cm−1. The procedure of spectra processing is demonstrated in Figure 3A.</p><!><p>High-wavenumber Raman spectra (2,600–3,800 cm−1) of mice lens. (A) Spectra processing procedure. (B): Average ratios of the intensity of protein and water brands (Ip/Iw) for three regions from all three lenses treated with different temperatures. Ip/Iw values increased from regions 1 to 3 and showed statistically significant differences between each region at 4°C, 25°C, and 37°C (****p ≤ 0.0001).</p><!><p>To quantify protein content distribution in different regions under different temperatures, we evaluated the ratio of the intensity of 2,935 cm−1 band as protein content and the intensity of 3,380 cm−1 band as water content (Ip/Iw) for each scanning point. Figure 3B depicts the average values (±S.D.) of Ip/Iw from all scanning points of the three lenses according to the three regions at 4°C, 25°C, and 37°C. For one lens, in regions 1, 2, and 3, the average Ip/Iw values were about 0.31, 1.35, and 1.81, respectively. This accorded with the rising of protein mass and the decreasing of water mass from lens cortex to nucleus. In regions 1 and 2, Ip/Iw values were not significantly different (p > 0.05), which revealed that protein total mass was nearly equal in these two regions with or without cold cataract. However, in region 3, Ip/Iw values were significantly different (p ≤ 0.0001).</p><p>In order to visualize the scanning result from different regions, we demonstrated scatter line plots for Ip/Iw values of every scanning point (scanning points 1–20) in three regions from every lens treated with each temperature in Figures 4A–C. For the 37°C group, scatter lines in all regions were relatively smooth. For the 25°C group, scatter lines in region 3 fluctuated more than those in regions 1 and 2. For the 4°C group, scatter lines were relatively smooth only in region 1, while scattered lines fluctuated obviously in regions 2 and 3. Generally, cold cataract impacted the protein content distribution in different regions under different temperatures.</p><!><p>The Ip/Iw values for different regions and different temperatures. (A–C) Scattered-line plots of Ip/Iw value for each scanning region at 4°C, 25°C, and 37°C. In each small plot, the display regions of the values were set equal as 0.6. (D–F) The value of fluctuation degree for regions 1, 2 and 3 at different temperatures, separately (*p ≤ 0.05, **p ≤ 0.01).</p><!><p>To further quantify the fluctuation degree (FD), we processed linear fit to every scatter line. The absolute value of the difference between each scatter point and the linear-fit value line were summed up as FD value. As Figure 4D shows, in region 1, FD values were close with an average of 0.22 under different temperatures. However, in region 2, as Figure 4E shows, the FD value was much higher at 4°C (0.63 ± 0.17) compared with25°C (0.27 ± 0.05) and37°C (0.27 ± 0.09). The FD values of the 25°C and 37°C groups were still close. In region 3, as Figure 4F shows, the FD values of the 4°C (0.79 ± 0.20) and 25°C (0.53 ± 0.16) groups were relatively higher than the 37°C group. This revealed that the opacity from cold cataract formation altered protein content distribution among lens fiber cells. The lower the temperature, the more uneven the protein and water content distribution presented with the formation of cold cataract.</p><!><p>In order to acquire water molecular information from Raman spectra, the water spectra were analyzed through curve fitting with the software Origin (OriginLab, United States). The strategy adopted for the curve fitting procedure was to use well-defined shape components of Gaussian functions, whose peaks were located at about (I) 3,230, (II) 3,400, (III) 3,530, and (IV) 3,650 cm−1. All three parameters for each Gaussian function were left to vary upon iteration. The statistical parameters were used as a guide to "best fit" The result of each fitted spectra was characterized by the adjusted R-square of ∼0.99 to ensure the stability of the procedure. Typical Gaussian peaks (peak I to peak IV) are demonstrated in Figure 5A.</p><!><p>Gaussian peaks and intensity changes without opacity. (A) Typical Gaussian fitting components as Gaussian peaks for Raman water spectra, ranging from 3,100 to 3,800 cm−1. I, II, III, and IV stand for Gaussian peaks I, II, III, and IV. (B) Raman intensity of four Gaussian peaks from three regions at 37°C. (C) Raman intensity of four Gaussian peaks at 37°C, 25°C, and 4°C in region 1.</p><!><p>At 37°C, the lenses were kept transparent in all regions. The average values of Gaussian peak amplitudes were evaluated according to all the scanning points (three lenses in total, n = 60). Figure 5B depicts the intensity change among the three regions under 37°C. From regions 1 to 3, the intensity of peaks I and II increased, while peaks III and IV decreased.</p><p>To reveal temperature dependence in water of normal lens, since there was no opacity in region 1, whether at 4°C, 25°C, or 37°C, Gaussian peak intensities from this region were compared. As Figure 5C shows, from 37°C to 4°C, the intensity of peaks I and II increased as temperature decreased, while peaks III and IV presented the opposite evolution with temperature.</p><p>Regions 2 and 3 from lenses treated with lower temperature, compared with region 1, presented various patterns of Gaussian components. In region 2, when temperature decreased from 37°C to 25°C, Gaussian peaks were still similar among different scanning points. Figures 6A,B show typical Gaussian well-fitted peaks of the lens water Raman spectra under 37°C and 25°C. However, at 4°C, Gaussian peaks were irregularly compared with 37°C and 25°C. The intensity of peaks III and IV was higher, while peak II decreased at some points. The intensity of peak I was higher or lower at some points (Figure 6C).</p><!><p>Changes in Gaussian peaks from regions 2 and 3 at different temperatures. (A–C) Typical Gaussian peaks from region 2 at 37°C, 25°C, and 4°C. (D–F) Typical Gaussian peaks from region 3 at 37°C, 25°C, and 4°C.</p><!><p>In region 3, Gaussian peaks kept fixed relatively only at 37°C, as Figure 6D shows. At 25°C, peaks became fluctuated. The intensity of Peak II decreased, while peaks III and IV increased (Figure 6E). At 4°C, the alteration in peaks was obvious and shared the same regulation with that in region 2 (Figure 6F).</p><!><p>Among previous researches, RS has been utilized to investigate changes in lens with cold cataract. Ondruska et al. studied the formation of dry and cold cataracts from duck, rat, and flounder lenses and found slight changes in Raman spectra (Ondruska and Hanson, 1983). Mizuno et al. investigated the tyrosine doublet in cold cataracts and discovered that some tyrosine residues possessed a change in their hydrogen bonding environment (Mizuno et al., 1984). Other studies basically evaluated protein and water mass in a large range to observe general changing trends (Bot et al., 1989; Huizinga et al., 1989). In this study, we illustrated the various opacity among three regions under different temperatures in P14 B6WT lenses. Region 1 stayed transparent under 4°C, 25°C, and 37°C, while region 2 became opaque only at 4°C. Region 3 became opaque at both 4°C and 25°C. These phenomena and the size of the various opacities with the formation of cold cataract correspond to previous studies from our group (Li et al., 2020). Ip/Iw values represent the protein mass distribution of three regions. Whether at 4°C, 25°C, or 37°C, protein content increases from region 1 to region 3. This result supported that the relative protein to water content of lenses increases from cortex to nucleus (Bot et al., 1989; Huizinga et al., 1989).</p><p>Heys et al. analyzed free and total water in human normal and cataractous lenses with thermogravimetric analysis and differential scanning calorimetry (Heys et al., 2008). Through RS, water spectra may not determine the water state in lens, but the structure information of water can be analyzed. In water spectra analyses with Gaussian profiles, peaks I and II represent fully four-hydrogen bonded water molecules, while peaks III and IV are associated to partly hydrogen-bonded free O-H (Crupi et al., 2008). In particular, peak I refers to the in-phase O-H stretching vibrations of hydrogen bonds from adjacent water molecules, while peak II is ascribed to out-of-phase O-H stretching vibrations (Walrafen et al., 1986). In region 1, as temperature decreased, although there was no cataract formation, the intensity of peaks I and II increased, while peaks III and IV decreased. This indicated that O-H in water became more stable with more full hydrogen bonded water molecules, which supported the same change in bulk water and confined water (Crupi et al., 2008). However, when opacification occurred (in region 2 at 4°C, in region 3 at 4°C and 25°C), peaks III and IV increased, while peak II was constrained at some scanning points. The changing patterns of the peaks were irregularly compared with the regions without cataract. These results supported that hydrogen bonds break from water molecules, and out-of-phase O-H stretching vibrations are weakened in lens fiber cells. This corresponded to the evidence that there was lower total water content in the center of advanced nuclear cataractous lenses, and the supported nuclear cataract formation may be associated with lower total hydration of the lens nucleus (Heys et al., 2008).</p><p>In this work, we used RS to scan the mice lens in the 20-μm range micron by micron for the first time. By focusing on the protein and water bands of Raman spectra, we discovered that lens opacity due to cold cataract can lead to the fluctuation of protein and water distribution. Compared with regions without cold cataract, in region 3, the Ip/Iw scatter line variance revealed uneven accumulations of proteins at 4°C and 25°C. In region 2, only 4°C of treatment can cause obvious fluctuations in the scatter line. These results showed that low temperature may alter the accumulation or aggregation of filensin proteins in mice lenses, which supported our previous research and the concept of supermolecular organization of protein complexes causing the formation of cold cataract (Ondruska and Hanson, 1983; Li et al., 2020). Furthermore, we used Gaussian peaks of Raman water spectra to investigate cold cataract at the molecular level for the first time. The results supported that hydrogen bonds in water molecules may become unstable and are likely to participate in protein aggregation during cold cataract formation at low temperatures. Mouse cold cataract presents a practical model for understanding the changes in fiber cells during lens development at young ages. Future work is supposed to address the molecular mechanisms on how protein and water distribution become uneven and how water participates in cold cataract formation at low temperatures.</p><!><p>We scanned across the fiber cells of the lens along visual axis for the first time with RS. By scanning different regions (20 μm for each) with/without cold cataract at different temperatures, protein and water content distribution was quantified. At 4°C, the protein and water distribution of both regions 2 and 3 were uneven. At 25°C, however, only region 3 showed uneven protein and water distribution. At 37°C, all regions were relatively even as comparison. The discovery testified that RS can be utilized to analyze changes in protein and water distribution across lens fiber cells. This proved that cold cataract formation is associated with the uneven protein and water distribution, revealing super-molecular mechanisms. Furthermore, Gaussian profiles of water Raman spectra demonstrated the activation of hydrogen-bonded free O-H in water molecules. The lens protein and water redistribution might be related to the unstable structure of water molecules so that water may participate in this process during cold cataract formation.</p>

PubMed Open Access

Fully oxygen-tolerant atom transfer radical polymerization triggered by sodium pyruvate

ATRP (atom transfer radical polymerization) is one of the most robust reversible deactivation radical polymerization (RDRP) systems. However, the limited oxygen tolerance of conventional ATRP impedes its practical use in an ambient atmosphere. In this work, we developed a fully oxygen-tolerant PICAR (photoinduced initiators for continuous activator regeneration) ATRP process occurring in both water and organic solvents in an open reaction vessel. Continuous regeneration of the oxidized form of the copper catalyst with sodium pyruvate through UV excitation allowed the chemical removal of oxygen from the reaction mixture while maintaining a well-controlled polymerization of N-isopropylacrylamide (NIPAM) or methyl acrylate (MA) monomers. The polymerizations of NIPAM were conducted with 250 ppm (with respect to the monomer) or lower concentrations of CuBr 2 and a tris [2-(dimethylamino) ethyl]amine ligand. The polymers were synthesized to nearly quantitative monomer conversions (>99%), high molecular weights (M n > 270 000), and low dispersities (1.16 < Đ < 1.44) in less than 30 min under biologically relevant conditions. The reported method provided a well-controlled ATRP (Đ ¼ 1.16) of MA in dimethyl sulfoxide despite oxygen diffusion from the atmosphere into the reaction system.

fully_oxygen-tolerant_atom_transfer_radical_polymerization_triggered_by_sodium_pyruvate

Introduction<!>Results and discussion<!>Conclusions<!>Conflicts of interest

<p>According to the IUPAC report, reversible deactivation radical polymerization (RDRP) is one of the top ten emerging technologies in chemistry that could change the world. 1 Atom transfer radical polymerization (ATRP) is one of the most widely used RDRP methods, providing access to well-dened, complex polymer architectures. [2][3][4][5][6][7] ATRP is catalyzed by transition metal complexes in their lower oxidation state. It is exceptionally tolerant to a wide variety of functional groups, solvents, and impurities. However, like any radical polymerization, ATRP is inhibited by oxygen. Recently, several avenues to design oxygen tolerant RDRP systems have been reviewed. 8 The most active copper catalysts with highly negative redox potentials allow a well-controlled polymerization at a loading of only 10 ppm relative to the monomer. [9][10][11][12] Even trace amounts of oxygen can inhibit polymerization by rapidly oxidizing the activator form of the catalyst Cu I /L to the inactive Cu II /L complex. 13 Furthermore, oxygen molecules can react with the propagating carbon-based radicals, thus terminating the polymerization process. 14 The sensitivity of ATRP to oxygen necessitates the use of specialized equipment or deoxygenation by inert gas sparging before the polymerization (Scheme 1). As a result, ATRP techniques can be cumbersome to non-experts. On top of that, inert gas sparging or freeze-pump-thaw degassing are oen incompatible with the synthesis of hybrid biomacromolecules, 15,16 as they may cause protein denaturation or a loss of enzymatic activity. 17 Scheme 1 Approaches for oxygen scrubbing and achieving oxygen tolerance in ATRP.</p><p>The Cu I /L ATRP catalyst activates the dormant C(sp 3 )-X polymer chain end, resulting in the formation of the X-Cu II /L complex and a carbon-centered radical. Both carbon-based radicals and Cu I /L species react with molecular oxygen with diffusion control to form peroxy radicals or hydroperoxides and Cu II /L complexes, respectively (Scheme 1). However, since Cu I /L is at a concentration thousands to millions times higher than the concentration of propagating radicals, oxidation of the Cu I / L activator to Cu II /L is predominant. Thus, continuous regeneration of the oxidized form of the catalyst Cu II /L with a reducing agent allows the chemical removal of oxygen from the reaction system (Scheme 1).</p><p>In 1998, we demonstrated that a well-controlled ATRP could occur in the presence of a limited amount of oxygen using a zero-valent copper powder as a reducing agent. 18 This concept was later extended to ATRP with copper wire [19][20][21] or copper plate [22][23][24][25] and other reducing agents, such as ascorbic acid, [26][27][28][29] tin(II) 2-ethylhexanoate, 30 tertiary amine, 31 nitrogen-based ligands, 32 phenols, 33 alcohols, 34 sodium dithionite, 35 and zerovalent iron. 36 Another area where signicant progress has been made towards oxygen-tolerant ATRP is photoinduced polymerization. [37][38][39][40][41] In photoinduced ATRP, catalyst regeneration occurs by excitation of the Cu II /L complex, followed by a single electron donation from the amine-based ligand. Photoirradiation of a copper catalyst in the presence of an electron donor in excess enables removal of dissolved oxygen. [42][43][44][45][46][47][48][49][50][51][52][53][54] Despite these great developments, the vast majority of reported methods are successful only when polymerization is performed in sealed vessels with a limited amount of oxygen in the reaction mixture. So far, only a few ATRP systems, mainly based on enzymatic degassing, can be carried out in a completely open reaction vessel, where oxygen continuously diffuses into the system from the atmosphere. [55][56][57][58] In 2018, inspired by the works of Yagci 59 and Stevens, 60,61 we developed a "breathing ATRP" of oligo(ethylene oxide)methyl ether methacrylate that used glucose oxidase (GOx) as a highly efficient scavenger for oxygen. 55 GOx catalyzes the oxidation of b-D-glucose to D-glucono-1,5-lactone and hydrogen peroxide. However, hydrogen peroxide reacts with Cu I /L in a Fenton-type reaction to form a hydroxyl radical and the Cu II /L complex. Hydroxyl radicals can initiate new polymer chains, decreasing average molecular weights (M n ) as compared to the theoretical values. To suppress this undesirable process, we developed a bio-inspired ATRP system in which GOx removed oxygen, while sodium pyruvate (SP) acted as a hydrogen peroxide scavenger and prevented the formation of new polymer chains. This study was later extended to "oxygen-fueled" ATRP by employing Horseradish peroxidase (HRP) as a catalyst for the generation of radicals from acetylacetone in the presence of hydrogen peroxide produced by GOx. This enzymatic cascade enabled a well-controlled ATRP in a reaction vessel open to the air. 56 However, these high-performance biocatalytic systems created a new challenge: the synthesized polymers or polymer bioconjugates were contaminated with enzymes, which are particularly difficult to separate from biohybrids. Also, the methods were limited to aqueous media. Recently, Keitz et al. harnessed an even more complex biological system, microbial metabolism, to develop an aerobic ATRP in water. 62 As with the enzymatic degassing, the use of cellular respiration machinery in bioconjugates synthesis can complicate the purication process. The development of efficient small molecule-based ATRP methods tolerant to oxygen that are compatible with water and organic solvents is therefore highly desirable.</p><p>Poly(N-isopropylacrylamide) (PNIPAM) is a temperatureresponsive, biocompatible polymer that has a lower critical solution temperature in water of $32 C. 63 This feature is widely used in the design of controlled drug delivery systems, 64 tissue engineering 65 and biosensing. 66,67 Low dispersity PNIPAM with varying molecular weights can be synthesized using a variety of ATRP techniques. [68][69][70][71][72] However, the methods reported so far exhibit at least one critical aw, such as the use of high loadings of copper catalysts, a relatively long reaction time, or oxygen intolerance. Recently, the disproportionation of Cu I /Me 6 TREN in water was shown to enable the ATRP of N-isopropylacrylamide (NIPAM) in open-air conditions. However, the use of high copper concentration (2000-8000 ppm relative to NIPAM) was necessary to attain high monomer conversions and low dispersity values. 73 Herein, we demonstrate the rst fully oxygen tolerant, photoinduced ICAR ATRP of NIPAM with ppm level of Cu catalyst in water, enabling a quantitative conversion of the monomer in less than 30 min. This simplied, non-enzymatic ATRP system uses sodium pyruvate as both a hydrogen peroxide scavenger and a "fuel" for the continuous regeneration of the catalyst and can be easily transferred to organic solvents.</p><!><p>Initial studies began by polymerizing NIPAM in water (targeting a degree of polymerization 200) under UV LED irradiation (l ¼ 394 nm, 2.6 mW cm À2 ), using 2-hydroxyethyl 2-bromoisobutyrate (HOBiB) as the initiator, CuBr 2 as the precatalyst, and tris [2-(dimethylamino)ethyl]amine (Me 6 TREN) as the ligand (Table 1). The reactions were carried out in sealed vials with a septum (see Fig. S1 in the ESI †) at 6 C and in the presence of limited amounts of oxygen (without degassing the reaction mixture). Table 1 shows the results of the polymerization of NIPAM and the effect of different components involved in the PICAR ATRP system.</p><p>A set of control experiments was performed to evaluate the inuence of SP on the ATRP process (Table 1, entries 1-3). The initial conditions used 250 ppm of CuBr 2 (with respect to the monomer) with a six-fold excess of Me 6 TREN ligand to Cu II and no SP. Aer 12 h of UV irradiation, the conversion of NIPAM measured by 1 H NMR was only 16%. Furthermore, size exclusion chromatography (SEC) analysis showed that the polymer had a high dispersity (Đ) of 1.86 (Table 1, entry 1). The use of Cubased ATRP catalysts in water typically results in a signicant dissociation of the [X-Cu II /L] + deactivator to the "naked" [Cu II / L] 2+ dication and a free halide anion. The [Cu II /L] 2+ complex cannot act as a true deactivator, leading to poorly controlled polymerizations. To counteract this problem, aqueous ATRP is performed in the presence of halide anions to suppress the deactivator dissociation. 74 The use of modied phosphate-buffered saline (PBS) solution containing bromide anions gave slightly better results (Table 1, entry 2; Đ ¼ 1.66). To our delight, when both the Br-based PBS and SP were used (Table 1, entry 4), a quantitative conversion was achieved within 30 min, and the polymerization was well-controlled (M n ¼ 31 700, Đ ¼ 1.16). These experiments showed the critical role of SP. The reaction without the addition of buffer components (Na 2 HPO 4 and KH 2 PO 4 salts) reached quantitative monomer conversion with a similar rate of polymerization. However, the dispersity of the resulting polymer was 1.42 (Table 1, entry 3). For aqueous ATRP, the optimal pH is 7.5. 75 UV irradiation induces the homolytic cleavage of SP (see further section on proposed mechanism), which leads to the protonation of the ligand, decreasing its ability to coordinate the metal center, resulting in a loss of control over the polymerization. Maintaining a constant pH $ 7.4 during the polymerization prevents this process.</p><p>Next, the performance of sodium pyruvate-based ATRP system was evaluated in the presence of varying amounts of CuBr 2 (Table 1, entries 4-7). Despite decreasing the amount of CuBr 2 to just 50 ppm relative to NIPAM, the reaction still proceeded to high monomer conversion (>97%) and yielding a polymer with a dispersity of 1.37 (Table 1, entry 6). Increasing the amount of CuBr 2 to 1000 ppm did not improve the outcome, yielding similar control as with 250 ppm of CuBr 2 (Table 1, entry 7).</p><p>In conventional photoinduced ATRP, a Cu II complex in the excited state reacts with an amine-based ligand, which acts as an electron donor, resulting in the formation of the activator Cu I /L and a radical cation from the donor. 39,76 Since this process consumes the ligand, it must be present in excess. In our PICAR ATRP, SP is the dominant electron donor. However, in the presence of dissolved oxygen, the ligand oxidation may still occur during photoirradiation. This explains why the ratio [CuBr 2 ]/[Me 6 TREN] ¼ 1/1 was not sufficient to achieve wellcontrolled polymerization while maintaining high conversion (Table 1, entry 9). The use of a 1/3 or 1/6 ratio allowed much better control over polymerization of NIPAM (Table 1, entry 8 and 4).</p><p>Increasing the concentration of SP from 100 mM to 200 mM caused a slight increase in PNIPAM dispersity (Table 1, entry 10; Đ ¼ 1.20). This could be attributed to a higher concentration of radicals resulting from the homolytic cleavage of SP under UV irradiation. The radicals thus formed could initiate new polymer chains or terminate polymerization by radical-radical coupling. Decreasing the SP concentration to 50 mM caused only a slight decrease in monomer conversion (98%), while maintaining the low Đ ¼ 1.16 (Table 1, entry 11). However, further tests were carried out with a concentration of SP of 100 mM to make the polymerization more tolerant to oxygen.</p><p>Several hypotheses have been proposed to explain the difficulty of obtaining good control in the polymerization of acrylamides by ATRP. [77][78][79][80] One possibility is the intramolecular cyclization reaction leading to the loss of C(sp 3 )-Br chain end. The u-Br chain end functionality was shown to decrease as a function of reaction time and was dependent on the structure of the amide group. 79 Low chain-end delity compromises the control over polymerization. The use of the pyridine-based ligands: the less active TPMA or the more active TPMA* 3 ligands (TPMA ¼ tris(2-pyridylmethyl)amine, TPMA* 3 ¼ tris([(4methoxy-2,5-dimethyl)-2-pyridyl]methyl)amine) 81 resulted in a signicant decrease in control over the polymerization (Table 1, entry 12 and 13). TPMA is the most versatile ligand for aqueous ATRP of acrylates and methacrylates. 75 However, for acrylamides, the [Cu I /TPMA] + catalyst does not provide a sufficiently high ATRP equilibrium constant (K ATRP ). Thus, the rate of the polymerization is slower, then the loss of C(sp 3 )-Br chain-ends via intramolecular cyclization. In turn, poor control provided by very active TPMA* 3 ligand could be explained by the too high value of the ATRP equilibrium constant. Higher K ATRP implies higher radical concentration at equilibrium, which favors bimolecular termination reactions, resulting in diminishing control over the polymerization. Next, the kinetics of the polymerization of NIPAM was investigated in an open reaction vessel (Fig. 1A). The reaction was performed in a Br-based PBS buffer with [NIPAM]/[HOBiB]/ [CuBr 2 ]/[Me 6 TREN] molar ratios of 200/1/0.05/0.30, in the presence of SP (100 mM). A short inhibition period of 15 min was observed, followed by a well-controlled (Đ ¼ 1.15), rapid polymerization with linear semi-logarithmic kinetics, that reached 97% monomer conversion in 15 minutes. We unexpectedly observed that polymerization in an open vessel (Fig. 1B) led to smaller deviation from the theoretical molecular weight value (M n,th ¼ 22 400, M n,GPC ¼ 25 400) than polymerization in a sealed vessel (M n,th ¼ 22 800, M n,GPC ¼ 31 700). Fast activation of initiators, leading to termination of initiating radicals could explain this deviation. 3 The performance of this system was further evaluated in a series of reactions in a closed vessel, with varying target degrees of polymerization (DP) of NIPAM (Table 2). The concentration of CuBr 2 was maintained at 250 ppm relative to the monomer. The results showed a high degree of control for targeted DP ¼ 100, 200, 400, 1000, and 2000 (Table 2, entries 1-5). In all cases, nearly quantitative monomer conversions were reached with Đ in the range 1.16-1.44. However, for higher DP ¼ 4000 and 10 000, a signicant deviation from the theoretical molecular weights and higher Đ values were observed (Table 2, entries 6 and 7). Moreover, SEC traces of the polymers showed a signicant tailing (ESI Fig. S5F and G †). The appearance of this tailing could be attributed to the continuous formation of new chains, plausibly generated by radicals formed from the photochemical homolytic cleavage of SP.</p><p>Then, polymerizations of NIPAM were conducted in the open reaction vessel, targeting DP of 100-2000 (Table 3). A high level of control over polymers was achieved under PICAR ATRP conditions when oxygen continuously diffused into the reaction system from the atmosphere, reaching 65-97% monomer conversions and providing polymers with monomodal, narrow molecular weight distributions (1.15 < Đ < 1.32). In open-air conditions, the Cu I /L activator is constantly oxidized to inactive Cu II /L, which results in lower monomer conversions. In turn, the increased X-Cu II /L deactivator concentration provides better control over polymerization.</p><p>The promising results of PICAR ATRP in an aqueous medium prompted us to utilize this fully oxygen-tolerant system in an organic solvent, which would signicantly extend the scope of this method toward hydrophobic monomers. Since the SP-triggered ATRP catalytic system is based on small molecules, it can be transferred to organic solvents much more easily compared to ATRP techniques based on enzymatic degassing. However, sodium pyruvate has limited solubility in organic solvents due to its ionic structure. We used a stoichiometric amount of tetrabutylammonium bromide (TBAB) to increase the solubility of SP in dimethyl sulfoxide (DMSO). This ) in the presence of oxygen (sealed vessel). b Monomer conversion was determined by using 1 H NMR spectroscopy. c See SEC traces in the ESI Fig. S3. All measurements were analyzed using GPC (dimethylformamide as eluent) calibrated to poly(methyl methacrylate) standards.</p><p>quaternary ammonium salt is commonly used as a phase transfer catalyst in many synthetic transformations. 82 The investigation was started by preparing a reaction mixture that contained all components needed for the polymerization (Fig. 2 ) was performed at room temperature. Aer 3 h, the conversion of MA measured by 1 H NMR was 84%. SEC analysis showed that the polymer had a low dispersity (Đ ¼ 1.16), and a molecular close to the theoretical value (M n,th ¼ 14 500, M n,GPC ¼ 16 700), indicating a well-controlled polymerization (Fig. 2). This experiment shows that even without a time-consuming, careful optimization, a highly efficient, fully open-air ATRP system could be quickly developed. Further optimization of this method and its applicability to other non-polar monomers will be the subject of a forthcoming publication.</p><p>To gain insights into the polymerization mechanism, we investigated the reactivity of SP toward Cu complexes. Recently we reported the sono-ATRP of MA in DMSO in the presence of sodium carbonate. 83 In this system, ultrasonication triggered the homolytic cleavage of the in situ formed (CO 3 )-Cu II /TPMA complex, generating Cu I species and a radical carbonate anion. Haddleton et al. observed a similar phenomenon for the photoreduction of (HCO 2 )-Cu II /Me 6 TREN complex. 84,85 In addition, Vaida et al. showed that the UV excitation of pyruvic acid in an aqueous medium causes photodecarboxylation, which forms radicals as intermediates. 86 Furthermore, a-keto acids can undergo decarboxylative acyl radical formation in transition metal-catalyzed radical cross-couplings. 87,88 Based on the above results, we propose that the SP reacts with Cu II species to yield a (CH 3 C(O)CO 2 )-Cu II /L complex by a simple anion dissociation/association process (Scheme 2). Subsequent UV excitation causes the homolytic cleavage of the carbon-carbon bond in the pyruvate moiety. This photolysis induces decarboxylation, which leads to the reduction of Cu II /L to Cu I /L and the formation of the acyl radical. This radical can regenerate the activator or initiate a new polymer chain by addition to the monomer. The role of a buffer medium is to control the pH and, thus, the concentration of the formed acyl radicals. 86 Furthermore, the reaction between the acyl radical and X-Cu II /L deactivator leads to the formation of an acyl halide, which undergoes rapid hydrolysis in a buffer. This prevents the initiation of new polymer chains and the protonation of the ligand. A control experiment without the HOBiB initiator showed that SP could initiate ATRP on its own, but the polymer had a broad molecular weight distribution and a high Đ ¼ 1.92. In order to conrm the role of SP in the catalytic system, UV-vis spectroscopy measurements were performed. Fig. S6A † shows a decrease in the absorbance of Cu II /TPMA complex under UV irradiation in the presence of SP. Fig. S6B † shows that the absorbance of Cu II /TPMA decreased much slower when SP was absent, which indicates that SP is necessary for the efficient reduction of Cu II species. This in turn is critical for ATRP in an ambient atmosphere. The proposed mechanism shown in Scheme 2 can be considered equivalent to PICAR (photoinduced initiators for continuous activator regeneration) ATRP. 89,90</p><!><p>We have developed the rst example of a photoinduced ATRP system that operates in an open reaction vessel and yields wellcontrolled polymerizations in both aqueous and organic solvents. Sodium pyruvate is the essential component in this novel method, acting as a hydrogen peroxide scavenger and enabling the continuous regeneration of the copper catalyst through UV excitation. This methodology allowed the synthesis of poly(N-isopropylacrylamide) in water with high monomer conversion (97%) and dispersity of 1.15 with 250 ppm of a catalyst in 30 min under an ambient atmosphere. Furthermore, the use of sodium pyruvate with tetrabutylammonium bromide enabled the polymerization of methyl acrylate in DMSO in a fully open vessel without compromising the control over the molecular weight distribution (Đ ¼ 1.16). Non-experts can easily apply this straightforward and robust protocol for the synthesis of well-dened polymers. Expanding the scope of this methodology to more complex polymer architectures and polymer-based biohybrids is currently under investigation.</p><!><p>There are no conicts to declare.</p>

Royal Society of Chemistry (RSC)

Synthesis, Structure-Activity Relationship, & Mode-of-Action Studies of Antimalarial Reversed Chloroquine Compounds

We have previously shown that a \'reversed chloroquine (RCQ)\' molecule, composed of a chloroquine-like moiety and a resistance reversal-like moiety, can overcome chloroquine resistance in P. falciparum (Burgess, S. J.; Selzer, A.; Kelly, J. X.; Smilkstein, M. J.; Riscoe, M. K.; Peyton, D. H. J. Med. Chem. 2006, 49, 5623; Andrews, S.; Burgess, S. J.; Skaalrud, D.; Kelly, J. X.; Peyton, D. H. J. Med. Chem. 2010, 53, 916). Here, we present an investigation into the structure-activity relationship of the RCQ structures, resulting in an orally active molecule with good in vitro and in vivo antimalarial activity. We also present evidence of the mode of action, indicating that the RCQ molecules inhibit hemozoin formation in the parasite\xe2\x80\x99s digestive vacuole in a manner similar to that of chloroquine.

synthesis,_structure-activity_relationship,_&_mode-of-action_studies_of_antimalarial_reversed_chloro

INTRODUCTION<!>CHEMISTRY<!>RESULTS AND DISCUSSION<!>In vivo efficacy against Plasmodium berghei<!>Accumulation of 1 in the DV of P. falciparum<!>In vitro heme binding and \xce\xb2-hematin inhibition<!>Hemozoin inhibition in vivo<!>SUMMARY AND CONCLUSION<!>Chemistry<!>2-(7-Chloroquinolin-4-ylamino)ethan-1-ol (2a)<!>2-(7-Chloroquinolin-4-ylamino)ethyl methanesulfonate (3a)<!>General procedure for the preparation of compounds 4\xe2\x80\x9313<!>N-(2-(4-Benzhydrylpiperazin-1-yl)ethyl)-7-chloroquinolin-4-amine (4)<!>N-(3-(4-Benzhydrylpiperazin-1-yl)propyl)-7-chloroquinolin-4-amine (5)<!>N-(3-(4-Benzhydryl-1,4-diazepan-1-yl)propyl)-7-chloroquinolin-4-amine (6)<!>7-Chloro-N-(2-(4-((4-chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethyl)quinolin-4-amine (7)<!>7-Chloro-N-(3-(4-((4-chlorophenyl)(phenyl)methyl)piperazin-1-yl)propyl)quinolin-4-amine (8)<!>7-Chloro-N-(3-(4-((4-chlorophenyl)(phenyl)methyl)-1,4-diazepan-1-yl)propyl)quinolin-4-amine (9)<!>N-(3-(4-(Bis(4-fluorophenyl)methyl)piperazin-1-yl)propyl)-7-chloroquinolin-4-amine (10)<!>N-(2-(4-(9H-Fluoren-9-yl)-1,4-diazepan-1-yl)ethyl)-7-chloroquinolin-4-amine (11)<!>N-(3-(4-(9H-Fluoren-9-yl)-1,4-diazepan-1-yl)propyl)-7-chloroquinolin-4-amine (12)<!>N1-(7-chloroquinolin-4-yl)-N2-(3-(10,11-dihydro-5H-dibenzo[b,f]azepin-5-yl)propyl)-N2-methylethane-1,2-diamine (13)<!>7-Chloro-N-(3-(piperazin-1-yl)propyl)quinolin-4-amine (14)<!>7-Chloro-N-(3-(4-tritylpiperazin-1-yl)propyl)quinolin-4-amine (15)<!>General method for the preparation of compounds 16 and 17<!>7-Chloro-N-(3-(4-(2,2-diphenylethyl)piperazin-1-yl)propyl)quinolin-4-amine (16)<!>N-(3-(4-(Adamant-2-yl)piperazin-1-yl)propyl)-7-chloroquinolin-4-amine (17)<!>N-(Dipyridin-2-ylmethylene)piperidin-4-amine (18)<!>7-Chloro-N-(2-(4-(dipyridin-2-ylmethyleneamino)piperidin-1-yl)ethyl)quinolin-4-amine (19)<!>7-Chloro-N-(3-(4-(dipyridin-2-ylmethyleneamino)piperidin-1-yl)propyl)quinolin-4-amine (20)<!>7-Chloro-N-(2-(4-(dipyridin-2-ylmethylamino)piperidin-1-yl)ethyl)quinolin-4-amine (21)<!>7-Chloro-N-(3-(4-(dipyridin-2-ylmethylamino)piperidin-1-yl)propyl)quinolin-4-amine (22)<!>2-(4-(4-Benzhydrylpiperazin-1-yl)butyl)isoindoline-1,3-dione (23)<!>4-(4-Benzhydrylpiperazin-1-yl)butan-1-amine (24)<!>N-(4-(4-Benzhydrylpiperazin-1-yl)butyl)-7-chloroquinolin-4-amine (25)<!>2-(4-Benzhydrylpiperazin-1-yl)ethanol (26)<!>General method for the preparation of compounds 27\xe2\x80\x9329<!>1-Benzhydryl-1,4-diazepane (27)<!>1-((4-Chlorophenyl)(phenyl)methyl)-1,4-diazepane (28)<!>1-(9 H-Fluoren-9-yl)-1,4-diazepane (29)<!><!>Mouse efficacy against P. berghei<!>Accumulation experiment<!>In vitro heme binding and \xce\xb2-hematin inhibition<!>In vivo hemozoin inhibition experiment<!>

<p>Malaria remains among the most important diseases, with nearly half of the world's population at risk of infection, and ∼250 million cases reported annually.1 Malaria kills nearly a million people each year, 90% of whom are African children. Of the different Plasmodium parasites known to give human malaria infections, P. falciparum is the most deadly, although the others still result in serious illness which can turn fatal. There are a number of drugs available to treat malaria, yet P. falciparum has developed resistance to almost all of them. Even the artemisinin class of drugs is now showing worrying signs of reduced efficacy,1–6 and so there is a continuing need for a pipeline of new antimalarial treatments to combat the disease.</p><p>Chloroquine (CQ) was first introduced in the 1940s and quickly became the drug of choice for the treatment of malaria. CQ has several advantages over other antimalarial drugs: its low cost made it available to everyone; its low toxicity meant it was safe for children and pregnant women, the most vulnerable victims of malaria; and its good efficacy meant the treatment regime was simple and easy to administer. However, resistance developed within about a decade and spread to such an extent that, today, the World Health Organization (WHO) recommends CQ not be used for the treatment of P. falciparum malaria, except in specific areas.1</p><p>CQ resistance in P. falciparum is strongly linked to mutations in the gene pfcrt that gives rise to the protein, PfCRT (P. falciparum chloroquine resistance transporter), located in the parasite's digestive vacuole (DV) membrane.7–10 In chloroquine resistant (CQR) P. falciparum there is reduced accumulation of CQ in the DV, due to increased efflux.9, 11 PfCRT is a putative member of the drug/metabolite transporter superfamily, and recent evidence suggests that the mutated forms are, in fact, the transporters directly responsible for exporting CQ from the DV of P. falciparum.12</p><p>There are molecules such as verapamil and desipramine, which have been found to reverse the effects of CQ resistance.11, 13, 14 These are known as Reversal Agents (RAs), or chemosensitizers. Many of these compounds are existing drugs, such as antidepressants or antihistamines, and at the high doses often required to achieve optimal reversal activity there can be problems with unpleasant side effects.15 A pharmacophore, consisting of two aromatic rings and an aliphatic nitrogen a few angstroms away, has been derived from a set of such RAs.16</p><p>Our previous work demonstrated that the hybrid molecule 1 can be synthesized, containing elements from both CQ and the RA pharmacophore (Figure 1).17 This prototype "reversed chloroquine" (RCQ) molecule gave IC50 values lower than CQ for both CQR and chloroquine sensitive (CQS) P. falciparum strains, and demonstrated oral efficacy against P. chabaudi in mice. A subsequent structure-activity relationship (SAR) study demonstrated that the linkage between the 7-chloro-4-aminoquinoline moiety from CQ, and the aromatic rings of the RA head group could be varied in length without serious loss of activity, and that the RA portion itself could be substantially varied without serious loss of activity against CQR or CQS P. falciparum malaria strains.18 The work presented here is the result of a more extensive SAR study, with further variations to both the linkage and the aromatic head group of the RA moiety. The result is an orally efficacious molecule with good in vitro and in vivo antimalarial activity.</p><!><p>The synthesis of 3b has been previously described,17 and 3a was similarly synthesized (Scheme 1). These were treated with 1-(diphenylmethyl)piperazine or 27–29 (Scheme 2) to give the RCQ compounds 4–12. RCQ compound 13, the 2-carbon linker analogue of 1, was synthesized by treating 3a with desipramine hydrochloride. The intermediate compound 14 was made by first treating 2b with methane sulfonyl chloride, then adding an excess of piperazine (Scheme 1). This was subsequently treated with trityl chloride to give 15. 16 and 17 were synthesized from diphenylacetaldehyde and 2-adamantanone respectively, by reductive amination onto 14.19 The dipyridyl analogues were synthesized by first treating 4-aminopiperidine with 2,2'-dipyridyl ketone to give 18 (Scheme 3). This was then treated with methane sulfonate esters 3a and 3b to give 19 and 20 respectively, which were reduced in the presence of sodium borohydride to give 21 and 22. Initial attempts to make 25, the 4-carbon linker analogue of 4 and 5, by an analogous route, failed due to intramolecular cyclization of the activated alcohol to form a pyrrolidine ring at the 4-position of the quinoline. Therefore, a different route was employed (Scheme 4). 1-(diphenylmethyl)piperazine was treated with N-(4-bromobutyl)phthalamide to give 23, then deprotected with hydrazine to give 24. This was treated with 4,7-dichloroquinoline to give 25. To make the unattached RA head group 26, chlorodiphenylmethane was treated with 1-(2-hydroxyethyl)piperazine in the presence of potassium carbonate (Scheme 5).</p><!><p>After the success of the prototype molecule 1 in overcoming CQ resistance, compounds 4 and 5 were designed to modify the structure of the RA head group slightly; moving away from the initial, tricyclic antidepressant, imipramine structure. In keeping with the published pharmacophore,16 the 1-(diphenylmethyl)piperazine retained the two aromatic rings and the protonatable nitrogen. However, the connector was now a piperazinyl ring which, it was hoped, would make the compounds more stable to metabolic cleavage. A secondary advantage was that it was easy to design a SAR study around this structure from inexpensive and readily available starting materials. Thus compounds 6–12, 15–17 were also synthesized, either varying the length of the chain from the quinoline to the piperazine ring, changing the piperazine to a homopiperazine, or varying the RA aromatic head group. Compound 13 gave the 2-carbon linker analogue of 1. As can be seen in Table 1, the in vitro results from these first compounds against P. falciparum malaria strains were favorable. While some did not quite match CQ against CQS D6, they all were more active than CQ against the two tested CQR strains.</p><p>The general cytotoxic effect was assessed by functional assay using mitogen-stimulated murine spleenic lymphocytes. The therapeutic indices for the RCQ compounds were calculated as the ratio of cytotoxicity IC50 to antimalarial IC50 against Dd2 (Table 1).</p><p>Although the in vitro data for 1, 4, 5–13 and 15–17 was promising, all of these compounds suffer from high ClogP values. This suggested that they could have limited water solubility, and so oral availability might be impaired, although formulating as salts and/or co-crystals, or other strategies may mitigate this concern. A modification to the aromatic head group was designed, using pyridines in place of the phenyl groups. Compounds 21 and 22 had ClogP values of 3.3 and 3.6 respectively, and IC50 values comparable to the best of the previous compounds (Table 1). For comparison, CQ has a ClogP value of 5.1. The therapeutic index values for these compounds are substantially greater than 10-fold above that of CQ. The result of the SAR is a collection of compounds with high potencies against CQS and CQR malaria strains, low cytotoxicities, and with ClogP values bracketing that of CQ.</p><p>It has previously been shown that simply changing the chain length of CQ can circumvent the PfCRT-associated CQR mechanism.20–22 All of the RCQ molecules thus far synthesized had either a 2- or 3-carbon chain between the tertiary nitrogen and the aminoquinoline, so 25, which has the same length linker between the quinoline ring and the aliphatic nitrogen, was made and evaluated by the in vitro methods applied above. As can be seen in Table 1, the activity of 25 against both CQS and CQR P. falciparum is better than that of CQ, and comparable to both 4 and 5, the 2- and 3- carbon analogues. These results, combined with other work from our group,18 demonstrate that the ability to overcome CQR by the addition of a RA head group to the 4-aminoquinoline ring is, in fact, independent of the chain length between them, at least if the chain length is between 2 and 4 carbons.</p><!><p>1, 4, 15 and 22 were tested in vivo in a P. berghei rodent model. Three sets of conditions were used: 1 dose of 30 mg/kg administered orally, 1 dose of 30 mg/kg administered subcutaneously, and 4 × 30 mg/kg dose delivered orally. The subcutaneous series provided a convenient alternative to the oral route, in case absorption across the intestine was problematic. The % activity was calculated as the difference between the mean percent parasitemia for the control and treated groups expressed as a percent relative to the control group:</p><p>%Activity = 100 × [(parasitemia control group – parasitemia treated group) / parasitemia control group].</p><p>The results for 1 × 30 mg/kg dose experiments were varied (Table 2). In terms of the activity of the drug, 1 did poorly when administered orally, but better when given subcutaneously. 4 and 15 were opposite to this, and 22 was fairly good both orally and subcutaneously. However, the average survival time of the mice in all cases was under 10 days, indicating that even if the drug dramatically reduced the parasitemia initially, there was recrudescence. In this protocol, CQ gave similar results. Improvements were seen in some cases of the 4 × 30 mg/kg dosage experiment: 1 showed little difference, 4 showed high activity and about a 10 day increase in survival days compared to control animals, and both 15 and 22 were encouraging, with 22 resulting in two of the three treated mice being parasite-free on day 30 post-infection. The change from phenyls in 4 to pyridyls in 22 seemed to result in a drug which was orally active and showed no obvious signs of toxicity when administered to mice. 15 was a surprise in that it showed good oral activity, but was poor when administered subcutaneously, even though it had a ClogP value of 8.9. It should be remembered that ClogP is a calculated value which gives only an indication of aqueous solubility, but should not be used on its own as an equivalent to bioavailability.23 Another possible reason for the good in vivo activity of 15 could be that the trityl group was being cleaved from the rest of the molecule after oral administration. This would result in the starting material, 14, which has a ClogP value of 3.4 (consistent with good oral activity) and low IC50 values against P. falciparum strains (Table 1).</p><p>Due to the difference in molecular weights, a dose of 30 mg/kg of CQ actually resulted in many more molecules being administered to the mouse, than in a dose of 30 mg/kg of any of the RCQ compounds. Accordingly, a second in vivo experiment was carried out for 21 and 22, wherein the dosage was adjusted such that each compound was administered in an equimolar amount to 30 mg/kg of CQ. In this test 22 was administered to 10 mice, to obtain a more reliable evaluation of oral activity in this mammalian model (Table 3).</p><p>21 had 3 out of 3 mice cured, and 22 had 9 out of 10, with the 10th mouse reaching 29 days. Parasitemia determination on day 30 showed that the surviving mice were parasite free.</p><!><p>When reversal agents such as verapamil or imipramine are co-administered with CQ, the enhanced CQ activity is limited to CQR parasites; no effect is seen against CQS P. falciparum.11–13 However, several of the RCQ compounds show IC50 values lower than CQ against even CQS parasites. One hypothesis to explain this enhanced potency against CQS parasites could be that the RA head group also had antimalarial potency, and that its intrinsic antimalarial effect was enhancing activity of the total RCQ molecule. To investigate this possibility, compound 26, which lacks the quinoline moiety, was tested against both CQS and CQR P. falciparum. As can be seen in Table 1, 26 showed no effect up to 2500 nM, and so the RA head group has no strong intrinsic antimalarial activity.</p><p>A second hypothesis to explain the enhanced activity of the RCQ molecules is that there was increased accumulation in the DV of the parasite. To test this, an accumulation experiment was devised, similar to one used by Kelly et al. to show accumulation of xanthones in the DV of Plasmodium parasites.24 The experimental design provides two results: first it allows the amount of drug accumulated in the DV to be measured; second, by demonstrating that the retention time is unchanged for the released drug, it shows that the vast majority of the drug is not modified by the parasite. As shown in Figure 2, 1 accumulated in the parasitized red blood cells (PRBCs) in a manner similar to that of CQ (Figure 2), in that there was a rapid initial uptake over the first minute, followed by a slow uptake until an equilibrium state was reached by about 60 minutes. After this time the accumulated amount remained nearly constant.</p><p>The initial concentration of 1 was slightly higher than CQ (5.5 µM to 4.9 µM), but after 1 hour the concentration of 1 in the medium was lower than CQ in both the D6 and Dd2 experiments. Addition of the ammonium chloride to the PRBCs resulted in a rapid return of each drug into the culture medium (data not shown). Both 1 and CQ returned to very close to their respective starting levels, and the HPLC retention times were not changed, indicating that the chemical composition of each of the drugs was, in fact, not altered while in the PRBCs over the course of the experiment.</p><p>By taking the difference between the amount of each drug in the medium after 60 minutes of incubation, and the amount after the addition of ammonium chloride to the medium, it is possible to deduce how much of each drug was taken up by the parasites. To estimate the concentration of the drug in the DV, it was first considered that each flask contained a 50 µL pellet of red blood cells (RBCs), and that each RBC has a volume of 80 fL.25 Therefore each flask contained 6 × 108 cells. Given the parasitemia level was about 10%, there were 6 × 107 PRBCs present. Assuming the DV has an average volume of 4 fL,25 the total DV volume for the population of parasites is 6 × 107 × 4 × 10−15 = 2.5 × 10−7 liters. The concentration of each drug in the DV can then be calculated (Table 4). This calculation is based on the assumption that the drug is completely released by the ammonium chloride. However, a small portion of the drug may still be associated with heme, and thus not be released to the medium.</p><p>The accumulation ratio for each drug can be estimated from this data, with the assumption that the drugs accumulate within the parasite DV; we provide evidence for this, below. From an initial medium concentration of ∼ 5 µM, 1 accumulates in the DV of D6 P. falciparum to an equilibrium concentration of about 65 mM, a 13,000-fold increase, yielding an accumulation ratio ([drug in the DV] / [drug in medium at equilibrium]) of 32,000. Most significantly, the accumulation ratio of 1 is more than triple that of CQ. This is in agreement with the IC50 values, in that the values for CQ are well over twice those of 1, even for CQS D6 P. falciparum (Table 1). However, the DV is known to swell as it accumulates quinoline-based antimalarial drugs,26, 27 and so the accumulation ratio presented here may be too large as the calculation is based on a DV volume of 4 fL. Yet whether the drug accumulation causes, or is enabled by, the swelling of the DV, does not detract from the fact that the molar uptake of 1 is more than double that of CQ, irrespective of DV volume. In any event, these experiments were carried out at significantly higher drug concentrations than the IC50 determinations presented in Table 1, and may reflect mechanisms in addition to simple differences in accumulation and inhibition of hemozoin formation.28, 29 In fact, the higher concentrations may represent at least a significant portion of the CQ blood concentration time-course during malaria chemotherapy.30, 31</p><!><p>It is known that CQ binds to heme dimers in vitro, and also can inhibit the formation of β-hematin, the in vitro analogue of hemozoin.32–35 A selection of RCQ compounds was tested as to whether addition of the RA head group affected the compounds' abilities to bind to heme and to inhibit the formation of β-hematin. As can be seen from Table 5, there was no significant difference between CQ and the RCQ compounds' ability to bind heme in solution (Table 5; Kd values). Although the numbers range from 1 to 8.3 µM, they are all in the micromolar range, with CQ about in the middle. A weak, positive correlation was noted between the RCQ in vitro potency (Table 1) and its Kd value (R2 ∼ 0.17). 26 was tested and showed no activity, suggesting that the quinoline portion of the molecules physically interacted with the heme.</p><p>Regarding β-hematin inhibition, the IC50 values ranged from 24 µM for CQ to about 2 µM for 4, 21, and 22 (Table 5). While these are all in the micromolar range, there is a trend toward (R2 ∼ 0.66) enhanced potency, coinciding with lower IC50 values for the RCQ compounds against P. falciparum strains (Table 1).</p><!><p>The hemozoin inhibition properties of 1 and 22 in cell culture were examined in a series of experiments, monitoring in parallel with microscopy (to characterize morphological change of the parasites) and a colorimetric assay (to provide a semi-quantitative assessment of hemozoin suppression). The images obtained by microscopy show hemozoin in the control samples, and even in the 100 nM CQ samples, but compound 22, at as low as 10 nM, appeared to preclude hemozoin formation in both D6 and Dd2 strains. 100 nM 1 showed some hemozoin present in the D6 sample, so inhibition was not complete even at this concentration in a CQS strain. This was also the case with the CQR Dd2 stain (not shown).</p><p>The images (Figure 3) also show an enlarged DV in the parasites incubated in the presence of drugs. In the drug-free control, the DV is fairly small relative to the parasite, and is almost entirely filled by several large crystals of hemozoin. It is known that the DV swells in the presence of quinoline-based antimalarial drugs,26, 27 and this can clearly be seen with both 1 and 22 (and to a lesser extent with CQ), where the almost clear DV is easy to distinguish from the darker parasite.</p><p>For the colorimetric assay, synchronized D6 and Dd2 P. falciparum parasites were incubated with various concentrations of each drug for 24 hours, then lysed by treatment with a saponin-containing lysis buffer. The hemozoin was extracted by centrifugation, then washed with acetone and PBS buffer to remove residual protein. The pellet was then dissolved in 0.2 N sodium hydroxide solution, and the absorbance at 400 nm was measured. Using an extinction coefficient of 91,000 cm−1M−1, the amount of heme was calculated.36–38 The amount of heme per parasitized erythrocyte was calculated based on the number of erythrocytes in the culture and the percent parasitemia obtained after growing synchronized culture for 24 hours. Baseline hemozoin production was calculated by processing the cell culture at 0 hour in an identical way.</p><p>The results from the colorimetric assays indicate that the RCQ molecules did indeed decrease the hemozoin production of P. falciparum, in a manner analogous to, but more potently than CQ (Figure 4). At 10 nM against D6, 22 nearly completely inhibited hemozoin production. Dd2 required a 100 nM concentration of 22 for the same percentage decrease, but this was much lower than the 1000 nM required by CQ.</p><p>The results for 1 against Dd2 are between those for 22 and CQ, a result consistent with the in vitro IC50 values (Table 1). The relatively low activity of 1 against D6 seems inconsistent with its low IC50 value against CQS P. falciparum. However, these results are for very high concentrations. While it is possible that hemozoin inhibition is not the only significant mode of action for the RCQ molecules, and that the low IC50 value is due, in part, to another antimalarial mechanism, further investigation would be needed before such a conclusion were reached.</p><p>These in vivo hemozoin inhibition experiments indicate that the RCQ molecules act in a manner similar to that of CQ against CQS P. falciparum. The enlarged DV caused by accumulation of the drugs, can be seen clearly, as can the reduction in hemozoin production. These effects are most pronounced for 22, requiring substantially lower concentrations than CQ to effect almost complete inhibition of hemozoin.</p><!><p>This work strongly supports the hypothesis that an improved drug can be made by combining elements of CQ and a reversal agent. The in vitro results clearly show that the RCQ compounds have great efficacy against P. falciparum, and the in vivo results demonstrate that the compounds are efficacious in a mouse model. In the in vivo test, 22 stands out as an excellent lead compound for full preclinical testing, with good activity via the oral route of administration, a low ClogP, and no obvious signs of toxicity. Further testing will also be carried out using a wider range of drug-resistant parasite strains to demonstrate that these compounds truly are promising lead compounds for all CQR Plasmodia.</p><p>While the mode of action of these compounds has not been fully elucidated, the experiments described above show that the RCQ compounds appear to act in a manner similar to that of CQ. Compound 22 showed the highest level of hemozoin inhibition, both in vitro and in vivo. Taken together, the results suggest that the addition of the RA moiety to the 4-aminoquinoline can enhance the mode of antimalarial activity, at least in part by acting to increase the accumulation in the parasite's DV.</p><!><p>All chemicals were obtained from Sigma-Aldrich Chemical Co. Purities of all final products were ≥95% as determined by HPLC, measuring by UV detection at 254 and 325 nm, using a Varian ProStar 325 UV/Vis dual wavelength detector. HPLC Method A was done with a Microsorb-MV 100 CN 5 µm 4.6 mm × 250 mm column, eluting with 100% methanol for 30 minutes unless otherwise stated. HPLC Method B was performed using a SUPELCO Ascentis RP-Amide 5 µm 4.6 mm × 150 mm column, eluting with 100% methanol for 30 minutes unless otherwise stated. HPLC Method C was performed using a SUPELCO Ascentis C18 5 µm 4.6 mm × 150 mm column, eluting with a 30 minute gradient, from 95:5 to 5:95 water with 0.1 % formic acid (v/v): acetonitrile. High resolution mass spectrometry was performed on a Bruker micrOTOF-Q instrument. Results were obtained using electospray ionization (ESI) in the positive mode, at a flow rate of 0.4 mL/min with 1:1 methanol water. 1H, 13C and 2D NMR experiments were run on a Bruker 400MHz AMX or AVANCE-II+ instrument, using the standard pulse sequences provided, including zg30, zgpg30 cosygpqf, hsqcetgpsi2, hmbcgplpndqf and noesyph, at 25°C.</p><p>The syntheses of 1, 2b and 3b have been previously described.17</p><!><p>A mixture of 4,7-dichloroquinoline (4.95 g, 0.025 mol) and ethanolamine (15.27 g, 15.0 mL, 0.25 mol) were heated with stirring at 130–140 °C for 24 hours. After cooling, the reaction was poured into water (150 mL) and filtered. After air drying the solid was boiled in methanol (100 mL), allowed to cool to room temperature then cooled in ice. The solid was filtered, then washed with a small amount of ice cold methanol to give 2a (3 g, 54%) as an off-white solid. HPLC (method A) tR = 6.99 min (99% pure). 1H NMR δ (ppm)(400 MHz, CH3OH-d4): 3.48–3.55 (2 H, m), 3.85 (2 H, q, J = 5.80 Hz), 6.60 (1 H, d, J = 5.67 Hz), 7.43 (1 H, dd, J = 9.02, 2.20 Hz), 7.80 (1 H, d, J = 2.19 Hz), 8.11 (1 H, t, J = 9.02 Hz), 8.38 (1 H, d, J = 5.64 Hz). 13C NMR δ (ppm)(100 MHz, CH3OH-d4): 46.2, 60.7, 99.7, 118.8, 124.3, 126.1, 127.6, 136.4, 149.7, 152.5, 152.9.</p><!><p>To a suspension of 2a (1.5 g, 6.7 mmol) in anhydrous dichloromethane (25 mL) under a nitrogen atmosphere was added triethylamine (2 mL, 14.3 mmol). The mixture was cooled to below 0 °C. Methanesulfonyl chloride (0.57 mL, 7.41 mmol) was added slowly, keeping the temperature below 5 °C, and the reaction was stirred in an ice bath for 1 hour. The reaction was added to a saturated NaHCO3 solution (100 mL), and the organic layer was separated and washed with saturated NaHCO3 solution (25 mL). The combined aqueous layers were extracted with dichloromethane (2 × 20 mL). The combined organic extracts evaporated to leave 3a (1.19 g, 59%) as an off-white solid.</p><!><p>A mixture of the respective piperazine or homopiperazine compound (1.2 equiv), triethylamine (2.0 equiv) and appropriate methylsulfonate ester (1.0 equiv) was heated to 70°C in THF for 3 days with stirring. After cooling to room temperature, 50% K2CO3 solution was added. The mixture was shaken and the THF layer was separated. The aqueous layer was extracted with ethyl acetate. The extracts were combined with the THF layer, and washed with water. After drying and evaporating, the residue was purified.</p><!><p>The title compound was prepared from 1-(diphenylmethyl)piperazine (0.4 g, 0.0016 mol), triethylamine (0.27 g, 0.0027 mol) and 3a (0.4 g, 0.00133 mol) in THF (12 mL) according to the general procedure. The crude compound was purified by recrystallization in ethyl acetate to give an off white solid (0.13 g, 21%). HPLC (method B) tR = 2.74 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 2.51 (8 H, d, J = 40.09 Hz), 2.78 (2 H, t, J = 5.91 Hz), 3.29 (2 H, q, J = 5.32 Hz), 4.26 (1 H, s), 5.96 (1 H, s), 6.35 (1 H, d, J = 5.36 Hz), 7.21-7.14 (2 H, m), 7.31-7.25 (4 H, m), 7.39 (1 H, dd, J = 8.90, 2.20 Hz), 7.46-7.40 (4 H, m), 7.65 (1 H, d, J = 8.92 Hz), 7.95 (1 H, d, J = 2.16 Hz), 8.52 (1 H, d, J = 5.31 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d) 38.9, 52.1, 52.9, 55.4, 76.2, 99.3, 117.4, 121.1, 125.3, 127.0, 127.9, 128.5, 128.8, 134.8, 142.6, 149.1, 149.8, 152.2. MS (ESI): m/z 457.2149 M + H (Calculated 457.2154).</p><!><p>The title compound was prepared from 1-(diphenylmethyl)piperazine (0.56 g, 0.0022 mol), triethylamine (0.43 g, 0.0042 mol) and 3b (0.65 g, 0.0021 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by recrystallization in ethyl acetate to give an off white solid (0.32 g, 31%). HPLC (method B) tR = 3.22 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 1.97-1.89 (2 H, m), 2.87-2.32 (10 H, m), 3.35 (2 H, q, J = 5.12 Hz), 4.42 (1 H, s), 6.29 (1 H, d, J = 5.40 Hz), 7.13 (1 H, dd, J = 8.92, 2.18 Hz), 7.28-7.20 (2 H, m), 7.36-7.29 (4 H, m), 7.46-7.41 (4 H, m), 7.61 (1 H, s), 7.78 (1 H, d, J = 8.95 Hz), 7.92 (1 H, d, J = 2.16 Hz), 8.48 (1 H, d, J = 5.35 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d) 23.3, 44.5, 51.6, 54.0, 58.9, 75.9, 98.4, 117.5, 122.4, 124.6, 127.2, 128.2, 128.5, 128.6, 134.6, 141.9, 149.1, 150.6, 152.2. MS (ESI): m/z 471.2293 M + H (Calculated 471.2310).</p><!><p>The title compound was prepared from 27 (0.22 g, 0.001 mol), triethylamine (0.18 g, 0.0018 mol) and 3b (0.20 g, 0.0006 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by chromatography on alumina eluting with ethyl acetate/hexanes 40:60) to give a solid (0.07 g, 24%). HPLC (method C) tR = 10.76 min (95% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.85–1.86 (4 H, m), 2.72–2.73 (6 H, m), 2.78 (2 H, d, J = 6.40 Hz), 2.87 (2 H, t, J = 5.56 Hz), 3.34 (2 H, q, J = 5.22 Hz), 4.63 (1 H, s), 6.29 (1 H, d, J = 5.39 Hz), 7.18–7.20 (2 H, m), 7.27-7.27 (5 H, m), 7.43 (4 H, d, J = 7.75 Hz), 7.62 (1 H, s), 7.72 (1 H, d, J = 8.92 Hz), 7.92 (1 H, d, J = 2.18 Hz), 8.48 (1 H, d, J = 5.34 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 24.5, 27.8, 44.5, 52.9, 53.0, 54.3, 57.0, 57.9, 75.7, 98.5, 117.6, 122.1, 124.8, 127.0, 128.0, 128.5, 128.6, 134.6, 143.2, 149.2, 150.6, 152.2. MS (ESI): m/z 485.2454 M + H (Calculated 485.2467).</p><!><p>The title compound was prepared from 1-[(4-chlorophenyl)(phenyl)methyl]piperazine (0.46 g, 0.0016 mol), triethylamine (0.27 g, 0.0027 mol) and 3a (0.4 g, 0.00133 mol) in THF (12 mL) according to the general procedure. The crude compound was purified by recrystallization in ethyl acetate to give an off white solid (0.32 g, 31%). The crude compound was purified by recrystallization in ethyl acetate/hexanes (70/30) to give an off white solid (0.20 g, 30%). HPLC (method A) tR = 7.30 min (96% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 2.50 (8 H, d, J = 44.37 Hz, 2.78 (2 H, t, J = 5.91 Hz), 3.29 (2 H, q, J = 5.32 Hz), 4.24 (1 H, s), 5.94 (1 H, s), 6.35 (1 H, d, J = 5.36 Hz), 7.31-7.19 (5 H, m), 7.41-7.35 (5 H, m), 7.64 (1 H, d, J = 8.93 Hz), 7.95 (1 H, d, J = 2.16 Hz), 8.52 (1 H, d, J = 5.31 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d) 38.9, 52.0, 52.8, 55.4, 75.4, 99.3, 117.3, 121.1, 125.3, 127.2, 127.8, 128.6, 128.7, 128.8, 129.1, 132.6, 134.8, 141.2, 142.1, 149.1, 149.7, 152.1. MS (ESI): m/z 491.1745 M + H (Calculated 491.1764).</p><!><p>The title compound was prepared from 1-[(4-chlorophenyl)(phenyl)methyl]piperazine (0.63 g, 0.0022 mol), triethylamine (0.43 g, 0.0042 mol) and 3b (0.65 g, 0.0021 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by recrystallization from ethyl acetate to give an off white solid (0.38 g, 34%). HPLC (method A – 40 min) tR = 9.67 min (98% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 1.92 (2 H, p, J = 5.38 Hz), 2.70-2.52 (10 H, m), 3.34 (2 H, q, J = 5.11 Hz), 4.37 (1 H, s), 6.29 (1 H, d, J = 5.40 Hz), 7.15 (1 H, dd, J = 8.90, 2.17 Hz), 7.32-7.22 (3 H, m), 7.38-7.30 (2 H, m), 7.42-7.35 (4 H, m), 7.52 (1 H, s), 7.76 (1 H, d, J = 8.95 Hz), 7.92 (1 H, d, J = 2.15 Hz), 8.48 (1 H, d, J = 5.35 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d) 23.3, 44.4, 51.6, 53.9, 58.8, 75.3, 98.4, 117.4, 122.3, 124.6, 127.5, 128.1, 128.6, 128.7, 129.3, 132.7, 134.6, 140.8, 141.2, 149.1, 150.5, 152.2. MS (ESI): m/z 505.1911 M + H (Calculated 505.1920).</p><!><p>The title compound was prepared from 28 (0.17 g, 0.00057 mol), triethylamine (0.18 g, 0.0018 mol) and 3b (0.20 g, 0.00064 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by chromatography on alumina eluting with ethyl acetate/hexanes (40:60) to give a solid (0.21 g, 71%). HPLC (method B) tR = 4.66 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 1.81–1.92 (4 H, m), 2.74–2.75 (8 H, m), 2.88 (2 H, t, J = 5.53 Hz), 3.37 (2 H, q, J = 5.13 Hz), 4.61 (1 H, s), 6.31 (1 H, d, J = 5.41 Hz), 7.26–7.28 (5 H, m), 7.35–7.40 (4 H, m), 7.53 (1 H, s), 7.72 (1 H, d, J = 8.93 Hz), 7.93 (1 H, d, J = 2.16 Hz), 8.50 (1 H, d, J = 5.36 Hz). MS (ESI): m/z 519.2079 M + H (Calculated 519.2077).</p><!><p>The title compound was prepared from 1-(bis(4-fluorophenyl)methyl)piperazine (0.52 g, 0.0018 mol), triethylamine (0.20 g, 0.0019 mol) and 3b (0.41 g, 0.0013 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by chromatography on alumina eluting with ethyl acetate/hexanes (40:60) to give a solid (0.12 g, 18%). HPLC (method B) tR = 3.3 min (98% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 1.97 (3 H, m), 2.57 (8 H, bs), 2.68 (3 H, t, J = 5.60 Hz), 3.40 (3 H, m), 4.38 (1 H, s), 6.34 (1 H, d, J = 5.47 Hz), 7.04 (4 H, t, J = 8.55 Hz), 7.22 (1 H, dd, J = 8.90, 2.05 Hz), 7.40 (4 H, dd, J = 8.47, 5.45 Hz), 7.67 (1 H, bs), 7.84 (1 H, d, J = 8.89 Hz), 8.00 (1 H, s), 8.51 (1 H, d, J = 5.44 Hz).</p><!><p>The title compound was prepared from 29 (0.24 g, 0.0010 mol), triethylamine (0.69 g, 0.0068 mol) and 3a (0.30 g, 0.0010 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by chromatography on alumina eluting with ethyl acetate/hexanes (40:60) to give a solid (0.05 g, 11%). HPLC (method C) tR = 10.88 min (95% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.79–1.80 (2 H, m), 2.67–2.70 (2 H, m), 2.74–2.78 (2 H, m), 2.84 (2 H, t, J = 5.93 Hz), 2.91 (4 H, dt, J = 12.45, 5.98 Hz), 3.23 (2 H, q, J = 5.25 Hz), 4.90 (1 H, s), 6.17 (1 H, s), 6.34 (1 H, d, J = 5.34 Hz), 7.26 (2 H, s), 7.37 (3 H, d, J = 7.82 Hz), 7.63 (1 H, s), 7.69 (3 H, d, J = 7.26 Hz), 7.96 (1 H, d, J = 2.18 Hz), 8.52 (1 H, d, J = 5.28 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 29.4, 39.7, 53.4, 54.9, 56.5, 71.6, 99.3, 119.8, 121.2, 125.3, 125.5, 127.1, 128.1, 128.8, 134.8, 140.8, 144.9, 149.1, 149.9, 152.1. MS (ESI): m/z 469.2143 M + H (Calculated 469.2154).</p><!><p>The title compound was prepared from 29 (0.31 g, 0.0012 mol), triethylamine (0.25 g, 0.0025 mol) and 3b (0.37 g, 0.0012 mol) in THF (15 mL) according to the general procedure. The crude compound was purified by chromatography on alumina eluting with ethyl acetate/hexanes (40:60) to give a solid (0.29 g, 50%). HPLC (method C) tR = 11.24 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.88–1.93 (4 H, m), 2.08 (1 H, s), 2.77 (3 H, s), 2.83 (2 H, t, J = 5.89 Hz), 2.94 (2 H, t, J = 5.29 Hz), 3.00 (1 H, s), 3.34 (2 H, t, J = 5.75 Hz), 4.92 (1 H, s), 6.27 (1 H, d, J = 5.53 Hz), 7.38 (2 H, t, J = 7.47 Hz), 7.64 (2 H, d, J = 7.49 Hz), 7.69 (2 H, d, J = 7.56 Hz), 7.96-7.96 (2 H, m),8.48 (1 H, s). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 23.3, 24.1, 27.6, 43.5, 50.2, 51.7, 53.5, 56.8, 57.4, 71.3, 98.3, 117.5, 119.9, 122.7, 125.1, 125.5, 127.2, 127.7, 128.3, 135.0, 140.9, 144.5, 148.3, 151.0, 151.3, 177.3. MS (ESI): m/z 483.2297 M + H (Calculated 483.2310).</p><!><p>Desipramine hydrochloride (0.44 g, 0.00145 mol) was dissolved in water (7 mL), and solid NaHCO3 (0.24 g, 0.0029 mol) was added with stirring. After addition of dichloromethane (8 mL), two clear layers resulted. The aqueous layer was removed and extracted with dichloromethane (2× 7 mL). The combined organic layers were evaporated to leave desipramine free base as a yellow oil. To this oil were added anhydrous THF (12 mL) and 3a (0.35 g, 0.00116 mol) followed by triethylamine (0.32 mL, 0.00232 mol). After being stirred at 50–60°C for 72 h, the reaction was allowed to cool to room temperature. The reaction was diluted with 50% K2CO3 solution (30 mL), and the THF layer was separated. The aqueous was extracted with ethyl acetate (2 × 10 mL). The extracts were combined with the THF layer, and washed with water (10 mL). After drying and evaporation, the residue was chromatographed on alumina (MCB type F20, 80–200 mesh), eluting with ethyl acetate:hexanes (70:30) to give a yellow oil (0.42 g, 77%). HPLC (method A) tR = 9.12 min (96% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d) 1.78 (2 H, p, J = 6.79 Hz), 2.23 (3 H, s), 2.46 (2 H, t, J = 7.00 Hz), 2.68 (2 H, t, J = 5.81 Hz), 3.03 (4 H, s), 3.26-3.16 (2 H, m), 3.79 (2 H, t, J = 6.54 Hz), 5.82 (1 H, s), 6.32 (1 H, d, J = 5.35 Hz), 6.84 (2 H, td, J = 7.14, 1.61 Hz), 7.09-6.95 (6 H, m), 7.23 (1 H, dd, J = 8.90, 2.19 Hz), 7.44 (1 H, d, J = 8.93 Hz), 7.95 (1 H, d, J = 2.16 Hz), 8.52 (1 H, d, J = 5.31 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d) 24.7, 31.0, 38.5, 40.9, 47.0, 53.3, 54.3, 98.2, 116.3, 118.6, 120.0, 121.5, 124.3, 125.3, 127.7, 128.8, 133.1, 133.7, 147.0, 148.1, 148.6, 151.0. MS (ESI): m/z 471.2316 M + H (Calculated 471.2310).</p><!><p>2b (2.7g, 0.0116 mol) was finely ground and suspended on dry THF (50 mL). Triethylamine (2.9g, 0.29 mol) was added and the mixture was cooled to below 0°C. Methane sulfonyl chloride(1.46g, 0.0128 mol) was added slowly, keeping the temperature below 5°C. After stirring in an ice bath for 1 hour, TLC (alumina plate, run in ethyl acetate) indicated no 2b left in the reaction. Piperazine (10g, 0.116 mol) was added, and the reaction was heated to reflux. After 2 hours TLC indicated the reaction was complete, and it was allowed to cool to room temperature. 150 mL of saturated NaHCO3 solution was added and the solution was extracted with ethyl acteate (3 × 50 mL). The combined organic extracts were washed with water (5 × 25 mL), dried and evaporated give a cream solid (1.2 g, 34%). HPLC (method C) tR = 2.81 min (93% pure). 1H NMR δ (ppm)(400 MHz, CH3OH-d4): 1.79–1.89 (2 H, m), 2.38–2.49 (6 H, m), 2.76–2.83 (4 H, m), 3.33 (2 H, t, J = 6.79 Hz), 6.44 (1 H, d, J = 5.69 Hz), 7.27–7.33 (1 H, m), 7.68 (1 H, d, J = 2.18 Hz), 7.98 (1 H, d, J = 9.01 Hz), 8.25 (1 H, d, J = 5.65 Hz). 13C NMR δ (ppm)(100 MHz, CH3OH-d4): 25.8, 42.6, 46.1, 54.6, 58.0, 99.6, 118.7, 124.3, 126.0, 127.6, 136.4, 149.6, 152.4, 152.8. MS (ESI): m/z 305.1538 M + H (Calculated 305.1528).</p><!><p>14 (0.3 g, 0.00098 mol) was dissolved in acetonitrile (15 mL) and potassium carbonate (0.2 g, 0.0015 mol) was added, followed by trityl chloride (0.25 g, 0.00089 mol). The mixture was heated to reflux with stirring for 3 hours, after which TLC indicated completion. After cooling to room temperature, the reaction was poured into water (50 mL) and ethyl acetate (20 mL) was added. The insoluble precipitate was filtered, washed with water and ethyl acetate, and dried. The ethyl acetate layer was separated and the aqueous layer extracted with ethyl acetate (2 × 20 mL). The combined organic phases were dried, filtered through an alumina plug, and evaporated. The residue was combined with the insoluble solid from above to give a white solid (0.15 g, 31%). HPLC (method A) tR = 8.63 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.90–1.97 (4 H, m), 2.53 (2 H, bs), 2.67–2.72 (2 H, m), 3.06 (2 H, bs), 3.16 (2 H, bs), 3.31 (2 H, q, J = 4.99 Hz), 6.22 (1 H, d, J = 5.41 Hz), 6.62 (1 H, dd, J = 8.95, 2.19 Hz), 7.26–7.34 (10 H, m), 7.52 (6 H, bs), 7.67 (1 H, s), 7.84 (1 H, d, J = 2.16 Hz), 8.43 (1 H, d, J = 5.35 Hz). 13C NMR δ (ppm)(100 MHz, CH3OH-d4): 22.8, 44.7, 47.8, 54.6, 59.6, 98.1, 117.2, 122.1, 125.0, 126.4, 127.2, 127.7, 127.8, 127.9, 129.4, 134.8, 146.9, 150.8, 151.5. MS (ESI): m/z 547.2637 M + H (Calculated 547.2623).</p><!><p>14 (0.5 g, 0.00164 mol) and the respective carbonyl compound were mixed together in dry THF (5 mL) then treated with sodium triacetoxyborohydride (0.52 g, 0.0025 mol) followed by acetic acid (0.1 g, 0.00175 mol).19 The mixture was stirred under nitrogen, and at room temperature, for 7 days. The reaction was quenched with saturated NaHCO3 solution (50 mL), and extracted into dichloromethane (3 × 10 mL). The extracts were washed with brine (10 mL), then dried and evaporated. The residue was chromatographed to give pure product.</p><!><p>The title compound was prepared from diphenylacetaldehyde (0.32 g, 0.00164 mol) according to the general procedure. The crude compound was purified by chromatography on silica, eluting with ethyl acetate/ammonium hydroxide (99:1) to give an off white solid (0.43 g, 54%). HPLC (method A) tR = 7.87 min (98% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.87–1.94 (2 H, m), 2.48–2.62 (6 H, m), 2.64 (4 H, s), 3.07 (2 H, d, J = 7.54 Hz), 3.34 (2 H, q, J = 5.12 Hz), 4.24 (1 H, t, J = 7.52 Hz), 6.30 (1 H, d, J = 5.41 Hz), 7.17–7.23 (2 H, m), 7.24–7.33 (9 H, m), 7.58 (1 H, s), 7.87 (1 H, d, J = 8.94 Hz), 7.93 (1 H, d, J = 2.15 Hz), 8.49 (1 H, d, J = 5.36 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 23.3, 44.5, 48.9, 53.5, 53.6, 58.8, 63.8, 98.5, 117.5, 122.5, 124.6, 126.3, 128.2, 128.4, 128.7, 134.6, 143.7, 149.1, 150.6, 152.2. MS (ESI): m/z 485.2479 M + H (Calculated 485.2467).</p><!><p>The title compound was prepared from 2-adamantanone (0.5 g, 0.00328 mol) according to the general procedure. The crude compound was purified by chromatography on silica, eluting with ethyl acetate/ammonium hydroxide (99.3:0.7) to give an off white solid (0.33 g, 46). HPLC (method A) tR = 11.75 min (96% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.42 (2 H, d, J = 11.72 Hz), 1.79 (5 H, m), 1.86–2.02 (5 H, m), 2.10 (5 H, m,), 2.60–2.66 (10 H, m), 3.38 (2 H, q, J = 5.12 Hz), 6.31 (1 H, d, J = 5.41 Hz), 7.32 (1 H, dd, J = 8.95, 2.14 Hz), 7.81 (1 H, s), 7.91–7.95 (2 H, m), 8.50 (1 H, d, J = 5.37 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 23.2, 27.3, 27.6, 29.0, 31.4, 37.3, 37.8, 44.7, 49.7, 54.3, 59.0, 68.3, 98.4, 117.5, 122.7, 124.7, 128.6, 134.6, 149.2, 150.7, 152.2. MS (ESI): m/z 439.2631 M + H (Calculated 439.2623).</p><!><p>2,2'-Dipyridylketone (25.95 g, 0.141 mol) was dissolved in toluene (500 mL) and 4-aminopiperidine (16.2 g, 0.162 mol) was added followed by p-toluene sulfonic acid (∼0.5 g). The mixture was heated to reflux for 3 days, with a Dean and Stark trap to remove water. After cooling to room temperature, the toluene was removed to leave a crude oil (37.5 g, 99%), which was used without further purification.</p><!><p>18 (1.74 g, 0.0065 mol crude) was dissolved in acetonitrile (20 mL) and 3a (1.31 g, 0.00436 mol) and K2CO3 (1.2 g, 0.00871 mol) were added. The reaction was stirred at 70°C for 2 days. After cooling to room temperature, water (100 mL) was added and the mixture was stirred for 30 minutes. The solid was filtered, washed with water and recrystallized from toluene/hexanes twice to give a solid (0.68 g, 33%). HPLC (method A) tR = 7.80 min (99% pure). MS (ESI): m/z 471.2054 M + H (Calculated 471.2058).</p><!><p>18 (1.02 g, 0.00382 mol crude) was dissolved in acetonitrile (12 mL) and 3b (1 g, 0.00318 mol) and K2CO3 (0.88 g, 0.00636 mol) were added. The reaction was stirred at 70°C overnight. TLC (alumina plate, run in ethyl acetate/methanol 9:1) indicated some 3b was still present, so a further solution of 18 (0.25 g, 0.000954 mol) in acetonitrile (5 mL) was added, and heating continued overnight. After cooling to room temperature, the solvent was evaporated and the residue was slurried in water (30 mL). The solid was filtered, washed with water and recrystallized from toluene/hexanes. The solid was dissolved in ethyl acetate/methanol 50:50 and stirred with alumina and charcoal for 30 minutes. After filtering through celite, the solvents were removed to give a solid (0.78 g, 50%). HPLC (method B) tR = 2.41 min (96% pure). MS (ESI): m/z 485.2230 M + H (Calculated 485.2215).</p><!><p>19 (0.53 g, 0.00113 mol) was dissolved in methanol (40 mL) and cooled in an ice/water bath. Sodium borohydride (0.13 g, 0.00338 mol) was added in portions and the reaction was stirred overnight at room temperature. After evaporating the methanol, water (40 mL) was added to the residue, and the resulting suspension was stirred for 30 minutes. The mixture was extracted with dichloromethane (3 × 20 mL) and the combined extracts were washed with water (10 mL), then dried and evaporated. Chromatography on alumina, eluting with dichloromethane/methanol (95:5) gave an oil (0.17 g, 34%). HPLC (method B) tR = 2.30 min (84% pure). 1H NMR δ (ppm)(CHCl3-d): 1.50–1.62 (2 H, m), 1.83 (1 H, s), 1.95 (2 H, d, J = 13.24 Hz), 2.04 (2 H, t, J = 11.64 Hz), 2.44–2.53 (1 H, m), 2.71 (2 H, t, J = 5.88 Hz), 2.89 (2 H, d, J = 11.30 Hz), 3.26 (2 H, q, J = 5.27 Hz), 5.24 (1 H, s), 6.09 (1 H, s), 6.34 (1 H, d, J = 5.37 Hz), 7.14 (2 H, ddd, J = 7.48, 4.87, 1.18 Hz), 7.36 (1 H, dd, J = 8.89, 2.19 Hz), 7.42 (2 H, dt, J = 7.88, 1.05 Hz), 7.62 (2 H, td, J = 7.67, 1.83 Hz), 7.64 (1 H, d, J = 8.94 Hz), 7.94 (1 H, d, J = 2.17 Hz), 8.51 (1 H, d, J = 5.32 Hz), 8.56 (2 H, ddd, J = 4.89, 1.81, 0.92 Hz). 13C NMR δ (ppm)(CHCl3-d): 33.0, 39.0, 51.9, 52.7, 55.3, 66.2, 99.2, 117.3, 121.2, 122.2, 122.4, 125.4, 128.7, 134.8, 136.7, 149.1, 149.2, 149.8, 152.1, 161.6. MS (ESI): m/z 473.2227 M + H (Calculated 473.2215).</p><!><p>20 (0.60 g, 0.00124 mol) was dissolved in methanol (40 mL) and cooled in ice. Sodium borohydride (0.14 g, 0.0037 mol) was added in portions, and the reaction was stirred at room temperature overnight. After evaporating the methanol, the residue was stirred with water (50 mL) for 30 minutes then extracted with dichloromethane (3 × 20 mL). The extracts were washed with water (20 mL), dried and evaporated to give a solid (0.59 g, 99%). HPLC (method B) tR = 2.49 min (97% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.57–1.70 (2 H, m), 1.87–1.95 (2 H, m), 1.99 (5 H, d, J = 11.77 Hz), 2.56 (2 H, t, J = 5.17 Hz), 2.59–2.68 (1 H, m), 3.00 (2 H, d, J = 11.29 Hz), 3.35 (2 H, q, J = 5.10 Hz), 5.25 (1 H, s), 6.29 (1 H, d, J = 5.42 Hz), 7.16 (2 H, ddd, J = 7.47, 4.88, 1.19 Hz), 7.44 (1 H, dd, J = 8.91, 2.18 Hz), 7.47 (2 H, d, J = 7.89 Hz), 7.65 (2 H, td, J = 7.67, 1.83 Hz), 7.74 (1 H, s), 7.84 (1 H, d, J = 8.96 Hz), 7.91–7.94 (1 H, m), 8.49 (1 H, d, J = 5.37 Hz), 8.59 (2 H, ddd, J = 4.89, 1.80, 0.92 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 23.7, 33.1, 44.5, 52.8, 53.1, 58.7, 66.5, 98.3, 117.6, 122.2, 122.4, 122.4, 125.0, 128.5, 134.6, 136.7, 149.1, 149.1, 150.7, 152.1, 162.1. MS (ESI): m/z 487.2355 M + H (Calculated 487.2371).</p><!><p>A mixture of N-(4-bromobutyl)phthalimide (0.5 g, 0.00177 mol), 1-(diphenylmethyl)piperazine (0.47 g, 0.00186 mol) and K2CO3 (0.61 g, 0.00443 mol) was stirred and heated in acetonitrile (25 mL) to reflux for 3 hours.39 After cooling, the acetonitrile was evaporated, and the residue partitioned between water (20 mL) and ethyl acetate (20 mL). The aqueous layer was extracted with ethyl acetate (2 × 10 mL) and the combined organic layers were dried and evaporated to give a solid (0.74 g, 92%), which was used without further purification.</p><!><p>23 (0.74 g, 0.00163 mol crude) was dissolved in ethanol (5 mL), and hydrazine hydrate (0.25 g, 0.0049 mol) was added.39 The mixture was stirred and heated to reflux for 3 hours, then allowed to cool to room temperature. The solid was filtered off, and the filter cake washed with cold ethanol. The filtrate was evaporated to give an oil (0.53 g, ∼100%), which solidified on contact with air. This was used without further purification.</p><!><p>4,7-dichloroquinoline (0.24 g, 0.0012 mol) was dissolved in ethanol (10 mL) and 24 (0.53 g, 0.00163 mol crude) was added, followed by triethylamine (0.33 g, 0.00326 mol). The mixture was stirred and refluxed for 10 days, then allowed to cool to room temperature. The ethanol was evaporated, and the residue partitioned between saturated NaHCO3 solution (20 mL) and dichloromethane (20 mL). The organic layer was separated and the aqueous layer was extracted with dichloromethane (2 × 20 mL). The combined dichloromethane layers were washed with saturated NaHCO3 solution (20 mL), then dried and evaporated. The resulting oil was chromatographed on silica, eluting with ethyl acetate/triethylamine (99:1), to give an off-white solid (0.10 g, 17%). HPLC (method B) tR = 2.97 min (95% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 1.67 (2 H, t, J = 6.86 Hz), 1.80 (2 H, p, J = 6.95 Hz), 2.37–2.54 (10 H, m), 3.24–3.33 (2 H, m), 4.19–4.24 (1 H, m), 5.38 (1 H, s), 6.38 (1 H, t, J = 6.53 Hz), 7.14–7.22 (2 H, m), 7.25–7.31 (4 H, m), 7.32 (1 H, dd, J = 8.92, 2.20 Hz), 7.41 (4 H, d, J = 7.61 Hz), 7.66 (1 H, d, J = 8.94 Hz), 7.95 (1 H, d, J = 2.18 Hz), 8.52 (1 H, d, J = 5.36 Hz). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 24.7, 26.4, 43.2, 51.8, 53.5, 57.7, 76.2, 99.0, 117.2, 121.1, 125.1, 127.0, 127.9, 128.5, 128.9, 134.8, 142.6, 149.2, 149.7, 152.1. MS (ESI): m/z 485.2456 M + H (Calculated 485.2467).</p><!><p>1-(2-Hydroxyethyl)piperazine (2 g, 0.0154 mol) was dissolved in DMF (20 mL), and potassium carbonate (4.27 g, 0.0308 mol) was added, followed by a catalytic amount of potassium iodide. The mixture was stirred at room temperature and chlorodiphenylmethane (3.12 g, 0.0154 mol) was added dropwise. After the addition, the reaction was stirred for a further 2 hours at room temperature, then warmed to 70°C, and held there overnight. After cooling to room temperature, water (100 mL) was added, and the mixture was extracted with diethyl ether (3 × 20 mL). The combined organic extracts were washed with brine solution (30 mL), then dried and evaporated to give an oil (1.7 g, 37%). HPLC (method B) tR = 2.25 min (95% pure). 1H NMR δ (ppm)(400 MHz, CHCl3-d): 2.43 (5 H, bs, J = 8.59 Hz), 2.54 (5 H, m, J = 5.45 Hz), 3.58 (2 H, t, J = 5.41 Hz), 4.22 (1 H, s), 7.14–7.19 (2 H, m), 7.24–7.30 (4 H, m), 7.38–7.43 (4 H, m). 13C NMR δ (ppm)(100 MHz, CHCl3-d): 52.0, 53.1, 57.7, 59.1, 76.2, 126.9, 127.9, 128.5, 142.7.</p><!><p>The halo compound was placed in chloroform and homopiperazine was added. The reaction was stirred and heated at reflux for 3 days. After cooling to room temperature, saturated NaHCO3 solution was added and the mixture was shaken. The organic layer was separated and washed with water. The aqueous layers were combined with the NaHCO3 layer and extracted with dichloromethane. The combined organic layers were dried and evaporated.</p><!><p>The title compound was prepared from Chlorodiphenylmethane (0.61 g, 0.003 mol) and homopiperazine (1.5 g, 0.015 mol) according to the general procedure. The crude compound was purified by chromatography on alumina, eluting with chloroform/methanol (95:5) to give an oil (0.35 g, 44%).</p><!><p>The title compound was prepared from Chloro(4-chlorophenyl)phenylmethane (0.71 g, 0.003 mol) and homopiperazine (1.5 g, 0.015 mol) according to the general procedure. The crude compound was purified by chromatography on alumina, eluting with chloroform/methanol (95:5) to give an oil (0.39 g, 43%).</p><!><p>The title compound was prepared from 9-bromofluorene (1.47 g, 0.006 mol) and homopiperazine (3 g, 0.03 mol) according to the general procedure. The crude compound was purified by chromatography on alumina, eluting with chloroform/methanol (95:5) to give an oil (0.55 g, 37%).</p><!><p>'normalized' IC50 RCQ compound (D6) = [6.9 / IC50 CQ (D6)] × IC50 RCQ compound (D6)</p><!><p>Compounds were formulated in a solution consisting of 70% Tween-80 (d = 1.08 g/mL) and 30% ethanol (d = 0.81 g/mL), followed by a 10-fold dilution in water. On day 0, heparinized blood (containing 100 µL of 200 u/mL Heparin) was taken from a donor NMRI mouse with approximately 30% parasitemia. The blood was diluted in physiological saline to 108 parasitized erythrocytes per mL. From this suspension 0.2 mL was injected intravenously (i.v.) into experimental groups of 3 female NMRI mice, and a control group of 5 mice. Compounds were administered in a volume of 10ml/kg either as single dose 24 hours after infection (day 1) either by oral gavage (p.o.) or subcutaneous injection, or as 4 consecutive daily p.o. doses 4, 24, 48 and 72 hours after infection (days 0–3).</p><p>On day 3 (with the single-dose regimen ) or on day 4 (with the quadruple-dose regimen), 1 µL tail blood was taken and dissolved in 1 mL PBS buffer. Parasitemia was determined with a FACScan (Becton Dickinson) by counting 100,000 RBCs. The difference between the mean value of the control group and those of the experimental groups was calculated and expressed as a percent relative to the control group (= activity). Animals receiving no compound would die typically 5–6 days post-infection and were therefore euthanized right after determination of parasitemia. The survival of the animals was monitored up to 30 days. Mice surviving for 30 days were checked for parasitemia and subsequently euthanized. A compound was considered curative if the animal survived to 30 days post-infection with no detectable parasites by microscopy, with a detection limit of 1 parasite in 10,000 erythrocytes (that is, 0.01%).</p><!><p>Synchronized PRBCs were obtained following two cycles of sorbitol-induced lysis of an asynchronous stock culture. Incubation for an additional 20–24 hours provided a population of mature trophozoites that were added to the culture medium at 2% v/v (about/10% parasitemia).</p><p>An aliquot of a 10 mM solution of 1 was added to a culture flask containing 5 mL of PRBCs suspended in complete medium (10% parasitemia), such that the initial medium concentration of 1 was ∼ 5 µM. Samples were removed from the flask at various intervals and centrifuged; the supernatant fluid was then removed and refrigerated, before analysis. 1 was added to flasks containing both CQS D6- and CQR Dd2-infected RBCs, and as a control, to a flask containing uninfected RBCs. For the purpose of estimating the amount of 1 accumulated within the DV, NH4Cl (10 mM) was added 10 min prior to sampling in order to basify the acidic subcellular compartments and cause the release of accumulated 1. The samples were analyzed by reverse-phase HPLC, using a C18 column, eluting with an isocratic mixture of 75% acetonitrile: 25% 5 mM phosphate buffer (pH 11). A parallel experiment was preformed with CQ, also at ∼ 5 µM concentration, for comparative purposes. With these conditions 1 had a retention time of 14 minutes, and CQ 5 minutes. Each drug sample was monitored at 325 nm, and was compared to a standard curve for quantification.</p><!><p>For heme-drug binding studies, a 1 mM stock solution of chloroquine or test compound was prepared in distilled water, methanol or dimethyl sulfoxide (DMSO), depending on solubility and sonicated to ensure complete dissolution. A 5 mM stock solution of heme was prepared by dissolving heme chloride in 0.1 mM NaOH by incubating at 37°C for 30 min. The solution was stored at 4°C for up to one month. At the beginning of each experiment, the stock heme solution was diluted to 5 µM in phosphate buffer (100 mM, pH 5.7) and allowed to equilibrate for four hours. The four hour equilibration allowed for the initial heme absorbance to stabilize prior to beginning the titration. Optical titrations with each compound were performed by successive addition of aliquots of its stock solution to the 5 µM heme solution. The pH was monitored throughout the procedure with only negligible (±0.05 pH units) changes. Equilibrium binding constants were determined by nonlinear least-squares analysis.41</p><p>Hemin chloride (16.3 mg) was dissolved in 1 ml of DMSO. The solution was passed through a 0.2 µm-pore membrane filter to remove insoluble particles and kept at 4°C for up to one month as a stock solution.42 In order to determine heme concentration of the stock solution, a sample was diluted in 2.5% sodium dodecyl sulfate in 0.1 M NaOH and an absorbance reading taken at 400 nm. The heme concentration was calculated using Beer's law with a molar absorptivity ε = 105 mol L−1 cm−1. The optimal heme and Tween20 concentrations for promoting heme crystallization were calculated by the procedure described by Huy, 2002.43 The RCQ compounds were screened for their inhibitory capacity, and IC50 values were determined. Assays were run in duplicate twice. Incubations were conducted in the dark to ensure that light did not interfere. A series of solutions were made consisting of 300 µl of varying concentrations of the compound under study in distilled, 700 µl of 1 M acetate buffer, 300 µl of a 200 µM heme solution freshly buffered by 1 M sodium acetate (pH 4.8) and 200 µl of 0.0375 g/L Tween20 solution. This provided a final 40 µM heme solution buffered by 0.67 M sodium acetate at pH 4.8 and 0.0005 g/L Tween20, with the test compound ranging in concentration from 0–1000 µM. The mixtures were incubated for 24 hours at 37°C,35 then mixed and transferred cuvette for a 415/630 nm absorbance reading. IC50 values were calculated by (Dmax – Dinitial ) / 2 where Dmax represents the lowest concentration of compound under study to provide maximal absorbance readings indicating maximal free heme, and Dinitial represents the lowest concentration of drug to provide any increase in absorbance over a solution with no drug.</p><!><p>P. falciparum strains D6 and Dd2 were synchronized to the ring stage (early trophozoites) with 5% sobitol solution.24 After the synchronization of the parasites, the erythrocytes were suspended in culture medium at 1% hematocrit and aliquots of 1 mM stock solution of drug were added to the culture flasks. Drug treated cultures, along with the no-drug control culture, were incubated for 24 hours at 37 °C under a gas mixture of 5% O2, 5% CO2, and 90% N2 then transferred to 15 ml centrifuge tubes. One 2 µL aliquot was used from each tube was used to obtain Giemsa-stained smear for determination of the parasitemia and morphology examination by microscopy.</p><p>Parasites were isolated by one freeze-thaw cycle at −20 °C and treated with a saponin-containing lysis buffer (Tris (20 mM; pH 7.5), EDTA (5 mM), saponin (0.008%; wt/vol), and Triton X-100 (0.08%; vol/vol)40 at 37 °C for 30 min. The hemozoin was pelleted by centrifugation at 215,000 g for 30 min at 25 °C. The supernatant was removed and the pellet consisting primarily of hemozoin was washed two times with acetone to remove residual proteins. Insoluble material was then washed three times with PBS (pH 7.4) buffer and collected by centrifugation at 215,000 g for 30 min at 25 °C. The pellet was dissolved with 0.2 N sodium hydroxide for 2 hours at 37 °C and periodic mixing of the sample. The absorbance at 400 nm was measured, and the amount of heme was calculated using an extinction coefficient of 91,000 cm−1M−1.36–38 The amount of heme per parasitized erythrocyte was calculated based on the number of erythrocytes in the culture and the percent parasitemia obtained after growing synchronized culture for 24 hours. Cultures at 0 hour processed with the identical method were used for determination of the baseline hemozoin production.</p><!><p>chloroquine</p><p>World Health Organization</p><p> Plasmodium falciparum chloroquine resistance transporter</p><p>digestive vacuole</p><p>chloroquine resistant</p><p>chloroquine sensitive</p><p>reversal agent</p><p>reversed chloroquine compound</p><p>structure-activity relationship</p><p>red blood cell</p><p>parasitized red blood cell</p>

PubMed Author Manuscript

Site-Selective Aliphatic C\xe2\x80\x93H Chlorination Using N-Chloroamides Enables a Synthesis of Chlorolissoclimide

Methods for the practical, intermolecular functionalization of aliphatic C\xe2\x80\x93H bonds remain a paramount goal of organic synthesis. Free radical alkane chlorination is an important industrial process for the production of small molecule chloroalkanes from simple hydrocarbons, yet applications to fine chemical synthesis are rare. Herein, we report a site-selective chlorination of aliphatic C\xe2\x80\x93H bonds using readily available N-chloroamides, and apply this transformation to a synthesis of chlorolissoclimide, a potently cytotoxic labdane diterpenoid. These reactions deliver alkyl chlorides in useful chemical yields with substrate as the limiting reagent. Notably, this approach tolerates substrate unsaturation that poses major challenges in chemoselective, aliphatic C\xe2\x80\x93H functionalization. The sterically- and electronically-dictated site selectivities of the C\xe2\x80\x93H chlorination are among the most selective alkane functionalizations known, providing a unique tool for chemical synthesis. The short synthesis of chlorolissoclimide features a high yielding, gram-scale radical C\xe2\x80\x93H chlorination of sclareolide and a three-step/two-pot process for the introduction of the \xce\xb2-hydroxysuccinimide that is salient to all the lissoclimides and haterumaimides. Preliminary assays indicate that chlorolissoclimide and analogues are moderately active against aggressive melanoma and prostate cancer cell lines.

site-selective_aliphatic_c\xe2\x80\x93h_chlorination_using_n-chloroamides_enables_a_synthesis_of_chl

INTRODUCTION<!>DEVELOPMENT OF THE ALIPHATIC C\xe2\x80\x93H CHLORINATION<!>APPLICATION TO THE SYNTHESIS OF CHLOROLISSOCLIMIDE<!>CONCLUSION

<p>The free radical chlorination of unactivated alkanes with elemental chlorine is industrially important for the preparation of a number of chlorinated small molecules.1 The vast majority of these applications involve hydrocarbons with only one type of C–H bond, however. This is a consequence of the promiscuity of chlorine free radical, which leads to poor site selectivities in free radical alkane chlorinations and a proclivity for undesired polyhalogenations with more complex substrates. 2 This contrasts the controlled, predictable nature of alkane brominations, which are often highly regioselective for the weakest C–H bond present, such as tertiary, allylic or benzylic positions.3 Alkyl chlorides are highly useful synthetic building blocks, and >2000 chlorine-containing natural products have been identified to date.4 New methods for practical, selective aliphatic C–H chlorinations hold significant potential for streamlining the synthesis and derivatization of broad classes of synthetically and medicinally valuable small molecules.5</p><p>Alternative strategies for intermolecular aliphatic C–H chlorination have been developed that avoid the intermediacy of chlorine free radical, and offer improved site selectivities. For example, prior studies have demonstrated that nitrogencentered radicals derived from N-chloroamines can facilitate site-selective C–H chlorination, but these reactions required the use of strong acid as solvent, and are therefore impractical for complex synthesis.6 Recent studies have indicated the potential for biomimetic alkane chlorination using manganese porphyrin catalysts,7 but the intermediacy of reactive high valent metal-oxo species presents challenges in chemoselectivity with functionalized substrates.</p><p>We have previously reported the development of a set of easily accessed, bench stable N-bromoamides for the site-selective, intermolecular bromination of unactivated C–H bonds.8 These reactions used substrate as limiting reagent and delivered products using elements of both steric and electronic control. Herein, we have extended our approach to C–H chlorination using household lamp irradiation and N-chloroamides that are trivially prepared from amides and NaOCl. Our studies have shown that in contrast to C–H bromination with N-bromoamides, background reactions (e.g., Cl• reactivity) were significant in these studies (Figure 1). We have developed a practical protocol that overcomes this unselective background reactivity. We have also demonstrated the unique site and chemoselectivities of our aliphatic C–H chlorination, including applications to substrates containing more reactive tertiary, allylic, or benzylic C–H bonds. Unsaturated substrates are rare in studies of intermolecular aliphatic C–H functionalization, and is a notable aspect of this approach.9 Finally, we demonstrate the practical utility of our chlorination method in the short synthesis of the potent cytotoxin chlorolissoclimide and analogues, wherein gram-scale, highly selective monochlorination of sclareolide plays a pivotal role.</p><!><p>Our studies commenced with the C–H chlorination of 1 equiv of cyclohexane (Table 1). As with aliphatic C–H bromination, methods for intermolecular aliphatic C–H chlorination using substrate as limiting reagent are extremely rare.7 A survey of classical C–H chlorinations demonstrated either low reactivity with N-chlorosuccinimide (entry 1, 5 equiv substrate), or uncontrolled reactivity with SO2Cl2 (entry 2). Chlorination using a biomimetic Mn-porphyrin system provided good conversion, however a significant amount of dichlorination product was formed (entry 3).7a The cyclohexane chlorination using the conditions previously reported for alkane bromination with N-chloroamide 1 (irradiation using 23W compact fluorescent bulbs) also provided moderate conversion and similar mono- versus dichlorination selectivity (entry 4). An alternative approach using radical initiation with benzoyl peroxide (BPO) was also suboptimal (entry 5).</p><p>At this stage, we hypothesized that the promiscuity of chlorine radical could be adversely affecting our reaction selectivity. This idea is supported by the prior studies of Greene, who demonstrated that the chain carrying species of aliphatic C–H chlorinations with N-chloroamides can vary widely depending upon the exact reaction conditions.10 Specifically, we questioned whether trace acid was reacting with reagent 1 to deliver amide and Cl2. Adding 1 equiv of Cs2CO3 improved the selectivity for monochlorination (90.5%), supporting this hypothesis (entry 6). Increasing the reaction temperature to 55 °C further improved the reaction selectivity to 96.9% monochlorination, potentially owing to greater solubility of the base.</p><p> </p><p>After arriving at our optimized conditions for the C–H chlorination, we next determined the deuterium kinetic isotope effect by the competition reaction between cyclohexane and d12-cyclohexane using reagent 1. The observed primary kinetic isotope effect was kH/kD = 4.9 under these conditions, which is consistent with irreversible hydrogen atom abstraction. For the sake of comparison, the bromination of cyclohexane under these conditions with the N-bromo derivative of 1 also resulted in a kH/kD = 4.9, consistent with an amidyl radical in both C–H abstractions.</p><p>Our studies continued with an investigation of the sterically dictated site selectivities of our C–H chlorination using methylcyclohexane as substrate (Table 2). Prior to conducting reactions with N-chloroamide 1, we surveyed the secondary (desired) versus tertiary (undesired) selectivity using known chlorination methods. Classical methods involving either N-chlorosuccinimide or sulfuryl chloride provided modest selectivities (ksecondary/ktertiary, ks/kt, = 0.31 and 0.28, respectively) after correcting for the number of tertiary (one) and secondary (ten) sites available (entries 1 and 2).11 Chlorination catalyzed by Mn(TPP)Cl provided similar selectivity (ks/kt = 0.38, entry 3).7 As observed in the reactions of cyclohexane in Table 1, chlorination using N-chloroamide 1 in the absence of base provides suboptimal selectivities, likely owing to background reactions involving Cl2 and chlorine free radical (entries 4 and 5). The addition of 10 mol % amylene, a known Cl2 scavenger, 12 greatly favors methylene functionalization (97.7%, ks/kt = 4.2), albeit at low conversion (entry 6). We found that added base could serve a similar role without decreasing conversion, with higher yield at 55 °C (entries 7 and 8). The high level of secondary selectivity in this functionalization of a simple cyclic hydrocarbon is higher than any known system for aliphatic C–H chlorination.</p><p>We extended our steric selectivity studies to additional hydrocarbon substrates such as norbornane, which under our standard chlorination conditions delivered a 54% yield of 2-exo-chloronorbornane as a single product (entry 9). As comparison, the C–H chlorination of norbornane with common reagents (e.g., Cl2 or SO2Cl2) leads to mixtures of the exo and endo isomers.11 Both trans- and cis-1,2-dimethyl cyclohexanes– benchmark substrates for sterically-selective aliphatic C– H functionalizations13–exhibited excellent methylene selectivity (entries 10 and 11). Adamantane C–H chlorination involving chlorine free radical is documented to be a poorly selective process, with kt/ks = 3.5.14 Reaction of adamantane under our standard reaction furnished the two regioisomers in a 19:1 ratio (kt/ks = 57), favoring functionalization of the less hindered tertiary site, and highlighting the unique selectivity profile of the current system.</p><p>Next we surveyed the potential to achieve an electronically site-selective C–H chlorination using an array of functionalized linear hydrocarbon substrates (Table 3). Using methyl hexanoate as a test substrate, reactions involving either sulfuryl chloride (entry 1) or Mn(TPP)Cl/NaOCl (entry 2) proceeded with relatively poor selectivity between the most electronrich γ and δ positions. As observed with methylcyclohexane in Table 2, reactions with N-chloroamide 1 under radical initiation (entry 3) also resulted in a poorly selective reaction. Under our optimized conditions in the presence of base, we significantly increase the selectivity for the most electron rich (δ) site in the molecule. Chlorination at the δ site accounts for 57.6% of all chlorination products (entry 4, 83% combined yield).</p><p>Other synthetically versatile, electron-withdrawing functionality effective at differentiating the methylene sites included protected amines, nitriles, alkyl chlorides, acetates, and sulfonate groups (entries 5–9). The δ selectivity in these studies ranged from 56% to 81%, with the phthalimide group providing the highest level of site selectivity. The general trend in these studies is greater δ selectivity with increased electron withdrawal of the substituent present. The chlorination of n-hexane indicates the possibility of a steric component to the C–H chlorination, with 65.5% 2-chlorohexane produced.</p><p>We further explored the site selectivity of the C–H chlorination with functionalized acyclic substrates containing more reactive C–H bonds at tertiary, benzylic, and allylic sites. The results in Table 4 clearly indicate that electronic (and possibly steric) factors are capable of deactivating these typically more reactive C–H bonds in favor of more electron-rich methylene sites. This electronically-dictated selectivity is substantial with multiple functional groups, and with methyl substitution at both the α and β positions of the chain (entries 1–4). The chlorination of phthalimide-protected norleucine methyl ester displays a major preference for the δ site (77.5% selectivity) owing to the strong polar deactivation of the sites adjacent to the amino acid functionality.</p><p>An area of significant interest was the possibility of achieving site-selective aliphatic C–H chlorination in the presence of substrate unsaturation. This chemoselectivity issue remains a roadblock in applying alkane functionalization to many complex substrates, particularly those containing alkenes. This is unsurprising given the propensity for electrophilic heterocycles or metal-oxo complexes–both widely used for alkane functionalization–to react with alkenes. Our preliminary studies in this area are promising (entries 5–7), demonstrating successful C–H chlorinations of substrates with both arene and alkene substitution. Of particularly note is the adamantane functionalization in the presence of a simple allyl group (entry 7). We anticipate that this unique aspect of aliphatic C–H functionalization with tuned amidyl radicals will facilitate applications across a broad range of complex substrates.</p><p>The ease of preparation of N-chloroamides, in addition to the useful levels of site selectivity in the reactions, offers attractive opportunities in the C–H chlorination of complex molecules (eqs 1 and 2). Functionalized adamantanes form the structural core of diverse small molecule drugs, yet there are few mild, site-selective protocols available for the C–H functionalization of these compounds. The chlorination of the N-phthalimide derivative of antiviral drug rimantadine (30) using N-chloroamide 1 provided chlorinated derivative 31 in good isolated yield (66%), with complete site-selectivity for the less-hindered tertiary C–H site (eq 1).</p><p>5α-Cholestane is a challenging substrate for site-selective C–H functionalization owing to the presence of 48 unactivated C–H bonds with little electronic differentiation considering the absence of heteroatomic functionality. The functionalization of 32 using 1 equiv of reagent 1 favors C3-chlorination (C3:C2 = 2:1) and provides an 81% yield of chlorinated products (eq 2). By comparison, the functionalization facilitated by the bulky, designed catalyst Mn(TMP)Cl (TMP = tetramesitylporphyrin) provides a C3:C2 of 1.5:1 in 55% yield using 3 equiv of NaOCl.7 We anticipate that the practicality, scalability, and site selectivity of this C–H halogenation are well suited for applications in target-oriented synthesis, as demonstrated by the concise synthesis of the antineoplastic agent chlorolissoclimide described herein.</p><!><p>In the early 1990s, the groups of Malochet-Grivois and Roussakis described the structures and cytotoxic activities of the succinimide-containing labdane diterpenoids chlorolissoclimide and dichlorolissoclimide (34 and 35, respectively, Figure 1).15,16 Initially, 35 was shown to have potent activity against both the P388 murine leukemia cell line (IC50 = 2.4 nM) and the KB human oral carcinoma cell line (33 nM).15a Later, both 34 and 35 were shown to interfere with the cell cycle at the G1 phase of non-small-cell bronchopulmonary carcinoma cells (NSCLC-N6), causing antiproliferation.15b</p><p>Since 2001, the groups of Ueda/Uemura and Schmitz have reported about 20 closely related labdane diterpenoids that they have called the haterumaimides (see 36, for example).17 Many of these compounds show equally impressive levels of cytotoxicity. In spite of the obvious potential interest in these compounds from the biological perspective, as well as some particularly interesting biogenetic peculiarities—both the C2-chloride and the succinimide are very unusual—only three groups have reported work toward these compounds. Jung and co-workers studied methods to introduce the two chlorides onto simplified decalin scaffolds.18 The González/Betancur-Galvis and Chai groups looked at methods to install the succinimide group onto aldehyde 38 (Scheme 1) derived from readily available (+)-sclareolide (37); the former study used an unselective aldol addition of a succinimide enolate,19 and the latter used Evans aldol chemistry to introduce the heterocycle via a 4-step sequence, but could not avoid isomerization of the C8–C17 exocyclic alkene into the endocyclic positions, nor could these isomers be fully separated from one another.20 In short, there have been no completed syntheses in this family of structurally and biologically intriguing natural products.</p><p>As part of a broader study of this family of diterpenoids, we questioned whether C–H functionalization methods might permit the conversion of sclareolide to chlorolissoclimide (Figure 3). In reverse order, key steps would include the stereocontrolled introduction of the β-hydroxysuccinimide—which had proved challenging in earlier studies19,20—stereoselective C7-oxygenation, and regio- and stereoselective C2-chlorination.</p><p>Previous reports strongly suggested that the C2 position of sclareolide is the most activated for C–H functionalization under radical conditions.21 Prompted by the efficient C2-bromination of sclareolide from the Alexanian group,8 a collaboration was borne to gain efficient access to 2-chlorosclareolide for the purposes of a concise synthesis of chlorolissoclimide. Using reagent 1, we converted 37 into 2-chlorosclareolide (39)7a,22 with remarkable efficiency, even on gram scale. The selectivity of this reagent is outstanding: only product 39 and traces of residual sclareolide can be observed in the 1H NMR spectra of the crude reaction product. Weinreb aminolysis of the lactone and dehydration of the tertiary carbinol—following a process previously performed on sclareolide23—afforded 40 in good yield. C7-oxygenation was performed by selenium dioxide-mediated allylic oxidation19,24 to afford the axial C7 allylic alcohol, which was subjected to Swern oxidation to give enone 41. Concurrent reduction of the Weinreb amide and the enone was efficient and stereoselective for the introduction of the equatorial C7-hydroxyl group. Silylation of this alcohol afforded 42, whose aldehyde was subjected to our optimized sequence for introduction of the β-hydroxysuccinimide.</p><p>Evans aldol addition of known imide 4320,25 to aldehyde 42 was initially low-yielding and inconsistent, which we attributed to non-productive coordination of the boron Lewis acid to the pendant ester of 43. This issue was resolved by pretreatment of 43 with dibutylboron triflate for 30 min at −78 °C prior to addition of Hünig's base, resulting in a reliable aldol addition. The labile TMS ether, which is important for reaction efficiency, is cleaved in this step. Direct ammonolysis of the crude imide (44) in methanol prevented undesired lactone formation as previously observed by Chai and co-workers;20 immediate imide formation via the presumed N-sodiated amide directly affords the β-hydroxysuccinimide without alkene migration and completes the first synthesis of (+)-chlorolissoclimide. This sequence is general and reliable, and this technical advance will prove important in the synthesis of the whole family of lissoclimides/haterumaimides. Notably, chlorolissoclimide is obtained in up to 14% overall yield via the nine-step sequence described in Scheme 1.</p><p>Variants of the same sequence have led to the synthesis of haterumaimide Q (36) and the 7-deoxy analogues of both 34 and 36 (45 and 46, respectively, Figure 4).26 We have evaluated all four compounds for their toxicity to aggressive prostate and melanoma cancer cell lines (DU145 and A2058, respectively). While we have found these compounds to be active at about the micromolar level, they are clearly much less potent toward these more relevant cell lines compared with the P388 murine leukemia cell line, against which all haterumaimides and lissoclimides have previously been tested.15,17 Clearly a larger panel of cell lines should be evaluated, given the previously reported potency of chlorolissoclimide against non-small-cell lung cancer (IC50 = 26 nM, see Figure 1). With respect to the two cell lines evaluated in this study, we recognize that this series of compounds affects both the prostate and melanoma cell lines about equally, and that the activities vary less than an order of magnitude depending upon the presence or absence of a C2-chloride or a C7-hydroxyl group.</p><!><p>In conclusion, we report a practical, site-selective approach to aliphatic C–H chlorination using N-chloroamides and visible light. While the chlorination of alkanes is commonly a poorly selective process owing to the promiscuity of chlorine free radical, these amidyl radical-mediated reactions provide sterically- and electronic-dictated site selectivities that enable chlorination of complex molecules with diverse C–H bonds. These studies also indicate the potential for chemoselective aliphatic C–H functionalization in the presence of alkenes and arenes. The trivial preparation of N-chloroamides, and the use of substrate as the limiting reagent in all cases, bodes well for applications in complex synthesis. In that vein, we also report the first synthesis of natural products in the lissoclimide/haterumaimide family of potent cytotoxins using this chlorination method as the first step. Our semi-synthesis of chlorolissoclimide starting with the gram-scale selective chlorination of (+)-sclareolide is short and efficient, and includes the first example of a stereocontrolled radical halogenation for the incorporation of a halogen-bearing stereogenic center of a natural product.27 The transformation itself is likely relevant to the biosynthesis of the 2-chlorinated lissoclimides and haterumaimides. That this chlorination reaction can support the synthesis of a complex natural product clearly demonstrates its practicality.</p><p>Additionally, in the context of the chlorolissoclimide synthesis, we have developed a straightforward and general solution to the β-hydroxysuccinimide motif that is common to all active members of this natural product family. Finally, we have learned that chlorolissoclimide (34) and analogues haterumaimide Q (36), 7-deoxychlorolissoclimide (45), and 7-deoxyhaterumaimide Q (46) are cytotoxic to aggressive melanoma and prostate cancer cell lines with IC50 values of about 1 μM.</p><p>Efforts to further improve the site selectivity of the C–H chlorination and applications to other complex substrates are underway. We are also in the process of expanding our work in the synthesis of haterumaimide natural products to better understand their structure-activity relationship. Each of these studies will be reported in due course.</p>

PubMed Author Manuscript

Signaling, Delivery and Age as Emerging Issues in the Benefit/Risk Ratio Outcome of tPA For Treatment of CNS Ischemic Disorders

Stroke is a leading cause of morbidity and mortality. While tissue-type plasminogen activator (tPA) remains the only FDA approved treatment for ischemic stroke, clinical use of tPA has been constrained to roughly 3% of eligible patients because of the danger of intracranial hemorrhage and a narrow 3h time window for safe administration. Basic science studies indicate that tPA enhances excitotoxic neuronal cell death. In this review, the beneficial and deleterious effects of tPA in ischemic brain are discussed along with emphasis on development of new approaches towards treatment of patients with acute ischemic stroke. In particular, roles of tPA induced signaling and a novel delivery system for tPA administration based on tPA coupling to carrier red blood cells will be considered as therapeutic modalities for increasing tPA benefit/risk ratio. The concept of the neurovascular unit will be discussed in the context of dynamic relationships between tPA-induced changes in cerebral hemodynamics and histopathologic outcome of CNS ischemia. Additionally, the role of age will be considered since thrombolytic therapy is being increasingly used in the pediatric population, but there are few basic science studies of CNS injury in pediatric animals.

signaling,_delivery_and_age_as_emerging_issues_in_the_benefit/risk_ratio_outcome_of_tpa_for_treatmen

Background<!>tPA in CNS Physiology<!>tPA in CNS Pathology of BBB Function<!>Neurotoxicity of tPA<!>tPA mediated signaling, cerebral hemodynamics, and outcome as an emerging area in CNS ischemia<!>Contemporary strategies used to increase benefit/risk ratio of thrombolytic therapy<!>Modulation of tPA signaling by novel PAI-1 inhibitors as an emerging therapeutic strategy for treatment of CNS ischemic disorders<!>Pediatric stroke as an emerging area of tPA therapy and clinical management<!>Coupling tPA to carrier RBC: emerging delivery strategy for cerebral thromboprophylaxis<!>Summary and perspectives for improving tPA benefit/risk ratio in treatment of CNS ischemic disorders<!>

<p>The Word Health Organization defines stroke as "rapidly developing signs of focal or global disturbance of cerebral function, with symptoms lasting 24h or longer leading to death, with no apparent cause other than that of vascular origin". Stroke is the third leading cause of morbidity and mortality after heart disease and cancer. Surviving stroke victims often face serious long-term disability and constitute a significant familial and societal economic burden. Stroke can be classified broadly into ischemic or hemorrhagic, with the former comprising 80% of cases. Ischemic stroke, in turn, can be further subclassified as thrombotic or embolic in origin. Thrombotic stroke occurs when a clot forms in an artery located within the brain whereas an embolic stroke results from a clot formed elsewhere in the body that is subsequently transported to the brain. Hemorrhagic stroke results from the disruption of vascular integrity or aneurysm sufficient to cause bleeding within the brain.</p><p>Generally speaking, 3 major approaches have been used in the treatment of acute stroke: neuroprotection, thrombolysis, and clot removal. To date, neuroprotection has been largely unsuccessful with the inability to translate potentially novel therapeutics found to be efficacious in animal models to the clinical arena (Lapchak and Araujo 2007; Fisher and Bastan 2008). In contrast, thrombolysis with tissue plasminogen activator (tPA) and mechanical removal of clot have both been approved by the FDA for the treatment of acute ischemic stroke. Based on studies (NINDS Study Group, 1995) that treatment within 3h of stroke symptom onset resulted in an increased number of patients with good outcome and a reduction in the proportion of patients with disability or death at 3 months, intravenous tPA remains the current standard of care for treatment of acute ischemic stroke within 3h of the ischemic event. Recent studies, however, have indicated that the time window for administration maybe safely extended from 3 to 6h post onset of the ischemic event (Hacke et al 1995; Lansberg et al 2009).</p><p>However, in addition to its salutary role in reperfusion, there is a growing body of scientific investigation indicating that tPA also exhibits deleterious action within the brain. Indeed, the original findings of the NINDS noted that the use of tPA was associated with an approximate 10% increase of intracerebral hemorrhage (ICH) (NINDS Study Group, 1995). As a result, clinical use of tPA in actual practice has been constrained to only roughly 3% of those eligible for such therapy (Lapchak and Araujo 2001; Lapchak 2002). Importantly, there is also a significant propensity for cerebrovascular thrombosis to recur. As a result, hospitalization is often recommended even in stable patients to preserve the option of timely re-administration of intravenous tPA, if necessary to treat reocclusion, an approach fraught with the potential for ICH (Fisher and Bastan 2008).</p><p>Studies in animal models demonstrate that endogenous tPA may increase the volume of injured tissue after stroke (eg in tPA null mice), provoke ICH, and exacerbate excitotoxic neuronal cell death by enhancing signaling through the NMDA glutamate receptor (Wang et al 1998, Nicole et al 2001). Although various modifications have been made to recombinant tPA, with the intent of improving its pharmacokinetic and pharmacodynamic properties, none have mitigated in the risk of ICH.</p><p>In this review, the beneficial and deleterious effects of tPA in the ischemic brain are discussed along with an emphasis on development of new approaches towards treatment of patients with acute ischemic stroke. In particular, the roles of tPA induced signaling and novel delivery systems for tPA administration based on coupling the drug to carrier red blood cells (RBC) will be considered as new approaches to increase its benefit/risk ratio. Additionally, the role of age will be considered as thrombolytic therapy is being increasingly used in the pediatric population, but there have been few basic science studies that investigate potential differences in the effect of CNS injury and its treatment as a function of age.</p><!><p>tPA and urokinase plasminogen activator (uPA) are serine proteases that are normally present in both the intravascular space as well as the brain parenchyma. They cleave the zymogene plasminogen to produce the active serine protease plasmin (Yepes et al 2008). In the intravascular compartment, blood clots are formed from the aggregation of activated platelets and formation of fibrin meshwork from fibrinogen. Fibrinolysis is achieved by plasmin generated primarily through the action of tPA. tPA is expressed in endothelial cells, neurons, and glia (Yepes et al 2009). The principal source of tPA in the intravascular space is the endothelial cell, from which it is released in the presence of fibrin to help preserve patency of the vasculature (Figure 1). Termination of tPA catalytic activity in plasma is produced by binding of serine protease (serpin) inhibitors, chiefly the PA inhibitor I (PAI-1) (Van Mourik et al 1992). Inactive tPA/PAI-1 complexes are cleared from the circulation by low density receptor-related protein (LRP) (Orth et al 1992). A neuronal specific inhibitor of tPA, neuroserpin, is the primary modulator of tPA activity in the CNS (Osterwalder et al 1996).</p><p>The neurovascular unit (NVU) is a dynamic structure consisting of endothelial cells, perivascular neurons and astrocytes, and the basal lamina (Abbott et al 2006; del Zoppo and Mabuchi 2003; Lo et al 2003). An important function of the NVU is to form a barrier known as the blood brain barrier (BBB), which regulates the bidirectional passage of substances between the brain and the intravascular space (Figure 1). The BBB function is determined mainly by the presence of tight junctions between endothelial cells and by the interaction between perivascular astrocytes and the basement membrane. Astrocytic LRP has a central role maintaining the integrity of the interaction between perivascular astrocytes and the basal lamina (Polavarapu et al 2007) (Figure 1). Interaction of tPA with LRP plays a role in regulating cerebrovascular tone (Nassar et al 2004; Akkawi et al 2006) and permeability of the NVU under nonischemic conditions (Yepes et al 2003). In the absence of ischemia, production and trans NVU transport of tPA is limited and readily constrained by PAI-1 and neuroserpin (Figure 1). After an ischemic event, however, tPA transport across the BBB can overwhelm the "buffering capacity" of serpins, resulting in pathologic action of tPA within the CNS and disruption of barrier function. Early after the onset of an ischemic insult, there is increased tPA activity around the NVU associated with the development of cerebral edema (Yepes et al 2003) (Figure 1). Cerebral ischemia also upregulates LRP experession in the abluminal side of the NVU and the deleterious action of tPA on the NVU is mediated by interaction with LRP (Yepes et al 2003; Polavarapu et al al 2007) (Figure 1). Furthermore, pro-inflammatory factors and cytokines may sensitize the CNS parenchyma to injurious effects of tPA and plasmin. For example, the interaction between tPA and LRP leads to an increase in MMP-9 expression and activity associated with the development of ischemic edema (see below) (Figure 1).</p><!><p>Degradation of the BBB occurs early after onset of ischemia, enabling transport of substances and passage of fluid which contributes to edema and hemorrhagic transformation (Figure 1). In humans administered tPA for treatment of acute ischemic stroke, an increased BBB permeability was observed (Kidwell et al 2008), indicating that the therapeutic application of this thrombolytic may, itself, have unintended deleterious actions. tPA may contribute to enhanced BBB permeability in the setting of stroke through interactions with multiple signaling pathways. For example, in a rat model of embolic stroke tPA has been observed to mediate ischemia induced increase in BBB permeability through enhanced expression and activity of matrix metalloproteinase-9 (MMP-9) (Aoki et al 2002; Lee et al 2007) (Figure 1). tPA mediated increased BBB permeability may occur both in an LRP or platelet derived growth factor but non LRP dependent manner (Yepes et al 2003; Su et al 2008). More recently, a model has been proposed wherein tPA interacts with NF-κB, MMP-9, and iNOS to increase BBB permeability during ischemia (Yepes et al 2009). In this model, tPA is envisioned as inducing phosphorylation of p65, an indicator of NF-κB activation, in an LRP dependent manner, which, in turn, increases expression of iNOS and MMP-9, both of which are known to increase BBB permeability (Yepes et al 2009). Release of uPA has also been observed to occur in an LRP dependent manner after global cerebral hypoxia/ischemia in the piglet, which subsequently induces release of the ERK isoform of mitogen activated protein kinase (MAPK) to contribute to histopathologic changes such as neuronal cell necrosis, hemorrhage, and edema (Armstead et al 2008).</p><!><p>Most studies indicate that tPA is neurotoxic (Figure 1), though some results have been variable. For example, ischemia induces an increase in tPA activity associated with cell death (Wang et al 1998) and the development of cerebral edema (Yepes et al 2003). Some of these deleterious effects of tPA equally have been observed in acute stroke patients (Kidwell et al 2008). Treatment with PAI-1 (Tsirka et al 1995), or neuroserpin (Yepes et al 2000) are neuroprotective in animal models of stroke, additionally supportive of the neurotoxicity of tPA. Some studies, however, have alternatively observed beneficial outcome with tPA (Kilic et al 1999; Meng et al 1999; Tabrizi et al 1999). Some of these discrepancies may relate to timing (tPA beneficial early, but detrimental when administered later after stroke onset) (Jiang et al 2000) or to infarct size, where smaller infarct predicts better outcome (Nagai et al 2002).</p><p>A link between tPA and glutamatergic neurotransmission has been made in that injection of kainic acid into the hippocampus is associated with cell death in wild type but not tPA null mice (Tsirka et al 1995). Other work has indicated that tPA cleaves the NR-1 subunit of NMDA to increase the influx of calcium (Nicole et al 2001; Fernandez-Monreal et al 2004), though subsequent studies suggest an interaction with the NR2B subunit of NMDA instead (Pawlak et al 2005). Regardless of the mechanism of action, it is widely accepted that tPA interacts with glutamate receptors, which are important mediators of excitotoxicity in ischemic stroke. For example, tPA is thought to control NMDA-dependent NO synthesis in an LRP dependent process and that this effect is critical for excitotoxic neuronal cell loss (Backsai et al 2000; Parathath et al 2006).</p><!><p>Activation of NMDA receptors elicits cerebrovasodilation, which may represent one mechanism for the coupling of local metabolism to blood flow (Faraci and Heistad 1998). More recently, it was observed that tPA is critical for the full expression of the flow increase evoked by activation of the mouse whisker barrel cortex (Park et al 2008). In particular, tPA was found to promote NO synthesis during NMDA receptor activation by modulating the phosphorylation state of nNOS (Park et al 2008). These findings suggest that tPA is a key factor in linking NMDA receptor activation to NO synthesis and functional hyperemia. Translationally, functional hyperemia is attenuated in Alzheimer's Disease patients and in mouse models of the disease (Iadecola 2004; Niwa et al 2000), along with decreased tPA activity (Melchor et al 2003). Therefore, impaired hyperemia due to diminished activity of tPA-NMDA receptor axis may be crucial to cognitive outcome in the setting of Alzheimer's Disease and neurovascular dysregulation following cerebral ischemia (Park et al 2008), where tPA expression has been observed to be decreased transiently by some investigators (Hosomi et al 2001). Nonetheless, in a non-stroke model of CNS injury, fluid percussion brain injury (FPI), endogenous upregulation of tPA contributes to impaired NMDA receptor-mediated cerebrovasodilation post insult (Armstead et al 2005a). Preliminary studies indicate that this results primarily from an upregulation of the JNK isoform of mitogen activated protein kinase (MAPK), with a more minor role for ERK MAPK upregulation (Armstead et al 2009c). MAPK, a family of at least three kinases, ERK, p38, and JNK, is one of the most distal signaling systems contributory to vascular tone (Laher and Zhang, 2001) and is thought to be critically important in cerebral hemodynamics after pediatric traumatic brain injury (TBI) (Armstead et al 2009a). Thus, tPA may either contribute to functional hyperemia in the uninjured state or oppose it in the setting of CNS pathology. In the latter case, NMDA induced pial artery dilation is reversed to vasoconstriction after FPI in the piglet (Armstead 2000). Since autoregulatory pial artery dilation during hypotension is similarly reversed to vasoconstriction after FPI (Armstead 1999) while the NMDA antagonist MK 801 prevented such impairment, these translationally relevant data indicate that this excitatory amino acid contributes to autoregulatory cerebrovascular dilation during hypotension (Armstead 2002). Taken together, then, tPA-mediated impairment of NMDA cerebrovasodilation via JNK MAPK upregulation results in disturbed autoregulation in the setting of TBI (Armstead et al 2005a; 2009c).</p><p>At the level of the NVU, tPA can act directly on the vasculature (Nassar et al 2004) to induce hemodynamic alterations that ultimately limit perfusion of the ischemic area despite restoration of vessel patency (Kilic et al 2001). The effect of tPA on vascular tone may result from direct interaction with vascular smooth muscle cells to which it signals via the integrin αvβ3 to elicit vasoconstriction in a non proteolytic manner (Akkawi et al 2006). tPA and uPA signaling mediation by integrin αvβ3 may also influence cerebrovascular tone through inhibition of important vasodilator stimuli such as autoregulation during hypotension and hypercapnia following global cerebral hypoxia/ischemia (Armstead et al 2005b; Kiessling et al 2009a). Signaling is terminated through binding of PAI-1 by tPA, internalization of the integrin-tPA-PAI-1 ternary complex via LRP mediated endocytosis and dissociation of tPA-PAI-1 from the integrin (Akkawi et al 2006).</p><p>Plasminogen activators may actually interact with diverse signaling systems to modulate cerebral hemodynamics in the setting of CNS injury. For example, cerebral hypoxia/ischemia causes upregulation of tPA and uPA, which impair cerebrovasodilation in response to hypercapnia and hypotension in an LRP and ERK MAPK dependent manner in piglets (Armstead et al 2008). In the same studies where considerable uPA and ERK MAPK expression was detected in neurons, there was also marked histopathology after cerebral hypoxia/ischemia (Armstead et al 2008). These observations support the concept that plasminogen activators act at the level of the neurovascular unit to impair reactivity to vasoactive stimuli and that overall outcome relates to complex interactions between the vascular compartment and the neuron. However, vasodilator responses to isoproterenol were unchanged after hypoxia/ischemia, indicating that plasminogen activator impairment of cerebrovascular reactivity was not an epiphenomenon. Preliminary data from a more translationally relevant animal model of CNS injury, photothrombosis, indicate that both endogenous and exogenously administered tPA impairs cerebrovasodilation to hypercapnia and hypotension via JNK, but not ERK, MAPK upregulation in the piglet (Kiessling et al 2009b). Alternatively, plasminogen produced neuronal injury in rat slices in culture and in a model of intracranial hemorrhage via ERK, but not p38 MAPK, while JNK MAPK might have been protective (Fujimoto et al 2008). Others have suggested that MMP upregulation observed to occur after focal cerebral ischemia (Tsuji et al 2005) may contribute to the dysfunction of the neurovascular unit (Lo et al 2004). These complex interactions between plasminogen activators and diverse signaling systems in focal, global, and hemorrhagic animal models of CNS injury suggest clinical presentations likely result from a highly heterogenous series of interrelated spatially and temporally regulated mechanistic pathways. Recognition that CNS injury may not be generic and homogenous suggests that design of therapeutics for treatment of CNS ischemic disorders should consider signal transduction mechanisms and etiology to effect improved clinical outcome.</p><!><p>Several avenues have been considered to increase the benefit/risk ratio of clinical critical care pathways currently used for thrombolytic therapy of acute ischemic stroke. One is to extend the time window for thrombolysis through development of newer more durable thrombolytics, such as alteplase and desmoteplase (Paciaroni et al 2009). However, these newer formulations do not effectively alter the side effect profiles that depend on proteolysis but simply extend the period of risk (Fisher and Bastan 2008; Meretoja and Tatlisumak 2008). Alternatively, consideration has been given towards the combination of intra-arterial with the traditional intra-venous administration of tPA, which may decrease toxicity, although initial results have been unremarkable (Fisher and Bastan 2008). Similar no improvement of benefit/risk has been provided by novel neuroprotectants, such as the free-radical scavenger NXY-059 (Shuaib et al 2007; Fisher and Bastan 2008). Use of mechanical clot removers are associated with the potential for vascular puncture and intracranial hemorrhage, and have no direct effect on secondary embolization to smaller vessels (Fisher and Bastan 2008).</p><p>Combination therapy has been studied in models of focal and global cerebral ischemia to reduce the neurovascular complications of tPA. For example, co-administration of the MMP-9 inhibitor minocycline with tPA decreased incidence of hemorrhage, improved neurologic outcome and decreased mortality in a rat suture occlusion model of focal cerebral ischemia (Machado et al 2009). Similarly, in a rat middle cerebral artery occlusion model of cerebral ischemia, tPA combined with normobaric hyperoxia reduced tPA-associated mortality, brain edema, hemorrhage, and MMP-9 augmentation compared with tPA alone (Liu et al 2009). While the latter studies commonly focus on mitigating MMP action to limit tPA neurotoxicity and hemorrhage, another approach might be to diminish tPA-associated disruption of the BBB and brain edema. For example, endothelin (ET)-1 binds to the ETA receptor to regulate BBB permeability, is upregulated after stroke, and blockade of ET-1 with an ETA receptor antagonist ameliorates stroke induced brain edema (Matsuo et al 2001). Co-administration of the ETA receptor antagonist S-0139 with tPA reduced infarct volume and improved neurological outcome in a rat embolic MCA occlusion model due to a synergistic effect on improvement of functional outcome as well as a reduction in tPA-associated thrombosis, hemorrhage, and BBB disruption (Zhang et al 2009).</p><!><p>An alternative approach, we have administered novel drugs such as a synthetic hexapeptide derivative of the naturally occurring tPA inhibitor, PAI-1, EEIIMD, which inhibits the vascular activity of tPA and uPA without inhibiting its fibrinolytic activity (Nassar et al 2002; Nassar et al 2004; Armstead et al 2005a). In a model of global cerebral hypoxia/ischemia in newborn pigs, co-administration of EEIIMD with tPA prevented the impairment of hypercapnic and hypotensive pial artery vasodilation (Armstead et al 2005b). In adult rat models of embolic stroke and middle cerebral artery occlusion, EEIIMD decreased infarct volume and hemorrhage while limiting reductions in cerebral blood flow, impaired cerebrovasodilation to activation of the NMDA receptor, and histopathologic changes such as brain edema, neuronal cell necrosis, and hemorrhage after fluid percussion brain injury (Armstead et al 2005a, 2006, 2009a) (Table 1). The beneficial effects of EEIIMD were not due to enhanced tPA clearance or inhibition of tPA's thrombolytic activity but appeared to be due to its mimicking the action of PAI-1 inhibition of tPA signaling through the LRP receptor (Akkawi et al 2006; Armstead et al 2006). For example, EEIIMD diminishes upregulation of ERK MAPK, contributory to improved cerebral hemodynamics after piglet fluid percussion brain injury (Armstead et al 2009a) (Table 1). Recent initial studies indicate that an 18 amino acid derivative of PAI-1, Ac-RMAPEEIIMDRPFLYVVR-amide, prevents impairment of hypercapnic and hypotensive pial artery dilation by inhibiting JNK MAPK upregulation, when administered either prophylactically (30 min prior to) or therapeutically (2h after) CNS photothrombotic injury in the piglet (Armstead et al 2009d) (Table 1). A key advantage of EEIIMD is the substantial reduction in the risk of hemorrhage in models of CNS ischemia (Armstead et al 2006, 2009a), a significant impediment to the wider use of tPA for treatment of acute ischemic stroke. It has been speculated that if these results can be extended to humans, they could usher in a new era of thrombolytic therapy for stroke (Dawson and Dawson 2006).</p><!><p>Ischemic stroke is also an important, yet understudied, contributor to morbidity and mortality in the pediatric population. The cost of pediatric stroke care is expensive given the lifetime expectancy need for clinical care in this patient population (Perkins et al 2009). Pediatric stroke may occur in as many as 1 in 4000 births (Nelson and Lynch, 2004) and complications due to hypoxia/ischemia are common (Ferriero 2004). Maternal and perinatal coagulopathy predispose to pediatric stroke (Gunther et al 2000; Kraus and Acheen 1999) with 30% of such events being due to thrombosis (DeVeber and Andrew 2001). The use of tPA (Kim et al 1999) in children has been limited and its benefit remains unclear (Benedict et al 2007; Janjua et al 2007). The use of tPA (Cremer et al 2008) in children is based on the assumption that studies in adults are generalizable, but the safety and efficacy of tPA in this setting have yet to be systematically investigated.</p><p>A number of considerations should be contemplated when extrapolating the experience with tPA in adults to children, such as dosing, benefit/risk ratio, pharmacokinetics and therapeutic window (Amlie-Lefond and Fullerton 2009). Indeed, the 2001 workshop report of the National Institute of Neurological Disorders and Stroke noted a deficiency in research in pediatric stroke related to the paucity of animal models and basic research investigation into ischemic disorders of the CNS in the pediatric population (Lynch et al 2002). Many studies of cerebral ischemia have been performed in rodent models. Piglets offer an important advantage in elucidating pathways involved in CNS ischemic injury by virtue of having a gyrencepahalic brain that contains substantial white matter similar to humans, which is more sensitive to ischemic damage than grey matter (Shaver et al 1996). Importantly, the use of a piglet model allows for study of additional physiologic variables and is therefore likely of greater clinical relevance than many rodent models. On the basis of interspecies extrapolation of brain growth curves (Dobbing 1974), the age of the newborn pig used in our studies of the effects of tPA in global cerebral hypoxia/ischemia and fluid percussion brain injury (Armstead et al 2006, 2008) roughly approximated the newborn-infant time period in the human. The identification of a molecular signaling target (ERK MAPK) (Armstead et al 2008) for modifying neuropathologic injury is potentially clinically relevant as there are, to date, no clinically proven neuroprotective interventions other than hypothermia for neonatal hypoxic/ischemic brain injury. Equally important might be the finding that tPA alone can exacerbate neuronal injury in the setting of hypoxia/ischemia (Armstead et al 2005b, 2008). This observation was made in adult models of focal arterial-occlusive stroke years ago, but its significance was diminished, indeed outweighed, by the overwhelming clinical trial evidence of benefit due to fibrinolysis. Yet, the use of tPA for adult acute stroke is highly constrained within a narrow therapeutic window so as to minimize the risk of hemorrhage. Our preliminary extension of investigation of tPA's benefit/risk ratio in pediatric CNS injury to the more translationally relevant model of piglet photothrombosis (Armstead et al 2009d) is based on the seminal paper establishing this model in the piglet (Kuluz et al 2007). Our studies showing differential roles of ERK and JNK MAPK mechanisms in tPA-associated impairment of cerebral hemodyanamics and histopathology in the setting of pediatric global cerebral hypoxia/ischemia compared to pediatric focal thrombotic injury support our belief that signaling is an emerging issue for optimizing tPA benefit in treatment of CNS ischemic disorders. Equally, these studies emphasize the importance of age, since the effects of tPA and their mediation via signaling systems may vary in pediatric and adult animal models of stroke. Conductance of basic science studies in animal models as a function of age will inform the clinical practice of stroke therapy in the pediatric population.</p><!><p>To be of use in thromboprophylaxis, a fibrinolytic agent should selectively lyse potentially occlusive clots during their formation without affecting hemostatic clots or exerting extravascular toxicity. However, all existing fibrinolytics are short lived (<30 min) and small (<10 nm diameter) agents, capable of diffusion into hemostatic clots. None can be used safely at therapeutic doses in patients with CNS ischemia. Contemporaneous studies from our group have shown that anchoring tPA on red blood cells (RBC) endows the resultant complex, RBC-tPA, with dramatically prolonged circulation time (many hours vs minutes for tPA), while spatially constraining it to the intravascular space with no harmful effects of the carrier RBC (Murciano et al 2003; Ganguly et al 2005; Ganguly et al 2006; Zaitsev et al 2006). RBC-tPA effectively lyses nascent thrombi that otherwise may cause sustained vascular occlusion, but its large size precludes it from entering and lysing preexisting clots and prevents it from extravasation, thereby limiting CNS toxicity. In rodent models of cerebrovascular thrombosis and traumatic brain injury, treatment with this RBC-tPA complex provided effective thromboprophylaxis, rapid reperfusion, neuroprotection, and reduction in mortality all without causing ICH (Danielyan et al 2008; Stein et al, 2009). It is presently uncertain, however, if the value of RBC-tPA for microclots encountered in the latter rodent models can be recapitulated in lysing thrombi of the size encountered in human stroke. Nonetheless, in unrelated studies using a piglet model of cerebral hypoxia/ischemia, RBC-tPA administered either prior to or post onset of the injury prevented impairment of cerebrovasodilation to hypercapnia and hypotension while concomitantly reducing histopathology in the parietal cortex and CA1 hippocampus (Armstead et al 2009b). While certainly some of the therapeutic potency of RBC-tPA in the setting of cerebral hypoxia/ischemia may relate to its long biological half-life, its influence on tPA-mediated signaling is probably of at least equal importance (Armstead et al 2009b). In support of the latter notion, RBC-tPA exerts anti-inflammatory (ERK MAPK inhibition) and neuroprotective effects while free tPA augmented pro-inflammatory signaling by potentiating LRP mediated upregulation of ERK MAPK, which aggravated CNS injury (Armstead et al 2009b). These data indicate that RBC-tPA may exert an additional mechanism of neuroprotection in cerebral hypoxia/ischemia by affecting pathological signaling in the CNS. The precise nature of the non-fibrinolytic protective and the injurious signaling seen with RBC-tPA and tPA respectively are currently not fully understood, but may be explained by differences in accessibility to subsets of tPA receptors and resultant signaling pathways within the vasculature and parenchyma. RBC-tPA may actually shift the MAPK isoform profile such that while parenchymal ERK MAPK is inhibited, other isoforms (p38 and/or JNK) are upregulated and mitigate adverse cerebral hemodynamic and histopathologic outcomes in the setting of cerebral hypoxia/ischemia. These studies suggest that RBC carriage may offer a unique opportunity to increase the benefit risk ratio of tPA within the CNS.</p><!><p>Ischemic stroke is a significant contributor to morbidity and mortality, and tPA remains the only FDA approved treatment for this CNS disorder. Despite its salutary role in reperfusion, tPA also has significant deleterious action including predisposition towards secondary hemorrhage to enhanced excitotoxic neuronal cell death and brain edema (Figure 2). Contemporary approaches towards increasing benefit/risk ratio include use of tPA variants like alteplase to extend the narrow therapeutic window of administration, use of novel neuroprotectants such as the free radical scavenger NXY-059, and co-administration with inhibitors of endothelin like S-0139 and MMP upregulation such as minocycline. A more novel approach to increase benefit/risk for tPA in treatment of CNS ischemic disorders involves the co-administration of PAI-1 derivatives that mimic endogenous PAI-1's ability to limit tPA signaling. In that context, emerging concepts that are anticipated to have an important influence on this field include tPA signaling, its modulation and its influence on neuronal cell integrity in the context of modifying the biology of the neurovascular unit concept, along with novel delivery systems such as RBC carriage that alter signaling and NVU function. RBC coupled tPA is viewed as a novel approach towards increasing benefit/risk ratio since it retains the positive fibrinolytic aspect of tPA action which restores perfusion while concomitantly mitigating negative aspects of tPA action such as intracerebral hemorrhage, edema, and neuronal cell necrosis via modulation of tPA signaling, such as MAPK (Figure 2). Extension of tPA therapy to treat CNS ischemic disorders in the pediatric population require pre-clinical assessment in pediatric animal models to assess the effect of aging on the response to ischemia and its management. Antagonists of deleterious tPA-mediated signaling and RBC carriage of tPA offer promising opportunities to enhance the efficacy and safety of this thrombolytic in the treatment of CNS ischemic disorders.</p><!><p>Interactions between tPA and LRP in the physiological and pathophysiological function of the neurovascular unit.</p><p>Schematic diagram depicting positive promoting (+) or negative opposing (-) actions of exogenous tPA and RBC-tPA in the setting of ischemic stroke.</p><p>Modulation of tPA Signaling and Outcome by PAI-1 Inhibitors in Models of Cerebral Ischemia</p><p>MCAO = middle cerebral artery occlusion, H/I = hypoxia/ischemia, FPI = fluid percussion injury</p>

PubMed Author Manuscript

Thermal chemical vapor deposition of epitaxial rhombohedral boron nitride from trimethylboron and ammonia

Epitaxial rhombohedral boron nitride films were deposited on α-Al2O3(001) substrates by chemical vapor deposition, using trimethylboron, ammonia, and with a low concentration of silane in the growth flux. The depositions were performed at temperatures from 1200 to 1485 °C, pressures from 30 to 90 mbar and N/B ratios from 321 to 1286. The most favorable conditions for epitaxy were: a temperature of 1400 °C, N/B around 964, and pressures below 40 mbar. Analysis by thin film X-ray diffraction showed that most deposited films were polytype-pure epitaxial r-BN with an out-of-plane epitaxial relationship of r-BN[001] ∥ w-AlN[001] ∥ α-Al2O3[001] and with two in-plane relationships of r-BN[110] ∥ w-AlN[110] ∥ α-Al2O3[100] and r-BN[110] ∥ w-AlN[110] ∥ α-Al2O3[1 ̅ 00] due to twinning.

thermal_chemical_vapor_deposition_of_epitaxial_rhombohedral_boron_nitride_from_trimethylboron_and_am

I. INTRODUCTION<!>II. EXPERIMENTAL DETAILS<!>A. Structural characterization of the BN films<!>B. Deposition process<!>IV. CONCLUSIONS

<p>The trialkylboron triethylboron (TEB, B(C2H5)3) is commonly used as boron precursor in chemical vapor deposition (CVD) of boron-based thin films as it is less corrosive than the halides BF3 and BCl3, and less poisonous than diborane (B2H6). In a seminal study, Lewis et al. 1 compared the trialylborons TEB, trimethylboron (TMB, B(CH3)3) and tributylboron (TBB, B(C4H9)3) and suggested that TEB was the most suitable for depositing boron carbon films, judged mainly by the high B/C ratio obtained. A recent study on the thermal gas phase chemistry of TEB in CVD 2 confirmed that the molecule is an efficient boron source at temperatures below 1000 °C. TEB has been employed for CVD of boron carbides 2,3 , phosphides and arsenides 4 and, of particular interest for this study, boron nitrides. [5][6][7][8][9][10][11][12][13] TEB decomposes primarily by β-hydride elimination, offering a low-temperature synthesis route for boron-rich films 1,2 . On the contrary, a drawback is that the ethyl ligands are suggested to form C2H4 upon β-hydride elimination 2 , which will be reactive as CVD precursors at the high temperatures needed for the growth of boron nitride (around 1500 °C) 5,9 and can therefore lead to carbon impurities in the BN films. In this regard, TMB is a promising alternative to TEB. TMB was recently shown to be an efficient boron precursor for high temperature deposition and suggested to form less reactive CH4 in an α-elimination decomposition. 14 deposited films from 10 B-enriched TMB and ammonia in a nitrogen ambient, 15 but did not report on any characteristics of the process. Here, we investigate CVD of epitaxial rhombohedral boron nitride (r-BN) using TMB and ammonia in hydrogen ambient at 3 temperatures ranging from 1200-1485 °C, pressures from 30 to 90 mbar and N/B ratios ranging from 321 and 1286.</p><!><p>BN films were deposited on α-Al2O3(001) for 120 min at temperatures of 1200, 1300, 1400, and 1485 °C in a hot-wall CVD reactor kept at a base pressure below 10 -7 mbar. The substrates were cut in 10x10 mm 2 pieces and were cleaned according to the following procedure: 3 min in an ultrasonic bath in acetone at 80 °C, 3 min in an ultrasonic bath in ethanol at 80 °C, followed by standard clean 1 (SC1, NH3:H2O2:H2O with relative concentrations 1:1:26 at 80 °C) 16 and standard clean 2 (SC2, HCl:H2O2:H2O with relative concentrations 1:1:22 at 80 °C) 16 . The substrates were placed in the center of a tantalum-carbide-coated elliptical susceptor. Prior to BN deposition, the α-Al2O3 substrates were heated to 1100 °C during 5 min in palladium membrane purified hydrogen gas (H2), after which ammonia (NH3, 99.999 %, further purified with respect to water by a getter filter) was introduced and the temperature ramped up to the selected growth temperature for 10 min to form an insitu aluminum nitride buffer layer as previously reported in 8,9,12 . H2 was used as carrier gas for the boron precursor TMB (99.99 % purity, Voltaix/Air Liquide Advance Materials, FL) as well as the nitrogen source NH3. TMB was flowed in a separate quartz liner to avoid the formation of the NH3:B(CH3)3 adduct 17 . The N/B ratio was varied between 321 and 1286. From previous works 18 , silane (SiH4, 99.999 % purity, 2000 ppm diluted in 99.9996 % H2) was inserted 2 min prior to growth. The process pressure was in the range of 30 to 90 mbar and regulated by a throttle valve.</p><p>The growth temperature was monitored by a pyrometer (Heitronics KT81R, calibrated by silicon melting).</p><p>The deposited films were characterized by thin film X-ray scattering, electron microscopy and ion beam analysis. All diffractograms and reflectograms were acquired using Cu Kα radiation. The 2θ/ω diffractograms were acquired with a PANalytical X'Pert PRO, using a Bragg-Brentano HD mirror with 1/2° divergence and anti-scatter slits as primary optics and an X'Celerator detector with a 0.5 mm anti-scatter slit, 0.04 rad Soller slits and nickel Kβ filter as secondary optics. In plane measurements, as azimuthal scans (φ-scans) and Glancing-Incidence Diffraction (GID) were acquired with a Phillips X'Pert MPD, using cross-slits (2x2 mm 2 ) with nickel Kβ filter as primary optics and a proportional detector (PW1711/96) equipped with a parallel plate collimator. The thickness of the film was estimated from scanning electron microscopy (SEM) using an accelerating voltage of 5 kV and an inlens secondary electron detector. The analysis of the composition was performed by time-of-flight energy elastic recoil detection analysis (ToF-E ERDA). The measurements were carried out with a 36 MeV 127 iodine ion beam. The incident angle of primary ions and exit angle of recoils were both 67.5° to the sample surface normal giving a recoil angle of 45°. The measured ToF-E ERDA spectra were converted into relative atomic concentration profiles using the Potku code 19 . and 54.5° originated from the diffraction of sp 2 -BN(00ℓ) and the second order diffraction (002ℓ), suggesting highly-oriented pyrolytic BN 20 or textured h-BN 21 or r-5 BN 22 on the nitridated α-Al2O3(001). We note that the growth temperature of 1400 °C is 100 °C lower than the temperature previously reported for TEB at similar growth conditions 9 . Increasing temperature to 1485 °C or decreasing it to 1300 °C, decreases the intensity of the 00ℓ peak and the second order diffraction peaks are no longer visible. At 1200 °C, no diffraction peak is visible. As for deposition with TEB 9 , the deposition of high-quality sp 2 -BN films seems to be constrained to a narrow temperature window, albeit at 100 °C lower temperature. In addition, to the highintensity 006 peak from the sapphire substrate in all investigated films, the 002 diffraction peak of w-AlN was detected from 1200 °C and with 100 and 110 peaks visible for growth at 1485 °C. SEM measurements of cross sections showed that the average film thickness increased from 896 ± 87 nm at 1200 °C to 1308 ± 194 nm at 1400 °C, corresponding to an average growth rate from 7.5 ± 0.7 nm/min to 10.9 ± The {110} planes of sp 2 -BN cannot be used to determine the BN polytype. By rotating the sample by 30°, it is possible to use GID to investigate the presence of the {100} planes of h-BN, as this family of planes is extinct in the case of r-BN. 22,23 The result is shown in Figure 2.(b), where only diffraction from the aluminum nitride buffer and the sapphire substrate is detected. This shows that it is possible to obtain polytype-pure r-BN films from TMB. In a few films, diffraction of h-BN inclusions could be detected by GID, as previously reported in 24 .</p><!><p>The twinning of the r-BN films was also investigated by acquiring azimuthal scans of the {101} of planes of r-BN. These planes have a three-fold symmetry as dictated by rhombohedral crystal system, whereas the φ-scan in Figure 3 shows six peaks for r-BN{101}. This originates from the presence of twin crystals that are rotated by 60°. From this result, two in-plane epitaxial relationship can be determined</p><p>Twinning of r-BN has been reported in previous works for films deposited on sapphire 9 and SiC 10 and is to be expected due to the 6-fold symmetry of the AlN buffer layer and of the hexagonal polytypes of silicon carbide, respectively. 9 FIG. 3. XRD φ-scans of r-BN{101} (2θ = 42.6835°, ψ = 77.61°) and α-Al2O3{202} (2θ = 46.161°, ψ = 72.20°). Diffraction from crystals oriented -30° with respect to the substrate is indicated by circles, diffraction from crystals oriented +30° is indicated by crosses.</p><!><p>In contrast to r-BN deposited from TEB 9 , epitaxial films were obtained in a wider range of N/B ratios and lower pressures using TMB at 1400 °C. At fixed pressure, NH3/TEB ratios below 460 and above 770 were shown to strongly affect r-BN epitaxy 9 , whereas NH3/TMB between 321 and 1286 resulted in epitaxial r-BN, without having any influence on the crystal quality from 2θ/ω scans for N/B ratios above 643, as shown in Figure 4 (a) by the full width at half maximum (FWHM) values from θ/2θ scans of r-BN(003). For comparison, the FWHM of 2θ/ω diffractograms of α-Al2O3(006) was 0.06°. Figure 4 (b) indicates an optimal N/B ratio of 964. This is higher than the value observed for decomposition of TEB (N/B around 10 615-640) and can be explained by the fact that at a lower process temperature the activation of the NH3 molecule is less favorable 25 . In Figure 4 (c), the crystal quality is shown to increase while decreasing the process pressure as illustrated by the proportional decrease in FWHM of θ/2θ scans of r-BN(003) with decreasing process pressure from 90 to 30 mbar. Interestingly, Figure 4 (d) shows that at fixed N/B ratios, the pressure does not affect the total amount of coherently diffracting domains along the c-axis, i.e. the proportion of crystallites is independent of the total pressure at these experimental conditions. i.e. 636 sccm NH3 in 6400 sccm H2 at 40 mbar. The TMB flow was 0.9 sccm and mole fractions were the same as in Figure 1. Lines are guide for the eyes.</p><p>Similarly as reported for TEB 18 , the deposition process using TMB is dependent on the background silicon concentration. In the absence of silane, the intensity of the (003) diffraction peak of r-BN is significantly reduced and the peak broadens as shown in Figure 5. ToF-ERDA gives 44.5 at% B, 46,1 at% N (B:N ratio of 1:1.04), 4.3 at% C , 3.8 at% O and 1.1 at% H in an r-BN film deposited at 1400 °C, 40 mbar, 0.9 sccm TMB and NH3/TMB ratio of 643. This can be compared to the B:N ratio of 1:0.98; O, H of 0.1 at%, 1 at%, respectively, and C being less than 0.1 at% (below detection threshold) for a film deposited at 1500 °C, 70 mbar, 0.7 sccm TEB and B/N = 643. 8 A reason for the increased carbon content in the film deposited from TMB may be the fact that TMB cannot undergo β-hydride elimination. This may restrict the removal of all ligands from the TMB molecule and result in a less favorable surface chemistry for the removal of the methyl groups, leading to the incorporation of carbon in the films. Similar trends have been observed for the pairs 13 trimethylaluminum/triethylaluminum and trimethylgallium/triethylgallium for AlxGa1-xAs 26 , GaAs 26 , InxGa1-xAs 27 and GaN 28 .</p><!><p>We demonstrate a deposition process for epitaxial r-BN on α-Al2O3(001) from a reaction between TMB and NH3 in hydrogen. Epitaxial growth was achieved at 1300 °C and with the best conditions at a deposition temperature of 1400 °C. The</p>

ChemRxiv

Constraints on the radical cation center of cytochrome c peroxidase for electron transfer from cytochrome c

The tryptophan 191 cation radical of cytochrome c peroxidase (CcP) compound I (Cpd I) mediates long-range electron transfer (ET) to cytochrome c (Cc). Here we test the effects of chemical substitution at the 191 position. CcP W191Y forms a stable tyrosyl radical on reaction with peroxide and produces spectral properties similar to that of Cpd I but has low reactivity toward reduced Cc. CcP W191G(or F) variants also have low activity, as do redox ligands that bind within the W191G cavity. Crystal structures of complexes between Cc and CcP W191X (X = Y, F, G), as well as W191G with four bound ligands, reveal similar 1:1 association modes and heme pocket conformations. The ligands display structural disorder in the pocket and do not hydrogen bond to Asp235, as does Trp191. Well-ordered Tyr191 directs its hydroxyl group toward the porphyrin ring, with no basic residue in range of interaction. CcP W191X (X = Y, F, G) variants substituted with zinc-porphyrin (ZnP) undergo photoinduced ET with Cc(III). Their slow charge recombination kinetics that results from loss of the radical center allow resolution of difference spectra for the charge-separated state (ZnP+, Cc(II)). The change from a phenyl moiety at position 191 in W191F to a water-filled cavity in W191G produces much smaller effects on ET rates than the change from Trp to Phe. Low net reactivity of W191Y toward Cc(II) either derives from the inability of ZnP+ or the Fe-CcP ferryl to oxidize Tyr or from a low potential of the resulting neutral Tyr radical.

constraints_on_the_radical_cation_center_of_cytochrome_c_peroxidase_for_electron_transfer_from_cytoc

INTRODUCTION<!>Mutagenesis<!>Protein Purification<!>Crystallography<!>Structure Determination<!>Saturation Kinetics<!>Cpd I Spectroscopic Characterization<!>CcP Turnover experiments<!>Transient Absorption Spectroscopy<!>RESULTS<!>Structures of W191(Y, F, G) CcP<!>Complementation of the W191G Pocket with Small Molecules<!>Saturation Kinetics<!>Species Formed on Reaction with Peroxide<!>Oxidation of Cc(II) by W191Y Cpd I<!>Photoinduced ET of ZnCcP:Cc W191 Variants<!>DISCUSSION

<p>The electron transfer (ET) partners cytochrome c peroxidase (CcP) and cytochrome c (Cc) provide an important model system for understanding inter-protein ET, protein-protein interactions and heme-oxygen chemistry.1–6 The catalytic mechanism of CcP:Cc proceeds as follows: peroxide reacts with the Fe(III) heme of CcP to form compound I (Cpd I), which consists of a Fe(IV) iron oxo species (Fe(IV)=O) and a radical cation localized on neighboring Trp191 (W•+). Two Fe(II) Cc proteins sequentially reduce CcP Cpd I back to Fe(III) and water. In the first step, W•+ is directly reduced by Cc(II). In the second step, the remaining Fe(IV)=O center reoxidizes Trp191, which is subsequently reduced by Cc(II)7–10 (Scheme 1). Thus, W•+ is the key electron acceptor for oxidation of Cc(II). Hoffman and colleagues developed CcP:Cc as a model ET system by incorporating Zn-porphyrin (ZnP) into either CcP or Cc in place of heme.2, 11–16 The photo-excited ZnP triplet state injects an electron across the molecular interface to reduce the Cc Fe(III) heme. ZnP+ and Cc(II) then recombine to regenerate the ground state. Similar to the native reaction, back ET between ZnP+ and Cc(II) is greatly accelerated by Trp191, which acts as a hole-hopping site by localizing the cation radical closer to the Cc Fe(II) heme3, 17–19 (Figure 1). Little of the charged separated state builds up in ZnCcP:Cc because the rate constant for back ET (keb) is much greater than the rate constant for forward ET (ke). However, the W191F substitution slows ET by at least two orders of magnitude and allows resolution of a ZnP+ Cc(II) intermediate.17, 20 In fact, charge recombination in W191F is slow enough to compete with complex dissociation, which produces a second kinetic phase at long times.20 Recent theoretical studies support the involvement of Trp191 oxidation in ZnCcP:Cc reaction kinetics.3, 19 Importantly, the back reaction of the ZnP/W•+ center with Fe(II)Cc involves similar donor-acceptor states, redox potentials and coupling pathways as the natural ET reaction between Cpd I and Fe(II)Cc3.</p><p>Conformational processes and dynamic docking of the CcP:Cc complex have also garnered much interest.5, 9, 21–30 Photo-induced ET reactions in crystals confirm that the crystal association mode has ET kinetics similar those observed in solution.18, 31, 32 Nonetheless, conformational dynamics within the ZnCcP:Cc complex likely generate ET competent states both in solution and in crystals.5, 18, 31, 33, 34 Altered binding interactions between CcP and Cc cause changes in ET kinetics that can be explained by accounting for Trp191 radical formation, electron coupling between donor and acceptor sites, redox potentials and reorganization energies.3</p><p>Electron-hole hopping through aromatic residues is an important process in many redox systems such as photosystem II, ribonucleotide reductase (RR), photolyase enzymes and cryptochromes.35–41 Protein ET rate constants are exponentially dependent on the distance of separation between electron donor (D) and acceptor (A) sites.42 Thus, if the acceptor can oxidize an intervening residue (the "hole"), one long ET step can be broken into two shorter "hops".37, 43–45 Introduction of appropriately placed Trp and Tyr residues in modified blue copper proteins44–46 and model systems36, 47 demonstrate the ability of aromatic residues to accelerate long-range ET. Incorporation of non-natural variants of Tyr into RR has also probed the effects of redox potential on multistep tunneling reactions.40 ZnCcP:Cc provides another system to explore residue oxidation in ET with the potential advantage of widening the reactivity of the hole-hopping site beyond Tyr and Trp residues. In particular, Goodin and colleagues have demonstrated that the Trp191Gly variant (W191G) produces a cavity in CcP within which heterocyclic cationic compounds will bind.48–51 One such compound, 2-aminothiazole acts as a reductant of the peroxide-oxidized heme.50 Replacement of a segment of the CcP polypeptide with a surrogate peptide allows for substitution of Trp191 with benzimidazole.52 Despite being a good structural mimic for the native residues, the surrogate peptide renders CcP inactive because the benzimidazole moiety cannot form a stable radical on reaction with peroxide.52 Herein, we extend this general approach of CcP cavity complementation to examine the ability of residue substitutions and exogenous compounds to support peroxidase activity and hole-hopping in ZnCcP:Cc. Structure determinations of the modified CcP:Cc complexes provide constraints for the interpretation of reactivity. We find that despite wild-type (WT)-like conformations and suitable redox potentials of various exogenous surrogates, only the native Trp191 residue supports peroxidase activity with Cc and rapid back ET in the ZnCcP system. Surprisingly, a Tyr191 variant also appears to form a Cpd I-like state, but does not oxidize Cc in the natural reaction or accelerate the photo-induced recombination process.</p><!><p>Cytochrome c peroxidase (CcP) was subcloned into the ppSUMO vector53, a pET28 derivative vector that introduces a His-tagged version of the SUMO protein to the N-terminus of CcP (obtained with thanks from Dr. Holger Sondermann; Dept. of Molecular Medicine, Cornell University). After a silent mutation (QuikChange, Agilent Technologies) was introduced to remove a natural BamHI site in CcP, the CcP gene was PCR amplified and inserted between the BamHI and XhoI restriction sites of ppSUMO. Two N-terminal Met-Ile residues were added to generate the "MI" version of CcP24. Point mutations were introduced with QuikChange (Agilent Technologies).</p><!><p>Cytochrome c was expressed and purified as described54. E. coli BL21 (DE3) cells were transformed with the Cc gene in a PBTR-1 vector and expressed overnight at 37 °C in lysogeny broth (LB) with 125 μg/mL ampicillin and 50 μg/mL δ-aminolevulenic acid to increase heme production. The PBTR1 vector54 contains the trc promoter which is constitutively active and does not require induction. Cells were harvested by centrifugation at 8000 RPM, and pellets were resuspended in 50 mM sodium phosphate, pH 8.0. The resuspended pellets were either frozen for storage or lysed by sonication. Lysate was spun at 22,000 RPM for one hour to remove insoluble cell detritus and the supernatant was loaded directly onto a HiPrep CMFF cation-exchange column (GE Healthcare Life Sciences) using an ÄKTA FPLC (Amersham Pharmacia). The column was equilibrated and washed with 50 mM sodium phosphate pH 8, and Cc was eluted by a five-column volume gradient of a high salt buffer (50 mM sodium phosphate, pH 8, 500 mM NaCl). All red-colored fractions were collected and concentrated using Millipore Amicon Ultra centrifugal concentrators (10 kDa cutoff) and then loaded onto a Superdex 75 size-exclusion column (50 mM sodium phosphate, pH 8, and 500 mM). Red-colored fractions were concentrated, flash frozen, and stored at −80 °C.</p><p>Cytochrome c peroxidase was expressed in BL21 (DE3) cells and grown at 37 °C in LB with 50 μg/mL kanamycin. Upon reaching an OD600 of 0.8 – 1.2, cells were induced with 100 μM IPTG and overexpressed at 24 °C for ~20 hrs. Cells were harvested by centrifugation at 8000 RPM, and the pellets were resuspended in lysis buffer (50 mM HEPES, pH 7.0, 150 mM NaCl and 5 mM imidazole). Cells were lysed by sonication. Insoluble cell detritus was separated out by centrifugation at 22,000 RPM for one hour. CcP was purified by a Ni-NTA column (Qiagen). To cleave the SUMO tag, the ULP-1 protease was added to the elution and incubated at 4 °C overnight. The eluent was then dialyzed into 100 mM potassium phosphate (KPi), pH 6, buffer and flowed over the Ni-NTA resin to separate the cleaved tag from the protein. CcP was loaded onto a HiPrep Q anion-exchange column (GE Healthcare Life Sciences) using an AKTA FPLC. A 10 column-volume gradient of 100 mM KPi against 500 mM KPi (pH 6.0) was used to separate the heme-containing CcP (FeCcP) from the apo-CcP. The apo-CcP was collected for zinc-protoporphyrin IX (ZnP) incorporation. FeCcP was concentrated and stored at −80 °C for enzymatic assays. To improve the yield of heme incorporation SUMO-cleaved CcP in 100 mM KPi (pH 6) was gently stirred with 1 molar equivalent of hemin (stock was dissolved in 0.1 M NaOH) at 4 °C overnight. The reaction was neutralized with 1 molar equivalent of 0.1 M acetic acid afterwards and centrifuged to remove precipitation. The solution was run through an equilibrated Superdex 75 SEC followed by anion exchange chromatography to separate the iron-containing protein from the apoprotein.55</p><p>For ZnP incorporation, the apo-CcP concentration was determined using the absorbance at 280 nm and the molar absorptivity coefficient ε280 = 55 mM−1 cm−1 56. A fivefold excess of ZnP and carbonyl-diimidazole (1:1 molar ratio) was mixed with apo-CcP in THF or DMF for 2 hours. Solvent was removed by rotovap and the activated ZnP resuspended in 500 μL of DMF. The ZnP solution was added to the apo-CcP and allowed to stir in the dark for 5 days at 4 °C. The solution was then centrifuged to remove protein and unbound ZnP that had precipitated. The sample was loaded onto the Superdex 75 size-exclusion column in 100 mM KPi pH 6.0 to increase purity and to remove non-specifically bound ZnP. The colored fractions were concentrated and loaded onto the HiPrep Q column to separate the apo-protein from the ZnP-incorporated protein (ZnCcP) using the protocol described previously to separate apo-CcP from FeCcP. ZnP incorporation was evaluated by comparing the UV-vis absorbance of the protein peak at 280 nm and the ZnCcP Soret peak at 432 nm (ε432 = 196 mM−1 cm−1)13, 57. Fractions with a ratio of A432/A280 > 2 were concentrated, flash frozen, and stored at −80 °C for crystallization and spectroscopy. Yields of ZnCcP were 80–90% of the initial apo-protein.</p><!><p>Prior to crystallization, Fe(III)CcP and Cc were combined in a 1-to-1 molar ratio at a final concentration of 1 mM each. The protein mixture was buffer exchanged into H2O to reduce ionic strength and thereby increase CcP/Cc binding. Initial crystal hits were obtained using the Phoenix robot (Art Robbins Instruments). Larger crystals were grown by vapor diffusion in either sitting or hanging drop trays against a reservoir containing 15 – 25% polyethylene glycol 3350, 175 mM NaCl, 5 mM n-octyl-β-D-glucoside, and 100 mM sodium acetate, pH 4.6 – 5.6. In some cases, streak seeding was used to increase size and crystal quality.</p><!><p>Diffraction data was collected at the Cornell High Energy Synchrotron Source (CHESS) at beamlines A1 and F2 on an ADSC Quantum 210 CCD. A mixture of 4 parts reservoir and 1 part ethylene glycol was used as a cryoprotectant for crystals. In soaking experiments with W191G CcP crystals, the protocol described by Goodin et al. 58 was followed. Briefly, potential small-molecule ligands were dissolved in 50% ethanol to make a 100 mM stock solution, with the exception of indole, which was dissolved in 100% ethanol. The crystals were soaked in a drop of well solution with a final concentration of 30 mM ligand for 30 s prior to soaking with cryoprotectant. Longer ligand soaks proved detrimental to diffraction. All data was indexed and scaled with HKL2000 59. All structures were phased using molecular replacement in PHENIX.60 Structures of W191G CcP were refined using CNS61 and all other structures were refined with the PHENIX suite.60 Building and adjustments were made with COOT62. Translation/Libration/Screw (TLS) parameters were applied in PHENIX to model Cc anisotropic disorder in the lattice.</p><!><p>The steady-state assay for CcP peroxidase activity was carried out as previously described63. Stock solutions of Cc were reduced on ice in the glovebox by incubating with 10 mM DTT for one hour. DTT was then removed by buffer exchange into 100 mM KPi pH 6.0, either by PD-10 desalting columns or ten rounds of concentration and dilution using Millipore Amicon Ultra centrifugal filters (10 kDa cutoff). Samples containing 2 nM peroxidase, 100 mM KPi pH 6.0, and 0 – 75 μM Cc were then prepared anaerobically to a volume of 1800 μL in gastight cuvettes (StarnaCell). Samples were placed in a Hewlett Packard 8909A peltier sample cooler kept at 24° C and stirred at 500 RPM. Spectra were recorded with an Agilent 8453 Spectrophotometer. Samples were blanked prior to data acquisition to monitor the change in absorbance over time. The reaction was initiated by addition of hydrogen peroxide to a final concentration of 170 μM. Oxidation of Cc was monitored at 550 nm and 540 nm and a constant baseline was set by normalizing to absorbance at 750 nm. The kinetics were monitored for 60 s, with data collected every 0.5 s. The initial range of data where the reaction progress is linear was chosen to represent the steady-state progress of the reaction, where Cc concentration greatly exceeded enzyme concentration. A linear fit was applied to this range and the slope was taken as the reaction velocity (v0) for that concentration. For every concentration of Cc, three samples were measured.</p><p>For measuring the effect of potential ligands on CcP activity, stock solutions of the ligands were prepared in 50% ethanol, or 100% ethanol in the case of indole. Samples were prepared as above, with a Cc concentration of 30 μM, a ligand concentration of 2 mM and final ethanol concentration of < 10 mM. To determine the Michaelis-Menten constants Vmax and KM, the average v0 was plotted versus concentration and fit to the equation v=Vmax[Cc]KM+[Cc] in Mathematica.64 Steady state parameters of W191G with ligands were compared to those of W191F with ligands to identify effects not attributable to cavity binding.</p><!><p>For UV/Vis spectroscopy, 30 μM CcP was prepared in 100 mM KPi, pH 6.0, and hydrogen peroxide was added to a final concentration of 1 mM. Stock solutions were diluted in 100 mM KPi, pH 6.0 to between 1–4 μM for recording optical spectra on an Agilent 8453 Spectrophotometer. For continuous wave EPR spectrosopy, CcP was prepared at ~0.5 mM to obtain stronger signals. Prior to data collection, samples were diluted into a buffer of 100 mM KPi, pH 6.0, 2 mM hydrogen peroxide, and 30% glycerol. Samples were loaded into EPR tubes, flash frozen minutes after addition of hydrogen peroxide, and measured with a Bruker EleXSys II spectrometer at 9 GHz with 1.5 Gauss modulation amplitude, 100 kHz modulation frequency and 25 – 30 dB microwave attenuation.</p><p>The L-tyrosine EPR standard was prepared as described65. L-tyrosine was dissolved in a sodium borate buffer, pH 10, degassed via three freeze-thaw cycles and flame sealed in a quartz EPR tube. While frozen in liquid nitrogen in a finger dewar, the sample was irradiated by a 600 W mercury lamp for 3 – 4 minutes in order to generate the free tyrosyl radical.</p><!><p>Following the method used for W191F66, Cc(II) was reduced with dithionite and buffer exchanged into 100 mM KPi pH 6. With continuous stirring at 25 °C, 4 μM H2O2 was added to 2 μM CcP and 30 μM reduced Cc in 100 mM KPi, pH 6, bringing the total volume up to 1800 μL. Spectral changes were monitored at 550 nm, 540 nm, and 434 nm over 15 min with a single baseline set at 800 nm. The change in Cc(II) concentration was determined spectrally by monitoring A550nm-A540nm and applying an extinction coefficient of 19.2 mM−1 cm−1. Traces were fitted to a monoexponential equation using MATLAB (The MathWorks, Inc., Natick, Massachusetts)</p><p>The offset t0 for the early phase was determined by an immediate drop in Abs550nm - Abs540nm after addition of peroxide. For the slow phase, the offset was established at the maximum of Abs434nm. Traces were truncated from the onset of the early phase to the onset of the slow phase in order to obtain kobs for the early phase. The kobs for the latter phase were determined from data truncated from the onset of the slow phase to 40 sec later. To determine the minimum amount of peroxide required for CcP ferryl generation 8 μM of CcP in 100 mM KPi, pH 6 was titrated with 8 – 24 μM H2O2 and the reaction monitored at 434 nm. Once formed, the CcP ferryl was stable for at least 2 hours on ice. H2O2 concentration was determined by titration with permanganate. Stock peroxide was diluted ten-fold and 1 mL was combined with approximately 30 mL water and 10 mL of 3 M sulfuric acid. With stirring, a 0.025 M solution of KMnO4 was added by buret until the midpoint was observed. Concentration of H2O2 was calculated from the volume dispensed. For multiple turnover experiments, Cc(II) was reduced with dithionite and buffer exchanged into 100 mM KPi pH 6. With continuous stirring at 25 °C, 5–10 μM H2O2 was added to 1 μM CcP and 30 μM reduced Cc in 100 mM KPi, pH 6, bringing the total volume up to 1800 μL. Initial rate values were determined by fitting the data to a monoexponential decay and then taking the first derivative at time t = 0. For examination by cwEPR, reduced Cc and W191Y CcP were combined in a 15: 1 ratio, respectively, with the final concentration of CcP at ~0.2 mM in a buffer of 100 mM KPi, pH 6, 25% glycerol. H2O2 was added to a final concentration of ~0.4 mM and the solution was rapidly mixed by stirring or vortexing. At 30 sec and 60 sec, ~70 uL of sample was transferred to a X-band EPR tube and flash frozen in liquid nitrogen. Data were collected as described earlier in the methods.</p><!><p>All preparations of spectroscopic samples were carried out under anaerobic conditions to avoid quenching of 3ZnCcP by oxygen. ZnCcP was diluted into 10 mM or 100 mM KPi, pH 7.0 to a concentration of 100 – 200 μM and combined with Fe(III)Cc in a 1:2 molar ratio. 4 μL drops were placed on siliconized glass coverslips (Hampton) and were glued to glass slides using a ring of epoxy to form a gas-tight seal, with an average path-length of ~0.5 mm. Samples were placed in the path of a probe light, provided by a 75-W Xe-arc lamp. Excitation light was provided either by a Opotek Opolette Nd:YAG laser tuned to 560 nm with approximately 2 mJ of power per 8 ns pulse, or a Continuum Surelight Nd:YAG laser providing light at 532 nm at approximately 5 mJ of power per 4 ns pulse. Fluorescent efficiency at various excitation wavelengths was measured by tracking the light emitted at 600 nm. Samples were preferentially excited in the Q-bands at 560 nm, although there was also sufficient cross-section at 532 nm for sample excitation. The two excitation energies produced identical kinetics, with both lasers firing at 20 Hz. Exposure by excitation light was controlled by a Hamamatsu A6538 Optical Laser shutter, and the absorbance of the probe light was measured with a Hamamatsu Photonic Multichannel Analyzer (PMA). Unless otherwise specified, spectra collected by the PMA were acquired from a 1 – 50 μs exposure time and averaged 20 – 200 times, depending on the strength of the signal.</p><p>The laser Q-switch firing acted as the master trigger, with a Digital Delay Generator DG355 (Stanford Research Systems) controlling timing between the other elements. Detection of scattered laser light set t0 (time = 0 s). Subsequent time points were then sampled randomly with an entire UV/Vis spectrum collected for each exposure. The time delay points were acquired in a random order to mitigate the effects of any photobleaching during data collection. To record a given time point, N reference spectra were collected at the specified delay time (relative to t0), where N is the number of spectra being averaged. Next, the laser shutter opens and N excited spectra were collected at the same delay. Difference spectra were calculated as ΔA=-log(ExcitedReference), accumulated and averaged over N.</p><p>To process the data, data vectors across the wavelength range were reordered in time and subjected to global analysis by Glotaran.67 Data below 375 nm were discarded because little light at those wavelengths was transmitted through the optics to the PMA, and data above 750 nm were discarded for lack of spectral features. A baseline correction at the triplet state isosbestic point of 546 nm was applied.17 Single-valued decomposition (SVD) of the multi-wavelength data was carried out to reconstruct the minimum number of spectroscopic (difference) states sufficient to describe the kinetic progress. In general, sequential reaction kinetics were assumed and as such, single or double exponential terms were used to connect the spectroscopic states in time.</p><!><p>In the following experiments, CcP Trp191 was substituted with Phe68, Gly48 and Tyr. For the W191G variant, a series of small molecules were also introduced into the cavity created by removal of the 191 side chain. In all cases, crystal structures of the CcP:Cc complexes were determined to aid interpretation of reactivity data. We then examined the peroxidase activity of the variants, their ability to form Cpd I and their photoinduced ET reactions in the context of ZnCcP:Cc.</p><!><p>The structures of W191(Y, F, G) CcP in complex with Fe(III)Cc were determined at resolutions that ranged from 2.0 – 2.4 Å (Table 1). For each of the variants, both CcP and Cc retained nearly identical conformations to those seen in the WT complex (Figure 2). In the absence of Cc, the W191G substitution causes enhanced flexibility in the 190 – 195 loop.69 Because this loop resides at the binding interface with Cc, there was concern that increased flexibility would impact the formation of the crystal complex. Nonetheless, W191G binds Cc in the expected position. The two copies of the CcP:Cc complex in the asymmetric unit are very similar to each other in structure (designated as chains A and C for CcP; chains B and D for Cc). Several of the Cc moieties were not well ordered in the complex structures. As observed previously18, the interfaces between CcP and Cc are generally well defined, but electron density weakens at the periphery of the complexes, largely due to Cc conformational variability within the lattice. This disorder is difficult to model well and somewhat degrades the refinement statistics relative to those expected for an average structure of similar resolution (Table 1).</p><p>In the CcP:Cc complexes, the altered side chains of W191F and W191Y occupy nearly the same position as W191 in the WT (Figure 2B), with the phenol group of W191Y and the phenyl group of W191F aligning with the imidazole of His175, which coordinates the heme and hydrogen bonds to Asp235.70 There is no polar moiety in hydrogen bonding range of the Tyr191 hydroxyl group (Figure 2B). (The Thr180 side chain is within 3.3 Å of the Tyr191 hydroxyl group, but the hydroxyl group hydrogen bonds with the backbone of Gly189). Instead, the Tyr191 hydroxyl proton may be stabilized by the π-electrons of the heme itself. In the W191G Cc complex, difference electron density confirms the presence of ordered water molecules in the cavity created by the loss of the W191 indole. However, the water positions differ somewhat from those of uncomplexed CcP,48, 49 and vary between Chain A and Chain C in these structures. No ordered water molecules were detected near the side chains of Tyr191 or Phe191 in the respective structures.</p><!><p>To promote ligand binding in the W191G cavity, crystals were soaked with 30 mM concentrations of several redox-active compounds (Figure 3). These compounds were selected with consideration of their structural similarity to aniline or indoline, which are ligands demonstrated to bind the pocket and have solution redox potentials similar to Trp.71, 72 Although the affinities of these compounds for W191G have not been measured, we reasoned that they would be in a similar range of those found for aniline and indoline (30 μM and 160 μM for CcP W191G, respectively 58). Of the compounds tested, four bound to W191G as evidenced by the presence of significant difference electron density in the cavity made vacant by the W191G substitution (> 2.0 σ in a Fo-Fc electron density difference map; Figure 4). Although the difference densities are changed relative to that seen in the water-filled cavity of W191G, they are ambiguous in each case; hence the compounds may bind with multiple configurations (Figure 4). Despite demonstrated affinity of indoline for the cavity, we did not observe any electron density for this compound in the crystal structures. Perhaps Cc association prevents movement of the 190 – 195 loop that is required for this larger ligand to access the pocket.52, 69 A barrier to access may have also prevented binding of indole and tryptophan, which were also not detected in the crystallization experiments. Co-crystallization of the complex with these ligands was also unsuccessful. Indole binding was possibly limited by its low solubility in the crystallization solutions.</p><!><p>WT CcP displayed the expected high peroxidase activity and typical Michaelis-Menten behavior (Vmax/E0 = 1600 ± 100 sec−1; Km = 30 ± 7 μM; 100 mM KPi) with respect to Cc concentration at 100 mM KPi, where complicating factors from second-site binding are negligible.20, 73 (We note that the Km value for WT is ~5x higher than those of previous studies63, 73, 74, perhaps due to the different buffer conditions used here). As expected,48, 66, 68 W191F and W191G had little detectable peroxidase activity, above that of Cc oxidation by hydrogen peroxide alone. The steady state rate of Cc(II) oxidation by W191F has previously been shown to be at least 103 fold less than that by WT and linear for only a few seconds66. We found similar behavior for both W191F and W191G, with the lack of linearity making rates difficult to determine. Under our conditions, W191Y also appeared largely inactive. Vmax/E0 was only 40 ± 20 s−1, but also showed a peroxide dependence (see below).</p><p>To determine if the small molecule compounds that bound in the W191G CcP cavity can rescue peroxidase activity, the rate of Cc oxidation was measured for W191G and compared to the value of W191F under the same conditions. As both W191G and W191F are inactive and Phe191 blocks the cavity, any peroxidase activity of W191G above that seen for W191F should reflect ligand binding. At ligand concentrations of at least an order of magnitude above the measured KD values (2 mM58), no detectable peroxidase activity of W191G was observed in the presence of any of the compounds listed in Fig. 3. These results corroborate previous studies on W191G complementation.48</p><!><p>CcP compound I formation is evidenced in the UV/Vis absorbance spectrum of WT and W191Y CcP by a shift in the Soret peak from 409 nm to 420 nm and appearance of Q-bands characteristic of the oxo-ferryl species (Figure 5).75 W191F also produces a similar Soret peak shift, from 409 nm to 420 nm, but the Q–bands are less well-defined50, 68 (Figure 5A). W191G displays the most unique spectrum with a 414 nm Soret peak for the ferric state. Although previous studies of the W191G reaction with peroxide found pronounced ferryl formation as indicated by a well-defined 414 nm Soret peak and Q-bands at 530 and 560 nm,48, 50 we find only a modest shift in the W191G Soret band to 417 nm with similarly small changes in the Q-bands (Figure 5A). The reason for this difference is unknown, but could reflect the heme incorporation process during protein expression.</p><p>We also tested for the ability of WT, W191Y, and W191F to produce a CW EPR spectra characteristic of aromatic residue oxidation (Figure 5B). As previously reported for W191G in CcP50 and related peroxidases,76 we observe a small amount of organic radical forms upon reaction with peroxide (~ 1/10 of the WT signal, Figure 5B), presumably due to the minor oxidation of aromatic residues remote from the active center.50, 77–79 In contrast, W191Y produces a strong EPR signal characteristic of a neutral Tyr radical80, 81 with an amplitude similar to that seen with WT (Figure 5B). Thus, despite its inability to oxidize Cc(II), W191Y forms a Cpd I-like species containing an oxo-ferryl and a tyrosine radical. Features of the W191Y radical species correspond well with those of free tyrosyl radicals generated from UV photolysis (Figure 5B).</p><!><p>To further investigate the inactivity of W191Y in the steady state oxidation of Cc(II), we examined the oxidation of W191Y Cpd I by Cc(II) under pseudo first order conditions and compared the results to those for W191F66. W191F does not form a radical species at position 191 upon reaction with peroxide and exhibits only very slow turnover under steady state conditions66. Titration of W191Y with hydrogen peroxide indicated that ~2 equivalents of H2O2 were necessary to fully form Cpd I, which was then stable for at least 5 min on ice (It is unclear why 2 equivalents of H2O2 are needed for the initial formation of Cpd I; under multiple turnovers by W191Y, one peroxide oxidizes 2 Cc(II) molecules – see below). Following the same procedure as used previously for W191F66, 30 μM Cc(II) was mixed with 2 μM CcP and 4 μM of H2O2 was added to initiate the reaction, which was then monitored at 550 nm (Cc(II)), 540 nm (an isosbestic point for Cc oxidation) and 434 nm (Fe(IV)=0). During the initial build-up of the Fe(IV)=O species at 434 nm, there was an early phase of Cc(II) oxidation with k1 = 0.17 ± 0.03 s−1. Following the maximum absorbance at 434nm [Fe(IV=O peak], the oxidation of Cc(II) was monophasic (Figure 6A) giving a rate constant of k2 = 0.08 ± 0.03 s−1. This behavior and value is similar to that for Cc(II) oxidation by the W191F ferryl-species under analogous conditions, which also shows a slow phase of Fe(IV)=O oxidation with k2 = 0.14 s−1)66. Furthermore, the spectral changes at 550 nm indicate that for W191Y ~ 1 equivalent of Cc(II) (2.5 ± 0.5 μM) is oxidized by W191Y Cpd I on this time scale. An equivalent of Cc(II) was also oxidized during the build-up of Cpd I in the initial phase of the reaction.</p><p>We then examined multiple turnovers of Cc(II) oxidation by reacting W191Y (1 μM) with a 10 fold excess of peroxide (10 uM, respectively) and 30 μM Cc(II) (Figure 6B). The resulting reactions produced 2 equivalents of oxidized Cc (22 ± 1 μM) and proceeded with a turnover number (V/E0) of 0.29 ± 0.03 s−1 1 which is greater than the rate constant for the analogous single turnover condition (0.08 ± 0.3 s−1). Notably, the ferryl species (434 nm) increases in amount as Cc(II) diminishes and then decays slowly when the peroxide is extinguished, concomitant with a small amount of Cc(II) oxidation. This behavior is somewhat different to that seen with W191F, where the CcP ferryl species remains constant until peroxide is depleted66. For W191F, it was suggested that the ferryl species reacts directly with peroxide to oxidize Cc(II), perhaps through the formation of additional protein-based radicals.</p><p>With W191Y, the ferryl appears to increase at 434 nm as the oxidation of Cc(II) slows, suggesting that in the early phase peroxide (and Cc(II)) react with an intermediate that precedes the ferryl in the reaction sequence. Furthermore, the multiple turnover rate (0.29 s−1) is considerably faster than the single turnover rate (0.08 s−1) and thus, with excess peroxide present, the reaction is not rate-limited by oxidation of Cc(II) by Fe(IV)=O. The reaction rate did show a peroxide concentration dependence, but the relationship was complex and not proportional. Although this behavior requires further investigation it is clear that the W191Y Cc(II) turnover rate is 103–104 times slower than of WT CcP82–84 due to altered reactivity of the Tyr radical.</p><p>Considering that W191Y does form a Tyr radical (Figure 5B) there are two possibilities for the reactivity of W191Y Cpd I. In the first case, Y• reacts quickly to oxidize one equivalent of Cc(II), but the remaining ferryl cannot reoxidize Y191 (as per eqn (3)). The second oxidation then involves reaction of the ferryl or some other intermediate with peroxide, which is fast compared to oxidation of Cc(II) by Fe(IV)=O (eqn 5). In the second case, the Y• potential is too low to oxidize Cc(II) at rates that greatly exceed oxidation by Fe(IV)=O or reaction of an intermediate state with peroxide. For the first case, if the Y• were especially stable, it may be observable during the single turnover experiment by EPR spectroscopy. However, no Y• was observed in the presence of Cc(II) during decay of the ferryl species (20–30 sec after reaction with peroxide). Thus, the Y• species has reacted prior to the reduction of Fe(IV)=O. Further investigation of Y• reactivity remains to be determined by methods with better time resolution.</p><!><p>We invoked the ZnCcP:Cc system to evaluate the ability of CcP position 191 to facilitate long-range ET2, 11–16 (Figure 1). Zinc protoporphyin IX (ZnP) provides a reactive, long-lived triplet state (3ZnCcP) when excited with 532 – 560 nm light. In isolation, 3ZnCcP will decay back to the ground state with rate constant kD ~ 100 s−1, 14, 16, 32, 57 whereas in complex with oxidized Cc, 3ZnCcP (Fe(III)Cc) is additionally quenched by heme-to-heme ET to Cc Fe(III) with rate constant ke. The total quenching rate constant (kp = kD + ke) is ~260 s−1 for the 1:1 complex with yeast Cc, depending somewhat on ionic strength, temperature and pH12, 14, 15, 17, 20, 57. The resulting charge-separated state containing the ZnP cation radical (ZnP+) and reduced Fe(II)Cc then recombines in an ET process (keb) that involves oxidation of W191 to an indole cation radical (W•+; Figure 1). On the time scale of ET with Cc, the radical equilibrates extremely rapidly between ZnP and W191 or may be considered delocalized between the two centers.3 In the WT system, keb greatly exceeds ke, and as a result, the charge-separated intermediate forms in vanishingly small amounts. To characterize ET reactivity of the ZnCcP:Cc complex, we employed a multichannel analyzer that allowed time-resolution of complete difference spectra for the ZnCcP:Cc complex following photoexcitation. Global analysis of the data sets defined reactive states and their kinetic parameters. A typical difference spectrum for 3ZnCcP in the absence of Cc is characterized by a broad, positive peak at 475 nm and two negative Q-bands at 555 nm and 592 nm85 (Figure 7). A strong negative peak at 432 nm also appears, consistent with triplet excited state spectra of isolated Zn-porphyrins.86 The 432 nm, 555 nm, and 592 nm features correspond to absorption maxima in the visible spectrum of ZnCcP (Figure 7), and their diminished intensity reflects changes in electronic state upon triplet formation.</p><p>All spectral features of 3ZnCcP alone decay with a rate constant of kD = 114 ± 4 s−1 (Table 2). To study ET quenching of 3ZnCcP by Fe(III)Cc we used both 100 mM KPi ionic strength conditions that favor the 1:1 complex and limit secondary binding of an additional Fe(III)Cc and 10 mM KPi conditions that stabilize the complex against dissociation but permit some secondary site binding20, 57. Although, both cases potentially bring complications, we find that changes in ionic strength do not greatly alter the kinetic parameters (Table 2). In 100 mM KPi conditions, addition of Fe(III)Cc in twofold excess increases the rate constant for triplet state decay to kP = (kD + ke) = 230 ± 12 s−1 (Table 2 and Figure 7). The increased quenching primarily results from ET to Fe(III)Cc14, 15, 32 (ke = kP – kD = 116 s−1), which is consistent with there being little change in rate constant (< 10 s−1) when reduced Fe(II)Cc is added. For WT ZnCcP:Cc, the keb indeed exceeds ke and thus an intermediate state could not be resolved (for individual acquisition times as low as 1 μsec). Consequently, the system was best modeled with a single difference spectra corresponding to the formation and decay of 3ZnCcP. The kinetic parameters (Table 2) are in line with those determined from single wavelength measurements in other studies.14, 15, 32, 57</p><p>In contrast to WT ZnCcP:Cc, W191F shows slower back ET17, 20 (Table 2). Here we resolve difference spectra for the charge-separated state (ZnP+/W•+:Cc(II)). Alone, W191F CcP produces difference spectra identical to WT CcP, and decays uniformly at the same rate. However, in the presence of Fe(III)Cc, an additional spectral species is detected (Figure 8). At ~10 ms, positive features begin to appear at 416 nm, 550 nm, and 625 – 690 nm. These absorption peaks increase while the 3ZnP signal diminishes, and then subsequently decay back to zero (Figure 8). Global analysis of the data reveals two distinct Evolution Associated Difference Spectra, or EADS87 (Figure 8), whose time-dependent linear combination effectively models the series of spectra.87 EADS1 is identical to the triplet state difference spectrum and fully describes the spectral features at t = 0 ms (Figure 7). EADS2 replaces EADS1 and is visible between 10 – 40 ms (Figure 8). EADS2 contains components characteristic of Fe(II)Cc (Soret peak at 416 nm and Q-bands at 522 and 550 nm) and a ZnP+ π-cation radical (625 – 690 nm) (Figure 8) and thus represents the charge-separated state. Global analysis predicts that the Fe(II)Cc and ZnP+ features rise and fall with the same kinetics. Thus, forward ET between 3ZnCcP(W191F) and Fe(III)Cc produces ZnP+ and Cc(II), which then recombine charge on a slower time scale (Table 2). To investigate the involvement of an additional intermediate, the data were modeled with three or more rate constants. However, any additional EADS was always degenerate to the first two and produced no new species of unique spectral qualities or time evolution. Previous studies of W191F (performed at 10 mM KPi) find a second slower kinetic phase for loss of ZnP+:Cc(II) that results from dissociation of the complex.17, 20 The overall timescale in our experiments (40 ms) is probably too short to characterize such dissociation well, and thus keb for intraprotein ET in the associated complex may be slightly underestimated due to this unaccounted contribution. Complex dissociation at ionic strength > 30 mM is rapid27 and could influence the apparent keb, especially considering that the ~10% yield of ET products in these experiments leaves a majority of competing unreacted proteins.20, 27 We thus also investigated the reaction at lower ionic strength (10 mM KPi) where the W191F complex is known to be stable in the msec time range.20 There was little change in the apparent rate constants between 100 and 10 mM KPi conditions (Table 2), with both the 10 mM and 100 mM apparent keb values (40–70 s−1) matching well with previously reported values of 40–74 s−1 17, 20.</p><p>The ET behavior of ZnCcP W191G is similar to that of W191F (Table 2). ZnP+:Cc(II) builds up and decays with similar kinetics (Figure 8); both ke and keb are smaller than for WT but comparable to those for W191F. Thus, substitution of an aromatic group for a water-filled cavity at position 191 has only a modest effect on the apparent forward and reverse ET processes; however, weaker binding between CcP and Cc for the W191G cavity mutant may increase dissociation kinetics, which could then contribute to the slightly lower observed keb.</p><p>Tyr at position 191 does not reductively quench 3ZnP because the 3ZnP excited state decay rate is unaffected in the absence of Cc. Like W191F and W191G, Cc(III) produces a modest increase in the 3ZnP decay rate (Table 2) indicating quenching by ET to Cc(III). Global analysis of the transient spectra in the presence of Cc(III) reveals two major EADS, similar to that observed with W191F and W191G, with similar absorbance increases in the 625 nm range of EADS2 (Figure 8). Consequently, there is no rate acceleration for the back ET reaction in W191Y, which reacts similarly to W191F(G) (Table 2). Like W191F, high (100 mM KPi) and low (10 mM KPi) ionic strength conditions give very similar ke and keb values (Table 2). Unfortunately, we were unable to study how the substituted anilines affect photo-induced ET with ZnCcP W191G because the ligands quenched 3ZnP independent of binding to the W191G cavity.</p><!><p>Trp191 oxidation is a key feature of the CcP peroxidase mechanism and also of the ZnCcP photo-induced ET reactions. Here we report that Tyr, a similar redox active residue at site 191 cannot support these activities, nor can a set of redox active small molecules bound in the W191G cavity. The inability of Tyr to rescue these reactions is despite the fact that it forms a stable radical adjacent to the heme on reaction with peroxide. The influence exerted by Trp or Tyr oxidation on back ET depends on the structural and electrochemical properties of donor, acceptor and hopping center. Indeed, the positioning and reduction potential of a hole-hopping site must fall within certain ranges to enhance long-range ET rates.37, 45, 88 When Cc(III) oxidizes 3ZnP, the resulting radical distributes between ZnP+ and W•+, with an equilibrium weighting (Kex) that depends on the difference in reduction potentials (ΔEo).</p><p>ET to ZnP+ from Cc(II) could conceivably take place with and without involvement of the 191 site: ZnPoCc(III)←keb1ZnPoW•+Cc(II)⇔krexkfexZnP+WoCc(II)ZnPoCc(III)←keb2ZnP+WoCc(II)</p><p>If the electron exchange reaction between ZnP+ and W is much faster than the back electron transfer reactions (i.e. kexf, kexr ≫ k1eb, k2eb) and the rate of ET to W•+ exceeds that to ZnP+ (k1eb ≫ k2eb), the observed back ET rate constant (kobs) will be the rate constant for transfer to W•+ weighted by the equilibrium constant for hole exchange between ZnP+ and W•+ (eqn (7)).</p><p>Known parameters justify the assumption of eqn(7). ET from Cc(II) to W•+ is much faster (k1eb = 2 × 106 s−1)8 than ET to ZnP+ directly (k2eb ~ 101 – 102 s−1, Table 2).17 Thus, k2eb sets a lower limit for any involvement of W191 to enhance ET from Cc(II). If the equilibrium constant for hole exchange, Kex < 10−4 (i.e. ΔEo > 240 mV, eqn(6)), there will be little advantage to hole hopping through the 191 site. For solution and crystalline complexes of ZnCcP with Cc(III), kobs ≥ 4000 s−1, 12, 18, 89 and thus eqn(2) implies that 120 mV < ΔEo < 180 mV. This difference in potential is consistent with measurements on the isolated moieties: Eo(ZnPo/ZnP+) = 1.2 V15; whereas Eo (Wo/W•+) = 1.1 – 1.4 V.45, 81, 90, 91 It is worth noting that the actual reduction potentials of W191•+ and ZnP+ in CcP are likely less than these values. W•+ potentials usually exceed 1 V45, 81, 90, 91 and most peroxidase Cpd II (Fe(IV)=O) potentials are > 0.9 V92,93, yet in WT CcP:Cc, the two-electron couple Eo(W•+Fe(IV)/WoFe(III)) = ½ [Eo(W•+/Wo) + Eo(Fe(IV)/Fe(III))] = 0.740 V.94, 95 Thus, the protein environment may substantially lower the reduction potentials of the 191 side chain and the porphyrin moiety.96, 97 Importantly, a lowered potential for W•+ is still consistent with a very small population of the charge-separated intermediate in the WT ZnCcP:Cc system. Provided that the reduction potential of the hopping site remains over ~200 mV higher than that of the donor Cc(II) (Eo(Cc) = 290 mV98, 99; i.e ΔG = −200 mV), the standard Marcus equation42 (k~keb(W•+)exp[-(λ+ΔG)24λkT]) predicts that the back ET rate to a 191 radical will remain ~100× higher than the forward rate of Cc(III) reduction (~ 102 s−1; Table 2). (This estimate is based on a reorganization energy (λ) of ~ 0.7 V for ZnCcP:Cc back ET3 and keb(W•+)= 2×106 s−1 for ET to W191•+ that is close to activationless8).</p><p>With the crystal structure of the W191Y complex ruling out any substantial structural perturbations caused by the substitution, the inability of Tyr191 to accelerate back ET like Trp may derive from one of the two considerations mentioned perviously. First, although, Eo(Y•+/Yo) will likely be comparable to Eo(W•+/Wo);43, 90, 91, 100–102 even a redox potential increase of 50–100 mV over that of Trp would decrease keb1 to the range of keb2. Unlike Trp191, which hydrogen bonds to Asp235, there is no hydrogen-bond acceptor for the Tyr hydroxyl and thus the Tyr redox potential may not be suitably reduced by the protein environment. In this case, during reaction of Fe(III) CcP with peroxide, Cpd I may react rapidly at Y• or Y•+ to oxidize one equivalent of Cc(II), but then the remaining ferryl species has insufficient potential to re-generate the Tyr radical. Oxidation of Cc(II) by the ferryl or another intermediate species would be slow relative to the reaction of this intermediate with peroxide itself. In the case of ZnCcP, ZnP•+ would also be too low in potential to produce any appreciable amount of Y•. In the second case, the potential of neutral Y• could be too low to oxidize Cc(II) at appreciable rates. The low pKa of Y•+ (~ −2)43, 101 favors deprotonation to the neutral radical Y•. The W•+ pKa is considerably higher than this at 3.2 – 4.5,103 and furthermore, the CcP heme pocket is known to stabilize the W•+ cation104 by favorable electrostatics96, 105 and hydrogen-bonding with the Asp235 carboxylate nitrogen.96, 106 In support of this scenario, during several turnovers the ferryl species does not build-up until peroxide and Cc(II) are depleted (Fig. 6B), which suggests that direct reaction of peroxide with the ferryl is not rate-limiting (eqn 5). However, the situation is complex and involves reaction of peroxide with something other than the ferric enzyme because the multiple turnover rate constant is faster than the single turnover rate constant. At this stage we view the first scenario as more likely because reduction of Y• in single turnover experiments precedes that of the ferryl, and Y• is not stable in the presence of Cc(II), albeit on relatively long timescales. Experiments with faster time resolution will help resolve this issue. The inability of the W191(F, Y) ferryl species to oxidize Cc(II) at rates comparable to WT CcP I supports the assertion that for WT ET proceeds primarily to the W191•+ center after it has been re-formed by reduction of Fe(IV)=O (Scheme I).</p><p>The aniline derivatives do not rescue peroxidase activity of the W191G CcP variant, even though they bind in the cavity and have potentials that should be in range of Trp. Furthermore, the pKas of the resulting aniline cation radicals are likely higher than those of Trp and Tyr (by 2 – 3 pKa units71, 103), hence, deprotonation of the radical cations does not explain their inactivity. The inability of the ligands to hydrogen bond with the Asp235 carboxylate may prevent the protein from sufficiently lowering their reduction potentials. This said, the bound ligands have a potential range of ~ 0.3 V (Figure 3), and thus at least some of them should be susceptible to oxidation by ZnP+. Thus, the more likely explanation for their inactivity stems from disorder and/or weak binding in the pocket. In particular, reorientation of the ligands in the W191G cavity, as evidenced by their heterogenous binding configurations in the crystals, may limit interactions conducive to oxidation or destabilize any resulting radicals by promoting side reactions. Moreover, the lack of covalent attachment to the protein may short circuit hopping as the ligands exchange to solvent on the overall ET timescales. Similar results were found with the CcP Asp235Asn substitution, which destabilizes the conformation of W191 and produces Cc turnover rates similar to those of W191F107–109.</p><p>The near equivalence of the WT, W191Y and W191F structures in complex with Cc lends strong support to the involvement of Trp oxidation in the charge recombination reaction. Interestingly, complete loss of the side chain in W191G produces kinetics similar to that of W191F. Even if complex dissociation is enhanced in W191G (at 100 mM KPi) the apparent keb values are not much less than those for W191F under conditions where the complex is stable (10 mM KPi). Thus, when hopping is inoperative, the ET rates are largely insensitive to large changes in the structure intervening the donor and acceptor sites. Calculations suggest that although many different bonding networks contribute to electron tunneling between the porphyrin centers, those that involve Trp oxidation are the most effective at accelerating long-rang ET3, 19 The W191F to W191G comparison supports the view that such effects are indeed large compared to those resulting from even quite substantial structural perturbations.</p><p>We conclude that a functional ET hopping site must not only meet requirements of potential and proton transfer, but also maintain a degree structural stability that can be best accomplished by covalent attachment or tight binding to the protein. Moreover, there is a narrow redox potential range over which hopping will be effective at accelerating ET rates and thus, stringent conditions must be met for multistep ET pathways to accelerate net charge transfer in proteins.</p>

PubMed Author Manuscript

A combined photobiological-photochemical route to C10 cycloalkane jet fuels from carbon dioxide via isoprene †

The hemiterpene isoprene is a volatile C5 hydrocarbon with industrial applications.It is generated today from fossil resources, but can also be made in biological processes. We have utilized engineered photosynthetic cyanobacteria for direct, light-driven production of bioisoprene from carbon dioxide, and show that isoprene in a subsequent photochemical step, using either near-UV or simulated or natural solar light, can be dimerized into limonene, paradiprene, and isomeric C10H16 hydrocarbons (monoterpenes) in high yields under photosensitized 2 conditions (above 90% after 44 hours with near-UV and 61% with simulated solar light). The optimal sensitizer in our experiments is di(naphth-1-yl)methanone which we use with a loading of merely 0.1 mol%. It can also easily be recycled for subsequent photodimerization cycles.The isoprene dimers generated are a mixture of [2+2], [4+2] and [4+4] cycloadducts, and after hydrogenation this mixture is nearly ideal as a drop-in jet fuel. Importantly the photodimerization can be carried out at ambient conditions. However, the high content of hydrogenated [2+2] dimers in our isoprene dimer mix lowers the flash point below the threshold (38 °C), yet, these dimers can be converted thermally into [4+2] and [4+4] dimers. When hydrogenated these monoterpenoids fully satisfy the criteria for drop-in jet fuels with regard to energy density, flashpoint, kinematic viscosity, density, and freezing point. Life-cycle assessment results show a potential to produce the fuel in an environmentally sustainable way. TOC graphic:Broader Context: The transportation sector is one of the major contributors to greenhouse gas emissions due to the use of fossil-based fuels. While the automobile industry is slowly shifting 3 towards electric vehicles, the aviation sector is still dependent on fossil fuels. To mitigate the environmental effects, biofuels have been introduced (in early stage of development) in the aviation sector. However, the biofuels production is dependent on biomass as a source of raw material which leads to competition with farmland and to rapid deforestation. Therefore, technology is needed to utilize CO2 as substrate for production of jet fuels, in a true carbon neutral process. Here, we report a combined photobiological and photochemical process for production of jet fuel equivalents, using CO2 as source of carbon and light as source of energy.A small hydrocarbon, isoprene, is produced by engineered photosynthetic cyanobacteria, and subsequently converted to C10 cycloalkanes by a photochemical process followed by catalytic hydrogenation. The C10 cycloalkane blends have all attributes to be used as drop-in jet fuels, ultimately enabling usage of the presently available infrastructure for aviation fuels, and lifecycle assessment (LCA) indicates that it would be possible to reduce climate impacts from jet fuel through this photosynthetic process. In the LCA, production of sodium nitrate dominated the impacts in all environmental categories, and therefore, use of an alternative nitrogen source from waste streams can be a potential solution for further reduction of the overall environmental impacts.

a_combined_photobiological-photochemical_route_to_c10_cycloalkane_jet_fuels_from_carbon_dioxide_via_

Introduction<!>Results and Discussion<!>Microbial production and trapping of isoprene:<!>Screening of triplet sensitizers:<!>Fig. 4<!>Dimerization induced by (simulated) solar irradiation:<!>Photodimerization of bio-isoprene:<!>Photodimerization mechanism:<!>Fig. 7<!>Hydrogenation and fuel performance:<!>Further modification of the C10 fuel:<!>Fig. 9 Isomerization of the cyclic [2+2] isoprene dimers to plausible cyclic [4+4] and [4+2]<!>System, efficiency and scale-up potentials:<!>Life cycle assessment:<!>Conclusions and Outlook<!>Conflicts of interest

<p>In order to mitigate global warming and reach the goals of the Paris agreement, a shift towards carbon neutral fuels is necessary. For year 2050, the International Air Transport Association (IATA) emission reduction roadmap projects a reduction in CO2 emissions from aviation by 50% compared to 2005 levels. 1 This may seem modest, yet, globally air traffic increased by 4.5 -8.7% per year during the period 2009 -2019, 2 and a low annual increase of merely 4% until 2050, resulting from changes in travel patterns due to covid-19 and the installment of alternative transportation infrastructures, 3 still implies more than a three-fold increase in air traffic by 2050 when compared to 2019 and approximately six-fold when compared to 2005. As the increase in air traffic is often considerably steeper in growing economies, fulfilment of the IATA goal requires prompt technological development and introduction of new sustainable aviation fuels far beyond the biofuels currently in use or at the stage to be introduced on the market.</p><p>Today, there are different technologies and feedstock alternatives to conventional jet fuels. [4][5][6] An emerging route to biofuels goes via direct production of hydrocarbons by engineered photosynthetic microorganisms, such as algae or cyanobacteria. [7][8][9][10] Cyanobacteria are photosynthetic bacteria which grow on water, minerals, and CO2 from the atmosphere, using sunlight as their energy source. Many cyanobacterial strains are amenable to genetic engineering, and thus, they are ideal hosts for biotechnological production of sustainable fuels. 11,12 Fossil-based jet fuels consist mostly of C8 -C16 hydrocarbons. More explicitly, they are mixtures of n-, iso-and cyclo-alkanes, small aromatics (< 25%) and alkenes (< 5%). 13,14 The fuel should be a proportional mixture of these compounds in order to follow the strict requirement for jet-fuels in terms of energy density, freezing point, and viscosity. In one typical jet fuel, JP-8, the proportion of C10 hydrocarbons is ~21%. 15 Hydrogenated monoterpenes (C10) and sesquiterpenes (C15) have long been considered as potential jet fuels due to their low viscosity and high energy density. Limonane (hydrogenated limonene) has been in focus among hydrogenated monoterpenes because of its availability from biomass fermentation and the low estimated cost of the resulting fuel (~0.73 USD/L). 16 Sesquiterpenes, e.g. bisabolene, farnesene and epi-isozizaene, are also molecules with potential utility. [16][17][18] While biotechnological production of monoterpenes and sesquiterpenes has been demonstrated in various microorganisms, the toxicity of these compounds to the cells is often problematic. 19 Mono-and sesquiterpenoids tend to accumulate in the biological membranes, due to their hydrophobic nature, and interfere with their integrity and function. 20 On the other hand, smaller hydrocarbons, e.g., alkenes such as iso-butene and the 5-carbon-atom hemiterpenoids, are more volatile and tend to easily escape through the cell membranes. 21,22 Their diffusion to the extracellular environment makes them less toxic to the cells and their harvest/capture is less costly since there is no need for cell disruption. We, therefore, suggest a two-step procedure in which these small volatile hydrocarbons (C5 and smaller) are produced photobiologically, followed by their photochemical oligomerization in a second separate step.</p><p>Isoprene is a volatile five-carbon hydrocarbon and can be an ideal precursor. It contains CC double bonds which are useful as sites for (photo)oligomerization, and its production by photosynthetic engineered cyanobacteria has been demonstrated. 21,23,24 Thus, hydrogenated isoprene oligomers could be ideal as drop-ins into presently used aviation fuels.</p><p>There are already well-established chemical methods using heterogeneous catalysts common in industry for oligomerization of alkenes and dienes, 25 which require high temperatures and pressures. Recently, Harvey and co-workers reported iron-catalyzed dimerization processes of alkenes and dienes, including isoprene, that run at ambient temperature and pressure and that produce [2+2] and [4+4] cycloadducts (Fig. 1). 26,27 Interestingly, the hydrogenated [4+4] dimers of isoprene have better fuel properties compared to conventional jet fuels (Jet-A), and a life-cycle assessment and technoeconomic analysis showed that the process can be further improved to reduce cost and emission to compete within the sustainable aviation fuel sector. 28 The [2+2] oligomerization of isoprene was not selective for dimers since also trimers and tetramers were formed in significant amounts.</p><p>Fig. 1 A) The two iron-based catalysts by Harvey and co-workers, 26,27 and B) the catalyzed oligomerization of isoprene. C) Photochemical dimerization of isoprene which resulted in the formation of [2+2], [4+2] and [4+4] photodimers. 29 Bonds formed in the reaction are marked in red.</p><p>We have explored to what extent isoprene can be dimerized photochemically through triplet sensitizers using as mild conditions as possible, ultimately with solar light and in ambient conditions. The photochemical dimerization of isoprene was reported already in the 60s by Hammond, Turro and Liu using benzophenone (5 mol%) as photosensitizer (Fig. 1C), leading to 65% conversion to isoprene dimers when irradiated for five days in a sealed tube. 29 Interestingly, the composition of the dimer mixtures, i.e., the distribution of [2+2], [4+2] vs.</p><p>[4+4] cycloadducts, depended on the triplet energies of photosensitizers, 30 yet importantly, trimers and longer oligomers were not formed. Combined with photosynthetic generation of isoprene from CO2, this could provide for sustainable production of hydrocarbons for jet fuels.</p><p>Here it can be noted that there are only a few earlier studies on the direct production of jet fuels from CO2. [31][32][33] An inexpensive heterogenous Fe-Mn-K catalyst prepared by the Organic Combustion Method was utilized for direct conversion of CO2 to jet fuel range hydrocarbons, with a CO2 conversion of 38% when run at 300 °C. 33 Recently, a model of thermochemical solar fuel production has been demonstrated where CO2 and H2O were captured from ambient air in a process that will be suitable for fuel production in desert regions. 34 Yet, we seek a process that requires as modest an energy input as possible. Hence, we now report on the first formation of C10 hydrocarbons, suitable as jet fuel drop-ins after hydrogenation, in a combined two-step photobiological-photochemical approach with CO2 as carbon source and with light, either as (simulated) solar or ambient light, as the energy source.</p><p>To ensure a sustainable production route, a system analysis perspective is needed as it allows us to understand the different impacts of the product throughout its entire life cycle. 35 Today, life cycle assessment (LCA) is employed as the main decision-support tool for implementing renewable energy technologies using a holistic framework, [36][37][38] and several earlier studies have assessed the environmental impacts of biofuel production from microalgae using LCA. [39][40][41][42][43][44][45][46][47] Furthermore, it has been shown that algae-derived biodiesel is the most efficient alternative in terms of land use as it avoids competition with food crops. 48,49 The environmental impacts of producing cyanobacteria-based biofuels have also been assessed. 38,40,50 Both Luo et al. and Quiroz-Arita et al. employed LCA to assess the life cycle energy and greenhouse gas (GHG) emissions of ethanol production via cyanobacteria, 40,50 and revealed that the ethanol purification process was the main energy consumer and a significant contributor to the carbon footprint of the process. Nilsson et al. assessed the environmental impacts of photosynthetic butanol production by genetically engineered cyanobacteria, 38 and found that in order to displace fossil fuels using butanol produced by cyanobacteria, significant metabolic engineering based improvements in carbon and energy conversion efficiency per cell are necessary.</p><p>As the process reported herein is based on a volatile product which spontaneously separates from the cell culture, we can eliminate the energy requiring distillation or processing of biomass, in contrast to ethanol and larger alcohols as well as direct biodiesel production. The process resembles a previously envisioned strategy on catalyzed oligomerization of ethylene produced by cyanobacteria, which was explored in a technoeconomic analysis study and revealed to yield economically viable biofuels in the long term. 51 We used LCA to assess the different environmental impacts of jet fuel production through the combined photobiologicalphotochemical route in order to identify the hot spots and improvement options. Our results should aid the further development of the novel emerging technology presented herein as it pinpoints the hurdles that need to be addressed, and thus, enable a faster realization of the technology at large scale.</p><!><p>The photobiological formation and trapping of the isoprene produced by the cyanobacteria are presented first, followed by optimization of the photoinitiated dimerization of isoprene (including bio-isoprene) to yield C10 hydrocarbons (monoterpenoids). The dimerization mechanism is analyzed through density functional theory (DFT) computations, unravelling why isoprene trimers are formed in only trace amounts. To be useful as a fuel, the monoterpenoids formed need to be hydrogenated and we determine various properties and assess the values of our hydrogenated monoterpenoids in relation to what is required for a jet fuel. We also carry out a life cycle assessment in order to pinpoint the different environmental impacts of bio-jet fuel production and to identify the related hot spots and improvement options. The results of the study will facilitate further development of the emerging technology presented.</p><!><p>Cyanobacteria, like other bacteria, are able to generate terpenoids via the methylerythritol-4-phosphate (MEP) pathway, but do not naturally produce isoprene (Fig. 2A). 52 In previous work, we have established engineered strains of the unicellular cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis), capable of light-driven isoprene production from CO2, via photosynthesis. This was achieved through the introduction of genes encoding an efficient isoprene synthase (IspS) and two enzymes upstream in the MEP pathway -DXS, 1-deoxy-D-xylulose-5-phosphate synthase, and IDI, isopentenyl-diphosphate isomerase (Fig. 2A). 24 DXS performs the first step of the pathway by combining the two substrates pyruvate and glyceraldehyde-3-phosphate to form 1-deoxy-D-xylulose 5-phosphate (DXP). IDI performs the interconversion of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the substrate for the isoprene synthase to form isoprene. 24,52 The reaction catalyzed by DXS includes a decarboxylation step, thereby serving as a gateway for the flux of carbon into the MEP pathway. The expression of IDI is likely necessary to maintain the balance between IPP and DMAPP, and thus enable the synthesis of essential terpenoids downstream in the terpenoid biosynthesis, when IspS expression would otherwise deplete the levels of DMAPP in the cell. 24 Here, we have used the engineered Synechocystis cells for photosynthetic production of isoprene in small-scale cultures. 20 mL of cyanobacterial culture were grown for four days in sealed 60 mL culture tubes under a constant illumination of 50 μmol photons m -2 s -1 , with addition of 50 mM NaHCO3 to the culture medium. Thereafter, the headspace gas was drawn through 20 mL of cold heptane to capture produced bio-isoprene from the cultures (Fig. 2B). Isoprene concentrations in the gas phase of the cultures were determined by gas chromatography comparing to an isoprene standard, before and after capturing of the gas phase.</p><p>For further experimental details, see Fig. S1, ESI †.</p><p>We achieved an isoprene titre of 1.60 mg L -1 culture after four days of cultivation under the abovementioned conditions. After capturing the isoprene in heptane in our customized trapping setup, the equivalent of 935 µg L -1 of culture remained in the cultivation tube, which translates into a capture efficiency of 41.4% (Fig. S2, ESI † and Table S1, ESI †). A second cycle of trapping resulted in the capture of ca. 490 µg L -1 culture and a higher efficiency (52.4%), for a combined trapping efficiency of ca. 70%. Additionally, we achieved higher capture efficiencies in a single trapping step for other tests, reaching as high as 89% of the isoprene produced. The bio-isoprene trapped in the heptane of the collector tubes was then used for the photochemical dimerization experiments (see section below on Photodimerization of bio-isoprene).</p><p>Throughout the experiments, we observed variability in the isoprene production by the engineered strain, likely due to genetic instability of the expression constructs. In order to improve long term isoprene production, we therefore generated another strain of Synechocystis, where the genetic constructs conferring ability to produce isoprene are expressed from the cyanobacterial chromosome rather than from a plasmid. This was achieved by integration into the slr0168 neutral site in the genome (Fig. 3A). 53,54 The resulting strain, ΔNS1::2MEP-EgIspS, was evaluated for isoprene production which was found to be stable for at least several weeks of cultivation (data not shown). Furthermore, since the isoprene production is performed in closed vessels where isoprene accumulates in the headspace, we hypothesized that over time the concentration of isoprene and oxygen in the culture tubes may become inhibitory for cell growth and productivity. We therefore performed a set of experiments where the headspace gas was vented from the cultures at different intervals. In these experiments, closed cultures of ΔNS1::2MEP-EgIspS were grown for 6 days with sampling and removal of the gas phase at 12, 24, 48 or 72 hour intervals, and growth and isoprene production was evaluated (see Fig. S3). In cultures with more frequent venting of the gas phase (12-48h cycles), growth as well as productivity continued for a longer time period, and total cumulative isoprene production and rates of production was higher than in cultures which was vented every 72h (Fig. 3B and C, Table S2, ESI †). Regardless of the periodicity of these cycles, the cumulative amounts of isoprene were always higher than when no cycling was applied. These results are in agreement with previous reports on butanol and isobutanol production in cyanobacteria, where semi-continuous cultivation with frequent dilution resulted in prolonged and enhanced productivity of the cultures. 55,56 The strategy of continuous or fed-batch cultivation with frequent product removal is thus a potential avenue for developing the isoprene production process on larger scale.</p><!><p>To establish a photochemical isoprene dimerization process that utilizes solar irradiation (natural or simulated) we started at the triplet sensitized diene dimerization reported by Hammond, Turro and Liu in the 60s. 17 Arylketones are excellent photosensitizers due to their relatively high triplet quantum yields and exceptional photostability. The excitation wavelength of arylketones can be tuned to the visible region by extension of π-conjugation of the aryl groups. Additionally, the triplet quantum yield of ketones can be greatly improved compared to the corresponding arene chromophore. 58 Such modulations push the excitation of the sensitizers toward the visible wavelength region where they can be activated by solar light (see below). In the screening of photosensitizers suitable for photodimerization of isoprene we used benzophenone (9), xanthone (10), thioxanthone (11), di(naphth-1-yl)methanone (12), naphthalen-1-yl(naphth-2-yl)methanone (13), and di(naphth-2yl)methanone (14), see Fig. 4. The synthesis of the photosensitizers is discussed in the ESI †. The triplet energies (E(T1)) of 9 -14 and isoprene, both experimentally determined and calculated using density functional theory (DFT) at the (U)B3LYP-D3/6-311+G(d,p) level, [59][60][61][62] indicate that these ketones are suitable for effective photosensitization because their E(T1)'s are slightly higher than that of isoprene (Fig. 4 and Fig. S4, ESI †). Furthermore, the T1 states of dinapthylketones (12-14) are of * character which prevents the competing H atom abstraction, 63 a photoreaction that many ketones with T1 states of n* character initiate. In a typical photoreaction, a mixture of inhibitor-free isoprene and aryl ketone was contained in a quartz test tube under argon and irradiated with 365 nm light (Fig. S5, ESI †). The solution was stirred during the photoirradiation in order to achieve uniform light exposure.</p><!><p>The photosensitizers used in this study as well as isoprene, and in parenthesis, their experimental triplet energies (kJ/mol, in red) and the calculated adiabatic triplet energies (kJ/mol, in blue) at (U)B3LYP-D3/6-311+G(d,p) level. 30,[63][64][65][66] The isoprene dimers formed were characterized by 1 H nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GCMS) analysis (Fig. S6 -S9, ESI †).</p><p>However, we confirmed the structure of the isomers by 1 H NMR as the GC chromatograms can give erroneous results on the relative product distribution due to thermal rearrangement of the dimers (see below). Seven isomeric isoprene dimers (2 -8) were observed, in line with findings reported by Hammond, Turro and Liu (Fig. 1C and Fig. S9, ESI † ). 29 It was also proposed by Hammond and Liu that cyclooctadienes 7 and 8 might have resulted from thermal rearrangements in the GC, 67 but our 1 H NMR data of the isoprene dimers (purified by silica gel column by using pentane as eluent) reveals that these two dimers originate from photoinitiated dimerization and cyclization. Here it can be noted that the distribution of the various isomers depends on the E(T1)'s of the photosensitizers used. It is also noteworthy that trace amounts of isoprene trimers were formed, but not any longer oligomers (Fig. S7, ESI †).</p><p>The screening of the photosensitizers was performed by using 2 mol% loading, unless otherwise mentioned in Table 1. Depending on the photosensitizer, with the quartz tube setup (Ø 13 mm, Fig. S5, ESI †) we observed 8 -41% conversion to isoprene dimers with di(naphthalen-1-yl)methanone 12 giving the highest conversion. A control experiment carried out without photosensitizer clarified its crucial role as the conversion dropped to 0.5% after 44 h of irradiation with  = 365 nm (Fig. S10, ESI †). a The actual loading was lower due to poor solubility of the sensitizer in isoprene.</p><p>Interestingly, the efficiency of the three dinaphthylmethanone isomers (12-14) to convert isoprene to its dimers varied from 21 to 44% due to the positional effect of naphthyl groups. Thus, the isomeric dinaphthylmethanones (12) acts as a better photosensitizer than benzophenone (9), while similar yield of isoprene dimers could be obtained with 13, and comparably the lowest yield could be obtained when 14 was used. If we compare the relative absorbance of the benzophenone (9) and the three dinaphthylmethanone isomers (12-14) at 365 nm, the maximum molar extinction coefficient is observed for 13 and minimum for 9 (Fig. S11-12, ESI †), and from the E(T1)'s of 12 -14 (Fig. 4) it is clear that 12 is the dinaphthylmethanone with a triplet energy closest to that of isoprene. Additionally, the absorption tails of the dinapthylmethanones go beyond 400 nm, which possibly enable solar light photosensitization.</p><p>As a result, the isoprene photodimerizations using dinaphthylmethanone sensitizers can be run with very low sensitizer loadings and as they absorb solar irradiation, it is apparent that particularly 12 is a suitable photosensitizer.</p><p>The yields of isoprene dimers when xanthone 10 and thioxanthone 11 were used as photosensitizers were significantly lower as compared to when benzophenone (9) was used, and we initially considered this to arise from their poor solubility in neat isoprene. To improve the solubility, we designed and synthesized 3,6-di(octyloxy)xanthenone (15) with solubilizing alkyl groups (for synthesis see ESI †). Yet, despite an improved solubility, the improvement in the isoprene-to-dimer conversion is minute (from 8 to 11%). Instead, the higher E(T1) of both 10 and 15 compared to 9 may cause less efficient triplet energy transfer to isoprene and, consequently, a less efficient isoprene dimerization. Indeed, the calculated triplet energy of 15 is higher than that of 10 by 10.9 kJ/mol, revealing that substitution allows for further tailoring of xanthone-type sensitizers, similarly as recently reported by Booker-Milburn and coworkers. 68 Optimization of dinaphthylmethanone sensitized dimerization: Having identified the most suitable photosensitizers among those selected, we determined the loading of 12 required for the optimal conversion of isoprene to its dimers. The photosensitizer loadings were screened from 0.5 down to 0.01 mol% with a similar setup as used above (see Table S3, ESI †). We could observe 21% yield of isoprene dimers in 44 h with the loading of 12 as low as 0.01 mol%. It is worth noting that the yield of the isoprene dimers does not correlate linearly with the loading of 12 as the light transmission through the solution, which is a function of the sensitizer concentration, influences the yields. We found that a loading of 12 of 0.1 mol% was adequate to get an optimized yield of the isoprene dimers. Additionally, we re-screened all photosensitizers (9-15) at 0.1 mol% concentration and the results confirmed that 12 was the most efficient photosensitizer at this concentration (Table 1).</p><p>Further improvement of the photodimerization was carried out in modified reaction setups. We first used a fluorinated ethylene propylene polymer (FEP) tubing (outer diameter: 3.2 mm, ~120 mL loop size) coiled around a water-cooled jacketed beaker (Fig. S13, ESI †). The FEP tubing setup extensively increased the surface area for the incident light, which in turn improved the light absorption. The water-cooled beaker also allows the reaction to run at ~10 ºC which prevents evaporation of the volatile isoprene. With this setup and with 0.1 mol% of loading of 12, we observed 89% yield of isoprene dimers (120 mL scale) when photo-irradiated for 44 h. We attempted to scale up the reaction to 400 mL by using wider FEP tubing (outer diameter: 7.9 mm) coiled around the water-cooled jacketed beaker (Fig. S13, ESI †) and we observed a 48% yield of isoprene dimers when using the reaction conditions described above. Here, the lower yield can be attributed to the increased tube diameter which prohibits an equal light distribution over the width of the tube.</p><p>The isomer distribution between the isoprene dimers, as quantitatively determined through the 1 H NMR spectrum, were found to be: 30 The lower triplet energy of dinapthylmethanone 12 than of 9 leads to preferential activation of s-cis isoprene, resulting also in high amounts of [4+2] cycloadducts (42.6%). The isoprene dimers and photosensitizer 12 could easily be separated by passing through a short silica gel column by using pentane as eluent or by distillation under reduced pressure (65 ºC at ~0.1 mmHg).</p><p>The isoprene dimers could be stored at 4 °C for a few months without noticeable decomposition (Fig. S15, ESI †). However, the conversion of kinetically stable [2+2] photodimers to the other thermodynamically more stable dimers was observed after a few months in storage (Fig. S16, ESI †) or upon heating over 100 °C in air. Also noteworthy is that under ambient conditions the photodimers tend to convert slowly over time to the corresponding immiscible epoxides and alcohols (Fig. S17-18, ESI †). Now, can the photochemical formation of isoprene dimers be run under ambient atmosphere? To explore this, we analyzed the photodimerization with the aforementioned setup (120 mL) and photosensitizer content for 44h under ambient conditions and we observed the same yield (86%) as before. The improved photosensitizing efficiency of 12 compared to benzophenone 9 is attributed to the higher absorption at 365 nm (Fig. S11, ESI †), lowest triplet energy difference as well as higher photodimerization quantum yield (ϕ = 0.91 for the dinaphthyl methanone 12 versus ϕ = 0.43 for benzophenone 9, see ESI † for details). It is also noteworthy that 12 is straightforwardly synthesized in a one-pot reaction using readily available and inexpensive reagents, and after the photoirradiation it can easily be recovered (up to 95%), purified, and used for another cycle. Finally, very low amounts of 12 as photosensitizer (0.1 mol% loading) are needed, which together with its recyclability, should significantly reduce the cost for large-scale production of isoprene dimers.</p><!><p>Our ultimate goal is to carry out the photodimerization of isoprene with solar irradiation (Fig. S21, ESI †).</p><p>Dinaphthylmethanone 12 might be an ideal photosensitizer as its absorption tail stretches until ~400 nm and the solar irradiation has significant light intensity at the surface of Earth at wavelengths longer than 350 nm (Fig. S22, ESI †). For this reason, we first performed the isoprene photodimerization in a solar simulator (1 sun, AM 1.5G) using a newly designed flat spiral coil made of FEP tubing for simulated solar irradiation of isoprene (Fig. 5). Now, we could obtain 61% yield of isoprene dimers (4 mL scale) when irradiated in the solar simulator for 44 h using 0.1 mol% of dinaphthylmethanone 12 as photosensitizer (Fig. S23, ESI †). Thus, the experiments demonstrate that the formation of isoprene dimers under sunlight irradiation is achievable. Furthermore, the higher yield that can be estimated after 20 h in the solar simulator (28%) can be rationalized by the fact that the solar simulator has a higher relative intensity in the 350 -400 nm range when compared to natural solar irradiation (see Fig. S22, ESI †).</p><!><p>The bio-isoprene produced by the Synechocystis cells and captured in heptane was mixed with dinaphthylmethanone 12 (0.02 M), filled into the flat spiral coil and irradiated in the solar simulator (24 h, 1 sun, AM 1.5 G). Even though the concentration of bio-isoprene was low, the reaction produced bio-isoprene dimers as confirmed by GCMS (Fig. 6), and experiments with commercially available isoprene (0.05 M solution in heptane) gave a similar distribution pattern of dimers (Fig. S26-27, ESI †). This proof-of-principle experiment shows the possibility to turn CO2 used as carbon source into C10 cycloalkanes with our combined photobiological-photochemical approach. Bio-isoprene dimerization was also attempted under natural sunlight, yet, no dimers were detected in GCMS.</p><p>This might result due to two factors; (i) the weaker intensity of the natural solar light compared to the simulated one in the 350-400 nm range (Fig. S22, ESI †), and (ii) the low concentration of the bio-isoprene in heptane. Thus, one next step is to increase the production of bio-isoprene so that a higher concentration can be achieved. This may be addressed via further metabolic engineering of the cyanobacterial strain to enhance flux of fixed carbon towards the isoprene product combined with more efficient trapping of isoprene from the culture.</p><!><p>The reaction mechanism for light-induced formation of the isoprene dimers involves six steps (steps 1-6, Fig. 7) which we explored through DFT computations at the (U)B3LYP-D3/6-311G(d,p) level [59][60][61][62] (for details on the computations and for additional results at M06-2X/6-311G(d,p) level 62,69 see the ESI †). The first step is the excitation and intersystem crossing (ISC) of the photosensitizer to its triplet state, followed by triplet energy transfer from the sensitizer to isoprene in the ground state, yielding isoprene in its T1 state. The T1 state isoprene can be described as a radical-pair composed of one resonance stabilized allyl radical and one methyl radical, and as such it exists in four different conformers with nearly equal energies, yet, separated by activation barriers of 63 -67 kJ/mol. One molecule of isoprene in its T1 state can add to an S0 state isoprene via a number of reaction paths. Among these, the addition of the methyl radical site of a T1 state isoprene molecule to an S0 state isoprene molecule proceeds over slightly lower activation barriers (step 3, lowest barrier ~56 kJ/mol) than the addition of the allyl radical part of T1 isoprene to an S0 state isoprene (lowest barrier ~61 kJ/mol). The triplet lifetime of isoprene has been determined to 5 s, 30,70 sufficiently long to allow a substantial amount to overcome the activation barrier for dimerization. The additions, which are markedly exergonic (-92 to -71 kJ/mol), lead to intermediate isoprene dimers that can be described as triplet state bis(allyl) radical pairs. Thus, once formed there will be no back reaction. As the two radical sites of the bis(allyl) radical pair are only weakly coupled, the singlet diradical is essentially isoenergetic with the triplet, and a rapid ISC should occur (step 5). Furthermore, the bis(allyl) radical pairs have high conformational flexibilities irrespective of electronic state because the conformer interconversions involve C-C single bond rotations (in the T1 state the rotational barriers are ~16 kJ/mol, step 4). Finally, when a singlet state bis(allyl) radical pair adopts a conformer with the two unpaired electrons at a sufficiently close distance they will combine into a C-C single bond (step 6), leading to the observed isoprene dimers with either cyclobutane, cyclohexene or cyclooctadiene rings (Fig. 1C).</p><!><p>The various steps in the reaction mechanism for the formation of the cyclic isoprene dimers (steps 1 to 6) and trimers (steps 7 and 8) with the lowest activation energies at UB3LYP/6-311G(d,p) level. ISC = intersystem crossing. For further details see the Supporting Information, section 6.</p><p>So why is further oligomerization hampered? As the bis(allyl) radical pairs are composed of two allyl radicals which are internally stabilized through -conjugation, they will be less reactive than triplet state isoprene which can be described as one allyl radical and one reactive methyl radical fragment. Thus, the rate for the addition of the bis(allyl) radical pair to an isoprene in its S0 state, leading to a trimer bis(allyl) radical pair, should be slow (step 7).</p><p>Indeed, the lowest activation barrier for the addition of the bis(allyl) radical pair to an S0 state isoprene is 76 kJ/mol, significantly higher than the addition of a T1 state isoprene to an S0 state isoprene (56 kJ/mol as seen above). A second potential route to trimers goes via addition of an T1 state isoprene to a C-C double bond of a cycloadduct (step 8), but this process should also be slow as it leads from a single carbon-centered radical to another. For this process we find a lowest calculated activation energy of 71 kJ/mol. Together with the fact that the ring-closure of the dimer bis(allyl) radical pair is a unimolecular reaction in contrast to the bimolecular reaction to trimer bis(allyl) radical pair, this explains why the further oligomerization to trimers, tetramers etc. is not competitive with the closure of the bis(allyl) radical pair to the cyclic dimers observed.</p><p>Finally, since the combined portions of isoprene dimers that are either [2+2] and</p><p>[4+4] cycloadducts make up more than half of the dimer mix, we also tested a T1 state concerted mechanism that would involve a transition state with a cyclic array of 4n electrons stabilized by through-space Baird-aromaticity, 71-73 however, we could not locate such a pathway. For further discussions, see ESI †.</p><!><p>The isoprene dimers are unsaturated, which is not ideal if they should function as a jet fuel as soot would form due to incomplete combustion when ignited. The isoprene dimers (here labelled ID-1) were therefore hydrogenated in presence of Pd/C as a catalyst at 10 atm hydrogen pressure, providing hydrogenated isoprene dimers (HID-1) in near quantitative isolated yields (see ESI † for detail procedure). These hydrogenated isoprene dimers appeared as a colorless liquid (Fig. S28, ESI †), and they were further characterized by 1 H NMR and GCMS analysis (Fig. S29, ESI †).</p><p>The disappearance of the alkene signals of the isoprene dimers in the 1 H NMR spectrum proves a complete reduction of the C-C double bonds, leading us to the cycloalkane-based jet fuel equivalent.</p><p>For this mixture of hydrogenated isoprene dimers, we determined the key fuel properties, i.e., the net heat of combustion (NHOC), kinematic viscosity, density, and flash point (Table 2). The measured density of HID-1 is 0.77 g/mL at 15 °C (Table S7 and Fig. S40, ESI †) which matches well with the lower required density of Jet-A. The density of the fuel is lower than that of dimethylcyclooctanes (DMCO) due to the presence of high amounts of isomers with cyclobutane rings. Moreover, the hydrogen content of the HID-1 (14.37%) is significantly higher than that of Jet-A due to the absence of aromatic and unsaturated moieties, which eventually gives a higher gravimetric NHOC value and produce clean burn without soot formation. The gravimetric NHOC is an important parameter for a jet fuel, and it should be above 42.8 MJ/kg according to the standard specification for jet fuels. 26 Additionally, the volumetric NHOC value of HID-1 is higher than that of conventional jet fuels (Jet-A). For the two C10 hydrocarbons (18, 19, 25 and 26) in Fig. 8 which have experimentally determined NHOC, 15,26 we find that computed values calculated with a DFT-based procedure by Major and co-workers 74 are in good agreement (for a further description of the method see the caption Fig. 8 and the Supporting Information). Thus, based on the computed NHOC of the C10H20 hydrocarbons contained in HID-1 we can also conclude that their energy contents are in line with expected for an aviation fuel. The NHOC values were computed following a DFT-based procedure by Major and co-workers developed for the M06-2X functional. 74 These values contain two corrections which are needed to achieve accuracy; (i) a correction for the addition of the enthalpy of vaporization of terpenes and water, and (ii) a correction of the enthalpy of O2. The enthalpies of vaporizations were calculated using the SMD solvation model. 75 Additionally, we have measured the kinematic viscosity of HID-1 from -40 °C to 20 °C as it is an important parameter in terms of safety and combustion of the fuel. 76 A higher viscosity leads to a poorer atomization of the fuel which leads to incomplete combustion and formation of soot, eventually reducing fuel efficiency. To achieve proper atomization and combustion of a jet fuel it is strongly recommended to have a kinematic viscosity value below 12.00 mm 2 /s at -40 °C. Rewardingly, the kinematic viscosity of HID-1 (1.71 mm 2 /s at -20 °C) is more than 4.5 times lower than the recommended value for conventional fuel (8.00 mm 2 /s), and it is even 2.4 times lower than that recently reported for DMCO (4.17 mm 2 /s at -20 °C)</p><p>which is closely related to the structure of the molecule (C10). The kinematic viscosity at -40 °C is 2.60 mm 2 /s (Table S5 and Fig. S39, ESI †), which is 4.6 and 3.1 times lower when compared to Jet-A and DMCO (7.95 mm 2 /s), respectively. The lower kinematic viscosity might result from the higher portion of alkylated cyclobutane isomers over cyclooctane isomers, and it will allow the drop-in to be blended with other conventional jet fuels at any ratio.</p><p>The freezing point of the jet fuel is also crucial for the safety and the flow of the fuel at high altitudes. We assessed the freezing properties of HID-1 by placing it in a dry ice/acetone bath (-78 °C) for 1.5 h and did not observe any cloudiness or crystallization, indicating that the freezing point of HID-1 is lower than -78 °C, i.e., it is much lower than the recommended value for conventional jet fuel (-40 °C). The low freezing point of HID-1 suggests that it is possible to use as a fuel in high altitude flight. Yet, a drawback of HID-1 is the flashpoint which was found to be 33.5 °C, lower than the specified value for conventional jet fuel (38 °C). The lower flash point may limit the use of HID-1 as jet fuel surrogate due to safety issues, although the commercially available Jet-B and TS-1 have much lower flash points (-18 and 28 °C, respectively) compared to the recommended value. 77 Yet, these fuels have very low freezing points allowing them to be used in extremely cold environments. The low flash point of HID-1 can be attributed to the isomers with cyclobutane rings as these are more volatile.</p><!><p>The fact that the flash point is slightly below the recommended value prompted us to consider modifications of the isoprene dimer mix ID-1</p><p>before the hydrogenation step. The boiling points of the various isomeric isoprene dimers (2 -8, Fig. 1C) were earlier reported by Hammond, Turro and Liu and it was revealed that the [2+2]</p><p>isomers have relatively lower boiling points than the others (Fig. S14, ESI †), 29 with 2 having the lowest. This should also contribute to the low flash point of HID-1 as the flash point of a hydrocarbon correlates with its vapor pressure. A further modification of ID-1 could be performed through moderate heating which led to the conversion of cyclobutane-containing isomers to cyclooctadiene-and cyclohexene-containing ones through Cope and other thermal rearrangements. 29 Here we probed two different temperatures, 135 and 160 °C, and subsequent hydrogenation gave the modified hydrogenated isoprene dimers HID-2 and HID-3 (see †ESI for detailed synthetic procedure, Fig. 9). The reaction mixtures were analyzed by 1 H NMR and GCMS measurements (Fig. S30-33, ESI †).</p><p>When ID-1 is heated at 135 °C for 1.5 h, leading to ID-2, the isomer 2 rearranges to isomers 5 and 8, where isoprene is formed as a byproduct to 5 % (Fig. S34, ESI †). In order to transform all [2+2] isoprene dimers into [4+2] and [4+4] isomers the temperature had to be elevated to 160 °C for 4 h, giving ID-3. Yet, in this case the amount of isoprene formed through a back-reaction increased to 11 %, even though 3 and 4 after prolonged heating remained in the post-modified ID-3 in trace amounts of 1 % and 2 %, respectively (Fig. S35, ESI †). It is worth noting that the post-modification of ID-1 can be justified, as the isoprene formed as a byproduct can be recycled. After removal of isoprene from ID-2 and ID-3, these dimer mixtures were 29 hydrogenated using the conditions described above leading to quantitative formation of HID-2</p><p>and HID-3 (Fig. S36-38, ESI †). Here it is noteworthy that the hydrogenation of isoprene dimers (ID-3) could be run at 1 atm H2 pressure to obtain HIDs (HID-3) in quantitative yield.</p><p>However, the reaction requires longer time (48 h) to complete and 1% p-cymene is formed due to aromatization of limonene (Fig. S69, ESI †).</p><!><p>isomers through thermal Cope and other rearrangements.</p><p>After the heat treatments, the flash points of HID-2 and HID-3 increased to 38.5 °C (Table 2), i.e., above the recommended value. The identical flash point of HID-2 and HID-3 can be rationalized as they are mixtures of hydrogenated cycloalkanes with very similar boiling points. The gravimetric NHOC values of HID-2 and HID-3 decreased to 43.57 and 43.59 MJ/kg, respectively, lower than that of HID-1 which is explained by the reduced amounts of cyclobutane isomers in the modified HID blends. Yet, the modified HID's have higher densities (both 0.809 g/mL at 15 °C) (Table 1, S7 and Fig. S40, ESI †) which leads to higher volumetric NHOC values (35.25 and 35.22 MJ/L, respectively). The volumetric NHOC values for modified fuels are 6.3% greater compared to conventional Jet-A (> 33.17 MJ/L), which should be an added advantage. With regard to the kinematic viscosities (3.16 and 2.92 mm 2 /s at -20 °C for HID-2 and HID-3, respectively) these are higher than that of HID-1 due to their lower contents of cyclobutanes (Table 1, S5 and Fig. S39, ESI †). Still, the values are more than 2.5 times lower than the largest recommended values, facilitating a good atomization of the HID's when used as fuels. Finally, both modified fuels have very low freezing points (<-78 °C), enabling high altitude flight (Table 1). The easy modulation of the ID-1 to ID-2 and ID-3</p><p>should be an advantage as they after hydrogenation should be ideal as drop-ins for conventional fuels for high-altitude jet engines.</p><p>There are also further favorable features of HID-1 -HID-3. Conventional jet fuels contain mixtures of aromatic compounds which have added benefits as they swell the nitrile rubber elastomer valves which helps to protect the integrity of the jet engine. However, modern elastic materials do not require the aromatic content to swell the elastomers, and recent studies have shown that cycloalkane blends have similar properties as aromatics and are able to swell nitrile rubber elastomer valves. 78,79 Additionally, the content of aromatic compounds in jet fuels leads to lower NHOC values as well as formation of carbon soot during the combustion which adversely affects the lifetime of the engine. Finally, aromatic compounds in jet-fuels are major health and environmental hazards. Thus, avoidance of such compounds is favorable for these reasons, and substantial interests have been focused towards development of biocycloalkane based fuels that mitigate the abovementioned problems. 80 The very recent review by Muldoon and Harvey further highlights the potential of bio-cycloalkane based hybrid fuels for future use in military and civilian aviation fuel industries. 80 In this context it can be noted that JP-10 (exo-tetrahydrodicyclopentadiene) is a synthetic C10 cycloalkane-based missile fuel. 81,82 Taken together, our jet fuel mixtures (HID-1 to HID-3), which are C10 cycloalkanes, fulfil all requirements for future, less environmentally hazardous jet fuels, they are devoid of aromatic content and have high NHOC values.</p><!><p>The emerging technology reported here is at a very early stage of development (approx. at technology readiness level 2 (TRL2)).</p><p>A technoeconomic analysis is therefore not yet meaningful. To clarify where future research and development need to focus, we instead identify technological challenges by using information from recent analyses of approaches that resemble our combined photobiologicalphotochemical one. We also performed a life-cycle assessment (see below).</p><p>In order to develop this platform into a commercial production system which is both energetically and economically sustainable, extensive improvements in performance are necessary on several levels. For the photosynthetic production of isoprene, the conversion from solar energy and CO2 to product needs to be more efficient. This will require further engineering of the host organisms, for improved photosynthetic efficiency and increased carbon fixation as well as for increased partitioning of carbon towards product formation. Furthermore, cultivation conditions need to be optimized for cell productivity. Cultivation and harvest systems also need to be further developed. While photobioreactors are commercially produced, albeit still mostly at smaller scale, efficient harvesting of a volatile product from the culture remains a challenge to solve.</p><p>A technoeconomic analysis of ethylene production by cyanobacteria has earlier been reported, 51 and it was estimated that gasoline-equivalents, produced by oligomerization of the bioethylene, could be sold at a price of $28.66 per gallon in the near-term and at a price of $5.36 in the long-term. The largest cost that determined the gasoline price was the capital investment for the photobiology reactors, followed by the electricity cost for the power intense cryogenic distillation. Isoprene, contrary to ethylene, will not require cryogenic distillation as it condenses at much higher temperatures than ethylene. A further difference is the subsequent oligomerization which in case of ethylene uses a Ziegler catalyst, a mature technology utilized widely within the petrochemical industry. Our photochemical dimerization of isoprene is not an established technology and needs extensive process development, yet, if carried out with natural solar light it will be much less power demanding than the catalytic approach for ethylene oligomerization. The efficiency of the photochemical step is such that we can assume that all isoprene produced photobiologically within one day can be dimerized photochemically within the same amount of time. Thus, the main limiting factor for the photoproduction is the photobiological production step.</p><p>One drawback of our first strain of cyanobacteria used for isoprene generation was genetic instability of the plasmid-borne DNA construct. We successfully circumvented this by instead inserting the genes required for isoprene production into the genome of the host cyanobacterium (Fig Fig. 3). This enables long-term stable production of isoprene, opening the possibility for continuous cultivation of the production strain for longer time periods. In a fully developed system at large scale, fed-batch or continuous cultivation combined with continuous product removal has the potential to increase productivity of the culture, while further strain engineering to enhance the productivity per cell will also be necessary.</p><p>As described above, the photochemical dimerization can be performed to very high yields (~90%) in batch setup using thin FEP tubing, yet the yield decreases when the tube diameter increases. Process optimization in which various conditions are varied (flow rate, irradiation intensity, tubing width, laminar vs. tubular flow, and reactor design) is required. One may also search for photosensitizers with smaller S1 -T1 energy gaps than compound 12, yet still with E(T1) above that of isoprene. Such sensitizers could absorb within the visible (blue)</p><p>wavelength region of the solar spectrum where the intensity is higher and still be able to transfer the triplet energy to isoprene and initiate the dimerization.</p><!><p>To assess whether large scale production of photosynthetic jet fuel according to our system may become an environmentally sustainable process, we have performed an LCA for the integrated photobiological-photochemical process, using one tonne of fuel as the functional unit (Fig. 10). For the cultivation and production of isoprene from cyanobacteria, we have used as a starting point a scenario described by Nilsson et al., 38 where the authors modelled cyanobacterial butanol production. In this system, cultivation takes place in a 750 m 3 array of serially connected vertical flat panel photobioreactors, covering 1 ha of land. We assume that the cyanobacterial cultivation would be performed in two phases. First, a pre-cultivation in 10% of the whole volume for five days to generate biomass, during which period product formation is inhibited. Second, the biomass is transferred to the reactor volume for a production phase of three weeks where production is induced, and 90% of fixed carbon is directed to isoprene production in the cells. Isoprene product is continuously removed and transferred to the downstream photochemical process. We make the following assumptions:</p><p>carbon fixation is at 1.2 g L -1 day -1 ; 83 inorganic carbon is supplied from a waste resource such as biomass combustion, thus providing a carbon source at no environmental cost to our system; 80% of the water from the reactor plant is recycled after each production round; electricity needed is supplied in accordance with the Swedish energy mix. In the scenario we modelled, nutrients other than CO2 are supplied based on the composition of BG11 growth medium. 84 With the mentioned carbon fixation rate and 90% of carbon allocated to isoprene formation, 38 the time of operation of the 750 m 3 reactor for generating one tonne of jet fuel at the end of the process is 2.4 days.</p><p>The bio-isoprene produced will subsequently be dimerized photochemically, and in our modelling, we utilised the input from the lab scale experiment and scaled up to produce 1 tonne of HID-2. Upon solar irradiation of isoprene (60 h) in presence of 12 as a photosensitizer to obtained isoprene dimers in 51% yield. The unreacted isoprene is distilled off to be used in the next cycle, while the isoprene dimers are separated by distillation under reduced pressure (~70 °C, 10 mmHg pressure). The photosensitizer is easily recovered from the residue by washing with pentane and methanol (~95% recovery, see above), and it was therefore excluded from the LCA since merely 0.1 mol% was used in the photoreaction.</p><p>Further, the isoprene dimers produced will be treated thermally at 135 °C under an inert N2 atmosphere to produce ID-2 in 92% yield. The residual isoprene produced during reaction should be distilled off and used again in the photoreaction cycle. Finally, we assume that heattreated isoprene dimers will be hydrogenated by using 10 wt% Pd/C (0.5 mol%) and H2 to obtain HID-2 in near quantitative yield, utilised as drop-in jet fuel and storable at the production site. The product could be separated by filtration of Pd/C to obtained jet fuel. The excess hydrogen used in this process would be recycled and used in the next hydrogenation cycle. The Pd/C (10 mol%) was not included in the LCA model due to low amount of loading (0.5 mol%)</p><p>and reusability of the catalyst.</p><p>The process and system boundaries modelled in the LCA are shown in Figure 10, and all inventory data summarized in Table S8, †ESI. Results from the LCA are presented in Table 3.</p><p>The climate impact was approximately 0.6 tonne CO2 eq./tonne biofuel (Table S9, †ESI), mainly attributed to emissions from the production of sodium nitrate used in the photobiological processes (Table 3). The climate impact is about 20% of that of fossil jet fuel (approx. 3.8 CO2 eq./tonne for conventional Jet A), 85 and is at the lower end of the range from 0.6 -2.7 tonne CO2 eq./tonne biofuel found in the study by Nilsson et al., 38 which investigated the environmental impacts of cyanobacteria-produced n-butanol using three different reactors. In other studies, some investigated bio-jet fuels had the best result at 0.8 tonne CO2 eq./tonne. 28 Fig. 10. Flow chart for the process and system boundaries for the LCA.</p><p>From the assessment of the overall environmental impact we see that under the assumptions made, the production of sodium nitrate completely dominates the impacts in all environmental categories (Table 3). This nitrate is used as a nutrient for cultivation of the cyanobacteria. The source of sodium nitrate in our model is the global market, and it is produced using fossil fuels. Use of alternative raw materials from waste streams, such as municipal waste water, as a source for nitrogen instead of sodium nitrate can be a potential solution for reducing the environmental impacts. 86 Increasing the photosynthetic efficiency of the cyanobacteria would also reduce the overall environmental impacts.</p><!><p>In this study, we demonstrated that it is possible to generate photosynthetically derived isoprene from CO2 using engineered cyanobacteria, capture the isoprene, and use it for subsequent biofuel generation via a novel photochemical process driven by sunlight. While further optimization of the engineered microorganisms is required for industrial applications, we were able to trap isoprene with high efficiencies relying on a simple, scalable capturing method. We could also show that repeatedly removing the product enhanced productivity from isoprene producing cyanobacterial cultures</p><p>In a subsequent photochemical step, the isoprene was dimerized into cyclic C10H20 isomers in nearly quantitative yields by usage of dinapthylmethanones as photosensitizers. The photoreaction could be run under ambient conditions, facilitating a fully renewable fuel production. Our current studies reveal that rather simple modifications of the reaction setup can greatly improve the yield of the photoreaction. Combined with a careful choice of photosensitizer this enables photodimerization of isoprene by use of solar light. The isoprene dimer mixture can be further modified by heating at moderately elevated temperatures (130 -160 °C), resulting in C10 hydrocarbon mixtures which after hydrogenation fulfil all criteria to function as drop-ins for conventional jet fuels. Indeed, the modified and hydrogenated isoprene dimers have better fuel properties than the commercially available Jet-A. The very low freezing points and low viscosity should make these fuels ideal for high-altitude flights.</p><p>It is usually a challenge to compare the results and environmental impacts of an emerging technology with a mature technology due to several uncertainties such as missing data, upscaling assumptions and modelling issues. 87 In case of production of photosynthetic biofuels using microalgae and cyanobacteria, the process is still in its early stages and significant productivity improvements can be expected. The results of the current LCA study will assist in further improving our novel two-step technology for bio-jet fuel production from cyanobacteria. Our LCA showed an overall positive result on the environmental sustainability of our system. It was noted that the production of nutrients, in particular nitrate, dominates the environmental impact categories. Cyanobacteria can also conceivably grow well on municipal wastewater as a source of nutrients, including nitrogen, 86 something we have not included in the above model and which would likely increase sustainability.</p><p>Hence, our results described are the very first steps toward a completely renewable jet fuel generated from CO2, water and solar light, provided that cultivation is carried out outdoors and that the hydrogenation and thermal rearrangement steps also utilize renewable energy. We report on the first proof-of-principle study of a combined photobiologicalphotochemical approach for jet fuel production. Extensive future research and development along various lines are needed, and several different short alkenes and dienes could be useful for similar processes. In the photochemical dimerization of isoprene presented in this study, we have produced in total ~3L of isoprene dimers. In an estimation, this amount would allow an Airbus A380 to fly (at least) ~174 m based on the fact that it is estimated to burn 13.78 kg/km and that the densities of our fuels are ~0.8 g/mL. 88 Considering this, there is a very long way to go before we have reached fully sustainable jet fuels produced by a combined photobiologicalphotochemical approach. Yet, every journey begins with a single step.</p><!><p>There are no conflicts of interests.</p>

ChemRxiv

Interactions of unconjugated bilirubin with vesicles, cyclodextrins and micelles: New modeling and the role of high pKa values

BackgroundUnconjugated bilirubin (UCB) is an unstable substance with very low aqueous solubility. Its aqueous pKa values affect many of its interactions, particularly their pH-dependence. A companion paper shows that only our prior solvent partition studies, leading to pKa values of 8.12 and 8.44, met all essential requirements for valid pKa determinations. Other published values, generally lower, some below 5.0, were shown to be invalid. The present work was designed to derive suitable models for interpreting published data on the pH-dependent binding of UCB with four agents, mentioned below, chosen because they are not, themselves, sensitive to changes in the pH range 4-10, and the data, mainly spectrometric, were of reasonable quality.ResultsThese analyses indicated that the high pKa values, dianion dimerization constant and solubilities of UCB at various pH values, derived from our partition studies, along with literature-derived pH- and time-dependent supersaturation effects, were essential for constructing useful models that showed good qualitative, and sometimes quantitative, fits with the data. In contrast, published pKa values below 5.0 were highly incompatible with the data for all systems considered. The primary species of bound UCB in our models were: undissociated diacid for phosphatidylcholine, dianion for dodecyl maltoside micelles and cyclodextrins, and both monoanions and dianion for sodium taurocholate. The resulting binding versus pH profiles differed strikingly from each other.ConclusionsThe insights derived from these analyses should be helpful to explore and interpret UCB binding to more complex, pH-sensitive, physiological moieties, such as proteins or membranes, in order to understand its functions.

interactions_of_unconjugated_bilirubin_with_vesicles,_cyclodextrins_and_micelles:_new_modeling_and_t

Background<!>Criteria for Acceptability and Pitfalls in Interpretation<!>Selection of Publications for Further Analysis<!>Ionization and Binding Equilibria of Unbound UCB Species<!><!>Ionization and Binding Equilibria of Unbound UCB Species<!>General Effects of Binding Affinities and pKa Values on Binding Curves<!>UCB Binding to Phospholipid Vesicles (Additional file 1, Table S1)<!>Interactions of UCB with Dodecyl Maltoside Micelles (Additional file 2, Table S2)<!>Interactions of UCB with Cyclodextrins (Additional file 2, Table S2)<!>Interactions of UCB with Bile Salts (Additional file 3, Table S3)<!>Conclusions<!>List of Abbreviations Used<!>Authors' contributions<!>Additional file 1<!><!>Additional file 2<!><!>Additional file 3<!>

<p>The true pKa values of unconjugated bilirubin (UCB) are an important determinant of the proportion of the three ionization species of UCB in present in solution, and of the overall aqueous solubility of UCB, at any given pH value [1]. In a companion paper [2] we re-examined many published studies that assessed pKa values for UCB in simple solutions, determined by a wide variety of methods. We critically assessed the reliability of the methods used, in relationship to minimal criteria for validity, as well as other considerations. We summarized the deficiencies in the many reports which suggested that pKa values of UCB were below 7.0 and even below 5.0 (see Table eight in Boiadjiev et al. [3]). The only experiments which fulfilled all the validity criteria were our solvent partition data [4,5], which indicated that the two pKa values were much higher, 8.12 and 8.44.</p><p>pKa values of UCB are clearly of great importance in determining the effects of pH on the interactions of UCB with other molecules [1]. In the present paper, we critically re-examine and reinterpret several reports dealing with the effects of varied pH on the interactions of UCB with phospholipid vesicles, cyclodextrins, and dodecylmaltoside and bile salt micelles. Some general approaches have been developed to deal with the binding equilibria involved. We show that these studies are incompatible with proposed low aqueous pKa values for UCB. In several cases, the reinterpretations, using high pKa's and incorporating the effects of binding ratios, self-association and pH-dependent supersaturation, lead to some interesting new models and findings.</p><!><p>Studies of UCB binding vs. pH should meet the same criteria of validity proposed in the companion paper that deals with UCB alone in simple systems [2]: 1) The UCB is pure; 2) There should be no significant degradation of UCB; 3) Measurements are made after a rapidly achieved equilibrium; 4) Unbound UCB concentrations are below to minimally above aqueous saturation; 5) The pH range studied should encompass all suggested pKa values of UCB, and sufficient data points should be available to permit mathematical modeling.</p><p>In addition, for studies of the effects of pH on UCB binding: 6) The large molecule or components of the aggregate must also be purified; 7) Changes with pH in the ionization and/or conformation of constituent regions of the large molecule or aggregate must not affect its intrinsic affinity for each UCB species. This last criterion renders it difficult to perform detailed mathematical modeling of pH effects on interactions of UCB with proteins, natural membranes and other biologically-relevant systems, which are often impure and include molecules whose conformation and binding properties for UCB are affected by pH in incompletely known ways.</p><p>Further discussion is needed regarding criterion 4, above, including the thermodynamic solubility of UCB crystals [4], and the possibility of stable supersaturation [6,7]. Brodersen & Theilgaard [7], showed that, after extensive sedimentation at 100,000 xg, the UCB concentrations were 0.1 μM at pH 7.40, 0.5 μM at pH 7.83, 17 μM at pH 8.05, and 34 μM at pH 8.2. Solubility calculated from our partition data [4] was 0.062 μM at pH 7.40, in reasonable agreement with the data of Brodersen's & Theilgaard [7], but our parition-derived solubilities at the higher pH values were considerably lower than theirs: 0.084 μM at pH pH 7.83, 0.112 μM at pH 8.05, and 0.148 μM at pH 8.2. Thus, for reasons discussed previously [4,6,7], stable supersaturation with UCB, up to fairly high UCB concentrations, may be expected at pH values of 8.0 and above, particularly with the short time intervals between solution preparation and data gathering in most spectroscopic studies. At modestly high concentrations of UCB, such supersaturation may actually be promoted by amphipathic additives, for example bile salts [8,9]. In the reinterpretations attempted below, we have made comparisons of results expected in the presence and absence of supersaturation and assuming high vs. low pKa values for UCB.</p><!><p>To find papers for possible review, we electronically searched PubMed (1967-date), and ISI and Chemical Abstracts databases back to 1950, using the keywords "bilirubin, bile pigments, binding, hydrogen-ion concentration, pH, pKa, ionization", as well as the reference lists in papers thus discovered. To locate papers published earlier than 1968, we manually searched T.K. With's two comprehensive compendia of studies related to bilirubin [10,11]. After eliminating papers that dealt only with bile pigments other than biladienes, or with bilirubin ester conjugates, papers were then eliminated that failed to meet the majority of the criteria of validity summarized above. In line with validity criterion 7, above, we then focused on studies of interactions of UCB with host systems not expected to be sensitive to pH in the range of 5.5 to 10. These 12 papers and one abstract are summarized in Additional files 1, 2 and 3 (Tables S1, S2 & S3), which specify experimental deficiencies for each study. Finally, we confined our detailed analyses to the four best studies; binding of UCB to phosphatidylcholine (PC) [12], dodecylmaltoside micelles [13], β-cyclodextrins [14], and sodium taurocholate [15].</p><!><p>The equilibria governing the binding of unconjugated bilirubin (UCB) involve its three unbound species, the protonated diacid (H2B), the monoanions (HB-) and the dianion (B=), one or more of which binds independently to a larger host system [1], such as phospholipid vesicles, cyclodextrins, or micelles. For each UCB species, this binding can then be described in terms of a distribution ratio, K = mols bound/mols unbound (free).</p><p>The ionization equilibria of the unbound species apply whether or not a binder is present. Thus, with a binder present, at any pH, the relative fraction (f) of each unbound monomeric species of UCB (fH2B, fHB-, and fB=) will be equal to f for that species, at the same pH, in an aqueous phase containing only monomeric UCB species [1]. Therefore, as described in the appendix in Hahm et al. [4], these unbound fractions are determined solely by the pH and the assumed pKa values of the monomeric species. The total concentration of unbound, monomeric UCB species (Bm) is given by Bm = [H2B] + [HB-] + [B=] = [H2B] × (1 + K1/[H+] + K1.K2/[H+]2). Note that these illustrative calculations and simulations (Figures 1, 2 and 3 and Table 1) deal only with pH effects on monomeric UCB species, so that fH2B + fHB- + fB= = 1.00. Additional effects of the formation of B= dimers [4] and supersaturation [7,9,16] have been considered below in the actual fitting of experimental data on UCB binding to phospholipid vesicles, cyclodextrins and micelles (Figures 4, 5 and 6 and Table 2).</p><!><p>Fractions (f) of unbound monomeric UCB at various pH values. Calculated assuming high pKa values for UCB of 8.12 and 8.44 [4]. Abbreviations: H2B, UCB diacid; HB-, UCB monoanions; B=, UCB dianion. fH2B + fHB- + fB= = 1.</p><p>Fraction of total UCB bound (F) at various pH values, assuming preferential binding of H2B: Effect of distribution ratio (K = bound/free UCB). Calculated assuming high pKa values for UCB of 8.12 and 8.44 [4]. H2B, fully protonated UCB diacid; shaded star, midpoint pH of titration curve.</p><p>Fraction of total UCB bound (F) at various pH values, assuming preferential binding of B=: effect of distribution ratio (K' = bound/free UCB). Calculated assuming high pKa values for UCB of 8.12 and 8.44 [4]. B=, UCB dianion; shaded star, midpoint pH of titration curve; open star, midpoint for TC binding (K' for B= = 730, [21]).</p><p>Modeling UCB binding to phosphatidylcholine vesicles at various pH values. Experimental data derived from Tipping et al., 1979 [12]. Curve A, calculated assuming pKa values for UCB of 8.12 and 8.44 and the dimerization constant for the B= dianion of 0.26 μM-1 [4]. Curve B, calculated assuming constant maximum solubility of H2B of 51 nM [4]. Curve C, calculated assuming pKa values of UCB of 4.2 and 4.9 [3,17-20]. Shaded star, mid-point pH of binding curve A.</p><p>UCB binding to β-cyclodextrin at various pH values. Experimental data (black squares) from Kano et al., 1995 [14]. Curve A, calculated ellipticity assuming pKa values for UCB of 8.12 and 8.44, with B= dimers [4]. Curve B, calculated ellipticity assuming pKa values of UCB of 4.2 and 4.9 [3,17-20]. Curve C, calculated ellipticity assuming constant maximum solubility of H2B of 51 nM [4].</p><p>Experimental vs. calculated CD ellipticities (103θ) of UCB in 50 mM sodium taurocholate solutions. Data for plotting taken from Table 2. *103θ values, obtained with 4.6 μM UCB, have been multiplied by the factor, 34/4.6 to render them comparable with data obtained at 34 μM UCB. Experimental values are from D'Alagni M, et al. [15]. Calculated values are derived as described in the text and summarized in Table 2.</p><p>Fractions (f) of unbound UCB species at different pH values, assuming low pKa values of 4.2 and 4.9*.</p><p>* Calculated assuming pKa values of UCB of 4.2 and 4.9 [3,17-20].</p><p>† Bilirubin species: H2B, diacid; HB-, monoanions; B=, dianion.</p><p>Circular dichroism (ellipticity, 103θ) of UCB in 50 mM sodium taurocholate solutions*</p><p>* Data from D'Alagni M, et al., 1994 [15]. For experimental problems, see Additional file 3, Table S3.</p><p>† 103θ values obtained with 4.6 μM UCB have been multiplied by a factor, 34/4.6, to render them comparable with 103θ values obtained at 34 μM UCB.</p><p>‡ fsHB- and fsB=, fractions of total UCB bound to TC as monoanion and dianion respectively, calculated utilizing the partition ratios of each UCB species in water [4] and 50 mM TC [21], plus fitted 103θ values for bound HB- and B=, derived from modeling the experimental changes in 103θ with pH, as described in the text.</p><!><p>Figure 1 shows the fractions of UCB species over the pH range of 4 to 10, using the high pKa values of 8.12 and 8.44 [4]. Table 1 shows the fractions derived by applying the low pKa values of 4.2 and 4.9, advocated in recent papers [3,17-20]. For such low pKa values, B= is the dominant fraction at pH values above 7.0 (fB= ≥ 0.992, close to unity), with fH2B < 0.001 at pH values above 6.0 and fHB- < 0.001 at pH values above 8.0. In stark contrast, for pKa values above 8.0 [4], the f values of H2B, HB- and B= are moderately high over wide ranges of pH and are of comparable magnitude at most pH values between 6 and 10 (Figure 1). At pH ≤ 6.0, only fH2B is above 0.99 and, over the pH range of 6.5 to 10.0, significant roles can be played by any of the three UCB species, each of which will show significant dependence on pH (Figure 1). The inescapable qualitative conclusion from these considerations is that numerous studies which show UCB interactions increasing with pH in range of 6.5-10.0 are far more compatible with the high pKa values of UCB (> 8.0) than the low ones (< 5.0).</p><!><p>The values of pKa affect some characteristic features of the uptake and binding curves of UCB as a function of pH. As the pH increases, the equilibrium among the unbound species of UCB will shift to the more ionized forms (Figure 1, Table 1). The binding of UCB will, therefore, increase with pH if the preferentially bound species is B= (as proposed for sodium taurocholate micelles [1,21]), but will decrease with pH if H2B is preferentially bound (as proposed for PC vesicles [4,12]).</p><p>The mid-point pH values of plots of binding vs. pH may shift significantly from the pKa of the unbound UCB ligand according to its affinity for the large molecule or aggregate, especially if the binding affinity for one of the three UCB species, H2B, HB- and B=, is strongly dominant. This effect is easily modeled by assuming that only one of the three species is bound and that the unbound UCB concentrations are low enough for self-association of the unbound UCB species to be negligible. The proportions of the unbound species, H2B, HB- and B=, are determined by the pKa values and the pH and calculated from the relevant terms in the model equation [4], as described above. If the relevant UCB species is not the only one bound, but is the predominant bound species, these analyses still apply qualitatively and the plots will closely resemble those derived by assuming that only a single UCB species binds.</p><p>Figures 2 and 3 show some simple representations of such modeling, using the high pKa values of 8.12 and 8.44 [4]; the fraction F = [UCB]bound/[UCB]total is plotted against pH. For Figure 2, it is assumed that only H2B binds, e.g. to a phospholipid vesicle [12]. The fraction F is calculated for values of the partition ratio K = [H2B]bound/[H2B]free, increasing from 0.2 to 104. As expected (Figure 2), with increasing pH, F decreases sigmoidally due to the decreasing fraction (fH2B) of the unbound UCB diacid (Figure 1). For each curve, its mid-point pH, where the binding of H2B is 50% of the maximum, is represented in Figure 2 by a shaded star. These mid-point pH values increase from 8.255 for K = 1, to 8.780 for K = 10, 9.251 for K = 102 and 9.778 for K = 103; for very low K values <<1 (e.g. for K = 0.2), the limiting mid-point pH is 7.994. This increase in the mid-point pH of the binding curves of H2B with increasing K is a general result that applies to binding to any large molecule and to any pKa values that are chosen. An example is binding of UCB to PC (Figure 4, curve A)</p><p>When the UCB dianion, B=, is the only interacting species, the uptake or binding is expected to increase with pH (Figure 3), due to the increasing fraction (fB=) of the unbound UCB dianion (Figure 1). Figure 3 plots F vs. pH at values of K' (= [B=]bound/[B=]free) of 0.2, 1, 10, 102, 103 and 104, assuming the pKa values are 8.12 and 8.44. The mid-point pH values of these sigmoidal curves (shaded stars), where binding of B= is 50% of the maximum, decrease from 8.348 for K' = 1, to 7.863 for K' = 10, 7.315 for K' = 102, 6.794 for K' = 103 and 6.287 for K' = 104. The limiting value of the mid-point pH, when K' values are <<1 (e.g. K = 0.2), is 8.574. This trend of decreasing mid-point pH values with increasing K' is also a general result for binding of B= to any large molecule for any pKa values chosen.</p><p>Many curves of UCB interactions as a function of pH show binding increasing with increasing pH (Figure 5; Additional files 1, 2 and 3, Tables S1, S2 & S3), indicating a primary role for the binding of B= and dictating that the mid-point pH values must be lower than the true pKa values of unbound UCB (Figure 3). Therefore, the large number of these titration curves with mid-point pH values between 6 and 8 are compatible with high pKa values of 8.12 and 8.44 [4], and quite incompatible with pKa values below 5.0. If pKa values were low, e.g. 4.2 and 4.9 [3,17-20], curves showing increasing binding of UCB with increasing pH and, therefore, binding of B=, would yield mid-point pH values lower than about 5.0.</p><p>The mid-point pH values of sigmoidal titration curves, like those in Figures 2 & 3, are sometimes misinterpreted as average pKa values. This is clearly wrong, since all of the curves in Figures 2 &3 assume the pKa values of unbound monomeric UCB to be 8.12 and 8.44.</p><!><p>Unilamellar vesicles of phosphatidylcholine (PC) are bilayers with a hydrophobic core of the fatty acid polymethylene chains, and hydrophilic exterior and interior surfaces composed of phosphryl-choline sidechains whose terminal -N+(CH3)3 groups are on the surfaces [22]. In the pH range 4 to 10, PC's are zwitterions, with both the amine and phosphoric acid groups fully ionized [23,24]; thus, changes in UCB binding to PC may be attributed to altered proportions of the three ionization states of unbound UCB in this pH range. Depending upon the molar ratios and experimental conditions under which the UCB and PC are mixed, either H2B or B= may be the UCB species that is preferentially bound [25].</p><p>pH effects on the interactions of UCB with PC vesicles have been reported in a number of studies [12,16,22,25-27]. Many of them involve time-dependent processes in non-equilibrium situations. Some used PC systems containing fluorescent probes, which are difficult to interpret due to uncertainties regarding the localization of the probes and the nature of their interactions with UCB.</p><p>The report by Tipping et al. [12], which assessed binding of UCB to egg PC vesicles from the effects of binding on the spectrum of UCB, most fulfills the above criteria of an acceptable study (Additional file 1, Table S1), although neither the UCB (10 μM) nor the PC (100 μM) were purified. We have, therefore, amplified our previous analysis [4] of this study.</p><p>The data points in Figure 4 (black squares) show values of ñ (moles of UCB bound per mole of total PC) vs. pH over the pH range of 6.54 to 9.54, obtained from Figure 10 in the paper of Tipping et al. [12]. ñ decreases from a maximum of 0.049 as pH increases, indicating (see Figure 2) that the binding of H2B is the predominant interaction. The concentration of bound UCB (Bb = ñ × 100 μM) is then used to calculate the concentration of unbound UCB (Bf = 10 μM - Bb). Bf ranges from 5.1 μM at pH 6.54 to 9.5 μM at pH 9.54.</p><p>At each pH, the fraction of unbound H2B (= [H2B]f/Bt) in the presence of 100 μM PC [12], is assumed to be equal to the fraction of H2B ([H2B]/Bw) in the aqueous phase in the absence of PC, calculated using Equation four in Hahm et al. [4]) and applying the high pKa values of 8.12 and 8.44 and the dimerization constant for the B= dianion of 0.26 μM-1.</p><p>Assuming that unbound UCB is in a solution state and that H2B is the only species that binds to the PC yields a simple model of distribution of H2B between the free and vesicle-bound state, ñ = K" [H2B]f. Applying this equation to the 9 data points of Tipping et al. [12], yields a mean K" = 0.00932 ± 0.00053 μM-1) and curve A in Figure 4). The fit of curve A with the experimental ñ values (black squares) is quite good (r = 0.953), and suggests that our assumptions are valid and that the H2B partition model is reasonable. The deviation of the data points from Curve A at the highest two pH values possibly reflects weak binding of the B= dianion [16,22,27], which constitutes over 80% of the UCB species above pH 9.0.</p><p>Tipping et al. [12] reported that "The spectral differences developed within the time required to mix the solutions and start recording and were the same whether multilamellar or unilamellar vesicles were used". This rapid analysis and establishment of equilibrium are necessary conditions for our detailed interpretation of the data. With the total concentrations of UCB = 10 μM and PC = 100 μM (molar ratio of UCB/PC, 0.1) used in the pH-variation study, the unbound UCB concentrations (Bf) at equilibrium were modestly above the aqueous solubility of UCB at all pH values ≤ 9.0 [4]. If supersaturation were absent, the concentration of H2B would be at its saturation value over the entire pH range, and ñ should maintain its value of 0.049 at pH 6.54 (the horizontal dashed line B, in Figure 4). The experimental data indicate, therefore, that stable supersaturation was indeed likely present in these short term experiments.</p><p>If, by contrast, the low pKa values of 4.2 and 4.9 [3,17-20], are used to fit the ñ value at pH 6.54, a much higher value of K", 94 μM-1, is needed to yield the calculated [H2B]. Using this constant and [H2B], determined from Bf and the low pKa values, yields curve C in Figure 4. As pH increases from 6.5 to 7, ñ decreases steeply, and is below 0.01 at a pH of 6.9 or higher. The low pKa values are, thus, not compatible with ñ vs. pH data determined for the binding of UCB by neutral PC vesicles [12]. Since, in this study, K ≈ 1 [12], the mid-point pH value of 8.1 (shaded star, Figure 4, curve A) would be close to the mid-point value of the curve for K = 1 in Figure 2, and thus compatible with our proposed high pKa values in the absence of a UCB binder [4].</p><p>Comments are warranted on an often-cited study, by Eriksen, et al., of the binding of UCB to egg PC vesicles [16]. The concentrations were 12.8 μM UCB and 130 μM PC. Figure two in that paper plots the changes with pH of the quenching by bound UCB of the fluorescence of diphenyl-hexatriene incorporated into the lipid bilayer, measured 3 hours after adding the UCB in an aqueous alkaline solution (pH 11). The sigmoidal curve exhibits decreasing fluorescence (increasing quenching by bound UCB) as pH increases, with a mid-point near pH 7.8. This evidence for increased affinity of UCB binding to PC as pH increases would indicate a primary role of binding of the dianion, B=. Since the dianion fraction for low pKa values is already 0.992 at pH 7.0 (Table 1), any significant increase in binding at higher pH values is incompatible with low pH values (see above). The increasing affinity of UCB for PC with increasing pH is in stark contrast to Tipping's data [12] and to our model based on uptake of H2B (above). However, examination of data in Table one of the Eriksen paper, reveals that results obtained "immediately" after adding the UCB at pH 7.4 and 8.3 are just the reverse of those obtained three hours later (as in their Figure two).</p><p>Eriksen's Figure one [16] indicates complex, pH-dependent time effects. Using the percent decrease in fluorescence, P, as a rough measure of the extent of binding of UCB, the data show a high value of P = 79% at pH 7.43 "immediately" after addition of UCB. Within one minute, there is rapid reversal of binding and P is decreased to 28%, followed by a far less rapid decrease in P to 23% after 3 hours. Results at pH 6.79 are similar. In contrast, at the highest pH in their Figure one, 8.34, the "immediate" value of P = 57% remains about the same for three hours. At the intermediate pH of 7.90, the "immediate" value of P = 73% increases a little over the first minute, remains about the same for four more minutes, and then decreases gently over the next two hours or so to a plateau of P = 22%. These complex time effects yield very different sequences of P values decreasing with pH: 7.43>7.90>8.34 for "immediate" measurements; the irregular sequence 7.90>8.34>7.43 between 1 and 20 minutes; 8.43>7.90>7.43 at 60 minutes; and 8.34>7.90 ≈ 7.43 at 3 hours. Some discrepancies among data in Eriksen's Table one, Figure one and Figure two [16], suggest irreproducible time effects.</p><p>These observations can be rationalized roughly on the basis of the progressive formation of aggregates of UCB, after the initial rapid binding of UCB. Cestaro et al. [22] have made a similar proposal, and Eriksen et al. [16] themselves warn that, as the pH falls, supersaturation of the aqueous medium with UCB must be avoided to prevent progressive formation of H2B aggregates. Their gradient ultracentrifugation data of 3 hour incubations (Figures three-d&e of Eriksen et al.) [16] showed large aggregates of UCB, with or without phospholipids, at pH 7.0 and smaller aggregates at pH 8.2. The dependence on pH, of the rates of formation of colloids and precipitates from supersaturated solutions of UCB itself, is clearly relevant. As discussed earlier, after extensive sedimentation at 100,000 xg, the measured supernatant concentrations of UCB at pH values of 7.40, 7.83, 8.05 and 8.2 were 0.1 μM, 0.5 μM, 17 μM and 34 μM respectively [7]. These are much higher than the calculated solubilities of UCB at the same pH values [4], which were low (0.062 μM, 0.084 μM, 0.112 μM and 0.148 μM) and increased only a little as pH increased. This indicates that the egg PC-UCB systems of Eriksen et al. [16] were highly supersaturated when first prepared, and that aggregates of UCB formed to relieve the supersaturation. This occurred to a much greater extent and much more rapidly at pH 7.43 than at pH 8.34, with intermediate behavior at pH 7.90. Any micro-precipitation of UCB, which may contain small proportions of PC, will reduce Bf and its interactions with PC vesicles. Vazquez et al. [28] have also reported significant reversal of UCB interaction with phospholipid membranes with time.</p><p>In view of the above considerations, and evidence that the uptake of UCB by PC vesicles is almost instantaneous (t1/2 = 2 msec) [29], we conclude that the "immediate" effects on P after addition of UCB to PC are least susceptible to time-dependent effects arising from the thermodynamic drive to decrease supersaturation. Indeed, even the "immediate" values of P may still be subject to some aggregation effects, particularly at pH 7.43. The qualitative trend in the "immediate" decrease of P with increasing pH is consistent with the results of Tipping et al. [12] and with our model, based on the binding of H2B and high pKa values for UCB. The conclusions are opposite to those given by Eriksen et al. [16], which were based on assumptions of binding of the dianion, B=, and low pKa values.</p><p>Other systems studied by Eriksen et al. [16] (their Table one and Figure one-b) also show exceptionally large and rapid decreases in binding of UCB with time at pH 7.4, intermediate behavior at pH 7.90, and minor effects at pH 8.34. These systems all contained 12.8 μM UCB, mixed with various types of phospholipids ± cholesterol, at varied concentrations: egg PC, 150 μM + cholesterol 15 μM; phosphatidlyethanolamine, 106 μM; phosphatidlyethanolamine, 102 μM + cholesterol 10 μM; sphingomyelin, 32 μM; and phosphatidylserine, 55 μM. The qualitative interpretations above apply to all of them. The data points are limited, and some of these systems involve charge effects, which introduce further uncertainties. In all systems, after 3 hours, UCB binding is greater at pH 8.3 than at pH 7.4. The "immediate" results, however, show a stronger reverse effect of pH, P = 82% at pH 7.4 vs. 25% at pH 8.3, for the egg PC-cholesterol system as compared to the egg PC system. They also show lower binding to sphingomyelin at the higher pH, indicating predominant binding of H2B. This common pattern leads us to conclude that the complex pH-dependence of precipitation of UCB from supersaturated solutions can lead to highly anomalous pH-dependence of UCB interactions with phospholipids.</p><p>The study of UCB binding to gangliosides by Vazquez et al. [30], reported little change in UCB binding going to pH 7.4 to 8.0, but a small increase in going from pH 7.0 to 7.4, suggesting an important role for UCB anions. Assuming low pKa values, the unbound dianion fraction changes only from 0.992 at pH 7.0 to 0.999 at pH 8.0, whereas the monoanion fraction decreases by a factor of 10 (Table 1). Therefore, these observations are incompatible with low pKa values, whereas with high pKa values, both the mono- and dianon fractions increase significantly over this pH range (Figure 1). Other problems with this study (Additional file 1, Table S1) are that: the systems were highly supersaturated with UCB, the UCB was neither purified nor protected against degradation, equilibrium was not attained rapidly, and the pH range studied was insufficiently broad to derive pKa values.</p><p>Zucker and Gössling used stop-flow kinetics to examine the rates of uptake of BSA-bound UCB by cultured HepG2 cells [26]. They found an inverse correlation between pH and the rate of UCB flip-flop, which they showed to be due to pH effects on the rates of dissociation of UCB from albumin and from the membrane bilayer. They concluded that "the identification of an inflection point at pH 8.1 is indicative of a pKa value for bilirubin in this range". Although this seemingly supports our contention that the pKa's of UCB are above 8.0 [4], these are kinetic and not equilibrium data, involve BSA (which is pH-sensitive in this range), and measure diffusion across a complex hepatocyte membrane. Studies of binding of UCB to whole cells [31], or natural membranes (e.g. red cell ghosts, mitochondria, synaptosomes) [28,32-35], likewise cannot be used to evaluate unequivocally the role of pKa values for UCB. These heterogeneous structures contain a mix of phospholipids, as well as proteins and other components, which may ionize and change conformation in the pH range studied and have unknown effects on membrane structure.</p><!><p>Non-ionic long-chain alkyl-saccharides, such as octylglucoside (C8G) or dodecylmaltoside (C12M), are readily purified and form micellar systems that are suitable for studies of the influence of pH on the uptake of UCB. Binding probably involves interactions of the -OH groups of the saccharide with the ionized -COO- groups of UCB, and induces marked changes in the ellipticity of the bound UCB [13].</p><p>Kano et al. [13] showed that solubilization of UCB (20 μM) in 1 mM C12M micelles (their Figure five) generated no circular dichroism effects from pH 4.5 to 6.2. (Note that their legend for Figure five misidentifies the saccharide as C8M, octylmaltoside; the text indicates it is C12M that was studied.) Kano's Figure three shows that UCB does not interact with C8G below the c.m.c. of 22 mM. The same would be expected of C8M, whose c.m.c. value should be similarly high, but the concentration of the alkyl-saccharide used in their Figure five is 1 mM, well below the presumed c.m.c. of C8M.) Above pH 6.2, the observed ellipiticity increased steeply with rising pH up to about pH 8.5, and leveled off to a constant value from pH 8.8 to 10. Since the conformation of the non-ionic C12M is unaffected by pH, their titration curve reflects increased binding of UCB dianion (B=) as the proportion of unbound dianion, fB=, increases with increasing pH.</p><p>The rise in ellipticity with pH has a mid-point of about pH 7.5. Reference to our Figure 3 shows that this is compatible with high pKa values of 8.12 and 8.44 and a UCB distribution ratio K (bound/free) between 10 and 100. If the low pKa values of 4.2 and 4.9 are invoked, the midpoint pH value would have to be lower than about 5, as discussed above. Moreover, at pH 6.5, the fraction of UCB dianion, fB=, would already be 97.5%, yet the observed ellipticity is near zero at that pH. In addition, the steep rise in ellipticity observed above pH 6.5 would not be expected. Thus, only the higher pKa values are compatible with the observed data.</p><!><p>Kano et al. [14] and Lightner et al. [36] studied CD of UCB in solutions of β-cyclodextrin (β-CDx) as a function of pH. Both studies used purified UCB and were conducted within minutes of mixing UCB with the cyclodextrins; Kano's studies also took precautions to minimize oxidation and photo-oxidation of the pigment. In each study, the molar circular dichroism absorption coefficient (Δε) was minimal at pH 6.0 and increased steeply as pH increased above pH 7.0, indicating that UCB dianions are the primary interacting species. Lightner's systems [36] were even more supersaturated than Kano's and measurements were provided only at integral pH values from 6 to 10, rendering Kano's data [14] preferable for analysis.</p><p>In the study by Kano et al. of UCB binding to β-CDx [14], no ellipticity was observed at pH 5.5, but Δε increased sigmoidally as pH increased from 5.5 to 9.5, with a steep slope between pH 7.0 and 8.5, and no further change between pH 9.5 and 10.8 (their Figure 3). Chiral recognition effects, registered at several pH values with the protonated 1-amino-β-cyclodextrin as the host molecule, (their Figure three [14]), support the conclusion that predominantly UCB dianion was bound.</p><p>At pH 10.8, an affinity constant of 23 L/mol was reported for UCB binding to β-CDx, indicating that 4.67 μM, or 18.7% of the total [UCB] (25 μM), was bound to 10 mM β-CDx at that pH. Assuming that the UCB binding is confined to B=, and that binding of UCB is proportional to the concentration of unbound, monomeric B=, the amount of UCB bound at lower pH values can be calculated using Δε values relative to Δε at pH 10.8. It is then possible to calculate Bf and, akin to modeling the data on binding of UCB to PC as described above, to apply Equation four of Hahm et al. [4] to calculate the unbound, monomeric dianion concentration, [B=], at each pH, assuming different pKa values.</p><p>Assuming the high pKa values of 8.12 and 8.44 and the dianion dimerization constant of 0.26 μM-1 [4] in calculating [B=]f from Bf, the four data points at pH values of 10.8, 9.5, 8.65 and 8.2 showed an excellent proportionality between Δε and [B=] using the equation Δε = Q × [B=]f, where the constant, Q = 1.099 ± 0.018 (n = 4, r = 0.974, S.D. = 0.17). Curve A in Figure 5 shows the results of fitting all the data to the above equation, Δε = 1.099 × [B=]. Curve A gives a reasonable fit for the experimental Δε at the five highest pH values (7.83 and above) and, of course, the very low pH of 5.5, where Δε = 0. Bf was below calculated solubilities at both pH 10.8 and 9.5, but there was likely stable supersaturation of the unbound UCB phase at the three lower pH values, based on previous observations [7], as discussed earlier. The rapid decrease in Δε below pH 8.65 is primarily due to the rapid decrease in the fraction of unbound monomeric UCB that exists as the dianion (fB=). The systematic discrepancies between the data and Curve A in the pH range of 7.3 to 6.9 (Figure 5) can be ascribed to many factors that are difficult to evaluate, including self-aggregation of the more highly supersaturated phase of unbound UCB (e.g. R = 369 at pH 7.0).</p><p>In comparison to the reasonable representation of the Δε data using pKa values above 8.0, the results obtained are very different (Figure 5, Curve B) if the lower pKa values of 4.2 and 4.9 [3,17-20], are used to calculate [B=] from Bf. The proportionality constant K, relating Δε to [B=] was determined by fitting the data at the pH values of 9.5 and 10.8, where the solutions are undersaturated with unbound UCB. K was then used to calculate Δε from the estimated [B=] at other pH values. Since the dianion fraction (fB=) of unbound UCB would be close to unity at pH 7.0 or above, and have the value of 0.79 even at pH 5.5, little variation is expected in [B=] and, therefore, in Δε over the alkaline pH range. This is shown by Curve B in Figure 5, which does not fit Kano's Δε vs. pH data [14]. This indicates that the low pKa values are highly incompatible with the experimental observations. The same conclusions apply to the data of Lightner et al. [36], which show similar pH dependence of binding at alkaline pH.</p><p>From the mean fit of the Δε data at pH 9.5 and 10.8 to the concentrations of [B=] calculated using pKa values of 8.12 and 8.44 and the dianion dimerization constant of 0.26 μM-1, it is possible to calculate Δε from [B=] at lower pH values, where total solution concentrations are above solubility, by assuming that all solutions are at true saturation, with the excess [B=] forming a suspension. For such systems, [B=] = 0.051 × 10(2pH-16.56). Here, 0.051 μM is the solubility of H2B and 16.56 = pKa1 + pKa2 [4,6]. The calculated curve C (Figure 5) shows a steep drop in Δε below pH 9.3. Curve C has little resemblance to the experimental data, suggesting that, at most pH values, a stable supersaturation was probably important in these relatively short-term experiments.</p><!><p>Taurine-amidates of bile salts, which have pKa values near 1.0, are the only bile salts which are fully ionized throughout the pH range from 4 to 10 [37], and are thus most suitable to assess the roles of pH and pKa values on UCB binding. Rapid solvent partition of purified UCB from undersaturated chloroform solutions into buffered aqueous solutions of 50 mM taurocholate (TC) (at least 80% in micelles), at ionic strength = 0.15, was performed over the pH range of 6.0 to 9.5 [1,21]. The data were fitted to the equation log P = log Po + [1 + 10(pH-A) + 10(2pH-B)], where Po is the partition ratio for H2B, and the values for A and B were 7.36 ± 0.5 and 14.08 ± 0.05 respectively.</p><p>Solubilities of UCB in 50 mM TC [21] and in water [4] were calculated from the fitted P data and an estimated solubility of UCB in chloroform of 1.14 mM [9]. (As argued before [4,6], a better estimate of the solubility of UCB in chloroform is 0.88 mM, which will decrease the estimated UCB solubilities in 50 mM TC and water by 23%, but not affect the shape of the titration curves.) Using the high aqueous pKa values of 8.12 and 8.44, the total and unbound concentrations of each UCB species in 50 mM TC was calculated from the solubilities in 50 mM TC/chloroform [21] and water/chloroform [21] respectively; their difference equaled the concentration of the bound species. From these, the distribution ratios (bound/free) of the three UCB species were calculated to be 1.41 for H2B, 12.9 for HB-, and 730 for B=, indicating predominant binding of B= [21], as concluded previously by Kano et al. [38]. From the relative contributions of bound B=, HB- and H2B, the apparent pKa values of UCB associated with the TC were estimated: 7.16 ± 0.5 for pKa1, 6.69 ± 0.5 for pKa2 and 13.85 ± 0.05 for the sum of pKa1 and pKa2, which is derived with somewhat greater precision.</p><p>The relationship of these pKa values, lower than the aqueous values of 8.12 and 8.44, to micelle-water distribution ratios for H2B, HB- and B=, has been discussed in our companion paper [2]. pKa values in the bile salt systems in the range of 6 to 7 have also been reported from studies using micellar electrokinetic capillary chromatography [39,40]. Since these authors did not determine micelle-water distributions, these were average values for the whole system, but appear to be compatible with Hahm's pKa values in 50 mM TC derived by solvent partition [21]. Utilizing essentially the same equations [1,40] relating changes in pKa values resulting from uptake in micelles and micelle-water distribution ratios for H2B, HB- and B= mentioned above [21], the pKa values of 6 to 7 in 50 mM TC are thus compatible with the high pKa values in water [4].</p><p>CD studies have been performed with unpurified UCB in 50 mM solutions of various bile salts [15,41,42]. In many cases, the UCB concentrations used appear to be above saturation. For 50 mM sodium taurocholate (TC), calculated Bf was above aqueous phase saturation at pH below 7.5 when total UCB was 4.6 μM and at pH below 8.3 when total UCB was 34 μM. Bile salts, however, have been shown to promote extensive, stable supersaturation of UCB [8,9]. In systems of UCB in 50 mM TC [15], the observed ellipticities were reasonably proportional to the two UCB concentrations, even though the calculated solubility in 50 mM TC was exceeded at several pH values. Similar proportionalities are seen over higher ranges of concentration of sodium glycocholate [15] and sodium deoxycholate [41] at pH values of 7.0 and above.</p><p>Since background information is available for 50 mM TC [21] and TC is expected to show no dependence upon pH, we have attempted a semi-quantitative interpretation of the pH effects for the ellipticity study of 4.6 μM and 34 μM UCB in 50 mM TC [15]. We assumed stable supersaturation in these short-term experiments (30 min or less) at pH values of 7.23 and higher. Table 2 presents ellipiticity values at 458 nm, estimated from their published graphs. The three measurements at 4.6 μM UCB have been multiplied by the factor, 34/4.6 to render them comparable to measurements at 34 μM UCB. The ellipticities are large and negative at the two lowest pH values of 7.23 and 7.35, become positive at pH about 8.1, and then show higher positive values as pH increases to 9.1 and above. This general trend was exhibited by all the bile salts studied [15,41,42].</p><p>As noted earlier, equations relating partition coefficients to pH for 50 mM TC [21] and water [4] allow the calculation of the fractions of total UCB solubilized by the TC as H2B, HB- and B=. The pH-dependence of the ellipticity values at 458 nm suggest strongly that the interactions with TC aggregates of the solubilized HB- cause negative ellipticities whereas interactions of the solubilized B= cause positive ellipticities. Table 2 shows the estimated fractions of total UCB (34 μM) that are bound to TC as HB- and B=. As expected, as pH increases, the fraction of total UCB that is bound to TC as HB- (fsHB-) decreases and the fraction bound as B= (fsB=) increases. In order to test our model, we use the equation below in which the constants A* and B* are measures of the effectiveness of (fsHB-) and (fsB=) in determining the value of 103θ:</p><p>The experimental ellipticity values in Table 2 are well described by this equation: n = 7, r = 0.992, SD = 1.1, A* = -98.7 ± 5.6, B* = 5.24 ± 0.54. Figure 6 shows that, at total UCB concentrations of 4.6 and 34 μM, the experimental ellipticities are in reasonable concordance with calculated ellipticities. Note that the two most positive ellipticity values for 4.6 μM UCB, at pH 8.13 and 9.12, and for 34 μM UCB at 9.35 and 11.60 were measured in unsaturated solutions. The significant changes in ellipticity at these high pH values are clearly due to significant changes in (fsHB-) and (fsB=) over this alkaline pH range. Remarkably, the measured ellipticity increased significantly, by about 2%, between pH 9.35 and 11.60, which is matched by a calculated increase in ellipticity of about 4%. Since, if low pKa values are assumed (Table 1), fB= is already close to unity at pH 8.5 (0.9997), and the changes in ellipticity at pH values above 9.0 are qualitatively incompatible with the low pKa values for UCB proposed by others [3,17-20]. The model proposed here for representing the circular dichroism values of UCB in 50 mM TC appears to be new and is likely to be useful also for other bile salt systems.</p><!><p>The analyses and interpretations presented above show that the high pKa values of 8.12 and 8.44 [1,4-6] are far superior to low pKa values of 4.2 and 4.9 [3,17-20] in rationalizing experimental data for the effects of pH on interactions of UCB with PC vesicles, cyclodextrins, and micelles of dodecyl maltoside and bile salts. The approaches outlined also demonstrate and take into account the possibly important roles of self-association of B= and pH-dependent supersaturation effects. They support further the conclusions of our companion paper [2] that analyzes the effects of pH on various properties of UCB alone in aqueous and organic solvent systems. That paper demonstrated that only our solvent partition studies [4,5] met all the requirements for valid experiments when using a poorly-soluble, unstable compound, such as UCB. Together, the two present papers clearly indicate that the pKa values of UCB are well above the pKa values of simple carboxylic acids (usually about 4.5). Theoretically these remarkably high values may result from the combined interactions of three factors that result from the unique, complex internal hydrogen-bonding of UCB, as presented elsewhere [5].</p><p>We do not claim that the high pKa values of 8.12 and 8.44, derived from our solvent partition study of UCB [4], are exact. The accuracy of these model-derived high pKa values of UCB is given on page 1131 of that paper. These values (mean ± S.D.) are: pK'1 = 8.12 ± 0.23; pK'1 + pK'2 = 16.56 ± 0.10; pK'2 = 8.44 ± 0.33. That paper also notes that "the analysis yields a less accurate estimate of the individual pKa values than of their sum, pK1 + pK2". The errors in these pKa values are insignificant when compared with the difference of 7.5 pH units between the sum (pK1 + pK2) of these high pKa values, and the sum (9.1) of the low pKa values of 4.2 and 4.9 proposed by others [3,17-20], with which our high pKa values were compared.</p><p>It should thus now be clear that such high pKa values not only acceptably explain the experimental findings on the effects of pH on the properties and interactions of UCB, but that the often quoted low pKa values [3,17-20], are incompatible with the experimental data. The implications are not trivial, since they invalidate older, but still widely held, interpretations and modeling of the binding and cytotoxicity of UCB [43-47] that were based on the assumptions that pKa values were below 5.0 and that B= was, therefore the dominant species of unbound UCB in the physiological pH range (Table 1). More recent reviews are available [1,48,49] that are based on the realistic pKa values above 8.0, and the consequent predominance of H2B among the unbound UCB species at physiological pH values (Figure 1).</p><!><p>UCB: unconjugated bilirubin; H2B: UCB diacid; HB-: UCB monoanions; B=: UCB dianion; fH2B: fHB-, and fB=, relative fractions of the unbound monomeric UCB species; F: [UCB]bound/[UCB]total; Bf: unbound (free) UCB concentration; R: the UCB saturation ratio = Bf/estimated solubility of UCB at a given pH; K: distribution ratio = mols bound/mols unbound (free); PC: phosphatidylcholine; c.m.c., critical micellar concentration; C12M: dodecylmaltoside; C8M: octylmaltoside; C8G: octylglucoside; β-CDx: β-cyclodextrin.</p><!><p>Both authors were equally involved in the conceptualization and writing of this paper, and both have read and approved the final manuscript. JDO performed the literature search and PM developed the mathematical models.</p><!><p>Studies of interactions of UCB with phospholipids. Details of the three publications that were considered, including the degrees of supersaturation with UCB, the analytical methods used, the charateristics of the binding curve, the experimental problems, and the citation.</p><!><p>Click here for file</p><!><p>Studies of interactions of UCB with alkyl saccharides and cyclodextrins. Details of the three publications that were considered, including the degrees of supersaturation with UCB, the analytical methods used, the charateristics of the binding curve, the experimental problems, and the citation.</p><!><p>Click here for file</p><!><p>Studies of interactions of UCB with bile salts. Details of the seven publications that were considered, including the degrees of supersaturation with UCB, the analytical methods used, the charateristics of the binding curve, the experimental problems, and the citation.</p><!><p>Click here for file</p>

PubMed Open Access

A Molecular Description of Flexibility in an Antibody Combining Site

Mature antibodies (Abs) that are exquisitely specific for virtually any foreign molecule may be produced by affinity maturation of na\xc3\xafve (or germline) Ab. However, the finite number of germline Ab available suggests that, in contrast to mature Ab, germline Ab must be broadly polyspecific so that they are able to recognize a wide range of ligands. Thus, affinity maturation must play a role mediating Ab specificity. One biophysical property that distinguishes polyspecificity from specificity is protein flexibility; a flexible combining site is able to adopt different conformations that recognize different foreign molecules (or antigens), while a rigid combining site is locked into a conformation that is specific for a given antigen. Recent studies (Proc. Natl. Acad. Sci. USA 104:8821\xe2\x80\x938826, 2007) have examined, at the atomic level, the structural properties that mediate changes in flexibility at four stages of affinity maturation in the 4-4-20 Ab. These studies employed molecular dynamics simulations to reveal a network of residue interactions that mediate the flexibility changes accompanying maturation. The flexibility of the Ab combining sites in these molecular systems was originally measured using 3-pulse photon echo spectroscopy (3PEPS). The present investigation extends this work by providing a concrete link between structural properties of the Ab molecules and features of the spectroscopic measurements used to characterize their flexibility. Results obtained from the simulations are in good qualitative agreement with the experimental measurements and indicate that the spectroscopic signal is sensitive to protein dynamics distributed throughout the entire combining site. Thus, the simulations provide a molecular level interpretation of the changes induced by affinity maturation of the Ab. The results suggest that 3PEPS spectroscopy in combination with molecular dynamics simulations can provide a detailed description of protein dynamics and, in this case, how it is evolved for biological function.

a_molecular_description_of_flexibility_in_an_antibody_combining_site

1. Introduction<!>2.1. Link between 3PEPS Signal and Transition Energy Fluctuations<!>2.2. Extracting Contributions to C(\xcf\x84) from Specific Protein Groups<!>3.1. Molecular Simulations<!>3.2. Evaluation of Correlation Functions<!>3.3 Covariance Analysis<!>4.1. Dephasing Time Scales Exhibit the Experimentally Observed Trends<!>4.2 Contributors to Dephasing are Distributed throughout the Combining Site<!>4.3. Correlations Due to Ab Motions Increase with Affinity Maturation<!>4.4. Specific Interactions are Altered as a Function of Maturation<!>5. Concluding Remarks<!>

<p>Antibodies (Abs) are the quintessential prototype for molecular recognition in biological systems, with the immune response generating Abs that selectively bind virtually any foreign molecule (or 'antigen'). These mature Abs are evolved from a finite set of naïve (or 'germline') Abs by iterative cycles of somatic mutation and selection, which combined are known as affinity maturation. The fact that the germline Ab repertoire is finite (being limited by the number of B cell lymphocytes) suggests that, in contrast to mature Abs, germline Abs must be broadly polyspecific.1 Polyspecificity would ensure that at least one member of the germline repertoire recognizes, at least with moderate affinity, any member of the virtually infinite set of antigens. Thus, affinity maturation is likely to play a role in generating Ab specificity.</p><p>A number of studies have been directed at establishing a link between physical properties of Abs and affinity maturation. The structural origins of affinity maturation have been elegantly studied by Schultz and Stevens, who characterized the structures of pairs of germline and mature Abs, both free and bound to their antigen.2–4 These studies revealed that, at least in some cases, affinity maturation pre-orders the Ab combining site to favor antigen binding. Li et. al. showed that maturation of Abs to hen egg white lysozyme was accompanied by burial of increased amounts of apolar surface and enhanced shape complementarity.5 Studies by Terzyan et. al. of a series of Abs that bind to the chromophore fluorescein (FL), including Ab 4-4-20 described in this work, suggest that structural changes in the combining site mediate the affinity increases that accompany maturation.6 Affinity maturation has also been shown to result in a more favorable entropy of antigen binding.7–11 These structural and thermodynamic data suggest that affinity maturation evolves polyspecific germline Abs in to more specific mature Abs, at least in part, by preordering the combining site in a conformation appropriate for antigen binding. This is consistent with the evolution of flexible Abs into more rigid receptors. A flexible combining site is able to adopt diverse conformations that recognize different antigens, while a rigid combining site is restricted to sample a more limited set of conformations, thus increasing specificity.</p><p>We are interested in more directly testing the hypothesis that affinity maturation tailors Ab dynamics and conformational heterogeneity. Toward this goal, we have employed Abs that were evolved to bind chromophoric antigens, such as FL12–15 and 8-methoxypyrene-1,3,6, trisulfonic acid (MPTS).16 We employed chromophoric antigens because they allow for the use of spectroscopic methods, such as three-pulse photon echo peak shift (3PEPS) spectroscopy,17–19 to characterize the protein that evolved to bind them12,16 yet are expected to be recognized like any other antigen. Our previous characterization of Ab 4-4-20 using 3PEPS spectroscopy revealed that the Ab is indeed significantly rigidified during affinity maturation.13–15 In general, 3PEPS experiments measure how the environment around a chromophore relaxes in response to a photoinduced change in the charge distribution of the chromophore. However, 3PEPS averages over all motions that are coupled to the chromophore's transition dipole, thus it is not possible to interpret the data in terms of specific protein motions. While 3PEPS experiments thus lend direct support to the hypothesis that polyspecific germline Abs are evolved into more specific mature Abs by tailoring protein dynamics, the detailed mechanism by which this occurred for 4-4-20 remains to be fully understood. Because the observables in a 3PEPS experiments are related to protein dynamics via fluctuations in the energy gap between ground and excited states of the chromophore, their origins may be investigated using molecular dynamics (MD) simulations. Thus, a combination of 3PEPS experiments and MD simulations promises to provide a detailed interpretation of Ab dynamics and how they are evolved during affinity maturation.</p><p>The antigen binding or 'combining' site of an Ab is located at the interface of two polypeptides called light chain and heavy chain. All Ab sequences are very similar except in the N-terminal domain of the light and heavy chains, which contain the 'variable regions', VL in the light chain and VH in the heavy chain (see Fig. 1). Within VL and VH there are six specific loops of hypervariable sequence, called the complementarity determining regions (CDRs), which form the combining site of the Ab. The light and the heavy chain each contribute three CDRs to the combining site (VL CDR1-3 and VH CDR1-3), which are supported by the so-called framework regions (VL FR1-4 and VH FR1-4). Previously we reported the sequence of mature 4-4-20 (VL4-4-20VH4-4-20) as well as its germline precursor (VLglVHgl).14,20 In this nomenclature, VL and VH refer to the light or heavy chain of the Ab molecule, respectively, while the superscript refers to the protein maturation state: 4-4-20 denotes the mature Ab while gl denotes the germline Ab. By characterizing the affinity for FL as a function of converting each somatic mutation in 4-4-20 back to its germline residue, we identified two approximate intermediates (VLglVH4-4-20 and VLH34RVH4-4-20) along the evolutionary pathway between the germline and mature Ab.13 The conversion of VLglVHgl to VLglVH4-4-20 is associated with ten amino acid substitutions in the heavy chain, conversion of VLglVH4-4-20 to VLH34RVH4-4-20 is associated with mutation of HisL34 (i.e. His34 of VL) to Arg, and conversion of VLH34RVH4-4-20 to VL4-4-20VH4-4-20 is associated with mutation of LeuL46 to Val.</p><p>Previously, MD simulations have been employed to understand the changes in flexibility which accompany the affinity maturation of 4-4-20.20 These studies suggested that a network of interacting residues mediate the rigidification induced by affinity maturation. (It should be noted that in those studies a different nomenclature for the Ab molecules was employed: VLglVHgl,VLglVH4−4−20,VLH34RVH4−4−20 and VL4−4−20VH4−4−20 respectively correspond to the GL, IM1, IM2 and AM designations used in that work). Here, we build on these results by elucidating the link between the structural properties of the Ab and features of the spectroscopic measurements used for their characterization. We employ MD simulations described above20 to compute correlation functions for the transition energy in each of the Ab-FL complexes. The results show good qualitative agreement with experimental findings regarding the differing time scales of fluctuations that modulate the FL environment and indicate that the 3PEPS signals reflect motions of the entire combining site. A covariance analysis further shows that Ab fluctuations in both CDR's and framework regions are correlated, and that this correlation is stronger in the mature than in the germline Ab. These results provide additional evidence that the Ab is rigidified during maturation. We anticipate that this information will prove useful in the implementation and analysis of similar studies of protein dynamics as well as provide insight into the determinants of molecular recognition and how it is evolved for biological function. In the subsequent section we provide the theoretical background for our studies; this is followed by a description of the simulation methodology. Our observations and a discussion of their implications are then presented. We conclude with a summary of our findings.</p><!><p>3PEPS experiments measure the position of the signal maximum of the time-integrated photon echo (the 'peak shift') emitted after applying a sequence of three resonant laser pulses to the sample (Fig. 2A,C).17 The first pulse creates a coherent superposition between the ground and excited states of the chromophores, and the resulting ensemble of photoexcited chromophores subsequently dephases (Fig. 2B). The second pulse creates a population, either in the ground or excited state, preventing further dephasing. The third pulse again creates a superposition of states, allowing the ensemble to rephase and emit an 'echo' signal. Molecules may also relax via a free-induction decay (FID) process, which does not require rephasing.21 The intensity of the time-integrated signal originates both from those molecules that rephase and those that relax via FID. Environmental fluctuations (vibrations of the chromophore and vibrations and diffusive motions of the protein) that occur during the time between interaction with the first and third laser pulse induce random phase changes, which prevent rephasing of the affected chromophores but do not affect the FID signal. Consequently, with longer delay time, proportionally more molecules relax by FID, causing an apparent shift of the peak position of the time-integrated signal toward zero (i.e. a decay of the peak shift, Fig. 2C).</p><p>Fleming,18,19 and Wiersma22 have established that in the high-temperature limit and for times longer than the bath-correlation time, the normalized 3PEPS decay approximates the two-point time correlation function of fluctuations in the electronic transition energy between ground and excited state, ε(t) = Ħωeg(t),</p><p> (1)C(τ)=〈δε(t)δε(t+τ)〉〈δε(t)2〉 where δε (t) = ε (t) −〈ε〉 are the fluctuations in ε (t) around its equilibrium, ensemble-averaged value, 〈ε〉, and τ≥0. Note that for chromophore or solvent responses on a timescale similar to the bath correlation time, as is the case for the Ab-FL complexes, C (τ) can be determined by means of a more complex data evaluation of the 3PEPS decay based on linear response theory;17,18,21 this approach was employed to generate the experimental C (τ) listed in Table 1 from the 3PEPS signals.13,14 This procedure also allows the use of molecular simulation data to recreate the actual peak shift itself.23–25 However, molecular simulations can also be used to delineate the impact of protein fluctuations on the chromophore environment via direct analysis of C (τ).26–28 The time scales of protein motions that cause phase shifts of the transition dipole also govern the decay of C (τ).29,30 However, the C (τ) determined from a 3PEPS experiment averages over all protein motions weighted by their coupling strength to the chromophore's transition dipole, which is a function of distance and charge of the moving entity. Computational methods allow for the deconvolution of C (τ) into the contributions of individual protein motions and to account for the electrostatic weights, as described in the following section. Similar deconvolution of C (τ) into distinct contributions arising from protein or solvent fluctuations has been carried out for the proteins myoglobin26,31,32 and monellin.27</p><!><p>To identify specific interactions that contribute to the decay of C (τ) we make use of the fact that the molecular mechanics force field used to evaluate ε(t) is strictly pairwise additive. Because there are no explicit multibody terms in the force field, ε(t) and δε(t) can be modeled as a sum of contributions from the energies εi(t) due to each atom i in the system, ε(t)=∑iεi(t) and δε(t)=∑iδεi(t), with δεi(t) = εi(t) − 〈εi(t)〉.</p><p>C (τ) can thus be written as: (2)C(τ)=∑in〈δεi(t)δεi(t+τ)〉〈(∑iδεi(t))2〉+∑i≠jn〈δεi(t)δεj(t+τ)〉〈(∑iδεi(t))2〉 where the summation runs over all n atoms in the system. For computational expediency, we carried out this analysis on a per residue basis rather than for individual atoms; i in the equations below thus will denote individual residues rather than atoms. Using the approach of Nilsson and Halle27, one can define a partial time correlation function, Cipartial(τ) to assess the contribution that an individual residue makes to the time dependence of C (τ). In these studies we evaluate Cipartial(τ) according to: (3)Cipartial(τ)=〈δεi(t)∑jn(δεj(t+τ))〉〈δεi2(t)〉1/2〈(∑inδεi(t))2〉1/2</p><p>One can rank the contribution each Cipartial(τ) makes C (τ) by normalizing this quantity via aiCipartial(τ) with ai=〈δεi2(t)〉1/2〈(∑inδεi(t))2〉1/2. With this choice C (τ) can be recovered via C(τ)=∑inaiCipartial(τ). Thus, the relative contribution of each residue to C (τ)is governed by δεi. The magnitude of δεi is a measure of the spatial amplitude of single-residue fluctuations weighted by the electrostatic coupling of the residue to the FL transition dipole.</p><p>In an attempt to understand the molecular origin of the dephasing correlation function, it is reasonable to ask whether C (τ) can be expressed in terms of time scales which arise primarily from the fluctuations of individual protein groups. One can employ Eq. 2 to probe this question further. The first term in Eq. 2 contains only contributions from self-terms while the second term contains the contributions from cross-correlations. By modifying the normalization factors employed above, we can construct a quantity we define as the additive correlation function, C (τ)ad: (4)C(τ)ad=∑inaiCi(τ) with ai=〈δεi2〉∑in〈δεi2〉;Ci(τ)=〈δεi(t)δεi(t+τ)〉〈δεi2〉.</p><p>C (τ)ad is composed of the time-correlation functions Ci (τ) of individual residues. One can thus isolate the impact that fluctuations of a given residue have on the time scales present in C (τ) by computing C (τ)ad to exclude cross-correlations and evaluating each C τ and ai. The constant ai determines the relative weight of an individual correlation function Ci (τ) in C (τ)ad. The values of each ai are related to the variance si2 for fluctuations in εi via: (5)ai=〈δεi2〉∑in〈δεi2〉=si2∑insi2</p><p>Note that the time dependence of δεi no longer needs to be explicitly considered as the ensemble averages are, in principle, independent of time. Since the value of the denominator above is the same for every atom, it can be ignored when evaluating relative magnitudes. Therefore, the relative contribution of individual Ci (τ) terms in C (τ)ad is completely described by computing si2. By extension, one can use si2 to rank the contributions of individual residues to C (τ)ad. By neglecting cross-correlations, C (τ)ad emphasizes the impact of fluctuations of individual residues. Thus, C (τ)ad provides the optimal venue to test whether fluctuations of certain residues in the Ab may give rise to distinct decay time scales in C (τ).</p><!><p>Simulations were initiated from a crystal structure of the antigen binding fragment (Fab) of the mature 4-4-20 Ab (PDB designation 1FLR)33 bound to FL. The charge distribution employed for the ground state of FL was determined by fitting point charges to the electrostatic potential of the molecule using the CHELPG fitting scheme.34 Before fitting the electron density for point charges, the geometry of FL was optimized in vacuum at the B3LYP/6-31G+(d) level of theory using the Gaussian 98 program.35 The resulting charges were scaled by 0.9 to be consistent with charge sets employed in the CHARMM molecular simulation program.36–38 Other parameters required for simulation of FL were obtained by assigning standard CHARMM atom types to FL atoms.</p><p>Structures of VLglVHgl and the two evolutionary intermediates were derived from the X-ray coordinates of VL4−4−20VH4−4−20 by performing appropriate amino acid substitutions using the Multiscale Modeling Tools for Structural Biology (MMTSB) Tool Set.39 Coordinates of atoms not present in the crystal structure were assigned standard values from CHARMM topology files. The assigned coordinates as well as converted residues were subjected to 1000 steps of energy minimization using the steepest descent algorithm while keeping the remaining atoms fixed. This was followed by 300 minimization steps during which no coordinates were constrained. In preparing the simulations, emphasis was placed on regions of the Ab complexes in the vicinity of the combining site because the 3PEPS measurements probe the local environment of the FL chromophore.12,13,15 Thus, the Fab was truncated at the boundary between the constant and variable domains to make the simulations less computationally intensive (Fig. 1).</p><p>The combining site was solvated with a 21 Å sphere of TIP3P water40 centered at the position of the center of mass of FL in the 1FLR crystal structure. Integrity of the solvent sphere was maintained through the use of a stochastic boundary potential.41 All bond distances between hydrogen and heavy atoms were constrained with the SHAKE algorithm.42 A spherical cutoff of 14 Å for non-bonded interactions was employed. The van der Waals interactions were gently switched to zero between 12–14 Å while forces corresponding to electrostatic interactions were shifted to zero at the cutoff distance. During an equilibration period of 200 ps the coordinates of the protein and ligand were restrained with mass weighted harmonic potentials which were gradually decreased from 5 to 1 kcal/mol Å2. At the end of equilibration, these restraints were applied only to residues in the truncated region more than 17 Å from FL. The restraints were maintained throughout the simulations to maintain the integrity of the truncated Fab. MD trajectories were propagated for 10 ns in the canonical (NVT) ensemble using the Nosé-Hoover thermostat with a relaxation time of 1ps. A time step of 2 fs was employed and coordinates written out every 100 steps. Equations of motion were integrated with the velocity Verlet scheme. Further simulation details were presented previously.20 It should be noted that, in principle, application of a thermostat can alter the time scales of fluctuations in molecular dynamics simulations. However, it is known that for well equilibrated systems (such as those employed in this work) the impact of the Nosé-Hoover thermostat on the fluctuations of any given particle is expected to be negligible.43 Moreover, the thermostat has a similar impact on every particle in the system, allowing relative changes in timescales of protein motions to be correctly reproduced.</p><!><p>Explicit water molecules were removed from the MD trajectories and replaced with Generalized Born (GB) implicit solvent.44 In principle, implicit solvent models such as GB provide the potential of mean force for the energetic contributions of solvent with respect to any given solute configuration, properly incorporating bulk solvent effects in a mean field sense. The use of an implicit solvent model in this context allows for contributions from water to be incorporated into ε in a mean field manner while minimizing the impact of solvent fluctuations on δε. These considerations are particularly important in the present study because the sphere of water used to solvate the combining site is much smaller than a bulk water system (see Fig. 1) and is therefore subject to much larger energetic fluctuations. This procedure reduces the noise in δε derived from solvent fluctuations and allows us to focus on contributions made by protein residues.</p><p>Thus, the total energy ε is given by: (5)ε=εsolute+ΔGsolv where the first term on the right is the molecular mechanics energy of the protein and ligand whileΔGsolv is the electrostatic free energy of solvation provided by the GB approach. A detailed description of the expressions used to evaluateΔGsolv has been provided in previous work.44</p><p>For each snapshot of these trajectories, a vertical excitation was performed by replacing the ground state charge distribution of FL with the corresponding charges for the excited state. Thus, ε (t) was modeled by the total energy difference between the ground and excited state charge distributions within a given snapshot from the MD simulations. A similar approach was employed by Halder et al. in studies of apomyoglobin and apoleghemoglobin.28 The excited state charge distribution of FL was determined by fitting point charges to the electrostatic potential obtained at the CIS/6-31G+(d) level of theory using the Gaussian 98 program35 and the CHELPG fitting scheme.34 As for the ground state (see above), excited state charges were uniformly scaled by 0.9. Ground and excited state charges for FL atoms are displayed in Fig. 3. The resulting time series for ε, ε (t), were employed to compute C (τ) for each Ab as described in Section 2.1 (Fig. 4). Note that C (τ)contains contributions from all components of the simulation system and includes cross-correlations between the energetic contributions of individual components. The C (τ) for each Ab are shown in Fig 4 while time constants extracted from the C (τ) are displayed in Table 1.</p><p>The individual correlation functions described in Eqns. 3 and 4 were computed by removing all atoms except for those of FL and the atoms of interest from the MD snapshots before placing each snapshot in GB implicit solvent (Fig. 5). For computational expediency this procedure was carried out on a per residue basis rather than for individual atoms. In principle, the electrostatic coupling between the protein/solvent environment and the chromophore transition dipole depends on charge-dipole, dipole-dipole and higher order terms. Such interactions decay much more rapidly with distance than Coulombic (charge-charge) interactions. Thus, one would expect residues more than 7 Å away from FL to exhibit negligible values of si2. Consequently, such residues were excluded from the analysis. Visual inspection was further employed to ensure that only residues clearly interacting with charged atoms in FL were included, reducing the number of residues considered to ten.</p><p>The various Ci (τ) were used to generate C (τ)ad, with the relative magnitudes of ai obtained by computing the variance εi (t) as described in Eqn. 5. For increased sensitivity, the contribution of FL atoms toεi (t) was removed before computing si2 for individual residues. The values of si2 were used to rank the contributions of individual residues. Time constants extracted from Cipartial(τ), Ci (τ) and C (τ)ad are shown in Tables 2 and 3.</p><p>All correlation functions were fit to multi-exponential decays with the program MEMexp,45 using the hybrid maximum entropy/nonlinear least square fitting mode to determine the number of exponentials required to fit a given correlation function.</p><!><p>The covariance of atomic displacements provides an efficient way to compare variability of the fluctuations present in proteins and reveals structural correlations between different components of the Ab-FL complexes. The covariance of spatial displacements of atoms in each Ab system, μij, were computed via μ = 〈(ri − 〈ri〉) • (rj − 〈rj〉)〉, where ri represents the displacement of atom i. These covariance values represent correlation coefficients for the fluctuations of individual atoms. In order to carry out this analysis, explicit solvent was first deleted and average structures for each Ab-FL complex were computed. Translation and rotation were removed from each trajectory by performing an RMS fit to heavy atoms of the average structure. Atomic fluctuations with respect to this average structure were determined for FL and Cα atoms, and these fluctuations were employed to generate a mass weighted covariance matrix46 using the CORMAN module of CHARMM. Cα correlations are representative of the correlations of each residue as a whole. Covariance matrices for VL4−4−20VH4−4−20 and VLglVHgl are shown in Fig. 6. In order to provide further insight into the patterns of correlation occurring in each system, we define the metric summed squared covariance, SSC. For a given site i, the SSC with respect to structural subunit J is defined as SSCi,J=∑j∈J,j≠iμij2, where j denotes a site within the subunit. SSC values compactly illustrate the extent of correlation between different regions of the Ab-FL complexes.</p><!><p>For the Ab-FL complexes, 3PEPS decays are typically observed on at least three different time scales; these have been qualitatively linked to protein flexibility.15 The fastest decays occur on a ~100 fs timescale and have been assigned to vibrations within local minima of the energy landscape. Dephasing on a ~1 to 10 ps timescale has been assigned to diffusive motions between these minima, and dephasing on even longer timescales to conformational dynamics, some of which is too slow to be time-resolved, thus leading to a non-zero asymptote in C (τ) at the longest observable τ. When comparing the 3PEPS decays of VLglVHgl,VLglVH4−4−20,VLH34RVH4−4−20, and VL4−4−20VH4−4−20, two major trends are observed: upon affinity maturation, amplitude shifts from long timescale decay components to short timescale components, while the time constants of the picosecond decays tend to become longer.12,13 This data is consistent with an increase in rigidity of the Ab combining site. A shift in amplitude to shorter timescales indicates that the protein samples less phase space through conformational dynamics, while increasing time constants of the picosecond decays indicate higher barriers between conformational substates.</p><p>The C (τ) computed for each Ab and the multi-exponential fits are shown in Fig. 4; corresponding τj and Aj are shown in Table 1. Since the total length of each simulation was 10 ns, the correlation functions were not computed for time periods exceeding 5 ns. Thus, τj values larger than approximately 2 ns denote processes that occur on time scales longer than we can reliably determine from the simulations. Moreover, the distinct reoccurrences in the C (τ) of VLglVH4−4−20 and VLglVHgl for times longer than 100 ps (see Fig. 4) indicate that 10 ns trajectories are not sufficient to capture all of the slower dynamics, since a non-monotonic decay in C (τ) on this time scale is most likely due to insufficient averaging. Nevertheless, comparison of simulated and experimental C (τ) shows that the simulations reasonably reproduce the experimentally observed trends (Table 1). For VL4−4−20VH4−4−20, C (τ) decays completely within ~1 ns, thus the simulations are expected to capture all of the dynamics, and we in fact find an excellent agreement between simulated and experimental C (τ). For VLH34RVH4−4−20,VLglVH4−4−20, and VLglVHgl, the simulated and experimental C (τ) do not agree as well, certainly due in part to the fact that the simulations apparently do not capture all of the dynamics for these Ab on longer timescales. Nevertheless, the data still reproduces the main experimental observation, i.e. that amplitude shifts from longer time scale motions to shorter timescale motions upon maturation. In VL4−4−20VH4−4−20, 96% of the amplitude of C (τ) decays on timescales shorter than 100 ps, compared to 54% in VLH34RVH4−4−20, 84% in VLglVH4−4−20, and 77% in VLglVHgl. In addition, the overall inhomogeneity of the Ab-FL complexes as measured by the absorption line width14 is qualitatively reproduced by the covariance s2 (Table 1). Thus, the MD simulations are sufficiently validated by the experimental results, and should be suitable for deconvolution of C (τ) into its components arising from motions of individual amino acids.</p><!><p>Having validated the MD simulations, we proceed to use the methodology presented in section 2.2 to correlate the experimental signals to specific protein dynamics. For brevity, we will focus on the mature VL4−4−20VH4−4−20 and germline VLglVHgl, i.e. inspect the overall changes that result from affinity maturation. To assess how well the experimentally measured signals relate to binding site flexibility, we will address two questions. First, are the experimentally observed dynamics representative of the entire combining site, or do they reflect motions of only a few, strongly coupled residues? Second, does the observed change in the experimental signals upon affinity maturation occur because the dynamics of individual residues change between germline and mature Abs, or because the FL transition dipole samples motions of different residues in the germline and mature Ab due to structural changes brought about by affinity maturation?</p><p>To address these questions we will first consider the Cipartial(τ) computed for ten residues in the combining site that clearly interact with charged atoms in FL (Fig. 5). The associated multiexponential fits are shown in Table 2. Note that the Kabat numbering system is employed for residue designations;47 conversion to crystallographic numbering is provided in the Supporting Information.14,20</p><p>We find distinct differences in the pico- to nanosecond decays of the Cipartial(τ) between the examined residues and as a function of Ab maturation. For all but one of the examined residues, the sum of decays in this time regime are of larger amplitude in VLglVHgl than VL4−4−20VH4−4−20 (Fig. 5), indicating that the phase space sampled by the individual residues decreases upon affinity maturation, in agreement with our results for the global C(τ). All residues exhibit decays on multiple time scales, and the time constants for the decays in Cipartial(τ) are rather similar between residues of the same maturation state. For example, each of the ten residues exhibit a ~100 ps decay in the germline Ab, while there is no decay within this time regime for the mature Ab (τ3 in Table 2).</p><p>It is apparent that the time scales present in each of the Cipartial(τ) are very similar to those observed in C(τ). Moreover, the general trends seen in the fits extracted from the Cipartial(τ) are quite similar to those extracted from C (τ). The relative contribution of high frequency components of the signal increases in each of the residues listed in Table 2 as maturation proceeds. In addition, the individual time constants tend to decrease. Thus, as observed for C(τ), there is a marked shift towards higher frequency oscillations as a result of maturation. These results indicate that the time scales observed in C(τ) arise from the contributions of multiple residues and cannot be decomposed into fluctuations from individual residues in the Ab. Thus, all residues participate in the fluctuations that give rise to C(τ) rather than specific residues giving rise to distinct time scales.</p><p>That said, it is insightful to consider the single-residue auto-correlation functions Ci (τ) and variances si2 shown in Table 3 in order to determine whether the FL transition dipole samples motions of different residues in the germline and mature Ab..</p><p>From the ten individual Ci(τ) and si2, we constructed the additive correlation functions, Cad′(τ)=∑i=1…10si2Ci(τ)/∑i=1…10si2, according to Eq. (4), and fit them to multi-exponential decays (Table 2).</p><p>There are two features of the data shown in Table 3 that should be highlighted. Firstly, as observed for the Cipartial(τ), the time scales present in each of Ci(τ) are very similar to those observed in C(τ). This result indicates that, even when combined in such a way as to enhance the impact of individual residues, the distinct time scales observed in C(τ) are not separable into contributions from individual residues in the Ab. Thus, all residues participate in the fluctuations that give rise to C (τ) rather than specific residues giving rise to distinct time scales.</p><p>The second interesting feature of the data in Table 3 is the manner in which the electrostatic coupling of each residue to the FL transition dipole changes as a function of maturation. The ten residues in Table 3 are ranked according to si2 in VL4−4−20VH4−4−20, which is a measure of the electrostatic coupling strength of a given residue to the FL transition dipole. Tyrosine residues YH100e and YH53 exhibit relatively large values of si2; together they provide a large fraction of the initial amplitude to Cad′(τ) in both the germline Ab and mature Ab. However, the relative magnitudes of si2 are non-negligible for all of the other eight residues examined. Thus, the chromophore exhibits appreciable electrostatic coupling to residues widely distributed throughout the entire combining site (see Fig 7). This indicates it provides a truly representative description of combining site dynamics. These results suggest that C(τ), and in turn the 3PEPS experiment, provides information about the dynamics of the entire combining site rather than of a few strongly coupled residues.</p><p>In addition to changes in the overall binding site dynamics displayed in Table 2, another source of the experimental observation may be that the FL transition dipole samples a differently weighted average of single-residue motions due to the varying si2 values in the germline and mature Abs. This question can be addressed through a comparison of the dynamics of the individual residues in the germline and mature Abs, quantified by the single-residue Ci(τ) and si2 (Table 3). The coupling strength to the FL transition dipole as measured by si2 changes significantly between the germline and mature Ab for seven of the ten residues, most prominently for YH53 (59.3 cal/mol vs. 28.8 cal/mol) and GH100f (33.8 cal/mol vs. 19.9 cal/mol). Thus, it is possible that the differential coupling of residues to the FL transition dipole plays some role in modulating the changes in experimental observables in addition to changes in the dynamics of the individual residues upon affinity maturation. With regard to Ab dynamics, the observation of similar time constants at different residues and comparable changes of these time constants upon affinity maturation strongly suggests that fluctuations of the corresponding residues are correlated. We now explore this issue in detail.</p><!><p>The decay of C(τ) is determined by fluctuations of the Ab combining site. Above we have shown that the timescales of single-residue fluctuations, while similar for residues of the same maturation state (mature or germline), change upon affinity maturation. However, to identify the mechanisms whereby the observed changes are manifest, it is not sufficient to inspect only single-residue motions. Protein flexibility and plasticity are determined by collective motions that arise from the coupling of adjacent residues via hydrogen bonds (H-bonds), hydrophobic packing, steric interactions, or salt bridges. In addition, previous studies indicate that presence of FL increases the levels of correlation between different regions of VL4−4−20VH4−4−20, demonstrating that correlated Ab fluctuations can be modulated by ligand interactions.48,49 Thus, correlations occurring in the Ab-FL complexes need to be considered in order to characterize the differences in flexibility between mature and germline Abs. The patterns of coupled fluctuations occurring in each simulation can be conveniently depicted via a covariance matrix, which identifies correlated motions in each Ab. An increase in the covariance between the motions of single residues indicates increased coupling, and such matrices may be employed to identify the networks of residues that exhibit coupled motions. The MD trajectories were used to compute the covariance matrices for each Ab-FL complex as described in section 3.3; results for VL4−4−20VH4−4−20 and VLglVHgl are displayed in Fig. 6. It is clear that the fluctuations in the two Abs exhibit different levels of correlation. In general, there are more regions exhibiting prominent correlation in VL4−4−20VH4−4−20 than in VLglVHgl, indicating that the mature Ab-FL complex is more structurally interconnected than the germline Ab-FL complex.</p><p>We employ summed squared covariance (SSC: see section 3.3) as a measure of how strongly fluctuations of a single residue correlate to fluctuations of FL or to the various structural subunits of the variable region (i.e. CDR loops or framework regions). Fig. 8A shows the differences in the SSC between germline and mature Abs for correlations between single residues and FL. Notably, the SSC between residues in the CDR loops and FL are significantly increased in the mature compared to the germline Ab, while smaller differences are observed for residues in the framework regions. The largest increase was found for residues in VL CDR1 and VH CDR3, which have been previously predicted to couple more strongly to FL in the mature Ab based on structural data.14 Specifically, a H-bond between RL34 of VL CDR1 and an enolic oxygen on the xanthenone ring of FL is introduced during affinity maturation by somatic mutation HL34R, as are increased packing interactions between residues in VH CDR3 and FL. The increase in correlation between residues of the CDR loops and FL may thus be an expression of the tighter binding of FL in the mature Ab. However, residues displaying increased correlation to FL in the mature VL4−4−20VH4−4−20 relative to that present in the germline VLglVHgl are found not only in the combining site but are distributed throughout both variable domains, demonstrating that affinity maturation leads to enhanced dynamic coupling throughout the Ab molecule (see Table S4, Supporting Information). This conclusion is further supported by the observed changes in SSC for correlations between residues and structural subunits of the variable region, which indicate varying flexibility of the protein itself. We find an increase in single residue-protein correlations upon affinity maturation for most residues (Fig. 8B). Increased single residue-protein correlations are not restricted to the CDR loops that directly contact FL, consistent with the widespread distribution of residues displaying increased Ab-FL correlations. For example, large increases in SSC values are observed for residues in framework regions VL FR3 and VH FR3, well outside the binding site. Thus, affinity maturation does not act on the dynamics of the binding site alone, but affects the dynamics of the framework regions as well. The observed increases in SSC values stem from increased correlations both within and between the structural subunits. Further analysis shows that the amount of correlation within structural subunits increases significantly for VH CDR3, VH FR3, VL CDR1 and VL CDR3 with a significant decrease only observed for VH FR1 (Table S3, Supporting Information). Correlations between structural subunits significantly increase as well, most prominently between several CDR loops, and between CDR loops and framework regions. Thus, it appears that the experimentally observed changes in binding site dynamics are the result of global changes in the dynamics of the variable domains. In this regard, the use of simulations strongly complements the experiments by allowing us to understand this process at the molecular level. The 3PEPS experiments, while conclusively demonstrating that the timescales for decay of C(τ) change upon affinity maturation, are sensitive only to motions in the vicinity of the chromophore due to the electrostatic weighting. However, the increased correlations observed for the affinity-mature Ab reveals that these motions are strongly affected by changes distal from the binding site. Stronger coupling between different regions of the mature Ab limits the conformational space accessible to the binding site, thus reducing its flexibility. Specifically, coupling within and between selected CDR loops increases upon affinity maturation, while the same CDR loops show increased correlations to FL motions. In addition, correlations are also increased between CDR loops and selected framework regions supporting these loops, suggesting that such couplings contribute to the observed rigidification of the binding site. Such a rigidification of the combining site via long-distance interactions was previously predicted based on the observation that some of the somatic mutations are located >20 Å away from the binding site and appear to introduce interactions that cross-link the β strands of the framework regions.14 Our results further support this interpretation.</p><!><p>The analyses above demonstrate altered correlations between different regions of the Ab-FL complexes, but do not reveal how these correlations arise. In order to address this question, we employed the information present in the covariance matrices to identify regions of the Ab-FL complexes which deserve closer scrutiny. To this end, the residues displaying greater correlation to FL in Fig 8A were examined to identify possible mechanisms of increased coupling (Table S4, Supporting Information). This list of residues includes MH100g, WH103, YH102, VL46 and RL34 which are part of a network of structural interactions described in previous studies.20 However, several of these residues were not formerly identified; many are also present in Table 2, suggesting that the increased correlation observed for these residues in VL4−4−20VH4−4−20 plays a role in generating altered decay behavior of C(τ) relative to VLglVHgl.</p><p>As discussed in the previous section, residues throughout VL CDR1 show a dramatic increase in correlation both to FL and to other residues upon affinity maturation. A residue of particular interest is YL32, which forms a H-bond with the phenylcarboxylate of FL. It also participates in a network of H-bonds with NL28 and NL30 (Fig. 9). The H-bond between YL32 and FL is fairly constant throughout the simulations. Likewise, the presence of a H-bond between YL32 and NL28 shows no distinct pattern as a function of maturation. However, the H-bond between YL32 and NL30 does exhibit a marked trend with maturation, as is apparent from the histograms of the distance between Nδ of NL30 and the hydroxyl group of YL32 shown in Fig. 9. Conformations with a H-bond between YL32 and NL30 are increasingly populated in concert with affinity maturation. This expanded H-bonding population is accompanied by an increase in the covariance value computed for the associated atoms, demonstrating that they become more strongly coupled. This observation suggests that these interactions in part mediate the increased rigidity which results from maturation, 'stapling' together regions of the loop containing VL CDR1 (also see Fig. 6). Since CDRs play a central role in antigen binding, rigidifying this region of the Ab will contribute significantly to decreasing the flexibility of the entire combining site. This effect may play a role in generating the patterns of correlation shown in Figs. 6 and 8. This hypothesis can be tested by replacing NL30 with a residue incapable of forming a H-bond to YL32; we anticipate carrying out such an experiment in the future. As the entire variable region is dynamically coupled (see Figs. 6 and 8), we expect that the effect of eliminating this H-bond will propagate throughout the entire combining site. We predict that removing the possibility for H-bonding at this position will increase the flexibility of the combining site and decrease structural and energetic coupling between the combining site and the chromophore. While this site in the Ab is likely not the only position that mediates flexibility of the Ab combining site (e.g. see Fig. 8), this example effectively illustrates the specific interactions which may determine flexibility in the Ab and provides a concrete link between the experimental observations via 3PEPS and properties of the Ab at the atomic level. This is especially significant because the observations described above would have been difficult to infer without a detailed molecular description of these systems.</p><!><p>The molecular simulation studies described in this work lend support to the assertion that 3PEPS spectroscopy provides a representative measure of protein dynamics in the vicinity of a given chromophore probe. In accordance with the 3PEPS measurements, Ab maturation is observed to be associated with more rapid dephasing time scales in MD simulations. However, the simulation methods possess the unique advantage of providing an atomistic description of the Ab groups involved, allowing for a detailed understanding of the associated molecular processes. The increased rigidity that accompanies Ab maturation is seen to be associated with specific modifications to Ab fluctuations and structural interactions, including H-bonding patterns. Moreover, it is apparent that the decreased flexibility which accompanies maturation is not limited to the combining site, but distributed throughout the entire variable region. In the specific case of affinity maturation of Ab 4-4-20, we find that the binding site of the mature Ab is significantly rigidified compared to the germline Ab, and that the rigidification is brought about by increased coupling within and between CDR loops and framework regions, including residues well outside the binding site. This observation may be functionally relevant. In the naïve VLglVHgl, flexibility of the binding site may allow recognition of a more diverse array of ligands, giving the immune system the capacity to respond to a variety of antigens (although with relatively low affinity). Once a bona fide antigen has been identified, the mode of binding changes to one that is more appropriately characterized by lock-and-key recognition in VL4−4−20VH4−4−20. In a highly evolved Ab such as VL4−4−20VH4−4−20, the ability to discriminate between different ligands is thus made possible by a relatively rigid binding cleft that exhibits a high degree of complementarity to the antigen. This could allow the immune system to neutralize specific antigens very efficiently without cross-reactivity. The approaches outlined above provide a broadly applicable procedure by which the origin of flexibility changes can be identified in other molecular systems. The ability to delineate these phenomena at the molecular level bodes well for the elucidation of general principles that govern the interplay between flexibility and molecular recognition.</p><!><p>Simulation system displaying truncated antigen binding fragment (Fab) from 4-4-20 Ab. The light and heavy chains of the Ab are shown in red and blue respectively, except for residues restrained during the simulation which are shown in black. Residues NL28, NL30, YL32, RL34 as well as the solvent sphere around the combining site are shown in ball-and-stick representation. The ligand FL is displayed in the center of the combining site.</p><p>Schematic representation of the 3PEPS experiment. (A) Experimental geometry. (B) Dephasing and rephasing of chromophores. The discontinued phase traces represent random phase changes due to structural fluctuations. (C) Determination of the peak shift decay from experimental data.</p><p>Model of FL displaying charges obtained for the ground (bottom) and excited (top) states overlaid on each atom. Charges on unlabeled atoms in the xanthene ring system are related to charges on labeled atoms via reflection in the z axis.</p><p>Multi-exponential fits to C(τ) for each Ab. In each panel the black solid, red dotted, and green dashed lines denote the computed C(τ), the multi-exponential fit and the residual, respectively.</p><p> Cipartial(τ) for the ten residues present in Table 2 in VLglVHgl (gray) and VL4−4−20VH4−4−20 (black).</p><p>Covariance matrix for FL atoms and Cα atoms in VL4−4−20VH4−4−20 (upper diagonal) and VLglVHgl (lower diagonal). Only values for residues that were not restrained during the simulations (see Fig. 2) are shown. Consequently, the matrix indices (numbers on the plot) do not correspond to successive indices in the crystal structure. Conversion to Kabat numbering is provided as Supporting Information. The light gray bars labeled F1-4 denote the positions of FR1-4 in VL and VH, the black bars labeled C1-3 denote the positions of CDR1-3 in VL and VH, the dark gray bars denote the position of FL, and the black dots indicate positions of somatic mutations. The position of residue NL30 described in the text is denoted by an arrow.</p><p>Residues observed to be correlated to FL. Residues listed in Table 2 are labeled and shown in ball and stick view while FL is shown in thick stick representation. Only part of the combining site is shown for clarity. Note that the residues are distributed throughout the entire combining site.</p><p>Differences in sum squared covariance (SSC) values between mature and germline Abs for correlations (A) between single residues and FL, (B) between single residues and all other residues under consideration (black bars), or all other residues of the same structural subunit (gray bars). The gray boxes indicate the positions of CDR loops, the black dots the positions of somatic mutations.</p><p>Hydrogen bonds that are altered as a function of maturation. Left: Representation of the combing site displaying FL and VL CDR1 (H-bonds are shown as dashed lines). The viewpoint in this panel corresponds to an approximately 180° rotation about the vertical axis relative to that shown in Fig. 7. Right: Distribution of the distance between Nδ of NL30 and the hydroxyl group of YL32 as a function of maturation. The number in each histogram denotes the covariance value computed for this pair of atoms during each simulation.</p><p>Multi-Exponential Fits to C (τ)</p><p>Due to the time resolution of simulation data (200 fs), the value for τ1 is not well determined.</p><p>From 13,14.</p><p>Half width full maximum of absorption band.</p><p>The offset for VLH34RVH4−4−20 combines all decays slower than ~300 ps (no nanosecond contributions observed for VLH34RVH4−4−20).</p><p>Multi-Exponential Fits to Cipartial(τ) for VL4−4−20VH4−4−20 and VLglVHgl</p><p>Due to the time resolution of simulation data (200 fs), the value for τ1 is not well determined.</p><p>Somatic mutation.</p><p>Multi-Exponential Fits to Ci(τ) and C′(τ)ad for VL4−4−20VH4−4−20 and VLglVHgl</p><p>Due to the time resolution of simulation data (200 fs), the value for τ1 is not well determined.</p><p>Somatic mutation.</p><p>Additive correlation function constructed from the ten residues under consideration, Cad′(τ)=∑i=1…10si2Ci(τ)/∑i=1…10si2. si2 is reported in cal/mol.</p>

PubMed Author Manuscript

Dictyostelium cells bind a secreted autocrine factor that represses cell proliferation

BackgroundDictyostelium cells secrete the proteins AprA and CfaD. Cells lacking either AprA or CfaD proliferate faster than wild type, while AprA or CfaD overexpressor cells proliferate slowly, indicating that AprA and CfaD are autocrine factors that repress proliferation. CfaD interacts with AprA and requires the presence of AprA to slow proliferation. To determine if CfaD is necessary for the ability of AprA to slow proliferation, whether AprA binds to cells, and if so whether the binding requires the presence of CfaD, we examined the binding and effect on proliferation of recombinant AprA.ResultsWe find that the extracellular accumulation of AprA increases with cell density and reaches a concentration of 0.3 μg/ml near a stationary cell density. When added to wild-type or aprA- cells, recombinant AprA (rAprA) significantly slows proliferation at 0.1 μg/ml and higher concentrations. From 4 to 64 μg/ml, the effect of rAprA is at a plateau, slowing but not stopping proliferation. The proliferation-inhibiting activity of rAprA is roughly the same as that of native AprA in conditioned growth medium. Proliferating aprA- cells show saturable binding of rAprA to 92,000 ± 11,000 cell-surface receptors with a KD of 0.03 ± 0.02 μg/ml. There appears to be one class of binding site, and no apparent cooperativity. Native AprA inhibits the binding of rAprA to aprA- cells with a Ki of 0.03 μg/ml, suggesting that the binding kinetics of rAprA are similar to those of native AprA. The proliferation of cells lacking CrlA, a cAMP receptor-like protein, or cells lacking CfaD are not affected by rAprA. Surprisingly, both cell types still bind rAprA.ConclusionTogether, the data suggest that AprA functions as an autocrine proliferation-inhibiting factor by binding to cell surface receptors. Although AprA requires CfaD for activity, it does not require CfaD to bind to cells, suggesting the possibility that cells have an AprA receptor and a CfaD receptor, and activation of both receptors is required to slow proliferation. We previously found that crlA- cells are sensitive to CfaD. Combined with the results presented here, this suggests that CrlA is not the AprA or CfaD receptor, and may be the receptor for an unknown third factor that is required for AprA and CfaD activity.

dictyostelium_cells_bind_a_secreted_autocrine_factor_that_represses_cell_proliferation

Background<!>Cell culture<!>Recombinant AprA and CfaD Expression and Purification<!>Quantification of secreted AprA<!>Proliferation inhibition by rAprA or conditioned growth medium<!><!>Determination of optimal binding time for rAprA<!>Steady state binding<!>Competitive binding<!>Recombinant AprA is bioactive<!><!>Recombinant AprA is bioactive<!><!>Recombinant AprA is bioactive<!><!>CfaD and AprA potentiate each other's ability to slow proliferation<!><!>Recombinant AprA binds to Dictyostelium cells<!><!>Recombinant AprA binds to Dictyostelium cells<!><!>Recombinant AprA binds to Dictyostelium cells<!><!>Discussion<!>Conclusion<!>Authors' contributions<!>Acknowledgements

<p>Much remains to be understood about the mechanisms that regulate the size of a tissue. In some cases, it appears that secreted diffusible factors allow cells in a group to sense the size of the group [1,2]. As the number of cells secreting the factor increases, the concentration of the factor increases [1,3]. The cells sense the concentration of the factor, allowing them to sense the size of the group of cells. If the factor inhibits cell proliferation, the resulting negative feedback loop could effectively stop proliferation once a specific group or tissue size is reached. The group or tissue size would then be determined by how much factor the cells secrete, the diffusion properties of the factor, and how sensitive the cells are to the factor. There are a few examples of this sort of negative feedback loop. For example, myostatin is a protein secreted by muscle cells, and myostatin concentrations rise as the amount of muscle in the body increases [4]. Myostatin inhibits myoblast proliferation, which keeps the amount of muscle in the body at a relatively constant level [5]. Mutation or disruption of myostatin results in abnormally large muscles [6,7]. Another example of a negative feedback loop is thyroid size regulation. Thyroid cells secrete thyroid hormone, which inhibits the release of thyroid-stimulating hormone [8] from the pituitary. TSH functions to stimulate the growth of the thyroid. Thus, if the thyroid is damaged, thyroid hormone levels would fall, allowing more TSH release to promote thyroid growth [9]. A third example of a negative feedback loop involves regulation of adipose tissue within the human body. The leptin protein is secreted by adipocytes and signals the amount of adipose tissue present in the body [10,11]. High leptin levels signal to the body that appetite is satisfied, which decreases adipose tissue accumulation to complete the feedback loop.</p><p>There are many tissues where there is evidence for the existence of a secreted factor that inhibits cell proliferation to regulate tissue size, but the identity of the factor and its signal transduction pathway is unknown. For example, in mammals the liver has the ability to regenerate to the correct size if any portion of the liver is removed, and this appears to be mediated by an unknown factor that is secreted into the blood [12]. The spleen is another example of a tissue whose size appears to be negatively regulated by unknown secreted factors [13]. Identifying these factors and their signal transduction pathways will aid in our understanding of tissue size regulation.</p><p>Dictyostelium discoideum is an excellent model system to study secreted factors and the regulation of proliferation and group size. Dictyostelium is a haploid unicellular eukaryote that feeds on soil bacteria. There are several secreted signals whose extracellular concentration is sensed by Dictyostelium cells to, in turn, sense the local density or number of other Dictyostelium cells. When cells starve, they stop dividing and begin secreting an 80 kDa glycoprotein called conditioned medium factor (CMF) [3,14-18]. As more and more cells in a population starve, the extracellular CMF concentration rises. When there is a high percentage of starved cells, as indicated to the cells by a high extracellular concentration of CMF, the cells aggregate to form multicellular structures called fruiting bodies. The aggregating cells form dendritic streams flowing toward a common center. To regulate the size of the fruiting bodies, the streams break up into groups if there are too many cells in a stream [19]. Cells sense if there are too many cells in a stream by sensing the concentration of counting factor (CF), a protein complex secreted by the aggregating cells [20-25].</p><p>CF is a 450 kDa complex of at least 4 different proteins [20,23,26-28]. Partially purified CF contains 8 proteins, and we have been systematically identifying which are true CF components and which are contaminants. We identified two proteins, AprA and CfaD, in the partially purified CF preparation that are not CF components [29,30]. AprA and CfaD are components of a 150 kDa complex and appear to bind to each other [30]. Disruption of either aprA or cfaD results in cells that have an abnormally high proliferation rate, while overexpression of either protein slows proliferation [29,30]. Adding either 10 ng/ml immunoprecipitated native AprA (at the time, we had not found conditions to make recombinant AprA) [29], or 20 ng/ml or higher concentrations of recombinant CfaD [30], also slows cell proliferation. Recombinant CfaD however does not affect the proliferation of aprA- cells, suggesting that CfaD needs the presence of AprA to inhibit proliferation [30]. Neither AprA nor CfaD affect growth rates per nucleus (effectively the mass increase per hour of cells) [29,30]. Because of the finite amount of available nutrients in a given patch of soil, and because cells will soon starve after they reach a high cell density, we have hypothesized that the functions of AprA and CfaD are to slow proliferation without slowing growth as the cells reach high density, so that when the cells do starve, the cells will tend to be large and have a relatively large store of nutrients [29,30].</p><p>While studying novel proteins with similarities to G-protein-coupled receptors, Raisley et al. [31] found that cells lacking CrlA, a putative a G protein coupled receptor, proliferate faster than wild type cells. Interestingly, we found that compared to its effect on wild-type cells, recombinant CfaD weakly inhibits the proliferation of crlA- cells [30]. This suggested that CrlA potentiates, but is not necessary for, CfaD signal transduction.</p><p>We recently found conditions in which we can express recombinant AprA (rAprA) [30]. In this report, we show that rAprA slows the proliferation of wild-type and aprA- cells, but has no effect on cfaD- or crlA- cells. However, rAprA binds to all four cell types, suggesting that CfaD and CrlA are necessary for AprA signal transduction, and that CrlA is a receptor for a different factor that regulates the ability of AprA to act as a chalone.</p><!><p>Wild-type Ax2, aprA- strain DB60T3-8 [29], cfaD- strain DB27C-1 [30], and crlA- strain JH557 [31] were cultured following Brock et al. [20] in HL5 medium (Formedium Ltd., Norwich, England). The growth of NC4 on bacteria was done as described in [30]. Calculation of doubling times was done as previously described [29].</p><!><p>Recombinant AprA (rAprA) and recombinant CfaD (rCfaD) were prepared following Bakthavatsalam et al. [30]. The concentrations of the purified proteins were determined as described in Gao et. al., [32].</p><!><p>The conditioned growth medium samples used for the AprA quantitation were aliquots of the samples we previously used to measure the accumulation of CfaD [30], allowing a direct comparison of the amount of AprA and the amount of CfaD secreted by cells. Samples of the conditioned growth media were run on 4–15% acrylamide gels (Biorad Laboratories, Hercules, CA) along with different known concentrations of rAprA. Western blots were stained with affinity-purified anti-AprA antibodies as described previously [29]. The AprA bands were then scanned and analyzed using ImageJ . The concentration of secreted AprA at each cell density was quantified by comparing against the known concentrations of rAprA.</p><!><p>To test the biological activity (cell proliferation inhibition activity) of rAprA or conditioned growth medium (prepared from wild-type cells grown to 1.2 × 107 cells/ml, where the measured rAprA concentration in the conditioned growth medium is 0.3 μg/ml), cells were grown in HL5 media to a density of 2 × 106 cells/ml, collected by centrifugation at 1,500 × g for 3 minutes, and resuspended in HL5 media to 5 × 105 cells/ml. Cells were then counted before and after 12 hours of incubation with rAprA (or an equal volume of buffer as a control) or conditioned growth medium (or an equal volume of HL5 as a control). A sigmoidal dose-response curve</p><p>Percent proliferation=100−(Max1+10((log⁡(EC50))−(log⁡(rAprA concentration))))</p><p>was then fit to the data to obtain Max and EC50 using nonlinear regression with Prism (GraphPad software, San Diego, CA). In Table 1, Max is called 'proliferation as percent of control at high rAprA'. The units/ml of proliferation-inhibiting activity was defined as the fold dilution of added rAprA or conditioned growth medium that caused a 20% decrease in the density of cells after the 12-hour incubation. The units/ml was calculated using the Max and EC50 values obtained from the above curve fitting, and solving for rAprA concentration with percent proliferation set to 80. For a known concentration of AprA, the units/ml of activity could then be converted to units/μg.</p><!><p>rAprA and wild-type conditioned growth medium slow the proliferation of wild-type (WT) and aprA- cells.</p><p>Cells of the indicated strain were grown in the presence of different concentrations of rAprA, and cells were counted after 12 hours. After fitting sigmoidal dose response curves (Figure 2), the activity of rAprA and the proliferation as a percent of control at high concentrations of rAprA were calculated. Similar fits were done for cells grown in different dilutions of wild-type conditioned growth medium (Figure 3). The activity of the AprA in conditioned growth medium was calculated using the observed AprA concentration of 0.3 μg/ml. All values are mean ± SEM from 3 independent experiments. ND indicates not determined.</p><!><p>To determine the saturation binding time of rAprA, cells were grown to a density of 2 × 106 cells/ml. Cells were collected by centrifugation at 1,500 × g for 3 minutes. Cells were briefly washed twice in ice cold HL5 and were resuspended in ice cold HL5 to a final concentration of 1.0 × 107 cells/ml and kept on ice. 0.5 μl of 300 μg/ml rAprA was added to 500 μl of cells, and this was gently mixed on a rotator at 4°C for 0, 1, 2, 5, 10, or 30 minutes. Cells were collected after the indicated times by centrifugation at 10,000 × g for 30 seconds and washed briefly in 500 μl of ice cold HL5. Following the wash, the cells were resuspended in 100 μl of SDS sample buffer and heated at 95°C before loading 10 μl onto a 4–15% gel (Biorad). Different concentrations of rAprA were used as a standard on the same gel, and proteins were transferred onto a PVDF membrane (Immobilin-P, Millipore corporation, Bedford, MA). A duplicate gel was stained with Coomassie to verify that there were roughly equal amounts of protein in each sample. To detect rAprA (which contains a myc tag), the blots were stained with a 1:10,000 dilution of anti-myc antibodies (Bethyl laboratories, Montogomery, TX) in 25 mM Tris/HCl pH 7.4, 150 mM NaCl/0.1% Tween-20 for 1 hour, and subsequent steps for Western blotting were done following [29]. The rAprA bands on the autoradiograph were scanned and the binding experiment intensities were compared against the standards to determine the concentration of bound rAprA. An association binding curve</p><p>Bound rAprA = Bmax(1-e-kt)</p><p>where t is time was then fit to the data using Prism.</p><!><p>A binding assay was performed as described above except that the cells were incubated with different concentrations of rAprA for 10 minutes at 4°C. One- and two- site binding curves with and without cooperative binding were then fit to the data using Prism.</p><!><p>For competitive binding assays, wild-type conditioned growth medium (CGM) was prepared as described previously [33]. The concentration of AprA in the conditioned medium was measured as described above. A binding assay was performed as described above with the exception that aprA- cells were used, and were resuspended in pre-chilled mixtures of HL5 medium and wild type conditioned growth medium before adding 150 ng/ml of rAprA. After 10 minutes of incubation, the amount of bound rAprA was determined as described above. A sigmoidal dose-response curve</p><p>Bound rAprA=BT−(BT1+10((log⁡(IC50))−(log⁡(native AprA concentration))))</p><p>where BT is the maximal rAprA binding in the competition assay and IC50 is the concentration of native AprA that causes 50% inhibition of the AprA binding, was then fit to the data using nonlinear regression with Prism. The Ki for the binding inhibition was then calculated from the IC50 using the equation of Cheng and Prusoff [34].</p><!><p>AprA is a secreted signal in Dictyostelium cells that slows cell proliferation [29]. To determine the extracellular concentration of AprA, we expressed and purified recombinant AprA (rAprA) for use as a reference standard (Figure 1A). The rAprA appeared as a single band at 60 kDa, which roughly corresponds to the sum of the predicted molecular mass of the his/myc tag on the rAprA (5.3 kD) and the predicted mass of the secreted portion of the AprA polypeptide backbone (53.1 kDa). Since the observed mass of the secreted portion of native AprA is 60 kDa [29], the observed mass of rAprA suggests that the secreted native AprA contains ~5–7 kDa of posttranslational modification, presumably glycosylation. Western blots of conditioned growth medium electrophoresed alongside known quantities of rAprA were stained with affinity-purified anti-AprA antibodies. We observed an increase in the accumulation of extracellular AprA with cell density during the growth of wild-type cultures (Figure 1B). As the cultures reached saturation at ~1.2 × 107 cells/ml, the AprA concentration rose to 0.3 μg/ml. This corresponds to an accumulation of 2.5 × 10-8 μg/cell. Similar assays showed that at a density of 2 × 106 cells/ml, both cfaD- and crlA- cells had accumulated 0.6 ± 0.1 μg/ml of extracellular AprA (data not shown), indicating that loss of CfaD or CrlA causes cells to accumulate, compared to wild-type cells, roughly ten times more extracellular AprA.</p><!><p>The concentration of extracellular AprA increases with cell density. A) An SDS-polyacrylamide gel of recombinant AprA (left lane) and molecular mass standards (right lane) was stained with Coommassie.B) Using different concentrations or recombinant AprA to make a standard curve, Western blots were used to determine the extracellular concentration of AprA as a function of cell density. Values are mean ± SEM (n = 3). The absence of error bars indicates that the error was smaller than the plot symbol.</p><!><p>It took 16 hours for wild-type cultures to proliferate from 0.5 × 106 cells/ml to 1.3 × 106 cells/ml, and during this time the extracellular AprA concentration increased by 0.021 ± 0.001 μg/ml (mean ± SEM, n = 3). Assuming that during log-phase growth the cell density ρ = Noekt, we see that with No = 0.5 × 106 cells/ml, k = 0.060/hour. Assuming a constant AprA accumulation rate/cell/hour = X, then</p><p>21 ng/ml=∫016Xρdt</p><p>Solving for X, we find that between 0.5 × 106 and 1.3 × 106 cells/ml, the extracellular AprA accumulation rate is 1.6 × 10-9 μg/cell/hour, or 2.6 × 10-11 μg/cell/minute, or 260 molecules of AprA/cell/minute. Similar calculations were done for the other cell density ranges shown in Figure 1B, as well as for the extracellular CfaD concentrations shown in Figure 3 of [30]. As shown in Table 2, with the assumption that there is no breakdown of extracellular AprA or CfaD, the accumulation rate of extracellular AprA per cell per hour fluctuates as the cell density in the population increases, with a general tend of decreasing as the cells approach saturation density. Conversely, the accumulation rate of extracellular CfaD per cell per hour increases as the cell density in the population increases (Table 2).</p><!><p>The accumulation of AprA and CfaD as a function of cell density.</p><p>The accumulation values are derived from the AprA quantitation in Figure 1 and from the CfaD quantitation in Figure 3 of [30]; both figures used the same set of samples for quantitation. Values are mean ± SEM from 3 separate experiments.</p><!><p>In the wild, Dictyostelium cells grow on soil surfaces. The parental strain used in these studies is an axenic strain derived from an isolate from North Carolina called NC4 [35]. We found that when NC4 strains grow on lawns of bacteria on agar plates, they secrete both CfaD and AprA, and that NC4 cells growing on bacteria accumulate approximately 4 times more CfaD per cell than Ax2 cells at 1.2 × 107 cells/ml in shaking culture [30]. Using rAprA to generate a standard curve, we found that when there are 3 × 107 NC4 cells on an agar plate, the agar contains 2.0 ± 0.1 μg of AprA (mean ± SEM, n = 3). This corresponds to an accumulation of 6.6 × 10-8 μg/cell, which is approximately 2.6 times higher than the accumulation for Ax2 cells at stationary phase. The data thus suggest that in the natural environment, cells accumulate somewhat more AprA and CfaD than axenic cells in shaking culture.</p><p>We previously observed that immunoprecipated native AprA slows the proliferation of wild-type and aprA- cells [29]. To determine if any eukaryote-specific posttranslational modification such as glycosylation is part of the AprA active site, we added rAprA to cells. After 12 hours, rAprA at concentrations at and above 0.1 μg/ml significantly slowed the proliferation of wild-type and aprA- cells (Figure 2A and Table 1). A recombinant version of the human serum protein Serum Amyloid P, made with the same expression vector in the same bacterial cell line, as well as bovine serum albumin, had no effect on cell proliferation (data not shown). Even at high concentrations (64 μg/ml), rAprA was only able to slow the proliferation of wild-type and aprA- cells rather than completely arrest their proliferation (Figure 2A). The doubling times we observed for wild-type and aprA- cells were 12.7 and 9.1 hours respectively, essentially identical to what we previously observed [29]. At 1 μg/ml rAprA, the doubling time for wild-type cells was 19.3 hours, and at 4 μg/ml the doubling time was 24.5 hours, similar to the 23.3 hour doubling time we observed for cells overexpressing AprA [29]. Interestingly, rAprA had essentially no ability to slow the proliferation of crlA- or cfaD- cells. These results demonstrate that rAprA is bioactive, that if AprA is glycosylated, the glycosylation is not essential for bioactivity, and suggest that CrlA and CfaD are required for the ability of rAprA to slow cell proliferation.</p><!><p>rAprA slows cell proliferation. A) Different concentrations of rAprA were added to the indicated cell types, and after 12 hours cells were collected and counted. WT is wild-type. Values are mean ± SEM (n = 3). The absence of error bars indicates that the error was smaller than the plot symbol. The lines are sigmoidal dose response curve fits to the data; the dashed line is the fit to aprA-. The inhibition of wild type and aprA- proliferation is significant with p < 0.01 at 0.1 μg/ml and higher rAprA concentrations (1-way ANOVA, Dunnett's test). B) A 2:1 mixture of rAprA and rCfaD was added to wild-type cells as in A above, so that at, for instance, 0.32 μg/ml rAprA there was an additional 0.16 μg/ml rCfaD. Values are mean ± SEM (n = 3). The line is a sigmoidal dose response curve fit to the data. The inhibition of wild type proliferation is significant with p < 0.05 at 0.02 μg/ml rAprA/0.01 μg/ml rCfaD and higher concentrations (t test).</p><!><p>Like AprA, CfaD is a protein secreted by growing Dictyostelium cells that slows cell proliferation [30]. CfaD appears to bind to AprA and requires AprA for bioactivity. To determine if rCfaD potentiates the activity of rAprA, we added mixtures of rAprA and rCfaD to cells. CfaD accumulates to ~0.08 μg/ml when cells are at 1.2 × 107 cells/ml, while AprA accumulates to ~0.3 μg/ml at this density. As a rough comparison, we thus added 2:1 w/w mixtures of rAprA:rCfaD to wild type cells (Figure 2B). A fit of a sigmoidal dose response curve indicated that at high concentrations, the mixture is able to slow proliferation to 72 ± 3% of control (mean ± SEM, n = 3). This is not significantly different from the amount that high concentrations of rAprA or rCfaD can slow proliferation (Table 1 and [30]). rAprA slows proliferation to 80% of control at ~0.27 μg/ml (Figure 2A and Table 1), while rCfaD slows proliferation to 80% of control at 0.05 μg/ml [30]. The mixture slows proliferation to 80% of control at 0.045 μg/ml rAprA/0.022 μg/ml rCfaD (Figure 2B). This suggests that the presence of CfaD decreases the concentration of AprA needed to slow proliferation, and vice versa.</p><p>The conditioned growth medium from wild-type cells slows aprA- cell proliferation, whereas conditioned growth medium from aprA- cells lacks this activity, indicating that AprA is a key component of the proliferation-inhibiting activity in wild-type conditioned growth medium [29]. To compare the proliferation-inhibiting activity of recombinant AprA to the AprA-associated activity in conditioned growth medium, cells were grown in different dilutions of conditioned growth medium collected from wild-type cells at 1.2 × 107 cells/ml. After 12 hours, wild-type conditioned growth medium at concentrations above 30% significantly slowed the proliferation of wild type and aprA- cells (Figure 3 and Table 1). Using the observed AprA concentration in conditioned growth medium (collected from wild-type cells at 1.2 × 107 cells/ml) of 0.3 μg/ml (Figure 1), the AprA activity, as measured in units/μg of the AprA in wild-type conditioned growth medium, on wild-type or aprA- cells was roughly similar to the activity of rAprA on wild-type cells; the differences were not statistically significant (p > 0.05, 1-way ANOVA, Tukey's test) (Table 1). However, at high concentrations, the wild-type conditioned growth medium caused a somewhat greater inhibition of proliferation than high concentrations of rAprA (Figures 2 and 3 and Table 1). Together, the results suggest that rAprA has roughly the same bioactivity as native AprA, but that there may be additional factors in conditioned growth medium that slow cell proliferation.</p><!><p>Wild-type conditioned growth medium slows cell proliferation. Conditioned growth medium was collected and diluted with fresh growth medium to the indicated concentrations. Wild-type (WT) and aprA- cells were then grown in the mixed media, and cells were counted after 12 hours. Values are mean ± SEM (n = 3). The absence of error bars indicates that the error was smaller than the plot symbol. Lines are curve fits of a sigmoidal dose response curve; the dashed line is the fit to aprA-. The inhibition of WT and aprA- proliferation is significant with p < 0.05 at 30% and higher conditioned growth medium concentrations (1-way ANOVA, Dunnett's test).</p><!><p>To determine if AprA is sensed by cell surface receptors, we examined the binding of rAprA to cells. The binding assays were done in HL5 growth medium, as we previously observed that we could measure binding of CMF to cells in this medium [16]. After trying a variety of binding times and concentrations to establish rough time and concentration conditions for the assays (Figure 4 and data not shown), the time course of rAprA binding was examined to establish steady state conditions for further binding assays. The amount of rAprA bound to cells reached near steady state levels by 10 minutes (Figure 5). Interestingly, even though rAprA was unable to inhibit the proliferation of cfaD- and crlA- cells, rAprA bound to these cells. Although there appeared to be differences in the binding rates, the difference in the binding time constant k between all pairs of cell types was not significant (p > 0.05, 1-way ANOVA, Tukey's test). In addition, although it appeared that there were differences in the amount of rAprA bound at 10 and 30 minutes to the different cell types, the difference between all pairs of cell types was not significant (p > 0.05, 1-way ANOVA, Tukey's test).</p><!><p>Binding of rAprA to cells. For a rAprA binding assay, wild-type cells were incubated for 10 minutes with the indicated concentrations ("Added to cells" in ng/ml) of myc-tagged rAprA. The cells were then washed to remove unbound rAprA, and were solubilized in SDS sample buffer. A western blot of the solubilized cells electrophoresed alongside different amounts of myc-tagged rAprA ("Standards") was stained with anti-myc antibodies (upper panel) while a duplicate gel of the cell samples was stained with Coomassie (lower panel). The heavy band in the Coomassie-stained samples is actin.</p><p>Time course of rAprA binding to cells. Cells of the indicated strains (WT is wild-type) were incubated with 150 ng/ml of rAprA for the indicated times at 4°C. Cells were collected and the bound myc-tagged rAprA was quantitated by western blots (staining for the myc tag), using known amounts of rAprA as standards. The plot symbols are the same as those in Figure 2. Values are mean ± SEM (n = 3). Lines are curve fits of an association binding curve; the dashed line is the fit to aprA-.</p><!><p>A key property of binding is that it is saturable. To examine whether the binding of rAprA to cells is saturable, cells were incubated with different concentrations of rAprA and the amount of bound rAprA was measured after 10 minutes. For wild-type and crlA- cells, the binding of rAprA appeared to saturate above a free rAprA concentration of 0.4 μg/ml (Figure 6 and Table 3). There appeared to be a higher level of binding to aprA- and cfaD- cells, the binding appeared to roughly saturate, and there appeared to be a lower KD for binding to these two cell lines. The binding appeared to be specific, as competition with 10 μg/ml of BSA had no discernable effect on binding (data not shown). For all four cell lines, binding curves were fit using nonlinear regression with an equation for one-site binding. F-tests comparing these fits to fits with a two-site binding model, or fits to binding models with a variable Hill coefficient, indicated that for each of the four cell lines there did not appear to be two classes of binding sites or cooperative binding; the Hill coefficient for binding to wild-type cells was 1.0. Taken together, the data suggests that rAprA shows saturable binding to cells.</p><!><p>Cells bind physiological concentrations of rAprA. Cells of the indicated strains (WT is wild-type) were incubated with different concentrations of rAprA. After 10 minutes, cells were collected and the bound rAprA was measured as in Figure 5. The plot symbols are the same as in Figure 2. Values are mean ± SEM (n = 3). For the 60 kDa rAprA, a bound rAprA value of 1.0 ng/5 × 105 cells is equivalent to 2.0 × 104 molecules/cell. The lines are curve fits to a one-site binding model with no cooperative binding.</p><p>The measured KD and Bmax for the binding of rAprA to vegetative cells.</p><p>For each cell line, the KD in μg/ml and the Bmax in ng/5 × 105 cells was obtained from the curve fits in Figure 6. The KD in nM and Bmax in molecules/cell were calculated using a molecular mass of 60 kDa for rAprA. All values are mean ± SEM, n = 3. For both the the KD's and the Bmax's, the difference between any two cell lines is not significant (p > 0.05, 1-way ANOVA, using either Tukey's or Bonferroni's test).</p><!><p>To compare the cell surface binding of rAprA to that of native AprA, we measured the inhibition of rAprA binding to aprA- cells by different concentrations of native AprA, using wild-type conditioned growth medium from cells at 1.2 × 107 cells/ml as a source of native AprA. As the concentration of wild-type conditioned growth medium in the binding assay increased, the amount of rAprA binding to aprA- cells decreased (Figure 7). Using the equation of Cheng and Prusoff [34] and the observed AprA concentration of 0.3 μg/ml in wild type conditioned growth medium from cells at 1.2 × 107 cells/ml, we found that native AprA had an inhibition constant (Ki) of 0.03 μg/ml, which is similar to the 0.03 μg/ml KD for the binding of rAprA to aprA- cells. Taken together, the data suggest that the binding affinity of rAprA to aprA- cells is roughly similar to that of native AprA.</p><!><p>Endogenous AprA competes with rAprA for binding to cells. The binding of rAprA to aprA- cells was measured in the presence of the indicated concentrations of wild-type conditioned growth medium from cells at 107 cells/ml, where the AprA concentration is 0.3 μg/ml. Values are mean ± SEM (n = 4). The line is a curve fit of a sigmoidal dose response curve.</p><!><p>Dictyostelium cells appear to regulate cell proliferation by secreting and sensing AprA and CfaD [29,30]. We found here that at 21°C, wild-type cells accumulate 260 molecules/cell/minute of extracellular AprA at low cell densities, and this decreases to 22 molecules/cell/minute as cells approach saturation density. Conversely, the CfaD accumulation rate appears to increase from less than 1 molecule/cell/minute at low density to 59 molecules/cell/minute near saturation density. These changes in accumulation rates as the cell density in a culture increases may be due to changes in nutrient or waste product levels, or changes in levels of signals such as AprA and CfaD. These accumulation rates are in the range of the 12 molecules/cell/minute for CMF [36], the 60 molecules/cell/minute for the accumulation of CF by wild-type cells [32], and the 250 molecules/cell/minute we observed for the accumulation of CF by smlA- cells [20].</p><p>Using the AprA secretion rate of 49 molecules/cell/minute for cells harvested at 2 × 106 cells/ml, we can calculate that, if the binding assays had been done at 21°C, in our 10-minute binding assays wild-type cells would accumulate 4.9 × 10-4 μg/ml of AprA. This is less than the lowest concentration of rAprA used for the binding curves (other than the buffer control) and well below the measured KD's (Table 3). We saw that, at the density that we harvested cells for the binding assays, crlA- and cfaD- cells accumulated roughly 10 times more AprA than wild-type cells. This could be due to an increased AprA secretion rate or a decreased AprA degradation rate, or some combination of the two. Assuming a ten-times higher AprA secretion rate, crlA- and cfaD- cells would accumulate 4.9 × 10-3 μg/ml of AprA in the 10-minute binding assays. This is still below the observed KD's. At 4°C (the condition for the binding assays), the secretion rate will be even lower than at 21°C, so the amount of AprA secreted by cells during the binding assay should not have strongly interfered with the binding assay results.</p><p>Cells overexpressing Apra or CfaD still proliferate, albeit slowly, and recombinant CfaD slows but does not stop the proliferation of cells [29,30]. Like CfaD, rAprA slows but does not stop cell proliferation, even when combined with recombinant CfaD at concentrations three times higher than seen in the conditioned growth medium of stationary phase cells (Figures 2A and 2B). One can imagine that in the wild, a Dictyostelium cell might find itself in a small enclosed space where secreted factors might build up to very high concentrations, and having high concentrations of a chalone completely stop proliferation would be disadvantageous. The observed response of cells to AprA and CfaD (slowing but not stopping proliferation) thus might allow cells to increase their mass and protein content as they reach a high density, without incurring the risk of unnecessarily stopping proliferation under some conditions.</p><p>The response of wild-type and aprA- cells is nonlinear: it takes approximately 0.01 μg/ml rAprA to decrease the cell density at 12 hours by 10%, approximately 0.1 μg/ml to decrease the density by an additional 10%, and more than 1 μg/ml to cause a further ~10% decrease (Figure 2A). rAprA appeared to have a higher activity (in units/μg) when added to aprA- cells compared to its activity on wild-type cells (Figure 2A and Table 1). A qualitative explanation for this is that wild-type cells are accumulating extracellular AprA while aprA- cells are not, so in the proliferation assay the wild-type cells are effectively starting at a higher extracellular AprA concentration compared to the aprA- cells. Since our definition of a unit of AprA activity is the inverse of the amount of AprA needed to inhibit proliferation by 20% at 12 hours, and given the nonlinear response of cells to AprA, it would thus take more AprA to slow wild-type cells by an additional 20% compared to the amount of AprA needed to slow aprA- cells by 20%. This would then predict that AprA would appear to have a lower activity on wild-type cells compared to its activity on aprA- cells, which is what we observed.</p><p>Immunoprecipitated native extracellular AprA at 10 ng/ml significantly slowed the proliferation of wild-type and aprA- cells [29], whereas higher concentrations of rAprA are needed to significantly slow proliferation. Native AprA has a higher molecular mass than recombinant AprA and thus appears to have some posttranslational modification, presumably glycosylation. This difference in posttranslational modifications may be the explanation for why native AprA appears to be more potent than recombinant AprA.</p><p>rAprA and rCfaD appear to potentiate each other's ability to inhibit proliferation (Figures 2A and 2B and Table 1). However, conditioned growth medium, which contains both AprA and CfaD, appears to have the same activity as rAprA, and thus has effectively less proliferation inhibiting activity than one would predict. This suggests that there may be some proliferation promoting activity in conditioned growth medium that counteracts the effects of AprA and CfaD. This proliferation promoting activity may well be due to a secreted growth factor activity (the factor has not been identified) that has been observed in Dictyostelium conditioned growth medium [37].</p><p>Wild-type cells show roughly steady-state binding after 10 minutes of incubation with rAprA, which is similar to the binding kinetics observed for countin [32] and conditioned medium factor (CMF) to Dictyostelium cells [16]. We found that recombinant AprA binds to wild-type cells with a KD of ~2.6 nM. This is stronger than the ~150 nM KD observed for folate binding to Dictyostelium cells [38], but quite similar to the 2.1 nM KD that we observed for CMF binding [16], and weaker than the 490 pM for CF50 binding [27], or 60 pM for countin binding [32]. Depending on the cell type, we observed ~6 – 9 × 104 AprA binding sites/cell. Although this is much higher than the ~50–60 countin or CF50 binding sites/cell [27,32], this is similar to the ~6 × 104 folate binding sites/cell [38] or ~4 × 104 CMF binding sites/cell [16]. Together, this suggests that the binding timecourse, KD, and number of binding sites/cell for AprA binding to cells are all within the range seen for the binding of other ligands to Dictyostelium cells. At 107 cells/ml, where the extracellular AprA concentration is 0.3 μg/ml, solving for the number of occupied cell-surface receptors using our observed KD and Bmax for wild-type cells, we see that there will be ~40,000 occupied receptors, or roughly 2/3 of the receptors will be occupied. This would then allow a strong activation of pathways downstream from the AprA receptor.</p><p>Although not statistically significant, it appears that aprA- and cfaD- cells have somewhat more AprA receptors than wild-type or crlA- cells, and that the AprA receptors in aprA- and cfaD- cells have a lower KD (stronger binding) than the receptors in wild-type or crlA- cells. A possible explanation for this is that there may be some degree of AprA-induced receptor desensitization and down regulation in wild-type and crlA- cells, and that CfaD is necessary for this effect.</p><p>AprA and CfaD appear to be part of the same extracellular complex, and the presence of AprA is required for rCfaD to be able to slow proliferation [30], and conversely the presence of CfaD is needed for rAprA to slow proliferation (Figure 2A). However, rAprA shows roughly normal binding to cells in the absence of CfaD (Figures 4 and 5). This suggests that CfaD does not regulate AprA's proliferation-slowing activity by regulating its ability to bind to cells, but rather CfaD activates some pathway downstream of AprA binding that permits AprA signaling. This is strikingly similar to what we observed for countin and CF50, two protein components of the extracellular signal CF [27]. Countin and CF50 need each other for activity, but still bind to cells in the other's absence [27]. rAprA also needs the presence of the receptor-like protein CrlA to slow proliferation (Figure 2A), but surprisingly rAprA shows roughly normal binding to crlA- cells (Figures 5 and 6). rCfaD slows the proliferation of crlA- cells, although to a lesser extent than rCfaD slows wild-type or cfaD- cells [30]. This suggests that CrlA is neither the AprA nor the CfaD receptor, but rather is part of a different pathway that for unknown reasons regulates the ability of AprA and CfaD to function as chalones to slow proliferation.</p><!><p>Together, the data suggest that AprA functions as an autocrine proliferation-inhibiting factor by binding to cell surface receptors. Like CfaD, the concentration of AprA increases with cell density, and also like CfaD, AprA slows but does not completely stop proliferation. Although AprA requires CfaD for activity, and the two factors potentiate each other's activity, AprA does not require CfaD to bind to cells, suggesting the possibility that cells have an AprA receptor and a CfaD receptor, and activation of both receptors is required to slow proliferation. We previously found that crlA- cells are sensitive to CfaD. Combined with the results presented here, this suggests that CrlA is not the AprA or CfaD receptor, and may be the receptor for an unknown third factor that is required for AprA and CfaD activity.</p><!><p>JMC carried out the binding assays and drafted parts of the manuscript. DB prepared recombinant AprA, did the AprA quantitation assays, and drafted parts of the manuscript. JEP did proliferation inhibition assays. RHG conceived the study, participated in the design of the study, performed statistical analysis, and helped to draft the manuscript. All authors read and approved the final manuscript.</p><!><p>We thank Dale Hereld for the crlA- cells, Jeff Crawford for recombinant human serum amyloid P, and Darrell Pilling for helpful suggestions. This work was supported by the National Institutes of Health grant GM074990.</p>

PubMed Open Access

Effects of nicotine conditioning history on alcohol and methamphetamine self-administration in rats

Background. Smoking constitutes a significant public health risk. Alcohol and methamphetamine use disorders are also highly co-morbid with smoking, further increasing negative health outcomes. An important question in determining the underlying neurobiology of nicotine poly-drug use is understanding whether having a positive history with nicotine effects alters later drug-taking behavior. Methods. The current experiments sought to elucidate whether having an appetitive nicotine conditioning history would affect later alcohol or methamphetamine self-administration. Adult male and female Long-Evans rats were first trained on a discriminated goal-tracking task in which the interoceptive effects of nicotine predicted sucrose reinforcement. As a control, pseudo-conditioned groups were included that had equated nicotine and sucrose experience. Rats were then shifted to either alcohol self-administration or methamphetamine self-administration. Results. Nicotine conditioning history had no effect on acquisition or maintenance of alcohol self-administration in males or females. In contrast, an appetitive nicotine conditioning history decreased methamphetamine self-administration in female rats, but not males. Conclusions. In female, but not male rats, an appetitive conditioning history with nicotine decreases methamphetamine, but not alcohol, self-administration. This dissociation suggests that the effects may be due to a specific increase in the reinforcing value of methamphetamine. This may have implications for better understanding the progression of drug use from nicotine to methamphetamine.

effects_of_nicotine_conditioning_history_on_alcohol_and_methamphetamine_self-administration_in_rats

Introduction<!>Subjects<!>Apparatus<!>Drugs<!>Nicotine Interoceptive Conditioning (Phase 1 for Experiments 1 and 2)<!>Experiment 1: Alcohol Self-Administration (Phase 2)<!>Experiment 2: Methamphetamine Self-Administration (Phase 2)<!>Methamphetamine Self-Administration Training.<!>Statistical Analyses<!>Nicotine interoceptive conditioning (Phase 1)<!>Sucrose Fading<!>Maintenance of Alcohol Self-administration<!>Nicotine interoceptive conditioning (Phase 1)<!>Methamphetamine Self-administration (Phase 2)<!>Discussion

<p>Smoking continues to be the leading cause of preventable death, carrying not only a significant health risk to individuals but also a tremendous public health cost annually (CDC 2014). Compounding this issue is evidence that smoking is highly co-morbid with other drugs of abuse including alcohol and methamphetamine. Indeed, as many as 80% of adults with an alcohol use disorder (AUD) and 97% of methamphetamine users smoke (Brecht et al. 2004; Chatterjee and Bartlett 2010). Despite high rates of tobacco use, nicotine alone has been shown to have weak primary reinforcing properties (Caggiula et al. 2009; Rose 2006). However, previous studies suggest that nicotine may act to enhance other reinforcers that in turn maintain nicotine use. For example, nicotine enhances responding to cues related to presentation of drug rewards, non-drug rewards, and brain stimulation (Arregui-Aguirre et al. 1987; Barrett et al. 2017; Chaudhri et al. 2006; Kenny et al. 2009; Olausson et al. 2004; Palmatier et al. 2007a; Paterson et al. 2008). This leads to the question of the role that experience with nicotine may play in initiating the use of other drugs such as alcohol or methamphetamine.</p><p>There is ample evidence in both humans and preclinical animal models that drug-seeking behavior is influenced by drug-associated cues. While these cues are often external/contextual, there is a great deal of interest in the role of interoceptive cues. That is, the interoceptive effects of a drug that become associated with other rewarding events (Bevins and Besheer 2014). Indeed, previous work from our laboratories have demonstrated that reward-seeking behavior can come under the control of drug interoceptive cues (Charntikov et al. 2014; Charntikov et al. 2017b; Murray and Bevins 2007a; b; 2009; Pittenger and Bevins 2013; Randall et al. 2016).</p><p>The purpose of the present work was to assess the impact of appetitive nicotine conditioning history on initiating self-administration of alcohol (Experiment 1) or methamphetamine (Experiment 2). To do so, a discriminated goal-tracking task was used in which the interoceptive effects of nicotine signaled whether sucrose would be presented non-contingently throughout the session. While there is a rich literature showing that reward-related cues can influence later drug taking behavior, these studies tend to depend on external cues, not internal drug-states. However, one such study by Cortright and colleagues (2012), demonstrated that male rats pre-exposed to nicotine through either contextual conditioning or through drug discrimination training, enhanced later self-administration of amphetamine in the absence of nicotine. Based on this, we hypothesized that an explicitly appetitive conditioning history with nicotine would enhance subsequent acquisition and maintenance of drug self-administration beyond that of rats without this conditioning history but with equal exposure to nicotine. Additionally, the current experiments were conducted in male and female rats in parallel. Given that females have been shown to self-administer more alcohol and methamphetamine than males (Randall et al., 2017; Roth and Carroll, 2004), we hypothesized that any enhancement from nicotine conditioning history would be evident in female, not male rats.</p><!><p>Adult Long-Evans rats (Envigo-Harlan) were used in these experiments. Experiment 1: n=42 males / 42 females. Experiment 2: n = 22 males / 22 females. Rats were approximately 7 weeks old upon delivery and were food restricted to maintain ~90% body weight. Water was available ad libitum in the home cage. The vivariums were maintained on a 12-h light/dark cycle, and experiments were conducted during the light cycle. Experiment 1 was conducted at the University of North Carolina – Chapel Hill and Experiment 2 was conducted at the University of Nebraska – Lincoln. All experimental protocols were approved by the Institutional Animal Care and Use Committee at the respective institution and conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals.</p><!><p>All experiments were conducted in standard operant conditioning chambers (31 × 32 × 24 cm; Med Associates, Georgia, VT) located within light-attenuating cubicles equipped with an exhaust fan that provided both ventilation and masking of external sounds. Chambers were fitted with a house light to provide general illumination. In both experiments, a liquid dipper receptacle was centered on the right wall for nicotine interoceptive conditioning phase. A 0.1-ml metal cup was attached to the dipper arm. For the alcohol self-administration phase of Experiment 1, chambers were fitted with a retractable lever on the left wall and a white cue light (2.54 cm diameter; 28V, 100mA) was centered 7-cm above the lever. A liquid receptacle was centered on that wall and located to the left of the lever. Lever responses activated a syringe pump (Med Associates) that delivered 0.1 ml of solution into the receptacle during a 1.66-s period. The white cue light located above the lever was illuminated during pump activation. During the initial nicotine interoceptive conditioning phase, a metal panel was placed to cover the entire left wall of the chamber to block entry into the liquid receptacle where alcohol self-administration would later be trained. Conversely, during the alcohol self-administration phase, a metal panel covered the entire right side of the chamber to block entry into the liquid dipper receptacle. In Experiment 2, the chambers were fitted with two retractable levers on either side of the centered liquid dipper receptacle on the right wall. A white cue light (2.54 cm diameter; 28V, 100mA) was centered 7-cm above each lever, 14.6 cm above the rod floor, and 3.5 cm from the nearest polycarbonate wall. The outside of each chamber was fitted with a balanced metal arm and liquid swivel. An attached spring leash hung into the chamber through the center of the ceiling. Tygon tubing extended through the leash and was connected to a 20-ml syringe mounted on an infusion pump (Med Associates) located outside of the sound-attenuating cubicle. During the nicotine interoceptive conditioning phase, levers were retracted (i.e., not available within the chamber); during the methamphetamine self-administration phase, a metal panel was inserted to block entry into the liquid dipper receptacle. For both experiments, all chambers were interfaced to a computer and data collection and presentation of experimental events was controlled with Med Associates Interface and Software.</p><!><p>(−)-Nicotine hydrogen tartrate (Sigma, St. Louis, MO, USA) and dissolved in 0.9% saline and brought to a pH of 7.4±0.2. Nicotine (0.4 mg/kg) was administered subcutaneously (SC) at a volume of 1 ml/kg. Alcohol (95%, Pharmaco-AAPER, Shelbyville, KY) was diluted (v/v) in distilled water along with sucrose (w/v) to achieve the desired concentration. D-methamphetamine hydrochloride (Sigma) was dissolved in 0.9% sterile saline and administered intravenously (IV) at a dose of 0.05 mg/kg/infusion and a rate of 0.04 ml/second based on each rat's individual weight.</p><!><p>In both experiments, rats were randomly assigned to the nicotine conditioned stimulus (CS) trained group or the pseudoconditioning CS (pseudo-CS) control group. For both groups, rats received nicotine (0.4 mg/kg) or saline injections 5 min prior to each 20-min session. The CS group was trained such that on nicotine sessions there were 36 sucrose presentations (26% w/v, 0.1 ml, 4-s access), with no sucrose presentation occurring less than 90 seconds from the start of the session. There were no sucrose presentations on saline sessions. The pseudo-CS group received sucrose presentations on half of the nicotine sessions and half of the saline sessions. For both groups, training sessions were randomly assigned for each rat with the limitation that no more than two of the same session type (sucrose; no-sucrose) or drug injection (nicotine; saline) were presented in a row. The rate of head entries into the sucrose receptacle before the first sucrose delivery (dipper entries per second) was the measure for conditioning. Rate of entries into the sucrose receptacle during the equivalent interval were also measured on sessions in which no sucrose was delivered. Training proceeded for 32 sessions (16 nicotine/16 saline; 16 sucrose/16 no-sucrose).</p><!><p>At the conclusion of nicotine interoceptive conditioning, rats began the alcohol self-administration phase. For alcohol self-administration, all reinforcers are delivered in the liquid receptacle on the left side of the chamber (opposite from the liquid dipper used for the interoceptive conditioning phase). A sucrose-fading procedure was used to train alcohol self-administration in which alcohol was gradually added to a 10% (w/v) sucrose solution. The exact order of fading was as follows: 10% (w/v) sucrose (10S), 2% (v/v) alcohol/10% (w/v) sucrose (2A/10S), 5A/10S, 10A/10S, 10A/5S, 10A/2S, 10A. There was one session at each concentration. Following sucrose fading, a 10% alcohol (v/v) solution was the reinforcer for the remainder of the study. Alcohol self-administration sessions (30 min) were conducted 5 days per week (M-F). The alcohol lever was maintained on a fixed ratio 2 (FR2), such that every 2nd response on the lever resulted in the activation of a syringe pump (Med Associates) that delivered 0.1 ml of alcohol. Rats underwent 14 sessions of alcohol self-administration.</p><!><p>At the conclusion of nicotine interoceptive conditioning, rats underwent surgery to implant a jugular catheter for intravenous methamphetamine self-administration. Rats were anaesthetized with a 1 ml/kg intramuscular injection of a ketamine hydrochloride (100 mg/ml): xylazine hydrochloride (20 mg/ml) cocktail (Midwestern Veterinary Supply, Des Moines, IA, USA). Tubing (SAI Infusion Technologies, Lake Villa, IL) of a silastic catheter constructed in house was implanted into the right external jugular vein. The tubing was threaded subcutaneously over the shoulder and connected to a metal cannula fitted within a polycarbonate back plate (Plastics One, Roanoke, VA) implanted under the skin just below the scapula. Buprenorphine hydrochloride (0.1 mg/kg; Sigma) was injected subcutaneously immediately following surgery and once per day for two more days for pain management. Catheters were flushed daily with 0.1 ml sterile saline mixed with heparin (30 U/ml; Midwest Veterinary Supply) and Baytril (5.0 mg/ml; Midwest Veterinary Supply; Lakeville, MN). Rats were allowed 7 days of recovery before beginning methamphetamine self-administration.</p><!><p>Following surgical recovery, all rats entered the methamphetamine self-administration phase. At the start of each 1-hr session, the house light was on and both levers were available. Lever assignment of 'active' and 'inactive' was counterbalanced across rats. Methamphetamine (0.05 mg/kg/infusion) was available under continuous reinforcement (fixed ratio schedule 1; FR1) in which one active lever press resulted in an infusion and initiated a 20-sec time out period; inactive lever pressing had no programmed consequence. During the time out, both levers were retracted, the house light was extinguished, and the cue light above the active lever was illuminated. After three FR1 sessions, the contingency was switched to a variable ratio 3 (VR3) reinforcement schedule in which, on average, every third lever press (range = 1-5) resulted in the drug and time out. Rats received 12 sessions on the VR3 schedule.</p><!><p>For the nicotine interoceptive conditioning phase, the dependent variable for all sessions was head entries per second prior to the first sucrose presentation. For sessions without sucrose presentations, the program measured head entry rate during an equivalent amount of time. For sucrose fading and maintenance of self-administration, the dependent variables were alcohol lever responses and alcohol intake (g/kg) in Experiment 1 and methamphetamine infusions for Experiment 2. Sex and each phase of training were analyzed separately, and all experiments were analyzed using repeated measures analysis of variance (RM-ANOVA) with nicotine treatment, conditioning group, and session as factors. Where interactions were present, post-hoc analysis (Tukey) was used to determine differences between specific points and conditions. Significance was set at p<0.05.</p><!><p>As shown in Figure 1 A, head entry rate on nicotine sessions was consistently greater than on saline sessions in the male nicotineCS group but not the male pseudoCS group. For the 3-way ANOVA, there was a main effect of Session (F[15,600] = 12.086, p < 0.001, ηp2 = .232), Drug (F[1,40] = 196.299, p < 0.001, ηp2 = .831), Drug by Session interaction (F[15,600] = 9.808, p < 0.001, ηp2 = .197) and Drug by Session by Conditioning History interaction (F[15,600] = 3.540, p < 0.001, ηp2 = .081). In the male nicotineCS group, there was a main effect of Session (F[15,300] = 6.451, p < 0.001, ηp2 = .244), Drug (F[1,20] = 174.353, p < 0.001, ηp2 = .897) and Drug by Session interaction (F[15,300] = 10.203, p < 0.001, ηp2 = .338). Post hoc analysis found that head entry rate was greater on nicotine sessions 3-16 compared to saline in the male nicotineCS group. In the male pseudoCS group, there was a main effect of Session (F[15,300] = 6.850, p < 0.001, ηp2 = .255), Drug (F[1,20] = 35.524, p < 0.001, ηp2 = .640) and Drug by Session interaction (F[15,300] = 3.107, p < 0.001, ηp2 = .134). Post hoc analysis found that head entry rate was only greater on nicotine sessions 4, 6-11, 14 and 16 compared to saline in the male pseudoCS group suggesting inconsistent and overall considerably less conditioning occurred compared to the male nicotineCS group.</p><p>As shown in Figure 1B, head entry rate on nicotine sessions was greater than on saline sessions in the female nicotineCS group. In contrast, there was no difference in head entry rate between nicotine and saline sessions in the female psuedoCS group. In the 3-way ANOVA, there was a main effect of Session (F[15,600] = 10.977, p < 0.001, ηp2 = .215), Drug (F[1,40] = 182.322, p < 0.001, ηp2 = .820), Drug by Session interaction (F[15,600] = 5.637, p < 0.001, ηp2 = .124) and Drug by Session by Conditioning History interaction (F[15,600] = 2.551, p = 0.001, ηp2 = .060). In the female nicotineCS group, there was a main effect of Session (F[15,300] = 6.317, p < 0.001, ηp2 = .240), Drug (F[1,20] = 518.168, p < 0.001, ηp2 = .963), and Drug by Session interaction (F[15,300] = 8.247, p < 0.001, ηp2 = .292). Post-hoc analysis found that head entry rate was greater nicotine sessions 3-16 compared to saline. In the female pseudoCS group, there was a main effect of Session (F[15,300] = 5.755, p < 0.001, ηp2 = .223) and Drug (F[1,20] = 13.115, p = 0.002, ηp2 = .396), but no Drug by Session interaction (F[15,300] = 1.010, p = 0.445, ηp2 = .048).</p><!><p>As shown in Figure 2A, males in both training groups showed an initial increase in lever responses as alcohol was added to the solution and decreased as sucrose was removed from the solution. There was a main effect of alcohol/sucrose Concentration (F[5,200] = 63.922, p < 0.001, ηp2 = .615) but no Concentration by Conditioning History interaction (F[5,200] = 0.532, p = 0.752, ηp2 = .013). Alcohol intake (g/kg) showed a similar pattern to lever responses (Figure 2B). There was a main effect of Concentration (F[5,200] = 122.130, p < 0.001, ηp2 = .753) but no Concentration by Conditioning History interaction (F[5,200] = 0.285, p = 0.921, ηp2 = .007).</p><p>As shown in Figure 2C, similar to males, females in both training groups showed an initial increase in lever responses as alcohol concentration increased and then decreased as sucrose concentration decreased. There was a main effect of alcohol/sucrose Concentration (F[5,200] = 47.636, p < 0.001, ηp2 = .544) but no Concentration by Conditioning History interaction (F[5,200] = 0.622, p = 0.683, ηp2 = .015). Similar effects were observed with alcohol intake (Figure 2D) with a main effect of Concentration (F[5,200] = 94.774, p < 0.001, ηp2 = .703) but no Concentration by Conditioning History interaction (F[5,200] = 1.212, p = 0.305, ηp2 = .029).</p><!><p>As shown in Figure 3A, alcohol lever responses increased across sessions in both groups of males. There was a main effect of Session (F[13,520] = 4.927, p < 0.001, ηp2 = .110) but not Session by Conditioning History interaction (F[13,520] = 0.570, p = 0.878, ηp2 = 0.014). Similarly, alcohol intake (Figure 3B) increased across sessions but did not differ between training conditions. There was a main effect of Session (F[13,520] = 4.470, p < 0.001, ηp2 = .103) but no Session by Conditioning History interaction (F[13,520] = 0.545, p = 0.896, ηp2 = .014).</p><p>As shown in Figure 3C, similar to males, alcohol lever responses increased across sessions in both female groups. There was a main effect of Session (F[13,520] = 4.397, p < 0.001, ηp2 = .099) but no Session by Conditioning History interaction (F[13,520] = 0.772, p = 0.690, ηp2 = .019). Alcohol intake in females (Figure 3D) was similar was a main effect of Session (F[13,520] = 4.637, p < 0.001, ηp2 = .104) but no Session by Conditioning History interaction (F[13,520] = 0.692, p = 0.772, ηp2 = .017).</p><!><p>As shown in Figure 4A, head entry rate on nicotine sessions was greater in the male nicotineCS group compared to saline whereas there was no difference in the male pseudoCS group. There was a main effect of Session (F[15,300] = 4.105, p < 0.001, ηp2 = .170), Drug (F[1,20] = 45.834, p < 0.001, ηp2 = .696), Drug by Session interaction (F[15,300] = 3.811, p < 0.001, ηp2 = .160), Drug by Conditioning History interaction (F[1,20] = 37.636, p < 0.001, ηp2 = .653), and Drug by Session by Conditioning History interaction (F[15,300] = 2.502, p = 0.002, ηp2 = .111). For the nicotineCS group, there were higher head entries on sessions 4-16 [p < .05; Main effect of Drug (F[1,10] = 118.181, p < .001, ηp2 = .922); Main effect of Session (F[15,150] = 2.848, p = .001, ηp2 = .222); Drug by Session interaction (F[15,150] = 4.984, p < .001, ηp2 = .333). For the pseudoCS group, there was only a main effect of Session (F[15,150) = 2.459, p = .003, ηp2 = .197). There was no Drug effect (F<1) or Drug by Session interaction (F[15,150] = 1.240, p = .248).</p><p>As shown in Figure 4B, similar to males, head entry rate on nicotine sessions in the female nicotineCS group was greater than saline sessions. There was a main effect of Session (F[15,300] = 10.302, p < 0.001, ηp2 = .340), Drug (F[1,20] = 99.975, p < 0.001, ηp2 = .833), Drug by Session interaction (F[15,300] = 5.792, p < 0.001, ηp2 = .225), Drug by Conditioning History interaction (F[1,20] = 52.489, p < 0.001, ηp2 = .724), and Drug by Session by Conditioning History interaction (F[15,300] = 2.522, p = 0.002, ηp2 = .112). The nicotineCS females had higher head entries evoked by nicotine compared to saline on sessions 5-16 [p < .05; Main effect of Drug (F[1,11] = 119.501, p < .001, ηp2 = .916); Main effect of Session (F[15,165] = 6.102, p < .001, ηp2 = .357); Drug by Session interaction (F[15,165] = 8.580, p < .001, ηp2 = .438)]. For the pseudoCS females, there were main effects of Session (F[15,135) = 5.414, p < .001, ηp2 = .376) and Drug (F[1,9] = 6.248, p = .034, ηp2 = .410) with nicotine evoking greater head entries than saline when collapsed across sessions (Means: nicotine = 0.124±.013; saline = 0.112±.014). There was no Drug x Session interaction (F<1).</p><!><p>As shown in Figure 5A, previous training with nicotine as an appetitive CS did not affect methamphetamine self-administration in males. There was a main effect of Session (F[13,260] = 2.930, p = 0.001, ηp2 = .128), however there was no Session by Conditioning History interaction (F[13,260] = 0.413, p = 0.965, ηp2 = .020). In contrast, in the females, nicotine conditioning history attenuated methamphetamine self-administration (Figure 5B). In the female groups, there was a main effect of Session (F[13,260] = 2.410, p = 0.004, ηp2 = .108) and Conditioning History (F[1,20] = 15.905, p = 0.001, ηp2 = .443), but no Conditioning History by Session effect, F(13,260)=1.572, p=.093, ηp2=.073</p><!><p>Drug experience plays a role in subsequent drug taking. Understanding the nuances of this interaction will be crucial to understanding how drug abuse and dependence progress. However, as the current experiments demonstrate, this is not a generalizable phenomenon. As shown in Experiment 1, having a previous appetitive conditioning history with nicotine had no impact on later acquisition or maintenance of alcohol self-administration in male or female rats. In contrast, Experiment 2 found that this same nicotine conditioning history led to an overall attenuation of methamphetamine self-administration in female, but not male rats. This finding identifies an intriguing sex effect and implicates the nature of the nicotine conditioning history – rather than the nicotine itself – in the subsequent methamphetamine use.</p><p>In both Experiments 1 and 2, reward-seeking behavior readily came under control of the interoceptive stimulus effects of nicotine in the nicotineCS group. That is, rats showed significantly greater rate of anticipatory head entries on nicotine sessions compared to saline because nicotine was a salient predictor of reward. By contrast, in the pseudoCS group, neither nicotine nor saline consistently predicted reward (Charntikov et al. 2012) which resulted in most rats showing no difference in head entry rate between nicotine and saline sessions. This acquired control of head entries into the dipper receptacle in nicotineCS trained rats was evident in both males and females, and, consistent with a recent paper from our lab (Charntikov et al. 2017a).</p><p>Following the nicotine interoceptive conditioning phase, rats began alcohol (Experiment 1) or methamphetamine (Experiment 2) self-administration, and at this point, a diverging pattern emerged between rats trained to self-administer alcohol and those trained to self-administer methamphetamine. Having a nicotine conditioning history did not affect the sucrose fading phase or maintenance of alcohol self-administration in female or male rats. This finding is consistent with a previous study assessing nicotine exposure in adolescence that found that nicotine exposure alone did not enhance alcohol self-administration (Madayag et al. 2017). However, several studies have shown that nicotine administered immediately prior to alcohol self-administration sessions increases alcohol-seeking (Larraga et al. 2017; Le et al. 2014; Smith et al. 1999), suggesting that proximity of nicotine treatment to self-administration sessions is important when assessing nicotine effects on alcohol self-administration.</p><p>In contrast to the alcohol self-administration study, female, but not male rats with the nicotine conditioning history (i.e., nicotineCS group) showed an overall reduction in methamphetamine self-administration relative to their same sex cohorts without this conditioning history (pseudoCS). These results suggest that the nicotine conditioning history had a selective and significant impact on the reinforcing value of methamphetamine in the females. That is, nicotine may have enhanced the reinforcing value of methamphetamine, causing a leftward shift in the demand curve for methamphetamine in females. As such, given an increased reward value, female rats required less methamphetamine overall. Similar findings have been observed in studies of brain stimulation in which nicotine decreases frequency threshold for reinforcement (Bozarth et al. 1998a; b; Ivanova and Greenshaw 1997). Alternatively, the reduction in self-administration in the females could instead be interpreted as decreased reinforcement efficacy of methamphetamine, perhaps due to enhanced anxiogenic effects of methamphetamine (Beirami et al. 2017; Schutova et al. 2009). It will be interesting for future work to replicate and investigate these ideas of altered reinforcing function of methamphetamine. Moreover, it has been previously suggested that the ability of nicotine to enhance reinforcement may depend on the initial reinforcing value of the stimulus being moderate (as opposed to weak) which may explain why methamphetamine, but not alcohol, was affected (Palmatier et al. 2007b). Furthermore, nicotine and methamphetamine have overlapping discriminative stimulus effects (Desai and Bergman 2010; Gatch et al. 2008), which may, in part, explain differences observed between Experiments 1 and 2. However, more experiments are needed to parse out this question.</p><p>The present study was designed to examine the impact of the nicotine conditioning history on subsequent methamphetamine (or alcohol) self-administration, and as such all rats received nicotine and saline injections. Therefore, we cannot determine whether nicotine history alone impacted methamphetamine (or alcohol) self-administration as there were no groups that were nicotine-naive (i.e., received saline only throughout the experiment). For example, it is possible that methamphetamine self-administration was enhanced in the pseudoCS group. This outcome would be interesting and would suggest that the reinforcing function of methamphetamine was changed following the unpredictable nicotine-sucrose conditioning history. Indeed, other studies have focused on that question and found that nicotine exposure can enhance self-administration of both alcohol and methamphetamine (Larraga et al. 2017; Le et al. 2014; Neugebauer et al. 2010; Smith et al. 1999). Our study contributes to that literature by demonstrating that beyond nicotine exposure, an explicitly appetitive conditioning history with nicotine can reduce subsequent methamphetamine self-administration in female rats while having no consequences on subsequent alcohol self-administration in male or female rats.</p>

PubMed Author Manuscript

Rapid Analysis and Authentication of Meat Products using the MasSpec Pen Technology

Food authenticity and safety are major public concerns due to the increasing number of food fraud cases. Meat fraud is an economically motivated practice of covertly replacing one type of meat with a cheaper alternative, raising health, safety, and ethical concerns for consumers.In this study, we implement the MasSpec Pen technology for rapid and direct meat analysis and authentication. The MasSpec Pen is an easyto-use handheld device connected to a mass spectrometer that employs a solvent droplet for gentle chemical analysis of samples. Here, MasSpec Pen analysis was performed directly on several meat types including grain-fed beef, grass-fed beef, venison, cod, halibut, Atlantic salmon, sockeye salmon, and steelhead trout, with a total analysis time of 15 seconds per sample. Statistical models developed with the Lasso method using a training set of samples yielded per-sample accuracies of 95% for the beef model, 100% for the beef versus venison model, and 84% for the multiclass fish model. Metabolic predictors of meat type selected included several metabolites previously described reported in the skeletal muscles of animals, including carnosine, anserine, succinic acid, xanthine and taurine. When testing the models on independent test sets of samples, per-sample accuracies of 100% were achieved for all models, demonstrating the robustness of our method for unadulterated meat authentication. MasSpec Pen feasibility testing for classifying venison and grass-fed beef samples adulterated with grain-fed beef achieved persample prediction accuracies of 100% for both classifiers using test sets of samples. Altogether, the results obtained in this study provide compelling evidence that the MasSpec Pen technology is as a promising alternative analytical method for the investigation of meat fraud.

rapid_analysis_and_authentication_of_meat_products_using_the_masspec_pen_technology

Introduction<!>Materials and Method<!>Optimization of the MasSpec Pen for meat analysis<!>Pilot study of MasSpec Pen meat speciation<!>Developing beef and venison authentication models using the MasSpec Pen.<!>Authentication of meat products using the MasSpec Pen.<!>Extending the MasSpec Pen use to the analysis of mixed meat samples.

<p>Food fraud is an increasing public health and commercial concern for consumers and vendors. Currently, food fraud costs the global food industry between an estimated $10 billion and $15 billion per year. [1][2] The 2013 European horse meat scandal, for example, caught international attention of consumers by revealing that meat products labeled as beef contained as high as 80-100% horse meat. [3][4] Thus occurrence led many countries to examine their own meat and food fraud policies to advance prevention of this crime. 5 The most common method for meat fraud is replacement, which involves the complete or partial substitution of a meat product with a less expensive adulterant, such as replacing beef for horse meat. 1-2, 4, 6-7 The substitution can be made by replacing the whole meat product, such as a steak or fish fillet, or by incorporating the adulterant into a ground meat product at a certain percentage. [1][2][6][7][8] In addition to the economic and criminal effects of this practice, meat fraud also affects consumers with meat allergies or other dietary, religious and cultural restrictions, while also representing an ethical violation of the trust of the costumers.</p><p>Physical and molecular evaluation of meat products is routinely performed to verify its authenticity and assure product quality before reaching the costumer. For example, in the United States, meat products from farms are continuously inspected by the United States Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) for authenticity and quality both before (ante mortem) and after (post-mortem) slaughter. For instance, meat will be inspected ante mortem for disease, illness, injury, identification, etc., and inspected postmortem for incidental or purposeful cross contamination, identification, bacterial growth, etc. Following visual inspection, randomly chosen meat products undergo speciation and hormone tests before reaching the consumer in a FSIS facility to detect the type(s) of meat and hormones present in a product. 1 Polymerase chain reaction (PCR) and liquid chromatography mass spectrometry (LC-MS) are the most commonly used techniques for meat authentication. In PCR assays, accuracies from 96% to 100% are reported for the identification of a variety of meat types. [8][9][10][11][12] PCR assays require targeted molecular probes for testing of the DNA material extracted from the meat product, and can often take four hours to several days to yield a result. 8,10 Mass spectrometry methods have been increasingly employed as a faster alternative test for meat speciation. 2,8 LC-MS techniques are commonly used to identify meat type based on quantitative analysis of lipids and proteins that are characteristic of each meat type.</p><p>Detection limits below 3% in terms of partial substitution limits have been reported using LC-MS, in a time frame normally ranging from three hours to two days for completion, depending on the sample preparation methods used. [13][14] LC-MS is also frequently used to identify the nature of the contaminants present in the meat, or food products. 2,6,15 For example, LC-MS has been used to detect adulterants that are added to a meat product for financial gain, such as soybean protein added to beef or chicken in pork meat. 2,6,14,16 Although LC-MS assays allow faster analysis when compared to PCR, the time required for sample preparation steps and chromatographic separation hinders its use for direct, real-time analysis of food products. 8,17 Moreover, PCR and LC-MS instrumentation is complex and often done off-site at a FSIS facility, thus requiring time for sampling, transportation to the analysis site, and storage.</p><p>Several ambient ionization MS techniques have been explored as methods for direct and on-site food analysis, including analysis of meat products in an effort to expedite and improve accessibility to fraud testing. [17][18][19][20][21][22][23][24][25] Liquid extraction surface analysis MS (LESA-MS), for example, has been explored for meat authentication based on proteomic analysis a sample digest from various raw, cooked, and processed meat products, followed by analysis using nanoESI. [26][27][28] Using this approach, M. Montowska and coworkers reported a predictive variation of 94.9% using orthogonal partial least squares discriminant analysis in the identification of cooked horse, pork, turkey, chicken, and beef (n=50) using peptide biomarkers. This study also reported partial substitution limits of detection of 10% for pork, horse, and turkey and 5% for chicken in beef matrices (n=50). 26 Rapid evaporative ionization MS (REIMS) has also been explored for meat authentication. In REMS, a handheld electrocautery device is directly used to thermally ablate the meat sample, leading to the formation of an aerosol that is transported to a mass spectrometer for lipidomic analysis. [29][30][31][32] REIMS was used to analyze a wide range of meat samples, including beef, horse, venison, and various fish species in multiple studies. [30][31][32][33] One of these studies by J. Balog, et al., yielded high prediction accuracies of 100% for horse and beef and 97% prediction accuracy at the breed level (n=20) using leaveone-animal-out-cross validation, as well as 5% detection limit for mixed samples of Wagyu beef and horse meat mixed into venison and grain-fed beef meat, of beef, venison, and horse meat, and of all four meats. 34 Here, we explore the use of the MasSpec Pen technology as an alternative approach for rapid and direct meat authentication of unadulterated and adulterated meat samples. [35][36] The MasSpec Pen is a handheld device coupled to a mass spectrometer that enables direct sample analysis based on a gentle liquid-extraction process.</p><p>Upon contacting the device onto a sample surface and pressing a foot pedal, a syringe pump delivers a discrete solvent droplet to a reservoir at the pen tip, where it is held in contact with the sample for 3 seconds. The solvent gently extracts molecules such as small metabolites and lipids from the sample into the droplet, which is then transported through polytetrafluorethylene (PTFE) tubing to the mass spectrometer for analysis. The entire process from contact to analysis is completed in seconds. 35 Notably, the gentle nature of the liquid-extraction process allows molecular analysis without apparent damage to the sample surface. We have previously shown that the molecular data acquired using the MasSpec Pen in conjunction with statistical modeling via the lasso method allows discrimination of normal and cancerous human tissues with overall accuracies over 96%. [35][36] Here, we show that the MasSpec Pen allows effective molecular analysis of various meat samples, enabling meat authentication in seconds.</p><!><p>Samples. Meat samples of grain-fed beef (n=13), grass-fed beef (n=13), pork (n=5), chicken (n=5), lamb (n=5), venison (n=13), cod (n=13), halibut (n=13), Atlantic salmon (n=14), sockeye salmon (n=13), and steelhead trout (n=13) were obtained from local grocery stores (Austin, TX) and stored at 4 o C until analysis. Each meat sample (or biological replicate) was from a different animal. Only samples that were verified as being regulated by the USDA were purchased. Mixed meat samples containing either grain-fed beef and venison (n=31) or grain-fed and grassfed beef (n=31) were made in the laboratory using pure meat products that were ground together at different ratios (0, 25, 50, 75, and 100%) using a meat grinder. Samples were processed through the grinder at least three times to yield a uniform distribution of each product and were stored at 4 o C until analysis. Prior to MasSpec Pen analysis, all samples were brought to room temperature and any excess moisture on meat samples was removed with a Kimwipe.</p><p>MasSpec Pen Analysis. The MasSpec Pen design and experimental setup have been previously described in detail. 35 Polydimethylsiloxane pen tips with a 4 mm reservoir diameter and a solvent droplet volume of 20 µL were used for all experiments. Various solvent systems (water, methanol:water blends, and acetonitrile:dimethylformamide (ACN:DMF) blends), extraction times (3, 5 and 10 seconds), and PTFE tubing lengths (0.5, 0.75, 1.0, and 1.5 m) were tested. Between analyses, a wash step was performed where the pen system was flushed with the solvent system. Tubing and pen tips were changed between meat types. For quality control, a background analysis was performed at the beginning and between analyses to monitor and minimize carryover. The MasSpec Pen was coupled to a Q Exactive Hybrid Quadrupole-Orbitrap and to a Q Exactive HF Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA) for analysis of the meat samples at ambient conditions. All analyses were conducted in the negative ion mode with a mass range of m/z 50-600 and 140,000 resolving power (at m/z 200). Eight or nine analytical replicates were acquired for each biological replicates of each meat type, by performing MasSpec Pen analysis of different regions of the meat piece. For each mixed meat sample, three to five analytical replicates were analyzed depending on the size of the sample. Test set samples were analyzed separately two months apart from the training set of samples and treated as an independent sample set.</p><p>Ions were tentatively identified based on high mass accuracy measurements (mass error ≤ 5 ppm) and higher energy collision induced dissociation (HCD) tandem MS analyses (Supplemental Figure 3). Statistical Analysis. Following analysis with the MasSpec Pen, three scans were averaged for each analytical replicate. The resulting mass spectrum was extracted from the XCalibur raw data. The relative standard deviation (RSD) of the method was calculated using the average and standard deviation of ratios of biological ions detected in the mass spectra for each meat type in the optimization study. For further statistical analysis, data were imported into the R programming language. All mass spectra were normalized according to total ion count. Following normalization, all background peaks and peaks not present in at least 10% of samples were excluded. Cosine statistical analysis was performed using the las package in R. Principal component analysis (PCA) was performed by centering the preprocessed data to mean zero and computing principal components in R. Lasso was applied to developed classification models using the glmnet package in the R language. Lasso is a logistic regression technique that selects a sparse set of features, specifically m/z values, to create a predictive model, or classifier, capable of discriminating between two or more classes, or in this case different meat types. 37 A training set of MasSpec Pen data was used to build each classifier using leave-one-out cross validation (Supplemental Figure 2). The test set of independent samples was used to evaluate the performance of the classification models. Performance of the classification models was measured by recall for each meat type and by an overall accuracy for each classifier.</p><!><p>We optimized the MasSpec Pen solvent system, extraction time, and tubing length to enable molecular analysis of meat samples. Solvent systems of water, methanol:water blends, and acetonitrile:dimethylformamide (ACN:DMF) were tested by analyzing grain-fed beef, pork, chicken, and lamb and evaluating the diversity of molecular species detected as well as the reproducibility of the mass spectra. Six biological replicates for each meat type and nine analytical replicates per sample were performed. Note that all the solvent systems tested were nondestructive to the meat tissue, thus allowing repeated analyses of the same sampling spot, if desired. Within the solvents tested, pure were observed in the mass spectra obtained with both solvents at high relative abundance. Although complex lipids are commonly observed with the MasSpec Pen in tissue analysis, the m/z range for analysis of meat products was restricted to m/z 600 to focus on the analysis of the detection of small metabolite species. Generally, we observed that ACN:DMF (1:1) allowed detection of a richer diversity of molecular species, with species such as deprotonated galactitol (m/z 181.070), deprotonated taurine (m/z 124.006), and deprotonated inosine (m/z 267.073) detected using ACN:DMF, but undetected with water. Additionally, ACN:DMF yielded higher reproducibility (RSD of 15% ± 4%, n=6 for each meat type) when compared to what achieved with water (RSD of 34% ± 7% for mass spectra of all meat types, n=6 for each meat type) (Supplemental Figure 1). Thus, ACN:DMF (1:1) was used as the solvent system for the remaining experiments performed.</p><p>Next, we optimized the MasSpec Pen extraction time by varying the time (3, 5 and 10 seconds) that the pen and the solvent droplet was in contact with grain-fed beef samples. To compare the mass spectra between varying extraction times, a cosine similarity analysis was performed. As previously reported in experiments with human tissues, 35 reproducible mass spectral profiles were obtained for the different extraction times explored, yielding an average cosine value of 0.985 ± 0.0.009. An extraction time of 3 seconds was selected for the analyses to expedite total analysis time per sample. Lastly, different PFTE tubing lengths (0.5, 0.75, 1.0. and 1.5 m) were also tested.</p><p>The mass spectra obtained of grain-fed beef samples using 0.5, 0.75, 1.0. and 1.5 m tubing lengths, resulted in an average cosine value of 0.97 ± 0.02, and shows that the mass spectra profiles are nearly independent of tubing length, which also corroborates previous findings 35 . Thus, the shortest tubing length of 0.5 m was used for all following analyses to conserve PTFE tubing material and expedite analysis time per sample. Using 0.5 m PFTE tubing length, droplet transport from the pen tip to the mass spectrometer was completed in 2 seconds. At the optimized conditions, each analysis was performed with ACN:DMF (1:1) solvent, 3 seconds of extraction time, 0.5 m PFTE tubing, for a total analysis time of ~15 seconds per sample, which is faster than PCR and LC-MS, the current gold standard methods. 2,6,8</p><!><p>We then performed a preliminary study to determine if the molecular profiles obtained with the MasSpec Pen were capable of distinguishing visually distinctive meat types, including grain-fed beef, pork, chicken, and lamb (five biological replicates per meat type, four analytical replicates per sample). As expected, different mass spectral profiles were obtained from the MasSpec Pen analysis of each meat type (Figure 1a). Deprotonated carnosine (m/z 225.099), for example, was detected in all meat types with high abundances in beef and pork, which was previously detected in various meat types using LC-MS. [38][39] Deprotonated anserine (m/z 239.115) and deprotonated succinic acid (m/z 117.018) were detected in beef, lamb, and chicken. Furthermore, deprotonated xanthine (m/z 151.025) was detected in chicken and beef at a higher abundance when compared to lamb and pork, while deprotonated taurine (m/z 124.006) was detected in relatively high abundance in all meat types. These molecules were previously detected using LC-MS in various meat types. [40][41] Using PCA, separation between the data obtained from the four meat types evaluated was achieved (Figure 1b), thus confirming that the molecular information obtained using from MasSpec Pen analysis is distinct between meat types.</p><!><p>We next used the MasSpec Pen to analyze commonly substituted meat products. One set of commonly substituted meat products are grain-fed beef and grass-fed beef, which are the same meat species with different feeding habits.</p><p>Typically, grass-fed beef is the more expensive of the two, thus grain-fed beef can be mislabeled as grass-fed for a larger profit. 42 Another set of commonly substituted meat products are grain-fed beef and venison, which is also performed for financial gain. For each meat type, ten raw samples were analyzed with at least eight analytical replicates per sample, yielding 254 spectra from the 30 meat samples.</p><p>Representative mass spectra are shown in Figure 2a for a sample of grain-fed beef, grass-fed beef, and venison. Qualitative differences in the relative abundances of several metabolic species previously reported in the skeletal muscles of animals can be seen when comparing the mass spectra obtained. For example, deprotonated carnosine (m/z 225.099), previously reported in skeletal muscles of beef and chicken using LC-MS, was detected at a high relative abundance in both beef profiles when compared to venison. [38][39] Additionally, deprotonated malic acid (m/z 133.013) and deprotonated anserine (m/z 239.115) are at an increased relative abundance in grain-fed beef when compared to venison and grass-fed beef. Moreover, a higher relative abundances of deprotonated xanthine (m/z 151.025) was detected in venison and grain-fed beef, while a lower relative abundances of deprotonated taurine (m/z 124.006) in grass-fed beef and venison, both of which have been previously observed in beef and fish meat using LC-MS. [40][41] Interestingly, a qualitative higher relative abundance of the chlorine adduct of hexose (m/z 215.032, [M+Cl] -) was detected in grain-fed beef, which could be associated with the diet of grain-fed cattle containing more sugars than the diet of grass-fed cattle. 43 Using the data collected, we created two two-class classification models, one for grain-fed and grass-fed beef, and one for grain-fed beef and venison using the Lasso method. 35,[44][45] For the beef two-class model, 176 spectra acquired from 20 samples were used as the training set of data using leave-one-out cross validation (LOOCV). A per-sample accuracy of 95% was achieved (Figure 2b), which is particularly encouraging as the samples are from identical species with the main difference being feeding habits. Notably, within the predictive m/z, deprotonated malic acid (m/z 133.013), deprotonated carnosine (m/z 225.099), and the chlorine adduct of hexose (m/z 215.032) were selected, which corroborate with the trends in ion relative abundances observed in the mass spectra. For the beef versus venison model, 165 spectra acquired from 20 samples were used as the training set, yield a per-sample accuracy of 100% (Figure 2c). The predictive m/z selected included ions deprotonated xanthine (m/z 151.025), deprotonated carnosine (m/z 225.099), and deprotonated anserine (m/z 239.115). To evaluate the performance of the model for meat authentication setting, a test set of samples were used, as discussed later in the manuscript.</p><p>Developing a fish authentication multiclass model using the MasSpec Pen.</p><p>We then examined the MasSpec Pen's performance for fish identification for a multiclass fish model. Here, we analyzed five common fish products, steelhead trout, sockeye salmon, Atlantic salmon, cod, and halibut. For each meat type, nine samples were analyzed as a training set of samples, with at least eight analytical replicate analyses performed for each sample, yielding 395 spectra from the 45 samples analyzed.</p><p>When evaluating the molecular profiles of the raw samples analyzed with the MasSpec Pen, qualitatively district mass spectra profiles were detected for each fish type, as shown in Figure 3a. For example, deprotonated taurine (m/z 124.006), was detected at varying relative abundances in each fish tested. 40 Additionally, m/z 267.074 (unidentified) is seen in a higher relative abundance in cod when compared to the other products. Deprotonated anserine (m/z 239.115) was qualitatively observed in a higher relative abundance for sockeye salmon, 39 while a higher relative abundance of deprotonated xanthine (m/z 151.025) was observed in halibut. Moreover, a qualitatively higher relative abundance of the chlorine adduct of hexose (m/z 215.032) was detected in Atlantic salmon, a farm-raised fish, when compared to wild-caught sockeye salmon, which could be due to their habitats and eating habits.</p><p>We then used Lasso to create a five-class model for the fish types analyzed (395 mass spectra obtained for the 45 samples) using LOOCV, yielding 84% overall accuracy per sample (Figure 3b). The confusion matrix with recall values for each fish type is provided in Supplemental Table 4. Notably, the highest recall was achieved for cod and halibut, which are the most commonly substituted fish meat products as cod loin is often substituted for halibut. 7,31 Larger confusion was seen when classifying the farm-raised Atlantic salmon and wild-caught sockeye salmon, which is expected as they are the same fish species and commonly substituted with each other and trout. 7,31 The predictive features selected included deprotonated taurine (m/z 124.006), the chlorine adduct of methyluric acid (m/z 217.011), and deprotonated xanthine (m/z 151.025) and reflected the trends in relative ion abundances seen in the molecular profiles. While a five-class classifier was built to include the five fish types analyzed, a twoclass and three-class classifier built using LOOCV were also explored for fish that are more commonly substituted in fraudulent activities. For example, a two-class classifier was built to distinguish halibut and cod, which are visually very similar and often substituted in meat fraud crimes, yielding 100% accuracy with 229 mass spectra acquired from 26 samples (Supplemental Table 5). A three-class classifier was also built to distinguish steelhead trout, sockeye salmon, Atlantic salmon, which are also visually similar and commonly substituted in meat fraud crimes. For this classifier, an overall accuracy of 90% was achieved (352 mass spectra and 40 samples) (Supplemental Table 5). Overall, the classifiers developed here for fish identification demonstrate the potential of the MasSpec Pen for identification of fish type and the investigation of fish fraud.</p><!><p>To evaluate the predictive performance of the classifiers built, we analyzed an independent test set of meat and fish samples using the MasSpec Pen following the same experimental approach. For the grain-fed beef, grass-fed beef, and venison, three biological replicates were analyzed with at least eight analytical replicates for each sample, yielding 71 mass spectra for 9 samples. For the five fish types, four samples were analyzed for each meat type with at least eight analytical replicates per sample, yielding 187 spectra for 21 samples. Using the previous statistical models to predict the meat type for each sample, test set accuracies of 100% were achieved for the beef model and the beef versus venison model (Figure 2b and 2c). Similarly, an accuracy of 100% was achieved for the fish multiclass model (Figure 3b). The accuracies are comparable with what previously reported for LESA-MS, 94.9% (n=50) for a training set, and REIMS, 100% (n=20) species identification for a training set. 26,30 Further, the accuracies achieved are comparable with current testing metrics using PCR (96%-100%). [8][9][10][11][12] The performance achieved with the MasSpec Pen in the test set of samples provides evidence the MasSpec Pen in conjunction with lasso is a robust method for analysis of meat products.</p><!><p>We next evaluated if the MasSpec Pen could be used to identify adulterated mixed meat samples. To this end, we mixed varying percentages of ground grain-fed beef (0%, 25%, 50%, 75%, and 100%, in weight), as an adulterant into samples of ground grass-fed beef or ground venison, and analyzed the ground samples using the MasSpec Pen.</p><p>For the grain-fed beef mixed into grass-fed beef, we analyzed nine 0% grain-fed beef samples, four 100% grain-fed beef samples, and six 25%, 50%, and 75% samples. For the grain-fed beef mixed into venison, we analyzed eight 0% grain-fed beef samples, six 100% and 25% grain-fed beef samples, and five 50% and 75% samples. When analyzed, each of the ground meat samples had five analytical replicates for each sample.</p><p>The mass spectra obtained from the analysis of the venison samples adulterated with grain-fed beef are shown in Figure 4a. Notably, relative abundances of deprotonated taurine (m/z 124.006), deprotonated malic acid (m/z 133.013), and deprotonated carnosine (m/z 225.099) were increasingly higher with increasing percentage of adulterant grain-fed beef in the ground venison samples. On the other hand, a decrease in the relative abundances of deprotonated succinic acid (m/z 117.018) and deprotonated inosine (m/z 267.074) was observed as the percentage of adulterant grain-fed beef increases in the ground venison samples. The changes in the relative abundance of the metabolites agreed with the trends observed in the raw meat samples. We then built statistical classifiers to test if classification of meat samples as adulterated was possible using the metabolic information obtained with the MasSpec Pen. To this end, we trained the Lasso method to identify adulterated and unadulterated meat by including mix samples (amount >25%) into the adulterated class and ground samples (0% mixed) in the unadulterated class.</p><p>Using the training set data of 76 mass spectra and 20 samples, an overall 93% per spectra accuracy and a 90% per samples accuracy was achieved (Figure 4b). The features were selected for prediction of the two classes and reflected trends in molecular profiles seen in Figure 4a. For example, deprotonated carnosine, which has been previously found to be present in skeletal muscles of beef, was selected as a predictive feature weighted towards the adulterated with grain-fed beef classification, while deprotonated inosine (m/z 267.074) was selected as a predictive feature weighted towards the unadulterated classification. [38][39] Within the training set, two unadulterated samples were misclassified as adulterated samples, while no adulterated samples were misclassified. We then tested the classifier on 43 mass spectra acquired from 10 samples, including 7 adulterated samples and 3 unadulterated samples. Overall accuracies of 98% per mass spectra and 100% per sample were achieved for the venison mixed samples test set (Figure 4b).</p><p>The mass spectra obtained from the analysis of the grass-fed beef adulterated with grain-fed beef are shown in Figure 5a. As the amount of grain-fed beef adulterant increased in the samples, the relative abundances of ions such as deprotonated taurine (m/z 124.006), deprotonated malic acid (m/z 133.013), and deprotonated D-erythro-Lgalacto-nonulose (m/z 269.088) also increased, while a decrease in the relative abundances of deprotonated succinic acid (m/z 117.018) and deprotonated xanthine (m/z 151.025) was observed. Similar to the adulterated venison classifier described above, a binary classifier was built for the beef samples in which grain-fed beef was used as an adulterant to grain-fed beef meat. Using a training set of 88 mass spectra and 21 samples, an overall accuracy of 92% per mass spectra and 90% per sample was achieved (Figure 5b). Within the several features selected by the model, including deprotonated malic acid and deprotonated anserine weighted towards the adulterated classification and deprotonated xanthine weighted towards the unadulterated classification. One unadulterated sample was misclassified as adulterated in the training set. When tested on 45 mass spectra acquired from 10 samples in the validation set, 100% accuracy per mass spectra and per sample was achieved (Figure 5b). Multiple LC-MS methods have previously reported detection limits down to 3% for various mixed meat samples. [13][14] Collectively, the results for both classifiers demonstrate feasibility in developing the MasSpec Pen and lasso as a robust method for the authentication of mixed meat samples, although further studies using lower percentages of substituted meat are needed to validate these findings.</p><p>In conclusion, in this study we describe the optimization and application of the MasSpec Pen technology for analysis and classification of meat products. We showed that the gentle nature of the MasSpec Pen analysis allows detection of a range of metabolic species directly from fish, beef, and venison without the need of sample digestions or alteration. Several of the metabolic species detected, including deprotonated carnosine, deprotonated xanthine, deprotonated inosine, deprotonated anserine, the chlorine adduct of hexose, and deprotonated taurine have been previously described in meat samples and related to eating habits and other metabolic process in animal skeletal muscle tissues. [38][39][40][41]43 Using statistical classification with the Lasso method, we demonstrate the robustness and high accuracy of the MasSpec Pen in identifying meat types in training (93% accuracy, n=85) and test sets (100% accuracy, n=33). Further, we showed that common meat replacement could items could be identified using our approach, including discrimination of meats commonly used for replacement fraud such as grain-fed versus grass-fed beef (100% accuracy in test set, n=6), and different fish types (100% accuracy in test set, n=21), with similar performance to what reported with other ambient ionization MS methods and other routine meat testing techniques. 8-12, 26, 30 Lastly, we showed feasibility for identification of adulterated mixed meat samples. Although the MasSpec Pen is limited to qualitative molecular evaluation with substantially lower molecular coverage when compared to LC-MS, the MasSpec pen analysis is completed in less than 15 seconds and does not require any sample pre-processing, which is appealing for routine use in testing of meat products. At a minimum, the 15-second testing time per sample provided by the MasSpec Pen is ~240 times faster than LC-MS (considering a total analysis time of 1 hour/sample), and ~720 times faster than PCR (considering a total analysis time of 3 hours/sample).</p><p>Further, the ease of use and maneuverability that a handheld device like the MasSpec Pen allows can facilitate implementation and use by users with various levels of expertise. Due to the gentle nature of the analysis, repetitive analysis of different products and regions within the sample of interest can be achieved. Future research would include expanding meat identification classifiers, improving the mixed meat results, and developing methods to quantify the amount of adulterant present in a sample. Furthermore, we will expand our method to other meat products, such as wild fish products and beef products from different countries. Lastly, while a high-performance Orbitrap mass spectrometer was used here for this exploratory study, we are currently exploring integration of the MasSpec Pen with a portable ion trap mass spectrometer for meat analysis to facilitate fieldable use outside of specialized laboratories. Collectively, our study shows compelling evidence that the MasSpec Pen provides a rapid and direct method for investigating meat fraud, allowing for accurate meat identification in less than fifteen seconds, thus providing a powerful alternative technique to traditional meat testing methods.</p>

ChemRxiv

Complex coacervates as extraction media

Various solvents such as ionic liquids, deep eutectic solvents, and aqueous two phase systems have been suggested as greener alternatives to existing extraction processes. We propose to add macroscopic complex coacervates to this list. Complex coacervates are liquid-like forms of polyion condensates and consist of a complex of oppositely charged polyions and water. Previous research focussing on the biological significance of these polyion-rich phases has shown that polyion condensates have the ability to extract certain solutes from water and back-extract them by changing parameters such as ionic strength and pH. In this study, we present the distribution coefficients of five commonly used industrial chemicals, namely lactic acid, butanol, and three types of lipase enzymes in poly(ethylenimine)/poly(acrylic acid) complex coacervates. It was found that the distribution coefficients can vary strongly upon variation of tunable parameters such as polyion ratio, ionic strength, polyion and compound concentrations, and temperature. Distribution coefficients ranged from approximately 2 to 50 depending on the tuning of the system parameters. It was also demonstrated that a temperature-swing extraction is possible, with backextraction of butanol from complex coacervates with a recovery of 21.1%, demonstrating their potential as extraction media.

complex_coacervates_as_extraction_media

Introduction<!>Materials<!>Experimental methods<!>Analytical methods<!>Butanol extraction and back-extraction<!>PEI/PAA complex coacervate formation and water content<!>Lipase enzyme distribution<!>Lactic acid distribution<!>Butanol distribution, extraction, and back-extraction<!>Lactic acid and butanol distribution in the enzyme-filled complex coacervates<!>Conclusion and outlook

<p>Solvent extractions are important processes in many industrial separation processes ranging from the chemical industry, the food industry, to the pharmaceutical industry. An application of liquid extraction that has been receiving increasing attention is in the field of bio-based chemical production. There are many different categories of bio-based chemicals and the feature that they often have in common is that typically large amounts of water are present. Removing water by evaporation is among the costliest operations in industry, and therefore when aqueous solutions are present, liquid-liquid extraction (LLX) may be applied. In LLX an additional liquid phase, typi-cally an organic solvent exhibiting preferential solubility for a specific solute, is used to selectively extract the solute from the initial liquid phase. Unfortunately, organic solvents that have been proven to be effective for extraction can be toxic for individual organisms and/or the environment. 1,2 There is great interest in the design of 'green solvents' that are more environmentally friendly in terms of production, usage, and disposal. For extraction from aqueous solutions, several alternatives to conventional organic solvents have been proposed in the past years such as ionic liquids (ILs), 3,4 deep eutectic solvents (DESs), 5 and aqueous two phase systems (ATPSs). [6][7][8] ILs are essentially molten salts with a relatively low melting point ( per definition, ≤100 °C). 9 ILs have shown a broad range of applications in part due to the customization possible as a result of the large variety of composite components. 3,10 They are generally less volatile in nature compared to organic solvents and the negligible vapour pressure eliminates solvent losses through evaporation. 11 Unfortunately, many ILs are potentially toxic and not biodegradable. 12 DESs are mixtures of hydrogen bond donors and hydrogen bond acceptors that form liquids on mixing and exhibit eutectic behaviour by having melting points lower than that those of their constituent components. They have been proposed as new extraction solvents and share many advantageous characteristics with ILs. 5,13 The toxicity of DESs varies, and in some cases the DES is even more toxic than its constituent components, 14,15 which is a factor to be taken into consider-ation when formulating DESs for sustainable extraction. Additionally, due to the fact that DESs are composite solvents, the molar ratio between the hydrogen bond acceptor and donor may change during the extraction. 16 This can result in solidification of the DES components and affect the subsequent extraction steps.</p><p>ATPSs function via segregative phase separation and consist of two ( partially) immiscible aqueous phases. The most common ATPSs are formed when two constituents (often polymer-polymer or polymer-salt (or even ILs 7,8 )) are mixed in an aqueous solution, resulting in two distinct segregated phases. Each of the segregated phases is rich in one of the two constituents. When used for the separation of molecules, one of these phases will be the preferred phase for the compound of interest, while the remaining impurities hopefully concentrate in the other phase. [17][18][19][20] ATPSs are currently extensively used for the isolation and extraction of various biological compounds ranging from small molecules, hormones, up to the isolation of entire cells. [20][21][22] Also, micellar systems have been proposed as the foundation for new greener extraction methods with extraction principles similar to those of ATPSs. 23 Similar to segregative phase separation, two phase systems can also be formed via associative phase separation such as complex coacervation (Fig. 1). This process occurs when oppositely charged polyions (a.k.a. polyelectrolytes) are mixed under conditions that allow them to associate. The formed complex coacervates (CCs) are macroscopic liquid-like aqueous polyion-rich condensates, which are in equilibrium with an aqueous polyion-poor phase, also called the supernatant. Depending on the chemistry of the polyions and the environmental conditions, solid-like condensates can also form, called polyelectrolyte complexes (PECs). In this study, we will make use of complex coacervates.</p><p>In previous studies, CCs and PECs have been reported with the property of partitioning certain proteins into the complex phase over the supernatant phase. [24][25][26] The ability to isolate proteins using single polyions is already well established, but a previous study has shown that in some cases the addition of a mixture of both polycations and polyanions can lead to better partitioning than the addition of only one species of polyions. For example, the addition of the polyanion poly (acrylic acid) (PAA) alone is not enough to extract the positively charged protein lysozyme from an aqueous solution, but with the addition of a polycation (and thus the formation of a PEC), the lysozyme could be extracted completely. 24 CCs therefore show emergent properties that their constituent components do not.</p><p>A potential advantage of associative phase separation of CCs and PECs over segregative phase separation of ATPSs is that the distribution coefficients of CCs can be dependent on the composition of the CCs, resulting in different partitioning behaviours for the same constituent polyions present in different ratios. 24,25 There are a handful of studies that show that PECs have the ability to partition certain proteins [24][25][26][27][28] as well as certain small molecule dyes 29,30 from an aqueous solution. In some cases, the distribution coefficients reported were in the order of 10 4 in favour of distribution in the PEC for a specific protein and polyion pair. 24 These studies hint at the potential of CCs and PECs as extraction media, though they are typically concerned with biomedical applications such as intracellular drug delivery. We have previously achieved success in using structurally simple polyions in order to selectively extract lysozyme from an aqueous solution in the presence of another protein. 24 Beyond varying the ratio of the polycation to the polyanion, there are other factors that influence the CC properties such as solution ionic strength, temperature, and varying concentrations of the system's constituents. There are no systematic studies that go into the details of the effect of such system parameters on the partitioning behaviour of the solutes.</p><p>The inspiration for CCs as extraction media comes from the partitioning behaviour of solutes between cellular fluids and membraneless organelle (MLO) compartments within living cells. MLOs consist of both negatively and positively charged biomacromolecules such as negatively charged RNA and positively charged intrinsically disordered proteins. 31 The MLO phase behaviour strongly resembles the phase behaviour of CCs. Our cells use MLO droplets to perform very specific biological functions, including the partitioning and release of specific targeted compounds in response to changes in the stimuli in the cellular environment. [32][33][34][35] While nature undoubtedly has a head start regarding the design of MLOs, their functionality in cells shows that there is currently untapped potential for CCs as media for extraction processes. Developing CCs with distribution coefficients that are strongly dependent on tunable stimuli and environmental parameters would be of great benefit to the development of extraction processes.</p><p>In this study, we investigate the extraction of several compounds from aqueous solutions using complex coacervates formed by branched poly(ethylenimine) (PEI) and poly(acrylic acid) (PAA). PEI-based nanocrystals have been used as extraction media for rare earth element recovery and are increasingly used as vehicles for drug delivery. 36,37 Higher molecular weight PEI is typically considered cytotoxic, though this effect can be decreased by using the low molecular weight (1.8 kDa) variant that is used in this study. 38 PAA is commercially used as a thickening agent and water absorber in the hygiene, cosmetic, agricultural, and food industries. In these contexts, PAA is usually known as sodium polyacrylate or waterlock.</p><p>We consider lactic acid (LA), butanol, and three varieties of industrial lipase enzymes as model compounds for the extraction from the aqueous supernatant into the PEI/PAA CC. These industrially relevant lipases are widely used in food, detergents, and pharmaceuticals 39,40 and represent up to 10% of the total global enzyme market. 41 Lactic acid extraction from an aqueous fermentation broth has received increased attention in the last few years amongst others due to the possibility of poly(lactic acid) being a sustainable alternative to many commonly used plastics. 42 The use of poly(lactic acid) as a competitor to modern plastics is currently restricted to application areas where the higher costs associated with purification and extraction from the fermentation broth can be tolerated. Several techniques have been in development for the recovery of LA from the fermentation broth aiming to reduce the production cost and decrease the impact of by-product formation during lactic acid production on the environment, and CCs may be a new technique to address the LLX of LA. 43,44 Butanol, being a popular solvent and a popular candidate for biofuels, can also be extracted from fermentation broths. 45 In this study, we create macroscopic CCs via associative phase separation of PEI and PAA. We investigate the effect of several parameters such as CC composition, reagent concentrations, and temperature on the partitioning of lipases, lactic acid, and butanol to demonstrate a proof of concept to draw attention to the use of CCs for extraction purposes.</p><!><p>Poly(acrylic acid) (PAA) sodium salt powder with a molecular weight of 6.0 kDa and branched poly(ethylenimine) (PEI) with a molecular weight of 1.8 kDa were purchased from Polysciences, Inc. Sodium chloride (NaCl, >99%), sodium hydroxide (NaOH, >98%), fuming hydrogen chloride (HCl, 37 ± 1 wt%), n-butanol (>99%), and lipase from porcine pancreas (PPL) were purchased from Sigma-Aldrich/Merck. NovoCor AD L lipase (CALA) and Novozyme CALB lipase (CALB) were donated by Novozymes A/S. Crystalline L-lactic acid was donated by Corbion N.V. Unless otherwise specified, water used for the solutions and dilutions was ultrapure Milli-Q water dispensed from a PURELAB flex system at a resistivity of 18.2 MΩ.</p><!><p>Complex coacervates were prepared by mixing prepared aqueous polyion solutions (PAA and PEI) for a total polyion concentration of up to 20 g L −1 in the presence of up to 400 mM NaCl. All solutions are set to pH 7 before mixing. In the case of lipases, they are added to the solution with the polyions at a lipase concentration of 67 µM, consistent with earlier studies. 25,26,33 Unless otherwise specified, butanol was added at 400 mM and lactic acid at 100 mM. In the case of butanol and lactic acid, the mixed polyion solution is first left to equilibrate overnight into a CC. Then it is centrifuged at 12 500 g for 30 minutes using the Centrifuge 5425 (Eppendorf ) to expedite the separation of polyion-rich complex coacervates from polyion-poor aqueous supernatant phases. The supernatant is then replaced with a new solution containing either lactic acid or butanol in an aqueous sodium chloride solution with the same NaCl concentration as during the preparation of the CC. Total volumes for each experiment were fixed at 500 µl unless otherwise specified.</p><p>The composition of the CC is defined via F − ;</p><p>where n − and n + are the concentrations of PAA and PEI monomers, respectively, which are mixed in solution. For example, at F − = 0.50, there is an equal molar amount of PEI and PAA monomers present, and at F − = 0.75, there are 3 PAA monomers for every 1 PEI monomer. The assumption being that at pH = 7 both polyions are fully charged due to the interaction between the two polyions. 24,25,46,47 Under this assumption, PAA has a mass of 76.7 g mol −1 of negative charge and PEI has a mass of 43.0 g mol −1 of positive charge.</p><!><p>The total mass of the complex coacervates was determined by comparing the mass of the sample tubes when emptied to that of those containing only the complex coacervates. The volume was determined under the assumption that the density of the complex coacervates is approximately equivalent to that of water. 24 This assumption is based on the densities of PEI (1.03 g ml −1 ) and 50% PAA solution (1.15 g ml −1 ) reported by the manufacturer. Considering that the majority of the CC consists of water, total CC density is within a few percent of water, in the calculated range of 1.02-1.04 g ml −1 .</p><p>The water content of the PEI/PAA complex coacervates was determined via thermogravimetric analysis (TGA) using a STA 449 F3 Jupiter (Netzsch) thermal analyzer on CCs formed at 10 g L −1 total polyion concentration. The temperature was increased from 30 to 120 °C at a rate of 5 °C min −1 and then kept constant at 120°for 40 min to evaporate the water present in the complex coacervates. The mass of the samples is recorded to obtain the mass loss corresponding with the evaporated water.</p><p>Prior to the determination of the concentration of the solute present in the experiment, the systems were centrifuged for 30 minutes at 12 500g in an Eppendorf Centrifuge 5425. Enzyme concentration from the supernatant was determined by evaluating the absorbance at 280 nm using a Shimadzu UV-2401PC spectrophotometer. Extinction coefficients for PPL and CALA were calculated to be 68 kM cm −1 and 54 kM cm −1 based on the peptide sequence. The extinction coefficient for CALB has been reported in literature as 41 kM cm −1 . 48 Butanol concentration was determined using a Thermo Scientific Trace 1300 gas chromatograph with two parallel ovens, an auto sampler TriPlus 100 Liquid Samples and an Agilent DB-1MS column (60 m × 0.25 mm × 0.25 μm) with an injection volume of 1 μL diluted in analytical acetone. A ramped temperature profile was used, in which the initial temperature was 30 °C, followed by a ramp of 10 °C min −1 to 140 °C. The second ramp of 50 °C min −1 to 340 °C finished the program, which lasted for 15 min. The flame ionization detector temperature was 440 °C. A column flow of 2 mL min −1 with a split ratio of 25, an airflow of 350 mL min −1 , a helium make-up flow of 40 mL min −1 and a hydrogen flow of 50 mL min −1 was used.</p><p>Lactic acid concentration was determined using a Grom Resin H + IEX column on a Metrohm 850 Professional ion chromatograph. The mobile phase was 1 mM H 2 SO 4 solution with a flow rate of 0.6 mL min −1 . The column temperature was 45 °C.</p><p>As the total amount of the added compound is known and the concentration of the compound in the supernatant is measured, the compound concentration in the complex coacervate can be calculated. The distribution coefficient is then determined via</p><p>where [X] CC and [X] SN are the concentrations of the compound in the complex coacervate and supernatant, respectively. The distribution coefficient changes depending on the varied parameter, resulting in a distribution profile.</p><!><p>PEI/PAA CC systems were prepared with a total polyion concentration of 50 g L −1 in 1 ml with a composition of F − = 0.26. The increased polyion concentration was chosen to produce more CC as a simulation of upscaling compared to the previous experiments. This mixture was centrifuged for 30 minutes at 1000 g. The aqueous supernatant was then replaced with 650 µl of 5.7% butanol and 10 mM NaCl solution. The samples were collected to determine the butanol concentration after 24 h of incubation at room temperature (RT), and again after 24 h of incubation at 70 °C. The supernatant was then decanted, and any excess supernatant drops were removed using pressured nitrogen gas. 600 µl of fresh 10 mM NaCl solution was added to the CC as a back-extraction phase, and the samples were collected from the back-extract after 24 h of incubation at 60 °C. Then, the samples were collected after another 24 h of equilibration at 40 °C, and once more after another 24 h at RT. The butanol concentration of all the samples was determined as described previously and the amount of butanol present in the CC was calculated taking into account the varying volumes of the supernatant due to sample extraction.</p><!><p>Complex coacervates are formed due to the interactions between the oppositely charged polymer chains, with the driving force being both entropy gain due to the release of counterions and electrostatic interaction. The fraction of the negative and positive charges is important for the total extent of CC formation. To narrow down the region of interest for evaluating the partitioning, we first evaluated the total CC formed as a function of the composition F − and looked at the water content for two CC compositions of interest.</p><p>In Fig. 2A, it is shown that the largest amount of CC was formed around F − = 0.25 to 0.50, with the highest values found at 0.26 and 0.36 with 23.1 ± 3.3 mg and 22.4 ± 3.3 mg, respectively. Fig. 2B shows the photographs of the relative quantities of CC as a function of F − .</p><p>As shown in Fig. 2C, we evaluated the water content of the two F − values with the highest CC formation as seen from Fig. 2A and found that for PEI/PAA CCs the water content varies drastically based on CC composition, with the water content for F − = 0.36 being 73.5%, and for F − = 0.26 being 51.9%. Comparing the remaining mass of the polyions in the CC to the total polyions added, it appears that for F − = 0.26 all the polyions form the CC mass, while for F − = 0.36 only approximately 60% of the polyions form the CC, with the rest presumably remaining in solution.</p><p>Intuitively, it might be expected that the largest volume of CC formation occurs at the composition F − = 0.50, where an equal amount of positive and negative monomers is present. However, this is not necessarily the case as demonstrated by the PEI/PAA CC system. One explanation for this discrepancy is that the interactions between polyions, water, and salts can affect the degree of ionization of the monomers.</p><p>Water content of CCs and PECs is typically reported to be between 60 and 80%. [49][50][51] We found using TGA that for PEI/ PAA CCs at a composition of F − = 0.36 the CCs fall within the reported range, though the water content at F − = 0.26 is approximately 10% lower than expected. The water content of CCs can impact the partitioning behaviour of solutes based on their preferential association with water. For example, lipases in general are known to prefer oil-water interfaces over fully aqueous environments. 52 Both PEI and PAA are not expected to decompose at the given conditions, temperature, and timescale. 53,54</p><!><p>In this section, the partitioning of several types of lipases in the PEI/PAA complex coacervates is described. In Fig. 3, the distribution coefficients (K D ) of three commonly used lipases PPL, CALB, and CALA as a function of the CC composition, the NaCl concentration, and the total polyion concentration are shown.</p><p>We found that the K D of all lipase types varies greatly as a result of the adjusted parameters. The charge ratio F − has the most significant consistent effect (Fig. 3A-C), showing distinct K D maxima at a composition of F − = 0.36 for CALB (K D maximum of 11.0 ± 0.9) and F − = 0.26 for CALA (K D maximum of 23.0 ± 0.5) and PPL (K D maximum of 19.2 ± 1.9). These maxima are partially consistent with the maximum values of CC formed (Fig. 2A); however, a small deviation in the composition results in a larger change in K D than that can be solely attributed to a difference in the CC quantity: for the region with the highest constant CC formation (F − ranging from 0.25 to 0.50), there are varieties in the K D for up to a factor 4 for CALA (Fig. 3A).</p><p>The distribution profiles were found to be dependent on the specific protein investigated. The results for these lipases corroborate the earlier studies that report similar nearly symmetrical distribution profiles (though centred around different F − values) for three proteins with poly(lysine)/poly(glutamate) CCs. 26 Other proteins with different polyion pairs show completely different distribution profiles altogether that are not necessarily symmetrical. 24,25 For now, there are no reliable methods to predict the distribution profile in advance as a result of the parameters. Many studies that look into the partitioning of proteins assume F − = 0.50 is the optimal composition for both PEC formation and partitioning and do not investigate the other charge ratios. 27,28,30 Based on the results presented here, there might be opportunities for working at other compositions that result in more desired K D values.</p><p>Both CALB and PPL show a similar distribution profile as a function of the NaCl concentration (Fig. 3E and F) with a slight K D increase initially, followed by a decrease. CALA however shows an immediate decrease, followed by a local maximum (Fig. 3D) at a comparatively high salt concentration.</p><p>By varying the NaCl concentration, the K D varies between approximately 5 and 15-20 for the investigated enzymes. We hypothesize that for CALB and PPL a partial screening of the polyion charges by the salt ions results in the CC being less densely packed, essentially increasing the distance between polyion chains and allowing the proteins (or other solutes) to enter the CC more easily. Polyion condensation in the presence of other ions (such as salt ions) results in ion association with charged monomer subunits of the polyion. This effectively screens the electrostatic interaction between the oppositely charged monomers of each polyion. Indeed, if the ionic strength of the solution becomes too high, the polyion structures dissolve completely as the degree of screening prevents the complex formation between polyions. 46 Between complete complex dissolution and the absence of additional ions beyond the counterions brought in by the polyions, there is a concentration region where the salt ions prevent part of the oppositely charged polyions from associating. Subsequently, this can influence the behaviour of the condensates.</p><p>All three enzymes showed a similar trend of K D decrease as the total polyion concentration increased. A possible explanation is that as the total mass of CC increases, this does not result in a proportional increase of the CC-water interface, limiting the penetration of the solutes into the CC.</p><p>It is worth mentioning that there are other advantages of concentrating enzymes in CCs or PECs beyond extraction purposes. It has been reported that the activity of proteins may be enhanced in CCs compared to the same proteins in regular aqueous solutions. 27,47 In addition, the polyions may protect the proteins from degradation, increasing the shelf life of (extracted) proteins. 55 The mechanism for this is unknown, though the ability to both highly concentrate the enzymes and increase their activity is particularly interesting for industrial applications.</p><!><p>The partitioning of lactic acid into CCs was studied, as lactic acid is an industrially relevant small molecule. The effects of the CC composition, the NaCl concentration, the total polyion concentration, the initial LA concentration, and the temperature on the lactic acid K D were studied, and the results can be observed in Fig. 4.</p><p>Unlike the distribution profiles for the lipase enzymes, we found only very little effect of the composition on the K D (Fig. 4A), which remained between 2 and 4. In contrast, the effect of NaCl on K D (Fig. 4B) of LA was more pronounced than those of CALB and PPL while following a similar distribution profile. Within our hypothesis of salt ions influencing the distance between polyion chains, the effect of the salt NaCl concentration may be more pronounced for LA, as it is substantially smaller than any of the lipases. By varying the NaCl concentration, we found the highest K D for LA at 7.4 ± 0.5 for 100 mM NaCl.</p><p>Similar to the trend with lipases, increasing polyion concentration had an adverse effect on the partitioning (Fig. 4C). However, altering the initial concentration of lactic acid only slightly affects the partitioning in the evaluated range (Fig. 4D), suggesting that the saturation point for the CC has not yet been reached as this would result in an expected decrease in K D at higher LA concentrations. 24 Fig. 4E shows that an increase in temperature has a small but consistent positive effect on the K D in the investigated range. This suggests that the extraction process is endothermic and that the driving force behind the partitioning is an increase in total entropy, perhaps similar to how an increase in entropy is the primary driving force for polyion-polyion association in the first place. 23 The optimal K D for LA in our PEI/PAA CC system at approximately 100 mM NaCl is comparable to or greater than many other liquid-liquid extraction systems. 44,[56][57][58] A disadvantage of some of these reported systems is their reliance on low pH 59 or the toxicity of the solvents. 58 While some established extraction methods, such as tri-n-octylamine in 1-octanol, 60 outperform CC systems with regard to LA for now, we show that the effects of system parameters for CC systems can substantially alter the K D . Interestingly, where the common method using tri-n-octylamine appears to decrease the distribution coefficient at higher temperatures, the opposite is true for PEI/PAA CCs. 56 There are many additional parameters that can be further fine-tuned, suggesting the ability to achieve much higher K D values.</p><!><p>We investigated the K D of butanol as a function of the CC composition (Fig. 5A) as well as the temperature (Fig. 5B). As butanol partitioning showed a remarkable temperature sensitivity, we evaluated the possibility of extraction and backextraction of butanol using CCs by alternating between RT and 70 °C (Fig. 5D).</p><p>Contrary to the lipases, we observe the highest K D as a function of CC composition quite distant from the optimal CC formation, resulting in the highest value of K D of 22.7 ± 0.7 at F − = 0.56. This K D is very similar to that of a reported task-specific IL and substantially higher than the standard of oleyl alcohol, which are K D = 21 and 3.4, respectively. 61,62 Whereas LA demonstrated only a minor temperature dependence of the K D (Fig. 4E), the butanol distribution shows a large difference between RT and 70 °C, roughly at a factor of 4-5. Out of the evaluated parameters, temperature is the most practical to change for the existing systems as it does not require the addition or removal of chemicals and is straightforward to implement. For this reason, we envisioned a PEI/ PAA CC system that was able to partition butanol within the CC to a greater degree at high temperatures and could then be coaxed to release butanol into a separate aqueous environment at lower temperatures such as RT. To evaluate such a system for extraction and back-extraction of butanol, we prepared PEI/ PAA CCs at higher concentrations of polyions (Fig. 5C). The resulting CCs had a mass of 62.2 ± 1.7 mg (average ± standard deviation, n = 4). A supernatant containing butanol was added to the CCs, and the temperature was increased from RT to 70 °C for butanol extraction. For back-extraction, the supernatant was replaced with fresh supernatant containing no butanol, and the temperature was decreased first to 60 °C, then to 40 °C, and finally to RT (Fig. 5D). Consistent with the observations of Fig. 5B, increasing the temperature to 70 °C substantially increases the butanol content in the CC. Fig. 5B shows an approximate quadrupling of the K D , whereas Fig. 5D only shows a CC butanol increase from 8.80 ± 0.03 to 20.39 ± 0.80 mg, corresponding with a decrease of the supernatant butanol concentration from 4.39 ± 0.00% at RT to 2.28 ± 0.14% at 70 °C. A possible explanation for this discrepancy is the difference in the total polyion concentration, as Fig. 3G/H/I and 4C show that increased polyion concentrations do not necessarily lead to an increase in partitioning.</p><p>By replacing the supernatant and lowering the temperature in the steps from 70 °C back to RT, 21.1 ± 0.6% of the butanol extracted into the CC could be back-extracted into a new aqueous solution. Interestingly, reverting the temperature back to RT did not completely revert the butanol equilibrium and a fraction of butanol remains within the CC.</p><p>Considering the large number of tunable parameters, it is likely that with alterations a back-extraction higher than 21.1% is achievable. For example, increasing the salt concentration has been used to back-extract proteins from polyion micelles and polyion precipitates by disrupting the polyion complex, 46 while varying the pH has been used to back-extract proteins, keeping the polyion precipitates intact. 24 Other experimental parameters such as increasing the number of temperature steps or increasing the equilibration time may also prove to be beneficial. Further research should find improved recovery methods as well as better understanding of the physicochemical mechanisms allowing for a larger fraction of the CCextracted butanol to be recovered.</p><!><p>We hypothesized that the presence of additional components in the CCs can influence the partitioning behaviour of LA and butanol in those CCs. For this reason, we investigated the distribution of LA and butanol in PEI/PAA CCs that already contained PPL, CALB, or CALA enzymes. Similar to the presence of salt ions, the presence of relatively large enzymes in the CCs may change the structure of the polyion complex by altering the distance between polyions and the properties of the CCwater interface. We fixed the compositions of the systems to the F − at which the maximum K D was found; F − = 0.36 for PPL and CALB, and 0.26 for CALA. Then, LA (Fig. 6) and butanol (Fig. 7) partitioning was studied as a function of the ionic strength at 25 and 50 °C.</p><p>In Fig. 6, we can see a stabilizing effect of the lipases on the LA distribution coefficients as they no longer strongly increase between 10 and 100 mM NaCl compared to the PEI/PAA CCs without the lipases shown in Fig. 4B. In addition, the presence of PPL slightly increases the 'stable' K D to approximately 5 compared to 3 without PPL. CALB increases the K D to approximately 4. For PPL and CALB, a higher temperature resulted in a slightly lower K D , comparable to values where the lipases were not present at all. Similar to Fig. 4E, there is no strongly noticeable difference between the investigated temperatures.</p><p>A much stronger effect is observed for the distribution of butanol shown in Fig. 7. For PPL and CALA, the K D values are comparable to CCs without the lipases at K D = 10-20, but with increased temperature the K D values increase to 40-50 for PPL and 30-50 for CALA. Interestingly, in the presence of CALB, the K D for butanol increases linearly with the NaCl concentration (Fig. 7B) at RT, but not at higher temperatures.</p><p>These 'doped' CCs show different distribution profiles than 'empty' CCs. Doped CCs may shield against the effect of increased NaCl concentration or simply increase the distribution coefficient by up to a factor of 3 compared to empty CCs. All in all, the concept of pre-filled CCs gives another parameter to tune and optimise the extraction potential of complex coacervates.</p><!><p>We present an exploratory study on new applications of complex coacervates. While the partitioning behaviour of CCs has been noted before, the step to develop them as an extraction medium has been absent. From previous studies as well as the results shown in this study, it has become clear that the partitioning behaviour of the compounds in CCs is a complex subject involving many tunable parameters that individually greatly influence the distribution coefficient between the aqueous environment and the CC.</p><p>In our study, we showed that the distributions of lipase enzymes, lactic acid, and butanol in PEI/PAA complex coacervates are strongly affected by the CC composition, ionic strength as determined by the NaCl concentration, polyion concentration, temperature, and presence of other compounds in the CC. However, the effect of any of these parameters depends on the partitioned compound examined.</p><p>For example, we found that the CC composition has a great influence on the K D of lipases (Fig. 3A-C), while it has only a minimal effect on the K D of LA (Fig. 4A). Even within the category of lipases, the effect of the NaCl concentration on the K D of CALA is much stronger than on the K D of PPL (Fig. 3D and F, respectively). The only consistent influences of the parameter found were that higher concentrations of polyions above 5 g L −1 or high concentrations of NaCl led to lower K D values, though a small amount of NaCl was often (but not always) beneficial. The highest K D experimentally found and the corresponding parameters for the 5 compounds are presented in Table 1.</p><p>We demonstrated that several relatively simple and tunable parameters can change the K D by a factor of 4 for lipases and butanol as a result of the changes in the CC composition (Fig. 3A-C) and temperature (Fig. 5B), respectively. It is unfortunate that many studies investigating the partitioning behaviour of solutes in CCs do not investigate different compositions, and instead fix it at F − = 0.50 where they might miss either compositions with greater partitioning or with greater PEC formation. 27,28,30 As is demonstrated with the PEI/PAA system, we have shown that it is far from a safe assumption that the optimal polyion complex formation takes place at F − = 0.50, let alone the assumption that the desired partitioning properties are optimal at this composition.</p><p>Special emphasis has been laid on temperature as a parameter that is easily physically tunable without adding or removing chemicals to or from the system. Using temperature, we created a PEI/PAA CC temperature-swing extraction system that can extract approximately half the butanol from an aqueous supernatant at 70 °C, and then back-extract 21.1% of the extracted butanol back into a new aqueous phase at RT in a single-step system. In this way, CC extraction media can be considered analogous to, for example, cyclic CO 2 absorption, where typical cyclic capacities are in the order of 5-15%. 63 However, considering the number of tunable parameters, it is almost certain that the cyclic capacity can be made much more efficient, and that extraction/back-extraction of a variety of small molecules as well as proteins is possible. While the results for our butanol extraction were not directly comparable in efficiency to some of the results shown by ATPS systems, 64 where up to 95% of a protein was purified in a single step, such high extraction numbers have been shown with different PECs for different proteins, 24 suggesting that a similar potential for CCs exists.</p><p>There are several limitations of this study. Some of the experimental protocols in these experiments, such as centrifuging at 12 500 g for 30 minutes, are impractical for industrial applications. These protocols were based on earlier fundamental research 24,25 and it is likely (but not verified) that centrifuging at far lower speeds and durations is sufficient. Indeed, the butanol (back-)extraction was performed without additional centrifugation steps after the addition of butanol to the system.</p><p>The reasons for the variation in K D values and the mechanisms determining the distributions in CCs or other PECs are not well understood. The partitioning behaviour is currently not well understood and cannot yet be accurately predicted. Currently, this means that extensive testing for the individual compound, polyion pair, and tunable parameters is required in order to learn how the parameters influence partitioning. It would be extremely beneficial for the development of CCs as extraction media if the fundamental mechanisms of partitioning in CCs were better understood. The ability to predict the influence of (combinations of ) parameters on partitioning prevents the necessity of high-throughput testing to optimise the parameters for the extraction of a particular desired compound. With a greater understanding of the underlying mechanisms, complex coacervates show promise as extraction media for a wide variety of compounds. The partitioning of solutes in CCs and PECs is the result of a complex interplay of at least 6 different compounds ( polyanion, polycation, water, two salt ions, and the solute of interest), and the temperature will affect the interactions between all these compounds, making it difficult to predict the partitioning behaviour. For proteins, it is expected that the charge and charge distribution are important, and hydrophobic interactions will also play a role. The temperature-dependent partitioning of butanol is promising, but systematic studies are required to unravel the detailed molecular mechanism.</p>

Royal Society of Chemistry (RSC)

Chemically-Induced Cell Wall Stapling in Bacteria

Summary Transpeptidation reinforces the structure of cell wall peptidoglycan, an extracellular heteropolymer that protects bacteria from osmotic lysis. The clinical success of transpeptidase-inhibiting \xce\xb2-lactam antibiotics illustrates the essentiality of these cross-linkages for cell wall integrity, but the presence of multiple, seemingly redundant transpeptidases in many species makes it challenging to determine cross-link function. Here we present a technique to link peptide strands by chemical rather than enzymatic reaction. We employ bio-compatible click chemistry to induce triazole formation between azido- and alkynyl-d-alanine residues that are metabolically installed in the peptidoglycan of Gram-positive or Gram-negative bacteria. Synthetic triazole cross-links can be visualized using azidocoumarin-d-alanine, an amino acid derivative that undergoes fluorescent enhancement upon reaction with terminal alkynes. Cell wall stapling protects Escherichia coli from treatment with the broad-spectrum \xce\xb2-lactams ampicillin and carbenicillin. Chemical control of cell wall structure in live bacteria can provide functional insights that are orthogonal to those obtained by genetics.

chemically-induced_cell_wall_stapling_in_bacteria

Introduction<!>Results and Discussion<!>Significance<!>Lead Contact<!>Material Availability<!>Data and Code Availability<!>EXPERIMENTAL MODEL AND SUBJECT DETAILS<!>Metabolic labeling and CuAAC<!>General fluorescence analysis<!>Peptidoglycan composition analysis<!>Antibiotic challenge<!>General Procedures.<!>Instrumentation.<!>Overall synthetic scheme.<!>Azidocoumarin pentafluorophenyl ester S3<!>Boc-d-Ala-azidocoumarin S5<!>d-Ala-azidocoumarin (1)<!>QUANTIFICATION AND STATISTICAL ANALYSIS

<p>Cell wall peptidoglycan is a mesh-like biopolymer that surrounds nearly all bacteria and is required to resist turgor pressure. The macromolecule consists of a glycan backbone and peptides, containing both l- and d-amino acids, that are cross-linked by d,d- and l,d-transpeptidases (Egan et al., 2015). The degree of transpeptidation can vary with species, growth phase and environmental conditions (Vollmer and Seligman, 2010). For example, the peptidoglycan of slow- or non-growing E. coli is more highly cross-linked and less susceptible to in vitro enzymatic turnover than that of actively-replicating E. coli (Glauner et al., 1988; Goodell and Tomasz, 1980; Lee et al., 2013; Pisabarro et al., 1985; Tuomanen and Cozens, 1987; Tuomanen et al., 1988). Cross-linking abundance is also predicted to impact the overall strength and stiffness of the cell envelope (Auer and Weibel, 2017; Huang et al., 2008; Loskill et al., 2014; Vollmer and Bertsche, 2008), cell shape (Huang et al., 2008; Sycuro et al., 2010; Yang et al., 2019), and assembly of macromolecular structures (Scheurwater and Burrows, 2011). The clinical success of transpeptidase-inhibiting β-lactam antibiotics highlights the importance of peptidoglycan cross-linking in bacterial physiology.</p><p>Despite the biological and medical significance of peptidoglycan transpeptidation, unraveling the roles of these linkages is challenging. Currently, the standard ways to manipulate cross-linking are to mutate or deplete the expression of the transpeptidase genes or to inhibit these enzymes with small molecules like β-lactams. However, the functional redundancy of transpeptidases and promiscuity of β-lactams (Spratt, 1975) pose challenges to rational control of peptidoglycan connectivity.</p><p>d-amino acids bearing reactive groups such as cysteines, alkynes, azides and tetrazines have been used to metabolically label the peptidoglycan stem peptide (de Pedro et al., 1997; Kuru et al., 2012; Pidgeon et al., 2015; Radkov et al., 2018; Siegrist et al., 2015; Siegrist et al., 2013). Once embedded, the presence of these probes can be revealed by chemical reaction with an exogenous label that bears a complementary reactive group (Siegrist et al., 2015). We hypothesized that we might also use functionalized peptide strands to manipulate cell wall cross-linking. More specifically, we reasoned that co-incubation of bacteria with azido- and alkynyl-d-amino acids would result in a subpopulation of labeled muropeptide strands in close enough proximity to undergo copper-catalyzed azide-alkyne cycloaddition (CuAAC) upon introduction of the appropriate reagents. Such structures would serve as synthetic, triazole cross-links.</p><!><p>We first tested our hypothesis using a loss-of-fluorescence assay. In this approach, bacteria are co-incubated in the presence of azido- and alkynyl-d-amino acids, washed and subjected to CuAAC (Figure 1A, left). We reasoned that the peptidoglycan-embedded functional groups should either react with each other or with the alkynyl- or azido-fluorophores in CuAAC solution. Bacteria incubated with a single d-amino acid probe, by contrast, should have muropeptides decorated with just one functional group, which in turn should react only with the complementary reactive fluorophore. In this assay, we interpret decreased labeling of co-incubated relative to singly-incubated bacteria to indicate that there are fewer peptidoglycan-embedded functional groups available to react with the fluorophores. This may occur because the reaction between azido- and alkynyl-muropeptides is favored or because there is competition between the d-amino acids for initial incorporation into the muropeptide. To control for the latter possibility, we also subjected metabolically-labeled bacteria to strain-promoted azide-alkyne cycloaddition (SPAAC; Figure 1A, right) with a cyclooctyne-appended fluorophore. In the absence of copper and other reagents, peptidoglycan-embedded azides and alkynes should not react with each other at an appreciable rate and only the azide-cyclooctyne reaction should occur. In the SPAAC reactions, therefore, we interpret changes in labeling to mean that the azido-d-amino acid outcompetes or is outcompeted by other d-amino acids for initial incorporation into the cell wall.</p><p>We used the loss-of-fluorescence approach to ask whether we could introduce triazole cross-links into the cell wall of Listeria monocytogenes, a Gram-positive, food-borne pathogen. We initially used pbp5::tn L. monocytogenes, a d,d-carboxypeptidase-deficient mutant that we previously showed has high levels of d-amino acid labeling (Siegrist et al., 2013). After incubating the bacteria in the presence of equal amounts of d-alanine (Dala), azido-d-amino acid (azDA or azDlys, the R groups of which respectively have one and four carbons), alkynyl-d-alanine (alkDA) or mixtures thereof, we washed away unincorporated amino acid and subjected the bacteria to CuAAC with either an alkynyl- (Figure 1B) or azido-fluorophore (Figure 1C). We assessed cellular fluorescence by flow cytometry. In both cases, the bacteria that were co-incubated in alkDA/azDlys had lower amounts of fluorescence than those incubated in azDlys (Figure 1B) or alkDA (Figure 1C) alone. We obtained similar results with the more bio-friendly CuAAC reaction (Yang et al., 2014) that employs the 3-[4-({bis[(1-tert-butyl-1H-1,2,3-triazol-4-yl)methyl]amino}methyl)-1H-1,2,3-triazol-1-yl]propanol (BTTP) ligand (Wang et al., 2011) (Figure 1D) or in wild-type L. monocytogenes (Figures S1A and S1B). These data suggested that bacteria co-incubated in azDlys and alkDA had fewer peptidoglycan-embedded functional groups available to react with complementary reactive fluorophores in solution (Figure 1A). Moreover, in bacteria subjected to SPAAC with cyclooctyne-fluorophore, the signal after alkDA/azDlys incubation was similar to that of azDlys alone or Dala/azDlys (Figures 1E and S1C). The SPAAC control reactions suggested that there was no appreciable competition between the d-amino acids for initial incorporation into the peptidoglycan. Taken together these data suggest that cell wall-embedded azides and alkynes can react with each other by CuAAC.</p><p>We next sought a more direct read-out for triazole cross-links. Fluorogenic molecules undergo a fluorescence enhancement upon chemical or enzymatic reaction. For example, CuAAC reaction of the non-fluorescent 3-azido-7-hydroxycoumarin (azidocoumarin) with terminal alkynes yields fluorescent triazole products (Sivakumar et al., 2004). As d-amino acids appended to fluorophores, including hydroxycoumarin, incorporate efficiently into peptidoglycan (Kuru et al., 2012), we decided to test whether swapping an azido-coumarin d-amino acid (azcDA) for an azido-d-amino acid would allow us to mark the presence of triazole cross-links (Figure 1F).</p><p>We began by synthesizing azcDA, which could be accessed readily by coupling Nα-Boc-d-2,3-diaminopropionic acid to azidocoumarin acid 1 (Weineisen et al., 2017) via pentafluorophenyl ester 2. Trifluoroacetic acid-mediated Boc deprotection afforded azcDA. Next we co-incubated pbp5::tn L. monocytogenes with Dala/azcDA or alkDala/azcDA, washed, and subjected the bacteria or not to a BTTP CuAAC reaction that lacked a complementary alkynyl-fluorophore. By microscopy we found that fluorescence of live, azcDA-labeled bacteria required the inclusion of alkDA in the initial metabolic labeling step as well as the subsequent CuAAC reaction (Figure 1G). These gain-of-fluorescence data were consistent with the loss-of-fluorescence results (Figures 1B–1D, S1A, S1B) and supported the notion that a CuAAC reaction can covalently join azides and alkynes metabolically installed in L. monocytogenes peptidoglycan to form synthetic cross-links.</p><p>We were initially unable to identify peptidoglycan modifications that were specific to alkDA/azDlys-treated, CuAAC-subjected bacteria and that had the exact mass of a triazole cross-link (Figure S1D). We hypothesized that our ability to detect such modifications—which theoretically could include muropeptides with additional, transpeptidase-mediated linkages—was complicated by the pre-existing complexity of L. monocytogenes peptidoglycan, which is both highly-cross-linked and tailored by N-deacetylases, O-acetyltransferases and amidotransferases (Aubry et al., 2011; Boneca et al., 2007; Rae et al., 2011). Therefore, we turned our attention to the model, Gram-negative bacterium Escherichia coli, as its peptidoglycan composition is considerably less complex (Vollmer et al., 2008). To simplify our analysis even more, we employed a strain, CS802–2, in which most of the genes encoding peptide-acting cell wall enzymes were deleted, including all 6 carboxypeptidases (Denome et al., 1999). The lack of tetrapeptides in this background prevents l,d-transpeptidation, so we expected d-amino acid incorporation to occur at the 5th position of the stem peptide (Cava et al., 2011). We first verified that CuAAC-subjected, alkDA/azDA-labeled CS802–2 E. coli, like wild-type E. coli, were less fluorescent than those labeled by azDA or alkDA alone (Figures 2A–2B, S1E–F) The decrease in d-amino acid concentration and labeling time compared to L. monocytogenes (described further below) correlated with a more modest reduction in fluorescence. We next metabolically labeled CS802–2 E. coli with different combinations of d-amino acids, washed away unincorporated amino acid, and performed BTTP CuAAC reactions in the absence of fluorophore. We then separated digested peptidoglycan by ultra-performance liquid chromatography (UPLC) and used MS/MS to identify molecules with the exact masses that corresponded to azDA- and alkDA-terminating pentapeptides in the appropriate samples. We identified peaks that were specific to alkDA/azDA-treated, CuAAC-subjected bacteria (Figure 2C) and had the exact masses of a 5–5 triazole dimer, trimer (+/− anh) or tetramer (Figures 2D–E, S2, S3). The presence of these species increased the total cross-linking by approximately 20% (Table 1). We note that muropeptide incorporation of azDA was ~2-fold more efficient than alkDA and associated with a general decrease in cross-linking (Tables 1 and S2B). Exogenous d-amino acids, including both Dala and non-canonical d-amino acids, have been shown or hypothesized to inhibit d,d-transpeptidation (Caparros et al., 1992; Lam et al., 2009). Importantly, however, CuAAC-dependent cross-linking occurred only in alkDA/azDA-treated samples (Table 1) and not in controls that had been treated with equimolar amounts of Dala/azDA (Figure S2B) or Dala alone (Table 1). These data confirmed our ability to introduce synthetic cross-links into bacterial peptidoglycan in a CuAAC-inducible manner.</p><p>Cell wall homeostasis is a balance between synthesis and turnover. Peptidoglycan-cleaving enzymes have been implicated directly (Park and Strominger, 1957; Schwarz et al., 1969; Tomasz et al., 1970; Tomasz and Waks, 1975) and indirectly (Cho et al., 2014; Kohlrausch and Höltje, 1991) in β-lactam cidality. Slow- or non-replicating, β-lactam-tolerant E. coli have highly cross-linked cell walls that are more resistant in vitro to lytic enzymes (Glauner et al., 1988; Goodell and Tomasz, 1980; Lee et al., 2013; Pisabarro et al., 1985; Tuomanen and Cozens, 1987; Tuomanen et al., 1988). We wondered whether β-lactam susceptibility might be influenced by pre-existing cell wall cross-linking, either in addition to, or as part of, the well-documented effect of bacterial growth rate (Eng et al., 1991; Lee et al., 2018; Lee et al., 1944; Toumanem et al., 1986). Since exogenous d-amino acids can modify the structure, amount and strength of peptidoglycan and inhibit bacterial growth (Caparros et al., 1992; Cava et al., 2011; Lam et al., 2009), and growth rate in turn correlates with β-lactam lethality (Eng et al., 1991; Lee et al., 2018; Lee et al., 1944; Toumanem et al., 1986), we first optimized d-amino acid concentration and incubation time (Figures S4A and S4B). We then labeled E. coli with different combinations of d-amino acids, washed and performed BTTP CuAAC. After CuAAC reagent washout, bacteria were resuspended in growth medium and challenged with the β-lactam ampicillin. Without CuAAC, ampicillin treatment resulted in similar killing regardless of what d-amino acid(s) the bacteria had been metabolically labeled with (Figure 3A). In E. coli subjected to CuAAC, ampicillin caused ~2–3 logs of killing for bacteria labeled with Dala, Dala/alkDA or Dala/azDA but less than 1 log for those labeled with alkDA/azDA (Figure 3B). These data suggested that triazole cross-links protect E. coli from ampicillin.</p><p>BTTP-liganded CuAAC (Wang et al., 2011) is more biocompatible than traditional TBTA CuAAC (Yang et al., 2014). While the BTTP CuAAC reaction that we previously optimized for mycobacterial species (Garcia-Heredia et al., 2018) did not change L. monocytogenes cell counts (Figure S4C), they partially inhibited the recovery of E. coli on solid medium (Figure 3). However the effect was consistent across the different d-amino acid combinations, indicating that the synthetic cross-links, not the CuAAC, were responsible for antibiotic rescue. In liquid medium, E. coli subjected to CuAAC had a distinct lag in growth relative to mock-reacted controls (Figures S4D–F). The length of the lag phase was significantly enhanced in CS802–2 E. coli that had been incubated in both azDA and alkDA e.g. bacteria with triazole linkages. Since longer lag phases are associated with antibiotic tolerance (Bertrand, 2019; Fridman et al., 2014) we asked whether the apparent protection afforded by alkDA/azDA labeling followed by CuAAC was transient and whether it was specific to β-lactams. During the post-CuAAC lag phase, synthetic cross-links protected bacteria from ampicillin and the closely-related antibiotic carbenicillin (Figures 3B, S4G, S4H) but not the translation-inhibiting aminoglycoside kanamycin (Figure 3C). This protection was lost following resumption of growth (Figure S4I). Thus an extended, post-CuAAC lag phase correlates with, but is likely not responsible for, the enhanced tolerance of alkDA/azDA-labeled E. coli to β-lactams.</p><p>Taken together, our data suggest that synthetic peptidoglycan cross-links protect against lethality induced by the broad-spectrum β-lactams ampicillin and carbenicillin. The total cross-linking density across CuAAC-treated bacteria is similar (Tables 1 and Figure S2B), suggesting that the unusual linkage (triazole) or position on the stem peptide (5–5) is instead responsible for protection. The classic view of β-lactam activity is that transpeptidase inhibition damages the cell wall by disrupting the balance between peptidoglycan synthases and hydrolases (Park and Strominger, 1957; Schwarz et al., 1969; Tomasz et al., 1970; Tomasz and Waks, 1975). β-lactams also induce a metabolically-taxing, futile cycle of cell wall synthesis and turnover (Cho et al., 2014; Kohlrausch and Höltje, 1991). Both models for β-lactam cidality posit that lethality directly or indirectly depends on the activity of peptidoglycan-degrading enzymes. An artificially-reinforced cell wall may resist β-lactam-induced damage because its structure is partially independent from transpeptidase-mediated synthesis. Additionally, or alternatively, synthetic cross-links may regulate peptidoglycan turnover. Indeed, while this manuscript was under review, Dik and colleagues proposed that non-canonical cell wall cross-links (derived from the reaction of exogenously-incorporated sulfonyl fluoride d-amino acids with endogenous m-DAP) can impede the processivity of lytic transglycosylases (Dik et al., 2020), enzymes that cleave the carbohydrate backbone of peptidoglycan. While we cannot rule out pleiotropic effects on other cell envelope or periplasmic structures, we hypothesize that synthetic triazole cross-links act as molecular speed bumps for lytic transglycosylases, blunting β-lactam cidality by keeping peptidoglycan degradation at bay. Consistent with the diverse roles for these enzymes in peptidoglycan homeostasis (Dik et al., 2017), the prolonged, post-CuAAC recovery in liquid medium of synthetically cross-linked E. coli (Figures S4E–F) may also reflect slowed cell wall turnover. Treatment with unnatural d-amino acids alone modestly enhanced both lag phase and β-lactam tolerance (Figure S4E–I), although the effects were not statistically significant. As noncanonical d-amino acid incorporation is not expected to alter cell wall turnover by lytic transglycosylases (Caparros et al., 1992), we speculate that these more-subtle peptidoglycan modifications impact E. coli physiology by a different mechanism(s) than the synthetic cross-links.</p><p>Given the promiscuity with which d-amino acids incorporate into the bacterial peptidoglycan (Radkov et al., 2018; Siegrist et al., 2015) stapling can be readily adapted for a wide variety of species. Molecular control of synthetic cross-link positioning may also be possible. The effect(s) of 5–5 cross-links on cell wall structure may be different from native, 4,3 or 3,3 cross-links. For example, 5,5 cross-links likely allow more flexibility and/or more space between glycan strands, which could in turn change the physical properties of the peptidoglycan. Unlike monopeptides, which can incorporate into the 4th or 5th positions (or both) of stem peptides (Kuru et al., 2012; Siegrist et al., 2013), d-amino acid dipeptides with functional groups on their N- or C-terminus are predicted to install these groups specifically at 4th or 5th position, respectively (Liechti et al., 2013). Our loss-of-fluorescence assay suggests that dipeptides functionalized with N-terminal azides and alkynes permit the introduction of synthetic, 4–4 cross-links into CS802–2 E. coli (Figure S1F), in addition to the 5–5 linkages afforded by monopeptide labeling. The development of alkyne- and azide-bearing DAP derivatives may also enable the introduction of triazole linkages at the 3rd position of the muropeptide. Finally, pulse-chase labeling in species with defined modes of growth can offer sub-cellular control of synthetic cross-links. Independent from and complementary to genetics, cell wall stapling is an orthogonal assay for dissecting the roles of peptidoglycan structure in bacterial physiology.</p><!><p>Bacteria are surrounded by cell wall heteropolymers that are essential for viability under most circumstances. The structure of the cell wall is well-conserved and consists of a glycan backbone cross-linked by d-amino acid-containing peptides. Cross-link-inhibiting β-lactams account for two-thirds of the global antibiotic market, underscoring the general importance of these linkages to bacterial physiology. For a given species, the density of cross-linking can vary with replication rate and environmental conditions. These changes in cell wall connectivity in turn correlate with other phenotypic properties of the bacterium. However most species have multiple, closely-related enzymes that catalyze cross-links, each with varying susceptibility to different β-lactams, making it difficult to control the density of these linkages by genetics or small molecule inhibition alone. In this work, we present a chemical technique to introduce synthetic cross-links to the cell walls of live bacteria. We use bio-compatible click chemistry to induce a reaction between azido- and alkynyl-d-alanine residues that are metabolically incorporated in the cell wall peptides of Gram-positive and Gram-negative species. The resulting triazole linkages can be visualized by substituting azido-d-alanine with azidocoumarin-d-alanine, an amino acid analogue that becomes more fluorescent after reacting with an alkyne. Stapling the cell wall of Escherichia coli enhances its tolerance to two different broad-spectrum β-lactams. Chemical manipulation complements genetic and small molecule perturbations as an independent means of investigating the role of cell wall connectivity in bacterial physiology.</p><!><p>Further information and requests for resources and reagents should be directed to Lead Contact, M. Sloan Siegrist (siegrist@umass.edu)</p><!><p>All unique/stable reagents generated in this study are available from the Lead Contact.</p><!><p>This publication did not use unpublished custom code, software, or algorithms.</p><!><p>E. coli was grown in Luria-Bertani Broth (LB) at 37 °C. L. monocytogenes was grown in Brain Heart Infusion Broth (BHI) at 37 °C.</p><!><p>E. coli were grown overnight at 37 °C. The next day cultures were back-diluted between 1:50 and 1:500 and d-amino acids (1.25 mM total per sample for monopeptides and 2.5 mM per sample for dipeptides) were added directly in the LB medium. Cells were grown until log phase (OD600 0.6–0.8) then centrifuged for 5 min at 5,000 × g at room temperature (RT). They were then washed with sterile-filtered PBS and subjected to BTTP CuAAC (200 μM CuSO4, 800 μM BTTP [Chemical Synthesis Core Facility, Albert Einstein College of Medicine, Bronx, NY], 2.5 mM sodium ascorbate (freshly prepared), with or without 25 μM of azido or alkynyl fluorescent dye as appropriate) or TBTA CuAAC (1 mM CuSO4, 128 μM TBTA [Click Chemistry Tools, Scottsdale, AZ], 1.2 mM sodium ascorbate (freshly prepared), with or without 25 μM of azido or alkynyl fluorescent dye [Click Chemistry Tools]) for 1 hr at room temperature, shaking. Samples were then centrifuged, washed thrice with PBS, and either fixed with 2% (v/v) formaldehyde or used in assays described below.</p><p>L. monocytogenes were grown overnight at 37 °C with the d-amino acids (2.5 mM total per sample) then centrifuged for 5 min at 5,000×g at RT. They were washed with PBS and subjected to CuAAC as described for E. coli.</p><!><p>Mean fluorescence intensities (MFI) of bacterial cell populations were obtained by flow cytometry from a BD DUAL LSRFortessa instrument.</p><p>Samples were imaged on an inverted Nikon Eclipse Ti microscope equipped with a Hamamatsu Orca Flash 4.0 camera and reconstructed with NIS Elements.</p><!><p>200 mL cultures of log-phase CS802–2 E. coli were treated with d-amino acids and subjected to BTTP CuAAC as describe above. Bacteria were centrifuged for 5 minutes at 5,000 × g at RT, wash twice with MilliQ water, resuspended in 1 mL MilliQ water then added drop-wise into 80 mL of boiling 4% SDS. Samples were vigorously stirred for 1.5 hr then cooled to RT. The insoluble fraction (PG) was pelleted at 400,000 × g, 15 min, 30 °C (TLA-100.3 rotor; OptimaTM Max ultracentrifuge, Beckman). SDS was washed out and the PG was resuspended in 200 μl of 50 mM sodium phosphate buffer pH 4.9 and digested overnight with 30 μg/mL muramidase (Cellosyl). Samples were incubated at 37 °C. PG digestion was stopped by 5 min incubation in a boiling water bath. Coagulated protein was removed by centrifugation. The supernatants were mixed with 150 μL 0.5 M sodium borate pH 9.5, and subjected to reduction of muramic acid residues into muramitol by sodium borohydride treatment (10 mg/mL final concentration, 30 min at RT). Samples was adjusted to pH 3.5 with phosphoric acid. Chromatographic analyses of muropeptides were performed on AQUITY Ultra Performance Liquid Chromatography (UPLC) BEH C18 column (130 Å, 1.7 μm, 2.1 mm by 150 mm; Waters), and peptides were detected at Abs. 204 nm using ACQUITY UPLC UV-Visible Detector. Muropeptides were separated using a linear gradient from buffer A (0.1% of Formic acid in water) to buffer B (0.1% of Formic acid in acetonitrile) in 218 min, and flow 0.25 mL/min. Muropeptide identity was confirmed by MS/MS analysis, using a Xevo G2-XS QTof system (Waters Corporation, USA). Quantification of muropeptides was based on their relative abundances (relative area of the corresponding peak). Cross-linking was determined by the following formula; crosslinking=dimmer+(trimmer/2).</p><!><p>CS802–2 E. coli that had been pre-labeled with d-amino acids for 6 hrs (OD600 0.6) (Figure 3) or overnight (Figure S4D–F) and subjected or not to BTTP CuAAC were washed with PBS and resuspended in LB medium to a normalized OD600 of 0.3 with or without 125 μg/mL ampicillin, 125 μg/mL carbenicillin, or 6.25 μg/mL kanamycin. After 1–5 hrs incubation at 37 °C, bacteria were washed twice with PBS and plated as 10-fold serial dilutions on LB agar.</p><!><p>Reactions were performed in round bottom flasks fitted with rubber septa under a positive pressure of nitrogen. Gas-tight syringes with stainless steel needles or cannulae were used to transfer air- and moisture-sensitive liquids. Flash column chromatography was performed as described by Still et al. using granular silica gel (60-Å pore size, 40–63 μm, 4–6% H2O content, Silicycle)(Still et al., 1978). Analytical thin layer chromatography (TLC) was performed using glass plates pre-coated with 0.25 mm 230–400 mesh silica gel impregnated with a fluorescent indicator (254 nm). TLC plates were visualized by short wave ultraviolet light (254 nm). Concentration of solutions under reduced pressure were carried out on rotary evaporators capable of achieving a minimum pressure of ~2 torr at 29–30 °C unless noted otherwise.</p><p>Dichloromethane, tetrahydrofuran, and N,N-dimethylformamide were were purified by the method of Grubbs et al. under a positive pressure of nitrogen(Pangborn et al., 1996).</p><!><p>Proton nuclear magnetic resonance (1H NMR) spectra were recorded with a Bruker Avance III 500 MHz spectrometer, are reported in parts per million, and are referenced to the residual protium in the NMR solvent (CDCl3: δ 7.24 (CHCl3), CD3OD: δ 3.31 (CHD2OD), DMSO-d6: δ 2.50 (DMSO-d5)). Data are reported as follows: chemical shift [multiplicity (s = singlet, d = doublet, t = triplet, sp = septet, m = multiplet), coupling constant(s) in Hertz, integration]. Carbon-13 nuclear magnetic resonance (13C NMR) spectra were recorded with a Bruker Avance III 500 MHz spectrometer, are reported in parts per million, and are referenced from the carbon resonances of the solvent (CDCl3: δ 77.23, CD3OD: δ 49.15, DMSO-d6: δ 39.51). Data are reported as follows: chemical shift. Infrared data (IR) were obtained with a Cary 630 Fourier transform infrared spectrometer equipped with a diamond ATR objective and are reported as follows: frequency of absorption (cm−1), intensity of absorption (s = strong, m = medium, w = weak, br = broad). Optical rotations were measured on a P-2000 JASCO polarimeter and compound concentrations are expressed in units of g/100 mL. High resolution mass spectra (HRMS) were recorded by the Harvard University Small Molecule Mass Spectrometry facility on an Agilent 6210 time-of-flight LCMS using an electrospray ionization (ESI) source.</p><!><p> </p><!><p>To a 25 mL round bottom flask charged with azidocoumarin acid S1(Weineisen et al., 2017) (56.0 mg, 214 μmol, 1 equiv) under a nitrogen atmosphere was added tetrahydrofuran (2 mL) at room temperature. N,N-diisopropylethylamine (74.5 μL, 428 μmol, 2.00 equiv) was added to the dark brown solution via syringe. This was followed immediately by addition of pentafluorophenyl trifluoroacetate (73.5 μL, 428 μmol, 2.00 equiv) via syringe and stirred at room temperature. After 30 min, the reaction mixture was concentrated under reduced pressure and the brown residue was purified by flash column chromatography on silica gel (eluent: 10% ethyl acetate in hexanes) to provide the azidocoumarin pentafluorophenyl ester S2 (41.0 mg, 45%) as a white solid. 1H NMR (500 MHz, CDCl3, 25 °C): δ 7.37 (d, J = 8.6 Hz, 1H), 7.15 (s, 1H), 6.93 (dd, J = 8.7, 2.5 Hz, 1H), 6.87 (d, J = 2.5 Hz, 1H), 5.04 (s, 2H). 13C NMR (126 MHz, CDCl3, 25 °C): δ 164.5, 159.1, 157.6, 152.9, 142.2, 141.2, 140.2, 139.2, 137.2, 128.7, 125.9, 124.7, 114.3, 113.3, 102.2, 64.8. 19F NMR (471 MHz, CDCl3, 25 °C): δ −152.2 (m), − 156.4 (m), −161.3 (m). FTIR (thin film, cm−1): 2128 (s), 1804 (m), 1722 (m), 1618 (m), 1521 (s), 1334 (m), 1118 (m), 1070 (m), 995 (m). HRMS (ESI, m/z): 428.0294 (calculated for C17H7F5N3O5 [M+H]+: 428.0300). TLC (15% ethyl acetate in hexanes, Rf): 0.32 (UV).</p><p> </p><!><p>To a 25 mL round bottom flask charged with azidocoumarin pentafluorophenyl ester S2 (30.0 mg, 70.2 μmol, 1 equiv) and Nα-Boc-D-2,3-diaminopropionic acid (S3) (28.6 mg, 140 μmol, 2.00 equiv) under a nitrogen atmosphere was added tetrahydrofuran (2 mL) at room temperature. N,N-diisopropylethylamine (14.7 μL, 140 μmol, 2.00 equiv) was then added to the solution via syringe followed by N,N-dimethylformamide (500 μL). After 20 min, the reaction mixture was concentrated under reduced pressure and the residue was purified by flash column chromatography on silica gel (eluent: 20% hexanes, 75% ethyl acetate, 5% acetic acid) to provide Boc-D-Ala-azidocoumarin S4 (21.0 mg, 67%) as a white solid. 1H NMR (500 MHz, d6-DMSO, 25 °C): δ 8.20 (t, J = 6.0 Hz, 1H), 7.63 (s, 1H), 7.59 (d, J = 8.5 Hz, 1H), 7.07–6.98 (m, 3H), 4.60 (s, 2H), 4.13–4.05 (m, 1H), 3.57–3.48 (m, 1H), 3.44–3.36 (m, 1H), 1.38 (s, 9H). 13C NMR (126 MHz, d6-DMSO, 25 °C): δ 172.2, 167.5, 159.7, 157.2, 155.4, 152.4, 128.9, 127.1, 122.6, 113.3, 113.1, 101.7, 78.4, 67.2, 53.4, 39.6, 28.2. FTIR (thin film, cm−1): 3366 (br-s), 2989 (br-m), 2128 (s), 1737 (m), 1670 (m), 1618 (m), 1521 (m), 1148 (m), 1055 (m). HRMS (ESI, m/z): 446.1324 (calculated for C19H20N5O8 [M−H]−: 446.1317). TLC (20:75:5 hexanes:ethyl acetate:acetic acid, Rf): 0.18 (UV). [α]D23 = +78 (c 0.25, DMSO).</p><p> </p><!><p>To a 25 mL round bottom flask charged with Boc-D-Ala-azidocoumarin S4 (18.0 mg, 40.2 μmol, 1 equiv) was added dichloromethane (2 mL) followed by trifluoroacetic acid (2 mL) at room temperature. After stirring for 15 min, the reaction mixture was concentrated under reduced pressure and the residue was purified by automated C18 reverse phase column chromatography (30 g C18 silica gel, 25 μm spherical particles, eluent: H2O+0.1% TFA (5 CV), gradient 0→100% MeCN/H2O+0.1% TFA (15 CV), tR=10.1 CV) to provide the trifluoroacetic acid salt of D-Ala-azidocoumarin (1) (11.0 mg, 79%) as a white solid. 1H NMR (500 MHz, d6-DMSO, 25 °C): δ 8.46 (t, J = 6.0 Hz, 1H), 8.37 (br-s, 3H), 7.64 (s, 1H), 7.59 (d, J = 8.7 Hz, 1H), 7.07 (d, J = 2.2 Hz, 1H), 7.02 (dd, J = 8.6, 2.4 Hz, 1H), 4.63 (s, 2H), 4.06–4.02 (m, 1H), 3.71–3.65 (m, 1H), 3.62–3.56 (m, 1H). 13C NMR (126 MHz, d6-DMSO, 25 °C): δ 169.2, 168.3, 159.7, 157.2, 152.4, 128.9, 127.2, 122.6, 113.4, 113.1, 101.6, 67.1, 52.2, 38.5. 19F NMR (471 MHz, D2O, 25 °C) δ −75.6. FTIR (thin film, cm−1): 2117 (s), 1707 (m), 1670 (m), 1618 (s), 1536 (m), 1431 (m), 1170 (m), 1141 (s). HRMS (ESI, m/z): 346.0796 (calculated for C14H12N5O6 [M−H]−: 346.0793). [α]D23= +68 (c 0.23, DMSO).</p><!><p>Statistical significance was evaluated using one- or two-way analysis of variance (ANOVA), followed by Tukey's multiple comparisons test using GraphPad Prism 8.4.0 software. Details in figure legends.</p>

PubMed Author Manuscript

Localization and expression of putative circadian clock transcripts in the brain of the nudibranch Melibe leonina

The nudibranch, Melibe leonina, expresses a circadian rhythm of locomotion, and we recently determined the sequences of multiple circadian clock transcripts that may play a role in controlling these daily patterns of behavior. In this study, we used these genomics data to help us: 1) identify putative clock neurons using fluorescent in situ hybridization (FISH); and 2) determine if there is a daily rhythm of expression of clock transcripts in the M. leonina brain, using quantitative PCR. FISH indicated the presence of the clock-related transcripts clock, period, and photoreceptive and non-photoreceptive cryptochrome (pcry and npcry, respectively) in two bilateral neurons in each cerebropleural ganglion and a group of < 10 neurons in the anterolateral region of each pedal ganglion. Double-label experiments confirmed colocalization of all four clock transcripts with each other. Quantitative PCR demonstrated that the genes clock, period, pcry and npcry exhibited significant differences in expression levels over 24 hrs. These data suggest that the putative circadian clock network in M. leonina consists of a small number of identifiable neurons that express circadian genes with a daily rhythm.

localization_and_expression_of_putative_circadian_clock_transcripts_in_the_brain_of_the_nudibranch_m

1. Introduction<!>2.1 Collection and housing of animals used for FISH<!>2.2 FISH<!>2.3 Imaging of FISH preparations<!>2.4 Collection and dissections of animals for qPCR experiments<!>2.5 RNA extraction and qPCR<!>2.6 Statistical analyses<!>3. Theory<!>4.1 Single-label FISH<!>4.2 Double-label FISH<!>4.3 qPCR<!>5.1 Clock transcript localization<!>5.2 Clock transcript expression<!>

<p>Many animals exhibit a daily rhythm of activity and tend to be more active either during the day (diurnal), or during the night (nocturnal). In addition, some animals have a crepuscular rhythm, expressing their strongest bouts of activity around sunrise and sunset (Dunlap, 1999; Panda et al. 2002). All of these rhythmic patterns are strongly correlated with, and typically synchronized to, environmental cues such as the 24 hr light-dark (LD) cycle (Bunney & Bunney, 2000). Moreover, in the absence of LD cues, most animals continue to show a daily (~ 24 hr) rhythm of activity, indicating the presence of an endogenous circadian clock (Aschoff, 1965). The molecular mechanisms that give rise to these ~ 24 hr clocks have been elucidated in many organisms ranging from cyanobacteria to humans (Takahashi, 1991, 1995; Dunlap, 1999; Young & Kay, 2001; Hastings et al. 2007; Allada & Chung, 2010). As a result of these studies, it is now clear that most organisms, from prokaryotes to mammals, share a transcription-translation feedback loop of clock genes and their protein products that are either identical or quite similar in principle.</p><p>While we now know a great deal about daily behavioral rhythms and the molecular basis of the clocks underlying these rhythms, less is known about the connection between the molecular clockwork pathways and the neural networks responsible for influencing the expression of specific physiological and behavioral rhythms. Gastropods, due to their large identifiable neurons and stereotyped behaviors, are often suitable model systems for these types of investigations. Therefore, one of our major long-term goals is to develop a nudibranch model system that will facilitate investigations of the interactions between clock neurons and the neural networks controlling specific behaviors, such as swimming in M. leonina (Thompson & Watson, 2005; Sakurai et al., 2014).</p><p>Daily rhythms of locomotion have been demonstrated in a number of gastropods, including Aplysia californica (Kupfermann, 1968; Jacklet, 1972), Bulla gouldiana (Block & Davenport, 1982), Bursatella leachi plei (Block & Roberts 1981), Helisoma trivolvis (Kavaliers, 1981), Limax maximus (Sokolove et al., 1977), Melanoides tuberculata (Beeston & Morgan, 1979), and M. leonina (Newcomb et al., 2004; 2014). Furthermore, the isolated eyes and retinal neurons of some of these species exhibit circadian oscillations of neural activity (Jacklet, 1969; Block & Roberts, 1981; Block & Wallace, 1982; Michel et al., 1993), suggesting that the eyes are the location of at least one clock system. Localization of putative circadian clocks has only been accomplished in two gastropods – A. californica and B. gouldiana, using immunohistochemistry with antibodies developed to a conserved region of the Drosophila melanogaster PERIOD protein (Siwicki et al., 1989). PERIOD-immunoreactive neurons were located in the eyes of each species, as well as in approximately 10 neurons in the cerebral ganglion of A. californica (Siwicki et al., 1989). Localization of period transcripts in B. gouldiana eyes has since been confirmed with in situ hybridization (Constance et al., 2002). In both of these species, period oscillates in certain tissues, based on western blots (Siwicki et al., 1989) and in situ hybridization (Constance et al., 2002). However, of all of these gastropods, the one with the best-studied central pattern generator (CPG) underlying a behavior that is expressed with a circadian rhythm is M. leonina (Thompson & Watson, 2005; Newcomb et al., 2014; Sakurai et al., 2014). Thus, it may be the most promising model system for investigating the link between clock neurons and a specific behavior.</p><p>M. leonina displays two modes of locomotion – crawling and swimming (Agersborg, 1923; Lawrence & Watson, 2002) and the CPG underlying swimming consists of four pairs of bilateral neurons (Thompson & Watson, 2005; Sakurai et al., 2014). M. leonina moves more at night (Newcomb et al., 2004) and this pattern of activity is controlled by an endogenous circadian clock (Newcomb et al., 2014). Furthermore, transcripts of clock genes in M. leonina have been recently sequenced (Cook et al., in press), making it possible to carry out experiments that were previously not possible. The goal of this project was to use these transcript sequences to accomplish two objectives: 1) determine the location of putative clock neurons that express these clock transcripts using RNA probes with fluorescent in situ hybridization (FISH); and 2) investigate the temporal expression profile of some of these clock genes.</p><!><p>Adult M. leonina were collected from eelgrass beds in the Puget Sound near the University of Washington's Friday Harbor Laboratories, from kelp forests near Monterey Bay, CA (Monterey Abalone Company), and from floating kelp masses around Catalina Island, CA (Marinus Scientific). Animals were shipped overnight to New England College and maintained in aquaria containing artificial seawater (Instant Ocean, 32–35 ppt salinity). The tanks also contained eelgrass or kelp shipped from the collection sites. All aquaria were kept at 10–12°C, with a 6 am:6 pm, light/dark cycle. Animals were given at least 3 days to acclimate to the LD schedule prior to dissection.</p><!><p>Sequence-specific RNA probes were developed by Biosearch Technologies for clock, period, and photoreceptive and non-photoreceptive cryptochrome (pcry and npcry, respectively), based on sequences derived from RNA transcriptomes (Cook et al., in press). In brief, 48 non-overlapping, 20-nucleotide RNA probes were designed for each transcript, with each probe being conjugated to a fluorophore. Amplification of the fluorescent signal results from multiple probes hybridizing to a single transcript. If less than five probes attach to a transcript, it will tend to blend into the background. FISH experiments were done in two stages. First, RNA probes for individual clock transcripts were used in individual brains to determine if each transcript could be identified (n = 35). These were accomplished with RNA probes conjugated to CAL Fluor 610 (peak excitation = 590 nm, peak emission = 610 nm). Dissections were done initially at 6 am, 12 pm, 6 pm, and 12 am, to determine if there were any differences in labeling over time. Dissection time had no apparent effect, so subsequent dissections were typically done around noon. Second, once each transcript was successfully identified, double-label experiments with two RNA probes conjugated to different fluorophores were used in individual brains (n = 21). In these experiments, one fluorophore was CAL Fluor 610 and the other was Quasar 670 (peak excitation = 647 nm, peak emission = 670 nm). Control experiments for each set of experiments involved omitting the RNA probes (n = 3).</p><p>Brains and attached buccal ganglia were removed, pinned in a dish, and fixed overnight in a 3.7% formaldehyde fixation buffer (in phosphate buffered saline) at 4°C. RNase-sterile techniques were used throughout this entire procedure to minimize degradation of RNA and nuclease-free water was used when making all solutions. The following day, brains were rinsed and then left overnight in 70% ethanol at 4°C. The brain was then incubated in 10% deionized formamide in Wash Buffer A (Biosearch Technologies) for 3 min on a shaker at room temperature, followed by incubation in the relevant FISH RNA probe (made against clock, period, pcry, or npcry). Initially probes for clock were diluted 1:100, 1:50 and 1:25 to determine the optimal dilution. Subsequently, all probes were diluted either 1:50 or 1:25 in 10% deionized formamide, made in Hybridization Buffer (Biosearch Technologies). Incubation in the FISH probe was done overnight on a shaker at 37°C. Double-label experiments included two FISH probes, with different fluorophores, each diluted to a 1:25 concentration in the final deionized formamide/Hybridization Buffer solution. Precautions were taken for the remainder of the protocol to keep light exposure to a minimum. Following overnight incubation in the FISH probe(s), brains were washed again in a 10% deioinized formamide solution, made in Wash Buffer A, on a shaker for 30 min at 37°C, followed by a 3-min rinse in Wash Buffer B (Biosearch Technologies) on a shaker at room temperature. Brains were then dehydrated in increasing concentrations of ethanol (50%, 70%, 90%, 95%, 100%, and 100%), for 10 min each, at room temperature. Dehydrated brains were then cleared using methyl salicylate, and mounted in Cytoseal (Electron Microscopy).</p><!><p>Single-label FISH images were obtained on a Zeiss LSM 880 confocal microscope, using an excitation wavelength of 594 nm. Multiple images of optical sections of a brain were obtained at a high resolution and stitched together with Zen software. Double-label FISH preparations were imaged on a Zeiss Axio Scope.A1 epifluorescence microscope, using emission filter sets that prevented bleed through between CAL Fluor 610 and Quasar 670 fluorophores. Images were obtained with a Zeiss Axiocam digital camera and viewed/captured with Zen 2012. Pseudocolors of red and green were used to facilitate confirmation of double-labeled neurons. For each preparation, the location and numbers of labeled neurons were noted on a brain map.</p><!><p>All the animals for the qPCR experiments were collected in Parks Bay off of Shaw Island, WA, (GPS: 48.565239, −122.980976), between March 2 and 11, 2017, and held in a flow-through seawater tank that was exposed to the ambient L:D cycle (which was roughly 12:12, due to the temporal proximity to the spring equinox). Animals were allowed at least 24 h after collection to adjust to their new conditions before the dissections began.</p><p>Prior to performing the dissections, all of the lab benches, dissection tools, and microscopes were cleaned with ethanol and RNase AWAY (Thermofisher Scientific). Over the course of three days, on two occasions, five animals were dissected every 3 hrs, for a total of 80 samples (n=10 for each time point). The dissections occurred at 6 am, 9 am, 12 pm, 3 pm, 6 pm, 9 pm, 12 am, and 3 am. During the night dissections (6 pm – 3 am), red light was used, as evidence suggests that M. leonina are not sensitive to this wavelength of light. Each dissection lasted ~ 10–30 min. The esophagus, to which the brain is firmly attached, was removed from each animal and placed in a tube containing 5 mL of RNAlater. The RNAlater samples were then covered with parafilm and stored at 4°C.</p><p>All 80 samples were shipped in a cooler, on ice, to the University of New Hampshire (UNH) overnight. At UNH the brain (paired cerebropleural and pedal ganglia) was removed from the esophagus and placed in PCR-clean Eppendorf tubes. The Eppendorf tubes were immediately flash frozen in a container holding a combination of 95% ethanol and dry ice, and then stored at −80°C until RNA extraction. All samples were processed within a month of arriving at UNH.</p><!><p>RNA was extracted and purified from each central nervous system (CNS) using TRIzol (Ambion, Fisher Scientific) and a Purelink RNA mini kit (Invitrogen, Thermofisher Scientific). Contaminate genomic DNA was removed with the on-column PureLink DNase treatment kit (Ambion, Thermofisher Scientific). Approximately 20 ng/μl total RNA was obtained for each sample based on measurements obtained with both a Nanodrop (NanoDrop Spectrophotometer ND-1000, University of New Hampshire, Durham, NH) and Qubit 2.0 (Flourometer, Invitrogen University of New Hampshire, Durham, NH).</p><p>DNA primers were designed using Primer3 (Table 1; Koressaar and Remm, 2007; Untergasser et al., 2012) so that amplicon sizes fell within an optimal range for qPCR assays (between 75 and 150 base pairs) for the four clock transcripts of interest (clock, period, pcry, and npcry) and the reference housekeeping gene alpha tubulin (atub). After an initial denaturation step (95°C for 10 min), cDNA synthesis was performed using a qScript cDNA supermix (Quantabio) following manufacturer protocols. A total of 3 μl of diluted cDNA was used for a 10 μl PCR reaction, and samples were amplified in triplicate for each primer. Complimentary DNA was amplified using an Agilent MX3000P qPCR system (Agilent) for 40 cycles (95°C for 30 s, 55°C for 60 s, 72°C for 60 s), using an Applied Biosystems SYBR select master mix (Applied Biosystems). These data were first normalized to the housekeeping gene alpha tubulin. The fold change over time was then graphed, which represents the expression levels of the genes per time point in comparison to the average expression level for each gene.</p><!><p>All qPCR statistical tests were performed with JMP13 (SAS, Cary, NC). Pairwise differences in expression level (ddCt values) between any two time points were determined with a students t-test.</p><!><p>Previous studies have indicated that M. leonina express a circadian rhythm of locomotion and also possess many of the core genes that control circadian rhythms in other organisms. Therefore, our working hypothesis was that we would see differences in the expression of these circadian genes in the brains of animals that were dissected in the night vs the day. Furthermore, in order to lay the foundation for future studies we sought to identify specific clock neurons using in situ hybridization. This effort was successful and makes it possible to carry out further intracellular investigations of clock neurons in M. leonina.</p><!><p>Seven of 13 brains labeled with anti-clock FISH probes exhibited two clock-positive neurons in each cerebropleural ganglion (Fig. 1A). This variability was likely due to the fact that we initially used three different probe concentrations (1:100, 1:50, and 1:25); this pair of cells were clear in all four brains labeled with the 1:25 probe concentration. There were also clock-positive neurons in the anterolateral region of both pedal ganglia in 10 of 13 preparations (Fig. 1A). The number of pedal neurons in these ten preparations was quite variable: with 8.3 ± 8.2 and 8.8 ± 6.2 (mean ± standard deviation) neurons in the left and right pedal ganglia, respectively. These clock-positive pedal neurons were present in all four brains labelled with the 1:25 probe concentration, although the number of neurons was similar in preparations labelled with weaker probe concentrations. After these clock FISH trials, we concluded that a dilution of 1:100 was not strong enough to provide consistent results. Therefore, all subsequent FISH experiments were done with probe concentrations that were diluted 1:50 or 1:25, and these yielded indistinguishable results.</p><p>Period labeling was similar to clock, with 7 of 9 preparations exhibiting two period-positive neurons in each cerebropleural ganglion (Fig. 1B). In the other two brains, one preparation had two period-positive neurons in the left cerebropleural ganglion, but only one in the right ganglion, and in the other brain, there was one period-positive neuron in the left cerebropleural ganglion and no labeling in the right ganglion. In 6 of 9 preparations, there were also period-positive neurons present in the anterolateral region of both pedal ganglia (Fig. 1B). The number of period-positive neurons was 3.9 ± 2.7 in the left pedal ganglion and 4.7 ± 2.2 in the right pedal ganglion.</p><p>In 5 of 6 brains labeled with probes for npcry, there were multiple bilaterally symmetric neurons in each cerebropleural ganglion (Fig. 1C). Three of these brains exhibited two labeled neurons in each cerebropleural ganglion, similar to clock and period, while a fourth preparation had three npcry-positive neurons in the left cerebropleural ganglion, and the fifth brain had three labeled neurons in both cerebropleural ganglia. The same five brains that exhibited npcry-positive neurons in the cerebropleural ganglia also had labeling for npcry in the anterolateral region of each pedal ganglion (Fig. 1C). There was an average of 4.8 ± 2.5 npcry-positive neurons in the left pedal ganglion, and 5.2 ± 2.3 neurons in the right pedal ganglion.</p><p>The fourth RNA probe for pcry labeled two neurons in each cerebropleural ganglion in all seven preparations (Fig. 1D). All seven brains also exhibited pcry-positive neurons in the anterolateral region of both pedal ganglia, with 9.3 ± 4.1 and 9.3 ± 4.9 neurons in the left and right pedal ganglia, respectively (Fig. 1D).</p><p>All of the FISH labeling was punctate at higher magnification (Fig. 2), illustrating labeling of individual transcripts by the accumulation of upwards of 40 custom FISH probes per transcript. This labeling was also relegated to the cytoplasm, and did not appear in the nucleus (Fig. 2).</p><!><p>As seen in Fig. 1, all four RNA probes labeled similar numbers of neurons in similar areas of the brain. To determine if multiple probes were labeling the same neurons, additional experiments were done with RNA probes that had different fluorophores, to enable double-labeled preparations. The following combinations of probes were used: clock and period (n = 6), period and npcry (n = 9), and npcry and pcry (n = 6). By association, these combinations enabled us to determine potential co-localization of all four RNA probes. Based on the single-label experiments above, RNA probes were used at a concentration of 1:25. In all preparations, there was 100% colocalization between probes (Fig. 3). The pattern of labeling (two bilaterally symmetric cerebropleural neurons and two small groups of bilaterally symmetric pedal neurons) and the numbers of labeled neurons in each area were similar to single-label FISH experiments (see Fig. 1). Control preparations (n = 3), that lacked RNA FISH probes, did not exhibit any labeling (not shown).</p><!><p>The brain (cerebropleural and pedal ganglia) exhibited oscillating expression of circadian clock genes over a period of 24 hr (Fig. 4). Note that all of the expression levels are expressed in comparison to the average expression for each gene, meaning that positive expression is higher than the average and negative expression is lower. There were significant variances in expression levels overall in a 24 hr period for pcry and period (Fig. 4C, D; ANOVA; p=0.0001 and p=0.0084, respectively), and fluctuations trending towards significance for clock (Fig. 4A; ANOVA; p=0.0893). No significant differences were seen overall for npcry (Fig. 4B; ANOVA; p=0.1253). Clock tended to have high gene expression at night, between 15 h (circadian time, middle of the night) and 2 h (right after sunrise), which gradually declined until 9 h (sunset) (Fig. 4A). The circadian clock gene npcry showed weak expression between 6 h (middle of the day) and 12 h (sunset), and then it subsequently increased from 18 h (middle of night) to 21 h (end of night) (Fig. 4B). Pcry had several time points with significant differences over the 24 h period (Fig. 4C), which was similar to the pattern for period (Fig. 4D). There were two troughs, at 3 h (day) and 21 h (night), and the expression slowly increased until 18 h (midnight) for both. There also was a peak in expression at 24 h for pcry and period.</p><p>In general, when comparing all circadian clock genes, there was a tendency for peak expression around 18 h (night). However, the expression patterns for clock and npcry indicate relatively moderate changes throughout the night whereas pcry and period show greater fluctuations in expression. For all of the genes, expression was fairly low during the day.</p><p>When comparing individual time points to one another, for each circadian gene of interest, some significant differences were apparent between specific pairs of time points (Fig. 5). For example, gene expression for clock quickly up-regulated from hour 12 (sunset) to hour 18 (midnight) (Fig. 4A, Fig. 5A) and clock gene expression at hour 18 (midnight) and 21(early morning) was very different than the expression at hour 6 (midday) and 9 (afternoon). These trends signify differences in expression from the day hours to the night hours for clock. Non-photoreceptive cryptochrome also showed gene up regulation from right after midnight to early morning with limited to no expression seen throughout the day (Fig. 5B). Trends toward expression differences were seen between hour 21(early morning) and the daytime hours (Fig. 5B) as the expression was slowly increasing over time until its peak at hour 21. The expression of Pcry at hour 18 (midnight) and hour 24 (sunrise) was significantly higher than every other hour, including all of the day hours (Figs. 4B, 5C). However, there was no difference between the expression at midnight and sunrise, suggesting that the expression began to down regulate from the early morning until sunrise (Fig. 5C). Like pcry, the significant differences in expression levels over time for period were between high levels at hour 18 (midnight) and 24 (sunrise) in comparison to all the other hours (Fig. 5D). Both pcry and period had troughs in expression at hour 3 (10 am; real time), and then leveled off until after sunset, before rapidly up regulating during the night. Every gene of interest showed significant differences between the expression during at least one daytime hour and one nighttime hour, suggesting differential expression over a 24-hour period and potentially daily rhythmic expression over time.</p><!><p>FISH labeling revealed a small group of neurons in the brain of M. leonina that consistently exhibited the presence of four putative clock gene transcripts (Figs. 1–3). These included two bilaterally symmetric neurons near the center of each cerebropleural ganglion, and a group of < 10 neurons in the anterolateral region of each pedal ganglion. Thus, the putative circadian clock in M. leonina likely consists of a network of ~20 neurons in two bilaterally symmetric regions of the brain.</p><p>A putative circadian circuit of ~20 neurons would rank M. leonina's clock as one of the smaller clocks in bilaterians, with the exception of several Lepidopteran insects that appear to have a central clock of only ~8 neurons (Sauman & Reppert, 1996; Wise et al., 2002; Sehadová et al., 2004; Sauman et al., 2005; Zhu et al., 2008). The central circadian clock is more than 2–3 times larger in crickets (~50 neurons; Shao et al., 2006, 2008), cockroaches (~50 neurons; Wen & Lee, 2008), and the housefly, Musca domestica (~50 neurons; Codd et al., 2007), more than seven times larger in D. melanogaster (~150 neurons; Allada & Chung, 2010) and three orders of magnitude larger in the suprachiasmatic nucleus of mammals (~20,000 neurons; Stephan & Zucker, 1972; Inouye & Kawamura, 1979; Ralph et al., 1990). Immunohistochemistry data from several decades ago suggest that ~10 neurons in the cerebral ganglion of A. californica contain PERIOD (Siwicki et al., 1989). However, western blots indicated that the antibodies bound to proteins that were 48 and 66 kDa in size, which is significantly smaller than most PERIOD proteins. There are two predicted PERIOD-like proteins for A. californica now available on NCBI (Accession numbers XP_005093378.1 and XP_012944985.1), and the predicted size of these proteins is 127 and 176 kDa, respectively (as determined by the ExPASy Compute pl/Mw tool [https://web.expasy.org/compute_pi/]). Furthermore, the only other known PERIOD protein in gastropods is for B. gouldiana (101 kDa; Constance et al., 2002). Therefore, it is possible that the antibody was not labeling PERIOD in the study by Siwicki et al., (1989), and that the number of neurons in a putative central clock in the brain of A. californica still remains to be determined.</p><p>Most prior electrophysiological and histological evidence point to the eyes as the location of the circadian clock in gastropods (Jacklet, 1969; Block & Roberts, 1981; Block & Wallace, 1982; Siwicki et al., 1989; Michel et al., 1993; Constance et al., 2002). In this study, our use of wholemount preparations meant that the FISH probes probably did not have access to retinal neurons inside the eye due to lack of permeabilization of the tough connective tissue surrounding the eye. Furthermore, M. leonina eyes exhibit strong autofluorescence over a wide range of excitation/emission wavelengths, including those of our fluorophores. Therefore, an additional clock in the eyes of M. leonina is still a distinct possibility, although M. leonina without eyes are still capable of maintaining a circadian rhythm of locomotion (Newcomb et al., 2014). There is also prior evidence to suggest that gastropods may have multiple circadian clocks. For example, in A. californica, some animals can maintain circadian rhythms of locomotion in constant darkness after eye removal, suggesting that there must be additional clocks outside of the eyes (Lickey et al., 1977). There is some evidence to suggest that the abdominal ganglia may contain a circadian clock in A. californica (Audesirk & Strumwasser, 1975; Beiswanger & Jacklet, 1975), but the ability of such a clock to maintain circadian activity in constant darkness has not been definitely demonstrated. Furthermore, lesion studies suggest that the circadian system in A. californica lies within the cerebral and buccal ganglia, and sensory structures associated with these regions of the brain (Roberts & Block, 1982).</p><p>Regardless of whether or not there is an additional clock in the eyes of M. leonina, the evidence presented here, as well as in a previous study (Newcomb et al., 2014) suggests that there are putative clock neurons in both the cerebropleural and pedal ganglia that may be important in regulating circadian rhythms of locomotion, such as swimming. The neural circuit underlying swimming in this animal consists of four bilateral pairs of neurons, one of which is in the cerebropleural ganglia and the other three are located in the pedal ganglia (Thompson & Watson, 2005; Sakurai et al., 2014). The two pairs of cerebropleural neurons identified in this study, which contain clock transcripts, are in the same region of the cerebropleural ganglia as Swim Interneuron 1 (Thompson & Watson, 2005). In contrast, the pedal clock neurons identified here are anterior and lateral to the pedal swim interneurons, although these pedal swim interneurons are known to have extensive arborizations in the pedal ganglia and neuropil (Thompson & Watson, 2005; Sakurai et al., 2014). Future studies are planned to investigate whether or not the newly identified clock neurons connect to, or modulate, the swim interneurons in M. leonina.</p><!><p>In general, the qPCR data obtained is consistent with the hypothesis that there are clock neurons in the M. leonina brain that utilize much of the same molecular machinery found in other animals. Npcry was up regulated at night compared to the day, and both period and pcry had peaks of expression around the same time, and slowly ramped up to this peak. Thus, in general, we see expression levels of clock genes that tend to increase in the late evening, and then decline late in the day and into the early evening.</p><p>The expression of M. leonina bmal was not measured in this study, but we hypothesize that bmal oscillates independently of period and npcry and, in turn, works with clock to shut off the expression of period; similar to what happens in the mammalian clock (Shearman et al., 2000). Although all of the circadian clock genes show low levels of expression during sunset, period and pcry have slight peaks shortly thereafter, and timeless (data not shown) shows trends mimicking this pattern. This could be when the genes are translated into proteins in the cytoplasm before beginning the negative feedback, shutting off their own transcription and promoting the upregulation of the other genes (Dunlap, 1999; Shearman et al., 2000; Allada & Chung, 2010).</p><p>The molecular machinery driving circadian rhythms has now been elucidated in a number of organisms. Based on these studies, there are several different ways that gene expression can lead to circadian rhythms of activity (Bunney & Bunney, 2000; Young & Kay, 2001). The two most well-studied and common patterns are found in the fruit fly and mouse. Both the molecules and oscillation patterns we have documented in brain of M. leonina are more similar to the mouse model than the D. melanogaster model. For example, the D. melanogaster model contains cycle, a circadian gene that binds with clock at the E-box promotor and helps regulate the transcription of period and timeless. In the Mus genus bmal, a homolog of cycle, binds to clock and regulates the transcription of period and npcry (Shearman et al., 2000). Although not included in this study, Cook et al., (in press) have demonstrated the presence of bmal in the M. leonina transcriptome. Therefore, the molecular mechanisms involved in the M. leonina circadian clock(s) contains the same genes present in the mammalian system and the connection/pathway of these genes is also similar. The times of up and down regulation for each gene vary from a mouse to M. leonina, but this is to be expected, because they live in different environments and express a different range and pattern of behaviors.</p><!><p>In the gastropod, M. leonina, clock gene transcripts are expressed in a relatively small number of neurons in the brain. There are a pair of putative clock neurons in the cerebropleural ganglia as well as a cluster of 5–10 neurons in each pedal ganglia.</p><p>M. leonina transcripts from the circadian genes cryptochrome (both photoreceptive and nonphotoreceptive), clock and period are all expressed differentially over time.</p><p>These data indicate that M. leonina might be a good model system for investigating the influence of identifiable clock neurons on the neural networks responsible for generating stereotyped behaviors at specific times of the day or night.</p>

PubMed Author Manuscript

Synthesis of Sequence-Specific DNA-Protein Conjugates via a Reductive Amination Strategy

DNA-protein cross-links (DPCs) are ubiquitous, structurally diverse DNA lesions formed upon exposure to bis-electrophiles, transition metals, UV light, and reactive oxygen species. Because of their super-bulky, helix distorting nature, DPCs interfere with DNA replication, transcription, and repair, potentially contributing to mutagenesis and carcinogenesis. However, the biological implications of DPC lesions have not been fully elucidated due to the difficulty of generating site-specific DNA substrates representative of DPC lesions formed in vivo. In the present study, a novel approach involving post-synthetic reductive amination has been developed to prepare a range of hydrolytically stable lesions structurally mimicking the DPCs produced between the N7 position of guanine in DNA and basic lysine or arginine side chains of proteins and peptides.

synthesis_of_sequence-specific_dna-protein_conjugates_via_a_reductive_amination_strategy

Introduction<!>General<!>Preparation of Radiolabeled DNA Duplexes<!>Reductive Amination to Generate DNA-Protein Cross-links<!>Gel Electrophoretic Analysis of DNA-Protein Cross-links Generated by Reductive Amination<!>Sample Processing for Mass Spectrometry Analysis<!>Characterization of DNA-Protein Cross-links by Mass Spectrometry<!>Synthesis and Characterization of 7-Deaza-7-(2-(N-acetyllysine)ethan-1-yl)-2\xe2\x80\xb2-deoxyguanosine and 7-deaza-7-(2-(N-acetylarginine)ethan-1-yl)-2\xe2\x80\xb2-deoxyguanosine Conjugates<!>Synthesis and Characterization of Nucleoside-Peptide Conjugates<!>Experimental strategy for the generation of hydrolytically stable model DPC substrates<!>Characterization of DPC formation by denaturing gel electrophoresis<!>Effects of reaction conditions on DPC yields<!>Influence of protein identity on DPC formation<!>Mass spectrometric characterization of DPCs<!>Synthesis and Structural Characterization of 7-Deaza-7-(2-(N-acetyllysine)ethan-1-yl)-2\xe2\x80\xb2-deoxyguanosine and 7-Deaza-7-(2-(N-acetylarginine)ethan-1-yl)-2\xe2\x80\xb2-deoxyguanosine Conjugates<!>Synthesis and Characterization of Nucleoside-Peptide Conjugates<!>Discussion

<p>Exposure to common antitumor drugs, environmental toxins, transition metals, UV light, ionizing radiation, and free radical-generating systems can result in cellular proteins becoming covalently trapped on DNA.1 The resulting DNA-protein cross-links (DPCs) are unusually bulky, structurally diverse, and highly heterogeneous DNA lesions involving a wide range of proteins of varying size, hydrophobicity, and cellular functions.1–4 Our previous mass spectrometry-based studies have revealed that a wide range of proteins can become covalently bound to genomic DNA upon treatment of human cells with clinically relevant concentrations of chemotherapeutic drugs (cisplatin and mechlorethamine) and metabolically activated carcinogens such as 1,2,3,4-diepoxybutane.2,3,5 Some examples of the participating proteins include HSP 90, tubulins, DNA helicases, PCNA, Fen-1, KU 70, Ku 86, Ref-1, PARP, and DNA polymerase δ.2,3 MS/MS sequencing has shown that DNA-protein cross-linking is non-random, with specific amino acid side chains (cysteine, lysine, histidine, or arginine) participating in covalent conjugate formation to the N-7 position of guanine in DNA (Scheme 1).2,3,6 DPCs are also formed as a result UV irradiation, treatment with formaldehyde,7–9 and endogenous exposure to reactive oxygen species, lipid peroxidation products, and transition metals, and have been shown to accumulate in the brain and heart tissues with age.7,10–17 Recent studies with laboratory mice deficient in the Fanconi Anemia DNA repair pathway have implicated DPC formed by formaldehyde in the observed cellular toxicity.18,19</p><p>Due to their enormous size as compared to other DNA lesions, DPCs are believed to compromise genetic stability and cellular viability by interfering with normal DNA-protein interactions required for DNA replication, transcription, and repair.1 We have recently engineered protein monoepoxide agents that specifically induce chromosomal DPC.20 These DNA-reactive proteins induced significant levels of mutations and toxicity when introduced into human cells,20 probably because the resulting DPCs block DNA replication and transcription. However, relatively little is known about the influence of DPC adducts on DNA and RNA polymerases or their repair mechanisms in mammalian cells. Consequently, there is a pressing need to examine DNA replication and transcription in the presence of specific DPC lesions and to identify DNA repair mechanisms responsible for their removal from mammalian cells.</p><p>Any mechanistic investigations of the biological effects of DPC lesions in human cells require the availability of structurally defined DNA substrates containing DPC lesions at a specified site of DNA. However, the access to such DPC substrates has been limited due to the synthetic challenge of covalently linking two complex biomolecules (DNA and proteins) in a site-specific manner. Previously, model DPCs have been generated by covalently trapping various enzymes on their DNA substrates. For example, the Schiff base intermediate produced between T4-pdg glycosylase/AP lyase and apurinic/apyrimidinic site of DNA can be reduced to form a stable T4-pdg-DNA conjugate.21 A disulfide trapping strategy was used to attach N149C mutant of human 8-oxoguanine DNA glycosylase I (hOGG1) protein to a DNA duplex containing alkanethiol tether at the N4 position of cytosine.22 DNA methyltransferease has been cross-linked to C6 position of 5-aza-cytosine.23 More recently, a reductive amination strategy was used to generate DNA-protein/peptide conjugates by the reaction of the N2-guanine aldehyde functionality derived from acrolein-induced 3-(2′-deoxyrobo-1′-syl)-5,6,7,8-tetrahydro-8-hydroxypyrimido[1,2a]purin-10(3H)-one (γ-HOPdG) with proteins and peptides.24,25</p><p>The most common site of DNA involved in DPC formation following treatment with bis-electrophiles is the N-7 of guanine (Scheme 1). However, to our knowledge, no methods exist in the literature to generate N-7 guanine conjugated DPCs. One formidable obstacle in accomplishing this goal is that N-7 guanine alkylation destabilizes the β-glycosidic bond of the modified nucleoside, leading to spontaneous depurination. In the present study, we have developed a new methodology to create hydrolytically stable structural mimics of N-7 guanine conjugated DPCs by reductive amination reactions between the Lys and Arg side chains of proteins and acetaldehyde functionalities of the modified 7-deazaguanine residues within DNA. The resulting model DPCs are structurally analogous to N7 guanine adducts generated by antitumor nitrogen mustards, 1,2,3,4-diepoxybutane (Scheme 1) and chlorooxirane.26</p><!><p>Synthetic oligonucleotides containing 7-deaza-7-(2,3-dihydroxypropan-1-yl)-2′-deoxy-guanosine (DHP-deaza-dG) and DHP-deaza-dG nucleoside were prepared as previously described.27 Fluorescein-dT phosphoramidite, protected 2′-deoxyribo-nucleoside-3′-phosphoramidites (dA-CE, Ac-dC-CE, dmf-dG-CE, dT-CE), Ac-dC-CPG ABI, dmf-dG-CPG ABI columns, and all other reagents required for automated DNA synthesis were purchased from Glen Research (Sterling, VA). Synthetic DNA oligonucleotides were synthesized by solid phase synthesis using an ABI 394 DNA synthesizer (Applied Biosystems, CA). All solvents and chemical reagents were obtained from commercial sources and used without further purification.</p><!><p>Single stranded oligodeoxynucleotides 5′-G TCA CTG GTA DHP-deaza-dG CA AGC ATT G-3′ and 5′-C AGT GAC CAT CDHP-deaza-dGT TCG TAA C-3′ (2 nmol in 12 μL of water) were radiolabeled with γ-32P ATP using standard methods. Following heating at 65 °C for 10 min to inactivate the enzyme, excess γ-32P ATP was removed using Illustra microspin G25 columns (GE Healthcare, Pittsburgh, PA). To obtain double stranded DNA, 5′-32P-enlabeled oligomers were mixed with equimolar amounts of the complementary strands in 10 mM Tris buffer (pH 7) containing 50 mM NaCl and heated at 90 °C for 10 min, followed by gradual cooling overnight.</p><!><p>32P-endlabeled DNA duplexes (50 pmol in 8 μL water) were oxidized in the presence of 50 mM NaIO4 (5 μL) in 15 mM sodium phosphate buffer (pH 5.4, 5 μL) for 6 h at 4 °C in the dark to unmask the aldehyde moiety on DHP-deaza-dG (Scheme 2). Excess NaIO4 was quenched with 55 mM Na2SO3 (5 μL). Proteins and peptides of interest (0.5 – 2.5 nmol) were incubated with the aldehyde-containing DNA duplexes (50 pmol) in the presence of 25 mM NaCNBH3 at 37 °C overnight to generate stable DNA-protein cross-links. Aliquots of the reaction mixtures were withdrawn and resolved by 12% SDS-PAGE with or without proteinase K digestion (6 units, 48 h at 37 °C).</p><!><p>12% SDS-PAGE gel plates were pre-run at a constant voltage of 150 V for 30 min in 1× SDS running buffer. DPC reaction mixtures were dissolved in an equal volume of 0.1% TFA or 10% SDS (2 μL). The samples were reconstituted in SDS loading buffer and heated at 90 °C for 5 min prior to loading. The gels were run at a constant voltage of 150 V at ambient temperature. Radiolabeled DNA strands and DPCs were detected with a Storm 840 phosphorimager (Amersham Biosciences Corp., Piscataway, NJ) or a Typhoon FLA 7000 instrument (GE Healthcare, Pittsburgh, PA). Covalent DPCs were observed as slowly moving bands on the gel, and the reaction yields were calculated by volume analysis using Image Quant TL 8.0 (GE Healthcare, Pittsburgh, PA).</p><p>To visualize the proteins participating in DPC formation to aldehyde-containing DNA, NuPAGE Novex 12% Bis-Tris gels (Life Technologies, Grand Island, NY) was pre-run at a constant voltage of 100 V for 30 min in 1× NuPAGE MOPS SDS running buffer (Life Technologies, Grand Island, NY). The reaction mixtures obtained from DNA-protein cross-linking were dissolved in 10% SDS (2 μL) and reconstituted in NuPAGE LDS sample buffer (Life Technologies, Grand Island, NY). The samples were heated at 70 °C for 10 min prior to loading onto a gel. The gels were run at a constant voltage of 100 V at ambient temperature. The unreacted protein and DPC bands were visualized by staining with SimplyBlue SafeStain (Life Technologies, Grand Island, NY).</p><!><p>DPCs were generated by reductive amination as described above using unlabeled DNA and proteins (15 to 20-fold excess) and either directly processed for mass spectrometric analysis or purified by PAGE as described below.</p><p>In direct processing experiments, the DNA component of DPCs was digested with PDE I (120 mU), PDE II (105 mU), DNase (35 U) and alkaline phosphatase (22 U) in 10 mM Tris-HCl/15 mM MgCl2 (pH 7) buffer containing at 37 °C overnight. The resulting protein-nucleoside conjugates were dried in vacuo, reconstituted in 100 mM NH4HCO3 (pH 7.9) (90 μL), and subjected to trypsin digestion using MS grade Trypsin Gold (Promega, Madison, WI) (2.5 μg) at 37 °C for 20 h. The digests were dried in vacuo, desalted using ZipTip with 0.6 μL C18 resin (Millipore, Billerica, MA), and the resulting peptides were reconstituted in 0.1% formic acid (10 μL) prior to MS analysis.</p><p>Alternatively, DNA-protein conjugates were first purified by 12% SDS-PAGE and stained with SimplyBlue SafeStain (Life Technologies, Grand Island, NY). DPC-containing gel bands were cut into slices and subjected to reduction using 300 mM DTT (10 μL) followed by alkylation with iodoacetamide (10 μL in 100 μL of 25 mM NH4HCO3, pH 7.9). Gel pieces were dehydrated with acetonitrile, dried under vacuum, reconstituted in 25 mM NH4HCO3 (pH 7.9) (75 μL) and incubated with PDE I (120 mU) at 37 °C overnight. Next, the samples were subjected to tryptic digestion and ZipTip desalting as described above, followed by MS analysis.</p><!><p>All HPLC-ESI+-MS/MS analyses were conducted with a Thermo Scientific LTQ Orbitrap Velos mass spectrometer interfaced with an Eksigent NanoLC-Ultra 2D HPLC system. Peptide mixtures (5 μL) were loaded onto a nano HPLC column (75 μm ID, 10 cm packed bed, 15 μm orifice) created by hand packing commercially purchased fused-silica emitters (New Objective, Woburn, MA) with Luna C18 5 μm separation media (Phenomenex, Torrance, CA). Liquid chromatography was carried out at an ambient temperature at a flow rate 0.3 μl/min using 0.1% formic acid (A) and acetonitrile (B). The solvent composition was changed linearly from 2% to 70% B over 60 min, then to 95% B over 1 min, kept at 95% B for further 5 min, and decreased to 2% B in 1 min. Finally, the flow rate was increased to 1 μl/min and kept at 2% B for an additional 7 min. Mass spectrometry was performed using the FTMS mass analyzer with a resolution of 60,000 ppm and with a scan range of m/z 300 – 2000. Peptide MS/MS spectra were collected using data-dependent scanning in which one full scan mass spectrum was followed by eight MS/MS spectra using an isolation width of 2.5 m/z, normalized CID collision energy of 35%, 1 repeat count, 20 s exclusion duration, with an exclusion mass width of ± 5 ppm.</p><p>Spectral data were analyzed using Thermo Proteome Discoverer 1.3 (Thermo Scientific, San Jose, CA) that linked raw data extraction, database searching, and probability scoring. The raw data were directly uploaded, without any format conversion, to search against the protein FASTA database. Search parameters included trypsin specificity and up to 2 missed cleavage sites. Protein N-terminus, lysine, or arginine residues were specified as possible modification sites by specifying the following dynamic modifications: (A) ethan-1,2-diyl cross-link to 7-deaza-7-ethan-1-yl-dG, +292.1172 Da (C13H16N4O4); (B) cross-link to 7-deaza-7-ethan-1-yl-guanine,+176.0698 Da (C8H8N4O); or (C) cross-link to 7-deaza-7-ethan-1-yl-dGMP, +372.0835 Da (C13H17N4O7P).</p><!><p>Synthetic 7-deaza-7-(2,3-dihydroxypropan-1-yl)-2′-deoxyguanosine27 (10 nmol) was oxidized in the presence of 50 mM NaIO4 (4 μL) in 1 M sodium phosphate buffer (pH 5.4, 6 μl) for 6 h at 4 °C in the dark to generate 7-deaza-7-(formylmethan-1-yl)-2′-deoxyguanosine. Excess NaIO4 was quenched with 55 mM Na2SO3 (4 μl). N-acetyl protected Lys or Arg (4 μL of 5 mM solution) was added to the reaction mixture, followed by 0.5 M NaCNBH3 (4 μL) at 37 °C overnight to generate amino acid-nucleoside conjugates. The amino acid-nucleoside conjugates were isolated by HPLC on a Supelcosil-LC-18-DB (4.6 × 250 mm, 5 μm) column (Sigma Aldrich, Milwaukee, WI) using a gradient of 0.1% formic acid (A) and acetonitrile (B). The solvent composition was changed from 0 to 24% B over 24 min, then to 75% B over 6 min, and finally to 0% B over 2 min. HPLC fractions containing major components were collected, concentrated in vacuo and analyzed by capillary HPLC-ESI+-MSn on an Agilent 1100 capillary HPLC-ion trap mass spectrometer (Agilent Technologies, Inc., Wilmington, DE).</p><!><p>Synthetic nucleoside-peptide conjugates were prepared using 7-deaza-7-(2,3-dihydroxypropan-1-yl)-2′-deoxyguanosine and angiotensin I (DRVYIHPFHL) or substance P (RPKPQQFFGLMNH2) peptides by reductive amination procedure as described above. The reaction mixtures were dried in vacuo and desalted using ZipTip with 0.6 μL C18 resin (Millipore, Billerica, MA). The resulting mixtures were reconstituted in 0.1% formic acid (10 μL) and analyzed on a Thermo Scientific LTQ Orbitrap Velos mass spectrometer interfaced with an Eksigent NanoLC-Ultra 2D HPLC system as described below.</p><p>Desalted reaction mixtures (2 μL) were loaded onto a nano HPLC column (75 μm ID, 10 cm packed bed, 15 μm orifice) created by hand packing commercially purchased fused-silica emitters (New Objective, Woburn, MA) with Zorbax SB-C18 5 μm separation media (Phenomenex, Torrance, CA). Liquid chromatography was carried out using 0.1% formic acid (A) and acetonitrile (B) as solvents at an ambient temperature at an initial flow rate 1 μl/min at 2% B for 5.5 min. The flow rate was reduced to 0.3 μl/min in 30 s and the solvent composition was changed linearly from 2% to 50% B over 20 min, then to 95% B over 1 min, kept at 95% B for further 5 min, and decreased to 2% B in 1 min. Finally, the HPLC flow rate was increased to 1 μl/min and kept at 2% B for an additional 6 min. Mass spectrometry was performed using the FTMS mass analyzer with a resolution of 60,000 ppm and with a scan range of m/z 300 – 2000 in the full scan mode. MS2 spectra were collected using the iontrap with an isolation width of 2.5 m/z, normalized CID collision energy of 35%, while MS3 spectra were collected using the orbitrap with an isolation width of 2.5 m/z, and a normalized HCD collision energy of 35%.</p><!><p>Since N7-guanine alkylation introduces a positive charge on the alkylated base, it destabilizes the N-glycosidic bond, leading to spontaneous depurination.28 Therefore, it is not practical to employ N7-guanine adducts in DNA replication and repair experiments. To avoid spontaneous degradation of our model DPC substrates, we have replaced the N-7 nitrogen of guanine with a carbon atom (7-deaza-G).27 To create a protein reactive group, the 2,3-dihydroxyprop-1-yl group was introduced at the same position (DHP-deaza-dG, compound 1 in Scheme 2). Treatment with periodate converts the diol group to the corresponding aldehyde (2 in Scheme 2), which then reacts with free amino groups of proteins (e.g., Lys or Arg side chains) to form a Schiff base (3 in Scheme 2). The latter can be quantitatively reduced with NaCNBH3 to produce a stable amine linkage (4 in Scheme 2). The aldehyde substrate (2 in Scheme 2) is a direct model for N7-(2-oxoethyl)-G, which is the major DNA adduct from exposure to chlorooxirane,26 and the resulting model cross-links are structurally analogous to DPCs formed by chlorooxirane and antitumor nitrogen mustards in cells (Scheme 3).</p><!><p>Our initial experiments were conducted with recombinant human AlkB protein, a DNA repair protein that contains multiple nucleophilic lysine and arginine residues and is known to bind to DNA.29,30 The formation of covalent AlkB-DNA conjugates was monitored by two independent methods. In the first approach, oligodeoxynucleotides containing the convertible nucleoside (DHP-deaza-dG) were radiolabeled with 32P-ATP. Following the cross-linking reaction, DPC formation was detected as the appearance of a new, low mobility band on denaturing PAGE (Figure 1). Alternatively, free proteins and DNA-protein conjugates were visualized by protein staining, and the presence of a cross-link was detected as a new protein band with reduced mobility (Figure 2).</p><p>As shown in Figure 1, the cross-linking reaction between DHP-deaza-dG containing DNA 18-mer and recombinant AlkB protein leads to the formation of covalent DPC conjugates as revealed by the appearance of a low mobility band on a denaturing polyacrylamide gel (Lane 3). This band is not observed in control experiments conducted in the absence of protein (Lane 1), and only trace amounts of conjugation are observed in the absence of the reducing agent (Lane 2). The DPC band disappears when the reaction mixture is subjected to proteinase K digestion, (Lane 4), confirming that it corresponds to covalent DNA-protein conjugates.</p><p>These results were further confirmed using protein staining to visualize the protein and the DPCs (Figure 2). Following reductive amination reaction between AlkB protein and DHP-deaza-dG containing 18-mer, a new band was observed with an increased molecular weight as compared to unreacted AlkB protein (22.9 kDa) (Lane 3). The size of the newly formed conjugate (~ 29 kDa) is consistent with the addition of 18-mer oligodeoxynucleotide (6.2 kDa) to the protein, and the cross-linking yield is dependent on DNA:protein ratio (Lanes 3, 5 and 6). In addition, several higher molecular weight bands (> 40 kDa) were observed due to the propensity of the GFP protein to oligomerize when present at a high concentration.</p><!><p>The experimental conditions for each of the reaction steps (Scheme 2) were optimized by varying the reaction temperature, reaction time, and molar ratios. We found that the highest DPC yields are observed when the NaIO4-mediated oxidative cleavage (step a) is conducted at 4 °C, and the optimal temperature for reductive amination reaction (steps b and c) was 37 °C (Figure S1).</p><p>When the effect of reaction times on DPC yields was assessed, the best results were achieved when the time of periodate-mediated oxidative cleavage (step a) was limited to 2–6 h (Figure S2). This can be explained by a limited stability of aldehyde under oxidizing conditions. In contrast, the best yields of reductive amination reaction (steps b and c) were achieved at extended reaction times (12 to 24 h) (Figure S2).</p><p>The influence of DNA:protein molar ratios on reaction yields was examined by keeping the concentration of one of the reagents (protein or DNA) constant while varying the equivalents of the other. When protein amounts were varied in respect to constant amounts of radiolabeled DNA, the highest DPC yields (~ 85%) were achieved when using an excess of the protein (Figure 3). When protein amounts were held constant and protein staining was employed to follow the reaction, increasing DNA concentrations similarly has led to increased DPC yields (Figure 2). These results suggest that the reversible Schiff base formation between the aldehyde functionality within DNA and the basic amino acid side chains of the protein can be driven forward towards product formation by employing a large molar excess of the other reagent. In a practical sense, generation of DNA substrates for replication and repair studies requires an excess of the protein in order to maximize the yields of DPC-containing DNA.</p><!><p>In theory, any protein containing lysine or arginine side chains can be cross-linked to DHP-deaza-dG containing DNA using the reductive amination strategy (Scheme 2). However, the reactivity and the accessibility of basic residues may vary depending on the protein identity. Therefore, the general applicability of our approach was examined using a range of proteins of different sizes and structures (Table 1 and Figure 4). We found that while some proteins (AlkB, NEIL1, Histone H4, GAPDH,) formed DPCs in a high yield (75 – 95%), significantly lower DPC yields (< 20%) were observed for others (trypsin, carboxypeptidase, myoglobin) (Table 1). In general, DNA binding proteins such as those involved in chromatin condensation and DNA repair produced DPCs in a higher yield than proteins that do not have an affinity for DNA. These results suggest that the formation of DPCs by reductive amination is facilitated by reversible DNA-protein interactions, which brings the two biomolecules into a close proximity to each other. Additional low mobility bands were observed for some proteins due to their dimerization (e.g. histone H4 and myoglobin, Figure 4).</p><p>To gain insight into the identities of the amino acid residues participating in DPC formation, the cross-linking reactions were carried out with peptides of differing amino acid composition. High abundance DPC bands were observed for Tat and Substance P, which are rich in lysine and arginine residues (Figure S3, lanes 2 and 4) suggesting that amino groups of Lys and Arg may be involved in crosslinking to DNA. In contrast, no covalent conjugates were detected for pepstatin, which has no Lys or Arg residues (Figure S3, lane 5). A single DPC band observed for hypertensin I, which has only one Arg residue and no Lys (Figure S3, lane 3). Taken together, these results suggest that amino side chains of Lys and Arg within proteins can participate in reductive amination reactions with DHP-deaza-dG containing DNA.</p><!><p>A mass spectrometry-based approach was employed to further characterize the structures of DNA-protein conjugates created by reductive amination and to identify the amino acids participating in reactions. The DNA components of DPCs to selected proteins (AlkB, RNase A, histone H4 and myoglobin) were digested with PDE I, PDE II, DNAse, and alkaline phosphatase. The resulting protein-nucleoside conjugates were cleaved with trypsin, and the peptides were analyzed by nano HPLC-ESI+-MS/MS on an Orbitrap Velos mass spectrometer. Tryptic peptides containing cross-links to deaza-dG were identified, and the cross-linking sites were determined by MS/MS sequencing (see an example in Figure 5). The mass spectral data were processed using Thermo Proteome Discoverer 1.3 (ThermoScientific, San Jose, CA) to identify the cross-linking sites. We found that for all four proteins examined, multiple lysine and arginine residues were engaged in the cross-linking reaction to aldehyde-containing DNA under reducing conditions (Tables 2, S1– S3, Figure S4).</p><p>HPLC-ESI+-MS/MS analysis of AlkB-DNA conjugates was repeated several times, yielding reproducible results. Peptide sequencing by HPLC-ESI+-MS/MS has revealed that two lysine residues (K127, K166) and five arginine residues of AlkB (R35, R161, R167, R204, R210) can participate in the AlkB-DNA cross-linking via reductive amination (Figure 6, Table 2). Examination of published crystal structures suggests that K134, R204, and R210 are located in the active site of AlkB, while K127, K166, and R167 reside in the DNA binding groove of the protein.29,30 These results suggest that specific AlkB-DNA binding facilitates covalent DPC formation. However, some of the residues participating in cross-linking (e.g. R35) are located outside of the DNA-binding domain. This can be explained by partial denaturation of the protein under the strongly reducing conditions used in our experiments.</p><!><p>To confirm the exact chemical structure of the DNA-protein cross-links generated via reductive amination (Scheme 2), synthetic 7-deaza-7-(2,3-dihydroxypropan-1-yl)-2′-deoxyguanosine (DHP-deaza-dG) was allowed to react with N-acetyl protected Lys and Arg, and the resulting nucleoside-amino acid conjugates were isolated by HPLC and characterized by mass spectrometry. In the case of N-acetyl-Lys, the major conjugation product was observed at m/z 503.2, corresponding to the [M + Na]+ ions of 7-deaza-7-(2-(N-acetyl-lysine)ethan-1-yl)-2′-deoxyguanosine. MS/MS fragmentation pathway of this conjugate (Figure 7) was dominated by the product ions at m/z 387.1 and m/z 485.2, which correspond to the loss of deoxyribose, and a water molecule, respectively (Figure 7). Similar results were observed for N-acetyl-Arg. MS2 fragmentation of protonated 7-deaza-7-(2-(N-acetylarginine)ethan-1-yl)-2′-deoxyguanosine, [M+H]+, revealed the loss of water ([M+H-H2O]+), acetylamine ([M+H-MeCONH2]+) and deoxyribose ([M+H-dR]+) (Figure S5). Taken together, these results are consistent with the cross-linking mechanism shown in Scheme 2, e.g. Schiff base formation between the lysine amino side chain and the aldehyde functionality within oxidized DHP-deaza-dG, followed by imine reduction to generate a stable amino linkage.</p><!><p>To further confirm the chemical structure and site of crosslinking of DPCs formed by this reductive amination strategy, nucleoside-peptide conjugates of 7-deaza-7-(2,3-dihydroxypropan-1-yl)-2′-deoxyguanosine (deaza-DHP-dG) to angiotensin I and substance P were characterized by mass spectrometry. MS2 spectrum of angiotensin I conjugate revealed that the site of crosslinking is side chain amino group of arginine (Figure 8A). The MS2 spectrum further revealed that 7-deaza-7-(1-aminoethan-2-yl)-2′deoxyguanosine is lost upon fragmentation of the doubly charged parent ion, [M+2H]+ = 794.9 m/z. The resulting singly charged daughter ion, [M+H]+ = 1279.8 m/z was further fragmented to observe additional b and y ions of the modified peptide that confirmed the site of crosslinking (Figure 8B).</p><!><p>Dynamic DNA-protein interactions are crucial for many cellular functions including chromatin packaging,31 cell division, 32 DNA replication, gene expression,31,33 DNA damage response, and DNA repair.31,33 Proteins reversibly interact with DNA by a combination of electrostatic forces, hydrogen bonding, and stacking interactions, and their ability to dissociate from DNA is critical for their cellular functions. However, exposure to common antitumor drugs, environmental toxins, transition metals, UV light, ionizing radiation, and free radical-generating systems can result in proteins becoming covalently trapped on DNA.1,4 This generates super bulky, highly heterogeneous DNA-protein cross-links (DPCs) that can block DNA and RNA polymerases, causing toxicity and/or mutations in affected cells.2,3,10,17,34–37</p><p>Our previous mass spectrometry-based proteomics studies have revealed that covalent DNA-protein cross-links (DPCs) involving the N7 position of guanine are readily formed in human cells treated with clinically relevant concentrations of chemotherapeutic drugs (e.g., platinum compounds and nitrogen mustards)2,3 and metabolically activated carcinogens (e.g., 1,2,3,4-diepoxybutane).2,3 Additionally, covalent DPCs have been shown to accumulate in an age-dependent fashion in the brain and heart tissues, probably a result of exposure to endogenous reactive oxygen species, lipid peroxidation products, and transition metals.17 If not repaired, DPCs may contribute to the development of cancer, cardiovascular disease, and age-related neurodegeneration.16,17,37–41</p><p>Conflicting data exist in the literature regarding the mechanisms of cellular repair of DPC lesions. Reardon et al. examined the ability of reconstituted bacterial and mammalian excision nuclease systems to recognize the ring-open T4 pyrimidine DNA glycosylase-DNA cross-links.42 While the excision of DNA-protein conjugates was not detected, DPCs to short polypeptides were recognized and cleaved by mammalian protein extracts, leading to the hypothesis that DPCs are proteolytically degraded prior to their repair via the NER pathway.42 Similar conclusions were drawn by the Lloyd group when using a bacterial UvrABC system21 and by Baker et al. who examined model DPCs containing bacterial DNA methyltransferase-DPC (37 kDa) attached to the C-6 position of cytosine.23 Quievryn et al. observed reduced rates of repair of formaldehyde-induced DPCs in the presence of a protease inhibitor.43 In contrast, Nakano et al. reported that cytosolic ATP-dependent proteases are not involved in DPC removal.44,45 These authors proposed that homologous recombination repair is responsible for removing the majority of DPCs generated via oxanine, while only DPCs involving small proteins (< 12 kDa in bacteria and < 8–10 kDa in mammalian cells) are repaired by NER.44,45</p><p>Structural factors such as protein size, identity and lesion structures (e.g. major or minor groove of DNA) are also likely to affect the DPC lesion′s ability to be bypassed by DNA and RNA polymerases.1,46 For example, E. coli polymerase I and HIV-1 reverse transcriptase were completely blocked by cis-[diamminedichloridoplatinum (II)] (cisplatin) cross-link to histone H1.47 The A family human polymerase ν was blocked by DNA-peptide cross-links located in the minor groove via N2-dG.25 In contrast, chemically and structurally similar lesions located in the major groove of DNA via N6-dA were efficiently and accurately bypassed by both human polymerase ν and E. coli polymerase I.25,48</p><p>It is likely that the discrepancies between the mechanisms of DPC repair and bypass reported by different groups reflect structural differences between the model DPCs examined. Indeed, these previous studies have employed DNA-conjugates of diverse structure and size, including those where the protein was directly attached to ring open abasic sites in DNA.4,44,49 This underlines the need to reexamine the replication and repair of DPC-containing DNA using substrates resembling the lesions formed in cells. The most common site of DNA involved in DPC formation is the N7 of guanine.2,4,6 However, to our knowledge, no methods have been previously reported in the literature to generate N-7 guanine conjugated DPCs.</p><p>As mentioned in the introduction, currently available synthetic strategies to generate site-specific DPCs are limited to several main strategies. Lloyd et al.21 and Sancar et al.21 employed a semi-enzymatic approach to trap T4 pyrimidine dimer glycosylase/AP lyase (T4-pdg) on abasic sites of DNA in the presence of sodium borohydrate. A similar methodology has been used to attach oxoguanine glycosylase (Ogg) to DNA strands containing 8-oxo-dG. DNA methyltransferase (Dnmt) has been trapped on DNA containing 5-fluoro-5-methylcytosine.23 Another approach involves the use of oxanine (Ox) in DNA that spontaneously reacts with amino groups of proteins to give a pyrimidine ring-open structure,41 a strategy that is specific to nitric oxide-induced Oxa lesion, requires a large excess of the protein (425 to 3000-fold), as well as long incubation times (up to 48 h).41,45 Finally, Schiff base formation between acrolein-induced γ-HOPdG adducts and lysine residues of proteins and peptides can be stabilized in the presence of NaCNBH3.15,24,25 Either the N2 guanine or N6 adenine aldehyde functionality derived from acrolein-induced 3-(2′-deoxyribos-1′-yl)-5,6,7,8-tetrahydro-8-hydroxypyrimido [1,2a]purin-10(3H)-one (γ-HOPdG) can be reacted with proteins and peptides to produce a Schiff base, which was subsequently reduced to the corresponding amine with sodium cyanoborohydride.24,25 To our knowledge, no synthetic methodologies are available to generate N7-gianine DPCs such as those formed in vivo upon exposure to environmental carcinogens and antitumor agents.2,3</p><p>In the present study, a post-synthetic reductive amination strategy was employed to create hydrolytically stable structural mimics of N-7 guanine conjugated DPCs by reductive amination reactions between the Lys and Arg side chains of proteins and acetaldehyde functionalities of modified 7-deazaguanine residues of DNA. The main advantage of this approach is that it generates sequence specific DPC lesions structurally analogous to the lesions formed in vivo. Reductive amination methodology is highly versatile, as it can be used to generate DPCs to most proteins and peptides containing Lys and/or Arg residues (Table 1). The resulting structurally defined and hydrolytically stable DPCs can be used to study the biological fate of DPCs in vitro to better understand the effects of these lesions in cells. Furthermore, experimental methods are being developed in our laboratory to incorporate these substrates into plasmid DNA and study their repair in cells to identify the mechanisms responsible for the removal of these lesions in vivo.</p><p>Model DPC lesions generated in this work resemble the adducts induced by antitumor nitrogen mustards (Scheme 3).2,6 We have previously identified 39 proteins that form covalent DPC in human fibrosarcoma (HT1080) cells treated with mechlorethamine.50 However, it is not known to what extent DPC formation contributes to toxicity of nitrogen mustards in cancer cells. The availability of hydrolytically stable model DPCs substrates will, for the first time, enable structural and biological evaluation of these super-bulky lesions. Based on our recent studies with DNA-reactive protein reagents that specifically induce DPCs in cells,20 we hypothesize that spontaneous and xenobiotic-induced DPCs, if not repaired, compromise the efficiency and the accuracy of DNA replication and are responsible for a major portion of the toxicity and mutagenicity induced by bis-alkylating agents, UV light, reactive oxygen species, and γ-radiation.</p><p>The model DPC substrates created by reductive amination (Scheme 2) are site-specific in respect to DNA, but may involve multiple possible cross-linking sites within the protein (Figure 6, Tables 2, S1–S3). This limited specificity in respect to the protein side chains should not affect the ability to employ these model conjugates in biological studies since the "real" DPC lesions formed in cells are also heterogenous in nature. However, it may not be practical to use this approach to generate DNA-protein conjugates for structural studies by NMR or X-ray crystallography, especially in the case of proteins that contain multiple basic residues available for reaction with DNA. Other types of conjugations that employ bioorthogonal reactive groups in each biomolecule (protein and DNA) may be more appropriate for this purpose. We are currently exploring the use of copper-catalyzed [3+2] Huisgen cycloaddition (click reaction) between azide-functionalized proteins51 and alkyne-containing DNA to generate site specific DPC conjugates.</p>

PubMed Author Manuscript

Small Molecule Tertiary Amines as Agonists of the Nuclear Hormone Receptor Rev-erb\xce\xb1

The structure activity relationship study of a small molecule Rev-erb\xce\xb1 agonist is reported. The potency and efficacy of the agonists in a cell-based assay were optimized as compared to the initial lead. Modest mouse pharmacokinetics coupled with an improved in vitro profile make 12e a suitable in vivo probe to interrogate the functions of Rev-erb\xce\xb1 in animal models of disease.

small_molecule_tertiary_amines_as_agonists_of_the_nuclear_hormone_receptor_rev-erb\xce\xb1

<p>Rev-erbα was originally identified as an orphan nuclear hormone receptor based on its canonical domain structure.1 Rev-erbβ was identified based on its homology to other nuclear receptors (NR) and has an overlapping pattern of expression with Rev-erbα. Rev-erbs have particularly high expression in the liver, adipose tissue, skeletal muscle and brain2–4 and are expressed in a circadian manner in these tissues.5–8 The Rev-erbs are unique within the NR superfamily in that they lack the typical C-terminal AF2 domain (helix 12), which is required for coactivator protein binding. Although these receptors lack the ability to activate transcription of target genes due to their inability to recruit transcriptional coactivator proteins, both have been shown to be effective repressors of transcription due to their ability to recruit transcriptional corepressor proteins such as NCoR and HDAC3.9, 10 It has been recently demonstrated that the porphyrin heme functions as a ligand for Rev-erbα and Rev-erbβ.9–2 Heme binds reversibly and specifically to the ligand binding domain (LBD) of Rev-erb. Binding induces a conformational change in the LBD that results in the ability of the receptor to recruit NCoR and thus repress target gene transcription. The nuclear hormone receptors, Rev-erbα and Rev-erbβ, regulate a number of physiological functions including the circadian rhythm, glucose and lipid metabolism, adipogenesis, and cellular differentiation.13, 14 The observation that these NRs are ligand regulated suggests that development of synthetic ligands may be possible.</p><p>Recently, the first nonporphyrin synthetic ligand for Rev-erbα, GSK4112/SR6452/1 (Figure 1) was identified.15, 16 This ligand acts as an agonist, mimicking the action of heme and resets the circadian rhythm in a phasic manner. It also represses expression of gluconeogenic genes in liver cells and reduces glucose output in primary hepatocytes. 1 was identified in a fluorescence resonance energy transfer (FRET) assay that significantly and specifically enhances the Rev-erbα-NCoR interaction with an EC50 value of 0.40 μM. We recapitulated this data showing that SR6452 was able to modulate the interaction of either Rev-erbα or Rev-erbβ with an NCoR CoRNR box peptide using Luminex technology.17, 18 SR6452 dose-dependently increased the interaction of both Rev-erbα and Rev-erbβ with the NCoR peptide, indicating that the ligand modulates the activity of both Rer-erb subtypes. Direct binding of an analog (12e) to Rev-erbα was also confirmed by circular dichroism analysis.18</p><p>The compound was reported to show no activity on related nuclear hormone receptors (LRH1, SF1, FXR, or RORα) using the same FRET assay and no activity on LXRα or LXRβ in reporter-gene assays. Unfortunately, the pharmacokinetic profile of 1 in rodents was poor hampering its use as an in vivo tool. Additionally, 1 had modest potency and limited efficacy in a cellular assay (in-house data). With the goal of interrogating the function of Rev-erbα in animal models of disease, we needed a more potent compound with improved potency and efficacy and an adequate in vivo profile. Based on trisubstituted amine 2, we initiated the structure-activity relationships (SAR) study described herein.</p><p>Based on the lead structure 1, the three portions of the molecule were individually modified in a step-wise fashion investigating R, R1, and R2 as in 2. The analogues 4–11 were synthesized in straightforward fashion starting from commercially available starting materials (Scheme 1). In one instance, reductive amination of t-butyl glycine (3) with p-chlorobenzaldehyde afforded secondary amine 4. Functionalization of the third amine substituent was carried out by a second reductive amination or sulfonylation or acylation as described to give products 5a–e. Alternatively, reductive amination of t-butyl glycine and 5-nitrothiophenecarboxaldehyde (6) uneventfully afforded secondary amine 7. This could then be converted to final products 8 in a similar fashion. Lastly, the 5-nitrothiophenecarboxaldehyde and p-chlorobenzylamine (9) could be condensed to yield amine 10, which was converted to final products 11.</p><p>Compounds were screened in a cell-based luciferase assay in a two-step format.18–20 Cells were co-transfected with an expression plasmid harboring full-length Rev-erbα and a luciferase reporter driven by the Bmal1 promoter. Compounds were first screened at two concentrations (1 μM and 10 μM) to determine the effect on repression of Bmal1 transcription. Rev-erb is a transcriptional repressor. Rev-erb agonists lead to recruitment of co-repressors, which leads to repression of transcription. Maximum inhibition at 10 μM is reported.21 The lower the value, the more efficacious the agonist is at repressing transcription. A value of 1.0 effectively means no repression. Compounds that appeared efficacious at 10 μM were then fully titrated in an eleven-point dose response format to generate EC50 values. In our in-house cell-based assay, GSK4112/SR6452/1 showed only modest potency and minimal efficacy (Table 1).</p><p>Given the potential for issues with the nitrothiophene residue in vivo, we assessed replacement of this group first. Several small heterocycles and carbocycles were tried as nitrothiophene isosteres, however none of them showed any improvement with regards to efficacy (Table 1). One might argue that the 4-pyridyl analog (5a) and the benzothiazole analog (5c) were equally efficacious as 1, however these early compounds were not fully titrated. Compounds 4, 5b and 5d showed no repression. Temporarily unsuccessful in replacing the nitrothiophene ring, we moved on to investigate the other two portions of the molecule.</p><p>Efforts were then focused on replacing the p-chlorobenzyl group (Table 2). The compounds shown are only a subset of those actually made however they are representative of the group. Substitutions on the benzyl group had modest effects on efficacy (8a–d) as did the naphthyl analogs (8e–f), however this did not translate into an improved EC50. Converting the amine to an amide or sulfonamide showed improved efficacy, and 8i was the first compound synthesized with an EC50 <1 μM. This represented a nice improvement over 1. Unfortunately, we were unable to assess the in vivo characteristics of 8i as this analog could not be detected in the mass spectrometer due to poor ionization under a number of conditions.</p><p>Finally, we began to modify the third segment of 1 and looked to modify the acetic ester side chain (Table 3). We found that the t-butyl ester residue was not important for activity, as the corresponding methyl ester (11a), primary amide (11c), and nitrile (11d) were all equipotent. Attempts to replace the ester group with aryl and heteroaryl residues (11e–g) were slightly misleading as improved efficacy at 10 μM did not translate into an improved EC50. Saturated ring systems were accommodated (11i–k), however only one showed improved cellular EC50 (11k). Amides and sulfonamides (11l–m) showed nice improvements in efficacy, however these analogs also displayed only modest EC50's. The most potent and efficacious analog identified was carbamate 11k. In an effort to further improve the activity of 11k, we investigated ring size and linker length (Table 4).</p><p>The first aspect investigated was to see if the methyl pyrrolidine side chain was optimal. We examined other 5-and 6-membered ring isomers (11n–r) with and without the methylene linker between the nitrogen atom and the ring. These analogs were synthesized simply via reductive amination with the corresponding ketone or aldehydes. All products are racemic at this stage, however, they could be made in enantiomeric fashion if desired. This might also lead to improvements in potency. The methyl pyrrolidine group in 11k was certainly better than either isomer of the piperidines (11n–o), however the analogs lacking the methylene spacer appeared to be equipotent (11p–r).</p><p>We next considered modifications to the carbamate group in 11k (Table 5). Deprotection of the t-butoxycarbonyl group (BOC) in 11k by exposure to acid was uneventful (Scheme 2). Installation of the R3-substituent via standard chemistry afforded products 12. Slightly smaller carbamates (12e,f) showed similar efficacy at 10 μM, but with nearly 3-fold improvement in EC50's. The corresponding ureas (12g–i) and sulfonamides (12j,l–m) were also equally efficacious and considerably more potent than 11k. Urea 12i and sulfonamide 12m were the most potent analogs synthesized. Amide 12c was equipotent to 11k. Removal of the BOC group and substitution with alkyl groups (12a–b) led to a substantial drop in efficacy. Clearly the additional hydrogen bond acceptors are important for activity.</p><p>As a secondary screen of in vitro activity, select compounds were tested in a Gal4-Rev-erb LBD cotransfection assay. 12e dose-dependently increased the Rev-erb-dependent repressor activity assessed in HEK293 cells expressing a chimaeric Gal4 DNA binding domain (DBD): REV-ERB ligand binding domain (LBD) α or β and a Gal4-responsive luciferase reporter. It's half-maximum inhibitory concentration (IC50) against Rev-erbα was 670 nM, in good correlation with its BMAL data.18</p><p>The in vivo properties of several analogs were examined in mouse (Table 6)22. As Rev-erbα is highly expressed in the central nervous system (CNS), brain penetration was also evaluated. Mice were given a 10 mg/kg IP dose of drug, and plasma and brain levels of drug were determined 2h later. The hydrophobic lead 1 had limited exposure in plasma, although CNS penetration was good. Urea 12h had somewhat better plasma exposure, with reduced brain penetration. The reduced brain penetration is not surprising given the increased polar surface area of the urea. Carbamate 12e had slightly better plasma and brain exposure as 1. Most surprising was sulfonamide 12j which displayed the best CNS exposure. These trisubstituted amines have several metabolic soft spots which may contribute to their poor exposure and it's not clear yet the liability of the nitrothiophene ring. Interestingly, upon increasing the dose to 50 mg/kg IP for 12e, plasma exposure and brain exposure increase significantly, although drug formulation was also modified. With an EC50 = 0.7 μM, given a 50 mg/kg dose, there is over 10-fold concentration of drug in brain at t=8h.</p><p>As can be seen from the curves in Figure 2, we have greatly improved the overall efficacy and potency of compounds in this series when compared to the lead GSK4112/SR6452/1. Compounds like 12e, 12h, and 12j also have good plasma and brain exposure such they might represent useful tools to study the function of Rev-erbα in vivo in models of disease. Progress in this area is on-going and will be reported in due course.</p><p>This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.</p><p>All assays are performed as contransfections with the dual glow luciferase and data are normalized to constitutively active renilla luciferase reporter activity. This provides normalization for any non-specific effects of the compounds and we also monitor renilla luciferase activity directly to determine potential toxicity.</p><p>CNS exposure was evaluated in C57Bl6 mice (n = 3). Compounds were dosed at 10 mg/kg intraperitoneally and after 2 h blood and brain were collected. Plasma was generated and the samples were frozen at −80°C. The plasma and brain were mixed with acetonitrile (1:5 v:v or 1:5 w:v, respectively). The brain sample was sonicated with a probe tip sonicator to break up the tissue, and samples were analyzed for drug levels by LCMS/MS. Plasma drug levels were determined against standards made in plasma and brain levels against standards made in blank brain matrix. All procedures were approved by the Scripps Florida IACUC.</p><p>GSK4112/SR6452 lead</p><p>Cell-based comparison between lead 1 and optimized analog 12e.</p><p>Reagents and conditions: a. NaBH(OAc)3, HOAc, Cl(CH2)2Cl, 4-Cl-PhCHO; b. RCHO, NaBH(OAc)3, HOAc, Cl(CH2)2Cl; c. RCOCl, TEA; d. RSO2Cl, TEA; e. NaBH(OAc)3, HOAc, Cl(CH2)2Cl, H2NCH2CO2tBu; f. 5-nitrothiophenecarboxaldehyde, NaBH(OAc)3, HOAc, Cl(CH2)2Cl.</p><p>Reagents and conditions: a. TFA, CH2Cl2; b. NaBH(OAc)3, HOAc, Cl(CH2)2Cl, R3CHO; c. R3COCl, TEA; d. R3SO2Cl, TEA; e. R3NCO; f. R3OCOCl, TEA, CH2Cl2.</p><p>Nitrothiophene Analogs</p><p>results are average of 2 or more experiments.</p><p>Value = fold change relative to DMSO control at 10 μM compound;</p><p>NT = not tested.</p><p>All standard deviations ≤ 25%.</p><p>p-Cl-Benzyl Analogs</p><p>results are average of 2 or more experiments.</p><p>Value = fold change relative to DMSO control at 10 μM compound;</p><p>NT = not tested. .</p><p>All standard deviations ≤ 25%.</p><p>Ester Analogs</p><p>results are average of 2 or more experiments.</p><p>Value = fold change relative to DMSO control at 10 μM compound;</p><p>NT = not tested. .</p><p>All standard deviations ≤ 25%.</p><p>Pipendine and Pyrrolidine Analogs</p><p>results are average of 2 or more experiments.</p><p>Value = fold change relative to DMSO control at 10 μM compound;</p><p>NT = not tested. .</p><p>All standard deviations ≤ 25%.</p><p>Carbamates, amides, ureas, and sulfonamides</p><p>results are average of 2 or more experiments.</p><p>Value = fold change relative to DMSO control at 10 μM compound;</p><p>NT = not tested. .</p><p>All standard deviations ≤ 25%.</p><p>In vivo properties of selected Rev-erbα agonists</p><p>Mice sacrificed at t = 2h. Brain and plasma levels of drug determined;</p><p>Mice dosed 10 mg/kg IP in 10:10:80 DMSO:Tween:water;</p><p>Mice dosed 50 mg/kg IP in 15% cremaphor EL;</p><p>brain levels at 8h;</p><p>b.p. = brain penetration.</p>

PubMed Author Manuscript

Chemistry and Structural Biology of DNA Damage and Biological Consequences

The formation of adducts by the reaction of chemicals with DNA is a critical step for the initiation of carcinogenesis. The structural analysis of various DNA adducts reveals that conformational and chemical rearrangements and interconversions are a common theme. Conformational changes are modulated both by the nature of adduct and the base sequences neighboring the lesion sites. Equilibria between conformational states may modulate both DNA repair and error-prone replication past these adducts. Likewise, chemical rearrangements of initially formed DNA adducts are also modulated both by the nature of adducts and the base sequences neighboring the lesion sites. In this review, we focus on DNA damage caused by a number of environmental and endogenous agents, and biological consequences.

chemistry_and_structural_biology_of_dna_damage_and_biological_consequences

Introduction<!>Conformational Interconversions of DNA Adducts<!>Aromatic Amines<!>8-Oxo-dG and 8-Oxo-dA<!>Etheno Adducts<!>1,N2-Propanodeoxyguanosine<!>4-Hydroxyequilenin<!>Polycyclic Aromatic Hydrocarbons<!>Configurational Rearrangements of DNA Adducts<!>Deamination of Butadiene-Derived N1-dA Adducts<!>Malondialdehyde<!>Enal-Derived Adducts<!>i) Acrolein<!>ii) Crotonaldehyde<!>iii) 4-Hydroxynonenal<!>Abasic Sites<!>Thymine Glycol<!>Oxidation of 8-Oxo-dG to Guanidinohydantoin and Spiroiminodihyantoin<!>Aflatoxin B1<!>Adduct-Induced DNA Cross-Linking<!>Butadiene-Mediated Cross-Linking<!>Summary

<p>DNA Damage, arising from both endogenous and exogenous chemical insults, is believed to represent an initiating step in chemical carcinogenesis. Considerable data exist with regard how specific DNA adducts perturb its structure, and how these structural perturbations interfere with the biological processing of DNA [1]. A major goal is to understand the basis whereby chemical modification of DNA triggers specific mutations. The field has advanced rapidly over the past several decades. One reason has been the development of automated DNA synthesis methodolgies [2], and progress in regioselectively and stereospecificaly incorporating specific types of damage into duplex DNA [1]. Collectively, these approaches enable the construction of site-specifically damaged DNA templates for structural and biological studies. Additionally, advances in applications of NMR spectroscopy [3] and X-ray crystallography [4] to the study of DNA enable high-resolution structures of damaged DNA to be obtained. In this review, we will focus on the conformational and configurational rearrangements of DNA adducts, derived from a number of environmental and endogenous DNA damaging agents, and in the context of biological consequences.</p><!><p>Conformational intercon-versions involve rotation about one or more chemical bonds, but do not require bond breakage. The conformational interconversions of interest here have sufficiently large activation barriers that, under physiologically relevant conditions, interconversion occurs slowly at ms or slower time scales, and it is anticipated that different conformers may elicit differential biological responses. In DNA, a major site of conformational interconverson for deoxynucleosides involves the N-glycosyl bond connecting the deoxyribose sugar to the heteroaromatic nucleobase. In B-form DNA, the N-glycosyl bond is maintained in the anti-conformation, thus orienting the sterically bulky nucleobase away from the deoxyribose sugar. In duplex DNA, this orients complementary nucleobases to form the canonical Watson–Crick base pairing interactions. Often, however, the introduction of sterically bulky substituents to DNA disrupts this conformational equilibrium, and shifts the N-glycosyl bond into the syn-conformation, in which the nucleobase is oriented toward the deoxyribose. In duplex DNA, this shifts the Watson–Crick H-bonding edge of the base toward the major groove, and hinders canonical Watson–Crick H-bonding. Characteristic examples of bulky adducts that undergo such conformational interconversions include those arising from C8-dG alkylation by aromatic amines, C8-dG oxidative-damage products 8-oxo-dG and 8-oxo-dA, 1,N2-etheno and -propano annelation products of dA and dG, and 4-hydroxy-equilenin.</p><!><p>Early evidence that conformational heterogeneity modulated the structures of DNA adducts came from studies of arylamine adducts. The subject has been reviewed by Cho [5] and Patel et al. [6]. Arylamines are environmental carcinogens; human exposures are associated with the etiology of bladder cancer [7]. The most studied example is N-acetyl-2-aminofluorene. 2-Aminofluorene (AF) is acetylated in vivo; the acetylated species then reacts with DNA [8–10] to give 2-amino-N-(deoxyguanosin-8-yl)fluorene (C8-dG AF) (Fig. 1). This has been detected in mammalian cells [11]. When the C8-dG AF adduct is placed opposite dC in the 5′-AXG-3′ sequence, it exists in two conformations, referred to as the external-AF and inserted-AF conformations [12]. The external-AF conformation features the anti-conformation, while the inserted-AF conformation features the syn conformation about the glycosyl bond (Fig. 2). In the external-AF conformation, the AF moiety is in the major groove. The X(anti)·C(anti) base pair exhibits Watson–Crick H-bonding [12] (Fig. 2). In the inserted-AF conformation, the syn glycosyl bond places the AF moiety into the DNA helix and displaces the damaged guanine base and the complementary cytosine [12], resulting in the disruption of Watson–Crick H-bonding at the X(syn)·C(anti) base pair (Fig. 2). A similar equilibrium is observed between external- and inserted-AF in the 5′-CXC-3′ sequence [13]. When the C8-dG AF adduct is placed opposite dA in the 5′-CXC-3′ sequence, modeling the intermediate associated with G→ T mutations observed for AF [14], the damaged nucleotide also adopts the syn conformation about the glycosyl bond, but the AF moiety orients in the minor groove [15]. The AF-dG adduct has also been examined in sequences derived from c-Ha-ras-protooncogenes with modification at codon 61, a site of G→T mutations, by Cho et al. [16], and Eckel and Krugh [12][17]. The amount of the major conformation of the AF-dG adduct is ca. 60% [16]; the aminofluorene moiety rotates toward the major groove. The major conformer adopted by the corresponding 4-aminobiphenyl-modified base [18] is similar.</p><p>Base substitutions are associated with these adducts, especially G→T mutations [14], although the correct incorporation of dCTP predominates during translesion bypass [19]. Frameshift mutations induced by AF adducts are associated with the E. coli NarI hot spot sequence (C-G1-G2-C-G3-C-C), in which −2 base deletions occur at G3 [20–25]. The NarI frameshift pathway is SOS-dependent but umuDC-independent, and DNA Pol II is responsible for the frameshifts [25]. When the AF-dG adduct is placed into the E. coli NarI sequence (C-G1-G2-C-G3-C-C), associated with −2 base deletions at G3 [20–25], its conformation is sequence-dependent [26]. The adduct favors the external-AF conformation when placed at G1 and G2, respectively, while an equal mixture of both conformers exists when AF is placed at G3. The conformational equilibrium is also affected by the next nearest neighbors [6]. Structures of the C8-dG adduct placed opposite to 3′-terminal primer cytosine have been obtained in a ternary complex with the Dpo4 polymerase [27]. The C8-dG AF adduct remained in the anti-conformation about the glycosyl bond with the AF moiety positioned in the major groove [27]. Overall, the conformational equilibrium of the C8-dG AF adduct, and the concomitant distortion of DNA structure, may contribute to both base substitution and frameshift mutations during DNA replication, in a sequence-dependent manner [12].</p><p>The heterocyclic amines 2-amino-3-methylimidazo[4,5-f]quinoline (IQ) [28–31] and 2-amino-1-methyl-6-phenyllimidazo[4,5-b]pyridine (PhIP) are produced, when protein-rich foods are cooked. IQ is activated to an N-hydroxy oxidation product [32–35] and reacts with DNA to produce C8-dG adducts that are observed in rodents and primates, as measured by 32P post-labeling [36]. A minor adduct forms at N2-dG [37]. In nucleosides, the C8-dG IQ adduct (Fig. 1) exists in the syn-conformation about the glycosyl bond [38], while the N2-dG IQ adduct adopts the anti conformation. When placed opposite cytosine in a duplex containing the E. coli NarI sequence, the C8-dG IQ adduct also adopts the syn-conformation (Fig. 3) [40], as does the C8-dG PhIP adduct (Fig. 4) [41]. This places the Watson–Crick edge of the modified dG into the major groove. The IQ moiety intercalates between the flanking C·G base pairs. These studies corroborated molecular-mechanics analysis of the C8-dG IQ-modified duplex [42]. As for the C8-dG AF adduct, the syn conformation of the C8-dG IQ adduct may contribute to error-prone replication, in a sequence-dependent manner. The structure of the N2-dG IQ adduct has not yet been examined in an oligodeoxynucleotide duplex. It has been proposed that differences in the accumulation and rates of removal of C8-dG IQ vs. N2-dG IQ adducts in rodents and non-human primates may be attributable to differences in conformation about the glycosyl bond in these two adducts. Adducts in the syn-conformation may be more easily recognized and excised as they induce greater distortion in the DNA [43]. Turesky et al. [43] showed that C8-dG IQ adduct was removed, whereas the N2-dG IQ adduct was persistent. In bacteria, mutations occur primarily at G·C base pairs [44][45], and IQ gives frameshift mutations in (CG)n repeats. Similar levels of mutations are observed in mammalian hprt [46] and ef-2 [47] gene assays.</p><!><p>Deoxyadenosine and deoxyguanosine are hydroxylated at C(8) to form 7,8-dihydro-8-oxoadenine (8-oxo-dA) and 7,8-dihydro-8-oxoguanine (8-oxo-dG) (Fig. 5) [48][49]. These lesions tautomerize [50][51]; the 6-amino-8-oxo and 6,8-dioxo [50–52] species predominate for 8-oxo-dA and 8-oxo-dG, respectively, in oligodeoxynucleotides [53][54]. DNA Synthesis proceeds past both lesions. High-fidelity polymerases insert dCTP or dATP opposite 8-oxo-dG in varying proportions dependent upon the polymerase, with extension preferentially occurring from 8-oxo-dG·A base pairs [55–58]. Consistent with this observation, 8-oxo-dG induces primarily G→T transversions in human cells [59]. The Y-family polymerases Dpo4 [60–62] and Pol η [63][64] preferentially insert dCTP over dATP opposite 8-oxo-dG, but favor extension from the 8-oxo-dG·C pair, thus allowing error-free bypass.</p><p>The 8-oxo-dG lesion perturbs base interactions and backbone conformations in single-stranded DNA [65]. In vitro, 8-oxo-dG pairs with all four dNTPs [66], but preferentially with A and C [58]. Studies of 8-oxo-dG and different complementary bases indicate that one base generally adopts the syn-conformation in the purine· purine pairs [67–69]. Thus, 8-oxo-dG mismatched with A adopts the syn-conformation, and the complementary A adopts the anti-conformation about the glycosyl bond [70][71], similar to G·A mispairs [69][72] (Fig. 6). This could explain the mis-insertion of dATP opposite 8-oxo-dG, yielding G→T transversions. In contrast, when 8-oxo-dG pairs with C, both 8-oxo-dG and C adopt the anti-conformation about the glycosyl bond, and the 8-oxo-dG·C pair forms Watson–Crick H-bonds [53][73]. Template distortion associated with 8-oxo-dG (anti) complementary to primer terminus dC has been observed for T7 [74] and for the Bacillus Pol I fragment BF [75]. In contrast, neither the template nor the polymerases were affected by 8-oxo-dG (syn) opposite primer terminus dA, possibly enabling the 8-oxo-dG·A base pair to evade proofreading by T7 [74] or Bacillus Pol I fragment BF [75]. This may also provide an explanation for extension by these polymerases from the 8-oxo-dG·A base pair [55–58]. Structures of 8-oxo-dG opposite A, C, T, or G, and the next nascent base pair in ternary complexes with the Dpo4 polymerase show that neither the template backbone nor the structure of the active site are perturbed by the 8-oxo-dG·C or 8-oxo-dG·A pairs [76]. However, the 8-oxo-dG·A pair adopts both the 8-oxo-dG (syn)·A (anti) and 8-oxo-dG (anti)·A (syn) alignments. This may explain the poor primer extension from the 3′-terminal primer base A by the Dpo4 polymerase. In the case of the 8-oxo-dG·C pair, the unperturbed Dpo4-active site explains the efficient primer extension. The OGG-2 glycosylase repairs 8-oxo-dG paired with G or A [77]. The E. coli glycoslyase MutY repairs 8-oxo-dG·G mispairs [78]. A duplex containing the 8-oxo-dG·G mispair has been examined. The 8-oxo-dG lesion adopts the syn-conformation about the glycosyl bond. The damaged base is inserted into the helix (Fig. 7) [79]. With 8-oxo-dA, dTTP is predominantly incorporated by Y-family polymerases, yielding error-free bypass [80]. In COS-7 cells, the mutagenicity of 8-oxo-dA is four times lower than that of 8-oxo-dG [81]; it induces mainly A→C transversions. The mutation frequency and spectrum associated with 8-oxo-dA depends on the sequence context of the lesion. In the 5′-TXG-3′ sequence, the mutation frequency was 1.2%. In the 8-oxo-dA·G mispair 8-oxo-dA adopts the syn-conformation, while the complementary G adopts the anti-conformation [82] (Fig. 8).</p><!><p>These arise from the reaction of vinyl chloride with DNA. The 3,N4-εdC adduct (Fig. 9) is mutagenic. In vitro studies showed that the mammalian polymerases α and δ predominantly incorporate dATP or dTTP opposite 3,N4-εdC, while polymerase β incorporates primarily dCTP [83]. In vitro replication studies with a DNA template containing 3,N4-εdC using the Klenow exo− fragment of DNA polymerase I showed primarily dATP and dTTP incorporation opposite the lesion [84–86]. The structures of 3,N4-εdC opposite dA, dT, dG, or dC have been determined [87–90]. When placed opposite dT in duplex DNA, 3,N4-εdC adopts the syn-conformation about the glycosyl bond, while the complementary dT adopts the anti-conformation [88] (Fig. 10, a). The 3,N4-εdC (syn)·T (anti) alignment is stabilized by a H-bond between T N(3)H and 3,N4-εdC C(4)–N. This base pair stacks with the flanking base pairs. The conformational change of 3,N4-εdC from the anti- to syn-orientation and maintaining coplanar alignment may explain the facilitation of misincorporation of dTTP opposite 3,N4-εdC by DNA polymerases.</p><p>When placed opposite dA, 3,N4-εdC and the complementary dA remain in the anti-conformation about the glycosyl bond [87]. The 3,N4-εdC (anti)·dA (anti) pair adopts a staggered conformation in which each nucleotide displaces 5′-side and intercalates between the bases on the complementary strand (Fig. 10, b). The partial intercalation of 3,N4-εdC (anti) and dA (anti) bases produces stacking between the aromatic rings of 3,N4-εdC and dA, and with flanking base pairs. Steric factors preclude a coplanar alignment between 3,N4-εdC and dA. Nevertheless, dATP is preferentially incorporated opposite 3,N4-εdC in vitro [86], and both dATP and dGTP are incorporated opposite this lesion in cells [91]. The structural data suggest that the partially intercalated structure may promote translesion synthesis past this lesion.</p><p>When placed opposite dG, both 3,N4-εdC and dG adopt the anti conformations [89]. The 3,N4-εdC is displaced and shifts towards major groove, while the complementary dG remains stacked (Fig. 10, c). The 3,N4-εdC (anti)·G (anti) alignment is stabilized by H-bonds involving dG. The structural studies corroborate the low mutagenicity of 3,N4-εdC in E. coli, suggesting that 3,N4-εdC (anti)·G (anti) pairing occurs during replication.</p><p>The 1,N6-εdA adduct (Fig. 9) induces A→C transversion. When placed opposite dG, 1,N6-εdA adopts the syn-conformation about the glycosyl bond, positioning the exocyclic ring toward the major groove, while dG adopts the anti-conformation [92]. The 1,N6-εdA (syn)·G (anti) alignment is stabilized by two H-bonds from the N(1)H imino H-atom and one H-atom from NH2 of dG to N(9) and N(1) of 1,N6-εdA, respectively (Fig. 11). The syn conformation of 1,N6-εdA is accommodated without disruption of flanking base pairs and may account for the incorporation of dGTP opposite 1,N6-εdA during replication. In mammalian cells, the alkylpurine-DNA-N-glycosylase recognize and repair 1,N6-εdA by a similar mechanism as proposed for 1,N2-εdG adduct (for a review, see [93]).</p><p>The 1,N2-εdG lesion (Fig. 9) is released from DNA by both the E. Coli mismatch-specific uracil DNA glycosylase and the human alkylpurine-DNA-N-glycosylase [94]. The flipping of damaged nucleotides out of the helix and into the glycosylase-active site provides a mechanism by which the DNA glycosylases interact with damaged DNA [93]. At neutral pH, 1,N2-εdG equilibrates between the syn- and anti-conformations about the glycosyl bond. At acidic pH, it adopts the syn-conformation, and the complementary dC adopts the anti conformation (Fig. 12, a) [95]. This 1,N2-εdG (syn)· dC (anti) pair is stabilized by Hoogsteen H-bonds. 1,N2-εdG introduces a localized perturbation involving the modified base pair and its 3′- and 5′-neighbor base pairs. The 3′-neighbor dG·dC base pair also equilibrates between Watson–Crick and Hoogsteen pairs (Fig. 12, b). At basic pH, both 1,N2-εdG and the complementary dC adopt the anti-conformation about the glycosyl bond (Fig. 12, c) [96]. The etheno moiety is inserted into the duplex, and dC is displaced. No H-bonding is observed between the base pairs. A similar conformational transition is observed for 1,N2-εdG opposite dC in the 5′-CXC-3′ sequence [97]. The decreased melting temperature of the DNA containing the 1,N2-εdG adduct [95][96] and its conformational exchange in duplex DNA, at neutral pH, may facilitate damage recognition [94].</p><p>Product analysis from the primer extension studies suggests that the Dpo4 polymerase uses several mechanisms, including dATP incorporation and also a variation of dNTP-stabilized misalignment to bypass 1,N2-εdG lesions [98]. Insertion of the correct nucleotide might be facilitated by the syn-conformation of 1,N2-εdG, which would allow Hoogsteen pairing with incoming dCTP. However, in structures of 1,N2-εdG inserted into a template containing the 5′-TXG-3′ sequence, and the formation of either binary or ternary complexes with the S. solfataricus DNA polymerase Dpo4, 1,N2-εdG adopted the anti-conformation about the glycosyl bond [98], as does the structurally similar 1,N2-propanodeoxyguanosine (PdG) adduct [99].</p><!><p>When incorporated into the 5′-(CpG)4-3′ frameshift hot-spot of the hisD3052 gene carried on an M13 vector, PdG (Fig. 13) induces frameshift mutations [100]. In both E. coli and simian kidney COS-7 cells, G→T transversions are observed; in SOS-induced E. coli, G→A transitions are also observed. The mutation frequency for single-stranded DNA that contains PdG is 100% in non-SOS-induced E. coli, 68% in SOS-induced cells, but only 8% in COS-7 cells [91]. The structure of PdG adduct has been studied in a variety of sequence contexts [101–110]. Its characterization paired to dC has been performed in two sequences, within a d(CG)3-iterated repeat and in the 5′-TXT-3′ sequence. In both, it adopts the syn conformation about the glycosyl bond, forming a PdG(syn)·C+(anti) Hoogsteen-like pair (Fig. 14). In the 5′-(CG)3-3′ iterated repeat, the 3′-neighbor dG also interconverts between syn- and anti-conformations, generating multiple structures. A correlation between the formation of tandem Hoogsteen base pairs and the two-base deletion mutations observed in the d(CG)3 context has been proposed. PdG also displays conformational exchange about the glycosyl bond when placed opposite dA. The syn-conformer forms two H-bonds with protonated dA; the anti-conformer partially intercalates between its partner dA and the 5′-neighbor nucleotide (Fig. 15). The PdG· G pair is similar to the PdG·A pair in that PdG adopts the syn-conformation about the glcyosyl bond and forms two H-bonds with the complementary dG.</p><!><p>A metabolic product of equilin and equilenin, formulated in the hormone replacement therapy drug Premarin [111][112], 4-hydroxyequilenin (4-OHEN), auto-oxidizes to form cytotoxic quinoids [111]. These alkylate DNA [113–118] to form cyclic dC, dA, and dG adducts [112][115][119][120]; structures of four stereoisomeric adducts have been identified [119][120] for each modified base [121]. The 4-OHEN-dC (Fig. 16) adduct predominates. Two stereoisomeric dA and three dG 4-OHEN adducts have been found in the mammary fat pads of rats upon 4-OHEN injection [115], and dA, dG, and dC adducts have been detected in breast tissues of patients [122]. The Watson–Crick edge of 4-OHEN-derived adducts is obstructed by the formation of the cyclic ring (Fig. 16).</p><p>The conformations of the four 4-OHEN-C stereoisomers have been examined in the 5′-GXT-3′ sequence by computational approaches [123][124]. The calculations suggest that the lesions are located in the major or minor groove with the modified cytosines adopting the syn- or anti-conformations, respectively. The configuration of the 4-OHEN-dC adducts orients the equilenin rings with respect to the 5′→3′ direction of the modified strand, and positions the equilenin CH3 and OH groups. Calculations have also been performed on stereosiomeric 4-OHEN-dA adducts [124][125]. The 4-OHEN-dC adducts differ structurally as compared to 4-OHEN-dA adducts in terms of H-bonding, stacking, bending, groove dimensions, solvent exposure, and hydrophobic interactions.</p><p>The 4-OHEN-dC adduct induces C→G and C→A transversions when a supF shuttle vector plasmid system is propagated in human cells [126]. Polymerases bypass the 4-OHEN-dC and dA lesions with efficiencies depending upon configuration and the identity of the damaged base [127–129]. Primer extension conducted with the polymerases Dpo4, Pol η, and Pol κ indicate that 4-OHEN-dC is bypassed with insertion of an incorrect dNTP or by strand slippage [128][130]. With Pol κ, both dCTP and dATP are inserted opposite stereoisomeric 4-OHEN-dC adducts, and primer extension with complementary dC is greater than that with complementary dA [128]. The insertion of dGTP is inefficient. With Pol η, the bypass frequencies of 4-OHEN-dC stereoisomers [128] differ by two orders of magnitude. With Pol η, both insertion of dATP and extension are greater than those for dGTP, the correct nucleotide. For 4-OHEN-dA, the bypass frequency also depends upon configuration [129]. However, both pols η and κ insert the correct nucleotide dTTP opposite 4-OHEN-dA [129]. Mismatched dATP and dCTP products are also observed for Pol κ and Pol η, respectively.</p><!><p>DNA Adducts arising from polycyclic aromatic hydrocarbons (PAHs) undergo more extensive conformational rearrangements, allowing the large planar aromatic moiety to intercalate into the DNA helix, or orient in the minor or major groove of the DNA. The complex subject of PAH structure–activity relationships has been reviewed by Geacintov et al. [131][132], and Broyde et al. [133]. Exposures to PAH are associated with cancer etiology [134–137]. Benzo[a]pyrene (B[a]P) is one of the most common PAHs [138]. It is metabolized to 9,10-epoxy-7,8-diol stereoisomers, principally the (+)-(7R,8S,9S,10R)-enantiomer (B[a]PED), although minor amounts of the (−)-(7S,8R,9R,10S)-enantiomer also form [139]. These diols alkylate the N2-dG position by trans-addition to C(10) of B[a]PED, but minor amounts of cis-addition are observed [140][141]. The lesions form efficiently in the presence of m5dC in 5′-CpG-3′ sequences that are recognized by DNA methyltransferases [142–144]. With respect to structure–activity relationships, the most studied adducts are (+)-trans-B[a]P-N2-dG, (+)-cis-B[a]P-N2-dG, (−)-trans-B[a]P-N2-dG, and (−)-cis-B[a]P-N2-dG [131]. For the (+)-trans- and (−)-trans-B[a]P-N2-dG adducts, the pyrenyl moieties orient in the minor groove, pointing either into the 5′- or the 3′-directions of the modified strand, respectively, relative to the modified guanine [145][146]. The (+)-cis-B[a]P-N2-dG adduct differs, having a base-displaced intercalative conformation with the modified guanine and the complementary cytosine displaced into the minor and major grooves, respectively [147]. Sequence-dependent and stereospecific conformational differences play an important role in the structural biology of PAH adducts [148].</p><p>If not repaired, the B[a]P-N2-dG adducts (Fig. 17) induce mutations [149–152]. In cell-free extracts, removal of the (+)-trans-B[a]P-N2-dG lesion by the NER apparatus depends on the base sequence in which it is embedded. The rate of incision of the lesion by the prokaryotic UvrABC system is two-fold greater in the 5′-TXT-3′ than in the 5′-CXC-3′ sequence. The former sequence exhibits a lower thermal stability [153]. In the 5′-CXC-3′ sequence, the pyrenyl moiety of (+)-trans-B[a]P-N2-dG resides in the minor groove in the 5′-direction along the modified strand [145]. In the 5′-TXT-3′ sequence, a similar motif is accompanied by increased conformational heterogeneity [154]. Furthermore, the 5′-CXC-3′ sequence is characterized by a rigid bend, whereas the 5′-TXT-3′ sequence is characterized by a more flexible bend [155]. The two sequences have also been investigated using molecular dynamics (MD) calculations [156][157]. The MD results highlight the importance of local dynamics in the vicinity of the lesion and show that the 5′-TXT-3′ sequence is more flexible, and exhibits weaker Watson–Crick H-bonding adjacent to the lesion, poorer stacking interactions, local roll/bending dynamics, and minor groove flexibility. In the 5′-TXC-3′ sequence, a similar minor groove conformation is observed, but a minor conformation involving insertion of the BP moiety into the duplex with disruption of Watson–Crick H-bonding at the lesion site is proposed [158]. Sequence effects on the minor groove conformations of this adduct in the 5′-CXG-3′ and 5′-GXC-3′ sequences have also been reported [159] in the 5′-GG-3′ mutation hotspot context. Cai et al. [157] have proposed that the amino groups in tandem 5′-GG-3′ sequences modulate the efficiency of NER.</p><p>Structures of DNA template·primers containing the (+)-trans-B[a]P N2-dG adduct, complexed with the S. sulfataricus Dpo4 polymerase, have been examined [160]. In one, the adducted base mispairs with adenine at the template·primer junction, and an incoming dATP is opposite template dT 5′ to the lesion. This corresponds to efficient incorporation of dATP; moreover, the X·A mispair is efficiently extended by the polymerase. Significantly, the B[a]P intercalates, occupying space corresponding to one base pair between the last two bases at the primer strand terminus [160]. The damaged base is in the syn-conformation about the glycosyl bond and shifts into the minor groove. Base pairing is disrupted both at the X·A mispair and the T·dATP insertion complex 5′ to the lesion. The 3′ orientation of B[a]P relative to the modified base allows B[a]P to stack with the neighboring bases with its long axis at an angle of 40° with respect to the DNA.</p><p>The B[a]P epoxy diols also react with DNA to form N6-dA adducts (Fig. 17) [161][162]. Repair studies of N6-dA adducts formed by fjord-region B[a]P epoxy diols vs. bay region epoxy-diol metabolites of benzo[c]phenanthrene (B[c]P) show that the bay region adducts are removed, while fjord region adducts are refractory to repair [163]. If not repaired, these N6-dA adducts are mutagenic, correlating with reports on B[a]P-induced mutagenesis at adenines [164–166]. The mutagenic spectra of N6-dA B[a]P adducts depend upon configuration at C(10) position, configurations of the OH groups at C(7), C(8), and C(9), and depend upon DNA sequence [167–169]. For N6-dA B[a]P adducts, C(10) (C(10) is the site of DNA alkylation) (R)-stereoisomers intercalate in the 5′-direction [170–173], whereas C(10) (S)-stereoisomers intercalate in the 3′ direction [174]. The structure of the (+)-cis-B[a]P-N6-dA adduct in a primer extension complex with the Dpo4 polymerase has been obtained, in which the adduct is paired with primer terminus dT, and the incoming dATP pairs with the next undamaged templating base, dT. Two conformations of the (+)-cis-B[a]P-N6-dA adduct are observed. In the first, the B[a]P is intercalated. In the other, it orients in the major groove perpendicular to the DNA base pairs. The (+)-trans-B[a]P-N6-dA adduct, mismatched opposite dG, adopts the syn conformation about the glycosyl bond [175] (Fig. 18). This could explain A→C transversions induced by the (+)-trans-B[a]P-N6-dA adduct.</p><p>Other N6-dA PAH adducts follow a similar pattern with respect to the configuration at the adducted C-atoms. For benz[a]anthracene, this is the C(1)-atom. The (1R)-adduct intercalates to the 5′-side of the damaged base [176], while the (1S)-adduct intercalates to the 3′-side [177]. Likewise, for trans-B[c]P, configuration at C(1) is crucial [178][179]. The (1S)-adduct permits the B[c]P ring to intercalate 3′ to lesion site without disrupting Watson–Crick H-bonding. The (1R)-adduct intercalates to the 5′-side of the modified base pair [178][179]. The greater thermal stabilities of duplexes containing fjord region N6-dA lesions correlate with lower susceptibilities of excision by NER [163]. Computational studies of a template·primer containing the (−)-(1S)-trans-B[c]P-N6-dA adduct in a complex with the Dpo4 polymerase suggest that B[c]P intercalation likely impedes replication [180]. The (+)-trans-benzo[g]chrysene-dA adduct also follows the pattern whereby (R)-configuration at C(14) correlates with 5′-intercalation [181].</p><!><p>Increasingly, the importance of interconversions of DNA adducts involving bond breakage and rearrangement has become recognized. Such rearrangements can be either chemically reversible, i.e., existing at equilibrium, or irreversible. Examples of specific adducts include abasic sites, the epimerization of thymine glycol lesions, deamination and the Dimroth rearrangement of N1-dA lesions, the rearrangement of 1,N2-dG lesions arising from malondialdehyde and α,β-unsaturated enals, and α/β anomerization of formamidopyr-imidine (FAPY) lesions.</p><!><p>Buta-1,3-diene (BD) is extensively used in the polymer industry, e.g., in the manufacture of styrene–butadiene rubber [182][183]. The metabolism of BD is depicted in Scheme 1. Reaction of butadiene epoxides with dA results in the formation of N1-dA adducts. These may undergo hydrolysis of the 6-NH2 group to give the analogous inosine derivatives [184] (Fig. 19). Deamination of dA represents a promutagenic event, because, during DNA replication, the resulting dI nucleotide is recognized as dG and preferentially pairs with incoming dCTP. When ligated into the single-stranded vector M13mp7L2 and transfected into repair-deficient E. coli, the 2′-deoxy-N1-[(2S)-1-hydroxybut-3-en-2-yl]inosine adduct codes for incorporation of dCTP [185]. Similar results are observed with COS-7 cells [186]. Structural analyses reveal that, following deamination of the N1-dA lesion, the glycosyl bond of the (S)-N1-BDO-dI adduct rotates into the syn-conformation, placing the BD moiety into the major groove. The complementary dT (anti) remains intrahelical at the adduct site. The results suggest that the tendency of the (S)-N1-BDO-dI adduct to code for incorporation of dCTP may be attributed to the propensity of this adduct to form a protonated Hoogsteen-pairing interaction with dCTP during replication [186].</p><p>Dimroth Rearrangement of N1-dA Adducts. Alternatively, N1-dA adducts of BD undergo chemical rearrangement to isomeric N6-dA adducts via a Dimroth rearrangement [187], in which ring cleavage between the N(1)- and C(2)-atoms of dA is followed by 180° internal rotation [188]. The consequence is that the initially formed N1-dA lesion, which is anticipated to be highly mutagenic due to the fact that it precludes Watson–Crick bonding during DNA replication, is converted to an N6-dA lesion (Fig. 19), which is projected to orient into the major groove of DNA, resulting in minimal structural perturbation and be less mutagenic. Structural studies of a series of N6-dA adducts of styrene oxide and butadiene epoxides suggests that, indeed, they induce minimal structural perturbation of DNA and are contained within the major groove [189–194]. Likewise, site-specific mutagenesis conducted in E. coli revealed that these adducts are only weakly mutagenic [195][196].</p><!><p>Malondialdehyde (MDA), produced by lipid peroxidation and prostaglandin biosynthesis [197][198], is mutagenic in bacterial and mammalian cells [199–201] and is carcinogenic in rats [202]. In Salmonella typhimurium, MDA induces insertions and deletions as well as base substitutions [200][203][204]. Replication of MDA-modified single-stranded M13 genomes in E. coli causes G→T, A→G, and C→ T mutations [199]. Adduction of MDA to deoxyguanosine produces 3-(2′-deoxy-β-δ-erythro-pentofuranosyl)pyrimido[1,2-a]purin-10(3H)-one (M1dG; Fig. 13) [205][206]. Like PdG, discussed above, M1dG is a 1,N2-dG annelation product, but chemically and biologically, it behaves much differently in DNA. Reddy and Marnett [207] incorporated M1dG into an oligodeoxynucleotide. The spectrum of mutations induced by M1dG using M13 vectors replicated in E. coli showed M1dG →A and M1dG →T mutations, and low levels of M1dG →C mutations; however, the mutation frequency was ca. 1%, when cytosine was placed opposite the lesion [208]. M1dG also induced −1 and −2 base deletions when positioned in an iterated 5′-(CpG)4-3′ sequence, but not when positioned in a non-iterated sequence in both E. coli and in COS-7 cells [209].</p><p>An explanation as to why M1dG is weakly mutagenic was elucidated when it was discovered that when M1dG is placed opposite dC it rearranges to N2-(3-oxoprop-1- enyl)-dG (OPdG; Scheme 2) [210]. Riggins et al. [211][212] concluded that ring opening of M1dG as a nucleoside or in oligodeoxynucleotides occurs via a reversible second-order reaction with hydroxide, catalyzed by the complementary dC. The closure of the resulting N2-(3-oxo-1-propenyl)-dG anion is pH-dependent and under neutral and acidic conditions ring-closure is biphasic, leading to the rapid formation of intermediates that slowly convert to M1dG in a general-acid-catalyzed reaction, in the presence of dC in the complementary strand. Structural studies in the 5′-(CpG)3-3′ sequence show that the OPdG propenyl chain is located in the minor groove, facilitating Watson–Crick H-bonding with dC [213]. Structural studies with OPdG adduct located in the 5′-TXT-3′ sequence lead to a similar conclusion [214].</p><p>Structural studies have also been conducted when M1dG and OPdG adducts are placed opposite a 2-base deletion (2BD) complementary strand in the 5′-(CpG)3 sequence [215–217]. M1dG is stable in the 2BD duplex and remains ring-closed when it is the 5′-nucleotide of a two-nucleotide bulge [215]. Both bulged nucleotides are in the anti conformation about the glycosyl bond and appear inside the helix, but lack H-bonding interactions [216]. On the other hand, the OPdG-2BD duplex undergoes bulge migration from the 3′-neighbor base pairs to the adduct region [217]. The bulge migration transiently positions OPdG opposite dC in the complementary strand and, consequently, hinders the conversion of OPdG to M1dG. Thus, in contrast to the rapid OPdG→M1dG conversion during denaturation [210], the ring-closure of OPdG in this 2-base deletion 5′-(GpC)3 sequence requires 140 days at room temperature. When the M1dG-containing primer-template was crystallized with the polymerase Dpo4 and dNTP, M1dG maintained the ring-closed form. It intercalated into the duplex and displaced the complementary dC to the minor groove [218].</p><!><p>Acrolein is mutagenic in bacterial and mammalian cells [204][219–221], and is carcinogenic in rats [222]. The binding pattern of acrolein-DNA adducts is similar to the p53 mutational pattern in human lung cancer, implicating acrolein as a major cigarette-related lung cancer-inducing agent [223]. Acrolein causes tandem G→T transversions in the supF gene on the shuttle vector plasmid pMY189. Crotonaldehyde is genotoxic and mutagenic in human lymphoblasts [224] and induces liver tumors in rodents [225]. 4-Hydroxynonenal (HNE) is produced from the metabolism of membrane lipids [226] and is a major in vivo peroxidation product of ω–6 polyunsaturated fatty acids [197][227]. HNE induces a DNA damage response in Salmonella typhimurium [228][229]. It also causes mutations in V79 CHO cells, and DNAs from liver specimens from individuals suffering from Wilson's disease and hemochromatosis contain mutations attributed to HNE-dG adducts [230]. Acrolein and related α,β-unsaturated aldehydes undergo Michael addition to dG, also yielding 1,N2-dG ring-annelation products. Like the M1dG lesion, these also undergo further downstream chemistry in DNA, in a sequence-dependent manner. Enal-derived adducts have been detected in human and rodent DNA [231–235]. Harris, Rizzo, and co-workers site-specifically incorporated acrolein, crotonaldehyde, and HNE derived γ-OH-PdG adducts into oligodeoxynucleotides [236–238].</p><!><p>Acrolein reacts with dG to produce γ-OH-PdG and α-OH-PdG (Fig. 13). Correct replication across γ-OH-PdG is efficient in both mammalian and bacterial cells [239–241]. In DNA, γ-OH-PdG undergoes ring opening when placed opposite dC, forming N2-(3-oxopropyl)-dG (Scheme 2) [242]. This facilitates Watson–Crick H-bonding with the complementary dC and could explain the weak mutagenicity of γ-OH-PdG. Nair et al. [243] showed that yeast Rev1 DNA polymerase incorporates the correct nucleotide dC opposite PdG, a model for γ-OH-PdG, with nearly the same efficiency as opposite an undamaged dG. But it cannot extend the primer. However, Pol ζ can carry out the subsequent extension reaction. The crystal structure, in complex with Rev1, showed that PdG rotated to the syn conformation about the glycosyl bond, and the incoming dCTP did not pair with PdG, but instead paired with Arg324 from yeast Rev1 polymerase.</p><p>Unlike γ-OH-PdG, α-OH-PdG (Fig. 13) blocks DNA replication in human cells, and it codes for dCTP incorporation, with minor G→A and G→T base substitution mutations when bypassed by Y-family polymerases [241]. The α-OH-PdG lesion is stable in DNA when placed opposite dC [244]. It adopts the syn-conformation around the glycosyl bond, forming a Hoogsteen-like pair to its complementary cytosine (Fig. 20). In vitro replication using Y-family DNA polymerases showed that Pol η and Pol κ catalyze mutagenic replication across α-OH-PdG, while Rev1 and Pol τ mediate accurate replication, with the later incorporating dATP and dTTP at low frequencies [245]. As for γ-OH-PdG, the ternary complex containing Rev1, PdG-modified DNA, and dCTP showed that PdG rotates to the syn conformation about the glycosyl bond, which positions PdG into a small hydrophobic cavity, while the incoming dCTP interacts with an Arg residue by forming two H-bonds [243]. Thus, the structure of α-OH-PdG reported by Zaliznyak et al. [244] identified the bonds that would keep the α-OH-PdG in the syn-conformation at the replication fork of Pol τ. This supports the formation of a non-mutagenic α-OH-PdG (syn)·C (anti) replication intermediate.</p><!><p>Similar methodology has been used to study the crotonaldehyde-derived γ-OH-PdG adduct, in which the α-CH3 group creates a new stereogenic center at C(6) [246]. Four stereoisomers are possible for the crotonaldehyde-derived γ-OH-PdG adducts; those with trans-configuration of γ-OH and α-CH3 predominate. In single-strand DNA, the major and minor epimers at C(8) interconvert. The ring-opened intermediate is undetectable in single-strand DNA. The diastereoisomeric adducts have been placed opposite dC and dT in DNA. The crotonaldehyde-derived γ-OH-PdG adducts exhibit higher stability than does the acrolein-derived adduct. When placed opposite dT, the ring-opened species is undetectable. On the other hand, both (R)- and (S)-α-CH3-γ-OH-PdG adducts undergo ring-opening to the N2-dG aldehydes and corresponding N2-dG aldehydrols when placed opposite dC (Scheme 2). However, the ring-opening is incomplete. Higher pH and temperatures favor the N2-dG aldehyde adducts. The structure of the (S)-α-CH3-γ-OH-PdG adduct shows the ring-opened (S)-α-CH3-N2-dG aldehyde adduct forms Watson–Crick pairing with the complementary dC, leaving the aldehyde moiety within the minor groove. The aldehyde of the N2-dG aldehyde adduct orients in the 3′-direction, while the (S)-α-CH3 group orients in the 5′-direction [247].</p><!><p>HNE reacts with the N2-amino group of dG to give four diasteromeric 1,N2-dG adducts (Fig. 21) [248–250], and all of them are detected in cellular DNA [231][251–256]. HNE is mutagenic [227] and carcinogenic in rodent cells [257]. HNE induces primarily G→T transversions, accompanied by lower levels of G→A transitions, in the supF gene of shuttle vector pSP189 replicated in human cells [258]. The site-specific mutagenesis studies showed that the (6S,8R,11S)- and (6R,8S,11R)-1,N2-HNE-dG adducts are mutagenic, inducing low levels of G→T transversions and G→T transitions. The initial studies revealed that, when 1,N2-HNE-dG adducts are placed opposite dC in duplex DNA, the exocyclic ring opens, leaving intact Watson–Crick base pairing for the coding face of the adducted dG (Scheme 2) [259]. This accounts for the low levels of mutations associated with these adducts. When mismatched with dA in DNA, (6S,8R,11S)-1,N2-HNE-dG maintains its exocyclic structure [260]. This duplex mimics the situation following incorrect incorporation of dATP opposite the (6S,8R,11S)-1,N2-HNE-dG adduct (G→T transversion). The adduct undergoes a conformational equilibrium between the syn- and anti-conformations about the glycosyl bond. At basic pH, the equilibrium shifts toward the anti-conformation where 1,N2-HNE-dG intercalates and displaces the complementary dA in the 5′-direction (Fig. 22). The HNE aliphatic chain is oriented toward the minor groove of the DNA. At acidic pH, the equilibrium shifts toward the syn-conformation in which the HNE moiety is located in the major groove (Fig. 22). The complementary adenine is protonated, and the (6S,8R,11S)-1,N2-HNE-dG (syn)·dA+ (anti) base pair is stabilized by Hoogsteen type H-bonding. Thus, at neutral pH, both the syn- and anti-conformations are present. Xing et al. [248] attributed the low levels of G→T transversions to the reorientation of the (6S,8R,11S)-1,N2-HNE-dG adduct into the syn conformation around the glycosyl bond, which might allow misincorporation of dATP opposite the lesion. The results confirm that such reorientation happens, when (6S,8R,11S)-1,N2-HNE-dG is mismatched with dA in DNA.</p><!><p>Hydrolytic cleavage of the nucleobase from the 2′-deoxyribose creates the abasic site, or apurinic and apyrimidinic sites (AP; Scheme 3). There probably is a constant level of ca. 10,000 abasic sites in typical human cells [261]. Most AP sites result from spontaneous depurination [261], but deamination of cytosine to uracil, which is then eliminated by uracil glycosylases, also occurs [262]. The abasic site is also an intermediate in the base excision repair process [263]. AP Sites are mutagenic [264] and cause mis-incorporation in bacterial and mammalian cells [265–271]. DNA Polymerases preferentially incorporate dATP opposite AP sites (sometimes referred to as the 3A-rule3) [272–274]. During translesion DNA synthesis by Y-family DNA polymerases, AP sites also cause frameshifts [275–278].</p><p>The AP site exists in three configurations, the cyclic hemiacetal 1 (a and b), the aldehyde 2, and the hydrated aldehyde 3 (Scheme 3). The equilibria of these species in different sequences, when AP sites are placed opposite all four 2′-deoxynucleotides [279][280], show that cyclic hemiacetal 1 predominates and constitutes over 99% of the population. Less than 1% of aldehyde 2 is also observed, whereas 3 is undetectable.</p><p>The equilibrium of AP species leads to the anomeric configuration interconversion of the deoxyribose. Stereoisomers 1a and 1b exist in equilibrium, when the AP site is placed opposite dA, dC, dG, or dT [279]. Beger and Bolton [281] reported that the β-anomer exists predominantly, when the AP site is placed opposite dC in the 5′-AXA-3′ sequence (X =AP site), whereas both α- and β-anomers exist, when AP is placed opposite dA. They proposed that a H2O-bridged H-bond with the complementary dC might contribute to the stereoselectivity. Based on the structure of the human ApeI endonuclease/THF-containing duplex, from which it was concluded that the β-anomer could not be accommodated in the active site of the enzyme, Mol et al. [282] proposed that the endonuclease only incises the α-anomer. However, other binding studies of AP surrogates with this enzyme show no differences for the two anomers and lead to the opposite conclusion that the configuration at C(1′) of deoxyribose is not important for enzymatic recognition [283].</p><p>AP Sites decrease the stability of DNA, depending especially on the identities of the flanking base pairs but only mildly on the orphan base [284][285]. The structures of α- and β-anomer of the AP site have been compared. Structural and dynamic studies have been performed, when AP or AP surrogates are placed opposite dA, dC, dG, or dT in different sequences [286]. The conformation around the AP site is more perturbed, when the base opposite to the AP is a pyrimidine than a purine [287][288]. The deoxyribose position depends on the type of orphan base, the configuration at C(1′) of the deoxyribose, and the flanking base pairs. Goljer et al. [289] reported that, when dA is the orphan base, the α-anomer of the natural AP site adopts an extrahelical conformation, whereas the β-anomer adopts an intrahelical conformation. Further studies by the same group, however, showed that both AP anomers stack interhelically in different sequences [281]. When AP is placed opposite dA, a H2O-mediated H-bond has been proposed [281]. This forms between the orphan dA and the hemiacetal OH group of the β-anomer, and maintains the deoxyribose moiety inside the helix. Formation of a H2O bridge is impossible for the α-anomer, which then adopts an extruded conformation [281]. When the AP site was placed opposite dC, the α-anomer was not present. Rather, two conformations of the β-anomer were observed, depending on the H2O-mediated H-bond between the OH group and the orphan cytosine N(3) or C(2)=O. Both H-bonds keep the β-anomer inside the helix [281]. Recent structural and dynamic studies with AP and AP surrogates confirm that both AP anomers are inside the helix [283][287][288]. However, the OH group of both α-AP and β-AP is more likely to form H-bonds directly with opposite dA, dC, and dT [287][288]. The biological effects of the anomers of the AP lesion remain unclear.</p><!><p>The oxidation of thymine and 5-methylcytosine produces 5,6-dihydro-5,6-dihydroxy-2′-thymine, thymine glycol (Tg) [290][291]. Tg has been detected in animal and human urine; human cells probably repair hundreds of these lesions per day [292][293]. Tg blocks DNA replication [294–296] and induces base-substitution mutations [297]. It inhibits DNA synthesis by many prokaryotic and eukaryotic DNA polymerases one nucleotide before and opposite the lesion site. Several DNA polymerases lacking 3′,5′ exonuclease activity can bypass the Tg lesion, albeit slowly [298–300].</p><p>Tg exists in DNA as two diastereoisomeric pairs of epimers, the (5R)-cis,trans-pair (5R,6S);(5R,6R) and the (5S)-cis,trans-pair (5S,6R);(5S,6S) (Scheme 4) [301–303]. The (5R) pair is more abundant and more stable [302]. For both the (5R)- and (5S)-pairs, the cis-epimers predominate at the nucleoside level [302]. The biological responses to Tg adducts are modulated by configuration. For example, the Y-family polymerase Pol η bypasses the (5R)-epimers more efficiently [304], whereas Pol κ bypasses the (5S)-epimers more efficiently [305]. The human hNTH1 glycosylase shows a greater preference for excising the (5R)-epimers [306], whereas the hNEIL1 glycosylase shows a greater preference for excising the (5R)-epimers [307][308]. Similar observations have been made for prokaryotic, yeast, and murine glycosylases [309]. The base excision repair of Tg lesion by DNA N-glycosylases/AP lyases is modulated by the cis/trans-epimerization of these two sets of diastereoisomers [310].</p><p>The (5R) epimers have been studied, when Tg is placed opposite dA or dG in the 5′-GXG-3′ (X=Tg) sequence [311]. For the duplex containing the X·A pair, the ratio cis-(5R,6S)/trans-(5R,6R) is 7 :3 at 30°. In contrast, for the duplex containing the X·G pair, the cis-(5R,6S)-epimer predominates; the trans-(5R,6R) epimer is undetectable. The introduction of (5R)-Tg in the 5′-AXA-3′ and 5′-GXC-3′ sequence contexts when paired opposite dA induces localized structural perturbations with the loss of H-bonding at the lesion sites [312][313]. Tg is displaced toward the major groove, increasing its exposure to the solvent. In contrast, when paired opposite dA in the 5′-GXG-3′ sequence, the cis-(5R,6S)-Tg lesion only minimally distorts the helical backbone [314]. Both Tg and the complementary dA insert into the helix and remain in a Watson–Crick alignment (Fig. 23,a and b). However, stacking between Tg and the 3′-neighbor G·C base pair is disrupted. Two conformations are obtained for the cis-(5R,6S)-Tg, depending on the axial or equatorial conformations of the Me group. The NMR-based MD simulations predict that the axial conformation of the cis-(5R,6S)-Tg is favored. An intrastrand H-bond observed between the Tg C(6)–OH and the N(7) position of a 3′-purine may account for the structural differences the cis-(5R,6S)-Tg in the 5′-GXG-3′ and 5′-GXC-3′ sequence contexts. Consistent with the structure in the 5′-GXG-3′ sequence, when (5R)-Tg-containing binary primer-template complex is co-crystallized with the replicative RB69 DNA polymerase, the cis-(5R,6S)-Tg epimer is intrahelical and forms a Watson–Crick base pair with the dA at the primer 3′-terminus. The Tg Me group is in the axial conformation, hindering stacking of the adjacent 5′-template guanine. These results provide a rationale for the observation that extension past the (5R)-Tg lesion by the Klenow fragment of E. coli DNA polymerase I or T4 DNA polymerase is prohibited.</p><p>The base excision repair of Tg lesions also depends upon the identity of the opposing base. The hNth glycosylase repairs the cis-(5R,6S)-Tg more efficiently when it is placed opposite adenine than when placed opposite guanine [310]. Thus, the solution structure of cis-(5R,6S)-Tg-containing oligodeoxynucleotide has also been refined in the 5′-GXG-3′ sequence when mismatched with dG (Fig. 23,c and d) [315]. Both Tg and the opposite dG remain stacked in the helix. The Tg·G base pair is in the wobble orientation. The Tg shifts toward the major groove and stacks below the 5′-neighbor base, its complement dG also stacks below the 5′-neighbor. Stacking between the Tg and the 3′-neighbor base pair is disrupted. In contrast to when placed opposite dA, the cis-(5R,6S)-Tg does not form strong intrastrand H-bonds with the imidazole N(7)-atom of the 3′-neighbor purine.</p><p>Alkaline hydrolysis of Tg produces urea adduct (Scheme 4), which exists as the minor species of thymine oxidation by exposure to the ionizing radiation [290][316]. Urea adducts constitute replicative blocks for DNA polymerases and subsequently inhibit chain elongation [295][317]. It also preferentially promotes misincoporation of dGTP [318]. To explain the biological effects, a structure of the nucleotide containing urea adduct has been studied, when dT or dG is placed opposite [319]. Whereas the conformation is undeterminable due to the broad NMR resonances when placed opposite dG, the urea deoxyribose exists as two conformations when placed opposite dT, depending on the cis/trans-orientation of the urea unit with respect to the deoxyribose. The trans-anti-isomer, which exists as the major species, is expected to be placed intrahelically and form H-bonds with opposite thymine. The cis-isomer is unable to form these H-bonds and exists as the minor species.</p><p>The urea adduct has also been placed in a −1 frame shift sequence [320]. Again, at equilibrium, two species are found in slow exchange. The urea adduct is either intra- or extrahelical within the right-handed DNA duplex, determined by the H-bonds formed by the cis/trans-isomers. In the minor intrahelical species, the cis-isomer forms two H-bonds with the bases in the opposite strand, whereas the trans-isomer does not. For the major extrahelical species, the trans-isomer forms two H-bonds, one is intrastrand with the guanine in the modified strand, the other is interstrand with the adenine in the opposite strand, whereas the cis-isomer only forms one H-bond. In the major species, the urea residue lies in the minor groove, and the neighboring bases are stacked over each other in a way similar to a normal B-DNA structure.</p><!><p>The fact that initially formed 8-oxo-dG lesions, discussed above, can undergo further oxidation, has become appreciated in recent years. The 8-oxo-dG is more prone to oxidation than is guanine [321], its downstream oxidation products are guanidinohydantoin (Gh), and two stereoisomers of spiroiminodihydantoin ((R)-Sp and (S)-Sp) (Fig. 24) [322–324]. Gh predominates in duplex DNA, while Sp predominates at the nucleoside level [325]. Sp has been detected in DNA from E. coli cells [326]. Gh-Containing DNA is bypassed by E. coli DNA pol I, which inserts dATP or dGTP opposite the lesion [327]. However, Gh and Sp are strong replication blocks to Pol α and human Pol β [327]. Gh is mutagenic with DNA polymerases incorporating purines, which results in G·C→T·A or G·C→C·G transversions [328][329]. A structure of the replicative RB69 DNA polymerase in complex with DNA containing Gh revealed that Gh is extrahelical and rotated toward the major groove [330]. In contrast to 8-oxoG, Gh in this structure was in a high syn-conformation and presented the same H-bond donor and acceptor pattern as thymine, which may explain why polymerases incorporate a purine opposite Gh during error-prone bypass [330].</p><!><p>Aflatoxin B1 (AFB) is a mycotoxin that contaminates agricultural products [331]. It is a mutagen in bacteria [332–334], a carcinogen in fish [335][336], and a carcinogen in rodents [337][338]. AFB is mutagenic, with G→T transversions being observed in a variety of prokaryotic and eukaryotic systems [334][339][340]. Aflatoxin exposures are implicated in mutations to the p53 tumor suppressor gene [341–344], and the mycotoxin is implicated in the etiology of human liver cancer [345–348].</p><p>The reaction of N7-dG at C(8) of the electrophilic AFB-exo-8,9-epoxide yields the N7-dG cationic adduct (Scheme 5) [349–352]. This adduct yields the characteristic G→T mutations but is only moderately mutagenic in E. coli [353]. The N7-dG cationic adduct is chemically unstable [354][355]. At physiological pH, depurination occurs, to release AFB-guanine and yield an apurinic site [355]. Of greater concern is the potential for base-catalyzed cleavage of the imidazole ring to yield the formamidopyr-imidine (FAPY) derivative. Two equilibrating AFB-FAPY species in duplex DNA exhibit different biological consequences in DNA replication: one of the two species is potently mutagenic, yielding G→T mutations, while the other blocks replication [356]. These have been assigned to the α- and β-anomers of the FAPY adduct [357][358]. In duplex DNA, the β-anomer predominates [358]. In single-strand DNA, however, a 2 :1 mixture of α- and β-anomers is observed [356].</p><p>Both the initially formed N7-dG cationic adduct and the FAPY rearrangement products have been examined as to structure in DNA. In all instances, the AFB moiety intercalates above the 5′-face of the damaged guanine [358–362]. When dC is placed opposite the N7-dG cationic adduct, Watson–Crick base pairing is maintained for the AFB-N7-dG·dC base pair. With regard to the highly mutagenic β-anomer of the FAPY species, Watson–Crick H-binding is conserved for the FAPY·dC and the flanking base pairs (Fig. 25). In duplex DNA with a 3′-neighboring adenine, both anomers adopt the (E)-conformation of the formyl O-atom, involving a H-bond between the formyl oxygen and the non-Watson–Crick H-bonded N6-amine H-atom of the neighboring adenine [362].</p><p>Significantly, the α-FAPY anomer decreases the stability of the DNA duplex, whereas the β-anomer increases the stability [362]. This suggests that it may be refractory to nucleotide excision repair processes in vivo. Favorable interstrand stacking is considered to be the main factor of the stability of AFB-β-FAPY. In contrast, the α-anomer disrupts the helical structure of DNA [362]. Intercalation of the AFB moiety induces perturbations in the phosphodiester and backbone torsion angles, ε and ζ, respectively, and the deoxyribose shifts to become parallel to the FAPY base and is displaced toward the minor groove. Intrastrand stacking between the AFB moiety and the 5′-neighbor thymine remains, but interstrand stacking is not observed. As a result, compared with the unmodified sequence, the α-FAPY anomer destabilizes the duplex.</p><!><p>DNA Interstrand cross-links represent one of the most serious types of damage, since replication and transcription require separation of the complementary strands. In eukaryotes, interstrand cross-link repair requires cooperation of multiple proteins that belong to different biological pathways, including NER, homologous recombination, TLS, double-strand break repair, and the Fanconi anemia pathway (reviewed in [363–366]).</p><p>The 1,N2-dG enal adducts formed by acrolein, crotonaldehyde, and HNE discussed above are capable of forming interstrand N2-dG:N2-dG cross-links. The formation of the cross-links is sequence dependent. Enals induce interstrand cross-links in the 5′-CpG-3′ sequence, but not in the 5′-GpC-3′ sequence [236–238][367]. The rearrangments of the γ-OH-PdG adducts to the ring-opened N2-dG aldehydes is critical for cross-link formation (Scheme 6). These N2-dG:N2-dG linkages can be chemically reduced [236][237]. This implies that they exist, in part, as imines. When DNA containing the cross-links is enzymatically digested to nucleotides, the cross-links are isolated as pyrimido-purinones [236–238]. However, in duplex DNA, the N2-dG:N2-dG linkages exist predominantly as carbinolamines [246][368][369]. The carbinol-amine linkage maintains Watson–Cricking base pairing [246][369]. The HNE-induced cross-link exhibits extraordinary chemical stability. Whereas less than 50% of acrolein and crotonaldehyde derived γ-HOPdG adducts are converted the cross-links [236][237], the HNE-derived (6S,8R,11S) γ-OH-PdG adduct is fully converted [238].</p><p>Formation of N2-dG:N2-dG cross-links is stereoselective. The (R)-carbinolamine linkage constitutes 80–90% of the cross-link induced by the γ-OH-PdG adduct [370]. Its structure has been refined in the 5′-CpG-3′ sequence (Fig. 26) [370]. The carbinolamine linkage is located in the minor groove, maintaining Watson–Crick H-bonding of the cross-linked base pairs. The anti-conformation of the carbinol OH group with respect to Cα of the tether minimized steric interactions, and allowed the formation of a H-bond between the carbinol OH group and the cytosine C(2)=O located in the 5′-neighboring G·C base pair. This H-bond might explain the stability of this cross-link, and the stereochemical preference for the (R)-configuration of the cross-link.</p><p>The preferential formation of the cross-link in the 5′-CpG-3′ sequence as compared to the 5′-GpC-3′ sequence is attributed to the longer distance between the two N2-dG atoms in the 3′-direction than in the 5′-direction, accompanied by greater destabilization of the DNA duplex by the cross-link in the 5′-GpC-3′ sequence [371][372]. Structures of the fully reduced trimethylene cross-links have been refined in both 5′-CpG-3′ and 5′-GpC-3′ sequences [373]. Whereas perturbation caused by the cross-link in the 5′-CpG-3′ sequence is minimal, the perturbation in the 5′-GpC-3′ sequence is significant (Fig. 27). Differential stacking of the base pairs at the cross-linking region explains the difference in stabilities of the trimethylene cross-links in the 5′-CpG-3′ and 5′-GpC-3′ sequence contexts, and, in turn, account for the sequence selectivity of the interstrand cross-link formation induced by the enal-derived 1,N2-dG adducts.</p><p>Configuration of the γ-OH-PdG adducts also modulates interstrand cross-link formation. The crotonaldehyde derived (R)-CH3-γ-OH-PdG adduct induces N2-dG:N2-dG interstrand cross-links more efficiently than the (S)-CH3-γ-OH-PdG adduct does [237]. The (S)-CH3-γ-OH-PdG places the aldehyde toward the 3′-neighbor A·T base pair in the 5′-CpG-3′ sequence [247]. Conformational adjustment is required to reorient the aldehyde group to the 5′-neighbor C·G base pair, and consequently results in the slow production of the cross-link. Model studies showed that re-orientation of the aldehyde in the 5′-direction led to the interference of the (S)-CH3 group with the 3′-neighbor A·T base pair and decreased the stability of the DNA duplex. The structures of stereospecific α-CH3-trimethylene cross-links, which are used as surrogates for the crotonaldehyde-derived carbinolamine cross-links, supported this conclusion (Fig. 28) [374]. The (S)-isomer of the α-CH3-trimethylene cross-link exhibits lower thermal stability than the (R)-isomer does. Both isomers of the cross-links are located in the minor groove and retain Watson–Crick H-bonds at the tandem cross-linked C·G base pairs. However, the α-CH3 group of the (R)-isomer is positioned in the center of the minor groove, whereas the α-Me group of the (S)-isomer is positioned in the 3′-direction, showing steric interference with the DNA helix.</p><p>Of the four stereoisomers of the HNE-derived γ-OH-PdG adducts, only the (6S,8R,11S)-configurated one induces interchain cross-links [238]. All HNE-derived γ-OH-PdG adducts undergo ring-opening to N2-dG aldehydes and derivatives when placed opposite dC [375]. The existence of small amounts of N2-dG aldehyde adducts, which have been detected for the (6S,8R,11S)- and (6R,8S,11R)-stereoisomers [376], accounts for the slow formation of the cross-link. The structures of ring-opened N2-dG cyclic hemiacetals of (6S,8R,11S)- and (6R,8S,11R)-configurations show the HNE moiety is located in the minor groove with the directions of aldehyde group differing for the two stereoisomers (Fig. 29). The (6S,8R,11S)-aldehyde orients to the 5′-neighbor C·G base pair and favors cross-link formation. In contrast, the (6R,8S,11R)-aldehyde orients to the 3′-neighbor A·T base pair. Re-orientation of the aldehyde unit in the 5′-direction to favor the interstrand cross-link formation is disfavored due to the interference of the HNE moiety with the 3′-neighbor [377].</p><!><p>The bis-electrophile 1,2,3,4-diepoxybutane (DEB) is considered to be the ultimate carcinogenic metabolite of buta-1,3-diene. Initial DNA alkylation by DEB produces 2-hydroxy-3,4-epoxybut-1-yl adducts. These can undergo further reaction to form DNA cross-links. Zhang and Elfarra demonstrated that the reaction of DEB with dG produced nucleoside adducts resulting from alkylation at N(1) and N(7) of dG, 2′-deoxy-1-(2-hydroxyoxiran-2-ylethyl)guanosine and 2′-deoxy-7-(2-hydroxyoxiran-2-ylethyl)guanosine [378]. Incubation of the N1 adducts with dG led to formation of diastereoisomers of 1-[4-(2-amino-1,7-dihydro-6-oxo-6H-purin-7-yl)-2,3-dihydroxybutyl]-2′-deoxyguanosine (N7-dG-N1-dG-BD). Incubation of the N7-dG adducts with dG led to formation of the bis-dG cross-link 7,7′-(2,3-dihydroxybutane-1,4-diyl)bis[2-amino-1,7-dihydro-6H-purin-6-one]. The sequence context of these cross-links is of particular interest, and not well understood. These cross-links predominate within 5′-GNC-3′/3′-CNG-5′ sequences, where N is any nucleotide [379]. The efficiencies of cross-linking are dependent upon configuration, with (S,S)- >(R,R)- >meso-DEB [380]. Other DEB-mediated cross-links have been reported. Tretyakova and co-workers reported regioisomeric dG-dA cross-linking products involving the N7-dG N-atom, and the N1-, N3-, N6-, and N7-dA N-atoms [381]. The 1-(hypoxanthin-1-yl)-4-(guanin-7-yl)butane-2,3-diol (N1 HX-N7-dG-BD) cross-link has also been identified [382]. It was proposed that the latter was formed by the hydrolytic deamination of 1-(adenin-1-yl)-4-(guanin-7-yl)butane-2,3-diol. Alternatively, DEB can form exocyclic lesions by alkylating two sites of the same DNA base. Zhang and Elfarra have identified bis-alkylation products of dG [378][383][384]. Tretyakova and co-workers identified bis-alkylation products of dA [385][386]. They proposed that DEB alkylates the N(1)-position of dA to form N1-(2-hydroxy-3,4-epoxybut-1-yl)-dA adducts, which undergo an SN2-type intramolecular nucleophilic substitution and rearrangement to give 1,N6-[2-hydroxy-3-(hydroxymethyl)propane-1,3-diyl]-2′-dA and 1,N6-[2-hydroxy-1-(hydroxymethyl)propane-1,3-diyl]-2′-dA. Both annelation products were identified in DNA treated with DEB in vitro and in liver DNA of mice exposed to BD by inhalation. Their formation provides a possible mechanism of mutagenesis at A:T base pairs. The initially formed 2-hydroxy-3,4-epoxybut-1-yl adducts may also interact with nucleophilic side chains within DNA-binding proteins to form DNA–protein conjugates, e.g., with DNA repair proteins [387]. Structural studies of these adducts have not been reported but will be of considerable interest. However, the synthetically accessible and stable N6,N6-dA intrastrand cross-links have been used as model systems to probe the structures of DEB-induced cross-links in the major groove of DNA, and their consequent biological processing. The structures reveal that the major conformational difference between the (R,R)- and (S,S)-BD cross-links (Fig. 19) regards the conformation of the C4 butadiene chain (27,28). The (R,R)-BD cross-link exists in the extended chain conformation with minimal perturbation of the DNA (27), while the (S,S)-BD cross-link creates a greater structural perturbation (28). Although both (R,R)- and (S,S)-BD cross-links were mutagenic in both E. coli and COS-7 cells, the (S,S)-BD cross-link exhibited a lower overall mutagenic frequency (20%) than that of the (R,R)-BD cross-link (54%) (29).</p><!><p>Early on, it was recognized that the alkylation of DNA, particularly when forming sterically bulky lesions, can be accommodated by conformational rearrangements at the damage sites, which do not involve bond breaking. Numerous examples now exist, particularly involving conformational rearrangement about the glycosyl torsion angle. These conformational interconversions have sufficiently large activation barriers that, under physiologically relevant conditions, interconversion occurs slowly at ms or slower time scales, and it is anticipated that different conformers may elicit differential biological responses. It has also been increasingly recognized that DNA damage may result in configurational rearrangements involving bond breakage; such rearrangements may be either reversible or irreversible and may alter the biological response to the damage. Finally, the ability of bis-electrophiles, such as α,β-unsaturated aldehydes or diepoxybutane, to cross-link DNA has been of considerable interest. Such cross-links, if not repaired, are anticipated to be highly genotoxic, interfering both with replication and repair processes.</p>

PubMed Author Manuscript

Reductive activation of the prodrug 1,2-bis(methylsulfonyl)-1-(2-chloroethyl)-2-[[1-(4-nitrophenyl)ethoxy]carbonyl]hydrazine (KS119) selectively occurs in oxygen-deficient cells and overcomes O6-alkylguanine-DNA alkyltransferase mediated KS119 tumor cell resistance

1,2-Bis(methylsulfonyl)-1-(2-chloroethyl)-2-[[1-(4-nitrophenyl)ethoxy]carbonyl]hydrazine (KS119) is a prodrug of the 1,2-bis(sulfonyl)hydrazine class of antineoplastic agents designed to exploit the oxygen-deficient regions of cancerous tissue. Thus, under reductive conditions in hypoxic cells this agent decomposes to produce the reactive intermediate 1,2-bis(methylsulfonyl)-1-(2-chloroethyl)hydrazine (90CE), which in turn generates products that alkylate the O6-position of guanine in DNA. Comparison of the cytotoxicity of KS119 in cultured cells lacking O6-alkylguanine-DNA alkyltransferase (AGT) to an agent such as Onrigin\xe2\x84\xa2, which through base catalyzed activation produces the same critical DNA G-C cross-link lesions by the generation of 90CE, indicates that KS119 is substantially more potent than Onrigin\xe2\x84\xa2 under conditions of oxygen deficiency, despite being incompletely activated. In cell lines expressing relatively large amounts of AGT, the design of the prodrug KS119, which requires intracellular activation by reductase enzymes to produce a cytotoxic effect, results in an ability to overcome resistance derived from the expression of AGT. This appears to derive from the ability of a small portion of the chloroethylating species produced by the activation of KS119 to slip through the cellular protection afforded by AGT to generate the few DNA G-C cross-links that are required for tumor cell lethality. The findings also demonstrate that activation of KS119 under oxygen-deficient conditions is ubiquitous, occurring in all of the cell lines tested thus far, suggesting that the enzymes required for reductive activation of this agent are widely distributed in many different tumor types.

reductive_activation_of_the_prodrug_1,2-bis(methylsulfonyl)-1-(2-chloroethyl)-2-[[1-(4-nitrophenyl)e

1. Introduction<!>2.1. Cell culture<!>2.2. Cell lines<!>2.3. Chemical syntheses<!>2.4. Toxicity studies<!>2.5. HPLC determination of the disappearance of KS119 in cell cultures under conditions of oxygenation and oxygen deficiency<!>2.6. AGT assays<!>3.1. Toxicity studies in cells lacking AGT<!>3.2. Toxicity studies in cells expressing AGT<!>3.3. Metabolism studies<!>3.4. Calculated possible levels of 90CE generated from KS119 and Onrigin\xe2\x84\xa2 under conditions of oxygenation and oxygen deficiency<!>3.5. O6-benzylguanine enhancement of KS119 cytotoxicity<!>4. Discussion

<p>It has been well documented that vascular anomalies in solid tumors can result in the formation of hypoxic/anoxic areas in which oxygen is severely limited or in some cases completely absent [1-4]. The presence of oxygen-deficient regions within tumors has significant implications for tumor growth, development, and invasiveness [5]. However, these oxygen-deficient regions possess properties which make them preferential targets for hypoxia directed prodrug chemotherapy. Thus, although oxygen-deficient regions in neoplastic tissue may be the furnace that drives tumor growth and lethal metastatic spread [6,7], these regions also may be considered to be sites of vulnerability through their capacity to actively convert certain prodrugs by reductive enzymatic activation into forms preferentially lethal for tumors relative to normal tissue, thereby producing specificity for neoplastic tissues.</p><p>Hif-1 is known to orchestrate the cellular response to oxygen deprivation [8,9]. This signaling protein must interface with the cellular oxygen sensing system and with angiogenesis inducers to modulate a complicated molecular program that carefully controls the cellular and tissue response to limited supplies of oxygen [10-12]. There is evidence that neoplastic cells can and must usurp or manipulate this process in order to spread throughout the body [6,7,12]. Thus, preferential targeting of tumors through selective prodrug activation by reductive enzymes in oxygen-deficient regions within malignant masses may not only permit therapeutic eradication of primary tumor masses, but perhaps more importantly, disrupt the processes which permit tumor spread and metastasis.</p><p>We have been devising ways to optimize the effectiveness of the 1,2-bis(sulfonyl)hydrazine prodrug Onrigin™ (cloretazine, laromustine, VNP40101M, 101M) and its active primary decomposition product, 90CE, against neoplastic tumors. This agent, which has completed phase II clinical trials for the treatment of acute myelogenous leukemia (AML), where it has clearly shown efficacy in de novo poor risk elderly AML patients, has also undergone initial evaluation against glioblastoma and small cell lung carcinoma [13-16]. Since Onrigin™ is activated by base catalyzed fragmentation to yield the activated product 90CE in both extracellular and intracellular compartments, we have attempted to increase the intracellular localization of 90CE in neoplastic cells, through the development of 1,2-bis(methylsulfonyl)-1-(2-chloroethyl)-2-[[1-(4-nitrophenyl)ethoxy]carbonyl]hydrazine (KS119, Fig. 1A), a second generation DNA O6-guanine targeting agent, which, after intracellular reductive activation in oxygen-deficient neoplastic cells, produces the primary cytotoxic product 90CE. Thus, KS119 was specifically designed to exploit oxygen-deficient areas of malignant tissue [17-19] by serving as a prodrug activated by reductive enzymes in tumors to release the active therapeutic component (Fig. 1B). The reductase enzymes in oxygen-deficient cells preferentially activate KS119 to generate 90CE by converting the 4-nitro substituent to the 4-amino or 4-hydroxylamino product which then spontaneously fragments within hypoxic regions of tumors; whereas, under aerobic conditions back oxidation of the one electron radical product occurs rapidly to regenerate the parental prodrug. The redox cycling that occurs under aerobiosis inherently directs the activation of KS119 to only occur within oxygen-deficient tumor cells, making it improbable that net activation will occur spontaneously in aerated tumor cells or by decomposition in extracellular areas.</p><p>In the studies reported herein, KS119 cytotoxicity was compared under oxygenated and enzymatically generated oxygen-deficient [20] conditions to agents such as 90CE and Onrigin™, which produce identical DNA lesions, but are not preferentially activated under oxygen-deficient conditions and, therefore, the activation of these agents is not localized to oxygen-deficient cells. Our results suggest that the tumor specificity attained by KS119 in oxygen-deficient cells can overcome the cellular resistance to O6-DNA guanine chloroethylating agents mediated by O6-alkylguanine-DNA alkyltransferase (AGT), even in cell lines with extremely high levels of expression of this repair protein. AGT is a DNA repair protein that catalyzes the transfer of alkyl groups from the O-6 position of guanine to cysteine-145 of the AGT protein, restoring the O-6 position of guanine to its native state [21]. There is no known acceptor that removes the alkyl group from cysteine-145 of AGT, so one AGT protein is required to repair each lesion; once alkylated, the AGT protein is degraded by the proteasome [21].</p><!><p>All cell lines were maintained under 5% CO2 in α-Minimal Essential Medium (α-MEM) or Dulbecco's Minimal Essential Medium (DMEM) supplemented with 10% FBS. All cell culture reagents were purchased from Invitrogen (Carlsbad, CA).</p><!><p>The O6-alkylguanine-DNA alkyltransferase (AGT) expressing cell lines were generated as previously described [22]. Briefly, CHO/AA8 Chinese hamster, EMT6 mammary carcinoma, and U251 glioblastoma cells which are deficient in AGT (containing below the level of detection of less than 600 AGT molecules/cell) were transfected using lipofectamine (Invitrogen, USA) using the manufacturer's recommended conditions, with a plasmid vector containing a neomycin resistance gene and a CMV promoter driven cDNA of the human AGT gene [22]. Cells were screened for G418 resistance and prospective transfectants were single cell cloned. Clones expressing AGT were identified by Western blot analysis with a monoclonal antibody specific for AGT (Neomarkers; Freemont, California). AGT levels were quantified using an [3H]-O6-benzylguanine binding assay [23].</p><!><p>The 1,2-bis(sulfonyl)hydrazine prodrugs were synthesized by previously published procedures [18,24]. All other drugs and chemicals were purchased from the Sigma Chemical Co. (St. Louis, MO).</p><!><p>Clonogenic survival assays were performed essentially as described previously [20]. Cells were seeded into 25 cm2 plastic tissue culture flasks at 5–8 × 105 cells per flask. When confluent, cells were treated with antineoplastic agents dissolved in DMSO in a total volume of 10 ml of medium for 24 h at 37 °C. For oxygen-deficient conditions, cells were incubated with 1,2-bis(sulfonyl)-hydrazines in the presence of 2 Units(U)/ml of glucose oxidase (Sigma G6641), 120 U/ml of catalase (Sigma, C1345) in high glucose DMEM (Invitrogen). Flasks were flushed with nitrogen for 10 s and the caps screwed on tightly. This facilitates oxygen depletion of the medium by glucose oxidase through removal of residual oxygen containing air and denial of the entry of additional air. After treatment, monolayers were rinsed, and cells were detached by trypsinization, suspended in culture medium, counted and sequential cell dilutions were plated in duplicate into 6-well plates at a density of 1 × 102, 1 × 103, or 1 × 104 cells per well. Seven to 10 days later, colonies were fixed, stained with crystal violet (0.25%) in 80% methanol and quantified. For studies involving O6-benzylguanine (O6-BG), cells were pretreated for 4 h in the presence of O6-BG prior to the addition of the cytotoxic agent. All analyses were corrected for plating efficiency in the presence of vehicle (DMSO) at concentrations equivalent to those used for exposure to the test 1,2-bis(sulfonyl)hydrazine. DMSO concentrations were ≤0.05%, and non-toxic. Cells under aerobic conditions were treated under similar conditions and cytotoxic agent concentrations, but in unsealed flasks without glucose oxidase and catalase. Cells were then washed, harvested by trypsinization, and assayed for survival using a clonogenic assay described previously [22,25,26]. In the absence of cells, no measurable direct metabolism of KS119 was detected in the presence of the glucose oxidase and catalase enzyme components of our oxygen deficiency generating system [20].</p><!><p>For these experiments, 107 cells/ml, were incubated for various times with KS119 under oxygenation or enzyme generated oxygen deficiency described above. For oxygenated studies, cells were incubated in 25 cm2 flasks in shallow 5 ml layers of growth medium containing 10% FBS (α-MEM for DU145 cells, or DMEM for EMT6 cells) with shaking at 37 °C. Experiments under oxygen-deficient conditions were performed in sealed 1.5 ml tubes in the presence of 2 U/ml of glucose oxidase (Sigma G6641), 120 U/ml of catalase (Sigma C1345) and 10 mM added glucose in 1 ml of growth medium. Cell supernatant samples containing KS119 were mixed with an equal volume of acetonitrile and allowed to stand at room temperature for 15 min to allow precipitation of most of the protein, then centrifuged at 10,000 × g for 5 min. The supernatant was then analyzed by HPLC using a 5 μm 220 mm × 4.6 mm C-18 reverse-phase column (RP-18, Applied Biosystems); elution was accomplished with 34.5% acetonitrile in buffer (0.03 M KH2PO4/1.0 mM NaN3, pH 5.4) for 5 min, followed by a 34.5–75.0% acetonitrile linear gradient in buffer, at a flow rate of 0.6 ml/min. Absorbance was monitored at 280 nm using a 168 UV/Vis detector (Beckman Coulter, Fullerton, CA). KS119 eluted at approximately 35 min.</p><!><p>AGT assays were performed essentially as described by Ishiguro et al. [23]. Determination of AGT activity relied upon stoichiometric covalent transfer of radioactive benzyl residues from [benzene-3H]O6-benzylguanine to AGT, and the numbers of AGT molecules/cell were calculated based upon radioactivity in a 70% methanol precipitable fraction, the specific activity of [benzene-3H]O6-benzylguanine and Avogadro's number as described [23].</p><!><p>KS119 was designed to be preferentially activated by bioreduction within cells under conditions of oxygen deficiency. The distribution and nature of the cellular reductive enzymes required for this activation are unknown, but in enzymatic systems, NADPH: cytochrome P450 reductase and xanthine/xanthine oxidase are capable of reducing KS119 in a one electron reaction (unpublished observations). Studies in three different cell lines have demonstrated that the activation of KS119 is prolonged and that only a portion of this agent (<40%) is converted to 90CE after exposure to cell monolayers for 24 h under conditions of oxygen deficiency [20].</p><p>We have compared the toxicity of KS119 to Onrigin™ and 90CE (Fig. 1B), all of which generate the same chloroethylating species that ultimately produce the G-C cross-linking DNA lesion, in three parental cell lines each lacking the expression of AGT. To accomplish this comparison, CHO/AA8 Chinese hamster ovary cells, U251 human glioblastoma cells, and EMT6 murine mammary carcinoma cells were treated with KS119, Onrigin™, or 90CE for 24 h under oxygenated conditions or those of enzyme generated oxygen deficiency and the resulting cell survival was measured by clonogenic assays. As shown in Fig. 2(A–F), KS119 was considerably more efficacious than either Onrigin™ or 90CE at equivalent concentrations under conditions of oxygen deficiency. This is particularly impressive since the total amount of KS119 being activated is very low under these conditions, requiring 24 h to reach <40%, compared to 90CE (half life = 30 s) and Onrigin™ (half life = 1 h), which are relatively rapidly activated independent of enzymatic action or the degree of oxygenation. These findings imply that KS119 can be activated by oxygen-deficient cells of solid tumors, thereby providing a selective advantage in generating tumor cell toxicity under conditions of oxygen deficiency, which is rare in normal tissue, but is known to be present in poorly vascularized and disorganized tumor tissue. Furthermore, intracellular activation can increase the effective delivery of the toxic agent, since the active cytotoxic metabolite is produced directly inside the oxygen-deficient neoplastic tissue where reductive activation occurs and is not produced in extracellular environments (i.e., the blood stream and normal tissue) where distribution throughout the host is occurring. Although, as expected, increased concentrations of Onrigin™ and 90CE have been shown to result in significant cell toxicity, as shown in Table 1, the 50% lethal concentration (LC50value) for KS119 is from 6- to 28-fold lower than that of Onrigin™ and 90CE under conditions of oxygen deficiency. The studies conducted imply that the reductase enzymes required for activation of KS119 to 90CE are widespread, being expressed in various cell types.</p><!><p>To ascertain the ability of the prodrug KS119 to overcome AGT mediated resistance to DNA O6-guanine alkylation, a series of AGT transfected cell lines, CHO/hAGT7, EMT6/hAGT18, CHO/hAGT135, and U251/hAGT325, which contain a range of human AGT levels (7000 to 325,000 molecules/cell), as well as a human prostate carcinoma cell line DU145, which naturally expresses a moderately high level of 42,000 AGT molecules/cell (Table 2), were evaluated for sensitivity to KS119. AGT levels were quantified in these cell lines by measuring the degree of [3H]-O6-benzylguanine binding to cysteine-145 of the AGT protein, an assay developed in this laboratory [23]. The two transfected cell lines, which stably expressed the lowest levels of AGT (CHO/hAGT7 and EMT6/hAGT18), exhibited sensitivity to KS119; while, under conditions of oxygen deficiency, relatively little or no inhibition was produced by this agent in the presence of aeration (Fig. 3A–D). Transfected cell lines with exceedingly high levels of human AGT expression, CHO/hAGT135 and U251/hAGT325 (135,000 and 325,000 molecules of AGT/cell, respectively) were also sensitive to this 1,2-bis(sulfonyl)hydrazine prodrug under conditions of oxygen deficiency (Fig. 4B and D). In addition, the DU145 human prostate carcinoma cell line which displays one of the highest levels of natural (untransfected) AGT expression among the cell lines surveyed in this laboratory [23] was found to be inhibited by this agent in clonogenic assays under oxygen-deficient conditions (Fig. 5). In contrast, the non-directed antitumor agents employed, Onrigin™ and 90CE, were not effective at similar concentrations, the maximum concentration of these 1,2-bis(sulfonyl)hydrazines employed in these experiments being determined by the aqueous solubility limit of KS119, which is approximately 40 μM.</p><!><p>Previous studies have indicated that KS119 was metabolized relatively slowly to an active product by cells in monolayers, with only 20–40% being activated over a 24 h period of exposure under conditions of oxygen deficiency [20]. Since high cell densities may possibly be a more accurate model of solid tumor masses than experiments conducted with single cell monolayers, we measured the extent of metabolic activation of KS119 using two different cell lines, the EMT6 mouse mammary carcinoma and the DU145 human prostate carcinoma (42% and 51% metabolized in a 3 h period, respectively), under conditions of enzymatically generated oxygen deficiency at 107 cells/ml (Fig. 6). Under the aerobic conditions employed in these studies much less metabolism of KS119 was detected over 3 h time periods with EMT6 (23% metabolism) and DU145 cells (5% metabolism). These studies demonstrate, in contrast to experiments with single cell monolayers, that under the appropriate conditions of high cellular density and oxygen deficiency deemed to be more comparable to that occurring in a tumor mass, KS119 can be relatively rapidly metabolized by neoplastic cells. These experiments measured the loss of KS119 and, although we assume that the disappearance of prodrug represents activation to 90CE, it is conceivable that metabolites might be formed that do not result in DNA cross-linking. Therefore, the relative loss of parental KS119 prodrug under both conditions of oxygenation and oxygen deficiency does not necessarily reflect the relative magnitude of DNA cross-linking that might be occurring.</p><!><p>We have estimated the possible levels of 90CE generated from KS119 and Onrigin™ under conditions of oxygenation and enzymatically generated oxygen deficiency compared to the flux of 90CE in these cell lines deficient in AGT (Table 1). Whereas 90CE would only be produced from KS119 inside cells under conditions of oxygen deficiency, Onrigin™ is capable of generating 90CE both intracellularly and extracellularly. Using the HPLC results from Fig. 6 for KS119 and previously published findings on Onrigin™ decomposition [27], we calculate that the cellular flux of 90CE generated by KS119 activation under conditions of oxygen deficiency is approximately 30-fold greater at maximal levels than that of Onrigin™ under similar conditions. These calculations are in close agreement to the LC50 ratios between KS119, Onrigin™ and 90CE under oxygen deficiency where the highest difference was found to be 28-fold (Table 1), close to the calculated maximum using HPLC measurements.</p><!><p>The inactivation of AGT by O6-BG has been tested clinically for its ability to enhance the therapeutic efficacy of the DNA O6-guanine alkylating agent carmustine; although O6-BG depleted tumor AGT without host toxicity, the combination of O6-BG and carmustine failed clinically because the depletion of AGT and subsequent enhanced alkylation of DNA guanine occurred in both tumor and normal tissue. This action resulted in an unacceptable increase in alkylating agent toxicity to patients, necessitating a large reduction in carmustine dosage such that the level of nitrosourea tolerated in the presence of O6-BG in this clinical trial had little anticancer activity (see for example Ref. [28]). The use of a primary DNA O6-guanine targeted prodrug such as KS119 which is preferentially activated in oxygen-deficient cells, as one arm of a combined treatment, with a pretreatment arm which non-selectively depletes AGT such as O6-BG, may eliminate this drawback.</p><p>We have also evaluated the capacity of KS119 in combination with O6-BG to produce antineoplastic activity against DU145 human prostate carcinoma cells. As shown in Fig. 7B, pretreatment of DU145 cells with 100 μM O6-BG for 4 h prior to 10 μM KS119 resulted in an almost two log drop in survival, from 8.6% to 0.1%, under conditions of oxygen deficiency, with little or no toxicity to these cells under oxygenated conditions (Fig. 7A). In contrast, the use of 10 μM 90CE or Onrigin™ following 100 μM O6-BG resulted in little or no cytotoxicity under oxygenated conditions and considerably less cytotoxicity compared to KS119 in oxygen-deficient cells.</p><p>Pretreatment with a lower concentration of 20 μM O6-BG or with 100 μM O6-BG followed by exposure to graded concentrations of KS119 resulted in a pronounced degree of cytotoxicity for the combination under conditions of oxygen deficiency (Fig. 8B and D); whereas, in the presence of oxygen little or no significant antineoplastic activity occurred (Fig. 8A and C).</p><!><p>KS119 is a second generation prodrug designed to produce 90CE, the initial DNA O6-guanine chloroethylating moiety of Onrigin™. Onrigin™, following base catalyzed activation, generates 90CE in both intracellular and extracellular compartments; whereas, KS119 only produces the active chloroethylating agent 90CE inside tumor cells, since its net activation requires enzymatic reduction in oxygen-deficient cells [19]. Thus, KS119 is a rationally conceived prodrug designed to be activated within hypoxic/anoxic areas of solid tumors, where net reduction can be readily triggered by cellular reductase enzymes, with KS119 exploiting the presence of areas of aberrant vascularization that result in partial (hypoxic) or complete (anoxic) oxygen deficiencies in cancerous tissue [1-4].</p><p>The therapeutic action of KS119 is not only facilitated by differences from Onrigin™ in the activation mechanisms, but also in the short half life (30 s) of 90CE, which is believed to generate a series of exceedingly reactive and short-lived cross-linking species, with little time to escape from the tumors. The findings in this report show that KS119 is markedly more cytotoxic than a molar equivalent concentration of 90CE itself or of Onrigin™ under conditions of oxygen deficiency, even when much of the KS119 prodrug is not metabolically activated.</p><p>The intracellular activation in neoplastic tissue allows KS119 to exert lethality even in cell lines with very high levels of AGT, thereby producing a cytotoxic effect not observed with related agents such as Onrigin™, which generates 90CE both intracellularly and extracellularly. The potency of KS119 may also be augmented by properties of the DNA O6-guanine chloroethyl lesion that it produces, since the chloroethyl adduct that forms, rapidly produces the cyclic product N1,O6-ethanoguanine, which is then slowly converted to a G-C cross-link (half life = 2 h; [29]), a lesion not susceptible to repair by AGT. However, even in the presence of exceedingly large concentrations of AGT, a small portion of the initial DNA O6-guanine chloroethyl adducts will "slip through" to an AGT non-reparable G-C cross-link prior to AGT effecting a repair, due to the competitive nature of these two reactions. This results in a small yet lethal number of cross-link lesions accumulating in a time dependent manner [29]. Thus, there are two competing fates for the initial lesion, i.e., AGT repair, or progression to a highly toxic cross-link. Greater AGT levels will result in a greater proportion of the initial lesions being intercepted; however, the sensitivity of the tumors containing exceedingly large quantities of AGT to KS119 makes it conceivable that some of the AGT may be in a form or cellular site not available to compete with the formation of the G-C cross-link. In contrast, greater targeting of KS119 will result in the formation of more initial lesions and eventual cross-links. Hence, cells expressing AGT have enhanced resistance to O6-guanine targeted chloroethylating agents while little AGT depletion is observed. This contrasts markedly to methylating agents where significant AGT depletion is seen at cytotoxic concentrations [29,30]. Since relatively few G-C cross-links appear to be required for cell lethality [29] the combination of oxygen-deficient cell selective activation of KS119 coupled with the chloroethylating agent "slip through" reaction may make this dual mechanism an unusually effective one in AGT containing cells, thereby explaining the ability of KS119 to eradicate oxygen-deficient neoplastic cells expressing extraordinarily large amounts of AGT compared, for example, to DNA guanine O6-alkylating prodrugs which are activated by mechanisms other than by the reductive properties of cells, such as Onrigin™ which employs base catalyzed fragmentation for activation. The enhanced delivery of the chloroethylating species produced by KS119 may also allow minor relatively non-toxic alkylations that most probably occur to reach lethal levels. This would be most apparent in the presence of very high levels of AGT where lethality from chloroethylation of the O-6 position of DNA guanine would be diminished. Secondary DNA lesions other than alkylation of the O-6 position of DNA guanine, such as the alkylation of the N-7 position of DNA guanine, the N-3 position of DNA adenine, or the alkylation of phosphate molecules, all of which are not susceptible to repair by AGT could be significant contributors to toxicity derived therefrom, as could non-DNA alkylations [29]. At the highest concentrations of KS119 these expected secondary effects may become more important, but based upon the findings presented in this report and in others [22], it is obvious that AGT induced repair is the primary mechanism of resistance to agents of the 1,2-bis(sulfonyl)hydrazine class, confirming the preeminence of O6-DNA guanine lesions in the generation of cytotoxicity by O6-DNA guanine targeted agents, and reinforcing the importance of developing strategies to overcome AGT mediated resistance to therapeutic agents of this type. Direct demonstration of the cross-links generated in AGT expressing cells by KS119 is desirable, but limitations in the sensitivity of our cross-linking assay [29], and in the solubility of KS119 currently do not allow detection of these hypothesized "slip through" cross-links. We are hopeful that either through enhancement of the sensitivity of our cross-link assay or better formulation of KS119 we can overcome these difficulties in future experiments.</p><p>The use of O6-BG followed by KS119 resulted in synergistic cytotoxicity to all of the cell lines evaluated under conditions of oxygen deficiency. The O6-BG was employed to deplete intracellular pools of AGT and was relatively non-toxic by itself, indicating that both arms of a combination need not be independently therapeutic, if one of the components of the admixture interferes with a resistance mechanism or by some other action enhances the efficacy of the primary therapeutic component. The finding that O6-BG combined with carmustine required an 80% decrease in the dosage of the nitrosourea in clinical trials because of intolerable myelosuppression [28] indicates that O6-guanine lesions are the primary contributors to both host and neoplastic toxicity despite the fact that DNA N-7 alkylations represent 75% of the alkylations produced by carmustine; whereas, KS119 and its chemical design progenitors, 90CE and Onrigin™, are distinguished from carmustine by their significantly higher ratio of O-6 to N-7 DNA alkylations, making them substantially more selective O6-guanine targeted agents than this clinically approved nitrosourea [27].</p><p>The methodology employed herein to deplete oxygen in cell culture, enzyme generated oxygen deficiency, does not precisely mimic in vivo conditions, but studies in mice using a combined radiation/KS119 treatment protocol suggest that KS119 can target hypoxic cells in AGT non-expressing animal tumors [19]. Further animal studies are necessary to determine if KS119 can demonstrate antineoplastic activity in tumors that express AGT.</p><p>The role of oxygen-deficient tumor regions in promoting cancer metastasis is becoming increasingly clear (4,6,7). Prodrugs that exploit oxygen-deficient regions in malignant tumors by undergoing reductive activation to form a cytotoxic moiety may not only attack therapeutic resistant hypoxic tumors but may also reduce or impede tumor metastasis by preventing the establishment of oxygen-deficient niches that facilitate the formation of nascent cancer metastases. The general structural design used for the development of KS119 can be readily extended to deliver other types of small molecule prodrugs, including enzyme inhibitors and other classes of cytotoxic agents.</p>

PubMed Author Manuscript

Classical/Non‐classical Polyoxometalate Hybrids

AbstractTwo polyanions [SeI V 2PdII 4WVI 14O56H]11− and [SeI V 4PdII 4WVI 28O108H12]12− are the first hybrid polyoxometalates in which classical (Group 5/6 metal based) and non‐classical (late transition‐metal based) polyoxometalate units are joined. Requiring no supporting groups, this co‐condensation of polyoxotungstate and isopolyoxopalladate constituents also provides a logical link between POM‐PdII coordination complexes and the young subclass of polyoxopalladates. Solid‐state, solution, and gas‐phase studies suggest interesting specific reactivities for these hybrids and point to several potential derivatives and functionalization strategies.

classical/non‐classical_polyoxometalate_hybrids

<!>Experimental Section<!>

<p>N. V. Izarova, B. Santiago-Schübel, S. Willbold, V. Heß, P. Kögerler, Chem. Eur. J. 2016, 22, 16052.</p><p>The chemistry of palladium‐containing polyoxometalates (POMs) has experienced impressive development over the past decade,1 with progress primarily concentrated on two areas. The first is defined by conventional PdII coordination complexes of lacunary polyoxotungstates (POTs), [PdII n(XmWpOq)r]Z−, where PdII ions in square‐planar environments coordinate oxygen atoms of vacant sites of POT ligands, resulting in a diverse range of structures incorporating one to four PdII centers.2 Such species are convenient precursors for highly stable suspensions of POT‐stabilized Pd0 nanoparticles, which can be obtained at mild conditions in aqueous media.3 Some of the Pd‐POT complexes were also shown to act as pre‐catalysts for various organic transformations.2m, 4 In these complexes, the PdII centers typically lack a direct connection, with the only exception in [PdII 4(α‐P2W15O56)2]16−, where two out of four PdII ions are bridged via O atoms of two phosphate groups.2p</p><p>In the second main area, formed by so‐called polyoxopalladates (POPds), the PdII centers, in contrast, act as addenda ions themselves. Here, the elementary PdO4 building blocks are condensed via corners and edges, typically also involving external RXO3 z− heterogroups stabilizing the discrete {PdxOy} entity.5, 6 About 50 of these non‐classical POMs are known today, incorporating up to 84 PdII ions. One of the most stable POPds archetype comprises species of general composition [MPdII 12O8(RXO3)8]z− ({MPd12}), where a heterometal ion Mz′+ in a cubic O8 environment is encapsulated in the cuboid‐shaped {PdII 12O8(RXO3)8} shell (RX=SeIV, OAsV, PhAsV, OPV, PhPV).6</p><p>Recently, Cronin and co‐workers also reported several polyanions that can be considered as complexes of seleno‐ and tellurotungstates {XnWmOp} with selenite‐ or tellurate‐supported multinuclear PdII‐based fragments.7 In two isomeric [HxPdII 10SeI V 10W52O206](40−x)− polyanions, two {Pd5Se2O2} units are coordinated to {B‐α‐SeW9O33} and {γ‐Se2W14O56} POT moieties. In [PdII 6TeI V 19W42O190]40− two identical {Pd3Te3O3} groups are stabilized by six {α‐TeW7O27} lacunary POTs.7</p><p>Yet, up to now there was no systematic investigation on how to achieve commensurate reaction conditions that allow to co‐condense, and thus cleanly interface, classical POTs and non‐classical POPds. We thus explored the possibility to prepare hybrid polyoxopalladatotungstates [XnPdII mWIV pOq]z−, where both PdII and WVI centers act as addenda centers of their individual POM units, without the need for any additional external stabilizing groups. Herein we report two first examples of such hybrid palladatotungstates, [SeI V 2PdII 4WVI 14O56H]11− (1) and [SeI V 4PdII 4WVI 28O108H12]12− (2), crystallized as hydrated mixed cesium/sodium salts Cs4Na3H4[Se2Pd4W14O56H]⋅ 18 H2O⋅0.3 CsOAc⋅0.2 NaOAc (CsNa‐1; OAc−=acetate) and Cs9.5Na2.5[Se4Pd4W28O108H12]⋅30 H2O (CsNa‐2), respectively, and their characterization in the solid state, aqueous solutions, and gas phase.</p><p>The polyanions 1 and 2 have been prepared in reactions of [SeI V 6WVI 39O141(H2O)3]24− ({Se6W39})8 with PdII nitrate in different aqueous media (Supporting Information, Scheme S1). The {Se6W39} precursor possesses a cyclic structure, where three {γ‐Se2W12O46} units are alternating with three trans‐{O=W(H2O)} groups. In aqueous solution it slowly decomposes, releasing {SexWyOz} fragments8 and thus could act as a source for preparation of diverse tungstoselenite complexes.9 The Cs+ counterions seem to play an important role for isolation of 1 and 2 as pure crystalline materials owing to relatively low solubility of the hydrated Cs+ salts. Alternatively, a Rb+/Na+ salt of 1 can be successfully prepared by replacing CsNO3 with RbNO3 in the synthesis of CsNa‐1. With no additional counterions only the hydrated sodium salt of paratungstate‐B ([H2W12O42]10−) could be isolated from the reaction medium for preparation of 1 as a crystalline product. The paratungstate‐B salt is also sometimes present as an impurity to CsNa‐1, which could be purified in this case by recrystallization from 0.25 m NaOAc aqueous solution (pH 6.7). Similar recrystallization of CsNa‐2 leads to formation of a mixture of CsNa‐1, CsNa‐2, and other undefined products. The purity and composition of the compounds was further confirmed by elemental analysis, PXRD, TGA, and XPS (see the Supporting Information for details).</p><p>CsNa‐1 crystallizes in the orthorhombic space group Pnnm. The polyanion 1 exhibits idealized C 2v symmetry and comprises an [α‐Se2W14O52]12− POT moiety ({α‐Se2W14}) supporting a {Pd4O4} fragment (Figure 1).</p><p>Structure of 1 (a) and the {(H2O)3Na}‐1 associate (b); comparison with the {Pd4O4(RXO3)4} fragment (c) in the cuboid‐shaped polyoxopalladate [MPd12O8(RXO3)8]z− (d). WO6 lime green, PdO4 blue polyhedra; Pd blue, Se/X yellow, O red, Na purple, M light blue. The R groups in {MPd12} are omitted for clarity.</p><p>The {α‐Se2W14} unit can be compared to a hypothetical tetralacunary Wells–Dawson‐type {α‐P2W14O54} fragment (Supporting Information, Figure S3), with two neighboring {W2O10} groups, composed of two edge‐shared {WO6} octahedra, removed from the inner {W6} belts of [α‐P2W18O62]6− ({α‐P2W18}; Supporting Information, Figure S3a/b), each one from one belt. The SeIV ions in {α‐Se2W14} adopt a trigonal pyramidal environment with the outwards oriented lone pair (Supporting Information, Figure S3d; Se−O 1.677(16)–1.725(15) Å). The formation of {α‐Se2W14} from the {γ‐Se2W12} building blocks of the {Se6W39} precursor requires attachment of two additional WVI ions to {γ‐Se2W12}, each of which is completing the outer {W3} cap of the POT fragment, combined with {γ‐Se2W14} isomerization by rotation of both {W3} caps by 60° (Supporting Information, Figure S3). The same {α‐Se2W14} building blocks have been recently observed in [Fe6Se6W34O124(OH)16]18− polyanions.9 At the same time, the arrangement of WVI centers in {α‐Se2W14} is different from that in the actual {α‐P2W14O54} moieties that, for example, form [H12Fe8P4W28O120]16− and [(W4Mn4O12)(P2W14O54)2]20−complexes.10 In fact, these {α‐P2W14O54} building blocks are the structural isomers to the hypothetical {α‐P2W14} units discussed above, and can be obtained from {α‐P2W18} polyanions by removing not {W2O10} but rather corner‐sharing {W2O11} units from its inner belts (Supporting Information, Figure S3c). It is also different in {γ‐Se2W14} moieties constructing the reported [HxPd10Se10W52O206]n−[7] (see above) and [Fe10Se8W62O222(OH)18(H2O)4]28−[9] complexes where the two {W3} caps are rotated by 60° relative to their orientation in the α isomer (Supporting Information, Figure S3e).</p><p>The four PdII centers in the {Pd4O4} fragment form a rectangle (Pd⋅⋅⋅Pd 3.360(2)–3.375(2) Å) and are linked by four μ2‐O sites (Figure 1 a). All square‐planar PdIIO4 (Pd−O 1.976(14)‐2.010(15) Å) include two cis‐positioned μ2‐O of the {Pd4O4} fragment as well as two OPOT atoms: two PdII centers bind to the {W3} caps and two to the belts of {α‐Se2W14} (Figure 1 a). Based on bond valence sums, the proton in 1 is disordered over the four μ2‐O atoms linking the PdII centers. These oxygens also coordinate to a {Na(OH2)3}+ counterion (Figure 1 b; Na−O 2.42(2)–2.53(2) Å).</p><p>The direct connection between the PdII centers by oxo ligands as well as the complete integration of the POPd {Pd4O4} moiety in the POM framework allow to consider 1 as a genuine hybrid polyoxopalladatotungstate. Interestingly, the structure of {Pd4O4} unit in 1 compares to the {Pd4O4(RXO3)4} face in the cuboid‐shaped {MPd12} POPds (Figure 1 c/d1), with the RXO3 n− groups stabilizing the {MPd12O8} core replaced by {α‐Se2W14}. Moreover, the Na+ attachment to {Pd4O4} in 1 is similar to the connection mode between the central Mz+ ion and the {Pd4O4(RXO3)4} face in {MPd12} nanocubes (Figure 1 b/d1). This suggests that the {Pd4O4} group in 1 possesses reactivity towards oxophilic heterometals.</p><p>The total number of metal centers in 1 allows for an analogy between 1 and Wells–Dawson‐type polyanions {α‐P2W18}.11 Both POMs comprise two central heteroatoms surrounded by 18 addenda metal ions. However the {Pd4} rectangle in 1 is rotated by 45° in comparison to the {WVI 4} rectangle in {α‐P2W18} if the latter is formally decomposed into the above‐mentioned hypothetical {α‐P2W14} fragment and four WVI centers (Supporting Information, Figure S4), possibly enforced by the square‐planar Pd coordination mode in 1 relative to the octahedral WVIO6 groups. This analogy prompted us to probe the possibility to form lacunary derivatives of 1 at conditions similar to those for formation of {α2‐P2W17} and {α‐P2W15} from {α‐P2W18}. These experiments, however, only resulted in Cs2Na3[H5Pd15Se10O10(SeO3)10]⋅ca. 20 H2O⋅POPd,12 which suggests that decomposition of 1 proceeds first through release of PdII ions, followed by POT decomposition.</p><p>However the possibility of existence of unstable lacunary derivatives of {α/β/γ‐Se2Pd4W14} polyanions is evident from the structure of 2 obtained indirectly by reaction of {Se6W39} with PdII in water. The compound CsNa‐2 crystallizes in the triclinic space group P 1‾ . The unit cell in CsNa‐2 contains two identical polyanions 2, each of which can be imagined as a dimer of two γ‐{(H2O)(OH)2PdII 2SeI V 2W13O49} ({γ‐Pd2Se2W13}) units connected by two trans‐{O=W(H2O)} groups (Figure 2). In line with the previous discussion, the {γ‐Se2W13} structure can be understood as a {γ‐Se2W12} unit, present in {Se6W39}, binding a WVI to complete one of the {W3} caps or, alternatively, as {γ‐Se2W14} (Supporting Information, Figure S3e), missing one WVI ion in its {W3} cap. The two PdII ions in {γ‐Pd2Se2W13} assume a square planar environment, each coordinating two cis‐positioned oxygens of {γ‐Se2W13}: one from the WVI ion in the {W3} cap and one from the {W4} belt (Figure 2 a). Furthermore, the two PdII ions are μ2‐OH‐bridged. One of the PdII ions additionally coordinates a terminal H2O, and its μ2‐O (Pd, W) ion in the trans‐position to the aqua ligand is protonated (Figure 2 a/c2; Supporting Information, Table S4). The second PdII ion is bound to trans‐{O=W(H2O)} group through the μ2‐O (Figure 2 b).</p><p>The structure of a {γ‐Pd2Se2W13} monomer (a) and a γ‐Pd2Se2W13{O=W(H2O)}2 moiety (b) in the polyanion 2 (c). WO6 lime green octahedra, PdO4 blue squares; Pd blue, Se yellow, O red spheres. The monoprotonated O atoms in the structure of 2 are highlighted in light purple, while aquo ligands are shown in pink.</p><p>Thus, the {γ‐Pd2Se2W13} fragment can be considered as a lacunary derivative of a hypothetical plenary {γ‐Pd4Se2W14} polyanion, lacking two PdII and one WVI centers. It is interesting to note that the orientation of {Pd4O4} fragment in this {γ‐Pd4Se2W14} POM, in case it exists, would be similar to that in {α‐P2W18} and not in {α‐Pd4Se2W14}. Along with the μ2‐O ligand connecting it to PdII (see above), the WVI center of each trans‐{O=W(H2O)} group also binds to an O atom of the neighboring {W4} belt of the same {γ‐Pd2Se2W13} monomeric unit as well as to the two O atoms of the incomplete {W2} cap group of the second {γ‐Pd2Se2W13} monomer, each of which belongs to different WVI ions (Figure 2 b/c2). Interestingly, one of the H2O ligands of the trans‐{O=W(H2O)} groups is directed inward the polyanion, while the second one is pointed outward (Figure 2 c). Thus, considering the protonation sites, 2 is of C 1 symmetry. Otherwise, it would possess a C 2 axis passing through the center of a line connecting the WVI centers of the two {O=W(H2O)} groups (Figure 2 c).</p><p>Owing to the presence of large Cs+ cations, the compounds CsNa‐1 and CsNa‐2 are only slightly soluble in water; however, their solubility is significantly increased in 0.25–0.5 m sodium and lithium acetate solutions (pH 6–7), especially upon heating to 65–70 °C. This allowed assessment of the solution behavior of 1 and 2 by 77Se NMR and UV/Vis spectroscopy (see the Supporting Information). Room‐temperature 77Se NMR of 1 in 0.25 m LiOAc solution (pH 6.2) exhibits a singlet at 1225.3 ppm (Figure 3), consistent with the presence of only one symmetrically non‐equivalent SeIV ion in the crystal structure of CsNa‐1 and with the observation of a singlet at 1202 ppm in the 77Se MAS NMR for this compound (Supporting Information, Figure S12). This indicates stability of 1 in aqueous medium in saturated solutions. The observed chemical shift is commensurate with those of ZnII (1222.5 ppm) and LuIII (1223.8 ppm)‐centered cuboid {MPd12Se8} POPds6c and is significantly upfield‐shifted compared to an aqueous SeO2 solution (pH 6.4; 1316.3 ppm). For comparison with other tungstoselenites, the {Se6W39} precursor (unstable in solution) gives a broad peak centered at 1289.1 ppm in 77Se MAS NMR.8a</p><p>Room‐temperature 77Se NMR spectrum of CsNa‐1 dissolved in 0.25 m LiOAc solution in H2O/D2O (pH 6.2).</p><p>The 77Se MAS NMR of CsNa‐2 (Supporting Information, Figure S13) shows two broad signals centered at 1255 and 1187 ppm (verified for two different spinning frequencies), in line with the symmetry of 2. Based on literature data for {Se6W39}8a and the data obtained for CsNa‐1 (see above), we tentatively assign the upfield signal to SeIV ions of the {Pd2SeW7} half of the {γ‐Pd2Se2W13} subunit (Figure 2 a), and the 1255 ppm peak to the SeIV ions positioned in the PdII‐free {SeW6} part of this motif. In contrast to 1, solution 77Se NMR of 2 exhibits two main signals at 1316.5 ppm and 1226.8 ppm with 1.8:1 relative intensities (Supporting Information, Figure S14). The chemical shifts of the signals are evident of decomposition of the polyanions with the release of selenite ions (signal at 1316.5 ppm) concurrent with formation of 1 (singlet at 1226.8 ppm), in line with the formation of CsNa‐1 crystals after recrystallization of CsNa‐2 from aqueous acetate solutions. These solution stability observations for 1 and 2 are further supported by SEM images obtained after drop‐casting of 10−4  m CsNa‐1 and CsNa‐2 solutions in ultra‐pure water onto HOPG surface (Supporting Information, Figure S5).</p><p>The exact composition of ion pairs based on 1 and 2 that potentially exist in solutions and gas phase was probed by mass spectrometry. The negative‐ion‐mode ESI‐MS spectrum of 1 (Figure 4) shows a set of peaks (III–VII), which can be attributed to various ion pairs {HxNay[Se2Pd4W14O56H]}3− based on the intact polyanion 1 (Table 1), by virtue of their m/z values and analysis of the corresponding calculated and observed isotope envelopes (see Figure 4, inset; Supporting Information, Figures S17–S24). Peak II could be attributed to an ion pair based on a monovacant derivative of 1, where one of the PdII centers is missing, while peak I belongs to a dilacunary species lacking two PdII ions with the μ2‐briding oxygen ion linking these metal ions together. This suggest that decomposition of 1 in gas phase (and possibly also in solution) proceeds via release of PdII centers in a first step.</p><p>ESI mass spectrum of 1 in H2O/acetone (80:20 vol %) solution in negative‐ion mode. Inset: comparison of the calculated and experimentally observed isotope envelopes for the most intense signal (III).</p><p>Assignment of the peaks observed in the ESI‐MS spectrum of 1.[a]</p><p>[a] Values are given for the most abundant isotopologue (see Figure 4). The small discrepancy in the experimental and calculated m/z values is due to the average element isotope composition was taken for the calculation of the masses. The precise assignment of the signals is made by comparison of the observed and calculated isotope envelopes (see the Supporting Information for details).</p><p>This is consistent with our observations of loss of PdII ions and the following POT moiety decomposition during our attempts to prepare lacunary derivatives of 1, but also suggests that such species could in principle exist if adequately stabilized. The ESI‐MS spectrum of 2 recorded at similar conditions (Supporting Information, Figure S25) only exhibits peaks attributed to singly charged POM decomposition products (see the Supporting Information for details), consistent with our NMR observations.</p><p>In summary, we have isolated and characterized two polyanions [SeI V 2PdII 4WVI 14O56H]11− and [SeI V 4PdII 4WVI 28O108H12]12− comprising both WVI and PdII addenda sites. As such, the new hybrid species bridge the conventional POT‐PdII coordination complexes and POPds. The analysis of the structural data for CsNa‐1 suggests reactivity of μ2‐O ions bridging PdII ions in its {Pd4O4} fragment towards oxophilic metals. Hence, the {Pd4O4} site in 1 could serve an analogy to a vacant site of lacunary POTs, that, in combination with solution stability of 1, could lead to a novel rich class of heterometal derivatives of mixed palladate–tungstates. On the other hand, the ESI‐MS results display a possibility for existence of lacunary species for 1 at appropriate conditions, with one or two PdII centers missing. This hypothesis is further supported by isolation of polyanion 2 which could be imagined as a dimer of two lacunary derivatives of hypothetical {γ‐Pd4Se2W14} species. Follow‐up work will focus on these possibilities.</p><!><p>Synthesis of CsNa‐1: Samples of Na24[H6Se6W39O144]⋅74 H2O8a (0.500 g, 0.042 mmol) and Pd(NO3)2⋅H2O (0.105 g, 0.423 mmol) were dissolved in 5 mL of aqueous 0.5 m NaOAc solution (prepared by addition of solid NaOH into 0.5 m HOAc solution in water until pH reaches 6.7) under vigorous stirring and heating at about 50–60 °C. The obtained clear dark‐red reaction mixture was stirred at 50 °C for 30 min and then cooled to room temperature. After that 0.5 mL of 1 m CsNO3 solution in H2O was added to the reaction mixture under stirring leading to immediate formation of light‐brown precipitate. The precipitate was collected by filtration and recrystallized from warm 0.25 m NaOAc (pH 6.7) resulting in an orange solution. Needle‐like brown‐yellow crystals of CsNa‐1 form within several days. The filtrate produced additional portion of CsNa‐1, although often contaminated by hydrated Cs/Na salt of paratungstate‐B (based on IR and single‐crystal XRD). In this case purification is achieved by recrystallization of the obtained solid material from 0.25 m NaOAc medium (pH 6.7). The crystals of the product were collected by filtration and washed with small amount of ice cold water. Total yield: 0.177 g (33 % based on Pd).</p><p>Elemental analysis calcd (%) for C1H42.5Cs4.3Na3.2O75Pd4Se2W14: Cs 11.30, Na 1.45, Pd 8.42, Se 3.12, W 50.89; found: Cs 11.53, Na 1.51, Pd 7.89, Se 3.11, W 51.64. IR (KBr pellet), ν˜ [cm−1]: 3424 (s, br); 1625 (m); 1420 (w); 1108 (w); 943 (s); 902 (s, sh); 874 (s); 840 (s); 819 (s); 774 (s); 713 (s), 676 (s); 502 (s); 451 (s). Raman (solid sample, λ e=1064 nm), ν˜ [cm−1]: 958 (s); 891 (m); 872 (m); 835 (m); 787 (w); 582 (w); 507 (w, br); 241 (w, br); 197 (m); 161 (m, br); 130 (m); 100 (m); 75 (m). 77Se NMR (H2O/D2O): 1225.3 ppm. 77Se MAS NMR: 1202 ppm. UV/Vis (0.25 m NaOAc buffer solution, pH 6.7): λ max (ϵ)=227 (74450), 273 (shoulder, 34153), 414 nm (1484 mol−1 dm−3 cm−1). CSD no.: 431484.</p><p>Synthesis of CsNa‐2: Na24[H6Se6W39O144]⋅74 H2O8a (0.200 g, 0.017 mmol) and Pd(NO3)2⋅H2O (0.026 g, 0.105 mmol) were dissolved in 2 mL of H2O under vigorous stirring and heating at about 50–60 °C. After the dissolution of all the reagents, the reaction mixture was stirred and further heated for 1 h and then cooled to room temperature and filtered. Three drops of 1 m aqueous CsNO3 solution were added to the obtained dark red–brown filtrate. The obtained pale brown precipitate13 was filtered and the evaporation of the resulting solution at room temperature led to brown crystalline material of CsNa‐2 within 1–3 days. Crystals were collected by filtration, washed with ice‐cold water and dried in air. Yield: 0.040 g (17 % based on W).</p><p>Elemental analysis calcd (%) for H72Cs9.5Na2.5O138Pd4Se4W28: Cs 13.31, Na 0.61, Pd 4.49, Se 3.33, W 54.24; found: Cs 13.22, Na 0.61, Pd 4.49, Se 3.39, W 54.2. IR (KBr pellet), ν˜ [cm−1]: 3423 (s, br); 1614 (s); 954 (s); 843 (s); 768 (s); 704 (s); 662 (s, br); 491 (m); 427 (s). Raman (solid sample, λ e=1064 nm), ν˜ [cm−1]: 970 (s); 914 (m); 902 (m); 885 (m); 866 (w, sh); 812 (m); 717 (w); 660 (m); 646 (m); 513 (w); 503 (w); 216 (m); 110 (m); 75 (m). 77Se MAS NMR: 1255 and 1187 ppm. CSD no.: 431485.</p><p>The Supporting Information for this article includes experimental and crystallographic details, powder X‐ray diffraction, XPS/SEM data, bond valence sum values; IR, Raman, UV/Vis, 77Se MAS and solution NMR spectra, and ESI‐MS with simulations.</p><!><p>As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.</p><p>Supplementary</p><p>Click here for additional data file.</p>

PubMed Open Access

Cocaine Esterase-Cocaine Binding Process and the Free Energy Profiles by Molecular Dynamics and Potential of Mean Force Simulations

The combined molecular dynamics (MD) and potential of mean force (PMF) simulations have been performed to determine the free energy profiles for the binding process of (\xe2\x88\x92)-cocaine interacting with wild-type cocaine esterase (CocE) and its mutants (T172R/G173Q and L119A/L169K/G173Q). According to the MD simulations, the general protein-(\xe2\x88\x92)-cocaine binding mode is not affected by the mutations, e.g. the benzoyl group of (\xe2\x88\x92)-cocaine is always bound in a sub-site composed of aromatic residues W151, W166, F261, and F408 and hydrophobic residue L407, while the carbonyl oxygen on the benzoyl group of (\xe2\x88\x92)-cocaine is hydrogen-bonded with the oxyanion-hole residues Y44 and Y118. According to the PMF-calculated free energy profiles for the binding process, the binding free energies for (\xe2\x88\x92)-cocaine with the wild-type, T172R/G173Q, and L119A/L169K/G173Q CocEs are predicted to be \xe2\x88\x926.4, \xe2\x88\x926.2, and \xe2\x88\x925.0 kcal/mol, respectively. The computational predictions are supported by experimental kinetic data, as the calculated binding free energies are in good agreement with the experimentally-derived binding free energies, i.e. \xe2\x88\x927.2, \xe2\x88\x926.7, and \xe2\x88\x924.8 kcal/mol for the wild-type, T172R/G173Q, and L119A/L169K/G173Q, respectively. The reasonable agreement between the computational and experimental data suggests that the PMF simulations may be used as a valuable tool in new CocE mutant design that aims to decrease the Michaelis-Menten constant of the enzyme for (\xe2\x88\x92)-cocaine.

cocaine_esterase-cocaine_binding_process_and_the_free_energy_profiles_by_molecular_dynamics_and_pote

Introduction<!>MD Simulations<!>Potential of Mean Force (PMF) Simulations<!>Experimental Procedure<!>MD-simulated ES Structures<!>Free Energy Profiles and Experimental Kinetic Data<!>Conclusion

<p>The naturally occurring and biologically active (−)-cocaine is considered to be the most addictive substance abused by millions of people worldwide.1,2,3,4 The disastrous medical and social consequences of cocaine addiction have made the development of an effective pharmacological treatment a high priority.5,6 It has been demonstrated that cocaine exerts its effects on central nervous system (CNS) through blocking the reuptake of neurotransmitter dopamine, thus potentiating the effects of dopamine in the synapse.7, 8, 9 The traditional pharmacodynamic approach has failed to produce a therapeutically useful small-molecule drug due to the difficulties inherent in blocking a blocker like cocaine.3,7,9 As an alternative, pharmacokinetic approach using an efficient cocaine-metabolizing enzyme has become a promising strategy for treatment of cocaine overdose and abuse. The enzyme strategy aims at accelerating the hydrolysis of cocaine and, therefore, eliminating cocaine quickly from the peripheral circulation.10,11,12,13,14 For this purpose, bacterial cocaine esterase (CocE)11,12,15,16,17,18,19,20,21,22,23,24 is a promising choice as a potential anti-cocaine agent for therapeutic treatment of cocaine overdose and abuse, because CocE is the most efficient natural enzyme against (−)-cocaine. Native CocE and the designed thermostable mutants are capable of protecting against cocaine-induced lethality.19,20,22,23 Thus, it is interesting to develop CocE mutants that are more efficient for the catalytic hydrolysis of (−)-cocaine.</p><p>Generally speaking, rational design of a highly efficient enzyme mutant is extremely challenging, particularly when the enzymatic reaction process consists of multiple steps.19,21,22,23,24 The catalytic efficiency (kcat/KM) of an enzyme for a substrate is determined by both the reaction rate constant (kcat) and the Michaelis-Menten constant (KM). The later is associated with the binding affinity of substrate with the enzyme. In order to design a mutant enzyme with an improved catalytic efficiency for a given substrate, one needs to design possible amino-acid mutations that can increase the binding affinity (associated with a smaller KM value) of the substrate and/or increase the corresponding kcat value. The general reaction pathway for CocE-catalyzed hydrolysis of (−)-cocaine has been uncovered by extensive molecular dynamics (MD) simulations and reaction-coordinate calculations using first-principles quantum mechanics/molecular mechanics (QM/MM) methods.21 The computational study have revealed that (−)-cocaine is bound in a site located on the interface of three domains of CocE,11,19 and the rate-determining step is the nucleophilic attack on the carbonyl carbon at the benzoyl group of (−)-cocaine by a water molecule. In our previous studies,19,21,24 we have designed and discovered several thermostable mutants of CocE through computational design, followed by in vitro and in vivo studies. The designed CocE mutants, i.e. T172R, G173Q, T172RG173Q, and L169K, have significantly increased thermostability of the enzyme in vitro and in vivo.19,22,23 Results obtained from previous studies19,20,22,23,24 have revealed that the enzyme can be stabilized by enhanced intra-molecular interactions resulted from these specific mutations. However, it is unclear about how these enhanced intra-molecular interactions affect the CocE-(−)-cocaine binding. Answer to this question is essential for us to better understand the binding mechanism of CocE, and such understanding will help us to rationally design novel mutants of this enzyme with higher catalytic efficiency against (−)-cocaine.</p><p>In the present study, we first performed MD simulations and potential of mean force (PMF) simulations to determine the free energy profiles for the substrate binding process of wild-type CocE and T172R/G173Q mutant. The combined MD and PMF simulations have revealed that these two enzyme-substrate (ES) complexes have similar free energy profiles, and the calculated binding affinity for (−)-cocaine with the thermostable mutant is lower than that of the wild-type CocE-(−)-cocaine binding. Based on the analysis of the simulated ES complexes and the calculated binding free energies, the L119A/L169K/G173Q mutant is predicted to have a significantly lower binding affinity with (−)-cocaine compared to the wild-type or the T172R/G173Q mutant. The computational prediction has been confirmed by wet experimental tests. The agreement between the computational and experimental data suggests that the PMF simulation is a reliable protocol to predict the binding free energy of new CocE mutant binding with (−)-cocaine. The novel insights obtained from the MD and PMF simulations should be helpful for future design of CocE mutants with an improved catalytic efficiency against (−)-cocaine.</p><!><p>The initial structures of the ES complexes were prepared based on the published X-ray crystal structures of CocE22 and the results of our previous molecular docking and MD simulations.19,21,24 The PDB code is 3I2J at resolution of 2.01 Å for wild-type CocE, 3I2G at resolution of 2.50 Å for G173Q, 3I2F at resolution of 2.50 Å for the T172R/G173Q mutant, and 3I2H at resolution of 1.65 Å for the L169K mutant.22 The initial structure of the L119A/L169K/G173Q mutant was prepared based on the X-ray crystal structures of the L169K and G173Q mutants. By superimposing the Cα atoms of the G173Q mutant with the corresponding Cα atoms of the L169K mutant, the atomic positions of the Q173 side chain were copied and the structure of the L169K/G173Q mutant was generated. Starting from the structure of the L169K/G173Q mutant, the structure of the L119A/L169K/G173Q mutant was generated through the L119A mutational modeling by using the X-leap module of Amber 9 program.25 The coordinates of the backbone atoms plus the Cβ atom of L119 were used for the corresponding atoms of A119. The redundant atoms on L119 side chain were deleted and the hydrogen atoms of A119 were added automatically. The generated structure of the L119A/L169K/G173Q mutant was then energy-minimized in order to optimize the interactions between the mutated residues and the surrounding residues. The energy minimization was performed by using the Sander module of Amber 9 program, via a combined use of the steepest descent/conjugate gradient algorithms, with a convergence criterion of 0.01 kcal mol−1 Å−1, and the non-bonded cutoff distance was set to 10.0 Å. The energy minimization was performed first on the mutated residues, and then on the residues within 5 Å around any of the mutated residues. The structure of the L119A/L169K/G173Q CocE-(−)-cocaine complex was constructed in a similar way as that for the other ES complexes. In order to further relax each constructed ES structure, MD simulations were performed by using the Sander module of Amber 9 program package.25 The general procedure of the MD simulations was similar to that used in our previously reported other computational studies.19,21,24 In particular, the molecular mechanics force field parameters and the partial charges of (−)-cocaine atoms were adopted directly from those developed in our previous studies.6,13,14,19,21,24 Briefly, the partial charges of (−)-cocaine atoms were calculated by using the restrained electrostatic potential-fitting (RESP) protocol implemented in the Antechamber module of Amber 9 program, following the electrostatic potential (ESP) calculation at ab initio HF/6-31G* level using Gaussian 03 program.26 Each of the ES complex structures was solvated in a rectangular box of TIP3P water molecules27 with a minimum solvent-wall distance of 10 Å. Sodium counter ions (Na+) were added to neutralize the solvated system. The solvated system was gradually heated to 298.15 K by weak-coupling method28 and equilibrated for 400 ps. During the MD simulations, a 10.0 Å non-bonded interaction cutoff was used and the non-bonded list was updated every 25 steps. The motion for the mass center of the system was removed every 1,000 steps. The particle-mesh Ewald (PME) method29,30 was applied to treat long-range electrostatic interactions. The lengths of covalent bonds involving hydrogen atoms were fixed with the SHAKE algorithm,31 enabling the use of a 2-fs time step to numerically integrate the equations of motion. Finally, the production MD was kept running for ~2.0 ns with a periodic boundary condition in the NTP ensemble at T = 298.15 K with Berendsen temperature coupling, and at P = 1 atm with isotropic molecule-based scaling.28,32</p><!><p>In order to explore the free energy profiles for the process of (−)-cocaine binding with wild-type CocE and its mutants, PMF simulations were carried out by using umbrella-sampling33 MD simulations. The classic PMF definition34 can be represented by a function of reaction coordinate as (1)ω(χ)=−RTln〈ρ(χ)〉−U(χ)+F. In Eq.(1), ρ(χ) is the probability density along the reaction coordinate χ, R is the gas constant, T is the simulation temperature, U(χ) is the biasing potential applied in the umbrella-sampling MD simulations, and F is the normalization constant. According to this approach, the reaction coordinate is usually divided into different regions, i.e., windows, and each of which is sampled separately. A biasing (umbrella) potential, i.e. U(χ), is applied for each window in order to obtain nearly uniform sampling of the potential energy surface. In the present study, the reaction coordinate was defined as the distance from the mass center of the non-hydrogen atoms of (−)-cocaine to the mass center of the non-hydrogen atoms on the side chains of residues H87, V121, and L146 of the enzyme. The total number of windows for each complex structure was about 70, depending on the starting structure of each system. Each window was separated by 0.3 Å, covering the reaction coordinate from ~11.43 Å to 32.93 Å. The biasing force constant applied in different windows of umbrella-sampling was 10.0 kcal/(mol·Å2). For each umbrella-sampling window, the initial complex structure was selected from the last snapshot of the PMF simulations of the previous window. The selected structure for each window was first equilibrated for 200 ps and then kept running for 800 ps for production sampling. The frequency for data collection was set to 1 fs, which was the same as that of the time step of umbrella-sampling MD.</p><p>After all the umbrella-sampling MD simulations were finished for each system, the data collected from separate simulation windows were combined along the reaction coordinate. These data were then used to calculate the PMF for the whole binding process with the weighed histogram analysis method (WHAM)35,36 using the code developed by Alan Grossfield (http://membrane.urmc.rochester.edu/Software/WHAM/WHAM.html).</p><p>Most of the MD and umbrella-sampling MD simulations were performed on a supercomputer (e.g. DELL Cluster with 388 nodes or 4,816 processors) at University of Kentucky's Computer Center. Some other modeling and computations were carried out on SGI Fuel workstations in our own lab.</p><!><p>Site-directed mutagenesis was generated by using QuickChange (Stratagene) and CocE cDNA cloned in the bacterial expression vector, pET-22b (+). The enzyme (the L119A/L169K/G173Q mutant of CocE) was expressed as 6×His-tagged proteins in E. coli BL-21 (DE3) cells grown at 37°C. Protein expression was induced with 1 mM isopropyl-β-thiogalactopyranoside (Sigma Aldrich) for ~15 h at 18°C. Cells were pelleted, resuspended in 50 mM Tris-HCl, pH 8.0, 150 mM NaCl, and a protease inhibitor cocktail (34 μg/ml each of L-tosylamido-2-phenylethyl chloromethyl ketone, 1-chloro-3-tosylamido-7-amino-2-heptanone, and phenylmethylsulfonyl fluoride, and 3 μg/ml each of leupeptin and lima bean trypsin inhibitor) and lysed using a French Press (Thermo Fisher Scientific, Waltham, MA). The 6×His-tagged enzyme was enriched using HisPur™ Cobalt Resin (Thermo Fisher Scientific, Waltham, MA) storage buffers containing 20 mM HEPES, pH 8.0, 2 mM MgCl2, 1 mM EDTA, and 1 mM dithiothreitol. The fractions were concentrated by using an Amicon Ultra-50K centrifuge (Millipore, Billerica, MA). The enzyme concentration was determined using CB-Protein Assay™ Kit (from CALBIOCHEM) with bovine serum albumin as a standard.</p><p>To determine the catalytic activity of the enzyme against (−)-cocaine, the initial rates of the enzymatic hydrolysis of (−)-cocaine at various concentrations were determined by using a sensitive radiometric assay based on toluene extraction of [3H](−)-cocaine labeled on its benzene ring, as we did for the catalytic activity of BChE mutants against (−)-cocaine.13,14,37,38,39,40,41 Briefly, to initiate the enzymatic reaction, 100 nCi of [3H](−)-cocaine was mixed with 100 μl of culture medium. The enzymatic reaction proceeded at room temperature (25°C) with varying concentration of (−)-cocaine. The reaction was stopped by adding 200 μl of 0.05 M HCl, which neutralized the liberated benzoic acid while ensuring a positive charge on the residual (−)-cocaine. [3H]benzoic acid (one of the reaction products) was extracted by 1 ml of toluene and measured by scintillation counting. Finally, the measured (−)-cocaine concentration-dependent radiometric data were analyzed by using Prism 5 (GraphPad Software Inc., San Diego, CA).</p><!><p>As suggested in our previous study on CocE-catalyzed reaction mechanism,21 the (−)-cocaine binding site is located on the interface of the three domains of CocE. Figure 1 depicts the most important distances tracked from the MD simulations and the typical ES structure of wild-type CocE-(−)-cocaine complex derived from the last snapshot of the MD simulations. The plots for the tracked positional root-mean square deviation (RMSD) of all non-hydrogen atoms versus the simulation time are provided as Supporting Information (Figure S2). The performed MD simulations were also served to obtain stable ES structure used as the starting structure in subsequent PMF simulations (discussed below). As shown in Figure 1A, the distance from the carbonyl carbon at the benzoyl group of (−)-cocaine to the hydroxyl oxygen at the side chain of residue S117 fluctuates around 3.2 Å. Such a distance is suitable for the nucleophilic attack by the hydroxyl oxygen on the side chain of residue S117, which initiates the first chemical reaction step of the catalytic hydrolysis. The carbonyl oxygen on the benzoyl group of (−)-cocaine interacts with the oxyanion hole residues Y44 and Y118 through hydrogen-bonding interactions. In the typical CocE-(−)-cocaine complex (Figure 1B), the benzoyl group of (−)-cocaine is located in a sub-binding site composed of hydrophobic residues W151, W166, L169, L407, F408, F261, and P150 of CocE, packing in parallel with the aromatic side chain of W166, and in perpendicular with the aromatic side chain of F261. The mode of binding for the benzoyl group of (−)-cocaine is supported indirectly by the observations from the X-ray crystal structures of CocE in complex with either the benzoic acid (PDB code 1JU4 at resolution of 1.63 Å) or phenyl boronic acid (PDB code 1JU3 at resolution of 1.58 Å).11 As revealed in these X-ray structures, the phenyl ring of either the benzoic acid or the phenyl boronic acid was bound in the similar sub-site as that of the benzoyl group of (−)-cocaine (Figure 1B) and interacted with several hydrophobic residues including W166 and F261 of CocE. The binding mode of the benzoyl group of (−)-cocaine is also consistent with earlier results from site-directed mutagenesis of CocE.12 As reported, each of the mutations W151A, W166A, F261A, L407A, and F408A had some negative impact on the substrate binding, ca. 2~80-fold increase in the experimentally measured KM value, indicating a dramatic decrease in the substrate binding affinity. According to our modeled wild-type CocE-(−)-cocaine complex structure (Figure 1B), the hydrophobic packing between the benzoyl group of (−)-cocaine and the surrounding residues would be dramatically weakened by mutating any of these residues into Alanine residue which has a much smaller side chain.</p><p>The methyl ester group of (−)-cocaine stays just above residue H287 of CocE with a distance of ~3.8 Å between the methyl carbon and the center of the side chain of residue H287 (Figure 1B). The methyl ester group is also in close packing with residues V116, M141, and L290 of domain I (residues from #1 to #144 and from #241 to #354) of CocE. The cationic head group of (−)-cocaine is partly exposed to the surrounding solvent, and is surrounded by the side chains of residues Y44, A51, Q55, and L169. As residue Q55 is within 5 Å around the cationic head group of (−)-cocaine, it can be expected that the Q55D or Q55E mutation will increase the binding affinity (−)-cocaine, as the Q55D or Q55E will enhance the electrostatic interactions between the mutated residue and the cationic head group of (−)-cocaine. This explains why the experimentally measured KM value for the Q55E mutant decreased 2.5 fold.12</p><p>The tracked important distances from MD trajectories and the MD-simulated ES structures are depicted in Figure 2 for (−)-cocaine binding with the T172R/G173Q mutant, and in Figure 3 for (−)-cocaine binding with the L119K/L169K/G173Q mutant. In general, the binding mode of (−)-cocaine with each of these CocE mutants is similar to that of (−)-cocaine with wild-type CocE. For example, the benzoyl group of (−)-cocaine is stabilized in the binding site by hydrogen-bonding interactions between its carbonyl oxygen (O33) and the oxyanion-hole residues Y44 and Y118 as seen from the tracked O33---Y44HH and O33---Y118H distances in Figures 2 and 3, respectively. However, the tracked O33---Y44HH distance in each of the mutant-(−)-cocaine complexes is longer than that in the wild-type CocE-(−)-cocaine complex as shown in Figures 2A and 3A, and as listed in Table 1 for the simulated average values (based on the MD trajectories). As tracked through the MD simulations, the average O33---Y118H distance in each mutant-(−)-cocaine complex is much longer than that in the wild-type-(−)-cocaine complex (Table 1). According to our previous studies on other receptor-ligand binding systems,42,43 the contribution of hydrogen bonding to the total binding free energy between a protein and a ligand can be calculated as the following equation: (1)ΔGHB=∑i=1N(β12Ri12−β10Ri10)=β12∑i=1N1Ri12−β10∑i=1N1Ri10, in which Ri is the H…O distance for the ith hydrogen bond between the protein and ligand, and the calibrated parameters β12 = 5.571 and β10 = 668.580. Using Eq.(1), the difference in binding free energy contributed from these hydrogen bonding interactions between the mutant-(−)-cocaine complex and the wild-type-(−)-cocaine complex can be conveniently calculated, i.e. ΔΔGHB = ΔGHB(mutant) − ΔGHB(wild-type), and the calculated results are also listed in Table 1. As shown in Table 1, the calculated ΔΔGHB values for the mutant-(−)-cocaine complexes are all positive. The data for the tracked hydrogen-bond distances and the calculated ΔΔGHB values indicate that the binding free energy for (−)-cocaine binding with each of these mutants should be higher than that of the wild-type CocE-(−)-cocaine binding.</p><p>As the residues #172 and #173 are not directly involved in the formation of the substrate-binding site of CocE, the T172R/G173Q mutations must affect the substrate binding indirectly. As shown in Figure 2 and listed in Table 1, the hydrogen bonding between the oxyanion-hole residues (Y44 and Y118) and the benzoyl group of (−)-cocaine in the T172R/G173Q mutant-(−)-cocaine complex is weakened compared to that in the wild-type CocE-(−)-cocaine complex (Figure 1), but the weakening effect on the substrate binding is not dramatic.</p><p>Concerning the L119A/L169K/G173Q mutant binding with (−)-cocaine, the residue #119 stays behind the catalytic residue S117 and the oxyanion-hole residue Y118 from the same α-helix, and does not directly contact with substrate (−)-cocaine. We tested L119A mutation as we initially expected this mutation to add more free space of the substrate binding site so that the (−)-cocaine binding could be improved. However, as shown in Figure 3, the tracked distance from the carbonyl oxygen at the benzoyl group of (−)-cocaine to the Cα atom of residue #119 in the L119A/L169K/G173Q mutant-(−)-cocaine complex (red curve in Figure 3B) is longer than that in the wild-type CocE-(−)-cocaine complex (black curve in Figure 3B). As listed in Table 1, the average distances for the hydrogen bonding between the benzoyl group of (−)-cocaine and the oxyanion-hole residues Y44 and Y118 in the L119A/L169K/G173Q mutant-(−)-cocaine complex are also longer than the corresponding hydrogen-bonding distances in the wild-type-(−)- cocaine complex. The data from the MD simulations on the L119A/L169K/G173Q mutant-(−)-cocaine complex suggest that the L119A/L169K/G173Q mutation will lead to a considerable decrease in the affinity for the mutant enzyme binding with (−)-cocaine.</p><!><p>In order to predict the binding free energy of (−)-cocaine with wild-type CocE and its mutants, the PMF simulations were performed starting from the MD-simulated ES complex structures. Based on the data collected from the umbrella-sampling MD simulations, the PMF for each of the ES structures was determined. Figure 4 depicts the PMF-calculated free energy profiles. The distance between the mass center of the non-hydrogen atoms of (−)-cocaine and the mass center of the non-hydrogen atoms on the side chains of residues H87, V121, and L146 of the enzyme was used as the reaction coordinate for the PMF calculations. Such selection of the reaction coordinate was based on the structural features of our modeled CocE-(−)-cocaine binding structures in the present study, as we found that the direction of reaction coordinate roughly went through the central point of the active site of CocE to reach the molecular surface of the enzyme.</p><p>To test whether the PMF simulations reached convergence, we calculated the binding free energy for the T172R/G173Q CocE-(−)-cocaine binding by using different lengths of the MD trajectory. As shown in Figure S1 of Supporting Information, the PMF for the T172R/G173Q CocE-(−)-cocaine binding was determined by using two different lengths of the MD trajectory for each window, i.e. 0.2−1.0 ns and 0.2−0.8 ns. There was no significant difference between the free energy profiles for the T172R/G173Q CocE-(−)-cocaine binding determined based on the 0.2−1.0 ns and 0.2−0.8 ns of the MD trajectory; the curve of the free energy profile corresponding to the 0.2−0.8 ns almost perfectly overlaps with that (corresponding to 0.2−1.0 ns) shown in Figure 4 (the blue), as seen in Supporting Information (Figure S2). These data suggest that 1.0 ns for each window of the PMF simulations should be sufficient for obtaining the converged results from the PMF simulations.</p><p>Based on the PMF-calculated free energy profiles (Figure 4), we cannot identify an obvious free energy barrier along the reaction coordinate in the simulated wild-type CocE-(−)-cocaine or T172R/G173Q mutant-(−)-cocaine binding process. According to the simulated free energy profile (black curve in Figure 4) for the process of wild-type CocE- (−)-cocaine binding and the MD-simulated ES structure, (−)-cocaine molecule can diffuse smoothly from external solvent to the active site of CocE, and its benzoyl group slides down to its sub-binding site around aromatic side chains of W151, W166, and F261. For the T172R/G173Q mutant-(−)-cocaine binding, the simulated free energy profile (blue curve in Figure 4) is also similar to that of the wild-type CocE-(−)-cocaine binding, except for the different starting point of the reaction coordinate. In order to check possible structural adaptation of the enzyme during the process of binding with (−)-cocaine, we tracked the size of the sub-binding site for the benzoyl group of (−)-cocaine along the reaction coordinate of the PMF simulations. Let us use the T172R/G173Q CocE-(−)-cocaine binding structure as an example for discussion here. We selected the distance from the center of mass of W166 side chain to the center of mass of F261 side chain as the criterion according to the structural features of the MD simulated T172R/G173Q CocE-(−)-cocaine complex (Figure 2). As shown in Supporting Information (Figure S2), the distance from the center of mass of W166 side chain to the center of mass of F261 side chain fluctuated at 11.5 Å ± 1.0 Å. Such small fluctuation suggests that the size of the sub-binding site for the benzoyl group of (−)-cocaine had no significant change along the binding process. For the L119A/L169K/G173Q mutant-(−)-cocaine binding, the simulated free energy profile (red curve in Figure 4) shows a local minimum when the reaction coordinate has a value around 23 Å. A detailed check of the umbrella-sampling MD simulations on the L119A/L169K/G173Q mutant-(−)-cocaine binding revealed that the carbonyl oxygen atom on the benzoyl group of (−)-cocaine was hydrogen-bonded with the positively charged head group on the side chain of residue K169 when the reaction coordinate was around 20 Å (see Supporting Information, Figure S5). The (−)-cocaine molecule starts to leave away from residue K169 as the value of reaction coordinate becomes smaller than 20 Å, and it reaches the edge of active site of CocE when the value of reaction coordinate becomes smaller than 16 Å. Further binding process for the L119A/L169K/G173Q mutant with (−)-cocaine starting from this local minimum has a local free energy barrier of 1.5 kcal/mol.</p><p>As shown in Figure 4, the calculated binding free energy (ΔGbind) is −6.4 kcal/mol for wild-type CocE, −6.2 kcal/mol for the T172R/G173Q mutant, and −5.0 kcal/mol for the L119A/L169K/G173Q mutant. The difference in the binding free energy between the T172R/G173Q mutant and wild-type CocE, i.e. ΔΔGbind = ΔGbind(mutant) − ΔGbind(wild-type), is 0.2 kcal/mol. The calculated ΔΔGbind for the difference between the L119A/L169K/G173Q mutant and the wild-type CocE is 1.4 kcal/mol. According to the calculated relative binding free energies, (−)-cocaine should have a much lower binding affinity with the L119A/L169K/G173Q mutant than that with wild-type CocE or the T172R/G173Q mutant, and the order of the binding affinity is wild-type CocE > T172R/G173Q mutant > L119A/L169K/G173Q mutant.</p><p>In order to know how well the calculated binding free energies are, we estimated the corresponding experimental binding free energies from available experimental data, i.e. the experimental values of Michaelist-Menten constant KM under the well-known rapid equilibration assumption as KM ≈ Kd (dissociation constant). Under the rapid equilibration assumption, we may have (2)ΔGbind(expt)=RTlnKd≈RTlnKM.</p><p>Our recently reported experimental kinetic analysis revealed that KM = 13 μM for the T172R/G173Q mutant against (−)-cocaine.44 Experimental determination of the KM value for wild-type CocE against (−)-cocaine has been a challenge due to the thermal instability of the wild-type enzyme. Nevertheless, according to the most recently reported kinetic analysis,22 the KM = 5.7 μM for wild-type CocE against (−)-cocaine. Thus, when T = 298.15 K, we may have ΔGbind(expt) = −7.2 kcal/mol for wild-type CocE binding with (−)-cocaine, ΔGbind(expt) = −6.7 kcal/mol for the T172R/G173Q mutant binding with (−)-cocaine. The experimentally-derived binding free energies are reasonably close to the corresponding PMF-calculated binding free energies.</p><p>Further, in order to examine the computational prediction on the L119A/L169K/G173Q mutant, we carried out site-directed mutagenesis to make the L119A/L169K/G173Q mutant and performed in vitro kinetic analysis. The kinetic analysis revealed that kcat = 2700 min−1 and KM = 0.3 mM for the L119A/L169K/G173Q mutant against (−)-cocaine in the room temperature. Compared to the T172R/G173Q mutant (kcat = 1082 min−1 and KM = 13 μM), the L119A/L169K/G173Q mutant has an increased kcat value of 2700 min−1 and an increased KM value of 0.3 mM. When KM = 0.3 mM, we have ΔGbind(expt) = −4.8 kcal/mol for the L119A/L169K/G173Q mutant binding with (−)-cocaine. The experimentally-derived binding free energy of −4.8 kcal/mol is close to the PMF-calculated binding free energy of −5.0 kcal/mol.</p><!><p>The combined molecular dynamics (MD) and potential of mean force (PMF) simulations have allowed us to determine the free energy profiles for the binding process of (−)-cocaine interacting with wild-type CocE and its mutants. The MD-simulated enzyme-substrate (ES) structures reveal that the binding mode for (−)-cocaine with each of the mutants (T172R/G173Q and L119A/L169K/G173Q) is generally similar to that of (−)-cocaine with wild-type CocE, e.g. the benzoyl group of (−)-cocaine is always bound in a sub-site composed of aromatic residues W151, W166, F261, and F408 and hydrophobic residue L407. The carbonyl oxygen on the benzoyl group of (−)-cocaine is hydrogen-bonded with the oxyanion-hole residues Y44 and Y118. The data obtained from the MD simulations indicate that the binding of (−)-cocaine with the L119A/L169K/G173Q mutant is less favorable compared to that of (−)-cocaine with wild-type CocE or the T172R/G173Q mutant.</p><p>The PMF simulations demonstrate that all of the three simulated ES structures have similar free energy profiles for the binding process, but with different starting points for the reaction coordinate. Based on the PMF simulations for the binding process, the calculated binding free energies for (−)-cocaine with the wild-type, T172R/G173Q, and L119A/L169K/G173Q CocEs are −6.4, −6.2, and −5.0 kcal/mol, respectively. The calculated relative binding free energies are reasonably close to the corresponding experimental values (−7.2 kcal/mol for the wild-type and −6.7 kcal/mol for the T172R/G173Q mutant) derived from the experimental KM values. The computational prediction for the L119A/L169K/G173Q mutant has been supported by experimental kinetic analysis showing KM = 0.3 mM (associated with ΔGbind = −4.8 kcal/mol) for the L119A/L169K/G173Q mutant against (−)-cocaine. The experimentally-derived binding free energy of −4.8 kcal/mol is in good agreement with the calculated binding free energy of −5.0 kcal/mol. The agreement between the computational and experimental data suggests that the PMF simulations may be used as a valuable tool in new CocE mutant design that aims to decrease the Michaelis-Menten constant and, thus, improve the catalytic efficiency of the enzyme for (−)-cocaine.</p>

PubMed Author Manuscript

Synthesis and evaluation of new iRGD peptide analogs for tumor optical imaging

Recently, a disulfide-based cyclic RGD peptide called iRGD, i.e. c(CRGDKGPDC), has been reported to interact with both integrin and neuropilin-1 receptors for cellular and deep tissue penetration to improve the imaging sensitivity and therapeutic efficacy. In this study, two new near-infrared fluorescent iRGD conjugates, i.e., Ac-Cys(IRDye\xc2\xae800CW)-iRGD (1), and its dual labeling analog DOTA-Cys(IRDye\xc2\xae800CW)-iRGD (2) were synthesized via the specific mercapto-maleimide reaction for tumor imaging. Both 1 and 2 showed significant tumor localization in optical imaging of MDA-MB-435 tumor-bearing mice. The potential of such iRGD compounds in tumor-targeted imaging and drug delivery deserves further exploration.

synthesis_and_evaluation_of_new_irgd_peptide_analogs_for_tumor_optical_imaging

<p>Integrins are a family of heterodimeric cell surface receptors that bind extracellular matrix proteins to mediate cell attachment and signaling. Currently, 24 integrin subtypes have been reported.1–3 Among them, some integrins such as αvβ3, αvβ5, and α5β1 have served as attractive targets for studying cancer pathology, imaging, and targeted therapy due to their over-expression on different types of tumors and related neovasculature for mediating tumor growth, angiogenesis, and metastasis.4–10 Importantly, various tumor imaging agents have been discovered and developed based on integrin receptors because integrin-targeted tumor imaging holds great promise to improve early detection, diagnosis, and therapy as well as discovery and development of novel targeted anticancer agents.</p><p>For a long time, RGD peptides are known for molecular recognition of integrin receptors.2 Diverse RGD peptides especially cyclic pentapeptide i.e. c(RGDfK) analogs exhibit remarkable binding affinity and selectivity with integrin αvβ3 and αvβ5. They have been applied widely to integrin targeting for cancer pathology, molecular imaging and drug delivery.11–19 It has recently been reported that a disulfide-based cyclic RGD called iRGD, i.e. c(CRGDKGPDC) discovered from phage display can interact with both integrin and neuropilin-1 receptors to mediate cellular internalization and extravasation as well as facilitate deep tissue penetration for improved imaging sensitivity and therapeutic efficacy.20, 21 These studies have inspired us to explore new strategies for integrin targeting and tumor imaging based on such iRGD peptides. Optical imaging has emerged as a powerful modality for studying molecular recognitions and molecular imaging in a noninvasive, sensitive, and real-time way. Some advantages of optical imaging include cost-effectiveness, convenience, and non-ionization safety as well as complementation with other imaging modalities such as positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI). Therefore, we have been interested in further exploring some novel iRGD analogs for tumor-targeted optical imaging. Herein, we report two new near-infrared (NIR) fluorescent Cys-containing iRGD conjugates i.e. Ac-Cys(IRDye®800CW)-iRGD (1), and its dual labeling analog DOTA-Cys(IRDye®800CW)-iRGD (2) (Figure 1). Both were synthesized via the specific mercapto-maleimide reaction and showed significant tumor localization in MDA-MB-435 tumor xenograft-bearing nude mice as revealed by optical imaging.</p><p>Based on the structure of iRGD peptide motif, we suggest it should be feasible to perform some N and C terminal modifications on iRGD first. Both N-acetylation and C-amidation of protein and peptide termini have been used as effective approaches to improve the stability and biological activities for some peptides.22, 23 Although N-terminal acetylation and/or C-terminal amidation reduce the overall charge and the solubility of the peptide, they can increase the permeability of the peptides to cells for intracellular, in vivo assay or in vitro functional studies. They can also increase the metabolic stability of the peptide toward degradation by some enzymes such as aminopetidases, exopeptidases or synthetase. To explore molecular design of novel iRGD peptide analogs for integrin targeting and tumor imaging, we first focused on some new iRGD analogs derived from N-terminal acetylation and C-terminal amidation based on the cyclic structure of iRGD. Therefore, we designed a new iRGD analog (3) containing one Cys residue at N-terminus, allowing for some specific chemical modifications on its mercapto group for exploring various biomedical applications as shown in Figure 2. For example, a near-infrared fluorescent iRGD conjugate (1) based on the reaction of mercapto group with a commercially available IRDye®800CW maleimide24 was designed.</p><p>As shown in Scheme 1, the protected linear peptide Cys(Acm)-Arg(Pbf)-Gly-Asp(OBut)-Lys(Boc)-Gly-Pro-Asp(OBut)-Cys(Acm) was first assembled on Rink amide resin using the conventional Fmoc chemistry. The disulfide formation was realized on solid support by using a solution of thallium trifluoroacetate in DMF.25, 26 A Fmoc-Cys(Trt) residue was further introduced at the N-terminus of the resin-bound protected iRGD peptide using Fmoc chemistry. Finally, the acetylated Cys-containing iRGD analog (3) was obtained by TFA cleavage (TFA/thioanisole/TIS).</p><p>As shown in Scheme 2, compound 3 was conjugated with a commercially available near-infrared fluorescent probe IRDye®800CW maleimide in PBS buffer (pH 7.2) to give 1 for optical imaging. As monitored by analytical HPLC, the conjugation progressed quickly and was complete within 5–10 min.</p><p>Multimodal molecular imaging has emerged as a powerful tool for cancer diagnosis, therapy. Because each imaging modality has its own unique strengths and weaknesses, the combination of different imaging modalities has the potential to overcome the respective limitations. Dual-modality optical/PET imaging agents may be attractive for coupling the high-resolution of optical imaging and the sensitivity of nuclear imaging to improve the cancer detection and diagnosis. Based on the above design and synthesis of 1, we further designed a NIR fluorescent iRGD analog (2) containing a metal chelator DOTA (1,4,7,10-tetra-azacyclododecane-N,N′,N″,N‴-tetraacetic acid). As shown in Figure 3, DOTA chelator was similarly introduced at the N-terminus of Cys(IRDye®800CW)-iRGD peptide instead of acetylation to give 2. Such an agent might be very useful as DOTA can be labeled with radioactive metals such as 64Cu to serve as a dual labeling imaging agent for dual-modality optical/PET imaging and for potential radiotherapy as well.</p><p>As shown in scheme 3, 2 can be synthesized similarly via a precursor DOTA-Cys-iRGD (4). DOTA chelator was introduced at the N-terminus of iRGD peptide in the presence of HBTU/HOBT/DIEA instead of acetylation. TFA cleavage afforded the iRGD analog (4) containing both DOTA and Cys motifs. Similarly, 4 was reacted with IRDye®800CW maleimide in PBS (pH 7.2) to give the dual labeling analog containing both IRDye®800CW and DOTA (2).</p><p>The purity and identity of all the compounds were fully identified by both analytical HPLC and LC-MS. The two NIR fluorescent compounds 1 and 2 showed similar UV-Vis and emission spectra with the dye material IRDye®800CW maleimide in PBS buffer (pH 7.2) (λmax: UV 774 nm, emission 789 nm).</p><p>We tested the ability of the two near-infrared fluorescent compounds 1 and 2 to target tumors in nude mice to evaluate their potential as in vivo molecular imaging agents. Both 1 and 2 were dissolved in PBS buffer (100 µL, 10 µM) and injected via tail vein into MDA-MB-435 tumor xenografts-bearing nude mice.31–33 The mice were monitored by a dynamic data acquisition for 60 min, followed by static acquisitions at 1, 2, and 4 h postinjection. Figure 4 showed some representative optical images of both 1 and 2. Both were found to preferentially localize in the tumor within 4 h post injection. Based on the time dependent tumor uptake curve, the tumor uptake of both 1 and 2 peaked at 10 min after probe injection. However, the tumor/muscle ratio kept increasing and reached the maximum level at 1 hour post injection. Based on the fluorescence intensity over bladder region, we deduced that the probes were mainly excreted through kidneys.</p><p>The complexity and diversity of integrin receptors in their structures and functions suggest that it is important to discover diverse novel ligands for targeting integrins.2, 5, 9, 16, 30, 34–37 Diverse tumor imaging agents have been discovered based on the targeting of integrin and some other receptors related to tumor angiogenesis, growth, and metastasis. Nevertheless, integrin-targeted imaging agents of deep tumor penetration should be attractive. As described above, we have successfully explored the molecular design, synthesis, and evaluation of some novel iRGD peptide analogs. The iRGD motif provides at least two sites at N- and C-termini for chemical modifications. Our results have clearly demonstrated significant tumor localization in vivo and the potential of such iRGD peptides as represented by compounds 1 and 2 in tumor targeting and optical imaging, especially 2 with potential in dual-modality imaging. In addition, the free mercapto groups of compounds 3 and 4 allow site-specific reactions for constructing novel diverse iRGD conjugates to further explore their potential in receptor targeting, tumor imaging, and drug delivery. For example, we have recently explored its applications for PET imaging using the 64Cu-DOTA-containing analogs and 18FBEM labeling. All the compounds showed remarkable tumor accumulation and retention in orthotopic MDA-MB-435 xenograft model as revealed by both optical and PET imaging modalities.</p><p>It is important to compare iRGD with the conventional cyclic RGD peptide c(RGDfK) in vitro and in vivo for their receptor targeting as well as cell tumor penetration in future work. The iRGD peptide containing 9 amino acid residues in its ring has very flexible structural conformations, which may not compete with the conventional lactam-based cyclic RGD peptide i.e. penta-peptide c(RGDfK) in integrin αvβ3 binding affinity and selectivity. Nevertheless, the novel structure and significant tumor localization suggest that iRGD compounds might exhibit some unique features for tumor imaging and other potential applications. In addition, the mechanism of iRGD for tumor targeting involves its binding with integrin αvβ3 first, followed by enzymatic hydrolysis to form an active CendR peptide that binds to neuropilin-1 and mediates an active transport system for extravasation and deep tumor penetration as reported.20, 21 Therefore, it is also important to study the biodegradation of our new iRGD compounds and further elucidate the mechanism of action for tumor-targeted imaging.</p><p>In conclusion, optical imaging has demonstrated the potential of iRGD for tumor imaging in mouse models. Further structural modification for improving tumor-targeted imaging and elucidating its mechanism is currently underway and the results will be reported separately. All these should facilitate the discovery and development of novel tumor-targeted imaging and therapeutic agents.</p><p>This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.</p><p> Supplementary Material </p><p>Supplementary material that may be helpful in the review process should be prepared and provided as a separate electronic file. That file can then be transformed into PDF format and submitted along with the manuscript and graphic files to the appropriate editorial office.</p><p>The two NIR fluorescent iRGD conjugates 1 (R: CH3) or 2 (R: DOTA) designed and synthesized for tumor optical imaging.</p><p>The structures of two new iRGD analogs Ac-Cys(IRDye800)-iRGD (1) and Ac-Cys-iRGD (3).</p><p>The structure of DOTA-Cys(IRDye®800CW)-iRGD (2) designed.</p><p>Optical imaging of MDA-MB-435 tumors with 1 (A, B, C) and 2 (D, E, F). A. In vivo optical tumor imaging with 1; B. Time dependent tumor uptake of 1; C. Tumor muscle ration of 1; D. In vivo optical tumor imaging with 2; E. Time dependent tumor uptake of 2; F. Tumor muscle ration of 2.</p><p>Synthesis of Ac-Cys-iRGD (3).</p><p>Conjugation of IRDye800-maleimide and Ac-Cys-iRGD (3) to form Ac-Cys(IRDye800)-iRGD (1).</p><p>Synthesis of DOTA-Cys-iRGD (4) and DOTA-Cys(IRDye800)-iRGD (2).</p>

PubMed Author Manuscript

Host–guest complexes of conformationally flexible C-hexyl-2-bromoresorcinarene and aromatic N-oxides: solid-state, solution and computational studies

Host–guest complexes of C-hexyl-2-bromoresorcinarene (BrC6) with twelve potential aromatic N-oxide guests were studied using single crystal X-ray diffraction analysis and 1H NMR spectroscopy. In the solid state, of the nine obtained X-ray crystal structures, eight were consistent with the formation of BrC6-N-oxide endo complexes. The lone exception was from the association between 4-phenylpyridine N-oxide and BrC6, in that case the host forms a self-inclusion complex. BrC6, as opposed to more rigid previously studied C-ethyl-2-bromoresorcinarene and C-propyl-2-bromoresorcinarene, undergoes remarkable cavity conformational changes to host different N-oxide guests through C–H···π(host) interactions. In solution phase CD3OD/CDCl3 (1:1 v/v), all twelve N-oxide guests form endo complexes according to 1H NMR; however, in more polar CD3OD/DMSO-d6 (9:1 v/v), only three N-oxides with electron-donating groups form solution-phase endo complexes with BrC6. In solid-state studies, 3-methylpyridine N-oxide+BrC6 crystallises with both the upper- and lower-rim BrC6 cavities occupied by N-oxide guests. Computational DFT-based studies support that lower-rim long hexyl chains provide the additional stability required for this ditopic behaviour. The lower-rim cavity, far from being a neutral hydrophobic environment, is a highly polarizable electrostatically positive surface, aiding in the binding of polar guests such as N-oxides.

host–guest_complexes_of_conformationally_flexible_c-hexyl-2-bromoresorcinarene_and_aromatic_n-oxides

<!>Introduction<!><!>Solid-state X-ray crystallography<!><!>Solid-state X-ray crystallography<!>Comparison of ditopic H–G complexes<!><!>Comparison of ditopic H–G complexes<!><!>Comparison of ditopic H–G complexes<!><!>1H NMR host–guest solution studies<!><!>1H NMR host–guest solution studies<!><!>Conclusion<!>

<p>This article is part of the thematic issue "Macrocyclic and supramolecular chemistry".</p><!><p>Resorcinarenes are macrocyclic compounds with a bowl-shaped cavity stabilised by circular intramolecular O···H–O hydrogen bonds (HBs) [1–2]. The combination of their confined cavity and conformational flexibility has driven the interest in these synthetic receptors [3], a subclass of calixarenes [4], for a wide range of applications in fields such as catalysis [5–9], sensors [10–11], coordination chemistry [12–13], biological systems [14] and especially for host–guest (H–G) chemistry [15]. Resorcinarenes can be modified at either the upper rim 2-position, lower rim, or both, to deliver supramolecular structures with the required structure for a given function [16–18]. We have shown that resorcinarenes are particularly suited hosts for both neutral and protonated N-heterocyclic compounds [19–20] and alkyl ammonium cations [21–25]. The resulting complexes have been extensively studied in both solid and solution state. The most common defined interactions involve encapsulation in the bowl-shaped upper rim (endo complexation) due to size complementarity between host cavity and guest shape, and are generally stabilised through multiple C–H···π interactions [26–28]. The cavity capacity to undergo induced conformational changes in response to the incorporation of various upper-rim substituents, differing lower-rim alkyl chain length, specific guests, and selective solvents, have made resorcinarenes an attractive platform for H–G applications. Through careful rational supramolecular design via self-assembly processes, our lab and others have combined simple 1:1 H–G building blocks into dimers [29–31], hexamers [32] or supramolecular chains (1D), sheets (2D), or lattice (3D) networks [15]. The detailed analysis of the molecular level interactions of these systems also has enabled our research to design constructs with specific individual molecular and electronic properties by tuning the structure of the interacting partners.</p><p>Over the past decade, the N-oxide family has attracted the attention of the H–G community in molecular recognition processes [33–35]. In order to tune the resorcinarene-PyNO H–G recognition events at the molecular level, a better understanding of the particular interactions is required. The N-oxide oxygen atoms potential to act as a HB acceptor for multiple simultaneous N–O···(O–H)host interactions raises the molecular complexity. These are the dominant non-covalent interactions, in both the solid and solution state, compared to endo cavity C–H···π(host) interactions that win in the presence of most other guests. Therefore, investigating H–G complexes relying on N–O···(H–O)host HBs is challenging especially in HB competitive solvents such as methanol and dimethyl sulfoxide (DMSO). In reports from our lab, we disclosed that the π-acidity of aromatic protons assist in orienting the N-oxide guest by C–H···π interactions, and that the HB accepting N–O group is positioned "up", extending out beyond the cavity to interact with solvent molecules. Our work, investigating the interactions of PyNO guests with various resorcinarene hosts, has investigated the impact of host cavity flexibility, guest's steric and electronic demands, and solvent effects, in both solution and the solid state [36–38]. For example, we recently studied C-ethyl-2-bromoresorcinarene (BrC2) [39] and C-propyl-2-bromoresorcinarene (BrC3) [40] to understand the effect of the electronic nature of the host cavity core and rigidity of the resorcinarene skeleton on the ability to host various PyNO guests. All of these studies have been focused on interactions between the guest and the host upper-rim cavity, either as endo guests or as exo complexes. However, in these studies, we have occasionally observed interactions between N-oxide hosts and the cavity formed by the lower rim alkyl chains. This cavity is well-known to provide additional binding sites for guest molecules [40–41]. Inspired, in the present study, we have investigated the H–G complexes of C-hexyl-2-bromoresorcinarene (BrC6) and twelve PyNO guest molecules (Figure 1). The incorporation of long chains in the lower rim creates a hydrophobic secondary lower-rim cavity. This provides the potential for the formation of simultaneous upper- and lower-rim endo complexes.</p><!><p>The chemical structures of C-ethyl-2-bromoresorcinarene (BrC2), C-propyl-2-bromoresorcinarene (BrC3) and C-hexyl-2-bromoresorcinarene (BrC6) as hosts and pyridine N-oxide (1), 2-methylpyridine N-oxide (2), 3-methylpyridine N-oxide (3), 4-methylpyridine N-oxide (4), 2,6-dimethylpyridine N-oxide (5), 2-methoxypyridine N-oxide (6), 3-methoxypyridine N-oxide (7), 4-methoxypyridine N-oxide (8), 2,6-dimethoxypyridine N-oxide (9), 4-phenylpyridine N-oxide (10), 4,4'-bipyridine N,N'-dioxide (11) and 2,2'-bipyridine N,N'-dioxide (12) as guests.</p><!><p>Nine X-ray crystal structures were obtained from BrC6 in combination with twelve PyNO guest molecules. Several attempts to obtain single crystals of BrC6 by itself, 1+BrC6, 2+BrC6 and 9+BrC6 in methanol were unsuccessful. In the following discussions, for example, 1+BrC6 indicates either from combination of guest 1 and BrC6 or exo complex while 1@BrC6 denotes the endo complexation process. However, considering the host flexibility, 'Δ' (Table 1), which is the measure of difference between centroid-to-centroid distances of opposite host aromatic rings, guests 1, 2, and 9 should easily fit into BrC6 cavity for endo complexation processes. The lack of a crystal structure for these systems should consequently not imply that they do not encapsulate. The Δ values for BrC6 in H–G complexes are >1.0 Å (Table 1) and range between 1.08 Å and 2.39 Å, which are relatively high when compared to BrC2 (range, 0.08–1.06 Å) and BrC3 (range, 0.32–1.81 Å) values. In solid-state crystals, the lower-rim hexyl chains which prefer different orientations due to C–C bond flexibility cause BrC6 to crystallise as non-centrosymmetric hosts in all H–G complexes. In our previous PyNO–BrC2 complexes, more than 50% of BrC2 hosts are centrosymmetric [39]. In other words, long lower-rim hexyl chains cause the high Δ values observed for BrC6, which facilitates a remarkably flexible cavity for various guests. For the following discussions, the position of the guest inside the BrC6 cavity is represented as 'h', defined as the measured distance from the centroid of the lower-rim host carbon atoms to the nearest endo guest non-hydrogen atom. In the X-ray structure of 3@BrC6 (Figure 2a), guest 3, oriented parallel to the host aromatic rings (h = 3.43 Å) is positioned in one corner of the cavity with only the proton meta- to the N–O group interacting with a host aromatic ring. This short contact C–H···π(host) interaction is about 2.65–2.85 Å long. In 4@BrC6 (Figure 2b), once again guest 4 is oriented parallel to the host aromatic rings (h = 3.38 Å) and the H–G recognition occurs by C–H···π(host) interaction at two sites through C2 proton (2.49–2.89 Å) and methyl group hydrogen atoms (2.93–3.0 Å). This behaviour is in contrast with H–G complex 4@BrC2, where the BrC2 rigid cavity only allows the methyl group of 4 to insert inside the cavity forming C–H···π interactions between methyl group hydrogens and the host aromatic rings [39]. Unlike 3 and 4, the sterically unhindered 5 sits deeper inside the cavity (Figure 2c) with h = 2.66 Å thereby forming numerous C–H···π interactions between protons meta- to the N–O group and host aromatic rings (2.86–3.0 Å).</p><!><p>Summary of solid-state host–guest endo/exo complexations, and cavity conformation flexibility in BrC6.</p><p>ah: Position of the endo cavity guest, calculated from the centroid of the lower rim host carbons to the nearest non-hydrogen atom of the guest; bCrystal structure not available; cAsymmetric unit contains two crystallographically independent BrC6 host molecules; dself-inclusion complex.</p><p>X-ray crystal structures of (a) 3@BrC6, (b) 4@BrC6, (c) 5@BrC6, (d) 6@BrC6, (e) 7@BrC6, (f) 8@BrC6, (g) BrC6 obtained from 10+BrC6, (h) 11@BrC6, and (i) 12@BrC6. The endo cavity N-oxide guests are shown in CPK models, and the host in capped-stick models. The lower-rim alkyl chains and selected hydrogen atoms were omitted for viewing clarity.</p><!><p>Guests 6 and 7 have never been previously analysed by us in our earlier resorcinarene–PyNO H–G studies [39–40]. As shown in Figure 2, despite the BrC6 cavity's flexible nature, the position of the methoxy substituent plays a crucial role for both guest orientation and the depth of the guest's occupation of the cavity. For example, in 6@BrC6 (Figure 2d) and 7@BrC6 (Figure 2e), guests 6 and 7 have h = 3.23 Å and 3.50 Å, due to their steric demands. However, in complex 8@BrC6 (Figure 2f) the unhindered para-methoxy group facilitates 8 to sit deep inside the cavity at h = 2.82 Å. The guest's parallel orientation to the host aromatic rings in 6@BrC6 is caused by either steric hindrance or unfavourable positioning. This prevents the formation of stronger C–H···π interactions; consequently, 6@BrC6 is only stabilised by weak C–C contacts at distances of 3.31 Å. However, 7 with similar 'h' values, due to the bulky methoxy group on the core aromatic ring, is tilted towards one side with the proton meta- to the N–O group able to manifest C–H···π interactions with distances of 2.52 and 3.0 Å. Of all the endo cavity interactions, the C–H···π(centroid) has the shortest contact (2.52 Å). As shown in Figure 2f, the core aromatic ring of 8 and those of BrC6 in 8@BrC6 are parallel to each other. As a result, the bromine of the C–Br bond and the C2-position establish short contacts of 3.52 Å. However, the prominent interactions responsible for locking the H–G complex are the C–H···π (ca. 2.92 Å) and C–H···O (ca. 2.61 and 2.71 Å) contacts between guest C3 hydrogens and the host carbon/hydroxy oxygens, respectively.</p><p>From our experience, the lack of π-acidic aromatic protons in guest 10 usually results in exo complexes [36–3739]. To our surprise, 10+BrC6 forms a self-inclusion complex of BrC6 by itself as shown in Figure 2g, the property usually preferred by resorcinarenes when solvate and guest molecules are absent inside the cavity. Note that the self-inclusion complex of BrC6 has exo methanol solvent hydrogen bonds to host hydroxy groups. This can possibly be explained by the longer lower-rim hexyl chains providing enough intermolecular host(C–H)···(H–C)host interactions to form a stable 3D crystal lattice. On the other hand, guest 11 with two N–O groups makes the C2-protons π-acidic enough to form an endo complex, 11@BrC6 (Figure 2h). The host BrC6 undergoes a remarkable conformation change elongated to one side to accommodate the rod-shaped guest 11. The h value for 11 in 11@BrC6 is ca. 4.0 Å, which is quite high when compared to values observed for small guest molecules in BrC6 H–G complexes. However, the large Δ and h values are typical for rod shape guests such as 11. Despite higher 'h' values, guest 11 is stabilised by several C–H···π interactions between C2 protons and host aromatic rings. The distances range between 2.72 and 3.0 Å, with C–H···π(centroid) on two sides being the shortest contacts with distances of 2.49 Å and 2.67 Å. In our previous report, 11+BrC2, due to the BrC2 rigid cavity the rod-shaped 11 form an exo complex [39]. In 12@BrC6 (Figure 2i), the C–C bond rotation in guest 12 allows one aromatic ring to reside inside the cavity at h = 2.83 Å. The H–G molecules are positioned primarily by the π···π contacts rather than C–H···π interactions, with a short C···C contact being ca. 3.20 Å. Furthermore, since 11 is able to undergo C–C bond rotation, BrC6 tends to maintain a nearly ideal crown geometry suggesting excellent conformational complementarity between 11 and BrC6.</p><!><p>In 3@BrC6, the asymmetric unit contains one host and four guest 3 molecules. Of the four guests, one resides in the upper-rim endo cavity, held in position by C–H···π interactions. The second sits in the lower rim between the hexyl chains and is stabilised through N–O···(H–C)Ar(host) and other weak non-covalent interactions. The remaining final two guests are exo cavity hydrogen bonded to the host's hydroxy groups. To our surprise, our previous X-ray crystal structures of 3@BrC3 and 3@BrC2 complexes obtained from acetone showed interactions with the putative guests (i.e., N-oxide and acetone molecules) by encapsulation within the upper-rim and lower-rim cavity [39–40]. Therefore, in an effort to better understand the host–guest interactions and the potentials of the secondary lower-rim binding mode, molecular mechanics (OPLS-2005) [42] calculations were performed on complexes, 3@BrC2, 3@BrC3 and 3@BrC6 using Jaguar (Schrödinger) [43–44]. Consequently, the structures are modelled for both exo and endo complexes in acetone. Of note, the X-ray crystal structure of 3@BrC6 (Figure 3e) is obtained from methanol and is presented here only for reference, while its corresponding computational model was optimised using acetone media. To ensure that we were adequately screening the host conformer space in these simulations, no constraints were enforced on either N-oxide or acetone molecules. The low energy structures obtained from these OPLS-2005 searches were then further analysed using DFT-based techniques [45–47]. The resulting optimised geometries of the 3@BrC2, 3@BrC3 and 3@BrC6 along with the M06-2X/6-31G(d,p)//ωB97X-D/6-311G(d,p) calculated relative energies of complexes with respect to the most stable complex 3@BrC6 by following isodesmic reaction schemes (see Supporting Information File 1, Table S3) are shown in Figure 3.</p><!><p>Comparison of X-ray crystal structures (a) 3@BrC2, (c) 3@BrC3, and (e) 3@BrC6 and their DFT-based optimised geometries (b) 3@BrC2, (d) 3@BrC3, and (f) 3@BrC6, respectively.</p><!><p>In the optimised structures, the inclusion complexes of 3@BrC6, 3@BrC3 and 3@BrC2 show that the N–O group of 3 in 3@BrC6, 3@BrC3 and 3@BrC2 is positioned outward from the host cavity similar to solid-state X-ray crystal structures as shown in Figure 3. Further, in the lower-rim, the C=O group of acetone in 3@BrC2 and 3@BrC3, and N–O group of 3 in 3@BrC6 are positioned closer to the lower-rim C–HAr forming non-classical H-bond, (C–H)Ar···O=C/O–N, interactions. All three optimised complexes evince C–H···π interactions in both lower- and upper-rim cavities and C–H···O=C/O–N interactions at the lower-rim pocket are responsible for the ditopic behaviour of BrC2/BrC3/BrC6 and 3. The relative energies for 3@BrC2, 3@BrC3 and 3@BrC6 are 10.9, 11.3, and 0 kcal/mol, respectively, and clearly 3@BrC6 tend to have the lowest energy and is the most stable among the three complexes. In the optimised 3@BrC6 structure, the upper-rim N-oxide oxygen atom are tilted towards the hydroxy group of the host molecule to form intermolecular negative charge assisted H-bonding, C–H···O [48], interactions with a distance of 1.49 Å.</p><p>In order to gain insights into lower-rim cavity binding sites from a qualitative analysis standpoint, a molecular electrostatic potential (MEP) surface map for 3@BrC6 was calculated. This shows that the host BrC6 lower-rim cavity is not neutral as might be expected, but instead contains a sharp positive electrostatic potential region as depicted with blue colour in Figure 4b. This provides an excellent opportunity for the negative potential regions of the N-oxide oxygen atom in guest 3 (red region in Figure 4a) to establish several intermolecular (C–H)Ar···O–N H-bond interactions at the lower-rim host pocket.</p><!><p>(a) The negative potential localised on the N-oxide oxygen in 3@BrC6 and, (b) the positive charge distribution in lower-rim host cavity [+0.06 to −0.06 a.u.].</p><!><p>In addition, we used Bader's quantum theory of atoms in molecules (QTAIM) [49] to analyse multiple non-covalent interactions (i.e., H-bonding and C–H···π) interactions in both the upper-rim endo cavity and the lower-rim site present in 3@BrC6. Based on QTAIM, the presence of a bond path between the donor and the acceptor atoms containing a (3, −1) bond critical point (BCPs; highlighted as small blue circles in Figure S1, Supporting Information File 1), confirm the existence of bonds in this system. In other words, the bond critical point and bond path connecting two atoms are evidence for a real interaction rather than a simple spacial relationship. At the bond critical points, the electronic charge density [ρ(r)], and its Laplacians (2ρ(r)) are important parameters to evaluate the nature and strength of interactions. Numerical values for these topological parameters related to several non-covalent interactions at both upper and lower rim of complex 3@BrC6 are shown in Table 2 (see Supporting Information File 1, Figure S1 for the related molecular graph). Based on QTAIM analysis, the presence of several C−H···π interactions are evident from the existence of the (3, −1) bond critical point (BCPs; small red circles) between the bond path connecting the hydrogen atoms in the alkyl chain of the lower cavity in BrC6 with the ortho, meta and para carbon atoms of the N-oxide aromatic ring (highlighted as (C–H)alkyl···π(ortho), (C–H)alkyl···π(meta), (C–H)alkyl···π(para)). In addition, C–H···π interactions are present in the upper rim of the host as observed from the existence of the (3, −1) bond critical point between the bond path connecting the aromatic C−H bonds of BrC6 with ortho, meta and para carbon atoms of the N-oxide aromatic ring (highlighted as (C–H)Ar···π(ortho), (C–H)Ar···π(meta), (C–H)Ar···π(para)). The ρ(r) values associated with these interactions ranged between 0.0046 to 0.0119 a.u. and the positive values of Laplacians (2ρ(r)) at the BCPs were from 0.0134 to 0.0397 a.u. suggesting the existence of a weak "closed shell" [50–52] character for non-covalent interactions (such as ionic bonds, HBs, stacking type and van der Waals interactions) between 3 and BrC6 (Table 2). This is completely consistent with the observations made from the crystal structures.</p><!><p>Values of the density of all electrons ρ(r) and Laplacian of electron density – 2ρ(r), (Hartree) at the bond critical points (3, −1) for selected significant lower-rim non-covalent C–H···π and H-bond C–H···O–N as well as upper-rim endo cavity C–H···π interactions in the model system 3@BrC6 as well as calculated energies of these bonds, E(x) (kcal/mol), proposed by Espinosa et al. [53–54].</p><p>aSee Supporting Information File 1 for more details and E(x) calculations.</p><!><p>Guest binding studies of the N-oxide guests (1–12) by the receptor BrC6 were investigated in solution via a series of 1H NMR experiments in different hydrogen bond competing solvents and solvent mixtures: acetone-d6, methanol/chloroform (CD3OD/CDCl3) 1:1 v/v and methanol/dimethyl sulfoxide (CD3OD/DMSO-d6) 9:1 v/v. The above solvent mixtures were chosen due to the poor solubility of some of the guests in pure methanol. DMSO is known to be an extremely HB competitive solvent and thus prevents the clear formation of host–guest complexes [40,55], while the less competitive chloroform tends to enhance capsular assemblies [55]. Only one set of resonances from the 1H NMR of the receptor BrC6 in all the solvents and solvent mixtures is observed, thus confirming a symmetrical crown conformation in solution (Figure 5). Our previous report studying the interactions between BrC3 and some N-oxides in acetone-d6 revealed moderate deshielding of the hydroxy groups of the BrC3 receptor and minor deshielding of the aromatic protons of the guest when complexes were formed [40]. This confirmed that the assembly was driven by hydrogen bonding [55–56]. Taking the example of BrC6 and 3, a similar moderate deshielding of the hydroxy groups of the BrC6 receptor and a minor deshielding of the aromatic protons of the guest signals are observed (Figure 5) confirming this assembly is also driven by hydrogen bonding. These shifts' changes are substantially increased when more electron-donating groups are present on the aromatic N-oxides such as with 5 (two methyl groups) and 9 (two methoxy groups, Figures S5 and S9, Supporting Information File 1). This is expected as the four electron-withdrawing bromine groups on the BrC6 receptor renders the receptor slightly electron deficient further facilitating π–π interactions. With the larger N-oxide guests 10–12, though the shift changes of the guest are not strong enough to conclusively indicate endo complexation, clear changes in the hydroxy groups suggest interaction via hydrogen bonding (Figures S10–S12, Supporting Information File 1).</p><!><p>An expansion of the 1H NMR (6.6 mM at 298 K, 500 MHz) of BrC6 complexes with 3. Spectra are produced from BrC6, 3 and an equimolar mixture of BrC6 and 3 in: (a) (CD3)2O, (b) CD3OD/CDCl3 1:1 v/v, and (c) CD3OD/DMSO-d6 9:1 v/v. Dashed lines highlight the observed shift changes of the resonances, labels are in ppm. (d) Bar chart showing the comparative shift changes of the guests in the different solvent media.</p><!><p>Due to fast H/D exchange processes on the NMR time scale at 298 K in protic solvents, the hydrogen bond interactions between host and guests were not observed. In CD3OD/CDCl3, complexation-induced chemical shift changes of the guests are observed which results from the electronic shielding effects of the core aromatic rings of the host cavity. As an example, significant up-field shift changes of up to 0.17 ppm for the c-proton, and smaller up-field shifts of 0.10 ppm for the a-proton in guest 3 were observed (Figure 5b). These shifts suggest that in solution, the N–O group of guest 3 is pointing outward from the BrC6 cavity during endo complexation. In the X-ray structure of 3@BrC6, only the c-proton of 3 has C–H···π(host) short contacts with distances ranging between ca. 2.65 Å and 2.85 Å. This supports the maximum chemical shift change of 0.17 ppm observed by 1H NMR experiments for the c-proton in guest 3. The 1H NMR experiments for guests 1, 2, and 4–9 (Figures S2–S9, Supporting Information File 1) show similar up-field chemical shift changes for the aromatic protons of N-oxides suggesting guests are inside the host cavity stabilised through C–H···π interactions. Very low shift changes for 11 clearly point to a minimal interaction with the host. This is contrary to the X-ray crystal structure, 11@BrC6, where 11 and BrC6 are locked by several C–H···π interactions, and of more prominently remarkably short C–H···π(centroid) interactions (2.49 Å and 2.67 Å). Interestingly, shift changes of up to 0.19 ppm for guest 12 are a clear indication for the endo complex. Chemical shift changes of up to 0.12 ppm for guest 10 suggest an endo complexation contrary to the X-ray. These observations also matches well with the presence and calculated values of energy for those interactions predicted by our computational analysis and match exactly with reported [48,53] HB interactions with medium strength as well as stacking type interactions with weak characters.</p><p>In CD3OD/DMSO-d6 9:1 v/v, under similar experimental conditions to CD3OD/CDCl3 9:1 v/v, no significant chemical shift changes were observed for nine of the twelve pyridine N-oxides. The above results clearly show the strong influence of DMSO in interfering with the host–guest complexation between BrC6 and the aromatic N-oxides. However, with guests such as 5 and 9, endo cavity host–guest interactions persist even in these very competitive environments (Table 3, Figures S5 and S9, Supporting Information File 1).</p><!><p>Summary of endo/exo host–guest complexations studied in solution by 1H NMR in comparison to the solid state by single crystal X-ray crystallography.</p><p>aH-bonds dominate the assembly in acetone and only deshielding observed; bCrystal structure not available; cSelf-inclusion complex.</p><!><p>Host–guest systems formed between C-hexyl-2-bromoresorcinarene (BrC6) and twelve aromatic N-oxides have been characterised using solid-state X-ray crystallography and 1H NMR solution studies in three different hydrogen-bond-competitive solvents. In the solid state, BrC6 undergoes large cavity conformational changes to accommodate the N-oxide guests compared to our previously studied host systems, C-ethyl-2-bromoresorcinarene and C-propyl-2-bromoresorcinarene, thus proving BrC6 as more reliable host system for a range of N-oxide guests. In solution through 1H NMR analyses in methanol/chloroform, significant shielding for aromatic N-oxide guests suggests endo complexation processes similar to solid state X-ray crystal structures were observed. In methanol/DMSO-d6 chemical shift changes were observed only for three N-oxide guests with suitable electron-donating groups on the core aromatic ring suggesting endo complexation, and for other N-oxide guests, DMSO solvation prevents the endo complexation processes. In acetone-d6, significant changes for host hydroxy groups suggest host–guest assemblies were driven by hydrogen bond interactions at the upper rim. DFT based calculations using M06-2X/6-31G(d,p)//ωB97X-D/6-311G(d) support the experimental results and show that the ditopic host–guest binding modes of 3-methylpyridine N-oxide+BrC6 is more favourable due to longer lower-rim hexyl chains compared to 3-methylpyridine N-oxide+C-ethyl-2-bromoresorcinarene and 3-methylpyridine N-oxide+C-propyl-2-bromoresorcinarene. The predicted low energy of 3-methylpyridine N-oxide+BrC6 with respect to the other complexes can be attributed to multiple intermolecular hydrogen bonding and stacking interactions at both upper and lower-rims.</p><!><p>Experimental details, 1H NMR solution-data, X-ray crystallography experimental details and computational data.</p>

PubMed Open Access

LigMerge: A Fast Algorithm to Generate Models of Novel Potential Ligands from Sets of Known Binders

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically \xe2\x80\x9cswapping\xe2\x80\x9d the chemical moieties of known ligands in order to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called \xe2\x80\x9cLigMerge\xe2\x80\x9d. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator-activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We are hopeful that LigMerge will be a helpful tool for the drug-design community.

ligmerge:_a_fast_algorithm_to_generate_models_of_novel_potential_ligands_from_sets_of_known_binders

Introduction<!>The LigMerge Algorithm<!>Finding the maximum common substructure (MCS)<!>Superimposition and fragment merging<!>Docking of LigMerge-generated compounds<!>Receptor preparation<!>Ligand preparation<!>LigMerge compound merging<!>Docking protocol<!>Custom decoy libraries<!>Results and Discussion<!>Peroxisome Proliferator-Activated Receptor<!>Reverse transcriptase<!>DHFR<!>Decoy-Library Docking<!>Conclusion<!>A schematic representing the LigMerge algorithm<!>The top LigMerge-generated compounds and the known inhibitors from which they are derived<!>The predicted binding poses of the top LigMerge-generated compounds docked into their respective receptors<!>Vina-score Histograms: LigMerge-Generated Compounds vs. Known Inhibitors<!>Vina-score Histograms: LigMerge-Generated Compounds vs. Random Decoys

<p>Given the exponential growth of computer speed and power, the role computers play in modern drug discovery, already important, is likely to increase in prominence in coming years. Virtual screening is one application of computer-aided drug design that is already commonplace. Rather than testing millions of compounds in high-throughput screens, experiments that are costly in both time and treasure, many researchers first use docking programs to predict small-molecule binding in silico. Virtual-screening approaches enrich a pool of candidate ligands for true binders; only a limited number of the best-scoring compounds is then tested experimentally, leading to greater hit rates and decreased cost (1–3). These methodologies have been used successfully to identify many experimentally validated ligands, including inhibitors of T. brucei RNA editing ligase 1 (4, 5), T. brucei UDP-galactose 4′-epimerase (6), T. brucei farnesyl diphosphate synthase (7), M. tuberculosis dTDP-6-deoxy-L-lyxo-4-hexulose reductase (8), and H. sapiens stromelysin-1 (9).</p><p>Critical to any virtual-screening project is the selection of a good database of small-molecule models whose real-world counterparts are readily available for experimental validation. These databases generally consist of compounds carefully designed to represent diverse scaffolds (i.e., diversity sets), compounds derived from common reactions (combinatorial libraries), compounds with known pharmacological properties (e.g., the set of all approved drugs), or analogs of known ligands.</p><p>In part due to the advent of high-throughput screening, many protein receptors are associated with a plethora of experimentally validated ligands (10). In designing novel small-molecule databases for virtual screening, it makes sense to consider the pharmacophoric features of known ligands. New ligands that combine the observed features of validated binders are more likely to be potent binders themselves.</p><p>BREED (11), an algorithm developed by Vertex pharmaceuticals, overlays known receptor-ligand complexes to generate novel ligands that bind with improved affinity. BREED is a receptor-based algorithm that relies on the presence of high-resolution crystal or NMR structures to overlay known ligands. To our knowledge, there is no stand-alone, ligand-based tool for recombining the three-dimensional structures of known ligands into novel potential binders.</p><p>Here we present a program called LigMerge that provides a fast and easy way to generate molecular models derived from known inhibitors without the need for information about the receptor. We expect the program will be useful for those designing custom virtual-screening, small-molecule databases when many ligands, potent or otherwise, have been identified experimentally or theoretically via virtual screening. LigMerge is implemented in Python and so is easily editable, customizable, and platform independent. A copy can be downloaded free of charge from http://www.nbcr.net/ligmerge/.</p><!><p>As input, LigMerge accepts two three dimensional, PDB-formatted compound models. PDB files are the only supported input format. SDF or MOL files must be converted to the PDB format before using LigMerge. These models are processed in three steps. First, the maximum (largest) common substructure of the two models is identified (Figure 1A and 1B). Second, the two models are translated and rotated so that these two substructures are superimposed (Figure 1C). Third, the two models are merged by mixing and matching the distinct fragments of each model attached at each common, superimposed atom (Figure 1D).</p><!><p>Exhaustive lists of atom indices/element types for all heavy atoms in the two structures are first generated (Figure 1A). Hydrogen atoms are not included in this analysis. Stretches of connected atoms comprised of the same sequence of elements occurring in both structures are identified and stored, regardless of geometry. As no structural information beyond connectivity is encoded in these lists, the criterion for consideration is necessary but not sufficient for identifying a common substructure. Many of the identified common fragments will eventually be rejected for having distinct geometries, but all true common substructures are nevertheless among those enumerated. The shortest stretches considered are three-atom fragments, as shorter fragments (i.e., single atoms or mere pairs of bonded atoms) cannot reasonably be considered distinctive common substructures. Consecutively larger fragments are likewise stored. While ideally MCS substructures of at least ten atoms are preferable to ensure as unique an overlay as possible, we judge three to be sufficient in extreme cases because, in addition to connectivity, the algorithm will eventually also account for the three-dimensional structures of these models. While three is set as the program default, the minimum number of common atoms can also be specified explicitly by the user.</p><p>Having identified candidate common substructures, the next step is to test for identical geometries (Figure 1B). To facilitate geometric comparison, a sorted pairwise distance matrix (i.e., a distance "fingerprint") describing the distance between all atom pairs is calculated for each fragment. Two fragments are considered geometrically identical if all pairwise distances are identical within a specified tolerance. Comparisons between fragments begin with the largest candidate substructures; subsequently smaller candidates are considered if larger candidates are found to be geometrically dissimilar. Setting the -output_mcs command-line parameter to true causes the program to output the maximum common substructure in addition to merged-compound models.</p><p>Without further consideration, the above protocol ignores questions of symmetry. For example, consider two models whose greatest common substructure is a toluene. Two symmetry-related superimpositions exist (i.e., two rotations about the axis defined by the methyl-phenyl bond). The -all_symmetry_relations command-line parameter can be used to specify whether the algorithm should consider all symmetry assignments when generating merged-compound models, or whether it should randomly choose a single assignment from those identified (Figure 1C). The -all_symmetry_relations command-line parameter only creates multiple ligands if the overlay of the determined MCS is ambiguous.</p><p>It is important to note that LigMerge ignores ligand flexibility when performing geometric comparisons. It is therefore prudent to use ligand models of compounds in docked or crystallographic poses, or to choose ligand models with inflexible segments (e.g., benzene rings). As the number of publicly available crystal structures is ever increasing and inflexible segments are common in bioactive molecules, we expect that these two limitations will not be too problematic.</p><!><p>All possible substructure assignments of maximum length and identical geometry are subsequently considered. For each, a transformation matrix is identified that minimizes the RMSD between substructures. Though only the common substructures are considered in generating this matrix, entire molecules are subjected to the transformation, essentially positioning the two models so that their maximum common substructures are superimposed (Figure 1C). When identifying the MCS, LigMerge is sensitive to the ligand conformation. If the user wishes to consider multiple ligand conformations, they need only provide multiple pdb files representing each conformation.</p><p>Moieties from each model, comprised of fragments with atoms bound to those of the common substructure, are next identified. The common-substructure atoms to which these moieties are bound are designated "handle atoms". If the command-line parameter -all_substituent_combinations is set to false, a random fragment is selected for each handle atom, and a single merged compound is generated by combining the common substructure and the selected fragments. Special consideration is given to "multiple-handle fragments," i.e. fragments that externally connect to two or more handle atoms. If fragments containing more than one handle atom are selected, these fragments essentially determine the selection at multiple handle-atom locations. If the command-line parameter -all_substituent_combinations is set to true, multiple merged structures with all possible combinations of fragments are generated and saved to separate PDB files (Figure 1D). If a specific fragment combination will create a molecule with steric clashes between the fragments, the merged molecule will not be generated and the fragment combination will be skipped.</p><!><p>To demonstrate the utility of the LigMerge algorithm, compounds generated by applying LigMerge to a variety of known binders were docked into three receptors: peroxisome proliferator-activated receptor (PPAR) gamma, HIV reverse transcriptase (RT), and dihydrofolate reductase (DHFR).</p><!><p>Crystal structures of peroxisome proliferator-activated receptor (PPAR) gamma in complex with ligand 570 (PDB ID: 1FM9 (12)), HIV reverse transcriptase in complex with inhibitor 14 (PDB ID: 3C6T (13)), and DHFR in complex with methotrexate (PDB ID: 3DFR (14)) were used for the virtual-screening studies. All crystallographic water molecules as well as the ligand molecules themselves were removed from the PDB files. Hydrogen atoms were added using PDB2PQR (15). In the case of DHFR, the hydrogen atoms associated with the NDP cofactor were derived from those present in the DUD database (10). All PQR files were then converted to the PDBQT format using MGLTools (16).</p><!><p>The BindingDB (17) was used to identify PPAR, RT, and DHFR ligands. SMILES strings of the thirty unique PPAR, RT, and DHFR ligands with the lowest IC50 values, respectively, were obtained from PubChem (18). The LigPrep module of Schrodinger's Maestro computer program was used to build the molecular models in three dimensions, to add missing hydrogen atoms, and to generate all possible protonation states in a pH range of 5.0 to 9.0. For PPAR, LigPrep generated 30 unique molecular models from the top 15 known binders and 64 models from the top 30 binders. For RT, LigPrep generated 37 unique molecular models from the top ten known binders and 105 models from the top 30 binders. For DHFR, LigPrep generated 66 unique molecular models from the top 30 binders.</p><!><p>The 30 models derived from the top fifteen known PPAR inhibitors, the 37 models derived from the top ten known RT inhibitors, and the 66 models derived from the top thirty DHFR inhibitors where processed using LigMerge with the -all_symmetry_relations and -all_substituent_combinations flags set to true. The -ligands_dir flag was used to automatically run LigMerge on all possible pairs of ligands in the specified directory. Following a second LigPrep run undertaken to minimize the structures, 896, 3959, and 3974 unique potential inhibitors were identified for PPAR, RT, and DHFR, respectively.</p><!><p>For PPAR, AutoDock Vina (19) was used to dock both the 896 LigMerge-generated models and the 64 models of known inhibitors into the 1FM9 binding site using a box size of 40.9 Å × 44.3 Å × 46.8 Å. For RT, the 3959 LigMerge-generated models and the 105 models of known inhibitors were docked into the 3C6T binding site using a box size of 18.0 Å × 18.0 Å × 18.0 Å. For DHFR, the 3974 LigMerge-generated compounds as well as the 66 models of known inhibitors were likewise docked into a crystallographic binding pocket (3DFR), using a box size of 42.9 Å × 44.8 Å × 44.0 Å.</p><!><p>For each of the LigMerge-generated ligand sets corresponding to the three receptors, the molecular weight (MW), logP, and polar surface area (PSA) were calculated using obprop (20). The set of 896 LigMerge compounds generated from known PPAR inhibitors had an average molecular weight of (611 ± 166) Da, an average logP of 7.1 ± 0.7, and an average PSA of (106 ± 38) Å2. The set of 3959 LigMerge compounds generated from known RT inhibitors had an average molecular weight of (493 ± 139) Da, an average logP of 4.6 ± 1.2, and an average PSA of (107 ± 40) Å2. Finally, the set of 3974 LigMerge compounds generated from known DHFR inhibitors had an average molecular weight of (478 ± 140) Da, an average logP of 3.6 ± 1.3, and an average PSA of (178 ± 73) Å2.</p><p>For each of the LigMerge-generated compound sets, an in-house script was used to generate a decoy set equal in size and chemical properties. MW, logP, and PSA statistics were calculated for each of the ~11,000,000 compounds of the ZINC "All Clean" dataset (21) using obprop (20). Subsets of the "All Clean" database were then identified with chemical properties similar to those of each of the LigMerge-generated sets in terms of both averages and standard deviations. In this way, decoy libraries were generated for PPAR (896 compounds), RT (3959 compounds), and DHFR (3974 compounds) that had average MW, logP, and PSA values within 1% of the values derived for the corresponding reference LigMerge data sets of same size. PPAR was the only exception; the average molecular weight of the PPAR decoy library was within 13% (MW = 532 Da) of the reference LigMerge-generated set because there was an insufficient number of high-molecular-weight compounds in the ZINC "All Clean" dataset. Additionally it was ensured that standard deviations for these quantities did not exceed values in the corresponding LigMerge-generated sets. The decoy libraries were docked into their respective receptors with the same parameters used to dock the LigMerge-generated compound sets.</p><!><p>LigMerge is an open-source, easy-to-use tool for generating novel compounds with structural features similar to those of known ligands. Compounds derived from known ligands are more likely to be true binders themselves. Once generated, LigMerge-derived compounds can be docked into receptor structures to identify likely inhibitors for subsequent synthesis and experimental validation.</p><p>To demonstrate the utility of the LigMerge algorithm, three protein drug targets with many known inhibitors were chosen as test systems: peroxisome proliferator-activated receptor (PPAR) gamma, HIV reverse transcriptase (RT), and dihydrofolate reductase (DHFR). For each of these systems, novel compounds were generated using LigMerge by combining features of known inhibitors. Predicted binding affinities were then assessed by computer docking. To demonstrate that LigMerge can generate a set of compounds enriched for high-affinity binders above and beyond screens of chemically similar molecules chosen at random, we also dock appropriate decoy databases into each of the three receptors studied to facilitate comparison.</p><!><p>A total of 896 LigMerge-generated compounds were derived from the top fifteen experimentally verified PPAR binders listed in the BindingDB database (17) as of October 2011. These compounds, together with the top thirty experimentally known inhibitors, were docked into an PPAR crystal structure using AutoDock Vina (19). The best-scoring LigMerge molecule (compound 1, Figure 2A) and known inhibitor (Figure 3A) had estimated binding affinities of −13.2 and −11.1 kcal/mol, respectively. In fact, there were 109 LigMerge-generated models that scored better than the best-known inhibitor.</p><p>The best-scoring binding pose of the top-ranked LigMerge molecule (compound 1) is shown in Figure 3A, together with the crystallographic pose of a known inhibitor (in yellow, taken from PDB ID: 2HFP(22). The similarities between the poses of these two compounds are noteworthy. The predicted pose of compound 1 positions benzene and anisole substructures coincident with those of the known inhibitor. Additionally, the trifluromethyl group of compound 1 is predicted to be proximal to a sulfonamide group of the known compound, and the trifluromethyl group of the known inhibitor is positioned proximal to the predicted location of a compound-1 carboxylate group. Others have suggested that fluorinated methyl groups might be bioisosteres of the carboxylate group (compare PDB structures 3AEB and 3AE6) and the sulfonamide group (compare PDB structures 2XBV and 2XBX,(23)). The similarities of these binding modes are not likely the result of mere chance; they give credence to the hypothesis that the LigMerge-generated compounds have improved docking scores specifically because they are based on known inhibitors and therefore build on pharmacophores known to be relevant to the receptor of interest.</p><p>Figure 4A shows a normalized histogram of the Vina scores associated with the LigMerge-generated and top-30/known-inhibitor compounds. The docking-score distribution of the LigMerge-generated compounds is markedly broader than that of the known inhibitors. As expected, LigMerge generated a number of compounds that scored worse than the known inhibitors; as compound models are generated through an exhaustive combinatorial process, it is unsurprising that some LigMerge compounds had reduced predicted binding affinities. However, the docking-score distribution of the LigMerge-generated compounds also extends further towards high affinities than that of the known inhibitors. About 5% of the known inhibitors had docking scores better than −11.0 kcal/mol, suggesting tight binding. In contrast, more than 15% of the docked LigMerge compounds scored in that range, suggesting a genuine enrichment for strong binders.</p><!><p>A total of 3959 LigMerge-generated compounds were derived from the top ten experimentally verified RT binders listed in the BindingDB database (17) as of October 2011. These compounds, together with the top thirty experimentally known inhibitors, were docked into an RT crystal structure using AutoDock Vina (19). The best-scoring LigMerge molecule (compound 2, Figure 2B) and known inhibitor (Figure 3B) had estimated binding affinities of −11.4 and −10.5 kcal/mol, respectively. In fact, there were 132 LigMerge-generated models that scored better than the best-known inhibitor.</p><p>The best-scoring binding pose of the top-ranked LigMerge molecule (compound 2) is shown in Figure 3B, together with the crystallographic pose of a known inhibitor (in yellow, taken from PDB ID: 3C6T(13)). The similarities between the poses of these two compounds are noteworthy. Aside from the fact that they are predicted to occupy the same general space in the binding pocket, the 3-flurobenzonitrile moiety of compound 2 docked at the same location as the analogous 3-chlorobenzonitrile moiety of the co-crystallized inhibitor. Additionally, the aromatic imidazo[1,5-b]pyridazine moiety of compound 2 docks at the same location, and in the same plane, as a crystallographic benzene moiety of the known inhibitor. Again, the similarities of these binding modes are not likely the result of mere chance; LigMerge-generated compounds likely have improved docking scores because they are based on known inhibitors rather than chosen at random.</p><p>Figure 4B shows a normalized histogram of the Vina scores associated with the models of both the top 30 known inhibitors and the LigMerge-generated compounds. While some LigMerge-generated compounds again performed a good deal worse than known inhibitors, as expected, LigMerge did generate a number of compounds with higher-scoring predicted affinities; over 4% of the LigMerge compounds scored better than −10.0 kcal/mol, compared to fewer than 2% of the known inhibitors.</p><!><p>A total of 3974 LigMerge-generated compounds were derived from the top thirty experimentally verified DHFR binders listed in the BindingDB database (17) as of October 2011. These compounds, together with the thirty experimentally known inhibitors themselves, were docked into a DHFR crystal structure. The best-scoring LigMerge molecule and known inhibitor (Figure 3C) had estimated binding affinities of −13.3 and −10.4 kcal/mol, respectively. LigMerge generated 608 models that scored better than the best-known inhibitor.</p><p>An analysis of the predicted binding pose of the top-scoring LigMerge-generated compound, compound 3 (Figure 2C), suggests that, as before, the enhanced predicted affinity over known inhibitors has not arisen by chance alone. The top predicted ligand was derived from the two best-scoring known inhibitors (Figure 2C): CHEBI232247 (24) and piritrexim analogue 10 (25), with IC50 values of 0.75 and 0.057 nM, respectively. Additionally, the top Vina pose of compound 3 places the moiety analogous to the pteridine-2,4-diamine of methotrexate, a known inhibitor (in yellow, taken from PDB ID: 3DFR(14), deep within the same folate-binding pocket. The novel ligand binds in ways that are similar to known inhibitors, as expected given that pharmacophoric information from known ligands has essentially been leveraged in the design of these novel compounds.</p><p>Histograms showing the Vina-score distributions of the 30 known DHFR inhibitors, as well as the LigMerge-derived compound models, are shown in Figure 4C. The distribution of the Vina scores associated with the LigMerge-generated compounds was again generally wider than that of the known inhibitors. As before, some of the LigMerge compounds were certainly incompatible with potent binding, but 15.5% of the compound models were predicted to bind more potently than any known inhibitor.</p><!><p>We propose that the LigMerge-generated compound set included compounds with improved predicted affinities over those of known inhibitors because LigMerge generates novel compounds from known inhibitors in an intelligent and systematic way. However, it could be that these improved compounds were identified simply because the set of LigMerge-generated compounds was much larger than the set of known inhibitors, making it statistically more likely that a high-affinity predicted ligand would be found. To rule out this possibility, we compared the docking performance of the three LigMerge-generated compound sets to that of decoy libraries similar in size and average chemical properties.</p><p>Figure 5 shows normalized histograms of the Vina-score distributions for LigMerge and decoy docking into PPAR (Figure 5A), RT (Figure 5B), and DHFR (Figure 5C). For PPAR and DHFR, the LigMerge distributions are clearly shifted towards higher binding affinities, suggesting legitimate enhancement beyond what would be expected by docking compounds chosen at random. In contrast, the LigMerge score distribution associated with RT is similar to that of the decoy library. This may well be a consequence of vina's inability to discriminate between native-like and non-native-like ligands for the HIV RT test system. These results demonstrate that for two out of three systems, LigMerge provided a useful enrichment for high-affinity predicted binders.</p><!><p>We here present an algorithm called LigMerge that considers two three-dimensional models of known or suspected small-molecule inhibitors and forms derivative models with similar chemical features. In the process of merging models, LigMerge first identifies the maximum common substructure (MCS). The MCS, which can be saved for later examination, may itself be a valuable tool for ligand evaluation. Next, the program aligns the two molecules by their mutual MCS so that they are partially superimposed. Finally, the chemical moieties attached to each superimposed atom of the maximum common substructure are recombined, producing composite molecules similar to known or suspected inhibitors, but with potentially higher affinities. LigMerge is freely available through the National Biomedical Computation Resource (NBCR) and can be downloaded at http://www.nbcr.net/ligmerge/.</p><!><p>A) Stretches of connected atoms consisting of identical elements in sequence are identified from two distinct compounds. B) Those stretches of connected atoms that have identical geometries are identified as common substructures. The maximum (largest) common substructure is subsequently identified (highlighted in a separate box). C) The two distinct compounds are aligned so that their greatest common substructures are superimposed. All possible superimpositions are considered. D) Novel compounds are generated by mixing and matching the moieties connected to each of the superimposed atoms of the maximum common substructure.</p><!><p>The maximum common substructures are highlighted in red. A) Peroxisome Proliferator-Activated Receptor gamma. B) Reverse transcriptase. C) Dihydrofolate reductase.</p><!><p>In all panels, some portions of the protein have been removed to facilitate visualization. Docked LigMerge-generated compounds are colored by element, and known co-crystallized compounds are colored yellow. Below, standard representations of the co-crystallized ligands. A) Compound 1, docked into PPAR. The crystallographic ligand (in yellow) is compound 2a, a known binder. B) Compound 2, docked into HIV reverse transcriptase. The crystallographic ligand (in yellow) is inhibitor 14, a known binder. C) Compound 3, docked into dihydrofolate reductase. The crystallographic ligand (in yellow) is methotrexate, a known binder.</p><!><p>The LigMerge-generated compounds are shown in gray, and the known inhibitors are shown in black. A) The histograms for PPAR gamma. B) The histograms for HIV RT. C) The histograms for DHFR.</p><!><p>The LigMerge-generated compounds are shown in gray, and the decoy compounds are shown in black. A) The histograms for PPAR gamma. B) The histograms for HIV RT. C) The histograms for DHFR.</p>

PubMed Author Manuscript

A Phase I, Randomized, Double-Blinded, Placebo- and Moxifloxacin-Controlled, Four-Period Crossover Study To Evaluate the Effect of Gepotidacin on Cardiac Conduction as Assessed by 12-Lead Electrocardiogram in Healthy Volunteers

ABSTRACTGepotidacin is a novel, first-in-class triazaacenaphthylene antibiotic in development for treatment of conventional and biothreat infections. This was a single-dose, crossover thorough QT study in healthy subjects who were administered intravenous (i.v.) gepotidacin as a therapeutic (1,000-mg) dose and supratherapeutic (1,800-mg) dose, placebo, and 400 mg oral moxifloxacin in 4 separate treatment periods. Gepotidacin caused a mild effect on heart rate, with a largest placebo-corrected change-from-baseline heart rate of 7 and 10 beats per minute at the end of the 1,000-mg and 1,800-mg infusion, respectively. Gepotidacin caused an increase of change-from-baseline QTcF (ΔQTcF), with a peak effect at the end of infusion. The largest mean placebo-corrected ΔQTcF (ΔΔQTcF) was 12.1 ms (90% confidence interval [CI], 9.5 to 14.8) and 22.2 ms (90% CI, 19.6 to 24.9) after 1,000 mg and 1,800 mg, respectively. ΔΔQTcF rapidly fell after the end of the infusion, with a mean ΔΔQTcF of 6.1 ms 60 min after the 1,800-mg dose. Exposure-response analysis demonstrated a statistically significant positive relationship between gepotidacin plasma levels and ΔΔQTcF, with a slope of 1.45 ms per μg/ml (90% CI, 1.30 to 1.61). Using this model, the effect on ΔΔQTcF can be predicted to be 11 and 20 ms at the observed mean peak plasma concentration after the infusion of gepotidacin at 1,000 mg (7 μg/ml) and 1,800 mg (13 μg/ml), respectively. In conclusion, gepotidacin caused QT prolongation in this thorough QT study, and a mean effect can be predicted to less than 15 ms at the highest expected plasma concentration, 9 μg/ml. (This study has been registered at ClinicalTrials.gov under identifier NCT02257398.)

a_phase_i,_randomized,_double-blinded,_placebo-_and_moxifloxacin-controlled,_four-period_crossover_s

INTRODUCTION<!>RESULTS<!><!>RESULTS<!><!>RESULTS<!><!>RESULTS<!><!>RESULTS<!><!>RESULTS<!>Safety.<!>DISCUSSION<!>MATERIALS AND METHODS<!>Cardiodynamic ECG assessment.<!>Statistical analyses.

<p>Gepotidacin, a first-in-class novel triazaacenaphthylene bacterial topoisomerase inhibitor, inhibits bacterial DNA replication and has in vitro activity against key pathogens, including drug-resistant strains associated with a range of conventional and biothreat infections. Gepotidacin selectively inhibits bacterial DNA replication by interacting in a unique way with the GyrA subunit of bacterial DNA gyrase and the ParC subunit of bacterial topoisomerase IV. This interaction appears to be highly specific to bacterial topoisomerases, as evidenced by weak inhibition of human topoisomerase IIα, supporting the selective activity of gepotidacin against the bacterial target. As a consequence of its novel mode of action, gepotidacin is active in vitro against target pathogens resistant to established antibacterials, including fluoroquinolones. Gepotidacin is available as oral and intravenous (i.v.) formulations and is currently being evaluated in phase II studies for acute bacterial skin and skin structure infections (ABSSSI) and gonorrhea (GC).</p><p>A potential for QT prolongation was identified in nonclinical studies with gepotidacin. The drug inhibited the human ether-à-go-go-related gene (hERG) ion tail current with a 50% inhibitory concentration (IC50) of 588 μg/ml, which is 96-fold greater than the highest anticipated clinical free-fraction plasma concentration (Cmax) of 6.1 μg/ml after an i.v. dose of 1,000 mg three times daily (TID), based on 33% plasma protein binding in human (GSK, unpublished data). In an ex vivo rabbit left ventricular wedge preparation, gepotidacin caused a concentration-dependent increase in the QRS interval, moderate QT prolongation, and an increase of transmural dispersion of repolarization at ≥135 μg/ml, 22-fold greater than the highest anticipated free-fraction Cmax. A torsadogenic potential was also noted in this assay at 67 μg/ml, approximately 11-fold greater than the highest anticipated clinical Cmax (GSK, unpublished data). In an in vivo cardiovascular study with i.v. gepotidacin in cynomolgus monkeys, moderate and reversible increases in heart rate, arterial blood pressure, and an index of cardiac contractility were observed at the highest tested dose (250 mg/kg of body weight), with a 7-fold exposure margin versus clinical concentrations. Mild QTc prolongation (4% to 9%) was also noted at 1.2- to 7.0-fold the anticipated highest clinical Cmax (6.1 μg/ml), as well as a widening of the QRS interval (GSK, unpublished data). Furthermore, in a meta-analysis of phase I data, QTc prolongation was seen in healthy subjects following i.v. doses which resulted in high plasma concentrations of gepotidacin. It was therefore considered important to conduct this thorough QT (TQT) study in parallel with phase II studies while maintaining cardiovascular monitoring in all subjects receiving gepotidacin.</p><p>This study was designed to meet the requirement for a TQT study, as defined in the ICH E14 document (1) with subsequent clarifications through the question and answers document (2). Peak plasma levels after an i.v. infusion of gepotidacin are higher than those after oral dosing; therefore, this route of administration was chosen for this study, with the intention of achieving supratherapeutic plasma levels. However, adverse events related to cholinergic effects, which have been attributed to acetylcholinesterase inhibition by the drug, have been observed after gepotidacin infusion (3). This effect seems to be related to Cmax but not to the exposure (area under the concentration-time curve [AUC]), and it seems to be mitigated by maintaining plasma concentrations of gepotidacin below 14 μg/ml. A 1,800-mg i.v. dose given as a 2-h infusion achieves this objective, had acceptable tolerability in previous phase I studies, and therefore was used as the supratherapeutic dose in this TQT study.</p><!><p>Fifty-five subjects with a mean age of 31 years (range, 18 to 55) and mean body mass index (BMI) of 26 kg/m2 (range, 19.9 to 30.7) were enrolled into the study. Twenty-seven subjects (49%) were females, and the majority were white (n = 39; 71%) or black or African American (n = 11; 20%).</p><p>Gepotidacin plasma concentration-time profiles are provided by treatment in Fig. 1. Mean gepotidacin plasma concentrations increased steadily during the 2-h i.v. infusion and then declined in a multiexponential fashion after the end of the infusion. The gepotidacin concentrations were generally detectable in plasma up to 48 h after the start of infusion.</p><!><p>Plasma concentration-time course after an intravenous infusion of 1,000 mg and 1,800 mg gepotidacin. Means ± SD are shown.</p><!><p>At the predosing baseline, there were data from 50 to 53 subjects in each treatment period. Electrocardiogram (ECG) parameters were well balanced across predose baseline time points, with mean heart rates (HR) between 60.6 beats per minute (bpm) and 62.1 bpm, mean QTcF between 401.7 ms and 402.8 ms, mean PR between 146.2 ms and 148.6 ms, and mean QRS between 103.5 ms and 104.0 ms.</p><p>The 2-h infusion of gepotidacin caused an increase of change-from-baseline HR (ΔHR), which peaked at the end of the infusion (2 h) at 9.0 bpm (90% CI, 7.9 to 10.2) after 1,000 mg and at 12.9 bpm (90% CI, 11.8 to 14.1) after 1,800 mg (Fig. 2A). At time points later than 4 h after the start of the infusion, the diurnal pattern of ΔHR was the same in all treatment periods. The placebo-corrected ΔHR (ΔΔHR) reached a largest mean value of 6.5 bpm (90% CI, 5.1 to 7.8) and 10.4 bpm (90% CI, 9.1 to 11.7), observed at the end of the 1,000-mg and 1,800-mg infusion, respectively.</p><!><p>(A) Change-from-baseline heart rate (ΔHR) across treatments and time points. Least-squares means and 90% CI from the statistical modeling are shown. (B) Change-from-baseline QTcF (ΔQTcF) across treatments and time points. Least-squares means and 90% CI from the statistical modeling are shown.</p><!><p>Since the largest ΔΔHR exceeded 8 bpm, QTci and QTcI were derived. The mean slope of QTcI, which was calculated from all QT/RR during 24 h at baseline, was 0.3219 (standard deviations [SD], 0.067), i.e., close to the correction factor for Fridericia (0.33) (4). The mean slope for QTci, derived from supinely resting data on the day before dosing, was 0.3872 (SD, 0.051), i.e., substantially steeper and therefore closer to the QTcB (0.5) (5). When the ability of each correction method (QTcF, QTci, and QTcI) to remove the heart rate dependence of the QTc interval was tested using the mean of squared individual slopes (SSS), the lowest on-treatment SSS was observed with QTcF and QTcI, with clearly larger values with QTci (Table 1). QTcF was selected as the primary endpoint, since it consistently produced somewhat lower values than QTcI (0.0017 versus 0.0018 on placebo and 0.0049 versus 0.0066 on 1,800 mg gepotidacin).</p><!><p>Average sum of squared slopes for different heart rate correction methods for QTc across treatmentsa</p><p>As proposed in the publication by Tornoe et al. (6).</p><!><p>Gepotidacin caused an increase of change-from-baseline QTcF (ΔQTcF), which evolved during and peaked immediately after the end of infusion (2 h), with a ΔQTcF of 12.8 ms (90% CI, 10.8 to 14.8) after 1,000 mg and 22.9 ms (90% CI, 20.9 to 24.8) after 1,800 mg (Fig. 2B). The largest placebo-corrected ΔQTcF (ΔΔQTcF) was also observed at the end of the infusion (Table 2), with 12.1 ms (90% CI, 9.5 to 14.8) after 1,000 mg and 22.2 ms (90% CI, 19.6 to 24.9) after 1,800 mg gepotidacin (Table 2). The mean peak ΔΔQT effect was somewhat larger in females than in males: 13.4 versus 11.1 ms after the 1,000-mg gepotidacin dose and 25.6 versus 19.5 ms after the 1,800-mg dose. After the end of the infusion, ΔΔQTcF fell rapidly (Table 2) with a mean ΔΔQTcF of 11.9 ms 30 min later (2.5 h) and 6.1 ms at 3 h; all mean values from 4 h onwards were below 5 ms in the 1,800-mg gepotidacin treatment period (Table 2). Results from the analysis of QTcI (data not shown) were very similar to those for QTcF.</p><!><p>Placebo-corrected change-from-baseline QTcFa</p><p>Results from the linear mixed-effects model. Primary endpoints were used.</p><p>Results are least-squares means with 90% CI in parentheses.</p><!><p>Assay sensitivity was confirmed by the observed QT prolongation after oral dosing of 400 mg moxifloxacin. The largest mean ΔΔQTcF was observed at 3 h (12.7 ms), with the lower bound of the 90% CI being above 5 ms at all prespecified time points (7.6 ms, 10.7 ms, and 10.5 ms at 2, 3, and 4 h, respectively) (Table 2).</p><p>In the exposure-response analysis, a linear model with an intercept provided a good fit to the data (Fig. 3A and B). A concentration-dependent effect of gepotidacin on the QTcF interval (ΔΔQTcF) was identified with a slope of the relationship of 1.45 ms per μg/ml (90% CI, 1.30 to 1.61) and an intercept of 0.55 ms (90% CI, −0.50 to 1.60). Using the exposure-response model, the mean effect on ΔΔQTcF can be predicted to be 11.2 ms (90% CI, 10.0 to 12.4) and 19.9 ms (90% CI, 18.0 to 21.7) at the observed geometric Cmax after the 1,000-mg (7.3 μg/ml; 90% CI, 7.02 to 7.66) and 1,800-mg (13.3 μg/ml; 90% CI, 12.72 to 13.85) dose, respectively. When gender was analyzed as part of the exposure-response (ER) model, the slope of the plasma concentration/ΔΔQTc relationship was somewhat smaller in females than in males (1.38 ms per μg/ml versus 1.54 ms per μg/ml), but the observed Cmax was higher, 14.8 versus 12.0 μg/ml, after the 1,800-mg gepotidacin dose. The predicted QT effect at the observed Cmax was therefore comparable: 20.6 ms in females versus 19.2 ms in males.</p><!><p>Relationship between gepotidacin plasma concentrations and placebo-corrected change-from-baseline QTcF (ΔΔQTcF). (A) Scatter plot with all observed ΔΔQTcF/plasma concentration pairs and exposure-response model predicted effect (red line) with 90% CI. (B) Exposure-response model-predicted ΔΔQTcF (means and 90% CI) and observed ΔΔQTcF (means with 90% CI) within each gepotidacin plasma concentration decile.</p><!><p>Infusion of gepotidacin had a small shortening effect on the PR interval, which coincided with the observed HR effect; the largest mean effect after the 1,800-mg dose of gepotidacin was observed at 2 h, with a ΔΔPR of −7.1 ms (90% CI, −9.0 to −5.1). Gepotidacin did not have a clinically relevant effect on the QRS interval; the largest ΔΔQRS was only 1.2 ms (90% CI, 0.8 to 1.5), observed 1.5 and 2 h after the start of the 1,800-mg infusion.</p><!><p>Adverse events were reported by 23 subjects (46%) after 1,000 mg gepotidacin and by 34 subjects (64%) after 1,800 mg gepotidacin. They were generally mild to moderate in severity and included nausea, abdominal pain, abdominal discomfort, vomiting, salivary hypersecretion, feeling hot, and oropharyngeal discomfort. After administration of gepotidacin, the moderate adverse events were vomiting (8 subjects [15%]), nausea (4 subjects [8%]), throat tightness (1 subject [2%]), and dizziness (1 subject [2%]), all after the 1,800-mg dose. Five subjects (9%) discontinued the study due to adverse events, including one case of throat tightness, 3 positive tests for Clostridium difficile, and one case of C. difficile infection.</p><p>A sensitivity analysis excluding subjects who experienced vomiting after the 1,800-mg gepotidacin dose did not materially change the results: the largest QT effect (mean ΔΔQTcF) excluding these subjects was 21.4 ms, observed at 2 h, compared to 22.2 ms in the full population. Cholinergic side effects such as salivary hypersecretion were few and mild, and it seems unlikely that these affected the QT analysis.</p><!><p>This TQT study demonstrated that gepotidacin caused QTc prolongation with a largest mean effect on ΔΔQTcF of ∼13 ms after a dose of 1,000 mg and ∼23 ms after 1,800 mg, given as an i.v. dose over 2 h. The peak effect was observed immediately after the end of the infusion and thereafter fell rapidly. The QTc effect was linearly related to plasma concentrations of the drug with a statistically significant slope of the relationship of 1.45 ms per μg/ml (90% CI, 1.30 to 1.61). A moderate heart rate effect of the drug was noted with a mean peak effect of approximately 10 to 13 bpm at the end of 1,000-mg and 1,800-mg infusions. The observation of QTc prolongation at high gepotidacin plasma levels is consistent with the nonclinical findings, and it confirms and further qualifies the observation made for the pooled phase I data.</p><p>Using these results as a basis, it is possible to gain insight and quantify the level of QT prolongation that can be expected in patients given different doses and formulations of gepotidacin. Based on the established exposure-response relationship from this TQT study, it can be predicted that the mean QT effect (ΔΔQTcF) in patients administered an oral dose of 1,500 mg will be around 7.5 ms (90% CI, 6.8 to 8.3 ms). Based on a short half-life of the drug, repeat dosing will lead to only small (9% to 18%) increases of Cmax; however, plasma concentrations may be higher in patients with impaired clearance of the drug due to, for example, drug interactions. Gepotidacin is a CYP3A4 substrate; therefore, the potential for drug interaction was evaluated in a study with itraconazole, a known strong inhibitor of CYP3A4 and a P-glycoprotein inhibitor. A therapeutically relevant dose of gepotidacin, 1,500 mg, was administered to healthy subjects as a single dose and then again after 3 days of dosing with 200 mg itraconazole. During maximum 3A4 inhibition on itraconazole, the geometric means of Cmax and AUC0–∞ for gepotidacin were approximately 40% to 50% greater than those for gepotidacin alone, indicating a weak drug interaction (GSK, unpublished data). Concomitant administration of gepotidacin and a strong CYP3A4 inhibitor is thought to represent the worst-case scenario in terms of plasma levels in patients on oral therapy. A 40% increase of Cmax in patients on 1,500 mg orally would lead to levels around 6.7 μg/ml, and the mean QT effect can then be estimated to ∼10 ms (10.3 ms; 90% CI, 9.3 to 11.3). Intravenous dosing regimens result in higher gepotidacin plasma concentrations. In patients with ABSSSI, the highest i.v. dose that has been explored in phase 2 studies to date is 1,000 mg infused over 2 h TID. The observed geometric mean Cmax was 8.9 μg/ml after this i.v. dose and 8.6 μg/ml after the corresponding oral dose of 2,000 mg TID in the same study. The QT effect at these plasma concentrations can be predicted to ∼13 ms (13.4 and 13.0 ms), with an upper bound of the 90% CI below 15 ms (14.8 and 14.4 ms).</p><p>Many macrolides and fluoroquinolone antibiotics have been shown to cause QTc prolongation. The QT effect of moxifloxacin has been well characterized through its use in several hundred TQT studies, and a therapeutic oral dose of 400 mg causes QTc prolongation of between 10 and 16 ms (7–10), an effect level that seems comparable with the expected QT effect at the highest therapeutic gepotidacin i.v. dose (1,000 mg; ∼9 μg/ml). Macrolides have been associated with QT prolongation and torsades de pointes (TdP) proarrhythmias in susceptible patients, and erythromycin, clarithromycin, and azithromycin are all listed on the Credible Medicines website as drugs with known risk of torsades de points (11). QT prolongation at the level that can be expected in patients on high therapeutic doses of gepotidacin, i.e., below 15 ms, is thought to carry a very low risk of proarrhythmias. However, it should be noted that higher plasma levels, and therefore a somewhat higher QT effect, may be seen in patients with impaired clearance of the drug due to intrinsic or extrinsic factors. Gepotidacin will be administered to patients in whom first-line therapy has failed or who are intolerant to available antibiotics. Given the medical need of these patients and with appropriate cautionary measures in place, such as correction of hypokalemia before initiation of therapy and exclusion of patients on other drugs known to cause QT prolongation, treating these patients with gepotidacin seems well justified. Furthermore, gepotidacin is an antibiotic that may serve as an important medical countermeasure against drug-resistant biothreat pathogens. As far as we know, gepotidacin remains the only new antibiotic to demonstrate efficacy in the FDA-accepted animal model of inhalational plague (Yersinia pestis). It is also the most advanced and only antibiotic with a novel mechanism able to address multiple other clinically important bioterror agents and therefore may become an important future societal medical countermeasure to protect against the release of resistant biothreat agents.</p><p>In conclusion, infusion of gepotidacin at doses of 1,000 mg and 1,800 mg over 2 h caused QTcF prolongation of 12 ms and 22 ms, respectively, at the end of the infusion, with a rapidly declining effect thereafter. Based on the exposure-response (QTc) relationship observed in this study, QT prolongation of approximately 13 ms (with an upper bound of the 90% CI of 15 ms) can be predicted at the highest achieved mean plasma levels in patients, i.e., around 9 μg/ml.</p><!><p>This TQT study was randomized and conducted in healthy subjects using a 4-period, actively and placebo-controlled, double-blind, crossover design. Subjects received all study drugs in separate treatment periods: placebo, gepotidacin at 1,000 mg and 1,800 mg i.v., and oral moxifloxacin at 400 mg. The primary objective was to evaluate the effect of single i.v. doses of 1,000 mg and 1,800 mg gepotidacin on the heart rate-corrected QT interval as determined by the change-from-baseline QTc (ΔQTc) compared with the placebo. A double-dummy approach was used to maintain blinding, i.e., on each dosing day, moxifloxacin or moxifloxacin placebo and i.v. gepotidacin or matched i.v. placebo was administered. In the first treatment period, subjects were admitted to the clinical research unit 2 days before dosing (day −2) and remained at the clinical research unit until completion of the last safety assessment on day 3. In the remaining periods, subjects were admitted on the day before dosing. Fifty-five healthy subjects, using standard clinical pharmacology criteria, were to be randomized to ensure 46 evaluable subjects completed the study.</p><p>Study treatments (A through D) were given in randomized sequence in separate periods: A, 1,000 mg gepotidacin i.v. over 120 min and oral moxifloxacin placebo; B, 1,800 mg gepotidacin i.v. over 120 min and oral moxifloxacin placebo; C (placebo), gepotidacin placebo i.v. over 120 min and oral moxifloxacin placebo; D (positive control), oral moxifloxacin at 400 mg and gepotidacin placebo i.v. over 120 min.</p><p>The therapeutic dose, 1,000 mg i.v. as a 2-h infusion, is the highest dose anticipated to be used in phase III clinical studies. The selection of the highest gepotidacin dose, 1,800 mg i.v. as a 2-h infusion, was based on cholinergic adverse effects previously observed with higher concentrations when 1,800 mg was given as a 1-h infusion. Therefore, it was well understood that achieved peak plasma levels with this dose would not generate truly supratherapeutic plasma levels, typically more than 3- to 6-fold above clinically relevant concentrations (2). Oral moxifloxacin (400 mg) was chosen as the positive control to demonstrate assay sensitivity. Moxifloxacin has been well characterized in numerous TQT studies (7), and the criteria for demonstration of assay sensitivity are based on an expected mean QTc prolongation of ∼8 to 15 ms, typically seen in these studies (see question 3.1 in reference 2). A minimum 7-day washout separated each treatment period. A follow-up visit was conducted 7 to 10 days after day 3 of the final dosing period.</p><!><p>Continuous 12-lead ECGs were recorded for 48 h starting 1 h before dosing in all treatment periods using a Global Instrumentation (Manlius, New York, USA) M12R ECG continuous 12-lead digital recorder. A 24-h ECG recording also was performed on the day before dosing in period 1 to allow for derivation of individualized QTc methods if a substantial heart rate effect was to be observed. Subjects were resting in the supine position during 15 min before and 5 min after each prespecified time point for ECG extraction. ECGs were extracted in up to 10 replicates at the following time points: at 3 time points predose (45, 30, and 15 min prior to starting the infusion), at 0.25, 0.5, 1.0, 1.5, 2.0 (end of infusion), 2.5, 3.0, 4.0, 6.0, 8.0, 12.0, 24, and 48 h after dosing, and at corresponding nominal time points on the day before dosing in period 1. Standard procedures were followed at the central ECG laboratory (iCardiac Technologies, Rochester, NY), which included that ECG analysts were masked to the subject, visit, and treatment allocation and that baseline and on-treatment ECGs for a particular subject were overread on the same lead by the same reader. Measurements of ECG intervals were performed using the high-precision QT technique. Up to 10 replicate ECGs were extracted at each time point, and QT and RR interval measurements were made initially by the underlying ECG algorithm, COMPAS, as previously described (12). All beats that were deemed high confidence based on criteria including heart rate stability and ECG pattern were measured by the computerized algorithm, while all other beats were overread and either accepted without adjustment or rejected. With the HPQT technique, up to 120 beats are measured per time point. The primary analysis lead was lead II. If it was not analyzable, then the primary analysis lead was changed to another lead for the entire subject data set.</p><!><p>Based on the observation of QTc prolongation at high intravenous concentration in phase I studies, an estimation approach was used, aimed to quantify the magnitude of the effect on QTc from single i.v. doses of gepotidacin.</p><p>All statistical analyses were performed using the statistical software SAS for Windows, version 9.3 (SAS Institute, Inc., Cary, NC). The mean from the 3 predose values (45, 30, and 15 min prior to infusion) was used as the baseline for each postdosing time point in the same period. For the placebo adjustment in the exposure-response (ER) analysis, the individual change from baseline for QTc (ΔQTc) on placebo calculated at a specific time point was subtracted from ΔQTc for the same subject on gepotidacin at the same time point to generate the placebo-corrected ΔQTc (ΔΔQTc).</p><p>The primary endpoint was ΔQTcF. If a substantial heart rate effect was observed on treatment with gepotidacin, the primary endpoints were to be selected based on 3 different correction methods' abilities to remove the heart rate dependence of QTc based on prospectively defined criteria. Secondary endpoints included change-from-baseline heart rate (ΔHR), PR (ΔPR), QRS (ΔQRS), categorical outliers for ECG intervals, and treatment-emergent T-wave morphology changes, as well as the relationship between gepotidacin plasma levels and the effect on the QTc interval and the safety and tolerability of i.v. gepotidacin following a single 1,000-mg and 1,800-mg dose.</p><p>In case the largest mean effect of gepotidacin on the placebo-corrected ΔHR exceeded 8 bpm, two variants of an individualized heart rate correction of QTc were to be derived from drug-free data on the day before dosing of period 1, in addition to QTcF. QTci was derived from QT/RR pairs extracted from the time points at which subjects were supinely resting on the day before dosing. QTcI was derived from all QT/RR pairs from the full 24-h recording. The QT/RR pairs from each subject were used for that subject's individual correction coefficient, derived from a linear regression model: log(QT) = log(α) + β × log(RR). The coefficient of log(RR) for each subject, βi, was then used to calculate the individually corrected QT for that subject with the following equation: QTc = QT/RRβi. The relationship between QTc (QTcF, QTci, and optimized QTcI) and RR interval then was investigated using on-treatment data (gepotidacin at 1,000 mg and 1,800 mg and placebo) by linear regression modeling: QTc = a + b × RR. The RR coefficient for each subject, βi, was then used to calculate the average sum of squared slopes (SSS) for each of the different QT-RR correction methods, using the method proposed by members of the FDA's Interdisciplinary Review Team for QT studies (6). The correction method that resulted in the slope closest to zero for on-treatment data was deemed the most appropriate HR correction method and therefore was used for the primary endpoint.</p><p>The primary analysis for gepotidacin was based on a linear mixed-effects model with ΔQTcF as the dependent variable and with time (categorical), treatment (gepotidacin at 1,000 mg and 1,800 mg, moxifloxacin, and placebo), and time-by-treatment interaction as factors and baseline QTcF as a covariate. Since this was a crossover design, period and sequence terms were also included in the model. Subject was included as a random effect for the intercept. Gender effect was added in the model for exploration; if period, sequence, and gender effects were not statistically significant at the alpha level of 0.05, they were excluded from the final model. An unstructured covariance matrix was specified for the repeated measures at postdose time points for the subject within-treatment period. For this analysis, the least-squares means and 90% confidence intervals were calculated for the contrast, termed gepotidacin − placebo. The same linear mixed-effects model was also applied to those QTc methods that have not been selected as the primary endpoint and to HR, PR, and QRS to compute the mean change between gepotidacin and placebo and the corresponding 90% CI.</p><p>The analysis to demonstrate assay sensitivity was based on ΔQTc on moxifloxacin. The model described for the primary analysis was used. For the time points of 2, 3, and 4 h, the contrast in treatment, ΔΔQTc = moxifloxacin − placebo, was tested against the one-sided null hypothesis of a ΔΔQTc of ≤5 ms on the 5% level. Multiplicity was controlled by using a Hochberg procedure (13). If, after this procedure, ΔΔQTc was significantly larger than 5 ms for at least one time point, assay sensitivity was considered shown. In addition, 2-sided 90% CIs were obtained for the contrast at all time points for descriptive purposes and was used in the figures.</p><p>The relationship between ΔΔQTcF and plasma concentrations of gepotidacin was investigated by a linear mixed-effects modeling approach, for which 3 models were considered: (i) a model with an intercept, (ii) a model with mean intercept fixed to 0 (with variability), and (iii) a model with no intercept. Time-matched concentration was included in the model as a covariate, and subject as a random effect for both intercept and slope whenever applicable. For diagnostic purposes, a plot of standardized residuals versus fitted values was used to examine departure from model assumptions. The normal Q-Q plots of the random effects and the within-subject errors were used to investigate the normality of the random effects and the within-subject errors, respectively. A final assessment of the adequacy of the linear mixed-effects model was provided by a goodness-of-fit plot (6). Via visual inspection of the goodness-of-fit plot, the assumption of linearity between ΔΔQTcF and plasma concentrations of gepotidacin and how well the predicted ΔΔQTcF matched the observed data in the regions of interest were checked. The linear exposure-response model that fit the data best (i.e., had the smallest Akaike information criterion and had predicted CIs similar to the observed CIs) was used to evaluate the exposure-response relationship.</p>

PubMed Open Access

Enhancement of photochemical heterogeneous water oxidation by a manganese based soft oxometalate immobilized on a graphene oxide matrix

Development of efficient and oxidatively stable molecular catalysts having abundant transition metals at the active site is an immediate challenge to synthetic chemists in order to photochemically split water into clean fuels oxygen and hydrogen to serve the ever-increasing energy demand. Herein we report a soft-oxometalate (SOM)-based heterogeneous photocatalytic system which effectively performs water oxidation giving oxygen. In the present work we placed a double sandwich type manganese-based polyoxometalate (POM), Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O, on an electroactive graphene oxide matrix and synthesized a new SOM [Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O@graphene oxide] 1 and performed water oxidation with it. The efficiency of photocatalytic water oxidation by SOM 1 is almost double than in the case of Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O alone. The rationale behind this lies in the electron accepting nature of the graphene sheets which effectively relay the electrons generated in the water oxidation reaction, thus facilitating the forward reaction and increasing the oxygen yield. Variation of catalyst loading, pH-dependent and time-dependent experiments are performed to study their effects on photocatalytic water-splitting. The reaction kinetics is sigmoidal in nature, suggesting the heterogeneous nature of catalysis.The composite catalyst system is observed to be stable towards the reaction conditions.

enhancement_of_photochemical_heterogeneous_water_oxidation_by_a_manganese_based_soft_oxometalate_imm

Introduction<!>Result and discussion<!>Scanning electron microscopy (SEM): morphology of SOM 1<!>UV-VIS spectroscopy of the POM constituent and SOM 1<!>Cyclic voltammogram of the catalyst<!>Photochemical water splitting<!>Confirmation of water oxidation and the effect of graphene oxide<!>Catalyst loading variation studies<!>Time depended studies of oxygen evolution reaction<!>Stability of the composite SOM catalyst<!>Catalytic recyclability of SOM 1<!>Mechanism of evolution of O 2 from water<!>Conclusion<!>Materials and reagents<!>Synthesis of graphene oxide<!>Synthesis of SOM 1<!>Photocatalytic water splitting<!>pH dependent water splitting<!>Characterization techniques

<p>Water oxidation is one of the most promising routes towards the global goal of alternative energy. [1][2][3][4] Many research groups have developed robust catalysts for efficient water oxidation. [5][6][7] Recently chemists have been interested in developing molecular water oxidation catalysts by using cheap and abundant transition metals. 6,8 Different chemical species are used as catalysts for that purpose, e.g. metal organic complexes, [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] nanomaterials, [25][26][27][28][29] and the recently developed polyoxometalates. 6,8,[30][31][32][33][34][35][36][37][38][39][40][41][42] Polyoxometalates (POM) show higher stability in an oxidizing environment compared to metal-organic complexes where organic ligands tend to get easily oxidized and thus offer better catalyst stability. Different routes of water splitting have been explored, such as chemical, 9,10 electrochemical 2,43-45 and photoelectrochemical methods. [46][47][48][49][50][51][52] However, photochemical water oxidation seems to be the most facile and clean technique. 6,13,53,54 A recent challenge in photochemical water oxidation by polyoxometalates is to enhance the oxygen generation and increase the turnover number (TON) of the reaction. 16 Up to now an iridium based complex has shown the maximum TON reported by Crabtree and Brudvig. 55 We are interested in observing whether the reaction efficiency can be enhanced without changing the active center of the catalyst. It is known that POMs can easily be immobilized on the electroactive surface to form stable composite systems. 56 So, we ask whether it is possible to employ a related composite system to perform water oxidation experiments. 29,33,57 Recently, the Hill group developed a similar method using graphene modified electrodes and ruthenium based POM as active catalyst. 57 The graphene modified electrodes show excellent catalytic activity and high stability toward the electrochemical water oxidation reaction at neutral pH. This work showed enhanced water oxidation reaction electrochemically. 57 Here we ask whether it is possible to make a soft oxometalate 56,[58][59][60][61] based on polyoxometalate-graphene oxide to enhance the efficiency of photochemical water oxidation.</p><p>In our present work we use a manganese based polyoxometalate Na O@graphene oxide] 1. This catalyst shows a turnover number of 22 at pH 8 for WO reaction. The SOM 1 dispersion is prepared by sonication.</p><p>Formation of the composite is confirmed by Raman spectra, SEM images and EDX data. Finally we use this SOM as a photocatalyst in water oxidation (Fig. 1). Interestingly, we observe that in the presence of the graphene oxide matrix the water oxidation activity of Mn-POM is almost doubled. A detailed account of synthesis and characterization of the composite catalyst and observations related to photochemical water oxidation studies are provided in the following sections.</p><!><p>Formation of the SOM 1 composite based on graphene oxide SOM 1 is prepared by following the classical route of immobilization of POM on an electroactive surface. 56 In our present study we initially prepared graphene oxide dispersion in water. To this dispersion Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O (Fig. 2) was added and the mixture was sonicated to finally get the composite SOM 1, which forms a stable dispersion. Composite formation was characterized by using Raman spectroscopy, SEM and EDX analysis.</p><!><p>GO shows nanosheet type morphology. It is observed from scanning electron microscopy (Fig. 3a). The SOM 1 shows nanospheres embedded on graphene oxide layers (Fig. 3b). The white bright spot indicates the clustering of POM, suggesting that POM units are attached to the surface of GO by electrostatic interactions, as GO has an electron deficient surface (positively charged) and POM are large polyanions (negatively charged). This further indicates the formation of the composite in the reaction system. From the EDX data we can also infer that the molecular integrity of POM is intact in SOM 1. [Note: manganese, phosphorus and tungsten are present in the expected correct ratio of POM in SOM 1 (Fig. 3c).] HATR-IR spectroscopy and stability of POM in SOM 1 HATR-IR spectroscopy of SOM 1 and also HATR-IR spectroscopy of the POM constituent were performed. It was observed that a few broad bands were obtained in each case in the IR spectrum. This broadness is possibly due to the low concentration of the sample in the dispersion. Here we observed common peaks for POM and SOM 1 at 1637, 693, 569, 496 cm À1 respectively (Fig. 4a). Thus from the IR-spectrum we can conclude that the POM constituent remains stable and intact in the SOM 1 after composite formation and no catalyst is degraded at all. We further performed UV-VIS spectroscopy to check changes in the energy gap of the POM constituent after composite formation.</p><!><p>We performed UV-VIS spectroscopy of SOM 1 and the POM constituent in water. For both POM and SOM 1 we got absorbance maxima at 250 nm (Fig. 4b). Thus it may be concluded that the band gap of the POM constituent does not change in the SOM 1 composite, which further proves the stability of the POM constituent in SOM 1 because if it was dissociated to other cluster units then there should have been a clear difference in the UV-VIS spectrum.</p><p>Raman spectroscopy and the nature of SOM 1</p><p>We now want to show the effective formation of SOM. Raman spectra (Fig. 4c) of Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O, graphene oxide and the SOM 1 composite were taken; for SOM 1 we observed 4 peaks at 516, 949, 1379, and 1628 cm À1 respectively. We assign these peaks as follows: 516 (n as,Mn-O ), 949 (n WQO ) and the other two peaks at 1379 and 1628 cm À1 for disorder and graphitic nature of graphene oxide respectively. These peaks are blue shifted compared to that of the spectrum of graphene oxide and Na shows peaks at 495 and 927 cm À1 which can be attributed to the following modes: (n as,Mn-O ), (n WQO ). The characteristic peaks at 1371 and 1618 cm À1 are on the other hand due to disorder and graphitic nature of graphene oxide respectively. This shift in the spectrum might indicate that there may be the possible presence of interaction between POM and the graphene oxide layer in SOM 1. In our system electrons are probably transferred from Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O to graphene oxide. 62,63 This was further explained by CV. We conclude from this shift that in SOM 1, graphene oxide may act as an electron acceptor and Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O may act as an electron donor. The Raman spectrum also reveals that SOM 1 is not a physical mixture of the constituents graphene oxide and POM but an assembly of the two at the molecular level.</p><!><p>To further monitor the stability of the POM constituent in SOM 1 we performed CV of SOM 1 and compared it with the CV of the POM constituent of SOM 1 (Fig. 4d) and it is clearly observed that both are identical, which indicates that the redox behavior of the POM constituent in SOM 1 remains unaltered and we can also conclude that the POM constituent is stable after composite formation. Also we observed that the peak current increased for SOM 1 which further indicates facile electron transport from the POM constituent to GO surface.</p><!><p>Photochemical water splitting experiments were performed under a UV lamp (l max = 373 nm) with the composite catalyst system. The composites were prepared as mentioned in the previous section. The oxygen evolution was monitored using a YSI optical sensor based dissolved oxygen meter and also by cyclic voltammetry. The maximum oxygen yield obtained was 19.2 mmol for 20% SOM 1 loading at pH 8 in phosphate buffer. The graphene oxide acts as an electron acceptor and traps the electrons released in the water oxidation reaction and facilitates electron transport as well.</p><!><p>In our present work SOM 1 absorbed light and was elevated to the excited state. This excited SOM 1 generated hole and electron pairs, and the holes oxidized water to oxygen in the presence of light. After photoillumination quantitative determination of evolved oxygen was performed by measuring the evolved oxygen (Fig. 5c) using a YSI optical sensor based dissolved oxygen meter. For further confirmation of evolution of oxygen cyclic voltammetry (Fig. 5a) was performed using samples after photoirradiation, where a sudden rise of current was observed near +1.2 V with respect to the Ag/AgCl reference electrode indicative of oxygen evolution from water. It implies oxidation of water.</p><p>We observe that the extent of oxygen evolution is almost doubled in the case of the SOM 1 composite catalyst as compared to water oxidation by Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O alone. Using POM alone the maximum amount of oxygen liberated is almost 3.2 mmol for 0.071 mmol loading of the catalyst, with a TON of around 46, whereas in the case of the SOM 1 composite catalyst system the amount of O 2 evolved is almost 6.5 mmol for 0.071 mmol loading of the catalyst, with a TON of 92, which is roughly double than that of the POM alone. We thus investigate the role of graphene oxide in water oxidation.</p><p>In the next set of experiments, Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á 43H 2 O loading was kept constant (10 mg/10 ml in all the catalyst dispersions) and the graphene oxide concentration was increased (Fig. 5b). Here we observe a similar type of sigmoidal curves and the maximum O 2 generation is almost 58 mmol for 10% SOM 1 loading. The comparative studies clearly show that the increase of graphene oxide loading has a prominent effect on water oxidation. We observed two different aspects: (i) up to a certain loading of graphene oxide (5 mg) O 2 evolution increases to a maximum of 58 mmol and (ii) thereafter the increase of graphene oxide loading has no further effect on O 2 evolution. We explain this as follows. With the increase in graphene oxide loading the extent of electron relay facilitated by graphene oxide increases, thereby increasing the effective O 2 evolution. However beyond a certain threshold of graphene oxide loading the active POM concentration in SOM 1 gets diluted. Hence the evolution of O 2 does not increase anymore.</p><p>Needless to say, graphene oxide invariably enhances the water oxidation activity. In SOM 1 we also infer that graphene oxide most likely (1) provides an enhanced active catalytic surface area and (2) facilitates electron transport and thereby enhances water oxidation effectively. Also one of the prominent reasons for the enhancement of water oxidation by using the GO matrix may be due to the increase in the effective surface area of the catalyst. In the case of SOM 1, the hydrodynamic radius is around 300 nm (from dynamic light scattering experiments) as compared to single SOM having a hydrodynamic radius of 130 nm. We have calculated the surface area by assuming the catalyst materials to form nanospheres in dispersion and for a spherical surface we calculated the area by the following equation: surface area = 4pR h 2 . Now we address the problem of how water oxidation is affected with variation in pH and loading of SOM 1 dispersion in the next section.</p><p>pH dependent study pH dependent water oxidation study reveals some interesting results (Fig. 5d). We observe that with the increase in pH the amount of evolved oxygen increases gradually. At pH 8 we observe the maximum yield. On further increasing the pH, oxygen evolution decreases abruptly and this may be attributed to the degradation of clusters at higher pH. This observation may be explained by the shift of equilibrium involved with oxygen evolution to the right with the increase in pH.</p><!><p>In this set of experiments, the graphene oxide concentration is kept constant (1 mg/10 ml in all the catalyst dispersions) and the SOM 1 loading is increased to observe the change in water oxidation. It is observed that with increasing POM loading oxygen evolution increases for a given pH. The nature of the oxygen evolution curve with catalyst loading reveals that initially with increasing catalyst loading oxygen evolution increases rapidly, but after exceeding a certain loading of POM on SOM 1 catalyst (Fig. 6), the rate of enhancement of oxygen evolution in the reaction decreases to some extent. This may be due to the stability factor of the dispersion. More precisely oxygen evolution decreases when phase separation is observed and when we cross the dispersion stability window. This decrease in oxygen evolution is also due to the decrease in the active surface area of the catalyst.</p><!><p>Time dependent water oxidation experiments show general sigmoidal kinetic patterns typical of heterogeneous catalysis reactions, where up to a certain limit of time oxygen evolution increases and reaches a plateau (Fig. 7a). There is an induction period of reaction which may be due to light absorption limitation. For excitation of SOM 1 it needs to cross a minimum energy barrier, which is attained after some time and therefore initially there is no reaction. When SOM 1 possesses minimum energy for excitation water oxidation starts (Fig. 7b). As water is taken in excess in the reaction, the reaction rate only depends on the intensity of light and not on the amount of water present in the reaction medium. At early times of the reaction, i.e., at low light intensity (up to 9.32 mW cm À2 ) there is no O 2 evolution. However, beyond a threshold light intensity (9.32 mW cm À2 ) O 2 evolution begins. The induction period (before threshold light intensity) probably simply reflects the time for O 2 product equilibration before the analysis of evolved O 2 . It increases in a sigmoidal fashion suggesting co-operative photo-activation of the SOM sites for water oxidation. However with the increase in energy density oxygen evolution reaches saturation. Hence in other words it might be said that water oxidation reaction requires a threshold energy density to begin with, then increases in a sigmoidal fashion implying co-operative photo-activation of the SOM sites, finally reaching saturation with energy density. Thus it implies that the water oxidation reaction is topped off after a certain time. The maximum TOF of the reaction is 0.75 min À1 which is comparatively less than that of the recently developed photochemical water oxidation using cobalt based POM. This difference in TOF may be due to the involvement of different redox couples in the reaction. Here we observe that the maximum amount of O 2 generated is almost 19 mmol for 20% of SOM 1 loading. Now we ask whether SOM 1 is stable in the course of the reaction. To determine its stability we measured the Raman spectrum of SOM 1 before and after the completion of the reaction. A detailed account of this study is provided in the next section.</p><!><p>Raman spectroscopic investigations were performed on the SOM 1 composite catalyst before and after the reaction. The spectra were observed to be identical (Fig. 8a). We also performed the HATR-IR (Fig. 8b) and UV-VIS (Fig. 8c) spectroscopy of the SOM 1 catalyst after the reaction. We also performed cyclic voltammetry (Fig. 8d) with the post reaction dispersion and in all the cases we observed identical spectra compared with the spectra obtained with the dispersion SOM 1 before the reaction. So we can possibly comprehend that the composite catalyst system remains intact during the water splitting reaction. Thus the reported catalyst system is stable under the water oxidation conditions that are used in this study. The POM constituent does not dissociate to form MnO 2 or some other fragment. Therefore during photochemical water oxidation reaction it is reasonable to believe that no MnO 2 is generated under reaction conditions which can possibly oxidize water. Only the POM constituent is clearly responsible for the water oxidation reaction. More experiments and analyses are needed to pin-point the active species, excited species lifetime and other deeper mechanistic details which will be performed by us in the future. We also took SEM images (Fig. 9) of the post reaction composite catalyst and observed almost a similar kind of morphology as in the images taken before the commencement of the reaction.</p><!><p>As the catalyst is stable after the reaction, we can effectively reuse this catalyst for further catalytic cycles. For this purpose we checked the recyclability up to 10 catalytic cycles and we observed that each time an equal amount oxygen is evolved in the catalytic cycle (Fig. 10). Therefore the catalyst is completely reusable.</p><!><p>Photochemical water oxidation with polyoxometalates generally takes place in the presence of an additional photosensitizer and a sacrificial electron acceptor. In our present work, the photocatalytic heterogeneous reaction possibly follows a completely different pathway. Here we need not add any photosensitizer and sacrificial electron acceptor. SOM 1 itself may be absorbing light and going to the excited state which oxidizes water to oxygen (Fig. 11), but elucidation of the actual photophysical mechanism will require additional studies. To further prove that the excited species oxidized water we added catechol in the reaction and observed that water oxidation ceased under these conditions. This may be due to the fact that catechol oxidation is more favorable compared to water oxidation and therefore water oxidation does not take place in the presence of catechol. The graphene oxide sheets are expected to act as electron acceptor platforms for the electrons generated in the water oxidation process 64,65 and also enhance the surface area of the POM constituent of SOM 1.</p><!><p>To summarize, we have demonstrated the water oxidation by Mnpolyoxometalate (POM) based soft-oxometalate (SOM 1) dispersion and the efficiency is almost doubled by immobilizing Mn-POM on an electroactive graphene oxide matrix. The catalyst system acts as a water oxidizing agent to generate oxygen under photochemical conditions. The graphene oxide layers possibly act as electron acceptors and surface area enhancers and facilitate water oxidation by SOM 1. Thereafter we describe the effect of catalyst loading and pH on photocatalytic water-splitting. From the kinetics of the reaction we show the operation of heterogeneous mode of catalysis. After demonstrating the stability of the catalyst in the course of the water splitting reaction we have proposed the plausible pathway of the catalyst action. Further work is in progress in our laboratory in order to design more SOM based water splitting catalysts.</p><!><p>All the materials were purchased from commercially available sources and used without further purification. All the glass apparatus were first boiled in an acid bath, then in water and finally rinsed with acetone. All the glass apparatus were properly dried in a hot air oven overnight. Doubly distilled deionized water was used to carry out all the experiments.</p><!><p>Graphene oxide was synthesized by the improved Hummers' method. [66][67][68] Hummers' method 69 (KMnO 4 , NaNO 3 , H 2 SO 4 ) is the most common method used for preparing graphene oxide.</p><p>A recent methodology study has modified the process to some extent and improved the efficiency of the oxidation process and this modified Hummers' method 68 was employed here to synthesize graphene oxide for our experiments. Concentrated H 2 SO 4 (69 ml) was added to a mixture of graphite flakes (3.0 g, 1 wt equiv.) and NaNO 3 (1.5 g, 0.5 wt equiv.), and the mixture was cooled using an ice bath to 0 1C. KMnO 4 (9.0 g, 3 wt equiv.) was added slowly to keep the reaction temperature below 20 1C as KMnO 4 addition is exothermic. The reaction was warmed to 35 1C and stirred for 7 h. Additional KMnO 4 (9.0 g, 3 wt equiv.) was added in one portion, and the reaction was stirred for 12 h at 35 1C. The reaction mixture was cooled to room temperature and poured into ice with 30% H 2 O 2 (3 ml). The mixture was then purified following the usual protocol of sifting, filtering, centrifugation, decanting with multiple washes followed by a final vacuum drying to give 4.0 g of solid product.</p><!><p>1 mg of graphene oxide was added into 10 ml of water and 2 ml of ethylene glycol was added to it for better separation of the graphene oxide sheets. Then it was sonicated for 3 hours at room temperature to prepare graphene oxide dispersion. After that, 10 mg of Na 17 [Mn 6 P 3 W 24 O 94 (H 2 O) 2 ]Á43H 2 O was added and the dispersion was sonicated for 3 more hours. The stability of the dispersion was checked and it was found to be stable.</p><!><p>Photocatalytic water splitting reactions were performed as follows. In the composite dispersion for water oxidation experiment buffer solution of pH 7 was added. The reaction mixture was then sealed and N 2 gas was purged for 3 hours to get rid of the trace amount of oxygen in it. Then the reaction mixture was kept in a photoreactor under UV-light (energy density of the photoreactor is À19.5 mW cm À2 with l max = 373 nm) for 2 hours. After irradiation we measured the amount of evolved oxygen in the reaction by using YSI optical sensor based dissolved oxygen meter standardized by using degassed double distilled water. Evolution of oxygen in the reaction was further investigated by performing cyclic voltammetry using the irradiated samples. In cyclic voltammetry we observed a sharp rise of the current-voltage curve near +1.2 V, which is typically indicative of O 2 generation by water splitting.</p><!><p>This experiment was performed by following the previous procedure using different buffer solutions in the pH range of 5 to 9. Measurement of oxygen evolution was carried out by a similar method mentioned earlier.</p><!><p>SEM-EDX microscopy. SEM measurements were done by drop-casting SOM 1 dispersion on a silicon wafer and drying under vacuum for 2 days. Then SEM imaging was performed and images were taken on a SUPRA 55 VP-41-32 instrument with the Smart SEM version 5.05 Zeiss software.</p><p>Cyclic voltammetry. A PAR model 273 potentiostat was used for CV experiments. A platinum wire auxiliary electrode, a glassy carbon working electrode with a surface area of 0.002826 cm 2 and an aqueous Ag/Ag + reference electrode which is filled with saturated KCl solution were used in a three electrode configuration. The scan rate was 0.5 V s À1 . The CV spectrum was recorded in the range of 0 to +1.3 V. Blank refers to the amount of oxygen present in distilled water in our mentioned reaction conditions. The pH of the medium was 7. 0.1 M KCl solution was used as a supporting electrolyte in all the experiments. All measurements were done at 298 K in an inert atmosphere.</p><p>Dynamic light scattering measurements. The average size of the particle was obtained using the dynamic light scattering method in a Malvern Zetasizer instrument. A very dilute solution of SOM 1 was prepared by further dilution of the SOM 1 dispersion and taken in a fluorescence glass cuvette with a square aperture and the instrument was set to take 15 runs before measuring the average hydrodynamic radius of the SOM 1 composite.</p><p>Raman spectroscopy. A LABRAM HR800 Raman spectrometer was employed using the 633 nm line of a He-Ne ion laser (l = 633 nm) as the excitation source to analyze the sample.</p>

Royal Society of Chemistry (RSC)

Mesoscopic superstructures of flexible porous coordination polymers synthesized <i>via</i> coordination replication

The coordination replication technique is employed for the direct conversion of a macro-and mesoporous Cu(OH) 2 -polyacrylamide composite to three-dimensional superstructures consisting of the flexible porous coordination polymers, Cu 2 (bdc) 2 (MeOH) 2 and Cu 2 (bdc) 2 (bpy) (bdc 2À ¼ 1,4-benzenedicarboxylate, bpy ¼ 4,4 0 -bipyridine). Detailed characterization of the replicated systems reveals that the structuralization plays an important role in determining the adsorptive properties of the replicated systems, and that the immobilization of the crystals within a higher-order architecture also affects its structural and dynamic properties. The polyacrylamide polymer is also found to be crucial for maintaining the structuralization of the monolithic systems, and in providing the mechanical robustness required for manual handling. In all, the results discussed here demonstrate a significant expansion in the scope of the coordination replication strategy, and further confirms its utility as a highly versatile platform for the preparation of functional three-dimensional superstructures of porous coordination polymers.

mesoscopic_superstructures_of_flexible_porous_coordination_polymers_synthesized_<i>via</i>_coordinat

Introduction<!>General considerations<!>Field-emission scanning electron microscopy (FE-SEM)<!>Synthetic procedures<!>Synthesis and characterization<!>Structural features and structural exibility of the replicated frameworks<!>Conclusions and future outlook

<p>The design and synthesis of porous coordination polymers (PCPs) or metal-organic frameworks (MOFs) has experienced an intensive focus in recent years, 1 due to their potential use in applications such as gas storage, molecular separations, and heterogeneous catalysis. 2 These compounds are assembled from metal-containing nodes bridged by organic linkers, which form porous structures that are characterized by high surface areas, as well as tunable pore dimensions and pore surface chemistry. While the ability to conveniently construct new materials from the combination of a metal salt and organic ligand (in the so-called modular approach) has provided researchers with a tremendously large library of compounds, there is an urgent need for versatile synthetic strategies for the convenient fabrication of PCPs in a structuralized form. 3 Here, synthetic routes have begun to emerge for the bottom-up preparation of zero-(e.g. hollow spheres), one-(rods), two-(lms), and three-dimensional (monolithic) superstructures of PCPs. 3a,b A feature common to the preparative methodologies of the systems reported so far is that they provide a precise control of the crystallization interface at which PCP formation occurs, resulting in the precipitation of the PCP with the desired structuralized architecture.</p><p>An elegant technique that has recently emerged for the preparation of three-dimensional superstructures of PCPs is the so-called coordination replication strategy. [4][5][6][7] In this method, a structuralized metal source (such as a metal oxide) is employed as a template, which undergoes conversion in a ligand solution into a three-dimensional PCP superstructure with retention of the original structure. While this technique has been successfully demonstrated with a small number of PCP systems so far, 4 investigations of the incorporation of molecular-scale exibility within structuralized systems with sophisticated dynamic properties are yet to emerge. While studies of this type are of high interest from a fundamental perspective due to the prospects of new phenomena emerging from the embedding of such dynamic building blocks in a structuralized form, the identication of suitable starting materials and PCP systems is challenging due to the difficulty in preparing metal-based compounds in well-dened structures, as well as the currently limited scope of structuring techniques.</p><p>In this work, we address these challenges via the structuring of exible copper-based PCPs, namely Cu 2 (bdc) 2 (MeOH) 2 , which has a two-dimensional interdigitated structure, and Cu 2 (bdc) 2 (bpy), which comprises a three-dimensional interpenetrated structure, into three-dimensional monolithic superstructures (bdc 2À ¼ 1,4-benzenedicarboxylate, bpy ¼ 4,4 0bipyridine). 8 A macro-and mesoporous Cu(OH) 2 -polyacrylamide (PAAm) monolithic material was chosen as a precursor for the coordination replication strategy, which was rstly successfully converted into a Cu 2 (bdc) 2 (MeOH) 2 monolith ("daughter" phase), followed by a PCP-to-PCP replication to fabricate a Cu 2 (bdc) 2 (bpy) monolith ("granddaughter" phase) via the pillar ligand (bpy) insertion process (see Fig. 1). Importantly, unique adsorptive and dynamic properties are observed following immobilization of the PCPs within the three-dimensional superstructures, and the potential origins of these effects are discussed in the context of both the composition and the structures of the monoliths.</p><!><p>Unless otherwise noted, all reagents were obtained from commercial vendors and used as received. While all syntheses were carried out in the air, the desolvated forms of each of the compounds were handled and stored in a nitrogen-lled glove box. Solvothermal syntheses were carried out in a DKN302 constant temperature oven (Yamato Scientic Co., Ltd) using glass vials sealed with Teon-lined lids. Nitrogen and methanol adsorption measurements were carried out on a BELSORP-max adsorption analyser (BEL Japan, Inc.) equipped with a constant temperature bath. Powder X-ray diffraction patterns were collected using a Smartlab X-ray Diffractometer (Rigaku Corp.) equipped with a Cu Ka source.</p><!><p>Scanning electron microscopy (SEM) images were collected using a JEOL JSM-7001F4 electron microscope. Powder and monolith samples were evacuated to remove any residual solvent molecules, and attached to a 13.5 mm substrate using double-sided carbon tape. The samples were then coated with osmium nanoparticles to a thickness of 5 nm, and transferred to the SEM instrument. The images were collected using an emission voltage between 10 and 15 kV.</p><!><p>Cu(OH) 2 -polyacrylamide monolith. The parent phase was synthesized by sol-gel processing as reported recently, 9 using a starting mixture of CuCl 2 $2H 2 O (1.53 g, 8.97 mmol), polyacrylamide (PAAm; 0.60 g, M w $ 10 000), water (1.10 mL), ethanol (0.30 mL), glycerol (2.40 mL), and propylene oxide (1.47 mL, 21.0 mmol). The as-synthesized form of the monolith was stored in 2-propanol, and was rinsed with methanol prior to the coordination replication procedure. Note that aer washing, some Cl À ions still remain in the composition (ca. 4 wt%), 9 but we refer to the starting structure as "Cu(OH) 2 -polyacrylamide monolith" for simplicity.</p><p>Fig. 1 A conceptual illustration summarizing the two-step replication procedure employed in this work. In the first step, a macro-and mesoporous Cu(OH) 2 -polyacrylamide (PAAm) composite is subjected to a coordination replication process via treatment with H 2 bdc (bdc 2À ¼ 1,4benzenedicarboxylate), resulting in a monolith consisting of the two-dimensional layered framework, Cu 2 (bdc) 2 (MeOH) 2 . During this step, there is a significant increase in the internal solid volume (versus void volume) due to the Cu 2 (bdc) 2 (MeOH) 2 crystals occupying a much greater volume compared to the precursor. In the actual monolith, this largely eliminates the macroporosity within the structure while keeping the external macroscopic dimensions. In the second step, the obtained monolith is subjected to a PCP-to-PCP replication procedure in the presence of 4,4 0bipyridine (bpy), which leads to the pillaring of the two-dimensional layers and formation of a monolith constructed from the three-dimensional, interpenetrated Cu 2 (bdc) 2 (bpy) framework. Inset: portions of the structures of each of the PCP compounds (one half of the interpenetrated framework of Cu 2 (bdc) 2 (bpy) is shown faded). Green, grey, blue, and red spheres represent Cu, C, N, and O atoms, respectively. H atoms, and solvent molecules (except for the directly coordinated atom) have been omitted for clarity.</p><p>Bulk Cu 2 (bdc) 2 (bpy). To a 500 mL round-bottom ask, H 2 bdc (210 mg, 1.26 mmol) and methanol (200 mL) were added, and the mixture was reuxed under Ar for 2 h. Aer this time, a commercially-obtained Cu(OH) 2 powder (121 mg, 1.24 mmol) was added, and the solution was reuxed for a further 3 days. Aer this time, a sky-blue precipitate was formed, and the reaction solution was cooled to room temperature. Then, a mixture of bpy (100 mg, 0.64 mmol) in methanol (100 mL) was added to the ask, and the solution was stirred vigorously for 3 days at room temperature. This induced a color change of the solid to pale-green. The resulting solid was isolated by vacuum ltration, washed with methanol (3  50 mL), and dried under a reduced pressure.</p><p>Cu 2 (bdc) 2 (MeOH) 2 -polyacrylamide monolith. An approximately 5 mm  5 mm  5 mm piece of the as-prepared Cu(OH) 2 -polyacrylamide monolith (Fig. 2, le) was inserted into a tapered glass tube (i.d. 8 mm), and placed into a 20 mL glass vial containing H 2 bdc (50 mg, 0.30 mmol) and methanol (20 mL). The vial was sealed and placed in an oven set to a temperature of 60 C for 12 h, aer which time the color of the monolith changed from green to sky-blue (see Fig. 2, center). The glass tube (containing the monolith) was then removed and placed in a fresh ligand solution of the same composition, and placed back in the oven for a further 12 h. This procedure was repeated until the total reaction time was 7 days, aer which time the fully replicated monolith was washed by immersion in neat methanol (50 mL) for 24 h to remove any unreacted H 2 bdc. The washing procedure was repeated three times, and the material was stored in neat methanol to avoid degradation of the resultant structure.</p><p>Cu 2 (bdc) 2 (bpy)-polyacrylamide monolith. In a 100 mL glass vial, the Cu 2 (bdc) 2 (MeOH) 2 -polyacrylamide monolith obtained in the previous step was immersed in methanol (45 mL). Then, a solution of bpy (10.0 mg, 64.0 mmol) dissolved in methanol (5 mL) was slowly added, and the contents of the vial were allowed to stand undisturbed at room temperature for 24 h.</p><p>Then, 5 mL of the solution was removed, replaced with a bpy solution with the same composition added initially, and the mixture was allowed to stand for a further 24 h. This procedure was repeated until the total reaction time was 5 days, aer which time the color of the monolith had changed to blue-green (Fig. 2, right). The solid was washed and stored using the same method as described for the Cu 2 (bdc) 2 (MeOH) 2 -polyacrylamide monolith.</p><!><p>The coordination replication technique is an attractive method for the structuralization of PCP materials, since a potentially wide variety of metal-based precursors can be shaped into a desired form via conventional fabrication techniques, such as sol-gel processing. Here, the main requirement for precursor materials is a slow dissolution rate relative to the crystallization rate of the target PCP crystals, such that crystal growth is spatially constrained at the interface between the solid precursor and the ligand solution. 4 This represents one of the main challenges in expanding the scope of coordination replication synthesis, and precursors that offer the correct balance between solubility and reactivity under the reaction conditions for the PCP formation process have remained limited so far.</p><p>Among the solid sources containing Cu 2+ considered for the formation of the Cu 2 (bdc) 2 (MeOH) 2 and Cu 2 (bdc) 2 (bpy) frameworks, Cu(OH) 2 was chosen for further study owing to its low solubility in polar organic solvents and high reactivity toward acids. 10 Consequently, synthetic conditions for the synthesis of the two compounds were developed using a commerciallyavailable bulk crystalline powder of Cu(OH) 2 . The screening of various parameters, including the reaction solvent, the metalto-ligand ratio, and the reaction time revealed that the addition of a stoichiometric quantity of Cu(OH) 2 to a reuxing solution of H 2 bdc in methanol afforded Cu 2 (bdc) 2 (MeOH) 2 aer a reaction time of 3 days. Next, a suspension of Cu 2 (bdc) 2 (MeOH) 2 was treated with an excess of bpy in methanol, resulting in the installation of bpy pillars between every second square grid layer to produce the interpenetrated Cu 2 (bdc) 2 (bpy) framework (see Fig. S3 †). SEM observation conrmed a plate-like crystal morphology (Fig. S4 †), and N 2 adsorption measurements (Fig. S5 †) at 77 K gave a BET surface area 11 of 1030 m 2 g À1 (Langmuir surface area: 1300 m 2 g À1 ) which is somewhat higher than the corresponding value of 700 m 2 g À1 measured previously for a sample prepared from a conventional method that uses CuSO 4 as the Cu 2+ source. 8a Following the successful demonstration of the synthesis of Cu 2 (bdc) 2 (MeOH) 2 and Cu 2 (bdc) 2 (bpy) from crystalline Cu(OH) 2 powders, a structuralized form of Cu(OH) 2 was required for coordination replication studies. Recently, a method for the preparation of an amorphous macro-and mesoporous Cu(OH) 2 -polyacrylamide composite material via sol-gel processing accompanied by phase separation, and its conversion to the prototypical and rigid PCP, Cu 3 (btc) 2 , was reported. 9,12 The amorphous nature of the Cu(OH) 2 within this parent phase is expected to have a similar (or enhanced) reactivity compared to crystalline Cu(OH) 2 , and was identied as a suitable candidate for further study. In this case, the synthetic procedure of the Cu(OH) 2 -polyacrylamide parent phase was adapted to prepare a monolithic solid featuring continuous macropores with a diameter of ca. 1 mm (see Fig. 2, le, and Fig. 3A). Analysis of the porosity of the parent monolith used in this work via N 2 adsorption isotherms afforded a type-IV prole typical of a mesoporous solid (Fig. S6 †). The determination of a pore size distribution based on this data revealed a maximum density at ca. 5 nm for the mesopores. Note that the large pores present within this monolith are expected to facilitate the diffusion of the organic linkers throughout the solid, which is required for full conversion during the coordination replication procedure.</p><p>The Cu(OH) 2 -polyacrylamide monolith was suspended and heated within a solution of H 2 bdc in methanol for an extended period of 7 days (with daily exchange of the mother liquor), which resulted in a color change of the solid from green to skyblue. Importantly, the external dimensions of the monolith and its mechanical integrity were retained despite the long period of treatment (see Fig. 2, center). 13 Observation of the surface of the monolith following replication by SEM revealed the growth of square plate-like crystals approximately 1 mm in width from the walls of the co-continuous structure (see Fig. 3B). SEM observation following slicing of a monolith sample to expose the cross-section (depth direction) of the structure showed crystals of the same morphology had uniformly formed throughout the material (see Fig. S7 †), but the macropores were almost completely eliminated. This is because the conversion from Cu(OH) 2 to Cu 2 (bdc) 2 (MeOH) 2 results in a volume increase of approximately 10 times (based on Cu 2+ density in the bulk, crystalline forms of both compounds). The complete conversion of the Cu(OH) 2 of the parent phase was further conrmed by thermogravimetric analysis (TGA), which did not exhibit a weight loss at the decomposition temperature of Cu(OH) 2 of ca. 80 C (see Fig. S8 †). The TGA data could also be used to estimate a polyacrylamide content of 15.0 wt%, which is close to the composition employed during the preparation of the Cu(OH) 2polyacrylamide monolith of ca. 20.0 wt%. Note that, in the preparation of the Cu 2 (bdc) 2 (MeOH) 2 monolith, the reaction conditions developed for the preparation of bulk powders of the same compound from crystalline Cu(OH) 2 was successfully used for monolith conversion. This agrees with our experience using the coordination replication method for the synthesis of Al-based PCP architectures from Al 2 O 3 phases, 4 which has demonstrated that amorphous or less-dense variants of an inorganic compound tend to dissolve faster or have higher reactivities since they have lower lattice energies. This results in the right balance between precursor dissolution and PCP crystallization, which is required for preservation of the structuring of the parent phase.</p><p>Next, the Cu 2 (bdc) 2 (MeOH) 2 monolith was immersed in a methanol solution of bpy to induce pillaring of the square grid layers of the two-dimensional framework to afford the three dimensional Cu 2 (bdc) 2 (bpy) compound. Aer several hours, the color of the monolith changed from sky-blue to blue-green (see Fig. 2, right). SEM data revealed the retention of the structuralization of the monolith following replication accompanied with a slight increase in the thickness of the crystals, which is consistent with the insertion of the bpy pillars between the dinuclear copper paddlewheels of every second square grid layer (Fig. 3C). Estimation of the composition of the monolith via TGA data revealed a polyacrylamide content of 2.0 wt% (Fig. S16 †), the loss of which, as discussed in the following section, has important consequences with respect to the properties of the monoliths.</p><!><p>Cu 2 (bdc) 2 (MeOH) 2 monolith. The properties of the replicated monolith were probed using a combination of powder Xray diffraction, SEM, TGA, infrared spectroscopy, and sorption experiments. Diffraction patterns obtained from a solvated fragment of the replicated solid were indicative of a highly crystalline framework phase, with reections that were wellmatched with those of solvated bulk Cu 2 (bdc) 2 (MeOH) 2 (see Fig. 4). Surprisingly, a signicant number of peaks were absent in the diffraction pattern of the monolithic phase. Assignment of the diffraction peaks observed for the monolith revealed that the 0k0, 00l, and 0kl reections were present, while all reections with a non-zero h component were signicantly broadened or absent. 14 The structure of the Cu 2 (bdc) 2 (MeOH) 2 compound is such that the crystallographic a-axis (i.e. the h00 reection) represents the periodicity of the stacking of the twodimensional square grids (see Fig. 1), and the absence of these reections can be attributed to its disruption (or "amorphization") upon integration into the monolith. This is analogous to a phenomenon observed in carbon-based materials with a turbostratic structure, in which 00l reections are prominently observed (with a broadened peak width) compared to its crystalline counterpart, graphite. 15 The origins of this unusual feature of the powder X-ray diffraction data were further probed by N 2 adsorption analysis at 77 K aer activation of the monolith at 150 C. 16 Fig. 5 displays data collected for the parent Cu(OH) 2 -polyacrylamide monolith, the Cu 2 (bdc) 2 (MeOH) 2 monolith and a bulk Cu 2 (bdc) 2 (MeOH) 2 powder sample. Remarkably, while the bulk material showed a negligible N 2 uptake owing to the inability of N 2 to open and access the interlayer spacing, the structuralized variant exhibited signicant uptake at low pressures, reminiscent of a type-I isotherm observed for a microporous solid. Indeed, a BET analysis of the sorption data (see Fig. S9 †) afforded a surface area of 520 m 2 g À1 , 17 which is signicantly greater than can be accounted for by the sorption properties of the parent Cu(OH) 2 -polyacrylamide phase and bulk Cu 2 (bdc) 2 (MeOH) 2 . This suggests that the structural inuence of the interactions between the Cu 2 (bdc) 2 (MeOH) 2 crystals and the polyacrylamide chains at the molecular scale in turn impart considerably different sorption properties to the PCP phase compared to its bulk counterpart.</p><p>The powder diffraction and adsorption data observed here can be reconciled by considering the role of the polyacrylamide polymer in the replicated system. The polyacrylamide content of the Cu 2 (bdc) 2 (MeOH) 2 monolith of approximately 15.0 wt% is a component required for the integrity of the three-dimensional structuralization. Here, it is expected that the anchoring of the crystals to the polymer occurs by way of Cu 2+ -amide interactions, which inherently requires the polymer to become partially incorporated between the layers of the framework (i.e. by coordination to the dinuclear paddlewheels). This is expected to disrupt the periodicity of the PCP in the crystallographic a-direction of the framework (while leaving the crystallinity of the bc plane unaffected), and the creation of uneven spacings between the square grid layers, some of which are sufficiently large for N 2 to be incorporated at low temperatures. This phenomenon is unique to Cu 2 (bdc) 2 (MeOH) 2 in a structuralized state, since such points of anchoring do not exist in the bulk form. Further, it demonstrates the importance of molecular scale interactions between the PCP crystals and the support in determining the adsorptive and dynamic behavior of the system as a whole.</p><p>The impact of structuralization in the Cu 2 (bdc) 2 (MeOH) 2 system was further investigated by destroying the architecture by mechanical grinding of the monolith into a ne powder. Although the crystallinity of the sample was preserved following this process (see Fig. 4), N 2 adsorption data at 77 K revealed the complete loss of microporosity once in a ground powder form (see Fig. S10 †). This can be ascribed to the pulverization of the crystals as observed by SEM (Fig. S11 †), which leads to most of the crystalline fragments no longer being bound by the polyacrylamide polymer. Indeed, while the microporous region of the N 2 isotherm no longer shows a signicant uptake, the prole exhibits a monotonic increase up to 190 cm 3 g À1 at a pressure of 1 bar, consistent with surface adsorption of N 2 to the polyacrylamide polymer surface. In addition, preparation of Cu(bdc) 2 (MeOH) 2 from a uniformly ground sample of the Cu(OH) 2 -polyacrylamide parent phase (prepared under the same reaction conditions as bulk Cu(bdc) 2 (MeOH) 2 ) yielded a sample of the same composition as the Cu(bdc) 2 (MeOH) 2 monolith. However, unlike the monolith form, the material displays little microporosity despite the presence of polyacrylamide in the overall composition (see Fig. S12 and S13 †). This further supports the observation that the immobilization of the Cu(bdc) 2 (MeOH) 2 crystals within the three-dimensional architecture provides the additional microporosity observed here.</p><p>Cu 2 (bdc) 2 (bpy) monolith. The composition, structure, and framework exibility of the replicated monolith was characterized using a combination of powder X-ray diffraction, SEM, and adsorption experiments. As shown in Fig. 6, powder X-ray diffraction data collected for an as-synthesized sample afforded reections corresponding to the open pore form of the framework simulated from single-crystal data. In situ activation of the sample under a He ow at 150 C led to a structural change in the framework to the corresponding closed pore form, which is consistent with the removal of the methanol molecules within the pores. Resolvation of the material in methanol resulted in a return to the open form phase with retention of the threedimensional superstructure. Note that this solvation-desolvation process could be repeated several times without loss of the integrity of the monolith, demonstrating the successful preparation of a monolithic structure consisting of reversibly exible building blocks. A methanol isotherm collected for an activated sample (see Fig. 7) exhibited a stepped isotherm with hysteresis in the desorption branch, which is typical for a gateopening type structural transition of the framework. Comparison of the methanol uptake over several cycles showed no degradation to the adsorption prole (Fig. S14 †), conrming the stability of the monolith with respect to exing of the framework.</p><p>Next, the effect of structuralization of the Cu 2 (bdc) 2 (bpy) compound in a monolith form was probed by comparing its methanol adsorption isotherm aer mechanical grinding of the framework. Surprisingly, in contrast to the Cu 2 (bdc) 2 (MeOH) 2 monolith, little change was observed aer the destruction of the structuralization with regard to both the gate-opening pressure and the quantity of methanol adsorbed (Fig. S15 †). Furthermore, comparison with a bulk powder of Cu 2 (bdc) 2 (bpy) also revealed an almost identical adsorption prole, revealing that both the structural exibility and the adsorption properties of the monolith are a good match to those of a bulk sample of the same compound. This is a somewhat surprising result given that, based on the unusual properties observed for the Cu 2 (bdc) 2 (MeOH) 2 monolithic system, the immobilization of the Cu 2 (bdc) 2 (bpy) crystals in a monolith form might be expected to inuence the adsorptive and dynamic properties of the system.</p><p>In order to elucidate the origin of this result, IR and TGA data were collected to evaluate the composition of the Cu 2 (bdc) 2 (bpy) replicate. As is clear from the IR data presented in Fig. 8, the spectrum observed for the activated form of the Cu 2 (bdc) 2 (bpy) monolith shows a close match with that of a bulk sample of the same framework. However, in comparison with the parent and Cu 2 (bdc) 2 (MeOH) 2 monolith, the C-N stretch at approximately 1660 cm À1 originating from the amide moiety of the polyacrylamide polymer is greatly diminished, suggesting that the polymer component is excluded from the structure during the insertion of the bpy pillars. This was further conrmed by the TGA data shown in Fig. S16, † which allowed the polyacrylamide content to be calculated as 2.0 wt%, compared with 20.0 wt% and 15.0 wt% in the parent Cu(OH) 2 -polyacrylamide and Cu 2 (bdc) 2 (MeOH) 2 monoliths, respectively. The loss of polyacrylamide from the structure is also consistent with a decrease in the mechanical robustness of the Cu 2 (bdc) 2 (bpy) monolith, emphasizing its key role in providing the effect of structuralization of the monolithic structure following replication.</p><p>The origin of the loss of polyacrylamide from the structure was probed via a number of control experiments. Immersion of the parent Cu(OH) 2 -polyacrylamide and Cu 2 (bdc) 2 (MeOH) 2 monoliths in methanol resulted in no change to the composition or the structuralization, which provided clear evidence of the stability of the monoliths (and its associated polymer content) under these conditions. Furthermore, immersion of the parent Cu(OH) 2 -polyacrylamide compound in a methanol solution of bpy resulted in no loss in the polyacrylamide component from the structure as evaluated by TGA data (Fig. S20 †). Thus, the polyacrylamide is only lost when the Cu 2 (bdc) 2 (MeOH) 2 undergoes pillaring by the bpy molecules during the second PCP-to-PCP replication step. While an exact mechanism for the loss of polyacrylamide is not yet available, a plausible sequence of events is as follows. In the conversion of the Cu(OH) 2 -polyacrylamide monolith to the Cu 2 (bdc) 2 -(MeOH) 2 replicate, the polyacrylamide directly binds to the Cu 2 (bdc) 2 (MeOH) 2 framework via amide sidechains as described above. This leads to the polymer chains, which are originally buried beneath a colloidal network of Cu(OH) 2 particles, to become exposed aer replication. This is due in part to the plate-shaped crystals of Cu 2 (bdc) 2 (MeOH) 2 that are not expected to uniformly protect the polymer chains from access at the molecular scale. Then, upon exposure of the monolith to a solution containing bpy, the amide moieties are displaced from the Cu 2+ centers, leaving the chains unbound and susceptible to dissolution out of the monolith. This dissolution process may additionally be assisted by a partial hydrolysis of the polymer chains, which is known to occur in the presence of basic species. Note that analysis of the reactant solution by IR and 1 H NMR did not reveal the presence of free acrylamide monomers, suggesting a complex decomposition pathway for the PAAm component into a variety of products. As such, aer the removal of the polyacrylamide component from the monolith, the limited intergrowth between the Cu 2 (bdc) 2 -(bpy) crystals leads to the structural and sorption properties of the monolith largely reecting those of a bulk powder, despite the retention of the monolithic structure.</p><!><p>The foregoing results have detailed the synthesis and properties of three-dimensional superstructures consisting of the exible Cu 2 (bdc) 2 (MeOH) 2 and Cu 2 (bdc) 2 (bpy) frameworks via coordination replication from a structuralized macro-and mesoporous Cu(OH) 2 -polyacrylamide composite parent phase. The synthesis of these monolithic systems expands on the scope of the coordination replication technique to include exible PCPs, but perhaps more importantly, provides monolithic systems that exhibit properties that differ from bulk powders as a result of structuralization. In the case of the Cu 2 (bdc) 2 (MeOH) 2 system, the anchoring of the two-dimensional framework by the polyacrylamide polymer leads to their immobilization within the superstructure, but also results in an amorphization of the interlayer direction of the framework structure. This provides the framework with an ability to adsorb N 2 , which is not observed in the absence of structuralization. For the Cu 2 (bdc) 2 (bpy) system, the framework exibility is preserved aer immobilization, leading to a exible monolith system. In this case, the sorption and dynamic properties largely reect the characteristics of the bulk form owing to the dissolution of the polymer phase during the PCP-to-PCP replication step. This emphasizes the importance of the polymer phase in maintaining the connectivity between crystals and in providing the system with the effects of structuralization.</p><p>The results presented here further demonstrate the versatility of the coordination replication technique, and it is envisaged that a greater library of structuralized PCPs will emerge in the near future for specic applications in areas such as molecular separations and heterogeneous catalysis. In addition, the new properties observed for the structuralized forms of the compounds suggest that new, rich phenomena could emerge as a result of detailed studies of this type. However, as revealed here, there is an urgent need for preparative routes to new parent materials that are optimized for coordination replication, and care is also needed in the selection of the target PCP system. Specically, a high degree of crystal intergrowth is desired in order to achieve cooperative effects stemming from material structuralization. While the polyacrylamide polymer serves as an adhesive between the crystals in this case, greater intergrowth between the PCP crystals themselves would preclude the need for the use of a composite system. For example, optimization of both the crystal size (i.e. smaller crystals) and morphology (i.e. block-shaped crystals) of the PCP phase is expected to facilitate a greater preservation of the original structure of the parent material with a greater degree of intergrowth. Such optimizations of the crystal parameters have already appeared in the case of bulk crystals via the coordination modulation technique, 18 and studies using this strategy for the fabrication of three-dimensionally structuralized systems composed of other functional PCP systems are already underway. mechanical strength measurements of the Cu 2 (bdc) 2 (MeOH) 2 monolith were not successful in this case. While large monoliths (cylindrical tablets with a diameter of 1 cm and a height of 0.5 cm) of the Cu 3 (btc) 2 framework were readily prepared within 30 min from the same starting precursor, 12 the conversion was found to be signicantly slower in the case of Cu 2 (bdc) 2 (MeOH) 2 . The use of starting monoliths of a sufficient size resulted in samples with unreacted cores even aer 14 days, likely due to preferential crystal growth at the exterior of the monolith resulting in macropore blockage, preventing diffusion of the organic linker throughout the solid. The signicantly different behavior of the two systems highlights potential differences in both the molecular scale replication mechanism and the nature of the crystal growth, which are areas worthy of systematic investigation in order to optimize precursor design for specic PCP systems. 14 Such effects are oen observed in oriented samples or those with highly anisotropic crystal shapes, although this is not expected for the replicated phase studied here due to the random distribution of spatial orientations of the crystals within the monolith. 15 Y. Hishiyama and M. Nakamura, Carbon, 1995, 33, 1399. 16 Note that this slightly lower activation temperature than for bulk powder samples allows the polyacrylamide component to be stably maintained within the framework, while allowing full removal of the methanol within the pores and bound to the Cu 2+ ions of the dinuclear paddlewheel units. 17 The macroporosity is largely eliminated and the mesoporosity signicantly diminished upon replication, which is due to the Cu 2 (bdc) 2 (MeOH) 2 crystals occupying a (up to 10 times) greater volume compared to the original Cu(OH) 2 component based on the density of Cu 2+ ions in the respective crystal structures. The plate-like morphology of the framework crystals may also provide a less contoured surface providing fewer cavities in the mesopore length scale. 18 (a) T. Tsuruoka, S. Furukawa, Y. Takashima, K. Yoshida,</p>

Royal Society of Chemistry (RSC)

Graphite Oxide Improves Adhesion and Water Resistance of Canola Protein–Graphite Oxide Hybrid Adhesive

Protein derived adhesives are extensively explored as a replacement for synthetic ones, but suffers from weak adhesion and water resistance. Graphite oxide (GO) has been extensively used in nanocomposites, but not in adhesives applications. The objectives of this study were to prepare functionally improved protein adhesive by exfoliating GO with different oxidation levels, and to determine the effect of GO on adhesion mechanism. GO were prepared by oxidizing graphite for 0.5, 2, and 4 h (GO-A, GO-B and GO-C, respectively). Increasing oxidation time decreased C/O ratio; while the relative proportion of C-OH, and C = O groups initially increased up to 2 h of oxidation, but reduced upon further oxidation. Canola protein-GO hybrid adhesive (CPA-GO) was prepared by exfoliating GO at a level of 1% (w/w). GO significantly increased (p < 0.05) adhesion; where GO-B addition showed the highest dry, and wet strength of 11.67 ± 1.00, and 4.85 ± 0.61 MPa, respectively. The improvements in adhesion was due to the improved exfoliation of GO, improved adhesive and cohesive interactions, increased hydrogen bonding, increased hydrophobic interactions and thermal stability of CPA-GO. GO, as we proposed for the first time is easier to process and cost-effective in preparing protein-based adhesives with significantly improved functionalities.Due to increasing concerns over environmental and human health implications of synthetic adhesives, researchers are looking for green materials/biobased adhesives using sustainable and renewable polymers [1][2][3][4] . Proteins are one of the most studied renewable polymers for adhesive applications 5 . Canola is the farm-gate crop in Canada while its meal after oil extraction finds limited value-added applications other than feed and fertilizer uses; thus research on canola protein gains the momentum as an alternative polymer source for adhesive preparation 5,6 . However, similar to other proteins, canola protein derived adhesives also suffered from weak water resistance and adhesion strength, which might limit their widespread applications 5,7 . Therefore, improving water resistance and adhesion strength of canola protein-derived adhesives is essential to succeed as a competitive alternative over synthetic ones. Our previous study found that exfoliating nanomaterials at lower addition levels could significantly increase the adhesion strength and water resistance of canola protein; especially, graphite oxide (GO) and nano crystalline cellulose (NCC) showed superior improvement over other nanomaterials 8 . The dry, wet and soaked adhesion strength of canola protein adhesives was increased from 6.38 ± 0.84 MPa, 1.98 ± 0.22 MPa, and 5.65 ± 0.46 MPa in the pH control samples to 10.37 ± 1.63 MPa, 3.56 ± 0.57 MPa, and 7.66 ± 1.37 MPa, respectively, for the 1% NCC addition (w/w, NCC/protein), and to 8.14 ± 0.45 MPa, 3.25 ± 0.36 MPa, and 7.76 ± 0.53 MPa for the 1% GO (w/w,GO/protein) addition 8 .Although NCC showed greater improvement than GO, NCC is more expensive than that of GO. Furthermore, GO shows excellent exfoliation properties in aqueous and organic solvents, as well as in different polymer matrixes due to hydrophilic nature of GO 9 . Previous studies on composite materials showed that the improvements in mechanical, thermal and electrical properties were directly related to the exfoliation properties of nanomaterials in polymer matrix 2,10 . Therefore, it is essential to use a nanomaterial with better exfoliation properties for adhesive preparation to improve mechanical strength of the adhesive 2 .Carbon based nanomaterials such as carbon nanotubes, graphene, graphite oxide and aerographite have been extensively studied recently in polymer and composite applications, mainly due to their excellent mechanical,

graphite_oxide_improves_adhesion_and_water_resistance_of_canola_protein–graphite_oxide_hybrid_adhesi

<!>Results and Discussion<!>Effect of different GO samples on protein structural changes.<!>Conclusions<!>Materials and Chemicals. Canola meal was provided by Richardson Oilseed Ltd. (Lethbridge, AB, Canada).<!>Canola protein extraction.<!>Graphite oxide preparation.<!>Preparation of canola protein-graphite oxide hybrid wood adhesive (CPA-GO).<!>Adhesion strength measurement.<!>X-ray diffraction (XRD).<!>Differential scanning calorimetry (DSC).<!>Fourier transform infrared spectroscopy (FTIR).<!>Transmission Electron Microscopy (TEM).

<p>thermal and conductive properties 11,12 . First isolated in 2004, graphene consists of two dimensional sheets of carbon molecules bonded via sp 2 -bonds 13 . Pristine graphene has unique material properties such as extremely high Young's modulus (∼1 TPa), fracture strength (∼130 GPa), thermal conductivity (∼5000 Wm −1 K −1 ) and specific surface area (2630 m 2 g −1 ) compared to other carbon based materials 14,15 . Graphite oxide (GO), an intermediary product in mass scale production of graphene, possess similar material properties to graphene 15 . GO represents advantages over graphene, mainly due to their simplicity of production through chemical methods, hydrophilic properties, and potential to convert into graphene or graphene oxide 15,16 either by chemical 17,18 or thermal 19 reduction methods before or after exfoliating in the polymer matrix. In addition, GO can form liquid crystals 20 and microscopic assembly of graphene once incorporated in polymer matrix 21 , which could help develop homogeneous polymer composite with improved mechanical properties 13,22 . The presence of oxygen containing functional groups imparts GO excellent hydrophilic properties, facilitating exfoliation in a polymer matrix 23 . Hydrophilic nature of GO is a vital property in preparing GO exfoliated adhesives using the solution intercalation method.</p><p>GO has been extensively explored in developing advanced nano-composites in combination with different polymers such as poly (vinyl acetate) 24,25 , chitosan 26 , natural rubber 27 , poly (methyl methacrylate) and epoxy 10,23 . However, there is scanty information available in literature on GO based adhesives, except one study found in literature regarding applicability of graphene in adhesive preparation. Khan et al. (2013) reported that incorporation of 3% graphene (dissolved in tetrahydrofuran) into poly (vinyl acetate) (PVA) adhesives improved both tensile strength (from 0.3 MPa to 0.75 MPa) and shear strength (from 0.5 MPa to 2.2 MPa) at dry conditions 28 . It is well known that the functionality of GO largely depends on the level of its oxidation 9,23,29 ; therefore, there is a need to study the effect of different GO oxidation levels on adhesion strength and water resistance. We hypothesized that adding GO with different oxidation levels will change adhesion strength and water resistance of canola protein derived adhesives. The objectives of this research were to prepare GO with different oxidation levels under various oxidation time, to determine the effect of GO with different oxidation levels on adhesion properties, and to explore the mechanism of GO in adhesion improvement.</p><p>In this study, GO with different oxidation levels were prepared by oxidizing graphite at different oxidation times. Prepared GO samples were exfoliated in canola protein to produce canola protein-graphite oxide (CPA-GO) hybrid wood adhesive. The effect of oxidation time on C/O ratio, surface functional groups, interlayer spacing, and thermal properties were characterized to identify their effect on GO dispersion in protein matrix, structural and thermal changes, adhesion strength and water resistance of CPA-GO.</p><!><p>The functionality of GO depends largely on the methods of preparation and conditions used in the process such as oxidation time and amount of oxidizer 23,29 . In composite research, tailoring conditions of GO preparation have proven to change material properties such as flexural strength and conductivity 29 . However, to best of our knowledge, there were no previous reports in the literature regarding the effect of GO on adhesives derived from biobased polymers/protein-based polymers.</p><p>Adhesion strength of canola protein-GO hybrid adhesives. Adhesives failure can happen in two occasions, either adhesively at adhesive-wood interface or cohesively within bulk adhesive material 28 . Since amorphous polymer generally has a limited mechanical strength 28 , cohesive failure is more prominent in biobased adhesives. The potential of nanomaterials in improving the bulk adhesion strength of canola protein adhesive was studied. Effects of adding GO on adhesion strength are shown in Fig. 1. All GO samples used in this study significantly increased (p < 0.05) the adhesion strength and water resistance compared to the negative control and the pH control samples. GO prepared at 0.5, 2, and 4 h of oxidation showed a dry adhesion strength of 10.63 ± 0.81, 11.67 ± 1.00, and 11.22 ± 0.82 MPa, respectively.</p><p>Increasing oxidation time reduced the C/O ratio of GO samples, but showed an increasing trend in dry adhesion strength. Similar trend was also observed in soaked strength, where the highest strength of 10.73 ± 0.45 MPa was observed in GO-B (2 h of oxidation) followed by GO-C and GO-A samples (10.22 ± 0.45, 9.82 ± 0.38 MPa respectively). The wet adhesion was significantly increased from 1.98 ± 0.22 MPa in the pH control sample to 4.85 ± 0.35, 4.85 ± 0.61 and 4.48 ± 0.28 MPa for GO-A, GO-B and GO-C samples respectively, but did not differ among different oxidation times. Protein contains both hydrophilic and hydrophobic residues which makes it an excellent amphiphilic biopolymer with well-known adhesiveness to various solid surfaces 30 . Liu et al. (2010) studied the interactions of GO with bovine serum albumin (BSA) and suggested that conjugated GO-protein complex can act as an adhesive matrix to interact with other solid materials 30 . Studies on PVA polymer composites showed improved interactions and mechanical strength after exfoliating graphene oxide at low concentrations 28,31 . Therefore, GO induced cohesive (protein-protein) and adhesive (protein-wood surface) interactions might be the main contributor to increased adhesion and water resistance observed in this study. Conversion of GO into more hydrophobic and stable reduced graphene oxide (rGO) might be another reason for the improved water resistance. Several authors reported thermal 32 or protein aided reduction 30 of GO into rGO in composite research, which improved the mechanical properties. Adhesive curing at elevated temperature, and the presence of canola protein might trigger the reduction of GO into rGO, thereby improve water resistance of the CPA-GO adhesive.</p><p>In comparison, canola protein modified with sodium bisulfite showed dry, wet and soaked adhesion strength of 5.28 ± 0.47, 4.07 ± 0.16, and 5.43 ± 0.28 MPa, respectively 6 . In another study, modifying canola protein with 0.5% sodium dodecyl sulphate (SDS) had dry, wet and soaked adhesion of 6.00 ± 0.69, 3.52 ± 0.48, and 6.66 ± 0.07 MPa, respectively. Grafting poly(glycidyl methacrylate) in canola protein was reported to improve the dry, wet and soaked adhesion to 8.25 ± 0.12, 3.80 ± 0.15, and 7.10 ± 0.10 MPa, respectively. Canola protein adhesives prepared with GO as developed in this study substantially improved both adhesion strength and water resistance.</p><p>Changes in elemental composition, functional groups of GO and their effect on adhesion. GO with variable elemental composition, C/O ratio and functional groups were previously developed via manipulating oxidation conditions 9,23,33 . In this study, we prepared GO with variable properties by changing oxidation time while maintaining other conditions constant. Oxidation conditions used in this study, elemental composition and C/O ratio of prepared GO samples are shown in Table 1. Native graphite mainly consists of carbon and oxygen at percentages of 97.65% and 2.35%, respectively, according to the XPS data (Supplementary information-S1). Graphite showed a C/O ratio of 41.55 while oxidizing graphite for 0.5, 2 and 4 h reduced C/O ratio to 2.06, 1.40 and 1.49, respectively. In addition, GO also contains small amount of sulfur (∼2%) and trace amounts of sodium, and manganese, as the residuals from GO processing. The presence of oxygen containing functional groups was confirmed by analyzing XPS high-resolution C1s spectra of graphite and GO samples. The original high-resolution C1s spectra and fitted peaks are shown in Fig. 2. XPS data processing for C1s spectra of graphite only showed a major peak centered at 284.5 eV which is attributed to sp 2 hybridized carbon, derived from C = C and C-C bonds with delocalized π electrons 29,33 . The other small peak at a binding energy of 285.3 eV resembles to sp 3 carbon hybridization 34 , which attributed to oxidation of graphite in the presence of atmospheric oxygen 35 .</p><p>GO-A sample shows four new peaks at binding energies around 285.5-288.5 eV, representing oxygen functional groups in addition to the characteristic sp 2 peak at 284.5 eV. Shift of binding energies from 284.5 eV to 285.4 eV, 286.5 eV, 287.2 eV, and 288.5 eV are attributed to the occurrence of carbon sp 3 , C-OH, C-O-C, and C = O functional groups respectively. Previous studies reported similar binding energy shift in GO [36][37][38] . Increasing oxidation time to 2 h (GO-B sample) further changed the composition of surface functional groups. Peak corresponding to the carbon sp 3 was disappeared while the relative proportion of C-OH and C = O peaks (286.5 eV and 288.3 eV respectively) increased. Furthermore, C-O-C peak appeared at the binding energy of 287.1 eV. Wang et al. (2012) also reported an increased proportion of C = O and C-OH groups at higher oxidation conditions in graphite oxide 29 . Further oxidation of graphite up to 4 h in GO-C increased the relative proportion of carbon sp 2 , C-O-C, and C = O groups, at the expense of C-OH groups; interestingly, the carbon sp 3 peak re-appeared at 285.4 eV binding energy. Degradation of oxygen functional groups in prolonged oxidation might be the reason for sp 3 hybridization observed in GO-C sample 33 .</p><p>FTIR spectra of GO samples prepared under different oxidation times are shown in Fig. 3. FTIR peaks were assigned to respective functional groups according to the previous data reported in the literature. In graphite, the peak appearing at 1586 cm −1 generally represents the stretching vibration of C = C bond (vC = C) 35,39,40 . However; after oxidation, the C = C stretching vibrations shifted to 1619 cm −1 , 1623 cm −1 , and 1621 cm −1 wavelengths for GO-A, GO-B and GO-C respectively. Chen et al. (2010), and Stankovich et al. (2006) also reported similar peak shifts in the range of 1618 cm −1 -1626 cm −1 probably due to the oxygen functional groups present in GO 41,42 . The absorption peaks of GO samples at 3424 cm −1 -3436 cm −1 are attributed to the stretching vibration of -OH groups (vO-H) either from -OH groups of absorbed water or -OH groups formed during the oxidation 35,41,43 .</p><p>Following oxidation, the presence of new peaks at 1729 cm −1 , 1731 cm −1 , and 1725 cm −1 wavelengths respectively in GO-A, GO-B, and GO-C samples was observed; probably due to the formation of oxygen containing functional groups, causing the C = O stretching vibrations (vC = O) 29,41,42 . The intensity of vC = O in GO samples was increasing at increasing oxidation level. Wang et al. (2012) also reported similar trend at increasing oxidation levels 29 . Higher degree of oxidation and oxidation induced cracks in GO edges were reported as the main reasons for increased intensity of vC = O 29,44,45 . C-OH bending vibration (δC-OH) peaks were observed in both GO-A and GO-B samples at 1411 cm −1 and 1423 cm −1 respectively, however the intensity was reduced in GO-C. Vibrations from either alcohols or carboxylic groups of GO were reported as the main contributors to δC-OH 39,40 . The peaks appeared at 1220 cm −1 -1230 cm −1 range were usually assigned to C-O stretching vibrations (vC-O 35,[39][40][41] , which attributed to carboxylic acid groups 29 , hydroxyl groups 39,41 , or epoxy groups 40,46 present in GO. The peaks appeared at 1730 cm −1 -1731 cm −1 range were probably attributed to the ester groups that formed during graphite oxidation 47 . The formation of various oxygen containing functional groups in GO might be responsible for the improved adhesion strength. For example, −OH groups in GO might increase −H bonding between adhesive matrix and wood surface; the epoxy groups (C-O-C) in GO can either homopolymerize with another epoxy group in GO, or react with functional groups such as −OH, −COOH on the wood surface, and −NH 2 , −SH in canola protein 48 , thus improving both adhesive and cohesive strength.</p><!><p>Effect of different GO samples on secondary structure of canola protein was studied by creating second derivative of FTIR spectra followed by peak fitting of Amide I peak 49,50 . GO induced protein secondary structural changes are shown in Fig. 4.</p><p>Exfoliating GO in canola protein has increased the relative proportions of unordered structures (1639-1642 cm −1 wavelength) and turn structures (at wavelength range of 1694-1697 cm −1 ) at the expense of β-sheets in the wavelengths of 1625 cm −1 , 1636 cm −1 and 1673-1675 cm −1 49, 50 . In comparison, GO-B and GO-C samples showed the highest relative proportions of unordered structures and turn structures, compared to the pH control and GO-A samples (Supplementary information-S2). The results observed in protein structural changes were compliment to the changes in adhesion strength of CPA-GO prepared with different GO samples. Increase in unordered structures will exposes more hydrophobic functional groups buried inside protein molecules which increase the hydrophobic interactions with wood surface 51 , thereby increase the water resistance and adhesion strength.</p><p>Changes in GO crystallinity and their effect on GO dispersion in protein matrix. The effect of oxidation time on glancing angle (2θ) and interlayer spacing (d) of GO samples are shown in Fig. 5. X-ray diffraction of graphite showed one major crystalline peak at a glancing angle of 26.28° with d spacing of 0.338 nm. Shao et al.</p><p>(2012) also reported a similar peak for graphite at a glancing angle of 26.54° and d spacing of 0.334 nm 23 .</p><p>After oxidation, the graphite crystalline peak was disappeared in GO-A (0.5 h) but two new peaks appeared at different glancing angles: the first major peak was appeared at glancing angle of 11.28° with d spacing of 0.785 nm while another minor peak was observed at glancing angle of 42.17° with d spacing of 0.214 nm. Shao et al. (2012) also reported the disappearance of the characteristic graphitic peak after oxidation and the formation of a new peak at a glancing angle of 11.3° with increased interlayer spacing of 0.80 nm 23 . Increasing graphite oxidation time from 0.5 h to 2 h significantly changed the crystallinity and d spacing of GO-B sample. Glancing angle of the characteristic GO peak has shifted from 11.28° to 9.40° while d spacing increased from 0.785 nm to 0.939 nm (for GO-A and GO-B respectively). Similar to GO-A, GO-B sample showed another peak at a glancing angle of 42.20° (d = 0.214 nm), and a new crystalline peak at 19.91° (d = 0.495 nm). Further increasing oxidation time to 4 h slightly shifted the glancing angle towards 9.94° while decreased d spacing to 0.889 nm.</p><p>The reduction in interlayer spacing has been previously reported due to the decomposition of oxygen containing functional groups in GO samples at prolonged oxidation 33,52 . In GO-C, another two peaks were visible at glancing angles of 42.29°, and 17.89° with d spacing of 0.214 nm and 0.495 nm respectively. In addition, the new peak at a glancing angle of 25.33° (d = 0.351 nm) in GO-C showed similarity to the characteristic graphite peak appeared in un-oxidized graphite. The re-appearance of graphite like crystalline peak at higher oxidation level indicate the decomposition of oxygen containing functional groups, re-forming carbon sp 2 bonds and reduction in crystallinity of GO-C samples 33,52 .</p><p>Proper exfoliation of GO in polymer matrix is one of the major factors affecting the improvement of adhesion strength and water resistance. Aggregation of nanomaterial upon mixing with protein will not improve the adhesion strength 2,53 ; therefore it is important to produce GO with appropriate exfoliation properties. All three GO samples prepared in this study exhibit improved exfoliation in canola protein matrix. X-ray diffraction patterns of GO samples and their dispersion in canola protein are shown in Fig. 6. Two common crystalline peaks were appeared in all three GO samples with diffraction angles (2θ value) around ∼9-11° and ∼42° and one additional crystalline peak was found at ∼25° diffraction angle for GO-C. The disappearance of crystalline peaks after exfoliation of GO in canola protein clearly indicated the uniform exfoliation of GO within protein matrix.</p><p>As shown in TEM images of exfoliated GO samples (Fig. 7), the appearance of single GO sheets in both CPA GO-A and CPA GO-B adhesive samples further supported the uniform exfoliation of GO in canola protein matrix. However, a slight aggregation of GO was visible in CPA GO-C. Addition of hydrophilic functional groups during graphite oxidation is the major reason for increased interlayer spacing of GO 33 . It was reported that increased interlayer space reduces binding energies of GO, which would facilitate the exfoliation of GO layers in the matrix 54 . Therefore, the uniform exfoliation of GO observed in this study, in particular for GO-B might be due to reduced binding energy resultant from increased interlayer spacing. Ultimately, proper exfoliation of GO will help in improving both adhesion strength and water resistance of the CPA-GO adhesive.</p><p>Change in thermal properties of graphite oxide and their effect on thermal stability of prepared adhesive. Effect of graphite oxidation time on GO thermal transitions is shown in Fig. 8. An exothermic transition was observed in all GO samples, but with different enthalpy requirement and temperature range. In GO-A (0.5 h) exothermic transition was observed at extrapolated onset and peak temperatures of 159.7 °C 190.0 °C respectively with 1.57 KJ/g ΔH. Increasing oxidation time to 2 h (GO-B) has changed the thermal transition to 145.6 °C, 164.9 °C and 1.16 KJ/g for extrapolated onset, peak temperature and ΔH respectively. Increasing oxidation time to 4 h (GO-C) shifted extrapolated onset and peak temperatures to 146.0 °C and 166.7 C° respectively where ΔH changed to 1.10 KJ/g. The reduction in ΔH and transition temperatures is a result of increased amount of oxygen containing functional groups. Schniepp et al. (2006) also reported similar changes in thermal transitions around ∼200 °C in graphite oxide and attributed them to decomposition of oxygen containing functional groups 36 . They have further analyzed the outlet gas generated from DSC, and showed that major products as CO 2 and H 2 O that were generated during decomposition of oxygen containing functional groups 36 .</p><p>Effect of different GO samples on thermal transitions of CPA-GO are shown in Table 2. Adding GO into canola protein increased both onset and peak temperatures, as well as the specific heat in transitions. The increased thermal stability is an essential property for adhesive application, as it required to process under higher temperature for adhesive curing 28 . Adding nanomaterials, especially graphene oxide, have been proven to increase thermal stability of protein in previous studies mainly due to improved protein-protein/protein-GO interactions, and 2007) also reported an increased thermal stability and denaturation temperatures of soybean peroxidase enzyme conjugated with graphene oxide nanosheets 55 . Addition of GO into canola protein increased the thermal stability of all CPA-GO samples compared to control samples, which can be related to the increased protein-protein/protein-GO interactions. CPA GO-A showed slightly higher onset and peak temperatures than that of CPA GO-B and CPA GO-C which can be a result of GO induced protein structural changes. Increased unordered structures were observed after adding GO-B and GO-C, at the expense of β-sheets and α-helix which can potentially reduce the thermal stability compared to GO-A.</p><!><p>GO samples with various C/O ratio and surface functional groups were prepared at different oxidation time. Oxidation of graphite for 0.5, 2 and 4 h reduced the C/O ratio of graphite from 41.55 to 2.06, 1.40, and 1.49, respectively. The relative proportion of C-OH and C = O groups as well as interlayer spacing of GO were increased at increasing oxidation time from 0.5 h to 2 h whereas both C-OH content and interlayer spacing were reduced at 4 h of oxidation. GO prepared with different oxidation times improved both adhesion strength and water resistance in all three GO samples; the dry, wet and soaked strength was increased from 6.38 ± 0.84 MPa, 1.98 ± 0.22 MPa, 5.65 ± 0.46 MPa in the pH control sample to 11.67 ± 1.00 MPa, 4.85 ± 0.61 MPa, and 10.73 ± 0.45 MPa, respectively for GO-B exfoliated adhesive. The improved adhesive and water resistance in GO added canola adhesive was due to increased interlayer spacing, improved exfoliation properties, and increased adhesive and cohesive interactions (protein-protein, protein-GO and adhesive-wood surface), hydrophobic interactions and thermal stability. Graphite oxide, instead of graphene, as we proposed for the first time in the study, is easier to process and more cost-effective in preparing protein-based wood adhesives with significantly improved functionalities.</p><!><p>All chemicals were purchased from Fisher Scientific (Ottawa, ON, Canada) unless otherwise noted. Graphite and cellulose were purchase from Sigma-Aldrich (Sigma Chemical Co, St. Louise, MO, USA). Birch wood veneer with thickness of 0.7 mM was purchased from Windsor Plywood Co (Edmonton, AB, Canada).</p><!><p>Proteins were extracted from defatted canola meal as described by Manamperi et al. (2010) with slight modifications 56 . Meal was ground to a fine powder using a Hosokawa milling and classifying system (Hosokawa Micron Powder Systems, Summit, NJ, USA) and then passed through a 100-mesh size sieve. Ground canola meal was mixed with mili-Q water in 1:10 (w/v) ratio; pH was adjusted to 12.0 by adding 3 M NaOH and stirred for 30 m (300 RPM, room temperature). The resulting dispersion was centrifuged for 15 m (10000 g, 4 °C). The supernatant was collected, pH was readjusted to 4.0 by adding 3 M HCl, stirred for another 30 m, and centrifuged at the same condition above to collect protein precipitate. The precipitate was washed with deionized water, freeze-dried, and stored at −20 °C for further use.</p><!><p>Graphite oxide nanoparticles (GO) were prepared as described by Hummers and Offeman method 57 with modification for oxidation time to produce GO with different oxidation levels. In brief, 5 g of graphite and 5 g of NaNO 3 were mixed in a glass beaker and 120 mL of concentrated H 2 SO 4 was slowly added while stirring in an ice bath at 200 RPM for 0.5 h, 2 h, and 4 h to prepare GO-A, GO-B and GO-C samples respectively. Then, 15 g of KMnO 4 was slowly added to the reaction mixture while maintaining the temperature at 35 ± 3 °C with stirring for 1 h. At the end of the reaction, 92 mL of deionized water was added and stirred for 15 m. Unreacted KMnO 4 and other leftover chemicals were neutralized by adding 80 mL of hot (60 °C) deionized water containing 3% H 2 O 2 . After cooling to room temperature, samples were centrifuged (10000 g, 15 m, 4 °C) and washed with deionized water to remove any leftover chemicals. Collected GO samples were sonicated for 5 m (at 50% power output); freeze dried, further dried in a vacuum desiccator with P 2 O 5 , and stored in air tight containers at -20 °C for further use.</p><!><p>GO with different C/O ratios was exfoliated in canola protein matrix according to our previously reported method. 1% (w/w, GO/protein) GO addition level was selected based on the optimum conditions developed in our previous method 8 . In brief, 3 g of canola protein was mixed with 20 mL of deionized water (15% w/v solution) and stirred for 6 h (300 rpm) at room temperature to disperse canola proteins; and then the pH was adjusted to 5.0 using 1 M HCl solution. GO samples (GO-A, GO-B and GO-C) were separately dispersed in 10 mL of deionized water (equivalent to a final GO/protein ratio of 1%, w/w, GO/protein) by stirring (300 rpm) 5 h at room temperature and another 1 h at 45 ± 3 °C, sonicated for 3 m by providing intermittent pulse dispersion of 5 s (at 3 s intervals and 60% amplitude) using medium size tapered tip attached to a high intensity ultrasonic dismembrator (Model 500, Thermo Fisher Scientific INC, Pittsburg, PA, USA), and then homogenized for 2 m (2000 rpm) using ULTRA TURRAX high shear homogenizer (Model T25 D S1, IKA ® Works, Wilmington, NC, USA). The prepared GO dispersions were slowly added to the protein dispersions dropwise while stirring for 15 m (300 rpm) to have a final protein concentration of 10% (w/v) in the adhesive mixture. The resulting adhesive mixtures were sonicated and homogenized as above and the pH of the adhesive was adjusted to 12.0 by adding 6 M NaOH solution. Negative control was prepared by dispersing canola protein (10% w/v) in deionized water and use as is while pH control was prepared by adjusting the pH of canola protein dispersions (10% w/v) to 12.0 similar to GO dispersed samples, without adding GO.</p><!><p>Hardwood veneer samples (Birch, 1.2 mm thickness) were cut into a dimension of 20 mm × 120 mm (width and length) using a cutting device (Adhesive Evaluation Systems, Corvallis, OR, USA). Veneer samples were conditioned for seven days at 23 °C and 50% humidity in a controlled environment chamber (ETS 5518, Glenside, PA, USA) according to the specifications of ASTM D2339-98 (2011) standard method 58 . CPA-GO hybrid adhesives were spread at an amount of 40 uL/veneer strand in a contact area of 20 mm × 5 mm using a micropipette. Veneer samples were air dried for 5 m and hot pressed for 10 m (at 120 °C and 3.5 MPa) using Carver manual hot press (Model 3851-0, Carver Inc., In, USA). Dry adhesion strength (DAS) was measured according to the ASTM standard method D2339-98 (2011) by measuring tensile loading required to pull bonded veneer using Instron machine (Model 5565, Instron, MA, USA) equipped with 5 kN load cell. Tensile strength data was collected using Bluhill 3.0 software (Instron, MA, USA). Wet adhesion strength (WAS) and soaked adhesion strength (SAS) was measured according to the ASTM standard method D1151-00 (2013) 59 using instron tensile loading. WAS values were measured after submerging bonded veneer samples for 48 h in water (23 °C) where SAS was measured after reconditioning submerged veneer samples for seven days at 23 °C and 50% relative humidity in a controlled environment chamber (ETS 5518, Glenside, PA, USA). Minimum of four bonded veneer samples per formulation were used in testing strength (DAS, WAS, SAS). All samples were clamped to Instron with a 35 mm gauge length and tested at 10 mm/m cross head speed.</p><p>X-ray Photoelectron spectroscopy (XPS). GO samples were characterized using X-ray photoelectron spectroscopy (XPS) for their elemental composition, carbon/oxygen (C/O) ratio and changes in the functional Binding energy of neutral carbon C1s spectra was adjusted to 284.5 eV as a reference. Oxidation time dependent changes in surface functional groups were characterized by curve fitting of high-resolution C1s spectra assuming a Shirley background and 70%/30% Gaussian/Lorentzian distribution shape. Four peaks were fitted for all other GO samples while five peaks were used in GO-A sample with a lower oxidation time.</p><!><p>X-ray diffraction (XRD) of GO and CPA-GO samples were performed using Rigaku Ultima IV powder diffractometer (Rigaku Co. Japan). Cu-Kα radiation (0.154 nm) was used to collect angle data (2ϴ) from 5 to 50 degrees. Interlayer spacing of graphite oxide was calculated using Bragg's equation 60 ; sin θ = nλ/2d where, λ, d and θ represent wavelength of the radiation, spacing between diffraction lattice (interlayer space), and glancing angle (measured diffraction angle) respectively 53,61 . XRD data was analyzed using Origin 2016 software (OriginLab Corporation, MA, USA) to identify effect of oxidation time on exfoliation of GO.</p><!><p>Thermal transitions of GO and CPA-GO adhesives were studied using differential scanning calorimeter (Perkin-Elmer, Norwalk, CT, USA). DSC instrument was calibrated for temperature and heat flow using a pure indium reference sample. Sample moisture was first removed by freeze-drying followed by drying with P 2 O 5 for two weeks in a hermetically sealed desiccator. GO and hybrid adhesive samples were accurately weighed into T-Zero hermetic aluminum pans (∼6 mg each), mixed with 60 µL of 0.01 M phosphate buffer, and hermetically sealed with lids. Heat flow differential of samples were recorded against the empty reference pan under continuous nitrogen purging. All samples were equilibrated at 0 °C for 10 m and thermodynamic data was collected while heating from 0 to 250 °C at a ramping rate of 10 °C m −1 . Data was analyzed using Universal Analysis 2000 software for thermal transition changes in adhesives and GO samples (Perkin-Elmer, Norwalk, CT, USA).</p><!><p>Effect of oxidation time on GO functional groups and GO induced protein secondary structural changes in adhesive samples were characterized using Nicolet 8700 Fourier transform infrared spectrometer (Thermo Eletron Co. WI, USA). Sample moisture was removed prior to FTIR analysis by freeze-drying and further drying with P 2 O 5 in a hermetic desiccator for two weeks. Samples were mixed with potassium bromide (KBr), milled into a powdered pellet prior to FTIR analysis. IR spectra were collected in 400-4000 cm −1 range using 128 scans at a resolution of 4 cm −1 . Collected data was analyzed using Origin 2016 software (OriginLab Corporation, MA, USA) to identify changes in functional groups. Second derivative spectra were generated using Savitzky-Golay smooth function (7 points window) and used for curve fitting to identify GO induced protein structural changes.</p><!><p>Effect of GO samples on exfoliation in canola protein matrix were characterized using transmission electron microscopy (TEM). Images were collected using Philips/FPI transmission electron microscope (Model Morgagni, FEI Co, OR, USA) coupled with Getan digital camera (Getan Inc, CA, USA). Adhesive samples were diluted to 100-fold with ethanol, and a single drop was casted onto 200 mesh holey copper grid covered with carbon film. After 30 seconds of air-drying, the remaining liquid was removed and copper grid was used for collecting TEM images.</p><p>Statistical Analysis. Adhesion strength data (DAS, WAS, and SAS) was analyzed using analysis of variance (ANOVA) followed by Duncan's Multiple Range (DMR) test to identify the effects of graphite oxidation time on adhesion strength. Collected data was processed using Statistical Analysis System Software (SAS version 9.4, SAS Institute, Cary, NC). Effects of different GO samples on adhesion strength were evaluated at the 95% confidence level.</p>

Scientific Reports - Nature

DNA Translocation by Human Uracil DNA Glycosylase: Role of DNA Phosphate Charge\xe2\x80\xa0

Human DNA repair glycosylases must encounter and inspect each DNA base in the genome in order to discover damaged bases that may be present at a density of less than one in ten million normal base pairs. This remarkable example of specific molecular recognition requires a reduced dimensionality search process (facilitated diffusion) that involves both hopping and sliding along the DNA chain. Despite the widely accepted importance of facilitated diffusion in protein-DNA interactions, the molecular features of DNA that influence hopping and sliding are poorly understood. Here we explore the role of the charged DNA phosphate backbone in sliding and hopping by human uracil DNA glycosylase (hUNG), which is an exemplar that efficiently locates rare uracil bases in both dsDNA and ssDNA. Substitution of neutral methylphosphonate groups for anionic DNA phosphate groups weakened nonspecific DNA binding affinity by 0.4\xe2\x80\x930.5 kcal/mole per substitution. In contrast, sliding of hUNG between uracil sites embedded in duplex and single stranded DNA substrates persisted unabated when multiple methylphosphonate linkages were inserted between the sites. Thus a continuous phosphodiester backbone negative charge is not essential for sliding over nonspecific DNA binding sites. We consider several alternative mechanisms for these results. A model consistent with previous structural and NMR dynamic results invokes the presence of open and closed conformational states of hUNG. The open state is short-lived and has weak or nonexistent interactions with the DNA backbone that are conducive for sliding, and the populated closed state has stronger interactions with the phosphate backbone. These data suggest that the fleeting sliding form of hUNG is a distinct weakly interacting state that facilitates rapid movement along the DNA chain and resembles the transition state for DNA dissociation.

dna_translocation_by_human_uracil_dna_glycosylase:_role_of_dna_phosphate_charge\xe2\x80\xa0

<!>Protein and Oligonucleotide Reagents<!>Experimental conditions<!>Synthesis of oligonucleotides containing methylphosphonate linkages<!>Determination of DNA Dissociation Constants by Fluorescence Anisotropy<!>Intramolecular Site Transfer Assay<!>Analysis of the Site Transfer Data<!>Approach<!>Calculating Site Transfer Probabilities<!>Effects of Neutral Methylphosphonate (M) Substitutions on Nonspecific DNA Binding<!>Sliding of hUNG Does Not Require a Continuous Polyanion DNA Strand<!>hUNG site transfer using physiological ion concentrations<!>Different Effects of M Substitution on Nonspecific DNA Binding, DNA Translocation and Uracil Excision<!>Boundary Estimates for 1D Translocation on Duplex DNA<!>Search and Recognition in the Cell Nucleus

<p>The integrity of the information content of genomic DNA depends on efficient and accurate repair of damaged DNA bases. In many cases, this task is initiated by base excision repair DNA glycosylases, which locate and cleave the glycosidic bond of rare mutagenic bases in DNA (1, 2). Unlike transcription factors or other DNA binding proteins, these unique repair glycosylases must rapidly encounter and inspect each base in the genome in the process of efficiently locating their damage targets. This unique search requirement, which is driven by the evolutionary necessity to patrol the genome, places stringent restraints on the thermodynamic and kinetic aspects of the enzyme-nucleic acid interaction that almost certainly differ from typical DNA binding proteins. If the glycosylase interacts too strongly with nonspecific DNA, then it spends too much time at non-target sites, if it interacts too weakly or moves too fast, then its residence time is not long enough to allow detection of DNA damage when it is encountered. These properties of an efficient damage search are one example of what has been called the "search-speed/stability" paradox (3, 4).</p><p>To resolve the paradox, DNA glycosylases have harnessed the most favorable mechanistic features of two distinct modes of facilitated diffusion: DNA hopping and sliding (2, 3, 5, 6). Frequent dissociation from the DNA chain most often results in reassociation at a nearby DNA segment (hopping), keeping the enzyme from wasting time unproductively searching regions where there is no DNA and allowing it to bypass bound proteins (7, 8). Once the enzyme has encountered a new DNA segment, it then has an opportunity to remain in contact with the chain and move along it in a one-dimensional sliding mode (3, 5, 6). An upper limit on the length of DNA over which sliding can occur is determined by the residence time of the enzyme on nonspecific DNA and the 1D diffusion constant (6). The importance of sliding, even over short segments of the DNA chain, is that the enzyme remains in contact with its substrate, thereby expanding the number of bases that can be inspected during each binding event. These two general modes of the search have been observed (or inferred) for many DNA glycosylases and other site-specific DNA binding proteins (7–20).</p><p>Although the fundamental importance of hopping and sliding in the damage search is well appreciated, a quantitative mechanistic understanding of the molecular features of the DNA chain that influence an enzyme's ability to hop and slide are poorly understood. In this regard, it is widely believed that the polyanion character of the DNA phosphate backbone provides an important nonspecific electrostatic handle allowing engagement of positively charged side chains on the enzyme. Such interactions may play a role in both hopping and sliding along nonspecific DNA, but also in other steps of the reaction such as specific recognition, making it challenging to sort out these individual effects (21–23). Specifically, electrostatic tracking along the phosphate backbone is often invoked as the primary translocation mode for DNA sliding, but a direct test of this mechanism has been absent. Here we investigate the role of charged DNA phosphate groups in the ability of human uracil DNA glycosylase (hUNG) to hop and slide along DNA during its search for uracil bases. The results show that a continuous backbone charge is not required for hUNG to track efficiently along a DNA strand, and that the transient sliding state has features that resemble the transition state for DNA dissociation.</p><!><p>hUNG was purified as previously described (9). Protein concentrations were determined by absorbance measurements at 280 nm using an extinction coefficient of 33.68 mM−1 cm−1. Oligonucleotides except for those containing methylphosphonate linkages were ordered from Integrated DNA Technologies (www.IDTDNA.com) in the crude desalted form and purified by denaturing PAGE. All oligonucleotide sequences are reported in the Supplemental Methods and concentrations were determined by UV absorption at 260 nm using extinction coefficients calculated from nearest neighbor parameters.</p><!><p>All measurements in this paper and the accompanying paper (insert reference upon publication) were made at 37 °C in a standard reaction buffer consisting of 20 mM HEPES pH 7.5, 0.002% Brij 35 detergeant (Sigma Aldrich), 3 mM EDTA (added from a 0.5 M pH 8.0 stock), and 1 mM DTT unless otherwise noted.</p><!><p>Oligonucleotides containing methylphosphonate linkages were synthesized using standard phosphoramidite synthesis procedures on an Applied Biosystems 390 DNA/RNA synthesizer. Nucleoside phosphoramidites and methylphosphonamidites were purchased from Glenn Research (Sterling, VA). After synthesis, the DNA was deprotected and cleaved from the silica support by the addition of 0.5 mL 45:45:10 acetonitrile/ethanol/ammonium hydroxide and allowed to incubate at room temperature for 30 minutes. 0.5 mL of ethylenediamine was then added and the DNA containing solution was allowed to sit overnight at room temperature. The DNA containing solution was separated from the silica support and dried under vacuum. After resuspension in 25 mM Tris-HCl pH 7.5 (Buffer A) the DNA was then purified from the failure products by HPLC by injection onto a Dionex™ DNA Pac anion exchange column and eluted with a linear gradient from 10% Buffer A to 90% Buffer B (25 mM Tris-HCl pH 7.5, 1M NaCl).</p><p>90mer oligonucleotide substrates used in the site transfer assays containing methylphosphonate linkages were first synthesized as smaller precursors and then a 3-piece ligation was performed to create the final product (Supplemental Fig. S1). Sequences of the precursor oligonucleotides are listed in the Supplemental Methods. For ligation, piece 1 (1.5 nanomoles) and piece 2 containing methylphosphonate linkages (2 nanomoles) were first phosphorylated at the 5′ end by incubation with T4 PNK (New England Biolabs) at 37 °C in a single reaction mixture (~200 μL volume in 1× DNA ligase buffer, New England Biolabs™). After inactivation of T4 PNK, Piece 3 (2 nmoles) and the Splint (2 nanomoles) were then added to the reaction mix and hybridized by heating to 95 °C for 5 minutes and allowed to cool slowly to room temperature by placing the heat block on the bench top. Fresh ATP was then added to 1 mM final concentration along with T4 DNA ligase (New England Biolabs) and the reaction mixture was incubated at 37 °C overnight. The reaction was then mixed with 50% formamide (final concentration) and the ligated product was purified by denaturing PAGE (Supplemental Fig. S1).</p><!><p>Binding of hUNG to non-specific DNA was determined using fluorescence anisotropy in a Spex Fluormax 3 fluorimeter at 37 °C. Concentrated hUNG in the standard reaction buffer containing 50 nM labeled DNA was titrated into a cuvette containing 50 nM labeled DNA in reaction buffer in order to avoid dilution of the DNA during the titration. After each addition the cuvette was placed in the fluorimeter and allowed to equilibrate for 2 minutes as the reading was found to stabilize after 60–90 seconds. For dissociation constants greater than 5 μM, KD values were determined by diluting a concentrated solution of hUNG in reaction buffer and 50 nM labeled DNA with a solution of labeled DNA only. The forward titration was found to overlay titrations performed by dilution indicating that anisotropy values were determined at equilibrium. Data were then fitted to a single site binding isotherm (anisotropy = Bmax × [hUNG]free/(KD + [hUNG]free) + Bmin), where Bmax and Bmin are the maximal and minimum anisotropies, and it was assumed that the free DNA concentration equals the total (which is a valid assumption given that the KD >> [DNA]total).</p><!><p>Site transfer measurements were performed identically as before (9, 11) with some modifications in the steps after reaction quenching to account for substrate and buffer differences. The DNA concentration in all site transfer measurements was 40 nM and the hUNG concentration ranged from 10 – 20 pM under the standard reaction conditions, and for the data presented in Fig. 6 from 300 pM to 1.5 nM.</p><p>For methylphosphonate containing duplexes (S5M and S6M), 30 picomoles of the top and bottom DNA strands were 5′-end labeled with 33P by incubation with T4 polynucleotide kinase and [γ33P] ATP in separate reactions. The reactions were then mixed and the strands hybridized by heating to 95 °C for 10 minutes in a dry heat block followed by slow cooling to room temperature by placing the heat block on the bench top. The hybridized DNA was then separated from the unicorporated [γ33P] ATP by gel filtration using P30 resin (BioRad™) and then desalted using P6 resin (BioRad™). Samples obtained before and after gel filtration were analyzed by native gel electrophoresis, where percent recovery was calculated from imaging of the band densities, and completeness of hybridization was confirmed. In general, the percent recovery was at least 80%. After reaction with hUNG and quenching by uracil DNA glycosylase inhibitor protein (UGI, New England Biolabs™) each individual reaction was treated with the nicking enzyme Nt.BbvCI and APE1 endonuclease as previously described (9, 11) resulting in discrete double-stranded fragments corresponding to the hUNG reaction products. Each sample was then separated by electrophoresis on a 0.5 millimeter thick 10% native gel (19:1 bis:acrylamide) run in 1× TBE buffer at 20 Watts in a model S2 sequencing gel for 1 hour and 40 minutes without pre-running the gel.</p><p>For S6Mss the 5′ and 3′ ends were labeled by incubation with [γ32P] ATP and 3′-deoxyadenosine 5′-triphosphate (cordycepin 5'-triphosphate)-[α-32P] using polynucleotide kinase and terminal transferase (New England Biolabs), respectively. Similarly as above for the duplex substrates, after radiolabeling the unincorporated nucleotides and excess salts were removed by gel filtration using P30 and P6 resins (Biorad™). After reaction with hUNG and quenching, the resulting abasic sites were cleaved by the addition of 0.25 M ethylenediamine pH 8.0 (final concentration) followed by immediate heating to 95 °C for 5 minutes. Formamide containing both xylene cyanol and bromphenol blue was then added to 65% final concentration and the samples were loaded onto a 10% denaturing gel (19:1 bis:acrylamide).</p><p>For the duplex substrate S5 under physiological salt conditions (140 mM potassium glutamate, 200 μM MgSO4, 10 mM Na-HEPES, pH 7.5) the uracil containing strand was first labeled with 32P at the 5′ and 3′ ends as described for S6Mss above. The labeled strand was then hybridized to the complementary strand and the unincorporated radiolabel was removed using P30 resin (Biorad™). Forty nanomolar of the duplex substrate was then reacted with hUNG and quenched at various time points using UGI as described above. To each aliquot, 3 μl of 0.25 M ethylenediamine pH 8.0 was added and the reaction was immediately heated to 95 °C for 5 minutes to cleave the DNA at the abasic sites. Formamide gel loading buffer was then added to 65% final concentration and the samples were heated at 95 °C for an additional 3 minutes. The samples were immediately loaded onto a pre-heated 10% (19:1 bis:acrylamide) denaturing gel in order to fully denature any residual structure.</p><!><p>All gels were exposed to a storage phosphor screen and digitized using a phosphorimager. For each reaction time course, product band densities were quantified in QuantityOne™ using the box method. More details concerning the data analysis are presented in the Results. All errors presented in the text are standard deviations derived from at least three independent measurements.</p><!><p>A method was recently described that allows measurement of the probability (Ptrans) that hUNG will successfully transfer between two uracil sites embedded in a single DNA chain separated by a known distance using a sliding or hopping pathway (Fig. 1a) (9). This is the first approach that allows dissection of the total transfer probability into the individual contributions from hopping (Phop) and sliding (Pslide), where Ptrans = Phop + Pslide (Fig. 1).</p><p>The method requires quantitative site transfer probability measurements (see below) in the absence and presence of a small molecule trap of the enzyme. Inclusion of the trap (the free uracil base) serves to capture all enzyme molecules that have dissociated from the DNA during the process of transferring between the two uracil sites (i.e. the enzyme molecules that have hopped off the DNA). The trap has no effect on enzyme transfers that follow the sliding pathway because the binding site for the trap is blocked when hUNG is bound to nonspecific DNA. Separation of the two pathways (Phop and Pslide) is possible because at zero concentration of trap transfer can occur by both hopping and sliding, but as the trap concentration increases, the hopping contribution diminishes in a hyperbolic fashion, ultimately approaching a limiting asymptote equal to Pslide (long dashed line, Fig. 1b). If the site spacing is large enough, no enzyme molecules will reach the second site without departing the DNA at least once, and transfer will be entirely ablated at high concentrations of trap (short dashed line, Fig. 1b). Conversely, at short site spacings all transfers may occur by sliding and therefore will be impervious to the trap (solid line, Fig. 1b).</p><p>The reader is referred to reference (9) for a detailed description of the method including control experiments that establish its utility for hUNG. The experimental observations that support the conclusion that uracil serves as a trap of a dissociated state of hUNG without disrupting DNA sliding are: (i) Two pathways for transfer between substrate sites are observed (uracil insensitive and sensitive). (ii) The uracil concentration dependence of Ptrans follows the expected hyperbolic kinetic behavior (Fig. 1b), including the non-zero plateau value at short site spacings and high uracil concentrations, as would be expected for concurrent hopping and sliding. (iii) The site spacing dependences of Pslide and Phop are consistent with those expected for hopping and sliding pathways. That is, the probability for hopping (uracil sensitive) follows a 1/r dependence on site spacing, while the probability for sliding (uracil insensitive) shows a bp2 dependence on site spacing. (iii) Transfer was completely eliminated at high uracil concentrations when the substrate sites were positioned on opposite DNA strands where an obligate dissociation/reassociation step is required for transfer. This was observed even though the opposite strand sites were closer in space than when positioned on the same strand. Additionally, at site spacings exceeding the sliding length, identical values of Ptrans were previously observed for sites positioned on the same and opposite strands, consistent with hopping (11). (iv) The hopping pathway was highly sensitive to increases in ionic strength while the sliding pathway was not (9). (v) High concentrations of uracil have no effect on the dissociation constant for nonspecific binding of UNG to DNA, but uracil blocks the catalytic activity of the enzyme (9). These observations are consistent with the trap having no effect on sliding and acting solely by trapping the active site of the dissociated enzyme.</p><!><p>To determine the probabilities for facilitated site transfer by hUNG, we use an initial-rate, steady-state assay that quantifies the fraction of enzyme molecules that excise one uracil site (primary excision events) and then successfully transfer and excise the other uracil in the same DNA molecule (secondary excision events) (Fig. 2a) (9, 24, 25). Primary or secondary uracil excision events will lead to discrete fragments of the double end-labeled DNA which may be resolved by polyacrylamide gel electrophoresis after post-reaction sample processing (see Materials and Methods) (Fig. 2b). If only primary excision events occur at site 1 or 2, the DNA fragments A + BC or AB + C will be produced in equal amounts with apparent velocities v1 = v2 if each site reacts identically. However, if intramolecular transfer occurs, the larger AB and BC fragments will be efficiently converted into the smaller fragments A and C (as well as the unobserved B fragment) with velocities v2→1 (reflecting 2→1 transfers) and v1→2 (reflecting 1→2 transfers). It is worth noting that the initial rates for formation of fragments A and C depend on both primary and secondary events, and therefore, v2→1 and v1→2 are not necessarily equivalent to the initial rates of appearance of fragments A and C. In general, the qualitative hallmark of intramolecular transfer is the production of greater amounts of the secondary excision products A and C at the expense of the single excision products AB and BC (20, 24).</p><p>The overall transfer probability (Ptrans) may be precisely calculated from the time dependent fragment concentrations. These concentrations are inserted into eq 1, which requires extrapolation to zero time to obtain the true transfer probability (24). The basis for this equation can be easily understood: the denominator counts all excision events and the numerator counts only secondary excision events. (1)Ptransobs=[A]+[C]−[AB]−[BC][A]+[C]+[AB]+[BC] Thus, the ratio reveals the fraction of all excision events that lead to successful transfers to the second site. (The term −[AB] − [BC] in the numerator corrects for the fact that fragments A and C can result from both primary excision events ABC → A + BC and ABC → AB + C, or secondary excision events AB → A + B and BC → B + C.) We find that this is a useful and straightforward analytical approach when there is no site preference for excision of the individual sites or no directional bias to transfer (9, 11). In the following paper we use a modified analytical approach that is useful when a site excision or transfer bias is present (insert reference upon publication).</p><!><p>Previous work suggested that a continuous DNA phosphate backbone was necessary and sufficient for DNA sliding because (i) hUNG sliding only occurred between uracil sites that were positioned on the same strand in duplex DNA, and (ii) sliding between uracil sites was observed on ssDNA substrates (reference (9) and accompanying paper) (insert reference upon publication). To begin to explore the role of phosphate backbone charge on DNA sliding, the effects of neutral M substitutions on nonspecific DNA binding were first determined using fluorescence anisotropy measurements (Fig. 3). Three 5′ fluorescein-labeled DNA constructs were investigated that contained mixed diastereomer M linkages at one or more positions, which were then compared with the corresponding all phosphodiester versions (Fig. 3a). The M-DNA constructs shown in Fig. 3a were chosen to match the intervening DNA strand segments used in the site transfer measurements described below (NS5M, NS6M), and also to evaluate the effect of removing as many as four phosphate charges (NS10M). Using single DNA strands as models for the intervening sequences in duplex DNA is justified because (i) structural studies indicate that hUNG primarily interacts with the phosphate backbone on the single strand of DNA that connects the two target bases (26), (ii) hUNG is known to slide along a single strand in duplex DNA (reference (9) and accompanying paper) (insert reference upon publication), and (iii) M substitutions in a single strand of duplex DNA still allow binding to the other strand and would complicate the interpretation of equilibrium dissociation constants.</p><p>Binding measurements revealed that single or multiple M substitutions in single stranded DNA decreased the binding affinity (Kd) of hUNG compared to matched all phosphodiester controls (Fig. 3b & c). The 2 fold-effect of a single M substitution in the center of a 5 mer strand (NS5M and NS5, ΔΔG = 0.42 kcal/mol) was increased 5-fold in the 6 mer strand containing two M substitutions (NS6M, ΔΔG = 1.01 kcal/mol, or ~0.5 kcal/mol per M linkage). Similarly, the 10mer strand containing four methylphosphonates (NS10M) showed a 14-fold deficit in binding (ΔΔG = 1.52 kcal/mol, or ~0.4 kcal/mol per M linkage). Qualitatively, these results demonstrate that the removal of phosphate charge has a damaging effect on nonspecific DNA binding, and raise the expectation that if site transfer by hUNG requires interaction with the phosphate backbone, the removal of these interactions should diminish successful sliding between two uracil sites. We defer to the Discussion possible further physical interpretations of the effects of M substitution on nonspecific DNA binding.</p><!><p>To address the question of whether a continuous backbone charge is a requirement for sliding along duplex DNA, we synthesized two 90mer M-substituted DNA substrates containing M linkages on the DNA strand connecting the two uracils (Fig. 4a). The intervening nonspecific DNA strand that connects the two uracil sites in these substrates corresponds exactly to the sequences of NS5M or NS6M described above. Importantly, the substrates in Fig. 4a were constructed with uracil spacings of five and six bp (S5M, S6M) because it has been previously shown that 40% and 20% of the hUNG site transfers occur by a sliding pathway at these spacings when uninterrupted phosphodiester linkages are present (9). In addition, we took care to position the M linkages far enough away from the uracil sites such that the footprint of the specific hUNG catalytic complex does not overlap these positions. This aspect of the substrate design is critical because it has been shown that specific M substitutions within two nucleotides of the uracil site can have a large damaging effect on catalysis (ΔΔG up to 10 kcal/mole) (22, 23).</p><p>We measured Ptrans, Pslide, and Phop for S5M and S6M, and compared these values to those previously measured for the analogous phosphodiester substrates S5 and S6 (Fig. 4) (9). As described above, measurement of Ptrans was obtained in the absence of the uracil trap, and measurements of Pslide were obtained in the presence of 10 and 15 mM trap (the two values were identical, confirming that the transfer measurements were in the plateau region depicted in Fig. 1b). For all of the data, Pslide is reported as the average value obtained at 10 and 15 mM uracil concentration (n = 3 for each concentration) and Phop is the difference between Ptrans and Pslide. Representative transfer data for S5M in the absence and presence of the uracil trap shows a significant degree of intramolecular transfer as revealed by excess A and C fragments (Fig. 4b). Addition of 10 or 15 mM uracil trap leads to a reduction in the successful transfer events, but transfer is not entirely ablated indicating that a sliding pathway is present (Fig. 4b). Extrapolation to zero time using eq 1 shows that Ptrans = 0.61 ± 0.08 and Pslide = 0.35 ± 0.07 for S5M, which are values indistinguishable from those previously reported for S5 (Fig. 4c and d). In addition, there was no difference between the transfer parameters of S6M containing two intervening M linkages and the all phosphodiester analog S6 (Fig. 4d). The transfer parameters for these phosphodiester and M-substituted substrates are summarized in Fig. 4d from which we conclude that ablating as many as one-third of the intervening negative charges connecting the two uracil sites has no measurable effect on Ptrans, Pslide, or Phop.</p><p>We next examined M linkages in the context of transfer of hUNG on single stranded DNA using a ssDNA substrate that contained two M linkages analogous to the duplex S6M (S6Mss). S6Mss was designed to have minimal secondary structure and to have no more than two adjacent adjacent Watson-Crick pairings to eliminate potential secondary structure (Supplementary Fig. S2). The data for S6Mss is summarized in Fig. 5 and show that M linkages have no effect on site transfer compared to the all phosphodiester ssDNA substrate S5ss. Comparing S6ss to S5ss is justified because Pslide for ssDNA has a flat dependence with site spacing between 5 and 10 ntds (9), [see also accompanying paper (insert reference upon publication)].</p><p>The absence of a requirement for a continuous phosphate charge in sliding or hopping between two closely spaced sites in dsDNA or ssDNA is striking in comparison with the damaging effect of M substitutions on the Km for a uracil-containing substrate (ΔΔG = ~1–4 kcal/mole depending on position) (22), the large and highly stereospecific 5–10 kcal/mol effects of single M substitutions on the activation barriers for uracil excision in single strand or dsDNA (22, 23), and the significant effects of M substitution on nonspecific DNA binding reported above. These differences suggest that the rapid kinetic process of nonspecific sliding does not involve the same phosphate backbone interactions observed in crystal structures of nonspecific and specific complexes between hUNG and DNA (26, 27).</p><!><p>Site transfer measurements published previously have been studied under conditions where [NaCl] ranged from 22 to 72 mM. In this range, sliding was found to be insensitive to salt, but hopping was fully ablated at salt concentrations exceeding 42 mM (9). However, it is desirable to evaluate these parameters under conditions that more closely mimic the intracellular ion concentrations. For this purpose we use a buffer consisting of 140 mM potassium glutamate, 10 mM Na-HEPES pH 7.5 and 200 μM MgSO4.</p><p>Measurements of equilibrium nonspecific DNA binding and hUNG catalytic activity under these conditions were first made. Compared to low ionic strength conditions, the equilibrium dissociation constant for nonspecific binding was increased ~100 fold (0.82 ± 0.26 μM to 85 ± 19 μM) (Supplemental Fig. S3a). Similarly, the catalytic activity of hUNG for a 90mer DNA duplex substrate containing a single uracil site was reduced ~300 fold under these conditions with a measured kcat/Km = 1 × 106 M−1s−1 (Supplemental Fig. S3b). This value may be compared with the previously measured kcat/Km = 3.4 × 108 M−1s−1 for the identical substrate at low ionic strength. We note that accurate determinations of kcat and Km were not possible under physiological salt conditions, but a good estimate of kcat/Km could be obtained from the linear increase in rate using 0 – 4 μM substrate (Supplemental Fig. S3b). Most of the effect on activity is assignable to Km because the maximal observed rates under physiological salt concentrations approached the kcat value of 5 s−1 under low salt conditions (9).</p><p>Site transfer measurements were then made using the physiological buffer with duplex substrate S5, which contains two uracils positioned 5 bp apart. At this spacing, intramolecular site transfer was still observed (Ptrans = 0.20 ± 0.04). The transfer probability was similar at high uracil (Pslide = 0.16 ± 0.06), indicating that within error of the measurements all transfers occur by sliding, although Pslide was reduced compared to the value at low ionic strength (Pslide = 0.37 ± 0.06). This result matches the previous finding that the hopping pathway is eliminated with a salt concentration of 42 mM while sliding persists unabated at the same concentration (9). Thus despite the 100-fold decrease in nonspecific binding affinity under mock physiological conditions, short-range sliding of hUNG on DNA can still occur. These findings imply that the binding interface of the sliding form of hUNG is immune to invasion by salt ions. This observation is consistent with the inability of the uracil trap to access the active site of hUNG during the process of sliding.</p><!><p>Comparison of the divergent effects of methylphosphonate (M) substitutions on nonspecific DNA binding, translocation between uracil sites, and catalysis by UNG leads to the conclusion that the requirements for a charged phosphate group in these processes are very different. Previous studies where stereospecific M substitutions were made in single stranded and duplex substrates of UNG have revealed that substitutions at the +1, −1, and −2 phosphates surrounding the uracil site (5′p+1Up−1Np−2N3′) result in stereospecific 101−108 fold damaging effects on catalysis (22, 23). Most of these large effects were attributed to the beneficial energetic effects of the anionic phosphate groups towards stabilization of the glycosyl cation transition state. The previously measured damaging effects of single M substitutions on the ground state Michaelis complex were not stereospecific and were less than the effects on the activation barrier (i.e. Km effects were in the range 10–100 fold) (22, 23). The even smaller damaging effect of a single M substitution on nonspecific DNA binding (~2-fold, Fig. 3c), would suggest that the nonspecific complex differs in its interactions with the phosphate backbone as compared to the Michaelis complex. Despite the apparent differences between these complexes revealed by M substitution, the high resolution crystal structures of the specific and nonspecific hUNG-DNA complexes show that the same phosphate groups form hydrogen bonds with neutral serine or histidine side chains, or backbone amide groups, and that there are few cationic groups ≤ 3.3 Å from phosphate oxygens (Fig. 7) (26). Thus, taken together, these energetic measurements suggest that as the enzyme moves forward along the reaction coordinate it forms increasingly important electrostatic interactions with the phosphate backbone. The interesting exception, as shown in this work, is the transient state for DNA sliding which apparently has no requirement for an uninterrupted charged phosphate chain.</p><p>What is the physical basis for the different effects of M substitution on nonspecific binding and DNA translocation? Although M substitution has only a minor effect on B DNA structural parameters (28–30), this substitution can change duplex hydration patterns (29, 31) and quite possibly reduce the ion count in the cloud loosely associated with the DNA (32, 33). Thus, these indirect outcomes of M substitution can make unique mechanistic interpretations of the observed effects elusive. In the present case, the small damaging effects of M substitution on nonspecific DNA binding (0.5 kcal/mol per substitution) could reflect direct disruption of the backbone hydrogen bonding in the complex (Fig. 7), or a reduction in minor groove hydration waters or ions around the neutral patch (29, 31). If these indirect effects prevail, then the reduction in binding affinity upon M substitution could arise from a smaller favorable entropy change resulting from fewer water molecules or ions being released to bulk solution upon complexation (34). Although such indirect effects might provide viable explanations for the reduced binding affinity of hUNG for M substituted DNA, they do not reasonably account for the absence of an effect of M substitution on DNA sliding because sliding occurs in a kinetic event after the ion cloud has been dispersed.</p><p>The absence of a functional requirement for a continuous negatively charged backbone in site translocation strongly suggests that the sliding form of hUNG cannot simply involve translocation of the crystallographic conformation of hUNG along DNA (26, 27, 35, 36). Rather, the data would suggest that the sliding conformation of hUNG is an open state that interacts loosely with the DNA backbone, with perhaps intervening water molecules (but not solute ions) that would serve to shield charge. This view of a loose, transiently bound conformation is consistent with CPMG NMR dynamic measurements indicating that UNG oscillates between an open and closed form on the millisecond time scale when bound to nonspecific DNA (37). The open form was proposed to function in stochastic sliding along the DNA chain, and the closed form resembles the crystallographic conformation, allowing hUNG to interrogate the integrity of base pairs. Indeed, a two state conformational change has been postulated as a general mechanism for site specific DNA binding proteins to overcome the "search-speed/stability" paradox (3, 4, 38), and recent structural evidence obtained with other DNA glycosylases suggests evidence for more than one conformation involved in search and recognition by these enzymes (12, 13). The findings reported here provide a first glimpse at the electrostatic properties of this transient state of hUNG.</p><!><p>Employing the measured values for the average lifetime of hUNG on nonspecific DNA (τbind = 3 ms), and its mean sliding length (Lslide = 4.2 bp), we previously used eq 2 to estimate the 1-dimensional diffusion constant (D1) of hUNG on nonspecific duplex DNA (D1 = 6 × 103 bp2 s−1 = 7 × 10−4 μm2 s−1) (9). This value was several orders of magnitude below the theoretical upper limit (~107 bp2 s−1 or ~1 μm2 s−1) (5, 39, 40). (2)D1=Lslide2τbind This calculation assumes that the entire bound lifetime of hUNG is available for DNA sliding. However the current data, which requires the presence of at least two nonspecific states of hUNG, also requires that only a fraction of the bound lifetime is available for sliding (i.e the time spent in the open state). An estimated lower limit for the population of the transient sliding state may be estimated based on the sensitivity of the NMR-relaxation dispersion dynamic measurements previously performed on the hUNG-nonspecific DNA complex (37). This methodology would not be able to detect a transient sliding state with a population of less than ~5% of the total, setting a lower limit for the time spent sliding of 0.05 × 3 ms ≥ 0.15 ms. It is difficult to set an upper boundary, but it must be considerably less than τbind = 3 ms. Using eq 2 and this lower limit for the sliding time, we calculate an upper limit for D1 ≤ 105 bp2 s−1. Thus, the previous and current estimates place D1 in the range ~104 to 105 bp2 s−1. Given this refined two-state view of sliding by hUNG, we suggest that the sliding state resembles the transition state for DNA dissociation. However, instead of falling off the DNA chain the enzyme closes on the DNA and completes a sliding transfer. This viewpoint of short range sliding as an aborted transition state for DNA dissociation differs considerably from other characterizations of protein sliding whereby the protein moves isoenergetically along the surface of the DNA (41–43). These aspects of the hUNG search mechanism are depicted in the model presented in Fig. 8.</p><!><p>Under optimal low salt reaction conditions hUNG is an evolutionarily optimized enzyme with a catalytic power that vastly exceeds any other DNA glycosylase (1). However, under conditions that more closely mimic the intracellular ionic environment, its ability to bind nonspecific DNA is severely hampered by a factor of around 100-fold, which exerts a profound effect on the mechanism of site location. One major ramification of the ionic strength effect on nonspecific DNA binding is that hopping becomes a less productive pathway. Each time hUNG dissociates from the DNA chain under high salt conditions, there will be a reduced probability that a reassociation attempt will result in a productive binding event. Thus many more attempts will have to be made, which will result in an increase in the search time contributed by hopping. In contrast, DNA sliding is largely refractory to increases in ionic strength, and the search time resulting from sliding will remain largely unchanged. This important property of sliding, even over the short ranges traveled by hUNG, is essential for increasing coverage of the genome and for the ultimate detection of damage (Fig. 8). An additional consideration within the nuclear environment is the effect of crowding, as well as excluded volume effects (44, 45). Such factors could favor compact sliding states and also increase the contribution of hopping because of the high local concentration of DNA chains. Consideration of such effects requires improved experimental models for search and recognition.</p>

PubMed Author Manuscript

Nucleophilic Water Capture or Proton Loss: Single Amino Acid Switch Converts δ‐Cadinene Synthase into Germacradien‐4‐ol Synthase

Abstractδ‐Cadinene synthase is a sesquiterpene cyclase that utilises the universal achiral precursor farnesyl diphosphate (FDP) to generate predominantly the bicyclic sesquiterpene δ‐cadinene and about 2 % germacradien‐4‐ol, which is also generated from FDP by the cyclase germacradien‐4‐ol synthase. Herein, the mechanism by which sesquiterpene synthases discriminate between deprotonation and reaction with a nucleophilic water molecule was investigated by site‐directed mutagenesis of δ‐cadinene synthase. If W279 in δ‐cadinene synthase was replaced with various smaller amino acids, the ratio of alcohol versus hydrocarbon product was directly proportional to the van der Waals volume of the amino acid side chain. DCS‐W279A is a catalytically highly efficient germacradien‐4‐ol synthase (k cat/K M=1.4×10−3 μm s−1) that produces predominantly germacradien‐4‐ol in addition to 11 % δ‐cadinene. Water capture is not achieved through strategic positioning of a water molecule in the active site, but through a coordinated series of loop movements that allow bulk water access to the final carbocation in the active site prior to product release.

nucleophilic_water_capture_or_proton_loss:_single_amino_acid_switch_converts_δ‐cadinene_synthase_int

<!>Introduction<!><!>Introduction<!><!>Results and Discussion<!><!>Results and Discussion<!><!>Results and Discussion<!><!>Results and Discussion<!>Experimental Section<!>

<p>M. Loizzi, V. González, D. J. Miller, R. K. Allemann, ChemBioChem 2018, 19, 100.</p><!><p>Present in all kingdoms of life, terpene synthases catalyse highly complex biosynthetic reactions, in which achiral, linear isoprenyl diphosphates are converted into complex often cyclic or polycyclic structures.1, 2 In most cases, these carbocationic reaction cascades are characterised by high stereo‐ and regioselectivity and involve changes in the hybridisation of up to half of the carbon atoms. The final carbocation can either lose a proton to generate a hydrocarbon product or react with water to produce a terpene alcohol.1 Subsequent biosynthetic reaction steps convert the products generated by terpene synthases into tens of thousands of terpenoids that act, among other things, as pigments; phytoalexins; semiochemicals or in primary metabolism as sterols, carotenoids and ubiquinones. Terpenoids have many important applications, for instance, as drugs, fragrances, pesticides, fuels or as food additives.1, 2</p><p>Examination of the structures of terpene synthases and the mechanisms of the catalysed reactions has revealed common structural features and distinct phases of activity.1, 2, 3, 4, 5, 6 Class I terpene synthases share a predominantly α‐helical fold with an active site lined with mostly hydrophobic and aromatic amino acid residues; they contain two conserved Mg2+‐binding motifs (DDXXD and NSE/DTE) on opposite sides of the active site.2 They provide a three‐dimensional template to bind the flexible substrate and chaperone the carbocationic intermediates along distinct reaction paths. Class I terpene synthases initiate the chemical reaction by catalysing the cleavage of the carbon–oxygen bond of the substrate to generate a tightly bound diphosphate (PPi)–carbocation pair.2, 7 Single‐crystal X‐ray structures of several sesquiterpene synthases complexed with (E,E)‐farnesyl diphosphate (FDP, 1), several analogues of 1, Mg2+ and PPi, together with molecular dynamics simulations, have provided strong support that loop movements and conformational changes are required to form the closed form of the enzyme, in which substrate 1 is in a reaction‐ready conformation.2, 3, 4, 5, 6, 7, 8 After conformational rearrangements of enzyme and substrate necessary to form the Michaelis complex, the chemical reaction occurs, with major contributions from carbocation stabilisation by the π electrons of aromatic amino acid residues, PPi carbocation interactions and general acid–base catalysis by PPi and/or the enzyme.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 The closed conformation of sesquiterpene synthases that generates hydrocarbon products also prevents access of bulk solvent, which avoids quenching of the reactive carbocationic reaction intermediates by water.1, 2, 3, 6, 7, 8, 9, 10, 11</p><p>In contrast to the wealth of knowledge available on the mechanistic details of sesquiterpene synthase catalysed reactions, in which the last cationic intermediate is deprotonated by PPi, little information is available for terpene alcohol synthases, such as epicubenol,13 hedycaryol,14 avermitilol,15 epicedrol16 and germacradien‐4‐ol synthases17 (GdolS), which generate their products through the reaction of the final carbocation with water. The mechanism by which sesquiterpene synthases discriminate between deprotonation or water capture has not been explored in detail. In enzymes that generate alcohols, deprotonation of the final cation must be prevented, and capture of the final carbocationic intermediate must be tightly controlled to prevent quenching of early cationic intermediates, whereas synthases that generate terpene alcohols must prevent deprotonation of the final carbocationic intermediate. Clearly, terpene synthases have evolved to control water access and reactivity. Tightly bound water molecules can be found in the active sites of terpene synthases in their closed conformation, even for synthases that do not generate alcohol products.2, 7, 9 This finding might suggest that these sequestered water molecules could be responsible for nucleophilic capture of the final carbocationic species.2, 14 However, a previously published investigation into the structure and mechanism of GdolS revealed that the reaction of the final carbocation most likely depended on specific loop movements of the enzyme that allowed bulk water to access the active site.18 GdolS must prevent deprotonation from C6 in intermediate 6, so as not to produce δ‐cadinene (7; Scheme 1), which in contrast is the pathway for catalysis by δ‐cadinene synthase (DCS).9</p><!><p>Catalytic mechanisms of the DCS (pathway a) and GdolS (pathway b) catalysed conversions of 1 to 7 and germacradien‐4‐ol (8).</p><!><p>In selina‐4(15),7(11)‐diene synthase from Streptomyces pristinaespiralis, a G1/2 helix break motif, combined with a diphosphate sensor–linker–effector motif that is conserved throughout bacterial sesquiterpene synthases, has been proposed to play a major role in substrate binding and active‐site closure.19 This "kink" in the G‐helix has also been noted as a potentially important catalytic feature in human squalene synthase20 and hedycaryol synthase.14 Interestingly, DCS was converted into a GdolS through saturation mutagenesis by Keasling and co‐workers.21 A re‐evaluation of that work reveals that the amino acid residues that generate this change of function are located in the G1/2 helix break motif (see below).</p><p>DCS from Gossypium arboreum produces 7 in the first committed step of the biosynthetic pathway to the phytoalexin gossypol (Scheme 1).22 Despite little sequence identity outside the conserved Mg2+‐binding motifs, DCS and GdolS share the typical structure of class I terpene synthases in the catalytic domain;9, 18 many aspects of their active‐site compositions and the respective catalytic reaction pathways from 1 to 7 or 8 are shared (Scheme 1). The co‐crystal structure of DCS, Mg2+ and the substrate analogue (2Z,6E)‐2F‐farnesyl diphosphate (2F‐FDP) revealed an unusual Mg2+‐binding motif, in which the NSE/DTE motif is replaced by a second DDXXD motif.9 Interestingly, depending upon the substrate used, DCS appeared to follow a 1,6; 1,10 or 1,11 ring closure;10 this suggested that these pathways were energetically similar and that DCS might have some inherent promiscuity, despite its high fidelity when acting on 1.10 Only the N‐terminal tail of the N‐terminal β domain of DCS is involved in catalysis and plays an important role in protecting the active site from water. Truncated proteins that lack the first 8 and 20 amino acids of the β domain produce increasing amounts of 8.23</p><p>The double‐mutant protein DCS‐N403P/L405H was shown to convert 1 into 8 (93 %) and an additional unidentified cyclic sesquiterpene alcohol.21 However, the catalytic activity of DCS‐N403P/L405H is severely compromised relative to that of the wild‐type (WT) enzyme.</p><p>In DCS‐N403P/L405H, the active site is exposed to solvent through a potential water channel created by alteration of the G‐helix residues.21 Analysis of the X‐ray structure of DCS reveals that the aromatic residues W279 and Y410 are closer to the isoprenyl chain of the substrate and on the opposite side of the active‐site contour relative to N403 and L405. W279 is on the C helix of DCS and within 7 Å of Y410, just below the G2 helix and towards the bottom of the active‐site cleft (Figure 1). These two residues are ideally placed not only to stabilise carbocationic intermediates during the formation of 7, but also to form hydrophobic interactions that may help to control the active‐site conformation of 1 and mediate active‐site closure and opening. Hence, to test the hypothesis that alteration of W279 can disrupt hydrophobic interactions with Y410 and 1 and allow increased water access to the active site, the contribution of W279 to catalysis was explored by sitedirected mutagenesis. Herein, we show that single amino acid changes can convert DCS into GdolS that produce up to 90 % 8 with high catalytic efficiency. The results suggest that W279 plays a key role in shielding the active site of DCS from solvent.</p><!><p>Left: view of the active‐site cleft of DCS, showing the bulk surface of the enzyme in blue. Mg2+ ions are depicted as silver spheres; Y410 and W279 are shown with their van der Waals radii in green and red, respectively. Right: sketch of the active site of DCS; N403 and L405 are at the hinge points of the G1/G2 helix break (magenta). Y410 is shown in green and W279 in red; helices G1, G2 and C as tan cylinders; and Mg2+ ions as silver spheres. 2F‐FDP is shown as bonds, but in this crystal structure its hydrocarbon tail did not bind within the cleft (PDB ID: 3G4F9).</p><!><p>DCS‐His6 was produced in Escherichia coli and its catalytic properties determined. Steady‐state kinetic experiments with radiolabelled [1‐3H]1 9, 10 revealed a turnover number, k cat, of 1.26×10−3 s−1; a Michaelis constant, K M, of 0.58 μm (Table 1) and a catalytic efficiency, k cat/K M, of (2.17±0.4)×10−4 s−1 μm −1, which was identical to the value previously measured for WT‐DCS with no His‐tag (k cat/K M=(3.1±0.2)×10−3 s−1 μm −1).9</p><!><p>Kinetic data and products generated from 1 with DCS and DCS‐W279 mutants.</p><p>[a] Percentage of total products.</p><!><p>Analysis of the pentane‐extractable products arising from incubations with 1 by GC‐MS showed that, in addition to 7, 11 % of 8 was produced by the His‐tagged enzyme. The products were identified by comparison with the GC retention times and mass spectra of authentic products generated through the incubation of 1 with WT‐DCS and GdolS from Streptomyces citricolor.18</p><p>To address the role of W279 during DCS catalysis, tryptophan was replaced by Glu, Gln, Asp, Leu, Met, Als and Tyr and the pentane‐extractable products generated from 1 were analysed by GC‐MS. Remarkably, DCS‐His6‐W279A produced only 11 % 7 and 81 % 8 (Table 1); a product ratio that represents an almost complete reversal relative to that measured for DCS‐His6. The values of k cat (3.12×10−3 s−1) and K M (2.23 μm) were similar to the values measured for DCS‐His6; this indicated that replacement of the hydrophobic and bulky indole group with hydrogen allowed water access to the active site to efficiently quench the intermediate (3Z,7E)‐germacryl cation (5) without loss of the catalytic efficiency. If W279 was replaced by tyrosine, the quantity of alcohol 8 formed was only slightly increased relative to that of DCS‐His6.</p><p>GC‐MS analysis of the pentane‐extractable products generated by DCS‐His6‐W279M and DCS‐His6‐W279L, in which residues with similar hydrophobicity, but with reduced van der Waals volume, replaced tryptophan,25 showed increased amounts of alcohol relative to that of DCS‐His6 (7 and 8 in approximately 2:1 ratio). These results show that the relative amounts of 7 and 8 are dependent on the volume of the side chain of residue 279. The values of K M for DCS‐His6‐W279M and DCS‐His6‐W279L were slightly increased, relative to that of the DCS‐His6 (Table 1). Smaller residues that were hydrophilic, rather than hydrophobic, were tested to examine the possibility that hydrophilic residues might form a repulsive interaction with Y410, leading to a poorly defined active site that compromised the catalytic activity. Alternatively, an increase in the mobility of the G1/G2 helix break motif might generate larger quantities of 8. Consequently, W279 was replaced with glutamine, glutamate and aspartate. When incubated with 1, DCS‐His6‐W279Q, DCS‐His6‐W279E and DCS‐His6‐W279D generated decreasing amounts of 7 and an increasing proportion of 8, with the ratio of the two products showing a near‐linear relationship between the van der Waals volume of the amino acid and alcohol production (Figure 2 and Table 1). This provides powerful evidence that the van der Waals volume of the C‐loop residue 279 is of central importance for product distribution in DCS catalysis.</p><!><p>Histogram of the percentage distributions of products 7 (▪) and 8 (▪) generated by DCS‐His6 and its mutants versus the van der Waals volume25 of residue 279 (▪).</p><!><p>These results establish an essential role for Trp279 in DCS catalysis. In the WT enzyme, residue 279 prevents water access to 5, which is also stabilised through cation–π interactions with the indole ring. Trp279, hence, facilitates ring closure to 6 and proton loss to generate 7. Replacement of Trp279 with tyrosine had only a modest effect on the outcome, with a 7 % increase in 8. However, if Trp279 was replaced with smaller, non‐aromatic residues the quantity of 8 relative to 7 increased in a manner that depended on the van der Waals volume of the residue (Figure 2), irrespective of the hydrophobicity of the amino acid in position 279. Replacement with Gln, Asp or Glu only significantly affected the product distribution and not the catalytic efficiency of the enzymes, thus suggesting that changes to the size of residue 279 might open a channel that allowed water access to the active site of DCS.21 The size of the channel appears to depend on the size of residue 279, so that small residues allow for the generation of larger amounts of 8. Inspection of the X‐ray crystal structure of DCS9 reveals the G1/G2 helix break motif first identified by Pandit et al. (Figure 1).20 This motif was missed in an earlier report in which a homology model for DCS based on epi‐aristolochene synthase was used.21 N403 and L405 sit at either end of the helix‐break motif, which suggests that the G1/G2 helix break motif is important for essential loop movements of terpene synthases, including those found in plants.19, 21 Aside from W279, N403 and L405, the active site of DCS is highly robust to site‐directed mutagenesis, in that changes to G276, I130, T407, C408, G409, L413, E455 and M523—all of which are located in or around the active site—do not lead to the generation of products other than 7.26 This is in stark contrast to many bacterial and fungal terpene synthases for which changes to the active‐site composition often lead to alternative products.3, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 Plant terpene synthases appear to possess a robust architecture that, in most cases, ensures product fidelity. There are, however, a few hot spots where mutation alters the reactivity dramatically and, in the case of DCS, these are found in regions that control the movements of loops involved in closure of the active site prior to formation of the Michaelis complex.8, 9, 19, 21 Specifically, in addition to the G1/G2 helix mutants reported previously,21 Trp279 mediates a loop movement to ensure that proton loss occurs in the final step of DCS catalysis, as opposed to allowing water to cation 5 to generate 8. Because W279 is located directly across the active site from Y410, these two residues may form a favourable hydrophobic contact with the substrate as it folds into its reactive conformation with the active site. The aromatic nature of W279 does not appear to determine product distribution because replacement with even negatively charged aspartate or glutamate, or neutral alkyl residues, such as Leu or Ala, do not alter the reaction products; they only increase the proportion of water captured in direct proportion to the van der Waals volume of the side chain. Subsequently, the diphosphate group may act as a general base for the final deprotonation step (Figure 3). If W279 is replaced with a smaller, non‐aromatic residue this process may be perturbed; C1 and C6 of 1 (Scheme 1 and Figure 3) are too far apart to facilitate the final ring closure and extra space in the active site opens a pore,21 whereby water can enter the active site and 5 is quenched (Figure 3). It is also notable that alteration of the C terminus through the addition of a hexahistidine tag led to the production of significant quantities of 8; this is consistent with a precise series of loop movements that effect closure of the active site of DCS. As mentioned above, water molecules have been observed in the crystal structures of several terpene synthases, in both open and closed conformations.2, 7, 9, 18 These water molecules, however, do not take part in reactions and simply cushion the substrate in the active site.23 It is perhaps surprising that water molecules remain tightly bound in an uncreative state, even in mutant enzymes in which the active site has been altered. In the case of DCS, we have never observed nucleophilic capture of the bicyclic cadinenyl cation (6; Scheme 1).</p><!><p>Representations of the active site of WT DCS (left)9 and DCS W279A (right) to illustrate the gap in the active site created by the disruption of the interaction between W279 and Y410 that assists in active‐site closure and formation of the catalytic active‐site contour. Water is proposed to ingress through this gap and attach at C3 of 2 to generate 8 (PDB ID: 3G4F9).</p><!><p>Terpene synthases generate many high‐value products with applications, for instance, as drugs, agrochemicals or fragrances. Understanding the intricate details of their catalytic mechanism will lead to improved methods for the production of naturally occurring terpenes39 and help the development of designer products with new or improved properties.40, 41, 42</p><!><p>Introduction of C‐terminal hexahistidine tag into DCS: The gene encoding WT‐DCS was available in a pET21d vector from previous work.9, 10 A single nucleotide deletion was required to bring the His6 coding sequence of pET21d in frame with the DCS coding sequence. A Quickchange site‐directed mutagenesis kit was used to introduce the desired deletion, according to the manufacturer's instructions. PCR primers were as follows: 5′‐GAACC AATTG CACTT GAGGA TCCGA ATTC‐3′ and 5′‐GAATT CGGAT CCTCA AGTGC AATTG GTTC‐3′. Plasmids were transformed into E. coli XL1 Blue and then purified from overnight cultures (lysogeny broth (LB) medium (10 mL) containing ampicillin (100 μg mL−1)) by using the miniprep kit, as described by the manufacturer. Deletion was confirmed by DNA sequencing.</p><p>Expression and purification: DCS‐His6 was produced in E. coli BL21(DE3) cells that harboured the cDNA for DCS‐His6 under control of the T7 promoter. E. coli BL21(DE3) cells were gently defrosted on ice before plasmid (1 μL; 60 ng μL−1) was added to the cell suspension. The resulting mixture was stored on ice (30 min), heat‐shocked in a water bath (42 °C, 30–35 s) and then returned to the ice (2 min). LB medium (1 mL) was added and the solution was incubated for 1 h at 37 °C with shaking (150 rpm). The cells were harvested by centrifugation (1 min, 3300 g), resuspended in a minimum amount of LB medium and spread on an agar plate containing ampicillin (100 μg mL−1). The plate was then incubated overnight at 37 °C. A single colony from the agar plate harbouring the transformed cells was used to inoculate LB medium (100 mL) containing ampicillin (100 μg mL−1) and the culture was incubated at 37 °C with shaking (150 rpm) overnight. The overnight culture (10 mL) was transferred to each of 6×500 mL of LB medium containing the same concentration of ampicillin as before. Cells were incubated at 37 °C with shaking (150 rpm). When an optical density (OD600) of 0.6 was reached, isopropyl β‐d‐1‐thiogalactopyranoside (IPTG) was added (0.5 mm final concentration) and the cultures were incubated for 24 h with shaking (250 rpm), at 20 °C. Cells were harvested by centrifugation at 5 °C (4200 g, 10 min). The supernatant solution was discarded and the pellets were stored at −20 °C.</p><p>Pellets were allowed to thaw at 5 °C and resuspended in cell lysis buffer (50 mL; 20 mm Tris‐Base, 5 mm β‐mercaptoethanol (βME), pH 8) and stirred gently for 1 h at 0 °C. Cells were then disrupted by sonication at 5 °C (40 % amplitude for 3 min with 5 s on/10 s off cycles) and the resulting suspension was centrifuged at 5 °C (17 000 g, 30 min). SDS‐PAGE analysis showed that protein was in the soluble fraction and the pellets were discarded. The supernatant solution was then loaded onto a 2 cm Amintra nitrilotriacetic acid (NTA) Ni2+ column (Expedeon, Over, UK) and eluted under gravity‐controlled drip flow. After 40 min, the column was washed with four column volumes (CV) of binding buffer (Tris⋅HCl 100 mm, βME 5 mm, NaCl 500 mm, imidazole 5 mm, pH 8). The column was then washed with a gradient of imidazole (from 5 to 300 mm, 20 CV) in binding buffer. DCS‐His6 eluted in the range 60–100 mm imidazole; column fractions were analysed by SDS‐PAGE. The fractions containing pure protein corresponding to a molecular weight of 64 000 (DCS‐His6) were pooled, dialysed overnight (10 mm Tris‐Base, 5 mm βME, pH 7.5; molecular weight cutoff (MWCO) 30000) and then concentrated to a final volume of about 5 mL (AMICON system, YM 30). The solution was aliquoted and stored at 0 °C. The concentration of protein was estimated by using the method of Bradford.24</p><p>Site‐directed mutagenesis of recombinant DCS‐His6 and mutagenic primers is described in the Supporting Information. The expression and purification of mutant DCS‐His6 enzymes was identical to that described for the WT.</p><p>Analytical incubations of DCS‐His6 and mutants with 1: Compound 1 (25 μL, 10 mm) was added to assay buffer (250 μL; 20 mm Tris, 5 mm βME, 10 mm MgCl2 at pH 7.5) followed by addition of enzyme (100 μL, 40 μm). The aqueous solution was overlaid with HPLC‐grade pentane (0.5 mL) and the resulting mixture was incubated with gentle agitation (18–24 h) at 25 °C. The incubations were repeated without enzyme as negative controls. The pentane extracts were then analysed by GC‐MS as described in the Supporting Information.</p><p>Steady‐state kinetics of DCS‐His6 and mutants: Kinetic assays were performed according to the standard, linear range, micro‐assay procedure previously developed for DCS (see the Supporting Information).9, 10</p><!><p>As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.</p><p>Supplementary</p><p>Click here for additional data file.</p>

PubMed Open Access

A Frequency-domain Approach to Determine Magnetic Address-Sensor Separation Distance Using the Harmonic Ratio Method

In this work, we describe an approach to determine the distance separating a magnetic address from a scanning magnetoresistive sensor \xe2\x80\x93 a critical adjustable parameter for certain bioassay analyses where magnetic nanoparticles are used as labels. Our approach is leveraged from the harmonic ratio method (HRM), a method used in the hard drive industry to control the distance separating a magnetoresistive read head from its data platter with nanometer resolution. At the heart of the HRM is an amplitude comparison of a signal\xe2\x80\x99s fundamental frequency to that of its harmonics. When the signal is derived from the magnetic field pattern of a periodic array of magnetic addresses, the harmonic ratio contains the information necessary to determine the separation between the address array and the read head. The elegance of the HRM is that there is no need of additional components to the detection platform to determine a separation distance; the streaming \xe2\x80\x9cbit signal\xe2\x80\x9d contains all the information needed. In this work we demonstrate that the tenets governing HRM used in the hard drive industry can be applied to the bioanalytical arena where submicron to 100 \xce\xbcm separations are required.

a_frequency-domain_approach_to_determine_magnetic_address-sensor_separation_distance_using_the_harmo

Introduction<!>Experimental Section<!>Sample Coupon Scans<!>Modeling<!>Conclusions

<p>Magnetoresistive (MR) sensors have emerged as an intriguing approach to detect surface bound biomarkers labeled with magnetic nanoparticles (MNPs) for bioanalytical and diagnostic purposes.1–14 These sensors, which undergo a decrease in resistance in an externally applied field,15 have become a mainstay in computer hard drives. The goal of translating MR sensors to the bioanalytical sciences derives from the ever continuing advances in their analytical sensitivity, speed, and compactness, all of which are important attributes of sensors destined for diagnostics, safety, and security applications.</p><p>There are two principal architectures used to take advantage of the detection capabilities of MR sensors in bioassay interrogation. The first uses the embodiment originally described by Baselt, et al.1 in which a thin passivation layer (e.g., tens of nanometers of Au or silicon nitride –Si3N4) deposited on the MR sensor protects it from a liquid sample.1,7 The passivation layer also serves as a surface that can be modified with molecular recognition elements (MRE), such as antibodies or single-stranded DNA complements, to capture a target biomarker from solution. Once captured, the biomarker is selectively tagged with MNPs that have also been MRE modified. The MR sensor detects the magnetic field generated by the captured MNP, HMNP, which provides the means for marker quantification. However, the strength of HMNP has a cubic dependence on the separation distance between sensor and MNP;16 the closer the MNP is to the sensor the greater the signal. Assuming that the MRE and biomarker both have a thickness of 10 nm and that the MNP label is 20–100 nm in diameter, the distance between the center of the MNP label and the MR sensor, given as d in Figure 1, is on the order of 40–80 nm. This places the MNP in close proximity to the MR sensor.</p><p>The second architecture, which has been a focus in our laboratory,13,14 is designed more along the lines of a hard disk drive reader. In this configuration, the assay is carried out on a sample coupon that is composed of multiple gold capture addresses interleaved with magnetic nickel reference addresses, and is physically separated from the MR sensor. The bioassay architecture is analogous to that described above where the final component is the MNP label. When the sample coupon is ready for readout it is scanned by the MR sensor.</p><p>This design differs from that originally put forth by Baselt et al. in three important ways. First, the test coupon is remote from the MR sensor, which enables the MR sensor to be used in a manner similar to that of a hard disk drive and eliminates the single use format of the earlier design. Second, future embodiments of the platform have the potential to be multiplexed with many different capture addresses for many different markers on a single, pre prepared coupon. Third, sample readout can be performed extremely rapidly by rotating the sample coupon across the sensor in air, emulating the way a computer hard drive reads magnetic data from a data platter.</p><p>The ability to detect the MNPs bound to a capture address on the test coupon hinges on minimizing the distance between the magnetic material and the MR sensor. This puts a premium on the ability to accurately control the separation distance between the sample coupon and MR sensor. Given that the magnetic field strength decays as the distance cubed, a 10× decrease in separation distance (e.g., from 10 μm to 1 μm) would theoretically increase the signal 1,000 times. However, some separation between the sensor and coupon is needed to avoid crashing the coupon into the MR surface. Typically, the physical size of the magnetic particle dictates the lower separation distance limit, which may be up to 5 μm in diameter. Therefore, a real time method to monitor and maintain a pre scribed stand-off distance is required for consistent and optimal magnetic readout.</p><p>In our previous MR based assay work, the separation distance was determined by bringing the coupon and sensor together until they were optically determined to be in contact. Once in contact, separation was achieved by manually rotating a z-axis lead screw. However, this method suffered from large "point of contact" estimation error, and lacked the automated control necessary for rapid, automated sample readout.</p><p>There are several methods that can be used to accurately measure the separation distances between two flat surfaces with submicron resolution, such as capacitive displacement sensors,17–20 thin film interferometry,20–23 and laser Doppler vibrometry,24–26 all of which require additional components on the detection platform. However, to realize ever smaller separation distances, the hard disk industry has developed techniques to accurately determine nanometer sized separation distances between the read head and magnetic media by simply measuring the "read back" signal of the MR sensor as it is scanned across the magnetic storage medium at a constant velocity. This is advantageous, as there is no requirement for extra platform components. The approach is based on the work of Wallace and can be described by the Wallace spacing loss equation (Eq. 1).27 Wallace showed that the amplitude of the read back signal (A(k)sensor) decays exponentially in the frequency domain as the separation distance between the MR transducer and the magnetic media increases,28 specifically: Eq. 1A(k)sensor=SsensorJmediume−kd where Ssensor is the sensitivity of the magnetic transducer, Jmedium describes the magnetic properties of the medium, and d is the separation distance between transducer and medium. The spatial frequency, k, is defined as k = 2π/λ = 2πf/v, where λ is the signal wavelength (i.e., the distance between two magnetic addresses or bits), f is the signal frequency, and v is the translation velocity of the transducer or medium with respect to each other (i.e., scan speed). As evident in Eq. 1, the signal amplitude in the frequency domain decreases exponentially as either d increases or λ decreases.</p><p>Several approaches, based on different forms of the Wallace spacing loss equation, have been developed and used to determine d, including the pulse width half max,29 read back signal modulation,30 and harmonic ratio methods (HRM).31–33 The advantages of the HRM over the other two methods are its independence of read head and media type (i.e., parallel or longitudinal magnetic media), and it can be used if the magnetization pattern unexpectedly changes, as long as it remains constant during the measurement.32 Moreover, by using a ratio approach (see below), factors that may affect signal amplitude (e.g., amplifier gain or head efficiency) are canceled out as they affect the harmonic amplitudes in exactly the same way. The HRM does, however, require that:34 the read head have a linear response over the range of measured signal amplitudes; the magnetic configuration should be two dimensional; the reader width or address width should be considerably wider than λ/2π; and the vertical field component should be measured to maintain fidelity with the Wallace predictions.</p><p>In the HRM approach, the sampled read back signal of a periodic, magnetic signature is converted to the frequency domain by use of the Fast Fourier Transform. The resulting fun damental frequency (f0) and harmonic frequencies (fi, i = 2, 3 etc.), each of which is an integer multiple of the fundamental frequency, are analyzed as the amplitude ratio of the fundamental to that of a given harmonic. An example of an amplitude ratio between the fundamental (k0 = 2π/λ) and the third harmonic (k3 = 6π/λ = 3k0) is given in Eq. 2. Eq. 2A(k0)A(k3)=Sk0/k3Jk0/k3e−k0de−3k0d=Sk0/k3Jk0/k3e2k0d</p><p>Inspection of Eq. 2 indicates that each amplitude ratio can be described by a unique analytical expression in which the y-intercept and slope are adjustable. The utility of this approach is well proven in hard drive applications where the magnetic media is contiguous and magnetic field transitions (i.e., data bits) occur over a finite length.</p><p>In this paper, we explore the extensibility of the HRM as a means to determine the separation distance between an MR sensor and a sample coupon, which is patterned with a one-dimensional array of alternating gold and magnetic nickel addresses. During the course of MR based bioassays, the gold addresses are used for analyte capture and the nickel addresses serve as c ex periments, only four nickel addresses are used. The experiments were performed by scanning an MR sensor across the four nickel addresses. The read back signal was then analyzed in the frequency domain to determine amplitude ratios at separation distances ranging from 105 to 5 μm. These amplitude ratios were fit to a two parameter exponential equation, and compared to the results from a finite difference model of the sample coupon. Using this method, we were able to determine the separation distance between the sample coupon and MR sensor between 5 and 105 μm with submicron resolution. While validated in our specific application, we expect the HRM can be extended to any number of applications as a technique to determine absolute separation distances.</p><!><p>A schematic of the test coupon immersed in Happ (100 Oe) and the MR sensor are shown in Figure 1. (Preparation of the test coupons has been previously described,7,13,14 and details are given in the Supporting Information.). A chromatic confocal imaging optical probe35 was used to verify the separation distance between the test coupon and MR sensor and to calibrate our z-axis stepper motor. Note that Happ and the scan direction are aligned with the x-axis of the coupon.</p><p>The 200×200 μm MR sensor36 (see Supporting Information) was translated across the test coupon as a function of separation between the test coupon and the MR sensor (z-axis) and data were acquired at 5.8 Hz. The resulting signal at each separation distance was transformed to the frequency domain using the Fast Fourier Transform (FFT) to determine the amplitudes of the resulting fundamental and harmonic frequencies. These data were compared to that predicted by a finite-element two dimensional model.</p><!><p>Four nickel addresses on a single test coupon were aligned directly over the MR sensor and scanned at a velocity of 31.1 μm/s relative to the stationary coupon at separations ranging from 5 to 105 μm in 10 μm increments. A subset of the resulting response is shown in Figure 2A at separation distances of 5, 55, and 105 μm. The signal transient across each address exhibits the same characteristic shape composed of a minimum – the center of each nickel address – surrounded by two smaller maxima – the leading and trailing edges of the nickel addresses (the evolution of the magnetic transient has been previously described14). Between each address, the signal re turns to the baseline voltage observed when the sensor is located beyond the magnetic field of the nickel addresses. As the separation distance decreases, the magnitudes of the maxima and minima features increase due to an increased flux density from the nickel addresses detected by the MR sensor. The trailing edge maxima features are slightly larger than the leading edge feature, which is likely due to a slight misalignment of the addresses as they are scanned across the MR sensor.</p><p>Shown in Figure 2B are the FFT results of the signal trans formation. The frequency spectra contain five frequencies, viz. the fundamental frequency (f0) at 22.2±0.1 mHz, and harmonic frequencies of f2 = 2f0 = 44.4±0.1 mHz; f3 = 66.7±0.1 mHz; f4 = 88.9±0.1 mHz; and f5 = 111.1±0.1 mHz. The fundamental frequency matches the expected frequency from the read ve locity (i.e., a scan rate of 31.1 μm/s and spatial wavelength of 1,400 μm). As the separation distance decreases, the amplitudes of the fundamental and harmonics increase at different rates, validating the utility of the HRM for our system.</p><p>Amplitude ratios were determined at each separation distance for the fundamental divided by the 2nd (f0/f2), 3rd (f0/f3), and 4th (f0/f4); the 5th harmonic was excluded due to the weak signal at large separation distances. Using a non linear least squares regression, each quotient as a function of separation distance was fit to a two parameter exponential, y=Qebd, a reduced version of Equation 2, with Q and b as fitting parameters. The resulting fits are shown in Figure 3 and the two fitting parameters are summarized in Table 1; the error for each fit is calculated at the 95% confidence level.</p><p>According to the reduced version of Eq. 2, the estimated b parameter should be a function of the signal wavelength. However, all of the calculated wavelengths (λ = 2π/b for f0/f2, 4π/b for f0/f3, and 6π/b for f0/f4) tabulated in Table 1 overestimate the signal wavelength, but appear to be converging toward the true wavelength of 1,400 μm at higher order harmonic quotients. The calculated values of Q represent the product of sensor sensitivity, S, and nickel address magnetic proper ties, J, as a function of harmonic quotient. This value is the sensor response as a function of harmonic ratio when d equals zero, i.e., the y intercept. These analytical expressions can be used as empirical relations to determine separation distances down to the 1–5 μm range that we require in our system. We speculate that the deviation from the HRM prediction is partially a result of the millihertz frequencies used in these experiments and partially due to the width of sensor and addresses being identical.32</p><p>The usefulness of each quotient, i.e., its slope – the sensitivity – depends upon the separation distance. At small separation distances where higher order harmonics have large amplitudes, f0/f4 is more analytically useful than the lower order harmonics. As the separation distance increases, the higher harmonic amplitudes become indistinguishable from the noise, the slope becomes infinite and it is no longer useful. However, at these larger separation distances the lower order harmonic ratios can still be used, giving the system a large dynamic range.</p><!><p>A two dimensional model was created of four nickel addresses in a layout that matched that of the sample coupon (see Supporting Information). A field of 100 Oe was applied in the x-direction (down the length of the coupon) and the induced field from the nickel addresses as a function of position was calculated at separation distances of 5 to 105 μm in 10 μm increments, in order to match the experimental setup. Since the MR sensor is finite (200×200 μm) and not a point sensor, the magnetic field data from the model was integrated over a 200 μm segment in the x-direction. This segment was then moved forward in 1 μm increments with the integration per formed at each increment. The integrated field was converted from position to time assuming a scan velocity of 31.1 μm/s.</p><p>The shape of the integrated magnetic field at separation distances of 5, 55, and 105 μm agrees with that observed experimentally (see Figure SI–1A). The predicted magnitude of the signal is larger than that observed experimentally, which will be discussed shortly. The magnitudes of the signal features increase as the separation distance decreases, which indicates that the frequency spectrum of the signal should have a dependence on separation distance.</p><p>Observed after transformation to the frequency domain (see Figure SI–1B) is a fundamental frequency (f0) at 22.2 mHz and five harmonics, which are each an integer multiple of the fundamental frequency (f2 at 44.4 mHz, f3 at 66.7 mHz, f4 at 88.9 mHz, f5 at 111.1 mHz, and f6 at 133.2 mHz). This is in agreement with the frequencies from our experimental data, though the modeling predicts the presence of a 6th harmonic which was not observed in our experimental data. The amplitudes of the fundamental and harmonic frequencies increase at different rates as the separation distance decreases, which match the trends of the experimental data.</p><p>Within error, a plot of the amplitude ratio fits for the 2nd, 3rd and 4th harmonics (see Figure SI–2A) yield the same values for the b parameter as determined from the experimental data (b1 = 0.0031, b2 = 0.0071, b3 = 0.0114) and thus the same predicted wavelengths. This indicates that the b parameter is fun damentally dependent on the signal wavelength as predicted from theory. As a result, the model is an effective method to qualitatively predict the impact of different coupon configurations and scan speeds that will be used as we continue to optimize the performance of our system.</p><p>Interestingly, the harmonic amplitudes are larger and the resulting ratios are smaller than those observed experimentally, which seems to imply that the experimental data was collected at a distance much greater than indicated in the modeling. We attribute this to a combination of factors stemming from the 2D model of a 200 μm sensor not matching the 3D physical situation of our Wheatstone bridge MR sensor configuration. Also the matched size of the sense pad and nickel address, i.e., 200×200 μm, may result in a loss of field resolution that would result in broadening of the signal features, and an apparent increase in separation distance in the harmonic analysis.</p><p>The model and experimental data can be brought into strong agreement, however, if the model data is evaluated using an effective distance. If we define an effective distance parameter, c, and add it to the exponential fits, as shown in Equation 3, we can estimate an effective distance that can be used to bring the theory and data closer into agreement. Eq. 3A(k0)A(k3)=Sk0/k3Jk0/k3e2k0(d+c)When the model amplitude ratio fit equations are evaluated at a distance of d+95 μm, the exponential fits are nearly equivalent to the experimental data (see Figure SI–2B).</p><p>Through the use of the model, we have validated our experimental results and developed a method to predict how the amplitude ratios will change as a function of separation distance and spacing between the nickel addresses on the sample coupons. By using an effective distance parameter of c = 95 μm, we believe that we will be able to accurately predict the amplitude ratios for different sample coupon configurations. Modeling also predicts that at even smaller separation distances – less than one micron – the utility of the f0/f5 and f0/f6 ratios will become apparent as their analytical sensitivities are predicted to be much greater than the lower order harmonic ratios at these distances. When sample architecture and labeling strategy permit, readout at submicron separation distances will improve our measurement resolution and improve limits of detection. Going forward, the model will be used to inform design decisions as we continue to develop our MR sensor platform for bioanalytical use.</p><!><p>Through this work, we have determined the HRM to be an effective method for determining separation distances between 5 μm and 105 μm for our MR biosensor platform. Using this method, we can achieve a prescribed separation between an MR sensor and sample coupon with submicron resolution. By transforming the signal to the frequency domain and monitoring the amplitude ratios of the fundamental frequency to a series of harmonics, we expect to be able to determine the separation distance with micron resolution and in near real time as we scan our sample coupons. From the data, it appears that this method can be extended over a large dynamic range, since the analytical sensitivity of a particular ratio differs de pending on the separation distance range of interest.</p><p>Also, we developed a finite difference model that can be used to help inform future platform development. From the model data, the wavelength dependence of the exponential factor, b, found experimentally was confirmed by theory. The amplitude ratio fit equations from the model data matched the empirical data when evaluated at an effective distance of d+95 μm, which is likely due to the non idealities of the 2D model when compared to the physical system. The frequency spectrum from the model data also suggested the presence of a 6th harmonic that we expect will be seen as the separation distance is decreased below a micron.</p><p>The HRM is an effective technique for determining the separation distance between two surfaces in relative motion and can potentially be applied to many systems, not just the MNP/MR based bioassay we show here. By either patterning a periodic magnetic signal or monitoring an inherent, periodic magnetic signal, separation distance can be accurately determined through the transformation of the read back signal and monitoring of the subsequent frequency content of the signal.</p><p>The HRM will be used in future embodiments of our MR biosensor platform in which a multiplexed sample coupon is rapidly rotated relative to the sensor for readout. By maintaining the interleaved pattern of nickel internal reference ad dresses and gold capture addresses on a sample coupon, the separation distance can be continually measured and controlled to maintain signal strength and fidelity. This translates to a larger magnetic signal to noise ratio and superior limits of detection than current designs. This rapid, multiplexed plat form has significant diagnostic and medical surveillance implications.</p>

PubMed Author Manuscript

Design, synthesis, and evaluation of novel heteroaromatic analogs of curcumin as anti-cancer agents

To improve the potential of curcumin to treat advanced hormone-refractory prostate cancer, three series (A\xe2\x80\x93C) of heteroaromatic analogs (thirty two compounds) with different monoketone linkers have been synthesized and evaluated for cytotoxicity against two human androgen-independent prostate cancer cell lines (PC-3 and DU-145). Among them, thirty analogs are more potent than curcumin against PC-3 cells, and twenty one analogs are more cytotoxic towards DU-145 cells relative to curcumin. The most potent compounds (44, 45, 51, and 52) also showed impressive cytotoxicity against three other metastatic cancer cell lines (MDA-MB-231, HeLa, and A549), with IC50 values ranging from 50 nM to 390 nM. All four most potent analogs exhibited no apparent cytotoxicity towards the MCF-10A normal mammary epithelial cells. Taken together, selective enhancement of cell death in prostate cancer cell lines and other aggressive cancer cell lines suggests that nitrogen-containing heteroaromatic rings are promising bioisosteres of the substituted phenyl ring in curcumin.

design,_synthesis,_and_evaluation_of_novel_heteroaromatic_analogs_of_curcumin_as_anti-cancer_agents

1. Introduction<!>2. Results and discussion<!>2.1. Chemistry<!>2.2. Cytotoxicity towards human androgen-independent prostate cancer cell lines<!>2.3. Cytotoxicity towards aggressive human cancer cell lines<!>2.4. Cytotoxicity towards MCF-10A normal mammary epithelial cells<!>3. Conclusion<!>4.1. General synthetic procedures<!>4.2. General procedure for the synthesis of 1-alkyl-1H-imidazole-2-carbaldehyde [35]<!>4.2.1. 1-Isopropyl-1H-imidazole-2-carbaldehyde (7)<!>4.2.2. 1-sec-Butyl-1H-imidazole-2-carbaldehyde (8)<!>4.2.3. 1-Isobutyl-1H-imidazole-2-carbaldehyde (9)<!>Method A [31]<!>Method B [34]<!>4.3.1. (3E,5E)-1-Methyl-3,5-bis((1-methyl-1H-imidazol-2-yl) methylene)piperidin-4-one (21)<!>4.3.2. (3E,5E)-1-Methyl-3,5-bis((1-isopropyl-1H-imidazole-2-yl) methylene)piperidin-4-one (22)<!>4.3.3. (3E,5E)-3,5-Bis((1-(sec-butyl)-1H-imidazol-2-yl)methylene)-1-methylpiperidin-4-one (23)<!>4.3.4. (3E,5E)-1-Methyl-3,5-bis((1-methyl-1H-pyrazol-4-yl) methylene)piperidin-4-one (24)<!>4.3.5. (3E,5E)-1-Methyl-3,5-bis((1-methyl-1H-pyrazol-5-yl) methylene)piperidin-4-one (25)<!>4.3.6. (3E,5E)-1-Methyl-3,5-bis(thiazole-2-yl methylene)piperidin-4-one (26)<!>4.3.7. (3E,5E)-1-Methyl-3,5-bis(thiazol-4-ylmethylene)piperidin-4-one (27)<!>4.3.8. (3E,5E)-1-Methyl-3,5-bis((2-methyloxazol-4-yl)methylene) piperidin-4-one (28)<!>4.3.9. (2E,6E)-2,6-Bis((5-methylisoxazol-3-yl)methylene) cyclohexanone (29)<!>4.3.10. (3E,5E)-1-Methyl-3,5-bis((3-methylisoxazol-5-yl) methylene)piperidin-4-one (30)<!>4.3.11. (2E,6E)-2,6-Bis((1-methyl-1H-imidazol-2-yl)methylene) cyclohexanone (31)<!>4.3.12. (2E,6E)-2,6-Bis((1-isopropyl-1H-imidazole-2-yl)methylene) cyclohexanone (32)<!>4.3.13. (2E,6E)-2,6-Bis((1-(sec-butyl)-1H-imidazol-2-yl)methylene) cyclohexanone (33)<!>4.3.14. (2E,6E)-2,6-Bis((1-isobutyl-1H-imidazol-2-yl)methylene) cyclohexanone (34)<!>4.3.15. (2E,6E)-2,6-Bis((1-methyl-1H-pyrazol-4-yl)methylene) cyclohexanone (35)<!>4.3.16. (2E,6E)-2,6-Bis((1-methyl-1H-pyrazol-5-yl)methylene) cyclohexanone (36)<!>4.3.17. (2E,6E)-2,6-Bis(thiazole-2-yl methylene)cyclohexanone (37)<!>4.3.18. (2E,6E)-2,6-Bis(thiazol-4-ylmethylene)cyclohexanone (38)<!>4.3.19. (2E,6E)-2,6-Bis((2-methyloxazol-4-yl)methylene) cyclohexanone (39)<!>4.3.20. (2E,6E)-2,6-Bis((5-methylisoxazol-3-yl)methylene) cyclohexanone (40)<!>4.3.21. (2E,6E)-2,6-Bis((3-methylisothiazol-5-yl)methylene) cyclohexanone (41)<!>4.3.22. (1E,4E)-1,5-Bis(1-methyl-1H-imidazol-2-yl)penta-1,4-dien-3-one (42)<!>4.3.23. (1E,3E)-1,3-Bis((1-isopropyl-1H-imidazole-2-yl)methylene) acetone (43)<!>4.3.24. (1E,4E)-1,5-Bis(1-(sec-butyl)-1H-imidazol-2-yl)penta-1,4-dien-3-one (44)<!>4.3.25. (1E,4E)-1,5-Bis(1-isobutyl-1H-imidazol-2-yl)penta-1,4-dien-3-one (45)<!>4.3.26. (1E,4E)-1,5-Bis(1-methyl-1H-pyrazol-5-yl)penta-1,4-dien-3-one (46)<!>4.3.27. (1E,4E)-1,5-Di(thiazol-2-yl)penta-1,4-dien-3-one (47)<!>4.3.28. (1E,4E)-1,5-Di(thiazol-4-yl)penta-1,4-dien-3-one (48)<!>4.3.29. (1E,4E)-1,5-Bis(2-methyloxazol-4-yl)penta-1,4-dien-3-one (49)<!>4.3.30. (1E,4E)-1,5-Bis(5-methylisoxazol-3-yl)penta-1,4-dien-3-one (50)<!>4.3.31. (1E,4E)-1,5-Bis(3-methylisoxazol-5-yl)penta-1,4-dien-3-one (51)<!>4.3.32. (1E,4E)-1,5-Di(pyridin-2-yl)penta-1,4-dien-3-one (52)<!>4.4.1. Cell culture<!>4.4.2. Trypan blue dye exclusion assay

<p>Prostate cancer has the highest incidence and the second highest cancer mortality in American men. The American Cancer Society estimates that 238,590 new cases of prostate cancer will be diagnosed and 29,720 men will die of prostate cancer in the United States in 2013 [1]. Current therapies (radical prostatectomy, chemotherapy, local radiotherapy, or hormonotherapy) are successful in treating localized, androgen-dependent, prostate cancer. However, treatment of hormone-refractory prostate cancer remains hindered by inevitable progression of resistance to first-line treatment with docetaxel. Consequently, novel drugs are needed to treat advanced hormone-resistant prostate cancer [2,3].</p><p>Curcumin or diferuloylmethane (1, Table 1), a polyphenolic molecule extracted from the rhizome of the plant Curcuma longa (turmeric), is a yellow spice used as curry ingredient and has been used for centuries in Ayurvedic, Chinese, and Hindu medicine systems. There is a huge difference in the rate of incidence of prostate cancer between Western (120 per 100,000 in Northern America) and East Asian countries (less than 10 per 100,000 in Asia) [4]. The increased risk of prostate cancer in the first generation of Asian men emigrating to the United States suggests a chemopreventive effect of Asian traditional food. Recent preclinical and clinical studies have demonstrated that curcumin has a number of anticancer properties [5,6]. The potential of curcumin to treat both androgen-dependent and androgen-independent prostate cancer has been demonstrated by the in vitro and in vivo studies [7,8]. A new philosophy that favors multitargeted drugs has recently gained momentum [9]. Curcumin serves as a good example of a class of compounds that is able to target multiple enzymes with a "magic shotgun" [10]. The anticancer effects of curcumin are associated with its influence on numerous growth factors within the cell [11,12]. The effect of curcumin on any particular growth factor is small, but its aggregate effect is significant. This characteristic is especially valuable for diseases like cancer that are complex, inflammation associated, and often evolve mutations in multiple genes. Because of its potential ability to treat hormone-refractory prostate cancer, its low molecular weight, lack of toxicity, and its mechanism of action against multiple targets, curcumin could be an ideal candidate as an androgen-independent agent against prostate cancer. However, its clinical development has been limited by its suboptimal pharmacokinetics and poor bioavailability caused by poor solubility in water and rapid in vivo metabolism [13]. It has been found that, with oral administration at the dose of 450–3600 mg/day in a phase I trial, the blood concentration of curcumin in plasma and target tissues falls under the detection limit [14].</p><p>Curcumin has extensively been used as a lead compound to design and synthesize analogs for the potential treatment of prostate cancer [15–28]. Some analogs, such as JC-9 [22], FLLL11, and FLLL12 (Fig. 1) [19] were found to be more potent than curcumin towards PC-3 prostate cancer cell line. The reported studies focused mainly on changes in the β-diketone structure and aryl substitution pattern of curcumin. It is believed from reported studies that the β-diketone moiety in the structure of curcumin appears to be a specific substrate of a series of aldoketo reductases and can be decomposed rapidly in vivo [29,30]. It has been evidenced that monoketone analogs generally have improved pharmacokinetic profiles over curcumin, and that some monoketone analogs with the acetone or cyclohexanone spacer confer increased cytotoxicity towards PC-3 cell lines [16,19].</p><p>To identify new curcumin analogs with improved bioavailability and potential to treat hormone-refractory prostate cancer, we replaced the substituted phenyl rings in curcumin with two identical basic N-containing heteroaromatic rings. We focused on basic nitrogen heteroaromatics to take advantage of their ability to exist in both the protonated and neutral form, allowing both solubility in aqueous media and enhanced potential to cross cellular membranes. It has been reported that pyridine analogs had better potency against MDA-MB-231 cancer cells [31], head and neck squamous cell carcinoma [32], and PC-3 prostate cancer cell line [20,33]. To the best of our knowledge, there is no cytotoxic study of N-containing five-membered heteroaromatic analogs against prostate cancer cells.</p><p>We have synthesized twenty-nine new compounds and three known compounds (Fig. 2), which are classified as three series according to their different linkers: 1-methylpiperidone (series A), cyclohexanone (series B), and acetone (series C). Among them, thirty one are five-membered heteroaromatic analogs and only one is six-membered analog (52). In this paper, we describe the synthesis of these curcumin analogs and the in vitro evaluation of their anticancer activities.</p><!><p>To engineer more effective analogs of curcumin for potential clinical use in treating hormone-refractory prostate cancer, three series of heteroaromatic curcumin analogs with three different monoketone linkers have been designed by replacing the two substituted phenyl rings in curcumin with two identical N-containing heteroaromatic rings. All these three series compounds are symmetrical monoketone curcumin analogs with N-methylpiperidone, cyclohexanone, or acetone as a linker, respectively (Fig. 2). Twenty nine of them are new, and three analogs (21, 31, and 52) are known. Analogs 21 and 31 have been synthesized by Yadav and coworkers for the evaluation of their cytotoxicity against ER-negative breast cancer cell line MDA-MB-231 [31]. The cytotoxicity of analog 52 towards colorectal carcinoma HCT 116/p53+/+ cells has been investigated [34]. However, no cytotoxicity of these three known compounds towards prostate cancer cell lines has been reported. Each of them was synthesized through a Claisen–Schmidt condensation of the corresponding aromatic aldehyde with the appropriate ketone. The structures of these analogs have been determined by interpretation of their NMR and HR-MS data. The cytotoxicity of all synthesized analogs has been evaluated against two human androgen-independent prostate cancer cell lines (PC-3 and DU-145). Most of these curcumin analogs exhibited significantly more potent cytotoxicity than curcumin towards PC-3 and DU-145 prostate cancer cell lines.</p><!><p>The starting aldehydes, 6, and 10–16, are commercially available. 1-Alkyl-1H-imidazole-2-carbaldehydes (7–9) were prepared from 1H-imidazole-2-carbaldehydes (5) using potassium carbonate as base (Scheme 1) according to the procedure described in the literature [35].</p><p>The general synthetic scheme for series A with N-methylpiperidone as a linker is shown in Scheme 2. The synthesis of this scaffold with various basic (N-containing) heteroaromatic rings was carried out via double aldol condensation of two equivalents of the appropriate aldehyde (6–8 and 10–16) with N-methylpiperidone using sodium methoxide as base following the procedure reported in the literature [31].</p><p>The synthetic strategy for series B with cyclohexanone as linker is shown in Scheme 3. The synthesis of 31–41 was carried out via double aldol condensation of two equivalents of the appropriate aldehyde (6–16) with cyclohexanone using sodium methoxide as base according to the procedure described in the literature [31].</p><p>As shown in Scheme 4, eleven curcumin analogs with acetone linker were prepared in two different methods. The imidazole analogs 42 and 43 can be synthesized from the respective aldehyde 6 and 7 using the same procedure as described in the literature [31]. However, the aldol condensation of acetone with aldehydes 8–17 using sodium methoxide as base only gave messy products. The mechanistic reason for the formation of 52 was postulated to be that water elimination is hindered by the neighboring nitrogen atom through an inductive effect which results in electron withdrawal from the carbon atom bearing the hydroxyl group [36]. Consequently, the analogs 44–52 were prepared from the corresponding aldehyde and acetone at 70 °C, using potassium carbonate as base and toluene–ethanol–water (4:4:2) as solvent, following the procedure reported by Long and co-workers [34]. We observed that not all compounds of this scaffold can be synthesized via this method. An improved method is currently explored in our laboratory and will be reported in due course.</p><!><p>To determine the in vitro cytotoxicities of the synthesized curcumin analogs, we performed prostate cancer cell viability assays in which the ability of the curcumin analogs to inhibit growth of PC-3 and DU-145 cell lines was measured. Both PC-3 and DU-145 cell lines are androgen-independent human prostate cancer cells. Curcumin and DMSO were used as positive and negative control, respectively.</p><p>Among thirty two heteroaromatic analogs of curcumin that have been prepared and evaluated, thirty analogs are more potent than curcumin against PC-3 cells, and twenty one analogs are more cytotoxic towards DU-145 cells than curcumin. As shown in Table 1, the IC50 values of these twenty one analogs against PC-3 cells and DU-145 cells are significantly lower than those of curcumin.</p><p>The analogs of series C in particular are more potent than parent curcumin in their cytotoxicity against PC-3 and DU-145 androgen-independent prostate cancer cell lines. Among the three scaffolds of analogs that have been prepared and evaluated, all compounds that contain scaffold C with acetone linker (only exception is 49) showed excellent cytotoxicity against both PC-3 and DU-145 prostate cancer cell lines with optimum IC50 value as 16 nM against DU-145 cells and 33 nM against PC-3 cells. They are 19 times and 60 times, respectively, more potent than curcumin.</p><!><p>To further evaluate the effects of the analogs on other types of aggressive cancers, the four most promising curcumin analogs (44, 45, 51, and 52) were selected for further evaluation of their cytotoxicities towards a metastatic breast cancer cell line (MDA-MB-231), an aggressive cervical cell line (HeLa), and a metastatic non-small cell lung cancer cell line (A549). As shown in Table 2, these four curcumin analogs are 7–9 times more potent than curcumin against MDA-MB-231 breast cancer cell line, 32–203 times better than curcumin against HeLa cervical cell line, and 94–294 folds more potent than curcumin against A549 non-small cell lung cancer line.</p><!><p>The four most promising curcumin analogs (44, 45, 51, and 52) were also selected for further evaluation of their toxicity towards normal cells. As shown in Fig. 3, all these four analogs demonstrate no apparent cytotoxicity towards MCF-10A normal mammary epithelial cells up to 1 µM.</p><!><p>In summary, we have prepared a panel of curcumin analogs in which both the central and terminal sectors of the molecule were modified. The central diketone moiety was replaced with three different monoketone linkers. The terminal oxygenated aromatic rings in curcumin were substituted with two identical basic heteroaromatic rings; most of them are five-membered heteroaromatic rings (only one exception). Three scaffolds, comprising twenty nine new compounds, of basic curcumin analogs have been evaluated for cytotoxic potency against two androgen-independent human prostate cancer cell lines (DU-145 and PC-3) by the trypan blue dye exclusion method. A number of important findings resulted from this study are: i) thirty analogs are more potent than curcumin against PC-3 cells, and twenty one analogs are more cytotoxic towards DU-145 cells relative to curcumin; ii) the scaffold containing two identical basic heteroaromatic rings with a dienone linker showed excellent cytotoxicity against both PC-3 and DU-145 prostate cancer cell lines; iii) the four most promising curcumin analogs are more potent than curcumin against three other metastatic human cancer cell lines: MDA-MB-231, HeLa, and A549; and iv) these four most potent analogs demonstrate no apparent cytotoxicity towards MCF-10A normal mammary epithelial cells. The structure-activity data acquired indicate the combination of two identical basic heteroaromatic rings with a dienone linker constitutes a promising scaffold to design novel curcumin analogs with promising cytotoxicity against aggressive prostate cancer cells but with no apparent toxicity towards normal mammary cells. Synthesis of more analogs for better understanding their structure–activity relationships is undergoing in our laboratory. Additional research is needed on mechanism study and the in vivo activity of potential compounds on tumor growth in appropriate animal models.</p><!><p>NMR spectra were obtained on a Bruker Fourier 300 spectrometer in CDCl3, CD3OD, or DMSO-d6. The chemical shifts are given in δ (ppm) referenced to the respective solvent peak, and coupling constants are reported in Hz. Anhydrous THF and dichloromethane were purified by PureSolv MD 7 Solvent Purification System from Innovative Technologies (MB-SPS-800). All other reagents and solvents were purchased from commercial sources and were used without further purification. Silica gel column chromatography was performed using silica gel (32–63 µ). Preparative thin-layer chromatography (PTLC) separations were carried out on 1000 µ AnalTech thin layer chromatography plates (Lot No.13401). Curcumin was synthesized by Claisen–Schmidt condensation of aromatic aldehyde with acetylacetone according to the procedure described in the literature [37].</p><!><p>To a solution of 1H-imidazole-2-carbldehyde (13 mmol) and potassium carbonate (16 mmol) in DMF (13 mL) was added alkyl bromide (16 mmol), and the reaction mixture was stirred at 50 °C for 6 h. The inorganic solids were removed by filtration, and the filtrate was diluted with water and extracted with diethyl ether. The combined organic extracts were dried over anhydrous magnesium sulfate, and the volatile components were evaporated under vacuum to give the respective product.</p><!><p>Yellow oil, 93% yield. 1H NMR (300 MHz, CDCl3) δ: 1.47 (d, J = 7.0 Hz, 6H), 5.41–5.55 (m, 1H), 7.31–7.33 (overlapped, 2H), 9.83 (s, 1H).</p><!><p>Yellow oil, 80% yield. 1H NMR (300 MHz, CDCl3) δ: 0.84 (t, J = 7.4 Hz, 3H), 1.46 (d, J = 7.0 Hz, 3H), 1.79 (quin, J = 7.0 Hz, 2H), 5.35 (sex, J = 7.0 Hz, 1H), 7.20 (s, 1H), 7.22 (s, 1H), 9.86 (s, 1H).</p><!><p>Yellow oil, 93% yield. 1H NMR (300 MHz, CDCl3) δ: 0.927 (d, J = 3.0 Hz, 6H), 2.08 (s, 1H), 4.22 (d, J = 4.4 Hz, 2H), 7.14 (s, 1H), 7.30 (s, 1H), 9.84 (s, 1H).</p><!><p>To a solution of the starting aldehyde (1.5 mmol) and ketone (0.75 mmol) in methanol (10 mL) was added the solution of sodium methoxide in methanol (5.4 M, 0.14 mL, 0.75 mmol), and the mixture was stirred for 4–18 h and monitored with TLC. When the reaction was completed, the following two work-up procedures were applied. Procedure 1: if precipitate was observed, the precipitate was filtered and rinsed with cold methanol. Procedure 2: if no precipitate was observed, then saturated solution of ammonium chloride was added, and the subsequent mixture was extracted with dichloromethane. The organic layer was dried over anhydrous MgSO4. The solvent was evaporated under vacuum to give a crude product, which was purified by preparative TLC (3–5% methanol in dichloromethane) or column chromatography (2% methanol in dichloromethane).</p><!><p>The reaction mixture of aldehyde (4 mmol), acetone (116 mg, 2 mmol) and K2CO3 (1.1 g, 4 mmol) in the mixed solvent of toluene–ethanol–water (10 mL + 4.0 mL + 2.0 mL) was stirred at 70 °C for 12 h. After cooling down to room temperature, the solvent was evaporated in vacuo. The resulting residue was partitioned between dichloromethane and water. The aqueous phase was further extracted with dichloromethane twice. The combined organic extracts were rinsed with brine and dried over anhydrous magnesium sulfate. The organic solvent was removed under vacuum to give a residue, which was purified by preparative TLC (5% methanol in dichloromethane) or column chromatography (2% methanol in dichloromethane).</p><p>The physical and spectroscopic data of mono-ketone curcumin analogs were listed below:</p><!><p>This compound was prepared by method A in 94% yield as a yellow solid: mp. 163–164 °C. IR (neat) νmax: 3096, 2941, 1614, 1578, 1475, 1262 cm−1. 1H NMR (300 MHz, DMSO-d6) δ: 2.49 (s, 3H), 3.80 (s, 6H), 4.12 (s, 4H), 7.22 (s, 2H), 7.41 (s, 4H). 13C NMR (75 MHz, DMSO-d6) δ: 33.3, 45.8, 57.2, 118.3, 125.0, 130.8, 133.6, 143.3, 186.2.</p><!><p>This compound was prepared by method A as a yellow-orange solid in 95% yield; mp. 133–137 °C. IR (neat) νmax: 3104, 2977, 1655, 1610, 1577, 1461, 1255, 1156 cm−1. 1H NMR (300 MHz, CD3OD) δ: 1.52 (d, J = 6.6 Hz, 12 H), 2.56 (s, 3H), 4.15 (s, 4H), 4.80 (sep, J = 6.6 Hz, 2H), 7.28 (s, 2H), 7.47 (s, 2H), 7.69 (s, 2H). 13C NMR (75 MHz, CD3OD) δ: 22.3, 46.8, 56.7, 118.8, 119.0, 130.4, 134.0, 141.8, 186.3. HR-MS (ESI) m/z: calcd for C20H28N5O [M + H]: 354.2294; found 354.2288.</p><!><p>This compound was prepared by method A in 90% yield as a yellow oil. IR (neat) νmax: 2969, 2934, 1652, 1551, 1459, 1259 cm−1. 1H NMR (300 MHz, MeOD) δ: 0.82 (t, J = 7.3 Hz, 6H), 1.51 (d, J = 6.6 Hz, 6H), 1.79–1.93 (m, 4H), 2.56 (s, 3H), 4.16 (s, 4H), 4.55 (sex, J = 6.7 Hz, 2H), 7.31 (s, 2H), 7.44 (s, 2H), 7.69 (s, 2H). 13C NMR (75 MHz, MeOD) δ: 9.4, 20.6, 30.3, 44.4, 53.9, 56.6, 119.1, 119.2, 130.5, 133.9, 142.5, 186.3. HR-MS (ESI) m/z: calcd for C22H32N5O [M + H]: 382.2607; found 382.2599.</p><!><p>This compound was prepared by method A in 43% yield as a yellow solid: mp. 179–180 °C. 1H NMR (300 MHz, CDCl3 + CD3OD) δ: 2.20 (s, 3H), 3.32 (s, 4H), 3.54 (s, 6H), 7.21 (s, 2H), 7.26 (s, 2H), 7.41 (s, 2H). 13C NMR (75 MHz, CDCl3 + CD3OD) δ: 37.8, 44.7, 56.0, 117.1, 126.8, 128.7, 132.3, 140.5, 185.4. HR-MS (ESI) m/z: calcd for C16H20N5O [M + H]: 298.1668; found 298.1674.</p><!><p>This compound was prepared by method A in 92% yield as a yellow crystal: mp. 155–157 °C. IR (neat) νmax: 2944, 1672, 1612, 1583, 1453, 1267, 1187, 925, 608 cm−1. 1H NMR (300 MHz, CD3OD) δ: 2.56 (s, 3H), 3.77 (br.s, 4H), 4.00 (s, 6H), 6.56 (d, J = 2.1 Hz, 2H), 7.57 (d, J = 2.1 Hz, 2H), 7.69 (s, 1H). 13C NMR (75 MHz, CD3OD) δ: 35.7, 44.5, 56.0, 108.6, 120.7, 133.3, 136.8, 138.3, 185.3. HR-MS (ESI) m/z: calcd for C16H20N5O [M + H]: 298.1668; found 298.1662.</p><!><p>This compound was prepared by method A in 71% yield as a yellow solid: mp. 110–111 °C. IR (neat) νmax: 3078, 2939, 1670, 1608, 1582, 1481, 1271, 1180 cm−1. 1H NMR (300 MHz, CDCl3) δ: 2.61 (s, 3H), 4.16 (s, 4H), 7.54 (d, J = 3.0 Hz, 2H), 7.76 (s, 2H), 8.03 (d, J = 3.0 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 45.9, 57.3, 122.4, 124.9, 135.7, 145.3, 163.1, 186.7. HR-MS (ESI) m/z: calcd for C14H14N3OS2 [M + H]: 304.0578; found 304.0566.</p><!><p>This compound was prepared by method A in 65% yield as a yellow crystal: mp. 131–132 °C. IR (neat) νmax: 3101, 2940, 1671, 1616, 1581, 1471, 1263, 1180 cm−1. 1H NMR (300 MHz, DMSO-d6) δ: 2.43 (s, 3H), 4.01 (s, 4H), 7.58 (s, 2H), 8.24 (s, 2H), 9.27 (s, 2H). 13C NMR (75 MHz, DMSO-d6) δ: 46.1, 57.4, 126.1, 126.4, 133.7, 152.6, 155.6, 187.8. HR-MS (ESI) m/z: calcd for C14H14N3OS2 [M + H]: 304.0578; found 304.0577.</p><!><p>This compound was prepared by method A in 6% yield as a yellow crystal: mp. 138–139.5 °C. 1H NMR (300 MHz, CDCl3) δ: 2.51 (s, 6H), 2.63 (s, 3H), 4.14 (s, 4H), 7.51 (s, 2H), 7.77 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 14.0, 44.9, 56.2, 124.3, 131.6, 137.6, 141.4, 162.1, 185.8. HR-MS (ESI, M + H) m/z: calcd for C16H18N3O3 [M + H]: 300.1348; found 300.1351.</p><!><p>This compound was prepared by method A in 49% yield as a yellow crystal: mp. 155–156 °C. IR (neat) νmax: 3129, 2943, 1685, 1636, 1598, 1426, 1267, 1181, 910, 783 cm−1. 1H NMR (300 MHz, CDCl3) δ: 2.47 (s, 6H), 2.52 (s, 3H), 3.90 (s, 4H), 6.11 (s, 2H), 7.42 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 12.2, 45.7, 57.5, 103.6, 121.9, 138.1, 158.8, 169.9, 186.5. HR-MS (ESI) m/z: calcd for C16H18N3O3 [M + H]: 300.1348; found 300.1345.</p><!><p>This compound was prepared by method A in 36% yield as a yellow crystal: mp. 162–164 °C. IR (neat) νmax: 3135, 2935, 1679, 1631, 1605, 1600, 1446, 1412, 1273, 1185 cm−1. 1H NMR (300 MHz, CDCl3) δ: 2.37 (s, 6H), 3.02 (s, 3H), 4.07 (s, 4H), 6.36 (s, 2H), 7.48 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 11.3, 45.6, 56.6, 109.5, 118.6, 136.0, 160.2, 166.0, 185.9. HR-MS (ESI) m/z: calcd for C16H18N3O3 [M + H]: 300.1348; found 300.1347.</p><!><p>This compound was prepared by method A in 72.5% yield as a yellow solid: mp. 190–192 °C. IR (neat) νmax: 3129, 3105, 3042, 2942, 1663, 1605, 1567, 1505, 1477, 1266, 1176, 742 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.89 (quin, J = 6.3 Hz, 2H), 3.37 (t, J = 5.4 Hz, 4H), 3.82 (s, 6H), 7.02 (s, 2H), 7.32 (s, 2H), 7.55 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 21.5, 28.5, 33.3, 118.6, 123.0, 130.3, 138.7, 144.3, 190.1.</p><!><p>This compound was prepared by method A in 74% yield as a yellow–orange solid: mp. 158.5–160 °C. IR (neat) νmax: 3104, 2977, 2933, 1660, 1608, 1567, 1463, 1248, 1157 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.52 (d, J = 6.6 Hz, 6H), 1.78 (quin, J = 6.0 Hz, 2H), 3.28 (t, J = 5.5 Hz, 4H), 4.62 (sep, J = 6.6 Hz, 2H), 7.03 (s, 2H), 7.20 (s, 2H), 7.54 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 21.7, 23.7, 28.6, 47.5, 117.3, 119.3, 130.6, 138.2, 143.0, 190.2. HR-MS (ESI) m/z: calcd for C20H27N4O [M + H]: 339.2185; found 339.2193.</p><!><p>This compound was prepared by method A in 72% yield as a yellow-orange solid. 1H NMR (300 MHz, CDCl3) δ: 0.84 (t, J = 7.3 Hz, 6H), 1.45 (d, J = 6.6 Hz, 6H), 1.74–1.88 (m, 6H), 3.35 (t, J = 5.1 Hz, 4H), 4.43 (sex, J = 6.8 Hz, 2H), 7.06 (s, 2H), 7.31 (s, 2H), 7.61 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 10.6, 21.8, 28.7, 30.9, 53.4, 117.6, 119.5, 130.7, 138.3, 143.6, 190.3. HR-MS (ESI) m/z: calcd for C22H31N4O [M + H]: 367.2498; found 367.2494.</p><!><p>This compound was prepared by method A in 72% yield as a yellow-orange solid. 75% yield. IR (neat) νmax: 3103, 2960, 1663, 1605, 1570, 1281, 1168, 1131 cm−1. 1H NMR (300 MHz, CDCl3) δ: 0.92 (d, J = 6.6 Hz, 12H), 1.85 (quin, J = 6.3 Hz, 2H), 2.06 (m, J = 6.7 Hz, 2H), 3.35 (t, J = 5 Hz, 4H), 3.87 (d, J = 7.4 Hz, 4H), 6.97 (s, 2H), 7.26 (s, 2H), 7.54 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 19.9, 21.6, 28.5, 30.4, 53.8, 119.4, 122.3, 130.1, 138.1, 143.9, 190.1. HR-MS (ESI) m/z: calcd for C22H31N4O [M + H]: 367.2498; found 367.2488.</p><!><p>This compound was prepared by method A in 83% as a yellow solid: mp. 188.5–190 °C. IR (neat) νmax: 2940, 1662, 1609, 1566, 1544, 1155 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.93 (quin, J = 6.0 Hz, 2H), 2.98 (t, J = 6.0 Hz, 4H), 3.96 (s, 6H), 7.59 (s, 2H), 7.65 (s, 2H), 7.72 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 21.9, 28.4, 39.2, 118.8, 127.2, 131.7, 132.7, 140.9, 189.0. HR-MS (ESI) m/z: calcd for C16H19N4O [M + H]: 283.1559; found 283.1567.</p><!><p>This compound was prepared by method A in 97% yield as a yellow solid: mp. 153–154 °C. IR (neat) νmax: 3097, 2943, 1664, 1607, 1450, 1265, 1172, 923 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.87 (quin, J = 6.3 Hz, 2H), 2.84 (t, J = 6.3 Hz, 4H), 3.97 (s, 6H), 6.47 (s, 2H), 7.51 (s, 2H), 7.66 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 21.6, 28.1, 37.1, 108.3, 122.1, 136.1, 137.5, 138.5, 188.5. HR-MS (ESI) m/z: calcd for C16H19N4O [M + H]: 283.1559; found 283.1553.</p><!><p>This compound was prepared by method A in 84% yield as a yellow solid: mp. 158.5–159 °C. IR (neat) νmax: 3073, 2929, 1654, 1594, 1562, 1463, 1259, 1178, 737 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.97 (quin, J = 6.3 Hz, 2H), 3.22 (t, J = 5.4 Hz, 4H), 7.53 (s, 2H), 7.87 (s, 2H), 8.02 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 21.2, 28.7, 122.0, 127.4, 138.6, 144.7, 163.5, 189.2. HR-MS (ESI) m/z: calcd for C14H13N2OS2 [M + H]: 289.0469; found 289.0457.</p><!><p>This compound was prepared by method A in 21% yield as a yellow solid; mp. 184.5–185 °C. IR (neat) νmax: 3091, 3073, 2933, 1661, 1610, 1557, 1265, 1143, 826 cm−1. 1H NMR (300 MHz, DMSO-d6) δ: 1.79 (quin, J = 6.3 Hz, 2H), 3.19 (t, J = 6.3 Hz, 4H), 8.17 (d, J = 1.8 Hz, 2H), 9.24 (d, J = 1.8 Hz, 2H). 13C NMR (75 MHz, DMSO-d6) δ: 22.0, 28.4, 125.3, 127.9, 136.6, 153.0, 155.1, 189.8. HR-MS (ESI) m/z: calcd for C14H13N2OS2 [M + H]: 289.0469; found 289.0463.</p><!><p>This compound was prepared by method A in 13% yield as a yellow crystal: mp. 154–155 °C. IR (neat) νmax: 3134, 2946, 1667, 1623, 1564, 1436, 1308, 1110 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.90 (quin, J = 6.3 Hz, 2H), 2.52 (s, 6H), 3.05 (t, J = 6.3 Hz, 4H), 7.52 (s, 2H), 7.74 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 13.9, 21.7, 28.4, 124.8, 136.4, 138.0, 140.2, 161.7, 189.1. HR-MS (ESI) m/z: calcd for C16H17N2O3 [M + H]: 285.1239; found 285.1246.</p><!><p>This compound was prepared by method A in 41.5% yield as a yellow solid: mp. 188–188.5 °C. IR (neat) νmax: 3112, 2960, 1681, 1590, 1451, 1428, 1309, 1258, 1168, 1138 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.88 (quin, J = 6.3 Hz, 2H), 2.47 (s, 6H), 3.07 (t, J = 6.3 Hz, 4H), 6.15 (s, 2H), 7.49 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 12.2, 21.4, 28.9, 103.4, 123.5, 140.9, 159.4, 169.6, 189.2. HR-MS (ESI) m/z: calcd for C16H17N2O3 [M + H]: 285.1239; found 285.1229.</p><!><p>This compound was prepared by method A in 90% yield as a yellow solid: mp. 192.5–193 °C. IR (neat) νmax: 2928, 1624, 1577, 1557, 1415, 1274, 1179, 1143 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.91 (quin, J = 6.0 Hz, 2H), 2.34 (s, 6H), 3.07 (t, J = 6.0 Hz, 4H), 6.13 (s, 2H), 7.48 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 11.3, 21.0, 28.2, 108.7, 120.3, 139.1, 160.0, 166.7, 188.5. HR-MS (ESI) m/z: calcd for C16H17N2O3 [M + H]: 285.1239; found 285.1234.</p><!><p>This compound was prepared by method A in 39% yield as a yellow solid: mp. 177–178 °C. IR (neat) νmax 2920, 1650, 1621, 1590, 1554, 1479, 1414, 1278 cm−1. 1H NMR (300 MHz, CDCl3) δ: 3.82 (s, 6H), 7.03 (s, 2H), 7.22 (s, 2H), 7.62 (d, J = 15.3 Hz, 2H), 7.49 (d, J = 15.3 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 33.3, 124.1, 126.6, 127.5, 130.4, 143.5, 188.1. HR-MS (ESI) m/z: calcd for C13H15N4O [M + H]: 243.1246; found 243.1247.</p><!><p>This compound was prepared by method A in 73% yield as a yellow–brown semi-solid. IR (neat) νmax: 3106, 2979, 2932, 1648, 1616, 1462, 1269 cm−1. 1H NMR (300 MHz, CDCl3) δ: 1.47 (d, J = 6.6 Hz, 12H), 4.69 (sep, J = 6.6 Hz, 2H), 7.13 (s, 2H), 7.21 (s, 2H), 7.49 (d, J = 15.0 Hz, 2H), 7.63 (d, J = 15.0 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 23.8, 47.6, 118.6, 126.8, 127.4, 130.9, 142.4, 188.3. HR-MS (ESI) m/z: calcd for C17H23N4O [M + H]: 299.1872; found 299.1865.</p><!><p>This compound was prepared by method B in 40% yield as a yellow solid. 1H NMR (300 MHz, CDCl3) δ: 0.84 (t, J = 7.3 Hz, 6H), 1.47 (d, J = 6.6 Hz, 6H), 1.75–1.86 (m, 4H), 4.46 (sex, J = 6.6 Hz, 2H), 7.10 (s, 2H), 7.25 (s, 2H), 7.58 (d, J = 15.0 Hz, 2H), 7.66 (d, J = 15.0 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 10.5, 21.9, 30.8, 53.5, 118.8, 126.6, 127.9, 130.6, 142.9, 188.3. HR-MS (ESI) m/z: calcd for C19H27N4O [M + H]: 327.2185; found 327.2180.</p><!><p>This compound was prepared by method B in 51% yield as a yellow solid. IR (neat) νmax: 3106, 2962, 1648, 1618, 1594, 1474, 1445, 1301 cm−1. 1H NMR (300 MHz, CDCl3) δ: 0.93 (d, J = 6.6 Hz, 12H), 2.05 (m, J = 6.8 Hz, 2H), 3.87 (d, J = 7.4 Hz, 4H), 7.02 (s, 2H), 7.19 (s, 2H), 7.47 (d, J = 15.0 Hz, 2H), 7.57 (d, J = 15.0 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 19.9, 30.6, 53.6, 123.4, 126.9, 127.4, 130.4, 143.3, 188.1. HR-MS (ESI) m/z: calcd for C19H27N4O [M + H]: 327.2185; found 327.2188.</p><!><p>This compound was prepared by method B in 61% yield as a yellow solid: mp. 111–112 °C. IR (neat) νmax: 2945, 1652, 1618, 1591, 1281 cm−1. 1H NMR (300 MHz, CDCl3) δ: 3.99 (s, 6H), 6.66 (d, J = 2.1 Hz, 2H), 6.89 (d, J = 15.3 Hz, 2H), 7.49 (d, J = 2.1 Hz, 2H), 7.64 (d, J = 15.3 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 37.1, 105.9, 126.8, 128.4, 138.4, 138.9, 187.3. HR-MS (ESI) m/z: calcd for C13H15N4O [M + H]: 243.1246; found 243.1239.</p><!><p>This compound was prepared by method B in 55% yield as a yellow solid: mp. 124–126 °C. IR (neat) νmax: 3112, 2923, 1652, 1615, 1594, 1477, 1330, 1311, 1094 cm−1. 1H NMR (300 MHz, CD3OD) δ: 7.33 (d, J = 15.9 Hz, 2H), 7.48 (d, J = 3.0 Hz, 2H), 7.82 (d, J = 15.9 Hz, 2H), 7.95 (d, J = 3.0 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 122.1, 128.6, 134.7, 145.1, 163.7, 187.6. HR-MS (ESI) m/z: calcd for C11H9N2OS2 [M + H]: 249.0156; found 249.0154.</p><!><p>This compound was prepared by method B in 50% yield as a yellow solid: mp. 139–140 °C. IR (neat) νmax: 3085, 1649, 1620, 1593, 1482, 1273, 1176, 1085 cm−1. 1H NMR (300 MHz, DMSO-d6) δ: 7.37 (d, J = 15.6 Hz, 2H), 7.80 (d, J = 15.6 Hz, 2H), 8.23 (d, J = 1.8 Hz, 2H), 9.23 (d, J = 1.8 Hz, 2H). IR (neat) νmax: 3104, 2977, 1645, 1611, 1578, 1478, 1427, 1285, 1077 cm−1. 13C NMR (75 MHz, CDCl3) δ: 122.1, 127.8, 134.5, 152.9, 153.8, 189.5. HR-MS (ESI) m/z: calcd for C11H9N2OS2 [M + H]: 249.0156; found 249.0166.</p><!><p>This compound was prepared by method B in 5% yield as a yellow crystal: mp. 140–141 °C. 1H NMR (300 MHz, CDCl3) δ: 2.52 (s, 6H), 7.20 (d, J = 15.6 Hz, 2H), 7.53 (d, J = 15.6 Hz, 2H), 7.75 (s, 2H). 13C NMR (75 MHz, CDCl3) δ: 13.9, 126.6, 130.7, 137.6, 139.9, 162.7, 188.6. HR-MS (ESI) m/z: calcd for C13H13N2O3 [M + H]: 245.0926; found 245.0936.</p><!><p>This compound was prepared by method B in 13% yield as a yellow crystal: mp. 153–154 °C. IR (neat) νmax: 3134, 1680, 1642, 1599, 1450, 974, 804 cm−1. 1H NMR (300 MHz, CDCl3) δ: 2.50 (s, 6H), 6.26 (s, 2H), 7.06 (d, J = 16.2 Hz, 2H), 7.65 (d, J = 16.2 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 12.3, 99.5, 130.5, 131.3, 160.0, 170.6, 188.0. HR-MS (ESI) m/z: calcd for C13H13N2O3 [M + H]: 245.0926; found 245.0919.</p><!><p>This compound was prepared by method B in 26% yield as a yellow crystal: mp. 167–169 °C. IR (neat) νmax: 3112, 2925, 1677, 1642, 1609, 1573, 1414, 1091, 994 cm−1. 1H NMR (300 MHz, CDCl3) δ: 2.37 (s, 6H), 6.41 (s, 2H), 7.18 (d, J = 15.9 Hz, 2H), 7.50 (d, J = 15.9 Hz, 2H). 13C NMR (75 MHz, CDCl3) δ: 11.4, 108.1, 126.5, 128.8, 160.6, 165.4, 187.2. HR-MS (ESI) m/z: calcd for C13H13N2O3 [M + H]: 245.0926; found 245.0921.</p><!><p>This compound was prepared by method B in 89% yield as a yellow solid: mp. 79–80 °C. IR (neat) νmax: 3050, 3004, 2926, 1655, 1628, 1600, 1581, 1466, 1430, 1330, 1195, 981, 785 cm−1. 1H NMR (300 MHz, CD3OD) δ: 7.44 (dd, J = 7.0, 3.9 Hz, 2H), 7.61 (d, J = 15.9 Hz, 2H), 7.77 (d, J = 7.0 Hz, 2H), 7.79 (d, J = 15.9 Hz, 2H), 7.91 (dt, J = 7.0, 1.2 Hz, 2H), 8.65 (d, J = 3.9 Hz, 2H).</p><!><p>The PC-3 prostate cancer cell line was routinely cultured in RPIM-1640 medium supplemented with 10% FBS, 4 mM glutamine, 1 mM sodium pyruvate, 100 IU/mL penicillin, 100 ug/mL streptomycin and 0.25 ug/mL amphotericin. Cultures were maintained in 5% carbon dioxide at a temperature of 37 °C. The DU-145 prostate cancer cells were routinely cultured in phenol red-free DMEM supplemented with 10% FBS, 4 mM glutamine, 1 mM sodium pyruvate, 100 IU/mL penicillin, 100 ug/mL streptomycin and 0.25 ug/ mL amphotericin.</p><!><p>PC-3 or DU-145 cells were plated in 24-well plates at a density of 20,000 each well in 10% FBS DMED medium. The cells were then treated with curcumin, or synthesized curcumin analogs separately at 6 different doses ranging from 0.01 µM to 10 µM for 5 days, while equal treatment volumes of DMSO were used as vehicle control. Cell numbers were counted with a cell viability analyzer (Beckman-Coulter). The ratio of drug treated viable cell numbers to vehicle treated viable cell numbers was defined as percentage viability. IC50 values were obtained from dose–response curves for each curcumin analog.</p>

PubMed Author Manuscript

Prolonged Amphetamine Treatments Cause Long-Term Decrease of Dopamine Uptake in Cultured Cells

Amphetamine (AMPH) is a systemic stimulant used to treat a variety of diseases including Attention Deficit Hyperactive Disorder, narcolepsy and obesity. Previous data showed that by binding to catecholamine transporters, AMPH prevents the reuptake of the neurotransmitters dopamine (DA) and norepinephrine (NE). Because AMPH, either used therapeutically at final concentrations of 1\xe2\x80\x9310 \xce\xbcM or abused as recreational drug (50\xe2\x80\x93200 \xce\xbcM), is taken over long periods of time, we investigated the prolonged effects of this drug on the uptake of DA. We found that, in LLC-PK1 cells stably expressing the human DA transporter (hDAT), pretreatments with 1 or 50 \xce\xbcM AMPH caused significant reduction in DA uptake right after the 15-h pretreatment. Remarkably, after 50 but not 1 \xce\xbcM AMPH pretreatment, we observed a significant reduction in DA uptake also after one, two or three cell divisions. To test whether these long-term effects induced by AMPH where conserved in a model comparable to primordial neuronal cells and native neurons, we used the human neuroblastoma cell line SH-SY5Y cells, which were reported to endogenously express both hDAT and the NE transporter. Pretreatments with 50 \xce\xbcM AMPH caused a significant reduction of DA uptake both right after 15 h and 3 cell divisions followed by neuro-differentiation with retinoic acid (RA) for 5 days. Under these same conditions, AMPH did not change the intracellular concentrations of ATP, ROS and cell viability suggesting, therefore, that the reduction in DA uptake was not cause by AMPH-induced toxicity. Interestingly, while 1 \xce\xbcM AMPH did not cause long-term effects in the LLC-PK1 cells, in the SH-SY5Y cells, it decreased the DA uptake after one, two, but not three, cell divisions and 5-day RA differentiation. These data show that besides the well-known acute effects, AMPH can also produce long-term effects in vitro that are maintained during cell division and transmitted to the daughter cells.

prolonged_amphetamine_treatments_cause_long-term_decrease_of_dopamine_uptake_in_cultured_cells

Introduction<!>Cell Cultures<!>Uptake Assays<!>Elisa Experiments<!>ATP and ROS Levels<!>Statistics<!>Prolonged Treatment with 50 but not 1 \xce\xbcM Amphetamine Reduces Dopamine Uptake up to 3-Cell Divisions in hDAT Expressing LLC-PK1 Cells<!>1 and 50 \xce\xbcM Amphetamine Reduce Dopamine Uptake up to 3-Cell Divisions in SH-SY5Y<!>Prolonged Treatments with 50 \xce\xbcM Amphetamine do not Affect Cell Viability<!>Prolonged Treatments with 50 \xce\xbcM Amphetamine do not Change Intracellular ATP or ROS in SH-SY5Y Cells<!>Discussion

<p>The neurotransmitters dopamine (DA) and norepinephrine (NE) belong to the catecholamine and phenylethylamine families of organic compounds and play an important role in fine-tuning a variety of animal behaviors such as movement, reward, cognition and attention. Following their synthesis, DA and NE are rapidly sequestered inside the neuronal vesicles by the vesicular monoamine transporter (VMAT), where they are packed until a depolarizing stimulus promotes the fusion of vesicles to the cellular membrane and the extracellular release of the neurotransmitters. In the synaptic cleft, DA and NE bind and activate their respective receptors and, thus, propagate dopaminergic and noradrenergic signaling. Although most of the released catecholamines diffuse away from the synapse [1], a good portion binds to the DA and/or NE transporters (DAT and NET) [2, 3]. This step prevents further stimulation of the receptors. Therefore, DAT and NET control the intensity and the duration of the signal propagated by DA and NE. Moreover, when DAT moves DA inside the neurons, it causes cell-membrane depolarization affecting, therefore, neuronal excitability [4, 5].</p><p>All substances that induce dependence increase the extracellular concentration of DA and NE [6–8]. Amphetamine (AMPH) for example, performs this task through two different mechanisms. As the chemical structure of AMPH is very similar to that of DA and NE, AMPH is carried inside the neurons by DAT or NET preventing, therefore, the reuptake of these catecholamines [9]. Once inside the neurons, AMPH forces DA and NE out of the storage vesicles by acting on VMAT [10]. The subsequent increase of cytoplasmic DA/NE induces DAT or NET to work in reverse resulting in the efflux of DA/NE into the synaptic cleft [11, 12]. The overall effect is, therefore, the accumulation of larger amounts of extracellular DA/NE with respect to that obtained using DAT or NET inhibitors, such as cocaine or methylphenidate [13].</p><p>Previous reports demonstrated that acute and brief (1 min) treatments with AMPH increase the surface expression of DAT [14, 15], whereas brief repeated or longer treatments (5–60 min) cause a decrease of surface expression of DAT, as measured by reduced DA uptake activity and DAT-mediated inward currents [16–19]. These effects were thought most likely be due to reallocation of the transporter from the plasma membrane to intracellular compartments [16, 20, 21], though German et al. reported that in vivo treatments with AMPH reduced the transport activity of murine striatal DAT without concomitant internalization of the transporter in ex vivo preparations [22].</p><p>The data mentioned above are examples of the several studies carried out over the last decades on the effects that acute AMPH treatments generate on DAT or NET activity. On the other hand, there are few data describing the effects generated by prolonged [23] AMPH treatments on the two transporters. Here we investigated the effects caused by 15-h treatments with 1 or 50 μM of AMPH on the uptake activity of hDAT heterologously expressed in the pig kidney cells or in the human neuroblastoma SH-SY5Y cells which endogenously express DAT and NET. We found that after 15 h treatment, both concentrations of AMPH reduced the accumulation of [3H]DA inside LLC-PK1 and SH-SY5Y cells. Interestingly, this effect was inherited by the daughter cells up to three cell divisions in the LLC-PK1 cells treated with 50, but not 1 μM AMPH, whereas in the SH-SY5Y cells, both doses caused a significant reduction of [ 3H]DA uptake in daughter cells after three and two cell divisions, respectively. Taken together, these data suggest that AMPH causes long-lasting effects that can be maintained within the cell and inherited during mitosis.</p><!><p>LLC-PK1 cells stably transfected with hDAT were kindly provided by Dr. Roxanne Vaughan and James Foster (University of North Dakota). LLC-PK1 cells were maintained in α-modified Eagle's medium (AMEM) containing 2 mM l-glutamine and 200 μg/mL G418 sulfate; whereas SH-SY5Y cells were grown in Dulbecco's Modified Eagle's Medium/F12 (DMEM). Both media were supplemented with 5% and 10% fetal bovine serum (FBS), respectively, and 1% penicillin/streptomycin. Cells were passaged according to the following protocol: after removing the media, cells were washed twice with 10 mL sterile phosphate buffer saline (PBS). After the PBS washes, 2 mL of trypsin solution was added to promote cell detachment from the flask. Trypsin was removed by aspiration and the cells were incubated in a 37 °C incubator for 5 mins. Once cells detached, the cell suspension was gently mixed with 10 mL DMEM and collected in a 15 mL tube. Cell pellets were collected by centrifugation at 1500 rpm at 4 °C for 5 min, re-suspended in 1 mL fresh DMEM media and equally divided in T-75 flasks containing 10–15 mL media.</p><!><p>300,000 or 150,000 cells were seeded in 24- and 12-, or 6-well plates, respectively. Different sizes of well-plates were used to accommodate the higher number of cells yielded after two and three cell divisions. Six-seven hours after being plated, cells were treated with 1 μM, 50 μM AMPH or control solution for 15 h. After 15 h, cells were washed three times with PBS and either used to measure [3H]DA uptake or grown in fresh media to cross one, two or three cell divisions in order to perform uptake assay in daughter cells. Uptake assays were performed as previously published in Carvelli et al. [5]. Briefly, LLC-PK1 cells were washed three times with Krebs-Ringer HEPES (KRH) buffer. KRH containing 0.1 mM tropolone, 0.1 mM ascorbic acid and 0.1 mM pargyline (KRH + TAP), was added to the wells to inhibit DA degradation and oxidation before performing the uptake assays. Finally, [3H]DA (PerkinElmer) was added in each well to obtain a final concentration of 20 nM. After 5 min, cells were washed with cold KRH + TAP three times and lysed with 1% triton solution. Lysates were collected in vials and radioactivity was counted using a β-counter. For SH-SY5Y cells, the experimental paradigm was similar to that utilized for LLC-PK1 cells. Cells plated in 24, 12, or 6 wells plates for 6–7 h were treated with 1 μM, 50 μM AMPH or control solution for 15 h. After the drug was washed off with three washes with PBS, one set of cells was immediately assayed for DA uptake. Another set of cells was let grow to pass one cell division as determined by cell counting. At this point, cells were treated with 10 μM retinoic acid (RA) in low serum media (DMEM containing 1% FBS) for 5 days, during which media was replaced once with fresh media containing 10 μM retinoic acid. Cells that went through two or three cells divisions also received the 5-day RA treatment before being assayed for [3H]DA uptake as described above for the LLC-PK1 cells.</p><!><p>To investigate possible residue of AMPH inside the SH-SY5Y cells, we used the Enzyme-linked Immunosorbent Assay (ELISA) kits (Abnova, TW). These consist of micro-wells coated with polyclonal anti-d-AMPH and d-AMPH conjugated to horseradish peroxidase (HRP). The principle of the assay is based on the competitive binding of AMPH and AMPH-HRP in proportion to their concentration in the reaction mixture. Cells were first detached with trypsin and collected by centrifugation. Then cells were washed with cold PBS three times, re-suspended in PBS to be sonicated (5 pulses for 5 s and then 10 pulses for 10 s) and subjected to centrifugation at 1500×g (4000 rpm) for 10 min at 2–8 °C to remove cellular debris. Supernatants were collected and stored at − 20 °C or − 80 °C to avoid loss of bioactivity and contamination. On the day of the experiment, samples were brought to room temperature and 10 μL of each sample, control and AMPH-treated, were incubated with 100 μL dilution of enzyme (Horseradish peroxidase) labeled d-AMPH derivative in micro-plate wells which are coated with fixed amounts of oriented high affinity purified polyclonal antibody. Samples were incubated for 60 min at room temperature in the dark. After removing the enzyme conjugate and washing the wells with 200 μL distilled water, the chromogenic substrate was added followed by an acid stop solution to cease the color produced from the substrate. Finally, the absorbance in each well was read within 1 h at a wavelength of 450 nm.</p><!><p>ATP changes were measured using the CellTiter-Glo Assay kit (Promega) following the protocol recommended in the kit. Briefly, 4000 and 8000 SH-SY5Y cells were plated in white opaque-walled 96-well plates (Falcon Cat. # 353296) in DMEM/F12 media with 10%FBS and 1% PenStrep. After 24 h, cells were treated with 50 μM AMPH or control solution in fresh media for 15 h at 37 °C in a 5% CO2 incubator. Cells were washed twice with PBS and phenol-red-free DMEM media was added to the cells. Cells were equilibrated at room temperature for about 30 min before adding the CellTiter-Glo reagents. The luminescent signal from each well, which is proportional to the amount of ATP, was measured using a microplate reader (BioTek Synergy H1) During each experiment, a standard curve was generated using increasing concentrations of ATP (0.025–0.25 μM). Data from each experiment were normalized to control samples with the lower number of cells and analyzed for normality tests and statistical significance using GraphPad Prism 7.04 software.</p><p>Reactive Oxygen Species (ROS) were quantified by using the Invitrogen Molecular Probe 2′,7′-dichlorodihydroflorescein diacetate reconstituted in DMSO (H2-DCFDA, Cat. # D339). 10,000 or 20,000 SH-SY5Y cells were plated in black 96 well plates (Costar #3603) and treated with 50 μM AMPH or control solution for 15 h. Cells were washed twice with PBS and incubated with 50 μM H2-DCFDA for 30 min in phenol-red free and serum free DMEM at 37 °C. The dye was removed, and fresh phenol-red free medium was added to the cells to recover. Cells treated with 50 μM Luperox TBH 70× (Sigma Cat. # 451839) for 1 h following H2-DCFDA were used as positive controls. Fluorescent intensity was measured with a microplate reader (BioTek Synergy H1) at excitation and emission wavelength of 480 and 530 nm respectively. Data from each experiment were normalized to control samples with the lower number of cells and analyzed for normality tests and statistical significance using GraphPad Prism 7.04 software.</p><!><p>Data collected form each experiment were imported in Graphpad Prism software and evaluated for normality and statistical significance. Normality tests were performed using KS, Shapiro-Wilk or D'Agostino and Pearson omnibus tests. Statistical significance was performed using the parametric one-way ANOVA test and Bonferroni's Multiple Comparison post-test. Data were produced by three independent experiments, otherwise indicated, and in each experiment, samples were tested in triplicates or quadruplicates. See figure legends for number of repetitions in each experiment.</p><!><p>Previous data showed that AMPH pretreatments of 1 or 60 min enhanced or diminished the amount of DAT at the cell membrane, respectively [14, 15, 19]. Here, we investigated whether continuous and prolonged treatments with AMPH caused changes in DA uptake and for how long these changes were maintained. About 260 mg of methamphetamine, taken as single dose by drug abusers, generate a pick of about 7.5 μM in the human blood [24] and concentrations seven times higher, i.e. 52 μM, in rodent brains [25]. Thus, we pretreated hDAT-expressing LLC-PK1 cells with 50 μM AMPH or control solution for 15 h and, then, thoroughly washed the cells to remove the drug. In a subset of samples, [3H]DA uptake experiments were immediately performed to quantify the amount of [3H]DA accumulated inside the cells. Another subset of cells was grown until their cell count was a value proximal to double, triple or quadruple of the initial number of cells counted before initiating the AMPH pretreatment. This indicates how many cell divisions the cells went through after the AMPH pretreatment. Cells tested right after 15-h AMPH pretreatment exhibited a statistically significant 37 ± 3% reduction in [3H]DA uptake with respect to samples pretreated with control solution (Fig. 1a; *p < 0.0001; one-way ANOVA test). Both control- and AMPH-pretreated samples displayed almost no [ 3H]DA uptake when the DAT inhibitor GBR12935 was included during the uptake assay, suggesting therefore that the accumulation of [3H]DA inside these cells occurs exclusively through DAT. Interestingly, the other subsets of cells that went through 1, 2 or 3 cell divisions after the AMPH pretreatment also exhibited a statistically significant reduction in [3H]DA uptake with respect to control-treated samples, 37 ± 3%, 39 ± 5% and 44 ± 2%, respectively, (Fig. 1b–d; *p < 0.0001; one-way ANOVA test). And, still under these conditions, the [3H] DA uptake was blocked by 10 μM GBR12935.</p><p>Using previously published data [24, 25], we calculated that concentrations of AMPH for therapeutic use (5–30 mg) yield about 1–6 μM AMPH in the brain. Thus, we investigated whether 1 μM AMPH produced effects similar to those obtained with 50 μM AMPH. As shown in Fig. 1e, 1 μM AMPH caused a statistically significant 20 ± 3% reduction in [3H]DA uptake when the assay was performed right after the 15 h pretreatment with AMPH (*p < 0.0001; one-way ANOVA). However, no difference between control- and AMPH-pretreatment was observed after the cells went through 1, 2 or 3 cell divisions (Fig. 1f–h). Taken together these data show that in LLC-PK1, 1 μM AMPH causes short-lived changed in DA uptake, whereas 50 μM AMPH produce a more penetrant effect which is maintained after three cell cycles.</p><!><p>Our initial experiments in LLC-PK1 cells showed that 1 or 50 μM AMPH significantly reduced DA uptake in parent cells and this effect was transmitted in daughter cells only by 50 μM AMPH. To test whether these long-term effects induced by AMPH were conserved in other cell types, we used the human neuroblastoma cell line SH-SY5Y which have been shown to endogenously expresses DAT as well as NET [26–28]. To test if prolonged AMPH pretreatments generate long-term effects on DAT activity also in SH-SY5Y cells, we used the same experimental paradigm used for the LLC-PK1 cells with the addition of one extra step. After the 15 h of AMPH pretreatment, cells were incubated with 10 μM retinoic acid (RA) for 5 days before performing the uptake assay. This step, as previously shown [29, 30], generates a higher number of differentiated neurons. Our pilot data showed that following RA-induced differentiation, 100 nM GBR12935, a specific DAT blocker, only partially blocked the DA uptake whereas, 100 nM desipramine, a specific NET inhibitor, completely blocked DA uptake in the SH-SY5Y cells (Fig. 2). These results suggest that most of the DA in the SH-SY5Y cells is taken up by NET. Therefore, 100 nM desipramine were co-incubated with [3H]DA to show the contribution of DAT/NET in our uptake experiments. We found that pretreatment with 50 μM AMPH significantly reduced DA uptake to 55 ± 0.14% (*p < 0.0001; one-way ANOVA) with respect to control-treated cells (Fig. 3a). This effect was maintained after 1- (45 ± 0.2%), 2- (51 ± 0.15%) and 3- (52 ± 0.08%) cell divisions (Fig. 3b–d; *p < 0.0001; one-way ANOVA). Similarly, pretreatments with 1 μM AMPH caused a statistically significant 25 ± 0.05% reduction (*p < 0.0001; one-way ANOVA) in DA uptake after 15-h AMPH pretreatment (Fig. 3e). Remarkably, we found that the effect of 1 μM AMPH was also inherited in daughter cells after 1- (26 ± 0.05%) and 2-cell divisions (15 ± 0.1%), but not after 3-cell divisions (Fig. 3f–h; *p < 0.0001; one-way ANOVA). 100 nM desipramine, co-incubated with 20 nM [3H]DA, caused 88–98% inhibition of uptake throughout our experiments (Fig. 3).</p><p>Although unlikely, the AMPH-induced reduction of DA uptake observed after the three cell divisions could be caused by residual AMPH not properly washed out from our samples. In this regards, it is worthwhile to note that the DA uptake experiments done after 1, 2 or 3 cell divisions, were performed after 1, 3 or 4 days from the AMPH pretreatment in the LLC-PK1 cells, and after 6, 8 or 9 days in the SH-SY5Y cells. Moreover, before the uptake assay, cells were subjected to changes of media and thoroughly washed with KRH buffer. Nevertheless, we tested the possibility that residual AMPH concentrations were left in our samples by measuring the amount of AMPH present in the SH-SY5Y cells after 15 h of 50 μM AMPH pretreatment and washed three times as we did during the uptake experiments. Using Elisa kits, we found that the concentration of AMPH in cells pretreated with AMPH was 0.013 ± 0.001 pM and this value was comparable to the amount of AMPH, 0.01 ± 0.003 pM, measured in cells pretreated with control solution which never encountered AMPH. These results show that the AMPH-induced reduction of DA uptake measured in our assays (Figs. 1 and 3) is not caused by residual AMPH left in the cell cultures as proved by our Elisa data.</p><p>Taken together, these data suggest that prolonged AMPH exposure in cultured cells expressing hDAT and/or hNET exogenously or endogenously causes long-term effects in the uptake activity of the transporters that are maintained during cell divisions.</p><!><p>Previous data showed that 24-h treatment with 3 mM AMPH cause cell toxicity and consequent death in the SH-SY5Y cells [31]. In our study, we used much lower concentrations of AMPH (1 or 50 μM) for 15 h. Nonetheless, we reasoned to test whether, under our experimental conditions, AMPH caused cell death. If that were the case, then the reduction of DA uptake observed after 15-h treatment and up to 3-cell divisions would be the result of a reduced number of DAT/NET-expressing cells. Thus, during our uptake experiments, extra samples were used in parallel to assess cell viability and to count the number of SH-SY5Y cells that went through the 15-h pretreatment with 50 μM AMPH or control solution. Cell viability, assessed by trypan blue staining combined with an automated cell counter, was in the range of 99–100% for both control- and AMPH-treated samples (Table 1). As shown in the same table, no difference in cell counting between the two groups was observed neither after 15-h or 1, 2, or 3-cell divisions. Moreover, imaging data showed that AMPH did not induce any obvious change in cell morphology in both SH-SY5Y and LLC-PK1 cells (data not shown). These results suggest that 50 μM AMPH for 15 h do not cause cell death in both LLC-PK1 and SH-SY5Y cells.</p><!><p>Administrations of high-dose (10–15 mg/kg) of AMPH and AMPH analogues in rodents increase reactive oxygen species (ROS) and decrease ATP levels [32]. Therefore, we tested whether the highest concentration of AMPH we used, 50 μM, altered ATP and/or ROS production. Using the CellTiter-Glo Assay kit (Promega) we measured the ATP levels in SH-SY5Y cells right after 15 h pretreatment with 50 μM AMPH or after 1-cell division, and found no change with respect to control-treated cells (Fig. 4a, b, compare white bars with gray bars). On the other hand, the ATP values doubled in samples containing twice as much the number of cells (*p < 0.001 and ∞p < 0.001; one-way ANOVA, Bonferroni post-test). These experiments show that while the luminescence signal, which is representative of intracellular ATP, is proportional to the number of cells tested, the treatment with AMPH does not change the intracellular levels of ATP.</p><p>Next, we tested whether prolonged treatments with 50 μM AMPH changed the intracellular amount of ROS by using the 2′,7′-dichlorodihydroflorescein diacetate (H2-DCFDA) molecular probe (Invitrogen). H2-DCFDA is cell permeant and, once inside the cell, its acetate groups are removed by intracellular esterases. If ROS are available, oxidation occurs and the fluorescent 2′,7′-dichlorofluorescin (DCF) is produced. Thus, an increase in fluorescence reflects higher amounts of intracellular ROS. As shown in Fig. 4c and d, no significant difference was observed between control- and AMPH-treated SH-SY5Y cells neither after 15 h treatment nor 1-cell division (compare white bars with gray bars). However, also in this case, a significant increase of ROS levels was detected in samples containing a higher number of cells (*p < 0.001 and ∞p < 0.001; one-way ANOVA, Bonferroni post-test) confirming the efficacy of the probe. Taken together, these data show that, when exposed to 50 μM AMPH for 15 h, the SH-SY5Y cells do not exhibit significant changes in ROS or ATP.</p><!><p>Since it was discovered, AMPH has been used to treat a variety of mental and physical conditions. Currently, low concentrations of AMPH (5–30 mg/pill) are successfully used to treat patients affected with narcolepsy, chronic fatigue syndrome and those affected with attention deficit disorders. On the other hand, data suggesting systemic toxicity caused by elevated and prolonged use of AMPH (260–1000 mg) have been reported both in animal models and postmortem human samples [33]. These opposite outcomes suggest that the concentration of AMPH is a crucial factor in determining the final effects of AMPH.</p><p>It is well established that AMPH increases neurotransmission of DA and NE by competing with the transport of these catecholamines and by displacing their storage vesicles. As a matter of fact, DAT and NET are direct targets of AMPH [34]. Moreover, previous reports have demonstrated that AMPH also alters the amount of DA removed from the synaptic cleft by changing the number of DAT at the plasma membrane [35]. For example, in rat striatal synaptosomes 5 μM AMPH increased surface DAT within 30 s and up 1-min following drug exposure [14]. On the other hand, 1-h treatment with 2 μM AMPH caused loss of DAT on the cell membrane which resulted in 40% reduction of DA uptake [16]. Under these conditions, the reduction of DA uptake caused by AMPH was DAT mediated and not produced by passive membrane diffusion of AMPH, since co-incubation with cocaine, a specific DAT blocker, prevented the AMPH-induced effects on DA uptake. All these data demonstrate that the alterations caused by short treatments of AMPH on DAT function have been well established. However, no study has been performed to test the effects that prolonged AMPH treatments have on the catecholamine transporters. Here we investigated the effect of 15-h continuous exposure of AMPH in cells expressing DAT and/or NET. We used 1 and 50 μM of AMPH because previous data showed that a single dose between 260 and 1000 mg methamphetamine (METH), which are the range of doses used by METH abusers, produce a pick of 7.5–28.8 μM METH in human blood [24]. And since the brain:serum ratio for AMPH measured in rats is about 7:1 [25], we calculated that the amount of AMPH in the brain of AMPH abuser is about 50–200 μM. Similarly, people who use 5–30 mg AMPH as therapeutic treatment are predicted to get about 1–6 μM AMPH in their brain.</p><p>To ensure that our results were representative of AMPH acting specifically at the catecholamine transporters, we first investigated the effects that 15-h pretreatments with 1 or 50 μM AMPH produced in LLC-PK1 cells stably expressing hDAT. At the end of the treatment and after thoroughly washing out AMPH from the samples, we found that both concentrations caused a significant and comparable decrease in DAT activity as measured by quantifying the radioactive [3H]DA accumulated inside the cells. Previous studies reported that acute or short-lasting treatments with AMPH reduces DAT activity by reallocating the transporter from the plasma membrane into intracellular compartments [16, 20, 21, 35]. We can speculate that also 15-h pretreatments with AMPH lower the capability of cells to reuptake DA by increasing DAT internalization from the cell membrane. Interestingly though, we observed that the reduced DA uptake observed in LLC-PK1 cells pretreated with 50 but not 1 μM AMPH was maintained up to three cell divisions, suggesting therefore, that continuous exposure to 50 μM AMPH also produce a non-previously reported effect which is retained during mitosis.</p><p>The LLC-PK1 cells used in our study were engineered to stably express hDAT. Thus, although integrated into the cell genome, the hDAT gene is not normally expressed in these cells. For this reason, we investigated whether the long-term effects generated by AMPH were also observed in a more physiologically relevant cell line. We chose to use the SH-SY5Y cells which are derived from human neuroblastoma cells. These cells contain both epithelial and primordial neuronal cells. The neuronal-type cells are in an early neuronal differentiation stage, characterized by the low presence of markers specific for cholinergic [36, 37], adrenergic and dopaminergic neurons including tyrosine hydroxylase [38], VMAT and DAT [26, 27, 39]. However, after treatment with RA, the SH-SY5Y cells exhibit morphological and biochemical parameters similar to those observed in differentiated neurons [29, 40, 41]. Thus, the SH-SY5Y cells are a good model to study if and how drugs affect neuronal differentiation.</p><p>Previous studies showed that 4–11 days of RA treatment increase the number of catecholaminergic neurons in the SH-SY5Y cells [42]. Accordingly, we found that after a 5-day RA treatment, SH-SY5Y cells efficiently accumulated [3H]DA and, while the NET specific inhibitor desipramine [43] completely blocked DA uptake, the GBR12935 blocker, which has a higher affinity to DAT than NET [44], only partially diminished DA uptake. This result may have two implications: (I) the RA-treated SH-SY5Y cells express only NET and the reduced ability of 100 nM GBR12935 to block DA uptake (Fig. 2) is due to the reduced affinity this blocker has for NET with respect to DAT; or (II) the RA-treated SH-SY5Y cells express more adrenergic neurons (NET-positive cells) than dopaminergic neurons (DAT-positive cells). In this second scenario, the 30% inhibition of DA uptake observed in presence of 100 nM GBR12935 (Fig. 2) is representative of the reduced number of DAT-expressing neurons in our cultures. Nevertheless, our data show that 100 nM desipramine effectively block DA uptake in the SH-SY5Y cells.</p><p>The most important result of our study is that both 1 and 50 μM AMPH pretreatments reduced the transportmediated DA uptake in SH-SY5Y daughter cells, and this effect occurred several days after the AMPH pretreatment. In fact, after the AMPH pretreatment and before the uptake assay, cells went through 1 to 3 cell divisions followed by a 5-day incubation with RA. This last step was required to increase the number of differentiated catecholaminergic neurons, i.e. to increase the number of neurons expressing DAT or NET. These results suggest that prolonged exposure to AMPH during development, when neural progenitor cells are formed, may change the asset of catecholaminergic neurons by reducing the number of neurons expressing DAT/NET or, alternatively, decreasing the expression of catecholamine transporters. One implication of these data is that prolonged use of AMPH during pregnancy might affect neuronal asset in the fetus. However, further experiments are needed to validate these conclusions.</p><p>Although unlikely, our experiments left open the possibility that residual concentrations of AMPH, after the 15-h pretreatment, were left behind in the cells. For this reason, we used an Elisa kit to measure the amount of AMPH and found no difference between control- and AMPH-pretreated cells. Therefore, we concluded that the AMPH-induced long-term effects seen in our experiments occurred during the 15-h exposure to the drug but, some-how, was maintained after the removal of AMPH.</p><p>We also investigated whether the reduction of DA uptake seen in daughter cells could be the result of cell death or changes in ROS and ATP as previous reports showed that high concentrations of AMPH for 24 h reduce viability of cultured cells [31]. The concentrations of AMPH used in our study, 1 and 50 μM, are much lower than those reported to be lethal in cell cultures (100 μM); yet we investigated whether 50 μM AMPH could induce cell death under our experimental conditions. We found that cells treated with AMPH for 15 h had 99–100% viability, presented no morphological modification and showed no change in cell counting yielded right after the pretreatment or after the various cell divisions with respect to control-treated cells. Moreover, the intracellular levels of ATP or ROS in SH-SY5Y were not changed by 15-h treatment with 50 μM AMPH (Fig. 4). Thus, our data demonstrate that the reduction of DA uptake we measured in our experiments is not due to residual concentrations of AMPH in the cells nor to AMPH-induced cell toxicity, rather they suggest that prolonged treatments with 1–50 μM AMPH may activate unknown mechanisms that can be inherited during cell divisions.</p><p>In conclusion, our results suggest that prolonged AMPH exposure during neuronal differentiation may affect the function/expression of DAT/NET in mature neurons. In fact, the experiments performed with SH-SY5Y cells were designed in a way that the treatment with AMPH preceded neuronal differentiation, i.e. when the SH-SY5Y cells exhibit features typical of an early neuronal differentiation stage or neural progenitor cells [36–39], but DAT/NET function was measured after RA-induced differentiation, i.e. when SH-SY5Y cells exhibit both morphological and biochemical features similar to those observed in differentiated neurons [29, 40–42]. Thus, we speculate that prolonged AMPH exposures during proliferation and differentiation of primordial neuronal cells change DA reuptake in mature neurons.</p>

PubMed Author Manuscript

Structure-based mutagenesis reveals critical residues in the transferrin receptor participating in the mechanism of pH-induced iron release from human serum transferrin

The recent crystal structure of two monoferric human serum transferrin (FeNhTF) molecules bound to the soluble portion of the homodimeric transferrin receptor (sTFR) has provided new details of this binding interaction which dictates iron delivery to cells. Specifically, substantial rearrangements in the homodimer interface of the sTFR occur as a result of the binding of the two FeNhTF molecules. Mutagenesis of selected residues in the sTFR highlighted in the structure was undertaken to evaluate the effect on function. Elimination of Ca2+ binding in the sTFR by mutating two of four coordinating residues ([E465A,E468A]) results in low production of an unstable and aggregated sTFR. Mutagenesis of two histidines ([H475A,H684A]) at the dimer interface had little effect on the kinetics of iron release at pH 5.6 from either lobe, reflecting the inaccessibility of this cluster to solvent. Creation of a H318A sTFR mutant allows assignment of a small pH dependent initial decrease in the fluorescent signal to His318. Removal of the four C-terminal residues of the sTFR, Asp757-Asn758-Glu759-Phe760, eliminates pH-stimulated iron release from the C-lobe of the Fe2hTF/sTFR \xce\x94757\xe2\x80\x93760 complex. The loss is accounted for by the inability of this sTFR mutant to bind and stabilize protonated hTF His349 (a pH-inducible switch) in the C-lobe of hTF. Collectively, these studies support a model in which a series of pH-induced events involving both TFR residue His318 and hTF residue His349 occurs in order to promote receptor-stimulated iron release from the C-lobe of hTF.

structure-based_mutagenesis_reveals_critical_residues_in_the_transferrin_receptor_participating_in_t

<!>Materials<!>Expression and Purification of sTFR Mutants<!>Immunoblotting<!>hTF/sTFR Complex Formation and Purification<!>Kinetics of Iron Release from hTF/sTFR Complexes at pH 5.6<!>Urea gel analysis of hTF/mutant sTFR Complexes<!>Recombinant sTFR production<!>pH-inducible changes in the sTFR<!>Iron release kinetics from hTF/H318A sTFR complexes<!>Iron release kinetics from hTF/[H475A,H684A] sTFR complexes<!>Iron release kinetics from hTF/sTFR \xce\x94757\xe2\x80\x93760 complexes<!>Kinetic analysis of the FeChTF/sTFR \xce\x94757\xe2\x80\x93760 complex as a function of pH<!>DISCUSSION

<p>The efficient delivery of iron to mammalian cells relies on the transferrin/transferrin receptor system. Iron (in the form of Fe3+) circulates in the blood bound to the ~80 kDa bilobal glycoprotein human serum transferrin (hTF). The two lobes of hTF (termed N- and C-lobes) are further divided into two subdomains (N1 and N2, C1 and C2). As with most other members of the transferrin superfamily, hTF is capable of binding two Fe3+ atoms deep within a cleft formed between the two subdomains of each lobe.1, 2 The sequestration of Fe3+ within the cleft of hTF is critical to preventing its hydrolysis or reduction to Fe2+ which promotes the production of harmful radicals via the Fenton series of reactions.3, 4 Iron-containing hTFs (diferric and the two monoferric hTF species) bind with nM affinity to the homodimeric transferrin receptor (TFR) located on the cell surface of all cells.5 Following clathrin-dependent endocytosis, a decrease in pH within the early endosome (to ~5.5–6.0) aids in the release of Fe3+ (to an as of yet unidentified biological chelator) from the hTF/TFR complex in a receptor-mediated manner.6, 7 Still within the endosome, the Fe3+ must be reduced to Fe2+ by a ferrireductase, such as Steap38, before being transported out of the endosome by the divalent metal transporter, DMT1.9 Iron-free hTF, referred to as apohTF, remains tightly bound to the TFR at endosomal pH, facilitating its proper sorting and recycling back to the cell surface.10 Upon exposure to the more alkaline pH of the blood (~7.4), apohTF is released or displaced by Fe2hTF from the TFR11 and becomes available to sequester more Fe3+.</p><p>The TFR plays a critical role throughout the entire process of cellular iron delivery. Iron release from hTF requires opening of the cleft and is accompanied by large conformational changes within each lobe (opening ~59° and 50° for the N- and C-lobes, respectively).1, 2 These significant conformational changes in hTF must be accommodated and compensated for by the TFR within the hTF/TFR complex. As well as being critical to the discrimination between iron-containing and apohTF at both neutral and endosomal pH, the TFR also significantly affects iron release from hTF at endosomal pH.12 Clearly, the interactions controlling this finely tuned system of cellular iron delivery must be elucidated to understand this process completely.</p><p>The crystal structure of the TFR ectodomain (PDB ID: 1CX8) 13 revealed that the homodimeric receptor is comprised of three distinct domains: a protease-like domain, an apical domain and a helical domain. Importantly, the TFR crystal structure was used to create a cryo-EM model of the hTF/TFR complex (PDB ID: 1SUV).14 The 7.5 Å resolution cryo-EM model provided the first structural insight into the hTF-TFR interaction. In the model, the N-lobe of hTF is situated between the protease-like domain of the TFR and the cell membrane, while a large portion of the C1 subdomain interacts with the helical domain of the TFR. However, given the relatively low resolution, the model lacked the precision needed to identify specific molecular interactions between hTF and the TFR.</p><p>The recently solved 3.22 Å crystal structure of recombinant monoferric N-lobe hTF (FeNhTF) bound to the soluble portion of the TFR (sTFR, residues 121–760, PDB ID: 3S9L)15 has provided more detailed information with regard to the binding interactions between hTF and the TFR (Figure 1A). As previously predicted from the crystal structure of the TFR bound to another ligand (the HFE protein),16 the FeNhTF/sTFR structure shows that the structure of the TFR changes significantly as a result of hTF binding. Since Cheng et al. 14 utilized the crystal structure of the unliganded TFR, these structural changes in the TFR as a result of hTF binding were not accounted for in the cryo-EM model. As observed in the HFE/TFR crystal structure,16 the geometry of a set of four histidine residues (His475 and His684 from each TFR monomer) is altered in the FeNhTF/sTFR structure (Figures 1C and D).15 Specifically, His475 and His475′ (which are 7.6 Å apart in the unliganded sTFR structure)13 are brought to within 3.6 Å of each other when hTF binds to the TFR.15 Given the physiologically relevant pKa of His residues (~6.0), it was suggested that repulsion of this histidine cluster at low pH would promote receptor-mediated iron release and/or release of the HFE protein at endosomal pH.15, 16 Additionally, an intersection formed between the apical and protease-like domains of one TFR monomer (TFR), the C-terminus (helical domain) of the other TFR monomer (TFR′) and the C1 subdomain of hTF was identified in the FeNhTF/sTFR crystal structure (Figure 1E).15 A number of interesting structural elements are located within this intersection including a metal binding site, previously identified in other TFR structures. 13, 16 In the FeNhTF/sTFR structure, a Ca2+ atom in this metal binding site is coordinated by two residues from the protease-like domain (Glu465 and Glu468) and three residues from the apical domain of the TFR (Asp307, Thr310 and Phe313, Figure 1B). Another significant feature of this TFR-TFR′-C1 intersection is the large movement of a long loop (residues 275–338) in the apical domain. Specifically, the movement of this loop causes residue His318 to move nearly ~18 Å in the FeNhTF/sTFR structure in comparison to the unliganded TFR structure (Figure 1F). Additionally, the location of α-helix 1 in the C-lobe of hTF, on which a number of residues involved in both the binding of hTF to the TFR (Asp356) 17 and pH-dependent receptor-mediated iron release from the C-lobe (His349)15, 18 are located, is shifted ~5 Å (nearly one full helical turn) in the FeNhTF/sTFR structure compared to the cryo-EM model.15 Although the last two amino acids (Glu759 and Phe760) of the TFR could not be placed in the FeNhTF/sTFR structure due to a lack of sufficient density at pH 7.5, it is plausible that α-helix 1 in the C-lobe of hTF could interact with the C-terminus of the TFR monomer at endosomal pH (Figure 1E).</p><p>Iron release from Fe2hTF can proceed via two pathways: N-lobe first followed by C-lobe (k1N→ k2C, where k1N is the rate constant for release of the first iron from Fe2hTF coming from its N-lobe and k2C the rate constant for release of second iron coming from its C-lobe) or alternatively iron release from the C-lobe first followed by the N-lobe (k1C →k2N). A comprehensive kinetic scheme of iron removal from diferric hTF (Fe2hTF) to apohTF was determined by monitoring the increase in intrinsic tryptophan fluorescence as iron is released from recombinant hTF constructs at pH 5.6.12, 19 In the absence of the sTFR, essentially all iron is released first from the N-lobe followed by the C-lobe.12 As first suggested by Aisen et al.,20 a switch in the order of iron release is observed in the presence of the TFR, such that iron is preferentially removed from the C-lobe first followed by the N-lobe (k1C → k2N).12 However, since this is only the case ~66% of the time,12 both pathways must be taken into account when fitting kinetic data from the Fe2hTF/sTFR complex. Under our defined conditions, the sTFR enhances iron release from the C-lobe ~7–11 fold and retards iron release from the N-lobe ~6–15 fold.12 Hence, binding to the TFR not only switches the order of iron release from hTF, but also balances the rates of iron release from the two lobes to maximize efficient delivery of iron to cells during the endocytic cycle.</p><p>Intrinsic tryptophan fluorescence allows iron release rates from each lobe of the hTF/sTFR complexes to be monitored, as well as protein conformational changes. A rapid initial drop in the fluorescence has been a puzzling feature of the kinetic profile of all hTF/sTFR complexes.21, 22 Since this initial decrease in fluorescence is observed even when the sTFR homodimer alone (without hTF) is exposed to putative endosomal conditions (pH 5.6) and is not observed in any hTF (without the sTFR), it was previously attributed to a pH-sensitive change in the sTFR.21 The time points for this initial quench in the tryptophan fluorescence are routinely removed prior to fitting kinetic data for iron release from the hTF/sTFR complexes.12</p><p>Based on the rearrangements within the sTFR observed in the structure of the FeNhTF/sTFR complex as discussed above, a number of sTFR mutants have been produced: H318A sTFR, [H475A,H684A] sTFR, [E465A,E468A] sTFR and a truncated form of the sTFR in which the last four amino acids, Asp757-Asn758-Glu759-Phe760, have been removed, namely sTFR Δ757–760. Using our established kinetic scheme of iron removal from hTF/sTFR complexes, 12 we have measured the iron release kinetics from various recombinant hTF constructs (non-glycosylated Fe2hTF, FeNhTF, and monoferric C-lobe or FeChTF) bound to the sTFR mutants to assess the effect of these mutations on function.</p><!><p>Dulbecco's modified Eagle's medium-Ham F-12 nutrient mixture (DMEM-F12) and fetal bovine serum (FBS) were obtained from the GIBCO-BRL Life Technologies Division of Invitrogen. Novex 6% TBE urea mini-gels, TBE running buffer (5X) and TBE-urea sample buffer (2X), and the iBlot dry blot transfer system were also from Invitrogen. Antibiotic-antimycotic solution (100X) solution and trypsin were from Mediatech, Inc. The QuikChange site-directed mutagenesis kit was from Stratagene. Pro293A-CDM serum-free medium, L-glutamine, and 4–20% acrylamide gels were from Lonza. Methotrexate from Bedford Laboratories was obtained at a hospital pharmacy. All tissue culture dishes, Corning flasks, and expanded surface roller bottles were from local distributors as were Amicon Ultracel 30 kDa molecular weight cutoff membrane microconcentrator devices. Ni-NTA Superflow resin came from Qiagen. Hi-prep 26/60 Sephacryl S-200HR and S-300HR columns were from GE Healthcare. Ethylenediaminetetraacetic acid (EDTA), Ponceau stain and Orange G were from Fisher. Nitrilotriacetic acid (NTA) and ferrous ammonium sulfate were from Sigma. The chemiluminescence detection kit was from Thermo Scientific.</p><!><p>Recombinant N-terminally His-tagged glycosylated sTFR and various mutants were produced as previously described.23 Briefly, mutations were introduced into the pNUT vector using the QuikChange site-directed mutagenesis kit. The mutagenic primers are shown in Table S2. Following transfection and selection with methotrexate, adherent baby hamster kidney (BHK) cells containing the mutant N-His tagged sTFR pNUT vector were transferred into expanded surface roller bottles.23 The culture medium (~200 mL/roller bottle) was collected every 2–4 days. The first two or three batches contain Dulbecco's modified Eagle medium/F12 with antibiotic-antimycotic solution and 10% fetal bovine serum, were collected and discarded. Subsequent batches (generally 4–6) containing Pro293A-CDM serum-free medium with L-glutamine and 1 mM butyric acid, were collected, pooled and saved at 4°C until purification. The amount of each sTFR mutant produced was determined by a solid-phase competitive immunoassay as previously described.23</p><p>Purification of the sTFR mutants followed the same protocol developed for the sTFR.23 Briefly, purification entailed concentration followed by the addition of 5X buffer to yield a final concentration of 1X Qiagen start buffer (50 mM Tris, pH 7.5, containing 300 mM NaCl, 20 mM imidazole, 10% glycerol, and 0.05% NaN3) before passage over a Ni-NTA column (1 × 10 cm) at a flow rate of 2 mL/min. Each sTFR sample was displaced from the column by the addition of 250 mM imidazole to the start buffer. Peak fractions were pooled, reduced using microconcentrators to less than 2 mL, filtered and loaded onto a Sephacryl S300HR 26/60 column equilibrated and run in 100 mM NH4HCO3 at a flow rate of 1.5 mL/min (V0 = ~93 mL). Fractions containing the sTFR (or mutant) were pooled and stored at 4°C in 100 mM NH4HCO3.</p><p>All recombinant non-glycosylated hTFs (diferric hTF, Fe2hTF; FeNhTF, monoferric N-lobe hTF in which mutation of iron binding ligands, Y426F/Y517F, prevents iron binding in the C-lobe); FeChTF monoferric C-lobe hTF in which mutation of iron binding ligands, Y95F/Y188F, prevents iron binding in the N-lobe) were produced as previously described.24</p><!><p>Non-reduced sTFR samples were separated by SDS-PAGE on 4–20% acrylamide gels pre-electrophoresed (20 min at 100 V) with Orange G buffer (0.25% Orange G, 30% glycerol). Samples were then loaded and electrophoresed on the gel (1.75 h at 120V). Proteins were transferred to nitrocellulose using the iBlot dry blot transfer system. Transfer of proteins to the membrane was confirmed by staining with Ponceau stain. The immunoblot was analyzed using the mouse IgG1 monoclonal antibody to the TFR, A4A6 (1 μg/mL, a generous gift from the laboratory of Dr. James Cook at the University of Kansas Medical Center). Bound antibody was detected using horseradish peroxidase conjugated goat anti-mouse IgG and a chemiluminescence detection kit.</p><!><p>The hTF/sTFR complexes were prepared by adding a small molar excess (~20%) of hTF (Fe2hTF or FeChTF) to 1.5 mg of each mutant sTFR. Following equilibration at room temperature for ~5 min, hTF/mutant sTFR complexes were purified by passage over a Sephacryl S300HR gel filtration column in 100 mM NH4HCO3 to remove excess hTF. Fractions containing the complex were concentrated to 15 mg/mL with respect to hTF.</p><!><p>Iron release from the hTF/mutant sTFR complexes was monitored at 25° C using an Applied Photophysics SX.20MV stopped-flow spectrofluorimeter as previously described.12, 18 One syringe contained the hTF/sTFR complex (375 nM) in 300 mM KCl and the other syringe contained MES buffer (200 mM, pH 5.6), KCl (300 mM) and EDTA (8 mM). Rate constants were determined by fitting the increase in fluorescence intensity versus time using Origin software (version 7.5) to standard models as described in detail previously.12, 18 When determining rate constants for iron release, the initial quench in the tryptophan fluorescence, attributed to a pH-inducible conformational change in the sTFR, was removed prior to fitting. All data were corrected to zero fluorescence intensity before fitting.</p><p>Analysis of the initial quench in tryptophan fluorescence required the derivation of a new model that is similar to the previously described A → B model, but also includes an initial decay term (Figure 2 of the Supporting Information). The equation used to fit the initial decay data, as well as the complete derivation and program code for Origin, are provided in the Supplemental Data. Again, all data were corrected to zero at the fluorescent minimum before fitting. Because the fluorescent increase following the minimum affects the fit, an equal number of data points on each side of the fluorescent minimum were included in the fitting process. The half-life (t1/2) was calculated by the following equation: t1/2 = ln(2)/k2.</p><!><p>The iron status of hTF bound to the sTFR mutants was examined by urea gel electrophoresis using Novex 6% TBE-urea mini-gels in 90 mM Tris–borate, pH 8.4, containing 16 mM EDTA as previously described.12, 18 Iron-containing complexes were mixed 1:1 with 2X TBE-urea gel sample buffer (final concentration 0.5 μg/μL). To determine the extent of iron removal from the various hTF/mutant sTFR complexes, an aliquot of each was added to iron removal buffer (100 mM MES buffer, pH 5.6, containing 300 mM KCl and 4 mM EDTA) and incubated at room temperature for 5 min. The iron removal process was halted by addition of 2X TBE-urea gel sample buffer. Samples (3.0 μg) were loaded and the gel was electrophoresed for 2.25 h at 125 V. Protein bands were visualized by staining with Coomassie blue.</p><!><p>As reported previously, the expression of the glycosylated sTFR in our BHK system generally produces between 30–40 mg of protein per liter of tissue culture medium (Table S1).23 Production of the H318A sTFR and sTFR Δ757–760 mutants was comparable to production of the wild type sTFR, while an ~50% decrease was observed in the production of the [H475A,H684A] sTFR mutant. However, mutation of the two glutamate residues in the protease-like domain of the sTFR ([E465A,E468A] sTFR mutant) significantly decreased the yield of receptor to <1.0 mg/L.</p><p>In the final step of purification, homodimeric wild type sTFR typically elutes from an S300HR size exclusion column as a broad peak centered at 165 mL.23 However, the [E465A,E468A] sTFR mutant (as indicated by monitoring the A280) eluted from the column immediately following the void volume (V0) of 93 mL (data not shown), indicating that it exists largely in an oligomeric state. While the wild type sTFR migrates as a monomer (Figure 2, lane 1) even under non-reducing SDS-PAGE conditions, immunoblot analysis confirmed the presence of homodimers as well as a range of higher order oligomers in the S300HR fractions of the [E465A,E468A] sTFR mutant (Figure 2, lanes 7–14). Unfortunately, the poor yield and oligomeric state of the [E465A,E468A] sTFR mutant precluded further experiments with this mutant.</p><!><p>As described in the Introduction, for all sTFR containing samples, an initial rapid drop in tryptophan fluorescence precedes the increase associated with iron release from Fe2hTF. Interestingly, the half-life of this pH-mediated rapid decrease in fluorescence of sTFR depends on whether Fe2hTF is bound (Fe2hTF/sTFR complex versus sTFR alone, Figure 3). The H318A sTFR and sTFR Δ757–760 mutations significantly affect the duration of this pH-sensitive change in the sTFR. Specifically, this pH-sensitive decrease in fluorescence is considerably lengthened in the presence of the H318A sTFR mutation, the half-life being ~8–15 fold longer, depending on whether Fe2hTF is bound (Figure 3). Conversely, the half-life of the pH-sensitive decrease in the fluorescence is slightly shorter in the presence of the sTFR Δ757–760 truncation and is unaffected by the presence of Fe2hTF (Figure 3). No effect on the pH-sensitive fluorescent decrease was observed in the [H475A,H684A] sTFR mutant, with or without Fe2hTF, in comparison to the wild type sTFR (data not shown).</p><!><p>Rate constants were determined by fitting the increase in fluorescence intensity versus time. This increase in intrinsic tryptophan fluorescence has been previously attributed to Trp residues in hTF and not the sTFR.21 Since no Trp or Tyr residues in hTF were mutated, no differences in the amplitude of the fluorescent signal in any of the hTF/mutant sTFR complexes in comparison to the hTF/wt sTFR complex were observed. TFR residue His318, part of a long loop in the apical domain, flips into the TFR-TFR′-C1 intersection upon hTF binding, moving nearly 18 Å relative to its position in the unliganded TFR structure (Figure 1F). Kinetic rate constants for conformational changes and iron release from various hTF/H318A sTFR complexes are presented in Table 1. In fitting the Fe2hTF/H318A sTFR kinetic data, rate constants for both pathways, k1N→ k2C and k1C→ k2N, were allowed to vary (Table 1). The rate constants, k1C, k1N and k2C are smaller in the Fe2hTF/H318A sTFR complex by 67%, 39% and 18%, respectively, relative to the control while the rate constant k2N is unaffected. The ratio, k1C/(k1C+ k1N,) corresponds to the fraction following the k1C → k2N pathway. For the wild-type Fe2hTF/sTFR complex, 66% of the iron is removed by the k1C → k2N pathway and 34% by the k1N → k2C pathway. In contrast, the rate constants for the Fe2hTF/H318A sTFR complex indicate that neither pathway is favored over the other, i.e., 51% of the iron is released via k1C → k2N pathway and 49% via the k1N → k2C pathway.</p><p>Normally, a rapid conformational change precedes iron release from the two monoferric complexes FeNhTF/sTFR and FeChTF/sTFR, exhibiting conformational rate constants k = 22.0 min−1 and 20.6 min−1, respectively. In the FeNhTF/H318A sTFR complex, a small decrease in the rate of the conformational change (k = 17.4 vs. 22.0 min−1) is observed, whereas the conformational change is completely absent in the FeChTF/H318A sTFR complex. The rate constant for iron release from the FeNhTF/H318A sTFR complex is decreased somewhat (k2N = 1.1 vs. 1.7 min−1, Table 1). Although the preceding conformational change is absent in the FeChTF/H318A sTFR complex, iron release from this mutant is relatively unaffected, i.e., k2C is similar to the k2C value obtained from fitting the Fe2hTF/H318A sTFR complex (5.5 ± 0.6 vs. 5.9 ± 0.9 min−1) and only 23% lower than the value of k2C = 7.2 min−1 for FeChTF/sTFR (Table 1).</p><!><p>Rate constants for iron release and conformational changes from the various hTF/[H475A,H684A] sTFR complexes are reported in Table 2. Relatively small differences are observed between the hTF/[H475A,H684A] mutant sTFR complexes and the respective control complexes except for k1C for the Fe2hTF complex which is reduced by ~ 45%.</p><!><p>The C-terminal residues of one TFR monomer are positioned between and make contact with both the C1 subdomain of hTF and the other TFR monomer (designated TFR′, Figure 1E) in the TFR-TFR′-C1 intersection. Kinetic rate constants for conformational changes and iron release from various hTF/sTFR Δ757–760 complexes are shown in Table 3. We were unable to fit the kinetic data for the Fe2hTF/sTFR Δ757–760 complex using the rate constants and two pathway model used for the Fe2hTF/sTFR control complex. Instead, iron release from the Fe2hTF/sTFR Δ757–760 complex was preceded by a very rapid conformational change (k1), followed by iron release from the N-lobe (k2=k1N) and very slow iron release from the C-lobe of hTF (k3=k2C). The specific assignment of these rate constants is established by urea gel analysis of the Fe2hTF/sTFR Δ757–760 complex (Figure 4), which indicates that, because of the small rate constant, a k2C=1.0 min−1, a population of FeChTF remains after subjecting the Fe2hTF/sTFR Δ757–760 complex to iron removal buffer. Thus, iron release from this construct proceeds by way of a single pathway (k1N→k2C), eliminating the need to include the other pathway (k1C→k2N) in fitting the kinetic data for the Fe2hTF/sTFR Δ757–760 complex.</p><p>As mentioned, iron release from the FeChTF/sTFR complex (k2C) is preceded by a conformational change (k1). While the rate constant for the conformational change is markedly increased (48.9 vs. 20.6 min−1) in the FeChTF/sTFR Δ757–760 complex relative to the control FeChTF/sTFR, the rate constant k2C for iron release is significantly decreased (3.2 vs. 7.2 min−1, Table 3). Additionally, in the FeNhTF/sTFR Δ757–760 complex, the rate constant for the initial conformational change (k1) is also increased (31.9 vs. 22.0 min−1), but the rate constant for iron release (k2N) is doubled in comparison to the FeNhTF/sTFR control (3.4 vs. 1.7 min−1) (Table 3). Thus, opposite effects of the Δ757–760 mutation on iron release are seen for the two monoferric hTFs.</p><!><p>The pH sensitivity of the FeChTF/sTFR Δ757–760 complex was examined by monitoring the iron release kinetics of the complex between pH 5.6 to 6.2. Both rate constants, k1 and k2C, of the FeChTF/sTFR complex are sensitive to pH: the rate constant reporting the conformational change (k1) is decreased as the pH increases from 5.6–6.2 (Figure 5, inset), while iron release does not occur at pH 6.0 and above (Figure 5). The pH profile of the FeChTF/sTFR Δ757–760 complex differs drastically in comparison to the control: the already rapid rate constant for conformational change increases slightly from pH 5.6 to 6.0, as does the rate constant for iron release (Figure 5).</p><!><p>The homodimeric TFR ectodomain is comprised of three distinct domains in each monomer. The protease-like domain (domain I, 121–188 and 384–606) contains two of the four Ca2+ binding residues (Glu465 and Glu468, Figure 1B) mutated in the present work, as well as, one of the histidines (His475, Figure 1C) comprising half of the histidine cluster that forms as a result of hTF binding. The apical domain (domain II, 189–383), in which a long loop (residues 275 to 338) containing His318 resides (Figure 1E), is not directly involved in binding hTF. When hTF binds to the ectodomain of the TFR, part of this loop markedly changes its position moving into proximity of the C-terminal residues of the other TFR monomer (Figure 1F).15 Specifically, Phe316′ moves 8 Å, while His318′ flips directly into the TFR-TFR′-C1 intersection (a movement of nearly 18 Å) bringing it to within 5 Å of the TFR C-terminus.15 Lastly, the helical domain (domain III, 607–760) responsible for dimerization, contains the other histidine (His684, Figure 1C) in the interface histidine cluster and the four C-terminal amino acids of the TFR (Asp757, Asn758, Glu759 and Phe760, Figure 1E).</p><p>Glu465 and Glu468 in the protease-like domain of the sTFR, and Asp307, Thr310 and Phe313 in the apical domain, participate in the octahedral coordination of a metal ion (identified as Ca2+ in BHK derived sTFR)(Figure 1B). The essential role of the bound metal in stabilizing the structure of the TFR is demonstrated by the poor production of the [E465A,E468A] sTFR mutant and the inability to isolate any non-aggregated sTFR, preventing further experimentation with this mutant. Although the exact role of the metal in vivo remains unclear, our results suggest that the coordination of this Ca2+ is important to the structural integrity of the sTFR.</p><p>The histidine cluster (His475 and His684 from each TFR monomer) formed when either HFE or hTF binds to the TFR (Figure 1C) has been suggested to be a pH-inducible motif which may trigger a conformational event in the TFR involved in the release of HFE or of iron from hTF within the endosome.15, 16 However, as previously noted,15 this histidine cluster, buried deep within the interface between the two TFR monomers, may be inaccessible to changes in pH. The relatively small differences in most of the kinetic parameters in the [H475A,H684A] sTFR complexes (Table 2) are inconsistent with the suggestion that this cluster is involved in the mechanism of pH-induced iron release, at least under the tested conditions (pH 5.6 with 300 mM KCl and 4 mM EDTA).</p><p>As mentioned, due to the inherent flexibility of the C-terminus, at neutral pH the final two amino acids of the TFR (Glu759 and Phe760) are not observed in the FeNhTF/sTFR crystal structure.15 Significantly, the C-terminus of one TFR monomer is located in the center of the TFR-TFR′-C1 intersection, directly between TFR′ residue His318 and hTF residue His349 shown to be a pH-inducible switch required for TFR stimulated iron release from the C-lobe (Figure 1E).15, 18 We propose that the proximity of these two pH-sensitive histidine residues to the C-terminus of the other TFR monomer provides an explanation for the many rather subtle kinetic effects discussed below.</p><p>A curious feature of the kinetic profiles of all hTF/sTFR complexes is the initial drop in the fluorescent signal as a result of the decrease in pH to 5.6. We have previously reported this phenomenon 21 and because it is only observed in sTFR containing samples and in sTFR alone, assigned it to a pH-induced conformational change in the sTFR. The significantly longer duration of this feature in the H318A sTFR mutant alone or complexed with Fe2hTF is clearly shown in Figure 3 and is consistent with protonated His318 promoting and reporting this change. When hTF binds to the ectodomain of the TFR, His318 moves into the interface, close to TFR residues Trp641 and Trp740 of the TFR′ monomer (~3.6 and 4.1 Å, respectively, Figure 1 of the Supporting Information). Because protonated histidine residues quench tryptophan fluorescence by electron transfer,25 it is reasonable to assign the pH-induced decrease in fluorescence of sTFR largely to His318 quenching of nearby Trp641 and/or Trp740 at low pH.</p><p>A new and interesting finding is that the binding of Fe2hTF significantly prolongs the duration of the fluorescent quenching event in the sTFR (~3.4 fold), a phenomenon that is even more pronounced in the presence of the H318A sTFR mutation (~6.4 fold, Figure 3). In contrast, binding of Fe2hTF does not increase the duration of the pH-inducible change in the truncated sTFR (Δ757–760) (Figure 3). Based on these results, we suggest that the C-terminus of the TFR interacts with His318′ of the TFR′ at neutral pH either through a hydrogen bond with Asn758 or a weak hydrophobic interaction with Phe760, but only when iron containing hTF is bound. This interaction between the C-terminus and His318 is likely destabilized at low pH (5.6), thereby freeing the C-terminus to interact with hTF via residue His349. Further support for this suggestion is provided by the kinetics of iron release from the H318A sTFR containing complexes (Table 1). Specifically, the conformational change that is normally found prior to iron release from the C-lobe is not observed in the FeChTF/H318A sTFR complex. In a manner reminiscent of the H349W or H349Y hTF FeChTF mutant/sTFR complexes, which also lack the conformational change,18 iron release from the FeChTF/H318A sTFR complex can occur without the pH-induced conformational change. Thus, elimination of the His318:C-terminus interaction as in the H318A sTFR mutant enables the C-terminus to interact with His349 hTF even at neutral pH.</p><p>Whereas the conformational change preceding iron release from the FeChTF/sTFR complex is not observed in the FeChTF/H318A sTFR complex, it is accelerated more than 2-fold in the FeChTF/sTFR Δ757–760 complex, k1 = 20.6 vs. 48.9 min−1 (Table 3). This finding supports the link between His318 of TFR′ and the C-terminus of the other TFR monomer and provides further evidence that the two interact and have an effect on the conformational change.</p><p>In contrast to the FeChTF/H318A sTFR complex, the conformational change is observed and little affected in the other monoferric complex, FeNhTF/H318A sTFR complex (Table 1). These results imply that the conformational change preceding iron release in the two monoferric complexes has a different physical basis despite having nearly identical rate constants (20.6 min−1 for the FeChTF/sTFR complex versus 22.0 min−1 for the FeNhTF/sTFR complex, Table 1).</p><p>Interestingly, since the C-terminus of the sTFR (residues 757–760) is only proposed to interact with the C1 subdomain of the C-lobe of hTF and residues of the protease-like domain of the other TFR monomer, it was expected that truncation of the sTFR C-terminus would not affect iron release from the N-lobe of FeNhTF. However, the rate constant for the initial conformational change (k1) is increased by 45% and the rate constant for iron release (k2N) is doubled in the FeNhTF/sTFR Δ757–760 complex in comparison to the FeNhTF/sTFR control (Table 3). These results indicate communication between the two lobes of hTF within the hTF/TFR complex. Given that His318 interacts with the C-terminus of the other TFR monomer at pH 7.5, removal of the C-terminus eliminates this interaction, allowing His318 to interact with Trp641 and/or Trp740 even at neutral pH (Figure 1 of the Supporting Information). Of possible significance, Trp641 is located on αIII-3 in the helical domain of the TFR which interacts with both the N1 and C1 subdomains in the FeNhTF/sTFR crystal structure.14 Thus, we postulate that the absence of the interaction between His318 and the C-terminus in the FeNhTF/sTFR Δ757–760 complex accelerates both the rate of conformational change and iron release from the N-lobe in comparison to the FeNhTF/sTFR control via the interaction between His318 and Trp641 within αIII-3.</p><p>It is well established that a pH-induced conformational change involving hTF residue His349 drives iron release from the C-lobe.14; 17 A previous in silico model26 of the hTF/TFR complex suggested that hTF C1-subdomain residue His349 interacts with hTF C2-subdomain residue Lys511 through a weak electrostatic interaction between the lone pair of electrons on the ND1 of His349 and the cation of Lys511. However, due to the ~4.0–5.0 Å distance between His349 and Lys511 in the recent crystal structure of diferric hTF (PDB ID: 3V83), this interaction is unlikely. Interestingly, mutation of Lys511 to alanine increases the rate of iron release from the C-lobe, by an unknown mechanism.16 Closer examination of the crystal structure of diferric hTF reveals that Lys511 lies within 3.2 Å of and likely forms a salt bridge with hTF C1-subdomain residue Glu372, possibly explaining the observed effects of the K511A mutation.</p><p>Notably, the rate constant for iron release from the control FeChTF/sTFR complex titrates with an apparent pKa of 5.8–5.9, consistent with titration of one or more histidine residues (specifically hTF residue His349).18 Intriguingly, the pH profile of the rate constants for the FeChTF/sTFR Δ757–760 complex (Figure 5) follows the same trend as the pH profile for the H349A FeChTF/sTFR complex.18 Moreover, the kinetics of iron release from the Fe2hTF/sTFR Δ757–760 complex (Table 3) are very similar to the published kinetic data for the H349A Fe2hTF/sTFR complex15: both exclusively follow the k1N→ k2C pathway preceded by a rapid conformational change (Table 3). Additionally, the rate constant for iron release from the C-lobe, k2C, is markedly reduced in the Fe2hTF/sTFR Δ757–760 complex. Hence, the inability to anchor the protonated His349 (as in the H349A hTF or the truncated sTFR) has a pronounced effect on iron release. These findings strongly support the suggestion that the TFR C-terminus stabilizes protonated His349 and promotes iron release from the C-lobe of hTF at pH 5.6 either through a cation-π interaction with the C-terminal Phe760 of the sTFR, or through formation of a salt bridge with Glu759 or Asp757. Individual mutation of the four C-terminal residues is required to establish the contribution of each to the TFR facilitated mechanism of iron release from the C-lobe of hTF.</p><p>In summary, the present data suggest that iron release from the hTF/TFR complex is controlled by a relay within the TFR-TFR′-C1 intersection (Figure 1E). The absence of the coordinated Ca2+ ion (Figures 1B and 2) or a glycosylation site in this region (Asn317)23 both have a negative effect on the structural stability of this region. At pH 7.5, His318′ (TFR′) interacts with the C-terminus of the other TFR monomer (Figure 1E). Upon exposure to the acidic environment within the endosome, the interaction between His318′ and the C-terminus is severed by the protonation of His318′, allowing His318′ to interact with and quench nearby tryptophan residues 641 and/or 740 (Figure 3 and Figure 1 of the Supporting Information). A conformational change which promotes receptor-stimulated iron release from the C-lobe of hTF (Figures 4 and 5) is then triggered by the interaction formed between protonated hTF residue His349 and the TFR C-terminus. Communication of these kinetic events to the N-lobe of hTF, possibly through helix αIII-3 of the TFR to which both the N1 and C1 subdomains are bound, prompts iron release from the N-lobe. Collectively, these results help to establish a molecular basis for the pH-induced events that dictate efficient release of iron from each lobe within the endosome in a physiologically relevant timeframe.</p>

PubMed Author Manuscript

Radical SAM Enzymes: Surprises along the Path to Understanding Mechanism

As the field of radical SAM enzymology has grown from a few examples in the 1990s to hundreds of thousands today, a fundamental question has remained: how does Nature use S-adenosyl-L-methionine to initiate radical reactions? This was a driving question when we first began studying pyruvate formate-lyase activating enzyme in 1993, and our journey for answers has brought us to many surprising discoveries, from a direct coordination of SAM to a unique iron in a [4Fe-4S] cluster, to our recent discovery of an organometallic intermediate and our ability to quantitatively produce and characterize the long-sought 5\xe2\x80\x99-deoxyadenosyl radical intermediate. These adventures, and what we have learned along the way about this fundamentally novel chemistry is described in this review.

radical_sam_enzymes:_surprises_along_the_path_to_understanding_mechanism

Introduction<!>A Site-Differentiated [4Fe-4S] Cluster and its Interaction with SAM<!>Roles of the [4Fe-4S] Cluster and SAM in Initiating Radical Catalysis<!>Probing the Mechanism of Radical Initiation in the Radical SAM Enzymes<!>Concluding Remarks<!>

<p>The group of radical S-adenosyl-L-methionine (radical SAM or RS) enzymes is among the largest enzyme superfamilies in nature, with hundreds of thousands of unique sequences identified spanning all kingdoms of life.1, 2 We published a JBIC mini-review on these enzymes in early 2001,3 essentially concurrent with a paper by Heidi Sofia identifying this superfamily for the first time.4 Our JBIC review described seven different enzyme systems that appeared to have mechanistic similarities in using iron-sulfur clusters and SAM to initiate radical reactions: pyruvate formate-lyase activating enzyme (PFL-AE), the anaerobic ribonucleotide reductase activating enzyme (RNR-AE), the benzylsuccinate synthase activating enzyme, lysine 2,3-aminomutase (LAM), biotin synthase (BioB), lipoate synthase (LipA), and spore photoproduct lyase (SPL).3 With the bioinformatics studies by Sofia and coworkers,4 and more recently by Babbitt, Holiday, and their coworkers,5, 6 this small group of enzymes has expanded to encompass a very large and diverse superfamily of enzymes catalyzing some of the most challenging reactions known in biology.1 RS enzymes have received growing attention in large part because they appear in so many important biological pathways, catalyzing a diverse array of reactions important for life processes.</p><p>Our interests have long centered on the fundamental chemistry of radical initiation: how does the ancient and ubiquitous [4Fe-4S] cofactor work in conjunction with SAM to generate a central 5'-dAdo• radical intermediate that initiates the chemistry on substrate in all these enzymes? Our thinking has been inspired by the early recognition by Perry Frey and coworkers,7–9 and Joachim Knappe and coworkers,10 that SAM-dependent radical enzymes bear key similarities to the adenosylcobalamin (coenzyme B12)-dependent radical enzymes. This review will focus on the structure and properties of the RS [4Fe-4S] cluster, how it interacts with SAM, and the current understanding of the mechanism of radical initiation.</p><!><p>The presence of a conserved CX3CX2C motif was one of the early indicators of similarity among radical SAM enzymes, and remains the most characteristic sequence feature, although variations in this motif are now known in the superfamily. This three-cysteine motif, together with the early evidence for a [4Fe-4S] cluster in these enzymes,11–16 pointed to the presence of a site-differentiated cluster, where one iron site is distinguished by having non-cysteine ligation. The first experimental evidence for site differentiation was provided through specific isotopic labeling of the [4Fe-4S] cluster of PFL-AE, coupled with Mössbauer spectroscopic analysis (Fig. 1).17 Specific isotopic labeling was accomplished by exposing purified PFL-AE containing a [4Fe-4S]2+ cluster to O2, which caused cluster oxidation and loss of the unique iron (the one not coordinated by cysteine) to provide a [3Fe-4S]1+ cluster bound to the protein. This protein was then made anaerobic again, and treated with mild reductant and 57Fe2+; the resulting protein had a [4Fe-4S] cluster with the unique site labeled with 57Fe. Addition of SAM to this protein resulted in a change in the isomer shift of the unique site from 0.42 to 0.72 mm/s, which we interpreted as resulting from SAM coordination to the unique Fe.17</p><p>Coordination of SAM to the unique iron of the [4Fe-4S] cluster was subsequently demonstrated using isotopic labeling of SAM together with electron-nuclear double resonance (ENDOR) spectroscopy (Fig. 2).18 SAM was labeled at the amino group with 15N, and at the carboxylate with 17O and with 13C, and when the labeled SAMs were added to PFL-AE in the [4Fe-4S]1+ state, ENDOR signals were observed that were attributed to direct coordination of the amino and carboxylate groups of SAM to the unique iron of the [4Fe-4S]1+ cluster.18, 19 This was the first demonstration for any radical SAM enzyme that SAM directly coordinated an iron of the [4Fe-4S] cluster; subsequent ENDOR studies on LAM showed the same coordination mode,20 and now numerous X-ray structures of RS enzymes with SAM bound have reiterated the same bidentate SAM coordination to the unique iron.21–26 Clearly, coordination of SAM to the unique iron of the activesite [4Fe-4S] cluster is a unifying structural feature of RS enzymes, but why? One answer seems to be that this places the sulfonium sulfur of SAM within orbital overlap of the [4Fe-4S]+ cluster, a close association also revealed through ENDOR spectroscopic studies.27 This close association is thought to provide a pathway for inner-sphere electron transfer from the reduced [4Fe-4S]+ cluster to the sulfonium of SAM to initiate reductive cleavage, as discussed further in the next section.</p><p>The X-ray crystal structure of PFL-AE provides a representative view of the active site of a RS enzyme.25 When the enzyme was crystallized in the presence of SAM and a 7-mer peptide corresponding to the site of H-atom abstraction (G734 of pyruvate formate-lyase) flanked by the three amino acids on each side, the SAM and peptide are found well-ordered in the active site, with SAM coordinated to the unique iron of the [4Fe-4S] cluster as previously determined by ENDOR studies (Fig. 3).18, 25 In this configuration, the sulfonium sulfur of SAM is 3.2 Å from the unique iron, and 3.9 Å from the nearest cluster sulfide, consistent with the orbital overlap observed in ENDOR studies.25, 27 The S-5'C bond is trans to the S(SAM)---Fe vector, a geometric arrangement common to all known canonical RS structures. The Cα of G734 is positioned 4.1Å from the C5' of SAM, seemingly perfectly positioned for H atom abstraction immediately upon formation of the 5'-dAdo• upon reductive cleavage of SAM.25 An interesting feature of the PFL-AE active site that is not universal to RS enzymes is the presence of a catalytically important monovalent cation that is bridged to the unique iron of the [4Fe-4S] cluster by the carboxylate of SAM (Fig. 4).28</p><!><p>Radical SAM enzymes require reducing conditions for activity. One of the earliest reports of this requirement was by Knappe et al. in 1969, where they showed that activation of pyruvate formate-lyase required a flavoprotein and other undefined components, which could be replaced by a chemical system of "high reducing power."29 We now know that flavodoxin can serve the function of providing the reducing power for a number of different RS enzymes, including PFL-AE.30–33 In the absence of the biological reductant, chemical reductants such as dithionite or photoreduction using deazariboflavin can be used for many of these enzymes. Perry Frey and coworkers showed that these strong reductants reduced lysine 2,3-aminomutase (LAM) to a state containing a [4Fe-4S]+ cluster, and that this cluster provides catalytic activity in the presence of SAM and substrate.34 We later showed that in PFL-AE, the reduced [4Fe-4S]+ provides the electron required for the reductive cleavage of SAM and the subsequent formation of the glycyl radical on PFL: there was a 1:1 ratio between the quantity of [4Fe-4S]+ in the PFL-AE prior to reaction, and the quantity of Gly• in PFL after reaction.35 These results provided important new insights into the role for the [4Fe-4S] cluster in RS enzymes: the [4Fe-4S]+ is the catalytically active state of RS enzymes, and it provides an electron to reductively cleave the S-C5' bond of SAM to generate the central 5'-dAdo• radical intermediate (Fig. 5). Given the orbital overlap between the sulfonium of SAM and the [4Fe-4S] cluster demonstrated through ENDOR studies,27 the electron is thought to be provided by inner-sphere electron transfer, with efficient configurational interaction between the [4Fe-4S]+ donor and the S-C5' σ* orbitals facilitating S-C5' bond cleavage.36</p><!><p>Although 5'-dAdo• has been implicated as the central intermediate in RS enzymes, it is a species that has never been directly observed; rather, its involvement in catalysis has been inferred by the transfer of isotopic labels from substrate into product 5'-deoxyadenosine or into SAM itself. The elusive nature of 5'-dAdo• was attributed to its high reactivity and expected very short lifetime. In order to provide a probe of this key intermediate, Perry Frey and coworkers developed an analog of SAM, 3',4'-anhydro-S-adenosyl-L-methionine (anSAM), which has a C3'-C4' double bond (Fig. 6).37, 38 They showed that anSAM is catalytically competent to function in place of SAM with LAM, however turnover with anSAM leads to buildup of the allylically-stabilized anAdo• radical, which they characterized by EPR. We subsequently used this anSAM/LAM system to generate samples with anAdo• in the active site with specifically isotopically labeled substrates; we observed that the 5'-C of anAdo• is in close proximity to the C3-H target of H-atom abstraction on lysine, and that C5' had moved only a total of 1.5 Å upon S-C bond cleavage.39 The active site appears to impose stringent control on this intermediate radical, allowing it to move only slightly upon S-C bond cleavage: in other words, the anAdo• radical and its close cousin the 5'-dAdo• radical are 'never free' during turnover.39</p><p>In our continuing efforts to provide evidence for the mechanism of radical initiation, we pursued rapid freeze-quench experiments of the PFL-AE reaction: PFL-AE in its catalytically active [4Fe-4S]+ state was mixed with PFL and SAM and quenched at 77 K on a millisecond timescale. EPR spectroscopy revealed a new radical species that was due neither to the starting [4Fe-4S]+ state of PFL-AE, nor to the product glycyl radical of PFL (Figure 7).40 Using electron-nuclear double resonance (ENDOR) spectroscopy in conjunction with SAM that was isotopically labeled with 13C, 2H, or 15N in specific atom positions, as well as PFL-AE labeled with 57Fe in the [4Fe-4S] cluster, we were able to show that this new radical species is an organometallic intermediate (named Ω) in which the S-C5' bond of SAM is cleaved, and the adenosyl moiety of SAM is directly bound to the unique iron of the [4Fe-4S]3+ cluster of PFL-AE through an Fe-C bond (Figure 8).40 The observation of Ω was a surprise, and led us to question the mechanism by which it forms, its relationship to the presumably universal intermediate 5'-dAdo•, and whether Ω is unique to PFL-AE or involved more broadly in the radical SAM superfamily.41 The formation of Ω could go via initial reductive cleavage of SAM to generate the 5'-dAdo• intermediate, followed by oxidative addition of the 5'-dAdo• to the [4Fe-4S]2+ cluster; alternatively, one could posit a nucleophilic attack of the unique iron of the [4Fe-4S]+ on the 5'C of SAM to directly yield Ω. While neither of these possibilities has been disproven, the typical geometry of SAM binding to the [4Fe-4S] cluster in RS enzymes places the 5'C pointing away from the unique iron, making the nucleophilic mechanism implausible in the absence of significant structural rearrangement.</p><p>The question of the ubiquity of Ω in the RS superfamily was answered when we carried out RFQ experiments on a series of RS enzymes spanning a range of subclasses in the superfamily: in every case tested, Ω was detected via EPR spectroscopy.42 The observation of Ω in divergent enzymes across the RS superfamily suggests that it is a central intermediate, fundamental to the mechanism of radical initiation.41, 42 The mechanistically central nature of Ω during radical initiation in RS enzymes requires a shift in the accepted paradigm for RS mechanisms: rather than reductive cleavage of SAM directly liberating 5'-dAdo• for hydrogen atom abstraction from substrate, we now propose that reductive cleavage of SAM is mechanistically coupled to Ω formation in these enzymes.41, 42 Further, Ω may serve as a way to store the nascent 5'-dAdo•, which is liberated upon homolytic Fe-C5' bond cleavage, in direct analogy to the Co-C5' bond homolysis to liberate 5'-dAdo• in radical-B12 enzymes.41, 42 Interestingly, an Ω-like organometallic intermediate has also been identified in the non-canonical RS enzyme Dph2,43 providing yet further evidence that organometallic chemistry may be an essential aspect of iron-sulfur cluster and SAM-based radical initiation.</p><p>Our ability to trap Ω led us to consider whether we might be able to generate the 5'-dAdo• by photolysis of Ω. The Co-C5' bond of adenosylcobalamin has been shown to be photochemically active,44, 45 however the putative 5'-dAdo• generated by photolysis was trapped near the paramagnetic Co(II) and thus not amenable to detailed characterization due to spin-spin coupling of the 5'-dAdo• and paramagnetic Co(II) center.46 In contrast, we reasoned that if we could photolyze the Fe-C5' bond of Ω, we would generate the 5'-dAdo• trapped near the diamagnetic [4Fe-4S]2+ cluster, enabling the characterization of the 5'-dAdo• radical via EPR spectroscopy. In the course of doing these experiments, we also carried out photolysis of the PFL-AE [4Fe-4S]1+/SAM complex in the absence of substrate, and we found that at 12 K it undergoes essentially quantitative photoinduced electron transfer to generate a 5'-dAdo• and [4Fe-4S]2+.47 This efficient photoinduced electron transfer in PFL-AE was quite surprising, as such a process has not been previously reported for any RS enzyme, or for iron-sulfur proteins more generally. That the process quantitively generated the elusive 5'-dAdo• radical, a central radical intermediate to both B12 and RS enzymes that had been sought by researchers for over half a century, was equal parts stunning and gratifying. While much remains to be learned about the photophysics and biological relevance of photoinduced ET in RS enzymes, this discovery has provided us with a powerful new tool to examine mechanism. Through use of isotopically labeled SAMs in this experiment, we unequivocally demonstrated that the radical species formed is 5'-dAdo•, and through spectroscopy and computation we provided a precise picture of the nature of this long-sought radical species.47</p><!><p>Helmut Beinert published a JBIC minireview in 2000 in which he described iron-sulfur clusters as "ancient structures, still full of surprises."48 That descriptor is even more true now: the discovery of the RS superfamily, and its vast reach in nature, has brought and continues to bring surprises in terms of the reactions catalyzed and the pathways in which these enzymes play key roles. Further, the fundamental chemistry of radical initiation in RS enzymes keeps reminding us to expect the unexpected. RS enzymes have brought us direct coordination of SAM to an iron of a [4Fe-4S] cluster as a unifying structural feature of the catalytically poised state of these enzymes.18 They have revealed the exquisite control possible in enzyme active sites, even when dealing with extraordinarily reactive primary carbon radical intermediates.39, 49 RS enzymes have revealed novel chemistry, such as the organometallic Ω intermediate: never before seen, but central to the mechanisms of these ubiquitous enzymes.40, 42 Most recently, RS enzymes have provided a novel photochemical route to generating radicals, allowing us for the first time to trap and characterize the 5'-dAdo• so broadly relevant in biological radical catalysis.47 So we agree with Beinert: iron-sulfur clusters really are "still full of surprises," and we are looking forward to the next surprises these ancient structures choose to reveal!</p><!><p>Mössbauer spectroscopic studies of PFL-AE provide evidence of SAM coordination. Left, the [4Fe-4S] cluster was specifically labeled with 57Fe in the unique site. Right, Mössbauer spectra in the absence (A) and presence (B) of SAM, and (C) difference of A-B. Adapted with permission from Krebs et al17</p><p>ENDOR spectroscopic studies of PFL-AE demonstrated that SAM interacts directly with the [4Fe-4S] cluster via coordination of the amino and carboxylate of SAM to the unique iron of the cluster.</p><p>Active site of PFL-AE, with SAM (bright green) bound to the unique iron of the [4Fe-4S] cluster. The 7-mer peptide substrate analog of the natural substrate PFL is shown in teal. PDB 3CB8. Adapted from Vey et al.25</p><p>Alternate view of the active site of PFL-AE, focusing on the monovalent cation site. The [4Fe-4S] cluster (rust and gold) is coordinated by SAM (teal with blue – nitrogen, red – oxygen, and yellow – sulfur) through the amino and carboxylate moieties. The carboxylate moiety also coordinates the monovalent cation K+, as do several amino acid residues.</p><p>In RS enzymes, a reduced [4Fe-4S]+ cluster provides the electron required for the reductive cleavage of SAM (step 1). The resulting 5'-dAdo• radical abstracts an H-atom from substrate R-H (step 2).</p><p>Reductive cleavage of SAM (top) and the analog anSAM (bottom). SAM reductive cleavage leads to the elusive 5'-dAdo• radical, while anSAM reductive cleavage gives the allylically stabilized anAdo• radical.</p><p>Comparison of the EPR spectra of the starting [4Fe-4S]+ cluster of the active state of PFL-AE, with SAM bound (left), the product glycyl radical of PFL (right), and the intermediate Ω.</p><p>Structure of Ω as determined by ENDOR spectroscopy.</p>

PubMed Author Manuscript

DEET and other repellents are inhibitors of mosquito odorant receptors for oviposition attractants

In addition to its primary function as an insect repellent, DEET has many \xe2\x80\x9coff-label\xe2\x80\x9d properties, including a deterrent effect on the attraction of gravid female mosquitoes. DEET negatively affects oviposition sites. While deorphanizing odorant receptors (ORs) using the Xenopus oocyte recording system, we have previously observed that DEET generated outward (inhibitory) currents on ORs sensitive to oviposition attractants. Here, we systematically investigated these inhibitory currents. We recorded dose-dependent outward currents elicited by DEET and other repellents on ORs from Culex quinquefasciatus, Aedes aegypti, and Anopheles gambiae. Similar responses were observed with other plant-derived and plant-inspired compounds, including methyl jasmonate and methyl dihydrojasmolate. Inward (regular) currents elicited by skatole upon activation of CquiOR21 were modulated when this oviposition attractant was coapplied with a repellent. Compounds that generate outward currents in ORs sensitive to oviposition attractants elicited inward currents in a DEET-sensitive receptor, CquiOR136. The best ligand for this receptor, methyl dihydrojasmolate, showed repellency activity but was not as strong as DEET in our test protocol.

deet_and_other_repellents_are_inhibitors_of_mosquito_odorant_receptors_for_oviposition_attractants

Introduction<!>AaegOrco cloning<!>In Vitro Transcription, Oocytes Microinjection, and Electrophysiology<!>Panel of Odorants<!>Mosquito Repellency Assay<!>Graphic Preparations and Statistical Analysis<!>Repellent-elicited outward currents<!>Repellent-elicited inward currents<!>Repellency activity of methyl dihydrojasmolate<!>Overall conclusions<!>

<p>Insect repellents have been used since antiquity to fend off disease-transmitting mosquitoes and other arthropods. They developed gradually from smoke generated by burning plants (e.g., lemon gum) and topical applications of essential oils (e.g., lemon eucalyptus extract) into repellent substances, including those isolated from plants (e.g., p-menthane-3,8-diol, PMD) and a broad-spectrum synthetic repellent DEET (N,N-dimethyl-3-methylbenzamide), which was discovered in the early 1940s from a screening of more than 7000 compounds (Moore and Debboun, 2006). Thereafter, other synthetic repellents have been developed, including IR3535, (ethyl-3-(N-n-butyl-N-acetyl)aminopropionate) and picaridin (butan-2-yl 2-(2-hydroxyethyl)piperidine-1-carboxylate) (Boeckh et al., 1996), but DEET remains the most widely used repellent substance worldwide (Moore and Debboun, 2006), particularly in the United States of America.</p><p>Repellents work primarily as spatial and contact repellents. Mosquitoes attracted to and flying towards vertebrate hosts (e.g., humans) may make oriented movements away from the source upon approaching chemically treated skin surfaces. In this case, the chemical is a repellent sensu stricto (Dethier et al., 1960). Because the repellent is acting from a distance (in the vapor phase (Barton-Browne, 1977)), it may be referred to as a spatial repellent (Gouck et al., 1967). When mosquitoes land on a chemically treated skin thus making contact before starting increasing locomotion activity or taking off, the chemical is called a contact repellent, which is sometimes referred to as excitorepellent, irritant, or contact irritant (Grieco et al., 2007). From a strict mechanistic viewpoint, these two groups of compounds should be named noncontact and contact disengagent (Miller et al., 2009) for spatial and contact repellents, respectively. It is now known that at least Culex and Aedes mosquitoes smell DEET (Stanczyk et al., 2010; Syed and Leal, 2008). More importantly, it has been demonstrated that an odorant receptor (OR) from the Southern house mosquito, Cx. quinquefasciatus, CquiOR136, is essential for reception of DEET as a noncontact disengagent (Xu et al., 2014). Recently, it has been demonstrated that as a contact disengagent, DEET is detected by sensilla on the tarsal segments of the legs of the yellow fever mosquito Aedes (=Stegomya) aegypti (Dennis et al., 2019), but the receptors remain elusive. Lastly, it has been suggested that DEET merely masks the reception of human emanations by Anopheles coluzzii (=An. gambiae M form) (Afify et al., 2019), thus reducing the attractiveness of the host.</p><p>Although its modes of action remain a matter of considerable debate, DEET is a gold-standard repellent. It also has many "off-label" properties that do not directly affect human-mosquito interactions. For example, DEET is a feeding deterrent (Lu et al., 2017), but if this were the primary mode of action, it would have little value in epidemiology. The great value of repellents is that they reduce biting rates, which represents a second order parameter in vector capacity (Norris and Coats, 2017). Another property that may have a value in epidemiology, albeit not by decreasing vector capacity, is the deterrent effect of DEET on oviposition, as first observed for Ae. aegypti (Kuthiala et al., 1992).</p><p>While using the Xenopus oocyte recording system to deorphanize odorant receptors (ORs) involved in the reception of oviposition attractants, we observed that DEET elicited outward currents in our preparations, in contrast to oviposition attractants and other compounds that generated inward (regular) currents. We have now systematically investigated this phenomenon using different ORs from three different species of mosquitoes. Here, we report that DEET, IR3535, and picaridin elicit outward (inhibitory) currents on OR involved in the reception of mosquito oviposition attractants in the Southern house mosquito, Cx. quinquefasciatus, and orthologues from the yellow fever and malaria mosquitoes. Dose-dependent outward currents were also observed with compounds in a panel that included plant-derived and plant-inspired repellents. Like DEET, IR3535 and picaridin (Xu et al., 2014), plant-inspired compounds, elicited robust inward current in the DEET receptor, CquiOR136, and showed repellency activity.</p><!><p>The pGEM-HE plasmids for the following ORs were obtained as previously reported: CquiOrco (Hughes et al., 2010), CquiOR21 (Pelletier et al., 2010), CquiOR2 (Hughes et al., 2010), CquiOR37, and CquiOR99 (Zhu et al., 2013). pSP64 Poly (A) or pT7TS vectors carrying AgamOrco (Pitts et al., 2004), AgamOR10 (Carey et al., 2010; Wang et al., 2010), AgamOR8 (Lu et al., 2007), AgamOR40 (Liu et al., 2010), and AaegOR10 (Bohbot et al., 2007) were generously shared by Dr. Larry Zwiebel, Vanderbilt University. To obtain a full-length coding sequence of AaegOrco, total RNA was extracted from Ae. aegypti female mosquitos provided by Dr. Anthon J. Cornel, UC Davis, Department of Entomology and Nematology, by using TRIzol (Invitrogen, Carlsbad, CA). cDNA was synthetized from 1 μg of total RNA using a GoScript™ Reverse Transcript kit, according to the manufacturer's manual (Promega, Madison, WI). Then, we performed PCR using AaegOrco gene-specific primers, AaegOrco-F 5'-accATGAACGTCCAACCGACAAAGTACCATG-3' with a Kozak sequence, AaegOrco-R 5'-TTATTTCAACTGCACCAACACCATGAAGTAGG-3'. The gene was cloned into pGEM-HE vector through the In-Fusion HD Cloning system (Clontech, Mountain View, CA). Amino acid sequence was identical to that in VectorBase.</p><!><p>In vitro transcription, oocytes microinjection, and electrophysiology were performed as previously described (Xu et al., 2014). Briefly, in vitro transcription of cRNAs was performed by using an mMESSAGE mMACHINE T7 kit (Ambion), according to the manufacturer's protocol. Plasmids were linearized with NheI, SphI, or PstI, and capped cRNAs were transcribed using T7 or SP6 RNA polymerase. cRNA samples were purified with LiCl precipitation solution and resuspended in nuclease-free water at a concentration of 200 μg/mL and stored at −80°C in aliquots. RNA concentrations were determined by UV spectrophotometry. cRNA samples were microinjected into stage V or VI Xenopus laevis oocytes (EcoCyte Bioscience, Austin, TX). Oocytes were then incubated at 18°C for 3–7 days in modified Barth's solution [in mM: 88 NaCl, 1 KCl, 2.4 NaHCO3, 0.82 MgSO4, 0.33 Ca(NO3)2, 0.41 CaCl2, 10 HEPES, pH 7.4] supplemented with 10 μg/mL of gentamycin, 10 μg/mL of streptomycin, and 1.8 mM sodium pyruvate. A two-electrode voltage clamp (TEVC) was used to detect currents. Oocytes were placed in a perfusion chamber (flow rate was 10 mL/min) and challenged with test compounds. Odorant-induced currents were amplified with an OC-725C amplifier (Warner Instruments, Hamden, CT), the voltage held at −80 mV, low-pass filtered at 50 Hz and digitized at 1 kHz. Data acquisition and analysis were carried out with Digidata 1440A and pClamp10 software (Molecular Devices, LLC, Sunnyvale, CA).</p><!><p>The following compounds were tested: skatole (CAS# 83-34-1), fenchone (CAS# 1195-79-5), 1-octen-3-ol (CAS# 3391-86-4), DEET (CAS# 134-62-3), IR3535 (CAS# 52304-36-6), PMD (CAS# 42822-86-6), picaridin (CAS# 119515-38-7), BDR-1 (farnesyl cyclopentanone, CAS# not available, n/a), BDR-2 ((E,E)-farnesol, CAS# 106-28-5), BDR-3 (methyl dihydrojasmonate = hedione, CAS# 24851-98-7), BDR-4 (methyl jasmonate, CAS# 39924-52-2), BDR-5 (γ-dodecalactone, CAS# 2305-05-7), BDR-6 (δ-tetradecalactone, CAS# 2721-22-4), BDR-7 (ethyl palmitate, CAS# 628-97-7), BDR-8 (isophorol, CAS# 470-99-5), BDR-9 (isophorone, CAS# 78-59-1), BDR-10 (prenyl dihydrojasmonate, CAS# n/a), BDR-11 (2-pentadecanol, CAS# 1653-34-5), BDR-12 (3,5,5-trimethyl cyclohexanol, CAS# 116-02-9), BDR-13 (methyl apritol, CAS# n/a), BDR-14 (methyl dihydrojasmolate, CAS# n/a), BDR-15 (dihydrojasmonic acid, CAS# 3572-64-3), BDR-16 (methyl apritone = miranone, 1206769-45-0), BDR-17 (dihydrojasminlactone, CAS# n/a), BDR-18 (dihydrojasmindiol, CAS# n/a), BDR-19 (ethyl dihydrojasmonate, CAS# n/a), and BDR-20 (2-pentadecanone, CAS#2345-28-0). To avoid possible mislabeling, after sample preparation for electrophysiology and behavior identity of test chemicals was confirmed by gas chromatography-mass spectrometry (GC-MS) using a 5973 Network Mass Selective Detector linked to a 6890 GC Series Plus + (Agilent Technology, Palo Alto, CA). The GC was equipped with an HP-5MS capillary column (30 m × 0.25 mm; 0.25 μm, Agilent Technologies), which was operated at 70°C for 1 min and increased at a rate of 10°C/min to 270°C, with a final hold of 10 min and a post-run of 10 min at 290°C.</p><!><p>The surface landing and feeding assay has been detailed elsewhere (Leal et al., 2017; Xu et al., 2014). In short, two 50-mL Dudley bubbling tubes painted internally with a black hobby and craft enamel (Krylon, SCB-028) were held in a wooden board (30 × 30 cm), 17 cm apart from each end and 15 cm from the bottom. The board was attached to the frame of an aluminum collapsible field cage (30.5 X 30.5 × 30.5 cm; Bioquip, Rancho Cordova, CA, USA). Two small openings were made 1 cm above each Dudley tube to hold two syringe needles (Sigma-Aldrich, 16-gauge, Z108782) to deliver CO2. To minimize handling of mosquitoes, test females had been kept inside collapsible field cages since the latest pupal stage. These female cages had their cover premodified for behavioral studies. A red cardstock (The Country Porch, Coeur d'Alene, ID, GX-CF-1) was placed internally at one face of the cage, openings were made in the cardboard and in the cage cover so the cage could be attached to the wooden board with the two Dudley tubes and CO2 needles projecting inside the mosquito cage 6 and 3 cm, respectively. Additionally, windows were made on the top and the opposite end of the red cardstock for manipulations during the assays and a video camera connection, respectively. The two cages were connected at least 2 h prior to bioassays. At least 10 min before the assays, water at 28°C started to be circulated with a Lauda's Ecoline water bath, and CO2 at 50 mL/min was delivered from a gas tank just at the time of the behavioral observations. Sample rings were prepared from strips of filter papers 25 cm-long and 4-cm wide and hung on the cardstock wall by insect pins to make a circle around the Dudley tubes. Cotton rolls (iDental, Fort Worth, TX, 1 × 3 cm) were loaded with 100 μl of defibrillated sheep blood purchased from UC Davis VetMed shop and placed between a Dudley tube and CO2 needle. For each run, one paper ring was loaded with 200 μL of hexane (control) and the other with 200 μL of test repellent (DEET or methyl dihydrojasmolate) in hexane. The solvent was evaporated for 1-2 min, blood-impregnated cotton plugs and filter paper rings were placed on the arena, CO2 was started, and the assays recorded with an infrared camera (Sony Digital Handycam, DCR-DVD 810). During the assay, the arena was inspected with a flashlight with a red filter. After 5 min, the number of females that have landed and continued to feed on each side of the arena was recorded. Insects were gently removed from the cotton rolls and the assays re-initiated after rotation of sample and control. Thus, repellence for each set of test mosquitoes was measured with the filter paper impregnated with the same sample at least once on the left and once on the right side of the arena.</p><!><p>Graphic illustrations were prepared with Prism 8 (GraphPad, La Jolla, CA). The number of mosquitoes in the treatment (T) and control (C) side of the arena was transformed into % protection, P% = (1-[T/C]) × 100, according to WHO (WHO, 2009) and EPA (EPA, 2010) recommendations. Tests comparing two repellents were conducted in tandem, with two replicates for DEET (treatment right and then left or left and then right), followed by two replicated from a test repellent (BDR-14), with this cycle being repeated multiple times. Data that passed the Shapiro-Wilk normality test were analyzed with the two-tailed, unpaired t test; otherwise, data were analyzed with the Mann-Whitney test. All data are expressed as mean ± SEM.</p><!><p>To revisit our earlier observation of repellent-induced outward currents on OR sensitive to oviposition attractants, we challenged CquiOR21/CquiOrco-expressing oocytes with DEET and then skatole. CquiOR21, formerly known as CquiOR10 (Leal et al., 2013), is narrowly tuned to the oviposition attractant skatole (Hughes et al., 2010). CquiOR21/CquiOrco-expressing oocytes generated robust inward (regular) currents when stimulated with 10 μM skatole, whereas 1 mM DEET elicited outward currents (Fig. 1). These outward currents were dose-dependent (Fig. S1A) and were not observed when oocytes were injected only with CquiOrco cRNA (Fig. S1B) or CquiOR21 cRNA (Fig. S1C).</p><p>We then tested how CquiOR21 would respond to other commercially available repellents, i.e., PMD, IR3535, and picaridin. In these new preparations, CquiOR21/CquiOrco-expressing oocytes responded to DEET and IR3535 with dose-dependent outward currents (Fig. 2A). Picaridin elicited minor outward currents at lower doses but robust outward currents at 1 mM dose. By contrast, PMD did not elicit outward currents; it was silent at lower doses and gave minor inward currents at the highest dose, 1 mM (Fig. 2A). We then interrogated CquiOR21 orthologs from the yellow fever mosquito, AaegOR10 (Bohbot et al., 2007), and the malaria mosquito, AgamOR10 (Carey et al., 2010; Wang et al., 2010). AaegOR10/AaegOrco- and AgamOR10/AgamOrco-expressing oocytes responded with a similar pattern to that observed with CquiOR21/CquiOrco-expressing oocytes (Fig. 2B, C). Specifically, DEET generated dose-dependent outward currents as did picaridin at 1 mM, whereas PMD elicited only minor currents. Over the years, we have deorphanized multiple ORs from Cx. quinquefasciatus and were surprised to observe that these outward currents generated only with preparations involving ORs sensitive to oviposition attractants. We then tested other ORs for oviposition attractants, namely CquiOR121 (=CquiOR2) (Leal et al., 2013; Pelletier et al., 2010), CquiOR37, and CquiOR99 (Zhu et al., 2013). Oocytes expressing each of these ORs along with the obligatory coreceptor Orco elicited dose-dependent outward currents when challenged with DEET (Fig. S2). We also challenged other ORs from the malaria mosquito, which are not involved in the reception of oviposition attractants. Like their Culex counterparts, ORs unrelated to oviposition attractants did not generate outward currents when challenged with DEET (Fig. S3). A previously reported larval OR, AgamOR40 (Liu et al., 2010) generated dose-dependent inward currents in response to DEET as well as to its best ligand, fenchone (Fig. S3A). By contrast, oocytes expressing AgamOR8 (Bohbot and Dickens, 2009; Lu et al., 2007) along with AgamOrco generated robust, dose-dependent inward currents in response to 1-octen-3-ol, but it was silent to DEET (Fig. S3B). Previously, it has been demonstrated that DEET modulates responses of other odorants to ORs (Bohbot and Dickens, 2010), but no outward currents were recorded. When odorants were present in combination with DEET at high I doses, the odorant-induced inward currents decreased significantly (Bohbot and Dickens, 2010), but DEET per se did not elicit measurable currents. At the time of this writing, a small DEET-induced hyperpolarization of a mosquito OR was reported (Dekel et al., 2019). Our fortuitous discovery of outward current elicited by DEET might occur mainly on ORs sensitive to oviposition attractants. However, we have recently reported outward currents elicited by multiple compounds, including repellents, on a Culex OR, CquiOR32, which is sensitive to a plant-derived compound with repellency activity, methyl salicylate (Xu et al.). It is, therefore, conceivable that the phenomenon expands beyond OR sensitive to mosquito oviposition attractants.</p><p>Next, we challenged CquiOR21, AaegOR10, and AgamOR10 with a panel of 20 compounds, which includes plant-derived and plant-inspired repellents. The compounds are part of pending worldwide (WO2013165477A1) and US (9314029) patent applications and have been previously tested as oviposition deterrents for an agricultural pest, the navel orangeworm, Amyelois transitella (Cloonan et al., 2013). The panel was provided to the experimenter (P.X.) with code names, i.e., BDR1-20. To make certain the compounds would be properly identified post hoc, one of us (W.S.L.) analyzed each sample by GC-MS before electrophysiology and behavior work.</p><p>None of the 20 compounds elicited inward currents (Fig. S4), and 4 compounds did not generate measurable currents, specifically BDR-7, 11, 15, and 20, which were later decoded by W.S.L. to the experimenter. They are ethyl palmitate, 2-pentadecanol, dihydrojasmonic acid, and 2-pentadecanone, respectively. Other compounds generated robust outward currents (equivalent to DEET-elicited currents) at least in one of the three ORs tested. They are BDR-3 (methyl dihydrojasmonate), BDR-4 (methyl jasmonate), BDR-10 (prenyl dihydrojasmonate), BDR-14 (methyl dihydrojasmolate), and BDR-19 (ethyl dihydrojasmonate) (Fig. S4). Of note, repellency activity for methyl jasmonate (Xu et al., 2014) and methyl dihydrojasmonate (Zeng et al., 2018) has been previously demonstrated. Using AgamOR10/AgamOrco-expressing oocytes (Fig. 3), we recorded dose-dependent outward currents generated by these compounds at 0.01, 0.1, and 1 mM.</p><p>We then investigated whether these outward currents would modulate CquiOR21 responses to skatole. Thus, CquiOR21/CquiOrco-expressing oocytes were challenged with skatole alone or in mixtures with one of the test compounds. Based on preliminary experiments showing that DEET modulates the response to skatole, we selected DEET as a positive control and tested two compounds from our panel, which generated strong/moderate and weak outward currents, i.e., BDR-4 and 5, respectively (Fig. 3, S4). Skatole was presented at a constant dose of 0.1 μM, and the tested compounds were added at decreasing doses from 1 mM to 15 μM (Fig. 4). When mixtures of skatole and DEET or BDR-4 at high doses (1 mM or 0.5 mM) were applied, outward currents were recorded, whereas attenuated inward currents were observed with mixtures containing BDR-5 at the same doses (Fig. 4). The effect of DEET and BDR-4 on CquiOR21 responses to skatole was clearly dose-dependent. When the test compounds were coapplied at 125 μM or lower, only inward currents were recorded. In the case of DEET and BDR-4, the inward currents were attenuated even when these compounds were presented at the lowest dose of 15 μM (Fig. 4). Although this dataset clearly shows that responses to skatole were modulated by DEET (and methyl jasmonate), it does not explain the mode of action of DEET as a noncontact disengagent (= spatial repellent). Mosquitoes responding to CquiOR21 are not host-seeking mosquitoes, but rather gravid females searching for oviposition sites. The observed modulation may explain at least in part the "off-label" activity of DEET as a deterrent for oviposition (Kuthiala et al., 1992). Next, we asked whether compounds modulating OR response to oviposition attractants would activate a DEET receptor mediating spatial repellency (Xu et al., 2014).</p><!><p>Previously, we have identified CquiOR136 as a DEET receptor in the Southern house mosquito (Xu et al., 2014), which is activated by the four major commercially available repellents, DEET, PMD, IR3535, and picaridin (Xu et al., 2014). CquiOR136/CquiOrco-expressing oocytes were challenged with our panel at three doses (10 μM, 100 μM, and 1 mM) (Fig. S5). IR3535, which elicits the strongest responses at 1 mM (Xu et al., 2014), was used as a positive control. BDR-3 (methyl dihydrojasmonate) and BDR-14 (methyl dihydrojasmolate), among other compounds, elicited robust inward currents (Fig. S5). We then constructed concentration-response relationships for all compounds in our panel (Fig. 5). These analyses clearly show that BDR-14 is the best ligand for CquiOR136 from all tested compounds thus far. More importantly, our data show that CquiOR136 is very sensitive to plant-derived compounds (Fig. 5). Specifically, CquiOR136/CquiOrco-expressing oocytes gave robust responses to methyl dihydrojasmolate, methyl dihydrojasmonate, ethyl dihydrojasmonate, dihydrojasminlactone, dihydrojasmindiol, and methyl jasmonate, which are plant metabolites or their derivatives (plant-inspired compounds). Methyl dihydrojasmolate (BDR-14) is a reduced form of methyl dihydrojasmonate (hydroxy vs. a carbonyl moiety), which in turn is the product of hydrogenation of the plant hormone methyl jasmonate. That this DEET receptor is very sensitive to these plant-derived and plant-inspired compounds is consistent with the notion that the primary function of CquiOR136 in the biology of Cx. quinquefasciatus is the reception of plant defense compounds and that DEET mimics these natural products (Xu et al., 2014).</p><!><p>Given the robust responses recorded of CquiOR136 to methyl dihydrojasmolate, we tested the repellency activity of this compound using our surface landing and feeding assay (Leal et al., 2017; Xu et al., 2014). First, we compared the repellency activity of methyl dihydrojasmolate to DEET with both compounds at 0.1%. At this dose, DEET showed ca. 80% protection, whereas no protection was achieved with methyl dihydrojasmolate (n = 5 each, unpaired, two-tailed t test, P = 0.0020) (Fig. 6A). At 1% dose, methyl dihydrojasmolate gave almost 60% protection, but significantly lower activity than DEET (n = 5 each, Mann-Whitney two-tailed test, P=0.0088) (Fig. 6B). We surmised that the lower protection rate obtained with methyl dihydrojasmolate might be due to differences in volatility. Measurements of spatial repellency are biased by differences in vapor pressures, with compounds with lower vapor pressure yielding lower protection, but longer duration. DEET has a higher vapor pressure than methyl dihydrojasmolate. Therefore, we compared methyl dihydrojasmolate at a higher dose (10%) with 1% DEET. Even with our attempt to compensate for vapor pressure, DEET showed a significantly better performance (n = 5 each, unpaired, two-tailed t test, P = 0.0445) (Fig. 6C). These findings suggest that comparatively DEET is a better spatial repellent, but we cannot unambiguously conclude whether DEET would have a better overall performance because high contact repellency activity may compensate for moderate spatial repellency.</p><!><p>Our data suggest that mosquito response to oviposition attractants may be modulated by repellents. When ORs sensitive to oviposition attractants were challenged with repellent, outward (inhibitory, hyperpolarizing) currents were generated. Responses of the OR detecting the oviposition skatole in the Southern house mosquito, CquiOR21 (=CquiOR10), were reduced when skatole was coapplied with DEET or methyl dihydrojasmolate. These inhibitory currents may explain at least in part, the deterrent effect of DEET on the attraction of gravid females (Kuthiala et al., 1992). It is noteworthy that DEET per se is an oviposition deterrent (Afify et al., 2014; Tikar et al., 2014). Therefore, DEET-mediated oviposition deterrence may have two modes of action (direct detection and interference with the reception of oviposition attractants). More importantly, the discovery of inhibitory currents demonstrates that the integration of chemical signals at the peripheral olfactory system is more complex than previously appreciated.</p><!><p>Fig. S1. Responses of a CquiOR21·CquiOrco-expressing oocyte to DEET and skatole. (A) Inward current elicited by 1 μM skatole and outward currents generated by DEET at 0.01, 0.1, and 1 mM (from left to right). Test of control oocytes injected with (B) CquiOrco or (C) CquiOR21 challenged with the same compounds at the same doses.</p><p>Fig. S2. Outward currents elicited by DEET on other oviposition attractant ORs. DEET was applied at 0.01, 0.1, and 1 mM (from left to right). Responses elicited by (A) CquiOR121, (B) CquiOR37, and (C) CquiOR99.</p><p>Fig. S3. Inward currents and no response recorded from other ORs challenged with DEET. (A) Responses recorded from an oocyte expressing the larval odorant receptor, AgamOR40, along with its coreceptor AgamOrco. Fenchone (black traces) and DEET (red trace), 0.01, 0.1., and 1 mM (from left to right). (B) Currents recorded from an oocyte co-expressing AgamOR8 and AgamOrco and challenged with DEET (red trace, 0.01, 0.1, and 1 mM) and 1-octen-3-ol (black trace: 0.1 and 1 μM, 0.01 and 0.1 mM from left to right).</p><p>Fig. S4. Comparison of outward currents recorded from OR orthologues. Responses elicited by compounds BDR-1 to BDR-20 on oocytes expressing (A) AgamOR10 and AgamOrco, (B) AaegOR10 and AaegOrco, and (C) CquiOR21 and CquiOrco. All compounds, including DEET, were tested at 1 mM. The profiles obtained with the three species are slightly different. In general, BDR-1, 2, 5, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, and 20 showed weak or no response. By contrast, BDR-3, 4, 10 (prenyl dihydrojasmonate), 14, and 19 (ethyl dihydrojasmonate) elicited moderate or robust outward currents.</p><p>Fig. S5. Traces obtained with CquiOR136/CquiOrco-expressing oocytes. Compounds were presented at 1 μM (upper trace), 10 μM (middle trace), and 100 μM (bottom trace).</p>

PubMed Author Manuscript

Optimization of a binding fragment targeting the \xe2\x80\x9cenlarged methionine pocket\xe2\x80\x9d leads to potent Trypanosoma brucei methionyl-tRNA synthetase inhibitors

Potent inhibitors of Trypanosoma brucei methionyl-tRNA synthetase were previously designed using a structure-guided approach. Compounds 1 and 2 were the most active compounds in the cyclic and linear linker series, respectively. To further improve cellular potency, SAR investigation of a binding fragment targeting the \xe2\x80\x9cenlarged methionine pocket\xe2\x80\x9d (EMP) was performed. The optimization led to the identification of a 6,8-dichloro-tetrahydroquinoline ring as a favorable fragment to bind the EMP. Replacement of 3,5-dichloro-benzyl group (the EMP binding fragment) of inhibitor 2 using this tetrahydroquinoline fragment resulted in compound 13, that exhibited an EC50 of 4 nM.

optimization_of_a_binding_fragment_targeting_the_\xe2\x80\x9cenlarged_methionine_pocket\xe2\x80\x9d_

<p>Human African trypanosomiasis (HAT), commonly known as sleeping sickness, is a neglected tropical disease caused by the protozoan parasite Trypanosoma brucei.1 The parasite is transmitted to humans through the bite of the tsetse fly. The disease progresses in two distinct stages: an initial acute stage where the parasitic infection is restricted to the hemolymphatic system and a late stage where the parasites cross the blood-brain barrier and reside in brain tissue.2 Current treatment options are severely inadequate for this disease.1,3 For the treatment of early stage infection, the two drugs, pentamidine and suramin, have toxicity and require injection.4 The late stage infection is particularly difficult to treat, as drugs must cross the blood-brain barrier to be effective. The two drugs available for the late stage infection, melarsoprol and eflornithine, are toxic, have limited ability to cross the blood-brain barrier, and require injection.4–6 New drugs that are safe and easy to administer are urgently needed for both stages of HAT.</p><p>We recently reported on structure-guided design of Trypanosoma brucei methionyl-tRNA synthetase (TbMetRS) inhibitors.7 Two series of compounds were designed and demonstrated to be potent TbMetRS inhibitors. The most potent compound in each series is shown in Figure 1. Compound 1 is a cyclic linker inhibitor with an EC50 of 39 nM and compound 2 is a linear linker inhibitor with an EC50 of 22 nM. In the previous study, the 3,5-dichlorophenyl moiety was fixed as the fragment to fill the so-called "enlarged methionine pocket" (EMP)8 and the investigation was mainly focused on the linker part. Here, we report on the optimization of the EMP binding fragment based on the cyclic and linear linker compounds 1 and 2 in order to identify the preferred moiety for binding the EMP. This led to the identification of inhibitors with significantly enhanced potency.</p><p>Analogues of compound 1 in which the 3,5-dichlorophenyl moiety was replaced by various 3,5-disubstituted phenyl or 2,3,5-trisubstituted phenyl ring were prepared as shown in Scheme 1. The synthesis was following previously reported procedures.7 In brief, (S)-tert-butyl piperidin-3-ylcarbamate reacted with 2-bromo-5-chloro-3H-imidazo[4,5-b]pyridine through nucleophilic substitution reaction, and the following Boc removal provided intermediate 4. Reductive amination of 4 with various substituted benzaldehydes afforded the final products 5a-5p.</p><p>An analogue of compound 2 in which the 3,5-dichlorophenyl moiety was replaced by 3,5-dichloro-2-ethoxy phenyl was also designed, and synthesized as shown in Scheme 2. Compound 7 was synthesized following the same procedure used for synthesizing compound 5. Additional analogues of compound 2 were prepared through introducing substituents onto the benzylic α-position of the 3,5-dichlorophenyl ring (Scheme 3). Reagent 2,2-dimethoxy-N-methylethanamine reacted with 2-bromo-5-chloro-3H-imidazo[4,5-b]pyridine under the same microwave assisted nucleophilic substitution reaction, but with extended reaction time. The resulted intermediate containing an acetal group was hydrolyzed under acidic condition to produce the aldehyde intermediate 8, which underwent reductive amination with methyl 2-amino-2-(3,5-dichlorophenyl)acetate to generate compound 9a. The methyl ester group of 9a was reduced by lithium aluminum hydride to generate compound 9b, while ammonolysis of the ester group produced compound 9c.</p><p>Inspired by the superior potency of bacterial MetRS and TbMetRS inhibitors that contained a tetrahydroquinoline group reported previously,9–11 a 6,8-dichloro-tetrahydroquinoline group was employed to replace the 3,5-dichlorophenyl moiety in compounds 1 and 2 to generate compounds 11 and 13. Their synthesis is shown in Schemes 4 and 5. For compound 11, amine and ketone were pre-reacted with Ti(OEt)4 as catalyst, and the reductant NaBH3CN was added 30 min later followed by an 8 h reaction under microwave conditions (Scheme 4). Compound 13 was synthesized through reductive amination using intermediates 8 and 12 (Scheme 5). Intermediate 12 was prepared following previously published procedures.9</p><p>All the compounds were first evaluated for enzymatic potency against TbMetRS using an ATP depletion assay as described previously.7,12 As shown in Table 1, most of the compounds are very potent inhibitors of TbMetRS, exhibiting IC50s below 50 nM (the enzyme concentration used in the assay). Compounds 5k and 5l were found to have significantly reduced inhibitory potency compared to 1, with IC50s >300 nM.</p><p>All the compounds were also tested for potency against T. brucei parasites using a growth inhibition assay as previously described.7,9,13 The results are shown in Table 1 and the inhibition curves of compounds with EC50 < 100 nM are shown in Figure s1 (Supporting Information). Compounds with larger groups at the 3- and 5-positions, such as bromo and cyano (5a-5c), were tolerated by the enzyme, but had higher EC50 values by 2–3 folds. All compounds that contain the 2,3,5-trisubstituted phenyl ring except 5p were less potent in the parasite growth inhibition assay. The ethoxy group was the best 2-position substituent as evidenced by 5g and 5p being more potent than their counterparts with other substituents. In fact the cellular potency of compound 5p was nearly identical to the EC50 of 1 (39 nM). Larger alkoxy groups at 2-position resulted in significant reductions in cellular activity, especially for branched alkoxy groups. In general, the larger the alkoxy group at the 2-position, the less cellular potency the compound exhibited (5g<5h<5j<5i). Compounds 5k and 5l that contain branched alkoxy groups at 2-position lost cellular potency significantly. Compounds 9a-9c that contain substitutions at the benzylic α-position in the case of the linear linker series exhibited weak cellular potency. The 6,8-dichlorotetrahydroquinoline moiety was a good match for the linear linker series, but not in the cyclic linker series as indicated by compounds 11 and 13. In the cyclic linker series, compound 11 was less potent than 1, while in the linear linker series, compound 13 was more potent than 2. Compound 13 was the most potent compound found in this study, exhibiting an EC50 of 4 nM. The calculated physicochemical properties of compounds with EC50 <100 nM against T. brucei are presented in Table S1 (Supporting Information). All these potent compounds possess Ligand Efficiency (LE) values > 0.3, indicating a good balance of size and lipophilicity. It is noteworthy that compounds 2 and 13 show the highest LE of 0.43, and the Ligand-Lipophilicity Efficiency (LLE) of 2.96 and 4.38, respectively. This indicates the optimization of 2 to 13 not only improved potency but also maintained LE and increased LLE.</p><p>To check for unexpected changes in the binding mode, we obtained crystal structure of TbMetRS in complex with compounds 13 at 2.3Å resolution (Figure 2A). The binding mode of compound 13 was compared to compound 2 bound to TbMetRS (Figure 2B). The 5-chloro-imidazopyridine group of compound 13 bound in the same manner to the auxiliary pocket (AP) as the corresponding part in compound 2, forming a hydrogen bond interaction with the catalytic residue Asp287 (Figures 2C, 2D). The linkers of both compounds superimposed almost perfectly (Figure 2B). The 6,8-dichlorotetrahydroquinoline ring of compound 13 also bound similarly to the EMP compared to the 3,5-dichloro-benzyl group in compound 2, as observed before for analogs with longer linkers.9 The dichloro benzene group of compound 2 was in essentially the same position as the corresponding part in compound 13. The availability of compound 13 bound crystal structure could help to explain the structure-activity relationship (SAR) data generated in Table 1 and guide future inhibitor design.</p><p>Potent compounds with EC50 <100 nM against T. brucei were selected to examine the host cell toxicity. The compounds shown in Table 2 were tested using a human lymphoblast cell line (CRL-8155) and a hepatocellular carcinoma cell line (Hep G2) following procedures described previously.7,14 Compounds 5b and 5c had CC50s (concentration to cause 50% cytotoxicity) close to or greater than 40 μM, whereas compounds 11 and 13 had CC50s of ~20 μM, and compound 5p had CC50 of ~10 μM. Overall, these compounds exhibited low toxicities to mammalian cell lines.</p><p>Compounds with good cellular potency were further tested for oral pharmacokinetic (PK) properties and/or brain penetration in mice (Table 2). The PK studies were performed following published procedures with compounds being administered by oral gavage at 50 mg/kg.7,9,15–17 The brain permeability was tested at a dose of 5 mg/kg IP as described previously.7,9 Compound 5b exhibited high plasma exposure comparable to compound 1 with a Cmax at 19.2 μM, and an AUC of 4687.5 min·μM. Unfortunately, like compound 1, the brain permeability of 5b was poor with undetectable brain levels at 60 min after IP injection. Compound 5p, the 2,3,5-trisubstituted analogue of 1, showed slightly improved brain/plasma ratio but less favorable oral PK compared to 1. Compound 11 had promising PK properties with a Cmax at 14.7 μM and AUC of 947.4 min·μM, whereas compound 13 showed low plasma exposure. Both the plasma and brain exposure of 13 are lower than those of 2, but its improvement in EC50 over 2 may compensate for the lower exposure in an in vivo efficacy model.</p><p>In summary, a series of 3,5-disubstitued and 2,3,5-trisubstituted benzyl groups, as well as a 6,8-dichlorotetrahydroquinoline moiety were used to replace the 3,5-dichlorobenzyl group as the EMP binding moiety based on previously discovered cyclic and linear linker TbMetRS inhibitors. It was found that substituents larger than chloro at 3-and/or 5-positions do not improve potency while 2,3,5-trisubstituted benzyl groups generally resulted in decreased potency. Substituents at the benzylic μ-position in the case of the linear linker series also led to a loss of potency. The 6,8-dichlorotetrahydroquinoline moiety however, afforded compound 13 in the linear linker series with improved potency against T. brucei parasites. We obtained the crystal structure of TbMetRS in complex with compound 13 which will help guide future inhibitor design. Compound 13 also had moderately good brain penetration in mice. This study identified potentially better fragments for binding the EMP than the 3,5-dichloro benzyl group in the cyclic and linear linker series of TbMetRS inhibitors.</p>

PubMed Author Manuscript

Distortion of the Major Histocompatibility Complex Class I Binding Groove to Accommodate an Insulin-derived 10-Mer Peptide*

Background: CD8+ T-cells play a central role in type 1 diabetes (T1D) by recognizing insulin peptides displayed by MHC.Results: A novel flexible MHC binding mode accommodates extra C-terminal peptide residues.Conclusion: Unusual peptide-MHC binding might explain weak TCR affinity of a natural T1D epitope.Significance: MHC peptide binding can be highly flexible around the F-binding pocket.

distortion_of_the_major_histocompatibility_complex_class_i_binding_groove_to_accommodate_an_insulin-

Introduction<!>CD8 T-cells<!>[3H]Thymidine Incorporation Proliferation Assay<!>ELISAs for Chemokine and Cytokine Production<!>Staining of Insulin-specific CD8 T-cells with H-2Kd·Peptide Tetramers<!>Construct Design<!>Protein Expression, Refolding, and Purification<!>pMHCI Biotinylation<!>pMHC Stability Assays<!>Surface Plasmon Resonance Analysis<!>Crystallization, Diffraction Data Collection, and Model Refinement<!>Insulin-reactive CD8 T-cells Are Stimulated by Native and Altered Insulin Peptides<!><!>Recognition of the Native G9G Peptide Is Characterized by Weak TCR Binding<!><!>Peptide Residues Glu-7 and Arg-8 Extend Out of the MHC Binding Groove for Potential TCR Contact<!><!>Interactions between the Peptide C Terminus and the MHC Binding Groove Determine pMHC Stability<!><!>Interactions between the Peptide C Terminus and the MHC Binding Groove Determine pMHC Stability<!>Tyr-84 in the MHC α1 Domain Swings Open Possibly to Enable Unusual Presentation of the G9GF Peptide<!>Discussion<!>Author Contributions<!>

<p>Type 1 diabetes (T1D)7 is an autoimmune disease affecting children and young adults where CD8+ T-cells have recently been shown to play a central role in pancreatic β-cell destruction (1–7). A number of CD8+ T-cell T1D epitopes from the key autoantigenic target proinsulin have been identified (6, 8, 9). How these autoreactive CD8+ T-cells escape thymic selection and cause pathology in the periphery is still under debate. However, some evidence suggests that the nature of the interaction between the clonally expressed T-cell receptor (TCR) and self-peptide-major histocompatibility complex class I (pMHCI) may drive this selection. The strength and/or duration of binding between the TCR and pMHCI (10), as well as different mechanical forces (11), can determine the threshold of T-cell activation. Accumulated data suggest that most self-reactive T-cells express TCRs that interact weakly with pMHC compared with pathogenic T-cells (5, 10, 12, 13). These observations are compounded by the low stability, predicted or demonstrated, for many autoimmune-pMHC interactions (5, 14–18). Other molecular investigations of T-cell-induced autoimmunity have also demonstrated suboptimal TCR binding through atypical TCR conformation, compared with most pathogen-specific TCRs (17, 19–21). These factors have previously been considered to be the basis for poor negative selection of autoreactive T-cells, which may escape from the thymus and become activated in the periphery, potentially through molecular mimicry (22, 23), and thence induce autoimmunity.</p><p>The non-obese diabetic (NOD) mouse model, which develops spontaneous diabetes, has been widely used for investigating T1D (24–27). There are many parallels between T1D in humans and NOD mice, and findings in these mice have paved the way for important discoveries in humans (27, 28). In NOD mice, in which both genetic susceptibility and environment play a role in disease development, both CD4+ and CD8+ T-cells, recognizing a number of different autoantigens (reviewed in Ref. 29) are important in autoimmune attack on pancreatic islet β-cells. We have previously cloned a diabetogenic CD8+ T-cell (G9C8) from the islets of young prediabetic NOD mice, which lyses islets in vitro and causes diabetes within 5–10 days after transfer to young non-diabetic NOD mice and NOD.scid mice (24, 30). The G9C8 T-cell clone recognizes insulin B chain amino acids 15–23, and T-cells reacting to this epitope can be highly represented in the small number of cells in the early infiltrate (8), although other specificities become more dominant later. It has been shown that epitopes within the insulin B chain have a prime role in the development of T1D, because substitution at position 16 of the B chain abolishes CD4+ (31) and CD8+ T-cell reactivity (8, 32). This region of the insulin B chain has also been identified as an important autoantigen in humans (26, 33, 34), offering an important model system for investigating the human form of the disease.</p><p>Here, we used cellular and biophysical methods to investigate the molecular interaction between the G9C8 TCR and the native insulin B chain 10-mer peptide, 15LYLVCGERGF24 (G9GF) and 9-mer peptide, 15LYLVCGERG23 (G9G) as well as a heteroclitic form of the peptide, LYLVCGERV (G9V), presented by H-2Kd. G9V was designed to improve MHC stability and has been shown to activate G9C8-like T-cells more strongly than the native G9G peptide (32), although the molecular basis for this increased potency has not been fully resolved. We solved the atomic structures of each of the peptides in complex with H-2Kd, demonstrating the peptide residues that interact with the MHC binding groove and identifying the solvent-exposed residues that are most likely to contact the TCR. These data provide the first molecular insight into CD8+ T-cell-induced β-cell destruction via recognition of the insulin B chain in this important disease model of T1D and demonstrate a novel flexible peptide-MHC binding mode that has broad implications for T-cell antigen presentation.</p><!><p>Insulin-reactive CD8+ T-cells (G9C8) were isolated from spleen cells from 5–8-week-old transgenic G9Cα−/− NOD mice (30).</p><!><p>Splenic CD8+ T-cells were purified using a Miltenyi MACS CD8+ isolation kit (>90% purity) and cultured at 10:1 with bone marrow-derived dendritic cells with the LYLVCGERGF (G9GF), LYLVCGERG (G9G), or LYLVCGERV (G9V) peptide in RPMI medium supplemented with 5% FCS, 2 mm l-glutamine, 0.05 mm 2-mercaptoethanol, penicillin/streptomycin. Each sample was plated in duplicate. After 48 h of incubation, cells were pulsed with 0.5 μCi of [3H]thymidine for 18 h, harvested, and counted to determine [3H]thymidine incorporation.</p><!><p>Supernatants were removed from the proliferation assay cultures prior to the addition of [3H]thymidine. MIP1β was measured by sandwich ELISA (R&D systems), whereas IFNγ was measured using a similar protocol (BD Biosciences) with the modification that the capture antibody was diluted in carbonate buffer and incubated at 4 °C overnight. Plates were blocked at 37 °C for 1 h, and the detection antibody was incubated for 1 h at room temperature.</p><!><p>Splenocytes from 6-week-old G9Cα−/− NOD mice were isolated, and red cells were lysed. 1 × 106 splenocytes were then preincubated with 50 nm dasatinib (Axon Medchem) for 30 min at 37 °C, and cells were washed in PBS with 2% FCS and stained for 15 min at 37 °C using 0.5 μg of each of the H-2Kd·peptide tetramers (National Institutes of Health tetramer facility): AYAAAAAAV (negative control), G9GF, G9G, or G9V. Cells were then washed again prior to the addition of CD8α FITC (clone 53-6.7, BD Biosciences), CD4 PE-Cy7 (clone RM4-5, eBioscience), CD19 PerCpCy5.5 (clone 1D3, eBioscience), CD11b BV421 (clone M1/70, Biolegend) and checked for viability using an eFluor 780 viability dye (eBioscience). Cells were incubated at 4 °C for 30 min prior to washing again before acquisition on a BD Biosciences FACSCanto II, with data analyzed with Flowjo version 7.6.5 software (Treestar) gating on Live CD8+CD19−CD11b−CD4−Tetramer+ T-cells. The mean fluorescence intensity was then calculated and further analyzed using GraphPad Prism version 4 software.</p><!><p>The TCRα and -β chains and the H-2Kd heavy chains (tagged and untagged with a biotinylation sequence) and the human β2m chain were generated by PCR mutagenesis (Stratagene) and PCR cloning. All sequences were confirmed by automated DNA sequencing (Lark Technologies). The G9C8 TCRα and -β chains, the H-2Kd heavy chains (residues 1–248) (α1, α2, and α3 domains), and β2m (residues 1–100) were also cloned. G9C8 TCRα and -β chains, the H-2Kd α chains, and β2m sequences were inserted into separate pGMT7 expression plasmids under the control of the T7 promoter (35).</p><!><p>Competent Rosetta DE3 Escherichia coli cells were used to produce the G9C8 TCRα and -β chains, the H-2Kd heavy chains, and β2m in the form of inclusion bodies using 0.5 mm isopropyl 1-thio-β-d-galactopyranoside to induce expression, and proteins were chemically refolded as described previously (36).</p><!><p>Biotinylated pMHCI was prepared as described previously (37).</p><!><p>Thermal stability of H-2Kd complexes was assessed by circular dichroism (CD) spectroscopy, monitoring the change in ellipticities at 218 nm. Data were collected on an Aviv Model 215 spectropolarimeter (Aviv Biomedical Inc., Lakewood, NJ) using a 0.1-cm quartz cell. Proteins were dissolved in PBS at concentrations of 2.5 μm. Melting curves were recorded in 0.5 °C intervals from 4 °C up to a maximum temperature of 90 °C when protein aggregation was observed. Melting curves were analyzed assuming a two-state trimer-to-monomer transition from the native (N) to unfolded (U) conformation N3 ↔ 3U with an equilibrium constant K = [U]3/[N3] = F/(3c2(1 − F)3), where F and c are the degree of folding and protein concentration, respectively. Data were fitted as described (38). Fitted parameters were the melting temperature (Tm), van't Hoff's enthalpy (ΔHvH), and the slope and intercept of the native baseline. Because all protein complexes aggregated upon unfolding, the ellipticity of the unfolded state was set as a constant of −4,500 degrees cm2 dmol−1 (39, 40).</p><!><p>Binding analysis was performed using a BIAcore 3000TM equipped with a CM5 sensor chip as described previously (41). Binding analysis was performed four times in independent experiments using pMHC monomers generated in house and from the National Institutes of Health tetramer facility. Approximately 200–500 RU of peptide-H-2Kd (in complex with G9GF, G9G, or G9V) was attached to the CM5 sensor chip at a slow flow rate of 10 μl/min to ensure uniform distribution on the chip surface. Combined with the small amount of peptide-H-2Kd bound to the chip surface, this reduced the likelihood of off-rate-limiting mass transfer effects. The G9C8 TCR was purified and concentrated to ∼140 μm on the same day of surface plasmon resonance analysis to reduce the likelihood of TCR aggregation affecting the results. For equilibrium analysis, eight serial dilutions were prepared in triplicate for each sample and injected over the relevant sensor chips at 25 °C. TCR was injected over the chip surface using kinetic injections at a flow rate of 45 μl/min using H-2Kd·AYAAAAAAV or HLA-A*0201·ALWGPDPAAA in different experiments as negative controls.</p><!><p>All protein crystals were grown at 18 °C by vapor diffusion via the sitting drop technique. 200 nl of each pMHCI (10 mg/ml) in crystallization buffer (10 mm Tris, pH 8.1, and 10 mm NaCl) was added to 200 nl of reservoir solution. H2Kd-G9GF crystals were grown in 4% PEG 4000, 0.1 m sodium acetate, pH 4.6, H-2Kd·G9G crystals were grown in 20% PEG 3350, 0.2 m sodium malonate, 0.1 m Bistris propane, pH 6.5, and H-2Kd·G9V crystals were grown in 20% PEG 6000, 0.2 m calcium chloride, 0.1 m Tris propane, pH 8.0 (42). All crystals were soaked in 30% ethylene glycol before cryo-cooling. All crystallization screens and optimization experiments were completed using an Art-Robbins Phoenix dispensing robot (Alpha Biotech Ltd., UK). Data were collected at 100 K at the Diamond Light Source (Oxfordshire, UK). All data sets were collected at a wavelength of 0.98 Å using an ADSC Q315 CCD detector. Reflection intensities were estimated with the XIA2 package (43), and the data were scaled, reduced, and analyzed with SCALA and the CCP4 package (44). Structures were solved with molecular replacement using PHASER (45). Sequences were adjusted with COOT (46), and the models were refined with REFMAC5. Graphical representations were prepared with PyMOL (47). The reflection data and final model coordinates were deposited in the Protein Data Bank (H-2Kd·G9GF, code 4Z78; H-2Kd·G9G, code 4WDI; and H-2Kd·G9V, code 4Z76).</p><!><p>We have previously demonstrated that the G9C8 T-cell clone can induce rapid onset T1D in NOD.scid mice (24). This pathology is governed by the ability of G9C8 T-cells to recognize a region of the insulin B chain protein that is conserved between humans and mice and is an autoantigen in both species (26, 33, 34). The G9C8 T-cell clone recognized both the native 9-mer (G9G) and native 10-mer (G9GF) versions of this peptide but generated a stronger response (proliferation, MIP1β, and IFNγ production) to the G9G peptide compared with the G9GF peptide (Fig. 1). The G9C8 T-cell clone was about 5 times more sensitive to the G9G peptide compared with G9GF in all assays. Interestingly, substitution of Gly for Val at residue 9 in the G9V peptide, distal from the central bulge of the peptide that is usually involved in TCR contacts, increased activation markedly compared with the G9G peptide (the G9C8 T-cell clone was at least 5 times more sensitive to G9V compared with G9G in all assays) (Fig. 1). These observations warranted further investigation of the molecular rules that govern recognition of this important autoantigen during T1D.</p><!><p>T-cell functional assays. Purified insulin-reactive CD8+ T-cells were incubated for 72 h with native 10-mer peptide, LYLVCGERGF (G9GF), native 9-mer peptide, LYLVCGERG (G9G), or heteroclitic peptide, LYLVCGERV (G9V), together with bone marrow-derived dendritic cells. [3H]Thymidine was added, and incorporation was measured upon harvesting 18 h later and counting on a micro-β-counter to measure T-cell proliferation (A). Supernatants from the cultures were removed prior to the addition of [3H]thymidine and used to measure MIP1β (B) and IFNγ (C) by ELISA.</p><!><p>We investigated the molecular interaction between the G9C8 TCR and the different peptide ligands by performing tetramer staining experiments. Although the G9GF peptide induced a low level of T-cell activation, the H-2Kd·G9GF tetramers did not robustly stain the G9C8 T-cell clone (Fig. 2A). The H-2Kd·G9V tetramer stained 87.9% of the G9C8 clone, in line with the strong activation observed with this ligand (Fig. 2A). Although the H-2Kd·G9G tetramer stained more weakly compared with H-2Kd·G9V, consistent with the T-cell activation analysis, the level of staining was still high (85.7% G9C8 clone staining). The biggest difference between the H-2Kd·G9G and H-2Kd·G9V tetramers was the mean fluorescence intensity, being substantially higher for H-2Kd·G9V (Fig. 2B). In order to further examine the strong staining of the H-2Kd·G9G tetramer compared with H-2Kd·G9GF, we determined the thermal stability of the soluble pMHC proteins using CD spectroscopy. Consistent with our previous findings (32), the lack of an optimal anchor at the C terminus of the G9GF and G9G peptides had a large negative effect on their stability compared with G9V, which showed a melting temperature over 20 °C higher than G9G and G9GF (Fig. 2C). Similar observations from other groups have been reported in which modification of the N-terminal positions of a melanoma peptide increased pMHC stability and immunogenicity (48). The similarly low thermal stability of both G9GF and G9G did not reveal an obvious mechanism for the high level of H-2Kd·G9G tetramer staining compared with H-2Kd·G9GF, although G9GF was slightly less stable than G9G overall. Thus, we performed surface plasmon resonance (Fig. 2, D and E) using recombinant soluble G9C8 TCR (Fig. 3) injected over a sensor chip coated with H-2Kd·G9GF, H-2Kd·G9G, and H-2Kd·G9V. The G9C8 TCR bound to the non-native H-2Kd·G9V with a comparatively strong affinity (KD = 13.6 μm). This was in contrast to the weaker binding affinity to the native G9GF and G9G peptides (KD ∼113 and ∼286 μm, respectively), mirroring the effect on TCR affinity by altering peptide anchor residues reported before (49, 50). These data further confounded the enhanced T-cell activation and tetramer staining of the G9G peptide compared with G9GF, because the G9C8 TCR bound to H-2Kd·G9GF with more than 2-fold stronger affinity compared with H-2Kd·G9G. However, because the G9GF and G9G peptides formed relatively unstable pMHC complexes and because the TCR affinity was weak, these affinities should be considered reproducible estimates rather than absolute values. As such, the tetramer staining and T-cell activation assays probably represent a more accurate comparative estimation of the affinity differences between the G9C8 TCR and the G9G/G9GF peptides.</p><!><p>Molecular characterization of G9C8 T-cell antigen recognition. A, staining of insulin-reactive CD8+ T-cells with peptide-tetramer complexes. Purified insulin-reactive CD8+ T-cells from the NOD mouse were incubated with H-2Kd·AYAAAAAAV negative control tetramer, H-2Kd·G9GF tetramer, H-2Kd·G9G tetramer, and H-2Kd·G9V tetramer, followed by anti-CD8 monoclonal antibody, and analyzed by flow cytometry. B, the mean fluorescence intensity of the tetramer staining is shown for different concentrations of each tetramer. C, CD thermal denaturation curves recorded at 218 nm are shown for selected pMHC samples. Dots, measured values fitted assuming a two-state trimer-to-monomer transition as described under "Experimental Procedures." The panel to the right shows bar graphs of the thermal stability with respect to melting temperature (top) and van't Hoff's enthalpy of unfolding (bottom). D–F, binding affinity of the G9C8 TCR interaction at 25 °C. Eight serial dilutions of the G9C8 TCR were measured; representative data from four independent experiments are plotted. Binding analysis was performed using pMHC monomers generated in house and from the National Institutes of Health tetramer facility. The equilibrium binding constant KD values were calculated using a nonlinear curve fit (y = (P1x)/P2 + x); mean plus S.D. values are shown. In order to calculate each response, the G9C8 TCR was also injected over a control sample (H-2Kd·AYAAAAAAV or HLA-A*0201-ALWGPDPAAA in different experiments) that was deducted from the experimental data (shown in the inset). D, G9C8 versus H-2Kd·G9GF. E, G9C8 versus H-2Kd·G9G. F, G9C8 versus H-2Kd·G9V.</p><p>G9C8 protein purification and analysis. A, gel filtration (size exclusion) using an S200 Superdex 25-ml bed volume column. A symmetrical peak at 14 ml (the expected elution profile for heterodimeric αβ TCR) was observed. B, SDS gel analysis of fractions corresponding to 1-ml sample collections from 11–18 ml (numbered 1–8 on the gel) from the corresponding gel filtration shown in A. Molecular weight markers with corresponding protein sizes are shown in the first lane. The non-reduced gel shows a single band of protein at ∼49 kDa, corresponding to the expected size of the G9C8 αβ TCR heterodimer. To ensure that both chains were present, we performed a reducing gel. Equal amounts of two protein species at ∼23 and 26 kDa were observed, corresponding to the expected size of the G9C8 TCRα and -β chains, respectively.</p><!><p>In order to further understand the mechanism underlying the weak affinity between the G9C8 TCR and H-2Kd·G9G, we solved the crystal structures of H-2Kd·G9GF, H-2Kd·G9G, and H-2Kd·G9V. H-2Kd·G9G crystallized in space group P1, and H-2Kd·G9V crystallized in two distinct space groups, P1 and P1 21 1 (only the P1 data set is shown here in detail), which showed identical features (data not shown). H-2Kd·G9GF crystallized in space group P 21 21 21 with three copies in the asymmetric unit (omit maps and density plots are shown in Fig. 4). All structures were determined to resolutions between 1.9 and 2.3 Å with crystallographic Rwork/Rfree ratios within accepted limits as shown in the theoretically expected distribution (51) (Table 1). Alignment of the three structures generated route mean square deviation values of 0.491 (G9G versus G9GF), 0.355 (G9G versus G9V), and 0.647 (G9V versus G9GF), demonstrating that the overall conformation of all of the structures was very similar. The G9G and G9V structures featured unambiguous density around the peptides (Fig. 4, A and B), which were presented in an extended conformation, primarily anchored at peptide residues 2 and 9, with Cys-5 acting as a secondary anchor in the center of the peptide and residues 6–8 extending away from the groove (Fig. 5, A and B). For G9GF, clear electron density was only observed for peptide residues 1–4 (Fig. 4C), indicating flexibility in the rest of the peptide. Indeed, although all three copies were identical at the N-terminal end of the peptide, in copies 1 and 2, the peptide appeared to be anchored mainly at position 9, with position 10 extending out toward the end of the MHC groove and performing a secondary anchoring role (G9GF-stretched) (Fig. 4C). In copy 3, the peptide appeared to be anchored at position 10 (G9GF-bulged) (Fig. 4C). Although unambiguous density was not observed for peptide residues 5–8 in the H-2Kd·G9GF structure, these residues were modeled in the same position and orientation as in the H-2Kd·G9G and G9V structures, guided by agreement with the final model, which indicated no negative density for these residues in this conformation. The solvent-exposed nature of peptide residues 1, 4, 6, and 7 in the H-2Kd·G9G and G9V structures (and possibly the G9GF structure) makes them the most likely TCR contact residues, supported by our previous data showing that modifications at peptide residues 1, 4, 6 (residue 6 could affect the conformation of residue 7), and 8 reduced T-cell recognition and could act as antagonists (32, 52). It is less likely that modification of these residues would affect peptide stability because of their apparent minimal role as anchor residues.</p><!><p>Omit map and density plot analysis. The left column shows omit maps in which the model was refined in the absence of the peptide and Tyr-84. Difference density is contoured at 3.0σ, positive contours are shown in green, and negative contours are red. The right column shows the observed map at 1.0σ after subsequent refinement using automatic non-crystallographic symmetry restraints applied by REFMAC5. A, the model for G9G, which has two identical copies of the pMHC motif in the asymmetric unit. Copy 1 is shown in gold, and copy 2 is orange. B, the model for G9V, which has two identical copies of the pMHC motif in the asymmetric unit. Copy 1 is shown in gold, and copy 2 is orange. C, the model for G9GF, which has three copies of the pMHC motif in the asymmetric unit. Peptide residues 1–4 are shown in the observed map (right). Copy 1 is shown in gold, copy 2 is light blue, and copy 3 is light purple. Note that the observed density (marine blue), has positive difference density close to the half-occupancy residues 4–10 and little negative density in the same region. The obvious disorder in the C-terminal 7 residues of the peptides does not extend to Tyr-84 in any of the copies.</p><p>Data collection and refinement statistics for pMHC structures</p><p>One crystal was used for solving each structure. Values in parenthesis refer to the highest resolution shell. Root mean square deviation targets are automatically assigned by REFMAC5 according to the appropriate level based on the maximum likelihood method: 0.019 Å for bond lengths and 1.94° for bond angles. r.m.s., root mean square.</p><p>Interactions between the peptide C terminus and the MHC binding groove determine pMHC stability. A, superposition of the G9G peptide (magenta sticks) and G9V peptide (green sticks). The H-2Kd α1 domain is shown in a gray schematic. Arrows below the peptides indicate whether each residue is positioned away from the binding groove for potential TCR contact (up arrow), a primary or secondary anchor (down arrow), or in between (no arrow). B, peptide residues Glu-7 and Arg-8 (magenta sticks) bulge furthest away from the MHC groove (gray schematic). C, H-2Kd binding groove is shown in a gray surface representation demonstrating the extended conformation of the G9G peptide (magenta sticks). Right, the interactions between the C terminus of the G9G peptide (magenta sticks) and the MHC F-pocket (gray sticks). D, the H-2Kd binding groove is shown in a gray surface representation demonstrating the extended conformation of the G9V peptide (green sticks). Right, interactions between the C terminus of the G9V peptide (green sticks) and the MHC F-pocket (gray sticks).</p><!><p>We next investigated the interactions between the different peptides and H-2Kd (Table 2). G9G made 17 vdWs and 4 HBs (Fig. 6C), and G9V made 21 vdWs and 4 HBs (Fig. 6D), both through peptide residue 9. Although G9G only lost four vdW contacts with anchor residue Gly-9 compared with G9V (anchor residue Val-9), there were knock-on effects at the N terminus of the peptide (Table 2). The first 5 residues of G9V made 102 vdWs and 12 HBs with the MHC binding groove, whereas the first 5 residues of G9G only made 102 vdWs and 9 HBs (Table 2). These observations demonstrate the importance of optimal anchoring at both the N and C terminus of the peptide and help explain the substantially lower thermal stability of the G9G and G9GF peptides compared with G9V (Fig. 2C).</p><!><p>Peptide-MHC contact table</p><p>SB, salt bridge; BSA, buried surface area between the peptide and MHC; r.m.s. deviation, root mean square deviation calculated by aligning each pMHC complex (α-chain, peptide, and β2m) in PyMOL. A 3.4 Å cut-off was used for HBs and salt bridges, and a 4 Å cut-off was used for vdWs.</p><p>MHC "opens the back door" to accommodate the extra C-terminal residue in the G9GF peptide. A, superposition of the G9G peptide (magenta sticks) and the G9GF peptide (yellow sticks) showing the extended position of the G9GF-stretched C terminus and the movement in the H-2Kd α1 domain (G9G (gray schematic) and G9GF (yellow schematic)). B, MHC residue Tyr-84 "swings" 8.2 Å in G9GF-stretched (yellow sticks) complex compared with the G9G (gray sticks) complex. C, interaction between G9G residue Gly-9 (magenta sticks) and MHC residue Tyr-84 (gray sticks). D, MHC residue Tyr-84 would cause a steric clash with G9GF residue Phe-10 (yellow sticks) when positioned as in the G9G complex structure. E, interaction between G9GF residue Phe-10 (yellow sticks) and MHC residue Tyr-84 (yellow sticks).</p><!><p>The dynamic nature of the G9GF peptide, evident from the lack of electron density for the central and C-terminal portion of the peptide, made analysis of peptide-MHC contacts unreliable. However, this instability, presumably mediated by the extra residue in the G9GF peptide, could contribute toward the lower relative stability of the H-2Kd·G9GF protein as well as altering interactions with the G9C8 TCR.</p><!><p>We expected that the additional residue at the C terminus in the G9GF peptide compared with G9G would require the central portion of the G9GF peptide to bulge further out of the groove to accommodate the extra residue, forcing G9GF into a completely different conformation compared with G9G, as is usually seen with longer MHCI-restricted peptides (53–55). Although the electron density was not definitive, our analysis indicated that the G9GF-bulged model anchored at position 10 mediating a slightly different conformation around peptide residues 6–8. However, in the more dominant conformation observed in the other two copies in the asymmetric unit (G9GF-stretched), the C terminus of the MHC α1 helix flexed by 1.8 Å, allowing Phe-10 to slide further down into the opening of the groove (Fig. 6A). Additionally, MHC residue Tyr-84 side chain underwent a large movement of 8.2 Å to swing out of the way of Phe-10, compared with its position in the G9GF-bulged, G9G, and G9V structures (Fig. 6B). These movements also altered the shape of the MHC F-pocket. Tyr-84 made three vdWs and one HB with the peptide in the G9G structure (Fig. 6C) but in that position would cause a steric clash with Phe-10 in the G9GF structure (Fig. 6D). In its alternative position in the G9GF-stretched structure, Tyr-84 could potentially form stabilizing interactions, including CH-π (π edge-to-face) interactions (56) with Phe-10, known to be important for peptide-MHC binding (48). Thus, the dynamic movement by Tyr-84 in the G9GF-stretched structure might allow Phe-10 to be accommodated by the MHC F-pocket (Fig. 6E) rather than forming a more prominent central bulge, observed in most other MHCI structures with longer (>9 residues) peptides.</p><!><p>The NOD mouse model of T1D is an important tool for investigating the role of T-cells in the destruction of islet β-cells in the pancreas. Other molecular investigations of T-cell-induced autoimmunity have shed light on the selection and mode of action of autoreactive T-cells. For example, we have recently demonstrated that a preproinsulin-specific human TCR derived from a CD8+ T-cell bound with extremely weak affinity and a highly focused binding footprint (5). Other studies of autoreactive T-cells in other disease models have also demonstrated suboptimal TCR binding, either through weak TCR affinity (20), poor pMHC stability (18), topologically unusual TCR binding (19, 20), or a combination (5, 20). These observations have led to the suggestion that autoreactive T-cells receive weak or unconventional signals in the thymus that lead to positive selection rather than deletion. Here, we show that the G9C8 T-cell clone is reactive to an autoantigenic peptide that is part of the insulin protein but that the native epitopes were both relatively unstable compared with a heteroclitic peptide with optimal anchor residues. The TCR from this clone bound with weak affinity to the native epitopes, resulting in lower functional avidity. This combination adds support to the notion that selection of this clone could occur through weak T-cell signaling in the thymus. T-cells that have high affinity TCRs for more stable insulin-derived epitopes would probably be deleted through negative selection, explaining their absence in the periphery. The high levels of insulin expressed by β-cells and the probable high levels of G9G/G9GF epitopes on the surface of these cells might bridge the activation threshold of G9C8-like T-cells, inducing the autoreactivity observed. Surprisingly, despite a weak monomeric affinity for the G9C8 TCR, H-2Kd·G9G tetramers could still robustly identify cognate T-cells. It is possible that the comparatively strong murine pMHC-CD8 affinity, compared with human pMHC-CD8 (57, 58), could play a role in stabilizing this weak affinity interaction at the cell surface (59, 60). Importantly, the G9C8 T-cell did not express an inherently weak binding TCR, because peptide substitution of Gly to Val at position 9 resulted in anti-viral-like affinity (10, 12, 13). The corresponding enhanced tetramer staining using the G9V peptide paves the way for the development of improved reagents to isolate, phenotype, and clonotype insulin-reactive CD8+ T-cells to better follow and determine their role in disease progression. Furthermore, this demonstration that the G9C8 TCR could bind to an altered ligand with >10 times higher affinity compared with the native ligands opens up the intriguing possibility that this T-cell clone could potentially be primed by a more immunogenic target and then cross-react with insulin B chain epitopes expressed by β-cells through a molecular mimicry type mechanism. Thus, this altered ligand could also be used to test the potential role of molecular mimicry on disease outcome.</p><p>The stability of the pMHC complex is critical in the presentation of epitopes to T-cells, because unstable pMHC will be present at lower concentrations or absent on the surface of antigen-presenting cells. To confound this issue, previously (49, 61, 62) and here, we found that modifications that altered pMHC stability also had a large effect on TCR binding affinity. H-2Kd is unusual compared with the binding motif for most other mouse alleles (that have an anchor at position 5 and the C terminus) in that it anchors at positions 2 and the C terminus, reminiscent of most human peptide-MHC binding motifs. Thus, our observations concerning the effects of TCR binding affinity upon altering the C-terminal anchor could be unique to this murine MHC allele. Because the peptide modifications were located at the C terminus, and the most solvent exposed peptide residues were Glu-7 and Arg-8, it is reasonable to speculate that the G9C8 TCR focuses on the C terminus of the peptide. This is consistent with our previous data demonstrating that modification of these residues (particularly Arg-8), along with Val-4, which was also pointing out of the groove according to our structural analysis, reduced T-cell activation (32, 52). This observation could explain the strong binding affinity between the G9C8 TCR and G9V, because Val-9 might stabilize the main TCR-peptide contact region, enabling more optimal contacts. Binding to this region of the peptide may also explain why, although the G9C8 TCR bound with a similarly weak affinity to H-2Kd·G9G and H-2Kd·G9GF, the G9C8 T-cell was more sensitive to the G9G peptide. The unusual presentation mode of the G9GF peptide may affect the dynamics of TCR binding, perhaps altering the formation of an optimal immune synapse or inhibiting the formation of TCR catch bonds that have recently been shown to play an important role in T-cell activation (11).</p><p>Our structural investigations also revealed a novel and unexpected mode of peptide presentation that has far reaching implications for T-cell antigen recognition in general. Although structures of different length versions of the same peptide have been published before, this is the first example in which the peptide alters the shape of the MHCI binding groove to accommodate an extra residue in the F-pocket. Additional residues at the N terminus and C terminus have been shown to have the following effects: 1) the central bulge of the peptide was altered because the extra residue could not be accommodated by the closed N-terminal end of the MHC binding groove (53–55); 2) the 9-mer version of the peptide assumed the same conformation as the 10-mer version of the peptide by using peptide residue 1, rather than residue 2, as the anchor (63); or 3) extra residues protruded from the groove at the peptide termini (64, 65). Here, the C-terminal end of the 10-mer G9GF peptide formed a dynamic interaction with the MHC binding groove. H-2Kd·G9GF crystallized with three molecules in the asymmetric unit, demonstrating two distinct conformations. The dominant conformation observed in two of the copies (G9GF-stretched) forced the MHC binding groove to open to accommodate the bulky side chain of Phe-10, resulting in MHC residue Tyr-84 swinging 8.2 Å and altering the shape of the MHC F-pocket. Usually, the central residues of longer peptides are squeezed into more extended conformations because of the closed nature of the MHCI binding groove, as observed in the G9GF-bulged model of the structure. In the G9GF-stretched structure, the movement around the MHC F-pocket enabled the C terminus of the G9GF peptide to slide further down the groove so that the N terminus of the peptide could adopt a potentially similar conformation to the G9G and G9V 9-mer peptides. The ability of the G9GF 10-mer peptide to "mimic" the conformation of the 9-mer peptides is likely to be an important factor facilitating recognition of the G9GF peptide by the G9C8 TCR. The dynamic nature of the MHC binding groove was highly unexpected and adds to other studies in which a distinct movement in the MHC helices and/or peptide has been observed (66, 67). Combined, these data provide important evidence demonstrating the highly flexible nature of peptide presentation by MHC. Interestingly, a previous study implemented mutation of Arg-84 for Ala-84 for the stable generation of a single chain pMHC (68). Our findings would suggest that this mutation could have a substantial effect on the shape and dynamics of the MHC F pocket, leading to potential changes in peptide presentation. The flexibility we observed around the F-pocket also has implications for so-called TCR-pMHC "catch bonds." A recent study demonstrated that, under force, some TCR and pMHC interactions can become stronger, resulting in enhanced T-cell activation (11). The formation of catch bonds suggests that the TCR, pMHC, or both undergo structural rearrangements when under force during binding at the cell surface, explaining the increase in binding strength. Our data, demonstrating the potential dynamic nature of the region around the MHC F-pocket, fits well with the notion of catch bond formation.</p><p>In summary, we show that insulin reactivity by a CD8+ T-cell clone, known to induce T1D, is characterized by weak TCR affinity to a highly unstable pMHC. The G9C8 TCR was able to bind more strongly to a peptide altered at the C terminus, demonstrating the potential of this T-cell clone to be triggered by a more immunogenic target. This observation also suggests that the interaction between the TCR and pMHC is likely to be focused toward the C terminus of the peptide, explaining the difference in sensitivity between the C-terminally altered peptide ligands investigated. Finally, we demonstrate a novel mode of flexible peptide presentation in which the MHC can effectively "open the back door" to accommodate extra C-terminal peptide residues.</p><!><p>C. M., J. A. P., E. D. L., P. J. R., K. B., A. T., and D. K. C. performed experiments. P. J. R. and D. K. C. performed the structural analysis. A. K. S., F. S. W., and D. K. C. conceived and funded the study and wrote the manuscript.</p><!><p>This work was supported by a Wellcome Trust ISSF grant (to F. S. W., D. K. C., and A. K. S.), United Kingdom Biotechnology and Biological Sciences Research Council Grant BB/H001085/1 (to A. K. S.), and Medical Research Council Grant G0901155 (to F. S. W.). The authors declare that they have no conflicts of interest with the contents of this article.</p><p>The atomic coordinates and structure factors (codes 4Z78, 4WDI, and 4Z76) have been deposited in the Protein Data Bank (http://wwpdb.org/).</p><p>type 1 diabetes</p><p>T-cell receptor</p><p>peptide-major histocompatibility complex class I</p><p>non-obese diabetic</p><p>1,3-bis[tris(hydroxymethyl)methylamino]propane</p><p>hydrogen bond</p><p>van der Waals interaction</p><p>β2 microglobulin.</p>

PubMed Open Access

An analysis of solution structure and signaling mechanism of LovK, a sensor histidine kinase integrating light and redox signals\xe2\x80\xa0

Flavin-binding LOV domains are broadly conserved in plants, fungi, archaea, and bacteria. These \xe2\x89\x88100 residue photosensory modules are generally encoded within larger, multi-domain proteins that control a range of blue light-dependent physiologies. The bacterium Caulobacter crescentus encodes a soluble LOV-histidine kinase, LovK, that regulates the adhesive properties of the cell. Full-length LovK is dimeric as are a series of systematically truncated LovK constructs containing only the N-terminal LOV sensory domain. Non-conserved sequence flanking the LOV domain functions to tune the signaling lifetime of the protein. Size exclusion chromatography and small angle X-ray scattering (SAXS) demonstrate that the LOV sensor domain does not undergo a large conformational change in response to photon absorption. However, limited proteolysis identifies a sequence flanking the C-terminus of the LOV domain as a site of light-induced change in protein conformation/dynamics. Based on SAXS envelope reconstruction and bioinformatic prediction, we propose this dynamic region of structure is an extended C-terminal coiled-coil that links the LOV domain to the histidine kinase domain. To test the hypothesis that LOV domain signaling is affected by cellular redox state in addition to light, we measured the reduction potential of the LovK FMN cofactor. The measured potential of \xe2\x88\x92258 mV is congruent with the redox potential of gram-negative cytoplasm during logarithmic growth (\xe2\x88\x92260 to \xe2\x88\x92280 mV). Thus a fraction of LovK in the cytosol may be in the reduced state under typical growth conditions. Chemical reduction of the FMN cofactor of LovK attenuates light-dependent ATPase activity of the protein in vitro, demonstrating that LovK can function as a conditional photosensor that is regulated by the oxidative state of the cellular environment.

an_analysis_of_solution_structure_and_signaling_mechanism_of_lovk,_a_sensor_histidine_kinase_integra

<!>Cloning and Expression Constructs<!>Protein Expression and Purification<!>UV/VIS Absorption Spectroscopy<!>Gel Filtration Chromatography<!>Small Angle X-ray Scattering<!>Structural Modeling and Sequence Alignment<!>Proteolytic Digests<!>Redox Potential Measurements<!>ATPase Activity Assays<!>LovK/FMN Redox Exchange<!>Conservation, diversity and signaling lifetime of LOV domains<!>Thermal recovery from the adduct state: a solvent accessibility model<!>Probing mechanism of LOV Domain signaling by size exclusion chromatography<!>Assessing LovK structure in the light and dark by SAXS<!>Photoexcitation destabilizes the LOV-HK linker sequence<!>Biochemical evidence that LovK can function as a sensor of cytoplasmic redox potential<!>

<p>Cellular adaptation requires protein domains that can perceive physical or chemical changes in the environment, and transduce detection of such events to downstream effectors. Flavin-binding light-, oxygen-, or voltage (LOV) domains, which were first identified in the phototropin (phot) plant photoreceptor family (1), are well-characterized protein photosensory domains. While phot LOV domains were initially postulated to serve as sensors of redox state (1) they were later shown to bind a flavin cofactor (2) and respond directly to the absorption of blue light via formation of a thermally-reversible cysteinyl-C(4a) adduct (3). Since these discoveries, LOV domains have generally become defined as a photosensory subset (4) of the larger Per-ARNT-Sim (PAS) domain superfamily (5). LOV domains are now known to be conserved across a diverse range of proteins in prokaryotes, eukaryotes, and archaea (4, 6).</p><p>Early studies on the structural mechanism of photon-dependent signaling in phot1 LOV domains revealed an allosteric switch, in which an amphipathic C-terminal α-helix packs against the LOV2 domain core in the dark state and unfolds and dissociates in the lit state (7). Later work revealed that N-terminal structural elements on phot1 LOV2 are also displaced upon photoexcitation (8). These solution and crystallographic analyses provided a framework for structural mechanisms of LOV signaling in phototropins, and insight from these studies have been successfully translated to the rational design of a chimeric LOV-Trp repressor that exhibits light-regulated DNA binding activity (9). However, recent examples of synthetic LOV-dihydrofolate reductase (10), LOV-Rac1 (11), and a LOV-histidine kinase (12) suggest multiple possible mechanisms through which LOV domains can regulate protein activity. Evidence for alternative modes of LOV signaling can be found in the fungal photoreceptor, VVD, in which cysteinyl-flavin adduct formation affects the equilibrium between monomer and dimer (13, 14). The structure of the LOV domain from the Bacillus subtilis σB regulator, YtvA (15–17) also suggests a mode of signaling that differs from phot LOV2, as YtvA does not have C-terminal Jα helix docked against the LOV domain but rather helices that extend away from LOV domain and connect to the C-terminal sulfate transporter anti-sigma factor antagonist (STAS) domain (18). An analogous, extended C-terminal helix is evident in the heme-binding PAS domain of the sensor histidine kinase, FixL (19).</p><p>While the biochemical and kinetic properties of isolated LOV domains from a variety of proteins have been broadly characterized, we generally do not understand the structural mechanisms by which LOV domains transduce signals within the context of the larger, multi-domain proteins in which they are encoded; recent studies on YtvA (20–22) and phototropin (23) have begun to address this question. Among the more prevalent classes of LOV proteins are the bacterial LOV histidine kinases (24). The Gram-negative aquatic bacterium, Caulobacter crescentus, encodes a soluble LOV-histidine kinase, LovK (Figure 1), that exhibits light-regulated autokinase activity and has been implicated in the regulation of cell adhesion (25). This simple, two-domain enzyme provides an excellent model to probe how LOV domains function in the context of multi-domain proteins. More generally, LovK provides a model to characterize the molecular basis of sensory transduction in histidine kinases.</p><p>Here, we report a systematic analysis of the structural/oligomeric properties, signaling lifetime, and redox properties of LovK. Small angle X-ray scattering (SAXS) of the complete sensory domain of LovK provides evidence for a structure in which the C-terminal sequence linking the LOV domain to the kinase is extended away from the LOV domain core. This extended conformation is consistent with proposed models of coiled-coil-mediated signaling between the sensor domain and the C-terminal histidine kinase (12, 26). SAXS of the LovK sensor domain under constant blue-light illumination demonstrates that light-dependent regulation of LovK kinase activity does not require large changes in tertiary/quaternary structure of the protein, which is consistent with recent vibrational spectroscopic studies of full-length LovK (27). However, limited proteolysis under dark and illuminated conditions defines the extended C-terminal linker between the LOV domain and kinase as a site of conformational change upon photon absorption. Finally, we demonstrate that the reduction midpoint potential of the FMN cofactor of LovK is similar to that of the cytosol, and that the redox state of the FMN cofactor affects the capacity of LovK to function as a photosensor.</p><!><p>The gene encoding LovK (CC_0285) was PCR-amplified from C. crescentus genomic DNA and cloned into the overexpression vector pET28a (Novagen, Madison, WI) to produce pET28a-LovK (25). The C70A LovK mutant was generated using PCR-based site-directed mutagenesis as previously described (25). Truncated versions of the LovK LOV domains were PCR-amplified from pET28a-LovK and digested with NdeI and XhoI (NEB, Ipswich, MA). The LovK (1–138) construct was ligated into pET28a, while all other constructs were ligated into a pET28 variant in which the N-terminal His6 tag is followed by a TEV cleavage site (gift of R. Keenan) (see Table 1 for construct boundaries).</p><!><p>The pET28 expression vectors were transformed into Rosetta(DE3)pLysS cells to create overexpression strains. Expression strains were grown at 37°C to an OD600 of 0.1 (1 cm path length), at which point the temperature was lowered to 16°C and expression was induced with 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG) for 16 hours. Cells were lysed by sonication in 20 mM Tris-HCl (pH 7.6), 500 mM NaCl, 20 mM imidazole, 1 mM β-mercaptoethanol, 5% glycerol. The recombinant protein was purified on Ni2+ Chelating Resin (GE Amersham Pharmacia, Piscataway, NJ) using a 20–500 mM imidazole gradient. Exposure to light during the purification led to loss of the flavin cofactor, so to avoid sample contamination with apo-LOV protein, affinity-purified sample was concentrated to 5 mL in the presence of excess FMN (Sigma-Aldrich, St. Louis, MO). Excess FMN and apo-LOV protein were removed via gel filtration chromatography; intact LovK was purified on a Hi Prep 26/60 Sephacryl S-200 column (GE Amersham Pharmacia) and the LOV domain truncations were purified on a 10/300 Superdex-75 column (GE Amersham Pharmacia). Gel filtration was performed in 20 mM Tris-HCl (7.6), 100 mM NaCl, 20 mM imidazole, 0.1 mM MgCl2. All further experiments were performed in this buffer unless otherwise specified. Thin-layer chromatography to identify LovK cofactor was performed as previously described (2).</p><!><p>Absorption spectroscopy was performed on a Shimadzu UV-1650 (Shimadzu Scientific Instruments, Columbia, MD) spectrophotometer using a 1 cm quartz cuvette. Purified LOV-proteins were kept in the dark for 24 hours prior to data collection, and photoexcited for 5 minutes with white light (1.1 mW/cm2). Spectra were collected at 120 second intervals to monitor the kinetics of thermal recovery. Recovery traces are the average of three independently-purified preparations of protein with error bars representing standard deviation. Recovery half-lives were calculated by fitting the kinetic absorption data in Prism (GraphPad Software, San Diego, CA) to the exponential function (1)A(t)=Ao+(AF−Ao)*(1−e(−kt)) where Ao is the initial absorbance at 446 nm, AF is the final absorbance at 446 nm (after complete recovery), k is the decay constant, and t is time.</p><!><p>All LovK gel filtration was performed on a Hi Prep 26/60 Sephacryl S-200 column. All LOV domain gel filtration was performed on a 10/300 Superdex-75 column. Columns were calibrated with proteins from a Low Molecular Weight Gel Filtration Calibration kit (GE Life Sciences, Piscataway, NJ). For dark-state experiments, protein was loaded into an aluminum foil-covered sample injection loop under dim red light (1.7 µW/cm2), and automatically injected onto the column. The column was kept in the dark for the duration of the experiment. For lit-state experiments, protein was photoexcited with white light (1.1 mW/cm2) for 5 minutes prior to loading into the sample injection loop, and the column was illuminated by (11.9 µW/cm2) white light during the experiment. Dual-wavelength monitoring of the column elutant at 280 and 447 nm confirmed that the FMN cofactor in the lit-state proteins was still photobleached when the protein eluted from the column.</p><!><p>SAXS data were collected at the Advanced Photon Source (Argonne National Laboratory, Argonne, IL) BioCAT beamline (Sector 18) on purified protein at concentrations of 0.4, 1.0, 2.0, and 4.0 mg/mL from a 1.5 mm flow capillary in which protein was flowed at 2 µl/s. Data were reduced in Igor Pro (WaveMetrics, Portland, OR). P(r) plots were calculated from the SAXS data using the indirect Fourier inversion algorithm of Svergun (28) in Gnom version 4.5a, and scattering envelopes were generated using DAMMIF (29). To measure SAXS in the lit state, protein samples were excited in the flow capillary with a blue AlGaInP LED (20° viewing angle, 8000 millicandela, 468 nm lambda max at 3.4 V; powered by a 3.3 volt, 4 amp AC adaptor) that was positioned orthogonal to the capillary throughout data collection as previously described (9). Complete adduct formation in the protein sample at this light intensity was confirmed spectroscopically prior to SAXS data collection.</p><!><p>A cartoon model of dark-state LovK(1–163) was constructed in PyMol (MacPyMol, DeLano Scientific LLC) by appending the N-terminal 30 residues of the crystal structure of dark state N. crassa VVD (PDB 2PD7) (30) to the N-termini of the monomers within the dimeric crystal structure of B. subtilis YtvA (PDB 2PR5) (18). This model was superimposed on the scattering envelope of dark-state LovK(1–163) in PyMol. Sequences of LOV domain constructs characterized in vitro were aligned with ClustalW (31) and displayed with Boxshade (http://sourceforge.net/boxshade/).</p><!><p>LOV proteins were diluted to 20 µM and digested with concentrations varying from 0.1 µg/mL (1x) to 1 µg/mL (10x) of trypsin in 50 mM Na phosphate (pH 7.4). Dark digests were performed under dim red light (1.7 µW/cm2) on protein that had been allowed to photorecover for 24 hours. Lit digests were performed on protein photoexcited with white light (1.1 mW/cm2) for 5 minutes before the addition of trypsin, and illuminated throughout the reaction. Aliquots were removed from the reaction at 5 minute intervals. The reaction was stopped by adding the aliquots to equal volumes of 100 mM Tris-HCl (pH 6.8), 200 mM dithiothreitol, 4% SDS, 0.2% bromophenol blue, 20% glycerol and boiling for 5 minutes. Samples were run on 5%–12% SDS-polyacrylamide gradient gels and visualized by silver stain.</p><!><p>Concentrated protein solutions were diluted to 20 µM in 100 mM potassium phosphate buffer (pH 7.0) in the presence of 200 µM xanthine and 1 µM benzyl viologen. Protein solutions were made anaerobic by repeated cycles of evacuation and equilibration with an atmosphere of purified argon in anaerobic glass cuvettes (32). Reduction potentials were measured using a xanthine/xanthine oxidase system to slowly reduce the sample in the presence and absence of an indicator dye (33). Experiments were monitored with a Shimadzu UV-2501PC spectrophotometer (Shimadzu Scientific Instruments, Columbia, MD) at 25°C, pH = 7.0. Phenosafranin (Em = −252 mV) and Safranin-T (Em = −290 mV) were used as reference dyes. Reduction potentials were calculated according to the Nernst equation (2)ln[(AREDSAMPLE−A(i)SAMPLE)/(A(i)SAMPLE−AOXSAMPLE)]=(EmDYE−EmSAMPLE)(ηSAMPLE*F/RT)+(ηSAMPLE/ηDYE)*ln[(AREDDYE−A(i)DYE)/(A(i)DYE−AOXDYE)] where A is absorption, η is the number of electrons transferred in the cell reaction, F is Faraday's constant (23063 cal−1 V−1), R is the universal gas constant (1.987 cal K−1 mol−1), T is absolute temperature, and Em is the reduction potential.</p><!><p>After purification and concentration, full-length LovK (1–368) was kept in the dark for 24 hours and diluted to 25 µM in kinase reaction buffer (50 mM Tris-HCl pH 7.6, 40 mM KCl, 10 mM MgCl2, 1 mM β-mercaptoethanol, 10% glycerol) in the presence or absence of 25 mM sodium dithionite and allowed to equilibrate in the dark for 30 minutes at room temperature. Reaction buffer was degassed for 48 hours prior to the addition of sodium dithionite, which was required for efficient reduction of the protein sample. Under these microaerobic conditions, the addition of sodium dithionite resulted in 50–60% reduction of the FMN cofactor. 'Dark' reactions were carried out under dim red light (1.7 µW/cm2), and 'lit' reactions were initiated after 2 minutes of illumination with white light (Hg fluorescent bulb; 1.1 mW/cm2). Reactions were initiated with the addition of 20 µM ATP, 5 µM [γ-32-P]ATP, allowed to proceed for 2 minutes, and were quenched by removing 4 µL aliquots and mixing with 2 µL 12 N formic acid. 'Blank' reactions containing no protein were performed in the presence and absence of sodium dithionite to ensure that the redox potential of the solution had no effect on the stability of the ATP. Aliquots were chilled on ice for 10 minutes, spun at maximum speed in a benchtop centrifuge for 2 minutes, spotted onto PEI Cellulose F plates TLC (Merck KgaA, Darmstadt, Germany), blotted with 50 µM ATP, and run for 7 minutes in 1 M formic acid, 1 M LiCl as previously described (25). Radioactive TLC plates were scanned using a Typhoon Trio Variable Mode Imager (GE Biosciences) and unhydrolyzed [γ-32P]ATP and free 32P were quantified with ImageQuant 5.2 software (GE Healthcare, Piscataway, NJ). ATP hydrolysis levels in the blank reactions were subtracted from the dark and lit-state reactions. Five replicates of each condition were performed for three independent protein purifications. All data were normalized to the average of the dark oxidized data. Statistical significance of ATPase activity differences were assessed by one-way ANOVA in Prism (GraphPad Software, San Diego, CA).</p><!><p>20 µM solutions of LovK and free FMN were reduced by molar equivalent concentration of sodium dithionite in anaerobic glass cuvettes as described above. Redox exchange between free oxidized FMN and reduced LovK was initiated by adding oxidized FMN to reduced LovK at a 1:1 molar ratio.</p><!><p>LovK has a dark-state flavin absorption maximum near 447 nm and vibronic bands near 425 and 475 nm (Figure 2A). Thin-layer chromatography of extract from denatured C. crescentus LovK demonstrates its cofactor is FMN (Table 2). As shown previously, LovK exhibits canonical LOV photochemistry (25): the 4a carbon of the FMN cofactor of LovK forms a covalent adduct with the conserved C70 residue, resulting in loss of 447 nm band and appearance of a band at 390 nm upon illumination (Figure 2C).</p><p>The rate constant at which the adduct species decays back to the ground state can be measured by monitoring the reappearance of the band at 447 nm (Figure 3); the reciprocal of this rate constant can effectively be considered the signaling lifetime of the protein. A number of prokaryotic and eukaryotic LOV domains have been characterized in vitro, and shown to exhibit signaling lifetimes (i.e. kinetics of cysteinyl-flavin adduct rupture) that range over five orders of magnitude (Figure 1). Some differences in the kinetics of adduct formation and rupture among LOV domains can be explained by variation in the residues that make up the binding pocket of the flavin cofactor (34–36), while other studies demonstrate that the signaling lifetime of LOV is modulated by non-conserved regions of structure outside the defined LOV domain core (37–39) (see Figures 1 and S1). To methodically test the effect of the non-conserved flanking sequence on the signaling lifetime of LovK, we expressed and purified full-length LovK and a series of systematic LovK truncations (see Figure 2B).</p><p>The kinetics of recovery from the adduct state depend not only on the buffer environment (40), but also on the structural context in which the LOV domain is contained (Figure 3). Full-length LovK (residues 1–368) has a long recovery, with a half-life of two hours (see Materials and Methods for purification protocols and buffer conditions under which kinetics were measured). The isolated LOV core (residues 25–138) recovers more quickly, with a half-life of 37 minutes. LovK (25–163), which contains the LOV core and the non-conserved linker sequence at its C-terminus, has a similar recovery half-life as LovK (25–138) at 28 minutes. A construct containing the LOV core and the non-conserved N-terminal flanking sequence, LovK (1–138), has dramatically faster recovery kinetics from the adduct state, with a half-life of only 2 minutes. However, it is not the case that this N-terminal region of structure has a general rate enhancement effect. LovK (1–163), a construct with fully intact N- and C-termini (but missing the kinase domain) is slower to recover than either LovK(25–138), LovK(1–138) or LovK(25–163) (Figure 3). Thus the effects of structure outside the LOV core on its signaling lifetime do not combine in a simple, additive way.</p><p>Steady-state lit minus dark difference spectra of these five constructs highlight the functional consequence of different adduct decay rates (Figure 2B). Namely, the maximum steady-state concentration of cysteinyl-flavin adduct is markedly decreased in LovK(1–138) relative to the other constructs under identical illumination conditions. This can be attributed to the fast rate (t1/2 = 2 minutes) of recovery from the adduct state in LovK(1–138). We also observe that the light-induced shift in absorbance at 280 nm, relative to 447 nm, varies between constructs (Figure 2B). We attribute this spectral difference to minor differences in the chemical environment surrounding buried aromatic amino acids and the flavin dimethylbenzene moiety in the five constructs.</p><!><p>A possible mechanism underlying the different recovery rates among the C. crescentus LovK constructs is differential accessibility of the FMN adduct to the surrounding buffer environment. It is known that imidazole, a biological base, enhances the rate of thermal decay from the adduct state back to the non-bonded "dark" state (40). While the mechanism of base-mediated rate enhancement has not been determined definitively, the relationship between recovery rate and base concentration is both linear and non-saturable (40). This is consistent with a simple bimolecular reaction model involving a direct effect of base in bulk solvent on the stability of the cysteinyl-flavin adduct in the interior of the protein, such that (3)d[adduct]dt=−k[adduct][base] We observe that the relative (i.e. fold-change) rate enhancement is identical for all constructs (Figure 4A). This suggests the absolute difference in base-catalyzed adduct rupture can be modeled by a simple solvent accessibility term, a, such that (4)d[adduct]dt=a(−k[adduct][base]) In this model, the slope of k versus concentration of base yields the solvent accessibility of the cysteinyl-flavin adduct, a, for each LOV domain construct (Figure 4B) (Table 3). The accessibility of the interior of the LovK LOV domain to solvent is similar in all of the truncation constructs except for LovK (1–138), which contains an intact N-terminus and no C-terminal linker. In the absence of sequence C-terminal to the LOV domain core, the 24 amino acids at the N-terminus of the LOV domain dramatically increase the rate of photorecovery (Figure 3, Figure 4B). This effect is attenuated by the addition of 25 residues C-terminal to the LOV domain core (Figure 3, Table 3). These data provide evidence that the two non-conserved regions flanking the LOV domain have opposing effects on protein signaling lifetime, perhaps by affecting the accessibility of solvent to the protein interior.</p><!><p>Signaling mechanisms have been proposed for LOV proteins that involve light-induced changes in oligomeric state (14), and/or localized unfolding of a region of structure (7, 41). An analysis of change in oligomeric state and hydrodynamic radius in C. crescentus LovK constructs (Figure 2B) in the dark and lit states by size exclusion chromatography reveals that LovK is principally dimeric, and does not undergo large structural changes upon illumination (Figure 5). Intact LovK(1–368) elutes at a volume consistent with a dimer in both the dark and lit states (Figure 5A), which does not support a model in which light-dependent changes in oligomeric state regulate activity of the kinase domain. The constructs containing the N-terminal flanking sequence (1–163 and 1–138) elute as apparent dimers; illumination of LovK(1–138) causes a slight shift in elution volume suggesting that this construct may undergo a modest change in structure in the lit state (Figure 5C). LovK(1–163) also elutes as a minor peak consistent with a trimer (Figure 5B). However, we do not believe this is a biologically-relevant oligomer, given that histidine kinases are known to function as dimers (42, 43), and that full-length LovK and other LOV constructs elute consistently as dimers in the light and dark. A possible explanation for this faster-eluting peak is the presence of a structural isoform in the population that is more elongated and less globular than the 'true' dimer. Together, these size exclusion data provide evidence that the signaling mechanism of LovK does not require a change in oligomeric state or large scale change in protein structure.</p><!><p>Analysis of LovK structure by small-angle X-ray scattering (SAXS) was confounded by the tendency of full-length LovK and 2 of the 4 truncated LOV domain constructs to form large aggregate species at the higher protein concentrations required for SAXS measurements (Figure S2). Preparations of LovK(1–163) yielded the highest quality SAXS data. Neither the Rg nor Io of LovK(1–163) change significantly upon saturating illumination of the protein with blue-light (Figure 6A and 6B), providing support for a model in which regulation of LovK kinase activity requires only minor structural rearrangements in the N-terminal sensory domain. A Guinier fit of LovK(1–163) data yielded a radius of gyration (Rg) of 27.6 Å (Figure 6B and Table 4). This result is consistent with dimeric LOV constructs from A. thaliana FKF1 (44) and phototropin 1 (41), which have radii of gyration in the range of 20–28 Å, and exhibit only minor increases (0.1–0.4 Å) in Rg upon photoexcitation.</p><p>While wild-type full-length LovK(1–368) aggregated at the high protein concentrations necessary for SAXS analysis, the "blind" mutant, LovK C70A, had an identical gel filtration elution profile as wild type and was less prone to aggregation. A Guinier fit of the SAXS data from LovK C70A yielded an Rg=42.7 Å (Table 4). The closest histidine kinase homolog for which SAXS scattering data exist is a PAS-histidine kinase fragment of ThkA from Thermotoga maritima. This construct, which forms a 76 kDa dimer in solution, has an Rg of 37.3 Å (45). The larger Rhodopseudomonas palustris bacteriophytochrome 4 histidine kinase forms a 167 kDa dimer with an Rg of 52.5 Å (46). Based on SAXS data from these histidine kinases, the measured Rg for C. crescentus LovK C70A is consistent with the expected 87 kDa dimer of His6-LovK C70A.</p><p>Ab initio shape determination (29) of the LovK(1–163) sensor domain from its SAXS scattering profile produced an elongated envelope, providing evidence that the sensor domain dimer of LovK forms an extended structure. We fit this envelope with a crystal structure of the B. subtilis YtvA LOV domain (18), which contains an extended C-terminal helix. At the N-terminus of our low-resolution model, we placed the N-cap of the VVD LOV protein (30). The reconstructed scattering envelope of LovK(1–163) is best fit by a model in which both the N- and C-terminal flanking sequence extend away from the LOV domain core (Figure 6C).</p><p>In the context of bacterial sensor histidine kinases, helical coiled-coils linking the N-terminal sensor domain to the C-terminal kinase have long been postulated to serve a functional role in signaling (26). Recent studies on an engineered LOV histidine kinase provide experimental support for a mechanism involving signal transmission via rotation of extended coil-coil helices (12). Moreover, sequence analysis of naturally occurring PAS/LOV-histidine kinases has shown that the linker regions between the boundaries of the PAS sensor and histidine kinase domains contain (7n) or (7n+2) coiled-coil signatures with hydrophobic residues in a 'heptad repeat' pattern (12). The linker region between the histidine kinase and LOV domain of C. crescentus LovK (residues 150–175) also has a coiled-coil signature according to the Lupas/Stock algorithm (47), and the Paircoil algorithm of Berger and Keating (48). Our SAXS data on LovK(1–163) evidence a model in which these putative coiled-coil residues extend out and away from the C-terminus of the LOV domain core.</p><!><p>Limited proteolysis of proteins can provide information about how their structure changes upon perturbation. In particular, this method can detect changes in protein structural dynamics or stability that are not necessarily accompanied by a large-scale change in average structure (49). Thus, limited proteolysis provides information that is complementary to methods such as size exclusion chromatography and SAXS. Complete trypsin digestion of LovK would yield 49 fragments of molecular weights ranging from 0.1–2.6 kDa (50). Limited trypsin digestion of dark-state LovK(1–368) reveals two major cleavage products, of ~29 and ~15 kDa (Figure 7), which are consistent with the predicted sizes of the isolated histidine kinase and LOV domains, respectively. Proteolysis of LovK(1–368) after illumination with white light is more rapid and yields a greater number of cleavage products. These data suggest the protein is more conformationally flexible and thus more vulnerable to proteolysis in the lit state.</p><p>Digestion of the four LovK truncation constructs reveals distinct patterns based on the presence or absence of different flanking sequences. The LovK(1–163) and (1–138) constructs, which include the N-terminal flanking sequence, are less susceptible to proteolysis than constructs without the N-terminus as evidenced by the higher trypsin:LOV ratio that is required to produce similar levels of cleavage per unit time (Figure 7). LovK(1–138), while relatively stable, does not show discrete proteolytic bands upon the addition of trypsin. Rather, this construct shows evidence of more non-specific degradation. The LovK(1–163) and (25–163) constructs, which include the C-terminal flanking sequence, show a pronounced increase in cleavage in response to illumination with visible light; constructs lacking this C-terminal extension do not show as pronounced a lit/dark difference in proteolysis (Figure 7). These data support a model in which structure at the N-terminus of the LOV domain is not significantly perturbed upon photoexcitation. Rather, it is the structure at the C-terminus, which links the LOV domain to the histidine kinase, that is the locus of the structural change in response to photon absorption and cysteinyl-C4(a) adduct formation. This result is analogous to the phototropin LOV2 signaling model in which illumination destabilizes a region of structure at the C-terminus of the LOV domain (7). However, the exact mechanism of signaling likely differs between LovK and phototropin, as the SAXS data support an extended conformation of the LOV C-terminus in LovK (see Figure 6).</p><!><p>The LovK FMN chromophore is capable of undergoing chemical reactions other than light-dependent cysteinyl-C4(a) adduct formation. Flavoproteins are more commonly known for their capability to transfer electrons and detect changes in cellular redox state. Examples of flavin-binding PAS domains that detect changes in cellular redox include NifL and Aer (51, 52). The light-sensing role of the LOV domain in vivo is, in principle, influenced by the redox state of the FMN cofactor. The electron-poor, oxidized form of FMN is favored to accept an electron in the triplet excited state and form a cysteine-flavin radical pair that precedes cysteinyl-flavin adduct formation (53) (Figure 8A). While it is known that the redox potential of the buffer environment does not influence the kinetics of LOV photorecovery from the adduct state (54), to our knowledge, the effect of the oxidation state of LOV domains on their capacity to serve as photosensors remains unexplored.</p><p>The cytoplasm of a bacterial cell is reducing, in the range of −260 to −280 mV under logarithmic growth conditions (55). We measured the potential for the two-electron reduction of full-length LovK at −258 mV, suggesting that the pool of cytoplasmic LovK will be at least partially reduced under normal growth conditions. With the exception of the LovK(1–138) construct (−303 mV), all of the truncated LovK constructs had two-electron reduction potentials in the −260 mV range (Table 5). As cysteinyl-flavin adduct formation in response to blue light absorption is only favored when FMN is in its oxidized form, increasing or decreasing the potential of the cytoplasm will, in theory, affect the responsiveness of a cell to blue light. In vitro ATPase activity assays of full-length LovK support a model in which a decrease in cytoplasmic reduction potential reduces light-dependent regulation of LovK. ATP hydrolysis activity of oxidized LovK was upregulated 1.6 fold upon illumination with white light (Figure 8B), which was consistent with our previous observation of an approximate 2-fold increase in ATPase activity in the lit state (25). Chemical reduction of LovK FMN (Figure 8B) (see Materials and Methods) to produce a mixed population of oxidized and reduced LovK did not affect its dark-state ATPase activity but attenuated the light-dependent increase in ATP hydrolysis. This result evidences a model in which the redox state of the cytoplasm can regulate LovK photoactivity (Figure 8C).</p><p>LovK has previously been implicated in the regulation of cell envelope composition (25), and envelope remodeling is known to be a common response to cellular stress (56). The cell envelope/adhesion phenotype of lovK mutants hints at a possible functional role for redox stress sensing by LovK. Indeed, extracellular stress can affect intracellular redox potential through a variety of mechanisms. Under normal growth conditions, cytoplasmic redox state is buffered mainly by the equilibria between the oxidized and reduced forms of thioredoxin and glutathione; these equilibria are affected by the availability of the reduced nicotinamide dinucleotides NADH and NADPH (57). Hypoxia can increase the cellular concentration of NADH, thereby lowering the cytoplasmic reduction potential. Oxidative stressors such as superoxide or nitric oxide can directly perturb the reduction potential of the cell or can affect it indirectly by altering the NADP/NADPH ratio (58, 59).</p><p>Thioredoxin, glutathione, and other redox-sensitive species in the cell display substrate specificity when participating in electron transfer reactions (57, 60). For example, ferric iron generated by oxidative stress is preferentially reduced by flavins in E. coli, rather than by NADH, thioredoxin, glutathione, or any of the other potential reducing agents in the cell (61). While molecules that may function as biological reductants of LovK in the cell are not known, we have in vitro evidence that NADH and NADPH do not likely to fill this role. Incubation of these reductants with equimolar concentrations of LovK failed to yield reduced protein over the course of several hours. However, the addition of one molar equivalent of oxidized FMN to a completely reduced sample of LovK resulted in a rapid (t1/2 < 1 minute) recovery of the absorption spectrum of oxidized LovK via reduction of free FMN (see Figure 8B). The ability of LovK to donate electrons to FMN suggests that flavins and other flavoproteins are candidates for such redox interactions in vivo.</p><p>The capacity of C. crescentus LovK to function as a conditional photosensor in the oxidized state may be a part of a more complex stress sensor pathway in which both light and cellular redox signals are coordinately integrated to affect adaptation to environmental stressors. Indeed, exposure to blue light is often coupled with exposure to UV light, a common cause of oxidative stress. We present a model in which LOV domains can have a dual role as sensors of light and cytosolic reduction potential, providing a possible explanation for the abundance of soluble LOV proteins encoded in the genomes of heterotrophic bacteria with no known photoresponse. LovK, and perhaps other LOV regulatory proteins, appear capable of integrating information from multiple environmental stimuli.</p><!><p>E.B.P was supported by National Institutes of Health Training Grant 5T32GM007183-34. S.C. acknowledges support for this project from NIH grant 1R01GM087353-2, the Arnold and Mabel Beckman Foundation (BYI), and the Mallinckrodt Foundation. Advanced Photon Source is supported by the DOE Office of Basic Energy Sciences (Contract No. DE-AC02-06CH11357). BioCAT is an NIH-supported Research Center (RR-08630). C.A.M. and B.A.P. are supported by NIH grant 2R01GM061087-8.</p><p> Supporting Information Available </p><p>An alignment of (A) N-terminal and (B) C-terminal LOV domain flanking sequence not presented in Figure 1 is available free of charge via the Internet at http://pubs.acs.org.</p><p>Light-oxygen-voltage</p><p>Per-ARNT-Sim</p><p>small-angle X-ray scattering</p><p>sulfate transporter anti-sigma factor antagonist</p><p>phototropin</p><p>isopropyl-β-D-thiogalactopyranoside</p><p>radius of gyration</p><p>retention factor</p>

PubMed Author Manuscript

Synthesis and biological evaluation of a new series of ortho-carboranyl biphenyloxime derivatives

(Z,Z’)-1,1′-(4-ortho-Caboranyldimethyl)-bis(2-methoxyphenylethan-1-oxime) intermediate 3 was synthesized by a three-step reaction with a final treatment with base to give a new series of ortho-carboranyl biphenyloxime derivatives (4–8). Compounds 7 and 8 showed high solubility and the in vitro study results revealed high levels of accumulation in HeLa cells with higher cytotoxicity and boron uptake compared to l-boronphenylalanine. Electronic supplementary materialThe online version of this article (10.1186/s13065-018-0444-z) contains supplementary material, which is available to authorized users.

synthesis_and_biological_evaluation_of_a_new_series_of_ortho-carboranyl_biphenyloxime_derivatives

<!>Experimental<!>Synthetic routes and experimental data<!>Cell viability assay (MTT assay)<!>Boron uptake<!>Results and discussion<!><!>Conclusion<!>

<p>Comparison of the o-Carborane and benzene</p><!><p>All manipulations were performed under a dry nitrogen atmosphere using standard Schlenk techniques. Tetrahydrofuran (THF) was purchased from Aladdin Pure Chemical Company and dried over sodium metal distillation prior use. The reactions were monitored on Merck F-254 pre-coated TLC plastic sheets using hexane as the mobile phase. All yields refer to the isolated yields of the products after column chromatography using silica gel (200–230 mesh). All glassware, syringes, magnetic stirring bars, and needles were dried overnight in a convection oven. Ortho-carborane (C2H2B10H10) was purchased from HENAN WANXIANG Fine Chemical Company and used after sublimation. The NMR spectra were recorded on a Bruker 300 spectrometer operated and the chemical shifts were measured relative to the internal residual peaks from the lock solvent (99.9% CDCl3 and CD3COCD3), and then referenced to Si(CH3)4 (0.00 ppm). The Fourier transform infrared (FTIR) spectra of the samples were recorded on an Agilent Cary 600 Series FT-IR spectrometer using KBr disks. Elemental analyses were performed using a Carlo Erba Instruments CHNS–O EA1108 analyzer (Additional file 1).</p><!><p>Synthesis of bis(3-methoxybenzyl)-ortho-carborane (1). A 2.5 M n-BuLi (4.0 mL, 10 mmol) solution was added via a syringe to a solution of o-carborane (1.44 g, 10 mmol) in 50 mL of THF at − 78 °C. A solution of 1-(bromomethyl)-3-methoxybenzene (4.22 g, 21 mmol) in THF 10 mL was added slowly to the reaction flask at − 78 °C, and the reaction temperature was maintained at − 78 °C for 1 h. The reaction mixture was then warmed slowly to room temperature, stirred for an additional 12 h, and quenched with distilled H2O (30 mL). The crude product was then extracted with methylene chloride (30 mL × 3). The organic layer was washed with H2O, dried with anhydrous Na2SO4, and filtered then concentrated. The residue was purified by flash column chromatography (ethyl acetate/hexane 1:10) to give compound 1 as a colorless oil: yield: 3.6 g (93%). IR(KBr pellet), cm−1, ν: (B-Ho-carborane) 2593. 1HNMR (CDCl3), δ, ppm: 3.2–0.8 (br, B-Ho-carborane, 10H), 3.61 (s, –CH2, 4H), 3.83 (s, –OCH3, 6H), 6.77 (s, 1-Hbenzene, 2H), 6.84–6.82 (d, J = 6.9 Hz, 2-Hbenzene, 2H), 6.90–6.88 (d, J = 6.9 Hz, 3-Hbenzene, 2H), 7.32–7.29 (m, 4-Hbenzene, 2H). Found, %: C 56.31; H 7.65. C18H28B10O2. Calculated, %: C 56.23; H 7.34.</p><p>Synthesis of 1,1′-(4-caboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one) (2). Acetyl chloride (1.4 mL, 20 mmol) was added via a syringe to a solution of aluminum chloride (2.6 g, 20 mmol) in 50 mL of methylene chloride at 0 °C and stirred for 30 min. A solution of compound 1 (3.5 g, 10 mmol) in methylene chloride 10 mL was added slowly to the reaction flask at 0 °C, and the reaction temperature was maintained at 0 °C for 30 min. The reaction mixture was then warmed slowly to room temperature, stirred for an additional 3 h, and quenched with a saturated NaHCO3 (30 mL) solution. The crude product was then extracted, and the organic layer was washed with H2O, dried with anhydrous Na2SO4, and filtered then concentrated. The residue was purified by flash column chromatography (ethyl acetate/hexane 1:8) to give compound 2 as a colorless oil: yield: 4.1 g (97%). IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2602. 1HNMR(CDCl3), δ, ppm: 3.2–0.8 (br, B-Ho-carborane, 10H), 3.64 (s, –CH3, 6H), 3.66 (s, –CH2, 4H), 3.95 (s, –OCH3, 6H), 6.82 (s, 1-Hbenzene, 2H), 6.89–6.86 (d, J = 7.8 Hz, 2-Hbenzene, 2H), 7.77–7.74 (d, J = 7.8 Hz, 3-Hbenzene, 2H). Found, %: C 56.42; H 6.67. C22H32B10O4. Calculated, %: C 56.39; H 6.88.</p><p>Synthesis of (Z,Z′)-1,1′-(4-caboranyldimethyl)-bis(2-methoxyphenylethan-1-oxime) (3). A solution of compound 2 (3.8 g, 8.1 mmol) and hydroxylamine (1.2 g, 17.8 mmol) in 40 mL of methanol was heated under reflux for 2 h. The reaction mixture was then cooled to room temperature, and the crude product was concentrated. The residue was purified by flash column chromatography (ethyl acetate/hexane 1:4) to give compound 3 as a colorless oil: Yield: 3.7 g (92%). IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2586. 1H NMR (CD3COCD3), δ, ppm: 3.16 (s, –CH3, 6H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.88 (s, –OCH3, 6H), 3.93 (s, –CH2, 4H), 6.97–6.95 (d, J = 7.5 Hz, 2-Hbenzene, 2H), 7.05 (s, 1-Hbenzene, 2H), 7.30–7.28 (d, J = 7.5 Hz, 3-Hbenzene, 2H). Found, %: C 52.68; H 6.81; N 5.69. C22H34B10N2O4. Calculated, %: C 52.99; H 6.87; N 5.62.</p><p>Synthesis of (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-dipyridin-2-ylmethyldioxime (4). A solution of compound 3 (0.7 g, 1.4 mmol) and potassium carbonate (0.4 g, 3.0 mmol) in 10 mL of acetonitrile was stirred at room temperature for 30 min. Subsequently, (2-bromomethyl)pyridine (0.5 g, 3.0 mmol) was added at room temperature, and then heated under reflux for 5 h. The crude product was then concentrated, and the residue was purified by flash column chromatography (ethyl acetate/hexane 1:4) to give compound 4 as a yellow oil: Yield: 0.8 g (88%). IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2607. 1HNMR (CD3Cl), δ, ppm: 2.31 (s, –CH2, 6H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.63 (s, –CH3, 4H), 3.84 (s, –OCH3, 6H), 5.37 (s, –CH2, 2H), 6.73 (s, 1-Hbenzene, 2H), 6.80–6.77 (d, J = 7.8 Hz, 2-Hbenzene, 2H), 7.29–7.24 (m, 3-Hbenzene and pyridine, 4H), 7.47–7.44 (d, J = 7.8 Hz, 3-Hpyridine, 2H), 7.76–7.70 (t, J = 7.8 Hz, 2-Hpyridine, 2H), 8.61–8.59 (d, J = 4.8 Hz, 1-Hpyridine, 2H). Found, %: C 59.36; H 6.63; N 8.35. C34H44B10N4O4. Calculated, %: C 59.98; H 6.51; N 8.23.</p><p>Synthesis of (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-phenoxyethyl)dioxime (5). A procedure analogous to the preparation of 4 was used and a colorless oil was obtained. Yield: 0.9 g (89%). IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2577. 1H NMR (CD3Cl) δ, ppm: 2.22 (s, –CH3, 6H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.64 (s, –CH2, 4H), 3.85 (s, –OCH3, 6H), 4.31–4.28 (t, J = 4.8 Hz, –CH2 alkyl-1, 4H), 4.56–4.52 (t, J = 5.1 Hz, –CH2 alkyl-2 4H), 6.75 (s, 1-Hbenzene-1 2H), 6.83–6.80 (d, J = 7.5 Hz, 2-Hbenzene-1, 2H), 7.00–6.95 (m, 1-Hbenzene-2, 6H), 7.34–7.29 (m, 2-Hbenzene-1 and 2, 6H). Found, %: C 61.47; H 6.92; N 3.84. C38H50B10N2O6. Calculated, %: C 61.77; H 6.82; N 3.79.</p><p>Synthesis of (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(3-phenoxypropyl)dioxime (6). A procedure analogous to the preparation of 4 was used and a colorless oil was obtained. Yield: 0.9 g (86%). IR (KBr pellet), cm−1, ν: (B–H) 2589. 1H NMR(CD3Cl), δ, ppm: 2.25–2.17 (m, –CH3 and -CH2 alkyl-1, 10H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.64 (s, –CH2, 4H), 3.85 (s, –OCH3, 6H), 4.16–4.12 (t, J = 6.0 Hz, –CH2 alkyl-2, 4H), 4.40–4.36 (t, J = 6.0 Hz, –CH2 alkyl-3, 4H), 6.74 (s, 1-Hbenzene-1, 2H), 6.82–6.79 (d, J = 7.8 Hz, 2-Hbenzene-1, 2H), 6.96–6.93 (m, 1-Hbenzene-2, 6H), 7.33–7.30 (m, 2-Hbenzene-1 and 2, 6H). Found, %: C 62.52; H 7.12; N 3.77. C40H54B10N2O6. Calculated, %: C 62.64; H 7.10; N 3.65.</p><p>Synthesis of (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-piperidin-1-ylethyl)dioxime (7). A procedure analogous to the preparation of 4 was used and a colorless oil was obtained. Yield: 0.8 g (82%) colorless oil. IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2591. 1H NMR (CD3Cl), δ, ppm: 1.47–1.45 (m, 1-Hpiperidine, 4H), 1.64–1.60 (m, 2-Hpiperidine, 4H), 1.88–1.86 (m, 3-Hpiperidine, 4H), 2.19 (s, –CH3, 6H), 2.53–2.51 (m, 8H), 2.76–2.72 (t, J = 6.0 Hz, –CH2 alkyl-1, 4H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.63 (s, –CH2, 4H), 3.85 (s, –OCH3, 6H), 4.36–4.32 (t, J = 6.0 Hz, –CH2 alkyl-2, 4H), 6.74 (s, 1-Hbenzene, 2H), 6.82–6.79 (d, J = 7.8 Hz, 2-Hbenzene, 2H), 7.31–7.29 (d, J = 7.8 Hz, 3-Hbenzene, 2H). Found, %: C 59.65; H 8.34; N 7.68. C36H60B10N4O4. C 59.97; H 8.39; N 7.77.</p><p>Synthesis of (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-morpholinoethyl)dioxime (8). A procedure analogous to the preparation of 4 was used and a colorless oil was obtained. Yield: 0.9 g (84%). IR (KBr pellet), cm−1, ν: (B-Ho-carborane) 2596. 1HNMR (CD3Cl), δ, ppm: 2.52 (s, –CH3, 6H), 2.55–2.54 (m, –CH2 alkyl-1, 4H), 2.77–2.72 (t, J = 6.9 Hz, –CH2 alkyl-2, 4H), 3.2–0.8 (br, B-Ho-carborane, 10H), 3.64–3.59 (m, 1-Hmorpholine, 8H), 3.76–3.73 (m, 2-Hmorpholine, 8H), 3.85 (s, –OCH3, 6H), 6.83–6.76 (m, 2-Hbenzene, 4H), 7.31 (s, 2-Hbenzene, 2H). Found, %: C 56.38; H 7.83; N 7.64. C34H56B10N4O6. C 56.33; H 7.79; N 7.73.</p><!><p>HeLa cells in a 3 × 104/mL cell suspension per hole in 96 well plates were digested by adding 100 μL of a cell suspension and culturing for 24 h to absorb the original culture medium followed by the addition of 200 μL configured compounds-4, 5, 6, 7, 8 and BPA (l-boronphenylalanine). Each concentration was made from 4 compound holes, and the holes around the 96 well plates were sealed with PBS, the negative control. The blank control group lacked the compounds. After 24 h, 20 μL of a MTT solution was added to each hole, and cultured for 4 h. Subsequently, DMSO 150 μL was added to the medium through a suction hole and shaken for 10 min. The OD of each hole was determined at 490 nM, and the sample inhibition rate in different concentrations was calculated: inhibition rate = (Control OD value/Delivery OD value)/Control OD value × 100%. Finally, the IC50 value of the sample was calculated using the related software.</p><!><p>HeLa cells (5 × 103) were incubated for 48 h in the presence of various concentrations of compounds 4, 5, 6, 7, 8, and BPA. After washing three times, the cumulative boron concentration was determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) [15, 16]. (± is the average value).</p><!><p>This paper reports the hydrophilic function of the ortho-carboranylbenzyloxime moiety, such as alkylmorpholine, alkylpiperidine, phenoxyalkyl and pyridine, on carbon–oxygen combined with chemical bonding. These compounds have higher solubility in polar solvents and increasing boron uptake in tumor cells within the organization for a drug evaluation.</p><!><p>Preparation of (Z,Z')-1, 1′-(4-Caboranyldimethyl)-bis(2-methoxyphenylethan-1-oxime)</p><p>Preparation of (Z,Z′)-1,1′-(4-Caboranyldimethyl)-bis (hydrophilic functional) derivatives(4–8)</p><p>Cytotoxicity (IC50) to HeLa cervical carcinoma cells</p><p>aThe results represent the means ± s.d.</p><p>Accumulation of compounds 4–8 into HeLa cells</p><!><p>In conclusion, we reported the series of ortho-carborane substituted bipolar-function derivatives, such as alkyl pyridine, alkyl phenoxide, alkyl morpholine, and alkyl piperidine, were synthesized. The target compounds coupling of the aryl-oxime with chain functional group proceeded successfully for introduction of an ortho-carborane moiety in the molecules, which can easily be further four-step substituted to high yield final compound. The effects of synthesized compounds on biology activity were assay in HeLa cells. Both cyclic alkyl derivatives of ortho-carborane and oxime containing compounds, 7 and 8, respectively, were exhibit high boron uptake and higher cytotoxicity than BPA (l-boronphenylalanine). This resulted in carborane compounds with improved water solubility for the BNCT agent. The knowledge gained from modified bipolar groups could facilitate both drug selection and evaluations.</p><!><p>Additional file 1: Figure S1. 1H-NMR bis(3-methoxybenzyl)carborane (1). Figure S2. 1H-NMR1,1′-(4-caboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one) (2). Figure S3. 1H-NMR (Z,Z′)-1,1′-(4-caboranyldimethyl)-bis(2-methoxyphenylethan-1-oxime) (3). Figure S4. 1H-NMR (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-dipyridin-2-ylmethyldioxime (4). Figure S5. 1H-NMR (1Z,1′Z)-1,1′-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-phenoxyethyl)dioxime (5). Figure S6. 1H-NMR (1Z,1′Z)-1,1'-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(3-phenoxypropyl)dioxime (6). Figure S7. 1H-NMR (1Z,1′Z)-1,1'-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-piperidin-1-ylethyl)dioxime (7). Figure S8. 1H-NMR (1Z,1′Z)-1,1'-(carboranyldimethyl)-bis(2-methoxy-4,1-phenylene-ethan-1-one)-O,O-di(2-morpholinoethyl)dioxime (8).</p><p>Electronic supplementary material</p><p>The online version of this article (10.1186/s13065-018-0444-z) contains supplementary material, which is available to authorized users.</p>

PubMed Open Access

Targeting InhA, the FASII Enoyl-ACP Reductase: SAR Studies on Novel Inhibitor Scaffolds

The bacterial type II fatty acid biosynthesis (FASII) pathway is an essential but unexploited target for drug discovery. In this review we summarize SAR studies on inhibitors of InhA, the enoyl-ACP reductase from the FASII pathway in M. tuberculosis. Inhibitor scaffolds that are described include the diaryl ethers, pyrrolidine carboxamides, piperazine indoleformamides, pyrazoles, arylamides, fatty acids, and imidazopiperidines, all of which form ternary complexes with InhA and the NAD cofactor, as well as isoniazid and the diazaborines which covalently modify the cofactor. Analysis of the structural data has enabled the development of a common binding mode for the ternary complex inhibitors, which includes a hydrogen bond network, a large hydrophobic pocket and a third \xe2\x80\x98size-limited\xe2\x80\x99 binding area comprised of both polar and non-polar groups. A critical factor in InhA inhibition involves ordering of the substrate binding loop, located close to the active site, and a direct link is proposed between loop ordering and slow onset enzyme inhibition. Slow onset inhibitors have long residence times on the enzyme target, a property that is of critical importance for in vivo activity.

targeting_inha,_the_fasii_enoyl-acp_reductase:_sar_studies_on_novel_inhibitor_scaffolds

Introduction<!>Structural and Mechanistic Basis for High Affinity Inhibition of Enoyl-ACP Reductases<!>Isoniazid-Based Inhibitors<!>Diaryl-Ether Inhibitors<!>Diazaborines<!>Pyrrolidine Carboxamides<!>Piperazine Indoleformamides<!>Pyrazole Derivatives<!>Arylamides<!>Fatty Acids<!>Imidazopiperidines<!>General Binding Model<!>Conclusion<!>

<p>The outer layer of the mycobacterial cell wall is comprised predominantly of mycolic acids, very long chain (C60-C90) fatty acids that are important for the ability of Mycobacterium tuberculosis to live and replicate inside macrophages, and also for the inability of many antibiotics to penetrate into the cytosol [1-2]. Consequently, compounds that antagonize the ability of mycobacteria to synthesize mycolic acids are promising leads for developing novel tuberculosis chemotherapeutics. The fatty acid precursors required for mycolic acid synthesis are generated by the mycobacterial type I (FASI) and type II (FASII) biosynthetic pathways (Fig. (1)). The FASI enzyme complex, which is homologous to the synthase found in mammalian cells, catalyzes the de novo synthesis of C20-C24 fatty acyl-CoAs that are subsequently extended by the FASII system producing fatty acids up to C56 in length.</p><p>In targeting essential bacterial pathways, there is always a concern that the organism can acquire the required metabolites from the environment and thus evade the impact of pathway inhibition. In this regard, a recent study raised questions about the essentiality of the FASII pathway in Streptococcus agalactiae and, by extension, Gram positive bacteria in general [3]. While doubt has now been cast on the generality of the conclusions reached in this study, at least with regard to the important nosocomial pathogen Staphylococcus aureus [4], it is important to note that mammals do not synthesize mycolic acids and thus the FASII pathway must play an essential role in mycobacteria. This belief is supported by the knowledge that the very effective front-line drug isoniazid targets InhA, the enoyl-ACP reductase in the M. tuberculosis FASII pathway [5-9], while Jacobs and coworkers have demonstrated that inactivation of InhA in M. smegmatis results in cell lysis [10].</p><p>The primary mechanism of resistance to isoniazid occurs from mutations in the mycobacterial catalase peroxidase enzyme KatG that is responsible for drug activation and not from mutations in InhA [11-12]. Consequently, inhibitors of InhA that do not require KatG activation should be active against most clinical strains of isoniazid-resistant M. tuberculosis [13]. InhA is a member of the short chain dehydrogenase reductase superfamily and is in the FabI class of enoyl-ACP reductases that are found in bacteria such as Escherichia coli, S. aureus and Francisella tularensis [7, 14]. Given the continuing need to develop antibacterial agents with novel mechanisms of action, there have been a number of efforts to develop FabI inhibitors [14], particularly the enzyme from S. aureus (saFabI) [15], and currently two Phase I trials are in progress for the saFabI inhibitors developed by Affinium Pharmaceuticals Inc. [16-17] and Fab Pharma SA.</p><p>In the present review we describe current attempts to develop potent inhibitors of InhA. Three classes of inhibitors are based on isoniazid, the diazaborines and triclosan, antibacterial agents that were subsequently shown to inhibit InhA. In addition we also summarize inhibitor discovery resulting from compounds identified by high-throughput screening (HTS) that includes the pyrrolidine carboxamides, piperazine indoleformamides, pyrazoles and arylamides, and conclude with attempts to use fatty acids to inhibit InhA. Despite the structural diversity in the different InhA inhibitor classes, two generalizations arise from an analysis of the data. Firstly, in almost every case the inhibitors bind to the enzyme in the presence of the oxidized and/or reduced cofactor, albeit in the case of isoniazid and the diazaborines as covalent adducts of the cofactor [14]. And secondly, high affinity inhibition is often coupled to ordering of the 'substrate binding loop' that is located close to the active site [14]. Given the importance of loop ordering and its relationship to the residence time of the inhibitor on the enzyme [18-19], we first briefly summarize the structural and mechanistic basis for high affinity inhibition of InhA and the FabI class of enoyl-ACP reductases.</p><!><p>A central theme in FabI inhibitor discovery concerns the role that inhibitor binding plays in ordering of a loop of amino acids close to the active site. This sequence of amino acids is known as the substrate binding loop, and the importance of loop ordering during FabI inhibition was first noted in studies on the inhibition of the E coli FabI by the diazaborine class of compounds [20]. Since then, X-ray crystallography has revealed ordered loops in a number of FabI:inhibitor complexes and those relevant to InhA are described in this review. Based on our own work, we believe that loop ordering is the structural event that occurs when FabI enzymes interact with slow onset inhibitors. In this type of inhibition, the initial rapid association for enzyme and inhibitor is followed by a slow step that results in formation of the final EI* complex (Fig. (2)).</p><p>A classic example of this effect is given by the inhibition of the E. coli FabI (ecFabI) by the diaryl ether antibacterial agent triclosan [22], which is a slow onset inhibitor of ecFabI [24-25]. The X-ray structures of ecFabI in a binary complex with the oxidized cofactor NAD+ and in a ternary complex with NAD+ and triclosan are shown in Fig. (3), where it can be seen that formation of the ternary enzyme inhibitor complex leads to ordering of the substrate binding loop (red).</p><p>Importantly, slow onset enzyme inhibitors have long residence times on their enzyme targets which, in a growing number of cases, is thought to be critical for in vivo drug activity [19, 21, 26-27]. In particular, compounds with long residence times will remain bound to their targets even when the free drug concentration is low, thus prolonging their biological activity. Recent studies from our lab on the FabI enzyme from the category A pathogen Francisella tularensis (ftuFabI) have revealed that the antibacterial activity of a series of ftuFabI inhibitors in an animal model of F. tularensis infection correlate with the residence time of the compounds on ftuFabI but not with their Ki values for enzyme inhibition or their MIC (minimum inhibitor concentration) values for inhibiting bacterial growth [18]. Thus in vivo drug activity in this case is mediated by the kinetics but not the thermodynamics of enzyme-inhibitor complex formation. This has important implications for drug discovery programs that are usually driven by thermodynamic estimates of compound potency (Ki or IC50 values), and, in the case of tuberculosis drug discovery, increases the motivation for developing inhibitors that promote loop ordering and are slow onset inhibitors of InhA. In the present review we summarize efforts to develop inhibitors of InhA with particular emphasis on the results of SAR studies within each compound class.</p><!><p>Isoniazid (INH) has been used as a front-line antitubercular drug for over 50 years [28]. Activation of INH by the endogenous catalase-peroxidase enzyme KatG is essential for INH activity [10-12, 29] and results in the formation of an acyl pyridine adduct with NAD (the INH-NAD adduct) (Fig. (4)). The 4S isomer of the adduct is a nanomolar slow-onset inhibitor of InhA with a residence time of 60 min [9], and the X-ray crystal structure of the enzyme inhibitor complex (1zid.pdb) [8] indicates that the substrate binding loop is ordered in agreement with the above discussion (Fig. (5)). Both enantiomers of the INH-NAD adduct, as well as those formed with NADP+ can undergo tautomerization (Fig. (4), I1-I3) [30], increasing the number of potential targets for the drug in M. tuberculosis. Although InhA was the initial INH target identified [5-7], the 4R INH-NADP adduct is a nanomolar inhibitor of the mycobacterial DHFR enzyme [31], while affinity purification has revealed 16 additional proteins in the M. tuberculosis genome capable of binding to the INH-NAD(P) adduct(s) [32]. There is also evidence that INH can inhibit non-NAD(P) binding proteins such as KasA, the β-ketoacylACP synthase in the FASII pathway [33]. Importantly, since mutations of KatG are the most common mechanisms of resistance to INH [12, 34], inhibitors that bind to the final drug target(s) should have activity against the majority of INH-resistant clinical strains [13].</p><p>In order to explore SAR involving the INH-NAD inhibitor, the benzoylhydrazine-NAD adduct (BH-NAD) has been synthesized and shown to competitively inhibit InhA with a Ki value < 1 nM [9]. Although this indicates that the acyl-pyridine can be replaced by a benzoyl group, the preliminary studies also showed that benzoic hydrazide is less readily activated than INH. In addition, a series of BH-NAD analogues have been synthesized (Fig. (6), I4, I5) [35], in which the ADPR portion of the molecule has been modified. However none of these analogues significantly affected InhA activity, indicating the importance of the nucleotide for InhA inhibition.</p><p>Finally, an iron complex of isoniazid, [FeII(CN)5(INH)]3- (Fig. (7)) was found to inhibit InhA with a Ki value of 70 nM [37]. Interestingly, this complex also inhibited growth of M. tuberculosis with an MIC value of 0.2 μg/ml. The iron complex inhibited InhA without forming an adduct with NADH, consistent with docking results that suggest the inhibitor may bind directly to the active site [38-39]. Additional inorganic complexes also demonstrated antimycobacterial activity [40-42], however the target of these compounds has not been confirmed.</p><!><p>Triclosan (Fig. (8)) is a biocide which is widely used in consumer products such as toothpaste and deodorant. While at high concentration triclosan may inhibit bacterial growth by acting as a membrane disruptant, the antibacterial activity of this compound at low concentration stems from the inhibition of FabI [22, 43-45]. Although the activity of triclosan against InhA and M. tuberculosis is relatively modest (Ki 0.2 µM, MIC 12.5 µg/ml) [13, 46], the lack of a requirement for KatG activation suggests that triclosan derivatives with increased affinity for InhA should be excellent leads for developing drugs that are active against MDR-TB and XDR-TB.</p><p>The interaction of triclosan with the FabI enzyme class, and in particular with InhA, has been thoroughly studied (reviewed in ref [14]). The compound is an uncompetitive inhibitor of InhA, binding preferentially to the enzyme:NAD+ product complex with a Ki value of 0.2 µM [46]. Slow onset inhibition is not observed when triclosan inhibits InhA, and, consistent with our hypothesis, the InhA substrate binding loop is thus disordered in the X-ray structure of the InhA:NAD+:triclosan ternary complex (2b35.pdb) (Fig. (9)). [13].</p><p>Key features involved in enzyme inhibitor recognition include hydrogen bonds between the A-ring hydroxyl group, Tyr158 and the NAD+ ribose, a hydrogen bond between the ether oxygen and the NAD+ ribose, and a stacking interaction between the aromatic A ring and the nicotinamide ring of NAD+ [22]. The importance of these interactions for the inhibition of other FabI enzymes by triclosan had been probed by site-directed mutagenesis, synthesis and computational studies [47-49], and the phenolic A-ring and ether oxygen are preserved in the analogues described below. However, ultimately it may be necessary to develop compounds that lack the hydroxyl group due to the likelihood that this functionality is susceptible to Phase II metabolism such as glucuronidation and sulfation [50].</p><p>In Table 2 we list the diaryl ethers in which the substituent on the A-ring has been systematically modified while making only minor changes to the B ring (PT01, PT03-PT08 [13, 51]; PT70 [52]; F2-F26, [53]).</p><p>Analysis of the SAR data in Table 2 reveals that the size and shape of R1 have the most effect on binding affinity. PT01, PT03-PT08 and F8-F10 indicate that an alkyl substituent of 5-8 carbons gives optimal inhibition of InhA and in vitro antibacterial activity. The X-ray crystal structures of PT03 [13] and C16-N-acetylcysteamine (C16-NAC) [54], a substrate analog, bound to InhA (Fig. (10A)) indicate that the PT03 A-ring can be superimposed on the acyl portion of C16-NAC, and that the last 3 carbon atoms of PT03 mimic the turn in the fatty acid chain of C16-NAC, implying that additional binding space is available near the end of the PT03 alkyl chain. In this regard, it can be seen that F17-F19, and F24-F26, in which an aromatic ring has been attached to the A-ring through a linker have similar IC50 values for InhA inhibition compared to F10. This indicates that additional space is available for inhibitor recognition, as can clearly be seen in the X-ray crystal structure of F7 bound to InhA (Fig. (10B)) [53]. Interestingly, F3, F6, F14-F16, and F20-F23, in which no linker is present between the additional ring and A-ring, all have IC50 values much larger than those with linkers, probably because the additional bulky substituent is too close to Phe149 and results in a steric clash [53].</p><p>PT03-PT05 were the most potent first generation diaryl ether InhA inhibitors developed in our lab . Compared to triclosan, the Ki value for PT05 has decreased 200-fold while the MIC value has decreased 10-fold. We anticipated that increased contacts between enzyme and ligand might result in loop ordering. However PT03-PT05 are not slow onset inhibitors and X-ray structural studies indicate that they do not promote loop ordering upon binding to the InhA:NAD+ complex. However, importantly these compounds have similar MIC values for drug sensitive and INH-resistant strains of M. tuberculosis, in agreement with the hypothesis that InhA inhibitors that do not require KatG activation should be active against drug-resistant bacteria. We also explored the mode of action of these compounds and demonstrated that overexpression of the putative target (InhA) resulted in a 10-fold increase in MIC. Finally, we compared the gene expression profile for PT04 and PT05 with that for triclosan and showed that two putative drug-resistance mechanisms upregulated by triclosan were not affected by the alkyl diaryl ethers. This suggests that the improved antibacterial activity may result partly from the ability to evade drug detoxification [13, 51].</p><p>In addition to SAR studies on the A-ring side chain, modification of the B-ring has also been performed [55]. With PT04 as the lead compound, a variety of substituents have been introduced at the ortho-, meta-, and para- positions on the B-ring. In addition, the B-ring has also been replaced with several heterocyclic aromatic groups (Table 3).</p><p>Many of the analogues in Table 3 were synthesized in an attempt to increase solubility of the non-polar lead compound. While decreases in logP were observed for some compounds [55], in general the main conclusion reached from the analoging studies was the inability of the active site to tolerate the introduction of bulky groups into the B-ring. Thus, while replacement of the B-ring with aniline led to only a modest decrease in potency, depending on the position of the amino substituent (e.g. PT13), subsequent derivatization of the amines with acyl groups led to dramatic decreases in both enzyme inhibition and MIC (PT16-PT21, PT28-PT30). A similar result was also observed when the B-ring was functionalized with a piperazine moiety (PT67, PT76). In addition, in most cases replacement of the B ring with a pyridine or pyrimidine ring also led to significant decreases in enzyme inhibition, although interestingly in the case of PT73, PT77 and PT42, without altering the MIC value by more than a factor of 3.</p><p>Comparison of the data in Tables 2 and 3 indicates that small substituents on the B-ring are well tolerated. In Table 2 all the substituents are either ortho or para to the ether linkage (F2-F26) and comparison of the biological data for the three aniline analogues (PT13-PT15) indicates that substitution of the meta position is less favorable. Analysis of the structural data reveals that the ortho substituent can occupy a space adjacent to both the nonpolar amino acids in the substrate binding loop and to the polar NAD+ ribose, thereby favoring groups that can participate in both polar and nonpolar interactions. This can clearly be seen in the X-ray structure of F7 bound to InhA where the meta-chloro group is within 4 Å of the backbone amide of Gly96, the methyl group of Ala98 and the 2' ribose hydroxyl (Fig. (11)).</p><p>The importance of the B-ring meta substituent can also be clearly seen when comparing SAR data for PT04 and PT70. Although the first generation alkyl diaryl ethers developed by us were significantly more potent than triclosan (PT01, PT03-PT08 [13]), in none of the cases were the compounds slow onset enzyme inhibitors. Recognizing the importance of residence time on in vivo activity [18-19, 21, 27], we speculated that introduction of a substituent meta to the ether linkage would limit rotation about the ether bond and thus reduce the entropic penalty for enzyme inhibition. Consequently, we synthesized PT70, which differs from PT04 by the presence of a meta methyl group. Significantly, in contrast to PT04, PT70 is a slow onset inhibitor of InhA with a residence time of 23 min[52]. In addition, PT70 binds to InhA with a K1 value of 22 pM, compared to 11 nM for PT04. Thus, introduction of the methyl group has both increased the thermodynamic affinity of the inhibitor for the enzyme, and resulted in a compound that triggers the slow step that leads to the final EI* complex. In keeping with our expectations, the substrate binding loop in the InhA:NAD+:PT70 is ordered (Fig. (12)) [52], whereas that for the corresponding complex with PT04 is not [13]. Inspection of the X-ray structure reveals similar enzyme-inhibitor interactions to those described for F7, in particular contacts between the B-ring methyl and the cofactor and substrate binding loop (Fig. (12)).</p><p>PT70 is currently under evaluation in an animal model of tuberculosis infection and additional efforts are currently under way to improve the ADME properties of these compounds.</p><p>In summary the diaryl ether skeleton is a promising foundation for the development of novel FabI-directed antibacterial agents. Compounds are already in Phase 1 trials for the treatment of staphylococcal infections, and confidence is high that this success can be repeated with inhibitors that target InhA for treating drug-resistant tuberculosis.</p><!><p>The diazaborines are bicyclic compounds that contain a boron atom and 1, 2-diazine in one heterocycle (Fig. (13)). Depending on the arene (X), diazaborines can be classified as thieno-, benzo-, furo-, and pyrrolo-diazaborine, when X is thiophene, benzene, furan, and pyrrole, respectively. A diazaborine was first identified in 1981 as a novel inhibitor of lipopolysaccharide biosynthesis synthesis [56] and subsequently the FabI enzyme in E. coli (envM) was shown to be the target for this class of compounds [57].</p><p>Although a large number of diazaborine derivatives have been were synthesized and evaluated for their antibacterial activity [58], SAR studies with M. tuberculosis have not been extensively explored. Davis et al. synthesized 6 compounds and determined their MIC values using two different types of media (Table 4) [59]. In general the antibacterial activity of the compounds was modest, the best MIC being 8 µg/ml. Comparison of B2 with B3 and B4 indicates that large substituents are preferred on N-2, together with a carbonyl (C=O or C=S) at C-3 (B3, B4, B5). Bioisosteres B3 and B4 have the same potency, and while a typical benzodiazaborine B1 has moderate antibacterial activity, surprisingly, the ring-opened amine B6 has similar activity to that of B1.</p><p>The mechanism of enzyme inhibition with specific focus on ecFabI has been studied in detail using X-ray crystallography and site-directed mutagenesis [20, 60-63]. The compounds form an adduct with the cofactor through a dative bond between the NAD+ 2'-ribose hydroxyl group and the diazaborine boron atom (Fig. (14)). Thus, in this respect they mirror the mechanism of action of isoniazid, although the diazaborines are not prodrugs. In addition, the boron hydroxyl group is hydrogen bonded to Tyr156 while the NAD+ nicotinamide ring forms a π-π stacking interaction with the bicyclic diazaborine ring which also has van der Waals interactions with Tyr156, Tyr146, Phe203, and Ile200. Extensive SAR studies have been performed on the mechanism of FabI inhibition together with antibacterial activity which have revealed that the 1, 2-diaza-moiety is essential. This raises an interesting question concerning the mode of action of the compounds in mycobacteria since the 1, 2-diaza group is not required for activity against M. tuberculosis (Table 4). One point of interest when comparing the E. coli and M. tuberculosis enzymes is the importance of the residue at position 63 (64 in InhA). Selection for resistance to diazaborine in E. coli results in replacement of Gly93 with a serine in ecFabI [57], while a similar experiment with isoniazid results in a Ser94Ala mutation in InhA [5].</p><!><p>As the interest in developing InhA inhibitors has grown, several groups have turned to HTS in order to identify novel leads. He et al. identified 30 hits from a HTS that inhibited InhA but that did not affect the activity of the FabI enzymes from E. coli or Plasmodium falciparum [64]. From these leads pyrrolidine carboxamides (Fig. (15)) were chosen for further optimization.</p><p>Table 5 contains SAR data from modifications made to the A-ring. Activity data for the various monosubstituted compounds showed that only meta- substitution was tolerated and that small electron withdrawing groups enhanced activity, while the meta-OH, -OCH3, -COCH3, -NHSO2CH3, and -CONH2 analogues having no activity. Consistent with this SAR, compound d11 with dual meta-substitution gave the best activity among all the derivatives. Compared to the corresponding monosubstituted compounds, an additional meta-CH3 or CF3 group did not affect the activity (d7, d12, d14). In addition, derivatives with linkers between the A-ring and the carboxamide (Fig. (16), g47-g49) did not have detectable activity, which suggests extension of the side chain is unfavorable.</p><p>Replacement of the A-ring with cyclopentyl, cyclohexyl, isobutyl, piperazine, morpholine, and a variety of other heterocycles resulted in a decrease or total loss of activity, which suggests that aromaticity of the A-ring is important for activity. Only r7 (Fig. (17), IC50 = 5.1 μM) gave better activity than s1, which implies that extension of aromatic ring structure may gain more hydrophobic contacts and thus improve activity.</p><p>Forty five aromatic A-rings were incorporated into the molecule via microtiter synthesis and screened in situ without isolation. Subsequently, 10 compounds were selected for synthesis and purification in order to permit the determination of IC50 values (Table 6). The results showed that polyaromatic moieties (p20, p21, p24) significantly increased activity. For compound p21 and p24, the substitution position and linkers between the two benzene rings are limited, and changes of position or linker may reduce the activity (data not shown, [64]). Considering the flexibility of the InhA binding pocket, it is plausible that bulky aromatic rings are accommodated by induced conformational changes.</p><p>In addition to altering the A-ring, SAR data for the C-ring was also obtained. Replacement of the C-ring with a benzene or substituted benzene ring decreased or totally abolished activity, suggesting that the chair form of the C-ring is preferred over a planar ring. To further explore the C-ring conformation, a combinational library with 15 A-rings and 8 C-rings including saturated rings of 5-8 carbons and multiple rings was prepared through microtiter synthesis and screened in situ for InhA inhibition. Selected compounds were subsequently synthesized in order to facilitate IC50 measurements (Table 7).</p><p>Analysis of the data in Table 7 indicates that analogues with cyclopentyl, phenyl, adamantyl, or bicyclohexyl C-rings have reduced activity or are inactive, while cycloheptyl (p65, p66) and bicyclo-[2.2.1]-heptyl (p62, p63) analogues have similar activity to the cyclohexyl compounds. p67 and p68 with cyclooctyl C-rings had slightly reduced activity and, taken together, the results suggests that the C-ring binds in a hydrophobic pocket that can only cyclohexyl, cycloheptyl, and bicyclo-[2.2.1]-heptyl groups. For no exception, the meta-biphenyl A-ring analogs are better than corresponding anthracenylmethyl compounds, which suggests more tridimensional space in active site.</p><p>The X-ray structure of compound d11 bound to InhA has been determined and reveals how the pyrrolidine carboxamides interact with the active site (Fig. (18)). Consistent with conclusions from molecular modeling, the pyrrolidine ring plays a central role in forming a hydrogen-bonding network with the active site. The pyrrolidine carbonyl is hydrogen-bonded to the 2'-hydroxyl of the nicotinamide ribose and to the hydroxyl of Tyr158; while the cyclohexyl ring makes van der Waals contacts with the side chains of Gly96, Phe97, and the nicotinamide ribose. In addition, the lactam ring interacts with the nicotinamide ring and the side chains of Met161 and Met199, while the meta-halogen is within 4.1 Å of Ala157, Gly104, Gyr158, and Met105. For the dichloro-substituted compound (d11), the second chloro group has hydrophobic contacts with Pro156, Met155, and Leu218 which likely explains the increased potency of this compound compared to the mono-substituted analogues. The structural data also reveal that the substrate binding loop is ordered, although no kinetic data are available to substantiate the possibility that d11 is a slow onset inhibitor.</p><p>Following chromatographic resolution, only a single enantiomer of p24, d11, and p64 was found to be active, the absolute configuration of which was determined using the X-ray structure of d11 bound to InhA (Fig. (18)). p64b, an enantiomer of p64, was the pyrrolidine carboxamide with the lowest IC50 so far identified (62 nM). However the MIC values of most compounds are larger than 125 μM, with d12 and p67 having the best activity, with MIC value of 62.5μM, while p9, p20, p63, and p65 have MIC values of 125 μM. The low whole cell antibacterial activity may be due to poor membrane permeability and/or efflux.</p><!><p>Screening has also led to the identification of piperazine indoleformamides has novel leads for developing InhA inhibitors [65-67]. SAR studies indicated that substitution of the indole imine reduced activity and that substitution of the piperazine ring was limited to the 4-N position (data not shown, [65]). Subsequent studies have focused on varying the group attached to the piperazine ring and selected compounds are listed in Table 8. In general, the introduction of bulkier groups resulted in better activity, with a fluorene substituent giving the best activity. Exploration of chemical space around the fluorene revealed that, generally, substituents at the 2- or 3- position are well tolerated, even for some bulky groups (Genz-13100, Genz-13108), while 4- substituent groups reduce binding affinity.</p><p>The X-ray structure of Genz-10850 bound to InhA indicates a series of both hydrogen-bonding and hydrophobic interactions between enzyme and inhibitor (Fig. 19). The indole imine is hydrogen bonded to a phosphate oxygen in NAD+ phosphate, which explains why an unsubstituted nitrogen atom is essential for binding. In addition, the amide carbonyl is hydrogen bonded to the 2'-OH of the nicotinamide ribose and also to the phenolic hydroxyl of Tyr158, which supports the importance of the indoleformamide scaffold. Extensive hydrophobic interactions are formed between the fluorenyl ring and Met103, Ala157, Ala198, Met199, Ile202, Ile215, and Leu218, explaining the increase in activity resulting from introduction of the fluorenyl group.</p><p>Although Genz-10850 inhibited InhA with an IC50 value of 160nM, the MIC for inhibiting bacterial growth was larger than 30 μM which may be due to poor membrane permeability [65] or efflux [67].</p><!><p>Pyrazole derivatives were also identified in the same screen that yielded the piperazine indoleformamides [65]. Table 9 lists compounds in which modifications were made primarily on the 1-N and 4-C positions of the pyrazole.</p><p>With R1 fixed as 4-chloro-2-nitrophenyl, trifluoromethylpyrimidine (Genz-5542) is the only R2 substituent that gave InhA inhibition over 80% at compound concentration of 40 μM. R1 was then further explored with substituted benzene rings when trifluoromethylpyrimidine was conserved as R2. Among the derivatives, the dinitrophenyl analog (Genz-8575) provided better activity than the parent compound, giving an IC50 of 2.4 μM and a MIC value of 2.5μM against M. tuberculosis H37Rv. Although the IC50 of Genz-8575 is 15-fold higher than Genz-10850, the MIC value is at least 12-fold smaller, implying that the pyrazole derivatives are a better scaffold than the piperazine indoleformamides.</p><!><p>The HTS performed by He et al.[64] identified 30 hits of which the arylamides (Fig. (20)) were the largest series of compounds. Optimization has primarily focused on the A- and B-rings (Table 10) [67].</p><p>Introduction of a para-methyl on the A-ring improved inhibition, and an additional meta-methyl increased activity by 3-fold, while a single meta-methyl reduced activity by 3-fold compared to the mono para-methyl compound. Bulky groups at the para-position of the A-ring abolished activity (a9-a12), which indicates a limit to the size of the group that can be accommodated by the active site at this position. Electron withdrawing meta-substituents are essential for inhibition, while many para- and ortho-substituted compounds showed no activity in the HTS (data not shown, [67]). Replacement of piperazine with piperidine or insertion of a methylene between B-ring and C-ring had no apparent effect on activity.</p><p>The similarity in structure between the arylamides and piperazine indoleformamides suggested that bulky groups might be tolerated on the piperazine ring of the former compounds. Further SAR work was thus undertaken through the microtiter synthesis of a focused library using a combination of aromatic acids and substituted piperazines. Following in situ screening, selected compounds were resynthesized and evaluated. Examples are shown in Table 11) [67].</p><p>The best inhibitors from this series incorporated piperazines with polyaromatic rings, indicating that the B-ring is involved in hydrophobic contacts with the enzyme. The smallest IC50 value (90 nM) was achieved by compound p2 with a benzene A-ring and fluorenyl B-ring.</p><p>The X-ray structure of b3 bound to InhA reveals the binding mode of the arylamides (Fig. (21). The amide carbonyl oxygen is hydrogen bonded to the 2'-hydroxyl of the nicotinamide ribose as well as the Tyr158 hydroxyl group. In addition the B-ring interacts with Phe149, Pro193, Leu218, and Val203 through hydrophobic contacts, which are clearly enhanced through the introduction of –Cl or -CF3 groups, or by replacing the B-ring with bulkier groups as shown by the SAR studies.</p><p>MIC values against M. tuberculosis were measured for selected compounds. a6 and p6 have the smallest MIC values (125 μM), while most inhibitors have MIC values above 125 μM. Since the structure of arylamides is very similar to the piperazine indoleformamides, it is likely that the arylamides also suffer from poor antibacterial activity due to problems crossing the membrane and/or efflux.</p><!><p>Since InhA catalyzes the reduction of 2-trans-enoyl ACPs, inhibition of the enzyme can in principal be achieved using unreactive analogs of the natural fatty acid substrate. Both 2-trans-hexadecenoyl-CoA and 2-trans-octadecenoyl-CoA are substrates for InhA, and the corresponding alkynoic acids are competitive inhibitors of InhA with complete inhibition achieved at a concentration of 43μM [68]. Interestingly, compared to their corresponding CoA thioesters, 2-hexadecynoic acid (L3) and 2-octadecynoic acid (L7) demonstrated much better inhibitory activity against M. tuberculosis H37Rv, with MIC values of 20 μM and 25 μM, respectively [69]. To generate SAR and explore their mode of action, 13 alkynoic acids were synthesized and MIC values against M. smegmatis mc2155 and M. bovis BCG were determined (Table 12) [69].</p><p>From Table 12 it can be seen that the antimycobacterial activity of the alkynoic acids is affected by both the position of the triple bond and the chain length of the fatty acid. L7 has the smallest MIC against M. smegmatis, while L14 and L15 have no activity at all, which suggests that the triple bond is essential for inhibitory activity. In addition, comparison of the data for hexadecynoic acids L3-L6 and octadecynoic acids L7-L10 indicates that the MIC values decrease as the triple bond is moved away from the carboxylic acid. This indicates that 2-alkynoic acids are the best scaffold, while fatty acids of 16-19 carbons give optimal antibacterial activity against the mycobacteria studied.</p><p>Further studies into the mode of action of the alkynoic acids using immunoblotting revealed that L3 and L7 directly inhibit InhA, while the use of 14C labeled L3 demonstrated the formation of two major metabolites, 3-ketoacyl CoA and 3-alkynoyl CoA, which inhibit fatty acid biosynthesis and β-oxidation respectively [69]. Since the 2-alkynoic acids have alternative effects on eukaryotes [70], there is some promise that they could be developed into specific antimycobacterials.</p><p>In addition to the studies with alkynoic acids, other types of fatty acids have been explored as potential leads for developing InhA inhibitors. Tasdemir and coworkers isolated several natural marine fatty acids from the Turkish sponge Agelas oroides with activity against InhA. FAMA, a mixture of 23:2 Δ5, 9 and 24:2 Δ5, 9 has an IC50 of 9.4 μg/ml, and FAMD, a mixture of saturated methyl-branched fatty acids has an IC50 of 8.2 μg/ml [71]. Also, four 2-methoxylated saturated fatty acids (L16-L19, Fig. (22)) were synthesized and tested against M. tuberculosis. Compound L17, with a 10 carbon chain, had the smallest MIC value (239.51 μM). However, the target of these fatty acids derivatives was not clarified [72]. Finally, several saturated and unsaturated fatty acids have also been shown to possess antimycobacterial activity [73-74]. However these compounds were not tested against M. tuberculosis, and their mode of action was proposed to involve insertion of the molecules into the cell membrane with a concomitant change in membrane permeability [70, 73].</p><!><p>Wall et al. used screening to identify the imidazopiperidines (Fig. (23a)) as a potential novel class of InhA inhibitors [75]. Sixteen derivatives were obtained through solid-phase synthesis and the biological activity of these compounds is given in Table 13.</p><p>Replacement of the imidazole group with benzene (Fig. (23b)) completely abolished inhibition (data not shown) and Table 13 concentrates on analoging around the imidazopiperidine scaffold. It was found that 4-methoxybenzyl is preferred at R2, while electron withdrawing substituents on the piperidine ring were favored. A combination of 4-methoxybenzyl at R2 and mono- or di-chlorobenzyl at R1 gave the best activity (m1, m2), however the lack of correlation between IC50 and MIC50 suggests that the compounds might have other target(s) in the cell or that uptake and/or detoxification may be an issue.</p><!><p>Comparison of the structural and mechanistic data enables a common binding mode for InhA inhibitors to be elucidated. With only one exception, the inhibitors described in this review bind in the presence of the cofactor, either in non-covalent ternary complexes or as adducts of the cofactor. For those that form ternary complexes and where Ki values have been determined, there is usually a preference for NAD+, and so these compounds are uncompetitive inhibitors binding to the InhA:NAD+ product complex. In addition, while a clear link exists between slow onset inhibition and ordering of the substrate binding loop for the diaryl ether inhibitors, in most cases the kinetics of enzyme inhibition has not been studied in detail although there are now many InhA:inhibitor X-ray structures in which the loop is ordered.</p><p>Of key importance for inhibitor binding is the formation of a hydrogen bonding network between a group on the inhibitor, Tyr158 and the 2'-hydroxyl on the nicotinamide ribose. Inhibitors with hydrogen bonding (HB) functionalities such as hydroxyl or carbonyl groups participate in this network [22, 25, 44, 62, 65]. In addition, since the natural substrates for InhA are significantly longer than for other FabI enzymes (24-56 carbons in length, [76]), the substrate binding loop in InhA is also larger, thereby enabling inhibitors with bulky hydrophobic groups such as fluorene to bind. Extensive hydrophobic contacts in this area include Met103, Gly104, Phe149, Ala157, Ala198, Met199, Ile202, Ile215, and Leu218 [13, 53, 65]. Finally, a third binding area can be identified close to the hydrogen bonding group in the inhibitor. This binding pocket is relatively constrained in size (size limited) and is also exposed to solvent as well as being close to polar groups such as the cofactor phosphodiester bridge and non-polar groups in the substrate binding loop (e.g. Ala198). Not surprisingly then, the SAR data shows that enzyme inhibition is highly sensitive to the chemical nature and size of the inhibitor substituent at this position, and groups that are small and that can also participate in both polar and non-polar contacts are preferred. In Fig. (24) the inhibitors from 7 different structures have been overlayed in order to highlight these three binding regions. Moving forward the challenge will be to use this information to optimize inhibitor affinity whilst at the same time engineering the compounds to reduce metabolic liabilities, improve compound ADME and enhance bioactivity.</p><!><p>In the past decade, beginning with the discovery that InhA is the target for isoniazid, there have been several efforts to develop potent InhA inhibitors as leads for novel tuberculosis chemotherapeutics. In addition to the approaches described in this review, de novo design [77], computational simulation [78-79], and selective optimization of side activities (SOSA) [80] have also been utilized. These activities are all underpinned by the expectation that InhA inhibitors that do not require activation by KatG will be active against isoniazid-resistant strains of M. tuberculosis. Importantly, at least two studies support this contention [13, 65], and several sub-nanomolar inhibitors of the enzyme have been developed. Current efforts are now focused on optimizing the in vivo properties of these lead compounds with an emphasis both on the chemical properties that affect ADME, as well as factors that affect antibacterial activity such as uptake and detoxification.</p><!><p>Fatty acid biosynthesis in M. tuberculosis.</p><p>The FASI pathway synthesizes C18+ acyl-CoAs that are subsequently extended by the dissociated FASII system. The FASI pathway catalyzes the de novo biosynthesis of fatty acids from acetyl-CoA and malonyl-CoA. In the FASII pathway chain elongation utilizes malonyl-ACP generated from malonyl-CoA by FabD, the malonyl-CoA:ACP transacylase. Entry of fatty acids into the FASII pathway is mediated by the β-ketoacyl-ACP synthase FabH which condenses the acyl-CoA with malonyl-ACP. The resulting β-ketoacyl-ACP is reduced by the NADPH-dependent reductase MabA, dehydrated by an unidentified enzyme, and reduced by the NADH-dependent enoyl-ACP reductase InhA. Thereafter, subsequent rounds of elongation are primed by the β-ketoacyl-ACP synthases KasA and KasB. Note that the acyl carrier protein in M. tuberculosis is sometimes referred to as AcpM. In addition, since InhA is a FabI enoyl-ACP reductase, this enzyme is sometimes referred to as the FabI from M. tuberculosis.</p><p>Two step mechanism for slow onset enzyme inhibition.</p><p>In this form of slow onset inhibition, initial rapid formation of EI is followed by a slow step leading to formation of the final EI* complex. Assuming that k−1>>k2 and k−2, then hence koff ≈ k−2 and residence time = 1/k−2. [21]</p><p>Structures of ecFabI in the absence and presence of triclosan.</p><p>The figure was made using the pdb files 1dfi [20] and 1qsg [22]for the binary and ternary enzyme complexes, respectively. In the binary FabI:NAD+ complex (left panel) the substrate binding loop is disordered and the adjacent chain has been colored red. In the ternary FabI:NAD+:triclosan complex (right panel), the substrate binding loop is ordered (red) and forms a helix that covers the bound inhibitor. The figure was made using PyMol [23].</p><p>Structure of the INH-NAD adduct</p><p>X-ray structure of the INH-NAD adduct (cyan) bound to InhA (1zid.pdb) [8]. The substrate binding loop is colored red. This portion of the protein (res 196-211) is disordered in the structure of triclosan bound to InhA (2b35.pdb [13]).</p><p>BH-NAD and its derivatives.</p><p>A series of 4-phenoxybenzamides have also been prepared in order to mimic key elements of the INH-NAD adduct (Table 1) [36]. However, as seen for the BH-NAD analogues, activity assays indicated that only compounds with a NAD moiety were able to inhibit InhA.</p><p>Structure of the [FeII(CN)5(INH)]3- complex</p><p>Triclosan</p><p>X-ray crystal structure of triclosan bound to InhA.</p><p>A: Overall structure in which the ends of the disordered substrate binding loop are colored red. B: Hydrogen bonding interactions between triclosan (yellow), NAD+ (cyan) and Tyr158 (gold). The figure was made with PyMol [23].</p><p>Superposition of the X-ray structures of InhA and NAD+ (cyan) in complex with (A) C16-NAC (slate, 1bvr.pdb) and PT03 (yellow, 2b36.pdb), and (B) C16-NAC (slate) and F7 3fng.pdb) [53]. In each case the surface for 2b36.pdb is shown in which the active site loop is disordered. The figure was made with PyMol [23].</p><p>X-ray structure of F7 bound to InhA (3fng.pdb) [53]). Interactions between the B-ring meta-chloro group and the protein/cofactor are shown with dotted black lines. The figure was made with PyMol [23].</p><p>X-ray structure of PT70 bound to InhA in the presence of NAD+. The substrate binding loop is shown with a mesh surface, and interactions are shown (red dots) between the B-ring methyl and the cofactor as well as the methyl side chain of Ala198 in the loop. The figure was made with PyMol [23].</p><p>Diazaborine scaffold</p><p>X-ray structure of a thienodiazaborine (TDB, yellow) bound to ecFabI [20]. The figure was made with PyMol [23].</p><p>General structure of pyrrolidine carboxamide.</p><p>Pyrrolidine carboxamides: Linkers between the A-ring and carboxamide.</p><p>Strucutre of compound r7.</p><p>X-ray structure of d11 bound to InhA in the presence of NAD+. The inhibitor is colored yellow. The protein surface is light orange while the surface of the loop is red. The figure was made with PyMol using the pdb file 2h7m [64].</p><p>X-ray structure of Genz-10850 (yellow) bound to InhA with NAD+ (cyan) (1P44.pdb) [65]. The surface of the substrate binding loop is colored red. The figure was made using PyMol [23].</p><p>Arylamides</p><p>X-ray structure of b3 (yellow) bound to InhA with NAD+ (cyan) (2nsd.pdb) [67]. The surface of the substrate binding loop is colored red. The figure was made using PyMol [23].</p><p>2-Methoxylated saturated fatty acids.</p><p>Imidazopiperidines and derivatives (a) Imidazopiperidines (b) analogues in which the imidazole has been replaced.</p><p>Common binding mode for InhA inhibitors.</p><p>Seven structures have been overlayed in which InhA is in complex with: two triclosan molecules (1p45.pdb), PT03 (2b36.pdb), C16-NAC (1bvr.pdb), F3 (3fng.pdb), Genz-10850 (1p44.pdb), d11 (2h7m.pdb), and b3 (2nsd.pdb). Key areas of interaction are highlighted and include groups involved in the hydrogen bond (HB) network (pink), the hydrophobic pocket (yellow), and the region surrounded by both polar and nonpolar groups (size-limited, slate).</p><p>INH-NAD Analogs</p><p>Diaryl-Ether InhA inhibitors.</p><p>IC50 values were determined with inhA at concentration of 1 nM, 100 nM and 5 nM, respectively.</p><p>IC50 values were determined with inhA at concentration of 1 nM, 100 nM and 5 nM, respectively.</p><p>IC50 values were determined with inhA at concentration of 1 nM, 100 nM and 5 nM, respectively.</p><p>unpublished data.</p><p>Ki for the inhibition of InhA by the INH-NAD adduct. [9] N.D. = not determined.</p><p>SAR study on B-ring of diaryl ethers.</p><p>Antitubercular activity of diazaborine derivatives.</p><p>MIC was measured in Bactec 6A media.</p><p>MIC was measured in Bactec 12B media.</p><p>Pyrrolidine carboxamide A-ring derivatives.</p><p>Percentage of InhA inhibition at 15μM.</p><p>Replacement of A-ring in the pyrrolidine carboxamides.</p><p>IC50 values for selected pyrrolidine carboxamides C-ring analogues.</p><p>N.D. = Not determined.</p><p>Piperazine indoleformamides.</p><p>Data were taken from references: p56-p60 [66], p4 [67], Genz- compounds [65]</p><p>Inhibition percentages of pyrazole derivatives.</p><p>Percent inhibition of inhA activity at compound concentration of 40μM.</p><p>SAR study on arylamides.</p><p>Selected compounds and their activity.</p><p>Antibacterial activity of alkynoic acids.</p><p>N.D. = not determined.</p><p>Enzyme inhibition and whole cell activity for imidazopiperidines.</p><p>The enantiomer with retention time < 7min.</p><p>The enantiomer with retention time > 7min.</p>

PubMed Author Manuscript

Hydrogen and Propane Production From Butyric Acid Photoreforming Over Pt-TiO2

Photocatalysis is a promising technology from economic, energetic, and ecological points of view because it takes advantage of solar light. Hence, it is one of the investigated green routes to produce hydrogen from renewable energy resources. Butyric acid (BA) is largely present in wastewater and as an intermediate product in anaerobic digestion and therefore it is an inexpensive resource, which can be converted to valuable chemicals. In this work, photoreforming of butyric acid (BAPR) under UV light in aqueous suspensions of platinum-modified titanium dioxide-based catalysts is reported for the first time. Titania nanotubes (TNT) synthesized and calcined at different temperatures (300, 400, 500°C) and commercial TiO2 (P25), decorated with platinum nanoparticles, have been tested and characterized through different techniques including X-ray powder diffraction, UV-vis diffuse reflectance and photoluminescence spectroscopy, transmission electron microscopy, BET and porosimetry analysis. The main identified products of the BAPR were H2, propane, CO2 and several organic acids (e.g., pentanoic and 3-methylhexanoic acid). It has been found that the morphology and crystallinity of the photocatalysts affected dramatically their optical properties and, consequently, the reaction rate and the product distribution. Specifically, the highest conversion of BA (XBA) and selectivity toward H2 (SH2) was recorded with P25-Pt (XBA = 26.9%, SH2 = 47.2% after 8 h of irradiation). TNT-400-Pt showed the highest selectivity toward propane (SC3H8 = 16.1%) with XBA = 23.4% and SH2 = 36.2%. The activity results in conjunction with the characterization of the catalysts highlighted that the main factor affecting the activity in terms of XBA and generation of H2 was the crystallinity, and in particular the presence of rutile phase in TiO2, whereas SC3H8 appears to increase when the electron-holes recombination is lower.

hydrogen_and_propane_production_from_butyric_acid_photoreforming_over_pt-tio2

Introduction<!>Synthesis of TiO2 Nanotubes (TNT)<!>Synthesis of TiO2 Calcined Nanotubes<!>Platinum Photodeposition<!>Photocatalyst Characterization<!>Photocatalytic Runs<!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!><!>Photocatalyst Characterization<!>Photocatalytic Runs<!><!>Photocatalytic Runs<!>Proposed Photocatalytic Mechanism<!><!>Proposed Photocatalytic Mechanism<!>Material and Electron Balances<!><!>Material and Electron Balances<!>Outlook and Conclusions<!>Data Availability<!>Author Contributions<!>Conflict of Interest Statement

<p>Nowadays, fossil fuels use is posing serious environmental issues (Raupach et al., 2007). Moreover, the global population is growing and global energy consumption is expected to increase. Thus, developing a process for energy production built on renewable resources is of utmost importance. Hydrogen (H2) is an energy carrier and an ideal fuel because it generates only water, and heat, which is in turn converted into electricity. With the development of fuel cells, the research on clean hydrogen production has found an additional application field. On the other hand, hydrogen is also a valuable chemical applied in several industrial processes.</p><p>Currently, 95% of the total hydrogen supply is provided from fossil fuels (Cargnello et al., 2011; Giddey et al., 2019). In the steam reforming process, methane (CH4) is converted into H2 at high temperature (typically 700 ÷ 900°C) (Halabi et al., 2010). This process is unsustainable on a long-term scale because of the limited availability of fossil fuels and the generation of greenhouse gases (mostly CO2). Consequently, many researchers are devoted to H2 production from alternative resources such as from biomass via catalytic steam reforming/gasification (Huber et al., 2003) and photoelectrochemical (Holladay et al., 2009) or enzymatic approaches (Lee et al., 2010). Most of these processes are often highly energy demanding owing to the strict operating conditions dictated by the use of enzymes (Cortright et al., 2011).</p><p>Solar energy, the largest renewable "infinite" resource, provides virtually unlimited energy to our planet (Lewis and Nocera, 2006). Therefore, the exploitation of sunlight is a very promising route in order to produce H2 and recent studies showed that it may be competitive with respect to traditional technology grounded on non-renewable sources (Rodriguez et al., 2014).</p><p>Photocatalytic H2 production from water is an interesting though challenging approach as it takes advantage of solar energy and operates under ambient pressure and temperature. The idea of using the photons in the solar spectrum to split H2O into H2 and O2 is known since 1972 (Fujishima and Honda, 1972). However, direct photosplitting of water has thermodynamic and kinetic limitations which can be overcome by adding oxygenated organic substrates, or "sacrificial agents," which act as water reductants. This process, operating under anaerobic conditions, is called photoreforming (PR).</p><p>In the last decades, many researchers studied PR for H2 production. Several organic compounds were used as sacrificial agent [methanol (Maldonado et al., 2018), ethanol (Puga et al., 2014), glycerol (Vaiano et al., 2018), glucose (Iervolino et al., 2016), raw biomass (Granone et al., 2018), and 2-propanol (Tanaka et al., 2012) are some examples], while the most investigated photocatalysts were titanium dioxide and cadmium sulfide, properly designed on a case-by-case basis. Sacrificial agents with hydrogen atoms in the α-position with respect to hydroxyl group proved to be effective for H2 production since OH groups capture the photogenerated h+, thus mitigating the e−/h+ recombination (Bowker, 2012).</p><p>Butyric acid (BA) is a volatile fatty acid abundantly present in wastewater and is one of the main by-products of biological natural fermentation processes (Mu et al., 2006; Yu and Mu, 2006). This makes BA an inexpensive resource that can be converted to valuable chemicals such as small hydrocarbons and H2 through heterogeneous photocatalysis. BA has been very poorly studied as a sacrificial agent in PR. To the best of our knowledge, only two very recent studies covered this topic (Zheng et al., 2017; Li et al., 2018). Zheng and co-authors succeeded in the photodegradation of butyric acid under UV light using a composite made by Cu2O/Bi2WO6 and generating alkanes (mainly, methane, ethane and propane) and H2 (Zheng et al., 2017). Li et al. (2018) investigated photocatalytic H2 evolution from different organic fatty acids (OFA) under visible irradiation over carbon-doped TiO2 nanoparticles. In terms of H2 production rate, the observed order was: propionic acid > butyric acid ≈ acetic acid > lactic acid, at equal electron densities; and lactic acid > acetic acid > butyric acid ≈ propionic acid, at equal molar concentrations. They also found that mixed organic acids inhibited H2 generation, except in the case of acetic-lactic acid mixture, which led to an increase in the H2 evolution rate. The composition and distribution of the reaction products were not explored.</p><p>Moreover, photoreforming of butyric acid (BAPR) over TiO2 and noble metals (e.g., Pt, Au, Pd) has not been investigated yet. The valence band (VB) and conduction band (CB) of titania are bent when a noble metal is added because of the formation of a Schottky barrier, arising from the difference in the Fermi levels between the metal and the semiconductor (SC) (Christoforidis and Fornasiero, 2017). The higher is the work function (ϕ), the greater is the Schottky barrier in the metal-SC heterojunction, resulting in a more efficient charges separation, which is a critical step in most photocatalytic processes. Platinum, which has a very high work function (ϕ = 5.93 eV), generally showed better performance as TiO2-co-catalyst (Fu et al., 2008; Chiarello et al., 2010). Indeed, photogenerated electrons are more efficiently utilized on the platinum sites (Izumi et al., 1980).</p><p>In this work, BAPR over Pt-TiO2 based photocatalysts has been investigated for the first time, focusing on the products selectivity, along with the correlation of activity and selectivity to the optical, structural, textural and morphological properties of the catalysts used. Titanium oxide nanotubes were synthesized by hydrothermal method, calcined at different temperature (300, 400, 500°C) and decorated with platinum. Commercial P25 was used as reference sample. All the catalysts were tested in a bench scale reactor under UV light.</p><!><p>Commercial TiO2 (Aeroxide P25, 1.2 g) and 75 mL of a 10 M NaOH aqueous solution were placed in a 95 mL teflon lined hydrothermal autoclave reactor. After stirring and ultrasound treatment for 1 h, the reactor was kept in the oven at 110°C for 12 h. Next, the sample was poured into six 50 mL polypropylene conical tubes (BD Falcon), washed with DI water and separated from the supernatant through a centrifuge (at 8000 rpm, model Heraeus Megafuge 8R, from Thermo Scientific) several times, until the conductivity of the supernatant was below 70 μS/cm. Afterwards, the 6 tubes containing the powder were filled with a 0.1 M HCl aqueous solution, sonicated for 15 min and centrifuged once. Afterwards, the samples were washed with DI water (as described above) till the conductivity of the supernatant was around 2 μS/cm. The conductivity was measured by a Delta Ohm pH-χ-O2 meter (model HD 22569.2) and SPT 01 G probe (from Delta Ohm). The obtained powders were kept in oven at 80°C for 12 h and, eventually, grinded.</p><!><p>TNT were calcined at different temperature in a furnace (Nabertherm P330) with the following temperature program: from 25°C to Tcalc°C at 5°C/min, 3 h at Tcalc. The calcination temperatures (Tcalc) were: 300, 400, 500°C. These 3 samples are labeled as TNT-300, TNT-400, TNT-500.</p><!><p>Platinum (1 wt%) was photodeposited on all the samples (P25, TNT, TNT-300, TNT-400, TNT-500). First, H2PtCl6·6H2O was dissolved in ethanol in order to obtain a solution containing 0.5 mg of Pt per 1 mL ethanol. Next, 8 mL of that solution were added to 200 mL of distilled water in a beaker, mixed for 10 min, sonicated for 10 min, mixed for 10 min. Then, 400 mg of photocatalyst were added and this suspension was mixed for 5 min and sonicated for 10 min. Afterwards, argon was bubbled into the suspension in order to remove the dissolved oxygen and 75 min later, a UV LED lamp (placed on the top of the beaker) was switched on. After 4 h irradiation and continuous bubbling of argon, the suspension was collected, dried in a rotary evaporator (model RE300) under vacuum at 70°C, kept in oven at 70°C for 12 h and grinded. These samples are referred as P25-Pt, TNT-Pt, TNT-300-Pt, TNT-400-Pt, TNT-500-Pt.</p><!><p>The crystalline structure of the samples was determined by an XRD PANalytical Empyrean diffractometer, a Cu Kα radiation of 1.54 Å, scan step-size 0.0167° and a 2θ scan range of 10–90°. The crystallite size was determined through the Scherrer equation with respect to the anatase and rutile major peaks at 25.33° and 27.50°, respectively (Howard et al., 1991; Swope et al., 1995), after the diffractogram background had been subtracted using the software HighScore Plus. In order to estimate the percentage of anatase and rutile in the TNTs, few XRD diffractograms were collected by mixing a certain photocatalyst with an equivalent mass (50/50 weight ratio) of CaF2 (Sigma-Aldrich, 99.9%). Before preparing such 50/50 weight ratio, TNT was kept 12 h at 85°C under vacuum whereas P25, P25-Pt, TNT-300, TNT-400, TNT-500 were kept at 160°C for 12 h under vacuum and CaF2 was kept at 200°C for 12 h. The ratio between the absolute crystallinity of sample and the absolute crystallinity of P25, for anatase (rA) and rutile (rR), were computed by applying equations reported by Jensen et al. (2004); the percentage of anatase in the crystalline phase was obtained by means of the Spurr and Myers equation (Spurr and Myers, 1957).</p><p>UV-vis diffuse reflectance spectra (DRS) have been collected by a UV/vis spectrophotometer (Shimadzu UV-2600) with an integrating sphere attachment and with BaSO4 as the reflectance standard.</p><p>The photoluminescence (PL) spectra in emission mode with an excitation at 300 nm were recorded using a Perkin Elmer LS 55 spectrometer between 320 and 580 nm (100 nm/min scan rate).</p><p>TEM and STEM-EDS analysis was performed by using Tecnai G2 and Titan FEI transmission electron microscopes, operating at 200 and 300 kV, respectively. The sample was prepared by suspending the powder in 2-propanol, ultrasounds treated, and finally dropping 5 uL of the suspension 3 consecutive times on a 400-mesh Cu grid provided by Tedpella, letting the solvent evaporate at room temperature.</p><p>Pore structure properties of the samples, including surface area, were obtained by means of a Flex Multiport Physisorption/Micropore Analyzer (Micromeritics, USA), using N2 as adsorbent. Before the analysis, the samples were degassed under vacuum at 80°C for 12 h. Surface area and total pore size distribution were obtained by applying Brunauer–Emmett–Teller (BET) theory. The microporosity was assessed through Horvath-Kawazoe model. The meso-pore volume was calculated by difference between total pore volume and micro-pore volume.</p><!><p>Photoreforming of BA was carried out in a Pyrex-made cylindrical batch photoreactor (total volume: 1090 mL) (see Supplementary Figure 1). The photoreactor was provided with valves in its upper section for the inlet and outlet of gases and for liquid and gas sampling. A 5 mM BA aqueous solution (temperature: 298 K; volume: 750 mL), containing 0.2 g/L of photocatalyst, was magnetically stirred and was bubbled with nitrogen 1 h before sealing the reactor by closing all the valves and switching the lamp on. The reactor was irradiated by 2 lamps made of LED strips (SMD 5050) with emission band between 360 and 370 nm. The lamps were placed outside the external walls of the reactor and inside a housing of Pyrex glass immersed in the reacting suspension and surrounded by a jacket where cooling water ensured a constant operational temperature and prevented LED strips from overheating. The absorbed electric power of the internal and external lamps were 7.2 and 12 W, respectively. The radiant power of both lamps was measured separately with a Delta Ohm 9721 radiometer in the ranges 315-400 nm: the external one was 6 W m−2 (measured at the bottom-center of the reactor) whereas the internal one was 2.28 W m−2 (measured at the external wall of the reactor). Each run was performed twice to confirm the reproducibility of the experiment.</p><p>Gas and liquid samples were withdrawn from the reactor at the beginning of and during the run. BA concentration was analyzed, after filtration of the liquid sample through a Whatman filter (0.2 μm TF), by means of a HPLC (UltiMate 3000, Thermo Scientific) equipped with a Phenomenex Rezex ROA—organic acid H+ (8%) column (size: 300 × 7.8 mm). The column was kept at 55 °C during the analysis and the eluent was a H2SO4 0.0025 M aqueous solution at flow rate of 0.8 mL/min. Identification and quantification of the main products in gas phase were performed by 2 different gas chromatographs (GC's). The content of H2 and CO2 were measured by a Shimadzu GC-2014 equipped with thermal conductivity detector (TCD) and a micropacked ST MP-01 column (size: 2 m length, 1 mm ID; packing material: shincarbon ST); the temperature of injector, column and detector were kept at 230, 30, 100°C, respectively. Nitrogen was used as carrier gas at a flow rate of 6.81 mL/min.</p><p>The composition of alkanes in the gas sample were investigated by a Shimadzu GC-2014 equipped with a flame ionization detector (FID) and a capillary column (Agilent 19095P-S25PT, 50 m × 0.535 mm × 15μm); the temperature of injector and detector were kept at 200°C, 250, respectively. The initial temperature of the oven was 60°C for 5.5 min, followed by a ramp of 20°C/min to 100°C, an isotherm at 100°C for 21 min, a ramp 20°C/min to 140°C, and an isotherm at 140°C for 10 min. Helium was used as carrier gas with a flow rate of 6.13 mL/min. The experimental set-up has shown a reproducibility < 95% in terms of measured concentrations.</p><p>At the end of the run, the organic fraction of the liquid suspension was extracted by using diethyl ether in a separator funnel. Then, the organic solution was concentrated by evaporating the solvent, filtered and analyzed by a GC (Agilent Technologies 7890B) equipped with a quadrupole mass spectrometer (MS, Agilent Technologies 5977A MSD). The separation of the organic compounds was achieved through a HP-5ms ultra inert column (Agilent 19091S-433UI). The carrier gas was helium with a flow rate of 1 mL/min. The oven temperature was programmed as follows: initial temperature set at 50°C for 3 min, ramp from 50 to 100°C at 15°C/min, isothermal at 100°C for 5 min, ramp from 100 to 300°C at 10°C/min, isothermal at 300°C for 5 min. The MS transfer line was kept at 300°C. Mass spectra were collected in scan mode for qualitative analysis by comparison with the NIST mass spectral library. The software 'NIST Mass Spectral Search Program' gives match factor and probability value. Match factor refers to the direct match between the unknown and the library spectrum. Probability value is derived assuming that the compound is represented by a spectrum in the libraries searched.</p><p>Total organic carbon (TOC) in the liquid sample was measured using a TOC-L (Shimadzu) analyzer. TOC conversion in liquid phase percent (XTOC,liq) was calculated as follows:</p><p>where TOC0 and TOCf are the TOC concentrations measured before and after irradiation, respectively.</p><p>The selectivity toward a compound i (Si) given in this paper has been computed as it follows:</p><p>where Ni is the moles of i and NBA is the reacted moles of BA, at the end of the reaction time (8h).</p><p>Details of the material balances discussed in this paper can be found in the Supplementary Materials.</p><!><p>The external diameter, internal diameter and length of the synthesized TiO2 nanotubes (TNT) were in the range: 7–8.5, 4–5.5, 80–120 nm, respectively (Figure 1). XRD diffractogram of TNT showed that the predominant crystal planes are A(101), A(200) and R(110), R(101), R(211) for anatase and rutile allotropic phases of TiO2, respectively (Figure 2). The morphology as well as the crystallinity of TNT changed dramatically with the calcination temperature (Tcalc). When Tcalc increases, the nanotubes evolve shrinking and shortening, eventually turning into a nanoparticle shape (Figure 1 and Supplementary Figure 2).</p><!><p>TEM images of TNT and TNT calcined at different temperature. Scale bar: 100 nm.</p><p>XRD patterns of different samples. The most important planes are indicated.</p><!><p>Table 1 reports the ratio between the absolute crystallinity of sample and the absolute crystallinity of P25, for anatase (rA) and rutile (rR), the percentage of anatase in the crystalline phase and the crystallite size of anatase and rutile. The size of anatase crystallite grows with Tcalc from 3.1 nm (for TNT) to 15.5 nm (for TNT-500), whereas rutile crystallite size was the same in TNT and TNT-300, then it increased in TNT-400 and TNT-500. The absolute crystallinity of TNT was significantly lower compared to P25 (rA = 12.2% and rR = 8.5%). After calcination, all the previously missing crystal planes of anatase can be seen in XRD pattern (Figure 2). Indeed, rA rose to 47.8% and 96.4% for TNT-300 and TNT-400, respectively; while rR lowered for TNT-300. In other words, only new anatase phase is formed at 300°C. TNT-500 contained less anatase and more rutile than TNT-400, because at ca. 400°C the anatase to rutile phase transformation is already taking place (Hanaor and Sorrell, 2011). Nevertheless, it should be said that the weight fraction of rutile is relatively low for TNT, TNT-300 and TNT-400 (Table 1) and, for this reason, it is more difficult to estimate the variation of rutile among the samples. However, no rutile was formed at 300 °C because, with respect to TNT, rR decreased and the crystallite size was the same (Table 1).</p><!><p>Ratio between the absolute crystallinity of sample and the absolute crystallinity of P25 (rA, anatase; rR, rutile).</p><p>Weight fraction of anatase in the crystalline phase of the sample. Crystallite size of anatase and rutile calculated by means of Scherrer's equation.</p><p>Jensen et al. (2004).</p><p>Spurr and Myers (1957).</p><p>Howard et al. (1991), Swope et al. (1995).</p><!><p>The morphology of the TNTs did not change after the Pt-photodeposition (see Supplementary Figure 3). However, the STEM-EDS analysis corroborated the presence of Pt (agglomerate size < 15 nm) decorating TiO2 surface (Figure 3).</p><!><p>STEM image of TNT-500-Pt. EDS spectra correspond to points 1, 2, 3, in the image, respectively. The Cu peak is caused by the grid, whereas the C peak is caused by the grid and the sample.</p><!><p>The textural properties of the samples are reported in Table 2. The Pt-photodeposition reduced the surface area of P25 and TNT. TNT-Pt surface area is one order of magnitude larger than P25-Pt. The surface area of TNT decreased with Tcalc although it is still significantly larger than P25-Pt (Table 2).</p><!><p>Textural properties of different samples.</p><!><p>The optical properties of the samples have been investigated by recording UV-vis DRS (Figure 4). In the range of UV-A wavelengths, TNT and calcined TNT absorb more than P25 following the order P25 < TNT < TNT-300 ≈ TNT-400 < TNT-500. This trend can be partially explained by considering that defective states on the surface are introduced upon annealing, because of the transformation from amorphous to anatase phase, during calcination at 300°C, and then, from anatase to rutile phase during calcination at 500°C (Patel and Gajbhiye, 2012). In fact, absorption edge of TNT-300 and TNT-400 are comparable while it is higher for TNT-500.</p><!><p>UV-vis diffuse reflectance spectra of different samples.</p><!><p>The broad emission peak about 400-435 nm in PL emission spectra (labeled as "PL peak," Figure 5), upon excitation at 300 nm, is an indication of the extent of steady state e−/h+ recombination. The maximum intensity of the PL peak followed the order TNT-400 < TNT-300 < TNT-500 < TNT ≈ P25. Two factors are thought to affect this trend, which is different compared to the above DRS analysis: morphology and crystallinity of the sample. Calcination of TNT at 300°C and 400°C improved the e−/h+ separation efficiency because of higher percentage of crystalline phase than TNT (Table 1). However, the separation efficiency worsened in TNT-500 due to the absence of nano-sized structures (Figure 1).</p><!><p>Photoluminescence spectra of different samples.</p><!><p>Titania VB and CB are bent when Pt0 is deposited on the catalyst surface because of the formation of a Schottky barrier arising from the difference in the Fermi levels between the metal and the semiconductor (Christoforidis and Fornasiero, 2017). This event has implications on the DRS spectra. The absorption in the visible range (400–700 nm) increases because of the Pt. Nevertheless, photons in the visible range are energetically weak and they can result in the promotion of electrons from the VB of the semiconductor to intraband gap states which cannot result in the reduction of many substrates due to thermodynamic constraints. In addition, electrons photo-promoted from VB to CB of TiO2 can rapidly recombine with holes. Reactivity tests, discussed in the next section, provide important information about the efficiency of the light absorbed.</p><p>Focusing on the wavelength range 360–370 nm (the irradiation sources used in the reaction system emit mainly in the same range), the light absorbed by the samples with Pt was in this order: TNT-Pt < TNT-300-Pt < TNT-500-Pt < P25-Pt < TNT-400-Pt. This trend matched the percentage of absolute crystallinity (considering both anatase and rutile, Table 1): higher crystallinity, wider light absorption. However, TNT-400-Pt did not respect such correlation.</p><p>A noble metal such as Pt lowers the excitonic PL process intensity owing to the capture of metal ions and, therefore, enhancing e−/h+ separation during irradiation (Liqiang et al., 2006). Thus, the weaker the excitonic PL spectrum, the lower the recombination rate of photo-induced charge carriers.</p><p>As expected, all the samples showed a minor photoluminescence when compared to the equivalent ones without Pt (Figure 5) but, now, the maximum intensity of PL peaks order resulted as follows: TNT-400-Pt < TNT-Pt ≈ TNT-500-Pt < P25-Pt < TNT-300-Pt. The fact that the highest PL peak intensity among the samples with Pt was recorded for TNT-300-Pt was unexpected. A reasonable explanation could be that the surface area of TNT-300-Pt is lower than TNT-Pt and, consequently, the concentration of Pt per unit surface area increases (Table 2). TNT-400 surface area is even lower than TNT-300 but the crystallinity (Table 1) was higher, thus overall the e−/h+ recombination rate decreased. Exhaustive studies are required by taking into account morphology, crystallinity and Pt content of the sample at the same time, but this was not the main purpose of the present work.</p><p>To conclude this section, it is worth noting that the sample TNT-400-Pt exhibited the lowest e−/h+ recombination rate, as well as the highest light absorption in the range 350-370 nm.</p><!><p>No production of H2 as well as no significant degradation of BA took place in the absence of Pt as cocatalyst, confirming what is extensively reported in literature for other sacrificial agents implemented in similar reaction systems (Gallo et al., 2012; Languer et al., 2013; Al-Azri et al., 2015; Christoforidis and Fornasiero, 2017; Guayaquil-Sosa et al., 2017).</p><p>The experimental results of BA photoreforming carried out in the batch photoreactor (Supplementary Figure 1) under UV light are shown in Figure 6 through 4 plots which report: BA conversion percentage (XBA), mmol of H2, propane (C3H8) and CO2 produced per gram of catalyst. In the Supplementary Figure 4, the peak area from the HPLC chromatogram of a significant unknown reaction by-product (ULP) is also shown. The whole following discussion refers repeatedly to Figure 6 and Table 3, unless otherwise stated.</p><!><p>Reactivity results. Time refers to the irradiation time. The legend applies to all four plots.</p><p>BA conversion (XBA), TOC conversion in liquid phase (XTOC,liq), selectivity toward H2 (SH2), selectivity toward propane (SC3H8), after 8 h of irradiation.</p><!><p>In the run with P25-Pt, the conversion of BA following a zero order kinetics was equal to 26.9% after 8 h. The main reaction products in gas phase were H2, CO2 and C3H8, whereas other by-products, including methane and ethane, were detected by GC-FID. The amount of these gaseous by-products corresponded barely to 1% of the produced C3H8. There were also unknown by-products in liquid phase. Indeed, XTOC, liq was half of XBA after 8 h, even if it should be noted that part of the TOCliq was converted to gas species (mainly C3H8).</p><p>When TNT-Pt was used, the BA degradation and the CO2 formation rates were around 15 times lower than P25-Pt, while the selectivity toward H2 and C3H8 (SH2, SC3H8) were almost negligible. This behavior of TNT-Pt can be ascribed to the low crystallinity (Table 1) and to absorption edge shifted to smaller wavelengths (Figure 4). Both drawbacks of the TNT were overcome by calcination at 300, 400 and 500°C. In terms of BA degradation and H2 evolution rates, none among TNT-300-Pt, TNT-400-Pt, TNT-500-Pt, performed better than P25-Pt, although both rates increased considerably when TNT samples were calcined. Remarkably, the SC3H8 was strongly affected by Tcalc. Indeed, BA conversion, H2 and CO2 production rates rose with Tcalc in a monotonic way, while SC3H8 followed the order TNT-300-Pt < TNT-500-Pt < TNT-400-Pt.</p><p>It is remarkable that calcined TNTs (after Pt deposition) shifted the selectivity from some by-products, namely the amount of ULP decreased, toward C3H8 with respect to the run with P25-Pt. For instance, the production of C3H8 after 8 h were the same in runs with TNT-300-Pt and P25-Pt, even though XBA was higher with in the latter case. This particular selectivity toward C3H8 became very evident at Tcalc equal to 400 and 500°C when the production of C3H8 after 8 h increased by 190% and 132%, respectively, compared to P25-Pt (per unit catalyst mass). This dramatic change in the reaction products distribution was corroborated by a significant enhancement in XTOC, liq.</p><p>Finally, it should be mentioned that the surface area and XBA followed exactly opposite trends. Thus, it can be assumed that the crystallinity plays a key-role in the photo-activity. For example, TNT-400-Pt surface area is larger than TNT-500-Pt, but the degradation of BA as well as the production of H2 were higher using TNT-500-Pt (Figure 6). However, as discussed above, the run with TNT-400-Pt highlighted SH2 and SC3H8 greater than the one with TNT-500-Pt (Table 3).</p><!><p>In the only previous study on BA photoreforming in which both the products distribution and the reaction mechanism were investigated (Zheng et al., 2017), the primary products were reported to be methane, propane, and ethane, with the last one being the most abundant alkane in the reaction system having either pure Bi2WO6 or pure Cu2O, or Bi2WO6/Cu2O composite as the photocatalyst. The results presented in this paper indicated that propane is the only significant gaseous product when powders of TiO2-Pt are used as photocatalyst in the BAPR. Thus, the photocatalytic mechanism must be different from the one previously published in literature, in which a drastically different catalyst was employed.</p><p>The formation of H2, CO2, C3H8, can be explained through the following possible photocatalytic mechanism (Equations 3–8):</p><p>During the first stage of this mechanism (Equations 3–5), the photo-generated electrons and holes give rise to the water-splitting process over TiO2 surface (Wei et al., 2012), hydroxyl radical and H2 are two of the products at this stage. If no sacrificial agent is added, the hydroxyl radical would accumulate into the system hampering H2 evolution (Zheng et al., 2017). BA reacts on TiO2 surface according to Equation (6) to form the radical CH3CH2CH2COO• (Betts et al., 2018). Powders of TiO2-Pt act as a multitude of small, short-circuited Pt-TiO2 electrode systems. Platinum particles distributed over titania surface lower the otherwise significant overpotential of hydrogen reduction (Kraeutler and Bard, 1978). The e−/h+ separation is boosted because of Equations (5) and (6), which occur on 2 different site (Pt and TiO2). Then, the radical CH3CH2CH2COO• decomposes quickly to CH3CH2CH2• and CO2 (Equation 7). Propane is formed through Equation (8). According to Kraeutler and Bard, alkanes are primarily produced by using H atom of the carboxylic group (Kraeutler and Bard, 1978).</p><p>Analysis of the liquid products by means of GC-MS suggested the compounds shown in Table 4 (MS spectra in Figure 7).</p><!><p>Compounds detected by means of GC-MS during the analysis of liquid samples.</p><p>Note that 3-methylhexanoic acid has 2 stereoisomers with 2 different retention times in the GC-MS chromatogram.</p><p>The unknown mass spectrum from GC-MS analysis (in red) associated with the library spectrum (in blue) for the following compounds: 3-methylhexanoic acid (both stereoisomers, A,B), heptanoic acid (C), 2-ethylbutanoic acid (D), pentanoic acid (E), 3-methylpentanoic acid (F).</p><!><p>These possible products could be generated through Equations (9–14).</p><p>When the propyl radical does not react with an e−/h+ pair (Equation 8), it triggers radical reactions with BA (Equations 9–13). Several products are generated (Table 4), depending on which position of BA the propyl radical attack takes place. Two different peaks in the GC-MS chromatograms resulted to be assignable to 3-methylhexanoic acid with a high probability, suggesting that both stereoisomers were formed. 3-Methylpentanoic acid could be formed directly from BA (Equation 13) or from pentanoic acid (Equation 14).</p><p>The traces of methane and ethane, which were shown to be formed in the previous section, could be due to the following reactions:</p><p>As mentioned above, this photocatalytic mechanism (Equations 3–16) has been hypothesized in order to explain the formation of the identified products. However, many other reactions can take place, especially when radical species are present in the reaction system. The unidentified species (ULP), which appeared in the HPLC chromatograms, could be one of the compounds in Table 4. The formation of alkenes cannot be ruled out since reactions as Equation 17, for instance, are possible under anaerobic conditions.</p><p>The selectivities SH2 and SC3H8 depend on the fate of h+ and e−. They can react over Pt generating H2 or over TiO2 by forming C3H8. In addition, charge carriers recombination is fundamental at this stage. P25-Pt, and TNT-400-Pt have similar DRS spectra (Figure 4) but the latter sample revealed the lowest e−/h+ recombination rate (Figure 5). TNT-400-Pt showed a lower figure of SH2 but higher SC3H8 (Table 3), when compared to P25-Pt. Several considerations should be mentioned here. First, the more C3H8 is formed through Equation (8), the less electrons are available for H2 evolution (Equation 5); also, 2 electrons and 2 protons are required to produce form hydrogen (Equation 5) while only one electron and one proton (and a propyl radical) are required to form C3H8 (Equation 8). Then, when the propyl radical is involved in Equations (9–11, 13), SC3H8 decreases, while more liquid by-products are formed (low XTOC, liq/XBA ratio, Table 3).</p><!><p>In order to further investigate the products, detailed carbon and electron balances were conducted over the batch reactor, based on the consumed BA (100% of C or e−) and products after 8 h irradiation (see Table 5). The number of electrons equivalents refer to the degree of reduction, namely to the number of electrons an electron donor (or acceptor) can donate (or accept) per mole of compound per unit of carbon (γ), in order for it to be completely oxidized (or reduced) to reference compounds (Shuler et al., 2017). These material balances aim to estimate the carbon fraction that ended up in products other than CO2, CH4, C2H6, C3H8, and in which phase (liquid or gaseous) such fraction is present. The selectivity appears clear toward liquid products (Table 4) rather than gaseous ones, when P25-Pt was used. In fact, the fraction of unknown products in liquid (CUL) and gaseous (CUG) phase, in terms of C-mol%, were 58.5 and 11 %, respectively. On the contrary, with the calcined TNT (TNT-300-Pt, TNT-400-Pt, TNT-500-Pt), CUL decreased and CUG increased (see Table 5). The last results confirmed that calcined TNT are more selective toward gaseous products such as alkanes (and probably alkenes). Overall, the percentage of total C as unknown products (CUT) was in the range from 52.1% (in the case of TNT-400-Pt) to 69.5% (with P25-Pt).</p><!><p>Carbon and electron balance after 8 h of irradiation.</p><p>Note that CKT = CKL+CKG; CUT = CUL+CUG; CKT+CUT = 100%; γ, degree of reduction per atom C.</p><!><p>The electron balances (shown in Table 5) indicate that the percentage of e− transferred from BA to unidentified compounds (eUT-, including those in Table 4) ranged between 74.1% (TNT-400-PT) and 87.5% (P25-Pt).</p><p>The degree of reduction of the compounds in Table 4 is between 5.20 and 5.43. The ratio eUT-/CUT (in mmol/mmol) gives an average γ of the unidentified reaction products (Table 5), which is in the range 6.30–7.12. This results suggest that other possible products in gaseous phase (or dissolved in liquid phase) could be linear and branched alkanes such as butane, pentane, 2-methylbutane, 2-methylpentane, which are yielded by decarboxylation of other species (see Table 4), because they have γ = 6.33 ÷ 6.50.</p><!><p>In this study the photoreforming of butyric acid under UV light was studied by applying both commercial and modified TiO2 in a batch reaction system. The main identified reaction products were H2, CO2, propane and organic acids with more carbon atom than BA (e.g., pentanoic and 3-methylhexanoic acid). The presence of platinum (1 wt%) in TiO2 was essential for PR to take place.</p><p>The different morphology and crystallinity of the samples affected dramatically their optical properties, and hence, the reaction rate of PR and the products distribution. The highest conversion of BA and selectivity toward H2 were recorded with P25-Pt (XBA = 26.9%, SH2 = 47.2% after 8 h). TiO2 nanotubes calcined at 400°C, after Pt photodeposition, showed the highest selectivity toward propane (SC3H8 = 16.1%) with still important figures for both BA conversion and SH2 evolution (XBA = 23.4%, SH2 = 36.2%).</p><p>The experimental results described in this work prove that production of valuable chemicals besides H2 is possible from butyric acid. Also several factors, such as morphology, crystallinity, charge carrier recombination, light absorption, and cocatalyst, appear to affect the activity and selectivity of the reaction. Currently, photocatalytic generation of H2 is not applied at industrial level since the small production rates make it unappealing. However, the coproduction of H2 and propane from an inexpensive abundant resource such as BA could boost the attractiveness of this process, promoting its future scale-up.</p><p>TiO2-Pt based catalysts are possible candidates for BAPR. However, the efficiency in terms of conversion and selectivity to target products can certainly be improved. For instance, designing photocatalysts active under both UV and visible light is still a challenge, especially for scale-up application. Narrowing the SC band gap by doping (Leung et al., 2010; Taylor et al., 2014; Sun et al., 2015) and incorporating visible light sensitizers [e.g., organic dyes (Manfredi et al., 2016), transition metal complexes (Duonghong et al., 1981) or another SC (Park et al., 2008; Wang et al., 2013)] are the two main strategies for attaining that goal. Indeed, oxidation of aromatic alcohols with H2 production was obtained via electron transfer from Pt nanoparticles, active under visible light, to the TiO2 CB (Zhai et al., 2011).</p><p>BAPR is proven to be able to upgrade low value butyric acid into a portfolio of higher value products. The average degree of reduction estimated for the large fraction of unknown products suggests a composition of high value either as chemicals or fuels.</p><p>In summary, efficient charge separation and absorption at wavelength > 400 nm are two main features that should be further investigated in order to make photoreforming of butyric acid a process suitable for scale-up application. An economic evaluation of photoreforming process will be also very relevant.</p><!><p>All datasets generated for this study are included in the manuscript/Supplementary Files.</p><!><p>GS performed the experimental activities and drafted the article. JR contributed to the data analysis and the mass balances. GP coordinated the research activities and contributed to the writing of the article. All the authors contributed to the research idea.</p><!><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>

PubMed Open Access

Effects of Chlorpyrifos and Trichloropyridinol on HEK 293 Human Embryonic Kidney Cells

Chlorpyrifos (CPF) [O, O-diethyl -O-3, 5, 6-trichloro-2-pyridyl phosphorothioate] is an organophosphate insecticide widely used for agricultural and urban pest control. Trichloropyridinol (TCP; 3,5,6-trichloro-2-pyridinol), the primary metabolite of CPF, is often used as a generic biomarker of exposure for CPF and related compounds. Human embryonic kidney 293 (HEK 293) cells were exposed to CPF and TCP with varying concentrations and exposure periods. Cell cultures enable the cost-effective study of specific biomarkers to help determine toxicity pathways to predict the effects of chemical exposures without relying on whole animals. Both CPF and TCP were found to induce cytotoxic effects with CPF being more toxic than TCP with EC50 values of 68.82 \xce\xbcg/mL and 146.87 \xce\xbcg \xe2\x80\xa2 ml\xe2\x88\x921 respectively. Cell flow cytometric analyses revealed that exposure to either CPF or TCP leads to an initial burst of apoptotic induction followed by a slow recruitment of cells leading towards further apoptosis. CPF produced a strong induction of IL6, while TCP exposure resulted in a strong induction of IL1\xce\xb1. Importantly, the concentrations of CPF and TCP required for these cytokine inductions were higher than those required to induce apoptosis. These data suggest CPF and TCP are cytotoxic to HEK 293 cells but that the mechanism may not be related to an inflammatory response. CPF and TCP also varied in their effects on the HEK 293 proteome with 5 unique proteins detected after exposure to CPF and 31 unique proteins after TCP exposure.

effects_of_chlorpyrifos_and_trichloropyridinol_on_hek_293_human_embryonic_kidney_cells

Introduction<!>Cell culture<!>CPF and TCP Dosage Experiments<!>Flow cytometry<!>Enzymatic Digestion of Cell Culture Fluid for LC/MS-MS Analysis<!>LC/MS-MS Analyses, Blasting and Database Search<!>Statistical analyses<!>Effects of CFP and TCP exposure on HEK 293 cell viability and morphology<!>Effects of CPF and TCP exposure on apoptosis for HEK 293 cells<!>Effects of CPF and TCP on the expression of IL1\xce\xb1 or IL6<!>Effects of CPF and TCP on the HEK 293 Proteome<!>Conclusions

<p>Toxicological risk assessments have traditionally focused on apical endpoints rather than the biologic changes leading to an adverse health outcome. These studies can be expensive, rely heavily on animals and require species extrapolation for human health risk assessment (NRC, 2007). The traditional toxicological approach of relying solely on whole animal testing to assess the effects of xenobiotics on human and ecological health cannot keep pace with the thousands of chemicals already in existence or the new chemicals constantly being introduced. The National Academy of Sciences report, Toxicity Testing in the 21st Century laid the foundation for a paradigm shift toward the use of other scientific tools to expand in vitro pathway-based toxicity testing and minimize whole animal approaches (NRC, 2007). These alternative methods such as bioinformatics analyses of exposed human cell cultures are gaining acceptance at predicting in vivo toxicity using a bottom-up-approach (Adeleye et al., 2015; Grafstrom et al., 2015; Rouquie et al, 2015), assisting in adverse outcome pathway (AOP) development (Ankley et al., 2010; OECD, 2013; Groh et al., 2015) and to identify biomarkers of exposure and effect. Biomarkers of exposure are used to assess the amount of a chemical that is present and may also provide information on the relative importance of different exposure pathways and associated risk. Biomarkers of effect are indicators of a change in biologic function in response to a chemical exposure and may provide direct insight into the potential for determining adverse health effects and pathways.</p><p>Chlorpyrifos (CPF) [O, O-diethyl O-(3,5,6-trichloro-2-pyridyl) phosphorothioate] is a broad-spectrum organophosphate (OP) insecticide used for many years for the control of economically important agricultural and urban pests (Eaton et al., 2008). CPF has served a key role in safeguarding food and feed crops, protecting public and veterinary health, and maintaining ornamentals and turf grasses around homes and public areas. Residential uses of CPF products were eliminated in the United States in 2000, with the exception of ant and roach baits in child resistant packaging and fire ant mound treatments (Grube et al., 2011; Stone et al., 2009). Prior to the ban, Dursban (20% CPF a.i.) was the most widely used household pesticide in the United States. In 2007, CPF was the most commonly used OP in the United States (Grube et al., 2011). Lorsban (15 – 75% CPF a.i.) is still in use for agricultural and urban turf purposes.</p><p>Direct dietary exposure to trace levels of CPF in foods (estimated to be 0.009 μg/kg body weight) is the main exposure route in the U.S. since the ban of indoor sprays (Trunnelle et al., 2014). Upon ingestion, CPF quickly passes from the intestines to the bloodstream and is distributed throughout the body and may be stored in fat tissues (Chaou et al., 2013). Acute poisoning from CPF results mainly from interference with the acetylcholine neurotransmission pathway (Ethan et al., 2008). CPF may also affect other neurotransmitters, enzymes and cell signaling pathways, potentially at doses below those that substantially inhibit acetylcholinesterase activity. The extent and mechanism of these effects remain to be fully characterized. CPF also passes quickly to the bloodstream after an inhalation exposure. Dermal exposure is not as problematic as only a small amount (3% of applied test dose) has been shown to be able to penetrate the skin.</p><p>Chlorpyrifos-oxon is formed by the bioactivation of CPF through the family of cytochromes P450 and is responsible for cholinergic toxicity and the inhibition of acetylcholinesterase (Eathan at al., 2008; Timchalk et al., 2006; Clegg and van Gemert, 1999). Detoxification of the oxon is through the A-esterases, as well, as the cytochromes P450 leading to the primary metabolite 3,5,6-trichloro-2-pyridinol (TCP) with subsequent glucuronidation for urinary elimination (Timchalk et al., 2006; US. EPA, 1986). Studies indicate that >90% of CPF is eliminated from the body within 48 hours although clinical signs of CPF–induced toxicity may persist for several weeks after exposure depending on the dose (ATSDR, 1997; Koch et al., 2001). The oral lethal dose (LD50) for CPF ranges from 32 to 1,000 mg/kg body weight in a variety of species (Eathan et al., 2008; ATSDR, 1997)</p><p>A major degradation pathway of CPF in the environment is the hydrolysis of the phosphorous ester bond to produce TCP (Racke, 1993). TCP is more persistent in the environment than CPF itself (U.S. EPA, 2002). Due to its high water solubility TCP may persist in soils and in aquatic systems (Amer et al., 1992; Yücel et al., 1999; Racke et al., 1988) posing exposure risks. TCP is also a marker of exposure and the environmental degradate for the foliar herbicide triclopyr (3,5,6-trichloro-2-pyridinyloxyacetic acid) and CPF-methyl, a grain fumigant.</p><p>Population based studies conducted between 1995 and 2001, found average urinary concentrations of 3 – 5 ppb TCP; however, concentrations 10–20 times higher have frequently been found (Ethan et al., 2008). TCP is not a specific biomarker of CPF exposure as residues of TCP can appear on fruits and vegetables, often at higher concentrations than CPF and the use of CPF-methyl on grains and the herbicide triclopyr may also contribute to the TCP levels in humans leading to overestimates of CPF exposure by up to 20-fold.</p><p>CPF and its metabolism may be linked to a number of serious health impacts dependent upon the species, dose and exposure period. Reports indicate that CPF and TCP exposure to various species may lead to DNA damage (Wang et al., 2014), altered gene expression (Estevan et al., 2013; Abdelaziz et al., 2010), increased apoptosis (Li et al., 2007; Li et al., 2009; Nakadai et al., 2006), increased number of micronuclei during embryonic development (Tian and Yamauchi, 2003), and an increased frequency of chromosomal aberrations and sister chromatid exchanges (Amer and Aly, 1992). Chronic human exposure to CPF may lead to acute renal failure (Cavari et al., 2013). CPF can also affect protein synthesis in brains of young rats (Slotkin et al., 2009) and has been shown to affect the proteome of the model organism Dictyostelium discoideum (Boatti et al., 2012). Zebrafish embryos exposed to CPF showed an increase in proteins associated with detoxification and stress response and a decrease for proteins related to cytoskeleton structure, protein translation, signal transduction and lipoprotein metabolism (Liu et al., 2015). CPF toxicity can also act through other mechanisms such as inflammation and oxidative stress (Salyha, 2013). Chlorpyrifos-induced brain injury may be mediated through pro-inflammatory pathways as indicated in mice given various dosages (20 – 140 mg/kg) of chlorpyrifos over exposure periods up to 24 hours. Expression of the pro-inflammatory mediators TNF-alpha, IL-6, MCP-1 and E-selectin were observed in several brain regions (Hirani et al., 2007). Exposure of human neuronal SH-SY5Y cells to CPF showed elevated expression of COX-2 with a subsequent increased production of pro-inflammatory cytokines such as TNA-alpha, indicating oxidative stress and inflammation (Lee et al., 2014). It has recently been reported that CPF resulted in DNA breaks leading to apoptosis in human kidney cells (Li et al., 2015). We expand on these findings by demonstrating the effects of dosage and exposure duration on human embryonic kidney (HEK 293) cells to both CPF and TCP and our preliminary findings on the effects of CPF and TCP on the HEK293 proteome.</p><!><p>HEK 293 cells were from the American Type Culture Collection (A.T.C.C., Manassas, VA). Cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum at 37 °C, with 5% CO2 in tissue culture flasks. The cell cultures were observed daily with an inverted microscope. When a culture was 70–80% confluent, the cells were washed with 1X Dulbecco's phosphate buffered saline (DPBS) and trypsinized by addition of 0.25% trypsin and 0.53 mM EDTA to foster cell detachment from the flask. The suspended cells were stained with trypan blue, counted with a TC10 automated cell counter (Bio-Rad, Hercules, CA) and used to seed new culture flasks.</p><!><p>Cells were seeded at 27,000 cells per well in 96-well tissue culture plates and cultured overnight in growth media. The cells in 2 mL growth media, were treated with either CPF in methanol over the concentration ranges of 1–100,000 ng/mL CPF (as indicated Fig. 1A) or with TCP in methanol over the concentrations ranges of 1–250,000 ng/mL TCP (as indicated in Fig. 1B). The QA controls of growth media alone and growth media with 5 μL methanol were always included as controls to verify the non-effects of methanol. After a 24 h exposure period, cellular viability was estimated using a Cell Counting Kit-8 assay (CCK-8; Dojindo Laboratories, Kumamoto, Japan). The procedure is based on the conversion of a tetrazolium salt to a formazan dye upon reduction by dehydrogenases in the presence of an electron carrier (Ishiyama et al., 1997; Tominaga et al., 1999). A CCK-8 solution (10 μl) was added to each well of the 96-well tissue culture plates, followed by incubation for 4 h at 37 °C, with 5% CO2. Absorbance (Abs) values at 450 nm were obtained using a microplate spectrophotometer (Molecular Devices). Dose response curves illustrating cell viability are shown in Figures 1a for CPF and 1b for TCP. As per the manufacturer's suggestion, cell viability was determined using the equation: % viability=Abs(sample)/Abs(control with vehicle)⋅100</p><!><p>Concentrations of CPF and TCP observed to cause cell death were determined from the above range finding experiments (red lines in Fig. 1). For flow cytometry analyses, cells were plated at 270,000 cells per well in 2 ml growth media in 6-well culture plates and incubated overnight at 37 °C, with 5% CO2. Cells were exposed to CPF (0, 10, 30, 60, and 90 μg/mL) or TCP (0, 50, 100, 150, and 200 μg/mL) in 5 μL methanol along with the methanol control. After 24 h of exposure, cells were observed using phase-contrast microscopy to characterize cell morphological changes as well as cellular distribution over the growth area and then imaged for analysis. Exposure duration data were obtained by exposing cells for 24, 48, and 72 h to either CPF (0, 30, and 60 μg/mL) or TCP (0, 50, or 150 μg/mL) with the methanol QA control. After each time point, cells were washed off the culture plates with DPBS and trypsin. An aliquot (10 μL) of suspended cells was stained with trypan blue and counted using a TC10 automated cell counter.</p><p>Cells were next labeled with annexin V and propidium iodide to detect the level of apoptotic cells during the exposure timeframe. Annexin V is an indicator for early apoptosis as it binds phosphatidylserine that is exposed to the outer leaflet of the plasma membrane during apoptosis while propidium iodide stains DNA indicating a later apoptotic phase (Vermes et al., 1995). The suspended cells were centrifuged at 150 x g for 5 min and the supernatant was removed. One million cells were washed twice with 5 mL of cold 1x PBS (phosphate-buffered saline, 10 mM PO43-, 137 mM NaCl, and 2.7 mM KCl, pH 7.4) and centrifuged at 300 x g for 5 min. Cells were suspended with the addition of 1 mL binding buffer (0.01 M Hepes, 0.14 M NaCl, 2.5 mM CaCl2, pH 7.4). Cells were then incubated in the dark for 15 min at room temperature following the addition of 5 μl of FITC-annexin V (BD Biosciences, San Jose, CA) and 5 μL of propidium iodide. Subsequently, 400 μL of binding buffer was added to each tube before detection by flow cytometry (BD FACS Calibur, BD Biosciences, San Jose, CA). Flow cytometry was performed as per the manufacturer's instructions. The QA control of cell media alone without methanol was used to normalize the flow cytometry gating for the exposure duration experiments to allow comparisons between days.</p><p>An antibody approach was used for detection of the cytokines, interleukins 1α (IL1α) and 6 (IL6). IL1α is expressed in HEK 293 cells where it is associated with inflammatory reactions (Luheshi et al., 2009). IL6 is also expressed by HEK 293 cells and has an active role in mediating febrile and acute phase responses (Spiegel and Weber, 2006; Mihara et al., 2012). One million cells were washed twice with 5 mL of cold 1x PBS and centrifuged at 300 x g for 5 min at 4 °C. Cells were resuspended in 100 μL cold 1x PBS and fixed by adding 900 μL of 4% paraformaldehyde for 10 min at 37 °C. Cells were chilled on ice for 1 min before being washed twice with 1 mL cold 1x PBS and centrifuged at 300 x g for 5 min at 4 °C. Cells were resuspended in 100 μL of cold 1x PBS and permeabilized by slowly adding 900 μL of 100% cold methanol to a final concentration of 90% methanol for 30 min on ice. Cells were centrifuged at 300 x g for 5 min at 4 °C and the supernatant was discarded. The cell pellet was washed twice using 1 ml of incubation buffer (0.5 g bovine serum albumin in 100 mL of 1x PBS) and then centrifuged at 300 x g for 5 min at 4 °C. Cells were resuspended and blocked for non-specific binding in 100 μL incubation buffer for 10 min at room temperature. 1 μL of either IL1 α or IL6 specific antibodies conjugated to FITC (Cat. Nos. 11–7118-41 and BMS130FI, eBiosciences, San Diego, CA) was added and incubated for 1 h at room temperature. Cells were rinsed twice by adding 1 mL of incubation buffer and centrifuging at 300 x g for 5 min at room temperature. Cells were suspended in 0.5 mL of 1x PBS and analyzed on the flow cytometer.</p><!><p>Cell culture fluid from the CPF (30 μg/mL) and TCP (100 μg/mL) 24-hour exposure period was used as these compound levels yielded a median response for cell viability. Cells were centrifuged at 13,336 x g for 10 minutes. The supernatants were discarded and 50 mL of 0.2% RapiGest, an acid-labile surfactant (Waters Corporation, Milford, MA, USA), in a 50 mM ammonium bicarbonate:1 mM calcium chloride digestion buffer, pH10, was added to the cell pellets and vortexed to mix. Tubes containing the disrupted cells were incubated at 99 °C for 10 minutes, centrifuged at 21,000 x g for 10 minutes, and the resulting supernatants collected. An enzymatic digestion was performed with sequence grade trypsin (Promega, Madison, WI, USA). The treated cell lysates and trypsin (~50 pmol) in digestion buffer (ammonium bicarbonate) were incubated at 37 °C for 16 hrs. The filtrates were hydrolyzed using 1.2 M HCl (10 μL) and dried using an Eppendorf vacuum-centrifuge with no heat. The dried digests were reconstituted to a final volume of 50 μL with 0.1% formic acid (FA), vortexed and centrifuged at 21,000 x g for 10 minutes. The supernatants containing the peptides were frozen at −70 °C if not used immediately.</p><!><p>Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed on the digested cell lysates using a nUPLC system (Waters Corporation, Milford, MA, USA), coupled to a linear ion trap (LTQ)-Velos Orbitrap tandem MS instrument (Thermo Scientific, San Jose, CA, USA). Peptides were separated using a nUPLC system directly coupled on-line to the MS instrument through an Advance Captive Spray source from Michrom Bioresources (Auburn, CA, USA). The spray voltage was set at 1500 V, and the capillary temperature was 200 °C. The nUPLC separation was performed using a Symmetry C18 trapping column and a BEH C18 analytical column (100 μm ID x 100 mm long with 1.7 μm packing) as described in (Moura et al., 2011 and 2013). The mobile phase consisted of: (solvent A) 0.2% FA, 0.005% trifluoroacetic acid (TFA) in water, and (solvent B) 0.2% FA, 0.005% TFA in acetonitrile (ACN). The gradient was set at 5% B for 5 min, followed by a ramp to 40% B over 90 min, then a ramp to 95% B in 1 min. The gradient was then held at 95% B for 5 min before returning to 5% B in 2 min, followed by re-equilibration at 5% B for 5 min. The flow rates were 5 μL/min for 5 min for trapping and 600 nL/min to complete the analytical run.</p><p>The MS instrument was programmed to perform data-dependent acquisition by scanning the mass range from m/z 400 to 1400 at a nominal resolution setting of 60,000 FMHM for parent ion acquisition in the Orbitrap as previously used in Moura et al, 2013. Tandem mass spectra of doubly charged and higher charged state ions were acquired for the top 15 most intense ions in each survey scan. All tandem mass spectra were recorded by use of the linear ion trap. This process cycled continuously throughout the duration of the nUPLC gradient. All tandem mass spectra were extracted from the raw data file using Mascot Distiller (Matrix Science, London, UK; version 2.2.1.0), and searched using Mascot (version 2.2.0). Mascot calculates the theoretical mass for each peptide using rules for enzyme cleavage, and was setup to search the entire National Center for Biotechnology Information (NCBI) nonredundent (nr) database in which trypsin is used as the digestion agent. Scaffold (v3.01, Proteome Software Inc., Portland, OR, USA) was used to further validate MS/MS-based peptide and protein identifications. The reported data represent three technical replicates and three analytical replicates. Mascot and Scaffold search parameters were used as described by Moura et al., 2011 and 2013, to yield a >95% protein identification probability. The BLAST-2-GO program (BioBam Bioinformatics S.L., Valencia, Spain, version 3.1) was used for protein blasting, mapping, annotating, and for the statistical assessment of annotation differences between sequence sets for further visualizing the MS/MS data.</p><!><p>Cell toxicity data were subjected to analysis of variance (ANOVA) followed by Tukey multiple comparisons tests. Results with P values < 0.05 were considered statistically significant. R software was used for all data analyses (R Development Core Team, 2008). The Basic Local Alignment Search Tool (BLAST) was used to calculate the sequence similarity of proteins determined from the three exposure scenarios. The BLAST algorithm enables a comparison of protein sequences which can be used to determine the proteomic effects of exposure (Altschul, et al., 1990). The process compares protein sequences to known sequence databases and calculates the statistical significance of matches.</p><!><p>HEK 293 cells were exposed for 24 h to either CPF (0, 10, 30, 60 and 90 μg/mL) or TCP (0, 50, 100, 150 and 250 μg/mL) and the QA methanol control. Cell viability was measured using a CCK-8 assay (Fig. 1). Loss of viability occurred as the dose concentration of either compound was increased (Fig.1). Linear regression equations across the effective dose range (red lines in Fig.1) revealed EC50 = 68.82 μg/mL for CPF and 146.87 μg/mL for TCP. A linear regression was used since a log-logistic concentration effect analysis would require additional data points that were not available in our experimental paradigm (Focke, et al., 2017). Untreated cells exhibited the characteristic flattened shape of adherent cells with round boundaries and tight cell junctions (data not shown). Distressed cells oftentimes swell or shrink resulting in irregular boundaries, or detach and aggregate into clusters floating in the medium. At the highest doses for both CPF and TCP, many cells appeared dead.</p><!><p>Cells exposed to CPF (0, 10, 30, 60, and 90 μg/mL) or TCP (0, 50, 100, 150, 250 μg/mL) for 24 h along with the QA methanol control were analyzed by flow cytometry to determine the extent of apoptosis. Representative data are shown in Fig. 2 and explained in the figure legend. Flow cytometry was able to effectively discriminate viable cells from those that were either undergoing apoptosis or that were dead. In summary, as the concentration of the compound increased (Fig. 2 D–F), the number of viable cells decreased while those that were either dead or that were undergoing apoptosis increased. These data are summarized in Fig. 3 (for CPF) and Fig. 4 (for TCP). There was no statistical difference between the cell culture media and methanol QA controls on cell viability or on the apoptotic state for either CPF or TCP exposure (ANOVA, P > 0.05), indicating that the methanol vehicle did not affect cell viability. In contrast, a 24 h exposure to 30 μg/mL CPF resulted in a decrease in viability and the onset of apoptosis (ANOVA, P < 0.05). Increased concentrations of CPF also resulted in greater decreases in viability and increased involvement of apoptosis in a dose-dependent manner (ANOVA, P < 0.05). Similarly, a 24h exposure to 50 μg/mL TCP resulted in decreases in viability and onset of apoptosis (ANOVA, P < 0.05). Increased concentrations of TCP also resulted in decreases in viability and increased involvement of apoptosis in a dose-dependent manner (ANOVA, P < 0.05).</p><p>Data analysis for early versus late apoptosis for the CPF exposure duration experiments showed the data are consistent with an initial bolus of cells entering apoptosis in response to high doses of CPF and TCP (Supplemental Figs. S1 and S2). Interestingly, there was no expected recruitment of additional late apoptotic cells following this initial bolus. Instead at the high dose of 60 μg/mL CPF, the percentage of cells in late apoptosis, although still high, is actually reduced in comparison to the value at 24 h for cells exposed to CPF for 48 and 72 h. This finding is consistent with a model of an initial bolus of cells entering apoptosis and perhaps slow attrition of the remaining cells. We note that our preparation wherein cells were washed prior to flow cytometry may have washed away dead cells. We believe this step may help explain why there is no accumulation of dead cells (Figs. S1–S2B) over the time course despite few cells at the highest dosages not being involved in apoptosis or being already dead (Figs. S1–S2A and labeled as viable cells).</p><!><p>We examined if CPF and TCP exposure would lead to inflammatory responses as indicated by the expression of the cytokines, IL1α or IL6. There was no statistical difference between the QA controls for either IL1α or IL6 expression in the CPF or TCP 24 h exposure experiments (ANOVA, P > 0.05). While lower doses of CPF and TCP elicited involvement of apoptosis (Figs. 3–4), the number of cells expressing greater IL1α was only increased when cells were exposed for 24h to 90 μg/mL CPF (Fig. 5; ANOVA, P < 0.05). The number of cells with increased IL6 expression increased when cells were exposed to 60 μg/mL CPF (Fig. 6). Interestingly, this pattern was not evident for IL1α or IL6 expression in response to TCP exposure. Here, expression of IL1α increased with 150 μg/mL TCP but IL6 did not increase until the dose was 250 μg/mL TCP for a 24 h exposure. These data might suggest a secondary effect wherein the large numbers of apoptotic and dead cells elicited an inflammatory response. We also examined how longer periods of exposure might influence the interleukin expression patterns at different concentrations (Figs. S3–S4). At higher compound doses longer periods of exposure resulted in more cells expressing IL1α (Fig. S3) and IL6 (Fig. S4).</p><!><p>Protein sequence similarity among the three exposure conditions of CPF, TCP and the QA control were calculated using the BLAST algorithm. The BLAST results were compared to the NCBI nr data base for human and trypsinized proteins. Using this proteomic approach, over 518 peptides were identified using HR-MS and assigned to 308 proteins common to all three exposure conditions (Supplementary Table S1). Groups of proteins consistently appeared only after cell exposure to the compounds. An in depth analysis of this large protein pool will identify proteins that are either up-regulated or down-regulated in response to CPF or TCP exposure. The identification of these proteins was out of the scope of this project as our interest was in the unique proteins seen after the different exposure scenarios. It is significant that 64 proteins were detected after exposure to CPF and TCP of which 55 were identified with high confidence. Five were unique to CPF exposure, thirtyone from TCP, and five were from the control (Fig 7). Twenty-eight proteins were common to both CPF and TCP exposures. The 39 proteins are listed in Supplementary Table S-1 with their accession numbers and descriptions which can be used for further study such as biomarker determination. Links to the Online Mendelian Inheritance in Man (OMIM) records are given for proteins that were definitely identified. OMIM summaries provide data to help understand gene function, although deeper searches of data bases and other sources are often warranted. The validated proteins were analyzed using BLAST-2-GO. The resulting pie charts (Figure 8A–C) represent the impacts of the unique proteins detected under exposure to both CPF and TCP as related to: (A) Biological Processes, (B) Molecular Function and (C) Cellular Components. Sequences for biological processes included metabolic and developmental processes, biological regulation, and response to stimulus (Fig 8A). A molecular function gene ontology analysis within the BLAST-2-GO program indicated that the majority of sequences found were for binding proteins (Fig 8B). Sequences in the GO analysis for cellular components ranged from those relating to membrane, organelle, and extracellular regions (Fig 8C). Sequences for six major enzyme classes (i.e., oxidoreductases, transferases, hydrolase, lyases, isomerases, and ligases) were also found (data not shown).</p><!><p>The effects of CPF and TCP exposure on human cells have been addressed previously for immune cells, hepatocytes, and very recently kidney cells (Nakadai et al., 2006; Li et al., 2009, Das et al., 2011; Li et al., 2015). Consistent with our findings, Li et al. (2015) found similar concentrations to those used here to induce apoptosis in HEK 293 cells. Exploiting a comet assay, these authors determined that there were DNA breaks as a result of exposure to CPF. What was not determined is whether prolonged exposure and/or exposure to TCP, the primary metabolite for CPF elimination, affected cells in the same manner, or if such exposures lead to changes in the protein profile as indicated in this study.</p><p>Our results demonstrate that both CPF and its primary metabolite, TCP, are toxic to HEK 293 cells (Fig. 1). Large scale changes in cellular morphology are evident and this toxicity appears to be mediated through induction of apoptosis as evidenced by presentation of phosphatidylserine on the outer leaflet of the plasma membrane following both CPF and TCP exposure (Figs. 3–4). Our data on prolonged exposure to these compounds suggest that there is an initial burst of apoptotic induction followed by a slowed attrition of the cells (Figs. S1–S2). These data may be indicative of the variation in cell vigor in a population. Less vigorous cells enter apoptosis early while more vigorous cells resist the pro-apoptotic effects of CPF and TCP.</p><p>Our data on the important cytokines, IL1α (Fig. 5) and IL6 (Fig. 6) reveal an intriguing outcome. Exposure to CPF appeared to elicit a greater IL1α and a somewhat blunted IL6 induction. However, exposure to TCP resulted in the opposite pattern. Both IL1α and IL6 have important roles in immune responses and inflammatory reactions (Luheshi et al., 2009, Spiegel and Weber, 2006, Mihara et al., 2012). Our data also suggest that while there is an apparent pro-inflammatory response as indicated by an increased numbers of cells expressing both IL1α and IL6, the dosage required to elicit these responses varies for these cytokines and is higher than the dose required to elicit entrance into apoptosis (Figs. 3 and 4). Such a finding suggests that apoptosis is initiated by an inflammation-independent mechanism. Only after a number of cells have been involved in apoptosis, does the inflammatory response become evident.</p><p>In conclusion, our results support those involving other cell types that CPF and TCP are toxic to human cells. A primary mechanism appears to be the induction of apoptosis. However, inflammatory responses appear to be a secondary outcome which may obfuscate elucidation of the mechanism. We further show that CPF and TCP can affect protein expression in human embryonic kidney cells resulting in 64 unique proteins. The differentiating effects of CPF and TCP exposures may be resolved through in depth omic analyses of data mining to assess the functional meaning of CPF and TCP exposures, particularly when applied within a systems toxicology approach, furthering the understanding of CPF and TCP toxicity and the identification of biomarkers exposure and effects (Tollefsen et al, 2014; and Willett et al., 2014; Lee et al., 2015) while minimizing the use of whole animals.</p>

PubMed Author Manuscript

Risk of hemoptysis in patients with resected squamous cell and other high-risk lung cancers treated with adjuvant bevacizumab

Purpose Bevacizumab improves survival in lung adenocarcinomas. The potential anti-tumor benefit of bevacizumab in squamous cell lung cancers (SQCLCs) is unknown because bevacizumab is contraindicated in patients with advanced SQCLC due to an increased risk of hemoptysis. The risk of hemoptysis may be eliminated in patients with resected SQCLCs. We evaluated the safety of adjuvant bevacizumab in patients with resected SQCLCs and other lung cancers at high risk of hemoptysis. Methods As part of a prospective, phase II trial, patients with lung cancers at high risk of hemoptysis (defined by SQCLC histology, tumor near the central blood vessels, or history of hemoptysis) were treated with adjuvant bevacizumab following neo-adjuvant chemotherapy and complete surgical resection. Bevacizumab 15 mg/kg was given once every 3 weeks for up to 1 year. Patients were followed for safety and survival. Results Thirteen patients with high-risk features were treated: 7 patients had SQCLC, 3 had central tumors, and 3 had previous hemoptysis. No hemoptysis of any grade was seen following treatment with bevacizumab. Five of 13 patients experienced grade 1 bleeding (epistaxis, gum bleeding). Hypertension and lymphopenia were seen. Conclusions In a cohort of patients with resected lung cancers at high risk of hemoptysis, including those with SQCLC, treatment with adjuvant bevacizumab did not result in hemoptysis of any grade.

risk_of_hemoptysis_in_patients_with_resected_squamous_cell_and_other_high-risk_lung_cancers_treated_

Background<!>Patients<!>Study design<!>Pre-operative treatment<!>Surgical resection<!>Post-operative therapy<!>Outcomes analyses<!>Patients<!>Response to neo-adjuvant chemotherapy<!>Bevacizumab compliance and toxicity<!>Relapse-free and overall survival<!>Post-operative bevacizumab in patients with squamous cell lung cancers<!><!>Discussion

<p>The dependence of solid tumor growth on the continuous formation of new blood vessels was initially hypothesized in the 1970s [1]. Angiogenesis has since been established as a hallmark of cancer [2]. At the physiologic heart of angiogenesis is a family of endothelial mitogens, vascular endothelial growth factors (VEGF), and their membrane-bound tyrosine kinase receptors (VEGFR) [3–5]. VEGF-A and its signaling through VEGFR-2 on endothelial cells are of particular importance [6–8]. Thus, the development of a monoclonal antibody targeting vascular endothelial growth factor (VEGF), bevacizumab (Genentech, South San Francisco, CA), was greeted with great optimism. Since then, studies have demonstrated clinical benefit when bevacizumab is combined with cytotoxic chemotherapy in advanced non-squamous non-small cell lung cancers (NSCLCs) [9, 10], leading to FDA approval for this indication in 2006.</p><p>Despite the benefit seen in non-squamous NSCLCs, the use of bevacizumab in squamous cell lung cancers (SQCLC) has been routinely avoided. This omission is rooted in an experience in a small cohort of patients treated with bevacizumab in the initial randomized phase II study of bevacizumab and chemotherapy in advanced NSCLCs [11]. In this study, 6 of 67 (9 %) patients suffered severe (grade ≥3) hemoptysis, which was fatal in four cases. The majority of severe hemoptysis was seen in the SQCLC group (4 of 13 SQCLC (31 %), compared to only 2 of 54 (4 %) patients with other histologies) [11]. SQCLC tumors have been subsequently excluded from most trials of bevacizumab in lung cancers. Notably, the rate of severe hemoptysis was lower in subsequent phase III and phase IV trials of bevacizumab in advanced NSCLCs which excluded squamous histologies (phase III: 2.3 % [9] and 1.2 % [10]; phase IV: 1 % [12]; compared to initial phase II: 9 % [11]).</p><p>The reasons for increased hemoptysis in patients with SQCLC treated with bevacizumab are unclear. Anatomically, the propensity of SQCLC to develop centrally near large blood vessels [13] and to cavitate [14] have been speculated as potential explanations. All 6 patients with severe hemoptysis on the initial phase II trial of bevacizumab in lung cancers had central tumors, and five had evidence of cavitation [11]. It may therefore be the structural characteristics associated with SQCLC rather than the inherent biology of its histology that predispose SQCLC patients to severe hemoptysis when treated with bevacizumab. A retrospective analysis of 877 patients with lung cancer found that rate of fatal hemoptysis was significantly higher in those with SQCLCs (7.4 %) versus those with adenocarcinomas (0.8 %) [15]. However, approximately half of all those with fatal hemoptysis had either centrally located (15/29) or cavitated disease (14/29). Univariate analysis demonstrated a significant correlation between hemoptysis and SQCLC histology, centrally located disease, as well as cavitation (p < 0.001, respectively), but no multivariate analysis was performed to determine whether SQCLC histology independently contributes risk. Of note, more recent reports have contested the predictive value of central tumor location or the presence of cavitation on the risk of hemoptysis in patients treated with bevacizumab [16]. Taken together, the etiology of the increased risk of hemoptysis for patients with SQCLC treated with bevacizumab remains unknown.</p><p>In an effort to determine whether the clinical benefit of bevacizumab in patients with advanced lung cancers could be translated to patients with resectable disease, we performed a phase II trial of neo-adjuvant bevacizumab, cisplatin, and docetaxel followed by adjuvant bevacizumab in patients with resectable lung cancers (BEACON, BEvacizumab And Chemotherapy for Operable NSCLC, NCT00130780). Arm A of this study included all patients with adenocarcinomas, and the results have been reported [17]. Patients deemed to be at high risk of severe hemoptysis with bevacizumab (SQCLC histology, history of gross hemoptysis, or large central tumors) received neo-adjuvant chemotherapy alone, but were eligible for adjuvant bevacizumab (Arm B). Adjuvant bevacizumab represents a unique setting to assess the risk of hemoptysis in patients with SQCLCs in whom the anatomic variables of central cavitating tumors are removed and an important opportunity to better understand the factors that contribute to the increased risk of hemoptysis.</p><!><p>All patients in this IRB-approved study gave written informed consent prior to enrollment. Patients had pathologically confirmed squamous cell lung cancer or any histology with a large central tumor near significant blood vessels and/or history of hemoptysis. Patients with clinical stage IB–IIIA (T1-3N0-2M0) by American Joint Committee on Cancer Staging 6th edition were eligible. Pretreatment evaluation included chest CT, PET scan, brain MRI, and pathologic mediastinal staging (mediastinoscopy or endobronchial ultrasound) if clinically indicated. Patients were required to have a Karnofsky performance status of ≥70 %, adequate organ function, and deemed resectable by a thoracic surgeon. Patients were ineligible if they were receiving anti-coagulation, had a history of stroke or myocardial infarction within the past year, uncontrolled hypertension (HTN), non-healing wound/ulcer/fracture, hearing loss, or peripheral neuropathy >grade 1.</p><!><p>This was a single institution, open-label, phase II study. The primary endpoint for the overall study—pathologic downstaging following treatment with chemotherapy and bevacizumab—was not applied to patients in Arm B as they did not receive bevacizumab pre-operatively. The primary endpoint of Arm B was safety. Secondary endpoints including overall survival and relapse-free survival following resection. This study was approved by the Institutional Review Board and all patients signed informed consent.</p><!><p>Patients were treated with neo-adjuvant intravenous cisplatin (75 mg/m2) and docetaxel (75 mg/m2). Treatment was given sequentially on day 1 of a 21-day cycle. Patients underwent a CT scan following 2 cycles of therapy. If at least a 10 % reduction in bi-dimensional tumor volume was achieved, patients received two additional cycles of chemotherapy (4 cycles total).</p><!><p>Following treatment, patients were re-evaluated for surgery by clinical examination, chest CT, PET scan, pulmonary function tests, and brain MRI. Surgical resection, if appropriate, occurred 3–8 weeks after chemotherapy.</p><!><p>Adjuvant bevacizumab (15 mg/kg) was administered intravenously starting 42–56 days post-operatively and continued every 21 days for 1 year (up to 18 cycles). If post-operative radiotherapy was indicated (e.g., N2 nodal involvement or a positive resection margin), bevacizumab was delayed until 28–52 days following the completion of radiation. No cytotoxic chemotherapy was given postoperatively. Patients were evaluated every 3 weeks with the administration of bevacizumab. CT scans of the chest and upper abdomen were performed every 4 months for recurrence.</p><p>Following completion of adjuvant bevacizumab, patients were followed with history, physical examination, and CT scans every 4 months for the first year, every 6 months in years 2–3, and annually thereafter.</p><!><p>Safety analysis of the Arm B cohort was pre-planned; toxicity was monitored and graded using National Cancer Institute Common Toxicity Criteria, version 3.0. Overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan–Meier method, with patients followed from time of surgery until death (in OS analysis) and until relapse or recurrence of disease, or death, whichever came first (in DFS analysis). Patients who did not experience the event of interest during study time were censored at the time of last known follow-up.</p><!><p>Twenty patients were enrolled between August 2005 and April 2011. Three patients progressed during neo-adjuvant chemotherapy and were taken off study. Of the 17 patients who completed neo-adjuvant chemotherapy and underwent surgical resection, 4 patients did not receive post-operative bevacizumab. Reasons for not receiving adjuvant bevacizumab included deep-vein thrombosis (DVT), wound infection, death, and patient refusal. The CONSORT diagram is shown in Fig. 1. The baseline characteristics of the 13 patients treated with post-operative bevacizumab are presented in Table 1.</p><!><p>Thirteen patients received neo-adjuvant chemotherapy with cisplatin and docetaxel and adjuvant therapy with bevacizumab. Pathologic response to neo-adjuvant chemotherapy was evaluated at the time of surgical resection. Seven of 13 (54 %) patients were downstaged (defined as an improvement in pathologic stage at the time of surgical resection compared to clinical staging at the time of diagnosis) following neo-adjuvant chemotherapy. The percent viable residual tumor was also assessed and ranged from 0 to 100 % (median 70 %). One patient had a complete pathologic response.</p><!><p>The median number of cycles received was 9 (range 2–18). Two patients completed all planned treatments. Reasons for early cessation of bevacizumab were toxicity (n = 4), disease recurrence (n = 2), and patient request (n = 5).</p><p>Toxicity experienced in the adjuvant bevacizumab treatment phase is summarized in Table 2. Five of 13 (38.5 %) patients experienced grade 1 bleeding (epistaxis, gum bleeding). One patient stopped therapy as a result of grade 1 epistaxis. No higher grade bleeding (grades 2–5) was reported in any patients. No patient developed hemoptysis of any grade.</p><p>Nine of 13 patients (69.2 %) experienced a grade 3+ adverse event. Two of these grade 3+ events, pneumonia and stroke, were unrelated to bevacizumab. The stroke predated the start of bevacizumab but was not detected until after therapy began. Grade 3 lymphopenia occurred in 3 patients (23.1 %), but no neutropenia was reported. One patient experienced a catheter-associated DVT. Grade 3 hypertension occurred in 2 patients (15.4 %).</p><!><p>The median duration of follow-up after surgery was 32.5 months (range 14–71 months). Seven of 13 patients relapsed following surgery. Four of 13 have died, three of which were as a result of recurrent cancer. Median overall survival was not reached; 2-year overall survival was 69 % (95 % CI 48–99) (Fig. 2a). Median disease-free survival was 23 months (95 % CI 10—not reached); 2-year disease-free survival was 46 % (95 % CI 26–83) (Fig. 2b).</p><p>Table 3 reports the clinical characteristics, treatment course, toxicity, and survival data for all patients on study.</p><!><p>Seven of 13 patients treated with post-operative bevacizumab had squamous cell histologies. Patient #6 had SQCLC histology seen on diagnostic biopsy, although had predominantly adenocarcinoma histology at the time of resection [18].</p><p>Three of 7 patients with SQCLCs had disease-free and overall survival >5 years (#2, with clinical stage IIB; #6, with clinical stage IB; and #8, with clinical stage IIIA disease at diagnosis).</p><p>Patients received between 2 and 18 cycles of treatment. Three of 7 patients had grade 1 bleeding, but no higher grade bleeding was reported.</p><p>One patient completed all planned therapy. Three patients stopped therapy earlier than initially planned without dose-limiting toxicity.</p><p>The three other patients stopped treatment early due to toxicity, one of which was unrelated to bevacizumab.</p><!><p>Patient #11 developed a left visual field disturbance and ataxia two days following surgical resection. This was initially attributed to cataracts, and no imaging was performed. His symptoms improved and he began adjuvant bevacizumab, receiving 2 cycles. Thereafter, as his visual field disturbance persisted, he was evaluated by an ophthalmologist. Formal visual field testing revealed left homonymous hemianopsia. An MRI of the brain revealed a subacute-chronic right occipital lobe infarct. He was then taken off study. In retrospect, his stroke was felt to predate bevacizumab administration and was unrelated to bevacizumab.</p><p>Patient #8 stopped adjuvant therapy as a result of progressive hypertension after 9 cycles of bevacizumab. She has remains recurrence free more than 5 years since surgical resection.</p><p>Patient #17 received 5 cycles of adjuvant bevacizumab without complication. He was then noted to have a decreased ejection fraction on echocardiography (EF 40–45 %), which was further evaluated with a cardiac catheterization. Left ventricular EF by this method was 60 %, and only mild, non-obstructive coronary disease was seen. He returned to therapy with bevacizumab, completing cycles 6 through 8. Following cycle 8, we was hospitalized with fever and shortness of breath and diagnosed with pneumonia. He required transient mechanical ventilatory support but recovered with antibiotics. Repeat echocardiography was normal. He ultimately recovered enough to receive cycles 9–12 of bevacizumab. Following cycle 12, the patient was again hospitalized with dyspnea. An electrocardiogram revealed a non-ST segment elevation myocardial infarction with elevated cardiac enzymes. He was admitted, treated medically, but clinically deteriorated, and died 2 days after admission. An autopsy revealed diffuse atherosclerotic occlusive coronary disease and the cause of death was reported as acute myocardial infarction.</p><!><p>This study is, to our knowledge, the first to address the safety of bevacizumab in patients with lung cancers at risk of developing life-threatening hemoptysis who have been rendered disease-free prior to the start of therapy. Class-specific toxicities of angiogenesis inhibitors such as bevacizumab, including venous thrombosis, rash, diarrhea, hypertension, and proteinuria, have been reported across many solid tumors. The risk of hemoptysis, however, is specific to lung cancers and, apparently, SQCLC in particular. Since the initial randomized phase II trial of chemotherapy with or without bevacizumab demonstrated an increased rate of hemoptysis in SQCLC [11], bevacizumab has been routinely avoided in patients with SQCLC. Nevertheless, it is not known whether patients with SQCLC are biologically predisposed to hemoptysis or if the clinical features associated with SQCLC, including central location and presence of cavitation, are the predisposing factors for this adverse event. This distinction is important, as bevacizumab in patients with SQCLC without structural predispositions to hemoptysis may be safe and potentially beneficial.</p><p>We found that in 13 patients who were initially deemed high risk for hemoptysis, 38 % experienced grade 1 bleeding (4 with epistaxis, 1 with gum bleeding), while no patients had any degree of hemoptysis during adjuvant treatment with bevacizumab.</p><p>Seven patients experienced grade 3 toxicities (lymphopenia, DVT, syncope, HTN, pleural effusion), each of which were possibly, probably, or definitely related to bevacizumab.</p><p>Two patients, both with SQCLCs, experienced grade 4–5 toxicities. One patient had a stroke which, in retrospect, predated treatment with bevacizumab and was unrelated to treatment. Another had grade 4 pneumonia, unlikely related to be bevacizumab, and grade 5 myocardial infarction possibly related to bevacizumab. The risk of myocardial infarction attributable to bevacizumab is uncertain. Across all indications, the rate of grade ≥3 arterial thromboembolic events (including myocardial infarction as well as angina and stroke) is 2.4 % compared to 0.7 % in the control arms [19], but no increase in myocardial infarction specifically was seen in a retrospective evaluation of 2,526 patients with stage IV colon cancer [20].</p><p>It is worth noting that strategies to safely incorporate bevacizumab in advanced SQCLC have been reported. The BRIDGE trial hypothesized that delayed integration of bevacizumab (after 2 cycles of chemotherapy) would allow initial cytoreduction and epithelial healing prior to starting bevacizumab and, therefore, reduce the risk of hemoptysis [21]. Only one patient had grade ≥3 hemoptysis (3.2 %, 90 % CI 0.3–13.5).</p><p>Although not reported in bevacizumab specifically, there may be safety concerns for the use of angiogenesis inhibitors in SQCLC that extend beyond hemoptysis. ESCAPE was a randomized, phase III trial of chemotherapy with or without sorafenib in advanced lung cancers [22]. SQCLC patients (223/926 enrolled, 24 %) receiving sorafenib had an increased risk of death compared to patients with SQCLC receiving placebo (HR 1.85 95 % CI 1.22–2.82). This was not attributable to hemorrhage as the rate of hemorrhage was the same across SQCLC patients regardless of treatment received. These data suggest that patients with SQCLC were harmed with sorafenib by heretofore unknown mechanisms.</p><p>The toxicity of angiogenesis inhibitors in patients with SQCLC treated with may not be uniform across this class of drugs. For example, an increased risk of hemoptysis and mortality was seen in patients with SQCLC treated with motesanib [23]. However, other studies of angiogenesis inhibitors in advanced lung cancers such as vandetanib [24–27] and cediranib [28, 29] have not reported increased toxicity in SQCLC patients.</p><p>Finally, it is important to mention that the biologic rationale for targeting VEGF in SQCLC has also been subject to some controversy. Two older studies suggested that intratumoral VEGF overexpression was a negative prognostic marker in SQCLC [30, 31], providing incentive to target VEGF therapeutically. A more recent meta-analysis was equivocal as to the prognostic relevance of VEGF in SQCLC [32]. Most recently, an analysis of VEGF, VEGFR-1, and VEGFR-2 reported higher composite expression in early stage SQCLCs to be associated with, contrary to earlier studies, improved prognosis [33].</p><p>In summary, our trial demonstrates that adjuvant bevacizumab did not lead to hemoptysis in a cohort of high-risk patients with lung cancers, 7 of whom had SQCLC, following curative surgical resection of their disease. These findings are consistent with previous notions that the risk of hemoptysis may be related to anatomic factors rather than inherent biology. In the absence of high-risk anatomic features, the risk of hemoptysis appears to drop substantially. We note that although the size of this cohort is small, it is the same (n = 13) as the cohort of patients with SQCLC who received chemotherapy and bevacizumab on the initial phase II trial in which the concern for hemoptysis originally arose. We eagerly await the results of the ECOG 1505 trial to more fully evaluate both the safety and the efficacy of bevacizumab in conjunction with chemotherapy in the adjuvant treatment of SQCLC and non-SQCLC.</p>

PubMed Author Manuscript

Replacement of Arg with Nle and modified D-Phe in the core sequence of MSHs, Ac-His-D-Phe-Arg-Trp-NH2, leads to hMC1R selectivity and pigmentation

Melanoma skin cancer is the fastest growing cancer in the US [1]. A great need exists for improved formulations and mechanisms to prevent and protect human skin from cancers and other skin damage caused by sunlight exposure. Current efforts to prevent UV damage to human skin, which in many cases leads to melanoma and other skin cancers. The primordial melanocortin-1 receptor (MC1R) is involved in regulating skin pigmentation and hair color, which is a natural prevention from UV damage. The endogenous melanocortin agonists induce pigmentation and share a core pharmacophore sequence \xe2\x80\x9cHis-Phe-Arg-Trp\xe2\x80\x9d, and it was found that substitution of the Phe by D-Phe results in increasing melanocortin receptor potency. To improve the melanocortin 1 receptor (MC1R) selectivity a series of tetra-peptides with the moiety of Ac-Xaa-Yaa-Nle-Trp-NH2, and structural modifications to reduce electrostatic ligand-receptor interactions have been designed and synthesized. It is discovered that the tetrapeptide Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2 resulted in a potent and selective hMC1R agonist at the hMC1R (EC50: 10 nM). Lizard anolis carolinensis pigmentation study shows very high potency in vivo. NMR studies revealed a reversed \xce\xb2 turn structure which led to the potency and selectivity towards the hMC1R.

replacement_of_arg_with_nle_and_modified_d-phe_in_the_core_sequence_of_mshs,_ac-his-d-phe-arg-trp-nh

1. Introduction<!>2. Design of novel tetrapeptides<!>3.1. Binding and cAMP studies<!>3.2. Pigmentation studies<!>3.3. NMR analysis<!>3.4. Docking study of peptide 5 at the hMC1R<!>4. Conclusion<!>5.1. Peptide synthesis<!>5.2. Bioassays<!>5.3. Pigmentation study<!>5.4. NMR spectroscopy<!>5.5. Docking studies

<p>Melanoma is among the utmost prevalent cancer diseases [1]. It is estimated that over 80% of malignant melanomas express higher levels of melanocyte stimulating hormone (α-MSH) receptors, human melanocortin 1 receptor (hMC1R) [2], a member of the melanocortin receptor family, which belongs to 7-transmembrane, G protein-coupled receptors (GPCRs) that control various physiological functions that are critical for survival [3]. In particular, the hMC1R is associated with skin pigmentation. Upon activation, the hMC1R in melanocytes and keratinocytes will form the pigmentation to block the UV radiation to prevent skin damage [4–19]. The endogenous melanocortin peptides are all agonists to hMCRs and include α-melanocyte stimulate hormone, α-MSH; β-melanocyte stimulate hormone, β-MSH; and γ-melanocyte stimulate hormone, γ-MSH. They all have a core pharmacophore structure of tetra-peptide (His-Phe-Arg-Trp) sequence [20]. Our numerous previous studies have demonstrated that the tetra-peptide -His-Phe-Arg-Trp- is a minimum sequence which has the capability of activating all hMCRs [21–27]. Malignant melanoma is the most fatal form of skin cancer. The involvement of MC1 receptor during the proliferation of melanoma cells suggests that α-MSH and its analogues may be candidates for melanoma prevention [18,19,28–31]. The current marketed drug for skin pigmentation disorder is an α-MSH analogue NDP-α-MSH called melanotan I (MT-I). However, NDP-α-MSH is a 13 amino acid peptide with no selectivity towards all the other hMCRs subtypes. Therefore, development of novel analogues with higher MC1R selectivity, a shorter sequence and more druggable properties are needed. Our novel designed tetra peptides show highly selective hMC1R agonist activity and skin pigmentation capability, which can be used to protect against melanoma.</p><!><p>It was previously discovered that the tetrapeptide Ac-His-D-Phe-Arg-Trp-NH2, which contains the tetrapeptide pharmacophore sequence of NDP-α-MSH, is the shortest melanotropin peptide required for binding and activation of melanocortin receptors [32]. However, it has poor potency and selectivity to all subtypes of hMCRs. Thus, modifying the tetrapeptide structure will be of critical importance to improve the potency and selectivity to hMC1R while keeping the short sequence. Modifications were mainly focused on three different sites of the Ac-His-D-Phe-Arg-Trp-NH2 template: 1. His was substituted with Pro in peptide 8–12. 2. We used either D-Phe with different halogenation groups (F, CF3, Cl, Br) at the para position or D-Nal(2′) to substitute the D-Phe position, which has been found to improve potency and modulate selectivity in the Ac-His-D-Phe-Arg-Trp-NH2 template [33,34]. It was noticed that substitutions such as halogenated D-Phe and D-Nal(2′) at D-Phe position were shown to reduce the tetrapeptide's ability to activate MC3R and MC4R, leading to partial agonism or even antagonism at MC3R and MC4R [33,34]. 3. We envisioned that enhanced selectivity towards the MC1R can be reached with reduced electrostatic interaction between the tetrapeptide and the respective aspartic acids on the MC3R and MC4R receptors. Previous receptor mutagenesis studies demonstrated that the electrostatic interaction between the Arg8 of the NDP-α-MSH and the Asp122, Asp126 of the hMC4R is of critical importance to achieve receptor activation, as evidenced by a more than 400-folds increase in the EC50 value for the Asp126Asn mutant [35]. Similarly, a key interaction between the Arg8 of the NDP-α-MSH and the Asp154, Asp158 of the MC3R is necessary, as Asp158Ala mutation on MC3R led to more than 350-fold increase for the EC50 value [36]. In contrast, Asp117 and Asp121 play much less role for interactions between hMC1R and NDP-α-SH, as evidenced by only around 10-fold increase on the IC50 and EC50 values for the Asp117Ala and Asp121Ala mutants [37]. Therefore, switching the arginine in the tetrapeptide to the neutrally charged amino acid norleucine, which has similar shape and size as arginine, should reduce binding towards the hMC3R and the hMC4R. Herein, a series of Nle8 tetra-peptides, Ac-Xaa-Yaa-Nle-Trp-NH2, were designed and synthesized. (Table 1).</p><!><p>The biological activities of the newly designed tetra-peptides were analyzed by binding and cAMP assays using stable HEK293 cell lines which express the hMC1R, hMC3R, hMC4R and hMC5R(Table 2). Our first step was replacing Arg8 with Nle8. As we expected, peptide 2, the Nle8 replaced tetra-peptide, lost 50% binding efficiency for all subtypes of hMCRs compared with the parent tetra-peptide, peptide 1. However, Peptide 2 kept 100% cAMP efficacy for the hMC1R, and the binding affinity for Peptide 2 towards hMC3R was greater than 1.0 μM. Thus, Peptide 2 tends to be more selective for the hMC1R. Introducing the bulky amino acid D-Nal (2′)7 into the Nle8 replaced tetra-peptide (peptide 3) abolished the binding affinity to all of the hMCRs. Nevertheless, it still retains 100% cAMP activity at the hMC1R. In order to improve the binding affinity for the hMC1R we introduced halogenated group (F, CF3, Cl, Br) into the D-Phe6 in the Nle8 replaced tetrapeptides (Peptides 4–7). Table 2 shows that peptides 5–7 have increased binding affinities towards the hMC1R along with binding efficiencies that are greater than 50%. In addition, peptides 4–7 retain 100% cAMP activity at the hMC1R. Among these four peptides, Peptide 4, Ac-His-D-Phe(4-F)-Nle-Trp-NH2, and Peptide 5, Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2, resulted in selective hMC1R agonists at the hMC1R with EC50: 25 nM and 10 nM respectively. Among these four peptides, peptide 5 (Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2) is a potent hMC1R agonist (EC50: 10 nM) with the strongest selectivity of at least 25-fold to other MCR subtypes.</p><p>In order to study the effects of modifying the MCR pharmacophore His-Phe-Arg-Trp sequence with respect to the introduction of Nle8 to achieve MC1R selectivity, His6 was replaced with Pro6 in Peptides 8–12. This was done to determine the influence of a more sterically constrained residue and its ability to further improve molecular recognition towards hMC1R. As shown in Table 2, Pro6 substituted tetra-peptides lost most of the binding and functional activities for all subtypes of the hMCRs, except for Peptide 11. Interestingly, Peptide 11 displayed a cAMP activity level of 63%, but only had a binding efficiency of 18%. Despite the fact that the partial agonism of cAMP levels, the poor binding efficiency suggests that the proline substitution for histidine in these tetrapeptide is not ideal for hMC1R selectivity.</p><!><p>Pigmentation studies were performed on lizard anolis carolinensis to analyze the in vivo pigmentation effect of Peptide 5. Lizards were given an intraperitoneal injection of the vehicle or peptide 5 at 3 μg/g. Peptide 5 induced pigmentation of the lizard within 1 h of injection (Fig. 1), while injection of the vehicle did not produce any pigmentation effect (data not shown). The natural green color was able to resume in less than 24 h.</p><!><p>Biological studies revealed that the Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2 (Peptide 5) is a selective hMC1R agonist. We performed a comprehensive NMR study of Peptide 5.</p><p>A complete assignment was achieved for all proton resonances based on the homonuclear 2D spectra protocol established by Wüthrich et al. (Table 3). The amide and aromatic proton resonances are well resolved in the 1D proton spectrum. The natural abundance 15N-HSQC facilitates differentiating the amide and aromatic proton resonances in the overlapping region (Fig. 2). Sequential NOE connectivities from N-terminal to C-terminal residues in the fingerprint region of 2D NOESY were assigned unambiguously. (Supporting Information) No chemical exchange or multiple spin systems due to minor conformations were observed in the spectra. The aromatic ring assignments were established by the intraresidual NOEs between beta protons to the spatially close protons in the aromatic rings. The 13C chemical shift of the Cα other than His1 Cα were also confirmed by the 13C-HSQC (His1 Hα overlapped with water peak).Chemical shift index method is routinely applied to identify the alpha-helices or beta-sheets using the 1Hα/13Cα chemical shifts variations relative to the values observed in the random coils. However, for the case of peptides with very short length, it is difficult to interpret the results of chemical shift index method. The lack of random coil reference values for non-natural amino acids is another hindrance. Nonetheless, the 1Hα/13Cα chemical shifts of His1 and Trp4 are very close to the values observed in random coils (His: Hα 4.73, random coil 4.63 ± 0.10; Trp Hα 4.58, Cα, 57.44 ppm, random coil 4.70 ± 0.10, 57.8 ± 0.7). The lack of significant up field shifts does exclude the possibility of the ring current effects due to the aromatic side chain stacking [42]. The inter-residual NOE connectivity's point to a β-turn structure. Other than the sequential dαN and dβN in the range of His1-Phe2- Leu3-Trp4, there exists the dαN (i, i+2) and dβN (i,i+2) between F2 and W4, and multiple dNN(i,i+2) and dNN (i,i+3) NOEs. The sequential HN-HN NOEs were observed along His1-Phe2-Leu3-Trp4. The direct HN-HN NOEs are also observed for His1-Trp4 and Phe2-Trp4. Another outward sign of the turn like structure is the long-range NOEs observed between N-terminal acetyl methyl group and Trp4 side chains, which results from the spatial closeness. The temperature coefficient for NH resonance of residue Trp4 is low (−Δδ/ΔT < 4.5 ppb/K), a hydrogen-bonding indicator [43]. The Nle3 NH is broad and weak, a sign that it is more exposed than other amide protons. Fig. 2.</p><p>A consistent preference for the β-turn structure has emerged from the distance-restrained simulated annealing calculations of the peptide 5. The majority of the ensemble of the 300 structures generated by the distance-restrained molecular dynamics calculation shows that the His1 Cα-Trp 4 Cα distance is less than 7 Å, and the distance between the His1 CO and the amide hydrogen of Trp4 is less than 2.5 Å. An ensemble of 10 representative NMR structures were selected based on the criterion of low NOE derived distances violations and low potential energy Fig. 3. The summary of the RMSD of the structures and the distance violations are shown in Table 4. The side chains packing in the calculated structure ensembles is not well converged, showing that the peptide is flexible in solution without a single rigid conformation. It is in agreement with the observation that the 1Hα, 13Cα chemical shifts are close to the values in random coils. Meanwhile the peptide has the preference for the β-turn conformations in its free energy landscape, reflected in the consistent β-turn conformation in the ensemble of the NMR derived structures.</p><!><p>In addition, to investigate the peptide topology which might lead to new conformations of selective melanotropins for the hMC1R, a molecular docking study was performed for the peptide 5 with Glide (Schrodinger LLC, New York). The NMR structure of the Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2 was docked into the hMC1R structure which was generated from the Mosberg lab [44]. The MC1R-Peptide 5 interaction sites (3 Å cut-off) showed the binding pocket is hydrophobic comprising of a series of aromatic residues (Phe and Trp residues) spanning TM3 -7. These functionally attached receptor residues are involved in aromatic-aromatic interactions with residues D-Phe and Trp of the tetra-peptides. Fig. 4 shows that hydrophobic residues on the 7th transmembrane domain (TM) of the receptor contributed to the major force for binding; the D-Phe(4-CF3)6 forms a π-π stacking with the 7TM Phe280 and the 7TM Phe277 in the hMC1R. Also, the Trp9 of the tetra-peptide and the TM4 Phe175, Phe179 and TM5 Phe195, have π-π stacking interactions. Nle8 has frequent interactions with the 6TM Phe257 and Leu261. Multiple mutagenesis studies revealed that loss of a single aromatic receptor residue might be easily compensated by a network of aromatic-aromatic interactions and not induce any problematic effects on ligand binding or receptor activation. [35] [36], Our docking study directly observed the multiple effect of aromatic interactions supports the concept of a hydrophobic hMC1R binding pocket. The hMC1R can be potently activated by compound 5 in comparison with weak activation of MSHs tetra-peptides (His-Phe-Arg-Trp). This suggests that increasing hydrophobicity with the presence of D-Phe and Trp and lipophilic amino acid residue Nle, is very important for the potency and selectivity for the hMC1R. Introducing para-halogenated D-Phe residues in the tetrapeptides will increase the dipole moment of the ligand receptor interaction. As a result, this increases the binding potency towards the hMC1R.</p><p>Finally, substitution of His with Pro was the initial goal in order to stabilize a β turn structure. However, the binding data show the loss of binding for all of these Peptides 8–12 to the hMC1R. Further investigation demonstrated that the position of Pro interferes with forming hydrogen bonding between the His1 CO and the amide hydrogen of Trp4. Analogues 5–6 indicate that the Nle position plays a critical role for the selectivity for the hMC1R, but does not enhance binding. To increase binding potency, halogenated groups were introduced on D-Phe6. It is known that in charge transfer compounds, halogens serve as acceptors and interact with a donor by transferring electronic charge. They can appear in biological systems amongst halogens. The π-electron clouds of benzene rings as well as with halogens and the delocalized pi;-electrons of peptide bonds of carboxy and amides stabilize π-π stacking. All of the halogen-containing compounds, including tetrapeptide 5, possess high lipophilicity, which improves in the following order: F < Cl < Br < CF3. The increased hydrophobicity of halogen-containing analogues with potent attractions between halogens and sulfur-containing receptor residues from transmembrane helices 3–6 may play an effective role in the stabilization of the firmly packed active receptor conformation. The hMC1R for halogen-containing peptides may be described with tighter packing of residues with the hMC1R binding pocket in comparison with other subtypes of human melanocortin receptors. Certainly, peptide 5 containing the bulky hydrophobic substituent p-CF3 increases stimulation activity only at the hMC1R, implying a more constrained geometry of its ligand binding pocket.</p><!><p>Structure-based drug design has become a useful approach to current drug discovery. In our long term peptide-based drug development, peptide truncation and amino acids scan have been used to discover the significant pharmacophore. Conformational constraints were applied to produce numerous stable and selective melanotropins, and the three-dimensional structure of ligands using NMR spectroscopy combined with computational based drug design have led to several selective compounds. In this research, we have combined previous knowledge on structure-activity relationships (SAR) of melanotropins as well as receptor mutagenesis studies to design tetrapeptide agonists selective to hMC1R. Our SAR results suggest that replacing the positively charged Arg residue with neutrally charged Nle is able to improve hMC1R selectivity with some sacrifice on potency in a tetrapeptide template, which is consistent with a recent discovery that replacing Arg residue with Leu in a γ-MSH template leads to a selective hMC1R agonist with only canonical amino acids [45]. Our further efforts to increase potency and binding affinity by adding parahalogenation groups to D-Phe led to the discovery of potent and hMC1R selective tetra-peptide peptide 5 (Ac-His-D-Phe(4-CF3)-Nle-Trp-NH2). Peptide 5 has an EC50 of 10 nM at hMC1R with at least 25-fold selectivity over other melanocortin receptors. An in vivo pigmentation study with lizards confirmed that peptide 5 can produce short-term skin pigmentation effect. Our NMR study revealed a β-turn conformation of peptide 5, which is stabilized by intramolecular hydrogen bond. Further docking studies identified extensive hydrophobic interactions between peptide 5 and hMC1R, which lead to hMC1R selectivity. Peptides are generally considered less toxic because they can be degraded into amino acids. As a result, we didn't measure or mention potential toxicity effects. We did use unnatural amino acids such as D-Phe with halogenations and Nle in this study, which could be potentially toxic to our body when metabolized. However, our peptide is potent in nanomolar range, which means that we can affect the physiological function in very low dose. Such dose is generally considered safe, even though it would require further experiments to confirm that. With its strong potency and selectivity to hMC1R as well as ease for synthesis, peptide 5 has great potential as a low side-effect product to induce skin pigmentation without sun for melanoma prevention.</p><!><p>All peptides in this study were synthesized manually follow our previous published work [46–49]. See supporting Information for detail.</p><!><p>Binding and cAMP Assays followed our previously published work and the data Analysis [46–49]. IC50 and EC50 values represent the mean of two experiments performed in triplicate. IC50 and EC50 estimates and their associated standard errors were determined by fitting the data using a linear least-squares analysis, with the help of GraphPad Prism 5 (GraphPad Software, San Diego, CA).</p><!><p>Lizards were purchased from Carolina online. Peptide samples were dissolved in saline at the concentration of 1 mM. The total amount of peptide was given through i.p. injection with 3 μg/g for each lizard. The methods follow previous publications [21,38–41].</p><!><p>The micelle samples were prepared by dissolving the peptide and 50 equiv of perdeuterated SDS in 0.6 mL of acetate buffer (10 mM, pH5.5) containing 10% D2O. The pH of each sample was further measured and adjusted to 5.5 by using trace amount of DCl or NaOD as necessary. The peptide concentration used for the NMR experiments was 4.7 mM.</p><p>NMR spectra were recorded on a Varian INOVA 600 MHz spectrometer equipped with a z-gradient 5 mm HCN coldprobe. Homonuclear 1H 2D spectra were recorded at 25 °C and calibrated relative to DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) internal reference. The water signal was suppressed by gradient echo or jump-return method. 2D DQF-COSY, TOCSY(70 ms mixing time), and NOESY spectra (100 ms mixing time) were recorded in the phase-sensitive mode with 4096 data points in t2 and 750 data points in t1. Shifted sine square window functions were applied in both dimensions. The 2D 15N HSQC and 13C HSQC at natural abundance were also recorded and referenced by the indirect method based on the gyromagnetic ratio. The 15N HSQC was recorded with 128 scans and 128 data points in F1 dimension, 40 ppm spectrum width centered around 120 ppm. The 13C HSQC was recorded with 128 scans and 300 data points in the F1 dimension, 160 ppm spectrum width centered around 80 ppm.</p><p>The assignment of the NMR spectra was done using the NMRFAM-Sparky software [50]. The NOE cross peak integrals were converted into upper distance bounds grouped as weak, medium and strong peaks. An ensemble of 300 structures was generated by the torsion-angle dynamics using the XPLOR-NIH simulated annealing protocol with the temperature range from 3500 K to 100 K [51], with NOE derived distance constraints (51 inter-residual and 19 intra-residual). A group of 10 structures were selected as the representative ensemble with the criterion of best fitted NOE derived distances (violations smaller than 0.20 Å) and lowest potential energy.</p><!><p>Molecular Docking Studies using the Glide programs (version 7.0, Schrodinger, LLC, New York, 2016). To analyze the docking results and execute the protocol, the Maestro user interface (version10.5, Schrodinger, LLC, New York, 2016) was employed. Docking was performed using the SP (Standard Precision Mode) protocol. This includes 1. Preparation of Protein. The protein was subjected to energy minimization using Schrodinger implementation of OPLS3 force field. 2. Preparation of Ligand. The ligand was prepared using the LigPrep 3.7 module of the Schrodinger suite using the standard protocol with OPLS3 force field. 3. Active Site Prediction. We employed Sitemap (version 3.8) to search for potential binding sites. Sitemap applies theoretical methods and predicts the most accurate binding site. Again, we used Sitemap after we had docked our ligand to evaluate the binding site. 4. Grid generation-docking calculation. Glide used a series of hierarchical filters to search for possible locations for the ligand in the active site region of the receptor. For the grid-based ligand docking, the receptor grid generation process was used. A grid box of 30 × 30 × 30 Å3 with a default inner box (10 × 10 × 10 Å3) was centered on the corresponding ligand. The receptor grid was defined as an enclosing box at the centroid of the ligand. Lastly, we performed a flexible docking calculation using the "Standard Precision" Glide algorithm and after the post-docking minimization we kept the pose with the best docking score.</p>

PubMed Author Manuscript

Structural disorder in expanding the \xe2\x80\x98functionome\xe2\x80\x99 of aminoacyl-tRNA synthetases

Over the last decade, aminoacyl-tRNA synthetases (AARSs) have emerged as a new class of regulatory proteins with widespread functions beyond their classic role in protein synthesis. The functional expansion concurs with the incorporation of new domains and motifs to AARSs and coincides with the emergence of the multi-synthetase complex (MSC) during the course of eukaryotic evolution. Notably, the new domains in AARSs are often found to be structurally disordered or to be linked to the enzyme cores via unstructured linkers. Further bioinformatic analysis performed here classifies the 20 human cytoplasmic AARSs into 3 groups based on their propensities for structural disorder. The analysis also suggests that, while the assembly of the MSC mainly involves ordered structural domains, structurally disordered regions play an important role in activating and expanding the regulatory functions of AARSs.

structural_disorder_in_expanding_the_\xe2\x80\x98functionome\xe2\x80\x99_of_aminoacyl-trna_synthetas

<!>New domains of AARSs and the MSC<!>New domains are often associated with or near structural disorders<!>Classification of human tRNA synthetases based on structural disorders<!>Structural disorders in new domains<!>Structural disorders in the linker region<!>No structural disorder<!>Functional significance of structural disorders in AARSs<!>Regulate the release from MSC<!>Regulate the activation of regulatory functions<!>Ensure conformational independence of the new domains and potentially coordinate regulation with translation<!>Mediate multiple interactions with high specificity and low affinity<!>Concluding remarks

<p>An ordered protein structure has a defined 3D architecture composed of secondary structurally elements, such as α-helices and β-strands, arranged in a certain manner. In contrast, disordered proteins or regions lack a well-defined structure and exist as dynamic conformational ensembles. Although structural disorders are common in various proteomes, their frequency increases with increasing complexity of the organisms. For example, Ward et al predicted that long (>30 residues) disordered segments occur in 2.0 % of archaeal, 4.2 % of eubacterial and 33.0 % of eukaryotic proteins (Ward, et al., 2004). Genome-scale bioinformatics analyses have shown that structural disorder is most common in proteins involved in signal transduction, transcription and translation regulation due to the ability of structural disorder to offer the malleability and adaptability required in signaling and regulation (Tantos, et al., 2012). A prominent example is the p53 protein, which interacts with many other proteins to carry out its signal transduction function. Most of these interactions are mediated by regions on p53 that are intrinsically disordered, for example, the C-terminus, which, upon interaction with different partners, adopts completely different structures (Oldfield, et al., 2008). In contrast, catalysis is often played by a globular protein, where a well-defined conformation or active site geometry is a pre-requisite for enzymatic function.</p><p>Aminoacyl-tRNA synthetases (AARSs) are multifunctional proteins possessing both enzymatic and regulatory functions. Members of the AARS family catalyze the first reaction in protein synthesis, that is, to ligate an amino acid to the 3'-end of its cognate tRNA by using the energy released from hydrolyzing ATP. Importantly, it is the AARS-catalyzed aminoacylation reaction that has established the rules of the genetic code by pairing each amino acid with a tRNA harboring the cognate anticodon trinucleotide. Therefore, tRNA synthetase is considered to be one of the most ancient protein families and is essential in all three domains of life (Woese, et al., 2000). While preserving this enzymatic role, eukaryotic AARSs have been shown to develop other functions during the course of evolution. Human cytoplasmic tRNA synthetases, in particular, regulate diverse functions in different pathways including angiogenesis, inflammation, development and tumorigenesis. Thus, AARSs have emerged as a new class of regulatory proteins with widespread functions beyond their classic role in protein synthesis. The goal of this paper is to propose that structural disorder plays an important role in expanding the 'functionome' of AARSs.</p><!><p>Comparing the protein sequences of eukaryotic cytoplasmic AARSs with their bacterial and archaeal counterparts, it is immediately obvious that eukaryotic AARSs are generally larger. The size increase is mainly due to the acquisition of new domains or motifs at either N- or C-terminus, and the acquisition continues during the evolution of higher eukaryotes (Guo, et al., 2010). Albeit some exception, these new domains are in general dispensable for the aminoacylation function of the synthetases and, instead, are intimately associated with developing regulatory functions of AARSs.</p><p>Also associated with the acquisition of new domains is the emergence of a multi-synthetase complex (MSC) in eukaryotes. A miniature MSC, composed of MetRS, GluRS and a nonsynthetase protein Arc1p, appears in basal eukaryotes such as Saccharomyces cerevisiae. From Drosophila to mammals, the MSC contains 9 AARS components (LysRS, ArgRS, GlnRS, AspRS, MetRS, IleRS, LeuRS and a bifunctional GluProRS) and 3 accessory proteins (p43/AIMP1, p38/AIMP2 and p18/AIMP3). A separate complex exists through the association of ValRS and elongation factor-1H (Motorin Yu, et al., 1987). The AARSs that are not contained in these two complexes are mostly freestanding. Formation of the MSC has been proposed to have a functional dualism: both facilitating protein synthesis by direct channeling of aminoacylated tRNA to the ribosome (Sivaram and Deutscher, 1990) and serving as a reservoir of various regulatory functions associated with its synthetase and non-synthetase components (Lee, et al., 2004; Ray, et al., 2007). Not surprisingly, acquisition of the new domains is also critical for the assembly of the MSC (Rho, et al., 1999).</p><!><p>Most of the structural information on AARSs has been derived from crystal structure analyses. From those analyses, it is clear that the new domains and motifs of AARSs are often disordered or linked to the conserved enzymatic core via a disordered linker. The first example is found in human TyrRS. Despite tremendous efforts, the protein could not be crystallized without chopping off a C-terminal extension added to TyrRS from insects to human. Structure of the enzyme core – mini-TyrRS – was solved at 1.18 Å, which marks the highest resolution achieved for a tRNA synthetase to this day (Yang, et al., 2002). The C-terminal extension itself, named EMAP-II-like domain for its high homology to a human cytokine – endothelial monocyte activating polypeptide II – that turns out to be a proteolytic product of MSC p43/AIMP1, can also be crystallized and exhibits a globular structure (Yang, et al., 2003). Therefore, a conformational flexibility might be introduced by the way that the EMAP-II-like domain is joined to the core enzyme that makes crystallization of the full-length protein difficult. Indeed, 22 residues (D343-I364) in between the enzyme core and the EMAP-II-like domain were disordered in the crystal structure of mini-TyrRS (Yang, et al., 2002).</p><p>TrpRS is the closest homolog of TyrRS and provides another example for a disordered linker in between the enzyme core and an appended domain. From fish to humans, TrpRS possesses a N-terminal extension named WHEP domain. Crystals were able to grow with the full-length human TrpRS but the helix-turn-helix WHEP domain was only resolved in one of two subunits of the dimeric protein structure (Yang, et al., 2003). The WHEP domain was asymmetrically resolved because of the half-of-the-sites binding of Trp-AMP in the same subunit to help engage the WHEP domain. However, even in the subunit where the WHEP domain was resolved, 21 residues (D61-E81) in the linker region between the WHEP domain and the enzyme core were disordered.</p><p>Structural disordering has also been observed within the appended domains of AARSs. Human LysRS contains a eukaryote-specific N-terminal extension. NMR studies in solution revealed that the extension is mostly unstructured (Liu, et al., 2012). Consistently, LysRS can be crystallized only when this extension was removed (Guo, et al., 2008). As another example, the C-terminal vertebrate-specific UNE-S domain is completely disordered in the crystal structure of human SerRS (Xu, et al., 2012). The N-terminal WHEP domains of GlyRS and HisRS were also missing from the electron density maps, and at least for GlyRS, the WHEP domain is disordered regardless of the space group that the protein is crystallized and whether or not in complex with Gly-AMP or other ligands (Guo, et al., 2009).</p><!><p>When a domain is disordered in the crystal structure, it is not immediately obvious whether the domain is intrinsically unstructured or the disorder is a result of a flexible linker. In this case, other complementary methods are needed for clarification. For example, although the WHEP domain in human HisRS is completely disordered in the crystal structure (Xu, et al., 2012), it exhibits the expected helix-turn-helix conformation in solution as detected by NMR (PDB 1X59). This suggests that the WHEP domain is essentially structured however adopts multiple conformations relative to the enzyme core and therefore is missing from the electron density map.</p><p>Theoretically, this ambiguity can also be clarified by bioinformatics approaches, as structural disorder is an intrinsic property encoded by the amino acid sequences of a protein. Amino acids can be grouped into three classes: order-promoting residues C, W, Y, I, F, V, L and probably H, T and N; disorder-promoting residues E, P, Q, S, R, K, M and probably D; and neural residues A and G (Radivojac, et al., 2007). Clearly, bulky hydrophobic residues promote order, whereas polar and charged residues have the opposite effect. Other distinct properties of disordered regions, such as richness in proline residues and low sequence complexity, have also been identified and incorporated into various algorithms to predict the presence of intrinsic disorders in proteins.</p><p>To systematically analyze structural disorders in human cytoplasmic tRNA synthetases, a prediction server metaPrDOS (http://prdos.hgc.jp/meta) based on a meta approach that integrates the results of seven different predication methods was used in this study (Ishida and Kinoshita, 2008). Remarkably, the results are consistent with what we have learned from crystal and NMR structural analysis (Figure 1). For example, with 5% false positive rate, the server predicted that 26 residues (A337-E362) in the linker region between the TyrRS enzyme core and the EMAP-II-like domain are intrinsically disordered. The server also predicted that 32 residues (A54-V85) in the linker region between the TrpRS enzyme core and the WHEP domain, the entire N-terminal extension (M1-V71) of LysRS and the C-terminal UNE-S domain (A474-A514) of SerRS to be structurally disordered. The WHEP domains of GlyRS and HisRS are predicted to be partially disordered, each having two internal stretches of ordered region corresponding to the two helices of the helix-turn-helix motif. Yet, not all new domains are disordered or near a disordered linker. Based on the prediction, the 20 cytoplasmic human tRNA synthetases can be categorized into three groups: A) disordered in new domains; B) disordered in linker regions; C) have no significant structural disorder associated with new domains or linkers (Figure 1). The classification and further analyses provide insights on the role of structural disorder in AARSs.</p><!><p>Five AARSs (i.e. LysRS, AspRS, SerRS, ThrRS and CysRS) exhibit significant structural disorders that are exclusively associated with their appended domains (Figure 1A). Majority of the new domains within this group, including the N-terminal extensions of LysRS, AspRS and ThrRS and the C-terminal extension of CysRS, started to appear in basal eukaryotes and were documented to exhibit tRNA binding or recognition capacities (Francin, et al., 2002). As exemplified by the N-terminal extension of LysRS (see below), such capacities are likely to be expanded or diverted to mediate interactions with other partners in higher eukaryotes.</p><p>Among the 5 AARSs of this group, LysRS and AspRS are components of MSC. While new domains are often involved in MSC assembly, the N-terminal extensions of LysRS and AspRS are the exceptions: both LysRS and AspRS interact with p38/AIMP2 in the MSC via their enzyme cores (Ofir-Birin, et al., 2013; Robinson, et al., 2000). The structurally disordered N-terminal extension of LysRS, instead, interacts with other protein partners both inside and outside the MSC. Inside the MSC, it interacts with p38 MAP kinase to become phosphorylated at Thr52, which triggers its release from MSC. The released LysRS is translocated to the plasma membrane to interact with the transmembrane region of 67LR laminin receptor to enhance laminin-induced cancer cell migration (Kim, et al., 2012). Although the exact binding site of 67LR on LysRS is not yet defined, the interaction involves the N-terminal region of LysRS that includes the N-terminal extension.</p><p>SerRS provided the first example illustrating that the regulatory function of a tRNA synthetase could be essential (Kawahara and Stainier, 2009). Three independent forward genetics studies in zebrafish established that SerRS plays a critical role in vascular development, and this role is independent of aminoacylation but dependent on the presence the UNE-S domain appended to vertebrate SerRS from fish to humans (Xu, et al., 2012). UNE-S contains a robust nuclear localization signal sequence that directs SerRS into the nucleus to regulate VEGF expression. Presumably, UNE-S interacts with a specific importin for mediating the nuclear import and the flexible conformation of the disordered UNE-S is key for this interaction. In fact, a point mutation (F383V) in SerRS linked to abnormal vasculature disrupts SerRS nuclear localization by sequestering the UNE-S domain (Xu, et al., 2012).</p><!><p>Significant structural disordering is also found in 10 other AARSs (i.e. GlyRS, HisRS, TyrRS, TrpRS, GlnRS, AsnRS, MetRS, ValRS and GluProRS). Compared to the unstructured N-terminal extension of LysRS and UNE-S domain of SerRS in the last group, the appended domains within this group have a preformed structure, but are linked to the enzyme cores via structurally disordered linkers of various lengths (Figure 1B).</p><p>The unstructured linkers are used as cleavage sites for proteolysis-based activation of the regulatory functions of TyrRS and TrpRS. Human TyrRS can be secreted and its regulatory functions outside the cell are self-inhibited until the EMAP-II-like domain is removed through proteolysis. While the EMAP-II-like domain itself exhibits cytokine functions similar to that of EMAP-II, mini-TyrRS manifests IL-8-like pro-inflammatory and pro-angiogenic cytokine activities through binding to chemokine receptor CXCR1 and CXCR2 (Vo, et al., 2011; Wakasugi and Schimmel, 1999). Apparently, linking the two functional entities with a flexible linker facilitates the activation due to the increased sensitivity of a flexible linker to protease cleavage.</p><p>TrpRS is highly upregulated by interferon-γ. The upregulation promotes both secretion and nuclear localization of TrpRS. When secreted, TrpRS possesses anti-angiogenic activities by inhibiting VE-cadherin to form cell-cell junction of endothelial cells (Tzima, et al., 2005). Similar to TyrRS, the anti-angiogenic activity of TrpRS is self-inhibitory until the WHEP domain is removed to expose the TrpRS active site for binding to VE-cadherin through its protruding Trp side chains (Zhou, et al., 2010). Here again, the activation of the anti-angiogenic activity of TrpRS is associated with proteolysis cleavage at a structurally disordered linker between the enzyme core and an appended domain.</p><p>The disordered linkers also harbor sites for posttranslational modification, as exemplified in the bi-functional GluProRS. The protein is linked together via 5 long disordered regions (36-, 47-, 33-, 40- and 68-residue, respectively) that joins the N-terminal GST domain, the GluRS core, the first, second and third WHEP domains and the C-terminal ProRS core (Figure 1B). Upon interferon-γ stimulation, the protein is phosphorylated at Ser886 between the second and the third WHEP domains, and at Ser999 between the third WHEP domain and the C-terminal ProRS. The phosphorylation events triggers the release of GluProRS from the MSC and the subsequent assembly of the "GAIT" (γ-interferon-activated inhibitor of translation) complex that includes GluProRS and three other proteins (ribosomal protein L13a, NS1-associated protein-1, and glyceraldehyde-3-phosphate dehydrogenase), resulting in translational silencing of VEGF expression (Arif, et al., 2009).</p><p>Intriguingly, GlnRS and MetRS are also components of the MSC, and ValRS exists in association with elongation factor-1H (Motorin Yu, et al., 1987). Each appended domain in GlnRS, MetRS and ValRS are linked to their respective enzyme core via a long and disordered linker (Figure 1B). Possibly, these linkers are also targeted for posttranslational modifications to trigger the dissociation of the synthetases from the MSC (or from the elongation factor) to participate in other regulatory processes.</p><p>A disordered region of 23 residues (R72-N94) in between the eukaryote-specific N-terminal domain (UNE-N) and the human AsnRS core is predicted by metaPrDOS. Similar structural disordering in a parasitic AsnRS (Brugia malayi) was revealed by NMR analysis (Crepin, et al., 2011). Interestingly, AsnRS is highly expressed in Brugia malayi, produced at least 10 times more than any other AARSs, and is secreted with the ability to bind IL-8 receptors, CXCR1 and CXCR2, and to activate their down-stream pathways (Ramirez, et al., 2006). The IL-8-like activity of the parasitic AsnRS resides in the UNE-N domain containing a β-hairpin-α-helix motif also seen in other CXC cytokines, such as IL-8 and SDF-1(Kron, et al., 2012). Although, in this case, the UNE-N domain does not need to be cleaved off from the synthetase core to exhibit cytokine activities, the flexibility of the disordered linker may still be important for ensuring the conformational independence of UNE-N. Interestingly, human AsnRS does not act on CXCR1 and CXCR2 (Ramirez, et al., 2006), but activates another chemokine receptor CCR3 (Howard, et al., 2002).</p><p>The flexible linkers are also implicated in diseases. The WHEP domain of HisRS is the main epitope recognized by HisRS autoantibodies in myositis-interstitial lung disease (Levine, et al., 2007). The disordered linker between the WHEP domain and the enzyme core harbors a granzyme B cleavage site (45LGPG48). The cleavage itself and/or the conformational freedom of the WHEP domain have been implicated in the initiation of the autoimmune disease (Levine, et al., 2007). GlyRS also has a WHEP domain that is flexibly linked to the enzyme core and its autoantibodies are associated with polymyositis and dermatomyositis complicated by interstitial lung disease (Stojanov, et al., 1996). In addition, GlyRS is implicated in Charcot-Marie-Tooth disease through genetic mutations. As demonstrated for one Charcot-Marie-Tooth disease-causing mutation (G526R), the conformation of the WHEP domain is dramatically affected in the mutant versus WT GlyRS (He, et al., 2011).</p><!><p>The appended domains of PheRS, ArgRS, LeuRS and IleRS do not exhibit significant structural disorder (Figure 1C). And as expected, no significant structural disorder is found in AlaRS, which is the only AARS that did not acquire an appended domain during the course of eukaryotic evolution. The lack of structural disordering seems to correlate with fewer numbers of regulatory functions. In fact, only one prominent regulatory function has been reported for this group of AARSs, which is the critical role of LeuRS as a leucine sensor for the mTOR-signaling pathway. Both human and yeast LeuRS activates TORC1 signaling through a leucine-dependent interaction with Rag GTPase (Bonfils, et al., 2012; Han, et al., 2012). However, the interaction is mediated through different sites on human versus yeast LeuRS, and the appended domain in human LeuRS (UNE-L) is not responsible for the interaction.</p><p>PheRS is the only human cytoplasmic tRNA synthetase encoded by two genes, forming a (αβ)2 heterotetrameric architecture. Eukaryotic PheRS-α is longer than its prokaryotic orthologs with a N-terminal extension UNE-F, however eukaryotic PheRS-β is shorter than its prokaryotic counterparts by the loss of the anticodon-binding domain B8. The structure of the N-terminal extension of the α-subunit was well resolved in the crystal structure of human PheRS to fold into three continuous DNA-binding fold domains and was predicted by structural modeling to interact with the D, T loops and the anticodon stem of the cognate tRNA. Consistently, truncation of the DNA-binding fold domains resulted in complete loss of tRNA aminoacylation while the activation of phenylalanine was not affected. Therefore, the role of UNE-F in PheRS-α is to compensate the loss of B8 domain in PheRS-β and is distinct from regulatory functions discussed here.</p><p>It is worth noting that ArgRS, LeuRS and IleRS are components of the MSC, and the UNE-L domain of LeuRS interacts with both the leucine zipper domain of ArgRS and the UNE-I domain of IleRS in the MSC (Ling, et al., 2005; Rho, et al., 1999). Other appended domains important for the MSC assembly such as the GST domains of MetRS and GluProRS also have preformed structures. In contrast, the disordered appended domain of LysRS and AspRS are not involved in MSC assembly. Therefore, it seems that the assembly of MSC mainly involves appended domains that are well structured, while the unstructured regions within the MSC play an important role in regulating the release of AARSs from the MSC and in mediating their regulatory functions outside the MSC.</p><!><p>From the above analysis, the role of structural disordering in developing the regulatory functions of AARSs can be summarized as below:</p><!><p>As exemplified by GluProRS and LysRS, release of the tRNA synthetase components from its cellular 'depot' (MSC) is regulated by posttranslational modifications (i.e. phosphorylation) on structurally disordered regions. Although phosphorylation can also act on side chains within structured regions of tRNA synthetases (Kwon, et al., 2011; Ofir-Birin, et al., 2013), a disordered region can more easily fold onto the modifying enzyme to facilitate substrate binding and to provide more efficient regulations (Iakoucheva, et al., 2004).</p><!><p>As for the same reason that structurally disordered regions provide better substrate display for posttranslational modifications, the disordered regions provide enhanced susceptibility for proteolysis cleavage. As shown by the examples of TyrRS and TrpRS, such cleavage is critical for the activation of otherwise masked regulatory functions of AARSs. In addition, the conformational advantage of an internally disordered region could dominant over distinct sequence specificities associated with different proteases to provide a unifying mechanism that converges various signaling pathways into the activation of a regulatory function of AARS. Indeed, proteases with different sequence specificities can cleave off the EMAP-II-like domain to activate the regulatory functions of TyrRS (Yang, et al., 2007).</p><!><p>In addition to their sensitivity to proteolysis-based regulations, a flexible linker allows the connecting domains to have the conformational freedom to recruit their own binding partners. In the context of AARS where a new regulatory domain is attached to an essential component of the translation machinery, this provides a potential coordination between regulation and translation. The concept, in some sense, is relevant to the regulatory function of TrpRS in the nucleus (Sajish, et al., 2012). In this case, the WHEP domain of TrpRS binds to DNA-PK and PARP-1 and bridges the two proteins for p53 activation. However, if Trp-AMP is bound to the TrpRS enzyme core, it induces a large conformational change of the WHEP domain that abolishes its ability to bind to DNA-PK and PARP-1. Therefore, the flexible linker not only allows the WHEP domain to freely interact with the large-sized DNA-PK and PARP-1 to activate p53 when the enzyme core is not in use, but also makes it possible for Trp-AMP, an aminoacylation reaction intermediate, to induce a large-scale interdomain conformational change that controls the nuclear function of TrpRS.</p><!><p>The N-terminal extension of human LysRS demonstrates the adaptability of a disordered region to different interaction partners from tRNA to MAP kinase and to 67LR laminin receptor. With multiple interaction partners, specificity is a question. Interaction with a disordered partner has the advantage of high specificity that is rendered by the induced-fit mechanism. For example, tRNA interaction induces part of the N-terminal extension of LysRS (S19-E45) to adopt a helical structure that aligns positively charged residues on one side of the helix to enhance the specificity (Guo, et al., 2010; Liu, et al., 2012).</p><p>The high specificity interaction with a disordered partner is coupled with low affinity resulted from large entropy penalty paid for the disorder-to-order transition (Wright and Dyson, 2009). The combination of high specificity with low affinity is widely exploited in regulatory processes and is required for cargo transportation. This could be the reason that the UNE-S domain of SerRS and the C-terminal region of LysRS (K574-V597), both of which harbor a nuclear localization signal sequence, are completely disordered.</p><!><p>Although the importance of intrinsic disorder has been broadly recognized in cell signaling and regulatory processes (Ward, et al., 2004), its role in expanding the 'functionome' of tRNA synthetases has not been recognized. It is clear that structural disordering is key to every aspect of the functional expansion, and majority of the AARS regulatory functions involve disordered structures in one way or another. Evolutionarily speaking, because of their smaller numbers of structural constraints, intrinsically disordered regions are capable of fast development of new functions (Rezaei-Ghaleh, et al., 2012). This explains, at least in part, the association of structural disordering with the 'new' functions of AARS and, in turn, suggests that identifying and understanding structurally disordered regions will guide the discovery of more regulatory functions of AARS.</p>

PubMed Author Manuscript

A thermodynamic study of the cadmium–neodymium system

AbstractCd vapor pressures were determined over Cd–Nd samples by an isopiestic method. The measurements were carried out in the temperature range from about 690 to 1200 K and over a composition range between 48 and 92 at % Cd. From the vapor pressures, thermodynamic activities of Cd were derived for all samples at their respective sample temperatures, and partial molar enthalpies of Cd were obtained from the temperature dependence of the activities. With these partial molar enthalpies, the Cd activities were converted to a common temperature of 873 K. By means of a Gibbs–Duhem integration Nd activities and integral Gibbs energies were calculated, using a literature value of ΔfG for the phase Cd6Nd as integration constant. A minimum of ΔfG ≈ −38 kJ g-atom−1 at 873 K was obtained for the phase CdNd, a value that compares well with other CdRE compounds.Graphical abstract

a_thermodynamic_study_of_the_cadmium–neodymium_system

Introduction<!>Literature review: phase diagram<!>Literature review: thermochemical data<!>Isopiestic measurements<!><!>Isopiestic measurements<!><!>Evaluation of the thermodynamic activity of Cd<!><!>Integral Gibbs energy<!><!>Conclusion<!>Experimental<!>

<p>Although nuclear energy is phased out in some countries many others still have to rely on nuclear power to provide the necessary electrical energy. One of the key issues for future use of nuclear energy, besides reactor safety, is a reliable waste management. At present several different reprocessing techniques are known. Traditional aqueous methods suffer from some drawbacks like limited solubility of fuel materials in acidic aqueous solutions and poor radiation stability of the organic solvents employed in the extraction process [1]. The so-called pyrochemical separation techniques appear to be more efficient methods for reprocessing of spent high burn-up fuels. The central step in these non-aqueous methods is the electro-refining process where in an electro transportable cell chopped fuel rods are reprocessed [2]. This electro transportable cell contains a steel anode in form of a basket, where spent fuels are inserted, and two different cathodes: a stainless steel cathode for the recovery of U and a liquid metal cathode (using Al [1], Bi [3], or Cd [3]) for the selective recovery of Pu and minor actinides (MA). The entire cell is completely filled with a liquid LiCl–KCl electrolyte with an additional pool of liquid metal at the bottom. A variety of liquid metals like Al [1], Bi [3], or Cd [3] have been explored for the extraction of the rare earth (RE) elements (in particular, light rare earth elements between La and Gd except Pm) which are partially oxidized, out of the electrolyte. The extraction behavior is primarily affected by the formation of intermetallic compounds, which makes a thorough knowledge of the various binary RE-metal systems imperative. The existence of intermetallic compounds as well as thermodynamic properties such as their stability is of considerable interest, both for a thermodynamic assessment of the corresponding binary system based on the CALPHAD1 method [4] and also for an optimization of the extraction process itself.</p><p>This was the starting point for the present study which wants to provide partial thermodynamic properties of binary Cd–Nd alloys, mainly based on Cd vapor pressure measurements according to an isopiestic method [5, 6]. Using a value of the Gibbs energy of formation of the phase Cd6Nd that had been obtained by a CALPHAD-type optimization [7], an estimate of the integral Gibbs energies of formation could be obtained over a large composition range.</p><!><p>Early experimental studies of the Cd–Nd phase diagram were done by Iandelli [8, 9], Johnson et al. [10, 11], and Bruzzone et al. [12]. Based on this experimental work and an earlier assessment by Gschneidner and Calderwood [13], a rudimentary phase diagram was published in Massalski's handbook [14]. Only very recently, the phase equilibria in the Cd–Nd system were studied in detail by Skołyszewska-Kühberger et al. [15]. Altogether seven intermetallic phases were identified: CdNd, Cd2Nd, and Cd45Nd11 with congruent melting behavior, and Cd3Nd, Cd58Nd13, Cd6Nd, and Cd11Nd with peritectic decomposition reactions. For the compound Cd2Nd, a transition into a high-temperature modification was found. In addition, the maximum solid solubility of Cd in α-Nd and β-Nd was determined with about 3 and 19 at %, respectively.</p><!><p>Only limited thermochemical information has been available for the Cd–Nd system. Koyama et al. [16] determined the activity coefficient of Nd in liquid Cd at infinite dilution at 723 K from an investigation of the distribution of Nd between Cd(l) and a liquid chloride salt. Similarly, Kurata et al. [3, 17] employed electrochemical measurements to determine the distribution behavior of Nd between a eutectic liquid LiCl–KCl mixture and Bi(l) or Cd(l). From the results they derived the activity coefficient of Nd in Cd(l) at 773 K. Based on this limited experimental information, Kurata and Sakamura [7] performed a CALPHAD-type optimization of the Cd-rich part of the Cd–Nd system and provided calculated Gibbs energy of formation values for the phases Cd11Nd and Cd6Nd.</p><p>Recently, Vandarkuzhali et al. [18] investigated the electrochemical behavior of NdCl3 at a liquid Cd electrode and derived the Gibbs energy of formation of Cd11Nd in the temperature range between 698 and 773 K. In addition, an overview of the thermodynamic properties of actinides and RE fission products (among them also Nd) in liquid Cd was provided by Zhang et al. [19], apparently without knowledge of Ref. [18].</p><!><p>Six successful isopiestic experiments were carried out for the Cd–Nd system, with reservoir temperatures between 687 and 893 K corresponding to total vapor pressures of Cd between about 2 and 150 mbar, respectively. The corresponding sample temperatures were between 688 and 1192 K. Since the vapor pressure of Nd is several orders of magnitude lower compared to that of Cd it can be neglected, and it can be assumed that the total pressure in the system is due to Cd maintained at a constant temperature in the reservoir. When the final equilibrium is reached in an isopiestic experiment the partial pressure of Cd over each sample at its sample temperature TS, pCd(TS), is equal to the vapor pressure of pure Cd at the reservoir temperature \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$T_{ ext{R}} ,p_{ ext{Cd}}^{0} \left( {T_{ ext{R}} } ight)$$\end{document}TR,pCd0TR:1\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$p_{ ext{Cd}} (T_{ ext{S}} ) = p_{ ext{Cd}}^{0} (T_{ ext{R}} )$$\end{document}pCd(TS)=pCd0(TR)</p><p>Under these circumstances, the Cd activity in the samples can be calculated by the following equation:2\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$a_{ ext{Cd}} (T_{ ext{S}} ) = rac{{p_{ ext{Cd}} (T_{ ext{S}} )}}{{p_{ ext{Cd}}^{0} (T_{ ext{S}} )}} = rac{{p_{ ext{Cd}}^{0} (T_{ ext{R}} )}}{{p_{ ext{Cd}}^{0} (T_{ ext{S}} )}}$$\end{document}aCd(TS)=pCd(TS)pCd0(TS)=pCd0(TR)pCd0(TS)</p><p>The vapor pressure of pure Cd as a function of temperature was taken from Binnewies and Milke [20]:3\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\log \left( {rac{{p_{ ext{Cd}}^{0} }}{ ext{bar}}} ight) = 8.7 - 5690 imes rac{ ext{K}}{T} - 1.07\, imes \,{ \log }rac{T}{ ext{K}}$$\end{document}logpCd0bar=8.7-5690×KT-1.07×logTK</p><!><p>Isopiestic experimental results; standard state: Cd(l)</p><p>Sample temperature vs. sample composition superimposed on the partial Cd–Nd phase diagram (the dashed line in two-phase field Cd2Nd + Cd3Nd is estimated and not supported directly by data points)</p><!><p>The majority of the samples were single phase, namely CdNd, Cd2Nd, Cd45Nd11, and Cd6Nd. As in several other RE-Cd systems [21–23], no single phase samples of Cd3Nd could be obtained in any of the runs suggesting that Cd3Nd is only slightly more stable than a two-phase mixture of its neighboring compounds. Thus, the activities of Cd in the adjacent two-phase fields Cd2Nd + Cd3Nd and Cd3Nd + Cd45Nd11 are only slightly different (cf. Fig. 4). Moreover, it was found that a majority of data points fall into the composition range of the phase Cd2Nd indicating that this must be one of the relatively most stable compounds in the Cd–Nd system, in agreement with its congruent formation from the liquid [15].</p><p>Some of the samples were obtained in various two-phase fields after equilibration. This was probably caused by slight variations in the sample temperatures during equilibration. These samples were quite useful for an estimate of the partial enthalpies of formation of Cd in these two-phase fields.</p><!><p>Natural logarithm of the Cd activity vs. reciprocal temperature in the phases CdNd (a) and Cd2Nd (b)</p><p>Partial molar enthalpy of Cd in the Cd–Nd system; standard state: Cd(l)</p><!><p>Two things should be pointed out. As can be seen in Fig. 2, the scatter of the data points is considerable which means that the derived \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\Delta ar{H}_{ ext{Cd}}$$\end{document}ΔH¯Cd values will exhibit an appreciable error limit, probably more than ±5 kJ g-atom−1. Furthermore, the numerical values of the partial enthalpies of Cd become positive for Cd contents of more than 75 at % Cd. This is somewhat surprising though not impossible since it depends on the shape of the curve of the integral enthalpy of formation.</p><!><p>Natural logarithm of the Cd activity in the Cd–Nd system at 873 K; standard state: Cd(l)</p><!><p>Rather limited information has been available up to now on the thermodynamics of the Cd–Nd system. To perform a Gibbs–Duhem integration one needs an integration constant, i.e., a value for the integral Gibbs energy or the partial Gibbs energy of Nd at a given composition. Vandarkuzhali et al. [18] determined the integral Gibbs energy of formation of Cd11Nd in the temperature range 698–773 K. Unfortunately, 773 K is rather at the lower limit of the present experiments, and only one single data point was obtained for the Cd11Nd-phase (see Fig. 1). On the other hand, Cd11Nd decomposes in a peritectic reaction at 793 K [15] and does not exist anymore at 873 K, the average temperature of all data points (see above).</p><!><p>Integral Gibbs energy of formation vs. composition at 873 K in the Cd–Nd system; standard states: Cd(l) and Nd(s)</p><p>Smoothed values of the Cd and Nd activities and of the integral Gibbs energy at 873 K in the Cd–Nd system; standard states are Cd(l) and Nd(s)</p><!><p>Thermodynamic activities of Cd were determined for the Cd–Nd system in the temperature range between about 690 and 1200 K and the composition range between 48 and 92 at % Cd based on an isopiestic vapor pressure method. Partial enthalpies of formation of Cd were derived from the temperature dependence of the activity. These data were used to convert the activity values to a common temperature of 873 K. Using a literature value of ΔfG for Cd6Nd as integration constant, it was possible to calculate Nd activities and integral Gibbs energies of formation at 873 K for the same composition range. A minimum of ΔfG ≈ −38 kJ g-atom−1 was obtained in the phase CdNd.</p><!><p>The principle and experimental details of the isopiestic method applied in this work were described previously by Ipser et al. [5, 6]. A schematic diagram of the particular setup used in the present investigation has been shown, for example, by Skołyszewska-Kühberger et al. [21] and Reichmann et al. [22]. The apparatus is essentially made of quartz glass. It consists of an outer tube of 38 mm OD with one end closed and the other end fitted with a ground joint which can be connected to a vacuum pump. A quartz glass crucible with 32 mm OD is placed at the bottom, serving as a reservoir for Cd. On top of the reservoir, a quartz glass spacer of suitable height and a quartz supporting tube (15 mm OD) are located where the tantalum crucibles containing pure Nd as samples are inserted. An inner tube of 7 mm OD with its upper end widened to 32 mm OD is used as a thermocouple well. The apparatus can be sealed under vacuum in its upper part.</p><p>Before use the entire apparatus was cleaned with an acid mixture (HF/HNO3/H2O), rinsed with distilled water, and dried. Afterward the fully assembled setup, including the empty tantalum crucibles (approximately 20), was degassed under vacuum (10−3 mbar) at 900 °C for 5 h. All preparations for the experiments were then carried out under Ar atmosphere in a glove box. The reservoir was filled with 25–35 g of Cd (99.9999 % Alfa AESAR, Karlsruhe, Germany), depending on the experimental reservoir temperature. Between 150 and 200 mg of pure Nd (99.9 % Alfa AESAR, Karlsruhe, Germany, and smart-elements, Vienna, Austria) were weighed into each Ta crucible with an accuracy of ±0.1 mg. The assembled apparatus was brought outside the glove box securely closed using a vacuum valve suitable to directly connect to a vacuum pump. The apparatus was then evacuated and sealed under a dynamic vacuum of better than 10−3 mbar.</p><p>The isopiestic equilibration experiments were carried out in different temperature gradients, applied by two-zone furnaces, for periods of about 4–8 weeks depending on the respective reservoir temperature. The temperatures of the samples (TS) and the reservoir (TR) were measured periodically by raising a Pt/Pt 10 % Rh thermocouple inside the thermocouple well. After equilibration, the isopiestic apparatus was quenched in cold water and cut open in air by a diamond saw. The individual samples (which had become Cd–Nd alloys during the equilibration) together with the crucibles were weighed in air. Immediately afterward they were brought into the glove box and weighed once again. No significant mass difference could be detected in any of the samples, and the sample compositions were derived from the mass difference before and after equilibration that was attributed to the uptake of Cd.</p><p>Representative samples were characterized by XRD with Cu Kα radiation on a Bruker D8 Advance Diffractometer with Bragg–Brentano pseudo-focusing geometry. Rietveld refinement was done by means of the TOPAS 3 software (provided by Bruker), making a full pattern refinement with empirical peak profile modeling. To check the calculated compositions, selected samples were also analyzed by energy dispersive X-ray spectroscopy (EDS) in a Zeiss Supra 55 VP scanning electron microscope (SEM).</p><!><p>CALPHAD CALculation of PHAse Diagrams.</p>

PubMed Open Access

Determinants of buildup of the toxic dopamine metabolite DOPAL in Parkinson\xe2\x80\x99s disease

Intra-neuronal metabolism of dopamine (DA) begins with production of 3,4-dihydroxyphenylacetaldehyde (DOPAL), which is toxic. According to the \xe2\x80\x98catecholaldehyde hypothesis,\xe2\x80\x99 DOPAL destroys nigrostriatal DA terminals and contributes to the profound putamen DA deficiency that characterizes Parkinson\xe2\x80\x99s disease (PD). We tested the feasibility of using post-mortem patterns of putamen tissue catechols to examine contributions of altered activities of the type 2 vesicular monoamine transporter (VMAT2) and aldehyde dehydrogenase (ALDH) to the increased DOPAL levels found in PD. Theoretically, the DA : DOPA concentration ratio indicates vesicular uptake, and the 3,4-dihydroxyphenylacetic acid : DOPAL ratio indicates ALDH activity. We validated these indices in transgenic mice with very low vesicular uptake (VMAT2-Lo) or with knockouts of the genes encoding ALDH1A1 and ALDH2 (ALDH1A1,2 KO), applied these indices in PD putamen, and estimated the percent decreases in vesicular uptake and ALDH activity in PD. VMAT2-Lo mice had markedly decreased DA:DOPA (50 vs. 1377, p < 0.0001), and ALDH1A1,2 KO mice had decreased 3,4-dihydroxyphenylacetic acid:DOPAL (1.0 vs. 11.2, p < 0.0001). In PD putamen, vesicular uptake was estimated to be decreased by 89% and ALDH activity by 70%. Elevated DOPAL levels in PD putamen reflect a combination of decreased vesicular uptake of cytosolic DA and decreased DOPAL detoxification by ALDH.

determinants_of_buildup_of_the_toxic_dopamine_metabolite_dopal_in_parkinson\xe2\x80\x99s_disease

<!>Sources of samples<!>Human brain tissue<!>VMAT2-Lo mice<!>ALDH KO mice<!>Catechol assay<!>Data analysis and statistics<!>Results<!>Discussion

<p>Severe depletion of the catecholamine, dopamine (DA), in the striatum (putamen and caudate) is the defining neurochemical characteristic of Parkinson's disease (PD) (Ehringer and Hornykiewicz 1960; Hornykiewicz 1998). Understanding mechanisms of death of nigrostriatal catecholamine neurons should spur development of innovative diagnostic, treatment, and prevention strategies for this major neurodegenerative disease.</p><p>Intra-neuronal metabolism of DA begins with deamination catalyzed by monoamine oxidase-A in the outer mitochondrial membrane to form the catecholaldehyde, 3,4-dihydroxyphenylacetaldehyde (DOPAL) (Eisenhofer et al. 2004). Because of ongoing DA biosynthesis in the cytosol, leakage of vesicular DA stores into the same compartment, imperfect recycling from the cytosol back into the vesicles, and reuptake of DA released by exocytosis (Fig. 1), DOPAL is produced continuously in DA neurons.</p><p>DOPAL is toxic, both in vitro and in vivo (Panneton et al. 2010; Mattammal et al. 1995; Burke et al. 2004, 2003). The toxicity may occur by at least four mechanisms – protein cross-linking (Rees et al. 2009), oxidation to quinones (Anderson et al. 2011), production of hydroxyl radicals (Li et al. 2001), and augmentation of toxicity exerted by other agents (Marchitti et al. 2007). DOPAL also potently oligomerizes and precipitates alpha-synuclein (Burke et al. 2008), and alpha-synucleinopathy is implicated in PD pathogenesis (Singleton et al. 2003; Polymeropoulos et al. 1997; Satake et al. 2009). According to the 'catecholaldehyde hypothesis' (Goldstein et al. 2012b; Goldstein 2012), DOPAL causes or contributes to the loss of DA-containing terminals that characterizes PD.</p><p>We previously reported that post-mortem putamen tissue from PD patients contains an increased concentration of DOPAL relative to DA (Goldstein et al. 2011). Determinants of DOPAL buildup in PD remain unknown. Addressing this issue was the main purpose of this study.</p><p>As indicated in Fig. 1 and by the kinetic model in the Appendix, several processes potentially determine DOPAL levels in dopaminergic neurons. In this study we focused on vesicular uptake of cytosolic DA by the type 2 vesicular monoamine transporter (VMAT2) and metabolism of DOPAL by aldehyde dehydrogenase (ALDH). In PC12 cells, blockade of vesicular uptake and inhibition of ALDH both increase endogenous DOPAL levels (Goldstein et al. 2012b).</p><p>From the kinetic model in the Appendix we derived that the tissue DA : DOPA ratio provides an index of vesicular uptake and that 3,4-dihydroxyphenylacetic acid (DOPAC) : DOPAL provides an index of ALDH activity. We validated these indices in striata of mice with very low VMAT2 activity (VMAT2-Lo) or double knockout of the genes encoding ALDH1A1 and 2 (ALDH1A1,2 KO). VMAT2-Lo mice are known to have decreased striatal DA : DOPAC (Taylor et al. 2009), but whether they have altered striatal DOPAL has not been reported. ALDH1A1,2 KO mice have decreased striatal DOPAC : DOPAL (Wey et al. 2012), but whether this reflects increased DOPAL, decreased DOPAC, or both has not been reported. We also predicted that ALDH1A1,2 KO mice have a buildup of 3,4-dihydroxyphenylethanol (DO-PET), because of a shift from ALDH to aldehyde/aldose reductase (AR) in the metabolism of DOPAL.</p><p>We applied these indices of vesicular uptake and ALDH activity in post-mortem putamen tissue from PD patients and control subjects without neurological disorders and estimated the percentage changes in these processes in PD.</p><!><p>Human brain tissue was from the posterior inferior putamen and mouse brain tissue from the striatum. All the animal research was done in compliance with ARRIVE guidelines.</p><!><p>Post-mortem brain neurochemical data were reviewed from 17 neuropathologically confirmed cases of end-stage idiopathic PD and 14 control subjects. The samples were provided by the University of Miami Brain Endowment Bank, which has IRB approval to obtain consents for brain donation and to acquire patient clinical records. For this study, de-identified specimens were used from an established biorepository of post-mortem brain tissues. The study was also conducted in accordance with guidance by the NCI/CCR/Laboratory of Pathology Tissue Resource Committee.</p><p>Post-mortem intervals (duration between death and brain freezing) were 24 h or less in all subjects. The control group was similar to the PD group in terms of age, gender makeup, ethnicity, and postmortem interval (Table 1). Among controls, the most frequent cause of death was cardiac (cardiac arrest, myocardial infarction, coronary atherosclerosis or thrombosis, or congestive heart failure, n = 10). Other causes of death among controls were respiratory failure and pneumonia, lung cancer, gall bladder cancer, or blunt force trauma. Among PD patients, maximum levodopa daily doses at the time of death ranged from 0–1500 mg.</p><!><p>Data were reviewed for seven mice with very low activity of the type 2 vesicular monoamine transporter (VMAT2-Lo) and from 21 control mice (Miller et al. 2001; Caudle et al. 2007). To create VMAT2-Lo mice, the VMAT2 locus (SLC18A2) was cloned from the 129/Sv genomic library and a 2.2 kb PvuII fragment from the third intron of the VMAT2 gene, and cloned into the blunt-ended NotI site of the construct (Caudle et al. 2007; Taylor et al. 2009). The targeting vector was introduced into 129/Ola CGR 8.8 embryonic stem cells and injected into blastocytes of C57BL/6 mice. Chimeric males (genotype confirmed by Southern blot analysis) were bred with C57BL/6 females. The procedures were approved by the Institutional Animal Care and Use Committee at Emory University.</p><!><p>Data were reviewed for a total of 34 mice with knockouts of the ALDH1A1 and ALDH2 genes and from 54 control mice that were heterozygotes or wild-type. ALDH1A1, ALDH2, and ALDH1,2 knockout mice were created and studied in accordance with the Institutional Animal Care and Use Committee of The University of Texas Health Science Center at San Antonio and the South Texas Veterans Health Care System. Mice null for ALDH2 were generated by gene trap mutagenesis and backcrossed to C57BL/6J mice for 10 generations. ALDH1A1 mutant mice were generated using a targeted deletion at exon 11 of the ALDH1A1 allele (Duester 2001) and backcrossed for eight generations to C57BL/6J. ALDH1A1 mutant mice were crossed with ALDH2 mutant mice to produce mice heterozygous for both genes (ALDH1A1,2 knockout). Cross-breeding of the mice heterozygous for ALDH1A1 and ALDH2 mutations generated the line homozygous for mutations in both genes and the wild-type line on an identical genetic background. The two lines were maintained by breeding male and female mice for each line. Male mice of three different age groups (young, 5–8 months; middle-aged, 12–14 months; and old, 18–27 months) were used. In this study the data for ALDH1A1,2 KO and control mice in these age groups were combined.</p><!><p>The same personnel (P.S.) conducted the tissue catechol assays in the laboratory of the Clinical Neurocardiology Section in intramural NINDS, under the Catecholamine Resource Initiative.</p><p>Catechol assays were done according to methodology previously developed and published by our group (Holmes et al. 1994, 2010). Briefly, frozen tissue samples were homogenized in a mixture of 20 : 80 of 0.2 M phosphoric : 0.2 M acetic acid and the supernate transferred to plastic cryotubes and stored at −80 degrees centigrade until assayed by batch alumina extraction followed by liquid chromatography with series electrochemical detection.</p><p>DOPAL standard was synthesized in the laboratory and provided by Dr Kenneth L. Kirk (NIDDK). Identification of the DOPAL standard was confirmed by mass spectrometry, nuclear magnetic resonance, and liquid chromatography with time-of-flight mass spectrometry.</p><p>Until now, it has been thought that concentrations of DOPA in mouse striatum are below the limit of detection of HPLC-electrochemical methodology, without incubating the tissue in an inhibitor of L-aromatic-amino-acid decarboxylase. Several factors are necessary to assay striatal tissue DOPA successfully (Figs 2 and 3) without decarboxylase inhibition. These include: (i) use of HPLC-electrochemical systems that are dedicated for catechols only, (ii) Type I water (18 meg-ohm resistance) and the purest HPLC grade reagents, (iii) carefully chosen and conditioned columns, (iv) filtering and degassing the mobile phase to ensure there are no particles or air bubbles, (v) alumina extraction to purify the catechols, (vi) post-column electrochemical detection with a series of flow-through electrodes (so that only reversibly oxidized species are detected), (vii) a policy of not assaying experimental samples until or unless chromatographs of standards and extracted standards are as clean as possible, and (viii) expert assay personnel.</p><p>Catechol concentrations in cell lysates were expressed in units of pmoles per mg wet weight.</p><!><p>Neurochemical data were graphed using KaleidaGraph 4.01 (Synergy Software, Reading, PA, USA). Differences between PD patients and their controls, VMAT2-Lo mice and their controls, and ALDH1A1,2 mice and their controls were assessed by two-tailed, independent-means t-tests conducted upon log-transformed data. As log-transformed data were used, all data with zero values were excluded. Mean values were expressed ± SEM. A p-value of less than 0.05 defined statistical significance.</p><!><p>In both human putamen and murine striatum, tissue concentrations of catechols varied by about 1000-fold. Concentrations of catecholamines and deaminated metabolites were higher in mice than in humans (Fig. 4), whereas DOPA was starkly lower in mice.</p><p>As expected, putamen tissue concentrations of DA and DOPAC were decreased drastically in PD patients compared to controls, by 94% each (p < 0.0001; Fig. 5a and b). From inspection of the histograms in Fig. 5, the red bars (PD patients) were very small with respect to the gray bars (controls). DOPAL and DOPET were also decreased (p < 0.0001 each) in PD, but to lesser proportionate extents (79 and 83%) than were DA and DOPAC (Table 1). DOPA was decreased in PD (p = 0.02) by 65%.</p><p>Although absolute concentrations of DOPAL were decreased in putamen from PD patients compared with controls, the PD group had about a 5-fold increase in DOPAL relative to DA (p = 0.007; Fig. 6a, Table 1).</p><p>The mean DOPAC : DOPAL ratio in putamen tissue was decreased by 70% in PD compared with controls (p = 0.0006; Fig. 6c, Table 1).</p><p>Consistent with predominance of ALDH over AR in the metabolic fate of endogenous DOPAL, in control subjects the mean ratio of DOPAC : DOPAL (11.3 ± 2.5) exceeded by far the ratio of DOPET : DOPAL (0.12 ± 0.03; p < 0.0001 by dependent-means t-test). In PD putamen, the mean ratio of DOPET : DOPAC, reflecting the relative contributions of AR versus ALDH in the fate of cytosolic DOPAL, was increased to about 10 times control (0.122 ± 0.062 vs. 0.012 ± 0.002, p = 0.01).</p><p>The mean ratio of DA : DOPA in striatum was markedly decreased in VMAT2-Lo mice compared with control mice (p < 0.0001; Table 2 and Fig. 7b). Meanwhile, the mean DOPAC : DOPAL ratio was markedly increased and DO-PET : DOPAC ratio decreased. VMAT2-Lo mice also had low striatal norepinephrine (NE) and dihydroxyphenylglycol (DHPG) and decreased NE : DHPG ratios (Table 2).</p><p>ALDH1A1,2 KO mice had increased DOPAL, DOPET, DOPAL : DA, and DOPET : DOPAC and decreased DOPAC and DOPAC : DOPAL compared to control mice (p < 0.0001 each; Fig. 7c, Table 2).</p><p>Inspection of the results in Tables 2 and 3 shows that with the exception of tissue DOPA, the two mouse strains differed completely in terms of the pattern of changes in the dependent neurochemical measures.</p><p>Applying the kinetic model and rate constants in the Appendix (Gjedde et al. 1991; Goldstein et al. 2002; Eisenhofer et al. 1996; Bonifacio et al. 2002), vesicular uptake was estimated to be decreased by 89% and ALDH activity decreased by 70% in PD putamen.</p><p>Among the PD patients, individual values for DOPAL : DA, DA : DOPA, and DOPAC : DOPAL ratios were unrelated to tissue DOPA content (r = 0.23, n = 14; r = 0.13, n = 17; r = 0.12, n = 14). DOPA content was also unrelated to the maximum levodopa dose prior to death (Fig. 8).</p><!><p>The major advance of this study is the derivation, validation, and application of a conceptual approach that helps explain why DOPAL is built up in the putamen in PD. As noted previously (Goldstein et al. 2011), DOPAL was increased by about 5-fold relative to DA. From the kinetic model in the Appendix, tissue DA : DOPA was used to indicate vesicular uptake and DOPAC : DOPAL ALDH activity. Data from mouse genetic models validated these indices. Applying the rate constants listed in the Appendix, we estimated that vesicular uptake was decreased by 89% in PD putamen. PD patients have about a 90% decrease in VMAT2 protein in putamen (Miller et al. 1999; Tong et al. 2011), and results of neuroimaging studies using positron-emitting analogs of tetrabenazine, a VMAT2 ligand, also indicate severely decreased vesicular sequestration in PD (Raffel et al. 2006; Okamura et al. 2010; Bohnen et al. 2006). Based on previously published data (DelleDonne et al. 2008), in PD patients immunoreactive tyrosine hydroxylase in the striatum is decreased by 65% compared with controls, whereas immunoreactive VMAT2 is decreased by 96%. The difference reflects decreased vesicular uptake in the residual terminals. The previously reported data lead to the inference that vesicular uptake is decreased by 88% in PD. This value agrees remarkably with the value of 89% based on the DA : DOPA ratios in this study.</p><p>From DOPAC : DOPAL ratios we obtained evidence also for a 70% decrease in ALDH activity in PD putamen. Postmortem substantia nigra from PD patients contains decreased gene expression and protein levels of ALDH1A1 (Mandel et al. 2005; Galter et al. 2003; Werner et al. 2008), but this could be a result rather than cause of loss of nigral dopaminergic neurons. Recently, it has been reported that blood of PD patients contains decreased gene expression for ALDH1A1 (Molochnikov et al. 2012; Grunblatt et al. 2010), supporting a pathogenetic role of decreased ALDH activity in PD. Low cerebrospinal fluid concentrations of DOPAC for given concentrations of DA (Goldstein et al. 2012a) are also consistent with decreased ALDH activity; however, this is a quite indirect measure of conversion of DOPAL to DOPAC. DOPAL itself is not detected reliably in human cerebrospinal fluid (Goldstein et al. 2012a).</p><p>PD patients had an increased ratio of DOPET : DOPAC in putamen tissue, consistent with a shift from ALDH to AR as an alternative means to detoxify DOPAL. The increased DOPAL concentration relative to DA suggests that such compensation is inadequate to prevent DOPAL buildup. Analogously, ALDH knockout mice had increased striatal DOPET : DOPAC ratios but markedly increased striatal DOPAL levels. These findings indicate that AR is relatively inefficient in DOPAL detoxification.</p><p>VMAT2-Lo mice had low DA : DOPAC ratios, explicable by increased oxidative deamination of cytosolic DA because of virtual absence of vesicular uptake. This explanation predicts elevated DOPAL and normal DOPAC : DOPAL, whereas DOPAL was low and DOPAC : DOPAL high. DOPAC : DOPAL in the VMAT2-Lo mice averaged about 200 times that in ALDH1A1,2 KO mice. From the kinetic model, VMAT2-Lo mice seem to have markedly increased ALDH activity. Incomplete compensation for increased DOPAL generation in VMAT-Lo mice or for decreased DOPAL detoxification in ALDH knockout mice may help explain why both strains develop aging-related neuropathologic and neurobehavioral abnormalities reminiscent of those in PD (Wey et al. 2012; Caudle et al. 2007).</p><p>Both the VMAT2 and ALDH mouse strains have been reported to show evidence of nigrostriatal neuron loss (Caudle et al. 2007; Wey et al. 2012); however, the magnitudes of these decreases do not come close to the magnitude of loss of putamen DA depletion seen in PD patients. Table 3 highlights similarities and differences among the PD, VMAT2-Lo, and ALDH1A1,2 groups. One gains the impression that neither mouse model reproduces the pattern of neurochemical abnormalities found in PD. Decreased DA, NE, DA : DOPA, and NE : DHPG fit with catecholamine depletion and a shift from vesicular uptake to oxidative deamination of cytosolic catecholamines, as in VMAT2-Lo mice, and decreased DOPAC : DOPAL and increased DOPET : DOPAC fit with decreased ALDH activity, as in ALDH1A1,2 mice. We speculate that in the setting of low VMAT2 activity, compensatorily increased ALDH activity protects dopaminergic neurons, and in the setting of low ALDH activity, an ongoing high rate of vesicular uptake protects those neurons. If so, then VMAT2-Lo mice should be susceptible to DA neuron death if treated with a drug that inhibits ALDH, and ALDH knockout mice should be susceptible to a drug that inhibits vesicular uptake.</p><p>Any of a variety of factors interfering with vesicular sequestration of cytosolic DA or with detoxification of DOPAL could lead to DOPAL buildup and thereby to loss of dopaminergic terminals (Panneton et al. 2010; Burke et al. 2008). As decreased vesicular uptake increases neuronal vulnerability to exogenous factors such as rotenone (Sai et al. 2008; Liu et al. 2005), amphetamines (Wang et al. 1997; Guillot et al. 2008), MPTP (Gainetdinov et al. 1998; Staal and Sonsalla 2000), and alpha-synuclein (Ulusoy et al. 2012), vesicular sequestration seems to play a key role in detoxifying compounds taken up into monoaminergic neurons (Guillot and Miller 2009). Meanwhile, lipid peroxidation products such as 4-hydroxynonenal potently inhibit ALDH (Rees et al. 2007), and nigral neurons from PD patients contain increased levels of this aldehyde (Yoritaka et al. 1996). Moreover, the insecticide benomyl recently has been shown to decrease ALDH and exert toxicity at dopaminergic neurons (Fitzmaurice et al. 2012). Finally, DOPAL potently oligomerizes and promotes precipitation of alpha-synuclein (Burke et al. 2008), which could set the stage for multiple pathogenic positive feedback loops.</p><p>Catecholaminergic neurons are rare in the nervous system, and the basis for relatively selective loss of striatal DA terminals in PD has been mysterious. The catecholaldehyde hypothesis provides a straightforward explanation. DOPAL formation is related directly to the leakage rate of DA from vesicular stores and therefore to the size of the stored pool. As one would expect from the striatum having the highest DA concentrations in the brain, DOPAL levels are also highest in the striatum (unpublished observations). Therefore, factors decreasing vesicular sequestration of cytosolic DA and decreasing detoxification of cytosolic DOPAL would be expected to be manifested by striatal dopaminergic denervation.</p><p>The results about DOPA, catecholamines, and deaminated metabolites demonstrate large species differences between humans and mice. Much lower striatal DOPA in mice than humans might be explained by more efficient conversion of DOPA to dopamine via L-aromatic-amino-acid decarboxylase, a pool of DOPA outside catecholaminergic neurons in human striatum, or greater efficiency of vesicular sequestration of cytosolic catecholamines in mice than in humans.</p><p>One might propose that a shift from vesicular sequestration to enzymatic deamination reflects a secondary response to loss of DA neurons, because of compensatorily increased release of DA or increased turnover of DA in the surviving neurons. From the kinetic model, compensatorily increased DA release from residual terminals, with or without reuptake of the released DA, cannot explain the elevated DOPAL : DA ratios found in PD. For instance, neither a 5-fold increase in DA release nor a 5-fold increase in DA release combined with a concurrent 80% decrease in reuptake would affect DOPAL : DA appreciably. Also, neither alteration can explain the decreased DOPAC : DOPAL ratios found in PD putamen.</p><p>Whether increased putamen DOPAL levels in PD cause or contribute to loss of DA terminals cannot be determined from this study. First, association cannot prove causation, and buildup of DOPAL with respect to DA does not imply that the buildup exerts cytotoxic effects. Second, post-mortem neurochemistry cannot provide information about a putative pathogenetic sequence during life. Third, as oxidative deamination of DA to form DOPAL necessarily entails hydrogen peroxide generation, it is impossible to separate these two potential contributors to toxicity. Nevertheless, in this study, ALDH knockout mice had about the same proportionate increase in striatal DOPAL as observed in PD putamen, and ALDH knockout mice develop aging-related neuropathologic and neurobehavioral manifestations resembling those in PD (Wey et al. 2012). In PC12 cells, combined inhibition of vesicular uptake and of DOPAL metabolism increases endogenous DOPAL by about the same extent as observed in PD putamen, and interference with DOPAL metabolism contributes to DA-induced apoptosis (Goldstein et al. 2012b).</p><p>An important issue is whether levodopa treatment before death in PD patients might have influenced the obtained results. We think such an influence was unlikely. First, tissue DOPA was unrelated to the maximum levodopa dose prior to death. Second, if tissue DOPA had been increased artifactually because of treatment, then based on the known fate of DOPA in dopaminergic terminals, this theoretically would have exerted little or no effect on values for the key dependent measures of the study – ratios of DOPAL : DA, DA : DOPA, and DOPAC : DOPAL. In confirmation of this view, among PD patients individual values for all these dependent measures were unrelated to tissue DOPA content. Third, to estimate the magnitude of the effect of ongoing levodopa treatment on putamen DOPA levels, we reviewed putamen DOPA data from four patients with end-stage Parkinsonism or autopsy-proven PD in whom the patients had either never received levodopa treatment or in whom levodopa treatment had been discontinued at least 5 days prior to death. Among these patients, putamen tissue DOPA averaged 0.77 pmol/mg, a value similar to that for the PD group reported here. From this analysis we estimate that the impact of levodopa treatment on absolute levels of putamen DOPA, if any, was small.</p><p>The catecholaldehyde hypothesis yields readily testable predictions related to pathogenetic mechanisms and experimental therapeutics. In VMAT2-Lo mice exposure to drugs that inhibit ALDH and in ALDH1A1,2 knockout mice exposure to drugs that inhibit vesicular uptake would be expected to accelerate loss of nigrostriatal dopaminergic neurons. Studies about effects of treatments that attenuate catecholaldehyde production, mitigate catecholaldehyde auto-oxidation, or interfere with catecholaldehyde-mediated protein cross-linking in these mouse models could provide further mechanistic and therapeutic insights.</p><p>In summary, in this study we obtained evidence that elevated DOPAL levels in PD putamen reflect markedly decreased vesicular uptake, as indicated by the ratio of DA : DOPA, and decreased DOPAL detoxification, as indicated the ratio of DOPAC : DOPAL. From the results we propose that strategies increasing the efficiency of vesicular sequestration or of catecholaldehyde detoxification, decreasing neuronal monoamine oxidase activity, or interfering with DOPAL-induced oligomerization of alpha-synuclein may prove useful in treatment or prevention.</p>

PubMed Author Manuscript

Pharmacophore Comparison and Development of Recently Discovered Long Chain Arylpiperazine and Sulfonamide Based 5-HT7 Ligands

The serotonin system exerts its effects on the CNS and many peripheral systems. Of the 14 serotonin receptors, the 5-HT7 receptor is the most recently discovered. The 5-HT7 receptor has been shown to be involved in stress reduction, depression, and nociceptive control. Despite the 20 years since the discovery of 5-HT7R, there are still few truly selective ligands. Two of the common scaffolds for 5-HT7R ligands are long chain arylpiperazines (LCAPs) and sulfonamide containing compounds. This review focuses on recently developed (2014\xe2\x80\x932016) 5-HT7R ligands, their selectivity for the receptor, and suggests possible new pharmacophore models for these ligands.

pharmacophore_comparison_and_development_of_recently_discovered_long_chain_arylpiperazine_and_sulfon

1.1. Serotonin<!><!>1.3 5-HT7 Receptors<!>1.4 Current Pharmacophore Models<!>1.5 Pharmacophore Model for LCAPs and Sulfonamide Containing Compounds<!>2. LONG CHAIN ARYLPIPERAZINES<!>2.1 Pharmacophore Model<!>2.2 Recently Discovered LCAP Ligands for 5-HT7<!>2.3 Important Binding Elements<!>3. SULFONAMIDE CONTAINING COMPOUNDS<!>3.1 Pharmacophore Model<!>3.2 Recently Discovered Sulfonamide-containing Ligands for 5-HT7<!>3.3 Important Binding Elements<!>4. COMPARISON OF LCAP AND SULFONAMIDE PHARMACOPHORES<!>5. CONCLUSIONS<!>

<p>Serotonin, or 5-hydroxytryptamine (5-HT), is a multifunctional, monoamine neurotransmitter with action in the central and peripheral systems of animals and is present as a secondary metabolite in a number of plants. The serotonergic system is important for the regulation of many processes in the central nervous system (CNS) and many peripheral systems. Serotonin has been linked to regulation of some aspects of the cardiovascular system, including blood pressure regulation, vasodilation or vasoconstriction, and the onset of arrhythmias.1–3 In the pulmonary system, serotonin affects both breathing and regulating respiratory drive.4,5 Serotonin has been shown to play a major role in many gastrointestinal disorders such as irritable bowel syndrome, nausea, and diarrhea.6–8 While nearly 95% of the body's serotonin is localized in the gut, one of the more widely studied roles of serotonin is its function within the central nervous system.9 Here the neurotransmitter has been shown to play a significant role in modulation of physiological functions. These functions include, thermoregulation, sleep, pain, hunger, cognition, emotions, learning, memory, and many others.10–16</p><!><p>The Gi-protein coupled receptors group is comprised of the 5-HT1R family (5-HT1A, 5-HT1B, 5-HT1D, 5-HT1E, and 5-HT1F) and the 5-HT5R family (5-HT5A and 5-HT5B). Being coupled to the inhibitory G-protein, these receptors inhibit the production of cAMP from ATP, which then has effect on downstream pathways.</p><p>The Gq-protein coupled receptors group is comprised of the 5-HT2R family (5-HT2A, 5-HT2B, and 5-HT2C). These receptors work by activating phospholipase C, which through a series of reactions, activates the phosphatidylinositol signaling pathway.</p><p>The Gs-protein coupled receptors include the 5-HT4R, 5-HT6R, and 5-HT7R. These receptors work by activating cAMP, which in turn activates the cAMP-dependant pathway.</p><!><p>In 1983, a "5-HT1-like" receptor was described based on its function in the relaxation of smooth muscle tissue of mammals.17 Ten years later, in 1993, the 5-HT7 receptor was discovered independently by three research groups.18–20 After some time, it became clear that this "5-HT1-like" receptor was, in fact, 5-HT7R.21 Since its discovery over 20 years ago, there has been extensive research into its role in signal transmission; however, because there are few ligands which are selective for 5-HT7R over other 5-HT1 receptors, there is still more to be learned about its role.</p><p>The 5-HT7 receptor has been reported in the CNS, as well as in the periphery. Within the CNS, it is found in high concentration in the brainstem, hypothalamus, thalamus, hippocampus, and cortex.22 Outside of the CNS, the 5-HT7R can be found in the ileum, spleen, endocrine glands, and arteries.23 5-HT7R antagonists have demonstrated a variety of effects including stress reduction and antidepressant effects.24,25 On the other hand, 5-HT7R agonists have been shown to inhibit nociceptive control, which could make them good candidates as analgesic drugs.26</p><p>One of the issues that has arisen in 5-HT7 research is that ligands for 5-HT7R are often also ligands for 5-HT1A, making it difficult to find selective ligands for the 5-HT7 receptor. 5-HT1AR and 5-HT7R have both been linked to anxiety, depression, thermoregulation and both may play a role in the drug action of TCAs and SSRIs.27–37 Despite previous difficulties in designing selective ligands for the 5-HT7 receptor, in the past few years, several selective ligands have emerged.</p><!><p>There are several pharmacophore models that have been constructed for the 5-HT7 receptor since its discovery in 1993. One of the first published models in 2003, (Fig. 1A), proposed five key features necessary for antagonism: 3 hydrophobic regions (HYD1, HYD2, HYD3), a positively charged center (PI), and a hydrogen bond acceptor (HBA).38 While the model has been adapted in the years since, these hydrophobic regions and the positively charged center have remained integral to the model. In 2006, the features which were attributed to selectivity and non-selectivity for 5-HT7 were studied and another pharmacophore model was elucidated (Fig. 1B).39 In 2004 Vermeulen et al. presented a pharmacophore model for agonist vs. antagonist activity(Fig. 1C).40 The major differences include the presence of two additional hydrophobic regions to select for antagonism. While the exact mechanism of the receptor is not yet fully understood, these pharmacophore models have led to the development of several classes of 5-HT7 ligands including long chain arylpiperazines (LCAPs), and sulfonamide containing compounds.</p><!><p>Herein, two new pharmacophore models for the LCAP and sulfonamide containing compounds are presented and were generated using MOE software.41 For each set of compounds, a database was prepared using the ligands referenced in this paper and the Flexible Alignment calculation was performed to align the parts of these ligands with similar features. The Pharmacophore Consensus application was used to calculate a pharmacophore query based on the flexible alignment, and the pharmacophore expressions which were satisfied by the highest percentage of ligands were selected as part of the model. Each ligand was then superimposed with the pharmacophore model and the individual interactions were analyzed. While the pharmacophore models generated in this review are based on recently developed selective ligands, they incorporate the knowledge from previous pharmacophore models. The recently developed selective ligands satisfy the older models and these new modified models.</p><!><p>One of the most thoroughly explored classes of 5-HT7 ligands is the long chain arylpiperazine. Based on the pharmacophore model described in Kołaczkowski et al. (Fig. 1A) the aromatic ring of the arylpiperazine occupies a hydrophobic pocket. Additionally, the tertiary amine can form hydrogen bonds with the nearby Asp residue. The other aromatic ring has pi-pi interactions when it binds in AR1.These three interactions are proposed to lead to selective 5-HT7R ligands, but, if a carbonyl is present that binds in HBA1, it is less likely that the compound will be selective. Commercially available LCAPs, such as LP-12 and LP-44, (Fig. 2) have demonstrated selectivity over the 5-HT1A receptor, and for this reason, are commonly used as leads for further SAR studies.</p><!><p>Using the recently discovered LCAPs described below, a new pharmacophore model for LCAPs which act as 5-HT7 antagonists was elucidated (Fig. 3).41 The critical components of this model include 3 hydrophobic/aromatic binding regions (HYD/AR1, HYD/AR2, and HYD/AR3), a large, non-aromatic hydrophobic region (HYD1) and a hydrogen bond donor (HBD) located inside of HYD1. These regions were determined to be critical as they were satisfied by at least 83% of the molecules described below. Moreover, each compound below satisfies at least 4 of the 5 pharmacophoric regions.</p><!><p>In 2014, Kim et al. developed a series of 20 methoxyphenyl-LCAPs. When the length of the carbon linker was varied, it was found that a 4-carbon chain resulted in the highest affinity for the 5-HT7 receptor.42 The substitution of the biphenyl group was then examined. The chlorophenyl substitution lead to the greatest increase in affinity. The 2'-Cl substituted compound 1, (Fig. 4), had the highest affinity for 5-HT7 (Ki = 8.69 ± 0.77 nM), while the 3'-Cl and 4'-Cl had much lower affinities. The arylpiperazine moiety satisfies both the HYD/AR2 and HYD/AR3. HYD1 is filled with the carbon linker and the amide, and the amide also satisfies the HBD, which may explain why the 4-carbon linker resulted in increased activity, as a longer or shorter chain would result in a molecule that no longer can satisfy both the HYD/AR regions and the HBD. HYD/AR1 is filled with the aryl ring adjacent to the amide, and interestingly, in 3D space, the chlorophenyl ring can extend back into the HYD1 region. Additionally, the 2-chloro substituent can rotate away from the hydrophobic region, while the 3- and 4-chloro derivatives would be closer to this region. Because of its affinity for the 5-HT7 receptor, compound 1 was subjected to a panel of radioligand binding assays to determine its affinity for other receptors (Ki 5-HT7 =8.69 nM, Ki 5-HT1A =20.0 nM, Ki 5-HT1B =131.0 nM, Ki 5-HT1D =418.0 nM, Ki 5-HT2A =478.0 nM, Ki 5-HT2C =26.0 nM, Ki 5-HT3 = >10,000 nM, Ki 5-HT5A =1178.0 nM, Ki 5-HT6 =1517.0 nM). It was determined that 1 is selective over all the other 5-HT receptors tested, including 5-HT1A, and acted as a 5-HT7 antagonist. This compound was then tested in vivo and demonstrated antidepressant effects at 25 mg/kg in the forced swimming test in mice.</p><p>In 2015, Pytka et al. synthesized two LCAP derivatives, 2 and 3 (Fig. 5), which showed high affinity for the 5-HT7 receptor (Ki = 156 nM, Ki = 34 nM, respectively) but with poor selectivity over the 5-HT1A receptor (Ki = 41 nM, Ki <1 nM, respectively), with antagonist activity at 5-HT7.25 However, because both of these receptors are known to play a role in depression and anxiety, the compounds were tested in vivo. In the forced swim test, 2 (5.0 mg/kg) decreased immobility time by 38% compared to the vehicle and increased swimming behavior by 185%. 3 (2.5 mg/kg) decreased immobility time by 38% and increased swimming time by 191%. Additionally, 2 and 3 both showed mild anxiolytic activity, but less than diazepam. These compounds both align with four of the five components of the pharmacophore model. HYD/AR1 and HYD/AR2 are satisfied by the two terminal aromatic rings. The piperazine ring and carbon linker fill the HYD region, although the ether could be the cause of lower affinity in this region. HYD/AR3 is not filled because the molecule does not extend far enough in that space. For these compounds, the piperazine amine, which is protonated at physiological pH, provides the proton to be a hydrogen bond donor, rather than an amide nitrogen. The methoxy-substituent on the phenyl ring does not interact with any of the key parts of the pharmacophore model, but is able to rotate back into the hydrophobic region (HYD1). Additionally, while substituting a phenyl with a chlorine shows no difference in interaction with the pharmacophore, it does however improve 5-HT7 activity, so it could be useful to explore chlorination of the HYD/AR2 moieties in other LCAPS.</p><p>In 2015, Canale et al. investigated the use of the cyclic amino acids Pro-amide and Tic-amide as modifications to the LCAP scaffold.43 Upon biological testing of a 26- membered library of LCAPs with cyclic amino acid moieties, compounds 4 and 5, (Fig. 6) emerged as a 5-HT1A partial agonist and 5-HT7 = 12 ± 22 nM and Ki 5-HT1A = 66 ± 22 nM, demonstrating a 6-fold preference for the 5-HT7R. When comparing the molecule to the pharmacophore model presented above, both 4 and 5 satisfy all 5 components of the molecule. HBD is satisfied by the piperazine nitrogen, much like 2 and 3 (Fig. 5). The HYD1 region is filled with carbon linker and amide, and all three of the HYD/AR regions are satisfied with either aromatic rings or the conjugated amide bond. It should be noted that although 4 has a longer carbon linker, the molecule could bend in space so that the HYD/AR2 and HYD/AR3 regions are satisfied. The substituents on the HYD/AR3 phenyl ring are the major differences between these compounds. The large hydrophobic benzyl ring provides more selectivity than the methylsulfide, which suggests that there is another hydrophobic pocket that is being filled. 5 was tested in vivo using the forced swim test and it was shown to reduce the immobility time of the mice by 24%, but did not affect the spontaneous locomotor activity.</p><p>Current studies have developed more selective 5-HT7 ligands with the LCAP structure. In 2016, Intagliata et al. synthesized a series of twenty 4-arylpiperazine containing compounds, six of which demonstrated high affinity for the 5-HT7 receptor and no measureable affinity for the 5-HT1AR.44 Compound 6, (Fig. 7, Ki 5-HT7 = 52.0 ± 15 nM), utilizes a phenyl group and benzyl group to fill the HYD/AR1 and HYD/AR3 (Fig. 7). It had been determined that the 2 carbon polylinker yielded higher affinity for the 5-HT7 receptors, so each of the subsequent compounds contained linkers of the same length as well as the benzyl moiety to fill the aromatic pocket. Several analogues of 6 with different substitutions on the HYD/AR3 pocket were synthesized. Compounds 7 (Ki 5-HT7 = 36.6 ± 2.92 nM), 8 (Ki 5-HT7 = 50.2 ± 12.3 nM), and 9 (Ki 5-HT7 = 24.2 ± 4.34 nM) utilized 4-chloro, 2-chloro, and 2-methoxyphenyl groups, respectively (Fig. 7). Compounds 10 (Ki 5-HT7 = 29.5 ± 8.21 nM), and 11 (Ki 5-HT7 = 23.5 ± 2.32 nM) utilized the nitrogen containing 2-pyridyl and 2-pyrimidyl groups, respectively, with the pyrimidyl group showing increased affinity compared to the pyridyl as well as the chloro and methoxy substituted derivatives. This series of analogues is different from the previously discussed compounds because it contains two piperazine rings which are able to satisfy both the HBD1 and the HYD/AR2 pockets. These are the most selective 5-HT7 ligands, so it is possible that the two-piperazine scaffold is important for increased selectivity. While the substitution of the benzyl ring was not explored in this study, increases in activity have been seen by introducing hydrophobic substituents to the 2 position of the aromatic ring in HYD/AR1, which can extend back to the HYD1 region, and this could be an area for further exploration.</p><p>Finally, in 2016, Kucwaj-Brysz et al. synthesized fifteen 5-HT7 antagonists based on the lead compound MF-8 (Ki 5-HT7 = 3 nM, Ki 5-HT1A = 121 nM, Ki D2 = 715 nM), with compounds 12 and 13 showing at least 10-fold affinity over both 5-HT1A and D2 (Fig. 8).45 This scaffold is unique in that the HYD/AR2 is filled by the hydantoin ring rather than a phenyl or piperazine. This is much more hydrophilic than a benzyl group and there is much more of an opportunity for hydrogen bonding. Compound 12 (Ki 5-HT7 = 89 nM, Ki 5-HT1A = 2969 nM, Ki D2 = 5187 nM) introduced a 3-methoxy group to the HYD/AR1 phenyl moiety, and while it showed a higher affinity for 5-HT7R over 5-HT1AR and D2R, its affinity for the 5-HT7 receptor was almost 30 times less than the lead compound. Compound 13 (Ki 5-HT7 = 56 nM, Ki 5-HT1A = 1304 nM, Ki D2 = 1814 nM), utilized 2,5-dimethyl substitutions which increased the affinity for the 5-HT7 receptor while maintaining significant selectivity over the 5-HT1A receptor.</p><!><p>Within the LCAP group of 5-HT7 ligands, there is still room for improvement in selectivity. It seems that the use of bis- arylpiperazine scaffolds to satisfy both HBD1 and HYD/AR2 may increase selectivity for the 5-HT7 receptor. Additionally, the use of phenyl rings with hydrophobic 2-substituents to fill the HYD/AR1 pocket may increase activity by allowing the hydrophobic group to reach back into the HYD1 pocket. Moreover, exploration of the substitution of the HYD/AR3 pocket may lead to more potent ligands. Halogens, methoxy, and methyl substituents have all resulted in increased activity in different compounds, however, it would be useful to determine whether it is merely the space-filling that is important, or if activity can be increased with hydrogen bonding in that region. Finally, exploration of the use of hydrogen bond acceptors in the HYD/AR2 region like 13, could lead to increases in activity for already active compounds.</p><!><p>Another common feature of selective 5-HT7R ligands is the presence of a sulfonamide to satisfy a hydrogen bond acceptor region of a pharmacophore model. Kołaczkowski et al. proposed that a selective ligand could be characterized by satisfying HBA1, AR1 and PI in their pharmacophore model, and this is the case for several of the recently developed ligands for the 5-HT7 receptor. Currently several commercially available sulfonamides are classified as 5-HT7R ligands such as SB-258719 and SB-269970 (Fig. 9).</p><!><p>It is likely that these sulfonamide-containing compounds bind to the receptor with the sulfonamide group in the same region. For this reason, a separate pharmacophore model for 5-HT7 antagonism was elucidated using the sulfonamide containing compounds described below (Fig. 10).41 In this model, there are three regions where a hydrogen bond acceptor is necessary (HBA1, HBA2, and HBA3). These regions are all surrounding a large hydrophobic region on one side (HYD1). There are two hydrophobic/aromatic regions (HYD/AR1 and HYD/AR2) on each side of HYD1. Finally, within HYD1 there is a hydrogen bond donor (HBD) that is usually satisfied by a tertiary amine.</p><!><p>In 2015, Canale et al. synthesized a library of N-Alkylated arylsulfonamides of (aryloxy)ethyl piperidines in order to study the effects of various N-alkylations on the affinity and selectivity towards the 5-HT7 receptor.46 Two compounds, 14 and 15 were found to be highly selective 5-HT7R antagonists (Fig. 11). Compound 14, (Ki 5-HT7 = 58 nM, Ki 5-HT1A = 9625 nM, Ki 5-HT2A = 557 nM, Ki D2 = 280 nM) showed very high selectivity over the 1A receptor and utilized the N-methyl substitution on the sulfonamide moiety. Additionally, compound 15, (Ki 5-HT7 = 49 nM, Ki 5-HT1A = 17,770 nM, Ki 5-HT2A = 1479 nM, Ki D2 = 230 nM), which possessed a N-cyclopropylmethyl substitution, showed very high selectivity over the 5-HT1A receptor. Both 14 and 15 satisfy the pharmacophore model presented earlier. The pyrazole ring fills HYD/AR2 and the oxygens of the sulfonamide fit within HBA2 and HBA3. The hydrophobic region (HYD1) is filled with the piperadine, carbon linker, and the substituted nitrogen. The difference between these two compounds is the cyclopropylmethyl group in 15 and the N-methyl in 14. This cyclopropylmethyl is larger and extends to the edge of the hydrophobic pocket, filling it without causing too much steric bulk outside of this region. The tert-butyl group may be effective because it is able to extend back into the large hydrophobic region as well. Because of their high affinity and selectivity for 5-HT7R, 14 and 15 were also tested in vivo using the forced swim test and 15 was also tested in the novel object recognition test. Both compounds showed the greatest decrease in immobility at a 1.25 mg/kg dose (between 70–80% of the control), with a U-shaped response curve in the FST. In the novel object recognition test, 15 also demonstrated pro-cognitive abilities at a 1mg/kg dose.</p><p>In 2016, the same group published the evaluation of another set of 39 arylsulfonamide compounds. Two compounds, 16 (Ki 5-HT7 = 19 nM, Ki 5-HT1A = 545 nM) and 17 (Ki 5-HT7 = 1 nM, Ki 5-HT1A = 98 nM) were identified as selective 5-HT7R antagonists (Fig. 12).47 While compounds 16 contained a piperidine ring, 17 has an azabicyclooctane ring system. For 16 and 17, there are several differences in the substitution, but the core structure remains the same. For these compounds, a fluorophenyl group fills the HYD/AR2 pocket. The sulfonamide oxygens still lie in HBA2 and HBA3, and a piperazine fills the large hydrophobic pocket. In 17, the addition of a carbon bridge on the piperazine helps to fill the hydrophobic pocket even more, increasing the affinity for 5-HT7R. Another difference between 16 and 17 is the cyclopentyl and isopropyl substituents on the benzyl ring that fills HYD/AR1. Compound 17 utilizes the isopropyl group which adds to the hydrophobicity ion this region, without increasing the bulk of the compounds as much as the cyclopentyl. This group however does not extend fully into the hydrophobic region, so it is possible that these substituents are extending into hydrophobic regions on the other side of the phenyl ring. Both compounds were subjected to three functional tests: the forced swim test, tail suspension test, and the four-plate test. The FST confirmed the antidepressant activity of 16 and 17 compared to known 5-HT7R antagonist SB-269970. Additionally, the antidepressant activity was also confirmed with the tail suspension test. In addition, the compounds both showed anxiolytic activity with the four-plate test.</p><!><p>In general, the sulfonamide containing compounds presented have higher affinities for the 5-HT7R than those without, therefore, it is possible that the HBA2 and HBA3 regions of the pharmacophore are the reason for this increased selectivity. One of the more interesting strategies was the introduction of the bicyclic ring system to fill the HYD1 region for compound 17.</p><!><p>The pharmacophore for the sulfonamide compounds is very similar to the LCAP scaffold, and there are several regions in which overlap occurs. Both of the pharmacophore models were overlaid (Fig. 13a) in order to see which regions were essential in both, or just one of the pharmacophore models. The regions which overlapped in both models (Fig. 13b) were the HYD/AR1 and HYD/AR2 regions, the hydrogen bond donor, and the large hydrophobic region. For the molecules that did not fit the pharmacophore completely, the regions that were satisfied were the regions that overlap.</p><p>When overlaying the two pharmacophores, it is interesting to see which areas do not overlap. First, the HYD/AR3 region for LCAPs is not in the pharmacophore model for sulfonamides. Looking at the compounds presented, none of the compounds presented extended into this region either. It would be interesting to explore compounds which contain a sulfonamide and extend out into this HYD/AR3 region. Additionally, the bicyclic ring system in 17 could be applied to LCAPs as well to fill the HYD1 region and possibly increase selectivity. Finally, the hydantoin ring used to fill HYD/AR2 in MF-8, 12, and 13, or other aromatic yet hydrophilic ring systems could be used (1) explore whether it is the aromaticity or the hydrophobicity that is more important in this region for both LCAPs and sulfonamides, then (2) potentially develop sulfonamides that combine this polar ring with the sulfonamide so satisfy the HBA2 and HBA3 regions.</p><!><p>In recent years, there have been several novel compounds developed which bind to the 5-HT7 receptor. While many strategies have been developed to create selective compounds, there is still much to be explored. This review discussed some of the most potent ligands for the 5-HT7 receptor, their selectivity profiles, and their effects in functional assays. Three pharmacophore models were elucidated, one for LCAPS, another for sulfonamide containing compounds, and a third for the combined LCAP and sulfonamide models. From these models, several areas for exploration were determined.</p><p>5-HT7 receptors are widespread throughout the brains, which suggest that it may have multiple roles in the CNS. There is still a need for novel, potent 5-HT7R agonist and antagonists in order to elucidate all of the roles that 5-HT7R serves in the CNS and periphery.</p><!><p>Serotonin, 5-Hydroxytriptamine</p><p>Serotonin receptor</p><p>Adenosine triphosphate</p><p>Cyclic adenosine monophosphate</p><p>Central nervous system</p><p>Forced swim test</p><p>G-protein coupled receptor</p><p>Long chain arylpiperazine</p><p>Selective serotonin reuptake inhibitor</p><p>Tricyclic antidepressants</p><p>Published pharmocaphore models by (A) López-Rodríguez et al., (B) Kołaczkowski et al., and (C) Vermeulen et al</p><p>Chemical structures of commercially available 5-HT7 ligands LP-12 and LP-44</p><p>Pharmacophore model for LCAPS</p><p>Chemical structure of 5-HT7 ligand 1</p><p>Chemical structure of 5-HT7 ligands 2 and 3</p><p>Chemical structure of 5-HT7 ligands 4 and 5</p><p>Chemical structure of 5-HT7 ligands 6–11</p><p>Chemical structure of 5-HT7 ligands MF-8, 13 and 14</p><p>Chemical structure of commercially available 5-HT7 ligands SB-258717 and SB-269970</p><p>Pharmacophore model for sulfonamide containing compounds</p><p>Chemical structure of 5-HT7 ligands 14 and 15</p><p>Chemical structure of 5-HT7 ligands 16 and 17</p><p>(A) Superimposed pharmacophore models for LCAPS (blue) and sulfonamides (red). (B) Combined pharmacophore model for LCAPs and sulfonamides</p>

PubMed Author Manuscript

Revealing Structure-Property Relationships in Polybenzenoid Hydrocarbons with Interpretable Machine-Learning

The structure-property relationships of polybenzenoid hydrocarbons (PBHs) were investigated with interpretable machine learning, for which two new tools were developed and applied. First, a novel textual molecular representation, based on the annulation sequence of PBHs was defined and developed. This representation can be used either in its textual form or as a basis for a curated feature-vector; both forms show improved interpretability over the standard SMILES representation, and the former also has increased predictive accuracy. Second, the recently-developed model, CUSTODI, was applied for the first time as an interpretable model and identified important structural features that impact various electronic molecular properties. The resulting insights not only validate several well-known "rules of thumb" of organic chemistry but also reveal new behaviors and influential structural motifs, thus providing guiding principles for rational design and fine-tuning of PBHs.

revealing_structure-property_relationships_in_polybenzenoid_hydrocarbons_with_interpretable_machine-

Introduction<!>The LALAS Representation<!>Data Sources<!>Interpretation of CUSTODI<!>Results and Discussion<!>The Performance of LALAS<!>Interpretation Based on Annulation Sequence<!>Interpretation Based on the LFV<!>Conclusions<!>Data and Code Availability

<p>In recent years, machine learning (ML) has been increasingly used in chemistry to address a wide variety of challenges, ranging from drug design 1,2 to automatic synthesis, [3][4][5] to accelerating traditional computations. [6][7][8] Whereas the success of earlier models was measured by efficiency and accuracy in prediction, current models are often aimed towards better "interpretability" -i.e., an ability to provide guiding principles and insight into domain relationships. 9 In other words, scientists wish to understand "what the model has learned", which may serve to validate existing chemical laws and intuitions, 10,11 or, hopefully, even lead to the discovery of new physical and chemical insights. 9,12,13 Recent reports have demonstrated that ML can "rediscover" concepts and conventional wisdom in chemistry and physics. Examples include: the effect of specific functional groups on solubility and HOMO level, 11 the hard and soft acids and bases (HSAB) principle for stability of inorganic complexes, and the identification of important normal modes for molecular dissociation 14 . Alongside these, there is discussion of how ML can lead to entirely new discoveries. 12 It should be mentioned, however, that in all of these cases, domain expertise is required, either to engineer the features given to the model or to place the "understanding" of the model in a domain-appropriate context.</p><p>In this work, we apply interpretable machine learning to the question of structureproperty relationships in the family of compounds known as cata-condensed polybenzenoid hydrocarbons (PBHs; sometimes also referred to as catafusenes or as polycyclic aromatic hydrocarbons, PAHs). These molecules are impactful in many areas, in particular in human and environmental health 15,16 and in organic electronics. [17][18][19] Due to their importance, these compounds have been extensively studied for many decades, both computationally and experimentally. They continue to garner attention for their potential to be used as organic semiconductors 20 and because they are precursors to nanographene sheets. 21 Understanding the properties of PBHs is crucial to both understanding their reactivity and designing new functional materials and new pathways for safe disposal of harmful ones. Thus, obtaining a deeper understanding of structure-property relationships governing the behavior of PBHs is of interest both from the conceptual aspect and from the practical one.</p><p>Beyond these reasons to study PBHs, there is also a fundamental issue. To paraphrase Randic: 22 in order to understand the behavior of polycyclic aromatic systems (PASs) in a general way, one must first understand the systems comprising the archetypal aromatic unit -benzene. We envision that the current study is the necessary foundation for future investigations of broader swaths of the PAS chemical space. The prevalence of PASs in both natural and man-made materials entails that factors affecting their molecular properties are important to consider in designing new functional molecules and materials.</p><p>We approach the subject of interpretable ML in the context of aromatic molecules from two directions: a) the introduction of a new type of molecular representation specifically suited to this kind of molecules and b) the application of a novel interpretable ML method, named CUSTODI, 23 which does not require any human-aided feature selection. We show that our new representation is suitable for extracting chemically meaningful insight and has similar performance to state-of-the-art techniques, but with shorter training times and fewer data required for training. The combination of these two new tools allows us both to validate structure-property relationships previously revealed using electronic-structure investigations and also to uncover additional relationships. These can then inform the rational design and/or fine-tuning of properties.</p><!><p>Our group has demonstrated in a series of reports over the past few years that catacondensed PASs can be broken down into their smaller components (monocyclic, bicyclic, and tricyclic), and the magnetic properties of the larger molecules can be predicted by summing the contributions of these smaller subunits using an additivity scheme. [24][25][26] For the particular case of the PBHs, molecular properties can be predicted by the type and order of the tricyclic components themselves, where the two tricyclic subunits differ only in their annulation: linear or angular, i.e., anthracene or phenanthrene, respectively. This conclusion allows for a reduction of the molecular structure to the sequence of tricyclic subunits (i.e., the annulation sequence). We have formulated this sequence as a textual representation of the molecule (Figure 1a), containing only the characters "L", "A", "(" and ")" (parentheses are used to denote branching points, where applicable; see Figure 1b for a selection of PBHs and their respective annulation sequences). The resulting names are strings of varying lengths comprising the letters "L" and "A", which we have accordingly named "LALA Strings" or "LALAS" (the terms "LALAS representations", "LALAS", and "annulation sequences" are interchangeable).</p><p>The annulation sequence, or LALAS, has been clearly demonstrated to be linked to molecular properties: molecules sharing the same annulation sequence are equiaromatic (i.e., the same aromatic behavior) in both the ground state and the lowest excited triplet state. 27 In addition, we have shown that the annulation sequences themselves demonstrate a clear connection to and enable prediction of numerous molecular properties, including relative stability, aromatic character, singlet-triplet energy gaps, and location of spin density in the triplet excited state. 27 The generation of a LALAS for a given molecule proceeds according to the following protocol (similar to IUPAC rules for naming branched alkanes), which we have automated in a modified version of Predi-XY. 28 The modified code for generating the LALAS is freely accessible online. a. For unbranched molecules, each tricyclic subcomponent is denoted as a letter "L" or "A", depending on the type of annulation. The choice of "left-to-right" or "rightto-left" is random, i.e., each molecule has (at least) two valid LALAS. E.g., the molecule LLA (Figure 1B) can also be read as ALL. b. For branched molecules, we search for the longest possible path through the molecule, and denote this the "main branch". E.g., the main branch of molecule LLA()is a chain of 5 rings. c. If there are branching points, they are denoted with "()" (e.g., LLA() in Figure 1B).</p><p>Note that branching points will always follow an "A", as they are by necessity connected to the middle ring of an angular annulation. Note, also, that the notation "()" implies a branch containing a single ring. d. Branches longer than a single ring will have their own sequence, which will be detailed within the parentheses (e.g., LAA(L)LL in Figure 1B). e. If there are two different paths of similar length, the one with more branching points is chosen as the main branch. We emphasize that, in contrast to SMILES or SELFIES, which describe molecules on an atom-connectivity basis, LALAS describe molecules using ring-based subunits. As such, they reduce the dimensionality of the molecular representation, while retaining important structural information. This trait could allow for significant improvement in efficiency of training ML models -in reducing both the training time and the required training set size. We also note that several graph-theoretical based notations for PBHs have been previously proposed, most notably by Gutman, 29 Balaban, [30][31][32] and Cyvin. 33 To the best of our knowledge, these have been used mostly for enumeration of isomers of various types of PBHs. The 3-digit code developed by Balaban in the 1960s, which is the most similar in approach to our own formulation, has been also used to identify correlations to molecular properties (e.g., ionization potential, IP, and electron affinity, EA). 34,35 In this work, LALAS representations were generated using a modified version of the Predi-XY code developed in our group 28 and were used in two ways: a) tokenization directly from the string format (LALAS) and b) as a basis for generating a LALA-based feature vector (LFV) for each molecule (vide infra).</p><!><p>With the advent of more efficient computational techniques, data-driven investigations have become increasingly common; however, it has been difficult to apply such methods to PBHs, as there is a paucity of suitable data. Recently, our group reported on the COMPAS Project: the construction of a novel COMputational database of Polycyclic Aromatic Systems. 36 The first instalment of the database, denoted COMPAS-1D, contains data on ~8,600 cata-condensed PBHs, including their optimized structures and a selection of electronic properties (calculated with DFT at the B3LYP-D3BJ/def2-svp level), as well as their respective SMILES representations and LALAS representations.</p><p>For the current study, we removed benzene and naphthalene from the dataset, as they are too short to have a LALAS. Both LALAS and SMILES representations were tokenized using two methods: one-hot 37 and CUSTODI. 23 The properties we extracted from the COMPAS-1D database for this study were: a) HOMO energy; b) LUMO energy; c) HOMO-LUMO gap; d) adiabatic ionization potential (AIP); e) adiabatic electron affinity (AEA); f) relative single-point energy.</p><p>LALA Feature-Vector (LFV)</p><p>In addition to the LALAS, we generated for each molecule a feature-vector based on curated structural features derived from the LALAS, denoted LFV. This set of chemically intuitive features (detailed in Table 1) was inspired by our collective experience studying PBHs and by structure-property relationships previously found in smaller datasets. 24,27 The purpose of using the LFV as input was threefold: (1) to validate the intuition we developed previously, (2) to check the predictive power of these descriptors, and (3) to compare the conclusions derived from this set of PBH-specific features to those derived from more general chemical representation. The CUSTODI Framework CUSTODI is a recently developed tokenization technique for text-based molecular representations. A full description of the method is beyond the scope of this report. In brief, the approach of CUSTODI is to find, using linear regression, the best fitting tokenization dictionary for a given target property. The resulting dictionary can be used for tokenization (CUSTODI representation) or for prediction (CUSTODI model), as shown in Figure 2. Both the CUSTODI representation and the CUSTODI model were used in this work. For further details on the method, the reader is referred to reference 23 . To perform a methodical comparison, four supervised learning models were used, ranging from standard to state-of-the-art (for further details on each model, please refer to Section S4 in the Supporting Information). Kernel ridge regression (KRR) and random forest (RF) were used in conjunction with CUSTODI tokenization and LALA features as input (denoted as CUSTODI[LALAS]); a recurrent neural network (RNN) was trained on onehot tokenization of the LALAS and SMILES (denoted One-Hot[SMILES] and One-Hot[LALAS], respectively); and a state-of-the-art 38,39 graph convolution (GC) model was used with the MolConv input. 40 The KRR and RF models were implemented using scikitlearn, 41 the RNN model by using tensorflow, 42 and the GC model by using DeepChem 43 with an identical architecture as the MoleculeNet benchmark. 38 The data was split into training and testing sets and hyperparameter optimization was performed using Bayesian optimization algorithm (Gaussian process) as implemented in the scikit-optimize python package. 44 Model selection was done using 5-fold cross validation. Exact details on the hyperparameters of each model are in Section S4.2 in the Supporting Information. The best model was retrained on the whole training set and used to estimate the model's performance. All properties were normalized using z-score normalization (0 mean and 1 standard deviation) and all tokenized strings were padded before insertion into the models.</p><!><p>The interpretation of CUSTODI is relatively straightforward: each tokenization value corresponds to a substring (e.g., atom or functional group), and these values are used to make the model's prediction (Eq. 1). In this work, each tokenization value corresponds to a tricyclic substructure within the PBH.</p><p>Where 𝑠 is the string representation of molecule 𝑖 in the database, 𝑐 is the 𝑖th character in 𝑠 , 𝑛 … is the number of occurrences of the substring 𝑐 … 𝑐 in 𝑠 , 𝑥 … is the substring's tokenization value and 𝑝 is the target property. From Eq. 1, the tokenization value is proportional to the significance of the represented substructure for a given property. The tokenization value 𝑥 is not independent of the number of occurrences 𝑛, and there is actually an inverse proportion between them. To account for this proportion, the importance of each substring is given by</p><p>So that the sum of all the importance terms is 1.</p><p>We emphasize that the analysis made here can be repeated for many chemical compounds and can produce similar intuition on the effects of various functional groups on properties. Unlike previous reports (a few are detailed in the Introduction, vide supra), CUSTODI does not rely on hand-crafted features. By iterating over all possible substrings, CUSTODI in essence performs its own data-driven feature-engineering. The main advantages are that this does not require any chemical intuition and tests all substructures in the dataset simultaneously. As a result, this reduces possible sources of bias and allows for identification of features that might not be obvious to experts. The disadvantages are that CUSTODI cannot search for varying-length substrings and will likely not identify features that involve non-adjacent structural components.</p><!><p>The two main aspects of the work are presented and discussed in the following sections: a) the performance of LALAS as a molecular representation and b) the use of LALAS in conjunction with interpretable ML models (CUSTODI and RF) to gain new chemical insights into PBHs.</p><!><p>As mentioned above, LALAS are specifically tailored to describe PBH compounds. To test the added value of this dedicated representation versus commonly used general-purpose representations, the performance of several models trained on LALAS was compared to the same models trained on other types of input (see Methods for further details on the selected models for comparison). The models employed are detailed in the Methods section and in Figure 3, which provides an illustration of all input+model combinations. The results obtained with a training set containing 7,674 molecules (90%) are illustrated in Figure 4 (the full fit results on the database are in Section S6 in the Supporting Information). In all other cases, the CUSTODI[LALAS] representation performed markedly better than the CUSTODI[SMILES] representation. What is more, when using CUSTODI[LALAS] as input, the RF model performed similarly to the best-performing RNN and GC models, which are considered much more sophisticated. A possible explanation is that the simpler syntax of LALAS better suits the linear approximation in CUSTODI, thus allowing for better tokenization dictionaries to be generated for LALA, as compared to SMILES.</p><p>The LFV did not show consistent behavior as an input: the RF model trained on LFVs performed the poorest; however, the KRR model trained on LFVs performed better than both CUSTODI[SMILES] and CUSTODI[LALAS]. This variance in performance is not surprising, as the RF and KRR models work best on significantly different latent spaces.</p><p>A visual inspection of the individual plots in Figure 4 indicates that the relative energy is the only property with a qualitatively different picture. For this property, it appears that there is a dramatic difference in the performance of the two RNN models, and the performance of RNN model trained on the One-Hot[LALAS] representation appears to be noticeably poorer than the model trained on the One-Hot[SMILES] representation. We must emphasize, however, that such an interpretation is misleading, considering that, in fact, all the models show very satisfactory accuracy: they predict the relative electronic energy with MAE < 0.002 eV, which is smaller than the margin of error of the DFT calculations.</p><p>Nevertheless, this apparent shift in performance (relative to the other molecular properties) led us to consider possible differences between the representations, which might affect the prediction of relative energy. One important difference is the way LALAS treat angular annulations. Angular annulations can have two types of direction -clockwise and counter-clockwise. Consecutive angular annulations in opposite directions create a zig-zag type of topology, which is planar in the ground state. 45,46 However, consecutive angular annulations in the same direction create cove, fjord, and eventually helix formations (for two, three, and four consecutive A annulations, respectively). These differences do not necessarily affect electronic properties (e.g., molecules with similar annulation sequences are equiaromatic -i.e., have similar aromaticity patternsregardless of the direction of the A annulations), however such substructures can affect relative energy as they have an increasing degree of curvature, which introduces helical strain, i.e., higher relative energy. Whereas SMILES representations include this information, LALAS do not differentiate between the types of angular annulations. Therefore, in principle, there could be performance discrepancies between the two; in practice, we observe that both perform exceedingly well on the given data.</p><p>Having analyzed the performance of the individual models in terms of prediction accuracy, we now turn to comparing the training time required for each of the models. Table S2 (Supporting Information, Section S2) gives the average time per molecule for each of the models. In general, we find that using the LALAS (or representations derived from LALAS) markedly decreases training time for the RF and RNN models (by factors of ~6 and ~5, respectively), and moderately decreases training time for the KRR and CUSTODI models (by a factor of ~2 for both).</p><p>Finally, a major advantage of LALAS is revealed when comparing the performance of models trained on smaller training sets. The top four best-performing input+model combinations were identified from Figure 4, and new models were trained on varying training-set sizes. Both LALAS and LFVs indeed show superior performance in small datasets compared to other tested methods. Using only 10% of the data for training, the RNN model trained on One-Hot[LALAS] achieved a normalized test set MAE of 0.12 eV, which is markedly lower than the other three models. At a training-set size of 40%, GC achieved similar results as the RNN, MAE = 0.11 eV, and at 70% all four models showed comparable results. This may be attributed to the concise nature of the LALAS: the LALAS of a given molecule is, on average, shorter by 86.5% (55 characters) than the SMILES of the same molecule. In addition, the complexity of the language is substantially reducedonly four types of characters. Thus, lower variance is expected for models trained on LALAS.</p><!><p>As mentioned above, the simplification that is inherent in LALAS affects not only model performance, but also interpretability, which is a main goal of this work. Whereas single atoms or atom-pairs can have meaning as functional groups in many organic molecules, in PBHs individual carbon atoms often do not carry significant chemical meaning. LALAS connect textual characters with chemically meaningful subunits, i.e., specific ring annulation patterns. This makes it amenable to interpretation when used for training textbased models such as CUSTODI (the methodology for interpreting the CUSTODI model dictionary is presented in the Methods section).</p><p>To extract the most meaningful insights from a given model, one should first ensure that the model shows good and reliable performance. Therefore, we initially performed a benchmarking procedure, to determine the optimal degree of CUSTODI for these data. This procedure is included in the hyperparameter optimization of the CUSTODI model, as the degree of CUSTODI is a hyperparameter of the model (see Methods for details). In other words: CUSTODI-1 was trained on subsequences of a single character (e.g., "L", ")"), CUSTODI-2 was trained subsequences containing either one or two characters (e.g., "L", "LA"), and CUSTODI-3 was trained on subsequences containing either one, two or three characters (e.g., "A", "(L", "ALA"). The best-performing model was found to be CUSTODI-2. The importance terms of the trained CUSTODI-2 model are presented in Figure 6. We emphasize that, while these terms can help assign the importance of the various structural features, they do not tell us in which way the features impact each property, i.e., increasing or decreasing the value of the predicted property. Such an analysis requires different treatment, which is the subject of ongoing work and will be disclosed in due course. Figure 6 shows that the properties HOMO, LUMO, and HOMO-LUMO gap have a similar dependence on particular substring sequences, which is not surprising. In addition, we observe a marked difference between the relative importance of the factors governing these three properties and those determining the relative energy of each molecule (note: the relative energy is calculated with respect to the respective lowest-energy isomer; for further details see 36 ). The adiabatic ionization potential (AIP) and adiabatic electron affinity (AEA) have some similarity to the three aforementioned electronic properties, which is in accordance with Koopman's theorem. 47 Yet, there are also dissimilarities, which demonstrate that the model is capable of distinguishing between the property types.</p><p>The main factor influencing the HOMO, LUMO, and HOMO-LUMO gap is the presence of linear annulations (L, 𝛽 ̅ 17.7%) and stretches of two consecutive linear annulations (LL, 𝛽 ̅ 9.1%; i.e., four benzene rings annulated linearly, akin to naphthacene). These properties are affected by the presence of angular annulations to a lesser extent (A, 𝛽 ̅ 11.6%), while the existence of branching points does not seem to be important. Our recent analysis of the COMPAS-1D dataset showed that the HOMO, LUMO, and HOMO-LUMO gap all depend on the length of the Longest L subsequence. Because CUSTODI-2 only looks as subsequences up to two letters long, we cannot see here the importance of longer Longest L subsequences. Nevertheless, all of these observations are in line with our previous observations on these compounds. 36 In contrast, the main factors influencing the relative energy are different. We observe the following dependencies: linear annulations (L, 𝛽 16.5%), consecutive series of angular annulations (AA, 𝛽 11.3%), and branching points following an angular annulation (")A", 𝛽 10.8%). The subsequence "AL" also appears, which implies that it is not only the presence of angular annulations that matters, but also what surrounds them, or at what point the A sequence is broken. These results are in line with our previous observations pertaining to prediction of the relative energy, which we attributed to the strain that is incurred by sequences of consecutive A motifs. Specifically, we noted that consecutive sequences of A annulations can be either helical or planar, depending on the direction of the consecutive As. While A annulations in opposing directions lead to "zig-zag" formation that is planar, stretches of A annulations in the same direction lead to the formation of helical structures (known as cove, fjord, and helix). The features entail helical strain which raises the relative energy. Therefore, it is not surprising to find them among the main influencers in the prediction of relative energy. Corroboration for this interpretation can be found in our analysis of the COMPAS-1D dataset, which has shown that the increase in relative electronic energy is correlated to the deviation from planarity. 36 In this context, we note that the relationship between angular annulations and stability has also been investigated with other computational and conceptual tools. For example, the same observations can be interpreted in the context of Clar's rule, 48,49 which states that isomers with a larger number of Clar sextets are more stable than those with fewer Clar sextets. In general, angular annulations and branching points allow for more Clar sextets to be generated, which can therefore influence the relative energy. We are currently investigating the link between Clar structures, aromaticity indices, and the relative energy, to see if this interpretation can be substantiated. Other computational analyses have also rationalized the greater stability of angular isomers in the ground state via graph-theory, 50 additional π-bonding, 51,52 and a greater number of resonance structures. 53 Though the L motifs are predicted by the model to have an importance effect, the direction of this effect is unknown. Hence, it can, in principle, be perceived in two ways: a) following the previous rationalization, the presence of L motifs can be seen as precluding the formation of such non-planar motifs and therefore contributing to stabilization; or b) the L motifs may contribute to destabilization, not via geometric deformation but rather through an electronic effect. Since it is well-established that the most stable isomers are the phenacenes (i.e., the "zig-zag" PBHs), 51,52 one may conclude based on this previous knowledge that the operative case is (b). Nevertheless, we are currently working on implementation of more sophisticated DL models that also reveal the direction of each feature's influence.</p><p>As mentioned above, the AEA and AIP mostly show similarity to the HOMO, LUMO, and HOMO-LUMO gap analyses, with some exceptions. The main difference is that for both AIP and AEA the angular annulation ("A", 𝛽 ̅ 17.7%) shows slightly greater importance than the linear annulation ("L", 𝛽 ̅ 14.2%). One possible explanation can be found in the work of Khatymov et al., who found that the stabilization of the LUMO is hampered due to specific symmetry features in the angular phenanthrene, which may be generalized to homologous series of angularly annulated PBHs. 54 As a result, within Koopmans' theorem (though just a crude approximation for our DFT-calculated values), the EA is expected to decrease in magnitude. An alternative, or complementary, explanation is that many of the molecules containing multiple A annulations have some degree of helicity, which may affect the charge delocalization. Therefore, the presence of As becomes an important factor for the predictive model. We note that, for all properties, the intercept has a large importance value, i.e., a large influence on the predicted value. As described in the Methods section, the intercept is a constant value that describes the bias of the CUSTODI model. In cases where the bias itself has a large value, relative to the individual tokenization values, the intercept has a strong influence. This can be understood in the following way: the CUSTODI model learns the "average value" of a property and the importance assigned to each of the subsequences represents the effect of the respective subsequence on that relative value.</p><!><p>The RF model has an inherent way of finding feature importance. 55 Our analysis focuses on the RF model trained on LFVs (Figure 7). The results show very similar patterns to those obtained with CUSTODI [LALAS]. Considering that LFVs are essentially domain expertise-based features which we extracted from LALAS, this implies that the CUSTODI model successfully captures the features directly from the textual representations, without the need for human intervention.</p><p>The RF model shows that the HOMO, LUMO, and HOMO-LUMO gap are mainly affected by the length of the longest stretch of linear annulations ("Longest L", 87%). Unsurprisingly, the AIP and AEA are also mainly influenced by the linear annulations ("Longest L", 80.2%). However, AEA and AIP are also affected by the number of rings, which is in line with previous reports of a size-dependency for these properties. 56 It is generally considered that the larger a conjugated system is, the better it is expected to stabilize excess charge through delocalization.</p><p>The relative energy displays a very different set of dependencies, chief among them are the longest stretch of angular annulations ("Longest A", 27.1%), the number of branches in the molecule ("No. Branching points", 21.2%), and the degeneracy of the longest linear sequence ("Longest L Degeneracy", 20.6%). The ratio of L motifs, the longest linear sequence, and the number of LAL sequences also have non-negligible influences (10%, 12.1%, and 6.4%, respectively). As mentioned above, we believe that the impact of the angular annulations can be attributed either to variations in helical strain or to the possible number of Clar sextets that can be formed. Similarly, the number of branches is influential because it is related to the tendency to form helical structures (an increase in branches precludes linear stretches and increases the likelihood of angular stretches in similar directions).</p><p>As we explained above, we hypothesize that, while the A motifs appear to raise the energy through geometrical deformation, the L motifs raise it via electronic effects. Thus, we observe a dependence also on several features describing the presence of L motifs. As opposed to the other properties, where only the longest linear stretch was important, here also the degeneracy (i.e., the longest stretch that appears more than once) is important. This indicates that the effect of individual linear stretches on the relative energy may be additive, while on other properties it is exclusive. Interestingly, the relative energy also shows a dependence on a specific substructure, "LAL". This particular subsequence was previously noted as behaving in an anomalous manner 24 in the prediction of magnetic behavior in PBHs. A similar analysis using the CUSTODI model trained on SMILES strings yielded no meaningful results, as the substrings used in CUSTODI models are short (this results from the hyperparameter optimization; see Section S4.2 in the Supporting Information for more details). The results of the influence analysis on SMILES strings are also provided in the Supporting Information (Section S5, Figure S1). Similarly, RF trained on CUSTODI[SMILES] did not afford any interpretable results.</p><!><p>In this work, we applied interpretable ML tools to investigate the structure-property relationships in the family of PBHs, which are archetypal polycyclic species. We introduced a new type of textual molecular representation, which is specifically suited for these molecules. This representation can be used either in string form (LALAS) or as the basis for a feature-vector (LFV). In addition, we applied a new type of interpretable ML method, CUSTODI. Comparison to standard models and input types demonstrated the added value of LALAS to both efficiency and interpretability.</p><p>The application of these two new tools to the newly reported database, COMPAS-1D, [ref:</p><p>database paper] allowed us to gain chemical insight into the structure-property relationships of PBHs. Four main conclusions were reached:</p><p>(1) most of the electronic properties of PBHs we studied are primarily influenced by the presence and length of consecutive linear annulations in the molecule; (2) the relative energy of isomeric PBHs is mainly affected by the presence of angular annulations and branching points in the molecule; (3) as expected from Koopmans' theorem, AIP and AEA have similar dependencies as HOMO, LUMO, and HOMO-LUMO gap, however, the former two are also sizedependent while the latter appear not to be; (4) there are "privileged" subsequences, one of which we identified -"LAL".</p><p>To a certain extent, (some of) these insights may be considered well-known "rules of thumb" or "conventional wisdom" in the chemical community. However, to the best of our knowledge, have never been demonstrated in a data-driven manner. Indeed, the agreement between the ML interpretation and generally accepted chemical behavior indicates that the models performed reliably well, and we have validated these rules of thumb with an unprecedented dataset containing ~8,700 PBHs. Nevertheless, there are also new insights, such as factors influencing relative energy of PBH isomers and the existence of "privileged" subsequences. We also emphasize that the importance analysis presented here indicated that the relationship between linear sequences and the various molecular properties is different. Specifically, for all of the properties except the relative energy, it appears that only the single longest linear stretch is important and how many times such a sequence appears does not matter; in contrast, for the relative energy, the degeneracy of these sequences does matter, which suggests that they might contribute cumulatively to destabilization.</p><p>Importantly, similar conclusions were obtained using the CUSTODI model, which was trained on LALAS without any preprocessing, and the RF model, which was trained on LFVs -domain-expert curated features. This serves to indicate that the CUSTODI model is capable of extracting the important structural features from this new representation automatically, without expert intervention. We emphasize that CUSTODI can be used in a similar manner on different string representations to derive structure-property relationships.</p><p>Both the RF and CUSTODI models describe the relative importance of various structural features/subunits, but they could not describe their effect -i.e., increase or decrease in magnitude. Our group is currently exploring the use of additional interpretable algorithms to provide further insight into this, as well as other, aspects. In particular, we are investigating the direct impact of individual structural motifs on different aromaticity indices. Additional emphasis is on the expansion of the LALAS representation concept to include peri-condensed and poly(hetero)cyclic aromatic systems and on generating the relevant data to enable further exploration and analysis of this chemical space.</p><!><p>The full code used in this paper appear in our GitLab repository at https://gitlab.com/porannegroup/lalas. The data was taken from the COMPAS Project repository at https://gitlab.com/porannegroup/compas.</p>

ChemRxiv

Estimation of acute oral toxicity in rat using local lazy learning

BackgroundAcute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance’s acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.ResultsIn this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R2 of 0.712 on a test set containing 2,896 compounds.ConclusionEncouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

estimation_of_acute_oral_toxicity_in_rat_using_local_lazy_learning

Background<!>Performance evaluation of LLL models<!><!>Performance improvement by constructing consensus model<!><!>Performance improvement by enriching the reference set<!><!>Performance improvement by enriching the reference set<!><!>Effects of applicability domain<!><!>Effects of applicability domain<!>Datasets<!>Feature sets and similarity measurement<!>Prediction models<!>Conclusion<!>kNN-based LLL Models<!><!>kNN-based LLL Models<!>Consensus model<!>Applicability domain (AD)<!>Abbreviations<!>Competing interests<!>Authors’ contributions<!>Additional file 1<!><!>Acknowledgements

<p>Estimation of rodent acute toxicity is an important task in the safety assessment of drug candidates. Median lethal dose (LD50), a dose causing 50% death of the treated animals in a given period when administered in an acute toxicity test [1], is a common criterion that measures acute toxicity of compound. However, due to ethical reasons, the animal experiments on rodent acute toxicity are highly controversial. European Union Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) has recommended the use of in vitro or in silico methods instead of animal testing of LD50[2]. This proposal drives the development of quick, reliable, and non-animal predicting methods such as quantitative structure-toxicity relationships (QSTRs).</p><p>Acute toxicity involves multiple biochemical mechanisms, and a large number of compounds have been reported for their LD50 information, which covers a significant portion of chemical diversity space. These complexities pose a big challenge to the building of a single QSAR model with high prediction accuracy. Taking the acute rodent toxicity as an example, Enslein et al.[3,4] developed multiple linear regression (MLR) models based on noncongeneric datasets, and found that the models had poor prediction power. To increase the prediction accuracy, Eldred et al.[5] and Guo et al.[6] built a few local models based on congeneric datasets. This type of models has improved accuracy, but their application ranges are limited. Zhu et al.[7] introduced the applicability domain (AD) in their study, and constructed consensus model from multiple individual models using k nearest neighbors (KNN), random forest, hierarchical clustering, and so on. The consensus model showed improved results as compared to the individual constituent models, while the prediction accuracy is still limited when the model coverage increases.</p><p>Due to the complex mechanisms of acute toxicity, we explored the similarity-based local models to study the rat LD50 data by oral exposure. The basic idea of such models follow that "structurally similar molecules are likely to have similar properties", which is suitable for modeling very complex boundaries between two classes [8]. In light of the idea, Yuan et al.[9] proposed a method "Clustering first, and then modeling". It means the training set members are firstly grouped together based on their structural similarity. Then, the test set member is assigned to a specific group according to its structural resemblance to the group members, and its toxicity value is next predicted using an on-the-fly constructed model from the group. This method shows good performance for the datasets with distinct clusters, but it has the disadvantage of requiring a priori knowledge of the number of clusters. In this study, we try to use local lazy learning (LLL) to solve this problem. Given a test compound, LLL method firstly find its k nearest neighbors in the training set by using a predefined property set (molecular fingerprints or descriptors), and then build local models using these compounds to predict the value of the test compound. This method can fully consider the structural information of every test compound, while doesn't rely on a priori knowledge of clusters. Moreover, to further improve the prediction accuracy, we try to enrich the reference data set and construct consensus models, which are critical for reducing the high variance of individual models. In the end, we analyze the application domain of the resulted models.</p><!><p>The use of LLL models makes it possible to explore many local structure-toxicity trends rather than global trends, which is expected to achieve an improvement in the prediction accuracy. Among the four types of LLL models, LLR prediction is based on a linear regression model with a single explanatory variable. In contrast, SA, SR, and GP predictions are directly based on the LD50 values of the query's neighbors. In assessing molecular similarity, we used three structural (ECFP4, FCFP4, and MACCS) and descriptor-based (DES) metrics to determine which compounds would be selected as neighbors of a query from different aspects. Since each LLL model can be combined with each type of the metrics, there are 16 individual models in all. During constructing kNN-like models, the choice of k is very critical. A small value of k can make noises have a higher influence on the result, while a large one makes it computationally expensive and does not follow the underlying assumption that similar compounds share similar toxicity. Here the LLR and GP models automatically learn a specific k for each query compound. In contrast, SA and SR models use a fixed number of neighbors, which is optimized using cross-validation on the whole reference set. Table 1 summarized the statistics of the models on the test set using reference Set I, together with the best results of Zhu et al.[7] for comparison. Among the four LLL models, LLR has the lowest R2 and the largest MAE (mean absolute error), and the results of GP and SR are slightly better than SA. It is not surprising to notice that LLR yielded inferior prediction accuracy. Compared with other models, LLR has the ability to make prediction outside the range of our reference data. However, it is also subject to greater uncertainties and a higher risk of producing meaningless results, as it relies on not only the existence of similar reference compounds, but also the choice of relevant variables to establish the local regression model. For the other three methods, the SA model assumed equal contribution of all neighbors. However, when constructing similarity-based models, it cannot be guaranteed that all neighbors are similar enough to the test compound. Therefore, the performance of SR and GP is expected to be improved by applying weighting strategy such that more similar neighbors contribute more to the prediction. In this respect, we may find that the prediction accuracies of different models agree with our expectation. Moreover, for the four similarity metrics, most results obtained from fingerprints (ECFP4, FCFP4, and MACCS) were found superior to the results from DES. This observation suggests that the structural similarity is more efficient than the descriptor-based similarity in selecting neighbors with equivalent toxicity level. Among the 16 individual LLL models, the best combination is GP + ECFP4. It yielded R2 of 0.491 for "Set_2896", 0.514 for "Set_2583", and 0.719 for "Set_743". Apart from being used to build predictive models, the fingerprints can also be analyzed to find out the fragments that may cause acute toxicity. The corresponding analysis with ECFP4 is provided in Additional file 1.</p><!><p>Performance of four LLL models using different similarity metrics on the test set using reference set I (Group I) versus the best model of the reference</p><p>aFor each coverage set, the best result among all individual models reported by Zhu et al. [7] was shown.</p><p>bThe reference did not report the prediction results of all compounds in the test set.</p><!><p>LLL models using different learning algorithm or similarity metrics can possess the high variance. For example, the descriptors used in this study characterize whether two compounds share similar physic-chemical properties, while fingerprints more focus on the 2D structure similarity, leading to that an individual model could only capture part of the relationship. Depending on the analogues retrieved by different similarity metrics, the learned decision boundaries and values can vary significantly. As a result, each compound has a chance of being successfully predicted for some individual models. This can be illustrated by the prediction of 5,6,7-Trichloro-2-(trifluoromethyl)-4- benzimidazolesulfonamide [10]. As shown in Figure 1, we may notice that all the individual model predictions show great variability, in which the toxicity is overestimated by using ECFP4 and FCFP4, but is underestimated by using MACCSS and DES. Accordingly, the average prediction from individual models is closer to the actual LD50 value. Therefore, to explore the complementary features of different modeling techniques for predicting the acute oral toxicity, we also constructed consensus models based on the above described individual LLL models. Given a query compound, its LD50 value in consensus model is predicted as the arithmetic average of all LD50 values from individual models. The statistical results of different consensus models on the test set were listed in Table 1. Clearly, all the consensus models showed improved performance as compared to their constitutional ones. Most of the prediction accuracies of the LLR, SA, SR, and GP consensus models were higher than those of the reference (e.g., R2 of "Set_2896" is 0.545 for GP versus 0.42 for the reference consensus model). Of note is the final consensus model. It obtained further improved R2 values ranging from 0.545 to 0.769 for "Set_2896", "Set_2583" and "Set_743", significantly higher than those of the reference consensus model. These results demonstrated that the LLL model based averaging scheme is an efficient way for enhancing prediction accuracy of acute toxicity prediction.</p><!><p>Example to show how consensus model improve the prediction results. Comparison of estimates for an herbicide (CAS: 89427-44-1) using individual and consensus models from Group I. The y-axis is prediction error.</p><!><p>The above results suggest that the use of LLL approaches can generally lead to an improvement in LD50 prediction accuracy. Then, we further inspected the performance of LLL models on the four different test sets. One can easily see that a significantly better prediction can be achieved for compounds which have more similar neighbors in the reference set. Clearly, a large and structurally diverse reference set is essential to the similarity based approaches. To enrich the chemical diversity space covered by the reference library, 2,271 compounds with LD50 values from EPA [11] and Accelrys Toxicity database [12] were combined with the reference set I to compose the reference set II. Table 2 summarized the statistics of all individual and consensus models on the test set by applying the expanded reference set. To make the comparison more clearly, we also plotted the distribution of performance on the whole test set ("Set_3874") using the two reference set. As shown in Figure 2, we may find that the increasing of the size and diversity of reference set evidently contributed to the improvement of results. For example, the model GP + ECFP4 using reference set II obtained a R2 of 0.587 on the whole test set, while the corresponding model using reference set I only gave a R2 of 0.413. In contrast to the subtle differences among the results of various LLL schemes, the enrichment of the reference set substantially improved the prediction performance of all models.</p><!><p>Performance of our models on the test set using the reference set II (Group II)</p><p>Distribution of R 2 values for the prediction of whole test set ("Set_3874") using different reference set.</p><!><p>The improvement can be illustrated with the prediction of the following test compound. Flocoumafen (cas: 90035-08-8) is an anticoagulant rodenticides [13] with a LD50 of 6.336. As shown in Table 3, the neighbors selected from the reference set I have low similarities to the compound, and the resultant prediction errors are large. However, the similarities of the neighbors selected from the reference set II to flocoumafen are significantly increased, and the toxicity range of neighbors (the range is from 4.762 to 6.515) is also closer to the test compound. With these similar reference compounds, the results of Group II had significant improvement compared to those of Group I. Apparently, the reference set should be expanded not only in terms of chemical diversity but also their activity distribution to afford higher prediction accuracy of LLL models.</p><!><p>The results of the test compound flocoumafen (cas: 90035-08-8) and its neighbors from the reference set I and II</p><p>aExperimental –log(LD50).</p><p>bThe Tanimoto distance of Flocumafen to its neighbor using ECFP4.</p><p>cThe predicted –log(LD50) value by the best individual model.</p><p>dThe predicted –log(LD50) value by the final consensus model.</p><!><p>A large reference set with more similar compounds can improve the prediction performance of models. However, we still need to determine in which range and to what an extent the LLL models can be reliably applied. In this study, the AD is defined as a Tanimoto (or Euclidean) distance threshold between the test compound and its nearest neighbor. If the calculated distance is beyond the threshold, the prediction of the test compound is considered to be unreliable. Through the Equation (7), we obtained the AD threshold of every individual model. In Group I, the distance thresholds are 0.595 for ECFP4, 0.477 for FCFP4, 0.257 for MACCS, and 0.035 for DES. In Group II, the distance thresholds are 0.498 for ECFP4, 0.378 for FCFP4, 0.195 for MACCS, and 0.025 for DES. Obviously, the expansion of the reference dataset made it possible to find more similar compounds for the test compound, and hence enlarged the model AD. In order to investigate the influence of AD on prediction error, we further applied the models of Group II to the test compounds within and outside AD, respectively. From the distribution of MAEs (see Figure 3), we can find that the MAEs follow the trend within < all < outside in all individual models, which is in line with our initial hypothesis that AD delimitation is required to assess the reliability of a prediction. Specifically, the models using ECFP4 have the best performance for compounds both within and outside AD, but have the smallest AD. In contrast, the models using "DES" have the worst performance but the largest AD. When using the same similarity metrics, GP in most cases showed the best performance among the four LLL model types, while the other three exhibited some differences for compounds within and outside the AD. For example, the MAEs of LLR are lower than SA and SR for compounds within AD, but are higher for those outside AD. All these observations suggested that the individual LLL models explain complementary portions of the variance in chemicals' LD50 data, which also account for the improvement in consensus modeling in this study.</p><!><p>The MAEs of all individual models for the test set in Group II. all (blue): the MAEs of all compounds in the test set; within (red): the MAEs of compounds within AD; outside (green): the MAEs of compounds outside of the AD; comp: the number of compounds within the AD.</p><!><p>As the final consensus model is constructed by taking the arithmetic average of all LLL models, its reliability to predict a test compound highly depends on its constituent models. Therefore, the "consensus prediction fraction" (i.e., the ratio of individual models being reliable to predict a new compound), is used to define the AD of the final consensus model [7]. In the current study, the final model comprises four kinds of individual LLL models, and if one of them can reliably predict a new compound, the "consensus prediction fraction" is 25% for this compound. Only if the "consensus prediction fraction" is larger than or equals to a predefined threshold, the final consensus model is considered as reliable. When the threshold is set to be 75%, there are totally 2,378 test compounds within the AD, on which the final consensus model has significantly improved performance. For example, for the final consensus model of Group II, the MAEs of all, within, and outside are 0.422, 0.358, and 0.523, respectively. Obviously, the application of AD can tell us when the final consensus model provides a reliable and better estimation of acute oral toxicity in rat.</p><!><p>The rat LD50 data by oral exposure were collected from Zhu et al.[7], United States Environmental Protection Agency dataset [11], and Accelrys Toxicity database 2011.4 [12]. The final dataset included 9,617 compounds after removing the duplicated and wrong structures. Among them, there are 3,472 and 3,874 compounds identical to Zhu's training set and test set, which will be used as the reference Set I and the test set, respectively. For comparison, we prepared three subsets representing different prediction coverage, in which the test compounds were ranked ascendingly according to their distance to their nearest neighbors, and then the first 2,896, 2,583 and 743 compounds were selected, respectively, to comprise multiunit test sets (hereafter called "Set_2896", "Set_2583", "Set_743"). In addition we also constructed an expanded reference set named the reference set II, which contains 5,743 compounds including the whole reference Set I, the compounds from EPA dataset and Accerlys Toxicity dataset. The original unit of LD50 was firstly converted to -log(mol/kg) to conform to the standard QSAR practice.</p><!><p>The initial structures of all compounds were optimized by Sybyl 6.8 [14] which used the Powell method with Tripos Force fields and Gasteiger–Hückel charges. Further structural optimization was performed using the AM1 semi-empirical method implemented in AMPAC 8.16 [15]. To measure the similarity between compounds, we tried both physicochemical descriptors and molecular fingerprints. For the former, totally 490 descriptors were calculated with Codessa 2.7.2 [16] and Discovery Studio 2.5 [17]. After removing those descriptors with zero variance or that cannot be calculated for some compounds, altogether 286 descriptors were remained (hereafter called "DES"). For any two compounds, their normalized Euclidean distance (Dis) [18] was defined below:</p><p>(1)Dis=∑i=1286XiA−XiB2286</p><p>where X iA and X iB are the normalized values of the i-th descriptor of A and B, respectively. For molecular fingerprints, the widely used ECFP4 [19], FCFP4 [19], and MACCS [20] were generated with RDKit [21]. Given the bit vectors of those fingerprints, the Tanimoto similarity (Sim) [18] between any two compounds were computed, and their Tanimoto distance (Dis') was given by the following transformation:</p><p>(2)Dis'=1−Sim</p><!><p>For each given query compound, four sets of k nearest neighbors were retrieved from the reference set using different feature sets. Then local lazy learning strategies were applied to construct local models, from which consensus model was built. All the computation was done using in-house C/Python programs.</p><!><p>In this study, four kinds of local lazy learning schemes were combined with four similarity metrics to predict the acute toxicity in rat. Different from the conventional global QSAR models built upon the entire diverse data set, these LLL models were constructed "on-the-fly" by only utilizing the analogical compounds of a query. Accordingly, the detailed and subtle local structure-toxicity relationships around the query compound can be captured, which might be otherwise overshadowed by the large amount of employed training compounds in global models. As the approach relies on a priori knowledge about the toxicity profile of a query's neighbors, its prediction accuracy can be improved by enriching the size and the structural diversity of the reference set, and the "on-the-fly" feature of LLL models also allows for a timely update and expansion. To reduce the high variance of individual LLL models, a consensus modeling scheme was employed, which further improved the accuracy of LD50 prediction. For the "Set_2896", the R2 of the final consensus model using reference set II was enhanced from 0.545 to 0,712, and the MAE of prediction was reduced to 0.385. Moreover, by introducing the concept of AD, the reliability of a predication can be evaluated. For the compounds within AD, their toxicity can be more accurately predicted. The outstanding performance of our approach suggests that LLL models are feasible and effective for in silico prediction of acute oral toxicity in rat. We expect this method would also be a useful tool to provide inspiration for discovering novel drug candidates with favorable safety profile.</p><!><p>Four sets of k nearest neighbors were provided for a test compound by ECFP4, FCFP4, MACCS, and DES. For each set, four types of LLL models were built, with details described as follows:</p><!><p>The flowchart of the LLR modeling.</p><p>Triangle inequality. An triangle can be constructed for any 3 compounds by using Euclidean or Tanimoto distance as the length of edge. D is the projection of vertex C on line AB.</p><!><p>a. Local lazy regression (LLR): For each test compound, only one most relevant descriptor was selected to build a linear equation based on k nearest neighbors. During the procedure, both the descriptor and the number of nearest neighbors were optimized. Initially let k = 5 and Q 2  = −108 (an arbitrary negative value that will be updated during the iteration), the algorithm was described as follows: (1). Select k nearest neighbors of the query compound (q) from the reference set. (2). Use one single descriptor to build a linear regression model, and perform a leave-one-out (LOO) cross-validation for the model. Note here the descriptor value calculated for the query was compared with those calculated for its neighbors. If the query's value falls outside the range of its neighbors, the descriptor was disregarded to avoid yielding an extrapolated value. (3). After traversing all descriptors, record the descriptor D(k) that leads to the highest LOO Q 2 k . (4). If Q2 k  > Q2, update Q2 and D (k), then add one more neighbor, and repeat steps 1–3 until k > 20; otherwise, the iteration is terminated, and let k = k-1. Finally, the query compound was predicted by a linear model using k nearest neighbors and the descriptor D (k) . Figure 4 shows the flowchart of the LLR modeling.</p><p>b. SA model: The predicted toxicity of the test compound is calculated as [22,23]:</p><p>(3)ypre=∑i=1nyi,obsn</p><p>where y i,obs is the experimental value of the i-th neighbor, and y pre is the predicted value of the test compound. The value of n was determined by using 10-fold cross validation of the reference set.</p><p>c. SR model: The predicted toxicity of the test compound is calculated by [22,23]:</p><p>(4)ypre=∑i=1nSi∑j=1nSj*yi,obs</p><p>where s i is the similarity value between the test compound and the i-th neighbor. The value of n was determined in the same way as being used to build the SA model.</p><p>d. GP model: For a query compound C and any two neighbors A and B, a triangle can be constructed in a multidimensional descriptor space (Euclidean space). As shown in Figure 5, the value of projection point D is used as an estimate of C, and under the assumption that the y-values is linearly changed along the line AB, the value of D can be calculated as follows:</p><p>(5)yD=yA−dADdAB*yA−yBifdAB≠0yD=yA+yB2otherwise</p><p>where d AB is the distance between neighbor A and B, d AD  = d AC * cos∠CAB, and y is –log(LD50) value of compound. Besides, as being proved by Lipkus [24] that Tanimoto distance from bit vectors also satisfies the triangle inequality, the GP model can also be applied to those using Tanimoto distance as a similarity measurement. Given k nearest neighbors of the query, any pair of the neighbors can build a GP model to yield a projection point D. We take the weighted average of all C2k individual estimates of the projection points as the final prediction:</p><p>(6)ypre=∑SAC·SBC·yD∑SAC·SBC</p><p>where S is the similarity between a pair of neighbors, y D is obtained by Equation (5). Of note the above definitions assume that none of the edges of a triangle is degenerated. If a query compound has a neighbor with a zero distance, its LD50 is directly estimated by that neighbor. Moreover, a proper k was automatically optimized for each test compound by applying the same strategy showing in Figure 4. This procedure is similar to that of LLR except that the initial k value was set to 3.</p><!><p>The LLL methods in this study are all similarity based, of which the decision boundary or value largely depends on the input points and their particular positions. However, individual models using different modeling methods or similarity measurements could vary significantly and capture different part of the relationship. To reduce the high variance, consensus model can be established by combining each individual model, which has been demonstrated to be an effective means to improve the performance of similarity based methods [7,25-29]. In this study, the strategy as used in Zhu et al.[7] was applied to build consensus model, in which the predicted toxicity for each compound equals to the arithmetical mean of all predicted values of individual models. For each type of LLL method, a consensus model was constructed by averaging the results using different similarity metrics, named as "LLR_consensus", "SA_consensus", "SR_consensus", and "GP_consensus", respectively. Besides, a consensus model named "Final_consensus" was built by averaging all the 16 individual models.</p><!><p>In the QSAR models, the AD defines the chemical space for which the model is considered to be applicable [30]. Here we use the distance-based AD definition [30,31], which assumes a prediction reliable if the concerned molecule is located in the neighborhood of the reference set compounds. The threshold of distance D T is defined as:</p><p>(7)DT=d¯+Z*σ</p><p>Where d¯ is the average Tanimoto or Euclidean distance between all compound and their nearest neighbor in the reference set, σ is the standard deviation of these distances, and Z is an arbitrary parameter to control the threshold level (here set to 0.5). If the distance of the compound to its nearest neighbor exceeds this threshold, this test compound is treated as an "outlier", and the prediction result is considered to be unreliable.</p><!><p>LD50: Median lethal dose; LLL: Local lazy learning; LLR: Local lazy regression; QSTRs: Quantitative structure-toxicity relationships; LOO: Leave-one-out; AD: Applicability domain; kNN: k nearest neighbors; MAE: Mean absolute error.</p><!><p>The authors declare that they have no competing interests.</p><!><p>Conceived and designed the experiments: MYZ and XML. Performed the experiments: JL, JLP, JAW, and QCS. Analyzed the data: JL, JLP, YB, MYZ, and XML. Wrote the paper: JL, JLP, MYZ, and XML. All authors discussed the results and commented on the manuscript. All authors have given approval to the final version of the manuscript.</p><!><p>Frequency analysis of fingerprints to find out potential substructures causing acute toxicity.</p><!><p>Click here for file</p><!><p>This work was supported by Hi-TECH Research and Development Program of China (Grant 2012AA020308), National S&T Major Project (Grant 2012ZX09301-001-002), and National Natural Science Foundation of China (81220108025, 81001399, 2013ZX09507001). We would like to thank Dr. Hao Zhu and Todd Martin for valuable dataset of rat LD50.</p>

PubMed Open Access

Metal-doped mesoporous ZrO₂ catalyzed chemoselective synthesis of allylic alcohols from Meerwein–Ponndorf–Verley reduction of α,β-unsaturated aldehydes

Meerwein-Ponndorf-Verley reduction (MPVr) is a sustainable route for the chemoselective transformation of a,b-unsaturated aldehydes. However, tailoring ZrO 2 catalysts for improved surface-active sites and maximum performance in the MPV reaction is still a challenge. Here, we synthesized mesoporous zirconia (ZrO 2 ) and metal-doped zirconia (M_ZrO 2 , M = Cr, Mn, Fe, and Ni). The incorporation of metal dopants into zirconia's crystal framework alters its physico-chemical properties such as surface area and total acidity-basicity. The prepared catalysts were evaluated in the MPVr using 2-propanol as a hydrogen donor under mild reaction conditions. The catalysts' remarkable reactivity depends mainly on their surface mesostructure's intrinsic properties rather than the specific surface area. Cr_ZrO 2 , which is stable and sustainable, presented superior activity and 100% selectivity to unsaturated alcohols. The synergistic effect between Cr and Zr species in the binary oxide facilitated the Lewis acidity-induced performance of the Cr_ZrO 2 catalyst. Our work presents the first innovative application of a welldesigned mesoporous Cr_ZrO 2 in the green synthesis of unsaturated alcohols with exceptional reactivity.

metal-doped_mesoporous_zro₂_catalyzed_chemoselective_synthesis_of_allylic_alcohols_from_m

Introduction<!>Materials<!>Synthesis of catalysts<!>Characterization of catalysts<!>Evaluation of catalytic performance<!>Surface properties of the M_ZrO 2 catalysts<!>Surface performance of the M_ZrO 2 catalysts in MPV reduction of citral<!>Substrate scope of mesoporous Cr_ZrO 2 catalyst<!>Discussion<!>Conclusion<!>Conflicts of interest

<p>The chemoselective reduction of a,b-unsaturated aldehydes to their corresponding allylic alcohols is one of the significant chemical transformations in synthetic organic chemistry. The unsaturated allylic alcohol (UAA) produced through this process is widely utilized as the primary feedstock in food and perfumery industries and intermediates in pharmaceutical industries. 1,2 However, the reaction is classically carried out using gaseous hydrogen in the presence of noble metals as catalysts with significant limitations such as high-pressure requirements and low selectivity to UAA. 3 The high-pressure involved in such a chemical process requires expensive equipment and an elaborate experimental set-up with associated safety risks. 4,5 The problems associated with these classical methods could be avoided in the Meerwein-Ponndorf-Verley (MPV) reduction.</p><p>The MPV is an alternative means of selective reduction of unsaturated carbonyls at atmospheric pressure using safe and readily available secondary alcohol as a hydrogen donor instead of high-pressure molecular hydrogen or hazardous reduction reagents such as LiAlH 4 and NaBH 4 . 6 The MPV among several reduction routes for functional group conversion has the following prevailing advantages: (i) easy to handle hydrogen donor without the requirement of heavy gas containment, (ii) cheap and environmentally friendly source of hydrogen (iii) enhanced selectivity under mild reaction conditions (iv) safer process (v) minimized waste (vi) reduced cost, e.g., maintenance, separation, and other production logistics (vii) economical and environmentally sustainable process. 7,8 However, to force the equilibrium reaction towards the formation of UAA, the MPV reduction requires excess sacrificial H-donating molecule, 9 which generates by-product. 10,11 The product separation through distillation is a tall task owing to the close boiling points of the a,b-unsaturated aldehydes or ketones, the UAAs, and the sacrificial alcohol. Hence, efforts should be directed towards attaining 100% selectivity and product yield and recycling the by-product generated from the sacrificial hydride donor. 11 The MPV reaction is highly chemoselective to the reduction of CQO bond in the a,b-unsaturated aldehydes but, the hydrogenation of conjugated CQC is preferentially favorable thermodynamically and kinetically over the CQO bond. 3,12 There is an ongoing research effort to develop an efficient catalyst system in MPV reactions. The catalytic performance of metal oxides depends on the surface's structural features or environments, influencing their adsorption strength and activation of the adsorbates. 24,25 Among a wide range of heterogeneous catalytic species utilized in MPV reduction, zirconia showed the most promising performance. 26 The potential catalytic activity of zirconia has increased its application in catalysis. It is known for its acidic properties, 27 high thermal stability, and corrosion resistance. 7,28 To further emphasize this fact, Alvarez-Rodriguez et al. pointed out that the zirconia catalyst is more effective than other conventional catalysts such as alumina or silica to produce unsaturated allylic alcohol from citral and cinnamaldehyde. 29 The surface active sites of zirconia has been verified to improve by addition of dopant. 30 Xie et al. prepared Cr-ZrO 2 using acidbase pair pathway with evaporation-induced self-assembly. 31 They found out that the catalytic performance in dehydrogenation of propane with CO 2 was influenced by the enhanced surface morphology, acidity and redox property of the Cr-ZrO 2 catalysts with different Cr doping percentage. Also, in the report of Wu et al. the catalytic activity of the Cr 2 O 3 -ZrO 2 prepared hydrothermally was attributed to the presence of Cr 6+ species. 32 From the above literature survey coupled with the crucial need for sustainable and environmentally benign catalytic hydrogenation processes, 11 it would be interesting to develop novel mesoporous zirconia-based catalyst systems with increased acidic sites for the transfer hydrogenation process under mild reaction conditions with high selectivity to unsaturated allylic alcohol. Also, a stable catalytic MPV process without the use of additives is imperative for the green and clean synthesis of UAA. The surface properties of zirconia, a major determining factor of its catalytic activity, may depend on the method of synthesis. 33 Several methods of preparing mesoporous zirconia have been reported, but the inverse micelles soft-templated technique is poorly represented. The inverse surfactant micelle approach enables the control of the surfactant-transition metal interactions, hydrolysis, and condensation of inorganic sols. 34,35 Transition metal oxides prepared using the inverse micelles approach are known for their exceptional structural properties such as large surface area (S BET ), porosity, and crystallinity. The large surface area avails the active sites for better catalytic activity than other porous materials with low S BET .</p><p>Hence, in this study, a triblock copolymer P-123 was employed as a structure-directing agent in the inverse micelles approach 34,35 for the synthesis of mesoporous zirconia and metal-doped zirconia. The mesoporous zirconia-based materials' catalytic potential was investigated as active phases in the MPV reduction of selected aromatic aldehydes to their corresponding alcohols (Scheme 1) without any additives or co-solvent. We optimized the MPV process variables. A deep insight into the effect of cation dopant on the physicochemical properties of ZrO 2 , including crystal phases, structural morphology, surface area, and acidity-basicity, was evaluated. Furthermore, the relativity of these properties to its catalytic performance in the MPV process was also revealed. Elucidation of the activity was based on the observed pseudo-first-order rate constants (k obs ) and calculated conversion Equations S1-3. Also, we show that all the synthesized catalysts exhibit excellent selectivity to the unsaturated allylic alcohol in 2-propanol as H-donor at 80 1C, 450 rpm, and atmospheric pressure. To the best of our knowledge, this is the first work that applied mesoporous Cr_ZrO 2 prepared via inverse micelle for the MPV process. The mesoporous Cr_ZrO 2 showed Scheme 1 MPV reduction of a,b-unsaturated aldehydes to their corresponding unsaturated allylic alcohol over the mesoporous zirconia-based catalyst.</p><p>considerable conversion with 100% selectivity to the unsaturated alcohols in reducing citral as a model reaction and other aldehydes. The synthesized Cr_ZrO 2 is a sustainable catalyst for the Meerwein-Ponndorf-Verley process.</p><!><p>Nitric acid (HNO 3 ) (69-70%) was purchased from Rochelle Chemicals (RSA). 1-Butanol (99.8%), ethanol (99.9%), poly(ethylene glycol)block-poly(propylene glycol)-block-poly(ethylene glycol) (PEO20-PPO70-PEO20 or Pluronic P-123), zirconium(IV) butoxide solution (80% in 1-butanol), manganese(II) nitrate tetrahydrate (97%), nickel(II) nitrate hexahydrate (99%), cinnamaldehyde (99%), citral (mixture of cis-and trans-isomers) (96%), benzaldehyde (99%), crotonaldehyde (99%), furfural (99%), 2-propanol (99.5%), decane (99%) were all purchased from Sigma-Aldrich. Ferric nitrate nonahydrate was purchased from SRL chemicals, and chromic(III) nitrate nonahydrate (98%) was purchased from UNIVAR, SAAR CHEM pty. All chemicals were of analytical grade and used as received without further purification.</p><!><p>The procedure already reported was followed for the synthesis of ZrO 2 . 34 Briefly, 15.34 g (0.040 mol) of zirconium butoxide was added to a solution containing 25.04 g (0.336 mol) 1-butanol, 4.0 g (6.8 Â 10 À4 mol) of P-123, and 4.0 g (0.064 mol) HNO 3 . The mixture was stirred overnight, and the clear gel was dried in an oven at 120 1C for 4 h. The yellow glassy thin flakes obtained were calcined in air at 350 1C for 5 h with a heating rate of 2 1C min À1 . The metal-doped zirconia was synthesized by adding the metal dopant (Cr, Mn, Fe, and Ni) precursor to zirconium butoxide in a molar ratio of 1 : 4. As in the case of pure ZrO 2 , the same thermal treatment was applied (Scheme S1, ESI †). The samples are tagged M_ZrO 2 , M = metal dopant.</p><!><p>Powder X-ray diffraction (p-XRD) analyses were carried out on a Philips XPERT-PRO diffractometer system operating with Cu Ka1, Ka2, and Ni Kb radiation (l = 1.5406, 1.54443 and 1.39225 Å, respectively) at 25 1C. Both low and wide 2y range (i.e., 4-901) angle diffraction patterns with a step of 0.1701 were measured. The Debye-Scherrer equation (eqn (S1), ESI †) was used to calculate the mean crystallite size. Micrometric ASAP 2460 sorption system gave the nitrogen sorption measurements. The samples were firstly degassed under flowing nitrogen at 100 1C for 18 h and under vacuum for 10 h at the same temperature before the experiments to remove any physisorbed moisture. The surface areas were calculated using the Brunauer-Emmett-Teller (BET) method. Transmission electron microscopy (TEM) for the confirmation of the mesostructure was achieved on a JEOL Jem-2100F electron microscope with an accelerating voltage of 200 kV. The pore diameter was measured using ImageJ software. A 10 mg of the catalyst was sonicated in 1 ml of methanol for 1 h, and a drop of the suspension was placed on a carbon-coated Cu-grid then allowed to dry before the TEM analysis. The prepared samples' surface morphology was identified on a Tescan Vega 3 LMH scanning electron microscope (SEM) using a scattering electron detector with a high voltage of 20.0 kV. Prior to analysis, the samples were placed on an aluminum stub and carbon-coated in an Agar Turbo carbon coater. The quantity of dopant M n+ was verified with energydisperse X-ray spectroscopy (EDX). The distribution of the metal species was identified by elemental mapping on SEM. Also, the dopant content in the solid samples was measured using a Spectro Acros ICP-OES spectrometer. The Fourier transform infrared spectroscopy (FTIR) spectra of the samples were recorded on a Bruker FTIR Alpha spectrometer in the 4000-400 cm À1 region. The samples were mixed with KBr, and the analysis was performed in the transmission mode under ambient conditions. The NH 3 /CO 2 temperature-programmed desorption (TPD) studies to determine the materials' acidity or basicity were performed on a Micromeritics AutoChem II. About 0.2 g of the sample was loaded in a quartz tube reactor. The loaded sample surface was degassed in a He gas flow at 200 1C for 1 h before the TPD measurement. We used a mixture of NH 3 or CO 2 and helium in the ratio of 10 : 90 as the probe gas at a flow rate of 50 ml min À1 . Measurements were performed in the temperature range of 30-550 1C at a temperature ramp of 10 1C min À1 and 3 1C min À1 for TPD-NH 3 and TPD-CO 2 , respectively. For identifying the Lewis and Brønsted acid sites on the samples, adsorbed pyridine FTIR analysis was carried out. Before the analysis, B0.03 g of the sample was activated by degassing under gaseous nitrogen at 300 1C for an hour and cooled to room temperature. After that, the activated catalyst was contacted with pyridine (200 ml) at 120 1C for 30 min. Subsequently, the physisorbed pyridine was evacuated under vacuum at ambient temperature for an hour, 36 and the sample was analyzed on a Bruker FTIR Alpha spectrometer. The H 2 -TPR (hydrogen-temperature programmed reduction) analysis was conducted on the same Micromeritics Autochem II. Approximately 30 mg of the catalyst was loaded in the quartz tube reactor and pretreated under Argon flow at 200 1C for 1 h to ensure the catalyst surface is clean before each test. After the pretreatment, H 2 /Ar (10 : 90) was passed over the catalyst at a 50 ml min À1 flow rate. The measurements were performed within the ambient temperature to 800 1C with a 10 1C min À1 ramping rate. The prepared samples' thermal stability test was performed on a PerkinElmer STA 6000 thermogravimetric analyzer (TGA). The degradation study temperature was varied from 25-900 1C with a ramping rate of 10 1C min À1 under air at a 20 ml min À1 flow rate. The UV-vis spectra of the samples were obtained on a microplate reader (PowerWave HT.Biotek microplate reader). Before obtaining the UV-vis absorption spectra, about 30 mg of the solid sample was sonicated in 2 ml methanol and decanted. After that, the supernatant was analyzed using a 24-well plate.</p><!><p>The liquid phase MPV reduction experiments were performed on a carousel reaction station multi-reactor (Radley Discovery Technologies) with twelve 50 ml vials. The 50 ml reactor vial was charged with 0.4 g M-ZrO 2 , 2.50 mmol of aldehyde, 1.00 mmol (200 ml) decane as an internal standard, and 130 mmol (10 ml) 2-propanol. Followed by reflux at 80 1C and stirring at 450 rpm with a 16.5 mm crossbar stirrer. After filtration, the filtrate was analyzed on a Shimadzu GC-2010 with flame ionization detector (FID) using a capillary column (Restek RTX-5; 30 m, 0.25 mm ID, thickness 0.25 mm) in N 2 carrier gas. The injection port and FID temperature were maintained at 200 1C and 350 1C, respectively. The products were further confirmed by a Shimadzu GC-MS QP-2010 using the same capillary column with the injection temperature at 200 1C. The ion source and interface temperatures were 200 1C and 250 1C, respectively. For the GC FID and MS, the column oven temperature program started at 40 1C (hold 2 min), then programmed at 20 1C min À1 to 280 1C (hold 5 min); the total analytical time was 19 min (details in Section S1.3.2, ESI †). The catalysts were screened with the MPV reduction of citral as a model reaction. The catalyst exhibiting the best activity was chosen for the transfer-hydrogenation of selected a,b-unsaturated aldehydes. The substrate conversion, product selectivity, and normalized activity were calculated (eqn (S2)-(S5), ESI †). Furthermore, the observed k obs for each experiment were calculated using Kinetic studio version 2.08 software. For the recyclability study, the catalyst was pretreated by calcining at 350 1C/5 h before reuse. No extra peaks were detected, which confirms the purity of the synthesized t-ZrO 2 . Upon doping t-ZrO 2 with a metal atom, an isomorphous substitution was observed. Suggesting that some surface Zr atoms in the M_ZrO 2 samples are substituted with the dopant atoms and the formation of a homogeneous solid solution of binary M x O y -ZrO 2 . This claim is supported by the gradual shift of the peak at 30.41 (101) of ZrO 2 towards a higher 2y degree. No identifiable peak is associated with the dopants, which is an indication that the dopants are well incorporated into the ZrO 2 matrix and high dispersion of the dopant species. The incorporation of cation into the crystal framework of zirconia significantly influenced its crystallinity. The tetragonal structure with reduced peak intensity remains in the presence of Mn and Fe, while we observed crystal distortion in the case of Ni and Cr dopants. The dopant species weakened the t-ZrO 2 peaks in the case of Mn and Fe and were significantly destroyed in Ni and Cr, forming disordered ZrO 2 . This observation implies the degree of dopant incorporation and distribution and M n+ -Zr 4+ interaction. The observed broader and weaker diffraction peaks in Mn_ZrO 2 and Fe_ZrO 2 explain the occurrence of higher surface area in correlation with the host ZrO 2 . Also, the crystallite size of t-ZrO 2 (5.50 nm) decreased upon doping with Mn_ZrO 2 (1.37 nm) and Fe_ZrO 2 (2.59 nm). The decrease in the crystallite size possibly contributed to expanding the surface area, as depicted in Table 1.</p><!><p>The experiments carried out to confirm the surface structure and porosity of pure and doped ZrO 2 using nitrogen sorption analysis shown in Table 1 revealed that the pure ZrO 2 exhibited pores with an average diameter of 2.53 nm within the meso range with a large BET surface area (S BET ) 206 m 2 g À1 . The BET surface area of the M_ZrO 2 catalysts depends on the final catalyst's crystallinity, which is a function of the dopant's nature. The S BET of pure ZrO 2 (206 m 2 g À1 ) increased when modified with Mn (221 m 2 g À1 ) and Fe (223 m 2 g À1 ) but decreased in the case of Cr (190 m 2 g À1 ) and Ni (193 m 2 g À1 ) dopants. The pore sizes are within the range of 2.5-3.6 nm, a characteristic feature of mesoporous oxides. The metal dopants' addition distinctly enlarged the pore size 2.5-3.6 nm and the pore volume 0.1 to 0.26 cm 3 g À1 . The mesostructuring is associated with the metal-metal grain boundary adhesion/ expansion during the formation of oxo-metal clusters at the stage of sol condensation, diffusion of volatile species, and subsequent removal of the surfactant template. The degree of grain segregation is directly proportional to the porosity of the material. [37][38][39] Also, the larger the porosity, the slower the grain growth, as depicted in the correlation of the pore size and the crystallite size (Table 1). The observed physisorption isotherms for all the catalysts in Fig. 2a are typical of Type IV compared with the IUPAC classification reported by Thommes et al. 40,41 This further indicates that all the catalysts are mesoporous materials with thin capillary pores. The ZrO 2 catalysts exhibited hysteresis loops P/P 0 typical of H2 type indicating the occurrence of cavitation controlled evaporation; this depicts that the materials possess a heterogeneous pore network with the neck size (W) distribution much more narrow than the size distribution of the cavities (W c ), that is, W o W c . [40][41][42] The pore size distribution of pure zirconia and M_ZrO 2 are shown in Fig. 2b. All the catalysts showed unimodal pore size distribution, with the M_ZrO 2 exhibiting a narrower pore distribution compared to the pure ZrO 2 catalyst. Significantly, the corresponding BET surface area, pore-size distribution, and pore volume is an indication that the catalysts are mesoporous with high surface area, and the surface mesostructure of ZrO 2 could be tailored by adding foreign atomic specie.</p><p>Moreover, the transmission electron microscopy (TEM) and high transmission (HRTEM) images of the zirconia systems as displayed in Fig. 3a, b, d, e and Fig. S1, S2 (ESI †) reveal that they are made of nanosized particles with intraparticle voids that were preserved upon the addition of different metal ions. The TEM images (Fig. 3a, d and Fig. S1, S2, ESI †) show that the pores are well distributed in the ZrO 2 matrix, supporting the evidence of the presence of pore and the pore enlargement upon doping as presented by the N 2 sorption experiment (Table 1 and Fig. 2b). A similar observation was reported in the work of Xie et al. 31 The surface morphologies of the catalysts are shown in Fig. 3c, f, and Fig. S3 (ESI †). Modification of the t-ZrO 2 surface morphology upon the introduction of dopants is insignificant; this is due to the homogeneity of the M_ZrO 2 solid structures, as observed in the XRD patterns. The EDX mapping (Fig. 3g The thermal stability of the catalysts (Fig. 4a) suggests that the catalysts are thermally stable. The dopant species enhance the thermal stability of the host ZrO 2 (8%), except Mn_ZrO 2 , which shows an approximately similar weight loss of 8.3%. The 5% degradation between 30-257 1C is attributed to the removal of adsorbed surface and bulk water molecules, while above 257 1C could be classified as degradation due to the decomposition of the organic surfactant residue. Above 683 1C, the spectra seemingly flatten out, suggesting minimal or no decomposition and the inorganic material's stability. The samples are also stable in the catalytic characterization and application temperature within this study's scope.</p><p>The catalysts' hydrogen consumption temperatures (Fig. 4b) and the minimum temperatures (Table 2) suggest the catalysts' reducibility. The pure zirconia showed a poor hydrogen uptake with a small peak around 652 1C, corresponding to the reduction of bulk lattice oxygen of zirconia. We found that doping enhanced the reducibility of ZrO 2 , with a significant shift in its reduction peak to lower temperatures; this depicts the rate of the redox reaction. However, the degree of reducibility is dependent on the kind of doping species. The reduction peaks between 261-360 1C likely represent the reduction of the metal dopant species: Cr 3+ -Cr 2+ , Fe 3+ -Fe 2+ , Mn 2+ -Mn 0 , Ni 2+ -Ni 0 . The reduction peaks demonstrated in the region of 430-486 1C and above 600 1C are typical of surface and bulk reduction of lattice oxygen of zirconia, respectively. The Cr_ZrO 2 demonstrated the superior reduction capacity of surface interaction with hydrogen at the lowest temperature of 261 1C. This is likely due to the strong synergistic interaction between the Cr 3+ and Zr 4+ (Cr x O y -ZrO 2 solid solution). The H 2 -TPR data supported the superior catalytic activity of Cr-Zr active phase species for the H abstraction-release mechanism in the MPV dehydrogenationhydrogenation reaction.</p><p>We investigated the surface acid-base properties of the prepared catalysts by NH 3 -and CO 2 -TPD analyses. The spectra are depicted in Fig. 5 and 6, respectively. The total acidity and basicity, along with their density, are summarized in Table 2. The total acidity and basicity were obtained from the peak area of NH 3 and CO 2 desorption, respectively. The acid or base sites density was derived by dividing the total acidity or basicity by the surface area (Table 1). The NH 3 /CO 2 desorption peak around 200 1C represents the acid/base sites of a weak strength, from 200 1C to 350 1C depicts medium strength acid/base sites, and above 400 1C corresponds to strong acid/base sites. 43 The surface basic properties of all the catalysts are depicted in Table 2 and Fig. 5. A similar chair-like CO 2 -TPD profile was reported for ZrO 2 44 and Cu/ZrO 2 /CaO. 45 The base concentrations of the prepared catalysts ranged from 0.3-1.1 mmol CO 2 g À1 . The basic sites distribution of the catalysts illustrated in Fig. 5 depicted that all the catalysts exhibited basic sites of both weak and strong strength, although with different peak intensities. The base concentration of the pure ZrO 2 (0.9 mmol CO 2 g À1 ) was approximately similar in the presence of Fe (0.9 mmol CO 2 g À1 ) but slightly increased upon doping with Mn (1.1 mmol CO 2 g À1 ) and Ni (1.0 mmol CO 2 g À1 ). An exception occurred in the case of Cr_ZrO 2 ; the Cr species significantly decreased the base concentration of pure ZrO 2 to 0.3 mmol CO 2 g À1 . The catalysts' basic density showed a similar trend, which was also confirmed by the reduction in the peak intensity representing the weak strength basic sites on the pure ZrO 2 in the case of Cr_ ZrO 2 . Fig. 6 shows the distribution of the acidic sites of weak to strong strength in the meso-ZrO 2 . Upon doping, the acidic sites underwent modulation. In the presence of Mn, Fe, and Ni, only two broad peaks representing weak and medium strength acid sites were observed. Whereas the Cr_ZrO 2 appears unique, which gave a more prominent shoulder desorption peak on the high-temperature side at ca 436 1C, suggesting a stronger surface acidic site. A similar trend of NH 3 desorption over Cr doped ZrO 2 was reported. 31 The Cr_ZrO 2 (0.7 mmol NH3 g À1 ) possessed the highest concentration of acidity among the catalysts (Table 2). The NH 3 desorption results suggest the proton-donating capacity of the surface acid site on the catalysts. The stronger the proton-donor tendency, the more strongly it binds with the base (NH 3 ), and the higher the required NH 3 desorption temperature. Hence, Cr_ZrO 2 possesses stronger electrophilic active sites (acid sites) needed for the selective adsorption of citral via the CQO bond.</p><p>Comparatively, the NH 3 -and CO 2 -TPD data revealed that all the catalysts exhibit both surface acidic and basic sites. However, these active sites are not equivalent, as observed in the data derived from the acid to base ratio (Table 2); the dominance in terms of strength, total concentration, and density depends on the metal-metal interaction nature. Meanwhile, Cr_ZrO 2 presents more acid sites density and strength than the pure ZrO 2 , which possibly originates from the interaction of the Cr-Zr species at the atomic level. The tuning of the surface acid-base character of ZrO 2 was achieved by incorporating metal ions, which facilitated the understanding of the effect of acid/base sites of the catalysts on the MPV reduction of aldehydes.</p><p>To further understand the surface components, types, and structures of the acid sites of the catalytic materials, investigation of the surface functionality and nature of acid sites was achieved through FTIR (Fig. 7a and b) and pyridine-adsorbed FTIR (Fig. 7c and d) spectroscopy methods, respectively. In Fig. 7a, the broad absorption band around 500-823 cm À1 is typical of Zr-O-Zr vibration in the tetragonal structure, and 46 This agrees well with the TG analysis (Fig. 4a), indicating residual surfactant is present in the catalysts. The IR peak at 1622 cm À1 suggests the bending hydroxyl group vibrations, while the broad and strong peak at 3435 cm À1 is a reflection of physically adsorbed moisture on the surface, hence showing the O-H stretching of water. 47 Upon doping, the peak intensity at 3435 cm À1 decreased, indicating a decline in hydroxy groups and hydrophilic property of ZrO 2 . 48 Like pXRD patterns (Fig. 1), the peak intensity at 500-823 cm À1 representing the t-ZrO 2 decreased upon the substitution of M n+ into ZrO 2 . Besides, the shifting of the bands towards the lower wavenumber was observed (for instance, 591 to 519 cm À1 ), which is most significant in Cr_ZrO 2 . This shifting is due to variation in the bond length when M n+ ions replace Zr 4+ ions. Hence, it confirmed the successful incorporation of the metal ions into the ZrO 2 lattice. In Cr_ZrO 2 catalyst, small peaks at 1210 and 1740 cm À1 are observed, attributed to the formation of Cr x O y , 49 due to the strong interaction between Cr-Zr. This could be responsible for the generation of more active sites on the surface of Cr_ZrO 2 . It is observed in Fig. 7b that the peak at 1740 cm À1 corresponding to the Cr species disappeared after five catalytic cycles, which suggests that the Cr_ZrO 2 perhaps undergoes a surface structural transformation in the course of further thermal pretreatments during reuse.</p><p>Pyridine is a sensitive probe molecule for the classification of Lewis acid and Brønsted acid sites. As depicted in Fig. S5.7c (ESI †), the pyridine-IR bands at 957, 1400, and 1615 cm À1 are typical of the Lewis acid site. Two different acidic strengths due to the Lewis acid sites are shown in ZrO 2 , Fe_ZrO 2 , and Mn_ZrO 2 , whereas Lewis acidity of three different strengths was observed for both Ni_ZrO 2 and Cr_ZrO 2 . The higher the assumed frequency of the IR bands, the stronger the acidity of the sites. 50 The IR band at 1538 cm À1 suggests Brønsted acid site while 1485 cm À1 indicates C-C oscillation of pyridine aromatic ring chemisorbed on both Brønsted and Lewis acid sites. 51 The ZrO 2 catalyst showed no peak typical of the Brønsted acid site, but two characteristic bands (1400 and 1615 cm À1 ) associated with pyridinium ions coordinately bonded to Lewis acid sites. 52 These two adsorption bands were retained with increased intensity upon the addition of Mn and Fe species. More broad bands for Lewis acid, Brønsted acid, and a combination of Lewis acid and Brønsted acid sites were found when ZrO 2 was doped with Ni and Cr, accompanied by an increased intensity on Cr_ZrO 2 catalyst. The pyridineadsorbed IR reveals that the total acidity obtained from the NH 3 -TPD data has a larger Lewis to Brønsted acid ratio, which is highest in the Cr_ZrO 2 and the main factor that governs its catalytic activity in this study. The results indicate the possibility of tuning the active sites of ZrO 2 by adding foreign atomic species.</p><p>The UV-vis absorption spectra of undoped and metal-doped ZrO 2 are shown in Fig. 8. The results indicate that the undoped ZrO 2 and M_ZrO 2 (M = Fe, Mn, and Ni) have no absorption peaks in the visible wavelength region of 300 to 700 nm. However, after Cr doping, a new absorption peak appears at around 360 nm; this is attributed to the band-gap transition of ZrO 2 due to Cr 3+ ions. 53,54 The Cr-3d electronic configuration results in the appearance of some localized states in the host band-gap and makes electron transfer easier than the undoped system. This agrees well with the H 2 -TPR data (Fig. 4b) of Cr_ZrO 2 . All these issues explain the reason for the generation of more Lewis acid sites on Cr_ZrO 2 compared to the undoped system.</p><!><p>3.2.1. Dopants vs. catalytic reactivity. The MPV reduction of citral with 2-propanol was selected as a model reaction to examine the activity of the prepared pure and metal-doped mesoporous zirconia (M_ZrO 2 ) catalysts. Table 3 indicates their respective performance. Interestingly, this work's catalytic systems gave 100% selectivity to the unsaturated allylic alcohol (nerol + geraniol). The pure ZrO 2 presented 62.6% citral conversion. Comparison with the pure ZrO 2 , the effect of metal dopant in terms of activity enhancement was only observed when ZrO 2 was doped with Cr species. The Cr_ZrO 2 gave optimal activity of 76.4% conversion of citral. The catalytic activities' observed trend follows the order Cr_ZrO 2 4 ZrO 2 4 Mn_ZrO 2 4 Fe_ZrO 2 4 Ni_ZrO 2 . The correlation of the surface area, acidity, and basicity with the catalytic activity in MPV reduction of citral is displayed in Fig. 9. Generally, the surface area controls the catalyst activity (high surface area, high catalytic activity). This is not the case in our proposed catalytic systems, as catalysts with higher surface area Mn_ZrO 2 and Fe_ZrO 2 gave low conversion of citral. Instead, we found that the catalysts' catalytic activity in the MPV reduction of citral is governed mainly by the kind of metal dopant, acidic site density, and reducibility. As shown in Table 2 and Fig. 9, the catalyst Cr_ZrO 2 with the highest acidity (acid:base ratio) presented the highest activity, whereas catalysts Mn_ZrO 2 , Fe_ZrO 2 , and Ni_ZrO 2 with higher basicity compared to that of Cr_ZrO 2 and the pure ZrO 2 decreased the activity of the host ZrO 2 . It could be deduced from the results that surface-active acid sites, Lewis acid in particular, play an essential role in the MPV reduction of citral. Also, the superior reduction capacity of Cr_ZrO 2 favors its performance. 3.2.2 Recyclability, leaching test, and characterization of Cr_ZrO 2 catalyst after reuse. Developing a recyclable catalyst without decomposing due to long-term reuse has become a critical factor in achieving a sustainable catalytic system. As shown in Fig. 10a, the reduction of citral hardly proceeds after removing the catalyst, indicating no residual active component in the reaction liquid. The recyclability test (Fig. 10b) reveals that the Cr_ZrO 2 is catalytically stable and reusable with excellent selectivity to UAA although, a slight variation in the activity during the 3rd and 4th reaction cycle was observed. The characterization of the spent gave more insights into the possible transformation in the catalyst during reuse. The FTIR (Fig. 7b) shows that the crystal phases remained, the nitrogen sorption results (Table 1 and Fig. S5.7a, b, ESI †) show a significant decrease in the S BET and pore size ascribed to the sintering effect due to repeated thermal treatment during the regeneration process. The adsorbed pyridine experiment (Fig. 7d) reveals a decline in the Lewis acid sites and a significant loss of the Brønsted acid site; this suggests that the Cr_ZrO 2 catalyst undergoes surface restructuring during catalyst pretreatment before reuse. Hence, the effect of Brønsted acidity could be negligible in this study.</p><p>Nevertheless, the TEM image (Fig. S7c and d, ESI †) shows the stability of the mesostructure after 5 consecutive catalytic cycles. Moreover, the comparison of the Cr content before (8.2 wt%) and after five use (8.1%) as quantified on the ICP/OES showed no leached Cr species. The leaching test (Fig. 10a) and the ICP results (Table 1) confirmed the homogeneity of the Cr_ZrO 2 solid structure. Hence, the mesoporous Cr_ZrO 2 is catalytically stable and reusable with retained activity.</p><!><p>The chromium doped zirconia exhibited the best catalytic activity in the reduction of citral with 76.4% conversion, Table 3. Hence, the catalytic scope of Cr_ZrO 2 in MPV reduction is extended to some unsaturated aldehydes to form their corresponding unsaturated alcohols Table 4. The Cr_ZrO 2 is most active in the MPV reduction of furfural. The appreciable reactivity suggests that the mesoporous Cr_ZrO 2 acid catalyst is highly active in the MPV process and 100% selective to unsaturated allylic alcohols.</p><!><p>Herein, a series of transition metal-doped mesoporous ZrO 2 (M_ZrO 2 , M = Cr, Mn, Fe, and Ni) catalysts were designed for the MPV reduction of aldehydes. Interestingly, the tunability of the surface properties of the resulting catalysts is governed by the metal dopant's nature. The S BET decreases from 206 to 189 and 193 m 2 g À1 upon doping with Cr and Ni, respectively. An improvement in S BET from 206-223 m 2 g À1 was observed when doped with Mn and Fe (221 and 223 m 2 g À1 , respectively). Also, upon doping, there was an enlargement of pore diameter from 2.53-3.63 nm and increased pore volume from 0.10-0.26 cm 3 g À1 (Table 1). The mesostructure properties of the synthesized pure zirconia ZrO 2 and the metal-doped zirconia M_ZrO 2 are typical of type IV hysteresis loops as shown by the BET isotherms (Fig. 2a). This indicates the successful design of a mesopore structure of the materials via a sol-gel approach. This study took advantage of the structure-directing ability of P-123 in the inverse micelles system, which serves as the nanoreactors. Also, a control condensation of the oxo-clusters was achieved by forming NO x species from the nitric acid's thermal decomposition. 34 The increase in the porosity (pore diameter and pore volume) upon doping and S BET in the case of Mn_ZrO 2 and Fe_ZrO 2 could be due to the metal-metal interactions during condensation of the inorganic sols and the condition of the reaction media. A similar phenomenon was explained by Grosso et al., 55 that the mesostructuring occurs during the formation of surfactant-templated inorganic materials by evaporation. The chemical composition of the film governs the meso-organization. Also, it depends on relative vapor pressure in the environment, the evaporation conditions, and the chemical conditions in the initial solution. The changes in the structure and crystallography of ZrO 2 resulting from doping species modified the concentration of active phases involved in the catalyzed reaction on the surface of the ZrO 2 based catalysts.</p><p>The NH 3 -TPD and CO 2 -TPD experiments showed that the synthesized catalysts possess acid-base properties that could be tuned. The acid/base strength and density are related to the nature of the metal dopant. The acid density decreases in this order Cr_ZrO 2 4 Ni_ZrO 2 4 Mn_ZrO 2 4 Fe_ZrO 2 4 ZrO 2 . On the other hand, the basicity of the M_ZrO 2 gave this trend Mn_ZrO 2 4 Ni_ZrO 2 4 Fe_ZrO 2 4 ZrO 2 4 Cr_ZrO 2 , which could be related to neither the electron density nor ionic charge. These results show that the presence of metal dopant in the ZrO 2 framework possibly tunes the active acid-base sites, resulting mainly from the metal-metal synergy between the metal dopant and the host ZrO 2 . It was revealed that the acid:base in ratio 3 : 1 is required for the chemoselective transfer hydrogenation of citral via the MPV system. To further understand the kinds of acid sites on the solid catalysts, the pyridine-adsorbed experiments indicated that the metal-metal synergy generated both surface Lewis and Brønsted acid sites with a higher concentration of Lewis acid sites having weak, medium, and strong strength. The Lewis acid sites are possibly generated by the concerted metal ions (Cr 3+ and Zr 4+ ) acting as the electron-acceptor. Also, the metal-metal synergy influenced the hydrogen consumption, with the Cr-Zr catalyst showing superior reducibility (H-abstraction capacity).</p><p>According to Stavale et al., the electronic structure and chemical properties of oxide materials, and their catalytic activities, could be tailored by doping with metal. 56 The approach takes advantage of the metal dopants' tendency to exchange electrons with the host oxide and surface-bound adsorbates. It has also been reported that dopant-modified metal oxides exhibit improved catalytic performance than their pure oxides. 57 In our case, the MPV process is catalytic driven; no activity was observed in the blank reaction. The catalytic activity of the materials in the MPV reduction of a,bunsaturated aldehydes is dependent on the concentration of the surface acidic sites. Among the transition metal dopants incorporated, the catalytic activity of pure ZrO 2 (62.6%) in terms of citral conversion in the model reaction was only improved by Cr_ZrO 2 (76.4%). The observed linear relationship between the surface acidity and activity of the synthesized catalysts suggests that the MPV reduction reaction is perhaps governed by the extent of acidity induced by the electronic interaction between Cr and Zr. The experiment performed with pure chromium oxide showed no activity after 24 h, this suggests that the synergistic interaction might be responsible for the enhanced activity in Cr_ZrO 2 . Also, the acid character of the Cr_ZrO 2 catalyst with the polarity of the citral molecule made it possible for citral to preferably adsorb through the carbonyl group. Hence, the transfer hydrogenation of the carbonyl to produce UAA is favored. A similar scenario in which the adsorption of citral on the Lewis acid site is via the carbonyl was reported. 58 The local structure of the Lewis acid sites on zirconia catalyst was also reported. 59,60 In view of these and the findings of this study, a possible mechanism for MPV reduction of citral on the Lewis acid sites of M_ZrO 2 is proposed (Scheme 2).</p><p>Distinctively, despite the high acidic properties of the catalytic system, no secondary products were formed. Acidic catalysts frequently favor secondary reactions as either dehydration of alcohol or aldehyde condensation. 61,62 All the prepared catalysts in this work exhibited excellent selectivity to unsaturated allylic alcohol as evidenced in the GC spectra (Fig. S8 and S9, ESI †) compared to their previously reported counterparts in Table 4; this is paramount to a sustainable catalytic process. Moreover, the MPV process in this work was carried out under milder reaction conditions in the absence of additives and gaseous hydrogen. The reactivity retained after five consecutive runs evidence the sustainability of the Cr_ZrO 2 catalyst.</p><p>Furthermore, the synthesized Cr_ZrO 2 in this work showed considerable reactivity compared to its counterparts in literature Table 5. Our catalyst gave a maximum selectivity of 100% to the UAA under milder reaction conditions in the absence of H 2 gas pressure. For instance, it is more reactive than the ZrSr-PN catalyst in the MPV reduction of cinnamaldehyde; the ZrSr-PN gave 24.0% conversion of cinnamaldehyde in 24 h while our Cr_ZrO 2 gave 60% conversion in 10 h. Also, in the MPV reduction of furfural, our Cr_ZrO 2 showed higher activity of 85% conversion in 4 h and 98.2% in 10 h at 80 1C than P-Zr 200 (55.3%), ME-Zr-200UW (67.6%), and Zr-SBA-15 (54%) after 24, 24 and 6 h, respectively. However, Pt/ZrO 2 synthesized by Wei et al. 4 gave better activity than our catalyst in cinnamaldehyde reduction and Ru/ZrO 2 in citral reduction but, this is due to the H 2 gas pressure used in their catalytic system.</p><!><p>Herein, we have demonstrated that incorporating metal dopants into zirconia's crystal framework alters its physico-chemical properties such as surface area, mesopore structure, crystallinity, basicity, acidity, reducibility, and thermal stability. The reducibility and the strength of the Lewis acid sites govern the activity of ZrO 2 based catalysts in the MPV reduction of citral. Specifically, the Cr dopant weakens the crystallinity of ZrO 2 . However, it improves the reducibility, acidity, and catalytic reactivity for MPV reduction of aldehydes. All the prepared zirconia-based catalysts in this work showed a remarkable selectivity of 100% to UAA. The surfaceinduced performance of the Cr_ZrO 2 is due to the enhanced active centers generated from the synergistic electronic interaction between CrO x and ZrO 2 . Hence, this work unveils that the reactivity of ZrO 2 depends solely on the intrinsic properties of its' surface structure rather than the specific surface expanse. Also, the Cr_ZrO 2 exhibited good stability and recyclability for at least five reaction cycles. We proposed a plausible mechanism of the MPV transformation over the Lewis acidic sites. This work presents the first application of a well-designed mesoporous Cr_ZrO 2 via an inverse micelle approach in the MPV reduction of aldehydes with exceptional reactivity and efficient reusability. The green production of UAA was successfully achieved under mild reaction conditions without pressurized hydrogen gas. The Cr_ZrO 2 is proposed to be a potential sustainable catalyst for industrial applications.</p><!><p>The authors declare no competing interest.</p>

Royal Society of Chemistry (RSC)

Molecular dissection of RbpA-mediated regulation of fidaxomicin sensitivity in mycobacteria

RNA polymerase (RNAP) binding protein A (RbpA) is essential for mycobacterial viability and regulates transcription initiation by increasing the stability of the RNAP-promoter open complex (RPo). RbpA consists of four domains: an N-terminal tail (NTT), a core domain (CD), a basic linker, and a sigma interaction domain. We have previously shown that truncation of the RbpA NTT and CD increases RPo stabilization by RbpA, implying that these domains inhibit this activity of RbpA. Previously published structural studies showed that the NTT and CD are positioned near multiple RNAP-σA holoenzyme functional domains and predict that the RbpA NTT contributes specific amino acids to the binding site of the antibiotic fidaxomicin (Fdx), which inhibits the formation of the RPo complex. Furthermore, deletion of the NTT results in decreased Mycobacterium smegmatis sensitivity to Fdx, but whether this is caused by a loss in Fdx binding is unknown. We generated a panel of rbpA mutants and found that the RbpA NTT residues predicted to directly interact with Fdx are partially responsible for RbpA-dependent Fdx activity in vitro, while multiple additional RbpA domains contribute to Fdx activity in vivo. Specifically, our results suggest that the RPo-stabilizing activity of RbpA decreases Fdx activity in vivo. In support of the association between RPo stability and Fdx activity, we find that another factor that promotes RPo stability in bacteria, CarD, also impacts to Fdx sensitivity. Our findings highlight how RbpA and other factors may influence RNAP dynamics to affect Fdx sensitivity.

molecular_dissection_of_rbpa-mediated_regulation_of_fidaxomicin_sensitivity_in_mycobacteria

<!>RbpA E17 and R10 synergize to promote Fdx activity against M. tuberculosis RNAP-σAin vitro<!><!>Multiple RbpA domains impact Fdx activity in vivo<!><!>Discussion<!>Media and bacterial strains<!>Protein preparation for biochemical assays<!>Fdx zone of inhibition<!>3-Nucleotide in vitro transcription assay<!>Data availability<!>Supporting information<!>Conflict of interest<!>Supplemental Figure S1

<p>Edited by Ursula Jakob</p><p>Mycobacterium tuberculosis is the causative agent of the disease tuberculosis, which resulted in an estimated 1.5 million deaths worldwide in 2019 (https://www.who.int/publications/i/item/9789240013131). New strategies are necessary to fight this global health crisis, including the development of novel therapies. Bacterial transcription is a druggable essential process in M. tuberculosis, demonstrated by the transcription inhibitor rifampicin's continued status as a cornerstone of tuberculosis treatment. Bacterial transcription is carried out by an RNA polymerase (RNAP) comprised of five subunits (α2ββ'ω), referred to as the core RNAP, and a sixth dissociable subunit (σ) that when bound to core RNAP forms a complex termed the RNAP holoenzyme. Mycobacterial transcription initiation in vivo also requires two additional essential RNAP-interacting proteins, RbpA and CarD (1, 2, 3, 4, 5, 6). RbpA and CarD regulate transcription initiation by binding to the RNAP and modulating the kinetics of RNAP-promoter open complex (RPo) formation and RNAP promoter escape (2, 5, 6, 7, 8, 9, 10).</p><p>RbpA is comprised of four structural domains, including the N-terminal tail (NTT), core domain (CD), basic linker (BL), and sigma interaction domain (SID) (4, 10, 11). Most of the characterization of RbpA has focused on the BL and SID. The RbpA SID domain directly interacts with σ region 1.2, σ nonconserved region, and σ region 2.3 in group I (M. tuberculosis σA) and group II (M. tuberculosis σB) σ factors (4, 5, 10, 12, 13, 14). The SID domain is both necessary and sufficient for RbpA to associate with the RNAP holoenzyme (5). An arginine at position 88 in the M. tuberculosis RbpA SID is critical for the interaction with σA and σB (5, 15). The M. tuberculosis RbpA BL contains several positively charged residues, including K73, K74, K76, and R79, that are positioned to interact with the negatively charged DNA phosphate backbone near the upstream edge of RPo (4, 10). Alanine substitution at either R79 in the BL or R88 in the SID has demonstrated that the interactions between RbpA and the RNAP and DNA are necessary for RbpA to increase RPo stability during transcription initiation (5, 10, 14). In vivo, R79A or R88A substitutions in RbpA result in upregulation of some genes and downregulation of other genes, suggesting that the outcome of RbpA activity may be promoter dependent, possibly due to differences in the kinetics of transcription initiation at each promoter (5, 16, 17).</p><p>Much less is known about the functions performed by the RbpA NTT and CD. Deletion of the RbpA NTT increases the ability of RbpA to stabilize RPo, and deletion of both the RbpA NTT and CD further increases RPo stability, indicating that both domains antagonize RbpA-mediated stabilization of RPo (5, 10). Structural analysis of RbpA bound to the M. tuberculosis RNAP-σA RPo shows that the RbpA NTT is positioned near the RNA exit channel, possibly contacting the RNAP β switch 3 region (Sw3), β flap, β′ lid, σA region 3.2 (σA3.2, also referred to as the σ "finger" domain), and the β′ zinc binding domain (ZBD), while the RbpA CD is positioned near the RNAP β′ zipper and RNAP β′ ZBD (10, 11). These RNAP structural domains have been characterized to varying levels in Escherichia coli, which lacks RbpA. The RNAP β Sw3 is one of five switch regions that are thought to undergo conformational changes during transcription initiation (18). RNAP β Sw3 is positioned near the template DNA −3 and −4 nucleotides, raising the possibility that RNAP β Sw3 could play a role in DNA template strand positioning (19). The RNAP β flap, which includes the flap tip helix that interacts with σ region 4, is important for positioning σ region 4 for interaction with the −35 element of the promoter (20) and represents a common binding interface for transcription factors that directly interact with σ (21, 22). The RNAP β′ lid separates the RNA/DNA hybrid as part of the RNA exit channel and is required for RPo stability and transcription in E. coli and Thermus aquaticus (23, 24). RNAP σ703.2 plays a role in initiating nucleotide triphosphate binding by positioning the DNA template strand for interaction with −4 and −5 nucleotides of the DNA template strand, which affects abortive transcription and promoter escape (25, 26, 27, 28). Both the RNAP β′ ZBD and β′ zipper facilitate RPo formation on promoters with −35 elements that form weak interactions with σ by making promoter contacts within the spacer region between the −10 and −35 motifs (29, 30).</p><p>The positioning of the RbpA NTT and CD near multiple different structural and functional domains of the RNAP-σA holoenzyme implies that the RbpA NTT and CD could impact RNAP activity through a number of mechanisms. However, it is unclear what contacts between the RbpA NTT/CD and the RNAP mediate the antagonism of RPo stability. In addition, structural studies indicate that the RbpA NTT is positioned in the RNAP-σA holoenzyme complex in such a way that it contributes to the binding site for the antibiotic fidaxomicin (Fdx) (11), which is used to treat Clostridium difficile infections. Fdx inhibits transcription initiation by binding the RNAP and blocking the closing of the RNAP clamp that occurs during RPo formation (11, 31). Deletion of the RbpA NTT decreases sensitivity of M. tuberculosis RNAP to Fdx in vitro and in vivo (11), which is proposed to be due to the loss of RbpA's contribution to the RNAP-Fdx binding interface. However, given that RbpA NTT also decreases RPo stability (5) and is predicted to interact with σA3.2, which is known to affect Fdx activity (11, 32), it is possible that RbpA may impact Fdx activity by additional mechanisms. In this study, we interrogate the roles played by residues within the NTT in RbpA-dependent Fdx sensitivity and find that the amino acids predicted by the structural studies to interact with Fdx do partially contribute to Fdx activity in vitro. However, we also find that RbpA's impact on Fdx activity in vivo extends beyond the role of the NTT in binding the antibiotic, revealing a dominant contribution for RNAP conformation in Fdx sensitivity.</p><p>RbpA E17 and R10 synergize to promote Fdx activity against M. tuberculosis RNAP-σAin vitro.A, schematic of RbpA's four domain structure including the location of the substituted residues, R10, E17, R79, R88, and the two M. tuberculosis truncation mutants, RbpA 26–111 lacking the NTT, and RbpA 72–111 lacking the NTT and CD. B, representative gels showing Fdx (0 μM, 0.01 μM, 0.1 μM, 1.0 μM, 10 μM, and 100 μM) inhibition of M. tuberculosis RNAP-σA production of three nucleotide transcripts alone or in complex with RbpAMtbWT, RbpAMtbR10A, RbpAMtbE17A, RbpAMtbR10A/E17A, RbpAMtbR79A, RbpAMtbR88A, RbpAMtb26–111, or RbpAMtb72–111 from a linear dsDNA template containing positions −80 to +70 of M. tuberculosis rrnAP3 (relative to the +1 transcription start site). C, dose–response curves of the experiments shown in (A). The curves are generated from at least four replicates from at least two different experiments. Percent inhibition at each Fdx concentration included in the plots compared to no drug is depicted as the mean ± SD. The IC50 for each replicate was calculated by nonlinear regression analysis with four-parameter (EC50, Hill Slope, top and bottom curve plateaus) fitting of log transformed Fdx concentration versus normalized response, with the mean IC50 and 95% confidence interval listed in the table. D, structural modeling of Fdx binding pocket on the RbpA-bound M. tuberculosis RNAP-σA from PDB structure 6BZO. Fdx and RNAP residues involved in the RNAP-Fdx binding interface are shown with PyMol stick representation while the rest of the structure is shown with PyMol cartoon representation. Polar interactions are indicated by red dashed lines, and potential van der Waals interactions are shown as gray lines. E, structural modeling of RbpA-bound M. tuberculosis RNAP-σA from PDB structure 6C04. RbpA R10 and RNAP σA D441 are shown with PyMol stick representation while the rest of the structure is shown with PyMol cartoon representation. The polar interaction between RbpA R10 and RNAP σA D441 is indicated by the red dashed line. CD, core domain; Fdx, fidaxomicin; NTT, N-terminal tail; RbpA, RNA polymerase binding protein A; RNAP, RNA polymerase.</p><!><p>Structural studies predicted that the NTT contributes contacts with Fdx when the antibiotic is bound to the M. tuberculosis RNAP-σA holoenzyme (PDB: 6BZO), specifically through a water-mediated interaction between RbpA E17 and Fdx (Fig. 1D) (11). To determine whether the predicted interaction between Fdx and RbpA E17 underpins NTT-dependent Fdx activity, we calculated the IC50 of Fdx in the presence of RbpAMtbWT versus an RbpAMtbE17A mutant protein. The activity of Fdx against the M. tuberculosis RNAP-σA in the presence of RbpAMtbE17A was nearly equal to Fdx activity against the M. tuberculosis RNAP-σA in the presence of RbpAMtbWT, indicating that alterations in the size and charge of the amino acid side chain at RbpA NTT position 17 do not impact Fdx activity against the M. tuberculosis RNAP-σA (Fig. 1, B and C).</p><p>The structure in Boyaci et al. (11) also highlights potential van der Waals interactions between RbpA R10 and Fdx in the RNAP-σA holoenzyme bound to double stranded forked DNA (PDB: 6BZO) (Fig. 1D); however, given the distance between RbpA R10 and Fdx, one would predict this to be a weak interaction. In a separate structure of RbpA bound to M. tuberculosis RNAP-σA in complex with two double-stranded forked DNA molecules that mimics the RPo (PDB: 6C04), the RbpA R10 positively charged side chain is positioned within 2.4 Å of the negatively charged side chain of σA3.2 D441, forming a polar interaction (11) (Fig. 1E). Fdx activity against E. coli RNAP-σ70 holoenzyme lacking σ703.2 is attenuated approximately 20-fold (32), indicating that σ703.2 contributes to Fdx inhibition of the E. coli RNAP. Therefore, if RbpA R10 interacts with σA3.2, this may also affect Fdx activity. To examine whether RbpA R10 contributes to M. tuberculosis RNAP-σA Fdx sensitivity, we measured Fdx IC50 against the M. tuberculosis RNAP-σA in the presence of RbpAMtbR10A. Similar to the RbpAMtbE17A mutant, we observed no change in Fdx IC50s against the M. tuberculosis RNAP-σA in the presence of RbpAMtbR10A compared to RbpAMtbWT (Fig. 1, B and C), indicating that the R10 residue is not required for RbpA NTT-dependent Fdx activity. To determine the effect of disrupting the contacts made by the both RbpA E17 and R10, we measured the Fdx IC50 against M. tuberculosis RNAP-σA in the presence of RbpAMtbR10A/E17A. Mutating both the R10 and E17 residues resulted in an approximately 3-fold increase in the Fdx IC50 compared to RbpAMtbWT, although this was still at least 5-fold lower than RbpA mutants lacking the entire NTT (RbpAMtb26–111 and RbpAMtb72–111) (Fig. 1, B and C). These data indicate that loss of one of these residues increases the importance of the other for Fdx activity, but additional mechanisms also contribute to NTT-dependent Fdx activity in vitro.</p><!><p>Multiple RbpA domains impact Fdx activity in vivo.A, ratio of the doubling times of M. smegmatis strains expressing RbpAMtbR10A, RbpAMtbE17A, RbpAMtbR10A/E17A, RbpAMsm28–114, or RbpAMsm72–114 as compared to the average doubling time for the strain expressing RbpAMtbWT. The mean ± SD from at least two independent experiments with three replicates per experiment. B, zones of inhibition (ZOI) by Fdx on bacterial lawns of M. smegmatis expressing RbpAMtbWT, RbpAMtbR10A, RbpAMtbE17A, RbpAMtbR10A/E17A, RbpAMtbR79A, RbpAMtbR88A, RbpAMsm28–114, RbpAMsm72–114, or RbpAMtb72–111 as the only copy of rbpA. C, mean radii of ZOI ± SD from at least two experiments with at least three replicates at 100 μM, 250 μM, and 500 μM Fdx is plotted. For A and C, statistical significance of differences was analyzed by ANOVA and Tukey's multiple comparison test. ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. All comparisons to RbpAMtbWT were included in the analysis, but only statistically significant comparisons are indicated in the figure. Fdx, fidaxomicin; RbpA, RNA polymerase binding protein A; RNAP, RNA polymerase.</p><!><p>To examine the Fdx sensitivity of each M. smegmatis strain, we used a zone of inhibition assay, similar to previous studies (2, 11). By spreading approximately 2.5 × 108 colony forming units of bacteria on an agar plate and spotting 10 μl of 100, 250, or 500 μM Fdx dissolved in dimethyl sulfoxide (DMSO) onto a disk placed onto the plate, the bacteria form a lawn after incubation at 37 °C for 2 days, and a zone absent of bacterial growth indicates growth inhibition by Fdx. DMSO had no effect on M. smegmatis growth in this assay and did not generate a zone of clearing on its own, whereas incubation of M. smegmatis with Fdx resulted in growth inhibition (Fig. 2B). We compared the radii of the zones of inhibition formed on each M. smegmatis mutant with Fdx and reproduced previous findings that deletion of the RbpA NTT results in resistance to Fdx in vivo (RbpAMtb72–111, RbpAMsm28–114, and RbpAMsm72–114 mutants in Fig. 2, B and C) (11), which is consistent with the in vitro findings (Fig. 1, B and C). In contrast, the RbpAMtbR10A, RbpAMtbE17A, and RbpAMtbR10A/E17A mutants were not more resistant to Fdx in vivo, despite the trend observed in vitro of RbpAMtbR10A/E17A displaying decreased Fdx sensitivity compared to RbpAMtbWT (Figs. 1, B and C and 2, B and C). Strikingly, the M. smegmatis RbpAMtbR79A and RbpAMtbR88A mutants, which have decreased affinity for DNA and the σ factor, respectively, were significantly more sensitive to Fdx treatment (Fig. 2, B and C). These in vivo data highlight the existence of other contributors to RbpA's effect on Fdx activity that exist in the bacteria but are not recapitulated in the in vitro assay.</p><!><p>RPostability in associated with Fdx sensitivity in vivo.A, representative gels of three nucleotide transcripts produced by M. tuberculosis RNAP-σA alone or in complex with RbpAMtbWT, RbpAMtb72–111, RbpAMtbR10/E17A, or RbpAMtbR88A from a plasmid DNA template containing positions −39 to +4 of M. tuberculosis rrnAP3 relative to the +1 transcription start site. B, ratio of transcript produced as compared to the average of "No Factor" replicates included on the same gel. Results are plotted as individual values with the mean ± SD shown. Statistical significance of differences was determined by ANOVA and Tukey's multiple comparison test. 'ns', not significant; ∗∗p < 0.01; ∗∗∗∗p < 0.0001. C, zones of inhibition (ZOI) by Fdx on bacterial lawns of M. smegmatis expressing CarDMtbWT or CarDMtbR25E as the only copy of carD. D, mean radii of ZOI ± SD from at least two experiments with at least three replicates at 100 μM, 250 μM, and 500 μM Fdx is plotted. Statistical significance was analyzed by two-tailed Welch's t test. ∗p < 0.05; ∗∗p < 0.01. Fdx, fidaxomicin; RbpA, RNA polymerase binding protein A; RPo, RNAP-promoter open complex.</p><p>Summary of the effects of RbpA and CarD mutants on fidaxomicin (Fdx) sensitivity and open complex (RPo) stability, compared to WT protein</p><p>The level of decrease in Fdx sensitivity in vitro with RbpAR10A/E17A is intermediate to that of RbpA72–111, when both are compared to RbpAWT.</p><!><p>Prior studies on RbpA have focused almost exclusively on the SID interaction with σ factor and the BL interaction with DNA, leaving the NTT and CD largely uncharacterized. Structural studies have provided tremendous insight into the potential interactions between the NTT and CD with multiple RNAP-σA holoenzyme domains as well as the antibiotic Fdx (10, 11, 37). Herein, we test the prediction that RbpA R10 and E17 contribute contacts with the antibiotic Fdx that are important for RbpA's NTT-dependent activity against M. tuberculosis RNAP-σA. We find that in vitro, combined mutation of both residues affects the IC50 of Fdx activity against the M. tuberculosis RNAP-σA (Fig. 1, B and C); however, it is still not clear whether RbpA R10 and E17 promote RbpA NTT-dependent Fdx activity through direct interaction with Fdx or through an alternative mechanism. Maintenance of partial Fdx activity against M. tuberculosis RNAP-σA bound by RbpAMtbR10A/E17A in vitro indicates that additional RbpA NTT residues, or perhaps the entire structural domain, mediate RbpA NTT-dependent Fdx activity. In addition, the RbpAMtbR10A/E17A mutant did not alter Fdx sensitivity in M. smegmatis (Fig. 2), indicating that those residues play less of a role in Fdx activity in vivo. The R88A substitution that weakens RbpA's interaction with the RNAP in vivo (5), and thus would be expected to decrease M. smegmatis sensitivity to Fdx since less RbpA would be associated with RNAP-σA, also unexpectedly increased M. smegmatis sensitivity to Fdx. Taken together, these observations reveal differences in the effects of RbpA mutants on Fdx sensitivity in vitro compared to in vivo and support a model where RbpA can impact Fdx activity independent of its direct contacts with the antibiotic.</p><p>These discrepancies between the measured sensitivities in vitro versus in vivo may be due in part to the limited scope of the in vitro assay used here and in previous studies to probe Fdx activity (11), where Fdx is added to RbpA and RNAP-σA holoenzyme before DNA addition. Whereas in the cell, RNAP-σA holoenzyme could be bound to DNA prior to Fdx binding. This limitation may bias the in vitro assay toward identifying the factors that affect Fdx binding to free RbpA-RNAP-σA holoenzyme complex. In particular, our in vivo results support an association between effects on RPo stability and Fdx sensitivity. Our work indicates that RPo stability is a newly characterized way that RbpA contributes to Fdx activity. During transcription initiation, RPo stabilization involves closing of the RNAP clamp module around downstream nucleic acid as the transcription bubble is formed (38). Structural studies indicate that Fdx inhibits transcription initiation by trapping the mycobacterial transcription initiation complex in an open-clamp conformation (11). In addition, Fdx is predicted to be unable to bind the closed-clamp conformation (11). Therefore, mycobacterial transcription factors such as RbpA and CarD that favor RPo formation (7, 8, 10) may impact Fdx sensitivity by reducing the lifetime of open-clamp RNAP complexes that Fdx can bind. Conversely, Fdx has also been shown to decrease the affinity of CarD to RNAP in vitro (39). CarD has a lower affinity to the open-clamp RNAP complex compared to the closed-clamp RNAP complex (RPo) (7). Thus, it is possible that Fdx lowers the fraction of CarD bound to RNAP-promoter complexes by reducing the amount of RPo formed at equilibrium. This work highlights the need to biochemically understand Fdx activity against the diversity of RNAP complexes that exist within the bacteria.</p><p>In addition to the initiation complexes formed following RNAP-σA binding to DNA, one could envision other factors that exist in vivo and not in vitro that could impact Fdx activity. The in vitro assays of Fdx activity also exclude RNAP holoenzymes containing alternative σ factors and additional RNAP interacting proteins present in the bacteria. Fdx has been shown to be more active at inhibiting the E. coli RNAP-σs holoenzyme compared to the E. coli RNAP-σ70 holoenzyme (32), suggesting that the presence of alternative σ factor–bound holoenzymes may also explain some discrepancies between our in vitro and in vivo findings. In addition to these direct effects on RNAP, truncation of the RbpA NTT and CD results in global dysregulation of gene expression in M. smegmatis (5, 10), which could also affect sensitivity to Fdx. Therefore, the effect of RbpA on Fdx activity in vivo is likely multifactorial. As such, analysis of RbpA mutants with substitutions in conserved residues within the NTT that are predicted to contact different domains in the RNAP-σA holoenzyme revealed diverse effects of RbpA on the Fdx sensitivity of M. smegmatis (Fig. S1). The impact of these mutants on transcription initiation is unknown, but further investigation into this area could shed more light on how association of RbpA on transcription initiation complexes contributes to antibiotic susceptibility.</p><p>Collectively, our results demonstrate that the RbpA NTT domain is a significant contributor to the Fdx sensitivity of the mycobacterial transcription machinery, consistent with previous studies. However, we also discover that the role for RbpA involves more than simply providing amino acids to the Fdx binding site. Our data support a model where multiple RbpA domains, including the NTT, can impact Fdx sensitivity through modulation of transcription initiation kinetics. Our studies reveal a role for another factor that also regulates RPo stability, CarD, in Fdx sensitivity. Fdx is currently used to treat infections caused by C. difficile, a bacterium that does not encode an RbpA homolog but does encode CarD and other factors that will regulate transcription by modifying RPo lifetime (1). Therefore, these studies also shed light on pathways that can be targeted to improve Fdx activity in the clinic.</p><!><p>All M. smegmatis strains were derived from mc2155 and grown at 37 °C in LB medium supplemented with 0.5% dextrose, 0.5% glycerol, and 0.05% Tween 80. M. smegmatis strains expressing RbpAMtbR4A, RbpAMtbR4E, RbpAMtbL6A, RbpAMtbR7A, RbpAMtbR7E RbpAMtbR10A, RbpAMtbS15A, RbpAMtbE17A and RbpAMtbR10A/E17A, RbpAMtbR79A, RbpAMtbR88A, RbpAMtb26–111, RbpAMtb72–111, RbpAMsm28–114, and RbpAMsm72–114 were engineered using pMSG430 plasmids that express each rbpA allele from a constitutive Pmyc1-tetO promoter and integrated into the attB site of the M. smegmatis ΔrbpA attB::tet-rbpA strain previously described (5, 33, 34). The primers used to make RbpA strains are in Table S1. RbpAMtbR79A, RbpAMtbR88A, RbpAMtb26–111, and RbpAMtb72–111 have been previously described in (5). The M. smegmatis ΔrbpA attB::tet-rbpA strains expressing RbpAMtbR4A, RbpAMtbR4E, RbpAMtbL6A, RbpAMtbR7A, RbpAMtbR7E, RbpAMtbR10A, RbpAMtbS15A, RbpAMtbE17A, RbpAMtbR10A/E17A, RbpAMsm28–114, RbpAMsm72–114, RbpAMtbR79A, and RbpAMtbR88A were named csm455, csm461, csm456, csm457, csm458, csm451, csm462, csm450, csm498, csm510, csm511, csm322, and csm314, respectively.</p><!><p>Plasmids containing the M. tuberculosis H37Rv genomic DNA encoding the different M. tuberculosis RNAP holoenzyme subunits were a gift from Jayanta Mukhopadhyay (Bose Institute) (40). Expression and purification were carried out in accordance with the methods described previously (5). Recombinant M. tuberculosis RbpA proteins were purified from E. coli as previously described using the pET-SUMO vector (primers used to make RbpA constructs for protein purification are in Table S2) (5). RbpA was stored at −80 °C in 150 mM NaCl, 20 mM Tris pH 8.0, and 1 mM β-mercaptoethanol. M. tuberculosis RNAP-σA holoenzyme was stored at −80 °C in 50% glycerol, 10 mM Tris pH 7.9, 200 mM NaCl, 0.1 mM EDTA, 1 mM MgCl2, 20 μM ZnCl2, and 2 mM DTT.</p><!><p>M. smegmatis cultures were grown to OD600 = 0.4 to 0.8. Based on the approximation that OD600 = 1.0 is equivalent to 5 × 108 mycobacteria, 2.5 × 108 cells were collected, resuspended in 100 μl of LB, and plated on LB agar plates. Whatman filter paper disks were applied to the plates, and 10 μl of 100 μM, 250 μM, or 500 μM Fdx (Selleck Chemicals) resuspended in DMSO or DMSO alone were added to the Whatman filter paper disks. The plates were incubated at 37 °C for 48 h, and the zones of inhibition were measured. The zone of inhibition for each replicate at each drug concentration is the average of four measurements approximately 90o apart.</p><!><p>For the Fdx studies in Figure 1, a linear 150 bp dsDNA template containing the M. tuberculosis rrnAP3 promoter was prepared by annealing and extending 85-mer oligonucleotide primers (Integrated DNA Technologies) with a 20 nucleotide overlap ranging from nucleotides 1,471,577 to 1,471,726 in the M. tuberculosis H37Rv genome (9) and HPLC purified as previously described (7). For the RPo stability assays in Figure 3, a plasmid DNA template containing the M. tuberculosis rrnAP3 promoter from the −39 to +4 positions relative to the +1 transcription start site, ranging from nucleotides 1,471,618 to 1,471,660 in the M. tuberculosis H37Rv genome, was used. Plasmid DNA was isolated by Midi-prep (Qiagen) and cleaned by alcohol precipitation. For all 3-nucleotide transcription assays, RbpA, M. tuberculosis RNAP-σA holoenzyme, and dsDNA template were incubated at 37 °C for 10 min. Reactions were initiated by adding 2.5 μl of a substrate mixture containing GpU, UTP, and 32P radiolabeled UTP and incubating at 37 °C for 10 min to allow for production of a 3-nucleotide product in 20 μl reactions that included a final concentration of 2 μM RbpA (saturating concentration based on (5, 8), 100 nM M tuberculosis RNAP-σA holoenzyme, 10 nM dsDNA template, 1 mM DTT, 0.1 mg/ml BSA (NEB), 200 μM GpU, 20 μM UTP, 0.2 μl of 32P radiolabeled UTP, 75 mM NaCl, 10.1 mM MgCl2, 2 μM ZnCl2, 18 mM Tris pH 8.0, 0.01 mM EDTA, 5% glycerol, and 0.1 mM β-mercaptoethanol. Reactions were stopped with 2X formamide stop buffer (98% [vol/vol] formamide, 5 mM EDTA and 0.05% w/v bromophenol blue). Reaction products were resolved by 22% polyacrylamide-urea gel electrophoresis and exposure to autoradiography film. Products were quantified using ImageJ. Dose–response curves were carried out the same way with the exception that Fdx was added to RbpA and M. tuberculosis RNAP-σA holoenzyme, incubated for 10 min at 37 °C, at which point linear dsDNA template was added and allowed to incubate at 37 °C for 15 min before initiating the reactions with the substrate mixture. The in vitro transcription reaction conditions are slightly different than those used in previously published work (11), including different salts in the buffers, different type of holoenzyme preps, and a different dsDNA template, all likely contributing to overall differences in the Fdx IC50 values. Nonetheless, the trends between samples are consistent between this manuscript and previously published work, and therefore, the different reaction conditions do not change the data interpretations or conclusions.</p><!><p>All data are contained in the manuscript and the supporting information file.</p><!><p>This article contains supporting information.</p><!><p>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</p><!><p>A, alignment of RbpA proteins from actinobacterial species. Conserved RbpA NTT residues targeted in this analysis are indicated by an asterisk, and the four RbpA structural domains are indicated. B, structural modeling of RbpA NTT interactions with the RNAP from PDB structure 6BZO. RbpA NTT residues targeted in this analysis and the M. tuberculosis RNAP-σA residues positioned to interact with RbpA are shown with PyMol stick representation while the rest of the structure is shown with PyMol cartoon representation. Polar interactions are indicated by red dashed lines. C, zones of inhibition (ZOI) by Fdx on bacterial lawns of M. smegmatis expressing RbpAMtbWT, RbpAMtbR4A, RbpAMtbR4E, RbpAMtbL6A, RbpAMtbR7A, RbpAMtbR7E, or RbpAMtbS15A as the only copy of rbpA. Mean radii of ZOI ± SD from at least two experiments with at least two replicates at 100 μM, 250 μM, and 500 μM Fdx is plotted. D, doubling times of M. smegmatis strains expressing RbpAMtbWT, RbpAMtbR4A, RbpAMtbR4E, RbpAMtbL6A, RbpAMtbR7A, RbpAMtbR7E, or RbpAMtbS15A normalized to the average doubling time of M. smegmatis expressing RbpAMtbWT. The mean ± SD from at least two independent experiments with at least two replicates per experiment. For both (C) and (D), statistical significance of differences was analyzed by ANOVA and Tukey's multiple comparison test. ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001. All comparisons to RbpAMtbWT were included in the analysis, but only statistically significant comparisons are indicated in the figure. RbpAMtbWT growth rate and ZOI data included in this figure are the same data included in Figure 2, A and C. Fdx, fidaxomicin; NTT, N-terminal tail; RbpA, RNA polymerase binding protein A; RNAP, RNA polymerase.</p>

PubMed Open Access

Responsive MRI agents for sensing metabolism in vivo

CONSPECTUS Magnetic resonance imaging (MRI) has inherent advantages in safety, three-dimensional output, and clinical relevance when compared with optical and radiotracer imaging methods. However, MRI contrast agents are inherently less sensitive than agents used in other imaging modalities primarily because MRI agents are detected indirectly by changes in either the water proton relaxation rates (T1, T2, and T2 *) or water proton intensities (chemical exchange saturation transfer and paramagnetic chemical exchange saturation transfer, CEST and PARACEST). Consequently, the detection limit of an MRI agent is determined by the characteristics of the background water signal; by contrast, optical and radiotracer-based methods permit direct detection of the agent itself. By virtue of responding to background water (which reflects bulk cell properties), however, MRI contrast agents have considerable advantages in "metabolic" imaging\xe2\x80\x94that is, spatially resolving tissue variations in pH, redox state, oxygenation, or metabolite levels. In this Account, we begin by examining sensitivity limits in targeted contrast agents and then address contrast agents that respond to a physiological change; these responsive agents are effective metabolic imaging sensors. The sensitivity requirements for a metabolic imaging agent are quite different from those for a targeted Gd3+-based T1 agent (for sensing cell receptors, for example). Targeted Gd3+ agents must have either an extraordinarily high water proton relaxivity (r1) or multiple Gd3+ complexes clustered together at the target site on a polymer platform or nanoparticle assembly. Metabolic MRI agents differ in that the high relaxivity requirement, although helpful, is eased because these agents respond to bulk properties of tissues rather than low concentrations of a specific biological target. For optimal sensing, metabolic imaging agents should display a large change in relaxivity (\xce\x94r1) in response to the physiological or metabolic parameter of interest. Metabolic imaging agents have only recently begun to appear in the literature and only a few have been demonstrated in vivo. MRI maps of absolute tissue pH have been obtained with Gd3+-based T1 sensors. The requirement of an independent measure of agent concentration in tissues complicates these experiments, but if qualitative changes in tissue pH are acceptable, then these agents can be quite useful. Finally, we describe examples of imaging extracellular pH in brain tumors, ischemic hearts, and pancreatic islets with Gd3+-based pH sensors and discuss the potential of CEST and PARACEST agents as metabolic imaging sensors.

responsive_mri_agents_for_sensing_metabolism_in_vivo

Introduction<!>The detection limit of targeted Gd3+-based T1 agents \xe2\x80\x93 how much does one need?<!>Gd3+ agents that respond to tissue biochemistry \xe2\x80\x93 metabolic imaging agents<!>Chemical exchange saturation transfer (CEST) agents<!>Summary and Outlook<!>

<p>Magnetic resonance imaging is a spectacular tool for providing high resolution anatomical images of rodents and humans but is markedly less sensitive than optical or radiotracer imaging modalities for most molecular imaging applications. However, it could be argued that the potential for MRI at a molecular level is equally exciting and it offers the inherent advantages of safety, three dimensional imaging, and high clinical relevance. Most chemists are familiar with small molecule MRI agents that alter image contrast by changing either the T1 or T2 of tissue water but many may not be aware of the limitations of such agents for molecular imaging in vivo. Gd3+-based agents are widely accepted by clinicians because they are easy to administer and they provide positive contrast (imaging brightening) in anatomical images rather than negative contrast (image darkening). Currently available agents consist of low molecular weight Gd3+ chelates that distribute into all extracellular space.1–3 For this reason, these agents produce a general brightening of all tissues in proportion to the extracellular space of each tissue. The standard clinical injection dose of a Gd3+ contrast agent is 0.1 mmol/kg and, depending upon variations in the extracellular space of various tissues, this corresponds to an average extracellular tissue concentration of about 0.5 mM. Given a typical relaxivity of 4 mM−1s−1 for a clinical Gd3+-based agent, this corresponds to an increase in T1 relaxation rate of 2 s−1, a value that is easily detected at common imaging fields. Rooney, et al.4 have demonstrated that extracellular contrast agents are easier to detect at higher magnetic fields due to an increase in tissue water T1, an important consideration as clinical imaging moves to higher fields. This indicates that lower doses of contrast agent will be equally effective at higher imaging fields. The detection limit of a targeted Gd3+-based T1 agent is less easily predicted because local tissue contrast depends upon many factors including the imaging pulse sequence, pixel size, water access to the Gd3+ complex at the targeted site, and the relaxivity of the targeted agent in vivo. Although accumulation of low molecular weight chelates in the extracellular space is sensitive to many diseases, the process is nonspecific and insensitive to many normal physiological processes regardless of imaging field.</p><p>The motivation for developing new MRI contrast agents is to add molecular, physiological or biochemical specificity to routine anatomical imaging. Numerous in vitro examples of T1 or T2 responsive MR agents have been reported but only a few have been demonstrated to work in vivo. The classic example of a responsive T1 agent first reported by Moats, Fraser and Meade5 was later demonstrated to respond as anticipated after injection into X. laevis embryos.6 Perez, et al.,7 later demonstrated the use small particulates of iron oxide as a platform for magnetic resonance T2 switches -this technology continues to evolve in a number of laboratories.8–10 Superparamagnetic iron-oxide nanoparticles have also been investigated for targeting tumors and tracking stem cells but these agents typically do not "respond" to their local tissue environment or to physiology and hence have not been applied to metabolic imaging in vivo.11 The goal of this short review is to address the question of sensitivity limits and options for T1 shortening agents for molecular targeting and to compare those requirements with those of physiologically responsive agents to measure metabolic events in vivo.</p><!><p>If one were to ask users what they think the detection limit of a typical Gd3+-based T1 agent would be, one would get widely diverse answers ranging from ~500 µM (0.5 mM) to perhaps as low of 50 µM. We recently addressed this question systematically to determine that the lower detection limit of a low molecular weight GdDOTA-peptide having an unbound (non-targeted) solution relaxivity (r1) of 10.7 mM−1s−1 was 9 ± 3 µM.12 Upon binding of this Gd3+-peptide conjugate to its receptor localized on agarose beads, the relaxivity of the agent increased to 17 mM−1s−1 and its detection limit dropped to 4 ±1 µM. These experiments allowed us to predict the lower detection limits of other molecularly targeted Gd3+ complexes, coming to the conclusion that if a targeted Gd3+ complex had a fully-bound relaxivity of 100 mM−1s−1, it should be detectable if the concentration of its target receptor molecule was 690 nM. A relaxivity value of 100 mM−1s−1 has proven difficult to achieve for Gd3+-polyamino-polycarboxylate complexes having a single water exchange site. One could consider using one of the novel fast water exchange systems designed by Raymond, et al. with predicted motionally restricted relaxivities ranging from 100–350 mM−1s−1, depending upon the number of inner-sphere water exchange sites.13 However, those systems have not been applied in vivo yet so we chose to test our predictions using rather standard polymeric structures based upon the clinically proven Gd3+-polyamino-polycarboxylate complexes. One could easily achieve a relaxivity of 100 mM−1s−1 by mutiplexing four q=1 Gd3+ complexes together so that each Gd3+ center contributes 25 mM−1s−1 to the molecular relaxivity. Perhaps even easier would be to multiplex eight complexes together each having a relaxivity of 12.5 mM−1s−1. This is relatively easy to achieve using simple q=1 complexes. Given these predictions, we proceeded to prepare a small lysine-based dendron consisting of eight individual GdDOTA units each having a r1 of 12.3 ± 0.5 mM−1s−1 (37°C, pH 7, 23 MHz) per Gd atom (Figure 1). To provide specificity, the dendron was covalently attached to a dimeric peptoid that binds with high specificity and affinity to the vascular endothelial growth factor receptor 2 (VEGFR-2),14 an important target in tumor metastasis. The r1 of the Gd8-dendron-peptoid conjugate increased to only 15.1 ± 0.2 mM−1s−1 per Gd3+ when fully bound to the VEGFR-2 on agarose beads indicating that the GdDOTA units have considerable motional freedom while bound to the receptor. Nevertheless, this corresponds to a molecular r1 of 120 mM−1s−1 per targeted Gd8-dendron-peptoid conjugate. With this system in hand, we then tested whether the predicted detection limits determined for the receptor on agarose beads could be reached in cells with known amounts of the VEGFR-2 receptor on their surface. Porcine aortic endothelial cells with overexpressed human VEGFR-2 (PAE/KDR) (2.5×105 receptors per cell) were used for imaging. This corresponded to a receptor concentration per cell (~5 μm cell radius) of about 790 nM, near the limit of detection predicted in reference 12.</p><p>PAE/KDR cells were exposed to 1 µM Gd8-dendron-peptoid in phosphate buffered saline (PBS) for 30 minutes at 4ºC followed by imaging. T1-weighted images of cells exposed to the agent (Figure 2 bottom-right) compared to non-exposed control cells (Figure 2 bottom-left) were brighter, consistent with agent binding. Given the known binding constant of this targeted agent to the VEGFR2 receptor, one can estimate that the local concentration of bound agent in this experiment was 650 nM with cells exposed to 1 µM Gd8-dendron-peptoid (agent in excess above the receptor concentration). An ICP-MS analysis of Gd3+ in the pelleted cells gave a measure value of ~700 nM, in agreement with the known receptor concentration. Although further details should be addressed, it is important to note that this very low sub-micromolar concentration in cells or in solution (Figure 2 top-right) can be detected by MRI as predicted by theory using a relatively simple agent with a molecular relaxivity of only 120 mM−1s−1.</p><!><p>Our long-term interest in developing MR methods to monitor tissue metabolism in vivo has led us to think about the design of contrast agents that respond to important metabolic indices such as tissue pH, tissue redox, hypoxia and metabolite levels. How do the requirements differ for these applications in comparison to those described above for antigen-targeted Gd3+-based T1 agents? First, biological indices such as pH, redox, and pO2 are a bulk property of tissue so the requirement of high relaxivity is not as important here. More important is the change in relaxivity (Δr1) that occurs in response to the metabolic parameter one wishes to monitor by MRI. For example, pH is an important index of metabolism in tissues because excess acid is a hallmark of abnormal metabolism in ischemic tissues, in certain secretory cells, and is certainly important in tumor growth and metastases. Numerous basic publications have reported different designs for pH sensitive Gd3+-based T1 agents15 but only a few of these have been applied in vivo. Thus, we will limit our discussions here to those few examples.</p><p>The T1 relaxivity of a Gd3+ complex is primarily determined by three factors: q, the number of water molecules in the inner coordination sphere of Gd3+, τM, the residence lifetime of these inner-sphere water molecules (how fast they are exchanging with other bulk water molecules) and τR, the rotational correlation time of the agent (how fast the complex tumbles in solution). To prepare a Gd3+-based pH sensor then, one only requires a chemical system wherein one or more of these variables (q, τM or τR) changes with pH. Examples of all three types can be found in the literature.16–18 However, the only design that has ever been applied in tissues (perfused tissues or organs or in vivo) is GdDOTA-4AmP5− (see structure in Figure 3), an agent that responds to pH by changes in proton exchange rate (variable τM). This agent is unusual because the single inner-sphere water molecule is actually exchanging quite slowly in this complex (τM = 26 µs) compared to typical clinical Gd3+ agents (where τM is typically ≈ 100 – 200 ns). However, this complex has four appended phosphonate groups that have pKa's in the range 6.5 to 8 and as these phosphonate groups become protonated below pH ≈ 8, the mono-protonated phosphonate groups hydrogen bond with the single Gd3+-bound water molecule and catalytically exchange the highly relaxed bound water protons with protons of bulk water.19 This has the same effect as an increase in water exchange rate at lower pH values even though the actual rate of water molecule exchange is not affected by changes in pH in this comples. Although this represents a rather unusual mechanism for Gd3+-based pH sensor, it should not be surprising that this acid-base catalytic system works so well in vivo because acid-base catalysis is a hallmark of many common enzymatic mechanisms in biochemistry.</p><p>A major obstacle in applying such systems to image tissue pH is the unknown concentration of the agent in tissue. The measured T1 contrast of course depends upon two factors, the tissue concentration and the r1 relaxivity (the pH dependent parameter). Given that one cannot assume the agent concentration is uniform throughout all tissues and furthermore may be changing with time, any measure of absolute pH requires a correction for any gradient in agent concentration at the moment the image is collected. Aime, et al.,20 have pointed out that the ratio, R2p/R1p, of water protons becomes independent of Gd3+ concentration for a motionally restricted agent (τR > 1 ns) but remains dependent on τM, τR and other magnetic parameters that normally affect relaxation in these complexes. They validated the method by demonstrating that the R1p of aqueous samples containing (GdDOTA)33-poly-L-ornithine was sensitive to pH due to a conversion of the poly-L-ornithine from a random structure at high pH values to a more ordered helical structure at lower pH values while R2p remains independent of pH. Thus, the R2p/R1p ratio is independent of agent concentration (at least at concentrations high enough to affect these parameters) but is also sensitive to pH, the parameter of interest. While this method is intriguing, the sensitivity of R2p/R1p to change in pH is relatively small and this may make it difficult to apply in vivo.</p><p>We have taken a somewhat different approach to image tissue pH by using two Gd3+-based agents with similar chemical characteristics (size and charge), one having a T1 relaxivity that is independent of pH and one having a pH dependent relaxivity. The chemical structures of two such compounds are shown in Figure 3. The r1 relaxivity of GdDOTP5− is insensitive to changes in pH over a wide range while the r1 relaxivity of GdDOTA-4AmP5− changes a modest amount, from 3.5 mM−1s−1 at pH 9.5 to 5.3 mM−1s−1 at pH 6.3 (see Figure 3 and Figure 5). T1-weighted dynamic contrast enhanced (DCE) images collected after a bolus injection of one agent followed by images collected after a bolus injection of the second agent provided the data needed to map tissue pH. By making an assumption that the two compounds have identical pharmacokinetics and tissue biodistributions, the image intensity differences at the maximum in the DCE curves may be used to estimate tissue pH. This "dual injection method" has been used to map extracellular tissue pH (pHe) mouse kidney21,22 and in a rat brain glioma (Figure 3).23 In the glioma model, an intriguing insight provided by the dual injection method includes observation of an inverse relationship between the time-to-maximal intensity (TMI) and pHe (Figure 3). This indicates that observation of a larger TMI, indicative of slower perfusion in that tumor, was correlated with lower pHe values.</p><p>Although this method works quite well in vivo, there are some drawbacks to the successive injections of two different agents. During the course of the injections, prolonged exposure to anesthesia may alter the blood pressure, which can result in significant differences in the TMI in the two injections.23 In addition, there is a temporal price to pay for two injections. It is necessary to wait until most of the contrast agent has exited the tumor before administering the second injection. These considerations make a case for the development of single injection method, which would enhance the clinical utility of a pHe sensitive contrast agent.</p><p>In many cases, it may not be necessary to measure absolute tissue pH to obtain diagnostically useful information. For example, if the goal is to detect abnormal pH regions of tissues, one might be able to expose the tissue to a pH sensor at a low enough concentration so that significant contrast effects are detected only if tissue pH is abnormally low (assuming the relaxivity increases at lower pH values as with GdDOTA-4AmP5−). Some disease processes such as malignancies may produce local increases in both extracellular volume and [H+] perhaps improving the threshold for early detection of a cancer or metastasis with water soluble, low molecular weight agents. To illustrate the simplicity of the method, we exposed two different perfused tissue preparations, ischemic rat hearts and pancreatic islets, to GdDOTA-4AmP5− and collected T1-weighted images (Figure 4A). Normoxic hearts perfused with 100 µM agent showed little to no contrast changes after addition of the agent while ischemic hearts showed regions of brightness which we attribute to regions of lower pH in ischemic regions generated during the hypoxic period. Similarly, rat islets embedded in agarose beads and perfused with 50 µM GdDOTA-4AmP5− showed little T1 enhancement until the islets were exposed to high concentrations of glucose to promote glucose stimulated insulin secretion (GSIS). Export of insulin from islets is known to be accompanied by release of protons and Zn2+ ions from insulin granules. This local increase in proton concentration was easily detected as a change in T1 after exposure of islets to glucose (Figure 4B). This relatively simple technology offers the opportunity to develop functional assays of islet biology in vivo.</p><p>Clearly, this simplified approach would work even better if Δr1, the difference in r1 between physiological pH and more acidic pH values, was even larger than that displayed by GdDOTA-4AmP5−. Standard theory predicts that the r1 of a Gd3+ complex undergoing fast water exchange will increase upon slowing molecular rotation or tumbling of the molecule in solution (increasing τR). However, GdDOTA-4AmP5− is a bit unusual in this context because it has a slowly exchanging water molecule at high pH and a catalytically enhanced proton exchange rate at lower pH values. Based on simple theory, one would predict that the r1 of the low pH species may become more magnified upon slowing molecular rotation than the r1 of the slow water exchange species at higher pH. If correct, then Δr1 could be significantly better for a motionally restricted version of GdDOTA-4AmP5−. To test this, a bifunctional derivative of GdDOTA-4AmP5− was synthesized and reacted with a generation five G5-PAMAM dendrimer having 128 surface amino groups. The product contained on average 96 molecules of benzyl-GdDOTA-4AmP5− on the surface of the dendrimer and the resulting macromolecule had an average hydrodynamic volume consistent with a molecular weight of ~140 kD.19 As anticipated, the r1 of resulting macromolecular sensor remained pH sensitive (Figure 5) with r1 increasing from 10.8 mM−1s−1 per Gd3+ at pH 9.5 to 24.0 mM−1s−1 per Gd3+ at pH 6. On a macromolecular basis, this corresponds to a change in r1 from 1037 mM−1s−1 at pH 9.5 to 2304 mM−1s−1 at pH 6. Thus, Δr1 for the dendrimer increased 2.2-fold over this pH range in comparison to a Δr1 of 1.5-fold for monomeric sensor over an identical pH range. This increase was significant but not as large as anticipated. It should be pointed out that the mobility of the dendrimer itself is known to be pH-dependent24 so part of the change in relaxivity observed in this system may be due to pH-dependent changes in molecular motion of the dendrimer itself and may not solely reflect the pH sensor attached to its surface. Nevertheless, these data show that one could use significantly less pH sensitive dendrimer (≈ 0.1 – 0.3 µM) to detect similar changes in pH by MRI as those demonstrated in Figure 4. This experiment has not yet been performed in vivo.</p><!><p>What other methods might be used to image abnormal pH regions in tissues in vivo? Ward, et al. were first to demonstrate that image contrast can be altered by taking advantage of chemical species that exchange protons with bulk water via a chemical exchange saturation transfer (CEST) mechanism.25 Typically, –NH protons in molecules are known to exchange more slowly at low pH than at high pH due to base catalysis of proton exchange so if the rate of exchange, kex, is comparable to the chemical shift difference between the exchanging proton and bulk water (Δω), then application of a frequency-selective presaturation pulse at the chemical exchange site prior to collection of an image will result in chemical transfer of some saturated spins into the water pool, thereby reducing the total amount of water detected in the imaging experiment. The amount of chemically transferred spins of course depends upon several factors including kex, bulk water T1, applied B1 power, and concentration of CEST agent.26,27 These features make CEST agents unique among the MRI contrast agents in that contrast is generated only when a presaturation pulse at the correct frequency of the chemical exchange site is applied. This means that image contrast may be manipulated by the operator! To obtain a CEST image, one collects two images, one after presaturation at the exchange site of interest and another at an equivalent frequency offset on the opposite side of bulk water. The difference between these two image intensities reports the effects of the exchanging CEST species.</p><p>A classic example of pH-dependent –NH proton exchange and its relationship to CEST imaging is given by the work of McMahon, et al.,28 summarized in Figure 6. These data illustrate the influence of a increased rate of –NH proton exchange catalyzed by base on the high resolution 1H NMR spectrum of poly-L-lysine (left) and the corresponding Z-spectra or CEST spectra (right). At pH 6, -NH proton exchange is rather slow (50 s−1) and this results in a sharp –NH proton resonance but a rather small CEST effect (top curve in right panel) but at pH 7.9 where –NH proton exchange was increased to 1250 s−1, the high resolution 1H resonance broadens and almost disappears into the baseline while CEST is larger (bottom curve in the right panel). This trend would continue at even higher pH values until exchange becomes too fast (kex ≫ Δω) and the CEST effect would once again disappear. This pH dependent effect then becomes the basis of using endogenous proteins that have a large number and variety of different –NH protons to detect changes in tissue pH by CEST imaging.</p><p>The first published example of using protein –NH exchange groups as an endogenous CEST reporter was given by the work of Zhou, et al.29 Given that the difference in chemical shift between the endogenous –NH proton resonances and bulk water (Δω) is relatively small (on the order of 3 ppm) and the water proton linewidth in vivo is considerably broader than that seen in Figure 6, it becomes necessary to perform an asymmetry analysis to separate out the proton chemical exchange effects from the effects of indirect saturation of water itself.30 Such analyses are further complicated by the magnetization transfer (MT) effects characteristic of tissues whereby dipolar interactions between water molecules associated with semi-solid macromolecules lead to a broad underlying resonance beneath the bulk water resonance. Given the assumption that the contribution of tissue MT is symmetric about the water resonance, collection of two images with a pre-saturation pulse applied at equal offset frequencies on each side of the bulk water resonance should in principle cancel out the MT contribution plus any contribution due to indirect saturation of water itself, leaving only the effects due to CEST. Even though these combined effects can be substantial, Zhou, et al. were able to detect an ischemic region in brain where the pH was substantially lower than in the surrounding tissues (Figure 7). In this case, the ischemic region is dark because –NH exchange from endogenous proteins is too slow to detect a CEST effect in those regions where the pH is below 6 or so while the surrounding healthier tissues have a CEST contribution due to –NH proton exchange. The corresponding histological tissue stain confirmed the result reported by CEST imaging.</p><p>The paramagnetic lanthanide ions (other than Gd3+) complexed by ligands with exchangeable protons also hold promise as exogenous CEST agents. Given the remarkable hyperfine shifting capabilities of the paramagnetic lanthanide ions, proton (-NH or –OH) or water molecule exchange sites in these complexes are typically frequency-shifted well away from the bulk water resonance (Δω) thereby making direct saturation at those sites considerably more distinct than activation of CEST exchange sites near the bulk water frequency. The resulting PARACEST agents have another advantage in that exchange can be faster while maintaining the intermediate-to-slow exchange condition required for CEST, Δω ≈ kex. These agents have not been used to measure pH in vivo at this point but they do hold promise for nontraditional imaging objectives such as tomographic images of temperature31 and extracellular glucose concentrations in tissues.32,33 Images of temperature or temperature gradients would have numerous applications in medicine and physiology because of the fundamental interactions among heat production, metabolism, blood flow and inflammation, and glucose imaging would be highly relevant to studies of nutrition and diabetes. As pH sensors, PARACEST agents do offer potential advantages over Gd3+-based agents that require infusion of two compounds to correct for any tissue concentration. One approach to this problem first described by Terreno et al.26 is to build into the same molecule two exchange sites, one insensitive to pH and another sensitive to pH. This was illustrated by the agent PrDOTA-(gly)4 − (Figure 8), a complex where water exchange is independent of pH (except for at the extremes of high and low pH) while CEST from the –NH protons displays a similar pH responsive profile as with other diamagnetic amide –NH protons. In principle then a pH map of tissues could be obtained after infusion of the single agent followed by CEST measurements using two different presaturation frequencies, the ratio of which would be a direct readout of pH. Unfortunately, this agent has not yet been tested in vivo.</p><p>A third generation G3-dendrimer conjugated with sixteen copies of YbDOTAM to increase the sensitivity of CEST was also recently reported (Figure 9).34 This yielded a molecular system with 48 exchangeable –NH protons that reduced the CEST detection limit into the range of 20 µM, a considerable improvement over simple PARACEST monomers such as that shown in Figure 9. Interestingly, the pH profile of the –NH exchanging protons differed between the monomer, a G1 dendrimer and the G4 dendrimer, thereby allowing fine tuning of the pH sensitive region over which the agent would be responsive. Additionally, a CEST agent concentration independent method for metabolite determination consisting of applying two different radiofrequencies for pre-saturation has also been reported and proven to work in vitro for pH mapping using the dendrimer system.35 This latter approach might provide the pathway for exogenous CEST agents to reach in vivo applications.</p><!><p>Responsive MRI agents for monitoring metabolism in vivo have only recently begun to appear in the literature and only a few have been demonstrated in vivo MRI maps of absolute tissue pH can be obtained by using Gd3+-based T1 agents but the experiments are complicated by the requirement of an independent measure of agent concentration in tissues. Various approaches can be taken to simplify the experimental protocol for obtaining an absolute measure of tissue pH but if one is simply willing to accept detection of low pH regions in tissue, then these agents can be quite useful. pH sensors based on CEST are in many ways more versatile because exchange sites can be built into molecules to make them concentration independent. Unfortunately, none of these have been applied in vivo at this point. The opportunities to apply clever chemistry to create novel metabolic imaging agents are vast but chemists should be encouraged to go beyond simply reporting in vitro examples and establish long-term collaborations with physiologists and imaging experts to see that such agents get applied in vivo. We have held a long-term goal of zdeveloping a simple MR imaging method to monitor pH in tumors, ischemic heart tissues, and islet function in the clinical setting and encourage other investigators to bring new ideas to the table.</p><!><p>A Gd8-dendron-peptoid used for detection of VEGFR-2 receptors.</p><p>Spin-echo T1-weighted coronal MR images of PBS alone (upper-left), 0.5 µM Gd8-dendron-peptoid agent in PBS (upper-right), PAE/KDR cells alone without exposure to the agent (lower-left), and PAE/KDR cells exposed to 1 µM agent (lower-right). Conditions: 4.7 T, TR=150 ms, TE=8.5 ms, FOV=30×30 mm, matrix=128×128; ave =12, 2 mm slice, samples in microtiter wells. A 5 pixel radius median filter was applied to the color image shown (Unpublished).</p><p>Representative time-to-maximal intensity (TMI) images and the calculated pHe map of a C6 glioma in the brain of a live rat. (a) In vitro measurement of r1 as a function of pH for GdDOTA-4AmP5−; (b) T1-weighted image of the brain prior to administration of either agent, (c) TMI image after administration of the non-pH sensitive agent, GdDOTP5−, (d) TMI image after administration of the pH-sensitive agent, GdDOTA-4AmP5−, and (e) the resulting calculated pHe image. Reprinted with permission from John Wiley & Sons, Inc: Magnetic Resonance in Medicine,23 copyright (2006).</p><p>A) MR image of a KCl-arrested rat heart during perfusion with 100 µM GdDOTA-4AmP5− – the gray scale (spin-echo TR300/te8 ms/2mm/128×128/FOV 40×40mm2) image shows a single slice through the left ventricular wall while the color overlay reflects the left ventricular muscle regions that became acidic during a 15 minute period of global ischemia (the colorized regions show only those voxels where the signal increased above the noise level due to a decrease in T1 after ischemia). B) MR image of alginate beads with encapsulated rat pancreatic islets (upper bead; 5% v/v cell-to-bead loading) or an empty bead as control (lower) perfused with 50 µM GdDOTA-4AmP5− – in this case, the color overlay shows only those voxels where the signal increased above noise after the glucose concentration in the perfusate was increased from 5 mM to 25 mM (gradient-echo images, TR8/TE5 ms/ FA=90/1mm/128×256/ave =2/FOV 20×40 mm2) Yellow in the fire-scale color overlays reflect the more acidic regions while purple the less acidic regions. (Unpublished).</p><p>T1 relaxivity versus pH profiles for GdDOTA-4AmP5− and a generation-5 PAMAM dendrimer containing 96 molecules of benzyl-GdDOTA-4AmP5− attached to its surface. Reprinted with permission from Wiley-VCH Publishers, Chemistry – A European Journal,19 copyright (2008)</p><p>High resolution 1H NMR spectra of the amide protons of poly-L- lysine as a function of solution pH (left) and the corresponding CEST spectra (right). Reprinted with permission from John Wiley & Sons, Inc: Magnetic Resonance in Medicine,28 copyright (2006).</p><p>An absolute pH map (left) and the corresponding tissue slice of ischemic rat brain stained with 2,3,5-triphenyltetrazolium chloride. The CEST image was collected at 4.7 T. The area of infarction visible on the right side of both images corresponds to the caudate nucleus (blue arrow), a region commonly affected by infarction following MCA occlusion. Reprinted with permission from Macmillan Publishers Ltd: Nature Medicine,29 copyright (2003).</p><p>PrDOTA-(gly)4: A CEST pH sensor with a built-in concentration indicator.</p><p>Conjugation of the simple tetraamide complex, YbDOTAM, to dendrimers of various size & generation results in –NH proton exchange-based CEST agents with differing pH sensitivities. Reprinted with permission from John Wiley & Sons, Inc: Contrast Media and Molecular Imaging,34 copyright (2007).</p>

PubMed Author Manuscript

Solution Structure, Mechanism of Replication, and Optimization of an Unnatural Base Pair

As part of an ongoing effort to expand the genetic alphabet for in vitro and eventual in vivo applications, we have synthesized a wide variety of predominantly hydrophobic unnatural base pairs and evaluated their replication in DNA. Collectively, the results have led us to propose that these base pairs, which lack stabilizing edge-on interactions, are replicated via a unique intercalative mechanism. Here, we report the synthesis and characterization of three novel derivatives of the nucleotide analog dMMO2, which forms an unnatural base pair with the nucleotide analog d5SICS. Replacing the para-methyl substituent of dMMO2 with a furanyl substituent (yielding dFMO) has a dramatically negative effect on replication, while replacing it with a methoxy (dDMO) or with a thiomethyl group (dTMO), improves replication in both steady-state assays and during PCR amplification. Thus, dTMO-d5SICS, and especially dDMO-d5SICS, represent significant progress toward the expansion of the genetic alphabet. To elucidate the structure-activity relationships governing unnatural base pair replication, we determined the solution structure of duplex DNA containing the parental dMMO2-d5SICS pair, and also used this structure to generate models of the derivative base pairs. The results strongly support the intercalative mechanism of replication, reveal a surprisingly high level of specificity that may be achieved by optimizing packing interactions, and should prove invaluable for the further optimization of the unnatural base pair.

solution_structure,_mechanism_of_replication,_and_optimization_of_an_unnatural_base_pair

Introduction<!>Unnatural base pair design and evaluation<!>Unnatural base pair synthesis - Insertion of dMMO2TP analogs opposite d5SICS<!>Unnatural base pair synthesis - Insertion of d5SICSTP opposite dMMO2 analogs<!>Unnatural base pair extension - Extension of dMMO2 analogs paired opposite d5SICS<!>Unnatural base pair extension - Extension of d5SICS paired opposite dMMO2 analogs<!>dDMO-d5SICS replication as a function of sequence context<!>Generality of unnatural base-pair recognition<!>PCR amplification of DNA containing the unnatural base pairs<!>Structures of the unnatural base pairs<!>Discussion<!>Conclusion<!>Synthetic Methods<!>Kinetic Assays<!>PCR amplification<!>Structural studies

<p>The natural genetic alphabet relies on the selective pairing of the four natural nucleotides, which is governed by a combination of hydrogen-bonding (H-bonding)[1] and shape complementarity.[2–4] However, a priori there is no reason that these forces should be unique in their ability to mediate base pairing. With the long term goal of expanding the genetic code, we[5–15] and others[3,16–22] have explored the development of unnatural nucleotides bearing nucleobase analogs that pair via hydrophobic and packing forces. Among the most promising predominantly hydrophobic base pairs that we have identified is that formed between dMMO2 and d5SICS (dMMO2-d5SICS, Figure 1a).[8] dMMO2-d5SICS is synthesized (by insertion of each unnatural triphosphate opposite the other in the template)[23] and then extended (by insertion of the next correct dNTP) with relatively high efficiency and fidelity by diverse polymerases,[13] including the exonuclease deficient Klenow fragment of E. coli DNA polymerase I (Kf).</p><p>The step that most limits the replication of DNA containing dMMO2-d5SICS is the insertion of dMMO2TP opposite d5SICS in the template. In general, we have found that the rates of insertion are most sensitive to triphosphate derivatization; thus our efforts to optimize dMMO2-d5SICS have focused on modification of dMMO2 (with the goal of optimizing the insertion of dMMO2TP opposite d5SICS). Because previous studies showed that the methoxy and sulfur substituents at the position ortho to the glycosidic linkage are essential for efficient extension of the nascent unnatural base pair,[6–8] our efforts focused specifically on meta and para derivatizations of dMMO2.[9,10] Indeed, we already demonstrated that both d5NaMTP and d5FMTP (Figure 1b) are inserted opposite d5SICS with higher efficiency and fidelity than dMMO2TP, and that dNaM-d5SICS is sufficiently well recognized for expansion of the genetic alphabet in vitro.[9] However, we anticipate that one of the most interesting in vitro applications of an expanded genetic alphabet will be the use of an unnatural base pair to site specifically modify DNA or RNA in a format consistent with enzymatic synthesis, and one limitation of dNaM is that the second aromatic ring precludes derivatization at the position most commonly used to attach linkers (i.e. the C5 position of natural pyrimidines[24]). While other positions might be found to derivatize dNaM with linkers, modifications of dMMO2 that improve replication without blocking the C5 position are desirable.</p><p>Previous kinetic[5–11] and structural[12] studies have prompted us to propose that the predominantly hydrophobic unnatural base pairs are replicated via a unique mechanism involving partial interstrand intercalation (Figure 2). In this mechanism, the unnatural triphosphates are recognized by at least partial intercalation of their nucleobases into the polymerase-bound template strand between the nucleobase of their cognate unnatural nucleotide and a flanking nucleobase. This mode of insertion likely results from the high hydrophobic packing and stacking potential of the unnatural nucleobases and the absence of interactions that favor edge-to-edge pairing, and also suggests that increased packing within the major groove underlies the more efficient insertion of dNaMTP and d5FMTP opposite d5SICS, relative to dMMO2TP. Importantly, the model also suggests that de-intercalation is required to position the primer terminus appropriately for continued extension, which is also favored by H-bond formation between a polymerase-based donor and the ortho substituents of d5SICS and dMMO2,[6–8] explaining why they are essential for extension. Thus, a subtle balance between intercalation and de-intercalation is required for the unnatural base pair to be synthesized and extended efficiently. (Despite the requirement of both intercalation and de-intercalation, for simplicity, we refer to this mechanism as the "intercalative mechanism.").</p><p>To test the intercalative mechanism of replication and to continue our efforts to optimize the dMMO2-d5SICS unnatural base pair, we now report the kinetic and structural characterization of base pairs formed between d5SICS and three dMMO2 derivatives that have been modified at the meta and/or para positions: dDMO, dTMO, and dFMO (Figure 1c). Complete steady-state kinetic analysis, as well as the efficiency and fidelity of PCR amplification show that derivatization with a thiomethyl, or especially with a methoxy substituent, significantly improves replication: dTMO-d5SICS and dDMO-d5SICS are replicated more efficiently than dMMO2-d5SICS. Unlike dNaM, the C5 position of dTMO and dDMO is available for derivatization, making them amenable for uses involving the site-specific modification of DNA. Surprisingly, the furan substituent of dFMO dramatically reduces the efficiency of each step of replication. To help understand these trends in replication, we also report the solution structure of dMMO2-d5SICS in duplex DNA, along with models of the derivative base pairs. The data strongly supports the intercalative model of replication and provides a rationale for the observed variations in the recognition of the dMMO2 derivatives.</p><!><p>The dDMO and dTMO nucleotides were designed to probe the effects of heteroatom substitution in the developing major groove at the primer terminus while leaving the C5 position unsubstituted. In contrast, like dNaM, the C5 position of dFMO is substituted, and this analog was designed to introduce a more rigidly positioned heteroatom while simultaneously increasing the potential for nucleobase packing. Oligonucleotides containing the unnatural nucleotides were synthesized to act as templates or primers so that both synthesis and extension of each unnatural base pair could be evaluated independently. Kinetic analyses were performed under steady-state conditions using Kf, and second order rate constants (efficiency, kcat/KM) were determined (individual kcat and KM values are reported in Supporting Information). PCR amplification was also performed to further evaluate the unnatural base pair and to gauge the potential practical utility. The unnatural base pairs are generally referred to as dY-dX, when no strand context is implied, while dY:dX is used to refer specifically to the strand context with dY in the primer strand and dX in the template strand.</p><!><p>To characterize the effects of major groove substitution, we first characterized the rate at which the dMMO2TP analogs are inserted opposite d5SICS in the template (Table 1). For comparison, dMMO2TP itself is inserted with a second order rate constant of 3.6 × 105 M−1min−1.[8] We found that dDMOTP and dTMOTP are inserted 5-fold more efficiently, resulting entirely from a decreased apparent KM for unnatural triphosphate binding. However, insertion of dFMOTP opposite d5SICS is more than 10-fold less efficient than insertion of dMMO2TP, due to changes in both the apparent kcat and KM. A complete characterization of mispair synthesis with d5SICS in the template was reported previously.[8]</p><!><p>To characterize the recognition of the unnatural nucleotides in the template, we examined the efficiencies with which Kf inserts d5SICSTP (Table 2). For reference, d5SICSTP is inserted opposite dMMO2 with an efficiency of 4.7 × 107 M−1min−1, and it is inserted opposite itself in the template with an efficiency of 1.2 × 105 M−1min−1. Insertion of natural dNTPs opposite dMMO2 in the template is not detectable (kcat/KM < 1.0 × 103 M−1min−1), except in the case of dATP, which is inserted with moderate efficiency (kcat/KM = 1.0 × 105 M−1min−1).[8] We found that insertion of d5SICS opposite dDMO is 3-fold less efficient than opposite dMMO2. However, dDMO also directs the synthesis of the mispair with itself or that with dA less efficiently than does dMMO2, without significantly increasing the synthesis efficiencies of any of the other mispairs.</p><p>We found that insertion of d5SICSTP opposite dTMO is 2-fold less efficient than opposite dMMO2. Interestingly, the thiomethyl substituent significantly increases the rate at which dATP is inserted, while only slightly increasing the rates at which the other mispairs are synthesized. Surprisingly, incorporating the major groove oxygen atom as a restrained furan (i.e. dFMO), as opposed to a free methyl ether (i.e. dDMO), dramatically reduces the efficiency of d5SICSTP insertion (by 100-fold). While dFMO does not direct Kf to synthesize the self pair (kcat/KM < 1.0 × 103 M−1min−1), it does direct the relatively more efficient insertion of each natural triphosphate.</p><!><p>Efficient and high fidelity replication of DNA containing the unnatural base pair also requires efficient continued primer elongation after incorporation of the unnatural nucleotide, and inefficient primer extension after incorporation of an incorrect nucleotide. We first examined the efficiencies with which Kf extends primers terminating with a dMMO2 analog paired opposite d5SICS or a natural nucleotide by insertion of dCTP opposite dG (Table 1). For comparison, Kf extends dMMO2:d5SICS (primer:template) with a second order rate constant of 1.9 × 106 M−1min−1. Changing the major groove substituent from the methyl group of dMMO2 to the methoxy group of dDMO results in a 4-fold increase in extension efficiency. However, the thiomethoxy and furanyl substituents result in approximately 2- and 40-fold reduced efficiencies. Extension efficiencies of primers terminating with a natural nucleotide paired opposite d5SICS have been reported previously.[8]</p><!><p>We next examined unnatural base pair extension with the dMMO2 analogs in the template paired opposite either the correct d5SICS nucleotide or one of the incorrect unnatural or natural nucleotides at the primer terminus (Table 2). For comparison, Kf extends primers terminating with d5SICS paired opposite dMMO2 with an efficiency of 6.7 × 105 M−1min−1. Kf does not efficiently extend primers terminating with the dMMO2 self pair or the dG:dMMO2 mispair; however, the mispairs with dA, dT, and especially dC are extended more efficiently.[8] We found that primers terminating with d5SICS paired opposite dDMO are extended 4-fold more efficiently than when paired opposite dMMO2. Primers terminating with the dDMO self pair are extended less efficiently than those terminating with the dMMO2 self pair. As with dMMO2, primers terminating with dG paired opposite dDMO are not extended at a detectable rate, while primers terminating with dA are extended slightly faster, and primers terminating with dT or dC are extended slower. Extension of d5SICS:dTMO is 3-fold faster than extension of d5SICS:dMMO2. Again, primers terminating with dG paired opposite dTMO are not extended, while the dA:dTMO and dA:dMMO2 mispairs are extended with similar efficiencies, and the mispairs with dT or dC paired opposite dTMO are extended an order of magnitude slower than when paired opposite dMMO2. Surprisingly, the d5SICS:dFMO pair is extended 70-fold less efficiently than d5SICS:dMMO2. Moreover, all of the mispairs between a natural nucleotide and dFMO are extended with rates slower than 7.8 × 103 M−1min−1, revealing that both correct pairs and mispairs with dFMO in the template are extended only poorly by Kf.</p><!><p>The steady-state kinetic data described above suggest that Kf recognizes dDMO-d5SICS better than dMMO2-d5SICS or the other derivatized unnatural base pairs. Because the practical utility of an unnatural base pair depends on its sequence-independent replication, we examined replication of dDMO-d5SICS in a second sequence context, hereafter referred to as sequence context II. In this context the unnatural nucleotide is positioned in the template between a 3′-dG and a 5′-dT (Tables 1 and 2), as opposed to between a 3′-dT and a 5′-dG as in the context examined above, hereafter referred to a context I.</p><p>For comparison, Kf inserts dMMO2TP opposite d5SICS in sequence context II with the same efficiency as in context I (~4 × 105 M−1min−1).[9] We found that sequence context has a slightly larger effect on dDMOTP insertion, with a 3-fold lower efficiency in context II than in context I (Table 1). Thus, while dDMOTP is inserted opposite d5SICS in context I better than dMMO2TP, the two triphosphates are inserted with the same efficiency in context II. Sequence context also has a larger effect on the synthesis of d5SICS:dDMO than on that of d5SICS:dMMO2, in this case the efficiency of synthesis is increased more than 6-fold, to the remarkable efficiency of 9.7 × 107 M−1min−1, which is the most efficient rate for the synthesis of any unnatural base pair identified to date. In fact this efficiency is only marginally less than that for a natural base pair in the same sequence context. While the efficiencies of mispairing with dDMO (i.e. self pair formation) and dA are also increased, they remain more than two-orders of magnitude less efficient, and the mispairs resulting from dCTP or dGTP insertion remain undetectable.</p><p>The effect of sequence context on the Kf-mediated extension of dDMO-d5SICS was also examined. For comparison, Kf extends dMMO2:d5SICS in context II approximately 6-fold less efficiently than in context I.[9] However, it generally extends each mispair with lower efficiency, as well. We find that dDMO:d5SICS is also extended 5-fold less efficiently in context II. In contrast, Kf extends d5SICS:dMMO2 in context II 3-fold more efficiently than in context I, while it extends each mispair less efficiently, with the exception of dT:dMMO2, which it extends approximately 3-fold more efficiently.[9] We find that Kf extends the d5SICS:dDMO heteropair with similar efficiencies in both sequence contexts. Similar efficiencies were also observed for the extension of the mispairs with dG, dC, and dT paired opposite dDMO in the template, but surprisingly, extension of the mispair with dA is an order of magnitude less efficient in context II than in context I.</p><!><p>While derivatization of the nucleobase scaffold commonly results in large effects on the recognition of the nucleotide as a triphosphate, modifications to the templating nucleotide are typically less perturbative.[9,10] Thus, it is surprising that Kf recognizes dFMO in the template so poorly, relative to dMMO2 or dTMO, both during unnatural base pair synthesis and extension. To determine whether this observation is specific for Kf, or whether it is inherent to the unnatural base pair itself, we characterized the ability of another A family polymerase, Taq, as well as a more diverged B family polymerase, exonuclease-negative Vent, to insert d5SICSTP opposite dMMO2, dTMO, or dFMO (Table 3). We found that Taq and Vent insert d5SICSTP opposite dMMO2 with an efficiency of 3.5 × 106 and 9.9 × 106 M−1min−1, respectively.[13] These two polymerases insert the same triphosphate opposite dTMO in the template with similar efficiencies of ~6 × 106 M−1min−1. However, just as observed with Kf, the efficiency of d5SICSTP insertion opposite dFMO by either Taq or Vent is greatly reduced relative to insertion opposite either dMMO2 or dTMO. Thus, for all three polymerases, d5SICSTP is inserted opposite dMMO2 and dTMO with similar efficiencies, but it is inserted opposite dFMO with an efficiency that is approximately two-orders of magnitude reduced. These results suggest that the factors disfavoring dFMO recognition are inherent to the unnatural base pair.</p><!><p>We recently showed that DNA containing dMMO2-d5SICS or dNaM-d5SICS in a variety of sequence contexts is PCR amplified with good efficiency and fidelity using multiple thermostable polymerases, including exonuclease-positive Deep Vent.[15] To further characterize the effects of the major groove modifications, the Deep Vent-mediated PCR amplification of DNA containing dDMO-d5SICS, dTMO-d5SICS, or dFMO-d5SICS was characterized to determine the amplification efficiency (fold-amplification of strand) and fidelity (percentage of strands that retain the unnatural base pair per doubling) (Table 4 and Figure S2; templates range in size from 134 to 149 nucleotides). As predicted by the steady-state data, dDMO-d5SICS is amplified with highest efficiency and fidelity, followed by dTMO-d5SICS, and then dFMO-d5ICS which is replicated with lower efficiency and fidelity than is dMMO2-d5SICS. With template D1, where the unnatural base pair is flanked by a natural dG-dC and dA-dT, dDMO-d5SICS is amplified with virtually natural like efficiency and fidelity. To examine the sequence dependence of amplification, dDMO-d5SICS was further characterized with templates D2-D6 (see Supporting Information for full sequences). As expected, both efficiency and fidelity decreased slightly with increasing dG-dC content of the flanking DNA, as it does with natural sequences,[25,26] but the fact that it remained high in the randomized sequence context of duplex D6 suggests that the efficiencies and fidelities are generally reasonable in different sequence contexts.</p><!><p>To help elucidate the factors underlying unnatural base pair recognition, we determined the NMR structure of a 12-mer duplex containing d5SICS and dMMO2 at the complementary positions 7 and 18 within the duplex (Figure 3a). Resonance assignments for the duplex followed conventional NOESY based methods.[27] The NOESY and DQF-COSY spectra suggest that the unnatural base pair adopts a single, well defined structure, with only small distortions localized to the region of the unnatural base pair. Following standard protocols,[28] a family of 15 structures were generated and used to generate an average structure (Figure 3b and c). In the average structure, both nucleobases of the unnatural base pair are positioned within the interior of a B-form duplex. Key cross-peaks in the NOESY spectra that support this conclusion include: d5SICS7 HD to dC6 H5, dC6 H6, and dC6 C1′H; dMMO218 CH3 to dG19 H8; d5SICS7 CH3 to A17 H8; and dMMO218 CH3/OCH3 to dG10 H8, in addition to cross-strand NOEs between d5SICS7 HB and dMMO218 CH3/OCH3 and HH (see Figures S3 and S4). However, slight distortions of the duplex, relative to a canonical B-form duplex, were apparent at the site occupied by the unnatural base pair. Specifically, relative to a canonical B-form duplex, the C1′-C1′ distance within the unnatural base pair is elongated ~3 Å, and the nucleobases are inclined ~10°, tilted ~30°, and tipped ~5°, with an increase in rise of ~1.5 Å. The deoxyribose rings of the d5SICS7-dMMO218 adopt a C2′-endo conformation, with an average sugar pucker (pseudorotation phase angle) of 137° (Figure S6).</p><p>The structure clearly reveals that the unnatural nucleobases pair via partial interstrand intercalation (Figure 4a). While d5SICS7 stacks well with dT8, it is not well packed with dC6, and instead reaches across the duplex and partially intercalates into the opposite strand between dMMO218 and dA17. Correspondingly, the nucleobase moiety of dMMO218 appears to stack rather poorly with both dA17 and dG19, and instead packs with d5SICS7 from the opposite strand. This mode of pairing appears to induce an approximately 5 Å stagger of the d5SICS7 nucleobase relative to that of dMMO218. The ortho sulfur and methoxy groups are oriented into the minor groove of the duplex, as predicted based on the expected anti geometry of the nucleotides, which is confirmed by cross-strand NOEs between dMMO218 CH3/OCH3 and d5SICS7 HB, between d5SICS7 HB and dMMO218 HH, as well as the absence of NOEs from HC, HD and HE of d5SICS7 to any proton of dMMO218. As further support of this nucleotide geometry, the aromatic protons giving rise to sequential NOEs between aromatic and C1′ protons along each strand include d5SICS7 HE and dMMO218 HF. The methoxy group of dMMO218 rotates out of planarity with the aromatic ring to achieve favorable van der Waals contact with the polarizable sulfur group of d5SICS7. This orientation necessarily places the ring substituted methyl groups of d5SICS7 and dMMO218 in close contact in the major groove. The sum of these interactions provides favorable hydrophobic packing but drives the nucleobase of dMMO218 out of planarity with dT17 and dG19. Within the d5SICS7-dMMO218 pair, the aromatic rings are oriented such that C4 of d5SICS7 is positioned nearly directly over C3 of dMMO218 (Figure 4a).</p><p>We next used the structure of d5SICS7-dMMO218 as a starting point to model the structures of the derivative base pairs in the same 12-mer duplex. Suitable parameters for the derivative nucleotides (dDMO, dTMO, and dFMO) were generated using DFT calculations (B3LYP/6-31G*),[29] and then dMMO218 was replaced and the resulting duplex was subjected to unconstrained minimization for up to 5000 steps in the Sander module of AMBER,[30] until the energy converged (Figure 4b – d). Like the parental unnatural base pair, each derivative base pair shows a similar level of interstrand intercalation. While the increased major groove bulk of dFMO18 appears to introduce some additional local perturbations, none of the structures predict significant distortions relative to the structure of DNA containing the parental base pair. The minor groove interactions between the methoxy and sulfur groups are conserved in all of the structures. While the overall structure of the base pairs in the major groove is also conserved, with the para-substituent of the dMMO2 analog stacking against the methyl/aromatic portion of d5SICS7, the models reveal differences in the stacking interactions that result from derivatization. The para-methoxy group of dDMO18 appears to rotate so that the methyl group packs against the methyl group of d5SICS7 and the oxygen lone pairs are oriented into the major groove. The increased size and hydrophobicity of the sulfur substituent of dTMO appears to preclude packing of the methyl group with d5SICS7, and instead the sulfur atom packs with d5SICS7 and the hydrophobic methyl group is oriented into the major groove. In contrast to dDMO, the cyclic aryl-ether bond of dFMO is unable to rotate and thus the lone pairs of the oxygen atom are forced toward the methyl group of d5SICS7. Furthermore, packing with the flanking dC6-dG19 pair isolates this oxygen and precludes it from potentially engaging in stabilizing interactions with water or metal ions within the major groove.</p><!><p>The effort to expand the genetic alphabet is predicated on the availability of an unnatural base pair that is well replicated and transcribed, and preferably also suitable for modification such that it may be used to enzymatically produce site-specifically modified DNA and/or RNA. The data reveal that dDMO-d5SICS is better replicated by Kf than is the parental base pair, dMMO2-d5SICS. In the steady-state experiments, dMMO2TP insertion opposite d5SICS limits replication, and the ortho methoxy group of dDMO increases the rate of this step, at least in sequence context I. In the opposite strand context, where increases in efficiency are less critical (as it is already very efficient), d5SICSTP is inserted opposite dDMO slightly less efficiently than it is opposite dMMO2 in sequence context I, but slightly faster in context II. In fact, the insertion of d5SICSTP opposite dDMO in context II is the most efficient reported for an unnatural base pair. Moreover, in both sequence contexts examined, extension of dDMO-d5SICS is more efficient than that of dMMO2-d5SICS by approximately a factor of four, except in the case of the extension with d5SICS in the primer in sequence context II, where both unnatural base pairs were extended with similar efficiencies. In addition, no mispairs between the unnatural or natural nucleotides and dDMO are synthesized more efficiently than those with dMMO2, and in fact, most are synthesized less efficiently. Finally, the mispairs with dDMO are also generally extended less efficiently than those with dMMO2, except for the mispairs with dA in sequence context I and dC in sequence context II. These individual steps combine to make dDMO-d5SICS replication significantly higher fidelity than dMMO2-d5SICS (Table 5).</p><p>The improved recognition of dDMO-d5SICS relative to the other unnatural base pairs, including dMMO2-d5SICS, is also apparent in the PCR data. Importantly, the efficiencies and fidelities of dDMO-d5SICS amplification appear to be sufficient for in vitro applications.[31] For example, dDMO-d5SICS appears to be uniquely suited for the site specific labeling of DNA (and possibly RNA[11]) within a format compatible with PCR (or transcription). Along with analogous modifications of (d)5SICS, this should allow the site-specific modification of DNA and RNA with two different functional groups, which should be useful for variety of in vitro applications, including SELEX with an expanded genetic alphabet,[32] as well as biophysical studies that rely on the modification of DNA with multiple biophysical probes.</p><p>The mechanism by which DNA polymerases replicate predominantly hydrophobic unnatural base pairs is of great interest for designing better base pairs, as well as for understanding the range of activities possible with these important enzymes. It has been suggested that shape complementarity is important;[2–4] however, it is critical to define in what context it is manifest (i.e. the mode of pairing). Shape complementarity is usually evoked within a natural, Watson-Crick-like mode of pairing, where two in-plane nucleobases interact in an edge-on manner. Each natural base pair thus adopts a similar shape that is thought to be uniquely well accommodated by DNA polymerases.[2–4] In contrast, the model proposed here (Figure 2) evokes a different mode of base pairing, where instead of interacting edge-to-edge, where little to no stabilization is available, the nucleobases partially interstrand intercalate during base pair synthesis, which is likely driven by their high stacking potential. However, extension of the nascent unnatural base pair requires de-intercalation to position the primer terminus 3′-OH appropriately for continued elongation. While de-intercalation is favored by a stabilizing H-bond between the polymerase and the ortho substituents of the nucleobase analogs,[33–38] the model emphasizes the balance of intercalation propensity that must be possessed by the pairing nucleobases: they must intercalate sufficiently for synthesis, but not so much that extension is inhibited. This model nicely explains a large body of previously reported kinetic[5–10] and structural data.[12]</p><p>The solution structure of the parental dMMO2-d5SICS pair, as well as the derivative model structures of the dDMMO-d5SICS, dTMO-d5SICS, and dFMO-d5SICS pairs in duplex DNA supports the intercalative model of replication (Figure 2). The structures clearly reveal that the nucleotides are accommodated within a B-form duplex, adopt anti-orientations about their glycosidic bonds, and importantly, pair in an intercalative manner. The data further reveal that the stacking interface between the nucleobases is comprised of the methyl group and proximal portion of the associated aromatic ring of d5SICS and the para substituent of dMMO2 or a dMMO2 analog. It should be emphasized that the structures suggest that the unnatural nucleobase analogs only partially intercalate, they do not fully insert into the opposite strand due to their size and the constraints imposed by the duplex (nonetheless, we refer to the interaction as intercalation for simplicity). Importantly, it is clear that the various substituents examined are predicted to be positioned within the stacking interface between the unnatural nucleobases, which accounts for their effects on replication. It should also be emphasized that the structural data is based on the analogs embedded within a duplex, and not at a primer terminus bound to a DNA polymerase. However, the fact that at least some of the specific interactions involved in base pair recognition are inherent to the base pair and not dependent on the polymerase supports the interpretation of the structure in terms of replication.</p><p>The structural models highlight the importance of how the different substituents affect the partitioning of the unnatural nucleobases between intercalated and de-intercalated states, which appear to be required for synthesis and extension, respectively. In the intercalated state, the major groove substituents form a central part of the nucleobase packing interface, but upon de-intercalation, these substituents are more solvent exposed in a more traditional-like major groove. The models suggest that the more efficient replication of dDMO-d5SICS results from an optimized balance of forces governing the stability of the intercalated and de-intercalated states. Synthesis is likely favored by optimized packing interactions between the major groove methyl groups of dDMO and d5SICS. In addition, the structure adopted by dDMO orients the oxygen lone pairs toward the major groove, where upon deintercalation, they may engage in stabilizing interactions with proximal water molecules and/or metals, thus favoring unnatural base pair extension. While anisole is generally not a strong metal ligand or H-bond acceptor due to electron delocalization, both interactions are favored when the conjugation is disrupted by rotation,[39–42] as is observed in the modeled structure of dDMO-d5SICS. The increased substituent size of dTMO appears to induce subtle structural changes without any significant affect on replication. In contrast, the cyclic structure of dFMO appears to force the oxygen lone pairs directly into the hydrophobic interface between the nucleobases, which is likely destabilizing.[43–45] Moreover, if the furanyl oxygen is solvated as the free triphosphate,[46,47] then this stabilizing solvation will be lost upon insertion without being replaced with any other favorable interactions. Moreover, de-intercalation is expected to force the hydrophobic methines further into the hydrophilic major groove, which is likely further destabilizing. Thus, with the aid of the structural models, the intercalative mechanism nicely explains the relatively large effects of the modifications on unnatural base pair synthesis and extension.</p><!><p>We have identified dDMO-d5SICS as an unnatural base pair that is better replicated than the parental dMMO2-d5SICS pair. In addition structural studies support an intercalative model of replication, as previously proposed based on kinetic data[9] and help to explain the observed effects of the various modifications. The intercalative mode of pairing is likely not limited to the analogs examined in the present work. Indeed, it is similar to that observed in the DNA zipper motif, where alternating natural nucleobases are interdigitated, as opposed to interacting in an edge-on manner.[48–56] Moreover, a similar mode of pairing has been observed previously by our group,[12] as well as by the Leumann group[57] with unnatural nucleotides bearing large aromatic nucleobase analogs. However, in these cases the extended aromatic surface area of the nucleobase analogs likely makes intercalation or extrusion from the duplex the only viable options. The intercalative mode of pairing observed between d5SICS and the dMMO2 derivatives occurs despite their potential in-plane accommodation. It is likely that such an intercalative mode of pairing is common to all analogs that are incapable of engaging in stabilizing edge-on interactions. It is also possible that some mispairs between natural nucleotides may be synthesized in a similar manner. Regardless of its potential contribution to the replication of natural DNA, the elucidation of the intercalative mode of pairing should prove invaluable for the further optimization of the unnatural base pairs as well as for our understanding of the potential substrate repertoires of DNA polymerases in general.</p><!><p>dFMO and dTMO were synthesized as described in Supporting Information and dDMO was synthesized as described previously.[7] Briefly, the corresponding arylbromides were lithiated and coupled to 3,5-di-(tert-butyldimethylsilyloxy)-2-deoxy-erythropentofuranose (Scheme S1). After deprotection with TBAF, anomeric mixtures of nucleosides were obtained, and the β-anomer was purified by column chromatography. Nucleosides were converted to triphosphates by POCl3 treatment in the presence of proton sponge, followed by reaction with tributylammonium pyrophosphate. Phosphoramidites of FMO and TMO were obtained from the free nucleosides by 5′ DMT protection and reaction with 2-cyanoethyl N,N-diisopropylchlorophosphoramidite. Oligonucleotides were synthesized by standard solid phase synthesis on controlled pore glass supports. Experimental details together with characterization of all nucleosides, phosphoramidites, oligonucleotides, and triphosphates are provided in the Supporting Information.</p><!><p>Primer oligonucleotides were 5′-radiolabeled with T4 polynucleotide kinase (New England Biolabs) and [γ-33P]-ATP (Amersham Biosciences) and annealed to template oligonucleotides by heating to 95 °C followed by slow cooling. Reactions were initiated by adding of 5 μL of 2× dNTP solution to a 5 μL solution containing polymerase (0.15–1.23 nM) and 40 nM primer-template in reaction buffer (see Supporting Information for details). After incubation at 25 °C (Kf) or 50 °C (Taq and Vent) for 3–10 min the reactions were quenched with 20 μl of loading dye (95% formamide, 20 mM EDTA, bromophenol blue, and xylene cyanole), reaction products were resolved by 15% polyacrylamide gel electrophoresis, and gel band intensities corresponding to the extended and unextended primers were quantified by phosphorimaging (Storm Imager, Molecular Dynamics) and Quantity One software (Bio-Rad). Plots of kobs versus triphosphate concentration were fit to a Michaelis-Menten equation using the program Origin (Microcal Software) to determine Vmax and KM. kcat was determined from Vmax by normalizing by the total enzyme concentration. Each reaction was run in triplicate and standard deviations for both KM and kcat were determined (see Tables S1–S4). Representative raw kinetic data are shown in Figure S1.</p><!><p>DNA duplexes used as templates in PCR amplification reactions were synthesized as described previously.[15] PCR amplification of duplexes D1–D6 (see Table 4 in the main text for details and Supporting Information for sequences) was carried out in 1× ThermoPol reaction buffer (New England Biolabs) with the following modifications: 6.0 mM MgSO4, 0.6 mM each natural dNTP, 0.4 mM each unnatural triphosphate, 1 μM each primer (see Supporting Information for sequences), and 0.03 unit/μL of DeepVent (exo+) in an iCycler Thermal Cycler (Bio-Rad) with a total volume of 25 μL under the following thermal cycling conditions: 94 °C, 30 s; 48 °C, 30 s; 65 °C, 8 min, 14 cycles. Upon completion, PCR products were purified utilizing the PureLink™ PCR purification kit (Invitrogen), quantified by fluorescent dye binding (Quant-iT dsDNA HS Assay kit, Invitrogen) and sequenced on 3730 DNA Analyzer (Applied Biosystems) to determine the fidelity of unnatural base pair replication (see Supporting Information and reference [15] for details).</p><!><p>Lyophilized duplex DNA containing the dMMMO2-d5SICS unnatural base pair was dissolved in buffer containing 10 mM sodium phosphate, pH 7.0, 100 mM NaCl, and 0.1 mM EDTA in 99.99% D2O to a final analyte concentration of 2 mM. All NMR spectra were acquired at 25 °C to resolve as much cross peak overlap as possible on a Varian Inova 500 MHz spectrometer. Proton resonance assignments were made according to established procedures. NOESY spectra with a mixing time of 300 ms were collected with a spectral width of 5913 Hz, 2048 complex points in t2 and 512 t1 increments (zero filled to 2048 on processing); for each t1 value 64 scans were averaged using a recycle delay of 2 s. The approach for computing the structure for the d5SICS-dMMO2 duplex was patterned as described.[28,58] Forcefield parameters for d5SICS and dMMO2 were calculated using Gaussian 98.[29] All energy minimization and restrained molecular dynamics (rMD) calculations were performed with the SANDER module of AMBER 9.[30] A total of 373 constraints were applied (including Watson-Crick hydrogen bonding constraints, 346 NMR-derived distance restraints and torsion restraints for each sugar moiety) during rMD. Structures of duplexes containing d5SICS:dDMO, d5SICS:dTMO, and d5SICS:dFMO shown in Figure 4 were modeled from the NMR determined d5SICS:dMMO2 structure. Briefly, each nucleobase (DMO, TMO, FMO) was subjected to DFT calculations to obtain charge distribution and geometrical parameters. These analogs were used to replace dMMO2 in the NMR structure and each duplex was then minimized (unconstrained) for up to 5000 steps in the Sander module of AMBER,[30] until the energy converged.</p>

PubMed Author Manuscript

Reactive Oxygen Species-Responsive Nanococktail With Self-Amplificated Drug Release for Efficient Co-Delivery of Paclitaxel/Cucurbitacin B and Synergistic Treatment of Gastric Cancer

Application of drug combinations is a powerful strategy for the therapy of advanced gastric cancer. However, the clinical use of such combinations is greatly limited by the occurrence of severe systemic toxicity. Although polymeric-prodrug-based nanococktails can significantly reduce toxicity of drugs, they have been shown to have low intracellular drug release. To balance between efficacy and safety during application of polymeric-prodrug-based nanococktails, a reactive oxygen species (ROS)-responsive nanococktail (PCM) with self-amplification drug release was developed in this study. In summary, PCM micelles were co-assembled from ROS-sensitive cucurbitacin B (CuB) and paclitaxel (PTX) polymeric prodrug, which were fabricated by covalently grafting PTX and CuB to dextran via an ROS-sensitive linkage. To minimize the side effects of the PCM micelles, a polymeric-prodrug strategy was employed to prevent premature leakage. Once it entered cancer cells, PCM released CuB and PTX in response to ROS. Moreover, the released CuB further promoted ROS generation, which in turn enhanced drug release for better therapeutic effects. In vivo antitumor experiments showed that the PCM-treated group had lower tumor burden (tumor weight was reduced by 92%), but bodyweight loss was not significant. These results indicate that the developed polymeric prodrug, with a self-amplification drug release nanococktail strategy, can be an effective and safe strategy for cancer management.

reactive_oxygen_species-responsive_nanococktail_with_self-amplificated_drug_release_for_efficient_co

Introduction<!><!>Synthesis of Thioketal-Paclitaxel and Thioketal-Cucurbitacin B<!>Synthesis of DEX-Thioketal-Paclitaxel and DEX-Thioketal-Cucurbitacin B<!>Micelles Preparation<!>In Vitro Drug Release<!>Reactive Oxygen Species-Triggered Micelle Degradation<!>Cellular Uptake<!>Reactive Oxygen Species Generation in Cancer Cells<!>In Vitro Cytotoxicity<!>In Vivo Antitumor Efficacy<!>Statistical Analysis<!>Paclitaxel and Cucurbitacin B Conjugation Synthesis and Characterization<!><!>Paclitaxel and Cucurbitacin B Conjugation Synthesis and Characterization<!>NPs Preparation and Characterization<!>Reactive Oxygen Species-Triggered Structure Change and Drug Release<!><!>Reactive Oxygen Species-Triggered Structure Change and Drug Release<!>Cellular Uptake<!><!>Reactive Oxygen Species Generation and Reactive Oxygen Species-Triggered Drug Release in Cancer Cells<!><!>Reactive Oxygen Species Generation and Reactive Oxygen Species-Triggered Drug Release in Cancer Cells<!>In Vitro Cytotoxicity Assay<!><!>In Vivo Antitumor Efficacy<!><!>Conclusion<!>Data Availability Statement<!>Ethics Statement<!>Author Contributions<!>Funding<!>Conflict of Interest<!>Publisher’s Note<!>Supplementary Material<!>

<p>In 2020, over 1,080,000 new cases of gastric cancer (GC) were diagnosed and 768,000 mortalities were reported making it the sixth most common cancer and the third cause of cancer-related deaths in the world (Sung et al., 2021). Chemotherapy remains the major treatment strategy among the various therapeutic strategies for gastric cancer (Ma et al., 2021). However, due to the physiological complexity and drug resistance of GCs, the treatment of GC with a single drug or even a stand-along therapy strategy has not been sufficient enough for inhibition of tumor proliferation (Shafabakhsh et al., 2020; Zhang Z. et al., 2020).</p><p>It has been reported that the chemotherapy strategy which utilizes a combination of multiple drugs, or the so-called "drug cocktail," can achieve a synergistic effect on the tumor cells (Lang et al., 2019). Furthermore, the drug cocktail maximizes the therapeutic effect through different signaling pathways, leading to higher therapeutic outcome and low side effects than the single chemotherapeutic drug strategy (Hu et al., 2016). However, various challenges of conventional combination therapy, such as different pharmacokinetics of the combined drugs, lack of tumor targeting, and unwanted side effects, have greatly hindered the application of the drug cocktail strategy in clinical practice (Qi et al., 2017).</p><p>Benefiting from the development of nanotechnology, the nanococktail strategy is developed by loading different drugs into a single nanocarrier. The strategy can effectively deliver therapeutic agents to the targeted sites with similar pharmacokinetics and reduced toxicity (Fang et al., 2018). The conventional nanococktails are prepared by encapsulating several drugs into a specific nanocarrier, such as polymeric micelles, liposome, and mesoporous silica nanoparticles (Meng et al., 2015; Zununi Vahed et al., 2017; Zhao et al., 2019). Although these nanococktails reduce the side effects and exhibit the synergistic effect in tumor therapy, premature drug release could potentially deteriorate the already compromised health condition of patients with the tumor (Li et al., 2019). Moreover, because of the different loading potentials against different drug compounds, the ratio of drugs in the nanococktail may not be easily modulated according to their required concentration (Chen et al., 2019).</p><p>Polymeric-prodrug-based nanomedicine involves conjugation of multiple drugs to a polymer through covalent bonds, such as thioketal (TK), disulfide, and ester bonds (Huang et al., 2017; Hao et al., 2020; Lu et al., 2020). Recently, nanomedicine has received great attention of researchers because it reduces the induced harm caused by premature drug release (Zhang et al., 2019; Zhang et al., 2021). When these nanoparticles reach the tumor tissue, the linkage between drug and the polymer is cleaved in response to endogenous (pH, reduction, reactive oxygen species (ROS), and enzyme) and exogenous (light and magnetic field) stimuli to selectively release the drugs (Zhang et al., 2019; Zhang et al., 2021). Therefore, the drug-related side effects can be significantly minimized because specific conjugated drugs can only be released after the cleave of the linker upon reaching the specific tumor tissue (Sui et al., 2019). However, the selectively and efficiency of stimuli-triggered drug release from the nanococktails are remarkably decreased due to the heterogeneity of different tumors (Ye et al., 2017). The benefits of polymeric-prodrug-based nanococktails are, hence, left with a catch-22 situation between their efficacy and safety (Sui et al., 2019). In the present study, a simple ROS-sensitive nanococktail was developed to improve drug release efficiency and selectively, enhance therapeutic efficacy, and reduce the side effects of polymeric-prodrug-based nanococktail drugs. Generally, it was evident that the developed nanococktail amplified the level of ROS in cancer cells, accelerated the drug release, and thus, broke the catch-22 limitation of nanococktail strategy.</p><p>Cucurbitacin B (CuB) is a typical tetracyclic triterpenoid compound, which exists widely in the plant kingdom (Garg et al., 2018). Accumulating evidence has shown that CuB can increase intracellular ROS level, hence inhibiting the growth of GC as well as colon, breast, and lung cancer cells (Yasuda et al., 2010; Ren et al., 2015; Luo et al., 2018; Xu et al., 2020). According to Wang et al. (2020) and Li et al. (2021), co-CuB can effectively enhance the level of ROS in cancer cells and, hence, accelerate and amplify degradation of ROS-responsive prodrugs. Therefore, co-delivery CuB for increasing intracellular concentration of ROS in cancer cells could greatly improve the efficacy of ROS-responsive nanococktail. Furthermore, CuB can potentiate the antitumor effect of numerous chemotherapiutic drugs, such as paclitaxel (PTX), methotrexate, and gemcitabine (Thoennissen et al., 2009; Lee et al., 2011; Marostica et al., 2017). Therefore, CuB-based nanococktail increases the ROS to promote drug release in cancer cells and also achieve a synergistic effect for the treatment of GC.</p><p>In the present study, a self-amplification release nanococktail (PCM) was developed by self-assembling of both ROS-responsive CuB (DEX-TK-CuB) and PTX (DEX-TK-PTX) polymeric prodrugs (Scheme 1). The two prodrugs were prepared by conjugating PTX and CuB to the hydrophilic dextran through an ROS-sensitive linkage several times. Reactive oxygen species (ROS)-responsive CuB (DEX-TK-CuB) and DEX-TK-PTX can co-assembly into micelles in an aqueous solution to form a self-amplification release nanococktail (PCM). Therefore, the side effect of CuB and PTX could significantly reduce because TK is very stable in the absence ROS. After entering the cancer cells, the TK linkage can be triggered by the endogenous ROS to break and release CuB and PTX. The released CuB could further induce massive generation of ROS, which then accelerates and amplifies release of more drugs. Therefore, the rapid and complete release of the drug ultimately enhances the efficiency of the nanococktail in the treatment of cancer.</p><!><p>Schematic illustration of the preparation and intracellular performance of PCM nanococktails.</p><!><p>The ROS-cleavable thioketal linker (TK) was first fabricated as described in the previous studies (Chen et al., 2016; Hu et al., 2017). In brief, 6.8 and 6.0 g of anhydrous acetone and anhydrous 3-mercaptopropionic acid, respectively, were mixed and stained with dry hydrogen chloride at room temperature for 6 h. At the end of the reaction, the mixture was cooled with an ice–salt mixture for crystallization. Thereafter, the crystals were filtered and washed with abundant hexane and cold water. The product was obtained after drying under vacuum (yield: 44.5%). The detection of molecular weight (MW) of TK was determined by mass spectrometry, and the MW was found to be 251.08, calculated to 251.05.</p><p>Subsequently, to obtain TK-TPX or TK-CuB, the prepared TK was conjugated to PTX or CuB, respectively. In brief, 4 mmol of TK was dissolved in 6 ml of acetic anhydride and stirred at room temperature in nitrogen atmosphere for 2 h. The solvent was then removed under reduced pressure and further dried under vacuum to obtain TK anhydride. Subsequently, all the TK anhydride, 4 mmol PTX, and 0.4 mmol DMAP were dissolved in dry DMSO, and the mixture was stirred in nitrogen atmosphere at room temperature for 14 h. The reaction product was purified through silica gel column chromatography using CH2Cl2 and ethyl acetate (3/1, v/v) as the eluent. A white solid (TK-PTX) was obtained with an yield of 71.3%. TK-CuB was also prepared using the same method by just changing the PTX with CuB (yield 73.5%).</p><!><p>TK-PTX and TK-CuB were conjugated to DEX though an ester reaction between prodrugs and DEX to generate DEX-TK-PTX or DEX-TK-CuB, respectively. In this study, the protocol of DEX-TK-PTX was described to present the polymeric prodrug synthesis details. Typically, the TK-PTX (108.8 mg, 0.1 mmol), EDC (28.8 mg, 0.15 mmol), DMAP (18.3 mg, 0.15 mmol), and DEX (3.5 g, 0.05 mmol) were dissolved in 40 ml anhydrous DMSO and stirred under nitrogen atmosphere at room temperature. After 24 h of reaction, the solution was transferred into a dialysis bag (MWCO: 1.5 kDa) and was dialyzed against DMSO for 24 h and then into distilled water for 48 h. Finally, the solution was lyophilized to obtain DEX-TK-PTX (yield = 65.4%). The DEX-TK-CuB was obtained using the same synthesis route with the yield of 61.7%.</p><p>The drug content in the polymeric prodrugs was detected using UV spectrophotometry using a standard curve method. The drug content was calculated using the following equation: Drug content(100%)=Mass of drug in prodrugMass of prodrug×100%.</p><!><p>A simple dialysis method was used to prepare the combination prodrug micelles. Typically, 2 mg of prodrugs (or a mixture of different prodrugs) was dissolved in 0.1 ml of DMSO. The prodrug solution was then dropped into 1 ml water under violent stirring. After 2 h stirring, the mixture was dialyzed against PBS in a Spectra/Por dialysis tube (MWCO: 3.5 kDa) at 4°C for 24 h to eliminate DMSO. Finally, the micelles were filtered through 0.45 μm syringe filters and stored at 4 °C. Furthermore, using the similar method, PTX and CuB co-loaded micelles (denoted PCM), only PTX-loaded micelles (denoted as PM), and only CuB-loaded micelles (named as CM) were obtained.</p><p>For coumarin-6-loaded micelle preparation, 10 μg of coumarin-6 and 2 mg of prodrugs were dissolved in DMSO and then prepared as described earlier.</p><p>The drug loading content (DLC) of PTX and CuB was detected by HPLC and calculated using the following Eq. 1: DLC(%)=Mass of drug in micellesMass of micelles×100%. (1)</p><!><p>A simple ultrafiltration centrifugation method was used to assay the release behaviors of PTX and CuB from PCM. The PBS (pH 7.4) containing 0.5% (w/v) Tween-80 with 0, 0.1, and 10 mM H2O2 was utilized as the release medium. In brief, freshly prepared PCM (containing 30 μg of PTX and 15 μg of CuB) was dissolved in 4 ml of release medium and cultured at 37°C with slight shaking. At pre-set time intervals, one sample was collected and centrifuged at 5,000 g for 10 min using a centrifugal filter unit (MWCO = 3.5 kDa). The UV spectrometry was used to detect the concentration of released CuB and PTX.</p><!><p>Micelles (2 mg/ml) were cultured in PBS (pH 7.4) with or without 10 mM H2O2 for 12 h at 37°C. After treatment, the size of micelles was then measured using DLS.</p><!><p>Human gastric cancer BGC-823 cells were seeded on a six-well plate with 5 × 104 cells per well and incubated for 24 h. The cells were then separately treated with coumarin-6-loaded PM, CM, and PCM, with the final concentration of coumarin-6 being 400 ng/ml. After culturing for 2 or 4 h, the cells were washed with PBS, fixed by 4% paraformaldehyde, stained with Hoechst 33,342, washed with PBS, and then, directly recorded on a fluorescence microscope.</p><!><p>The CuB-mediated ROS generation in cancer cells was studied using a fluorescence microscope and flow cytometry. For fluorescence microscope assay, BGC823 cells were seeded on a six-well plate with 6 × 104 cells per well and incubated overnight. Then, the cells were pretreated with or without 4 mmol/L of N-acetyl cysteine (NAC) for 1 h. Subsequently, cells were separately cultured with PBS, CuB, PTX, CM, PM, and PCM2 which were equal to 1.0 μg/ml of CuB for 8 h. After treatment, the cells were stained with DCFH-DA for 20 min at 37°C, washed with PBS for three times, fixed by 4% paraformaldehyde, and then, quickly observed under a fluorescence microscope.</p><p>For flow cytometry assay, the BGC823 cells were seeded on a six-well plate with 6 × 104 cells per well and incubated overnight. Then, some cells were incubated with free CuB, CM, PM, and PCM2 for various concentrations (equal to 0.5, 1.0, and 2.0 μg/ml of CuB) or containing 1.0 μg/ml CuB for different times (4, 8, and 12 h), respectively. At the end of incubation, the cells were stained with DCFH-DA for 20 min at 37°C and then detected through flow cytometry.</p><!><p>The BGC823 cells or human SGC7901 cells were seeded into a 96-well plate at the density of 5,000 cells per well. After cultured for 24 h, the cells were incubated with CuB, PTX, PTX+CuB, CM, PM, or PCM with different concentrations (0.0001, 0.001, 0.01, 0.1, 1, 50, 100, and 200 μg/ml, equal to PTX) for 48 h. A 20 μL of MTT solution (5 mg/ml) was then added into each well and incubated for further 3 h. Finally, the old medium was replaced with 100 μL DMSO to dissolve the formed formazan salts, and the adsorption of each well was recorded on a microplate reader at 490 nm.</p><!><p>For BGC-823 xenograft model construction, 6 × 106 cells resuspended in 200 μL of PBS solution were subcutaneously inoculated in the right flank. When the tumor volume reached about 100 mm3, mice were randomized into seven groups of six mice each with similar mean tumor volumes between the groups and then treated with PBS, CuB, PTX, PTX+CuB, PM, CM, and PCM at 5.0 mg/kg PTX and 1.7 mg/kg CuB. The formulations were administered through the tail vein on days 0, 3, 6, and 9, respectively, as a total for four times. The body weight, tumor length ( a ), and width ( b ) were detected every 2 days. The tumor volume was calculated as volume = 1/2 × a × b 2.</p><p>After 3 weeks, all mice were sacrificed to harvest the tumors which were weighted, and their tumor suppression ratio (TSR) was calculated according to the Eq. 2:  TSR(%)=Tumor mass of PBS group – tumor mass of treatment groupTumor weigh of PBS group×100%. (2)</p><!><p>Each experiment in the present study was carried out triplicate. The results were presented as mean ± SD. Inferential statistics were carried out using the t-test or one-way analysis of variance (ANOVA) followed by Tukey's post hoc test to compare the means. p < 0.05 was defined as a statistically significant difference.</p><!><p>The DEX, a naturally water-soluble bacterial polysaccharide, has been widely used in medicine because of its excellent biocompatibility and biodegradability (Raveendran et al., 2016; Zhang X. et al., 2020). The abundance of hydroxyl groups enables DEX to be conjugated with drugs to increase the solubility, prolong the circulation time, and improve the stability of drugs (Zhang T. et al., 2020; Zeng et al., 2020). Moreover, in comparison with poly(ethylene glycol), DEX shows better stability and less tendency to nonspecific protein adsorption as a drug carrier (Li et al., 2017). Therefore, DEX is an ideal and safe biomedical material.</p><p>In this work, the polymeric prodrugs were prepared by modifying PTX or CuB to DEX though an ROS-sensitive TK linker via a two-step esterification reaction. The synthesis route was as presented in Supplementary Scheme S1. First, the ROS-cleavable TK linkage was prepared based on the previous report (Chen et al., 2016; Hu et al., 2017). The MS results were in consistence with previous reports (Chen et al., 2016; Hu et al., 2017). Subsequently, TK-modified PTX (TK-PTX) and -CuB (TK-CuB) were obtained. To increase the yield and decrease the by-product, the anhydride TK was synthesized and used instead of TK due to its high reactivity with hydroxyl groups. The structure of TK-PTX and TK-CuB were verified through 1H NMR (Figures 1A,B) and MS (Supplementary Figure S1). In the 1H NMR spectrum of TK-PTX (Figure 1A), the peaks that appeared at 2.9 and 3.1 ppm, which belong to the two methylene groups of TK, demonstrated the successful reaction between TK and PTX. Moreover, as compared with free PTX, the 2′-CH proton peaks of TK-PTX shifted from 4.7 ppm to 5.6 ppm. This suggests that the reaction between PTX and TK took place preferentially at the 2′-hydroxyl of PTX (Wang et al., 2016). Similarly, the characteristic peaks of both TK and CuB emerged in the 1H NMR spectrum of TK-CuB. This suggested that TK-CuB was successfully prepared (Figure 1B). In addition, the MS results of TK-PTX and TK-CuB exhibited peaks at m/z = 1,087.1 and m/z = 791.7 matching with the [M-H]-, respectively. This was utterly consistent with the theoretical calculation value (Supplementary Figures S1A,B). This result further demonstrated that TK-PTX and TK-CuB were successfully synthesized.</p><!><p>Characterization of prodrugs. (A) 1H NMR spectra of PTX, TK-PTX, and DEX-TK-PTX in DMSO-d6. (B) 1H NMR spectra of CuB, TK-CuB, and DEX-TK-CuB in DMSO-d6. (C) UV spectra of PTX, TK-PTX, and DEX-TK-PTX. (D) UV spectra of CuB, TK-CuB, and DEX-TK-CuB.</p><!><p>Finally, it was found that DEX reacted with TK-PTX and TK-CuB to generate the DEX-TK-PTX and DEX-TK-CuB, respectively. The 1H NMR spectrum of DEX-TK-PTX showed all the expected resonance peaks characteristic of PTX and DEX, such as the peak at 7.8 ppm corresponding to the protons of benzene in PTX and the CH peak at 0.55 ppm related to those of DEX (Figure 1A). A similar phenomenon could also be observed in the 1H NMR spectrum of DEX-TK-CuB (Figure 1B). These results indicate the successful synthesis of DEX-TK-CuB. The UV spectra of DEX, PTX, DEX-TK- PTX, CuB, and DEX-TK-CuB were presented as shown in Figures 1C,D. It was evident that the DEX had no UV absorption in the range of 250–400 nm, and both PTX and DEX-TK-PTX showed the maximum absorption at 227 nm. Similarly, it was found that the CuB and DEX-TK-CuB exhibited maximum absorption at 293 nm. The PTX and CuB content in DEX-TK-PTX and DEX-TK-CuB were 18.8 ± 1.0 and 19.5 ± 1.2%, respectively, detected using HPLC by a standard curve method.</p><!><p>This was carried out through conjugation of the hydrophobic CuB and PTX to the hydrophilic DEX to obtain amphiphilic DEX-TK-CuB and DEX-TK-PTX. The two amphiphilic polymeric prodrugs (DEX-TK-CuB and DEX-TK-PTX) provided an opportunity for self-assembly into micelles in the aqueous medium. As a proof of the conception, the CMC values (the fundamental parameter of micelles) of two prodrug micelles were determined using Nile red as the fluorescence probe. As shown in Supplementary Figures S2A,B, the CMC values of the two prodrugs were calculated to be 12.7 (DEX-TK-PTX) and 10.8 μg/ml (DEX-TK-PTX). The low CMC values of both prodrugs suggested that the excellent antidilution stability in vivo of the formed micelles.</p><p>Subsequently, the micelles containing PTX, CuB, and their combination were prepared using a simple dialysis method and denoted as PM, CM, and PCM, respectively. To achieve the highest antitumor efficiency, a series of PCMs with different molar ratios of PTX to CuB were prepared and named as PCM1 ∼ PCM5. PCM1 to PCM5 have a similar particle size, size distribution, and zeta potential (Supplementary Table S1). It was found that, at 48 h, PCM2 induced the highest cytotoxicity against BGC823 cells with the IC50 value of 2.78 ug/mL. Therefore, PCM2 (PTX/CuB = 3: 1, mass/mass) was selected as the final micelle for the following studies.</p><p>The physical properties of PM, CM, and PCM2 were measured through TEM and dynamic light scattering (DLS). The TEM images revealed that all the three micelles of PM, CM, and PCM2 had clear spherical morphology with uniform distribution. The hydrodynamic particle size of PM, CM, and PCM2 in PBS was (82.6 ± 3.4) (80.8 ± 3.0), and (78.7 ± 2.5) nm, respectively, as determined by DLS. It was found that these micelles had a narrow distribution as evidenced by those whose PDI was lower than 0.22 (Supplementary Table S1). The appropriate particle size is conducive for the accumulation of micelles in tumor tissues through the enhanced permeability and retention (EPR) effect, ultimately achieving high-efficiency drug delivery (Goos et al., 2020). Additionally, the stability of micelles in PBS (pH 7.4) or PBS (pH 7.4) containing 10% FBS was tested using the DLS method (Supplementary Figure S3). After an incubation period of 48 h, the size of PM, CM, and PCM2 had no significant changes in the two conditions of PBS (Supplementary Figure S3A) and PBS containing 10% FBS (Supplementary Figure S3B). This indicated that these micelles have an excellent stability.</p><!><p>In this work, the PTX and CuB was covalently conjugated to DEX through an ROS-cleavable TK linker. In the ROS environment, the TK linker would be broken, resulting in release of the modified drugs and degradation of micelles. To investigate the ROS-responsive capability of prodrug micelles, size change and drug release behavior of micelles in different ROS conditions were studied by using H2O2 as a typical ROS stimulus (Hu et al., 2017). First, the time-dependent size change of PM, CM, and PCM2 in 1 mM H2O2 was monitored by DLS. An increase in size of three micelles was observed after incubation with 1 mM H2O2 (Figure 2B). After incubation for 8 h, the average size of PM, CM, and PCM2 changed from 50, 60, and 70 nm to 120, 350, and 7,202 nm, respectively. This suggested the good responsiveness of the micelles to ROS. The potential mechanism is that the cleavability of TK by ROS leads to the hydrophobic drug removal from the micelle core, resulting in the hydrophobic core of micelles being transformed to hydrophilic, and then induces the disassembly of micelles.</p><!><p>Micelle characterization. (A–C) Size distribution and TEM images of PM (A), CM (B), and PCM2 (C). (D) Changes in size of PM, CM, and PCM2 after treatment with 10 mM H2O2 for 0, 2, 4, 8, and 16 h, respectively. (E,F) Cumulative release of PTX (E) and CuB (F) from PCM2 after incubation with various dose of H2O2. The data in D, E, and F are presented as the mean ± SD, n = 3.</p><!><p>Subsequently, the ROS-triggered drug release behavior of PCM2 was further tested in the present study. It was found that the TK linkers between drugs and DEX maintained stability and less than 5% of the drugs was released from PCM2 even after incubation for 60 h. On the contrary, about 43 and 55% of PTX and CuB, respectively, were leaked within 60 h after incubation with 0.1 mM H2O2. While PCM2 was treated with 10 mM H2O2, the cumulative release of PTX and CuB at 60 h reached 74 and 84%, respectively. These results confirmed that the concentration of H2O2 had a positive and significant influence on the amount and the rate of drugs released, which was the basement of self-amplification drug release in the tumor intracellular conditions.</p><!><p>In this study the cellular internalization process of PCM2 against BGC-823 cells was monitored using a fluorescence microscope (Figure 3). After a 2 h incubation period, the weakly green fluorescence could be observed in the cytoplasm in the three micelle treatment group. This demonstrated that the micelles could effectively be internalized by BGC-823 cells. When the incubation time was increased to 4 h, the green fluorescence signal in the three micelle group was also increased, and this suggested the time-dependent cellular uptake. In addition, it was found that the fluorescence intensity had no significant difference within the same incubation period. This indicated that the three kinds of micelles could be effectively internalized by cancer cells and there was no remarkable difference in the uptake.</p><!><p>Fluorescence microscope images of BGC-823 cells after incubation with coumarin-6-loaded PM, CM, and PCM2 micelles for 2 or 4 h, respectively.</p><!><p>In the hypothesis of the present study, the released CuB in cancer cell could induce a large amount of ROS generation, which in turn could accelerate and amplify the release of drug. To confirm this feature, the PCM intracellular ROS production capacity of different formulations was determined using DCFA-DA as the probe. It was evident that the cell-permeable nonfluorescent DCFA-DA could be easily and quickly oxidized to dichlorofluorescein (DCF) with green fluorescence using the intracellular ROS. First, the time- and dose-dependent ROS production in human gastric cancer BGC823 cells after treatment with free CuB were quantified through flow cytometry. It was found that the mean fluorescence intensity (MFI) in CuB-treated BGC823 cells was enhanced by increasing the incubation time or treatment dose (Supplementary Figures S4A,B). This demonstrated that the CuB could effectively induce ROS generation in BGC823 cells.</p><p>Therefore, the ROS regeneration potential of CM, PM, and PCM2 against BGC823 cells was further observed by using a fluorescence microscope and quantified through flow cytometry. Furthermore, it was found that a stronger green fluorescence could be observed in CM-treated cells which demonstrated that the CM could effectively induce ROS production in cancer cells as compared with the control group (Figure 4A). Notably, it was found that the PM-treated cells showed slightly enhanced fluorescence intensity as compared with cells in the control group and this could have been induced by the released PTX. The qualitative results show that the PCM2-treated cells exhibit a stronger fluorescence intensity compared with PM and CM under the synergy of CuB and PTX. Furthermore, the results of fluorescence microscopy were well consistent with those of flow cytometry (Figure 4B). Moreover, the generated ROS in CuB, CM, and PCM2 groups can be scavenged by the ROS scavenger, NAC, further demonstrating CuB could effectively trigger ROS generation in cancer cells. Therefore, the results of this study demonstrated that the CuB-based formulations can effectively promote ROS generation in cancer cells.</p><!><p>Intracellular ROS generation. (A) Fluorescence microscopic images of BGC823 cells after treatment with PBS, CuB, CM, PM, or PCM2 for 4 h equal to 1 μg/ml of CuB with or without NAC pretreatment. (B) Mean fluorescence intensity (MFI) of BGC823 cells after being treated with free CuB, CM, PM, or PCM2 (equal to 1 μg/ml of CuB) for 4 h (C,D) Intracellular release of PTX after incubation with PM and PM2 (C) and intracellular release of CuB after incubation with CM and PM2 (D). Data in (B–D) were exhibited as the mean ± SD, n = 3.</p><!><p>To further verify whether the CuB-mediated ROS production could also promote drug release, the released PTX and CuB in CM (17 μg/ml of CuB)-, PM (50 μg/ml of PTX)-, and PCM2 (17 μg/ml of CuB and 50 μg/ml of PTX)-treated cells were quantified using HPLC. It was found that the released drugs in each formulation were increasing with the extension of incubation time (Figures 4C,D). The release of PTX in the PCM2-treated group at the incubation period of 4, 8, 12, and 24 h was 2.6-, 2.2-, 2.1-, and 1.8-fold higher than that of PM, respectively. However, the release of CuB in the CM- and PCM2-treated group at the same incubation period had no remarkable difference. These results suggested that CuB can effectively promote the drug release in cancer cells.</p><!><p>The cytotoxicity of different drug formulations was evaluated in the current study using BGC823 cells and SGC7901 cells through the MTT method and the IC50 values were simultaneously calculated (Figures 5A–C). Results of this study show that both free CuB and CMs had a weak cell-killing ability with a high IC50 value, which was 10.52/10.49 μg/ml and 14.33/12.87 μg/ml in BGC823 cells and SGC7901 cells, respectively. Furthermore, it was found that the IC50 value of PM was 7.69 and 8.0 μg/ml in BGC823 cells and SGC7901 cells at 48 h, which was lower than that of PTX due to the delayed PTX release. This was because PTX's main antitumor active group C2′-hydroxyl was blocked (Wang et al., 2020). On the contrary, it was found that PCM2 exhibited a better cytotoxicity in comparison with PTX and showed a similar cancer cell-killing capability in comparison with the combination of PTX and CuB. The results of this study show that the IC50 value of PCM2 was 2.78/2.63 μg/ml in comparison with 0.87/1.00 μg/ml for PTX and 0.33/0.29 μg/ml for the combination of PTX and CuB (equal to PTX) against BGC823 cells and SGC7901 cells, respectively. Subsequently, to evaluate the synergistic effect of CuB and PTX, the combination index (CI) was calculated according to the Chou-Talalay equation (Li et al., 2020): CIx = D1/Dx1 + D2/Dx2. In the present study, Dx1 and Dx2 are represented as the IC50 value of PTX and CuB alone, respectively. The D1 and D2 were defined as the dose of PTX and CuB in the co-treatment group at the IC50 value. Furthermore, the CI > 1, CI = 1, and CI < 1 were denoted as antagonism, additive effect, and synergism, respectively. The CI50 value of PCM2 against BGC823 cells and SGC7901 cells was 0.39 and 0.30 which demonstrated a strong synergic effect and good sequential prodrug bioactivation.</p><!><p>In vitro antitumor effect. Cell cytotoxicity (A,B) and IC50 value (C) of CuB, PTX, PTX+CuB, PM, CM, and PCM2 against BGC823 (A) and SGC7901 (B) cells at 48 h. Data in (A,B) are shown as the mean ± SD, n = 6.</p><!><p>The in vivo anticancer effects of PCM2 were further investigated using BGC823 tumor-bearing mice. As shown in Figures 6A–C, the tumor volume of mice treated with saline rapidly increased from 100 mm3 to about 1,216 mm3 within 21 days. For mice treated with PTX, CuB, and CM, tumor volume increased to 739 mm3, 916 mm3, and 590 mm3 at the end of 21 days of treatment. The TSR of PTX, CuB, and CM was 42.1, 31.6, and 46.4%, indicating poor antitumor effect. The tumor volume of mice treated with a combination of PTX and CuB was 440 mm3, and the TSR was 68.0%. This may be due to poor solubility, low drug accumulation in tumors, and short duration in blood circulation (Sauraj et al., 2018). In contrast, the tumor volume of mice treated with PM was moderately reduced to about 503 mm3 on day 21, and its TSR was 60.7%. Notably, among the evaluated formulations, PCM2 exhibited the best antitumor effect, with a tumor size of as low as ∼125 mm3 for the same therapeutic period and its TSR as high as 88.3%. The high antitumor effect was ascribed to the rapid on-site prodrug bioactivation triggered by ROS generated in response to CuB. Changes in bodyweight were recorded to reflect the safety of PCM2. As displayed in Figure 6D, the bodyweight of mice administered with free PTX or free CuB was slightly higher compared to that of mice that received PTX+CuB treatment within 21 days. Contrarily, no remarkable body weigh lost was discovered in mice treated with micelle formulations, suggesting that PCM2 was safe.</p><!><p>In vivo antitumor effects. (A) Changes in tumor volume during the treatment cycle. (B) Dissected tumor weight at day 21 in different groups. (C) TSR of PTX, CuB, PTX+CuB, PM, CM, and PCM2. (D) Changes in body weight during the treatment cycle. Data are exhibited as the mean ± SD, n = 5.</p><!><p>In summary, an ROS-responsive, self-amplification drug release nanococktail (PCM) was successfully developed in the present work by co-assembling an ROS-sensitive PTX prodrug and CuB prodrug. The nanococktail was effectively uptaken by tumor cells and induced intracellular ROS generation which further enhanced drug release. The developed PCM inhibited the growth of tumor cells in vitro and in vivo without causing significant systemic toxicity. Overall, polymeric-prodrug-based nanococktails with self-amplification drug release possess good antitumor effects and have low drug-related toxicity.</p><!><p>The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.</p><!><p>The animal study was reviewed and approved by the Animal Care and Use Committee of the Nanjing Medical University.</p><!><p>This project was conceptually designed by TL. The majority of the experiments were performed by LP and LZ, assisted by HZ. Data analysis and interpretation were carried out by LP, LC, YS, and TL. This manuscript was prepared by TL and YS. All authors discussed the results and commented on the manuscript. All authors contributed to the article and approved the submitted version.</p><!><p>This work was supported by the Rejuvenating Health through Science and Education Project of Wujiang District (wwk201711) and Institute-Level Scientific Research Project of Jiangsu Shengze Hospital (SYK201817).</p><!><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p><!><p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p><!><p>The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2022.844426/full#supplementary-material</p><!><p>Click here for additional data file.</p><p>combination index</p><p>CuB loaded micelles</p><p>critical micelle concentration</p><p>cucurbitacin B</p><p>dichlorofluorescein</p><p>2,7-dichlorodihydrofluorescein diacetate</p><p>dextran</p><p>CuB polymeric prodrug</p><p>PTX polymeric prodrug</p><p>drug loading content</p><p>dynamic light scattering</p><p>4-dimethylaminopyridine</p><p>dimethylsulfoxide</p><p>1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride</p><p>enhanced permeability and retention effect</p><p>fetal bovine serum</p><p>gastric cancer</p><p>high performance liquid chromatography</p><p>half maximal inhibitory concentration</p><p>mass spectrometer</p><p>molecular weight</p><p>molecular weight cut off</p><p>N-acetyl cysteine</p><p>phosphate buffer</p><p>PTX and CuB co-loaded micelles</p><p>PTX loaded micelles</p><p>paclitaxel</p><p>reactive oxygen species</p><p>transmission electron microscopy</p><p>thioketal linker</p><p>the conjugation of TK and CuB</p><p>the conjugation of TK and PTX</p><p>tumor suppression ration</p>

PubMed Open Access

Nanostructured electrode enabling fast and fullyreversible MnO 2 -to-Mn 2+ conversion in mild buffered aqueous electrolytes

On account of their low-cost, earth abundance, eco-sustainability, and high theoretical charge storage capacity, MnO 2 cathodes have attracted a renewed interest in the development of rechargeable aqueous batteries. However, they currently suffer from limited gravimetric capacities when operating under the preferred mild aqueous conditions, which leads to lower performance as compared to similar devices operating in strongly acidic or basic conditions.Here, we demonstrate how to overcome this limitation by combining a well-defined 3D nanostructured conductive electrode, which ensures an efficient reversible MnO 2 -to-Mn 2+ conversion reaction, with a mild acid buffered electrolyte (pH 5). A reversible gravimetric capacity of 560 mA•h•g -1 (close to the maximal theoretical capacity of 574 mA•h•g -1 estimated from the MnO 2 average oxidation state of 3.86) was obtained over rates ranging from 1 to 10 A•g -1 . The rate capability was also remarkable, demonstrating a capacity retention of 435 mA•h•g -1 at a rate of 110 A•g -1 . These good performances have been attributed to optimal regulation of the mass transport and electronic transfer between the three process actors, i.e. the 3D conductive scaffold, the MnO 2 active material filling it, and the soluble species involved in the reversible conversion reaction. Additionally, the high reversibility and cycling stability of this conversion reaction is demonstrated over 900 cycles with a Coulombic efficiency > 99.4 % at a rate of 44 A•g -1 . Besides these good performances, also demonstrated in a Zn/MnO 2 cell configuration, we discuss the key parameters governing the efficiency of the MnO 2 -to-Mn 2+ conversion. Overall, the present study provides a comprehensive framework for the rational design and optimization of MnO 2 cathodes involved in rechargeable mild aqueous batteries.

nanostructured_electrode_enabling_fast_and_fullyreversible_mno_2_-to-mn_2+_conversion_in_mild_buffer

INTRODUCTION<!>RESULTS AND DISCUSSION<!>CONCLUSION<!>EXPERIMENTAL SECTION<!>GLAD-ITO Mesoporous Electrodes.<!>Preparation of the<!>III. Electrochemical characterization of the MnO 2 -GLAD-ITO electrode in a pure KCl electrolyte

<p>The development of rechargeable aqueous batteries for large-scale electrochemical energy storage devices is driven by their ability to achieve appropriate energy densities with safe, sustainable and inexpensive chemicals. 1,2 Among the different aqueous batteries, the rechargeable Zn/MnO 2 battery, able to operate in a mild aqueous electrolyte, is certainly the most attractive, especially since the breakthrough of Pan et al. in 2016, 3 who demonstrated a high reversibility and excellent cycling stability when using a mild acidic electrolyte containing both ZnSO 4 and MnSO 4 . Since then, the concept has been explored in many studies, varying the nature of the MnO 2 cathode and the chemical composition of the Zn 2+ -based aqueous electrolyte. 4 Despite all these efforts, however, the gravimetric capacities of mild aqueous Zn/MnO 2 batteries remain capped to 350-380 mA•h•g -1 , [4][5][6] which is far from the maximal theoretical value of 617 mA•h•g -1 associated with the reversible 2-electron reduction of Mn(IV) into Mn(II).</p><p>Recently, we have revealed that the charge storage mechanism at MnO 2 electrodes is not based on an insertion process but instead on a reversible conversion principle, wherein the weak Brønsted acid AH and conjugated base Apresent in an aqueous buffered electrolyte assist the following proton-coupled electron transfer reaction at the electrode interface: 7 MnO 2(s) + 4 AH + 2 e - Mn 2+ (aq</p><p>According to this reaction, the 2-electron gravimetric capacity of MnO 2 should be accessible in a mild, non-corrosive buffered aqueous media, which according to the Pourbaix diagram lies within the domain of thermodynamic stability of Mn 2+ . 8 In our previous work, the maximal gravimetric capacity achieved in a buffered electrolyte of pH 5 was 450 mA•h•g -1 (obtained from MnO 2 thin films electrodeposited onto planar electrodes). 7 Although much better than the capacities previously reported for a range of MnO 2 -cathodes in mild unbuffered aqueous electrolytes, this gravimetric capacity remains far from the 570 mA•h•g -1 value recently achieved in a strongly acidic electrolyte (i.e., 0.1 M H 2 SO 4 ). 9 In this case, a similar conversion reaction is at work, but with H 3 O + /H 2 O acting as the proton donor/acceptor couple. Further studies are thus required to better understand the key factors and parameters currently limiting the MnO 2 -to-Mn 2+ conversion in mild aqueous electrolytes. Our previous work leads us to believe that two issues are critical: (i) the efficiency of the electrical wiring between the MnO 2 solid phase and the underlying current collector, and (ii) the appropriate regulation of both the electron transport/transfer at the electrode/MnO 2 film/electrolyte interface and the mass transport of all soluble species involved in reaction 1 (i.e., Mn 2+ , AH, and A -). 10 Indeed, if misbalanced, the Coulombic efficiency (CE) lies below 100 %, inducing a progressive decrease of the electrode gravimetric capacity upon cycling because of the incomplete exploitation of MnO 2 . 7 Concerning the electron transfer between the semi-conductive MnO 2 and the current collector, a few studies have reported on the poor adhesion and mechanical stability of MnO 2 on conductive substrates of various chemical composition and roughness. 11,12 Furthermore, because of the moderate electronic conductivity (10 -3 -10 -4 S•cm -1 ) of MnO 2 , 13,14 the ohmic drop is expected to rise together with the film thickness. All these issues can potentially be solved by using a 3D nanostructured current collector, with a high aspect ratio, well-suited for the conformal electrodeposition of thin films of MnO 2 to provide shorter ion diffusion distances and better electron harvesting paths. 10,15 Such a strategy has previously been successfully exploited to improve the performances of MnO 2 -based supercapacitors, [16][17][18] as well as MnO 2 cathodes toward reversible Li + insertion in organic electrolytes. 19 To achieve good conformal electrodeposition of MnO 2 over a 3D porous conductive substrate, the latter must necessarily have a well-opened structure with accessible porosity to avoid any mass transport limitation of the soluble species involved in reaction 1. The approach also requires proper matching of the reactant concentrations and the conversion fluxes, which are linked to both the cycling rate and the specific area developed by the 3D conductive scaffold. In addition, due to the stoichiometry of the conversion process (4 equivalents of proton donor required, see eq 1), strong pH gradients may be generated at the electrode/electrolyte interface, inducing the concomitant precipitation of insoluble phases such as zinc hydroxide sulfates (ZHS), as evidenced in several studies. 3,[20][21][22][23][24] We believe this point contributes to the limited gravimetric capacities previously observed with unbuffered mild aqueous Zn/MnO 2 batteries. 4 To solve this issue, one strategy is to regulate the pH at the metal oxide/electrolyte interface, using a mild acidic buffered electrolyte at a sufficiently high concentration. 7 In the present study, we demonstrate the decisive advantage of using a nanostructured current collector to fully exploit the reversible MnO 2 -to-Mn 2+ conversion from a buffered aqueous electrolyte containing Mn 2+ . We also show that this conversion reaction remains highly efficient even in a Zn/MnO 2 battery cell configuration, which involves a Zn foil anode paired to the MnO 2 cathode and the presence of Zn 2+ ions, in addition to Mn 2+ , in the buffered electrolyte. For such purpose, we took advantage of 3D nanostructured indium-doped tin oxide (ITO) electrodes (1μm-thick film deposited over a standard flat ITO-coated glass substrate) prepared by glancing angle deposition (GLAD), a technique which allows for the growth of metal oxides nanostructures in different shapes and morphologies. 25 These model mesoporous electrodes are characterized by a reproducible morphology with high aspect ratio and opened porosity, 26 wellsuited for modification by electrodeposition as previously shown with conductive polymers. 27 In addition, their transparency allows for in-situ UV-vis spectroelectrochemical monitoring, providing a real-time quantitative analysis of the amount of MnO 2 that electrodeposits/electrodissolves during the galvanostatic cycles.</p><!><p>The GLAD-ITO electrodes (1 µm-thick film) were prepared according to the published technique 25 using a deposition angle of 80° (see Experimental section) and rapid rotation. This combination leads to growth of vertically-oriented ITO nanopillars on an underlying commercial ITO substrate (Figure 1). The nanopillars are separated from one another with a void spacing in the range of tens of nanometers throughout the entire film thickness, 28 attesting to a well-opened mesoporosity. 29 An electroactive surface area enhancement of 45 was estimated from the capacitive current determined by cyclic voltammetry (CV) at different scan rates (see Supporting Information).</p><p>The pores of the GLAD-ITO electrodes were filled with MnO 2 by galvanostatic electrodeposition, controlling the amount of MnO 2 through the deposition time (see experimental section). The resulting MnO 2 -modified electrodes were then characterized by SEM, XPS, EDX and UV-vis absorption spectroscopy as detailed in the Supporting Information. Cross-sectional SEM and EDX images reveal that MnO 2 uniformly grows inside the porosity of the GLAD structure and locally forms interwoven nanofibers (Figure 1). The top-view images in Figure 1 show that MnO 2 exhibits a typical carambola-like morphology, similar to that obtained from electrodeposited films on planar ITO electrodes. For the deposited charge of 240 mC•cm -2 , the ITO nanocolumns remains clearly discernible from above (Figure 1), while at higher loads, once the pores of the GLAD ITO are filled, the MnO 2 deposit continues to develop far outside the GLAD-ITO structure, making the ITO nanocolumns no longer discernable. This behavior is well evidenced in Figure 1, where a 0.8 μm-thick MnO 2 film is observable by both SEM and spatially-resolved EDX on top of the GLAD-ITO electrode charged at 400 mC•cm -2 . (An even thicker film, reaching 1.6 µm, is observed for the electrode charged at 800 mC•cm -2 , see Figure S1D). Such a transition between filling of the GLAD-ITO pores with MnO 2 and subsequent growth far beyond the GLAD-ITO structure is also identified in the galvanostatic electrodeposition curves, showing after a certain delay a sudden 20 mV jump in the potential.</p><p>This potential jump is moreover linearly correlated with the GLAD-ITO thickness as the potential jump shifted from 150 to 90 and then 60 mC•cm -2 when the GLAD-ITO thickness decreased from 1 to 0.6 and then 0.3 µm, respectively (Figure S1A).</p><p>In order to investigate the benefit of a 3D nanostructured current collector on the reversible conversion of MnO 2 into Mn 2+ , 1 μm-thick GLAD-ITO electrodes were loaded with an intermediate amount of deposited charge of 100 mC•cm -2 (or 48.4 µg•cm -2 ) to ensure the MnO 2 was fully restricted to the interior of the porous structure. The maximal capacity stored in these MnO 2 -modifed electrodes was estimated from the average oxidation state (AOS) of the Mn centers, which in turn was determined from the linear correlation between the amount of electrodeposited Mn centers obtained by ICP quantification and the effective charge passed during the electrodeposition (see Supporting Information and Figure S1B for details). An AOS value of 3.86 was deduced, which is indicative of 1.86 electrons stored per Mn center. This value is in quite good agreement with the AOS of 3.75 determined by XPS analysis of the Mn(3s) peak splitting energy (Figure S1E). We thus conclude a maximal recoverable gravimetric capacity of 574 mAhg -1 for the full conversion of the electrodeposited MnO 2 , and we define the C-rate thanks to this value (1 C corresponding to 0.574 Ag -1 ).</p><p>MnO 2 -GLAD-ITO electrodes charged at 100 mC•cm -2 were galvanostatically cycled in buffered electrolyte and concomitantly monitored by in-situ UV-vis spectroscopy. The results reported in Figure 2 demonstrate the full exploitation of MnO 2 when working in a 1 M acetate buffer at pH 5, containing 0.1 M MnCl 2 . Indeed, full discharge of the capacity is reported with a near-perfect overlay of the first 20 galvanostatic cycles performed at 11 A•g -1 (19 C-rate), except for the initial discharge which is associated with a slightly lower capacity. Simultaneously, the electrode absorbance oscillates evenly and stably between 0.7 and 0, underlying the excellent reversibility of the MnO 2 electrodeposition/electrodissolution process. The absorbance value recorded at the end of each discharge cycle is close to the absorbance baseline (dashed line in Figure 2B), which demonstrates the complete reductive dissolution of MnO 2 . The small and stable voltage hysteresis (< 0.1 V) observed between the well-defined charge and discharge plateaus at rates lower than 11 A•g -1 is indicative of a rather fast reversible conversion process, occurring under nearly thermodynamic equilibrium (at least at rates < 11 A•g -1 ), as defined by the following Nernst equation: 7</p><p>(2)</p><p>where is the standard potential of the MnO 2 /Mn 2+ redox coupled and the activity of soluble Mn 2+ ions. Hence, based on this equation, the highly stable charge and discharge potentials, leading to well-defined horizontal plateaus, suggest that no significant pH or Mn 2+ gradients develop at the electrode/electrolyte interface within the range of rates (see Supporting Information for details). This mass, equivalent to 145% of the mass electrodeposited during each charging step, indicates that only 40% of the MnO 2 is reversibly exploited. This leads to a significant loss in the gravimetric capacity, reaching only 235 mA•h•g -1 after a few cycles. These observations are in line with what we previously reported at 2D MnO 2 -ITO electrodes using the same electrolyte but with lower amounts of electrodeposited MnO 2 . 7 Another striking difference between the 3D MnO 2 -GLAD-ITO and 2D MnO 2 -ITO electrodes is the shape of the galvanostatic discharge curves, exhibiting one or two plateaus, respectively.</p><p>At the MnO 2 -GLAD-ITO electrode, the discharge potential centered on a well-defined value of 0.49 V is also remarkably stable until ca. 80% of the MnO 2 is electrodissolved. On contrario, the discharge curves recorded at the 2D MnO 2 -ITO electrode exhibit, after a few cycles, a less welldefined supplementary plateau at a much lower potential of 0.25V, corresponding to roughly 80 % of the total discharge process (Figure 2C). This shift in potential contributes to a huge increase in the voltage hysteresis, thereby severely affecting the charge storage energy efficiency. We previously attributed this second plateau to the formation of a more resistive fraction of MnO 2 at the planar electrode, a fraction which is probably much less electrically connected to the underlying current collector. 7 The lack of a second plateau with the 3D MnO 2 -GLAD-ITO electrode tends to confirm this assumption. It also supports the idea that the 3D nanostructured substrate facilitates the electrical wiring of MnO 2 by shortening the electron transport distances across the semi-conductive MnO 2 , and possibly also by strengthening the interactions between MnO 2 and ITO due to the nanocolumns' roughness visible in SEM images (Figure 1). This result clearly highlights the benefit of the nanostructured electrode to facilitate the reversible electrodissolution/electrodeposition of MnO 2 into/from Mn 2+ , and this benefit should persist as long as the conversion reaction remains within the porosity of the GLAD-ITO electrode. This latter assertion is corroborated by the cycling experiments in Figure S4 carried out with a progressive increase of the deposited charge (i.e., the amount of MnO 2 was increased until it grew far beyond the GLAD-ITO boundary). In the corresponding discharge curves, a second plateau at a lower potential of 0.3 V gradually emerged as the deposited charge increased. The development of this secondary plateau is also accompanied by a progressive and significant decrease in the Coulombic efficiency.</p><p>The efficiency and rate capability of the conversion process at MnO 2 -GLAD-ITO electrodes was further investigated by cycling electrodes at different rates from 1.4 to 110 A•g -1 (i.e., from (i.e. 38 C). Accordingly, a small fraction of poorly active MnO 2 accumulates at the electrode at the fastest rate, as attested by the nonzero absorbance values A recorded at the end of the discharge steps (Figure 3B). The gravimetric capacities of MnO 2 -GLAD-ITO electrodes cycled at different rates are reported in Figure 3E. The data show that for rates < 11 A•g -1 (i.e., < 19 C), the capacity remains almost constant at 560 ± 10 mA•h•g -1 and close to the maximal value of 574 mA•h•g -1 , while at higher rates, the capacity decreases only slightly, remaining at 435 mA•h•g -1 for 190 C. This remarkable result demonstrates the high rate capability of this conversion process, which is quite attractive for the development of sustainable high-power storage systems. It is worth noting that as the rate increases, the voltage hysteresis also gradually increases from 60 to 340 mV, with a limited ohmic drop contribution (estimated to be 30 )</p><p>which only represents 23 % of the total hysteresis at 190 C (Figure 3D). At the higher rates, we also notice an upward and downward drift of potential during the charging and discharging steps, respectively. Based on the Nernst equation (eq. 2), we assume this arises from local pH and/or Mn 2+ gradient changes, driven by mass transport limitations. At the lower rates, while the overall discharge curves overlap, the small increase in the voltage hysteresis results mainly from an increase of the charging potential. This behavior suggests that electrodeposition is intrinsically a slower process than electrodissolution.</p><p>Overall, these results demonstrate that the combined use of a 3D nanostructured electrode with a mild acidic buffered electrolyte allows for full exploitation of an electrodeposited film of MnO 2 . Such a combination is decisive to provide fast and reversible MnO 2 -to-Mn 2+ conversion, despite the multi-step mechanism, including proton-coupled electron transfer reactions as well as formation/breaking of metal-oxygen bonds. 30 The gravimetric capacity of 560 mA•h•g -1 we report here at pH 5.0 is the highest yet reported in mild acidic electrolytes, 4 and remains competitive with the values recently achieved under much stronger acidic conditions (pH 1). 9</p><p>Furthermore, the conversion reaction can be carried out at outstandingly high C-rates, competing with the rate-performances achieved at MnO 2 -based supercapacitors, 16 but with an incomparably greater charge storage capacity. Indeed, as illustrated in the Supporting Information, the present MnO 2 -GLAD-ITO electrode in an unbuffered 1 M KCl electrolyte displayed a capacitance of 240 F•g -1 , which is equivalent to 60 mA•h•g -1 for a potential window of 0.9 V (Figure S2), i.e.</p><p>9-times lower than in a buffered electrolyte.</p><p>The 3D MnO 2 -GLAD-ITO electrode was next assembled in the spectroelectrochemical cell with a Zn foil counter electrode to mimic a Zn/MnO 2 battery. The cell included an Ag/AgCl reference electrode to follow the potential of both MnO 2 and Zn electrodes while simultaneously monitoring the MnO 2 absorbance. Under these conditions, the following overall charge storage conversion reaction is expected:</p><p>In addition to the acetate buffer (1.5 M, pH 5) and MnCl 2 (0.1 M), 0.25 M ZnCl 2 was added to the electrolyte, as required for the Zn-to-Zn 2+ conversion reaction. The electrochemical performances of the Zn/MnO 2 assembly are given in Figure 4. An initial series of 20 galvanostatic cycles were recorded at an intermediate rate of 7.2 A•g -1 (referred here per gram of MnO 2 ). Well-defined single charge and discharge plateaus were observed at average potential values of 1.65 and 1.50 V vs. Zn 2+ /Zn, respectively, which are very similar to those previously obtained in the Zn 2+ -free electrolyte. The voltage hysteresis slightly increases by 60 mV upon cycling, but without significantly affecting the good energetic efficiency (EE), which remains higher than 86 % over the 20 cycles (blue dots in Figure 4F). Remarkably, after a few preconditioning cycles, the coulombic efficiency remains over 99 %, and from the residual absorbance of the cathode at the end of the discharge steps, we can conclude that only a very small amount of a less active fraction of MnO 2 accumulates, estimated to 6.5 µg•cm -2 ,and mainly arising within the first few cycles. Accordingly, the MnO 2 gravimetric capacity that can be fully exploited here is 500 mAh•g -1 , slightly less than in the absence of Zn 2+ but much higher than has previously been reported for mild aqueous Zn/MnO 2 batteries. 4 Finally, the long-term cyclability of this aqueous Zn/MnO 2 cell configuration was confirmed through the near absence of capacity fading over 400 cycles at 20.6 A•g -1 (i.e., 36 C, Figure 4E), with a Coulombic and an energetic efficiency that rapidly stabilize at 99.7 ± 0.2 % and 82.6 ± 1.3 %, respectively (red dots in Figure 4F). These results clearly demonstrate that the excellent performances of the MnO 2 -GLAD-ITO electrodes are conserved in a Zn/MnO 2 assembly, and that the addition of Zn 2+ in the buffered aqueous electrolyte does not significantly interfere with the reversible MnO 2 -to-Mn 2+ conversion reaction. This thus paves the way to the design of high-performance Zn/MnO 2 batteries in noncorrosive mild aqueous electrolytes.</p><p>In order to demonstrate the great benefit of using a buffered electrolyte in the aforementioned experiments, the Zn foil/MnO 2 -GLAD-ITO assembly was cycled in an unbuffered aqueous electrolyte (adjusted to pH 5) containing only the inorganic salts required for the conversion reaction (i.e., 0.1 M MnCl 2 and 0.25 M ZnCl 2 along with 0.85 M KCl). The electrochemical performances recorded at 7.2 A•g -1 are reported in Figure 4C. First, it is worth noting that both the Coulombic and the energetic efficiency are significantly deteriorated as compared to those previously obtained in a buffered electrolyte, remaining below 95 % and 79 %, respectively, over the 20 cycles (green dots in Figure 4F). Still, the reversible conversion mechanism is supported by the absorbance change monitored at the cathode, certifying the reversible electrodissolution/electrodeposition of MnO 2 . The absorbance measurements also testify to a significant accumulation of a less active form of MnO 2 , estimated to be 37 µg•cm -2 after 20 cycles (i.e., 76 % of the mass deposited during each charging step), leading to a significant loss in the MnO 2 exploitation and consequently to a lower gravimetric capacity of 310 mA•h•g -1 .</p><p>It is important to keep in mind that in the absence of acetate buffer, the available proton donors are the hexaaquo complexes [Zn(H 2 O) 6 ] 2+ and [Mn(H 2 O) 6 ] 2+ resulting from the solvation of their parent divalent inorganic salts (here ZnCl 2 and MnCl 2 ). These complexes are characterized by a weak Brønsted acidity with a pK a value of 9.0 and 10.6, respectively. 31 As we have previously demonstrated, 7 both complexes can act as proton donors to assist the electrodissolution of MnO 2 into Mn 2+ according to the following electrochemical reaction:</p><p>where M is either Zn or Mn. This is typically what we observed in Figure 4C, where, after a few cycles, the galvanostatic discharges lead to a similar areal capacity than in the 1. Mn(OH) 2 ) precipitate, thereby stabilizing the local pH, an effect that is observed through the appearance of a stable plateau at the end of the discharge curves (the beginning of this plateau is indicated by an asterisk on Figure 4C). 24 Such precipitation of insoluble hydroxides over the MnO 2 cathode most likely contributes to a loss in the conversion efficiency, probably by slowing down the ongoing reductive dissolution of MnO 2 . Evidence for the precipitation of such zinc hydroxides has been previously reported in several studies and also related to local pH changes. 3,[20][21][22][23][24] However, none of these studies explicitly identified the proton source, preventing a clear explanation to the local pH changes and thus the associated precipitation of zinc hydroxides, both governed by the pK a s and local activities of the weak acid and conjugated base acting as proton donor/acceptor.</p><p>Concerning the charging curves, which involve MnO 2 electrodeposition with the concomitant local release of several equivalents of protons, the first plateau observed at 1.45 V (Figure 4C) suggests a stabilization of the local pH at an intermediate mild acidic value. This effect can be rationalized from the dynamic equilibrium that is expected to persist through the continuous neutralization of the protons released by the Brønsted bases which are generated during the previous discharge step (and which include the precipitated hydroxides). This interpretation is further supported by the absence of a similar plateau when the galvanostatic charge is immediately applied to a MnO 2 -GLAD-ITO electrode without prior discharge (orange curve, Figure 4C), leading to a direct rise of the charging potential up to a plateau at 2 V vs. Zn 2+ /Zn, which is close to the charging potential previously reported for an acidic Zn/MnO 2 battery (i.e., 2.2 V in the presence of a 0.1 M H 2 SO 4 electrolyte). 9 This result is thus indicative of the strong pH gradients that develop during the electrodeposition of MnO 2 from an unbuffered electrolyte.</p><p>Besides the demonstration of the beneficial effect of buffered electrolytes on the stabilization of the potentials of MnO 2 electrodeposition and electrodissolution, the present work also provides a clear explanation of the good performances recently achieved for a Zn/MnO 2 battery using an unbuffered aqueous electrolyte containing zinc and manganese acetate salts. 32 While the authors focus on the coordinating role of the acetate ions to explain the high performances they obtained and the low MnO 2 electrodeposition potential they observed (1.8 V vs. Zn 2+ /Zn), the present results rather suggest that the increased performance is due to a local buffering effect.</p><p>Indeed, the protons locally released during the electrodeposition are neutralized by the acetate ions present in the electrolyte, generating acetic acid in dynamic equilibrium with the remaining acetate ions. In the following discharge step, this acetic acid thus behaves as the proton donor to efficiently assist the reductive electrodissolution of MnO 2 . It is worth noting that significant shifts of the charging and discharging potentials were reported in such unbuffered aqueous electrolytes, which most likely arises from significant variations in the local concentrations of acetate and acetic acid, and thus in the local pH.</p><!><p>In the present work, we demonstrate the beneficial combination of a nanostructured conductive 3D substrate and a buffered electrolyte to fully exploit the reversible, two-electron MnO 2 -to-Mn 2+ conversion mechanism in mild acidic conditions. The corresponding cathode is characterized by a high gravimetric capacity (560 mA•h•g -1 ), high rate capability (435 mA•h•g -1 at 190 C), low charge/discharge hysteresis (thus high energetic efficiency) and high cyclability (up to 900 cycles with a CE > 98%). Such performances are preserved in a Zn/MnO 2 cell configuration (i.e., 500 mA•h•g -1 at 12 C and a stable Coulombic efficiency of 99.7 % over 400 cycles at 36 C), outperforming all the mild aqueous Zn/MnO 2 assemblies so far described, with the additional advantage of a highly stable potential of 1.5 V over almost the complete discharge.</p><p>These great performances arise from properly balancing the electrolyte composition (to avoid mass transport limitation) and the MnO 2 mass loading regarding both the surface enhancement and the porosity of the nanostructured conductive 3D substrate (to avoid long-range electron transport throughout the semiconductive MnO 2 film and thus ohmic drop issues). The present demonstration was performed using transparent model GLAD-ITO electrodes, allowing for insitu monitoring of the conversion process. These model electrodes, however, are unsuitable for charge storage applications, and therefore, further developments require scaling-up the conversion process with inexpensive, high surface area conductive substrates to get beyond the low gravimetric capacities of mild aqueous Zn/MnO 2 batteries.</p><!><p>Chemicals. HNO 3 (Suprapur, 65%), acetic acid (Reagent plus, > 99%), KOH, HCl (Normapur, 37%), KCl (GR for analysis), ethanol absolute (EMSURE) and ZnCl 2 were purchased from Sigma-Aldrich/Merck. MnCl 2 tetrahydrate (99%) was purchased from Alfa Aesar. Acetone (Normapur) and chloroform (Normapur) were purchased from VWR Chemicals.</p><!><p>Porous ITO thin films were prepared by the glancing angle deposition (GLAD) method followed by thermal treatment as previously described. 25 Briefly, nanostructured ITO films were deposited from ITO evaporant (Cerac, 91:9 In 2 O 3 /SnO 2 99.99% pure) in an electron-beam physical vapor deposition system (Axxis, Kurt J Lesker) on ITO-coated glass substrates (8-12 Ω/, Delta Technologies Ltd.). Throughout the deposition, substrates were maintained at an 80° angle with respect to impinging evaporant flux, while constantly rotating as a feedback-controlled function of the deposition rate. The film thickness was adjusted between 0.3 and 1 µm. Following deposition, the GLAD-ITO samples were thermally annealed in a two stage process, first under air at 500 °C and subsequently under 5% H 2 /Ar flow at 375 °C, to improve and stabilize the optical and electrical properties. For such deposition conditions, the film porosity was previously estimated to be 0.5 and its density to be 4 g•cm -3 . 25 Prior to the electrochemical experiments, the GLAD-ITO electrodes were cleaned by soaking them successively in chloroform, acetone, and ethanol, each time for 30 min at 50°C.</p><p>After the electrodes were left to dry, a geometric area of 0.50 ± 0.04 cm 2 (N = 40) was delimited by depositing an insulating layer of nail polish. All gravimetric intensities (A•g -1 ) were calculated from the current density (mA•cm -2 ) applied to the electrode and the expected electrodeposited mass of MnO 2 , deduced from eq. 4. The Coulombic efficiency , energetic efficiency ( ), and gravimetric capacity ( in mA•h•g -1 )</p><!><p>were calculated using the following equations:</p><p>where Q i,ch and Q i,disch are the areal charging and discharging capacities of the i-th cycle in mC•cm -2 , E i,ch and E i,disch the corresponding charging and discharging energy densities in W•h•cm -2 determined from the product of Q and the average charge/discharge voltages, m ch the areal MnO 2 mass deposited during the charging step (i.e., 48.4 µg•cm -2 for a constant charge of 100 mC•cm -2 ), and m acc the total areal inactive MnO 2 mass accumulated at the end of the experiment (the latter being determined either by ICP analysis or from the absorbance of the electrode after discharge).</p><p>Film characterization. Scanning electron microscopy and energy dispersive x-ray spectroscopy were performed on a Hitachi S5500. XPS spectra were recorded using a K-Alpha+ system (ThermoFisher Scientific, East-Grinsted, UK) fitted with a micro-focused and monochromatic Al Kα X-ray source (1486.6 eV, spot size of 400 µm). The pass energy was set to 150 and 40 eV for the survey and the narrow high resolution regions, respectively. The spectra were calibrated against the (C-C/C-H) C(1s) component set at 285 eV. The chemical composition was determined using the manufacturer's sensitivity factors within the Avantage software (version 5.977). The average oxidation state (AOS) of the Mn centers in the electrodeposited MnO 2 thin-film was estimated on the basis of the XPS Mn(3s) peak splitting energy (ΔBE) and using the following correlation: 33,34</p><p>The amount of MnO 2 electrodeposited on ITO was analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES; Thermo Scientific iCAP 6300 ICP spectrometer) after dissolution of the MnO 2 film in concentrated nitric acid under ultrasonication and then dilution with purified water to have a final 6.5% v/v nitric acid concentration.</p><p>electrodeposited MnO 2 ( , Figure S1C). The modeling of the experimental data, arising from 20 independent electrodes, gives us the following linear relationship: (S2) The obtained gravimetric extinction coefficient is weaker than that previously determined from thinner MnO 2 films (18.6 × 10 -3 cm 2 µg -1 with < 20 µg.cm -2 ) on 2D ITO, S3 probably because the linear approximation tends to lose its validity for high surface concentrations. Nevertheless, eq. S2allows a rough estimate of the low surface concentrations of MnO 2 on the GLAD-ITO surface, which is typically observed at the end of a galvanostatic discharge. Thus, we are able to calculate the gravimetric capacity of the MnO 2 film at the end of a galvanostatic cycling experiment (see experimental section eq. 5).</p><!><p>The double-layer electrical capacitance of a MnO 2 -GLAD-ITO electrode loaded at 100 mC•cm -2 was investigated in a 1 M KCl electrolyte adjusted to pH 5.0. Under these conditions, the electrode exhibits the typical features characterizing the reversible charging/discharging of an electrical double-layer, showing a linear time-dependence of charge and discharge curves with the potential (Figure S2A). The high stability absorbance switching (between 0.76 and 1.35) together with the high Coulombic efficiency of 99.1 ± 0.2 % over 400 cycles also agrees with the charging/discharging behaviour of a true capacitance (Figure S2B). From these data, a specific gravimetric capacitance of C f = 240 ± 5 Fg -1 is obtained (Figure S2C), which lies in the range of capacitances commonly reported for electrodeposited MnO 2 thin-films. S8-S12</p>

ChemRxiv

Stroke outcome in the ketogenic state - a systematic review of the animal data

As a predictor of potential clinical outcome, we performed a systematic review of controlled studies that assessed experimental stroke outcome in rodents maintained on special diets (calorie restriction, ketogenic diet) or following the direct administration of ketone bodies. Pre-clinical studies were identified by searching web databases and the reference lists of relevant original articles and reviews. Sixteen published studies (a total of 733 experimental animals) met specific criteria and were analyzed using Cochrane Review Manager software. This resulted in objective evidence to suggest beneficial effects of the ketogenic pathway on pathologic and functional outcomes following experimental stroke.

stroke_outcome_in_the_ketogenic_state_-_a_systematic_review_of_the_animal_data

<!>Study Identification<!>Data extraction<!>Data analysis<!>Design of studies<!>Overall efficacy of inducing a ketogenic state<!>Type of parameter used to assess outcome<!>Reported study quality<!>Duration of intervention<!>Discussion

<p>For decades we have known that caloric restriction and intermittent fasting extend the lifespan of rodent species (and probably primates) by reducing free radical production, inflammation, and/or increasing resistance to stress. Such strategies would, therefore, be predicted to benefit both cardio- and cerebro-vasculature (Mattson and Wan, 2005; Yamada, 2008). Other dietary regimens can also be beneficial to neurologic function. For instance, the ketogenic diet, in which fat replaces carbohydrate, is clinically useful in the treatment of epilepsy and there is growing interest in extending use of this diet to other neurological disorders (http://www.clinicaltrials.gov/ct2/results?term=ketogenic+diet&pg=1), including acute injury such as stroke and trauma (Gasior et al., 2006). To provide a prediction of potential clinical outcome we performed a systematic review and meta-analysis of controlled rodent studies that assessed stroke outcome where the use of ketone bodies as an energy source had been promoted. These include ketogenic diets, as well as calorie restriction, which promote the use of triacylglycerols and fatty acids. In addition, ketone bodies can be directly administered. All three dietary interventions are evaluated here.</p><!><p>Experimental animal studies assessing the effect of dietary interventions related to ketone body utilization on outcomes following experimental stroke were identified from Embase, PubMed, Web of Science and Google Scholar by searching for all published articles up to the end of April 2012. The earliest study for analysis was that of Go et al. (1988). Search keywords included combinations of caloric restriction, cerebral ischemia, ketogenic diet, rodent. Additional publications were identified from reference lists of all identified articles and narrative reviews. Pre-specified exclusion criteria were used to aid selection and prevent bias, and studies were only included if (i) experimental ischemia was induced, (ii) animals were exposed to a ketogenic state, (iii) there was a control group, and, (iv) an appropriate outcome was measured following stroke.</p><!><p>The term 'intervention' refers to any intervention that induces a ketogenic state in animals such that the brain is deprived of its usual energy source, i.e. glucose, and instead uses ketones as an alternative. A ketogenic state, experimentally, can be induced by; (i) restriction of caloric intake, (ii) ketogenic diet or (iii) exogenous administration of ketones. From the relevant studies data were extracted on animal species, number, type of brain injury, type of intervention, timing of intervention relative to onset of experimental injury, and outcome post-stroke. Outcomes included lesion volume, brain water content, neuron counts, survival rate and functional measures. Data for functional outcomes included: (i) open field activity (distance travelled), (ii) 8-arm radial maze (working memory errors) and, (iii) novel object recognition task (discrimination index = time spent exploring novel object/total exploration time).</p><p>A comparison (C) was defined as the assessment of outcome in intervention and control groups, whereby intervention constituted either a dietary intervention (caloric restriction, ketogenic diet) or administration of a ketone body at a stated time point relative to the induction of ischemia. For each comparison, data were extracted for mean outcome, standard deviation and the number of animals per group. Data were not extracted if mean values were not reported, i.e. if only median and confidence intervals were given. If published studies (S) used multiple groups, for example to assess dose-response relationships, then data were individually extracted. If numerical data were not reported in the text, they were extracted from enlarged versions of the graphs. The methodological quality of each study was determined by minor modification to the ten-point scale of O'Collins et al. (2012).</p><!><p>The data were analysed using Cochrane Review Manager (version 5), as in a previous animal meta-analysis (White & Murphy, 2011). The effect of ketogenic state, as compared with control, on post-stroke outcomes was assessed using the standardized mean difference, whereby the difference in effect between intervention and control treatment is divided by the total standard deviation. A standardised mean difference of zero represents a lack of intervention effect, whereas a positive value indicates a beneficial effect and a negative value demonstrates a detrimental effect of the intervention. This allows comparisons to be made even though different methods of measurement and/or different animal models are used. Statistical heterogeneity was accounted for through the use of the DerSimonian and Laird (1986) pooling model of random effects. The data were grouped and stratified meta-analyses based on: (i) type of intervention used to induce ketogenic state, (ii) quality score, (iii) type of outcome assessed following ischemia, (iv) quality score, and (iv) duration (chronic or acute) of intervention. For those tests in which an increase in nominal value represented an improvement in outcome (e.g. neuron counts following FluoroJade labelling) the inverse of the extracted data was used for data comparisons. Studies were weighted by sample size and the results are expressed as standardised mean difference with 95% confidence intervals. The significance was set at α = 0.05 and P values of <0.05 from meta-analyses were considered to be significant.</p><!><p>Based on the stated search criteria, we identified 19 studies that investigated the effect of a ketogenic state on outcome following cerebral ischemia. However, three of these (Chiba et al., 2010, Combs et al., 1987; Marie et al., 1990) were excluded, as mean values and the distribution of data (standard error or standard deviation) were not reported. The main characteristics of the 16 included studies are reported in Table 1. All of these reported the effect of inducing a ketogenic state, versus control, on one or more outcomes following cerebral ischemia. The 16 included studies represent published data from 12 research groups; four research groups published two studies each with the remaining 8 studies coming from separate research groups. Data from a total of 733 experimental subjects were included for analysis.</p><p>In terms of the type of intervention used to induce a ketogenic state, three studies utilised a ketogenic diet (Puchowicz et al., 2008; Tai et al., 2008; Xu et al., 2010), five studies exogenously administered ketone bodies (Gueldry et al., 1990; Massieu et al., 2003; Robertson et al., 1992; Suzuki et al., 2001, 2002), and the remaining 8 studies along with the study by Robertson et al., 1992 used calorie restriction. Of the five studies that exogenously administered ketone bodies, two administered β-hydroxybutyrate (Suzuki et al., 2001, 2002), two administered 1,3 butanediol (Gueldry et al., 1990; Robertson et al., 1992), one administered acetoacetate (Massieu et al., 2003), and the study by Robertson et al. (1992) also administered triacetin. When applying an intervention to induce a ketogenic state, the majority of studies did so prior to the onset of cerebral ischemia. Ten of the included studies introduced the intervention at least 7 days prior to the onset of cerebral ischemia whereas 4 studies (Gueldry et al., 1990; Go et al., 1988; Robertson et al., 1992; Suzuki et al., 2001) introduced the intervention less than 7 days prior to the onset of cerebral ischemia. The remaining two studies (McEwen & Paterson 2010; Suzuki et al., 2002), along with the study by Massieu et al. (2003), examined the effects of inducing a ketogenic state following the induction of ischemia.</p><p>In order to assess the effects of a ketogenic state on outcome following cerebral ischemia, the majority of studies utilised various models to induce cerebral ischemia; thirteen used vessel occlusion (filament occlusion of the middle cerebral artery, or clip occlusion of a number of vessels including the middle cerebral artery), one study used microspheres (Gueldry et al., 1990), one study used a model of glutamate excitotoxicity via application of iodoacetate (Massieu et al., 2003), and one study used a hypoxic chamber (Xu et al., 2010). Male rat models (F344, Long Evans, Sprague-Dawley, Wistar) were used in 12 of the 16 studies; the remaining 4 used either a mouse strain (Arumugam et al., 2010; Suzuki et al., 2001; Yoon et al., 2011) or gerbils (McEwen & Paterson, 2010).</p><p>In terms of assessing outcome following cerebral ischemia, the majority of studies used lesion volume (see Table 1), five used % water content (Go et al.,1998; Gueldry et al., 1990; Puchowicz et al., 2008;Suzuki et al., 2001, 2002), three used neuron count (McEwen & Paterson 2010; Roberge et al., 2008a, 2008b), two studies used survival rate (Go et al., 1988; Suzuki et al., 2001), and one study used neurological score (Arumugam et al., 2010). A number of studies also used measures of functional outcome, including the elevated maze (Roberge et al., 2008b), open field activity (Bobyn et al., 2005; McEwen & Paterson 2010), radial maze (Roberge et al., 2008a), and novel object recognition (Xu et al., 2010).</p><!><p>We determined initially that there was an overall significant protective effect of a ketogenic state on outcome following cerebral ischemia (1.24, 1.55 - 0.93, P < 0.001), regardless of individual study characteristics. Our literature search indicated that studies tended to induce a ketogenic state either by intervention (calorie restriction or a ketogenic diet) or through the exogenous administration of ketones. Thus, we also determined if a beneficial effect was seen following either intervention (1.12, 1.55 – 0.69, P < 0.001) or exogenous ketone administration (1.42, 1.84 – 1.0, P < 0.001; Fig. 1).</p><!><p>The effect of intervention on cerebral ischemia outcome was analysed according to type (Fig 2). First, data were grouped according to intervention, i.e. all, intervention, or exogenous administration, and then analysed to determine if they had a significant beneficial effect on either pathology or function (Fig. 2A). Pathological outcomes included lesion volume, brain water content and neuronal counts, whereas functional outcomes included all measures of behaviour. Regardless of whether a dietary intervention or administration had been used to induce a ketogenic state, a significant beneficial effect was seen on both outcomes (P < 0.01). However, a variety of outcomes were used in the included studies and we went on to analyse each separately (Fig. 2B). Induction of a ketogenic state was found to have a beneficial effect on functional tests (1.46, 2.19 – 0.73 P < 0.001), lesion volume (1.29, 1.7 – 0.88, P <0.001) and brain water content (1.04, 1.65 – 0.43 P < 0.001). However, a ketogenic state did not have a beneficial effect on neuron count, as assessed by FluoroJade labelling (−0.03, 0.39 - −0.44, P = 0.9), which included data from 3 studies, 4 comparisons and 91 experimental subjects.</p><!><p>The quality scores of the included studies ranged from 1 (low) to 5 with a median score of 3. In order to determine whether the quality score of an individual study had any impact on whether or not the intervention had a beneficial effect following cerebral ischemia, included studies were analysed according to quality score (Fig.3). Only those with a score of 2, 3 or 5 demonstrated a significant (P < 0.01) beneficial effect of the intervention. Studies that had the lowest quality score had no significant beneficial effect (P = 0.07). In addition, three studies received a quality score of 4 and these showed no beneficial effect (P = 0.08).</p><!><p>When comparing data across all studies it was determined that there was a beneficial effect of intervention to induce a ketogenic state, regardless of whether this occurred pre- or post-experimental ischemia (Fig. 4). A beneficial effect was observed when the intervention began more than 7 days prior to the onset of experimental ischemia (1.44, 1.88 – 1.0 P < 0.001) or less than 7 days prior to the onset of experimental ischemia (1.42, 1.95 – 0.89 P < 0.001). In addition, a significant beneficial effect was observed if the intervention began following the onset of experimental ischemia (0.82, 1.38 – 0.26 P = 0.004).</p><!><p>To restate the major findings from our analyses, we found beneficial effects on pathologic and functional outcomes of dietary intervention, or exogenous ketone administration, either prior to or following experimental stroke.</p><p>Neuropathologies, such as a stroke, cause a mismatch between energy demand and supply: blood flow goes awry, oxygen levels fall, and mitochondria malfunction. A period of fasting results in a short-term ketosis, and increased reliance on ketone bodies appears to be a form of cerebral metabolic adaptation (Manzanero et al., 2011). Ketone metabolism is enzymatically simpler and more efficient than glucose or pyruvate metabolism (Veech, 2004), and is reported to increase global cerebral blood flow (Gasior et al., 2006; Prins, 2008). Indeed, ketone bodies are the only circulating substrates in addition to glucose known to contribute significantly to cerebral metabolism. The precise mechanisms whereby caloric restriction, the ketogenic diet, and ketone bodies provide protection in ischemic stroke are not clear but there is notable improvement in mitochondrial function, a decrease in inflammation, and an increase in expression of neurotrophins such as BDNF and bFGF (Maalouf et al., 2009; Manzanero et al., 2011). Whether this relates to the higher level of potential energy available in the C-H bonds of beta hydroxybutyrate compared to pyruvate is unclear but potentially important to recovering ATP levels during reperfusion (Veech, 2004). More recently, the sirtuins have also been implicated. These histone deacetylases localize to different subcellular compartments and have a variety of substrates. Activity depends on nicotinamide adenine dinucleotide (NAD+), which links this family of enzymes to cellular energy levels. Indeed, some sirtuins (SIRT3) are located within mitochondria. Caloric restriction could activate and/or increase expression of sirtuins, which then modulate proteins involved in cell survival and apoptotic cell death (Maalouf et al., 2009; Morris et al., 2011).</p><p>Caloric restriction and the ketogenic diet appear to represent cost-effective and efficient strategies through which stroke incidence and/or subsequent pathology could be reduced.</p>

PubMed Author Manuscript

The N-terminal amphipathic helix of Endophilin does not contribute to its molecular curvature generation capacity

N-BAR proteins such as endophilin are thought to bend lipid membranes via scaffolding (the molding of membranes through the crescent protein shape) and membrane insertion (also called wedging) of amphipathic helices. However, the contributions from these distinct mechanisms to membrane curvature generation and sensing have remained controversial. Here we quantitatively demonstrate that the amphipathic N-terminal H0 helix of endophilin is important for recruiting this protein to the membrane, but does not contribute significantly to its intrinsic membrane curvature generation capacity. These observations elevate the importance of the scaffolding mechanism, rather than H0 insertion, for the membrane curvature generation by N-BAR domains. Furthermore, consistent with the thermodynamically required coupling between curvature generation and sensing, we observed that the H0-truncated N-BAR domain is capable of sensing membrane curvature. Overall, our contribution clarifies an important mechanistic controversy in the function of N-BAR domain proteins.

the_n-terminal_amphipathic_helix_of_endophilin_does_not_contribute_to_its_molecular_curvature_genera

Introduction<!>Results and Discussion<!>The role of H0 densities on the membrane<!>The role of membrane binding capacity<!>The role of lattice formation<!>The implication of curvature sorting in Clathrin-mediated endocytosis<!>Conclusion<!>Protein preparation<!>Liposome preparation<!>Liposome tubulation assay<!>GUV shape stability assay<!>Curvature sorting assay

<p>The shape diversity of cell membranes is regulated in part by the large family of BAR (Bin/Amphiphysin/Rvs) domain containing proteins1. Endophilin belongs to the class of N-BAR proteins. Members of this type contain an N-terminal helix (also named H0) that amphipathically inserts into the membrane as well as a BAR domain that can homo-dimerize to form a crescent-shaped structure. This BAR domain dimer is thought to act as a scaffold to enable membrane curvature remodeling2. In this contribution, we use the term "scaffolding" to describe the molding of the membrane through the crescent shape of the protein in general and the concave membrane-binding interface in particular. The role of H0 helix insertion versus the BAR dimer scaffolding mechanism in membrane curvature generation and sensing has remained controversial.</p><p>Several reports have argued that both H0 amphipathic insertion and BAR dimer scaffolding can drive membrane bending2–7 and that H0 insertion promotes membrane scission6,7, although the latter has recently been questioned8. Steric repulsion arising from protein-protein crowding has also been considered as a driving force for membrane curvature generation9. This mechanism is general and would be effective for any peripheral membrane protein if present at sufficiently high density. However, we have shown that N-BAR domains such as endophilin induce membrane curvature changes at low protein coverage where the crowding effect is negligible10.</p><p>Based on continuum mechanical models11, molecular dynamics (MD) simulations12, and experimental evidence13, H0 amphipathic insertion has been described as solely sufficient to generate membrane curvature if H0 helices are present at a high enough membrane surface density. On the other hand, MD simulation studies from Cui et al.14 and Blood et al.15 suggest that amphipathic helix folding through membrane interaction of N-BAR domains either requires the membrane to be already bent, or requires the amphipathic helices to be more concentrated than physiological conditions to induce curvature. Furthermore, results from Blood et al.15 imply that the close association of the charged concave surface of the N-BAR domain is required for its membrane curvature induction and that membrane curvature is not driven by the membrane-embedded amphipathic helices. However, amphipathic helices were shown to be essential to maintain close association of the concave surface of the N-BAR domain with the membrane15,16. Consistent with this notion, MD simulations from Arkhipov et al.17, later refined by Lyman et al.18, also suggested that BAR dimer scaffolding, rather than helix insertion, is the key player in membrane bending by N-BAR domains. That said, MD simulations typically investigate local membrane curvature generation in the neighborhood of either a single, or a small number, of BAR proteins whereas experimental studies such as those presented here assess global curvature generation in terms of budding and tubulation events. This fact, and the coarse graining underlying most MD simulations of curvature sensitive proteins, challenges the comparison between experimental and simulated phenomena. However, experimental reports exist that are consistent with the notion that the H0 helix is not essential for membrane curvature generation through N-BAR domains19, and that the H0 peptide alone cannot alter liposome morphology20.</p><p>In membrane curvature sensing, Bhatia et al. reported that amphipathic motifs are essential for the membrane curvature sensing of BAR domains21,22, based on sensing local, curvature-dependent membrane bilayer defects23,24. On the other hand, the asset of the dimeric BAR domain structure in favoring the geometry of curved membrane was also discussed by Doucet et al.25, emphasizing a possible role of the BAR domain scaffold in membrane curvature sensing.</p><p>Clearly, the role of the H0 helix in both curvature generation and sensing requires clarification. In this contribution, we designed mutants and used in vitro biophysical tools to specifically study the contributions of amphipathic helix insertion to endophilin N-BAR domain membrane binding, curvature generation capacities, and membrane curvature sensing. We revealed that the amphipathic helix dominates the membrane binding of endophilin N-BAR, which influences the overall tubulation capacity of endophilin N-BAR. However, our results also show that amphipathic insertion does not contribute to the molecular curvature generation ability of endophilin N-BAR. Finally, we revealed that the endophilin BAR domain (without H0 helix) is capable of sensing membrane curvature, while the H0 helix contributes to the nonlinear curvature sorting of endophilin N-BAR.</p><!><p>To distinguish the role of H0 hydrophobic insertion and BAR dimer scaffolding, we designed endophilin N-BAR variants with varied H0 properties (hydrophobicity, length) to specifically study the contribution from H0 insertion.</p><p>As shown in Figure 1A, the endophilin N-BAR domain was modified either by single-site mutagenesis or truncation. Residue F10 is the most hydrophobic residue (Fig. 1B–C) within the H0 wild-type (WT) domain and was reported to insert into the membrane2. We thus mutated this residue to either one with greater hydrophobicity (F10W) to enhance H0 membrane insertion or with a small residue (F10A) to reduce H0 insertion. In addition to single site mutagenesis, we progressively truncated the H0 helix to investigate the effect of H0 length. Circular dichroism proved that the mutations did not affect the helicity of the protein (Fig. 1D) for any of the variants.</p><p>To delineate the role of the H0 helix in endophilin function, we first compared the membrane binding capacity of the endophilin variants. Our results show that the F10W mutant binds to the membrane stronger than the WT protein under the same bulk concentration, while the F10A mutant binds significantly weaker (Fig. 2A). This observation is expected due to the different side chain hydrophobicities. The binding capacity of the truncation variants under the same bulk concentration decreased with decreasing length of the H0 helix (Fig. 2A, equilibrium density: WT > D1-6 > D1-10 > D1-14 > D1-18 > D1-24). These observations indicate that the H0 helix plays a key role in the binding of endophilin N-BAR to the membrane29.</p><p>The linear relationship (Fig. 2B) between logarithmic densities and helix length is consistent with a roughly linear dependence of the free energy of binding on helix length. However, the membrane binding density of the H0 total deletion variant (D1-24) showed no significant difference from that of the D1-18 variant, which likely implies that residues 19–24 do not significantly contribute to membrane binding. The absence of a discernible contribution to membrane binding from residues 19–24 suggests that this region does not significantly interact with the membrane, consistent with the predicted absence of helical structure in this region (Fig. 1B). Furthermore, the basal degree of membrane binding of the D1-24 truncate implies that H0 interaction with the membrane is the dominant, but not the only contributor, to endophilin N-BAR's membrane binding. Other mechanisms such as 1) H1 insert helix (H1i, residues: 59–87) membrane binding and 2) electrostatic interactions between the positively charged concave dimer surface and the negatively charged membrane, could contribute to attract the protein to the membrane. Indeed, the S75D (within H1i) mutant showed reduced membrane binding (Fig. 2A), as expected30.</p><p>We next compared the intrinsic membrane curvature generation capacities of the variants to WT protein. A giant unilamellar vesicle (GUV) shape stability assay31,32 served to quantify the protein density required to induce membrane curvature changes on membrane tension-controlled GUVs. In this assay, a single vesicle was aspirated from a GUV dispersion, set under a specific membrane tension (Fig. 3A, see Materials and Methods for calculation of tension, σ), and then transferred to a protein solution, followed by confocal imaging to monitor the protein density on the GUV as well as GUV geometry changes (Fig. 3B). Fig. 3B shows that when N-BAR domain binding reached a threshold density level, the projection length inside the glass pipette began to decrease and tubules formed towards the GUV exterior (Fig. 3B, red arrow, note that individual tubules are not resolved but result in fluorescent blur near the vesicle31,32). The point where the apparent area, Area(t)=4πRv2(t)+2πRPLP(t), of the GUV starts to decrease (red arrow indicates the transition point in Fig. 3C) corresponds to a membrane-curvature-instability-transition protein density (Fig. 3C), which combined with the selected membrane tension is an indicator of the intrinsic membrane curvature generation capacity of the protein (see Materials and Methods for details)32. Note that transition densities of the three shortest variants could not be determined because their low membrane binding capacity required bulk solution concentrations high enough to cause background fluorescence intensities that interfered with the measurement of fluorescence levels at the membrane.</p><p>In sharp contrast to the significant contribution of the H0 helix to membrane binding, the endophilin variants with either enhanced or inhibited membrane binding capacity showed no significant difference in the transition density at the same membrane tension (Fig. 3D), suggesting that all variants use the same mechanism to initiate bending of membranes.</p><p>We observed through classical negative stain transmission electron microscopy (TEM) imaging of membrane tubulation that variants with inhibited membrane binding are less efficient in liposome tubulation. This assay provides the most commonly used readout for membrane curvature generation (Fig. 4). This apparent discrepancy with the findings shown in Fig. 3 is easily explained by distinguishing between the intrinsic molecular capacity of a membrane bound protein to generate curvature (assessed in Fig. 3D), and the efficiency with which a bulk solution can tubulate vesicles (assessed via classical TEM based tubulation assays). Only the latter is affected by the membrane binding capacity of the protein of interest. This conclusion is confirmed by the observation that the logarithmic tubulation efficiency (Fig. 4C) follows an essentially linear relationship (slope in Fig. 4C is close to 1) with the logarithm of equilibrium density on the membrane (Fig. 2), implying that the overall tubulation efficiency of an endophilin N-BAR solution is linearly correlated with the membrane binding capacity of the protein.</p><p>Taken together, our findings so far are consistent with the notion that the amphipathic insertion of the H0 helix is not responsible for membrane bending through N-BAR domains. To exclude the possibility that H1i, rather than H0, engages in amphipathic wedging through endophilin33, we examined the H1i mutant S75D. This mutation has previously been shown to reduce the membrane insertion of the H1i helix30. As shown in Fig. 3D, this effect does not affect endophilin's curvature generation capacity.</p><p>Our present and previous32 findings of endophilin function are all consistent with a recent characterization of endophilin membrane association, H0 insertion, and membrane deformation34. However, our current analysis implies that the process of endophilin H0 membrane insertion, which was observed to coincide with membrane deformation34, is a consequence, rather than a cause, of membrane deformation through endophilin. This implication is supported by a previous simulation report showing that H0 folding and insertion is much less energetically favorable in flat membranes, but that it is facilitated in curved membranes with packing defects14.</p><p>To understand the role of the H0 helix in sensing membrane curvature, we compared the membrane curvature sorting of endophilin WT and its H0 deletion mutant with a GUV-pulled tether system (see Materials and Methods for details regarding this method)35.</p><p>In contrast to the known non-linear sorting of endophilin WT N-BAR domains35, we observed (Fig. 5) that the H0 deletion variant followed linear sorting with membrane curvature (higher membrane tension corresponds to higher membrane curvature on the pulled tether), indicating that the BAR domain without H0 insertion is capable of sensing membrane curvature. This observations is consistent with previous reports showing that F-BAR36 and I-BAR37 domains lacking in terminal amphipathic helices also sort on membrane tubules.</p><p>In the range of relatively low membrane curvature (1/Rt < 0.03 nm−1), the curvature sorting of the H0 deletion variant is comparable to WT protein (Fig. 5B). However, when membrane curvature further increases (1/Rt > 0.03 nm−1), the curvature sorting of the H0 deletion variant was observed to be significantly weaker than WT protein (Fig. 5). This observation can be rationalized by the fact that 1) higher membrane curvature creates more membrane bilayer defects and 2) amphipathic motifs (such as H0 helix in endophilin) sense such curvature-dependent membrane bilayer defects21–24.</p><p>Taken together, these results imply that curvature sorting of the N-BAR domain is driven by both the H0 helix (which may contribute to non-linear sorting of endophilin35), and by the BAR domain.</p><!><p>Our observation that amphipathic H0 helix insertion/wedging does not contribute to curvature generation is not consistent with earlier suggestions that the H0 helix of the N-BAR domain might be solely responsible for generating membrane curvature. This discrepancy can be re-solved by considering the fractional coverage of H0 helices on membranes. MD simulations from Blood et al. indicated that the H0 helix of the N-BAR domain cannot bend the membrane at an H0 density of 150 lipids/embedded helix (corresponding to ~ 5.7% protein coverage assuming helix cross sectional area = 6 nm2 and area per lipid = 0.7 nm2). Furthermore, they showed that membranes can only be bent through H0 helices at 30 lipids/embedded helix, an unrealistically high density which would be equivalent to ~ 28.5% helix coverage on the membrane15. Consistent with these results, MD simulations from Arkhipov et al. also showed that at 12.5 –18.8% coverage fraction, the amphiphysin H0 cannot induce membrane curvature. These authors further noted that the high H0 density of ~ 30% would compromise the ability of N-BAR domains to bend the membrane because the scaffolding effect of the BAR dimers would be inhibited38. The membrane-curvature-instability-transition protein densities shown in Figure 3D are around ~ 2000/μm2, corresponding to < 2% H0 coverage, i.e. a range where MD simulations have predicted absence of curvature generation through H0 alone.</p><p>Furthermore, the endophilin density of ~ 2000/μm2 corresponds to an overall protein coverage of ~ 6.7%, which is smaller than the coverage fractions cited by Stachowiak et al., where 20 – 30% coverage was shown to be required to bend membranes through crowding at negligible tension (i.e. no pipette aspiration)9. Our previous membrane tension-controlled studies also demonstrated that at this relatively low protein coverage (~ 6.7%), the crowding effect is negligible for the membrane curvature generation of endophilin10.</p><!><p>The previous reports arguing that H0 amphipathic insertion drives membrane curvature could be due to the missing distinction between the role of the H0 helix in associating N-BAR proteins to the membrane versus its capacity to generate membrane curvature. Previous contributions hypothesized that H0 insertion is essential for membrane curvature generation based on the observation that N-BAR variants with compromised H0 insertion showed lower liposome tubulation efficiencies2,7. Our findings explain this observation through the reduced membrane binding capacity of H0 truncated mutants. Consistent with this notion, Peter et al.'s liposome tubulation assay to assess the tubulating activity of wild-type N-BAR potentially explains the discrepancy between the controversial observations: buds on liposomes, tubules, and small vesicles were increasingly observed with higher protein concentrations19. This notion is further supported by Blood et al.'s MD simulation studies, where H0 insertion played a key role to ensure close association of the charged concave surface of N-BAR domain to membrane and thus drive membrane curvature. Without amphipathic helix insertion, N-BAR domain's membrane binding was compromised and failed to drive membrane curvature15,16. Taken together, this implies that only studies that determine the density of proteins on the membrane can assess the intrinsic, molecular curvature generation capacity of a membrane binding protein.</p><!><p>The H0 helix was reported to mediate the formation of stable endophilin N-BAR lattices on the membrane39,40, which was hypothesized to be important for its membrane curvature generation39. However, we observed that the D1-10 mutant, which was reported to show a higher degree of lattice disorder compared with the WT protein39, showed uncompromised membrane curvature induction capacity (Fig. 3D). This observation is consistent with a report for the N-BAR protein BIN1 showing that a low long-range order N-BAR coat was capable of inducing flexible membrane tubules41. Taken together, these observations are consistent with the notion that formation of highly ordered stable lattices is not essential for the curvature generation of N-BAR proteins.</p><!><p>The diameter of a clathrin-coated vesicle ranges from 70 nm to 150 nm42, which corresponds to the region 1/Rt < 0.03 nm−1 in Fig. 5B. In this region, we find that both endophilin WT and D1-32 show vanishingly small sensitivity to membrane curvature changes. On the other hand, the neck area (diameter: 25 nm – 30 nm43) of the clathrin-coated pit is more highly curved and corresponds to the region 1/Rt > 0.06 nm−1, where the WT shows strong membrane curvature sorting while the H0 deletion mutant's membrane curvature sorting is much weaker compared to the WT. This observation firstly explains why endophilin is recruited to the neck area of the clathrin-coated pit. Secondly, although the H0 deletion variant is still capable of sensing membrane curvature, its capacity is relatively weak and potentially compromises the specific recruitment of endophilin to the neck area, implying that the H0 helix is essential for endophilin's physiological function in clathrin-mediated endocytosis.</p><!><p>In summary, we have quantitatively studied the membrane binding, curvature generation, and curvature sorting of endophilin N-BAR and its variants with modified H0 amphipathic insertion abilities. We revealed that, for N-BAR domains, the H0 helix plays a key role in membrane binding, but does not influence the protein density required to initiate a membrane curvature transition. Our observations demonstrate that the H0 amphipathic insertion/wedging mechanism of the N-BAR domain of endophilin does not directly induce membrane curvature; instead, other mechanisms, such as BAR dimer scaffolding, appear to be more important for membrane curvature generation. Furthermore, we revealed that the H0 truncated variant was capable of sensing membrane curvature, indicating that sensing as well does not exclusively depend on H0. Overall, this contribution shed light on the controversial state of the biophysical mechanism of endophilin function.</p><!><p>A plasmid encoding GST-tagged endophilin N-BAR domain with mutations C108S and E241C for minimally perturbing fluorescence labeling, noted as WT in this manuscript, was generated as described in reference44. This plasmid was further used to generate mutants: 1) single site mutagenesis: F10W, F10A and S75D; 2) truncation mutagenesis: deletion of N-terminal residues 1-X (D1-6, D1-10, D1-14, D1-18, D1-24 and D1-32). The fusion proteins were expressed in BL21(DE3) RIL CodonPlus bacteria (Stratagene, La Jolla, CA). After cell lysis, the supernatant was first applied to a GSTrap FF affinity column (GE Healthcare, Marlborough, MA), and the eluted fusion proteins were cleaved with PreScission Protease44. After cleavage, the target protein contains a 5 residue tail (GlyProLeuGlySer) at the N-terminus corresponding to BamH I restriction site and protease cutting site, which is predicted to not form secondary structure27. The digestion product was then subjected to a HiTrap Q HP column (GE Healthcare, Marlborough, MA) and a HiLoad Superdex 200 PG column (GE Healthcare, Marlborough, MA)44. All proteins were labeled with Alexa Fluor® 488 C5 Maleimide (Thermo Fisher Scientific, Philadelphia, PA) at residue C241 and excess dye was removed by HiTrap Desalting columns (GE Healthcare, Marlborough, MA). Proteins were stored in buffer containing 20 mM HEPES, 150 mM NaCl, and 1 mM TCEP at pH = 7.4. HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) was obtained from SIGMA-ALDRICH® (Allentown, PA), NaCl (sodium chloride) was obtained from Thermo Fisher Scientific (Philadelphia, PA), and TCEP-HCl (tris (2-carboxyethyl) phosphine hydrochloride) was obtained from Pierce/Thermo Fisher Scientific (Rockford, IL). Note that the protein concentrations indicated in this manuscript refer to monomer concentrations, while the protein densities on the membrane refer to homodimer densities.</p><!><p>Lipids DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine), DOPS (1,2-dioleoyl-sn-glycero-3-phospho-L-serine), DOPE (1,2-dioleoyl-sn-glycero-3-phosphoethanolamine), and DSPE-Bio-PEG2000 (distearoylphosphatidylethanolamine – N - (biotinyl (polyethylene glycol) 2000)) were obtained from Avanti Polar Lipids (Alabaster, AL). Texas Red® DHPE (Texas Red® 1,2-Dihexadecanoyl-sn-Glycero-3-Phosphoethanolamine, Triethylammonium Salt) was obtained from Invitrogen/Life Technologies (Grand Island, NY).</p><p>For preparation of large unilamellar vesicles (LUVs)45, a lipid mixture containing 45% DOPS, 30% DOPE and 25% DOPC was first dried with compressed air and then desiccated at least 2 hours before rehydration. The protein storage buffer was used to rehydrate lipids at a concentration of 1 mg/ml. Rehydrated lipids were vortexed occasionally at room temperature for 1 hour. Next, the dispersions were extruded 13 times through a single polycarbonate membrane of 400 nm pore size (Whatman/GE Healthcare). The resulting LUVs were stored at 4°C and used within two days.</p><p>Giant unilamellar vesicles (GUVs) containing 1) 45%DOPS, 30%DOPE, 24.5%DOPC and 0.5% TexasRed DHPE and 2) 64%DOPC, 25%DOPG, 10% PI(4,5)P2, 0.3% TexasRed DHPE and 0.7% DSPE-Bio-PEG2000 were electroformed in 300 mM sucrose as described in refs31,32,45,46.</p><!><p>0.1 mg/ml LUVs were co-incubated with 10 μM protein solution for 30 min and then put on TEM grids (Formvar/Carbon 200 mesh, Copper grids, Electron Microscopy Sciences, Hatfield, PA) for 2 min. Before negative staining with 2% (w/v) uranyl acetate, the grids were first rinsed in buffer and blotted with filter paper to remove excess materials. After staining, the grids were dried gently and then imaged with a JEM 1011 transmission electron microscope (JEOL).</p><!><p>The GUV shape stability assay was carried out as described in ref31,32. As illustrated in Figure 2A, a GUV with controlled membrane tension46, σ=ΔP2(1Rp−1Rv),was aspirated and transferred from a GUV dispersion to a protein solution. During the transfer, the glass pipette used for GUV aspiration was casein-coated to reduce attachment of protein/lipid membrane to the glass pipette47, and the GUV was protected from dehydration with an outer capillary44. The transfer is followed by confocal imaging (Objective: 60× W 1.1 NA, Olympus, Center Valley, PA) to monitor the protein binding and GUV shape changes. The apparent area of the GUV was defined to be: Area(t)=4πRv2(t)+2πRPLP(t),which was used as an indicator of the GUV membrane curvature changes since tubulation or vesiculation from the GUV causes a change in the apparent area of the GUV. When protein density reached a GUV-shape-instability transition point, the apparent area started to decrease. This transition density, combined with the chosen membrane tension, is an indicator of the intrinsic membrane curvature generation ability of the protein31,32. The GUV-shape instability transition threshold was rigorously defined as described in ref31. Briefly, the standard deviation (SD) and the average value of the apparent area of pre-transition points were calculated and the threshold was determined by subtracting 2X SD from the average value, where the corresponding transition protein density was extracted.</p><p>In this assay, the GUV dispersion and protein solutions were prepared by diluting from concentrated stocks with buffer containing 400 mM sucrose, 400 mM glucose, and protein buffer with a ratio of 1:1:1. All measurements were done at room temperature.</p><p>The fluorescence intensity obtained from imaging was converted to protein density on the membrane using a calibration with standard BODIPY labeled lipids, where the brightness difference between BODIPY and Alexa 488 dyes was taken into account31,32,48.</p><!><p>As described in refs35,49, a GUV-pulled tether system was used to test the membrane curvature sorting of proteins. Briefly, GUV dispersions were co-incubated with proteins to reach binding equilibrium, then streptavidin-coated polystyrene beads were added to the mixture, and the whole solution was placed in a glass chamber and mounted onto a confocal microscope. Two micropipettes were inserted into the chamber: one to aspirate a GUV, and the other one to aspirate a streptavidin-coated bead, attached to the membrane via biotin-streptavidin coupling, to pull a cylindrical tether from the GUV with ~ 20 μm in length. The membrane tension on the pulled-tether-GUV system was controlled by adjusting the height of a water reservoir, and aspiration pressures were detected with a differential pressure transducer (Validyne Engineering, Los Angeles, CA). The radius of the pulled-tube was calculated from membrane tension based on the model used in Ref.48. The fluorescence intensities of labeled protein (Alexa 488) and lipid (Texas Red DHPE) on the tether were recorded through xz scans of the cross-section of the tether (with a stepwidth of 0.07 μm and a total imaging depth of 6 μm) under varied membrane tension, and the ratio (Ir=Igreen/Ired) was normalized by the ratio on the vesicle (Ir0 =Ives-green/Ives-red). The aspiration pressure was changed to obtain Ir/Ir0 as a function of tube radius on a pulled-tether. For each pulling step, the images were taken at least 90 s after pressure change at constant tether length to make sure the lateral tension reached equilibrium. Buffers used for these measurements were the same as those used in the GUV shape stability assay.</p>

PubMed Author Manuscript

Rational design of selective allosteric inhibitors of PHDGH and serine synthesis with in vivo activity

Summary Metabolic reprogramming in cancer cells facilitates growth and proliferation. Increased activity of the serine biosynthetic pathway through the enzyme phosphoglycerate dehydrogenase (PHGDH) contributes to tumorigenesis. With a small substrate and a weak binding cofactor (NAD+), inhibitor development for PHGDH remains challenging. Instead of targeting the PHGDH active site, we computationally identified two potential allosteric sites and virtually screened compounds that can bind to these sites. With subsequent characterization, we successfully identified PHDGH non-NAD+ competing allosteric inhibitors that attenuate its enzyme activity, selectively inhibit de novo serine synthesis in cancer cells, and reduce tumor growth in vivo. Our study not only identifies novel allosteric inhibitors for PHGDH to probe its function and potential as a therapeutic target, but also provides a general strategy for the rational design of small molecule modulators of metabolic enzyme function.

rational_design_of_selective_allosteric_inhibitors_of_phdgh_and_serine_synthesis_with_in_vivo_activi

Introduction<!>Allosteric Site Prediction and Identification of Novel Allosteric Inhibitors<!>Cellular Effects of PKUMDL-WQ-2101 and PKUMDL-WQ-2201<!>Compound Activity is Selective for PHGDH<!>PKUMDL-WQ-2101 and PKUMDL-WQ-2201 Inhibits Tumor Growth of Amplified Cell Lines in vivo<!>Discussion<!>Significance<!>Allosteric site prediction and virtual screening<!>Molecular cloning, protein expression and purification<!>Enzyme assay<!>Surface Plasmon Resonance (SPR) experiments<!>Competition experiments<!>Mutagenesis experiments<!>Cell culture<!>Proliferation Assays<!>MTT assays<!>Synergistic experiments between PKUMDL-WQ-2101 and PKUMDL-WQ-2201 in enzymatic assays and cell-based assays<!>Flow cytometric analysis of cell cycle<!>Generation of CRISPR-Cas9 PHGDH Knockout Cells<!>Immunoblotting<!>U-13C-glucose stable isotope labeling<!>Metabolite extraction<!>Peak extraction and data analysis<!>MDA-MB-468 and MDA-MB-231 xenograft mouse models

<p>It has long been known that tumor cells exhibit altered glucose metabolism characterized by increased glucose uptake and incomplete oxidation to lactate in the presence of oxygen (Warburg, 1956; Zhao et al., 2016a). With the surge of interest in understanding cancer cell metabolism, it is now widely accepted that metabolic rearrangements accompanying malignant transformation also involve numerous other pathway alterations such as the increased flux of the pentose phosphate pathway (PPP), elevated rates of lipid biosynthesis, high glutamine consumption, maintenance of redox homeostasis, and alterations in autophagy (Pavlova and Thompson, 2016). Therefore, targeting the metabolic enzymes in these pathways provides a promising strategy for cancer therapy.</p><p>The gene encoding phosphoglycerate dehydrogenase (PHGDH), an enzyme that catalyzes the first committed step of serine biosynthesis, is also involved in metabolic reprogramming in cancer. PHGDH was identified as a focus of recurrent copy number gain across a large set of tumors (Beroukhim et al., 2010). The PHGDH gene that is located at chromosome 1p12 showed copy number gain in 16% of all cancers including 40% of melanoma and some triple negative breast cancers (Locasale et al., 2011; Possemato et al., 2011). Cancer cells with PHGDH amplifications are sensitive to PHGDH depletion, which indicates that the enzyme is required for the growth of certain tumor cells.</p><p>Recent studies have identified different regulatory mechanisms that can activate PHGDH through both transcriptional regulation and changes in its activity via posttranslational modifications (Ma et al., 2013; DeNicola et al., 2015; Ou et al., 2015; Ding et al., 2013). Additional studies have found several underappreciated functions for de novo synthesis of serine and the use of one-carbon metabolism including epigenetic maintenance and NADPH production that is important for biosynthesis and controlling the levels of reactive oxygen species (Fan et al., 2014; Mentch et al., 2015). Together these findings demonstrate that PHGDH is an attractive anti-cancer target, and that designing PHGDH inhibitors may be a fruitful enterprise.</p><p>Human PHGDH contains four domains: nucleotide-binding, substrate-binding, regulatory and intervening domains. Currently only the crystal structure containing the first two domains is available (PDB code: 2G76, Turnbull, 2006). The substrate-binding pocket of PHGDH is rather small, approximately 100-200 Å3, and the physiological concentration of its cofactor NAD+ is as high as 0.3 mM (Yamada et al., 2006). These properties likely increase the difficulties of the design of substrate-competitive inhibitors. Meanwhile, considering NAD+ or NADH is a widely used cofactor, which also easily causes the problem of specificity, we focused on designing allosteric inhibitors for PHGDH that do not compete with the native ligand. Allosteric regulation can be achieved by various effectors, ranging from small molecules to macromolecules (Merdanovic et al., 2013) and can have high specificity, as allosteric binding sites are usually not evolutionarily conserved. Computational methods for rational design of allosteric effectors were emerging (Wagner et al., 2016; Ma et al., 2016) and a number of successful application examples have been reported. For example, using the two-state Go model based allosteric site prediction method that we developed (Qi et al., 2012), we obtained novel allosteric inhibitors for Escherichia coli (E. coli) phosphoglycerate dehydrogenase (Wang et al., 2014). Novel enzymes activators were also found using combined computational and experimental approach (Meng et al., 2016), providing an alternative way to control disease-related molecular networks (Pei et al., 2014).</p><p>In the present study, we first computationally identified two potential allosteric sites in PHGDH and used them to virtually screen a compound library. Selected compounds were tested for their inhibition activities using recombinant enzyme, cancer cell-lines, and tumor xenograft models. Two distinct compounds with activity in cells were found. Their specificity was confirmed using CRISPR-Cas9 gene-targeting PHGDH, chemical compound pull-down in cancer cells, and metabolomics. Recently, three studies have reported compounds that have activity against PHGDH by using high-throughput experimental screening. One series of PHGDH inhibitors showed activities in enzymatic and cell-based assays, but the binding mechanism, selectivity towards PHGDH, and efficacy in vivo were unclear (Mullarky et al., 2016). Another series of inhibitors with bioactivities in enzymatic and cell-based assays, as well as a xenograft model, do not have clear binding sites (Pacold et al., 2016). The third series of inhibitors were found by fragment screen that bind to the adenine subsite with only millimolar protein binding affinities and no further biological activities were reported (Unterlass et al., 2016). To our knowledge, the present study is the first successful example of using a structure-based approach to discover allosteric inhibitors that directly and specifically target PHGDH.</p><!><p>Two potential allosteric sites, I and II, were identified computationally using a cavity detection algorithm based on defined geometric criteria (Yuan et al., 2013; Yuan et al., 2011) (Figure 1A). Site I is close to the active site and the NAD+/NADH-cofactor binding site, with a volume of 847 Å3 and a predicted maximal pKd of 8.71. It shares residues Gly 78, Val 79, Asp 80, Asn 81 and Val 82 with the active site. Site II is located in the substrate binding domain, with a volume of 463 Å3 and a predicted maximal pKd of 7.79. Molecular docking across a large virtual compound library was then conducted (Halgren et al., 2004; Friesner et al., 2004). Ninety-eight compounds were selected and then acquired to test their abilities to regulate PHGDH activity.</p><p>PKUMDL-WQ-2101 in site I and PKUMDL-WQ-2201 to 2203 in site II were identified to significantly affect the PHGDH activity in a concentration dependent manner (Figures 1B, 1C, and S1A. SPECS IDs of these four compounds are shown in Table S1), and their KD values were determined by using Surface Plasmon Resonance (SPR) (Figures 1D, and S1B-S1G). SPR experiments also demonstrated these inhibitors did not aggregate under the experimental conditions.</p><p>To test whether the compounds indeed bind to site I and II, respectively, we selected PKUMDL-WQ-2101 in site I and PKUMDL-WQ-2201 in site II, and performed competition experiments and mutagenesis studies. The competition experiments between these compounds and cofactor NADH indicated that they did not bound in the cofactor site (Figures 1E and S1H). Inhibition ability of PKUMDL-WQ-2101 and PKUMDL-WQ-2201 for the C-terminal truncated PHGDH containing only the substrate binding domain and the nucleotide binding domain demonstrated that the C-terminal regulatory and intervening domains did not contribute to binding and the compounds bound to the N-terminal domains used for virtual screening (Figure S1I and S1J). Based on the docking structures, mutants R134A and K57AT59A for PKUMDL-WQ-2101, and mutants T59A and T56AK57A for PKUMDL-WQ-2201 were made and tested (Figures 1F-1I). All these mutants retained their secondary structures (Figure S1K) and exhibited reduced responses to the corresponding inhibitors. For PKUMDL-WQ-2101, the PHGDH-inhibiting activities were dramatically reduced for mutants R134A (IC50 = 141 ± 4 μM, max inhibition = 49%) and K57AT59A (IC50 = 128 ± 10 μM, max inhibition = 47%) compared to that for WT PHGDH (IC50 = 34.8 ± 3.6 μM, max inhibition = 67%) (Figure 1G). For PKUMDL-WQ-2201, its inhibition abilities for PHGDH mutants were also significantly decreased. The IC50 values for T59A and T56AK57A were 69 ± 40 and > 300 μM, respectively, while the IC50 value for WT PHGDH was 35.7 ± 8.6 μM (Figure 1I). In addition, the inhibition ability of PKUMDL-WQ-2101 to T59A and T56AK57A (key residues in site II) or the inhibition ability of PKUMDL-WQ-2201 to R134A and K57AT59A (key residues in site I) were also measured to verify the specificity of inhibitor binding (Figures 1G and 1I). These results support that PKUMDL-WQ-2101 and PKUMDL-WQ-2201 bound in site I and site II, respectively.</p><p>Combination therapy is emerging as a promising strategy to generate synergistic therapeutic effects, reduce side effects of monotherapy, overcome multidrug resistance (MDR), and reduce dose of each drug require (Yang et al., 2015; Botham et al., 2014), which can also be used to demonstate whether two compounds bind to the same site or to different sites. Synergism can be quantified through the calculation of Combination Indices (CI) (Chou, 2006). When the concentration of PKUMDL-WQ-2101 was kept at 25 μM (about IC50 value), synergistic interactions were observed with PKUMDL-WQ-2201 concentration ranging from 1 to 200 μM (Figures 1J and S2A). In contrast, when the concentration of PKUMDL-WQ-2202 was kept at 25 μM, PKUMDL-WQ-2201 and PKUMDL-WQ-2202 showed antagonism rather than synergy, indicating that they compete for a single site (Figures 1J and S2B). Together, these synergistic results further confirm our docking results about the binding sites of the compounds.</p><!><p>The effects of the compounds against a panel of cancer cell lines along with one immortalized human breast epithelial cell line were evaluated. PKUMDL-WQ-2101 and PKUMDL-WQ-2201 showed dose-dependent suppression effects on the cell viability at micromolar concentrations, with good selectivity for PHGDH amplified breast cancer cell lines (Figures 2A and 2B), while PKUMDL-WQ-2202 and PKUMDL-WQ-2203 showed weak bioactivity in cell based assays with EC50 values more than 200 μM (Figure S3). The antitumor activities of PKUMDL-WQ-2101 in the two PHGDH amplified breast cancer cell lines (MDA-MB-468 and HCC70) were 7.70 and 10.8 μM, which were 3- to 4-, 8- to 12-, and 14- to 20-fold more active than its antitumor activities in PHGDH non-dependent cell lines, MDA-MB-231, ZR-75-1 and MCF-7 cell lines, respectively. For PKUMDL-WQ-2201, the EC50 values were 6.90 μM in MDA-MB-468 and 10.0 μM in HCC70 cell lines, which were 13- to 18-fold more active than that of ZR-75-1. No bioactivities in the other three PHGDH non-amplified breast cancer cell lines tested were measurable. Meanwhile, PKUMDL-WQ-2101 and PKUMDL-WQ-2201 exerted weak cytotoxic effects on the MCF-10A cell line, which was consistent with previous observations of PHGDH requirements using genetic approaches (Locasale et al., 2011). The antitumor activities of PKUMDL-WQ-2101 and PKUMDL-WQ-2201 to MDA-MB-468 cells may be caused by their influence on cell cycle (Figures 2C and 2D).</p><!><p>To further evaluate the activity and selectivity of the compounds, we developed a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) mediated PHGDH gene knockout (KO). We designed a single-guide RNA (sgRNA) with a protospacer adjacent motif (PAM) sequence specifically targeting a coding region in exon 8 of the PHGDH gene, predicted to result in a frame shift mutation and loss-of-function (Shalem et al., 2014; Mali et al., 2013) (Figure 3A). A clonal population of SKOV3 ovarian cancer cells was obtained and able to grow in the absence of PHGDH. Complete knockout was confirmed with immunoblotting in reference to a cell line created by targeting a sgRNA against GFP (Figure 3B). A 6-day growth curve revealed the ability of PHGDH KO cells to grow, albeit more slowly than the GFP KO control cells (p<0.01, two-tailed multiple t-test) (Figure 3C). We then evaluated the compounds PKUMDL-WQ-2101 and PKUMDL-WQ-2201 on these cell lines. The GFP KO cells exhibited sensitivity to PKUMDL-WQ-2101 (IC50= 37.3 μM) (Figure S4A) and, albeit to a lesser extent to PKUMDL-WQ-2201 (IC50 = 291.5.3 μM) (Figure S4B). To further understand the specificity of these compounds, 6-day proliferation assays were carried out in SKOV3 control and PHGDH KO cells. GFP KO cell growth was significantly suppressed after treatment with PKUMDL-WQ-2101 (p<10-3, two-tailed student's t-test) (Figure 3D), whereas PHGDH KO cells were able to proliferate in the presence of the compound (Figure 3E). Similarly, albeit to a lesser extent, proliferation in SKOV3 control cells was suppressed after 6 days in the presence of PKUMDL-WQ-2201 (p<0.05, two-tailed student's t-test) (Figure 3F), whereas PHGDH KO cell growth remained unaffected (p>0.99, two-tailed student's t-test) (Figure 3G). Chemical compound pull down assays were also carried out to verify PKUMDL-WQ-2101 with the best binding affinity was specifically bind to PHGDH in MDA-MB-468 cells (Figures S4C-4E). These results indicated that the cytotoxicity to these compounds appears to a large extent specific to PHGDH and serine synthesis.</p><p>We then investigated the effects of PHGDH KO on serine metabolism. Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) and stable isotope labeling were used to monitor the conversion of uniformly labeled U-13C-glucose to metabolites in the serine metabolic network in both SKOV3 GFP KO control and SKOV3 PHGDH KO cells (Figure 4A). We detected 13C-glucose incorporation in both serine and glycine in SKOV3 GFP KO cells, but not in SKOV3 PHGDH KO cells, confirming that the knockout fully abrogated de novo serine synthesis (Figure 4B). Both compounds (about IC50) also produced comparable metabolic effects on the serine metabolic network and reduced glucose incorporation into serine and glycine metabolites by more than 50% (Figures 4C and 4D). We further investigated pathways downstream of serine upon inhibition of PHGDH with PKUMDL-WQ-2101 and PKUMDL-WQ-2201. Given that serine is essential for nucleotide synthesis (Locasale, 2013), we investigated whether 13C-glucose consumption into nucleotides was altered after treatment. We analyzed the mass isotopomer distribution (MID) of one pyrimidine and purine synthesis by measuring uridine triphosphate (UTP) and adenosine triphosphate (ATP), and determined whether a difference in the mass shift of 1 or 2 (m+1 or m+2), known to result from incorporation of serine or glycine, was observed. We also monitored any changes in m+6 or m+7, which correspond to labeling from both the pentose phosphate and serine biosynthesis pathways. Upon treatment with both PKUMDL-WQ-2101 and PKUMDL-WQ-2201, decreases in m+2, m+6, and m+7 glucose labeling were observed in UTP and ATP, indicating a direct effect of PHGDH inhibition on nucleotide synthesis (Figure 4E and 4F). A sharp decrease in the m+5 peak was also observed due to a decrease in ribose labeling from the pentose phosphate pathway, suggesting that PHGDH ablation likely exerts effects on nucleotide synthesis through affecting glycolysis or occurs indirectly as a product of on-target cytotoxicity of the compound. We excluded interpretation of the m+1 peak due to the confounding influence of natural abundance isotopes. We also analyzed glucose incorporation into glutathione, another metabolite belonging to a pathway downstream of serine and glycine synthesis, and decreases in m+2 were found in cells treated with both compounds (Figure 4G). All together, these data suggest that PHGDH inhibition by PKUMDL-WQ-2101 and PKUMDL-WQ-2201 decreases de novo serine synthesis and metabolism downstream of the serine synthesis pathway, with effects comparable to PHGDH genetic deletion.</p><!><p>Previous studies have questioned whether PHGDH inhibition is required for longer term tumor maintenance (Chen et al., 2013). To further understand the role of PHGDH in tumor growth and maintenance in vivo, MDA-MB-468 and MDA-MB-231 cells were injected into the fourth mammary pad of NOD.CB17 Scid/J mice. Tumor volumes were monitored every 2 days. We found that both PKUMDL-WQ-2101 and PKUMDL-WQ-2201 exhibited substantial inhibitory effects on MDA-MB-468 xenografts compared with vehicle-treated mice after 30 days of drug delivery (Figure 5). For MDA-MB-231 xenografts, neither PKUMDL-WQ-2101 nor PKUMDL-WQ-2201 affected tumor growth (Figure S5A and S5B) further confirming the specificity of the compounds. The compounds appeared to also be tolerated as all mice were able to maintain normal body weight over the course of the experiments (Figure S5C-S5E). Though the two compounds synergized each other to potently induce enzyme activity inhibition and the death of cell line in culture, the combination strategy was not applied to the mice model, due to complicated pharmacokinetic issues of in vivo compound concentration and clearance time, which may not exactly match. Nevertheless, these findings confirm the bioactivity, tolerability, and selectivity for PHGDH in vivo.</p><!><p>Using a structure-based drug design approach, we successfully identified compounds that bound to the predicted allosteric sites and effectively inhibited the enzyme activity of PHGDH. These compounds exhibited sub-micromolar to micromolar binding affinities and inhibited cancer cell growth in the micromolar range. PKUMDL-WQ-2101 and PKUMDL-WQ-2201 showed good activity and selectivity to PHGDH over-expression breast cancer cells. The use of CRISPR-Cas9 mediated PHGDH KO in SKOV3 cells provided a genetic evaluation of the relative on-and off- target effects of each compound, whereby PKUMDL-WQ-2101 had high selectivity for PHGDH control but not KO cells. PKUMDL-WQ-2101 and PKUMDL-WQ-2201 were proven to suppress PHGDH amplified breast cancer cell growth in mice. Our study provides the first successful example of PHGDH allosteric inhibitor discovery using a structure-based approach.</p><p>The identified PKUMDL-WQ-2101 and PKUMDL-WQ-2201 compounds are novel allosteric inhibitors for PHGDH with unique structures. No biological activities for these compounds have been reported before. Though PKUMDL-WQ-2101 was predicted as pan-assay interference compounds (PAINS) (Baell and Holloway, 2010), due to the hydroxyl-phenyl-hydrazone group in its structure, the promiscuity has been eliminated by changing ionic strength or adding DTT in the enzymatic assay, SPR, and mutagenesis experiments, for the hydroxyl-phenyl-hydrazone group was generally believed to have the tendency for aggregation (McGovern et al., 2002), spectroscopic absorption (Auld et al., 2008), chelation (Ainscough et al., 1999) and reactivity, thus inactive proteins. In addition, the catalytic process of PHGDH or PSAT1 does not need the participation of metal ions, and PKUMDL-WQ-2101 showed good selectivity to PHGDH amplified breast cancer cells. Furthermore, PKUMDL-WQ-2101 showed bioactivity in vivo, confirming that PHGDH is required for tumor maintenance. These experimental results confirmed that PKUMDL-WQ-2101 specifically bound to PHGDH and inhibited its enzymatic activity.</p><p>Allosteric regulation needs communication between the allosteric site and the distant functional site. Therefore, high binding affinity of allosteric ligands may not necessarily cause strong influence on protein function (Nussinov and Tsai, 2014). In the case of PKUMDL-WQ-2101, while its Kd value is 0.56 ± 0.10 μM, its PHGDH inhibition activity IC50 value is 34.8 ± 3.6 μM. Previous studies showed that a large number of protein conformations in solution pre-exist and can be characterized by the energy landscape (Kar et al., 2010). Allosteric effectors may change the distribution of these conformations. PKUMDL-WQ-2101 and PKUMDL-WQ-2201 inhibited PHGDH activity mainly by forming hydrogen-bond networks with site I and II, respectively, limiting the movement of the rigid domains, preventing the active sites from closing, thus stabilizing PHGDH in the inactive conformation. Nevertheless, further experimental or computational studies are needed to better understand this inhibitory mechanism.</p><p>Three recent studies have reported compounds with activities against PHGDH. One study reported an example of a PHGDH inhibitor by screening a library of 800, 000 drug-like compounds (Mullarky et al., 2016). The best compound, CBR-5884 inhibited PHGDH enzymatic activity with an IC50 of 33 ± 12 μM in a time-dependent manner. CBR-5884 was speculated as a covalent inhibitor binding to a Cys in the non-active site and disrupting the enzyme oligomerization state. At 30 μM, CBR-5884 inhibited the growth of MDA-MB-468 cells by 35% to 60% in serine-replete media, and by 80% to 90% in serine-deplete media. Neither a direct binding test nor postulated binding site was reported. CBR-5884 was unstable in mouse plasma and could not be used for in vivo testing. Another study reported three PHGDH inhibitors by first screening a 400,000-compound NIH Molecular Libraries Small Molecule Repository (MLSMR) library and then optimizing the lead compounds (Pacold et al., 2016). The best compound, NCT-503, exhibited an IC50 value of 2.5 ± 0.6 μM and showed some selectivity in PHGDH amplified breast cancer cell lines and had bioactivities in a xenograft model. Although NCT-503 was found not substrate competitive, its specific binding site remains unknown. The third study reported 15 fragments with PHGDH inhibition activities by first screening a library of 600 fragments, then validating the fragments by using the thermal shift assay, isothermal titration calorimetry (ITC) competition experiments and X-ray crystallography (Unterlass et al., 2016). All the 15 fragments bound in the adenine subsite with millimolar binding affinities. However, fragment activities in cells and tumors were not reported. In the present study, we successfully discovered novel allosteric inhibitors for PHGDH using structure-based design approach with the best IC50 of 28.1 ± 1.3 μM for enzyme inhibition. PKUMDL-WQ-2101 and PKUMDL-WQ-2201 were confirmed to specifically bind to PHGDH in PHGDH amplified breast cancer cells with EC50 values less than 10 μM in serine-replete media, which was better than that of CBR-5884 and similar to that of NCT-503. Furthermore, PKUMDL-WQ-2101 and PKUMDL-WQ-2201 also suppressed tumor growth in mice. We started from purposely designing allosteric inhibitors for the predicted allosteric sites, while CBR-5884 and NCT-503 were found from high-throughput screening. Nevertheless, all the compounds inhibit PHGDH by an allosteric effect, demonstrating that allosteric inhibition is a promising strategy to suppress its activity. More allosteric inhibitors for PHGDH can be expected in the future.</p><p>In the past decade, considerable efforts have been devoted to identify agents to suppress oncogenesis and tumor progression (Hanahan and Weinberg, 2011), and then develop drugs to selectively kill cancer cells based on their metabolic alterations. Several drug candidates were successfully discovered and entered into clinic trials, such as AZD3965 (Birsoy et al., 2013; Sonveaux et al., 2008) and TCD-717 (Clem et al., 2011). Some anti-metabolite agents even have been used in clinic for a long time, such as 5-fluorouracil, methotrexate, and gemcitabine (Galluzzi et al., 2013). We are hopeful that PKUMDL-WQ-2101 and PKUMDL-WQ-2201 may be an additional starting point for further targeting cancer metabolism. In conclusion, we have successfully discovered PHGDH allosteric inhibitors targeting the predicted allosteric sites by using virtual screening and experimental validation. The compounds reported can be further optimized and developed for next-generation anti-cancer therapies.</p><!><p>Cancer cells reprogram metabolism to support their growth and proliferation. During the past decades, targeting cancer metabolism has emerged as a promising strategy for the development of selective anti-cancer agents. The gene encoding phosphoglycerate dehydrogenase (PHGDH), an enzyme that catalyzes the first critical step of serine biosynthesis is involved in metabolic reprogramming in cancer. The PHGDH gene that is located at chromosome 1p12 showed copy number gain in 16% of all cancers including 40% of melanoma and some triple negative breast cancers. Cancer cells with PHGDH amplifications are sensitive to PHGDH depletion, which indicates that the enzyme is required for the growth of certain tumor cells. Although the importance of PHGDH as a cancer target has been proposed, the lack of small molecules inhibitors hinders further exploration. The high cellular concentration and widely used of its cofactor (NAD+) and small size of the active site make inhibitor discovery targeting the active site difficult. We used a computational approach to scan for possible allosteric sites and used them to virtually screen for allosteric inhibitors. Two novel allosteric sites on PHGDH were identified. Compounds that directly bind to these sites, inhibit PHGDH enzyme activity, suppress cancer cell proliferation and in vivo tumor growth were found. The best compound binds to PHGDH with a dissociation constant of 0.56 μM, which selectively inhibits PHGDH amplified breast cancer cell line with EC50s less than 7.7 μM. The inhibitors were also characterized using metabolomics on PHGDH-amplified, CRISPR-Cas9-generated PHGDH knockout cell lines, and mice to demonstrate the specificity and activity in vivo. Our study not only identifies novel allosteric inhibitors for PHGDH with in vivo activity to probe its function and potential as a therapeutic target, but also provides a general strategy for the rational design of small molecule modulators of metabolic enzyme function.</p><!><p>Potential allosteric sites in PHGDH N-terminal fragment structure containing the substrate binding and the nucleotide binding domains (PDB code: 2G76) were identified using the CAVITY program (Yuan et al., 2013; Yuan et al., 2011) and then applied to screen for potential allosteric inhibitors. The program Glide Standard Precise (SP) mode and Extra Precise (XP) mode were used to do the molecular docking studies and screen the SPECS library (Friesner et al., 2004; Halgren et al., 2004). The top 5% compounds from the XP mode were chosen for manual selection and purchased from SPECS for experimental testing.</p><!><p>The full-length PHGDH or PSAT1 open reading frame (Seajet Scientific, Beijing, China) was amplified by polymerase chain reaction (PCR), ligated into the pET21a(+) vector, transformed to the BL21 (DE3) strain of Escherichia coli (E. coli), and purified using a nickel-nitrilotriacetic column (HisTrap HP; GE Healthcare) and then a gel-filtration column (Sephacryl S-200 HR, GE Healthcare). For details, see the Supplemental Experimental Procedures.</p><!><p>Due to the unavailability of PHGDH direct-substrate phosphohydroxypyruvate (PHP), the enzyme activity of PHGDH was measured accompanied with the upstream of PSAT1 catalytic reaction (Hart et al., 2007). For details, see the Supplemental Experimental Procedures.</p><!><p>The binding affinities of compounds towards PHGDH were assayed using the SPR-based Biacore T200 instrument (GE Healthcare). PHGDH was immobilized on a CM5 sensor chip by using standard amine-coupling at 25°C with 1× running buffer PBS-P (GE Healthcare), as described previously (Wang et al., 2014). For details, see the Supplemental Experimental Procedures.</p><!><p>To investigate competition effects between the compounds and the cofactor NADH, we performed compound-cofactor competition experiments as follows:</p><p>Before Pser was added to start the reaction, the enzyme sample was pre-incubated with cofactor and the compound for 10 min at 25°C. The compound was kept at a constant inhibitory concentration (50 μM), while NADH concentration was gradually increased from 5 to 40 μM. At these concentrations, the compounds inhibited PHGDH activity by ∼50% when the NADH concentration was 150 μM.</p><!><p>All mutagenesis experiments were carried out according to the instructions of the QuikChangeSite-Directed Mutagenesis (SBS Genetech Co., Beijing, China). The plasmid pET-21a(+)-containing wild-type (WT) PHGDH was mutated to obtain the mutants. The DNA sequences of all mutants were verified by DNA sequencing. The protein expression and activity assays of the mutants were performed as described for the WT.</p><!><p>MDA-MB-468, MDA-MB-231, and ZR-75-1 from China Infrastructure Cell Line Resources, and SKOV3 and HCC70 from ATCC were maintained in RPML-1640 culture medium (Gibco) supplemented with 10% fetal bovine serum and 1% penicillin/ml/streptomycin. MCF-7 from China Infrastructure Cell Line Resources and HEK293T from ATCC were maintained in Dulbecco's modified Eagle's medium (DMEM, Gibco) supplemented with 10% fetal bovine serum and 1% penicillin/ml/streptomycin. MCF-10A from China Infrastructure Cell Line Resources was maintained in DMEM/F12 (1:1) medium (Gibco) and supplemented with 5% horse serum, 10 μg/ml insulin, 0.1 μg/ml cholera toxin, 0.5 μg/ml hydrocortisone, and 0.02 μg/ml epidermal growth factor (EGF).</p><!><p>SKOV3 GFP KO and PHGDH KO cells (10,000 cells/well) were plated into 24-well culture plates in triplicate. After 24 hours, cells were treated with DMSO or compound. Each day, cells were counted by trypan-blue exclusion test for cell viability at a 1:1 ratio using a hemocytometer.</p><!><p>MDA-MB-468 (5000 cells/well), HCC70 (5000 cells/well), MCF-7 (3000 cells/well), MDA-MB-231 (2000 cells/well), ZR-75-1 (4000 cells/well), and MCF-10A (3000 cells/well) in exponential growth were plated into 96-well culture plates and allowed to adhere overnight. The number of viable cells was assessed by spectrophotometry at 490 nm using a BioTek Synergy4 microplate reader after 3-days treatment, and calculated as the percentage of absorbance of treated cells relative to that of solvent controls.</p><p>For SKOV3 WT/KO cells (30,000 cells/well) were plated in a 96-well plate. The following day, media was aspirated and replaced with 100 μl phenol-red free RPMI-1640 (Gibco) and 12mM Methyl thiazolyldiphenyl-tetrazoliumbromide (MTT, Thermo Fisher Scientific) was added to the cells. After 4 hours, the media containing MTT was aspirated and 50 μl DMSO was added to dissolve the formazan and read at 540nm.</p><!><p>For enzymatic assay, one concentration among 0, 1, 5, 12.5, 25, 50, 100, 200 μM of PKUMDL-WQ-2101 was successively mixed with different concentrations of PKUMDL-WQ-2201 (0, 1, 5, 12.5, 25, 50, 100, 200 μM), and the mixture was then pre-incubated with enzyme samples to test their effects on PHGDH activity.</p><p>For cell based assay, MDA-MB-468 cells (5000 cells/well) were plated in 96-well plates, allowed to adhere overnight and incubated with the different combinations of PKUMDL-WQ-2101 (0, 0.1, 0.5, 1, 2.5, 5, 7.5, 10 μM) and PKUMDL-WQ-2201 (0, 0.1, 0.5, 1, 2.5, 5, 7.5, 10 μM) for three days. The EC50 values of the combinations were measured by MTT methods.</p><!><p>MDA-MB-468 Cells (300, 000 cells/well) in exponential growth were plated into 6-well culture plates and then treated in triplicate with or without various concentrations of PKUMDL-WQ-2101 and PKUMDL-WQ-2201. After 24 h, cells were harvested by trypsinization and centrifugation, and then washed twice with 1× PBS, fixed in 70% ice-cold ethanol, and kept at 4°C overnight. The fixed cells were afterwards washed in 1× PBS and resuspended in 1× PBS containing 0.5% triton-x-100, 50 μg/ml Prodiumiodide (PI) and 50 μg/ml DNase-free RNase A. The cell suspension was incubated in the dark for 30 min at 37°C and analyzed using a BD FACSCanto™ cytometer.</p><!><p>LentiCRISPR transfer plasmid (Addgene Plasmid 49535), LentiCRISPR- EGFP sgRNA 1 (Addgene Plasmid 51760), PMD2.G VSV-G envelope expressing plasmid (Addgene Plasmid 12259), and PsPAX.2 lentiviral packaging plasmid (Addgene Plasmid 12260) were purchased. The target sequence of the sgRNA is GCTCTGAGCCTCCTTGGTGC (exon 8 of PHGDH). The plasmids were virally transfected into HEK293T cells using polyethylemine (PEI) (Polysciences, Inc) and transduced into SKOV3 cells as previously described (Shalem et al. 2014). Single-cell colonies of puromycin-resistant cells were selected and validated by western blotting.</p><!><p>Protein was extracted from cells using 1X RIPA buffer (Rockland Immunochemicals, Inc.) and centrifuged at 2000 rpm for 30 minutes at 4°C. Protein concentrations were measured using Bradford Protein Assay (Bio-Rad) and loaded onto 7.5% SDS-PAGE gels transferred to PVDF membranes. Membranes were blocked in 5% dry milk in TBST and incubated with anti-β-actin (Cell Signaling 8H10D10) 1:2000 or anti-PHGDH (Sigma-Aldrich WH0026227M1) 1:1000. Horseradish peroxidase conjugated anti-mouse (Rockland 611G4302), 1:2000 was used as secondary antibody. Chemiluminescent signals were detected with Clarity Western ECL Detection Kit (Bio-Rad) and imaged using a ChemiDoc MP System (Bio-Rad).</p><!><p>SKOV3 cells (300,000 cells/well) were plated in a 6-well plate and allowed to adhere to the plate. Cells were then replaced with RPMI-1640 media containing 11mM U13C-glucose (Cambridge Isotope Laboratories, Inc.) and incubated for 24 hours. For U13C-glucose tracing with drug treatments, cells were first treated with their corresponding compounds for 24 hours, followed by media replacement with 11mM U 13C-glucose and corresponding drug treatment. Metabolites were then extracted.</p><!><p>Metabolite extraction and subsequent Liquid-Chromatography coupled to High-Resolution Mass Spectrometry (LC-HRMS) for polar metabolites of HCT116 cells were carried out using a Q-ExactiveOrbitrap Plus as previously described(Liu et al., 2014). For details, see the Supplemental Experimental Procedures.</p><!><p>Raw data collected from LC-Q Exactive Plus MS is processed on Sieve 2.0 (Thermo Scientific). Peak alignment and detection are performed according to the protocol described by Thermo Scientific. For a targeted metabolite analysis, the method "peak alignment and frame extraction" is applied. An input file of theoretical m/z and detected retention time of 197 known metabolites is used for targeted metabolite analysis with data collected in positive mode, while a separate input file of 262 metabolites is used for negative mode. m/z width is set to 10 ppm. The output file including detected m/z and relative intensity in different samples is obtained after data processing. If the lowest integrated mass spectrometer signal (MS intensity) is less than 1000 and the highest signal is less than 10,000, then this metabolite is considered below the detection limit and excluded for further data analysis. If the lowest signal is less than 1000, but the highest signal is more than 10,000, then a value of 1000 is imputed for the lowest signals. Serine and glycine samples were normalized by comparing relative labeling of glucose-derived labeled metabolites from treated with vehicle samples. For all other samples, mass isotopomer distributions (MID) were calculated and samples were normalized by comparing the ratio of glucose-derived labeled metabolites to unlabeled metabolites within each sample. Quantitation and statistics were calculated using Microsoft Excel and GraphPad Prism 6.</p><!><p>PKUMDL-WQ-2101 and PKUMDL-WQ-2201 bioactivity assay in vivo - All animal experiments were performed in compliance with guidelines of the Animal Welfare Act and the guide for the care and use of laboratory animals following protocols approved by the Institutional Animals Care and Use Committee (IACUC). MDA-MB-468 or MDA-MB-231 cells were injected into the fourth mammary fat pad of NOD.CB17 Scid/J mice at 2×105 or 5×105 cells per injection site, respectively (Vital River Laboratory Animal Technology Co., Ltd., Beijing, China). For MDA-MB-468, when the average tumor volume reached 30 mm3, the mice were randomized into 7 groups (n=5): vehicle control (10%DMSO, 20% EL and 70% PBS, IP); 20, 10, and 5 mg/kg/day PKUMDL-WQ-2101 or PKUMDL-WQ-2201 (IP), respectively. For MDA-MB-231, after the tumor was palpable, the mice were randomized into 3 groups (n=5): vehicle control (10%DMSO, 20% EL and 70% PBS, IP); 20 mg/kg/day PKUMDL-WQ-2101 (IP); 20 mg/kg/day PKUMDL-WQ-2201 (IP). The tumor volume was calculated using the formula width (mm)2× length (mm) × 0.5.</p>

PubMed Author Manuscript

Relationship between the Molecular Coil Dimension and the Energy Storage Modulus of Polymer Solution Configured with Oilfield-Produced Sewage

Polymer viscoelastic solution is the non-Newtonian fluid and widely used in oil production. In the process of seepage, the mechanism of the polymer solution with different molecular coil dimensions (Dh) flooding on remaining oil is unknown. By using the dynamic light scattering instrument, the molecular coil dimension of the polymer solution is tested. By using the HAAKE rheometer, the creep recovery test data of the polymer solution under the same creep time condition are obtained. The effects of polymer solutions with different Dh on residual oil are observed, by using the visible pore model. The results show that the higher the molecular weight (Mw) of the polymer, the larger the size of the molecular coil dimension. The elasticity characteristics of the polymer solution are sensitive to the molecular coil dimension. As Dh of polymer molecules becomes larger, the contribution of the elastic portion to the viscosity of the polymer solution increases. The higher the Mw of polymer is, the longer the molecular chain is and the size of Dh is larger. On the condition of the polymer solution with different Mw with 2.5 g/L, when Dh is between 320.0 nm and 327.8 nm, the ratio of the elastic part in the apparent viscosity exceeds the proportion of the viscous part, and the polymer solution composition after these data can be used as a comparative study of elasticity for residual oil use. In the visible pore model, the pore-throat ratio is 3.5, the ER of water flooding is 54.26%, the ER of the polymer solution with Dh = 159.7 nm is 75.28%, and the increase of ER is 21.02% than that of water flooding. With the increase of Dh to 327.8 nm, the final ER of the experimental polymer solution is 97.82%, and the increase of ER of the polymer solution than that of water flooding is 43.56%. However, in the model with a pore-throat ratio of 7.0 and the same polymer solution with Dh = 327.8 nm, the increase of ER of the polymer solution is only 10.44% higher than that of water flooding. The effect of the polymer solution with the same Dh is deteriorated with the increase of the pore-throat ratio.

relationship_between_the_molecular_coil_dimension_and_the_energy_storage_modulus_of_polymer_solution

1. Introduction<!>2.1. Test Instruments<!>2.2. Creep Recovery Test Principle<!>2.3. Test Results and Analysis<!>3. Oil Displacement Experiments and Analysis<!>3.1. Experimental Materials and Experimental Conditions<!><!>3.3.1. Rheological Parameters of the Polymer Solution<!>3.3.2. Analysis of Oil Displacement Experiment Results<!>

<p>With the gradual expansion of the application scale of polymer flooding and composite system, the water resources used for the preparation of the polymer solution are becoming increasingly tense; at the same time, a large number of produced water bring great pressure on environmental protection [1–4]. The viscoelastic properties of the polymer solution are the key factors affecting oil recovery and the type of residual oil [5–9]; the test results of the geometrical form of the polymer molecules in the polymer system configured with the treated sewage directly affect the polymer system and the pore adaptability, thereby affecting the microscopic residual oil use effect [10–14]. In this paper, the use of oilfield-produced sewage to prepare the polymer solution has become an inevitable choice for the oilfield production. By using the dynamic light scattering instrument, the viscosity, elasticity, and the molecular coil dimension (Dh) are studied. As an effective parameter, the creep recovery test can be used to study the viscoelasticity of the polymer solution. The relationship between molecular storage modulus of the polymers (prepared by the oilfield-produced water) is analyzed, and both the Mw and the concentration of polymer (Cp), combined with the core model designed by the pore structure of the actual reservoir, guide the hydrolysis of the indoor oil flooding experiment, and the changes of micro remaining oil in sandstone of pore size level is studied, and the best polymer system for oil displacement suitable for experimental pore construction parameter data is given.</p><!><p>The instrument used is a wide angle dynamic/static light scattering system type BI-200SM (Brookhaven Instruments Corp, USA),as shown in Figure 1; the main components of the system include a laser correlator (Type BI-9000AT), signal processing apparatus, and argon ion laser (power: 200 mW and wave length: 532.0 nm). The dynamic light scattering instrument used in this research can directly test Dh. Dh can be used to characterize the crimping degree of polymer chains and molecular coil dimension.</p><p>The instrument HAAKE RS150 rheometer (Germany) used in this test for creep recovery is shown in Figure 2. The constant temperature is the water temperature s at 45°C in this test, and data are automatically controlled by a computer. The C60/1Ti cone plate is adopted in the test (Taper: 1°), the shear rate itables 0.1∼1000 (1/s), and the cone gap of the test system is 0.052 mm.</p><!><p>In the creep recovery experiment, as shown in Figure 3, during the creep period (time from 0 s to 60 s), with a constant stress (stress = 0.005 Pa) applied, the straining increases with time. The stress is removed after 60 s; until 360 s is the recovery period, the straining decreases sharply first and then reach a stable value. The stable straining (γss) is the contribution of the viscosity parts of the polymer solution, and the recoverable straining (γmax − γss) is the contribution of the elasticity parts. The percentage of total straining (γmax) can be decomposed into viscosity parts and elasticity parts. The proportion of the straining contributed by elasticity parts (Ee) and the strain contributed by viscosity parts (Ev) to the total straining reflect the elasticity and viscosity of the viscoelastic polymer solution; the calculation formula is shown in the following formula:(1)Ev=γssγmax×100%,Ee=γmax−γss γmax×100%.</p><!><p>Figure 4 shows the Dh distribution of different Mw with a polymer concentration of 0.5, 1.0, 1.5, 2.0, and 2.5 g/L. Table 1 shows the Dh equivalent peak of different Mw with the polymer of different concentrations.</p><p>Figure 5 shows the result of the creep recovery test of the polymer solution under the same creep time condition, which includes the Mw of 950 × 104, 1200 × 104, and 2500 × 104 with the Cp from 0.5 to 2.5 g/L.</p><p>Figure 6 and Table 1 show the proportion of the viscosity and elasticity parts with different Dh, Mw, and Cp. It can be seen from the creep recovery test results that with the increase of the molecular coil dimension, deformation decreases gradually and the proportion of the elastic part of the solution increases gradually and even exceeds the viscous part. This is due to the increase of the polymer molecular coil dimension. The molecules in the system are more easy to wind together and the ability to resist the external force is strong, at the same time due to the increase of electrostatic repulsion within the molecular coil; so, it is not easy to be deformed.</p><p>From the Dh distribution curve of different Mw with same Cp, we can see that, the higher the Mw of polymer is, the longer the molecular chain and the more complex the conformation is, and the size of Dh is larger. On the condition of the polymer solution with different Mw with 2.5 g/L, when Dh is between 320.0 nm and 327.8 nm, the ratio of the elastic part in the apparent viscosity exceeds the proportion of the viscous part, and the polymer solution composition after these data can be used as a comparative study of elasticity for residual oil use.</p><!><p>The effects of different elastic polymer solutions with different Dh on residual oil are observed, using the visible pore model.</p><!><p>The experimental temperature is 45°C, and the experimental oil is a type of simulated oil prepared from crude oil (viscosity value is 10.0 MPa·s, 45°C).</p><p>The Mw of polyacrylamide (HPAM) used in the experiments is 2500 × 104, and the concentration of the polymer solution is spate 0.5 g/L, 1.5 g/L, and 2.5 g/L.</p><p>Salinity mineralization of water used in experiments is 6.778 g/L.</p><p>The model used in the visual experiment is a transparent glass homogeneous core etched according to the actual core pore structure. The model have two kinds: model 1 has a pore size of 105.0 microns and a throat size of 30.0 microns; the pore throat ratio of model 1 is 3.5. Model 2 has a pore size of 210.0 microns and a throat size of 30.0 microns; the pore throat ratio of model 2 is 7.0. The injection rate is 0.02 ml/hr.</p><!><p>The microscopic model injects oil</p><p>The displacement speed of the simulated oil layer is constant speed water flooding, and displace the water until the oil is not seen at the exit</p><p>Inject the viscoelastic polymer solution at a constant speed to drive the oil, record the dynamic image during the displacement process, and record the images before and after the flooding</p><p>Calculate the oil displacement efficiency (ER) under each displacement condition</p><p>Clean the core and finish the experimental equipment</p><p>Continue the experiment changing the concentration of the polymer solution and repeat the first five steps</p><!><p>The rheological curves of the polymer solution system are shown in Figure 7, the determined Mw of the polyacrylamide (HPAM) used in the experiments is 2500 × 104, and the concentrations of polymer solution (Cp), respectively, are 0.5 g/L, 1.5 g/L, and 2.5 g/L. The storage modulus (G′) and dissipation modulus (G″) of polymer solutions are tested. The test results are shown in Figures 8 and 9. It can be ground from the viscous relationship curve, and the viscosity gradually increases by increasing the concentration of the polymer.</p><!><p>The ER results of oil displacement experiments for the polymer solution with different Dh under different pore-throat ratio visual models are shown in Table 2. The results of residual oil in the visual pore model are shown in Figures 10 and 11.</p><p>In the visible pore model, the pore-throat ratio is 3.5, the ER of water flooding is 54.26%, the ER of the polymer solution with Dh = 159.7 nm is 75.28%, and the increase of ER of the polymer solution is 21.02% than that of water flooding. With the increase of Dh of 327.8 nm, the final ER of the experimental polymer solution is 97.82%, and the increase of ER of the polymer solution than that of water flooding is 43.56%. However, in the model with a pore-throat ratio of 7.0, the same polymer solution with the Dh = 327.8 nm, the increase of ER of the polymer solution is only 10.44% higher than that of water flooding. The effect of the polymer solution with the same Dh is deteriorated, as the pore-throat ratio increases.</p><p>The experimental results show that, in the same pore-throat ratio model, as the Dh of polymer solution increases, the residual oil in the visual pore model gradually decreases. As the pore-throat ratio increases, the ER of polymer solution with the same Dh gradually deteriorates, and a polymer solution with a larger Dh is needed. The residual oil displacement pictures show that under the same flow velocity conditions and the same pore throat conditions, the creep recovery conditions after the polymer macromolecules flow out of the throat are similar. The deformation of the modulus part restores the force acting on the external stress feedback, and the shear force on the Newtonian fluid in the pores gradually increases.</p><!><p>The higher the Mw of the polymer, the larger the size of the molecular coil dimension.</p><p>The elasticity characteristics of polymer solution are sensitive to the molecular coil dimension. As Dh of the polymer molecules becomes larger, the contribution of the elastic portion to the viscosity of the polymer solution increases.</p><p>In the same pore-throat ratio model, as the Dh of polymer solution increases, the residual oil in the visual pore model gradually decreases. As the pore-throat ratio increases, the ER of polymer solution with the same Dh gradually deteriorates, and a polymer solution with a larger Dh is needed.</p><p>Under the same flow velocity conditions and the same pore throat conditions, the deformation of the modulus part restores the force acting on the external stress feedback, and the shear force on the Newtonian fluid in the pores gradually increases.</p>

PubMed Open Access

P90 RIBOSOMAL S6 KINASE 2, A NOVEL GPCR KINASE, IS REQUIRED FOR GROWTH FACTOR-MEDIATED ATTENUATION OF GPCR SIGNALING\xe2\x80\xa0

The 5-hydroxytryptamine 2A (5-HT2A) receptor is a member of the G protein-coupled receptor superfamily (GPCR) and plays a key role in transducing a variety of cellular signals elicited by serotonin (5-HT; 5-hydroxytryptamine) in both peripheral and central tissues. Recently, we discovered that the ERK/MAPK effector p90 ribosomal S6 kinase 2 (RSK2) phosphorylates the 5-HT2A receptor and attenuates 5-HT2A receptor signaling. This raised the intriguing possibility of a regulatory paradigm whereby receptor tyrosine kinases (RTKs) attenuate GPCR signaling (i.e., \xe2\x80\x98inhibitory cross-talk\xe2\x80\x99) by activating RSK2 [Strachan et al. (2009) J. Biol. Chem. 284, 5557-5573]. We report here that activation of multiple endogenous RTKs such as the epidermal growth factor receptor (EGFR), the platelet-derived growth factor receptor (PDGFR), and ErbB4 significantly attenuates 5-HT2A receptor signaling in a variety of cell types including mouse embryonic fibroblasts (MEFs), mouse vascular smooth muscle cells (mVSMCs), and primary cortical neurons. Importantly, genetic deletion of RSK2 completely prevented signal attenuation, thereby suggesting that RSK2 is a critical mediator of inhibitory cross-talk between RTKs and 5-HT2A receptors. We also discovered that P2Y purinergic receptor signaling was similarly attenuated following EGFR activation. By directly testing multiple endogenous growth factors/RTK pathways and multiple Gq-coupled GPCRs, we have now established a cellular mechanism whereby RTK signaling cascades act via RSK2 to attenuate GPCR signaling. Given the pervasiveness of growth factor signaling, this novel regulatory mechanism has the potential to explain how 5-HT2A receptors are regulated in vivo, with potential implications for human diseases in which 5-HT2A or RTK activity is altered (e.g. neuropsychiatric and neurodevelopmental disorders).

p90_ribosomal_s6_kinase_2,_a_novel_gpcr_kinase,_is_required_for_growth_factor-mediated_attenuation_o

<!>Materials<!>Serum Dialysis<!>Cell Culture and Transfection<!>cDNA Constructs<!>Microarray Analysis and Pathway Generation<!>Isolation of Mouse Aortic Vascular Smooth Muscle Cells<!>Isolation of Primary Cortical Neurons<!>Lentivirus Production and Infection<!>Immunoprecipitation and Western Blotting<!>Fluorometric Imaging Plate Reader (FLIPRTetra) Analysis of Intracellular Ca2+ Release<!>Analysis of Intracellular Ca2+ Release in Primary Cortical Neurons<!>RSK2 is required for EGF-induced attenuation of 5-HT2A receptor signaling<!>RSK2 is required for PDGFR-mediated attenuation of endogenous 5-HT2A receptor signaling in primary mVSMCs<!>IGF-1 weakly activates RSK2 in MEFs and does not attenuate 5-HT2A receptor signaling<!>RSK2 is required for EGF-mediated attenuation of endogenous P2Y purinergic receptor signaling<!>Growth factors essential for normal brain function attenuate 5-HT2A receptor signaling in cortical neurons<!>Discussion<!>RSK2 is required for inhibitory cross-talk between RTKs and the 5-HT2A receptor in a variety of cell types<!>IGF-1 fails to robustly activate RSK2 and does not attenuate 5-HT2A receptor signaling<!>RSK2 is required for growth factor-mediated regulation of multiple GPCRs-evidence from P2Y purinergic receptors<!>

<p>The GPCR superfamily mediates essential functions in organisms as diverse as unicellular choanoflagellates and humans (1, 2). In humans, GPCRs comprise approximately 2% of the genome to transduce signals elicited by both endogenous and exogenous ligands (3-5). Not surprisingly, GPCR dysregulation is associated with many human diseases (6), thus explaining why GPCRs are successful therapeutic targets and remain the focus of intense drug discovery efforts (7).</p><p>The Gq-coupled 5-HT2A receptor, in particular, is one of 14 GPCRs that mediates the pleiotropic actions of 5-HT in both peripheral and central tissues (8, 9). The 5-HT2A receptor is an important therapeutic target for a large number of psychiatric and medical diseases (9), and is also the site of action of most, but not all hallucinogens which function as 5-HT2A receptor agonists (10, 11)(Keiser et al., 2009, in press). Additionally, atypical antipsychotics (e.g., clozapine) are thought to mediate their therapeutic actions, at least in part, by antagonizing 5-HT2A receptors (12).</p><p>We recently discovered a novel regulatory mechanism whereby RSK2 interacts with 5-HT2A serotonin receptors and attenuates receptor signaling via direct receptor phosphorylation (13, 14). RSK2 is a multifunctional ERK/MAPK effector activated downstream of growth factor signal cascades involving RTKs (15). This raised the intriguing possibility of a new regulatory mechanism whereby RTKs attenuate GPCR signaling (referred to here as 'inhibitory cross-talk') by activating RSK2. These studies led to the initial discovery that activation of the EGFR attenuates 5-HT2A receptor signaling, presumably via RSK2 activation (14). These preliminary data were intriguing for several reasons including: (1) they suggested for the first time that the 5-HT2A receptor is part of an emerging regulatory paradigm whereby activated RTKs attenuate GPCR signaling (16-22), and (2) they were the first to identify RSK2 as a novel mediator of inhibitory cross-talk between growth factor-activated RTKs and a GPCR.</p><p>In this paper, we show that activation of various endogenous RTKs (i.e. EGFR, PDGFR, and ErbB4) significantly attenuates 5-HT2A receptor signaling in multiple cell types (i.e., in MEFs, mVSMCs, and primary cortical neurons). In contrast, insulin-like growth factor 1 (IGF-1), which only weakly activates RSK2, fails to attenuate 5-HT2A receptor signaling. Together with evidence that genetic deletion of RSK2 is sufficient to prevent RTK-mediated signal attenuation in all tested cellular backgrounds, these findings support a novel role for RSK2 in inhibitory cross-talk between RTKs and the 5-HT2A receptor. Significantly, we also discovered that P2Y purinergic receptor signaling, which is regulated by RSK2, was similarly attenuated following EGF receptor activation in wild-type (RSK2+/+) MEFs. By testing several endogenous growth factors/RTK pathways and multiple Gq-coupled GPCRs, we have now established a cellular mechanism whereby RTK signaling cascades attenuate GPCR signaling through RSK2. These findings provide an initial framework for a conserved regulatory mechanism whereby RTKs act via RSK2 to attenuate GPCR signaling, and given the complexity of cellular signaling, have the potential to explain how these receptors are regulated in vivo.</p><p>Moreover, because null mutations of RSK2 lead to Coffin-Lowry Syndrome which exhibits behaviors characteristic of 5-HT2A dysregulation including a schizophrenia-like psychosis and cognitive impairment (23), these findings may explain, in part, some of the clinical manifestations of this neurodevelopmental disease..</p><!><p>Cell culture reagents including fetal bovine serum (FBS), Dulbecco's modified essential medium (DMEM), Trypsin-EDTA, F-12 nutrient mixture, OptiMEM, neurobasal medium, B27 supplement, Hank's balanced salt solution (HBSS), sodium pyruvate, penicillin, and streptomycin were supplied by Gibco (Invitrogen, Carlsbad, CA). Serotonin, 5-methoxy-N,N-dimethyltryptamine (5-methoxyDMT), human EGF, human PDGF-AB and PDGF-BB, human TGF-α, IGF-1, papain, probenecid, bovine serum albumin (BSA), low molecular weight poly-L-lysine, sodium tetraborate, L-glutamine, and all other standard reagents were supplied by Sigma-Aldrich Corp. (St. Louis, MO). Boric acid was supplied by EMD Chemicals (Gibbstown, NJ). MDL100907 and lisuride were acquired as previously detailed (12). Collagenase II was obtained from Worthington Biochemical Corp. (Lakewood, NJ) and elastase (grade II) was supplied by Roche Applied Science (Indianapolis, IN). Restriction endonucleases were supplied by New England Biolabs (Ipswich, MA). [3H]-Ketanserin was obtained from PerkinElmer Life and Analytical Sciences (Waltham, MA). Protein A/G agarose was supplied by Santa Cruz Biotechnology, Inc. (Santa Cruz, CA).</p><!><p>We extensively dialyzed FBS to remove the 5-HT present in serum. Briefly, 500 mL FBS was placed into dialysis tubing (Spectra/Por 3500 MWCO, Spectrum Laboratories, Rancho Dominguez, CA) and equilibrated with 4 L of cold dialysis buffer (120 mM NaCl, 10 mM Tris-HCl, pH 7.5 at RT) for 24 hr at 4°C with stirring. The buffer was changed five times totaling 120 hr of dialysis. The dialyzed FBS was then sterile-filtered (0.22 μm, Millipore) and aliquots were stored at −20°C until further use. HPLC-electrochemical detection analysis of the dialyzed serum determined that, when used at a concentration of 5%, our dialyzed culture medium contained 33 times less 5-HT than commercially dialyzed FBS (i.e., 0.039 nM vs. 1.3nM 5-HT).</p><!><p>The RSK2+/+ and RSK2 knockout (RSK2−/−) MEFs stably expressing similar levels of 5-HT2A receptors were generated previously by Sheffler et al. (13) using MEFs originally isolated from RSK2+/+ and RSK2−/− mice (24). Mouse VSMCs and cortical neurons were isolated as detailed below. HEK293T cells were obtained from the American Type Culture Collection (Manassas, VA). All cell lines were cultured at 37°C in a humidified environment in the presence of 5% CO2. Specifically, HEK293T and mVSMC cell lines were maintained in standard medium (DMEM supplemented with 10% FBS, 1 mM sodium pyruvate, 100 units/mL penicillin, and 100 μg/mL streptomycin). Polyclonal populations of MEFs stably expressing FLAG-tagged rat 5-HT2A receptors were cultured in standard medium supplemented with 4 μg/mL puromycin to maintain selection pressure. Primary cortical neurons were maintained in complete neurobasal medium (neurobasal medium, 1× B27 supplement, 0.5 mM L-glutamine, 25 μM glutamate, 100 units/mL penicillin, and 100 μg/mL streptomycin). Fugene6 (Roche) was used exactly as described by the manufacturer to transiently transfect sub-confluent HEK 293T cells.</p><!><p>For generation of stable cell lines, the rat 5-HT2A receptor containing a cleavable N-terminal H. influenza hemagglutinin membrane insertion signal sequence (25) and N-terminal FLAG (DYKDDDDK) affinity tag (FLAG-5-HT2A) (26) was subcloned into the pBABE retroviral vector containing a puromycin resistance gene (FLAG-5-HT2A-pBABEpuro) (27). Briefly, 5′ EcoRI and 3′ SalI restriction sites were introduced into the FLAG-5-HT2A sequence via the following PCR primers: 5′AAAGAATTCGCCACCATGAAGACGATCAT3′ (EcoRI highlighted) and 5′AAAGTCGACTCACACACAGCTAACCTTTTC3′ (SalI highlighted). The FLAG-5-HT2A-pBABEpuro construct was sequence-verified (Case Western Reserve University Genomics Core Facility, Cleveland, OH) and determined via competition radioligand binding assays to bind 5-HT with characteristic affinity (http://pdsp.med.unc.edu/pdsp.php)(28).</p><p>For infection of primary cortical neurons, the rat 5-HT2A receptor containing the green fluorescent protein (GFP) inserted between amino acids 452 and 453 within the C-terminus (5-HT2A-GFP-CT) (29) was subcloned into the FUGW lentiviral construct (5-HT2A-GFP-CT-FUGW) (30). Briefly, 5′ XbaI and 3′ EcoRI restriction sites were introduced into the 5-HT2A-GFP-CT sequence via the following primers: 5′AAAATCTAGAGCCACCATGGAAATTCTTTGTGAAG3′ (XbaI highlighted) and 5′TTTTGAATTCTCACACACAGCTAACCTTTTCATTC3′ (EcoRI highlighted). The resulting 5-HT2A-GFP-CT-FUGW construct was sequence-verified by automated sequencing (UNC-Chapel Hill DNA sequencing facility, Chapel Hill, NC).</p><!><p>Microarray studies were performed previously by Sheffler et al. (13) to compare gene expression profiles in RSK2+/+ and RSK2−/− MEFs. For pathway analysis of RSK2−/− and RSK2+/+ fibroblast gene expression patterns, GenMAPP and MAPPFinder software packages were used as previously detailed (13, 31, 32).</p><!><p>Mouse aortic VSMCs were isolated from 12-week old mice (three mice per genotype), as previously detailed (33). Briefly, mice were sacrificed by cervical dislocation and immediately perfused with 25 mL of 1× HBSS (without Ca2+ and Mg2+). Under sterile conditions, the abdominal/thoracic aorta extending from the ilial bifurcation to aortic arch was carefully microdissected and rinsed with HBSS. The pooled aorta were then incubated with collagenase buffer (175 U/mL in HBSS, filtered through 0.2 μm polyethersulfone membrane) for 15 min at 37°C in the presence of 5% CO2. After the adventitial layer was removed, the aorta were incubated with DMEM supplemented with 10% FBS overnight at 37°C in the presence of 5% CO2. The next day the aorta were cut into 2 mm segments and incubated with digestion buffer (175 U/mL of collagenase and 0.125 mg/mL of elastase in HBSS, filtered through 0.2 μm polyethersulfone membrane) for 1 hr at 37°C in the presence of 5% CO2. Following digestion, the tissue was dissociated with a glass Pasteur pipette, DMEM supplemented with 10% FBS was added, and the cells were collected via centrifugation (200 × g for 8 min). The cells were re-suspended in DMEM supplemented with 20% FBS, transferred to a T-25 cm flask, and the cells were incubated overnight at 37°C in the presence of 5% CO2. The next day the cells were carefully washed with DMEM supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin and cultured normally with medium changes every three days. Cells were determined to be SMCs via immunofluorescence assays using the rabbit polyclonal smooth muscle α-actin antibody exactly as described by the manufacturer (1:1000, Abcam Inc., Cambridge, MA). For all experiments, mVSMCs were used between passages three and seven.</p><!><p>Cortical neurons were prepared as previously detailed (34-36). Briefly, pups (postnatal day<2) were genotyped and euthanized by decapitation. The entire frontal cortex of RSK2+/+ and RSK2−/− animals was microdissected from whole brain, followed by digestion in neurobasal medium containing 0.1% papain and 0.2% BSA at 37°C for 20 min. The medium was then replaced with complete neurobasal medium and the digested tissue was mechanically dissociated via trituration with a glass Pasteur pipette. The supernatant was transferred to a new sterile 1.5 mL tube and cells were collected via centrifugation (200 × g for 5 min). The cell pellet was then resuspended in conditioned complete neurobasal medium. Cells were counted and seeded at a density of 50,000 cells/well onto poly-L-lysine-coated 96 well plates (0.1 mg/mL low molecular mass poly-L-lysine, 0.625% boric acid, 0.955% sodium tetraborate) and cultured normally.</p><!><p>Lentiviral infection of primary cortical neurons was performed essentially as previously described (35). In brief, a pre-determined mixture of 5-HT2A-GFP-CT-FUGW and the viral packaging constructs VSVG and Delta 8.9 (ratio= 3.3 FUGW: 2.5 Delta 8.9 : 1 VSVG) were co-transfected into HEK293T cells using Fugene6. Forty-eight hours after transfection the medium containing virus was removed, pooled, and a virus pellet was obtained via centrifugation (26,000 × g for 5 hr). The virus pellet was re-suspended in PBS, concentrated approximately 40-fold using Amicon UltraCel 100K filters (Millipore), and then tested for infection and expression of 5-HT2A-GFP in HEK293T cells. A pre-determined amount of concentrated lentivirus was then applied to primary cortical neurons cultured for 7-10 days in vitro.</p><!><p>Immunoprecipitation of RSK2 and detection of Ser(P)-386 following growth factor treatment was performed as previously described (37). Briefly, RSK2+/+ and RSK2−/− cells were treated with EGF (100ng/mL) or IGF-1 (10nM) for various times and solubilized with cold lysis buffer (50 mM Tris-HCl, 150mM NaCl, 1% tergitol, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, EDTA-free protease inhibitors, 50mM NaF, 50mM β-glycerolphosphate, 5mM sodium pyrophosphate, and 0.1mM sodium orthovanadate, pH 8.0) for 20 min at 4°C. Supernatants were collected via centrifugation (15,000 × g, 30 min) and equal amounts of Protein A/G-cleared lysate were incubated with mouse monoclonal anti-RSK2 (2μg) for 2 hr at 4°C, followed by incubation with Protein A/G agarose for 2 hr at 4°C. Immunopurified complexes were extensively washed with lysis buffer, eluted with 2× SDS sample buffer (125 mM Tris-HCl, 4% sodium dodecyl sulfate, 20% glycerol, 200 mM dithiothreitol, 0.2% Bromphenol Blue, pH 6.8), and stored at −80°C until further use.</p><p>Proteins were immunoblotted using standard procedures (28). Specifically, proteins were resolved on 10% SDS-PAGE gels, electroblotted onto nitrocellulose membranes (BioRad Laboratories, Hercules, CA), and blocked with standard blocking buffer (Tris-buffered saline, 0.1% Tween-20, and 5% nonfat dehydrated milk) for 1 hr at RT. Membranes were then incubated with primary antibodies diluted in standard blocking buffer or phospho-specific blocking buffer (5% BSA and TBST). Specifically, RSK2 was detected using the goat polyclonal RSK2 antibody (1:1000, Santa Cruz Biotechnology, Inc.) and the rabbit polyclonal Ser(P)-386 antibody (1:1000, Cell Signaling Technology, Inc., Danvers, MA). The EGFR was detected using the rabbit polyclonal EGFR antibody (1:500, Santa Cruz Biotechnology, Inc.) and the mouse monoclonal Tyr(P)-1068 antibody (1:500, Cell Signaling Technology, Inc.). Phosphorylated IGF-1 R was detected using the rabbit polyclonal Tyr(P)-1158/1162/1163 antibody (1:1000, Upstate Millipore).Membranes were washed extensively with Tris-buffered saline + 0.1% Tween-20 (TBST) and subsequently incubated for 1 hr at RT with secondary horseradish peroxidase-conjugated antibodies raised against mouse, rabbit and goat IgG (1:1000, Vector Laboratories, Burlingame, CA). Membranes were washed extensively and proteins were detected using SuperSignal West Pico chemiluminescent substrate (Thermo Fisher Scientific, Inc., Rockford, IL). Immunoreactive bands were imaged and quantified using Kodak Imaging software (Eastman Kodak, New Haven, CT). Sum pixel intensity values were analyzed via the one-tailed, paired t test (significance defined as p<0.05)(Graphpad Software, Inc., La Jolla, CA).</p><!><p>Intracellular Ca2+ release was measured in MEFs and mVSMCs via FLIPRTetra assays using a Ca2+ assay kit (Molecular Devices, Sunnyvale, CA) as previously detailed (13, 14). Briefly, MEFs were plated at a density of 25,000 cells/well into black-wall, clear-bottom 96-well tissue culture plates (Greiner Bio-One, Monroe, NC), whereas mVSMCs were plated at a density of 10,000 cells/well into black-wall, clear-bottom 384-well tissue culture plates (Greiner Bio-One). The cells were cultured in dialyzed medium (DMEM, 5% FBS dialyzed to <0.05 nM 5-HT, 1 mM sodium pyruvate, 100 units/mL penicillin, and 100 μg/mL streptomycin) and serum-free medium (DMEM, 0.1% BSA, 100 units/mL penicillin, and 100 μg/mL streptomycin) for 24-40 hr before the assay for normal and growth factor desensitization experiments, respectively. For both experiments, the cells were incubated with Ca2+ assay buffer (20 mM HEPES, 1× HBSS, 2.5 mM probenecid, and Ca2+ assay reagent, pH 7.4) for 60 min at 37°C prior to initiating the FLIPR program. However, for growth factor desensitization experiments the cells were incubated with growth factors diluted in Ca2+ assay buffer (for 30 min and 60 mintime points). After dye loading, the FLIPRTetra was programmed to add agonist approximately 10 seconds after establishing baseline relative fluorescence unit (RFU) values (excitation 470-495, emission 515-575 nm). RFU values were collected every second for 5 min and the average baseline values were subtracted from maximum RFU values. Values were expressed relative to the maximal untreated response in each cell line and analyzed by nonlinear regression to generate fit parameters of potency (EC50) and maximal signaling (Emax) (Graphpad software). The F test was used to determine the statistical significance (defined as p<0.05) of the fit parameters in growth factor-treated vs. untreated cells.</p><!><p>Cortical neurons were isolated, cultured and imaged as described previously (35, 36). In brief, 48 hr after lentivirus infection each well was imaged for total GFP fluorescence using the BD Pathway 855 high content imaging microscope equipped with environmental control. Ca2+ flux was then determined using the FLIPR Ca2+ assay kit (Molecular Probes) as detailed by the manufacturer. In brief, prior to live cell imaging, cells were washed 1× with phosphate buffered saline followed by 60 min incubation with Ca2+ assay buffer (20 mM HEPES, 1× HBSS, 2.5 mM probenecid, 0.57 mM ascorbic acid, and Ca2+ assay reagent, pH 7.4). Assays using growth factors were performed similarly except that growth factor was added during the dye loading step. Cells were maintained at 37°C during the entire period of observation and were imaged for 20 sec prior to drug addition to obtain baseline dye fluorescence. The liquid handling capability of the BD Pathway 855 was used to add 10× drug and then fluorescence images were obtained for 120 sec. To control for subtle differences in receptor expression, Ca2+ responses were normalized to GFP intensity/well using custom written macros for Excel (Microsoft, Redmond, WA) and Image J (U. S. National Institutes of Health, Bethesda, Maryland). Values were expressed as fold over baseline and a two-tailed, paired t test was used to determine the statistical significance (defined as p<0.05) of responses in growth factor-treated vs. untreated cells.</p><!><p>We recently determined that RSK2 interacts with the 5-HT2A serotonin receptor and attenuates signaling via direct receptor phosphorylation (13, 14). Moreover, preliminary data suggested that EGFR activation attenuates 5-HT2A receptor signaling, presumably by activating RSK2. Considering the potential for describing how 5-HT2A receptors are regulated in cells, and perhaps in vivo, we applied pharmacological and genetic approaches to determine if various RTKs, including members of the EGFR family, require RSK2 for attenuating 5-HT2A receptor signaling.</p><p>First, we activated the EGFR (also known as ErbB1) in RSK2+/+ and RSK2−/− MEFs with two canonical agonists (i.e., EGF and TGF-α) and monitored 5-HT2A receptor responsiveness using 5-HT2A agonists of varying intrinsic efficacies. Consistent with our initial EGFR findings (14), EGF significantly attenuated 5-HT2A receptor signaling in RSK2+/+ MEFs. As shown in Figure 1A, 100ng/mL EGF maximally activated the EGFR and RSK2 in these cells. When receptor signaling was assayed, we found that EGF pretreatment resulted in significant rightward shifts in 5-HT concentration-response curves (CRCs) as early as 30min, with maximal effects reached within 60 min (Figure 1B, Table 1). According to classical concepts of receptor pharmacology, these rightward shifts in 5-HT CRCs denoted decreases in agonist potency, most likely resulting from attenuation of receptor signaling given the short time scale of the experiment (38). To best illustrate this decrease in 5-HT potency, we compared the 5-HT2A-mediated Ca2+ responses elicited by a sub-maximal, EC50 concentration of 5-HT (i.e., 10nM). As shown in Figure 1C, 10nM 5-HT elicited significantly lower Ca2+ responses after treating with EGF for 30 min (peak Ca2+ release= 99.7 +/− 0.1% vs. 79.7 +/− 3.1% in untreated and EGF-treated RSK2+/+ MEFs, respectively; N=3 to 9, p<0.05) and 60 min (peak Ca2+ release= 99.7 +/− 0.08% vs. 63.7 +/− 6.0% in untreated and EGF-treated RSK2+/+ MEFs, respectively; N=7 to 9, p<0.05). Although it was clear that EGF decreased 5-HT signaling, these results could be explained by non-specific effects associated with a single, supramaximal concentration of EGF. To address this concern we determined that EGF attenuated 5-HT2A receptor signaling with an IC50 of 1.3 ng/mL (Figure 1D), a value that is within the concentration range typically observed for EGFR-mediated signaling events (39).</p><p>In contrast to these results in RSK2+/+ MEFs, EGF cannot attenuate 5-HT2A receptor signaling in RSK2−/− MEFs. Specifically, EGF did not significantly alter 5-HT potency in RSK2−/− MEFs as evidenced by superimposed 5-HT CRCs (Figure 1E and Table 1). Moreover, 5-HT2A-mediated Ca2+ responses elicited by an EC50 concentration of 5-HT were not significantly decreased after treating with EGF for 30 min (peak Ca2+ release= 99.7 +/− 0.2% vs. 93.9 +/− 4.2% in untreated and EGF-treated RSK2−/− MEFs, respectively; N=3 to 6, p>0.05) and 60 min (peak Ca2+ release= 99.7 +/− 0.2% vs. 96.8 +/− 6.3% in untreated and EGF-treated RSK2−/− MEFs, respectively; N=6, p>0.05) (Figure 1F).</p><p>It was conceivable that differences in EGFR signal transduction between RSK2+/+ and RSK2−/− MEFs could account for the differential effects of EGF. To evaluate this possibility, we compared the mRNA expression profiles of genes constituting the EGFR signal transduction pathway in RSK2+/+ and RSK2−/− MEFs. As shown in Figure 2, analysis of microarray studies revealed no substantial differences in gene expression profiles between RSK2+/+ and RSK2−/− MEFs that could account for lack of attenuation in RSK2−/− MEFs. Only two differences were apparent in RSK2−/− cells: (1) a decrease in RSK2 mRNA (as predicted in knock-out cells) and (2) an increase in Sos2 mRNA. Additionally, at the protein level we found that EGFR activation was similar between RSK2+/+ and RSK2−/− MEFs after 5 min of EGF treatment (472 +/− 131% vs. 491 +/− 96% for EGFR phosphorylation in RSK2+/+ and RSK2−/− MEFs, respectively; N=3, p>0.05) (Figure 1A). Taken together, these findings indicate (1) that RSK2 is a critical mediator of inhibitory cross-talk between EGF and 5-HT2A receptors in MEFs and (2) that the effects are not due to compensatory changes in expression of EGFR signaling partners.</p><p>We further confirmed that RSK2 was required for inhibitory cross-talk by activating the EGFR in RSK2+/+ and RSK2−/− MEFs with another selective and potent EGFR agonist, TGF-α. Similar to our results with EGF, TGF-α attenuated 5-HT2A receptor signaling in RSK2+/+ MEFs. Specifically, 1hr treatment with TGF-α decreased 5-HT potency, as illustrated by significant rightward shifts in 5-HT CRCs (Figure 3A and Table 1) and significant decreases in 5-HT2A-mediated Ca2+ release elicited by an EC50 concentration of 5-HT (peak Ca2+ release= 99.7 +/− 0.2% vs. 76.9 +/− 2.2% in untreated and TGF-α-treated RSK2+/+ MEFs, respectively; N=5, p<0.05) (Figure 3B). Moreover, TGF-α attenuated 5-HT2A receptor signaling in RSK2+/+ MEFs with an IC50 of 4.1 ng/mL (Figure 3C), again consistent with EGFR-mediated signaling events. In agreement with our previous experiments using EGF, TGF-α treatment did not attenuate 5-HT2A receptor signaling in RSK2−/− MEFs (Figure 3D and Table 1). Moreover, we did not detect large decreases in 5-HT2A-mediated Ca2+ release elicited by an EC50 concentration of 5-HT (peak Ca2+ release= 99.8 +/− 0.2% vs. 91.7 +/− 1.2% in untreated and TGF-α-treated RSK2−/− MEFs, respectively; p<0.05) (Figure 3E).</p><p>In addition to full agonists, partial agonists display characteristic and predictable signaling behaviors under conditions of decreased receptor responsiveness. Notably, in this context, full agonists commonly exhibit effects on potency but lesser effects on maximal signaling, while partial agonists commonly display decreases in maximal signaling (i.e., large downward shifts in CRCs) (38). Thus, we predicted that EGF treatment would decrease the maximal signaling of the weak partial agonist lisuride with minimal effects on potency; whereas the potency of the strong partial agonist 5-methoxyDMT would be significantly decreased. Consistent with these predictions, EGF treatment significantly decreased the maximal signaling of lisuride in RSK2+/+ MEFs (Figure 4A, Table 1). Furthermore, 5-methoxyDMT displayed behaviors intermediate between the full agonist 5-HT and the weak partial agonist lisuride, exhibiting a minor decrease in maximal signaling and a significant decrease in potency (Figure 4C, Table 1). In agreement with a requirement for RSK2, we did not observe significant shifts in the CRCs of either partial agonist in RSK2−/− MEFs (Figures 4B and 4D, Table 1). Taken together, these pharmacological and genetic approaches strongly support the hypothesis that EGFRs act via RSK2 to attenuate 5-HT2A receptor signaling in MEFs.</p><!><p>The growth factor PDGF is a potent mitogen, chemoattractant, and survival factor that activates RSKs downstream of PDGFR activation in VSMCs (40, 41). VSMCs also endogenously express 5-HT2A receptors which produce measurable Ca2+ responses in FLIPR assays (Figure 5A) (42). Therefore, VSMCs isolated from RSK2+/+ and RSK2−/− mice represented an intact model system whereby we could test: (1) whether inhibitory cross-talk occurs between additional RTKs and endogenously expressed 5-HT2A receptors, and (2) to what extent this requires RSK2.</p><p>In these studies we activated PDGFRs with PDGF-AB and PDGF-BB, the principal PDGF ligands in serum (43). As evidenced by significant downward shifts in 5-HT CRCs following 60 min treatments with PDGF-AB (Figure 5B) and PDGF-BB (Figure 5D), activation of the PDGFR resulted in attenuation of 5-HT2A receptor signaling (Table 2). To best illustrate this we showed that PDGF-BB treatment significantly decreased 5-HT2A-mediated Ca2+ responses in RSK2+/+ MEFs elicited by a saturating concentration of 5-HT (i.e., 10μM) (peak Ca2+ release= 90.2 +/− 0.7% vs. 67.0 +/− 8.5% in untreated and PDGF-BB-treated RSK2+/+ MEFs, respectively; N=4, p<0.05) (Figure 5E). As expected, PDGF treatments did not significantly reduce the maximal signaling of 5-HT in RSK2−/− mVSMCs (Figures 5C and 5F). Moreover, PDGF-BB treatment failed to significantly decrease 5-HT2A-mediated Ca2+ responses in RSK2−/− MEFs elicited by a saturating concentration of 5-HT (peak Ca2+ release= 90.3 +/− 3.8% vs. 77.9 +/− 7.6% in untreated and PDGF-BB-treated RSK2−/− MEFs, respectively; N=4, p>0.05)(Figure 5G). Together with our results using two different EGFR agonists, these results strongly suggest that RSK2 is required for inhibitory cross-talk between multiple growth factor signaling pathways and the 5-HT2A receptor.</p><!><p>We have demonstrated using various RTK agonists (i.e. EGF, TGF-α, and PDGF), cell lines (MEFs and VSMCs), and GPCR ligands (i.e. 5-HT, 5-methoxy-DMT, and lisuride) that RTKs require RSK2 to attenuate 5-HT2A receptor signaling. However, it was unknown whether insulin or IGF-1, which have been shown to attenuate the signaling of GPCRs including the closely related 5-HT2C receptor (22), also attenuate 5-HT2A receptor signaling.</p><p>In initial experiments testing insulin, we determined that insulin showed no effect on 5-HT2A receptor signaling, despite modest activation of RSK2 (data not shown). However, upon closer examination we discovered that insulin receptors are not expressed at detectable levels in RSK2+/+ and RSK2−/− MEFs (Figure S1), suggesting that RSK2 activation is mediated via the IGF-1 R which has low affinity for insulin (44). Since both RSK2+/+ and RSK2−/− MEFs express equal amounts of IGF-1 R (Figure S1), we next determined if IGF-1 could attenuate 5-HT2A receptor signaling. As shown in Figure 6A and 6B, IGF-1 treatment did not result in large shifts in 5-HT CRCs in RSK2+/+ or RSK2−/− MEFs (Table 1), identical to our results with insulin. Importantly, these results could not be explained by a general deficiency in RTK signaling since a 1hr treatment with EGF attenuated 5-HT2A signaling in parallel control experiments (peak Ca2+ release= 99.9 +/− 0.1% vs. 80.7 +/− 2.3% in untreated and EGF-treated RSK2+/+ MEFs, respectively; N=3) (Figure 6C). Additional experimentation showed that the IGF-1R is activated by IGF-1 (Figure S2). Together, these findings suggested that the mechanism(s) underlying inhibitory cross-talk between an RTK and a GPCR (e.g., RSK2 activation) engenders some level of specificity. One such possibility was that IGF-1 treatment only modestly activated RSK2 (24). Indeed, as shown in Figure 6D and quantified in Figure 6E, maximal activation of RSK2 by IGF-1 was significantly less when compared to EGF (0.546 × 106 +/− 0.107 × 106 vs. 1.25 × 106 +/− 0.25 × 106 for IGF-1 and EGF, respectively; N=3, p<0.05). Thus, robust activation of RSK2 by RTKs seems to be required for inhibitory cross-talk with the 5-HT2A receptor. Regardless of the mechanism, it is likely that some degree of specificity exists given the potential physiological importance of RTK-GPCR crosstalk.</p><!><p>RSK2 attenuates the signaling of additional GPCRs endogenously expressed in MEFs, including P2Y purinergic receptors (13). Therefore, we hypothesized that EGFR activation, in addition to regulating 5-HT2A receptors, could also attenuate P2Y receptor signaling in a RSK2-dependent manner. By testing this hypothesis we could begin to address whether this novel regulatory mechanism is conserved across multiple GPCRs that show sensitivity to RSK2 regulation.</p><p>As shown in Figure 7A, EGF treatment significantly reduced ATP signaling in RSK2+/+ MEFs (Table 1). Specifically, we observed significant decreases in ATP maximal signaling (Emax=99.7 +/− 2.0% vs. 82.5 +/− 3.2% in untreated and EGF-treated RSK2+/+ MEFs, respectively; N=5, p<0.05) and potency (7.0 μM vs. 13 μM for untreated and hEGF-treated, respectively; N=5, p<0.05) following 60 min EGF treatment. Similar to our observations in mVSMCs (Figure 5), decreased maximal signaling of the full agonist ATP was consistent with desensitization of endogenously expressed P2Y receptors. However, treating RSK2−/− MEFs with EGF failed to significantly decrease ATP maximal signaling or potency (Figure 7B, Table 1). These data are important because they provide the first evidence for a common regulatory mechanism whereby RTKs act via RSK2 to regulate the signaling of multiple GPCRs.</p><!><p>We have presented multiple lines of evidence to show that 5-HT2A signaling is indeed attenuated following activation of several endogenous RTKs in multiple cell types. In addition to expression in peripheral tissues, RTKs are widely expressed throughout the brain (e.g., in the cortex) and are activated by endogenous ligands such as EGF and NRG-1 (45). Since 5-HT2A receptors are also highly expressed in the cortex (46), it was tempting to speculate that cross-talk between RTKs and 5-HT2A receptors could explain how 5-HT2A receptors are regulated in cortical neurons.</p><p>To test this possibility, we developed a live cell imaging technique to measure 5-HT2A receptor signaling in cortical neurons in the presence and absence of growth factors. As shown in Figure 8A and 8B, uninfected neurons were unresponsive to the 5-HT2A/2C selective agonist DOI, despite robust Ca2+ responses following depolarization with 80 mM KCl (Figure 8C). However, DOI elicited measurable Ca2+ responses in neurons only after infection with GFP-tagged 5-HT2A receptors (Figure 8D and 8E), thus ensuring specificity of the DOI response. We then quantified these DOI-induced responses in untreated (Figure 8E) and growth factor-treated (Figure 8H) neurons via manual segmenting (Figure 8F and 8I). As shown in Figure 8J and quantified in Table 3, treatment with either EGF or NRG-1 significantly reduced the Ca2+ response elicited by DOI. In these studies we present the first evidence that inhibitory cross-talk occurs between RTKs and GPCRs in neurons. Most importantly, our data show that RTKs attenuate 5-HT2A signaling in neurons-a finding with enormous potential for explaining how 5-HT2A receptors are regulated in the brain.</p><!><p>The three major findings in this paper are: 1) multiple endogenous RTK receptors and their ligands attenuate 5-HT2A receptor responsiveness in several physiologically relevant cell types; 2) RSK2 is required for RTK-mediated attenuation of 5-HT2A receptor signaling, and 3) RTK activation similarly attenuates P2Y purinergic signaling in a RSK2-dependent manner. By directly testing multiple endogenous growth factors/RTK pathways and multiple Gq-coupled GPCRs, we have now established a cellular mechanism whereby RTK signaling cascades attenuate GPCR signaling through RSK2. Importantly, these findings support a novel paradigm of inhibitory cross-talk between RTKs and GPCRs and extend it to include a larger mechanism whereby RTKs act via RSK2 to regulate the signaling of multiple GPCRs.</p><!><p>Consistent with evidence for inhibitory cross-talk between RTKs and select GPCRs (i.e., the β1-, β2-, α1B-, and α1D-adrenergic receptors, and 5-HT2C receptor) (16-22), our data demonstrate that activation of the EGFR attenuates 5-HT2A receptor signaling in MEFs, VSMCs and cortical pyramidal neurons. Moreover, we discovered that this novel regulatory pathway requires RSK2. We verified that the EGFR requires RSK2 to attenuate 5-HT2A receptor signaling by observing the signaling of both full and partial 5-HT2A agonists in RSK2+/+ and RSK2−/− cells. Since changes to GPCR responsiveness affect each agonist class differently, this approach allowed us to unambiguously identify RTK-mediated effects on receptor signaling. Explicitly, full agonists have a large receptor reserve and are resistant to changes in the population of functional receptors (i.e., resulting from receptor desensitization or down-regulation). As a result, full agonists signal maximally but with lower potency under conditions of receptor desensitization in cells over-expressing a GPCR (i.e., CRCs are right-shifted) (38). However, both the maximal signaling and potency of full agonists is decreased under conditions of receptor desensitization in cells with endogenous GPCR expression (i.e., CRCs are predominantly shifted downward with minor rightward shifts). Partial agonists, on the other hand, have low receptor reserve and are more sensitive to changes in the population of functional GPCRs. As a result, partial agonists typically signal with lower efficacy and potency under conditions of receptor desensitization irrespective of receptor expression (38). In line with these predictions, we observed that EGF significantly decreased full agonist (i.e., 5-HT) potency and partial agonist (i.e., lisuride) efficacy in high-expressing RSK2+/+ MEFs. Additionally, growth factor treatment significantly decreased the maximal signaling of full agonists (i.e., 5-HT and ATP) when their cognate receptors were expressed at endogenous levels in RSK2+/+ mVSMCs and MEFs. Taken together, our results in RSK2+/+ cells are consistent with RTK-mediated attenuation of 5-HT2A receptor signaling. Importantly, none of these predicted effects were observed in RSK2−/− MEFs, thus supporting the hypothesis that RTKs act via RSK2 to attenuate 5-HT2A receptor signaling.</p><p>Alternatively, these results could be explained by differences in gene expression profiles between RSK2+/+ and RSK2−/− cells. However, microarray data show that the expression of genes required for EGFR signal transduction are not significantly different between RSK2+/+ and RSK2−/− MEFs. Thus, the simplest explanation for our results remains that RSK2 is a critical mediator of cross-talk between the EGFR and 5-HT2A receptor.</p><p>In addition to our results in RSK2+/+ MEFs, we observed that 5-HT2A signaling was significantly decreased following activation of endogenous RTKs in mVSMC and cortical neuron primary cell lines. Considering the physiological importance of inhibitory cross-talk, it is attractive to speculate that growth factor signaling may be relevant for regulating the 5-HT2A receptor in the CNS. In support of this, members of the EGFR family are widely expressed throughout the brain and regulate a variety of functions including proliferation, differentiation, maturation, and survival of a variety of neurons (45). Interestingly, the ErbB4 neuregulin receptor, which is a member of the EGFR family, is expressed throughout the mature brain and is known to reside in some of the same cortical layers (47) as the 5-HT2A receptor (48). Moreover, ErbB4 interacts with PSD-95, a post-synaptic density protein that associates with and regulates 5-HT2A receptor signaling and trafficking in vitro and in vivo (26, 29, 35, 49). Thus, considering the pervasiveness of growth factor signaling in the brain, as well as its overlapping expression with 5-HT2A receptors, RTK signaling could modulate 5-HT2A receptor signaling in vivo. Intriguingly, aberrant signaling of both RTKs and 5-HT2A receptors has been associated with neuropsychiatric disorders such as depression and schizophrenia (47, 50-52). Together, these findings suggest that a more complete understanding of the mechanism(s) underlying inhibitory cross-talk between RTKs and 5-HT2A receptors is of considerable therapeutic importance.</p><!><p>In stark contrast to our results using EGF, PDGF, and ErbB4 receptor agonists, we discovered that IGF-1 did not attenuate 5-HT2A receptor signaling in either RSK2+/+ or RSK2−/− MEFs. In order to interpret these negative results, we showed that EGF treatment retained the ability to attenuate 5-HT2A-mediated Ca2+ release in parallel control experiments. These data suggest that, unlike EGF receptor activation, IGF-1 signaling does not desensitize 5-HT2A receptors. The reasons for this are unknown, although our data showing that IGF-1 weakly activates RSK2 when compared to EGF suggest that a threshold level of RSK2 activation must be reached in order to elicit 5-HT2A receptor desensitization.</p><p>Other mechanisms have been proposed to explain IGF-1-induced GPCR desensitization including phosphorylation of tyrosine residues in the second intracellular loop of the β2-adrenergic receptor and Akt-mediated phosphorylation of the β1-adrenergic receptor (16, 19). However, 5-HT2A receptors are not known to be phosphorylated on tyrosine residues and are not substrates for Akt, perhaps explaining why IGF-1 has no effect on 5-HT2A receptor signaling.</p><!><p>In addition to the 5-HT2A receptor, endogenous P2Y purinergic receptor signaling is regulated by RSK2 (13). Here we provide the first evidence showing that, like 5-HT2A receptors, EGFR activation attenuates P2Y-purinergic receptor signaling in a RSK2-dependent manner. Thus, it appears that RSK2 is a critical mediator of inhibitory crosstalk between multiple RTKs and GPCRs. Interestingly, the β1-adrenergic and PAR-1 thrombinergic receptors are also regulated by RSK2 (13), and it remains to be determined if these receptors are regulated by RTKs in a RSK2-dependent manner. This is an especially intriguing question for the β1AR since it is already known that activation of the IGF-1R regulates β1-adrenergic receptor signaling through activation of PI3 kinase and Akt (16).</p><p>A question of important physiological relevance is whether specific RTK signaling pathways influence the signaling of all or only select groups of GPCRs. Our results, along with those of others, indicate that signaling from some Gq-coupled receptors (i.e., 5HT2A, P2Y, α1b-adrenergic, and α1d-adrenergic) are similarly attenuated by one RTK, the EGFR (20, 21). Other RTKs, such as insulin and IGF-1 receptors, are well-known to decrease the signaling of some Gs-coupled GPCRs such as β1- and β2-adrenergic receptors (16, 53). However, insulin and IGF-1 receptors attenuate signaling from only some (i.e., 5-HT2C), but not all (i.e., M1 muscarinic or 5-HT2A) Gq-coupled GPCRs ((22), Figure 6). Therefore, a robustness of this signaling crosstalk is evident and RTK inhibitory crosstalk to GPCRs will likely emerge as a receptor-specific phenomenon. Ultimately, further studies testing many RTKs and GPCRs will help elucidate if this crosstalk is a conserved phenomenon.</p><p>In summary, multiple lines of evidence suggest that RSK2 is a critical mediator of inhibitory cross-talk between RTKs and the 5-HT2A receptor. Specifically, this study presents the first evidence that 5-HT2A receptor signaling is attenuated by the growth-factor-mediated activation of RTKs endogenously expressed in multiple cell types including physiologically relevant mVSMCs and cortical neurons. Moreover, genetic deletion of RSK2 was sufficient to block these effects, thus demonstrating that RSK2 is required for the inhibitory cross-talk between RTKs and 5-HT2A receptors in all relevantcell types examined. Intriguingly, we discovered that the P2Y purinergic receptor, whose signaling is also regulated by RSK2, is similarly attenuated following EGFR activation in RSK2+/+ MEFs. Taken together, these findings provide the initial framework for a conserved regulatory mechanism whereby multiple RTKs act via the ERK/MAPK effector RSK2 to attenuate GPCR signaling. Most importantly, inhibitory cross-talk between RTKs and 5-HT2A receptors could provide insight into how these receptors are regulated in vivo.</p><!><p>Figure S1. Genes involved in IGF-1 R signal transduction are expressed at similar levels in RSK2+/+ and RSK2−/− MEFs. The microarray data quantifying gene expression in RSK2+/+ and RSK2−/− MEFs was produced previously by Sheffler et al. (13). Here we overlaid the mRNA expression levels of IR and IGF-1 R signal transduction genes in RSK2+/+ and RSK2−/− MEFs with gene-expression color criterion and fold-changes from the programs GenMAPP and MAPPFinder. Gray colored genes are equally expressed in RSK2+/+ and RSK2−/− MEFs. Green colored genes show greater than a 2-fold increase in expression in RSK2−/− MEFs compared to RSK2+/+ fibroblasts. Red colored genes show greater than a 2-fold decrease in expression in RSK2−/− MEFs compared to RSK2+/+ fibroblasts. As shown, the gene encoding the IR was not expressed in RSK2+/+ and RSK2−/− MEFs. However, the genes encoding the IGF-1 R and downstream effectors were expressed at comparable levels in RSK2+/+ and RSK2−/− MEFs. Raw microarray data and gene information can be found in the Supporting Information zip file.</p><p>Figure S2. IGF-1 activates the IGF-1 R in RSK2+/+ MEFs. Here we determined using cell lysates that 10 nM IGF-1 activated the IGF-1 R (Tyr(P)1158/1162/1163) in RSK2+/+ MEFs. Shown is an immunoblot representative of three independent experiments.</p>

PubMed Author Manuscript

Quantum-State Controlled Reaction Channels in Chemi-ionization Processes: Radiative (Optical–Physical) and Exchange (Oxidative–Chemical) Mechanisms

ConspectusMost chemical processes are triggered by electron or charge transfer phenomena (CT). An important class of processes involving CT are chemi-ionization reactions. Such processes are very common in nature, involving neutral species in ground or excited electronic states with sufficient energy (X*) to yield ionic products, and are considered as the primary initial step in flames. They are characterized by pronounced electronic rearrangements that take place within the collisional complex (X···M)* formed by approaching reagents, as shown by the following scheme, where M is an atomic or molecular target: X* + M → (X···M)* → [(X+···M) ↔ (X···M+)]e− (X···M)+ + e– → final ions.Despite their important role in fundamental and applied research, combustion, plasmas, and astrochemistry, a unifying description of these basic processes is still lacking. This Account describes a new general theoretical methodology that demonstrates, for the first time, that chemi-ionization reactions are prototypes of gas phase oxidation processes occurring via two different microscopic mechanisms whose relative importance varies with collision energy, Ec, and separation distance, R. These mechanisms are illustrated for simple collisions involving Ne*(3P2,0) and noble gases (Ng). In thermal and hyperthermal collisions probing interactions at intermediate and short R, the transition state [(Ne···Ng)+]e− is a molecular species described as a molecular ion core with an orbiting Rydberg electron in which the neon reagent behaves as a halogen atom (i.e., F) with high electron affinity promoting chemical oxidation. Conversely, subthermal collisions favor a different reaction mechanism: Ng chemi-ionization proceeds through another transition state [Ne*······Ng], a weakly bound diatomic-lengthened complex where Ne* reagent, behaving as a Na atom, loses its metastability and stimulates an electron ejection from M by a concerted emission–absorption of a “virtual” photon. This is a physical radiative mechanism promoting an effective photoionization. In the thermal regime of Ec, there is a competition between these two mechanisms. The proposed method overcomes previous approaches for the following reasons: (1) it is consistent with all assumptions invoked in previous theoretical descriptions dating back to 1970; (2) it provides a simple and general description able to reproduce the main experimental results from our and other laboratories during last 40 years; (3) it demonstrates that the two “exchange” and “radiative” mechanisms are simultaneously present with relative weights that change with Ec (this viewpoint highlights the fact that the “canonical” chemical oxidation process, dominant at high Ec, changes its nature in the subthermal regime to a direct photoionization process; therefore, it clarifies differences between the cold chemistry of terrestrial and interstellar environments and the energetic one of combustion and flames); (4) the proposed method explicitly accounts for the influence of the degree of valence orbital alignment on the selective role of each reaction channel as a function of Ec and also permits a description of the collision complex, a rotating adduct, in terms of different Hund’s cases of angular momentum couplings that are specific for each reaction channel; (5) finally, the method can be extended to reaction mechanisms of redox, acid–base, and other important condensed phase reactions.

quantum-state_controlled_reaction_channels_in_chemi-ionization_processes:_radiative_(optical–physica

<!>Introduction<!><!>Computational Methodology<!>Optical Potential Formulation<!><!>Optical Potential Formulation<!>Adiabatic and Nonadiabatic Effects in the Open-Shell Atom Phenomenology<!><!>Chemi-ionization Reaction Mechanisms<!>Predictions and Experimental Results<!><!>Predictions and Experimental Results<!><!>Penning Ionization Electron Spectra<!>Dependence of the Observables on Optical Potential Features<!><!>Dependence of the Observables on Optical Potential Features<!><!>Conclusions<!>

<p>FalcinelliS.; VecchiocattiviF.; PiraniF.Adiabatic and Nonadiabatic Effects in the Transition States of State to State Autoionization Processes. Phys. Rev. Lett.2018, 121, 16340330387669.1New insights are provided on the electronic adiabatic and nonadiabatic effects in the stereodynamics of state to state atomic and molecular collisions, controlling relevant properties of the transition state of chemi-ionization reactions.</p><p>FalcinelliS.; PiraniF.; CandoriP.; BrunettiB. G.; FarrarJ. M.; VecchiocattiviF.A new insight of stereo-dynamics of Penning ionization reactions. Front. Chem.2019, 7, 44531275926.2Recent developments in the experimental study of chemi-ionization reactions are presented to cast light on basic aspects of the stereodynamics of the microscopic mechanisms involved.</p><p>FalcinelliS.; VecchiocattiviF.; PiraniF.General treatment for stereo-dynamics of state-to-state chemi-ionization reactions. Commun. Chem.2020, 3, 64.3A theoretical approach able to formulate the optical potential for Ne*(3P2,0) noble gas atom chemi-ionizations as prototype oxidation processes and to evaluate the state-to-state reaction probability is proposed.</p><!><p>Anisotropic intermolecular forces, associated with alignment and orientation effects produced by atomic and molecular polarization, modulate the fate of molecular collisions. A knowledge of these phenomena is relevant to control the stereodynamics of elementary processes occurring in the gas phase and at the gas–surface interface,4−18 but a general theoretical and computational foundation is still lacking.</p><p>This Account focuses on the role of valence atomic orbital alignment in determining the selectivity of electronic rearrangements that affect the stereodynamics of gas-phase chemi-ionization reactions (Penning ionization phenomena).19−22 Our study provides complementary information to the nuclear stereodynamics deeply investigated in seminal works.23−25 Indeed, present atom–atom reactions are directly triggered by the electronic rearrangements and indirectly affected by nuclear motions: possible electronic–nuclear couplings emerge as Coriolis effects.</p><p>Chemi-ionization reactions occur in collisions of open shell species, electronically excited in energetic metastable states, with neutral partners, giving rise to spontaneous ejection of electrons and subsequent ion formation. The reactions proceed without a barrier and are described by an anisotropic optical potential, W, defined in eq 1 as a combination of a real (Vt) and an imaginary (Γ) part that control, respectively, entrance–exit channel trajectories and disappearance probability of neutral reactants by ionization.19−22,26,271The strength of both the real and imaginary components varies with the center-of-mass separation and relative orientation of interacting partners. The imaginary component Γ mediates the passage from neutral reactants to ionic products through an electronic rearrangement within the reaction transition state (TS).</p><p>Chemi-ionization processes studied under electronically state-selected conditions are important for catalysis, plasmas, photodynamics, and interstellar and low-temperature chemistry and play an important role in applied research topics such as soft ionization in mass spectrometry.28−31 Such reactions are the primary step in flames,32,33 classified here as prototypes of strongly exothermic elementary oxidation processes, for which the details of the stereodynamics are provided by Penning ionization energy spectra (PIES) of spontaneously emitted electrons and by total and partial ionization cross sections.12,21 These experimental observables are very sensitive probes that highlight the crucial features of TSs such as geometry and orbital energetics.</p><p>This Account focuses on reactions of metastable Ne*, with a valence electron excited to a 3s orbital. Its open-shell ionic core Ne+ exhibits the same electronic configuration, 2p5, of the high electron affinity fluorine atom, with 2P3/2,1/2 fine structure levels. When Ne* approaches an atomic or molecular target M with sufficient collision energy (Ec), it forms an interacting complex within which a spontaneous electron jump from one of the HOMOs (highest occupied molecular orbitals) of M to the open shell ionic core of Ne* can occur, releasing enough energy to eject the 3s electron with a defined kinetic energy. Therefore, measured PIES34,35 provide direct information on electronic rearrangements occurring inside the TS.36 Moreover, the ionization probability and PIES are strongly dependent on symmetry, energy, and relative spatial orientation of the atomic or molecular orbitals involved in the electron exchange.</p><p>A number of laboratories including our own fully highlighted the reaction dependence on the orbital orientation of various molecular systems.37−40 However, in the case of the anisotropic Ne* reagent, an important open question concerns the selective role of the half-filled 2p atomic orbital within the collision complex the alignment of which affects the TS structure. To emphasize basic aspects of the stereodynamics promoted by selective electronic rearrangements, we have focused on prototype atom–atom reactions between Ne* and the heavier noble gases (Ng = Ar, Kr, Xe). The limited internal degrees of freedom of Ng, the absence of fragmentation in Ng+ product, and the availability of detailed experimental findings such as cross sections, branching ratios (BRs), and PIES facilitated the investigation.</p><p>Ours is an innovative theoretical approach based on identification and modeling of the basic components of the interaction. Their formulation uses fundamental physical properties as scaling parameters (polarizability, ionization potential, electronic affinity, spin–orbit (SO) splitting) of the participating collisional partners, providing a computational method based on simple-operating interdependent relationships.</p><p>Our study on Ne*–Kr1,2 serves as a paradigm for emphasizing similarities and differences in the reaction stereodynamics of the complete Ne*–Ng family.</p><p>The computational method, which provides an integrated picture of the stereodynamics of this series of chemi-ionization reactions, is based on two important markers, Cx and Cy, which quantify the Σ character degree in excited and lowest electronic states, respectively, of the molecular ion (Ne···Ng)+ coupled by CT. Such markers, identifying how the molecular symmetry degree of the state-selected collision complexes (which evolve in the TS ones at the turning point region) changes with the interatomic distance R, represent how quantum levels of reagents and products couple during each collision event. They describe how the SO levels of reagents and products are perturbed at large R and destroyed at shorter R by increasing strength and anisotropy of the electric field associated with the interaction. Only strong electric fields decouple the electronic orbital angular momentum from the spin and effectively align valence orbitals along R, promoting the formation of real molecular states.</p><p>Therefore, the markers map all reaction dynamics changes as a function of Ec and concomitant changes in the ranges of R probed.</p><!><p>Entrance and exit channels belong to a manifold of states of the same system, properly coupled by the configuration interaction. Their characterization provides the correct sequence in energy of quantum levels accessible, including also those of the TS, and their dependence on R; the real and imaginary parts of the optical potential are interdependent, being related to adiabatic and nonadiabatic effects, respectively, arising from electronic rearrangements occurring within the collision complex.</p><p>The microscopic mechanisms triggered by the selectivity of interaction components have a marked Ec dependence:</p><p>Subthermal conditions promote reactions classified as photoionization processes, where only long-range noncovalent interactions (induction, dispersion, and polarization) are effective. They determine the formation of weakly bound diatomic adducts [Ne*······Ng] (the TS in this case) where Ne* behaves as a sodium atom perturbed by the Ng presence: this breaks the validity of the optical selection rules, allowing ionization to occur by a concerted emission–absorption of a "virtual" photon.3,41</p><p>Hyperthermal conditions favor processes that evolve as chemical oxidation reactions, where the TS is a molecular complex of which the accessible levels are represented by proper molecular quantum numbers. In this case, the collision complex [Ne*······Ng] formed at large R does not ionize and evolves toward shorter R where the Ne* polarization makes the stronger ion–dipole interaction effective, trapping the reactants via the formation of [(Ne···Ng)+]e− TS:2Here, the behavior of neon is dominated by its ionic core (behaving as a fluorine atom) inducing the oxidation of the Ng via an electron transfer. Under thermal conditions, the two types of reactions occur simultaneously, and their relative role varies with Ec depending on both reaction channel and Ng characteristics.</p><!><p>The proposed methodology exploits the following steps suggested by our recent research.1−3</p><!><p>The real part, Vt, of eq 1 assumes that each entrance channel is determined by the weighted sum of two limiting representations.1−3 At large R, the system exhibits a substantial isotropic behavior, typical of an alkaline atom interacting with Ng and promoting a photoionization (physical) process. At intermediate and short R, the anisotropy of the ionic core of Ne* emerges, behaving as a F atom, which promotes an oxidation reaction. The interaction in the entrance channels must take into account the anisotropic contributions from the open shell "P" nature of Ne*,1−3,21,22,41−47 whereas the exit channels are affected by the P nature of the Ng+ product.</p><p>The investigation of the interaction of open P shell atoms or ions with a closed shell 1S0 species47,48 suggests a Vt representation defined in terms of proper quantum numbers that accounts for the relative alignment or orientation of reagents and products within the interatomic electric field, which is the proper quantization axis of the system. The resultant interactions provide effective adiabatic potentials that include VΣ and VΠ contributions mixed by SO effects. The Σ and Π molecular states are defined by the electronic quantum number Λ = 0 and Λ = 1, where Λ describes the absolute projection of the orbital angular momentum decoupled by the spin along R. For a full description of present anisotropic interactions, it is sufficient to use42 a weighted sum of V0 and V2 Legendre-expansion radial coefficients:47,4834V0 represents the isotropic component, with all anisotropic contributions included in the V2 term. The latter, accounting for the quantized spatial orientation of valence orbitals of the open shell species within the interacting complex, controls the sequence in the manifold of adiabatic potential energy curves47,48 (PECs) associated with all quantum states accessible, including their stabilities and anisotropies. Accordingly, for all channels, the effective PECs have been formulated3 and indicated as V|J,Ω⟩ (J is the total (orbital + spin) electronic angular momentum quantum number, while Ω is the absolute projection of J along R).</p><p>While the isotropic V0 term is a noncovalent interaction component, the anisotropic V2 originates primarily from "chemical" contributions. In entrance channels, V0 accounts for the gradual passage of the system, as R decreases, from neutral–neutral [Ne*······Ng] to ion–neutral [(Ne···Ng)+]e−, that is, a molecular ion core surrounded by a Rydberg electron (eq 2).1−3 In exit channels, it is determined by an isotropic Ne···Ng+ ion–neutral interaction. In both cases, V0 depends on size repulsion, polarization, and dispersion/induction attraction contributions. In contrast, V2 identifies the anisotropic configuration interaction (CI) between entrance and exit channels differing for one electron exchange. V2 is represented by an exponential decreasing function of R,3,47,48 reflecting the "canonical" dependence of the integral overlap between atomic orbitals exchanging the electron. For entrance and exit channels, the modulus of the exponential function must be the same, while its sign is negative for the exit channel and positive for the entrance channel. The different signs relate to bonding and antibonding effects by charge or electron transfer (CT) that arise from the CI between entrance and exit channels of the same symmetry.1−3,47,48 CI makes entrance and exit channels of each system as belonging to the same correlated manifold of states. The formulation of the potential functions is summarized in Supporting Information (SI).</p><p>For entrance and exit channels of the same system, this approach leads to a different correlation between atomic states, representative of the behavior at long R, where |V2| ≪ SO energy splitting, and molecular states emerging at short R, where |V2| ≫ SO splitting48 (Figure 1 and Figure S2). The Σ and Π molecular character degree associated with each V|J,Ω⟩ curve at all R values can be evaluated by relations (see SI) that depend on the ratio between V2 strength and SO splitting and agree with the following asymptotic conditions (Figure 1): at short R, all PECs must represent states having pure Σ or Π molecular character, while at large R, where the SO coupling is dominant, a mixing of molecular characters occurs.</p><!><p>(a) Electronic features of reagents and collision complex. (b) CI between states of entrance and exit channels differing for one electron exchange, defining CT contributions for Σ states. Real part of W for Ne*–Ar (c) and Ne*–Xe (d) represented by adiabatic PECs.</p><!><p>As previously noted, the adoption of Cx and Cy coefficients quantifies the Σ character degree in entrance and exit channels, respectively: emphasizing all basic electronic rearrangements within the collision complexes, they represent important markers of the reaction dynamics modulation under different conditions. Their characterization is important to provide suitable correlation between atomic and molecular states and to obtain a simple-operating formulation of the imaginary part Γ of the optical potential internally consistent with that of the real part Vt. Indeed, the relative role of Σ and Π molecular character in entrance and exit channels must be properly taken into account to define their couplings and state-to-state Γ components.</p><!><p>The electronic structure of the Ne* reagent is depicted in Figure 1, where the "floppy" cloud of the outer 3s electron and the nature of the open shell of the ionic core are emphasized. These features determine basic characteristics of the collision complex with the Ng and of the reaction TS. Electronic rearrangements driving the reaction arise from polarization of the 3s electron cloud, CT, and modifications of angular momentum couplings of valence electrons within the collision complex. Such rearrangements are accompanied by adiabatic and nonadiabatic effects, which play a crucial role in the collision dynamics.</p><p>Anisotropic adiabatic effects arise from the strength and selectivity of CI within the collision complex, promoted by CT, that couple entrance and exit channels of the same symmetry. Such effects, determining the anisotropy of Vt, account for the adiabatic conversion of atomic states, represented by |J,Ω⟩ quantum numbers, into molecular states of Σ and Π symmetry. While the atomic states are representative of reagents and products at large and intermediate R, the molecular states of the interacting system emerge at chemical bonding length scales. The resulting PECs for Ne*–Ar and Ne*–Xe systems are plotted in Figure 1 (for Ne*–Kr see ref (3)). The figure depicts also CI and CT for Σ states: the corresponding components for Π states are much smaller1,2 because of the reduced overlap integral between atomic half-filled orbitals exchanging the electron, aligned orthogonal to R. Sequence and stability of levels, obtained by general guidelines,48 are consistent with results of Dehmer49 and the natural bond order method.50</p><p>The components of the imaginary part of the optical potential depend on the strength and radial dependence of nonadiabatic effects. They arise from polarization, selective CI, changes in electronic angular momentum couplings, and SO and Coriolis contributions1 as determined in the recent analysis of the Ne*–Kr case.47 This procedure exploits the characterization of Cx and Cy discussed above. Figure 2a indicates that at large R such coefficients maintain their asymptotic values and the system is not reactive. As R decreases, the system is initially affected by weak noncovalent components of the interaction: the Cx and Cy coefficients are slowly varying, with a perturbation of Ne* sufficient to promote within [Ne*······Ng] emission–absorption of a "virtual" photon41 initiating a photoionization mechanism. Conversely, at short R stronger "chemical" interaction components promote pronounced changes in angular momentum couplings with the passage from atomic to molecular states: the TS, [(Ne···Ng)+]e−, becomes a molecular ion surrounded by a Rydberg electron. In this region, the reactions become true chemical (oxidation) processes. Therefore, Cx and Cy, the important markers controlling the relative role of reaction mechanisms accounting for the variation with R of the Σ character of the state-selected TS, have been obtained with a procedure detailed in SI and are shown in Figure 2a for Ne*–Ar and Ne*–Xe systems. The R interval where they show the fastest variations corresponds to the region where the interaction anisotropy becomes comparable to the SO coupling and an emerging transition from atomic and to molecular states occurs. The behavior of V|2,2⟩ and V|3/2,3/2⟩ curves, effective in the entrance and exit channels, respectively, is not discussed in detail since they show at all distances pure Π character.</p><!><p>(a) Vertical axes give values of the Σ character in entrance (Cx) and exit (Cy) channels as a function of R. Π character is defined as complement to 1 of the Σ one. All states accessible to the system are indicated by |J,Ω⟩ quantum numbers. The |3/2,3/2⟩ states are not included since they exhibit a pure Π character at all R. Dashed and full lines refer to Ne–Xe and Ne–Ar systems, respectively. The larger interaction anisotropy of the Ne+–Xe system makes the variation of Cx more prominent with respect to Ne+–Ar, while the larger SO coupling of Xe+ with respect to Ar+ hinders the passage to the molecular state causing less variation of Cy. (b) Cartoon representing the main features of Σ–Σ, Π–Π, Σ–Π, and Π–Σ couplings promoted by nonadiabatic effects operative during the collisions.</p><p>direct mechanism (driven by "chemical" forces), ΔΛ = 0, with coupling terms called AΣ–Σ and AΠ–Π on the basis of the molecular character (Σ or Π) of initial and final states;</p><p>indirect mechanism (controlled by of "physical" forces), ΔΛ= ±1, promoted by electronic polarization, SO, and Coriolis effects and stimulated by mixing between initial and final states of different symmetry, whose coupling terms are defined as AΣ–Π and AΠ–Σ.</p><p>State-to-state Γ components defined in terms of |J,Ω⟩ quantum numbers of Ne*(3PJ) reagent and of Ar+(2PJ) (a) and Xe+(2PJ) (b) products.</p><!><p>The two mechanisms show different radial dependence,1−3 and therefore their relative roles vary with Ec. The direct mechanism dominates at shorter distances, accessible in higher Ec, and arises from the chemical oxidation of the Ng controlled by the [(Ne···Ng)+]e− TS, where the Ne* ionic core behaves like a fluorine atom, while the indirect one emerges at lower relative energies when the collision probes larger distances and evolves along PECs dominated by the behavior of Ne* as a sodium atom. The indirect mechanism includes radiative contributions as proposed in pioneering works.27,41,42</p><p>All coupling terms AΛ–Λ′ are represented by exponential functions1−3 given in SI. The couplings AΣ–Σ and AΠ–Π exhibit a pronounced radial dependence since they, as the V2 component, relate to the variation of valence orbital overlap integrals with R. However, arising from noncovalent interaction and Coriolis contributions, AΣ–Π and AΠ–Σ show a less pronounced radial dependence.1−3 Coupling with the continuous states of the emitted electron,41,42 which slowly varies with R, is accounted in the pre-exponential factor of AΛ–Λ′.</p><p>Strength and radial dependence of state-to-state Γ terms have been defined exploiting AΣ–Σ, AΠ–Π, AΣ–Π, and AΠ–Σ terms. Similar to Ne*–Kr,3 for the present systems strength and radial dependence of AΣ–Σ and AΠ–Π have been estimated from strength and radial dependence of CI, which couples and mixes states of the same symmetry to which partial or full molecular character can be properly assigned.</p><p>Therefore, considering the correlation diagram between atomic and molecular states3 reported in SI, we obtained explicit relations for state-to-state Γ|JΩ→J′Ω′⟩ terms of the optical potential (see SI), represented as weighted averages of AΛ–Λ′ couplings, where relative weights in each channel are given as combination of Cx and Cy.</p><p>Such state-to-state Γ|JΩ→J′Ω′⟩ components, reported in Figure 3, exhibit similar behavior for both systems, although for Ne*–Xe they are stronger suggesting the occurrence of more efficient chemi-ionization processes. For the direct mechanism, this is due to the effect of larger atomic overlap of Xe with respect to Ar, while for the indirect mechanism, the larger electronic cloud of Xe causes a higher perturbation on the external electronic configuration of Ne* with subsequently more probable violation of the selection rules favoring its radiative decay.</p><!><p>Present optical potential formulations have been exploited to calculate, within a semiclassical method,21,22,26,27 state-to-state ionization cross sections over a wide Ec range. Such calculations directly provide also the product BRs, defining the relative probability of selected channels.3Figures 4, 5, 6, and 7 compare theoretical predictions with experimental data from several laboratories including our own. All experimental data have been obtained in high-resolution molecular beam experiments: our apparatus has been illustrated in previous papers31,35 and SI. Therefore, this treatment attempts to give, for the first time and for all Ne*–Ng systems, an internally consistent rationalization of most relevant experimental findings1,3,35,42−45,51</p><p>Pronounced differences in state-to-state total ionization cross sections, which directly relate to the different strengths and radial dependences of Γ|JΩ→J′Ω′⟩ components, are obtained. Representative experimental results42 with non-state-selected reagents in a wide Ec range are reported in Figures 4 and 5 (black points). Good agreement between theoretical predictions and experimental data, both in their absolute values and in Ec dependences, is obtained for values of the cross sections averaged over the statistical distribution of quantum states accessible in the experimental conditions.42</p><!><p>State-to-state total ionization cross section for Ne*–Ar as a function of Ec. The comparison with early experimental results (black points, data from ref (42)) refers to state averaged conditions and emphasizes differences with respect to state-to-state results, while their statistical average is consistent with the experimental determination.</p><p>State-to-state total ionization cross section for Ne*–Xe as a function of Ec. The comparison with early experimental results (black points, data from ref (42)) refers to state averaged conditions and emphasizes differences with respect to state-to-state results, while their statistical average is consistent with the experimental determination.</p><!><p>Cross section ratios, , representing the relative formation probability of the ionic aggregate [Ng+–Ne] (associative ion) with respect to the Ng+ (Penning ion), are also determined. The state-to-state BRs for associative to Penning ionization, , as a function of Ec, are plotted in Figure 6 with non-state-selected experimental data (black points) from our laboratory.51 The experimental data compare well with the statistical average of the present calculations. However, the most important comparison is performed in Figures 6 and 7 with data by the Osterwalder group43−45 (open circles) recorded using a state-selected Ne* beam in J = 2 and Ω = 2, 1, 0 sublevels, indicating a good agreement with the state-to-state selectivity predicted here.</p><!><p>State-to-state associative/Penning ratios predicted for Ne*–Ar. The comparison involves experimental results (black points, data from ref (51)) referred to state averaged conditions. Recent data, measured with Ne*(3P2) beams state-selected in Ω = 2, 1, 0 quantum states, are also reported (open circles) for a further comparison (data from refs (43−45)).</p><p>State-to-state associative/Penning ratios predicted for Ne*–Xe. The comparison includes only results of recent experiments (open circles). Data from ref (45).</p><!><p>Ne*–Kr PIES experiments1−3 permitted us to separate contributions of different entrance and exit channels, referred to specific J levels of the Ne*(3P2,0) reagent and the Kr+(2P3/2,1/2) product. The large SO splitting in Kr+ and Xe+ allowed individual contributions to be resolved in the PIES data, measured as a function of Ec for both Ne*–Kr and Ne*–Xe systems.1 Similar experiments with Ne*–Ar have not been done, since SO splittings in Ne* reagent (0.097 eV) and in Ar+ product (0.177 eV) are comparable, making it difficult to separate the contribution of different SO states in entrance and exit channels.</p><!><p>The proposed methodology clearly indicates how the various experimental findings depend on basic features of real and imaginary parts of the optical potential and how their role is modulated by Ec and the selected channel. While Vt controls the dynamics of reagent approach and product removal defining the R region mainly probed by the system at each Ec, Γ determines the reaction probability for each assumed configuration of the TS in the probed R region. Important selectivity in the reaction dynamics emerges by deconvoluting from each state-to-state Γ component the contributions assigned to each Λ and Λ′ quantum number pair. These pairs describe the molecular symmetry of the system before and after the electron exchange. The channel |2,2⟩ →|3/2,3/2⟩ always shows the smallest cross sections that tend to vanish at low Ec. This behavior can be rationalized by noting that this channel is exclusively governed by AΠ–Π, a very weak coupling term effected by an electron exchange between valence orbitals aligned perpendicularly to R and the overlap of which is small and rapidly vanishing with R. For all other channels, the observed behavior arises from a combined effect of Σ and Π molecular character in the interaction driving the collision. The Ne*–Ar system has been considered as representative of the complete phenomenology, and the analysis has been focused on three different entrance channels, namely, |0,0⟩, |2,0⟩, and |2,2⟩, and on the same exit channel, |3/2,1/2⟩. Note that the |2,1⟩ channel behaves similarly to |2,0⟩. Deconvoluted results, obtained as a function of R, are plotted in Figure 8a. We emphasize that small and large distances identify, respectively, R regions mainly probed by experiments at high and low Ec, respectively. From the figure, it appears that for the |0,0⟩ entrance channel the direct mechanism of Σ–Σ type is dominant at short distance, where the system assumes full Σ molecular character in both the initial and final states. In this selected quantum configuration, the Ne*–Ar chemi-ionization is dominated by the electron exchange inside the molecular [(Ne···Ng)+]e− TS, promoting a chemical (oxidation) phenomenon. For |2,0⟩, the same mechanism is prevalent only at intermediate R, where the transition from atomic to molecular states is emerging and Cx and Cy associated with the Ne+ reagent ionic core and Ar+ product change quickly, as shown in Figure 8b, since SO couplings are destroyed in the electric field associated with the anisotropic interaction potential.</p><!><p>(a) Individual contributions to Γ, associated with different molecular symmetries Λ and Λ′ of entrance and exit channels of Ne* + Ar, plotted as a function of R. (b) Some details of the (0,0 −3/2,1/2) channel: the Σ character degree in entrance (Cx) and exit (Cy) channels (Figure 2) are plotted as a function of R. (c) Reaction probability P(b) at each impact parameter b and distance intervals probed at three selected Ec (thermal–hyperthermal range). For Ec < 3–4 meV (subthermal conditions, T ≤ 50 K) mainly probed distances are significantly larger than 4 Å, where Cx and Cy are weakly perturbed and tend to assume asymptotic-statistical values. Vertical gray area confines the turning points region for reactive oxidation collisions.</p><!><p>For all channels considered here, including the |2,2⟩ entrance one, the Π–Π direct (exchange-oxidation) mechanism plays a minor role at short R, since the exit channel tends to assume pure Σ symmetry. Contrarily, in all cases indirect Σ–Π and Π–Σ mechanisms become dominant for R ≥ 3.5 Å, where small changes in Cx and Cy, induced by weak interactions, tend to reduce the validity of the optical selection rules stimulating radiative effects.41,42</p><p>By exploitation of semiclassical cross section calculations, it is possible to characterize the R regions mainly probed at each Ec. Figure 8b,c reports this information for the (0,0 −3/2,1/2) channel and clearly confirms that, under hyperthermal conditions, the direct mechanism (chemical-oxidation) is dominant (lower R), while under thermal conditions direct and indirect mechanisms become competitive (intermediate R). Under subthermal conditions, where only a full quantum mechanical cross section calculation is appropriate, only the indirect mechanism (radiative-physical) is effective (larger R), being driven exclusively by weak isotropic long-range interactions. Figure 8b,c also shows the turning point region where oxidation collisions show the greatest reactivity.</p><p>Experimental findings related to ratios (Figures 6 and 7) probe other details of state-to-state components of the optical potential. In particular, the highest value for the |2,2⟩ entrance channel at low and intermediate Ec arises from the softer repulsive wall of V|2,2⟩, emerging at intermediate R highlighted in Figure 1c,d, which allows a more prominent approach of reactants that favors the trapping in the potential well of the exit channels. This observation represents a stereodynamical feature clearly evident in experiments of the Osterwalder group.43−45 At high Ec, the associative/Penning ionization ratio, , falls off fast, as experimentally observed,51 since the reactions provide ionic products confined in the repulsive wall of the exit channels, which lead to dissociated ions. At very low Ec, ratios are affected by the weak long-range attraction, where the anisotropic nature of the Ne+ core is shielded by the isotropic behavior of the excited 3s electron.</p><p>Finally, the most important evidence of reaction mechanism modulation is provided by the Ec dependence of measured PIES, resolved for J levels of entrance and exit channels. Specifically, very low Ec leads to the exclusive formation of diatomic adducts [Ne*······Ng] (eq 2 and Figure 9a), binding by weak noncovalent interactions, and the observables are consistent with those of pure photoionization spectra (PES) generated by a radiative (physical) phenomenon (Figure 9b). At high Ec, the appearance of chemical interaction components modifies the TS in the molecular [(Ne···Ng)+]e− structure (Figure 9a), where Ne+–Ng and Ne–Ng+ configurations couple by CT. Consequently, significant changes in peak shape and position appear in measured PIES with respect to PES (Figure 9b), indicating the emergence of a chemical-oxidation reaction. Figure 9 emphasizes these changes for Ne*–Kr pointing out the main characteristics of the two mechanisms discussed for chemi-ionization reactions (for Ne*–Xe, see details in SI).</p><!><p>(a) Schematic view of two mechanisms in chemi-ionizations. (b) Ne*–Kr PIESs where vertical blue lines indicate peak positions from Ne(I) photoionization spectrum (PES): at higher Ec electron spectra are very different from PES indicating a chemical interaction inside the formed [(Ne···Ng)+]e− TS (oxidation mechanism), while at very low Ec, they become very similar to PES since the [Ne*······Kr] TS evolves via a photoionization process.</p><!><p>This new treatment provides unique information on the stereodynamics of chemi-ionization reactions, which are relevant in flames, astrochemistry, plasmas, and nuclear fusion.1−3,53−56 Electronic angular momentum couplings and orbital alignment are properly accounted for to describe the selectivity of each state-to-state channel. Since collision complexes are rotating adducts, their features must be consistent with angular momentum couplings confined in specific Hund's cases.52 The emergence of the direct (exchange-oxidation) mechanism corresponds to the passage from Hund's case c to Hund's case a, while the indirect (radiative) mechanism operates when the transition concerns Hund's e to Hund's c cases.</p><p>The proposed methodology (i) identifies two important markers (Cx and Cy), which permit a description of adiabatic and nonadiabatic effects through the use of simple phenomenological equations, (ii) includes previous theoretical descriptions since 1970,27,41,42 (iii) is a simple and general treatment reproducing experimental results from our and other laboratories since 1981,1,3,42−45,51,53,54 (iv) clarifies that exchange and radiative mechanisms are not alternative but simultaneously operative with a relative weight that changes with Ec and depends on the investigated state-to-state channel, and (v) clarifies for the first time that chemi-ionizations are prototype gas phase elementary oxidation processes that can be probed by PIES, a spectroscopy of TS not allowed in the condensed phase.</p><!><p>Potential energy formulation, the definition of state-to-state Γ|JΩ→J′Ω′⟩ components, and the PIES of Ne*–Xe (PDF)</p><p>ar0c00371_si_001.pdf</p><p>The authors declare no competing financial interest.</p><p>Stefano Falcinelli was born on March 13, 1963, in Senigallia (Italy), received his Ph.D. in Chemistry from University of Perugia (1994), did postdoctoral research at Stanford University (USA) with R.N. Zare, and is currently Associate Professor of Chemistry at University of Perugia (Italy).</p><p>James Martin Farrar was born in Pittsburgh (USA) on June 15, 1948. After receiving his Ph.D. in Chemistry (1974) at University of Chicago (with Yan-tseh Lee), he did postdoctoral research (1974–1976) at University of California, Berkeley, with Bruce H. Mahan. Since 1986, he is Full, now Emeritus, Professor of Chemistry at University of Rochester (USA).</p><p>Franco Vecchiocattivi was born in Rome (Italy) on July 27, 1945, graduated in Chemistry in 1968 at University of Rome, and was at University of Chicago (1973) with Y.T. Lee. Since 1995, he is Full Professor of Chemistry at University of Perugia (Italy).</p><p>Fernando Pirani was born in Fabriano (Italy) on September 28, 1949. Graduated in Chemistry in 1973 at University of Perugia (Italy) where, since 2002, he is Full Professor of Chemistry.</p>

PubMed Open Access

Techniques for measuring cellular zinc

The development and improvement of fluorescent Zn2+ sensors and Zn2+ imaging techniques have increased our insight into this biologically important ion. Application of these tools has identified an intracellular labile Zn2+ pool and cultivated further interest in defining the distribution and dynamics of labile Zn2+. The study of Zn2+ in live cells in real time using sensors is a powerful way to answer complex biological questions. In this review, we highlight newly engineered Zn2+ sensors, methods to test whether the sensors are accessing labile Zn2+, and recent studies that point to the challenges of using such sensors. Elemental mapping techniques can complement and strengthen data collected with sensors. Both mass spectrometry-based and X-ray fluorescence-based techniques yield highly specific, sensitive, and spatially resolved snapshots of metal distribution in cells. The study of Zn2+ has already led to new insight into all phases of life from fertilization of the egg to life-threatening cancers. In order to continue building new knowledge about Zn2+ biology it remains important to critically assess the available toolset for this endeavor.

techniques_for_measuring_cellular_zinc

Introduction<!>Probes and Sensors<!>Progress and challenges in applying FRET-based protein sensors to measure Zn2+<!>Re-examination of data collected using small molecule probes<!>Total elemental imaging and mapping<!>Mass-spectrometry techniques<!>LA-ICP-MS<!>SIMS<!>X-ray fluorescence techniques<!>Synchrotron X-ray fluorescence<!>X-ray absorption spectroscopy<!>EDS<!>Perspectives<!>Quantitative analysis of zinc distribution using complementary approaches<!>Conclusions

<p>Zinc is an essential metal, and the proper balance of zinc is critical to the health of organisms [1]. At the molecular level, the coordination of zinc ions (Zn2+) to individual proteins and enzymes either to stabilize protein structure or to create a catalytic center has been well characterized [2]. Further, bioinformatics studies predict that 10% of human proteins require Zn2+ for their structure and function [3], indicating that Zn2+ is necessary for the proper function of thousands of proteins. Work at the cellular level strives to connect our molecular understanding of Zn2+ biology to observations correlating organismal Zn2+ status and health. Mammalian cells maintain a total concentration of Zn2+ in the hundreds of micromolar [4]. While most of this Zn2+ is bound to proteins and inaccessible, a pool of labile Zn2+, which is non-protein bound and complexed to a variety of small molecule ligands [5], has been detected in the cytosol with a concentration in the hundreds of picomolar. Furthermore, there is growing evidence of a labile Zn2+ pool in organelles [4, 6]. The concentration of Zn2+ in cells and across organelles is maintained by a complex set of 24 Zn2+ transporters [7, 8]. This pool of labile Zn2+ is available to bind to newly synthesized proteins, but the importance of Zn2+ to organismal and proteomic stability and the cellular energy allocated to the transportation of Zn2+ has led to the hypothesis that Zn2+ may also serve as a signal [9].</p><p>In order to gain insight into the biology of Zn2+ at a fundamental level it is important to understand both the distribution and dynamics of accessible Zn2+ in cells. Research into the distribution of Zn2+ generally strives to generate a detailed quantitative map of where Zn2+ is located in order to define possible sources and sinks of labile Zn2+ and identify whether there is a heterogeneous distribution of total Zn2+. To rigorously assign Zn2+ to a specific organelle, experimental approaches must be high enough resolution to unambiguously distinguish organelle structures (such as electron microscopy techniques) or the probe being used to measure Zn2+ must be restricted to a specific organelle. Fluorescent probes and elemental mapping techniques (see below) applied can both be used to develop such a Zn2+ map. As detailed in this review, the two approaches provide complementary information on different types of samples: live cells in the case of probes, and fixed samples or fixed time points in the case of elemental mapping techniques. On the other hand, research into the dynamics of Zn2+ must be carried out in live cells using fluorescent sensors in order to obtain temporal information about Zn2+ fluxes. Ideally, such tools will be sensitive to small changes in Zn2+ concentrations. For measuring both distribution and dynamics, tools must respond specifically and selectively to labile Zn2+.</p><p>A growing number of fluorescent small molecule probes and protein-based sensors are being developed to measure both the dynamics and subcellular distribution of labile Zn2+ in live cells. A wide range of sensors has been developed with diverse characteristics including: targeting to different organelles, signal detection at various wavelengths, and binding to Zn2+ with altered affinities. The application of probes and sensors in live cell imaging experiments requires many controls to evaluate whether the sensor is selectively measuring labile Zn2+, targeted only to the cellular area of interest, perturbs Zn2+ homeostasis and regulation, and validate that the signal changes are sensitive only to fluctuations in Zn2+ [6]. Thus, it is both wise and prudent to employ complementary imaging methods on fixed samples or fixed time points to corroborate and further define the cellular distribution of zinc, without adding probes to cells. These data can strengthen and confirm interesting observations gathered through the development and use of Zn2+ sensors and probes.</p><p>In the past several years, several elemental mapping methods including mass-spectrometry-based and X-ray fluorescence-based techniques have greatly improved in sensitivity and spatial resolution, allowing for the study of trace metal distribution at the cellular and subcellular levels [10–13]. By analyzing the significance of heterogeneous metal distributions at high resolution and quantifying biologically relevant changes in these distributions, researchers have gained insight into physiology, pharmacology, toxicology, pathology, and other disciplines. Besides providing a snapshot of total elemental distribution, mass-spectrometry approaches are capable of resolving individual isotopes of Zn2+ and other metals, and X-ray techniques can be used to determine chemical speciation. The information obtained from these approaches can complement studies using tools for measuring labile Zn2+ pools in live samples.</p><!><p>Several types of systems have been developed to study Zn2+ in cells by fluorescence microscopy. We refer the reader to several recent reviews that provide comprehensive coverage of these probes and sensors [4, 6, 14]. In this review, we will briefly summarize the types of sensors available for Zn2+ detection, and move on to a discussion of the experimental challenges associated with using probes and important controls that should be carried out to minimize misinterpretation of data. Many of the available tools fall under two general classes of probes: small molecule probes and protein sensors based on Förster Resonance Energy Transfer (FRET) [6, 14, 15]. The strengths and weaknesses of these two classes of probes, as well as summary of commonly used probes are highlighted in Figure 1. Small molecule probes usually increase in fluorescence upon chelation of Zn2+. The strengths of small molecule probes are that they can be cell permeable and therefore are easy to apply to cells, they are bright and yield a high fluorescence signal over background autofluorescence of cells, and can be made to fluoresce at various wavelengths. Small molecule probes have also been adapted to give ratiometric signals that allow for normalization for changes in fluorescence that are not due to chelation of Zn2+ [16, 17]. FRET-based protein sensors have also been developed to measure Zn2+ in cells. These sensors consist of two fluorescent proteins (FPs) and a Zn2+ coordinating site that is designed to change the relative orientation and distance of the FPs upon binding leading to a change in FRET signal from the donor FP to the acceptor FP. The ratiometric nature of these sensors allows for correction for protein concentration, sample thickness, and movement. The sensors are genetically encoded, can be targeted to organelles, and, through mutation, can be tuned to bind Zn2+ with a variety of affinities. Different colored FRET based sensors have been derived from the many available FPs, increasing flexibility in experimental protocols.</p><p>Beyond these two general classes of probes, other platforms have been developed that rely on different strategies to detect and report Zn2+. Hybrid sensors combine a synthetic portion with a genetically encoded portion. The Lippard lab has used SNAP-tag to genetically target the small molecule sensor ZinPyr-1 [18]. In a similar vein, the Fierke group created sensors that combine the Zn2+ binding enzyme, carbonic anhydrase, with a small molecular fluorophore and a FP [19]. These systems aim to combine the advantages of both small molecule and protein-based systems: the modularity and brightness of the small molecules with the targetability and ratiometric signals of protein based systems. A new DNA based probe for Zn2+ has recently been developed [20]. This system relies on a photocaged DNAzyme, which can be activated with light and cleaves in the presence of Zn2+ separating a fluorophore, fluorescein, from a quencher, dabcyl. This process creates a molecular beacon for Zn2+. Further development of this platform might allow for a new class of Zn2+ sensing molecules. It is useful to note the diversity of strategies for sensor design, as different sensors have different strengths and application of complementary sensor platforms can strengthen cell-based studies. Below we turn to recent work that points to the need for careful controls in the application of these molecules and the importance of understanding of the underlying chemistry that allows these molecules to detect Zn2+. We also refer the reader to an excellent recent review that discusses the complex solution chemistry and speciation of zinc ions in biological environments [5]</p><!><p>FRET-based sensors have been applied to measure the concentration of Zn2+ in the cytosol and organelles in a variety of cell lines. Figure 2 outlines the in situ calibration that is carried out in order to use ratiometric sensors to convert the FRET ratio to a normalized parameter for comparing relative levels of labile Zn2+ in different cells or under different conditions. Briefly, once cells are expressing the sensor, the 'resting' FRET ratio or the signal of the sensor before manipulation of Zn2+, is measured. Subsequently, the FRET ratios of the apo-sensor and the fully saturated sensor must be measured in order to determine the fractional saturation of the sensor at rest in each individual cell. To measure the FRET ratio of the apo sensor, the Zn2+ concentration is lowered to its extreme by adding excess cell permeable Zn2+ chelators, N,N,N',N'-Tetrakis(2-pyridylmethyl)ethylenediamine (TPEN) or Tris(2-pyridylmethyl)amine (TPA). To measure the FRET ratio of the Zn2+-saturated sensor, the Zn2+ concentration is elevated by adding excess concentrations of Zn2+ combined with either a cell permeabilizing agent (digitonin or saponin) or an ionophore (pyrithione) [21]. The ratio of the maximum signal of the sensor over the minimum signal of the sensor is called the dynamic range (DR).</p><p>The in situ calibration is typically used to calculate the fractional saturation of the sensor in individual cells, and can be used along with the KD value to estimate the concentration of Zn2+ in a particular location. The fractional saturation of the sensor provides a relative comparison of Zn2+ in different cells or under different conditions, where higher saturation suggests higher levels of labile Zn2+ and lower saturation suggests lower levels. Estimation of the Zn2+ concentration requires further assumptions and data processing, including accurate measurement of the sensor KD. The dissociation constants of these sensors are usually determined through in vitro titration of the sensors with known amounts of Zn2+, and fitting of the resulting data using a ratio-based approach [22]. This approach assumes that the donor flourophore signal decreases at the same rate that the acceptor flourophore signal increases during FRET. Recently, Pomorski et al. have shown that many ratiometric sensors do not conform to this assumption and have developed a more rigorous intensity-calibrated approach for fitting the titration data that should be applied to determining the apparent binding constants of ratiometric sensors [23]. It is worth noting that the reported KD values of many FRET sensors will likely need to be adjusted using the approach of Pomorski et al. In this review, we report the currently published KD values for consistency. It has also been suggested that the isosbestic wavelength of FRET sensors can be used as an internal standard to determine the binding affinities of sensors; this approach decreases the dynamic range by using the intensity change at a single wavelength [24]. Finally, some attempts have been made to measure in situ KDs, but this is challenging due to the complexity of the cellular environment.</p><p>Four different FRET-based protein sensor platforms, Zif, Zap, eCALWY, and eZinCh, have been applied using the above procedure to measure the concentration of Zn2+ in the cytosol of a variety of cell types. Interestingly, these sensors have very different modes of Zn2+ sensing. The ZifCY and ZapCY sensors, developed by the Palmer Lab, connect two FPs, truncated ECFP and Citrine, through zinc finger zinc-binding domains (ZBDs) that are unstructured in the unbound state with a sensor architecture of: donor FP – ZBD – acceptor FP [25–27]. Coordination of Zn2+ to the zinc-binding domain structures the linker between the two FPs leading to an increase in FRET signal. Mutation of the zinc binding domain of the ZapCY proteins led to the development two sensors with different apparent zinc disassociation constants at pH 7.4: ZapCY1, KD' = 2.5 pM, and ZapCY2, KD' = 811 pM [26]. The FPs have also been exchanged in the ZapCY platform to give a range of colors of sensors [28]. The Merkx Lab has developed two sensor platforms, the eCALWY and eZinCh sensors. In the eCALWY sensors, the FPs were engineered by making two mutations in the FPs (S208F and V224L) creating intermolecular contacts between the two FPs [29]. Two copper binding proteins, Atox1 and WD4 were modified to decrease binding of copper and linked between the two FPs, yielding a Zn2+-specific sensor with the following sensor architecture: donor FP – Atox1 – linker – WD4 – acceptor FP. Coordination of Zn2+ by the two copper binding domains breaks the contacts between the FPs lowering the FRET signal. In the final platform, eZinCh, the FPs have been engineered to create a Zn2+ binding site between the two FPs using two mutations (A206H and S208C) in each FP. These mutated FP are linked by an unstructured, flexible linker with the overall architecture: donor FP – linker – acceptor FP [30]. When Zn2+ is coordinated to the sensor there is an increase in the FRET ratio. Members of both the eCALWY and eZinCh platforms have been developed to bind zinc with a variety of binding constants and have a variety of colors of FPs [14, 30–32]. Although these platforms have very different modes of sensing, they converge on a labile Zn2+ concentration in the hundreds of picomolar in the cytosol of many cell types.</p><p>There are a few concerns that should be addressed in order to ensure the sensors are functioning well: the sensor should be localized to the area of the cell of interest, the concentration of the sensor should be such that it does not perturb the labile Zn2+ pool, the apparent disassociation constant of the sensor for Zn2+ should be close to the concentration of labile Zn2+ in the cellular compartment of interest, and the binding modality should be such that the sensor doesn't complex Zn2+ bound to proteins [6, 33]. A few key experiments suggest that genetically encoded FRET-based protein sensors are measuring the native labile Zn2+ pool in the cytosol. The lessons learned from measuring Zn2+ concentrations in the cytosol may help to inform protocols for measuring Zn2+ in other organelles.</p><p>Qin and coworkers critically compared FluoZin-3 AM and ZapCY2 to demonstrate that ZapCY2 was measuring the labile Zn2+ pool [34]. First, the localization of the sensors was compared. By incorporating a nuclear exclusion signaling sequence, ZapCY2 can be localized only to the cytosol and not to other organelles. In comparison, the small molecule probe, FluoZin-3 AM, co-localizes with a Golgi marker as well as localizing in the cytosol. To ensure that the concentration of ZapCY2 was not perturbing the Zn2+ pool, the concentration of Zn2+ measured by both ZapCY2 and FluoZin-3 AM was measured as a function of sensor concentration. While raising the extracellular concentration of FluoZin-3 AM from 0–10 µM appeared to deplete the Zn2+ pool, the fractional saturation of ZapCY2 remained constant across a range of intracellular sensor concentrations from ~0–80 µM demonstrating that ZapCY2 does not detectably deplete the Zn2+ pool. Depletion of the Zn2+ pool by FluoZin-3 AM may be caused by the high concentration of the probe that accumulates inside the cell as hydrolysis of AM-ester probes traps the probes intracellularly. By this process, the intracellular concentration of these probes reaches hundreds of micromolar which may allow the probes to interfere with the homeostasis of the hundreds of picomolar concentrations of labile Zn2+ [35]. In contrast to the dye that is added acutely to cells and measured a few hours later, the long-term expression of the genetically-encoded sensors over the course of days at a relatively low concentration (~10 µM) may allow these sensors to become part of the cellular Zn2+ buffer and therefore non-perturbing to the labile Zn2+ pool.</p><p>Another important concern to address when using sensors to quantify Zn2+ is whether the sensor is truly accessing the labile pool. The Zn2+ coordination environment is flexible in both geometry and number of ligands [5]. Consequently, probes have the potential to form ternary complexes by binding to Zn2+ that is already coordinated to a protein. The propensity of ternary complex formation is likely much lower for FRET-based sensors than small molecular probes, due to the steric bulk of FPs and protein-based zinc binding domains compared to small chelates, however this has yet to be tested. Sensors with low KD values or present at high concentrations could also shift the native equilibrium of Zn2+ thereby perturbing the labile pool. In either scenario the sensor would access Zn2+ from outside of the labile pool. If this were the case then sensors of various affinities would be able to access different pools of Zn2+ and measurement of Zn2+ with sensors possessing different affinities for Zn2+ would give rise to different results for the amount of Zn2+. Vinkenborg and co-workers tested this hypothesis by applying a panel of eCALWY variants to the cytosol of INS-1 cells with a range of apparent Zn2+ disassociation constants (KD') from 2 pM to 2.9 nM at pH 7.4 [29]. A plot of the fractional saturation of each variant against the disassociation constants of the variants gives a sigmoidal shape and a consistent cytosolic concentration of ~ 400 pM. Similar experiments were carried out by Qin et al in HeLa cells, yielding a cytosolic concentration of ~ 180 pM [34]. These experiments provide evidence that both sensor platforms are accessing the same pool of labile Zn2+ regardless of the affinity of the sensor.</p><p>These kinds of experiments strengthen the measurements collected through the application of sensors. As more sensors are developed and more measurements are made in a variety of cell types and in many organelles it is important to think critically about the various aspects of the sensors that could be affecting the measurements. Next we will examine measurements of Zn2+ concentrations using FRET-based sensors in the endoplasmic reticulum (ER), Golgi apparatus, and mitochondria where measurement of Zn2+ with different classes of probes has been less consistent. The variability in these measurements points to a need for critical examination of the fundamental chemistry of these tools, and to an interesting area of zinc biology as the distribution and dynamics of Zn2+ in the organelles continues to be studied.</p><p>There are a number of challenges associated with measuring Zn2+ in organelles, including ensuring that the sensor is properly localized, measuring Zn2+ in a small crowded environment with an uncertain Zn2+ buffering capacity, and measuring Zn2+ in different chemical environments (pH, redox state, etc). Table 1 summarizes the estimates of organelle Zn2+ obtained using three different sensor platforms and reveals the variability in estimates of Zn2+ in organelles. ZapCY1 and ZifCY1 were the first FRET-based genetically encoded sensors to be targeted to the ER and the Golgi in HeLa cells. Colocalization with canonical markers of the ER and Golgi was used to demonstrate localization to the targeted organelle [26]. Treatment of the cells with TPEN resulted in a small decrease in the high affinity sensor, ZapCY1 (KD'= 2.5 pM, pH 7.4), and no response in the low affinity sensor, ZifCY1 (KD'= 1 µM, pH 7.4), suggesting that Zn2+ concentration in the organelles was lower than the detection limit of ZifCY1. In situ calibrations suggested that ZapCY1 (dynamic range in the ER = ~2) was 26% saturated in the ER and 18% saturated in the Golgi. Using these data and the in viro KD', the concentrations of labile Zn2+ in the ER and the Golgi were estimated to be 0.9 ± 0.1 pM and 0.6 ± 0.1 pM, respectively. When ZapCY1 and ZifCY1 were targeted to the mitochondria each sensor reported a very different concentration of labile Zn2+ in the organelle [27]. Because of the very low dynamic range of ZifCY1 in the mitochondria, this sensor was re-engineered by replacing the YFP with circularly-permuted Venus to increase the dynamic range. The circular permutation of Venus changes the orientation of the fluorophore with respect to the CFP, altering the FRET efficiency between the two FPs. This resulted in the development of a high dynamic range (DR=2.5), but lower affinity sensor, mito-ZifCV1.173. Importantly, Zif sensors that incorporated other versions of circularly-permuted Venus had very low dynamic ranges and appear more saturated with Zn2+ in comparison to the high dynamic range sensors (~40% vs ~10%) although all the sensors had the same Zif1 binding domain and probably similar affinities for Zn2+. This observation highlights the need for high dynamic range sensors (above ~1.2 based on these data) for accurate measurement of Zn2+ concentration. Because the environment in the mitochondrial matrix is crowded and has a variable pH, the KD' of ZapCY1 was determined in situ to be 1.6 pM. This value matched the in vitro KD measured at the same pH. ZapCY1 was saturated 16 ± 10 % in the mitochondria (dynamic range = ~2), and, using these data and the in situ KD' for the sensor, the concentration of Zn2+ in the mitochondria of HeLa cells was estimated to be about 0.14 pM. Although the range of sensor concentration was limited in the organelles, perturbation the labile Zn2+ concentration at increasing levels of sensor expression was not detected [26, 27].</p><p>The Merkx Lab has also applied their sensors to the mitochondria and ER. Three different sensors were targeted to the ER of a variety of cell lines (eCALWY-4, KD' = 630 pM; eCALWY-6, KD' = 2.9 nM; eZinCh-2, KD' = 1.0 ± 0.1 nM) [30, 36]. The dynamic range of the sensors in the ER varied from 1.1 to 1.4, depending on the sensor and the cell type, yielding a range of Zn2+ concentrations from 800 pM to 7.2 nM. eZinCh-2 and eCALWY-4 were both used to measure the labile Zn2+ concentrations in the mitochondria of HeLa cells where the dynamic range in mitochondria ranged from 1.2 to 1.9 and estimations of Zn2+, determined using the in vitro KD' values, ranged from 3.3 ± 1.2 pM applying eZinCh-2 to 180 ± 300 pM applying e-CALWY-4 [30].</p><p>In summary, while it is easy to point to the variability in Zn2+ estimates obtained using genetically encoded sensors and find fault with the FRET-based protein sensors [4], a better understanding the details of why the variability occurs will lead to the development of more robust tools that can be targeted to organelles to uncover the details of Zn2+ biology.</p><!><p>Over the last 30 years small molecule probes have been engineered for and applied in a variety of contexts to study Zn2+ biology in cells and tissues. Over this period, progress has been made in designing probes to gain enhanced photophysical properties, to tune the affinities of probes for Zn2+, to be ratiometric, to be reaction-based, and to target different organelles. These improvements have led to the application of probes to complex biological questions and led to important new knowledge about Zn2+ homeostasis and signaling [6]. However, recent work has led to re-examination of data collected using small molecule probes that emphasizes the need to better understand the chemistry of these probes and to develop clear controls for verifying the reactivity and properties of probes in cells. Studies carried out by the Petering Lab have pointed out the subtleties of the interaction of small molecule probes, Newport Green, TSQ, and Zinquin with Zn2+ in the cell [37, 38]. Briefly, it was always assumed that these sensors substituted for weakly bound ligands in cells, coordinating labile Zn2+, and increasing in fluorescence intensity. However, examination of the dyes under a variety of conditions shows that these probes have multiple interactions with both labile and bound Zn2+ that cause detectable fluorescence increases. Although papers have claimed to use Newport Green to image native Zn2+ dynamics, the dye has a micromolar affinity for Zn2+, and it is therefore difficult to find an application where it detects changes in the labile Zn2+ pool, given that estimates of cytosolic Zn2+ are in the hundreds of picomolar range. TSQ and Zinquin were both found to form fluorescent ternary sensor-Zn-protein adducts, meaning that the probes access and report Zn2+ that is tightly bound to proteins in addition to labile Zn2+. This observation is interesting in light of recent work done by the Merkx Lab that re-examined data that was collected in fixed cells using Zinquin with live cells and genetically encoded FRET sensors. Work with Zinquin suggested that Zn2+ was mobilized from ER stores by activation of the Zn2+ transporter Zip7 in the presence of EGF and ionomycin in Tamoxifen-resistant MCF-7 cells. Through application of eCALWY-4 and eZinCh-2 in the cytosol and ER, no such dynamics of Zn2+ were seen [31]. The application of Zn2+ sensors allows for the start of interrogation of the biology of Zn2+, but studies like these are excellent reminders that other imaging techniques can lend increased rigor to the data obtained with sensors.</p><!><p>Numerous analytical techniques are available for visual mapping of elemental distribution in biological samples. Here we will discuss several approaches that can be divided into two classes: mass spectrometry-based and X-ray fluorescence-based imaging techniques. A schematic of the differences between these techniques is outlined in Figure 3. Due to space limitations, this review will cover only a subset of the various methods in these categories, focusing on the most widely used techniques reported in the literature. We will describe the basic properties of each imaging technique and provide a few examples of how they have been applied to the field of Zn2+ biology. An important distinguishing feature of these techniques is that they provide a measure of the total amount of a metal of interest, as opposed to just the labile or accessible metal pool, and most techniques permit measurement of multiple elements at once, allowing for correlation between metals and other abundant biological elements, such as phosphorus. These techniques are traditionally applied to fixed samples of cells, or biological specimens such as plants or seeds at a fixed time point. While these techniques cannot be applied to live cell imaging, they can offer complementary elemental snapshots at multiple time points.</p><!><p>Mass-spectrometry based techniques involve identification of specific chemicals within a sample by separation of ions according to their mass-to-charge ratio. Early mass-spectrometry applications did not provide spatial resolution, but newer approaches often contain mapping capabilities, thus enabling researchers to define the spatial distribution of metal ions. Mass-spectrometry-based spatial imaging allows for a point by point mass-spectrum distribution map generated by ablation of a sample at thousands of spots [13]. The different types of massspectrometry techniques can be distinguished by their method of ionization. Here we will discuss laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and secondary ion mass spectrometry (SIMS), the major two mass-spectrometry imaging techniques that are used to study the localization of metals in biological samples.</p><!><p>LA-ICP-MS has been widely applied in the life sciences for analyzing biological samples for metal localization in medical studies, such as tumor growth analysis, and biochemistry of animal model diseases [13, 39]. This method uses high-energy focused lasers to ablate particles from a solid sample; the particles are then carried by gas (Ar or He) into an ICP-MS platform where ionization occurs and charged ions are detected by their mass-to-charge ratio. Although LA-ICP-MS has a relatively low spatial resolution compared to other imaging techniques (1 µm limit), it offers very high sensitivity (ng/g) and multi-elemental imaging capability [11, 40, 41]. Particularly unique to both LA-ICP-MS and SIMS (discussed below) is the ability to detect multiple isotopes of individual elements, allowing for snapshots of dynamic trace element transport and distribution [40].</p><p>One notable application of this technology was the use of laser ablation coupled to multi-collector-ICP-MS (LA-MC-ICP-MS) to measure Zn2+ isotope incorporation in rat brain thin sections in an effort to identify the rate of uptake into different regions of the brain [42]. This is particularly important because the mechanisms of Zn2+ acquisition and distribution at the tissue and organismal level are not well understood. Further, alterations in brain Zn2+ homeostasis have been linked to the development of neurological diseases and thus it is critical to understand Zn2+ metabolism in the brain [43]. In this study, animals were injected with solutions enriched in two stable isotopes of Zn2+, 67Zn and 70Zn, at two time points compared to control animals injected with either saline only or a Zn2+ solution of natural isotopic composition [44]. Isotope ratio maps revealed a heterogeneous distribution of Zn2+ isotope ratios in different physiological features of brain tissue (hippocampus, amygdala, cortex, and hypothalamus) suggesting different turnover kinetics in specific regions of the brain. In another study using LA-ICP-MS, researchers analyzed brain tissue sections and found that the 31P/66Zn ratio decreased beyond tumor boundaries, demonstrating that changes in this ratio can be used to identify healthy vs. cancerous tissue [45]. Similarly, a study of liver sections demonstrated that metals can be used as disease biomarkers; Fe and Cu levels are higher in diseased liver tissue, while Zn2+ levels are decreased, and these alterations can be used to predict changes from healthy to diseased liver status [46]. Further examples demonstrating the application of LA-ICP-MS include cerebral imaging metals including Zn2+ in the pathophysiology in the mouse models of Parkinson's disease and Alzheimer's disease [47–49].</p><!><p>Another analytical mass-spectrometry technique used for imaging elements and their isotopes is SIMS. SIMS uses a focused primary ion beam to dispel particles from a sample surface. The ions that are ejected after this surface bombardment are called secondary ions, which are subsequently analyzed in a mass spectrometer. The primary strength of SIMS is that it can offer excellent spatial resolution, particularly for NanoSIMS, which has a spatial resolution of 50 nm. In general, the width and energy of the beam dictates the achievable resolution; a wide, high intensity beam allows for depth profiling, while a narrower, low intensity beam may only erode the surface, making quantification difficult [11]. Further limitations include: the technique does not provide information about chemical state [11] and SIMS analysis requires samples to be in a vacuum, and hence samples must either be cryo-fixed or must be fixed at low temperatures, dehydrated, and resin-embedded [10]. For such preparations, care must be taken to ensure elemental distribution is not altered as a result of sample preparation. Although SIMS can be used to detect most elements in the periodic table and their different isotopes, a few elements such as Zn2+, Cd, and Mn have poor secondary ion yield and are therefore difficult to detect at standard concentrations (Zhao, 2014, Moore 2012). SIMS has, however been used to successfully determine Zn2+ localization in Poplar leaves from plants grown on Zn2+-contaminated soil [50], suggesting that although SIMS may not be not ideal for detecting low levels of Zn2+, it may be appropriate to study biological samples with elevated Zn2+ levels. Further, in studies where another approach is applied to study Zn2+ localization, SIMS may offer the ability to study localization of other lighter metals of interest such as P and Si that cannot be examined as effectively by other approaches, such as X-ray fluorescence (discussed in detail in the next section of this review) [51]. In cases where X-ray fluorescence and SIMS are used in combination, adjacent thin sections can be analyzed.</p><!><p>In addition to mass-spectrometry techniques, the use of X-ray fluorescence techniques provide a complementary approach for mapping metal distribution in biological specimens, and offer the additional advantage of obtaining information on multiple elements and chemical speciation of elements of interest. X-ray fluorescence methods rely on the photoelectric effect; when an atom is exposed to high-energy radiation, electrons are ejected from inner shells, leading to core vacancies, which are filled by outer-shell electrons. As outer shell electrons decay, energy is released in the form of fluorescence. Because the energy of the emitted photons is characteristic for each element, and the intensity is proportional to concentration, X-ray fluorescence provides information on the identity and amount of a given element in a sample. Samples can be raster scanned line by line through a focused incident X-ray beam to yield a high resolution, high sensitivity elemental map [10, 11]. X-ray fluorescence techniques are categorized according to the type of incident radiation. Here we will discuss synchrotron X-ray fluorescence (SXRF), which uses X-rays, and electron-probe energy-dispersive spectroscopy (EDS), which uses electrons and is often combined with a scanning electron microscope (SEM). Other techniques beyond the scope of this review include micro-particle-induced X-ray emission (μPIXE), which uses charged atoms such as protons.</p><!><p>SXRF possesses a number of advantages for mapping metals and other elements in biological samples. Synchrotron facilities have high-intensity photon sources that are more than ten times brighter than those of conventional X-ray tubes [10], thus giving rise to high sensitivity and submicron spatial resolution [13, 52]. As opposed to mass spectrometry-based techniques, SXRF is generally non-destructive and does not require vacuum. Further, the sensitivity of SXRF increases with increasing atomic number, making it well-suited to study trace elements and heavy metals/metalloids, including Zn2+.</p><p>A recent application of SXRF fluorescence demonstrates the strengths of this approach for mapping metals and other elements with sub-cellular resolution, where the location and redistribution of metals was monitored during mitosis of mammalian cells. Briefly, washed and dried NIH 3T3 cells attached to Si-nitride windows were analyzed by SXRF and researchers found that Zn2+, Cu, and S remained colocalized throughout mitotic states and that Zn2+ increased during mitosis compared to interphase, suggesting a role for Zn2+ in the cell cycle [53]. Because SXRF imaging has the potential to study metal distribution at the subcellular level in studies such as these, important questions are raised about the significance of variable sub-cellular metal accumulation and speciation. To confirm subcellular localization, SXRF may be performed in combination with organelle localization using fluorescent markers. For a recent review on the application of this concept, see Roudeau et al., 2014 [54].</p><!><p>In addition to mapping elemental localization by SXRF, X-ray absorption spectroscopy (XAS) is another synchrotron-based technique that can be used to analyze the chemical speciation of individual elements. This capability is unique to synchrotron-based approaches and is not offered by the previously discussed mass-spectrometry techniques. An XAS analysis involves progressively increasing the incident X-ray beam energy at a specific point in a sample and collecting X-ray fluorescence at each individual energy. The XAS spectrum includes the X-ray absorption near-edge structure (XANES) and the extended X-ray absorption fine structure (EXAFS). XANES covers the energy range from about −50 to +200 eV of the absorption edge and is sensitive to the oxidation states of elements, while EXAFS covers energies from the absorption edge to approximately +800eV and is well-suited to elucidate coordination chemistry, including the identity and number of coordinating atoms and their interatomic distance [10]. Depending on the researcher's specific needs, these techniques can be applied to several regions of interests in samples using a micro-focused beam or alternatively applied to bulk tissues using a larger mm beam. It is important to note that in order to properly identify speciation within a sample, an XAS spectrum must be compared to known standards and thus researchers may be limited by the availability of appropriate standards.</p><p>Synchrotron beam-lines generally allow for collection of both SXRF mapping and XANES or EXAFS allowing researchers to map metal distribution and perform more detailed analysis of the chemical speciation in specific regions of the sample. For example, SXRF analysis of frozen murine macrophage cells identified Zn2+ hotspots, or putative Zn2+ vesicles, called "zincosomes" [55]. These spots were then analyzed by XAS to elucidate chemical speciation information of the zincosomal Zn2+. XANES and EXAFS revealed that Zn2+ was bound to one sulfur atom at a distance of 2.28 Å and two and a half histidine atoms and one oxygen atom at 1.97 Å. For a few recent reviews on the application of X-ray absorption spectroscopy for metal speciation, refer to Gräfe et al. 2014, West et al., 2015, and Zhao, et al. 2014 [10, 56, 57].</p><!><p>Although Synchrotron-based X-ray fluorescence techniques are powerful, access to Synchrotrons can limit the widespread availability of these techniques. An additional approach for elemental mapping by X-ray fluorescence is electron-probe energy-dispersive spectroscopy (EDS), also called electron probe microanalysis (EPMA), which uses an electron beam to bombard a sample. As with Synchrotron-induced fluorescence, the X-rays emitted following bombardment of a sample with an electron-beam, are characteristic for individual elements and the intensity is proportional to the amount of the element in the sample. EDS is usually used in combination with a scanning electron microscope (SEM), a transmission electron microscope (TEM), or a scanning transmission electron microscope (STEM) to allow for analysis of additional morphological features of the sample. Simultaneous high resolution imaging of subcellular structures in combination with elemental analysis within these structures is particularly useful for studying composition of whole cell samples that are a few micrometers thick [13]. Recently, this technique has been applied by fitting a cryo-compatible STEM with an EDS detector. Researchers demonstrated the ability to spatially map K, Fe, and Zn2+ in red blood cells with STEM-EDS [58]. They found an even distribution of these metals throughout RBCs and because of the combination with STEM, they were also able to study these metal distributions in the context of the flexible RBC morphology. We will discuss an additional example of STEM-EDS in the section entitled "combinatorial imaging approach".</p><!><p>Each technique highlighted above offers a different degree of sensitivity, specificity, and spatial resolution; the advantages and limitations of each should be carefully considered for individual experimental design. Besides the fundamental pros and cons of each approach, access to equipment may present additional limitations for researchers. For example, a STEM-EDS system may not be available at a researcher's home university. Further, many of the X-ray fluorescence techniques, such as SXRF, require access to specialized synchrotron facilities. Access to these facilities usually necessitates a prior arrangement of beam time through award of successful written proposal submitted well in advance. Even with awarded time, researchers are allotted a limited period of time and each scan may take upwards of several hours. Researchers may have to compromise resolution of their elemental maps to decrease scanning time in order to study more samples. Because of issues such as these, studies using these techniques are often limited in sample size/biological replicates. Thus, these approaches are better suited for targeted hypothesis-driven experiments, rather than discovery-based screens or high-throughput studies.</p><!><p>As emphasized above, different tools permit measurement of different metal pools; small molecule probes and sensors can be used to quantify the subcellular distribution of labile Zn2+, whereas mass-spectrometry and X-ray fluorescence-based techniques can be used to map the distribution of total Zn2+. Each analytical tool offers a unique piece of information such that combination of multiple techniques provides a powerful means to generate a comprehensive picture of Zn2+ distribution.</p><p>An elegant example of this combinatorial approach is illustrated by the recent study of Zn2+ fluxes involved in the fertilization mammalian egg. Previous studies found that labile Zn2+ is released from eggs during a meiotic checkpoint in what is referred to as a "Zn2+ spark" [59]. To elucidate the molecular mechanism of these Zn2+ sparks, researchers used a combination of four approaches to resolve zinc distribution and measure Zn2+ concentrations in single cells before and after sparks. Using a synthetic fluorescent Zn2+ probe and an extracellular zinc dye for live-cell imaging at different stages of egg development, researchers that Zn2+-rich compartments are the source of Zn2+ that is exocytosed during Zn2+ sparks. Further, elemental composition of meiosis II (MII) eggs was analyzed at an ultrastructural level with a STEM microscope equipped with EDS detectors, allowing for anatomical and elemental imaging. Thin sections of fixed, resin-embedded MII eggs were analyzed with STEM-EDS. Bright, vesicularlike structures near the ooplasmic membrane were found in Z-contrast STEM images, indicating elements of higher molecular weight. The EDS spectra measured in these regions identified by STEM compared to nearby cytoplasmic regions revealed an increased Zn2+ intensity in the vesicular-like bodies. Authors point out that STEM-EDS is not very quantitative, so they further turned to high-resolution SRXF to confirm their results by mapping thin sections of the MII eggs. In these experiments, they also saw Zn2+-enriched compartments, consistent with both the live-cell imaging and the STEM-EDS analysis. Finally, quantification by highly sensitive SRXF allowed for determination of total Zn2+ concentration in the vesicular stores. Application of four complementary technique allowed the authors to conclude that Zn2+-containing vesicles near the membrane are the source of Zn2+-sparks, and the amount of Zn2+ within these vesicles is consistent with the amount of Zn2+ released.</p><!><p>We have discussed the methods and considerations for imaging labile and total Zn2+ using various tools and approaches. As others have pointed out, there is room for improved communication between the chemists who develop tools such as FRET sensors, experts of particular imaging techniques (i.e. synchrotron beamline scientists), and the biologists who apply them [11, 60]. With the extensive and ongoing development of a vast array of chemical and analytical tools available to study labile and total Zn2+ pools, the scientific community is now well-poised to answer fundamental questions about Zn2+ biology. Understanding the dynamics and distribution of metals is essential for deciphering how particular distribution patterns are set up, which genes control these processes, how they differ between cell-types, and how they are altered in healthy vs. diseased states. To aid in answering these questions, Zn2+ pools can be depleted or elevated, manipulated by pharmacology, or altered by genetics (i.e. knockdowns or overexpression of particular Zn2+ transport/homeostatic genes). By combining these biologically relevant perturbations with the chemical and analytical analysis methods, we will continue to elucidate fundamental aspects of Zn2+ biology.</p>

PubMed Author Manuscript

Prediction of HIV-1 Protease/Inhibitor Affinity using RosettaLigand

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R=0.71 and SE=5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R=0.85 and SE=4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

prediction_of_hiv-1_protease/inhibitor_affinity_using_rosettaligand

Introduction<!>176 experimental PR/PI binding energies have been collected<!>171 high resolution template PR structures have been collected<!>Threading of sequence onto structure for comparative modeling<!>High resolution refinement of comparative models<!>Low resolution initial placement of ligand<!>Docking of PIs into comparative models<!>Predicting \xce\x94\xce\x94Gs using the standard approach<!>Predicting \xce\x94\xce\x94Gs using the constant-unbound approach<!>Predicting \xce\x94\xce\x94\xce\x94G focuses on the influence of mutation on binding affinity<!>Optimization of RosettaLigand score term weights<!>Partitioning data by location of PR mutations<!>Assessment of uncertainty in experimental binding affinity data<!>Comparative models have been built for 176 PR/PI complexes with known binding energies<!>RosettaLigand docking protocol allows local flexibility<!>Usage of experimental data for weight optimization<!>Analysis of optimized scores<!>Predicting \xce\x94\xce\x94Gs using the standard approach<!>Predicting \xce\x94\xce\x94Gs using the constant-unbound approach<!>Optimized score term weights predict binding affinity in independent data set<!>Analysis of data partitioned by location of PR mutations<!>Conclusion<!>Future Directions

<p>The binding affinity of a drug to its protein target is defined by the free energy difference between the bound and unbound state. Mutation of the protein or chemical modification of the ligand can alter this energy difference directly – i.e. by adding or subtracting interactions between the two partners – or indirectly – i.e. by stabilizing or destabilizing protein or small molecule in either bound or unbound conformation (1). For the unbound state often ensembles of protein and small molecule need to be considered (2) while the bound state is often considerably more rigid. HIV-1 protease (PR) interaction with its inhibitors is a model case for this scenario while examples for the opposite scenario – rigid protein increases flexibility upon binding – are also known (3, 4).</p><p>Current computational methods are capable of predicting direct effects reasonably well through an analysis of all interactions between protein and ligand. However, the same methods often fail to predict indirect effects. For instance it remains difficult to predict how mutations outside the binding pocket are propagated throughout the protein and to the binding site (5). These indirect effects are likely to have greater destabilizing influence on a rigid-bound state then on a flexible unbound state.</p><p>We hypothesize that in the scenario of a rigid bound and flexible unbound state, prediction accuracy of indirect effects on binding affinity can be improved through a simple approximation. Figure 1 summarizes the effects of mutations on binding free energy in two scenarios: The top row represents the scenario wherein the unbound state exists as one stable low energy conformation. The bottom row represents the rugged energy landscape (jagged red line) of a flexible unbound state with multiple energetic minima. In a thought experiment we compare a binding site mutation that is assumed to interfere only with direct interactions between ligand and protein with a non-binding site mutation that is assumed to only affect stability of the protein, but does not change the protein-ligand interaction. In reality combinations of these two scenarios exist.</p><p>In the first scenario – a rigid unbound state engages the ligand and remains rigid, a mutation within the binding site that disrupts protein-small molecule interactions will lower the binding affinity (Figure 1B). A mutation outside the binding pocket would have an equal effect on the free energy of bound and unbound conformation as they are identical. As a results the ligand affinity is unaltered (Figure 1C). In the case of a flexible unbound state, mutations inside the binding pocket that interrupt protein-ligand interactions would again be expected to lower binding affinity (Figure 1E). However, mutations outside the binding pocket are expected to have a greater destabilizing effect on the single rigid bound conformation than on the unbound state which consists of an ensemble of structures. While mutations which affect low-energy structures that contribute to the unbound state will certainly affect the overall free energy of the unbound state. However, we hypothesize that this effect is small as mutations will affect only a fraction of the low-energy conformations the unbound state can assume. If the ensemble is large enough, influence on free energy will be small. This hypothesis suggests that the free energy of the unbound state can be approximated with a constant in this scenario. The result of this difference is a net change in binding energy due to mutation outside the binding pocket (Figure 1F). It is obvious that this approximation is only valid for proteins that are very flexible in the unbound state and convert to a rigid bound conformation. HIV-1 PR is an example.</p><p>HIV-1 PR is a homodimer with a flexible binding site (Figure 2). Over 200 high resolution crystal structures of HIV-1 PR mutants in complex with HIV-1 PR inhibitors (PIs) are deposited in the protein databank (PDB, resolution better than 2.0 Å) (6). These mutants exhibit limited structural diversity verifying the well-defined rigid bound conformation of the protein (7). However, the two flap regions exhibit up to 7Å of movement in the unbound state (Figure 2) (8, 9). The unbound state is therefore best described as a large ensemble of structures (10). We hypothesize that it is for this reason that PR/PI docking studies have had difficulty predicting binding free energy (ΔΔGs). The free energy of the unbound state (ΔGu) is not accurately reflected by a single structure or a tight ensemble.</p><p>Cheng et al. assessed 16 scoring functions utilized in protein/ligand docking (11) for prediction of PR/PI ΔΔGs. Correlation coefficients ranged from R=0.17 to R=0.34. RosettaLigand predicted ΔΔGs with a correlation of R=0.41 (12). AutoDock predictions correlated with R=0.38 on a set of 25 HIV-1 PR/PI structures from the PDB, with binding data available (13).</p><p>At the same time HIV PI therapies are greatly hampered by drug resistance mutations. Only recently, conformational ensembles were used to assist in designing PIs with broad enough specificity to avoid escape mutations (14). The authors of this study evaluated chemical modifications to known PIs using electrostatic charge optimization. They chose not to include induced-fit effects or ligand flexibility.</p><p>In this study we use RosettaLigand to predict the effect of PR mutations inside and outside the binding pocket. Predicted ΔΔGs are compared with experimentally determined ΔΔGs. These include 34 HIV-1 PR mutants and eleven PIs. We demonstrate that by assuming the unbound state constant with respect to mutation we can achieve a correlation coefficient of R=0.71 over a wide array of PR/PI ΔΔG data. Improved prediction of PR/PI binding affinity may help clinicians select the optimal PI for treatment and help design PIs with broad specificity that avoid resistance mutations.</p><!><p>PR/PI binding energies (ΔΔGs) were obtained from the Binding Database (www.bindingdb.org) (15). These 176 binding energies include experimental conditions and HIV-1 PR mutant sequence information, but lack structural information. They include a total of eleven distinct PIs and 34 distinct PR sequences. 106 of these datapoints resulted from isothermal titration calorimetry (ITC) measurements. The remaining 70 datapoints are enzyme inhibition constants (Kis).</p><p>These Kis were converted to binding energies using the equation ΔG = RT ln Ki, where R is the gas constant, 8.314 J K−1mol−1, and T is temperature in Kelvin. Ki values before and after conversion are summarized in Table S1. Since temperatures were rarely reported, we assumed 25°C (298K) for the conversion.</p><!><p>171 crystal structures of HIV-1 PR bound to various ligands were obtained from the PDB. These structures each have resolution better than 2.0 Å. PDB codes, resolution, bound ligands, and citations for all 171 of these structures are listed in Table S2. A multiple sequence alignment of these 171 structures is given as Figure S1.</p><!><p>34 distinct sequences were associated with the 176 experimental PR/PI binding energy data points. The 3-letter residue codes found in each of the 171 backbones were replaced with 3-letter residue codes for each of the 34 sequences, thus generating 5,814 models. Missing side-chain coordinates were constructed using Rosetta:</p><!><p>Rosetta's high-resolution refinement protocol searches for low-energy structures in the conformational vicinity of the starting model (16, 17). Backbone torsion angles are perturbed. Next side-chain rotamers are optimized (18). Finally backbone and side-chain torsion angles are adjusted using a gradient-based energy minimization. This process is repeated multiple times, using a Monte Carlo accept/reject criterion (19).</p><!><p>After a structural alignment was used to superimpose all comparative models, ligands were placed in the binding pockets of these models according to their positions in homologous crystal structures. Next 1,000 placements of the ligand were sampled to find a starting pose that has acceptable attractive and repulsive scores. A soft repulsive energy term was used during initial ligand placement (12).</p><!><p>Six cycles of side-chain rotamer sampling were coupled with small (0.1 Å, 0.05 radians) ligand movements. Each cycle included minimization of ligand torsion angles with harmonic constraints (where 0.05 radians of movement is equal to one standard deviation). Each ligand torsion angle has a constraint score which is calculated as: f(x)= (x−x0)/(standard deviation). Amino acid side chains were repacked using a backbone-dependent rotamer library (20). During a final minimization, backbone torsion angles were optimized with harmonic constraints on the Cα atom positions (0.2 Å standard deviation). Each C-alpha atom has a constraint score which is calculated as: f(x)= (x−x0)/(standard deviation).</p><p>The RosettaLigand standard scoring function with hard repulsive forces was used during the final minimization step. Score terms include the 6–12 Lennard-Jones potential (21), the Lazaridis-Karplus solvation model (22), a side-chain rotamer score, based on the Dunbrack rotamer set (20), a pair potential based on the probability of seeing two amino acids close together in space (23), and an explicit orientation hydrogen bonding model (24).</p><p>All computation was performed on the Vanderbilt University ACCRE cluster (www.accre.vanderbilt.edu). Rosetta revision 32372 was used for all calculations. Command line arguments and input options are given in the Supporting Information.</p><!><p>The standard approach calculates ΔΔGs as the difference between the free energy of a docked model (ΔGb) and the free energy of the unbound model with equivalent sequence (ΔGu) after energy minimization. This setup corresponds to Figure 1A–C wherein the unbound state and bound state free energies are equally susceptible to disruption by mutation (Eq. I). For each of the 34 mutant PR sequences the lowest energy unbound comparative model was chosen to represent ΔGu. The lowest energy docked model for a given PR/PI pairing was chosen to represent ΔGb. The difference between these values was taken as a prediction of ΔΔG.</p><!><p>The constant-unbound approach corresponds to Figure 1D–F and calculates ΔΔG by assuming ΔGu to be unknown but invariant with mutation (Eq. II). The lowest energy docked model for a given PR/PI pairing was chosen to represent ΔGb. [I]ΔΔG=ΔGb−ΔGu [II]≈ΔGb−const</p><!><p>To determine how well RosettaLigand can predict changes in binding free energy (ΔΔΔG, see Figure 3) upon protein mutation i→j, pairs of predicted or experimental ΔΔGs sharing the same PI but different PR sequence were subtracted to obtain ΔΔΔGs (Eqs. III, IV). ΔΔΔGs predicted by Rosetta were compared with experimental ΔΔΔGs to obtain ΔΔΔG correlation. This strategy removes influences from the changes of the ligand thereby focusing on predicting the influence of mutations. [III]ΔΔΔG=ΔΔGi−ΔΔGj=(ΔGi,b−ΔGi,u)−(ΔGj,b−ΔGj,u) [IV]≈ΔGi,b−ΔGj,b</p><!><p>The docking calculations performed so far were based on the original RosettaLigand scoring function ( 2006)(12) where the scoring term weights had been optimized across a set of diverse protein/ligand complexes. In the past it has been demonstrated that optimized scoring functions are needed to accurately predict free energies with Rosetta(25). Therefore an optimized weight set for PR/PI complexes was developed. Score term weights were optimized separately for standard binding affinity predictions and constant-unbound predictions. Score term weights were also optimized separately for ΔΔG predictions and ΔΔΔG predictions. Hence, a total of four optimized weight sets were produced (Table 1). First, docking results were filtered by taking the top 5% of models by total energy and the top model by interface energy. A leave-one-out cross-validation analysis was used to determine the weights that produce the strongest correlation with experimental data. A multiple linear regression was used to determine weights that optimize the correlation between experimental and predicted binding affinity. The weight set was then applied to predict binding affinity of the data-point left out. In a round robin scheme, each data point was left out. The correlation coefficients and standard deviations relate to the predictions made for these independent data points. The final optimal weight sets reported are averaged over all cross-validation experiments (Table 1). Weight optimization was implemented in Mathematica (26).</p><!><p>We partitioned the 34 sequences shown in Figure 4 into four distinct groups, based on the presence and location of "exceptional" mutations. Exceptional mutations are defined as amino acids that are uncommon or rare in a multiple sequence alignment – i.e. if 17 out of 34 sequences have an A in a position and the other 17 have a V, neither is an exceptional mutation. A sequence that has an S in the same position would be counted as an exceptional mutation A/V→S. Exceptional mutations were selected using ClustalW alignment software (gray boxed residues in Figure 4). The first group includes sequences with no exceptional mutations (sequences 4, 5, 22, and 26). The second group has only exception mutations within or near the binding site (red residues in Figure 2) and includes sequences 1, 8, 16, 19, 21, 24, 29, 30, and 33. The third group has only exceptional mutations outside the binding pocket and includes sequences 2, 3, 9, 11, 12, 23, 27, and 28. The fourth includes sequences that have exceptional mutations within and outside the binding site (sequences 6, 7, 10, 13, 14, 15, 17, 18, 20, 25, 31, 32, and 34).</p><p>We also partitioned sequences based on whether exceptional mutations fell within or outside of the flexible flap region. We define this region as comprising residues 37–61 (27). By this definition, 24% of PR lies in the flap region. Sequences with only exceptional mutation in the flap region include sequences 19 and 24. Sequences with only exceptional non-flap mutations include 1–3, 8, 9, 11–18, 20, 21, 23, 25, 27–33. Sequences with exceptional mutations in and out of the flap region include 6, 7, 10, 20 and 34.</p><!><p>As seen in table S1 for a few PR/PI pairs binding affinities have been determined multiple times. In these cases we use average values which reduces the total number of experimental ITC values from 106 to 99 while the total number of Ki datapoints is reduced from 70 to 62. We further use replicate data to estimate the accuracy of experimental values. The standard error for ITC replicates is 4.69 kJ/mol. The standard error for converted Ki replicates is 7.21 kJ/mol. We will use these numbers as estimates for the experimental uncertainty. As noted in the previous section, we assume a temperature of 25°C in order to convert Kis to ΔΔGs. This assumption introduces additional uncertainty for ΔΔGs calculated from Kis. nevertheless, the standard deviation between ΔΔG values converted from Ki data and matching ITC values is 1.07 kJ/mol, confirming the validity of the conversion.</p><!><p>The 34 distinct mutant sequences found in our experimental data contained between 3 and 14 mutations per monomer to match the wild-type HIV-1 PR sequence (28). These 34 mutant sequences were aligned and mutations at residues known to confer drug resistance are highlighted in red boxes (Figure 4). Each of the 34 sequences was threaded onto the backbones of all 171 template structures yielding 5,814 comparative models. These 5,814 ligand free structures were relaxed 10 times each using the Rosetta energy function (see methods). These 58,140 relaxed structures served as starting structures for RosettaLigand docking simulations.</p><!><p>For each 176 experimentally determined PR/PI binding affinities, the 171 times 10 comparative models with matching sequence were docked with the respective ligand. A total of 300,960 unique input structures were used for ligand docking. Local induced-fit effects were considered through full PR and PI flexibility in the binding site: The RosettaLigand docking predictions allow ligand flexibility by minimizing ligand torsion angles. Backbone torsion angles near the PR/PI interface were also minimized.</p><p>For each input, the docking protocol was repeated 20 times. For each set of predictions for a given PR/PI datapoint, docking results were filtered by taking the top 5% of models by total energy and the top model by interface energy.. Figure S2 compares top scoring Rosetta models with experimental PR/PI complex structures from the PDB that share the same PI to confirm accuracy of the modeling procedure.</p><!><p>RosettaLigand uses a scoring function that has been optimized to give optimal docking results for a wide variety of ligands (12). For accurate prediction of free energies the weights of the scoring function need to be adjusted (25). For the purposes of optimizing the RosettaLigand scoring function weights and then testing the predictive power, we split our experimental datapoints into two groups. The 99 datapoints acquired by ITC were used to optimize weights because of their higher accuracy. Score term weights were optimized using leave-one-out cross-validation using 98 datapoints to fit the weights and predicting the 99th (see Table 1). The 62 Ki values converted to ΔΔGs were used as a second independent test of the scoring function.</p><!><p>The van der Waals attractive and solvation energies contribute most to an accurate prediction of free energy. Van der Waals attractive scores assess the shape complementarity of ligand and protein. The solvation score penalizes the burial of polar atoms not engaged in hydrogen bonds. Score terms that capture protein/ligand hydrogen bonding effects were also given a substantial weight. Hydrogen bonds can contribute substantially to binding affinity. Interestingly we find a significant negative weight for the amino acid pair potential. We attribute this negative weight to the fact that amino acid electrostatic interactions are disrupted in the PR binding site upon PI binding. Removal of the amino acid pair potential from the scoring function does however not result in significantly reduced prediction accuracy (data not shown).</p><!><p>The standard approach calculates ΔΔGs as the difference between the free energy of a docked model (ΔGb) and the free energy of the unbound model with equivalent sequence (ΔGu) (see methods). Score terms were reweighted to optimize predicted ΔΔG correlation with experimental data (weights are shown in Table 1, columns labeled "Standard Approach"). After reweighting, the predicted and experimental ΔΔGs correlate with R=0.40 (Figure 5A), while ΔΔΔGs correlate with R=0.47 (Figure 5C).</p><!><p>The constant-unbound approach predicts ΔΔG as a function of ΔGb alone. Assuming constant free energy for unbound PR the ΔΔG and ΔΔΔG correlations improve to R=0.71 and R=0.85 (Figure 5B, D) after score term reweighting (Table 1, columns labeled "Constant Unbound"). The standard error of prediction is with 5.91 kJ/mol and 4.49 kJ/mol, respectively, in range of the experimental uncertainty (4.69 kJ/mol, Table 2). ΔΔΔG correlations reported above are calculated by subtracting ΔΔGs sharing the same PI but different PR sequence. ΔΔΔG correlations calculated by subtracting ΔΔGs sharing the same PR sequence but different PIs yield a correlation of R=0.61±0.04 with a standard error of 7.28 kJ/mol.</p><!><p>Optimized weight sets shown in Table 1 were generated from ITC data only. In order to show that high correlation statistics were not an artifact of leave-one-out weight optimization, optimized weights were applied to ΔΔG predictions for experimental Ki data. RosettaLigand predictions correlate well with the 62 ΔΔGs in this independent dataset (R=0.70, see Table 2). The standard error in our predictions is 7.22 kJ/mol which correlates with the previously determined experimental uncertainty for this dataset (7.21 kJ/mol).</p><!><p>We partitioned the experimental data according to whether mutations were found in the binding site of HIV-1 PR or elsewhere. Averaging replicates reduces the total number of experimental ΔΔG values from 176 to 149. These data points were assigned to one of the four groups. Group one contained no exceptional mutations and included 15 datapoints. Group 2 included 17 datapoints with only mutations in the binding site. Group 3 includes 44 datapoints with only mutations outside the binding site. Group 4 includes 73 datapoints with mutations inside and outside the binding site. Corresponding Rosetta predictions were reweighted using the previously optimized weights (weights from Table 1, "constant-unbound") and predicted ΔΔG within each group were compared with experimental values.</p><p>Standard errors between Rosetta predicted ΔΔG and experimental data are shown in Table 2. Note that the small and variable sample size makes correlation coefficients unsuitable for comparison. Generally, ΔΔΔG predictions outperform ΔΔG predictions. Further, predictions are most accurate for sequences with no mutations or only non-binding site mutations. Accuracy decreases as binding site mutations occur. While the latter effect exemplifies the larger influence of binding site mutations for affinity, the former data point confirms our hypothesis that assuming PR ΔGu to be invariant with respect to mutation allows for accurate prediction of effects of non-binding site mutations on PR/PI affinity.</p><p>We also partitioned data based on whether mutations were found in the flexible flap region (residues 37–61)(29). While our flap region definition comprised 24% of the protein, only 2 of the experimental data points contained only flap region mutations, 35 data points had mutations in flap and non-flap regions, and 97 data points contained only non-flap region mutations. It appears that predictions are more accurate for mutants that contain both, flap and non-flap mutations (Table S3). This finding supports our hypothesis that assuming PR ΔGu to be invariant with respect to mutation allows for accurate prediction of effects of non-binding site mutations on PR/PI affinity. The lack of only-flap region mutants complicates interpretation of this analysis.</p><!><p>Both, ΔΔG and ΔΔΔG predictions improve for PR/PI complexes using the constantunbound approach (to R=0.71 and R=0.85 respectively, after score term reweighting). This is expected since unbound HIV-1 PR exhibits a high degree of flexibility (10) and stabilizes upon ligand binding. Therefore the free energy of the unbound state is less sensitive to individual mutations. This result is significant because it demonstrates a simple way to improve binding free energy predictions for proteins with a flexible unbound state. By assuming differences in the unbound state of closely related structures are negligible, binding free energy prediction is possible considering the bound state of the protein only. This finding becomes even more important if one considers that a crystal structure of the unbound protein is often not available in such a scenario.</p><p>Clearly if it was possible to accurately predict the free energy of the unbound state, one could further improve binding affinity predictions. However, currently limited structural information is available to describe the conformational ensemble that represents unbound state of PR mutants.</p><p>As expected ΔΔΔG predictions outperform ΔΔG predictions. These relative binding energies focus on effects of mutations on the same ligand thereby removing the need to accurately predict differences in ΔΔG among PIs. Because Rosetta scoring terms have been parameterized for optimizing amino acid side chain placement, Rosetta excels at ΔΔΔG predictions.</p><p>Note that the standard approach that uses a single bound and unbound state resembles closely a lock-and-key paradigm with local induced fit in the biding site. The constant unbound approach resembles a conformational selection paradigm coupled with local induced fit in the biding site.</p><!><p>During docking we allowed backbone flexibility within the binding site. A future study may need to incorporate global backbone flexibility during docking, to allow mutations outside the binding site to affect the conformation of the binding site. The Rosetta database only includes de-protonated aspartic acid. In a study by Wittayanarakul et al. the protonation state of the catalytic aspartate residues at position 25 was important for more accurate binding free energy calculates (30).</p><p>Further, for several PIs, a water molecule mediates interaction with flap residues Ile-50 and Ile-50', stabilizing PR in the closed conformation (31, 32). This water molecule is not modeled in the present study. However, given that both interactions are present in all PR/PI complexes cancellation of errors allows an accurate prediction of PR/PI affinity already with the setup presented here. A future direction would be to add protonated aspartate to the Rosetta residue type library and simultaneously optimize the positing of the PI and the bridging water molecule.</p>

PubMed Author Manuscript

Design, Synthesis and Crystal Structures of 6-Alkylidene-2\xe2\x80\x99-Substituted Penicillanic Acid Sulfones as Potent Inhibitors of Acinetobacter baumannii OXA-24 Carbapenemase

Class D \xce\xb2-lactamases represent a growing and diverse class of penicillin inactivating enzymes that are usually resistant to commercial \xce\xb2-lactamase inhibitors. As many such enzymes are found in multi-drug resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa, novel \xce\xb2-lactamase inhibitors are urgently needed. Five unique 6-alkylidene-2\xe2\x80\x99-substituted penicillanic acid sulfones (1, 2, 3, 4, and 5) were synthesized and tested against OXA-24, a clinically important \xce\xb2-lactamase that inactivates carbapenems and found in A. baumannii. Based upon the roles Tyr112 and Met223 play in the OXA-24 \xce\xb2-lactamase, we also engineered two variants (Tyr112Ala and Tyr112Ala,Met223Ala) to test the hypothesis that the hydrophobic tunnel formed by these residues influences inhibitor recognition. IC50 values, against OXA-24, and two OXA-24 \xce\xb2-lactamase variants ranged from 10 \xc2\xb1 1 (4 vs. WT) to 338 \xc2\xb1 20 nM (5 vs. Tyr112Ala, Met223Ala). Compound 4 possessed the lowest Ki (500 \xc2\xb1 80 nM vs. WT) and 1 possessed the highest inactivation efficiency (kinact/Ki = 0.21 \xc2\xb1 0.02 \xce\xbcM-1s-1). Electrospray ionization mass spectrometry revealed a single covalent adduct, suggesting the formation of an acyl-enzyme intermediate. X-ray structures of OXA-24 complexed to four inhibitors (2.0-2.6 \xc3\x85) reveal there is formation of stable bicyclic aromatic intermediates with their carbonyl oxygen in the oxyanion hole. These data provide the first structural evidence that 6-alkylidene-2\xe2\x80\x99-substituted penicillin sulfones are effective mechanism-based inactivators of class D \xce\xb2-lactamases. Their unique chemistry makes them developmental candidates. Mechanisms for class D hydrolysis and inhibition are discussed, and a pathway for the evolution of the BlaR1 sensor of Staphylococcus aureus to the class D \xce\xb2-lactamases is proposed.

design,_synthesis_and_crystal_structures_of_6-alkylidene-2\xe2\x80\x99-substituted_penicillanic_acid

INTRODUCTION<!>Chemical syntheses<!>Genetic constructs and host strains<!>Antibiotic susceptibility<!>Kinetic parameters<!>Electrospray ionization Mass spectrometry (ESI-MS)<!>Protein purification and crystallization<!>Data collection, structure determination and refinement<!>Inhibitor Design and Chemical Syntheses<!>Microbiological Studies<!>Kinetic parameters<!>ESI-MS<!>Crystallography<!>CONCLUSIONS<!>

<p>β-Lactamase enzymes (E.C. 3.5.2.6) are one of the most important mechanisms of resistance to β-lactam antibiotics in Gram-negative bacteria (1-10). As the prevalence of resistant pathogens is increasing in our health care institutions, this formidable challenge is creating significant concern among the medical and scientific community (2,11). Currently, there are more than 870 unique naturally occurring β-lactamases (2). Based on protein sequence similarities, four major β-lactamase classes are described (classes A, B, C, and D) (12-15). The class A, C, and D enzymes use serine as the active site, reactive nucleophile. Class B enzymes are metallo-β-lactamases that use one or two Zn+2 atoms to catalyze the hydrolysis of the β-lactam bond (4). In general, serine β-lactamases inactivate β-lactams by following a two step reaction mechanism: Eq 1E+S⇌k­1k1E:S→k2E­S→H2Ok3E+PHere, E represents the β-lactamase, S is the β-lactam substrate, E:S is the Henri-Michaelis complex, E-S, the acyl-enzyme, and P is the inactive product.</p><p>The most rapidly growing and diverse group of β-lactamases are the class D enzymes (16). As a group class D β-lactamases, also called "oxacillinases", hydrolyze penicillins, cephalosporins, and carbapenems (Figure 1, Panel A). Unlike class A enzymes, class D β-lactamases are typically resistant to inhibition by clavulanate, sulbactam, and tazobactam. In order to preserve our current β-lactam antibiotics, potent inhibitors of the class D oxacillinases are urgently needed (17,18).</p><p>Among the many experimental β-lactamase inhibitors that were developed, Chen et al. designed the 6Z-(α-pyridylmethylidene) penicillin sulfone series and showed them to be potent compounds with the ability to inactivate a wide spectrum of serine β-lactamases (19). Buynak, et al. later showed that the incorporation of a 2′ substitution (Figure 1, compound 1, Panel B) could both improve synergy and augment the inhibitory spectrum of this series (20-25). Based upon a consideration of the potency of 1 and related compounds against class A β-lactamases compared to clavulanate, sulbactam and tazobactam, a reaction mechanism was hypothesized.</p><p>To elucidate this mechanism, an analysis of SHV-1 inactivated by 1 was undertaken (26). A complex of 1 with SHV-1 (PDB 3D4F) showed that the inhibitor's C6 (heteroaryl)alkylidene group plays a critical role in the formation of a planar bicyclic aromatic intermediate. Moreover, the pyridyl nitrogen of the C6 substituent nucleophilically adds to the intermediate imine, leading to the formation of a bicyclic aromatic species (indolizine). Furthermore, the crystal structure showed that the acyl-enzyme ester carbonyl of the intermediate is resonance stabilized by the conjugated π system and the carbonyl group of the intermediate is positioned outside the oxyanion hole. The decreased deacylation rates of this species are likely due to resonance stabilization, the location of the carbonyl at an increased distance from the hydrolytic water, and improperly positioned for nucleophilic attack by the enzyme's backbone nitrogens.</p><p>A recent investigation involving the synthesis and microbiological evaluation of more than 100 substituted analogs of the general 6-(pyridylmethylidene)penam sulfone inhibitory subtype revealed that many of these derivatives, particularly those incorporating 2′ substitutions, were also potent submicromolar inhibitors of the class D OXA-24 carbapenemase (27). In addition, many showed synergy with carbapenems against resistant microorganisms. These findings are especially noteworthy as the commercially available inhibitors are ineffective against class D β-lactamases. A comparative susceptibility and kinetic study of OXA-1, -10, -14, -17 and 24 β-lactamases with 1 and related compounds established the potential efficacy of these inhibitors. This analysis was consistent with a branched pathway model for inhibition (Scheme 1 and ref. (28)) and served as a springboard for more in-depth analyses.</p><p>Here we present the design, synthesis and characterization of novel potent 6Z-(α-pyridylmethylidene)penam sulfone inhibitors (2, 3, 4, and 5) against OXA-24 β-lactamase. These inhibitors were complexed with OXA-24 (also known as OXA-40). A previous report by Santillana et al. showed that carbapenem substrate specificity is largely determined by a hydrophobic barrier that is established through an arrangement of the Tyr112 and Met223 side chains (29). Tyr112 and Met223 residues define a tunnel-like entrance to the active site. To test the contribution of these important residues to the inhibitor profile the Tyr112Ala, and Tyr112Ala/Met223Ala β-lactamases were also assayed in inhibition studies. We show for the first time that 6-alkylidene-2′-substituted penicillin sulfone inhibitors are effective mechanism-based inactivators for this challenging class of β-lactamases; their unique reaction chemistry makes then suitable lead compounds for further development.</p><!><p>The design, synthesis and evaluation of 1 were previously reported (25,26). Compounds 2, 3, 4, and 5 were synthesized as part of a drug discovery effort (27). The chemical structures of the compounds studied and details of each synthesis are illustrated in Figure 1 Panel B and Schemes 1, 2, and 3 (Supplementary Information, SI) and discussed below.</p><!><p>blaOXA-24 (also named blaOXA-40) was isolated from a strain of A. baumannii RYC 52763/97 (30). The wild type, WT, blaOXA-24 and mutated genes were subcloned into pGEX-6p-1 (BamHI and EcoRI restriction sites) to generate a fusion protein between GST and the OXA-24 lacking the signal peptide. The recombinant β-lactamase was then purified to homogeneity using the GST Gene Fusion System (Amersham Pharmacia Biotech, Europe GmbH). The mature purified β-lactamases lacking the GST fusion protein appeared on sodium dodecyl sulphate-polyacrylamide gels as a band of approximately 29 kDa (≥95% purity).</p><p>blaOXA-24 and the mutated blaOXA-24 genes [blaOXA-24-Tyr112Ala, blaOXA-24-Met223Ala, and blaOXA-24-Tyr112Ala, Met223Ala] were directionally subcloned into the pAT-RA plasmid (rifampin resistance) at the SmaI and EcoRI restriction sites under the control of the blaCTX-M-14 β-lactamase gene promoter. Once the correct constructs were confirmed by DNA sequencing, the different plasmids were electroporated into the carbapenem-susceptible clinical strain, A. baumannii JC7/04 (29).</p><!><p>Antibiotic susceptibility profiles were determined in cation-adjusted Mueller-Hinton Broth by microdilution testing following Clinical Laboratory Standards Institute (CLSI) criteria (31). Five different inhibitor compounds, 1, 2, 3, 4, and 5 were tested for their capacity to inhibit A. baumannii strain JC7/04 that possessed the different blaOXA-24 genes. The inhibitors were tested at two concentrations, 4 and 16 μg/mL, in the presence of either imipenem (Merck) or meropenem (AstraZeneca). As a comparator inhibitor, tazobactam was used (Wyeth) at two different concentrations, 4 and 16 μg/mL. MICs were determined in the presence of 50 μg/mL of rifampin (Sigma). The MICs reported are the result of three independent experiments.</p><!><p>The half maximum inhibitory concentrations (IC50s) were determined using two different approaches. In a first set of experiments, we used 3-[(3-carboxy-4-nitrophenyl)sulfanylmethyl]-8-oxo-7- [(2-thiophen-2-ylacetyl)amino]-5-thia-1-azabicyclo[4.2.0]oct-2-ene-2-carboxylic acid, CENTA (Figure 1, Panel A), (Calbiochem, EMD, San Diego, CA) at 25 μM as an indicator substrate (32). We incubated the different inhibitors and β-lactamases for 10 min (complete inactivation) at 37°C before measurement of the remaining enzymatic activity (32). The β-lactamases were used in the reactions at 0.13, 0.12, and 0.072 nM concentrations for OXA-24 WT, the OXA-24 doubly substituted enzyme, and OXA-24 Tyr112Ala, respectively. These determinations were performed at 25°C, in a Nicolete Evolution 300 spectrophotometer (Thermo Electron Corporation, Waltham, MA) with quartz cuvettes of optical path length 1 cm, (each determination was measured in triplicate).</p><p>In addition, we also used nitrocefin, NCF (Figure 1, Panel A) (100 μM) as the indicator substrate, incubating the enzymes and inhibitors at room temperature using 3.5 nM OXA-24 and 7 nM of each variant. Residual velocities were determined after 10 minutes. The data were plotted as 1/velocity (1/ν) as a function of inhibitor concentration (I), fitted to a linear equation, and the value of IC50 determined by dividing the y-intercept by the slope of the line.</p><p>Steady state kinetic parameters were determined using an Agilent™ 8453 Diode Array spectrophotometer (33). The kinetic determinations were performed at room temperature in 50 mM sodium phosphate supplemented with a saturating concentration of sodium bicarbonate (20 mM) (34). First, the kinetic parameters, Vmax and Km, were obtained with non-linear least squares fit of the data (Henri-Michaelis equation) using Enzfitter™ (Biosoft Corporation, Ferguson, MO): Eq 2ν=Vmax[S]/(Km+[S])</p><p>We determined the Ki for the inhibitors by measuring initial steady state velocities in the presence of a constant concentration of enzyme, (E; 7 nM) with increasing concentrations of inhibitor competed against 100 μM of the indicator substrate nitrocefin (NCF). The competition assay between the inhibitor, I, and substrate, S, in the reaction can be represented by Scheme 1. Assuming this competitive mode of inhibition, initial velocity (ν0) measurements immediately after mixing yield a Ki which closely approximates Km, and is represented by the following equation: Eq 3ν0=(Vmax∗[S])/(Km∗(1+I/Ki+[S])</p><p>The data were plotted as 1/ν as a function of inhibitor concentration, fitted to a linear equation, and the value of Ki determined by dividing the y-intercept by the slope of the line. The Ki (observed) value was corrected to account for the affinity of NCF for the OXA β-lactamases (35). Eq 4Ki(corrected)=Ki(observed)/[1+([S]/KmNCF)]</p><p>The first-order rate constant for enzyme and inhibitor complex inactivation, kinact, was measured directly by monitoring the reaction time courses in the presence of inhibitors (I) 1, 2, 3, 4, and 5. A fixed concentration of enzyme (E; 7 nM), NCF, and increasing concentrations of I were used in each assay. The kobs were determined using a non-linear least squares fit of the data to Equation 5 using Origin 7.5®: Eq 5A=A0+νft+(ν0­νf)[1­exp(­kobst)]/kobs</p><p>Here, A is absorbance, ν0 (expressed in variation of absorbance per unit time) is initial velocity, νf is final velocity, and t is time. Each kobs was plotted versus I and fit to determine kinact (33,34).</p><!><p>ESI-MS of the intact OXA β-lactamases inactivated by 1, 3, 4, and 5 was performed on an Applied Biosystems (Framingham, MA) Q-STAR XL quadrupole-time-of-flight mass spectrometer equipped with a nanospray source as described previously (33,34). Experiments were conducted by first desalting the reaction mixtures using C18 ZipTips (Millipore, Bedford MA) according to the manufacturer's protocol. The protein sample was then diluted with 50% acetonitrile/ 0.1% trifluoroacetic acid to a concentration of 10 μM. This protein solution was then infused into the mass spectrometer at a rate of 0.5 μL/min and data were collected for 2 min. Spectra were deconvoluted using the Applied Biosystems (Framingham, MA) Analyst program.</p><!><p>From purified OXA-24 β-lactamase tetragonal crystals were grown by the hanging drop vapor diffusion method in a crystallization solution containing 0.1 M sodium acetate, 28% PEG 2000 MME buffered with 0.1M HEPES (pH 7.5). Diffraction-quality crystals were obtained by mixing equal volumes of the crystallization solution with protein at a concentration of 6 mg/ml. Bipyramidal shaped crystals grew in a period of 5-8 days reaching their maximal dimensions (0.15 × 0.15 × 0.10 mm). The crystals belong to space group P41212 with one molecule in the asymmetric unit (Table 1). The first trials to co-crystallize the β-lactamase in complex with several of the selected inhibitors were unsuccessful, for these crystals the inhibitor was poorly defined in the electron density maps. To solve this, crystals were subjected to different soaking times while varying the concentration of the inhibitor, which was intended to stabilize the crystals and to minimize cracking. Final optimised conditions were determined at incubation times as short as 5 min in crystallization conditions containing also 3mM of the corresponding inhibitor. Furthermore, 3 was incubated with the enzyme for 15 min at 10 mM concentration to test time-dependent inhibition (3b).</p><!><p>Crystals were transferred into a crystallization solution containing 15% (v/v) PEG 400 for cryoprotection before immersion into liquid N2 for data collection. X-ray diffraction data for 1, 2, 3, and 5 inhibitor complexes were collected at the European Synchrotron Radiation Facility (ESRF, Grenoble) beamlines ID14-EH1, ID14-EH2 and ID14-EH4 using single frozen crystals (100 K). Inhibitor 3b data was collected at beamline X-29 at National Synchrotron Light Source Brookhaven National Laboratory Upton, NY. Diffraction images were indexed and integrated with MOSFLM (36). Data for the 3 complex were processed using HKL2000 (37). Data scaling, merging and reduction were carried out with programs of the CCP4 suite (38). Relevant statistics are presented in Table 1.</p><p>The structure of native OXA-24, crystallized at pH 7.5, was determined by difference Fourier techniques using the protein atomic coordinates of the original OXA-24 β-lactamase crystallized at pH 4.5 (PDB entry 2JC7) (29). Carbamylation of Lys84 was clearly visible in the electron density maps and could be built with confidence using COOT (Emsley and Cowtan, 2004) (39). Moreover, this new crystal form shows the absence of the sulfate ion in the active site of the enzyme, making it a better target that could help address the structural studies of the selected inhibitors.</p><p>The model was subjected to several rounds of refinement with the program REFMAC (40) whereas interactive model building used COOT (39). Water molecules were modelled according to residual density profiles and geometrical requirements for hydrogen bonding. The crystallographic R-factor of the model is 19.7% for all unique reflections from 8.0 to 1.97 Å resolution (Rfree = 24.4%).</p><p>The structures of OXA-24 in complex with four different penicillin sulfone based inhibitors were solved by Fourier synthesis employing the coordinates of the native enzyme. Refinement of these structures was carried out with CNS (41) and REFMAC (40). After rigid-body refinement and model fitting, the position of the inhibitors was clearly defined in the active binding site, covalently attached to the active serine Ser81, from the electron density maps. Topology and parameter values for each of the ligands, 1, 2, 3 and 5, were generated using the Dundee PRODRG2 server (42). Several rounds of refinement were combined with model rebuilding in COOT after inspection of electron density maps. All residues are in the most-favored and additionally allowed regions of the Ramachandran plot. A summary of refinement statistics is presented in Table 1. Structural figures were done using Chimera (43) or Pymol (44). Electrostatic potential surfaces were calculated with GRASP (45).</p><!><p>With the knowledge of the mechanism of inhibition depicted in Scheme 1 and previous results regarding structure activity relationships, SARs, due to modification of the 2′β position (25), we synthesized compounds 1-5 according to the following plan.</p><p>As shown in Scheme 1 (SI), the carboxylic acid and amino functionalities of the commercially available 6-aminopenicillanic acid (6-APA) were sequentially protected by treatment with diphenyldiazomethane and allyl chloroformate, respectively, and the sulfur was oxidized to the corresponding sulfoxide with mCPBA. Utilizing the method of Kamiya (46), the protected penam sulfoxide 6 was heated in the presence of mercaptobenzothiazole to generate an intermediate sulfenic acid, which was trapped in situ to generate the disulfide 7. The thiazolidine was regenerated by treatment with silver acetate in the presence of chloroacetic acid to stereoselectively produce the 2′β functionalized penam 8, together with the cepham 9. After deprotecting the C6 and C7 amines, this mixture was converted to the corresponding diazo compounds, then treated with a catalytic amount of rhodium octanoate in the presence of excess propylene oxide to generate the corresponding 6-oxopenicillinates and 7-oxocephalosporinates, 12 and 13, respectively (47). Reaction with α-pyridylmethylenetriphenylphosphorane selectively produced the olefins of the Z-geometry. Separation of the penam and cephem isomers was achieved by oxidation of the mixture with mCPBA, which quickly oxidizes the cephalosporin to the sulfone 17, while leaving most of the more hindered sulfur of the penicillin at the sulfoxide oxidation state 16. Subsequent oxidation of the penicillin sulfoxide 16 to the corresponding sulfone, removal of the chloroacetate protecting group, and activation of the 2′β alcohol as the p-nitrophenyl carbonate produced intermediate 20. Intermediate 20 was then reacted with the appropriate amine and the benzhydryl ester removed to produce the inhibitors 4 (compound 22) and 5 (compound 24).</p><p>As shown in Scheme 2 (SI) , the functionalized pyridyl moiety was prepared from commercially available 2,4-lutidine, which undergoes a selective oxidation of the C4 methyl group on treatment with potassium permanganate (48). Generation of the corresponding acid chloride and reaction with allyl alcohol produces ester 26, which was converted to the corresponding pyridine-N-oxide 27 on treatment with mCPBA. Treatment of this oxide with phosphorus oxychloride produced chloromethyl pyridine 28 (49), which was subsequently converted to ylide 29. Reaction of this ylide with the 6-oxopenicillinate 31 produced a benzyhydryl 6Z-(α-pyridylmethylidene) penicillinate 32, which was converted to the corresponding sulfone and deprotected to produce acid 34. This acid was then converted to the unstable acyl azide, which was immediately thermolyzed in methanol to effect the Curtius rearrangement to the isocyanate, which was then trapped to produce carbamate 35. Deprotection of the benzhydryl ester with TFA-anisole produced inhibitor 2.</p><p>Lastly, the "east-west" dual-functionalized inhibitor 3 was synthesized as shown in Scheme 3 (SI). Thus the functionalized 6-oxopenicillinate 12, (mixture with the corresponding cepham 13), was treated with ylide 29, to produce a mixture of 6-alkylidenepenam, 37, and 7-alkylidenecepham, 38, as shown. As before, treatment with 1.5 eq of mCPBA affected the oxidation to the corresponding cepham sulfone and, primarily, the penam sulfoxide, which were separable. Further oxidation of the sulfoxide 39 to the sulfone 41, followed by removal of the chloroacetate and the allyl ester protecting groups produced hydroxyacid 43. Treatment of this material with excess p-nitrophenyl chloroformate simultaneously converted the acid to the p-nitrophenyl ester and the alcohol to the p-nitrophenyl carbonate. Reaction with ammonia in dioxane produced the amidocarbamate intermediate 45, which was subsequently deprotected to produce inhibitor 3 (compound 46).</p><!><p>In order to be an effective partner inhibitor for clinical use, the mechanism-based β-lactamase inactivator must effectively and rapidly penetrate the outer membrane of Gram-negative bacteria in sufficient concentrations to lower MICs into the susceptible range (28). To establish a comparison, we employed the carbapenems that are used clinically (imipenem or meropenem) with the inhibitors at two different concentrations, 4 and 16 μg/ml. We also used tazobactam at the same concentrations as a comparator β-lactamase inhibitor. In order to ensure an appropriate contrast, we also expressed each of the different constructs in an isogenic host, the carbapenem-susceptible A. baumannii JC7/04 strain (29). In contrast to laboratory strains of E. coli, this model system ensures a more realistic appraisal of the efficacy of the compounds against the pathogen containing the blaOXA-24 (50).</p><p>Against A. baumannii JC7/04 without OXA-24 expressed, the meropenem MICs are 1.0 μg/ml, well within the susceptible range (31). In the genetic background where blaOXA-24 in A. baumannii strain JC7/04 is expressed, high-level carbapenem resistance is observed (Table 2a and 2b, meropenem and imipenem MIC = 32 μg/ml). When the inhibitor tazobactam, at 4 μg/ml, was combined with meropenem or imipenem, we did not detect a reduction in MICs (only slight and no significant inhibition with imipenem). This is consistent with the clinical observation that β-lactam-tazobactam combinations are not effective against carbapenem resistant isolates (51,52). As each inhibitor possesses a β-lactam scaffold, we first tested each inhibitor without a partner antibiotic. Our results showed that 1-5 do not possess any intrinsic antibiotic activity against A. baumannii JC7/04 with pAT-RA or pAT-RA with blaOXA-24 (MIC ≥ 64 μg/ml). With respect to the inhibitory activity of the compounds, 1 or 5 at 4 μg/ml combined with meropenem we showed a noticeable reduction in MICs (32 to 4 μg/ml). Overall, MICs decreased greater when the inhibitors were tested at a concentration of 16 μg/ml (Table 2b). This enhanced, "dose-dependent" effect was slightly more pronounced for meropenem than imipenem. Overall, 1 combined with imipenem or meropenem reduced MICs better than any other combination.</p><p>We next measured the activity of the carbapenems against the variants possessing the Tyr112Ala, Met223Ala substitution (Table 2b). As is shown by MICs, imipenem and meropenem resistance is reduced for the strain possessing the doubly substituted enzyme (WT is 32 μg/ml and the doubly substituted enzyme = 2 or 4 μg/ml). This supports the observation that the two residues, Tyr112Ala and Met223Ala, also play a critical role in resistance to carbapenems (29). The MICs were slightly reduced when each of the inhibitors was combined with meropenem or imipenenem against the doubly substituted enzyme (especially at 16 μg/ml).</p><!><p>In the steady state experiments summarized in (Table 1 (SI), the OXA-24 β-lactamase and variants studied were purified to greater than 95% homogeneity. The WT enzyme, OXA-24 β-lactamase, hydrolyzed NCF with kcat/Km values 19.2 ± 2.6 μM-1s-1 (Table 1, SI). This robust activity was similar to the hydrolysis of NCF by OXA-1 (28,33) and ranks with the high catalytic efficiency demonstrated by class A β-lactamases towards penicillins and certain class C enzymes towards cephalosporins (53-55). Categorically, OXA enzymes are regarded as carboxy-penicillinases and amino-penicillinases (16). Since OXA-24 is characterized as a carbapenemase, we next measured the catalysis of imipenem as a reference carbapenem. In keeping with susceptibility testing (MICs = 32 μg/ml), the kcat/Km value was 1.7 ± 0.2 μM-1s-1 (Table 1, SI). Interestingly, the kcat of imipenem was much lower when compared to the kcat of NCF. This finding is consistent with the observation made by Queenan and Bush that OXA carbapenemases have a high apparent affinity for carbapenems, but a low turnover (10).</p><p>The single (OXA-24Tyr112Ala) and doubly substituted enzymes (OXA-24Tyr112Ala, Met223Ala) demonstrated less robust activity (14% activity of the WT vs. NCF; 14-24% of the WT vs. imipenem). Each of the variants showed higher Km values for NCF (OXA-24 Tyr112Ala, Km = 238 μM and OXA-24 Tyr112Ala Met 223Ala Km = 143 μM).</p><p>In order to enrich our understanding of the mechanism of inhibition, two approaches were used to assess the efficacy of the inhibitors against OXA-24 and the OXA-24 variants. As discussed previously, the appropriate biochemical correlates that translate into effective β-lactamase inhibition in the clinical setting are complex and multi-factorial (cell penetration, pharmacodynamics, pharmacokinetics, etc.) (17). These considerations are fundamental to the assessment of each inhibitor as OXA-24 is resistant to the commercially available inhibitors and is expressed in the Acinetobacter spp. background.</p><p>To begin, we first measured IC50s at 10 minutes using CENTA, as this parameter informs us of the relative effectiveness of an inhibitor. The IC50 values for all four inhibitors against the OXA-24 β-lactamase ranged from 127 ± 42 nM (1) to 237 ± 7 nM (5) (Table 2a, SI). We also noted a slight increase in the IC50 values with respect to the four inhibitors when tested against the singly and doubly substituted enzyme, but this increase was not enough to confer inhibitor resistance (≤ 1 μM).</p><p>In a similar manner to CENTA, we determined IC50s with NCF. As the chemical properties and affinities of CENTA are different than NCF (both are indicator substrates), this served as a confirmation of the affinities of the compounds for OXA-24 and variants. As is evident from the data in Table 2b (SI), by using NCF we find the inhibitors demonstrated a 10 fold greater affinity (lower IC50s) than with CENTA.</p><p>To more precisely identify the correlates of inactivation and inhibition, we next determined the Ki and the inactivation efficiency (kinact/Ki) of each inhibitor (Table 3 and Table 3 (SI)). We examined the activity of each of these inhibitors against the WT enzyme and the two variants.</p><p>Notably, 4 and 1 showed the lowest Ki for WT followed by 3, 2, and 5. Compound 1 possessed the highest inactivation efficiency (kinact/Ki = 0.21 ± 0.02 μM-1s-1) (Table 3). With regards to the two variant OXA β-lactamases, we see for the five inhibitors an overall lowering of inactivation efficiencies (kinact/ Ki) due to decreases in kinact and increases in Ki (Table 3, SI).</p><p>We note that there is a difference between the kinetic parameters obtained measuring IC50 and Ki measurements vs. microbiological values. We reconcile this difference by positing that the ability and rate of each of the inhibitors to penetrate across the outer membrane of this strain of A. baumannii may be different (50). Factors such as permeability coefficients, diffusion rates, presence or absence of specific porins may play an important role here and merit further studies (56,57). The design of 1 attempts to enhance transport across the cell membrane (26).</p><!><p>To establish the nature of the inactivation products, ESI-MS was performed with a Q-Star quadrupole time-of-flight mass spectrometer equipped with a nanospray source. We inactivated OXA-24 β-lactamase with each of the inhibitors. The incubation (inactivation) time was 15 min (900 s). The deconvoluted spectra are presented (Figure 1, SI) and our results are summarized in Table 4 (SI). The ESI-MS measurements (29,071 ± 3 amu) were in agreement with the theoretical mass of the OXA-24 β-lactamase, which is 29,073 (this includes the five additional amino acids at the N-terminus as a result of the cloning procedure). The preparative method did not permit us to identify a mass increase consistent with the addition of a CO2 group (carboxylation at Lys84) to the β-lactamase.</p><p>Covalent attachment of each inhibitor to the OXA-24 β-lactamase and variant enzymes was demonstrated in each of the spectra. During the time period studied, we did not find evidence of the fragmentation of the inhibitors, as was seen in the inactivation of TEM-1, CMY-2, SHV-1, the Arg244Ser variant of SHV-1, and the Ser130Gly variant of SHV-1 with tazobactam and clavulanate (58-64). These observations were further rationalized after examination of the mechanisms of inactivation.</p><!><p>We next solved the crystal structures of OXA-24 complexed with different penicillin sulfone based inhibitors (1, 2, 3 and 5). This offered us an unprecedented opportunity to compare the inactivation mechanisms of different inhibitors of the same chemical series with different Kis. Structures were solved by Fourier synthesis employing the coordinates of the native enzyme. After rigid-body refinement and model fitting, the position of the inhibitors was clearly defined in the active site covalently attached to Ser81. The overall fold of the complexes were similar to the native OXA-24 structure (29).</p><p>When complexed to inhibitors, the conformations of the active site are very similar to that observed in the native enzyme, although several changes can be appreciated. The side chains of Tyr112 and Arg261 re-orient slightly (~ 0.6 Å) to accommodate the sulfinic and carboxylate groups of the inhibitors (Figure 2).</p><p>The initial omit electron density maps strongly indicated the formation of an acyl-enzyme intermediate containing a bicyclic aromatic ring system composed of two fused aromatic rings, a five-membered ring fused to a six-membered pyridine ring (Figures 3A-E). The electron density and structures of 3 soaked at different time points (3, 3b, Figures 3A, B, D) indicate that the bicyclic inhibitor intermediate is quite stable during the 5-15 min time period. This aromatic indolizine system points towards the solvent region and sterically blocks the entry to the catalytic cleft on the left side of the tunnel-like cavity. Contrary to what was found in SHV-1 and SHV-2 class A β-lactamases complexed with 1 (26), the carbonyl oxygen of the intermediates of 2, 3, and 5 is located in the oxyanion hole forming hydrogen bonds to the backbone amide of Trp121 (2.9 Å), in a similar manner to that found in the structure of the class C GC1 β-lactamase with the reaction product of the sulfone DVR-II-41S (65). The side chains of the ring-derived system extending from C2 in the three inhibitors also show other significant contacts with the enzyme.</p><p>We stress that the network of interactions which clearly contributes to the stability of the intermediate is strictly conserved for the inhibitors 3, 2, and 5 (Figures 4A-C). The sulfinate anion is in close proximity to the guanidinium group of Arg261 establishing strong salt-bridge interactions (2.67 Å and 3.02 Å). Besides this electrostatic interaction, OXA-24 still also makes strong hydrogen bonds to the hydroxyl groups of Ser128 (3.04 Å) and Ser219 (2.52 Å), two highly conserved residues at the active site. The orientation adopted by the sulfinate anion emulates the positioning of the carboxylate moiety of the antibiotic, as was reported in other acyl-enzyme complexes with meropenem (66,67). As a consequence of this orientation the carboxylate group folds back over the tunnel establishing a strong hydrogen bond with the hydroxyl group of Tyr112 (2.8 Å) one of the key residues that conform the tunnel-like entrance to the active site in OXA-24 (Figures 2A and 2B, SI) (29). Apart from this common interaction network, the remaining contacts between the intermediate and the enzyme vary depending on the chemical nature of the inhibitor. Thus, the effect of the substituents at the pyridylmethylidene moiety in the stabilization of the acyl-enzyme intermediate can only be described for compounds 2 and 3. Whereas the carbamoyl group of compound 3 is in close contact with the hydroxyl group of Thr111 (3.06 Å), the methoxycarbamoyl substituent on compound 2 has not revealed additional interactions with the enzyme (Figures 4A and B).</p><p>On the other hand, the more complex tailored substituents at position C2 of the penicillin sulfone ring do not show favorable interactions to provide an additional stabilization of the intermediate with exception of the 3,4-(dihydroxyphenyl)acetate of 1. Unfortunately, the electron density maps for the 1 inhibitor do not show a clearly defined position for the catechol moiety of the inhibitor and also the population of the central core is not full, perhaps indicating disorder or partial occupation of the site (data not shown). However, it can be said that the bulky group at C2 runs almost parallel to the right side of the active site by stacking the catechol moiety with various residues on the hydrophobic surface of the enzyme (Figure 2A, SI). Stacking of 1's catechol moiety has also been observed in its complex with SHV-1 (26).</p><p>The combined results imply that the stable acyl-enzyme complexes from the 6-alkylidene-2'-substituted penicillin sulfone inhibitors are formed by a sequence of events in which initial nucleophilic attack of Ser81 Oγ on the carbonyl of the penicillin ring releases the β-lactam nitrogen lone pair, thus enabling the opening of the neighboring sulfone ring. Another rearrangement provided by the pyridyl nitrogen bonded to the former C5 of the resultant imine leads to the formation of the crystallographic observed indolizine system.</p><p>In each of the inhibitor complexes, the π system of the acyl-enzyme carbonyl is orthogonal to the π system of the bicylic indolizine. Thus, it is clear that the hydrolytic stability of the covalent ester linkage is not due to a resonance interaction with the nitrogens (i.e. β-aminoacrylate or 'enamine') or due to interaction of the ester carbonyl with the aromatic system. What is stabilizing these acyl-enzymes toward hydrolysis? Notably absent in all structures is a hydrolytic water molecule proximal to both the carboxylated lysine and to the acyl-enzyme carbonyl carbon. It has been observed that, unlike the corresponding class A β-lactamases, a crystallographically observable hydrolytic water molecule is not seen in the class D apoenzymes, and it is assumed that such a water must diffuse into the site after formation of the acyl-enzyme (68). However, in the case of these inhibitors, the active site is occupied with the conformationally rigid bicyclic indolizine moiety, prospectively preventing the entry of extraneous water, and thus stabilizing the acyl-enzyme (Figures 2B and 2C, SI).</p><p>Based upon previous insights proposed by Golemi et al., a likely mechanism for the inactivation of OXA-24 β-lactamase is shown in Scheme 2 (69). An interesting feature of the mechanism is that, relative to the normal hydrolytic process (Panel B), the inhibitor provides the enzyme with one extra proton, which is lost from C5 of the inhibitor in order to achieve aromaticity (Y → Z, Panel A). This would then leave the carboxylated lysine as its conjugate acid and thus unable to activate water for hydrolysis of the acyl-enzyme. Note that, in the case of the penam substrate (bottom), the carbamic acid can be deprotonated by the proximal amine (previously N4 of the penam), while, in the case of the inhibitor, N4 is rendered significantly less basic due to its conjugation with the indolizine and interaction with the acyl-enzyme carbonyl.</p><p>This mechanism also illustrates the ability of the OXA-24 carboxylated lysine to repetitively cycle between its conjugate acid and base forms and acyl- and apo state, thus differentiating these catalytic class D β-lactamases from the highly homologous sensor domain of the BlaR1 protein from Staphylococcus aureus. In the case of BlaR1, the formation of the acyl-enzyme (and consequent formation of the carboxylysine conjugate acid) results in decarboxylation, thus removing the proximal base and fixing the sensor in the acylated ('on') state as shown in Scheme 3 (70). Through an unknown mechanism, this acylated form results in a conformational change of the transmembrane domain and relays a signal to the cytoplasm leading to the proteolytic degradation of a repressor protein, thus derepressing the blaZ gene and leading to production of more β-lactamase. In contrast to the catalytic action of the class D β-lactamases, the BlaR1 protein is irreversibly acylated by penicillins, presumably as a consequence of the lysine decarboxylation which occurs upon acylation (and formation of the conjugate acid of the carbamic acid) (71,72).</p><p>Ab initio quantum chemical studies have shown that the activation energy for the decarboxylation of carbamic acids is lowered by 44 kcal mole-1 by the presence of one molecule of water in the transition state as shown in Scheme 3 (73). As shown in Figure 5, comparison of the respective active sites reveals a key structural difference between the BlaR1 sensor and class D β-lactamases is the presence of a highly conserved hydrophobic residue at the β-lactamase position 130 (OXA-24 numbering, Val or Ile, corresponds to position 120 using DBL consensus numbering), in contrast to a hydrophilic residue (Asn in case of S. aureus, or Thr, in the case of Bacillus licheniformis (74)) at corresponding BlaR1 position. Additionally, as shown, two proximal water molecules were found in the 1XA1 BlaR1 apoenzyme prospectively providing the precise water molecule involved in this decarboxylation (75).</p><!><p>OXA carbapenemases are becoming one of the most clinically important resistance determinants found in A. baumannii (16). Most clinical isolates of A. baumannii are resistant to narrow-spectrum and extended-spectrum cephalosporins due to the expression of the chromosomally-encoded AmpC β-lactamase, called ADC, Acinetobacter Derived Cephalosporinase (ADC) (51,76). An examination of the current clinical experience shows that OXA-23, -24, -48, -51, -69, and -72 seem to be very prevalent among outbreaks of carbapenem resistant A. baumannii (16). At present, there are only two carbapenem hydrolyzing class D enzymes (OXA-48 and OXA-24) crystal structures that are available for study.</p><p>As shown by Santillana et al. the crystal structure of OXA-24 β-lactamase revealed several unique features (29). In contrast, the structure of OXA-48 at 1.9 Å was overall similar to the crystal structure of OXA-10, a class D β-lactamase that does not hydrolyze imipenem (77). The present work captures for the first time the crystal structure of novel 6-alkylidene-2'-substituted penicillanic acid sulfones as active site inhibitors of OXA-24. This work significantly extends the previous studies with 1 in class A enzymes to a class of β-lactamases that has escaped inhibition by commercial inhibitors (26).</p><p>Five important insights are obtained from this analysis. Firstly, with all inhibitors, we see the formation of a stable un-fragmented adduct with the carbonyl oxygen positioned deep in the oxyanion hole. Normally, one would expect that this conformation would facilitate hydrolysis as opposed to inhibition. Yet, the unique reaction chemistry followed by these C6 substituted compounds, formation of a bicyclic aromatic ring that may impede approach of an attacking water molecule. This adds an additional enhancing feature to these molecules (a theme reminiscent of DVR-II-41S, see reference 65). This is in stark contrast to what was seen with SHV-1 β-lactamase (26) because of the positioning of the intermediate carbonyl oxygen.</p><p>Secondly, the negatively charged sulfinate group on each of these compounds is positioned to interact via a stabilizing salt bridge with the residue Arg261. The strong H bonds to Ser128 and 219 also add to this stability.</p><p>Thirdly, the carboxylate of the inhibitor, folding over to make an H bond to Tyr112, further enhances the stability of the complex.</p><p>Fourthly, a new and previously unappreciated feature of these penicillin sulfones may be their capability to provide an extra proton to the active site thus generating the conjugate acid of the carboxylysine, and interrupting the catalytic process.</p><p>Lastly, a comparison of the environment surrounding the carboxylysine of the class D β-lactamases with that of the highly homologous BlaR1 sensor protein reveals that the class D β-lactamases have a significantly more hydrophobic environment. The more hydrophilic environment of the carboxylysine of BlaR1 may provide an explanation for its rapid decarboxylation, since it is known that the transition state for decarboxylation of carbamic acids is significantly lowered by incorporating one molecule of water in the process. Relative to ongoing catalysis, this decarboxylation also requires at least one additional proton thus potentially providing a mechanism to induce the large conformational shift needed for intracellular signaling.</p><p>Taken together, our findings point to an effective strategy to inhibit not only this OXA carbapenemase but also other serine based enzymes that inactivate β-lactam antibiotics, and provide several intriguing hypotheses to be explored with respect to the highly homologous BlaR1 sensor protein. A comparative analysis against other OXA carbapenem hydrolyzing and extended-spectrum OXA β-lactamases enzymes is warranted as the pathways to evolution of these enzymes are different (77). The quest to find inhibitors active against a wide range of carbapenem hydrolyzing enzymes will remain a persistent challenge as the diversity in class D grows.</p><!><p>Chemical structures of substrates, commercially available inhibitors (sulbactam, tazobactam and clavulanic acid), and 6-alkylidene-2'-substituted penicillanic acid sulfone compounds used in this study. The chemical structure of penicillin G is used as a model for all penicillins. In like manner, cephaloridine and imipenem are represented as models for cephalosporins and carbapenems.</p><p>Stereoview of the superposition at the active binding site of native OXA-24/40 (gray) and 1 (yellow), 2 (cyan), 3 (orange), 5 (green) complexes. The secondary structure of the enzyme is in gray. For clarity, only side-chain residues implicated in binding are represented. In the active site major changes are not observed upon inhibitor binding, but several modifications are seen in active site residues directly involved in inhibitor accommodation.</p><p>Binding mode of the substituted penicillin sulfone inhibitors in the active site of OXA-24 b-lactamase. (A) initial Fo-Fc omit maps for 3b contoured at 3.0 σ. Final 2Fo-Fc electron density maps for (B) 3b at 2.6 Å resolution. (C) 3 at 2.1 Å resolution in stereoview, (D) 2 at 2.0 Å resolution and (E) 5 at 2.0 Å resolution. Contour levels are at 1.0 σ. The maps show a clear density for an intermediate containing an indolizine moiety as a result of the formation of a five-membered ring fused to the pyridine ring extending from the 6-alkylidene substituted penicillin sulfone inhibitors.</p><p>Active site and inhibitor interactions. Detailed interactions in the active site of OXA-24 with (A) 2, (B) 3 and (C) 5. The indolizine aromatic ring is visibly anchored into the tunnel-like cavity of the binding site through its conjugated acyl group covalently attached to the catalytic serine residue Ser81. The secondary structure of the enzyme is in blue. The side chains of the ligand-binding residues and inhibitors are represented in ball-and-stick mode. Selected interacting residues are labelled and hydrogen bonds are indicated by dotted lines.</p><p>Comparison of the active site carboxylysine environment of OXA-24 (left, 3G4P) with BlaR1 (right, 1XA1).</p><p>Based upon the analyses of the C2/3-substituted penicillin and cephalosporin sulfone series against OXA β-lactamases, the following model was proposed (28). In this model, E:I represents the formation of the pre-acylation complex and E-I, the acyl-enzyme species. The acyl-enzyme (E-I) can proceed to hydrolysis (E + P1) or undergo rearrangement to a transiently inhibited species (E-IT). The E-IT intermediate may then return to E-I, proceed to hydrolysis (E + P2), or form an inactivated acyl-enzyme (E-I*). The rate constants, k, describing each of these steps are also represented.</p><p>Proposed mechanism of OXA-24 β-lactamase inhibition by 5 based upon the intermediates hypothesized to be formed in the model illustrated in Scheme 2. Panel A and Panel B.</p><p>Proposed mechanism for the formation of the BlaR1 acyl-enzyme and requirement for additional proton</p><p>Data Collection and Refinement Statistics</p><p>Highest resolution shell is shown in parentheses.</p><p>a. MIC values of the carbapenem-susceptible A. baumannii strain JC7/04 transformants</p><p>Tazobactam, 1, 2, 3, 4, and 5 tested at 4μg/ml;</p><p>pAT-RA plasmid pAT-RA in A. baumannii without bla OXA-24 gene. The MICs of tazobactam, 1, 2, 3, 4, 5 without meropenem or imipenem were > 64 μg/ml.</p><p>Tazobactam, 1, 2, 3, 4, and 5 tested at 16 μg/ml;</p><p>pAT-RA plasmid pAT-RA in A. baumannii without bla OXA-24 gene.</p><p>Kinetic Parameters of Inhibition</p><p>Tazobactam Ki was determed by Drawz, et al and was 271 ± 37 μM (ref).</p>

PubMed Author Manuscript

Recent advances in transition metal-catalyzed N -atom transfer reactions of azides

Transition metal-catalyzed N-atom transfer reactions of azides provide efficient ways to construct new carbon\xe2\x80\x93nitrogen and sulfur\xe2\x80\x93nitrogen bonds. These reactions are inherently green: no additive besides catalyst is needed to form the nitrenoid reactive intermediate, and the by-product of the reaction is environmentally benign N2 gas. As such, azides can be useful precursors for transition metal-catalyzed N-atom transfer to sulfides, olefins and C\xe2\x80\x93H bonds. These methods offer competitive selectivities and comparable substrate scope as alternative processes to generate metal nitrenoids.

recent_advances_in_transition_metal-catalyzed_n_-atom_transfer_reactions_of_azides

Introduction<!>Transition-metal catalyzed N -atom transfer to sulfides<!>Aziridination<!>Aminochlorination/aminoalkoxylation<!>C\xe2\x80\x93H bond amination<!>Functionalization of sp2-C\xe2\x80\x93H bonds<!>Towards a general aliphatic C\xe2\x80\x93H bond amination method<!>Conclusions and future outlook

<p>Azides participate in a wide range of reactions that construct new carbon–nitrogen or nitrogen–heteroatom bonds.1 Their reactivity has motivated considerable research interest since the discovery of phenyl azide in 1864.2 Azides have enjoyed a renaissance in recent years since their potential was recognized for use in "click" reactions, which rapidly increase molecular complexity by bringing together two molecules together in a reliable and stereoselective fashion.3,4 Their use in transition metal-catalyzed N-atom transfer reactions, however, has garnered considerably less attention than other nitrenoid precursors despite the attractiveness of azides.5–7 Their appeal stems from (1) their ready availability from sodium azide;8,9 (2) they require no additive besides catalyst to form the nitrenoid reactive intermediate; and (3) the by-product of the N-atom transfer reaction is N2 gas. These positive attributes provide motivation to develop high yielding and stereoselective N-atom transfer reactions, and this perspective describes the recent progress to achieve these atom-economical and environmentally benign processes.</p><p>Azides were recognized early as nitrene precursors.1,10,11 Thermolysis or photolysis of biaryl azides was reported by Smith and co-workers in 1951 to afford substituted carbazoles.11d This result was extended to styryl azides (e.g. 1) by Sundberg and co-workers and to vinyl azides (e.g. 3) by Hemetsberger and co-workers. While good to excellent yields can be achieved with aryl- or vinyl substituents, carbazole formation is not generally selective: thermolysis or photolysis of biaryl azide 5 produced a 1 : 1 mixture of the two possible carbazole products (Scheme 1).12</p><p>The reactivity of the aryl azide depends on the identity of the ortho-substituent. When a methylene spacer was placed in between the two π-systems, the temperature controlled the amount of electrophilic cyclization versus C–H bond amination: at 190 °C, azepine 9 was formed; whereas, flash vacuum pyrolysis (350 °C) produced 10 and acridine 11 (Scheme 2).13 Extending the spacer length by an additional methylene eliminates azepine formation to produce indoline 13 or indole 2, albeit with reduced yields.14b,15,16 In contrast to aryl azides, few selective intramolecular cyclizations of nitrenes derived from sulfonyl-,17 acyl-,18 or phosphoryl19 azides exist. Even fewer synthetically useful examples of intermolecular thermal or photochemical N-atom transfer have been reported.18,20–22</p><p>Thermal- and photochemical reactions of azides demonstrated their potential as precursors for nitrenes. While these reactions do provide access to a variety of N-heterocycles, they are limited by the hyper reactivity of the nitrene, which can lead to poor selectivity and extensive decomposition. Attenuating the reactivity of the nitrene by developing transition metal-catalyzed decomposition reactions of azides to provide nitrenoids would address these limitations. The development of mild, low temperature reactions would enable azides to realize their potential in N-atom transfer reactions.</p><!><p>The thermal or photochemical transfer of nitrene from an azide to the sulfur-atom in a sulfide or sulfoxide produces sulfoximides and sulfimides.23 Due to the harsh conditions required to liberate nitrene, however, the yield of these processes is low. These unproductive side reactions and the hazards associated with the thermolysis of azides make this reaction a good target for catalysis. Iron(II) chloride was reported by Bach and co-workers to dramatically improve the synthetic efficiency of this process by reducing the reaction temperature from 90 °C to 0 °C (Scheme 3).24,25 These mild conditions allowed tert-butoxycarbonyl azide (Boc–N3) to be used as the source of the nitrogen-atom. In addition to sulfides and sulfoxides, other nucleophiles could be used in this reaction including silyl enol ethers, although the yield was modest.</p><p>The synthetic potential of this transformation was realized when allylic sulfides were used as substrates (Scheme 4).26 Bach and co-workers reported that FeCl2-catalyzed N-atom transfer to the allylic sulfide was followed by a facile [2,3] sigmatropic rearrangement to efficiently produce α-substituted allylic amines 17 in a stereochemically defined manner. While the functional group tolerance of this reaction was broad, the synthetic utility of this transformation was diminished by the low reactivity of chiral α-substituted allylic sulfides. Van Vranken and co-workers extended this reaction to include propargylic substrates to enable access to N-allenylsulfenamides (e.g. 17e).27</p><p>The observed reactivity trends led the authors to report that the mechanism of this transformation involved a redox process (Scheme 5).26 Iron-mediated decomposition of Boc-azide produces iron nitrenoid 18, which is followed by a stepwise N-atom transfer to form 16 via the iron(III) intermediate 19. A subsequent [2,3] rearrangement furnishes the allylic amine. This stepwise mechanism accounts for the formation of the sulfonamide and ulfenamide by-products, which are commonly observed.</p><p>Since the initial report of this transfer reaction by Bach in 1998, synthetic efforts have been aimed at achieving the asymmetric N-atom transfer to sulfides. If allylic sulfides could be used, this reaction would enable the synthesis of chiral, non-racemic allylic amines from achiral sulfides. In 2001, Katsuki and co-workers reported that ruthenium salen complexes effectively catalyzed the sulfimidation of alkyl aryl sulfides with excellent enantioselectivity (eqn (1)).28,29 While several different arylsulfonyl azides exhibited excellent enantioselectivities, Boc-azide afforded the iminosulfide in reduced yield. This reaction tolerated a variety of functional groups including nitro- and methoxy groups, but was limited to aryl alkyl sulfides: benzyl methyl sulfide reacted slowly and with reduced selectivity.</p><p>The enantioselective imidation of allylic sulfides using ruthenium salen complexes triggered a [2,3] sigmatropic rearrangement to produce allylic amines with enantioselectivities greater than 80% ee (Scheme 6).30 While the initial N-atom transfer to the sulfide occurred with % ee greater than 90%, some loss of optical activity occurred in the sigmatropic rearrangement. The resulting N-arylthioarylsulfonamides were hydrolyzed to allylic amines upon exposure to KOH in methanol without loss of optical activity. The reaction tolerated a broad range of aryl sulfide substituents as well as either E- or Z-olefin substituents. Importantly, the reaction was stereospecific: the chirality of 23c and 23d depended on the identity of the olefin isomer. Since nitrenoid transfer to the starting allylic sulfide was identical for either the E- or Z-isomer, the authors interpreted these results to indicate that the transition state of the subsequent [2,3] rearrangement is unique to the starting olefin isomer. While trisubstituted olefins did participate in the reaction, no chiral induction of the [2,3] rearrangement was observed. The lack of stereoselectivity led the authors to conclude that multiple reaction mechanisms are possible in the rearrangement.</p><!><p>In 1967, Kwart and Khan reported the first metal-catalyzed nitrogen-atom transfer from an azide to an olefin (eqn (2)).31 They reported that copper powder promoted the decomposition of benzenesulfonyl azide in cyclohexene. In addition to cyclohexene aziridine, they also obtained C–H amination products as well as benzenesulfonamide. The product distribution is consistent with a nitrene or metal nitrenoid reactive intermediate.32 A radical mechanism could also account for the observed products.33 After these reports, Migita and co-workers reported palladium-catalyzed N-atom transfer reactions to olefins,34 and Groves and co-workers reported a stoichiometric N-atom transfer from a nitridomanganese(V) porphyrin to cycloalkenes.35</p><p>After these seminal reports, the use of iminoiodinanes supplanted azides as nitrene precursors in metal-mediated N-atom transfer reactions to olefins.36 Evans and Faul and co-workers found that tosyl azide was not as effective of a nitrene precursor as Ph=NTs—use of the iminoiodinane produced phenyl aziridine 24 in 96% yield whereas, using tosyl azide as the nitrene precursor produced only a 12% yield of 24 (Scheme 7).36b36d These observations were subsequently echoed by Jacobsen and coworkers in their report of copper diimine-catalyzed aziridination of olefins.36c While these iodine(III) reagents dramatically improved the efficiency of N-atom transfer to olefins, they were not as atom economical or green as azides because a stoichiometric quantity of iodobenzene was also produced.</p><p>Inspired by the aforementioned reports of stoichiometric N-atom transfer from azides to olefins using transition metal porphyrins, Cenini and co-workers investigated the possibility of a catalytic reaction of aryl azides with olefins in the presence of metal porphyrin catalysts.37 An examination of a wide range of metal porphyrins revealed that nitrene transfer from p-nitrophenyl azide to cycloalkenes was catalyzed only by ruthenium- and cobalt complexes. The best results were obtained with ruthenium tetraphenylporphyrin (RuTPPCO) and cobalt octaethylporphyrin (CoOEP).38 The electronic nature of the aryl azide influenced the product distribution: when the nitro-group was substituted with a methoxy group, the only product observed was the corresponding aniline. Reduction of the aryl azide to the aniline is a common byproduct observed in the thermal- or photochemical decomposition of azides.14 Formation of this product has been attributed to the intermediacy of a divalent nitrogen reactive intermediate.14,39</p><p>Using the most active ruthenium tetraphenylporphyrin complex, N-atom transfer to a variety of olefins was attempted (Scheme 8).37 Similar to the results of Kwart and Khan,31,32 a range of products were observed for cyclohexene. Switching the olefin to cyclooctene resulted in the formation of aziridine 26 as the major product in 25% yield. The major by-products were p-nitroaniline and the azo compound. Monosubstituted olefins also produced aziridines as the major product: 1-hexene formed aziridine 28 in 29% yield. Substrates lacking α-protons provided more promising results: styrene afforded 29 as the only product in 89% yield. trans-Stilbene, on the other hand, did not produce any amination products.</p><p>Substantial research effort by the Katsuki and Zhang groups has resulted in the development of methods that achieve the asymmetric formation of aziridines from monosubstituted styrenes with excellent yields and very good enantioselectivities. Prior to their work, two seminal reports by Jacobsen and Mueller,36c,40 established that asymmetric N-atom transfer from azides to olefins could be achieved with modest enantioselectivities. Katsuki and co-workers reported that the asymmetric formation of aziridines from sulfonyl azides could be achieved using a chiral ruthenium(II) salen complex (eqn (3)).41 This complex was previously shown to catalyze the formation of iminosulfides with excellent enantioselectivities and large turnover numbers. Because iminosulfide formation was believed to occur via N-atom transfer, the authors anticipated that aziridines could be formed from nucleophilic olefins. N-Tosyl aziridines were formed from terminal conjugated olefins with good yields and high enantioselectivity. Non-conjugated olefins (e.g. 1-octene) or trisubstituted olefins did not produce aziridines.</p><p>After examining crystal structures of 30, Katsuki and coworkers designed a new ruthenium(II) salen complex, 32, which they anticipated would be a more reactive aziridination catalyst that would suppress the undesired allylic C–H bond amination reaction.42 A crystal structure of 30 showed that the meta-hydrogens on the phenyl group were close (3.59 Å) to the N-atom of a bound acetonitrile ligand. Since acetonitrile would be replaced with the nitrene group when exposed to an azide, the meta-hydrogens might react with the nitrene to decompose the catalyst. Replacing these meta-hydrogens with fluorine atoms would render the catalyst more robust by eliminating this potential decomposition pathway. In line with this hypothesis, higher turnover numbers (TON) without attenuation of enantioselectivity was observed when ruthenium salen 32 was used to catalyze the aziridination of terminal olefins (eqn (4)). For the aziridination of para-nitrostyrene, a turnover number of 5 was observed using 30, whereas, a TON of 34 was observed using the fluorinated 32.</p><p>One weakness these aziridination reactions share is that efficient reaction rates occurred only when para-tolylsulfonyl azide was used. Relatively harsh conditions (e.g. NaNp, DME)43 are required to remove this group from the aziridine product. The discovery of a more active and robust catalyst enabled the Katsuki group to examine other azides bearing more easily removable groups on nitrogen (eqn (5)).44,45 They examined nitrobenzenesulfonyl azide (NsN3) and 2-(trimethylsilyl)ethanesulfonyl azide (SESN3) as a potential sources for the nitrenoid because both the p-Ns-group46,47 and SES-group48,49 can be removed under mild conditions. While both azides were shown to be competent sources of nitrene, higher enantioselectivities were observed when SES–N3 was used. Significantly higher turnover numbers, however, were obtained using p-NsN3 —up to 746 as compared to 260 for SESN3.</p><p>With this more reactive catalyst, aziridination of non-conjugated olefins could be achieved (Scheme 9).42,45 The fluorinated ruthenium salen complex (32) successfully catalyzed the formation of aziridines 33 and 34 from alkyl substituted alkenes although higher temperatures were required (eqn (4)). No competing allylic C–H bond amination was reported under these conditions. Even disubstituted alkenes, such as indene, were competent substrates.</p><p>The Zhang group has pursued complementary approaches to the synthesis of chiral aziridines from azides. Like the Katsuki group, Zhang and co-workers have been motivated to develop a reaction wherein the product aziridine contains an easily removable group on nitrogen. Towards this end, they have developed several aziridination methods that employ diphenylphophoryl- or nosyl azide.</p><p>The Zhang group chose diphenylphosphoryl azide as their nitrogen-atom source because the nitrogen–phosphorous bond in the product aziridine is readily hydrolyzed.50–52 Low catalyst loads and high chemical yields were realized when a D2-symmetric chiral porphyrin was used as a ligand for cobalt (Scheme 10).52 This ligand contains chiral, non-racemic cyclopropyl-substituted amide side chains, which prevent the cobalt amido complex from decomposing in the reaction. It is easily synthesized from a tetrabromo-substituted porphyrin through a palladium-catalyzed quadruple amidation.53 While the asymmetric induction for diphenylphosphoryl N-atom transfer was moderate, it does represent a launching point for future method development.</p><p>Increased yields and broader substrate scope was observed when the azide substituent was changed from diphenylphosphoryl to sulfonyl.54 For this transformation, a non-chiral tetraamide porphyrin P2 was used as the ligand on cobalt. While cobalt tetraphenylporphyrin did not react with the sulfonyl azide, efficient N-atom transfer was obtained with the cobalt P2 porphyrin complex. A variety of substituted styrenes were reported to demonstrate the broad scope of their method (Scheme 11). They found that changing the electronic- or steric nature of the styrene did not lead to attenuated yields. Their optimized conditions tolerated substrates with ortho-substituents as well as strongly electron-withdrawing groups. N-Atom transfer from para-nosylazide was possible to a broader range of substrates than using diphosphoryl azide. One weakness of this system, however, was that aliphatic and disubstituted styrenes were not tolerated as substrates.</p><p>A model was suggested to account for the enhanced reactivity of the cobalt P2 complex.54 The cobalt porphyrin complex was designed to place the amide N–H bond close to the SO2 group present on the nitrene (Fig. 1). The subsequent hydrogen bond formation was anticipated to stabilize the formation of the postulated metal nitrenoid intermediate and enhance its electrophilicity. While no experimental evidence was provided to back up these assertions, computational modeling produced a minimal geometry of the cobalt nitrenoid that had an N–H–O bond distance of 2.9 Å. This short distance indicates that significant hydrogen bonding could occur between the nitrenoid and the amide. This hydrogen-bonding model may account for the increased reactivity of the cobalt P1 complex for the aziridination of styrenes with phosphoryl azide.</p><p>An important advance was realized by the Zhang group when they used their D2 symmetric cobalt porphyrin complex to catalyze the formation of aziridines from trichloroethoxysulfonyl (TCES) azide and an alkene (Scheme 12).55 In pursuit of discovering conditions that provided chiral aziridines with easily removable groups on nitrogen, the Zhang group found that trichloroethoxy-sulfonyl azide was a promising nitrene source. Optimization of the porphyrin ligand revealed that the highest yields and enantioselectivity could be achieved using porphyrin P5. While higher yields were observed using other D2-symmetric porphyrins, this porphyrin afforded the highest enantioselectivities. Examination of additives revealed that the addition of palladium acetate led to improved yields without loss of enantioselectivity.</p><p>These optimized conditions enabled a large range of alkenes to be transformed into aziridines with high yields and good enantioselectivities (Scheme 13). A screen of styrenes revealed that the electronic nature of the styrene did not affect the yield or selectivity of the process. The nitrenoid could be transferred to styrenes with ortho-substitution as well as gem-disubstituted styrenes to give 38e and 38f. For the latter substrates, attenuated yields were obtained without much diminishment of enantioselectivity. Even aryl-substituted silyl enol ethers could be used as substrates, as in 38g, although no enantioselectivity was observed. Even dienes and alkyl-substituted olefins were tolerated as substrates.55 This report by Zhang is the first to show that these substrates could be converted to aziridines without competing side reactions, such as dimerization of the nitrenoid and allylic C–H bond amination of the olefin.</p><!><p>The intramolecular aminochlorination of an olefin can be achieved using substoichiometric amounts of iron(II) chloride to decompose azidoformates (Scheme 14).56,57 Bach and co-workers reported that monosubstituted alkenes 39 could be transformed to oxazolidinones diastereoselectively using 10–30 mol% of FeCl2 in the presence of a slight excess of trimethylsilylchloride (1.5 equiv). Irrespective of the R-group, the diastereoselectivity of the reaction was consistently about 9 : 1. Propargyl substrates are also tolerated as substrates. Insight into the mechanism of this transformation was gained from the reactivity of disubsituted alkenes 42. While nearly perfect stereochemical fidelity was obtained with a phenyl substituent, the n-propyl substrate afforded a 1 : 1 mixture of products.</p><p>To account for the lack of stereospecificity, Bach and co-workers proposed a mechanism involving the intermediacy of radical intermediates (Scheme 15).56,57 Iron-mediated decomposition of the azidoformate produced Fe(III) nitrene 46, which underwent a 5-exo-trig cyclization to give 47. Even though chlorination was proposed to occur intramolecularly, this atom-transfer process occurred more slowly than bond rotation. Experiments to determine the lifetime of radical 47, were not successful.</p><!><p>Achieving the selective transformation of an unactivated C–H bond to a C–N bond would enable the rapid functionalization of readily available hydrocarbons. The use of azides as the nitrogen source in C–H bond amination reactions has motivated several research groups because the only by-product of C–N bond formation is N2. Azides also do not require additives to access divalent nitrogen, or to neutralize by-products of the activation. Together, these attributes simplify purification of the reaction mixture as well as ensure that the pH of the reaction remains constant to eliminate the need of base. Consequently, the resulting process has the potential to be quite environmentally benign if the appropriate solvent/catalyst pairing is discovered. Towards the goal of achieving selective C–H bond amination significant progress has been made in the functionalization of both sp2- and sp3-C–H bonds.</p><!><p>The Hemetsberger–Knittel–Moody indole reaction represents a rapid synthesis of 2-carboxylate-substituted indoles in two-steps from commercially available aryl aldehydes.10 Thermolysis of the vinyl azide substrate produces the indole in good yields. The Driver group reported that the rate of reaction could be accelerated by rhodium(II) perfluorobutyrate to form indoles at ambient temperature (eqn (6)).58 Other rhodium(II) tetracarboxylate complexes, such as Rh2 (OAc)4 and other transition metal salts (e.g. FeX2, ZnX2, CuX2, AgX, AuX) were found to be ineffective as catalysts.</p><p>Rhodium(II) perfluorobutyrate proved to be an effective catalyst to enable the synthesis of a range of indoles 48 from vinyl azides 47 (Scheme 16). The reaction tolerated a variety of functional groups, including halogen-, trifluoromethyl-, and methoxy substituents as well thiophenes, furans, and pyrroles. The Lewis acidity of the rhodium(II) carboxylate limited the substrate scope to those that contained non-Lewis basic functional groups: azides bearing cyano-, and dimethylamino groups did not produce indoles.</p><p>Rhodium(II)-catalyzed N-heterocycle formation was extended to include pyrroles (Scheme 17).59 The functional group tolerance of this reaction mirrored the indole reaction and a variety of di-and trisubstituted pyrroles could be produced in good yield.</p><p>In contrast to indole synthesis, a variety of transition metal salts were able to catalyze pyrrole formation (Scheme 18).59 In addition to rhodium(II) carboxylates, copper(II) triflate and zinc iodide were found to be competent catalysts. In the presence of 5 mol% of zinc iodide, the pyrrole formation occurred readily at room temperature. Comparison of reaction conversions revealed different substrate preferences: higher yields were obtained with dienylazides bearing electron-deficient aryl groups using rhodium perfluorobutyrate (50b, 50c), while zinc iodide was preferred for substrates with electron-donating dienyl azide substituents (e.g. 50a).</p><p>The Driver group also developed a complementary synthesis of N-heterocycles with C2-aryl or alkyl substituents from aryl azides 51 through the construction of the C2 C–N bond (Scheme 19).60 While the analogous thermal and photochemical reactions are known,11 they are rarely used to make N-heterocycles because the rate of decomposition of the aryl nitrene competes61 with the desired C–H amination reaction. Using rhodium(II) perfluorobutyrate or octanoate as catalyst, a range of indoles (52) were formed at 60 °C from aryl azides (51). In addition to indoles, a variety of carbazoles could be formed from the corresponding biaryl azides.62</p><p>Examination of the reactivity of biaryl azides revealed a significant advantage to using rhodium(II) carboxylates as catalysts to form N-heterocycles (eqn (7)).62 Thermolysis or photolysis of biaryl azide 51k produced a nearly 50 : 50 mixture of the two possible carbazoles.12 In contrast, rhodium(II) octanoate significantly improved the selectively to form carbazole 52k as the major product. The Driver group interpreted this selectivity as evidence that the metal complex was involved in the C–N bond forming step of the mechanism.</p><p>Further insight into the mechanism was obtained from the observed reactivity of both the E- and Z-styryl azide isomers 51l (Scheme 20). While the E-isomer was found to readily produce 2-phenylindole, Smith and co-workers reported that thermolysis of the Z-isomer resulted in the formation of a tarry substance in addition to a significantly lower yield of indole.63 In contrast, exposure of either the E- or the Z-isomer to 5 mol% of rhodium(II) carboxylate produced 2-phenylindole cleanly. The Driver group interpreted this result as evidence that indole formation occurred through a stepwise process where C–N bond formation preceded C–H bond cleavage.</p><p>Further insight into the mechanism came from a study on the reactivity of triaryl azides 53 (eqn (8)).64 The ratio of products would be analyzed using the Hammett equation, and the results were anticipated to provide insight into the nature of C–N bond formation. The original hypothesis was that C–N bond formation occurred through electrophilic aromatic substitution and a linear correlation to σmeta-constants was expected. Because the methoxy-group is at the meta-position to the reaction center, it would act as an inductive electron-withdrawing group if an electrophilic aromatic substitution (EAS) mechanism was occurring (Scheme 21).</p><p>In contrast to these expectations, no correlation with σmeta-constants was observed. On the basis of this data, electrophilic aromatic substitution was eliminated as the mechanism for C–N bond formation. Instead, bisecting straight lines were observed when the product ratios were plotted against Hammett σ+-constants (Fig. 2).65 Related V-shaped graphs have been interpreted as evidence for a change in the identity of the rate-determining step, or as evidence that a change in the mechanism is occurring.66</p><p>The Driver group interpreted the correlation with σ+-constants as evidence that the product-determining step occurred before C–N bond formation (Scheme 22). Linear correlation with σ+- constants would be expected if the more electron-rich aryl group assists in the formation of the rhodium nitrenoid (56 to 60). Considering the rhodium nitrenoid as ortho-azaquinoid 60 provides an explanation for the selectivity in C–N bond formation. A resonance structure of the resulting ortho-azaquinoid (61) places the positive charge on the ortho-carbon to trigger a four-π-electron-five-atom electrocyclization67 to form the C–N bond in 69. This electrocyclization must occur faster than tautomerization of the metalloimine (61 to 62),68 which would abolish selectivity in C–N bond formation. A 1,5-hydride shift from 63 then affords the carbazole product.</p><p>The requirement of a contiguous π-system was examined with 70 (eqn (9)). If electron assistance was required for rhodium nitrenoid formation, azide 70 was expected not to react. As anticipated, exposure 70 to reaction conditions resulted in no reaction.</p><p>The Jia group recently reported that ruthenium trichloride could be used as a catalyst to promote N-heterocycle formation from aryl- and vinyl azides (Scheme 23).69 While carbazole and pyrrole formation occurred at 85 °C, slightly higher temperatures (105 °C) were required to form indoles from vinyl azides 71. In comparison to the use of rhodium(II) carboxylate complexes, slightly elevated temperatures were required to form the desired N-heterocycle. This limitation is mitigated by the significantly reduced cost of the catalyst and increased functional group tolerance of the reaction—Lewis basic groups such as dimethylamine (71b) were allowed. The stereoselectivity of this Ru-catalyzed N-heterocycle formation has not yet been reported.</p><p>To provide insight into the mechanism of this process, density functional theory calculations were performed on a variety of possible reactive intermediates.69 The Jia group interpreted these calculations to suggest that a Ru(III)/Ru(V) catalytic cycle occurs and that the C–N bond is formed through a similar electro-cyclization mechanism as reported by Driver and co-workers (Scheme 24). In support of this mechanism, aryl azide 70 did not react when exposed to ruthenium trichloride.</p><!><p>In 1999, ruthenium- and cobalt porphyrin complexes were reported by Cenini and co-workers to catalyze sp3-C–H bond amination reactions using para-nitrophenylazide as the source of the nitrogen-atom.37,38 While cycloheptene and cyclooctene were effectively converted to the corresponding aziridine, exposure of cyclohexene to either catalyst formed allyl amine 77 (eqn (10)). The major by-product was reduction of the azide to form para-nitroaniline. The product distribution was affected by the electronic nature of the aryl azide: when an electron-rich azide was used (e.g. p-methoxyphenylazide), the yield of allylic C–H bond amination was reduced.</p><p>Cobalt(II)-catalyzed benzylic C–H bond amination was also investigated using para-nitrophenylazide (Scheme 25).70,71 While a variety of cobalt(II) porphyrins catalyzed the formation of amine 78, empirical evidence revealed that changing the hydrocarbon substrate required a screen of different porphyrin ligands to achieve the highest yield.</p><p>Further insight into the mechanism of allylic C–H bond amination came from the isolation of a ruthenium diimido complex 80 (Scheme 26).72 Exposure of ruthenium tetraphenyl porphyrin to aryl azide 79 resulted in the formation of diimido complex 80, whose structure was determined by X-ray crystallography.73 This complex catalyzed N-atom transfer from aryl azide to cyclohexene. Exposure of an excess of cyclohexene to 80 resulted in the formation of allylic amine 82 and 1,3-cyclohexadiene. X-Ray crystallography revealed that the ruthenium product was diamide complex 81. This complex was also a competent amination catalyst. Reaction of 81 and cyclohexene produced allylic amine and cyclohexane. The ruthenium was then oxidized to produce the diimide complex to complete the catalytic cycle. This data contrasts with the kinetic and mechanistic data obtained from Cenini's previous study of cobalt porphyrin-catalyzed N-atom transfer to alkenes.38,71</p><p>Significant advances in this field have been achieved by the Zhang group.74–76 They reported both intra- and intermolecular sp3-C–H bond amination using cobalt(II) porphyrin complexes. In 2007, they reported that cobalt tetraphenylporphyrin catalyzed the intramolecular amination of benzylic C–H bonds (Scheme 27).74 Cobalt tetraphenylporphyrin emerged as the best catalyst from a series of transition metal porphyrin complexes. No reaction, or in-efficient reaction rates were observed using vanadium(IV), iron(III), copper(II), chromium(III), zinc(II), manganese(III), nickel(II), or ruthenium(II) porphyrin complexes. The optimal reaction conditions were determined to only require 0.5 mol% of Co(TPP) to decompose the aryl sulfonyl azide. The scope of benzyl C–H bond amination was found to be broad. Tertiary-, secondary-, and primary C–H bonds could be functionalized to produce N-heterocycle 84. In addition to alkyl groups, the reaction also tolerated bromo- and nitro-substituents.</p><p>The Zhang group investigated the selectivity of their C–H bond amination by screening aryl sulfonyl azide 85 (eqn (11)).74 The Du Bois group has reported that the related sulfonamide nitrenoid rhodium complexes prefer reaction with C–H bonds to form six-membered N-heterocycles.77 To compare the selectivity of their cobalt-catalyzed process, the Zhang group investigated 85, which contains two reaction centers. Amination of the weaker benzylic C–H bond would produce a five-membered ring, whereas reaction with the β-C–H bond would produce a six-membered ring. The Zhang group reported that the ratio of 86 to 87 depended on the identity of the porphyrin ligand, with Co(OEP) promoting a more selective reaction. Control of the product ratio indicates that the metal complex is involved in the C–N bond-forming step of the mechanism.</p><p>The Zhang group also reported that intramolecular benzylic C–H bond amination could be accomplished from aryl phosphoryl azides (Scheme 28).75 The optimal reaction conditions were determined using azide 88a. The Zhang group found that the reaction efficiency strongly depended on the identity of the porphyrin ligand. In contrast to their sulfonyl azide study, cobalt tetraphenylporphyrin was found to be inactive. Instead, effective conversion to phosphoramidite 89a was accomplished using the catalyst formed from porphyrins P2, P6, or P7. The higher activity of these complexes was attributed to their participation in hydrogen bonding of the amide N–H with the oxide of the phosphoryl group on the nitrenoid.54 The yield of the process depended on the steric nature of the R group on the amide. The highest yields were observed with a methyl- or ethyl substituent. Increasing the size of the amide (R =i-Pr) resulted in a diminished yield. Further increasing the size to a t-Bu group completely inhibited the reaction. No product formation was also observed with an N-phenylamide (P9) ligand.</p><p>The scope of this reaction was quite broad (Scheme 29).75 The reaction could form either a six- or a seven-membered ring, the latter ring size being particularly noteworthy for C–H bond amination because it is rarely observed. Six-membered phosphoramidites could be formed in high yield from primary, secondary-, or tertiary benzylic C–H bonds. The successful conversion of 88c revealed that aziridination was not a competitive reaction. Even phosphoryl azides containing Lewis basic groups such as a tertiary amine (88f) were competent substrates. If the benzylic position was fully substituted, seven-membered ring formation readily occurred without increasing the catalyst loading or reaction temperature. The cyclic phophoramidite amination products can be reduced with LiAlH4 or methanolyzed to produce value added amine-containing products.75</p><p>In addition to phosphoryl- and sulfinylazides, intramolecular benzylic C–H bond amination of aryl azides is also possible. The Driver group found that iridium(I) catalyzed the formation of indolines 91 from electron-deficient aryl azides 90 at room temperature (Scheme 30).16 Cyclooctadiene iridium methoxide dimer was identified empirically from a screen of nearly 200 different transition metal salts and complexes known to catalyze the functionalization of C–H bonds. Indoline formation was sensitive to the electronic nature of the starting azide. While electron-deficient aryl azides were efficiently converted, no reaction was observed with electron-rich substrates. In contrast, the reaction was not affected by the electronic nature of the β-aryl substituent: good conversions were observed with both electron-rich and electron-poor substituents. One weakness of this method was the strict requirement for Schlenk experimental techniques: in the absence of rigorous air and water exclusion, the major product of the reaction was aniline.</p><p>A primary kinetic effect was observed in the intramolecular competition experiment of 91f- d2 (Scheme 31). 16 Driver and co-workers interpreted this result as evidence that C–H bond amination occurred stepwise through either an H-atom abstraction–radical recombination through diradical 93f-d2 or a concerted mechanism via TS-94. Unfortunately, the lack of reactivity of the cyclopropyl-substituted 90g prevented further mechanistic conclusions to be drawn.</p><p>An important advance was realized by the Zhang group when they reported that intermolecular C–H bond functionalization could be achieved using Troc-azide as the N-atom source and the commercially available cobalt tetraphenylporphyrin complex as the catalyst (Scheme 32).76 To achieve this intermolecular reaction, a range of azides were screened as potential nitrogen-atom sources. Only tosyl-azide and Troc-azide were found to be competent starting materials and significantly higher yields were observed with Troc-azide. The facile removal of the Troc-protecting group from the product amine is another significant advantage offered by this process.78 This N-atom transfer reaction was sensitive to the electronic- and steric nature of the azide. Replacing the methyl group on the sulfonyl azide with a nitro group inhibited the reaction. Increasing the steric nature of the Troc-azide by adding two methyl substituents also resulted in no reaction.</p><p>The scope of the reaction was examined using Troc-azide as the N-atom source (Scheme 33).76 The reaction worked best for the functionalization of secondary benzylic C–H bonds. Attenuating the electron density of the aryl substituent led to a slightly reduced yield (97d), whereas adding an ester group to the benzylic carbon (97e) led to a significantly lower yield. No reaction was observed with substrates containing primary benzylic C–H bonds (e.g. toluene) or non-benzylic C–H bonds (e.g. cyclohexane).</p><p>Intermolecular nitrene transfer was also recently reported by the Warren laboratory using a copper ketiminate complex 98 (eqn (12)).79 While this complex is not commercially available, it can be synthesized from Cu(Ot-Bu)2 using inert experimental techniques from copper tert-butoxide.80,81 The catalyst was quite selective: the reaction of ethylbenzene with adamantyl azide produced only benzyl amine 99.</p><p>A variety of hydrocarbons were screened to investigate the scope of this reaction (Scheme 34).79 While higher yields were observed with secondary benzyl C–H bonds than primary or tertiary C–H bonds. For primary C–H bonds, the product amine reacted to afford adamantyl aldimine 100e. In addition to benzyl C–H bonds, cyclohexane could be converted to cyclohexyl amine. For this substrate, slightly longer reaction times were required. While excellent yields were observed if the reaction was performed neat in hydrocarbon, lowering the amount of the substrate to one equivalent resulted in diminished yields for toluene and cyclohexane. For more reactive C–H bonds (such as secondary benzylic C–H bonds) the yield was not as attenuated.</p><p>A catalytic cycle for this process was postulated on the basis of several isolated copper species (Scheme 35).79 Reaction of copper ketiminate with adamantyl azide forms copper nitrenoid 101. This species can react with an additional copper ketiminate to produce the isolable 102, or with a C–H bond to produce the copper amine 103. Dissociation of the amine regenerates the copper ketiminate complex. Density functional theory calculations were performed to investigate the nature of copper nitrenoid 101. The ground state of 101 was determined to be a singlet biradical and to exist 18 kcal mol−1 below the triplet state.</p><!><p>Substantial progress has been made in the development of transition metal-catalyzed N-atom transfer reactions using azides as the nitrogen-atom precursor. The attractiveness of azides in these processes stems from their ready accessibility, controllable reactivity, and environmentally benign by-products. High yields, good selectivities, and broad substrate scope prove that azides are excellent choices for the nitrenoid precursor in aziridination and sulfimination reactions. Because these reactions can be catalyzed by a range of different transition metal complexes, the reaction conditions can be tailored to match the demands of the process. Considerable strides have also been made using azides in C–H bond amination processes. Intramolecular processes that form new C–N bonds are appealing, alternative methods for the construction of N-heterocycles. Selective intermolecular C–H amination processes are just emerging and represent future targets for the development of new chemistry, which use azides with easily removable groups on nitrogen. Achieving transition metal-catalyzed intra- and intermolecular asymmetric C–H bond amination reactions also remains a future goal in this area. Tandem processes triggered by an initial N-atom transfer from azides to create multiple bonds also remain underdeveloped. The positive attributes of using azides will insure continued research interest for their use in N-atom transfer reactions.</p>

PubMed Author Manuscript

Protective Effects of Notoginsenoside R1 via Regulation of the PI3K-Akt-mTOR/JNK Pathway in Neonatal Cerebral Hypoxic–Ischemic Brain Injury

Notoginsenoside R1 (NGR1) is a predominant phytoestrogen extracted from Panax notoginseng that has recently been reported to play important roles in the treatment of cardiac dysfunction, diabetic kidney disease, and acute liver failure. Studies have suggested that NGR1 may be a viable treatment of hypoxic-ischemic brain damage (HIBD) in neonates by reducing endoplasmic reticulum stress via estrogen receptors (ERs). However, whether NGR1 has other neuroprotective mechanisms or long-term neuroprotective effects is unclear. In this study, oxygen-glucose deprivation/reoxygenation (OGD/R) in primary cortical neurons and unilateral ligation of the common carotid artery (CCL) in 7-day-old postnatal Sprague Dawley (SD) rats followed by exposure to a hypoxic environment were used to mimic an HIBD episode. We assessed the efficacy of NGR1 by measuring neuronal damage with MTT assay and assessed brain injury by TTC staining and brain water content detection 24–48 h after OGD/HIE. Simultaneously, we measured the long-term neurophysiological effects using the beam walking test (5 weeks after HI) and Morris water maze test 5–6 weeks after HI. Expression of PI3K-Akt-mTOR/JNK (24 h after HI or OGD/R) proteins was detected by Western blotting after stimulation with HI, NGR1, LY294002 (PI3K inhibitor), 740Y-P (PI3K agonist), or ICI 182780(estrogen receptors inhibitor). The results indicated that NGR1 exerted neuroprotective effects by inhibiting neuronal apoptosis and promoting cell survival via the PI3K-Akt-mTOR/JNK signaling pathways by targeting ER in neonatal hypoxic–ischemic injury.

protective_effects_of_notoginsenoside_r1_via_regulation_of_the_pi3k-akt-mtor/jnk_pathway_in_neonatal

Introduction<!>Drug Preparation<!>Animals<!>Cell Culture and Drug Treatment<!>Oxygen Glucose Deprivation/Reoxygenation<!>Hypoxic-Ischemic Brain Damage Model<!>Cell Viability Assessment<!>Measurement of Cell Membrane Integrity<!>Morris Water Maze<!>Beam Walking Test<!>Evaluation of Brain Damage 6 Weeks After Modeling<!>Evaluation of Infarction Volume<!>Brain Water Content Detection<!>TUNEL Staining<!>Western Blots<!>Statistical Analysis<!>NGR1 Attenuated OGD/R-Induced Cortical Neuron Damage Mediated by Estrogen Receptors<!><!>NGR1 Attenuated OGD/R-Induced Cortical Neuron Damage Mediated by Estrogen Receptors<!>NGR1 Attenuated HI-Induced Brain Injury in Newborn Rats Mediated by Estrogen Receptors<!><!>NGR1 Attenuated HI-Induced Brain Injury in Newborn Rats Mediated by Estrogen Receptors<!>NGR1 improved neurobehavioral function Mediated by Estrogen Receptors<!><!>NGR1 improved neurobehavioral function Mediated by Estrogen Receptors<!>In Vitro and In Vivo<!><!>In Vitro and In Vivo<!><!>NGR1 Downregulated JNK Signal Pathway via Estrogen Receptors in Vitro and in Vivo<!><!>NGR1 Downregulated JNK Signal Pathway via Estrogen Receptors in Vitro and in Vivo<!>NGR1 Exerted Neuroprotective Effects via Estrogen Receptors and PI3K<!><!>Discussion

<p>Hypoxic-ischemic brain damage (HIBD) in neonates is an important risk factor for many severe human neurological dysfunctions, such as motor and learning disabilities, cerebral palsy, epilepsy, and even death [1–3]. In spite of the major advances in modern medical technology and the increased understanding of fetal and neonatal pathologies, neonatal hypoxic–ischemic encephalopathy (HIE) is still an unresolved serious condition that leads to significant mortality and long-term morbidity [4–7]. Presently, there are no well-established effective therapies for neonatal HIE [8]. Hypoxic–ischemic brain injury directly results in a large amount of neuronal death. Research suggested that an important way causing neuronal loss was apoptosis, especially in the penumbra area [9]. Malagelada et al. [10] found that there were at least 50% of dying cells which performed morphological characteristics of apoptosis in OGD-treated cortical neuron cultures. Therefore, enhancing neuronal survival, reducing apoptosis have become the most important strategies for solving neurological diseases [11].</p><p>Notoginsenoside R1 (NGR1) is a predominant phytoestrogen extracted from P. notoginseng. NGR1 was recently reported to possess anti-inflammatory, antioxidant, and anti-apoptotic properties, and may play important roles in the treatment of cardiac dysfunction [12–15], acute liver failure [16], and diabetic kidney disease [17]. Meng et al. [18] found that 3-day pretreatment with NGR1 significantly reduced cerebral infarct volume in an adult rat model, while pretreatment with NGR1 for 24 h prevented apoptosis induced by oxygen glucose deprivation/reoxygenation (OGD/R) in primary cortical neurons. Our past study [19] indicated that NGR1 treatment exerted neuroprotective effects in the acute phase of a neonatal HIBD model. It is worth noting that neonatal HIBD often leads to long-lasting neurological deficits such as mental deficiency, cerebral palsy, and learning disabilities, which develop in the immature brain. These consequences have seriously affected the quality of life of children with HIE. Whether NGR1 treatment can promote the long-term recovery of neurological function after HIBD has not yet been reported and is worth exploring.</p><p>Research [13, 15, 18, 19] has indicated that NGR1 may perform its functions through estrogen receptors (ERs). The classic ERs have two major subunits, estrogen receptor α (ERα) and estrogen receptor β (ERβ). Within the brain, ERα/β are found in cognitive brain regions associated with learning and memory, such as the cerebral cortex, hippocampus, and basal forebrain [20, 21]. A number of studies have shown that ERs play an important role in organ ischemic injury. Liu et al. [22] found that calycosin exhibited an anti-apoptotic effect via ERα/β and improved Akt phosphorylation in cardiomyocytes. Hsu et al. [23] suggested that 17β-estradiol (E2) treatment reversed hepatic injury following hemorrhagic shock and resuscitation through ERs-related p38 MAPK-dependent HO-1 upregulation. Wang et al. [24] reported that E2 offered protection against retinal ischemic injury by inducing upregulation of SDF-1 expression through activation of ERs. Activating ERs were found to provide protection for CA1 neurons in ischemic injury, while ICI 182780 (the broad-spectrum ERs antagonist) abolished the protection [25].</p><p>As an important signal transduction pathway, PI3K-Akt-mTOR/JNK is involved in many cellular processes, including cell apoptosis, survival and proliferation [26, 27]. Phosphatidylinositol 3 kinase (PI3K) is an intracellular phosphatidylinositol kinase which consists of a catalytic subunit (p110) and a regulatory subunit (p85) [28, 29]. Protein kinase B (Akt), a serine/threonine kinase, is a primary downstream target in the transduction pathway of PI3K signaling. Akt is a key information molecule that promotes cell survival, inhibits apoptosis [30] and maintains normal functions [31]. Activated Akt can transmit signals to a variety of downstream substrates. The common downstream proteins include TSC1/2-Rheb-mTOR [32], pro-apoptotic factor JNK, NFκb, and frontal transcription factor FKHR [33]. Mammalian target of rapamycin (mTOR) is a serine/threonine kinase that can benefit cell growth, survival, and metabolism [32]. The main targets of activated mTOR are ribosomal protein S6 kinase (p70S6K) and eukaryotic initiation factor 4E binding protein 1 (4E-BP1). Among them, p70S6K is mainly involved in cell-cycle regulation and contributes critically to cell survival. Activated p70S6K promotes the synthesis of ribosome translation regulator protein, resulting in the positive regulation of protein synthesis. Through the phosphorylation of 4E-BP1, mTOR regulates cap-dependent protein translation and promotes the proliferation of neurons. JNK which also can be regulated by Akt directly or indirectly controls a number of transcriptional and non-transcriptional processes, including inflammation and cell death or survival [26, 34–39].</p><p>Many studies have shown that PI3K-Akt-mTOR/JNK signaling plays a major role in cerebral hypoxic–ischemic injury [26, 32, 40, 41]. Some researchers [42–44] have found that Akt signaling, which is activated after transient cerebral ischemia, inhibits delayed neuronal apoptosis and promotes cell survival. Activation of the mTOR pathway is sufficient for promoting both neuron survival and axon regeneration [45, 46]. Research [26, 47, 48] indicates that the JNK pathway is also involved in ischemia-induced neuronal apoptosis. Hence, a number of researchers have proposed that JNK may be a target for the treatment of neuronal necrosis and that the inhibition of the JNK signaling pathway may reduce the apoptosis caused by ischemic brain damage [49–51].</p><p>Some studies have reported that NGR1 could protect the heart from septic shock via the activation of ERα and PI3K/Akt signaling [13]. NGR1 activated Nrf2/ARE signaling and upregulated phase II antioxidant enzymes in PC12 cells via ERs [52]. Our previous findings suggested that NGR1 could inhibit endoplasmic reticulum stress-induced neuronal apoptosis and brain damage via ERs [19]. However, it remained unclear whether NGR1 could exert neuroprotective effects and reduce neuron apoptosis via ERs by acting on the PI3K-Akt-mTOR/JNK signal pathway in a neonatal hypoxic–ischemic brain damage (HIBD) model.</p><p>In this study, we investigated the neuroprotective effects of NGR1 in a neonatal HIBD model, especially concerned whether NGR1 had a contribution to the long-term recovery of neurological function in the HIE. Furthermore, we explored the neuroprotective mechanisms of NGR1 by inhibiting neuronal apoptosis and promoting cell survival via the ERs and PI3K-Akt-mTOR/JNK signaling pathway.</p><!><p>NGR1 (chemical structure C47H80O18, molecular weight = 933.13, purity > 98%) was from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO). ICI-182780 (an estrogen receptor inhibitor), LY294002 (an inhibitor of PI3K) and 740Y-P (an agonist of PI3K) were from Tocris (London, UK), Selleck Chemicals (Houston, Texas, USA),and Selleck Chemicals (Houston, Texas, USA), respectively.</p><!><p>Seven-day-old Sprague–Dawley (SD) male rats and rat fetuses (18 days) were provided by the Animal Department of Chongqing Medical University (Chongqing, China). All experiments were put into practice in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All protocols were ratified by the Animal Ethics Committee of Chongqing Medical University and efforts were made to reduce animal suffering.</p><!><p>The experiment was conducted according to previously described methods [19, 53]. Dissociated cultures of cortical neurons were harvested from time-mated embryonic day 18 (E18) rat brains using established protocols. Cerebral cortices were excised and hatched in Ca2+- and Mg2+-free HBSS solution. The tissues were mechanically separated and then digested in 0.25% trypsin (with 0.02% EDTA) for 7 min at 37 °C. After trypsinization was terminated, the digests were centrifuged for 5 min at 1000 rpm. The centrifuged cells were resuspended in Neurobasal medium (Gibco, Gaithersburg, MD) with 2% B-27 supplement (Gibco) and 2 mmol/l l-glutamine (Invitrogen, Gaithersburg, MD). Cells were subcultured in 96-well plates (5 × 104 cells/well) for 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, in 24-well plates (1 × 105 cells/well) for lactate dehydrogenase (LDH) determination and in 6-well plates (1 × 106 cells/well) for other experiments. Plates were precoated with polyethylenimine (0.05 mg/ml, Sigma-Aldrich) overnight at 37 °C. Cultures were maintained in a Heraeus CO2 incubator (Thermo Fisher Scientific, Rockford, IL) containing 5% CO2 and 95% air at 37 °C. Cultures were used for experiments on the fifth day in vitro. Cells were treated with NGR1 (10 µmol/l) [19] when subjected to oxygen glucose deprivation and reoxygenated. ICI 182780 (0.1 µmol/l) [19] was used to preprocess cells 2 h before OGD. LY294002 (20 µmol/l) and 740Y-P (20 µmol/l) were applied to cells 1 h before OGD. ICI 182780, LY294002, and 740Y-P were dissolved in dimethyl sulfoxide (DMSO). DMSO acted as a vehicle with a concentration of 1%.</p><!><p>OGD/R was accomplished using day-5 cultured primary cortical neurons to imitate cerebral ischemic/reperfusion injury. OGD/R was achieved using a modification of a previously described procedure [19]. After the cells were washed once with phosphate-buffered saline (PBS), culture plates were replenished with glucose-free Dulbecco's Modified Eagle's Medium. Cultures were placed in an anaerobic chamber (Thermo Fisher Scientific) and incubated in an anaerobic gas mixture (1% O2, 5% CO2, and 94% N2) at 37 °C. After 1.5 h, cultures were returned to a normoxic environment from the anaerobic chamber. Simultaneously, the culture plates were refilled with Neurobasal medium, and cultures were allowed to reoxygenate for 4–24 h.</p><!><p>HI was imitated by unilateral ligation of the common carotid artery (CCL) followed by 2.5 h of hypoxia in 7-day-old SD rats. Rat pups were anesthetized with isoflurane (2.5%) and supine fixed in the thermostat console. A longitudinal midline incision disinfected by iodophor disinfectant was made in the anterior neck. After the right common carotid artery was identified and freed from the surrounding tissues, without any damage to the right vagus nerve, it was double ligated and transected between the ligatures. The pups were then returned to a heating pad for 1 h for recovery. Simultaneously, an airtight chamber containing 7% humidified oxygen and 93% N2 was prepared using a heating pad to maintain the temperature at 35–39 °C. Then the HI animals were placed in the chamber for 2.5 h. Sham animals received an incision but did not undergo CCL treatment, and the pups were placed in a similar container but not exposed to a hypoxic environment. After modeling, all pups were returned to their dams. NGR1 (15 mg/kg q 12 h, for 2 days) [19] was administered to the pups by intraperitoneal injection after CCL immediately, before exposure to the hypoxic environment. ICI-182780 (2 mg/kg) was administered to pups 2 h before CCL treatment by intraperitoneal injection [19].</p><!><p>An MTT assay was used to test cell viability. Four or 24 h after the OGD/R injury, cells were incubated with MTT (0.05 mg/l) for 4 h at 37 °C. The culture medium was then completely removed, and all wells were filled with 100 µl DMSO to dissolve the formazan crystals. Absorbance was surveyed at 570 nm using a microplate reader (Bio-Rad Model 680, Bio-Rad, Hercules, CA). Cell viability was calculated using the formula (mean experimental absorbance/mean control absorbance) × 100%.</p><!><p>The rate of LDH release was used to estimate the membrane integrity of cells. The supernatant of each well was collected, and the LDH content was determined using an LDH assay kit according to the manufacturer's instructions (Nanjing Institute of Jiancheng Biologic Engineering, Nanjing, China). For the positive control, the supernatant of the cells was collected after cells were lysed using 0.25% Triton X-100. The level of LDH release was calculated using the formula (experimental LDH activity/positive control LDH activity) × 100%.</p><!><p>Neurocognitive outcomes were measured by using the Morris water maze (WM) test with a computerized video tracking system (BW-mwm101, Shanghai BioWill Co., Ltd., China) 5–6 weeks after modeling. The WM consisted of a circular pool 120 cm in diameter and 47 cm in height, containing water 30 cm deep. A hidden submerged platform (9 cm diameter) was placed in the second quadrant 2.5 cm below the water surface for rats to step on and escape from the water. Rats could identify the position of the platform using visual clues placed on the walls. The time to locate the submerged platform (defined as the latency, with cutoff time 60 s) was measured. Every day, each rat performed four trials starting from different quadrants. The test lasted for 5 days. On testing day 6, each rat performed a probe trial (60 s cutoff) without a platform. All of the activities were video recorded, and the animals' swimming paths were measured for quantification of time, frequency, and latency [54, 55] using the ANY-maze Animal Behavioral Video Analysis System (Shanghai Bio-will Co., Ltd, China).</p><!><p>Coordination and integration of motor movement was assessed with a beam (80 cm × 2.0 cm × 2.5 cm; 60 cm above floor) walking test 5 weeks after modeling. Each rat was tested 3 times, for 2 min each time. The ratio scale was modified from Ohlsson [56] and Feeney [57]. Balance performance on the beam was graded as follows: 0, the rat falls down and cannot walk on the beam; 1, the rat is unable to walk on the beam but can sit on the beam; 2, the rat falls down while walking; 3, the rat can traverse the beam, but the affected hind limb does not aid in forward locomotion; 4, the rat crosses the beam with more than 50% foot slips; 5, the rat traverses the beam with fewer than 50% foot slips; 6, the rat successfully crosses the beam with no foot slips.</p><!><p>Hemispheric weight loss has been used as an important variable for assessing brain atrophy in neonatal HI model [58]. After Morris water maze test, the brains were extracted and the hemispheres were cut along the center line and weighed on a high-precision balance. The brain weight ratio (%) was calculated using the formula (weight of ipsilateral hemisphere/weight of contralateral hemisphere) × 100%.</p><!><p>2,3,4-tiphenyl tetrazolium chloride (TTC) (Sigma-Aldrich, MO) staining is a reliable way to evaluate infarction volume. Using this method, the brain sections were prepared as follows: First, the brains were removed and frozen at − 20 °C for 10 min. Next, consecutive 2 mm coronal sections were obtained by slicing the brains with Brain Matrix (ASI Instruments, Warren, MI). The subsequent incubation of the sections was performed in a dark environment with 25-min immersion in 2% TTC solution at 37 °C. Finally, the sections were immersed in a 4% formaldehyde solution. TTC stained normal areas of brain deep red but did not stain infarcted tissue. Infarction volumes were measured and analyzed with ImageJ software (NIH Image, Version 1.61, Bethesda, MD, USA) as described previously [19].</p><!><p>Rats were sacrificed 24 h after HI for brain water content measurement. The wet weight of the brain sample was measured immediately after harvest. The brain was then placed in an oven at 105 °C for 24 h and weighed again to determine the dry weight [59]. Brain water content (%) was calculated using the formula[(wet weight − dry weight)/wet weight] × 100%.</p><!><p>Coronal brain slices were stained with neuron-specific nuclear protein (NeuN) and terminal deoxynucleotidyl transferase-mediated nick-end labeling (TUNEL) to measure apoptotic neurons 24 h after HI. After dewaxing by xylene, sections were subjected to gradient hydration. The slices were incubated with anti-NeuN (1:50, Abcam) and Alexa Fluor 555-labeled goat anti-mouse IgG (1:100, Beyotime Institute of Biotechnology). Afterward, samples were added to the TUNEL reaction mixture (Thermo Fisher Scientific) for an incubation time of 60 min at 37 °C in a humidified atmosphere in the dark. Then, DAPI was used to incubate the samples for 2 min. Apoptotic cells were photographed under a microscope (Olympus) with an excitation wavelength of 450–500 nm (green) and a detection wavelength of 515–565 nm (red). Three coronal brain sections were selected from each brain (six animals in each group), and the numbers of positive cells (neurons) in the ipsilateral cerebral cortex was counted for each section at high magnification in five visual fields. The proportion of TUNEL-positive cell nuclei was determined by dividing the number of TUNEL-positive nuclei by the number of total nuclei.</p><!><p>Protein expression was evaluated through Western blot analysis. Cells or brain tissues (Respectively taking the contralateral hemisphere and ipsilateral hemisphere) were homogenized by lysis buffer (Beyotime Institute of Biotechnology). The insoluble material was removed by centrifugation at 12,500 rpm for 15 min at 4 °C. The supernatants of the lysate were collected to measure the protein concentration with a BCA Protein Assay Kit (Thermo Fisher Scientific). Protein samples were denatured for 5 min at 100 °C after being mixed with sodium SDS gel-loading buffer. Then, samples were separated by SDS–polyacrylamide gel electrophoresis and transferred to a polyvinylidene membrane (the specific conditions of electrophoresis and transfer varied according to the molecular weight of the target protein). Membranes were blocked for 2 h in 5% nonfat dry milk in Tween/Tris-buffered saline (TTBS) at room temperature. The membranes were then incubated with the primary antibody. After incubation overnight at 4 °C, the membranes were washed with Tris-buffered saline and incubated with a secondary antibody for about 2 h at room temperature. Bands were scanned and densitometrically analyzed by automated ImageJ software (NIHImage, Version 1.61).</p><!><p>All data are expressed as mean ± SEM statistical analyses were carried out by SPSS version 17.0 (SPSS, Chicago, IL). One-way analysis of variance was used to evaluate the significance of differences among experimental groups. A p value of 0.05 was regarded as the level of statistical significance.</p><!><p>As the main component of the phytoestrogen from P. notoginseng, NGR1 protected the cortical neurons from injury induced by OGD/R, but this effect could be blocked by ERs blocker ICI 182780. Neuronal damage was measured by MTT assay and LDH leakage performed at 4 or 24 h after OGD/R (Fig. 1). The results showed that NGR1 (10 µmol/l) significantly improved neuronal cell viability (83.17 ± 13.68 vs. 65.71 ± 13.60%, p < 0.05, at 4 h after OGD/R; 86.01 ± 9.17 vs. 62.85 ± 18.31%, p < 0.05, at 24 h after OGD/R) and reduced the LDH leakage rate (19.23 ± 3.24 vs. 26.92 ± 5.86%, p < 0.05, at 4 h after OGD/R; 28.31 ± 8.34 vs. 39.75 ± 10.20%, p < 0.05, at 24 h after OGD/R) in the cortical neuron OGD/R model compared with the OGD/R group.</p><!><p>The effects of NGR1 treatment on neuron injury after OGD/R via estrogen receptors. a and b At 4 and 24 h after OGD/R, NGR1 increased cell viability compared with the OGD/R group, ICI 182780 pretreatment could abolish this effects. The OGD/R + NGR1 + ICI 182780 group had lower cell viability compared with the OGD/R + NGR1 group. c and d At 4 and 24 h after OGD/R, NGR1 treatment reduced LDH release in neurons and ICI 182780 reversed this effects. Data are expressed as the mean ± SEM for n = 6. *p < 0.05; **p < 0.01; ***p < 0. 001</p><!><p>However, ICI 182780 could suppress these neuroprotective effects of NGR1. In the OGD/R + NGR1 + ICI 182780 group, the cell viability was significantly reduced (67.19 ± 14.28 vs. 83.17 ± 13.68%, p < 0.05, at 4 h after OGD/R; 65.81 ± 17.36 vs. 86.01 ± 9.17%, p < 0.05, at 24 h after OGD/R), and the LDH leakage rate was significantly increased (25.18 ± 4.76 vs. 19.23 ± 3.24%, p < 0.05, at 4 h after OGD/R; 39.36 ± 8.02 vs. 28.31 ± 8.34%, p < 0.05, at 24 h after OGD/R) compared with the OGD/R + NGR1 group. There was no significant difference in cell viability or LDH leakage rate between the DMSO vehicle group and the OGD/R group.</p><!><p>Brain edema was detected at 24 h after HI (Fig. 2a), as indicated by increased brain water content. Compared with the sham group (85.46 ± 2.43%), the ipsilateral hemisphere water content was significantly increased in the HI group (93.36 ± 3.41%, p < 0.001 vs. the sham group). The ipsilateral hemisphere water content was significantly reduced by treatment with NGR1 (90.12 ± 2.78%, p < 0.05 vs. the HI group), but this effect could be reversed by ICI 182780 (93.09 ± 2.63%, p < 0.05 vs. the HI + NGR1 group).</p><!><p>The effects of NGR1 on brain injury after HI via estrogen receptors. a The water content in the ipsilateral hemisphere was significantly decreased in the NGR1 treatment group compared with the HI group. There was also a significant increase in water content in the HI + NGR1 + ICI 182780 group compared with the HI + NGR1 group. (sham n = 7, HI n = 9, NGR1 n = 9, HI + NGR1 + ICI 182780 n = 8, HI + DMSO n = 7; + means ipsilateral, − means contralateral). b and c NGR1 could reduce the infarction area, but the neuroprotective effect was blocked by ICI 182780. The HI + NGR1 + ICI 182780 group showed a larger infarction area than the NGR1 treatment group (sham n = 6, HI n = 9, NGR1 n = 9, HI + NGR1 + ICI 182780 n = 8, HI + DMSO n = 7). d and e The number of TUNEL-positive cortical neurons were greater in the HI group than in the HI + NGR1 group, but the administration of ICI 182780 could inhibit the protective effect of NGR1. A large number of TUNEL-positive cortical neurons were also found in the HI + NGR1 + ICI 182780 group (n = 6). Data are expressed as mean ± SEM. f The ipsilateral hemisphere weight was significantly decreased in the HI group compared with the NGR1 treatment group 6 weeks after HI. ICI 182780 could block this effect. There was also a significant reduction of ipsilateral hemisphere weight in the HI + NGR1 + ICI 182780 group compared with the HI + NGR1 group (sham n = 8, HI n = 9, HI + NGR1 n = 9, HI + NGR1 + ICI 182780 n = 9, HI + DMSO n = 9). *p < 0.05; **p < 0.01; ***p < 0.001</p><!><p>Infarct volume was used to evaluate brain damage at 48 h after HI injury. As shown in Fig. 2b, c, HI caused an increased magnitude of infarction in the right hemisphere (34.49 ± 9.49%), and the infarct volume was significantly reduced in the HI + NGR1 group (22.49 ± 11.63%, p < 0.01 vs. the HI group). The result supported the neuroprotective effect of NGR1. Quantitative comparisons of the infarct volumes of the HI + NGR1 group and the HI + NGR1 + ICI 182780 group showed that the degree of infarction was intensified in the latter (31.74 ± 8.90%, p < 0.05 vs. the HI + NGR1 group).</p><p>The cortical neuronal apoptosis was observed at 24 h after HI injury. Few TUNEL-positive cortical neurons were found in the sham group, while in the HI group, neuronal apoptosis was 37.35 ± 10.16%. In comparison, neuronal apoptosis was 21.10 ± 11.00% in the HI + NGR1 group (p < 0.01 vs. the HI group), however the neuroprotective effect of NGR1 could be reversed by ICI 182780 (33.48 ± 9.53%, p < 0.05 vs. the HI + NGR1 group) (Fig. 2d, e).</p><p>In order to observe the long-term effect of NGR1 on HIBD, the hemisphere weight was estimated at 6 weeks after surgery [38]. The HI injury caused severely brain atrophy, marked by a decrease in the right-to-left hemispheric weight ratio in HI group(0.35 ± 0.20, p < 0.001 vs. the sham group), but the brain atrophy was significantly improved in the HI + NGR1 group (0.64 ± 0.18, p < 0.01 vs. the HI group) (Fig. 2f). Blockage of ERs reversed the neuroprotective effect (0.48 ± 0.19, p < 0.05 vs. the HI + NGR1 group).</p><!><p>Balance performance was severely impaired in the HI group at 5 weeks after HI insult (Fig. 3a). In contrast, rats treated with NGR1 showed significantly improved balance performance compared with the HI group (3.44 ± 1.01 vs. 2.33 ± 1.12, p < 0.05). However, the protective effect of NGR1 was blocked by ICI 182780. The result showed significantly reduced scores in the HI + NGR1 + ICI 182780 group (2.56 ± 1.13, p < 0.05 vs. the HI + NGR1 group).</p><!><p>Neurobehavioral effects of NGR1 5–6 weeks after HI via estrogen receptors. a Balance performance was severely impaired in the HI group at 5 weeks after HI, but NGR1 treatment significantly improved balance performance. The protective effect of NGR1 was blocked by ICI 182780. b–h The Morris water maze test was performed 5–6 weeks after HI. The results showed that the latencies of the HI group were significantly higher than those of the sham group (*HI group vs. sham group p < 0.05, #HI group vs.HI + NGR1 group p < 0.05, &HI + NGR1 group vs. HI + NGR1 + ICI 182780 group p < 0.05) (b–f). The percentage of time spent in the target quadrant g and the frequency of crossing the target platform h were significantly higher in the sham group than those in the HI group; NGR1 treatment could increased the percentage of time and the frequency compared to the HI group. However, the protective effects could be reversed by ICI 182780 (b, g–h). Data are expressed as mean ± SEM. Sham n = 8, HI n = 9, HI + NGR1 n = 9, HI + NGR1 + ICI 182780 n = 9, HI + DMSO n = 9. *p < 0.05; **p < 0.01; ***p < 0.001</p><!><p>NGR1 could improve spatial learning and memory function recovery, as indicated by the Morris water maze test which was detected 5–6 weeks after neonatal HI injury. The rats' escape latency reflected their spatial learning and memory impairments. The results (Fig. 3b–f) showed that the latencies of the sham group were significantly shortened after 2 days of training, which indicated that the sham group rats had intact learning and memory capacities. At the end of the fifth day of training, almost all rats could aim to move in the direction of the platform. After the platform was removed, some sham group rats went directly to the location of the platform and wandered nearby, which suggested that the rats had remembered the location of the platform. However, the HI group rats mostly swam in the pool without showing obvious signs of proximity to the platform. The latencies of the HI group in each of the four quadrants were 50.11 ± 15.19, 40.23 ± 15.53, 38.43 ± 13.32, 39.89 ± 15.46 s, respectively. They were higher than those of the sham group (13.21 ± 7.70, 4.98 ± 4.20, 5.12 ± 3.46, and 5.01 ± 4.88 s, respectively; p < 0.05 vs. the HI group). Moreover, in the sham group, the percentage (Fig. 3g) of time spent in the target quadrant (55.02 ± 12.90 vs. 24.78 ± 11.13%, p < 0.001) and the frequency (4.56 ± 1.32 vs. 0.75 ± 0.77, p < 0.001) of crossing the target platform (where the platform was previously located) were significantly higher than in the HI group (Fig. 3h). These results indicated that the spatial learning and memory function of HI group rats had been severely weakened as a result of the injury. NGR1 showed neuroprotective effects by significantly decreasing the rats' latencies(33.43 ± 13.23, 20.57 ± 9.90, 20.78 ± 8.78, and 27.44 ± 11.43 s, respectively; p < 0.05 vs. the HI group) and increasing the percentage of time spent in the target quadrant (36.51 ± 13.49%, p < 0.01 vs. the HI group) and the frequency of crossing the target platform (1.72 ± 1.09, p < 0.01 vs. the HI group). However, the protective effects could be reversed by ICI 182780. The latencies of the HI + NGR1 + ICI 182780 group (44.46 ± 13.33, 33.78 ± 15.45, 34.54 ± 11.54, and 35.54 ± 15.31 s, respectively) were significantly higher than those of the NGR1 treatment group (p < 0.05). The same results were found in the percentage of time spent in the target quadrant (27.88 ± 9.61%, p < 0.05 vs. the HI + NGR1 group) and the frequency (1.03 ± 1.11, p < 0.01 vs. the HI + NGR1 group) of crossing the target platform. The results suggested that NGR1 might exert its protective effects by targeting ERs.</p><!><p>PI3K is an intracellular phosphatidylinositol kinase that plays a major role in cerebral hypoxic–ischemic injury by regulating its downstream signaling pathway. Western blot analysis was used to detect expression levels of PI3K at different times after hypoxic–ischemic injury in vitro (primary cortical neurons) and in vivo (ipsilateral hemisphere). As shown in Fig. 4a, expression of PI3K (1.54 ± 0.60 in the control group) was significantly decreased at 12 (0.88 ± 0.42, p < 0.05 vs. the control group), 24(0.35 ± 0.31, p < 0.01 vs. the control group), and 48 h (0.42 ± 0.47, p < 0.01 vs. the control group) of reoxygenation cortical neurons. In vivo, expression of PI3K in the ipsilateral hemisphere was significantly decreased at 24 (0.51 ± 0.34 vs. 1.32 ± 0.78, p < 0.05) and 48 h (0.30 ± 0.32 vs. 1.12 ± 0.69, p < 0.05) post HI compared with the contralateral hemisphere (Fig. 4b).</p><!><p>Expression of PI3K during OGD/R and HIBD. Representative Western blots for PI3K in primary cortical neurons and in HI rats. a PI3K was expressed at low levels 12, 24, 48 h after OGD/R. b Compared with the contralateral hemisphere, PI3K was expressed at low levels in the ipsilateral hemisphere 24 and 48 h after HI. (*p < 0.05; **p < 0.01 compared with control/sham groups, n = 5, mean ± SEM)</p><!><p>Akt is an important downstream target in the PI3K signal transduction pathway which can promote cell survival, inhibit apoptosis and maintain normal function as a key information molecule. As one of the important substrates for Akt, mTOR plays an important role in cell survival and differentiation. Among its downstream target proteins, 4EBP1 and p70S6k are the key signaling molecules, involved in cell-cycle regulation and promoting the synthesis of ribosomal translation regulatory proteins.</p><p>To detect activity of PI3K/Akt/mTOR signal path, primary neurons or hemisphere tissue were harvested at 24 h after OGD/R or HI injury for western blots. As seen in Fig. 5, the OGD/R group showed significant decrease in PI3K (0.34 ± 0.07 vs. 1.09 ± 0.46 p < 0.01), phospho-Akt (0.21 ± 0.10 vs. 0.86 ± 0.42, p < 0.01), phospho-mTOR (0.46 ± 0.21 vs. 2.58 ± 1.28, p < 0.001), phospho-4EBP1 (0.24 ± 0.09 vs. 1.00 ± 0.40, p < 0.01), and phospho-p70S6k (0.57 ± 0.33 vs. 1.63 ± 0.53, p < 0.01) compared with the control group. Treatment with NGR1 (10 µmol/l) increased the expression levels of PI3K (1.06 ± 0.40, p < 0.01 vs. the OGD/R group), phospho-Akt (0.88 ± 0.46, p < 0.01 vs. the OGD/R group), phospho-mTOR (1.83 ± 0.43, p < 0.01 vs. the OGD/R group), phospho-4EBP1 (1.05 ± 0.54, p < 0.01 vs. the OGD/R group), and phospho-p70S6k (1.55 ± 0.83, p < 0.05 vs. the OGD/R group). However, pretreatment with ICI 182780 before NGR1 treatment in vitro resulted in the down-regulation of PI3K (0.49 ± 0.32, p < 0.05 vs. the OGD/R + NGR1 group), phospho-Akt (0.30 ± 0.15, p < 0.05 vs. the OGD/R + NGR1 group), phospho-mTOR (0.42 ± 0.25, p < 0.01 vs. the OGD/R + NGR1 group), phospho-4EBP1 (0.33 ± 0.18, p < 0.01 vs. the OGD/R + NGR1 group), and phospho-p70S6k (0.60 ± 0.39, p < 0.05 vs. the OGD/R + NGR1 group) protein expression compared with the OGD + NGR1 group. As shown in Fig. 6, there was significant decrease in PI3K (0.36 ± 0.16 vs. 1.00 ± 0.35, p < 0.01), phospho-Akt (0.18 ± 0.09 vs. 0.52 ± 0.15, p < 0.01), phospho-mTOR (0.79 ± 0.22 vs. 1.92 ± 0.82, p < 0.01), phospho-4EBP1 (0.21 ± 0.18 vs. 0.96 ± 0.34, p < 0.01), and phospho-p70S6k (0.76 ± 0.49 vs. 2.40 ± 1.00, p < 0.01) compared with the sham group. Treatment with NGR1 (15 mg/kg) increased the expression levels of PI3K (0.98 ± 0.42, p < 0.01 vs. the HI group), phospho-Akt (0.41 ± 0.05, p < 0.05 vs.the HI group), phospho-mTOR (1.5 ± 0.41, p < 0.05 vs. the HI group), phospho-4EBP1 (0.70 ± 0.30, p < 0.05 vs. the HI group), and phospho-p70S6k (1.81 ± 0.29, p < 0.05 vs. the HI group). However, pretreatment with ICI 182780 before NGR1 treatment in vivo resulted in the down-regulation of PI3K (0.37 ± 0.09, p < 0.01 vs. the HI + NGR1 group), phospho-Akt (0.19 ± 0.17, p < 0.05 vs. the HI + NGR1 group), phospho-mTOR (0.82 ± 0.16, p < 0.05 vs.the HI + NGR1 group), phospho-4EBP1 (0.22 ± 0.14, p < 0.05 vs. the HI + NGR1 group), and phospho-p70S6k (0.85 ± 0.47, p < 0.05 vs. the HI + NGR1 group) protein expression compared with the HI + NGR1 group. The results indicated that NGR1 might regulate the PI3K-Akt-mTOR signal pathway via ERs in hypoxic–ischemic brain injury.</p><!><p>Effects of NGR1 and ICI 182780 on PI3K-Akt-mTOR-4EBP-1/p70S6K expression 24 h after OGD/R. Representative Western blots a for PI3K, phospho-Akt/Akt, phospho-mTOR/mTOR, phospho-p70S6K/p70S6K, and phospho-4EBP-1/4EBP-1 in primary cortical neurons. Western blot results showed that the expression of PI3K (b), phospho-Akt (c), phospho-mTOR (d), phospho-p70S6K (e), and phospho-4EBP1 (f) was reduced in the OGD/R group compared with the control group. NGR1 (10 mmol/l) enhanced the expression of PI3K phospho-Akt, phospho-mTOR, phospho-p70S6K, and phospho-4EBP1 in vitro. Pretreatment with ICI 182780 before NGR1 treatment could block the promoting effect. *p < 0.05; **p < 0.01; ***p < 0.001; n = 5, mean ± SEM</p><p>Effects of NGR1 and ICI 182780 on PI3K-Akt-mTOR-4EBP-1/P70S6K expression 24 h after HI. Representative Western blots a for PI3K, phospho-Akt/Akt, phospho-mTOR/mTOR, phospho-P70S6K/P70S6K, and phospho-4EBP-1/4EBP-1 in vivo. Western blot results showed that the expression of PI3K (b), phospho-Akt (c), phospho-mTOR (d), phospho-p70S6K (e), and phospho-4EBP1 (f) was significantly decreased in the HI group compared with the sham group. NGR1 (15 mg/kg) enhanced the expression of PI3K phospho-Akt, phospho-mTOR, phospho-p70S6K, and phospho-4EBP1 in vivo. Pretreatment with ICI 182780 before NGR1 treatment could block the promoting effects. *p < 0.05; **p < 0.01; n = 5, mean ± SEM</p><!><p>The phosphorylation of JNK and c-JUN were examined 24 h after OGD/R or HI injury. Western blot analysis showed that OGD/R injury resulted in remarkably increased expression of both phospho-JNK (1.38 ± 0.56 vs. 0.34 ± 0.14, p < 0.01) and phospho-c-JUN (1.56 ± 0.63 vs. 0.31 ± 0.24, p < 0.01) in primary cortical neurons compared with the control group. NGR1 treatment significantly decreased the expression levels of phospho-JNK(0.63 ± 0.33, p < 0.01 vs. the OGD/R group) and phospho-c-JUN(0.72 ± 0.57, p < 0.05 vs. the OGD/R group), and the effects of NGR1 were blocked by ICI 182780. Pretreatment with ICI 182780 before NGR1 treatment led to higher levels of phospho-JNK (1.18 ± 0.36) and phospho-c-JUN (1.65 ± 0.40) than those in OGD/R + NGR1 group (p < 0.05) (Fig. 7a–c).</p><!><p>Effects of NGR1 and ICI 182780 on JNK-c-JUN expression 24 h after OGD/R and HI. Representative Western blots for phospho-JNK/JNK and phospho-c-JUN/c-JUN in primary cortical neurons (a) and for phospho-JNK/JNK and phospho-c-JUN/c-JUN in vivo (d). Western blot analysis showed that compared with the OGD/R or HI group, NGR1 inhibited the expression of phospho-JNK and phospho-c-JUN in vitro (b, c) and in vivo (e, f). Pretreatment with ICI 182780 before NGR1 treatment could block the inhibiting effects of NGR1. *p < 0.05; **p < 0.01; n = 5, mean ± SEM</p><!><p>Similarly, in the HI group, the expression of phospho-JNK (0.99 ± 0.44 vs. 0.28 ± 0.20, p < 0.001) and phospho-c-JUN (1.26 ± 0.56 vs. 0.41 ± 0.22, p < 0.001) increased in the ipsilateral hemisphere compared with the sham group, and NGR1 attenuated the activation of phospho-JNK (0.47 ± 0.28, p < 0.01 vs. the HI group) and phospho-c-Jun (0.70 ± 0.24, p < 0.05 vs. the HI group). Pretreatment with ICI 182780 before NGR1 treatment led to higher levels of phospho-JNK (0.94 ± 0.25, p < 0.01 vs. the HI + NGR1 group) and phospho-c-JUN (1.19 ± 0.30 p < 0.05 vs. the HI + NGR1 group) than those in the NGR1 group (Fig. 7d–f).</p><p>The results indicated that NGR1 might inhibit the activity of JNK/c-JUN signal pathway by acting ERs and reduced the neuronal apoptosis.</p><!><p>The preceding results showed that NGR1 could exert neuroprotective effects by regulating the PI3K-Akt-mTOR/JNK signal pathways, but these effects could be reversed by blocking the ERs. Previous research [60–64] showed that PI3K could interact with ERs. To further explore the relationship between NGR1, PI3K and ERs, LY294002 (PI3K inhibitor) and 740Y-P (PI3K agonist) were used.</p><p>As shown in Fig. 8, with a optimum concentration of LY294002 treatment (20 µmol/l) [40] (Fig. 8a), the OGD + NGR1 + LY294002 group showed lower cell viability (46.99 ± 17.50 vs. 75.53 ± 18.94%, p < 0.05) and more LDH leakage (39.40 ± 7.40 vs. 28.18 ± 6.40%, p < 0.05) than the NGR1 treatment group, which suggested that the neuroprotective effects of NGR1 were inhibited (Fig. 8c, d). At the same time, the phosphorylation of Akt (0.18 ± 0.12 vs. 0.46 ± 0.18, p < 0.05) and mTOR (0.31 ± 0.16 vs. 0.88 ± 0.28, p < 0.01) was lower in the OGD/R + NGR1 + LY294002 group than that in the OGD/R + NGR1 group, while the phosphorylation of JNK (0.96 ± 0.32 vs. 0.49 ± 0.17, p < 0.05) was higher than that in the OGD/R + NGR1 group (Fig. 8e–h). To further explore the role of ERs in the PI3K signal pathway, the optimal concentration of 740Y-P was tested and found to be 20 µmol/l (Fig. 8b); this concentration was used in the following investigation. The results showed that ICI 182780 could reverse the neuroprotective effects of NGR1 and aggravate neural injury. However, when 740Y-P was used in the OGD/R + NGR1 + ICI 182780 group, the expression of phospho-Akt (0.46 ± 0.17 vs. 0.16 ± 0.11, p < 0.01) and phospho-mTOR (0.99 ± 0.39 vs. 0.35 ± 0.23, p < 0.01) was activated and the expression of phospho-JNK (0.18 ± 0.17 vs. 1.28 ± 0.50, p < 0.001) was inhibited compared with the OGD/R group (Fig. 8e–h). Simultaneously, the results showed higher cell viability (69.70 ± 17.52 vs. 47.34 ± 21.36%, p < 0.05) and less LDH leakage (24.27 ± 9.30 vs. 38.97 ± 10.20%, p < 0.05) in the OGD/R + NGR1 + ICI 182780 + 740Y-P group compared with the OGD/R + NGR1 + ICI 182780 group(Fig. 8c, d). These results indicated that ERs might regulate the activation of Akt-mTOR/JNK through interaction with PI3K, and NGR1 might cause PI3K activation to decrease cell damage after OGD/R by targeting ERs.</p><!><p>Effects of LY294002/740Y-P during OGD/R. a The optimal concentration of LY294002 was 20 µmol/l. b The optimal concentration of 740Y-P was 20 µmol/l. LY294002 treatment could accelerate LDH leakage c and reduce cell viability d in the OGD/R + NGR1 + LY294002 group compared to the NGR1 treatment group. 740Y-P treatment could promote cell viability and inhibit LDH leakage in the OGD/R + NGR1 + ICI 182780 + 740Y-P group compared to the OGD/R + NGR1 + ICI 182780 group. (*p < 0.05, n = 6, mean ± SEM). Representative Western blots e for phospho-Akt/Akt (f), phospho-mTOR/mTOR (g) and phospho-JNK/JNK (h) in primary cortical neurons. In the OGD/R + NGR1 + LY294002 group, the phosphorylation of Akt and mTOR was lower than that in the OGD/R + NGR1 group, with a higher phosphorylation of JNK than that in the OGD/R + NGR1 group; In the OGD/R + NGR1 + ICI 182780 + 740Y-P group, Akt/mTOR phosphorylation was higher and JNK phosphorylation was lower than that in the OGD/R group. *p < 0.05; ** p < 0.01; ***p < 0.001; n = 5, mean ± SEM</p><!><p>HIE is a common neurologic disease in newborns, but there is currently a lack of promising therapy [3]. Many studies have shown that estrogen provides neuroprotective effects in experimental cerebral ischemia [20, 21]. These protective effects are mediated by ligand interactions with two primary classical ERs, ERα and ERβ [65]. Research has shown that the distribution patterns of ERα and ERβ are similar in male and female brains. Especially in the cortical and hippocampal regions [66], sex differences were found to be absent [67]. However, studies suggested that estrogen exhibited universal protection against experimental ischemia injury via ERs in female but not male brains [68]. The differences may be due at least in part to the fact that circulating estrogens have free access to all brain regions. As a phytoestrogen, NGR1 has been found to exhibit a number of treatment effects and exert direct anti-inflammatory and anti-apoptotic effects on cardiomyocytes [26], vascular endothelial cells [69], podocytes [70], and neurons [18, 44] through acting ERs. Some scholars reported that NGR1 treatment significantly improved cognitive function in the APP/PS1 double-transgenic mouse model of Alzheimer's disease [71]. One study demonstrated neuroprotective effects of NGR1 in an adult rat model of cerebral ischemia/reperfusion [18]. However, research has revealed that the immature brain responded differently to treatment than the mature brain in laboratory animals [3]. In fact, therapies designed to ameliorate brain injury in adults may worsen outcomes in neonates [72]. Hence, effective therapies for neonatal HIE need to be explored. Although some preliminary experimental results are available [19], whether NGR1 exerts short-term or long-term protective effects and the underlying mechanisms are largely unknown. Therefore, the evaluation of the early effects and long-term therapeutic effects of NGR1 is of great clinical significance.</p><p>In the present study, a series of experiments were designed to explore the neuroprotective effects and underlying mechanisms of NGR1 in a neonatal hypoxic-ischemic injury model. The pivotal findings are as follows. (1) NGR1 significantly attenuated neuronal injury in the neonatal HI model in vitro and in vivo. Most importantly, NGR1 had contributed to the long-term recovery of neurological function in the HI rats. (2) NGR1 exerted neuroprotective effects through regulating the PI3K-Akt-mTOR/JNK signal pathways by targeting ERs.</p><p>HIE [11] can develop as a result of circulatory and energy metabolism disorders, leading to a series of pathophysiological processes, including oxidative stress, mitochondrial impairment, apoptosis, and necroptosis. These injuries in the developing brain often lead to lasting neurological impairments, such as cerebral palsy, epilepsy, mental retardation, and learning and memory disorders. Therefore, reducing neuronal death and promoting neuronal survival and proliferation are important strategies for reducing the occurrence of long-term neurological sequelae [26]. Our results indicated that NGR1 possessed protective effects both in vitro and in vivo. NGR1 was observed significantly to improve neuronal cell viability and reduce the LDH leakage rate 4 and 24 h after OGD/R (Fig. 1). The inhibition of cortical neuronal apoptosis was observed 24 h after HI injury and the decrease of infarct volume was examined 48 h after HI injury in HI + NGR1 group (Fig. 2). These findings are consistent with a recent study in an adult cerebral ischemia–reperfusion brain injury model, which found that NGR1 therapy reduced brain damage after ischemia [18]. However, that study used a higher concentration of NGR1 (25 mmol/l in vitro and 20 mg/kg in vivo) than our study (10 mmol/l in vitro and 15 mg/kg in vivo). There may be two reasons for the difference. (1) We used cells from different culture days and rats of different ages. (2) NGR1 was administered after OGD/R or HI in our study, not as a pretreatment. Importantly, our results indicated that NGR1 contributed to the long-term recovery of neurological function in the neonatal HI model in addition to reducing apoptosis. NGR1 treatment reduced brain atrophy 6 weeks after HI injury (Fig. 2). Moreover, the results of beam walking (5 weeks after HIE) and the water maze test (5–6 weeks after HIE) showed that NGR1 significantly restored limb coordination and improved learning and memory in the impaired rats (Fig. 3).</p><p>Hypoxic–ischemic brain injury directly results in a large amount of neuronal death. Therefore, reducing neuronal death and promoting neuronal survival and proliferation are important strategies for reducing the occurrence of long-term neurological sequelae [26]. Apoptosis is reported to be responsible for a significant proportion of the HI-induced neuronal loss [72], and multiple apoptosis-related signal pathways, such as PI3K-Akt-mTOR/JNK, are involved in neuronal death after stroke [34, 40, 41]. Our results showed significant inhibition of the PI3K-Akt-mTOR-4EBP1/p70S6k signal pathway at 24 h following OGD/R or HI injury (Figs. 5, 6). At the same time, JNK—another important signaling protein downstream of Akt, which can be inhibited by Akt directly or indirectly—was significantly activated. These results suggested that neuronal apoptosis might be related to the inhibition of PI3K-Akt-mTOR and the activity of JNK-c-JUN during HIBD. Some other researchers [44, 49–51, 73, 74] have found similar results indicating that cerebral ischemia induced the robust activation of JNK signaling and inhibition of PI3K-Akt-mTOR pathway activity. NGR1 treatment could increase the expression of PI3K, phospho-Akt, and phospho-mTOR (Figs. 5, 6) and reduce the activity of the JNK signaling pathway 24 h after OGD/R or HI brain injury (Fig. 7). These results indicated that NGR1 could likely reduce neuronal apoptosis by regulating the activity of the PI3K-Akt-mTOR/JNK signal pathways. NGR1 treatment could improve the cell survival rate in vitro and reduce infarct volume and promote long-term neurobehavioral recovery and improvement in vivo by inhibiting neuronal apoptosis. Previous studies showed that mTOR accelerated angiogenesis [75] and neuronal regeneration [76] in many neurologic injuries in addition to reducing neuronal apoptosis. Perhaps the long-term protective effects of NGR1 were also related to its activation of mTOR and promotion of neuroregeneration.</p><p>We further explored whether NGR1 achieved its neuroprotective effects via ERs. As a predominant phytoestrogen extracted from P. notoginseng, NGR1 was previously found to perform its function through acting ERs [13, 15, 18, 19]. Mounting evidence showed that ERα and ERβ expression was reduced during neuronal ischemia [19, 77]. Kraczkowski [78] indicated that the downregulation of ERs might be related to the ontogenesis of brain µ-opioid receptors during HIBD. As an ERs agonist, NGR1 may act on ERα/β and improve the role of ERs during HIBD [18, 19]. Our results indicated that pretreatment with ICI 182780 reduced the survival rate of cortical neurons in vivo and increased brain edema and cerebral infarction volume in vitro compared with the HI + NGR1 group. Moreover, the long-term protective effects of NGR1 were suppressed by ICI 182780. These results suggested that NGR1 exerted its protective effects via ERs.</p><p>Studies on a variety of cells—such as endothelial cells [79], MCF-7 breast cancer cells [80], and neurons [81–83]—have found that ERs can interact directly with PI3K or bind to the PI3K p85 subunit through scaffold proteins such as CAV-1, connective proteins such as Src and Shc, and growth factors, then activate the downstream Akt, causing a series of signal pathway cascades, such as the Akt-mTOR/JNK signal pathway [60–63]. Our results showed that pretreatment with ICI 182780 could inhibit the activity of PI3K-Akt-mTOR and increase the activity of the JNK signal pathway. These results suggested that NGR1 regulated the PI3K-Akt-mTOR/JNK signal pathways via acting ERs. In order to further validate this finding, we used LY294002 (PI3K inhibitor) and 740Y-P (PI3K agonist) to perform related experiments. The results (Fig. 8) revealed that the protective effects of NGR1 were significantly inhibited after adding LY294002, the expression of phospho-Akt and phospho-mTOR decreased and that of JNK increased in the OGD/R + NGR1 + LY294002 group. However, 740Y-P could reverse the inhibition of NGR1's neuroprotective effects induced by ICI182780. Simultaneously, phospho-Akt expression increased and phospho-JNK expression decreased in the 740Y-P agonist group. These results suggested that NGR1 might exert a neuroprotective effects by targeting ERs and regulating PI3K.</p><p>In conclusion, the present study demonstrated that NGR1 inhibited neuronal apoptosis and promoted neuronal survival, exerting an important neuroprotective effects against HIBD in neonates through targeting ERs and regulating the PI3K-Akt-mTOR/JNK signal pathway. Our findings suggested that NGR1 might be a potent new therapeutic compound for neonatal hypoxia–ischemia brain damage treatment.</p>

PubMed Open Access

The amide linker in nonpeptide neurotensin receptor ligands plays a key role in calcium signaling at the Neurotensin receptor type 2

Compounds acting via the GPCR neurotensin receptor type 2 (NTS2) display analgesia in relevant preclinical models. The amide bond in nonpeptide NTS1 antagonists plays a central role in receptor recognition and molecular conformation. Using NTS2 FLIPR and binding assays, we found that it is also a key molecular structure for binding and calcium mobilization at NTS2. We found that reversed amides display a shift from agonist to antagonist activity and provided examples of the first competitive nonpeptide antagonists observed in the NTS2 FLIPR assay. These compounds will be valuable tools for determining the role of calcium signaling in vitro to NTS2 mediated analgesia.

the_amide_linker_in_nonpeptide_neurotensin_receptor_ligands_plays_a_key_role_in_calcium_signaling_at

<p>The identification of novel analgesics is an ongoing challenge of discovery science. In particular, there is a need for new treatment options for alleviating chronic pain as no more than half of patients get adequate relief from currently existing medications.1 It is thus significant that compounds acting via the GPCR neurotensin receptor type 2 (NTS2) are reported to be effective in managing both acute and chronic pain in animal models.2–4 This analgesia is synergistic with opioid mediated analgesia and may offer compounds that either supplant opioids or that work in concert with existing opioid receptor-based drugs.5,6 It has also been shown that the NTS2 receptor produces analgesia without the side effects of hypothermia and hypotension that are the hallmark of NTS1 interaction.4,7,8 Collectively, these findings point to the NTS2 receptor as an attractive target to explore for treating acute and chronic pain with a lower adverse effect profile.</p><p>The NTS2 receptor is one of two GPCR's that modulate the action of the tridecapeptide neurotensin (NT, pGlu-Leu-Tyr-Glu-Asn-Lys-Pro-Arg-Arg-Pro-Tyr-Ile-Leu), which acts as both a neuromodulator and neurotransmitter in the CNS and periphery, overseeing a host of biological functions including regulation of CNS dopamine, hypothermia, hypotension and, nonopioid analgesia.9–11 Compared with the NTS1 receptor, few selective compounds have been identified for the NTS2 receptor over the last forty years. The peptide ligands that established our current understanding of the physiological role of NTS2 include JMV-43112,13 and NT79.4 In addition to this, highly potent and ultra-selective peptide-peptoid hybrids with selectivity ratios reaching 12,000 and 22,000-fold have been identified recently.14,15. 1</p><p>In search of NTS2 selective nonpeptide analgesics, we concluded that the delayed progress of NTS2 research compared with NTS1 might be due to the variable results obtained when the receptor is expressed in different cell lines. The literature reports dealing with functional assays of NTS2 receptors yielded contradictory data, exhibiting cell-type expression- and species-dependent pharmacological properties with opposing patterns. Indeed, NT has been reported to be an agonist, an inverse agonist as well as a neutral antagonist depending upon the cell expression system.16–19 To better understand this issue, we analyzed the calcium mobilization at NTS2 with the goal of establishing a link between FLIPR assay activity and analgesia in vivo. Our initial goal was to identify a group of nonpeptide compounds that included full agonists, partial agonists and antagonists believing that this spectrum of activity would provide the tools necessary to establish the desired in vitro to in vivo correlation. We recently reported our initial efforts towards this goal that began with our establishment of a baseline of FLIPR activity for compounds commonly described in NTS2 receptor research. We found that compounds SR48692 (1a) and SR142948a (2) Chart 1, which are known antagonists in vivo and at NTS1, were full agonists at NTS2 in agreement with literature reports.16,18–20 We also found that the NTS2 selective compound Levocabastine (3), an analgesic in vivo, was found to be a potent partial agonist in vitro.3 NT on the other hand, which is also an analgesic in vivo, was found to be an antagonist in the FLIPR assay. Collectively, this preliminary data set aligned partial agonist and antagonist activity with analgesia in animal models.</p><p>We reported recently using the same FLIPR assay to expand the pool existing of NTS2 selective nonpeptide compounds to include NTRC-739 (4), NTRC-808 (5), and NTRC-824 (6).21–23 The first two are potent partial agonists while the latter compound mimics the activity of the endogenous ligand NT in the FLIPR assay. Compounds 4 and 5 were identified during SAR studies that focused on changes to the perimeter of 1a. In the current report, we examined the effect on calcium signaling resulting from changes to the central amide bond of 1a and 1b. This study led us to compound 7, the first competitive(surmountable) antagonist that we have identified using the NTS2 FLIPR assay. The details of this effort are provided herein.</p><p>In the pioneering studies with 1a that led to the first NTS1 antagonist pharmacophore model, Quéré demonstrated that the amide group forms an intramolecular H-bond with the pyrazole nitrogen in the crystal structure of 1a.24,25 This structure thus plays an important role in molecular conformation and overall structural rigidity. It could also act as an H-bond donor/acceptor and could therefore contribute significantly to the process of ligand/receptor recognition. Given this background, we imagined that the amide could also play a critical role in calcium signaling and receptor recognition at NTS2.</p><p>To carry out our investigation, we synthesized and tested target compound 12 (Scheme 1), the reduced amide variant of 1b, and also compounds 7 and 16b–18b (Scheme 2) that possessed the reversed amide group (a comparison of reversed and conventional amide structures is shown in the Table 1 Chart).</p><p>The synthesis of the reduced amide target compound 12 was accomplished as illustrated in Scheme 1. Thus, methyl 1-(7-chloroquinolin-4-yl)-5-(2,6-dimethoxyphenyl)-1H-pyrazole-3-carboxylate21 (8) was reduced using lithium aluminum hydride (LAH) to give alcohol 9 that was subsequently oxidized using MnO2 to give aldehyde 10. This intermediate was then coupled reductively with L-cyclohexylglycine tert-butyl ester to give 11. Target compound 12 was then available via deprotection of 11 using trifluoroacetic acid (TFA) in methylene chloride.</p><p>The synthesis of target compounds 7 and 16b–18b was accomplished using a Curtius rearrangement and the appropriate pyrazole-3-carboxylic acid (13a–13d) as illustrated in Scheme 2.21 Thus, acids 13a–13d were heated with diphenylphosphoryl azide (DPPA) in ethanol and 1,4-dioxane and triethylamine under reflux overnight and the resulting carbamates were subsequently hydrolyzed using 10% NaOH in ethanol to give intermediates 14a–14d. From this point the target compounds were obtained by coupling 14a–14d with 1-(ethoxycarbonyl)cyclohexanecarboxylic acid (21, Scheme 3) using O-benzotriazol-1-yl-N,N,N',N'-tetramethyluronium hexafluorophosphate (HBTU) and triethylamine to give ester intermediates 15a–18a. Hydrolysis of these ethyl esters using 1N LiOH in dioxane provided target compounds 7, 16b, 17b and 18b.</p><p>The synthesis of 1-(ethoxycarbonyl)cyclohexanecarboxylic acid (21) used to prepare target compounds 7, 16b–18b was accomplished as illustrated in Scheme 3.26 Thus, the alkylation/cyclization of tert-butyl ethyl malonate (19) and 1,5-dibromopentane with NaH gave the cyclic mixed diester 20.27 Subsequent deprotection using trifluoroacetic acid (TFA) then gave 21. We initially tried to hydrolyze the ethyl ester first leaving the tertiary butyl ester in place, but found that this product was difficult to isolate from a basic work up. Doing the TFA deprotection first, on the other hand, led to 21 that could be used in crude form following removal of solvents.</p><p>In Table 1, we present the data obtained from the FLIPR assays and binding assays for our test compounds. This includes the data for the reduced amide 12 followed by the data for the reversed amides 7 and 16b–18b. Accompanying the data for 12, we provided the data (obtained previously) for the unreduced amide 1b (Chart 1). The data for the reversed amides are presented along with the data previously obtained for their conventional amide compounds (4, 22–24). This is displayed above the data of their reversed amide counterparts so that the impact of the structural change is more readily appreciated.</p><p>We found that compound 12, with a reduced amide bond, showed no activity at either NT receptor in the FLIPR assays or the binding assays. Since compound 1b is quite active, we find that this functional group, intact, is necessary for activity at NT receptors. There are several explanations for this observation. It is believed that this functional group acts as an H-bond donor/acceptor and thus helps stabilize the ligand to receptor interaction. From this perspective, the data suggests that this H-bond donor/acceptor is a key feature of the ligand recognition architecture. An alternative explanation exits however. Rather than resulting from the loss of an H-bond interaction, it could be that the fundamental shift in character of the amide nitrogen from neutral to basic may be the cause of the loss of activity. This would enable zwitterion formation and in turn would change not only the shape of the ligand but also the ionic character of the resulting molecule. This could, in turn, render it unrecognizable to the associated binding domains of the NT receptors and thereby eliminate binding and thereby signaling.</p><p>Unlike 12, the reversed amide derivatives (7 and 16b–18b) were active at both NTS1 and NTS2 and also demonstrated SAR. Considering the NTS1 receptor first, we found that all of the reversed amide compounds lacked agonist activity (data not shown). This is inline with the parent compounds (4, 22–24) that that were either inactive or antagonists at NTS1. Thus, reversing the amide does not convert antagonists into agonists at this receptor. However, the antagonist activity (Ke data) found for the reverse amides 16b and 7 was much weaker than that found for the conventional amide compounds (22 and 23). This degradation in activity suggests that the amide group is a key component for antagonist recognition and blockade of NT mediated calcium signaling at NTS1.</p><p>The 4-fluorophenyl substituted reversed amide derivatives 17b and 18b did not show antagonist activity in the NTS1 FLIPR assay but neither did their conventional amide analogues (24 and 4). Thus, reversing the amide does not over ride the NTS2 selectivity promoted by the 4-fluorophenyl substituent. In the binding assay at NTS1, this trend continued with 17b showing less affinity for NTS1 compared with the conventional amide compound 24.</p><p>In the NTS2 FLIPR assay, we found that the reverse amide 16b showed a significant change in behavior relative to 22. Here, the potent full agonist activity of 22 was supplanted with weak antagonist activity in the reversed amide 16b. While this activity was weak, it was remarkable since its antagonist behavior was different from that reported previously for NT or 6. In this case, 16b showed competitive (surmountable) antagonist activity whereas both NT and 6 demonstrated insurmountable antagonism of the calcium release mediated by compound 2.21,22 In fact, 16b was the first competitive antagonist observed in our studies of nonpeptide NTS2 compounds. The napthyl substituted compound 7 showed substantially improved antagonist activity compared with 16b (Ke of 181 versus 3046 nM respectively). As before, the potent partial agonist activity of the conventional amide (23) gave way to competitive antagonist activity in the reversed amide 7.</p><p>In Figure 1, the antagonist behaviors of compounds 6 and 7 versus the agonist 2 are illustrated. Compound 7 shows competitive (surmountable) antagonist activity versus the agonist 2 as it shifts the agonist response curve to the right. On other hand, compound 6 demonstrated insurmountable antagonist activity versus compound 2 in the FLIPR assay as it shifts the curve of 2 to the right but also lowers the maximal response of 2 as its concentration is increased. NT demonstrates behavior similar to 6 (not shown in Figure 1).22</p><p>In the binding assay, compound 7 revealed that it possessed good affinity for NTS2 but quite low affinity for NTS1 and while it does not show the >100-fold separation characteristic of a selective compound, it possesses a 79-fold preference for NTS2 versus NTS1. Together with its competitive antagonist nature in the FLIPR assay, its affinity and preference for NTS2 makes analogue 7 a useful addition to the toolkit needed to probe the relevance of calcium signaling at NTS2 to analgesia in vivo.</p><p>Unlike the naphthyl substituted compound 7, the 4-fluorophenyl substituted reverse amide compounds 17b and 18b did not display a shift from agonist to antagonist at NTS2, Table 1. Instead, 17b possessed a profile of activity similar to that of the conventional amide 24 but with roughly half of the activity across the various assays. The reverse amide 18b, on the other hand, was found to be virtually inactive at both NT receptors. Though limited in scope, the data for 17b is in line with earlier results that suggested that compounds with the 4-fluorophenyl substitution do not behave like those with the naphthyl substitution, perhaps as a consequence of binding to the receptor in an alternate manner. Viewed from this perspective then, the lack of a shift from agonist to antagonist activity for 17b suggests that the amide group is not situated in a manner conducive to this transformation providing additional evidence that these two types of compounds bind NTS2 differently.</p><p>Overall, the information provided by this study, together with the work of Quéré, demonstrates that the amide bond in compounds derived from 1a plays a critical role in mobilization of calcium and receptor recognition at both GPCR NT receptors. At the NTS2 receptor, we found that reversing the amide can trigger antagonist activity that is of a competitive nature. This feature differentiates compound 7 from the insurmountable antagonists described in previous work.22 As such, compound 7 provides an additional tool to further our understating of the relevance of calcium signaling at NTS2 to analgesia in relevant animal models. These studies are currently in progress and will be reported in the near future.</p><p>This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.</p><p> Supplementary Material </p><p>Supplementary material including experimental details for compound synthesis and biological testing as well as catalog numbers of the compounds identified for testing in our database mining, can be obtained from the online version at …</p>

PubMed Author Manuscript

Discovery of Benzyl Tetraphosphonate Derivative as Inhibitor of Human Factor Xia

The inhibition of factor XIa (FXIa) is a trending paradigm for the development of new generations of anticoagulants without a substantial risk of bleeding. In this report, we present the discovery of a benzyl tetra-phosphonate derivative as a potent and selective inhibitor of human FXIa. Biochemical screening of four phosphonate/phosphate derivatives has led to the identification of the molecule that inhibited human FXIa with an IC 50 value of ~7.4 μM and a submaximal efficacy of ~68 %. The inhibitor was at least 14-fold more selective to FXIa over thrombin, factor IXa, factor Xa, and factor XIIIa. It also inhibited FXIa-mediated activation of factor IX and prolonged the activated partial thromboplastin time of human plasma. In Michaelis-Menten kinetics experiment, inhibitor 1 reduced the V MAX of FXIa hydrolysis of a chromogenic substrate without significantly affecting its K M suggesting an allosteric mechanism of inhibition. The inhibitor also disrupted the formation of FXIa -antithrombin complex and inhibited thrombin-mediated and factor XIIa-mediated formation of FXIa from its zymogen factor XI. Inhibitor 1 has been proposed to bind to or near the heparin/polyphosphate-binding site in the catalytic domain of FXIa. Overall, inhibitor 1 is the first benzyl tetraphosphonate small molecule that allosterically inhibits human FXIa, blocks its physiological function, and prevents its zymogen activation by other clotting factors under in vitro conditions. Thus, we put forward benzyl tetra-phosphonate 1 as a novel lead inhibitor of human FXIa to guide future efforts in the development of allosteric anticoagulants.

discovery_of_benzyl_tetraphosphonate_derivative_as_inhibitor_of_human_factor_xia

Introduction<!>Figure 2.<!>Rationale for Screening Phosphonate/Phosphate Derivatives (1-4) Against Human FXIa<!>FXIa Inhibition Potential of Phosphonate/Phosphate Derivatives (1-4)<!>Benzyl Tetraphosphonate 1 is a Selective Inhibitor of Human FXIa Over Other Coagulation Proteins<!>Effect of Inhibitor 1 on Clotting Times of Normal and FXI Deficient Human Plasma<!>Allosteric Inhibition of FXIa by Benzyl Tetraphosphonate 1<!>Inhibition of FXIa: Activation of the Physiologically Relevant Substrate FIX by Tetra-Phosphonate 1<!>Effect of Benzyl Tetraphosphonate 1 on FXI(a) Interactions with Macromolecules<!>Molecular Modeling of Potential Binding of Benzyl Tetraphosphonate 1 to the Anion -Binding Site in the Catalytic Domain of FXIa<!>Discussion<!>Conclusions and Outlook<!>Experimental Section Materials<!>Inhibition of FXIa in Chromogenic Substrate Hydrolysis Assay by Phosphonate/Phosphate Derivatives (1-4)<!>Effect of Benzyl Tetraphosphonate 1 on Other Coagulation Factors<!>Effect of Benzyl Tetraphosphonate 1 on Clotting Times in Human Plasmas<!>Michaelis-Menten Kinetics for Chromogenic Substrate (S-2366) Hydrolysis by Human FXIa in the Presence of Benzyl Tetraphosphonate 1<!>Effect of Benzyl Tetraphosphonate 1 on FXIa-Mediated Activation of FIX<!>Effect of Benzyl Tetraphosphonate 1 on FXIa -Antithrombin Complex Formation<!>Effect of Benzyl Tetraphosphonate 1 on FXI Activation by Thrombin<!>Effect of Benzyl Tetraphosphonate 1 on FXI Activation by FXIIa<!>Molecular Modeling Studies of FXIa and Benzyl Tetraphosphonate 1

<p>Thrombosis is a condition in which the blood unnecessarily and/or excessively clots in blood vessels and/or heart chambers leading to life-threatening pathologies. Thrombosis can initiate in a vein-driven manner as it is in deep vein thrombosis and pulmonary embolism. [1][2][3] It can also be of arterial origin as in ischemic heart disease and stroke. [1][2][3] Thrombosis has also been linked to a host of other chronic and serious diseases including inflammation, [4] cancer, [5] neurodegenerative diseases, [6] and microbial infections. [7] Importantly, micro-and macro-vascular as well as venous and arterial thrombotic conditions have been implicated in the ongoing pandemic of coronavirus disease of 2019 (COVID-19). [8][9][10] In fact, COVID-19-associated thrombotic events appear to often lead to poor clinical outcomes of hospitalization, ICU admission and mechanical ventilation, and death. [11] In this arena, important components that variably contribute to thromboembolic diseases are the platelets and the procoagulant factors of the coagulation system. Therefore, drugs that are clinically used to treat thrombosis either target platelet proteins i. e. antiplatelets or inhibit the procoagulant factors of the coagulation system i. e. anticoagulants. Considering the origin of the pathological clots, antiplatelets are generally used in arterial thrombosis [12] whereas anticoagulants are more frequently used in treating and/or preventing venous thromboembolism. [13,14] Combinations of the two classes of antithrombotics are also used. [15,16] On the anticoagulants front, clinically available anticoagulants include the indirect anticoagulants of warfarin and heparins as well as the direct anticoagulants of thrombin inhibitors (argatroban, dabigatran, and bivalirudin) and factor Xa inhibitors (rivaroxaban, apixaban, edoxaban, and betrixaban). Despite their structural diversity, all available anticoagulants inhibit thrombin and/or factor Xa (FXa), two serine proteases in the common coagulation pathway (Figure 1). [13][14][15] This leads to a very efficient inhibition of the pathological formation of the blood clot, yet it also disrupts hemostasis. Thus, all currently used anticoagulants are associated with serious bleeding events [17][18][19][20][21][22] which complicate their effective and safe use in several patient populations such as those with atrial fibrillation [23][24][25] or chronic kidney diseases. [25][26][27] Therefore, the development of anticoagulants that do not cause bleeding is the main goal of contemporary drug discovery programs in the field. [28][29][30] In this direction, several other procoagulant factors including factors VIIa (FVIIa), [31] IXa (FIXa), [32] XIa (FXIa), [28][29][30]33] XIIa (FXIIa), [33] and XIIIa (FXIIIa) [34] have been considered to design and develop new effective anticoagulants with limited-to-none bleeding risk. In particular, FXIa appears to be gaining momentum owing to several epidemiological, animal, and human observations which collectively indicate that FXIa activity contributes to thrombosis but not to hemostasis. [28][29][30]33] In fact, given the promise of FXI(a) as a drug target for safer anticoagulants, several FXI(a)-targeting agents are under development and these include small molecules, [35][36][37][38][39][40][41] monoclonal antibodies, [42][43][44][45] antisense oligonucleotides, [46,47] and aptamers. [48,49] Many of those inhibitors are active site inhibitors and few are allosteric inhibitors. [30] Biochemically, FXIa is a serine protease homodimer that belongs to the intrinsic pathway of coagulation (Figure 1). Physiologically, FXIa activates factor IX to FIXa so as to eventually amplify thrombin generation. [50] Importantly, the zymogen form of FXIa i. e. factor XI (FXI) is activated by thrombin or FXIIa in the presence of negatively charged macromolecules such as heparin, inorganic polyphosphates, and dextran sulfate via a template-mediated mechanism. [51] The negatively charged macromolecules binds to anion-binding sites on the activating enzymes as well as on FXI. Interestingly, while the negatively charged heparin facilitates the activation of the zymogen FXI, heparin also directly and allosterically inhibits the active enzyme FXIa. [52] Furthermore, the inorganic polyphosphates have also been found to bind to the same anion binding sites of FXI and acts as cofactors for its autoactivation and for its activation by thrombin and FXIIa. [53] Accordingly, to identify a new line of FXI(a)-targeting anticoagulants, we have considered the aromatic mimetics of inorganic polyphosphates so as to allosterically inhibit the function of human FXIa by targeting its anion-binding sites. In this arena, we tested four phosphonate/phosphate derivatives (1-4) (Figure 2) to evaluate their potential to inhibit human FXIa. As a result, we have identified the benzyl tetraphosphonate derivative (1) as the first aromatic mimetic of inorganic polyphosphates that allosterically inhibits human FXIa, as determined in the corresponding in vitro experiments of chromogenic substrate hydrolysis assay and Michaelis-Menten kinetics. The benzyl tetraphosphonate 1 inhibited human FXIa with an IC 50 value of ~7.4 μM and a submaximal efficacy of ~68 %. The molecule has demonstrated at least 14-fold selectivity toward FXIa over other procoagulant factors of thrombin, FIXa, FXa, and FXIIIa. The inhibitor also selectively prolonged the activated partial thromboplastin time (APTT) of human plasma. Interestingly, inhibitor 1 concentration-dependently inhibited the physiological function of FXIa i. e. FIX activation and inhibited thrombin-mediated and FXIIa-mediated activation of FXI. The inhibitor disrupted the formation of FXIaantithrombin complex in the presence of heparin, suggesting that it may compete with heparin for binding to or near the anion-binding site(s) of FXIa. Overall, we put forward benzyl tetraphosphonate derivative 1, the first potent, selective, and partial allosteric inhibitor of FXIa, to be considered in the development of effective anticoagulants with a limited risk of bleeding complications.</p><p>Figure 1. The coagulation process is depicted. Among the most important factors are FVIIa of the extrinsic coagulation pathway, FXIIa/FXIa/FIXa of the intrinsic/contact activation pathway, and thrombin and FXa of the common coagulation pathway. Thrombin and FXa are the molecular targets of all currently available anticoagulants. FXIa is the molecular target in this study. Targeting human FXIa is expected to yield effective anticoagulants without the risk of bleeding because FXIa contributes to thrombosis, but not hemostasis. Physiologically, FXIa activates FIX to FIXa which subsequently forms the intrinsic tenase complex that further activates FXa, and then, thrombin. FXIa is produced by inorganic polyphosphate-mediated activation of FXI via the action of thrombin and FXIIa. FXIa can also be produced by autoactivation.</p><!><p>Chemical structures of phosphonate and phosphate derivatives (1-4) that were screened against human FXIa in this study.</p><!><p>Several approaches have been utilized to discover and/or rationally design inhibitors of FXIa. These approaches include small molecules, polypeptides, aptamers, and monoclonal antibodies. [30] While most of the small molecules are active site inhibitors, sulfated nonsaccharide mimetics of heparin, reported as SPGG [54,55] and SCI, [56] are allosteric inhibitors of FXIa. They appeared to inhibit FXIa by targeting its anion-binding sites, particularly the site in the catalytic domain. Interestingly, SCI exhibited potent anticoagulant activity with no bleeding complications in rat models of thrombosis. [57] Although their allosteric inhibition mechanism is unique for achieving a high level of functional selectivity, these molecules are highly negatively charged with at least 10 sulfate groups, a structural feature that may compromise their druggability. Thus, we have considered phosphonate and phosphate derivatives 1-4 (Figure 2) to be screened against human FXIa to identify new inhibitors of FXIa with fewer number of negative charges. Given the fact that anion-binding sites of FXI(a) recognize both sulfated heparins as well as inorganic polyphosphates, we have hypothesized that phosphonate and phosphate derivatives will likely exhibit an allosteric inhibition mechanism similar to that exhibited by the sulfated nonsaccharide mimetics of heparin. We have also hypothesized that the phosphonate and phosphate derivatives will likely enjoy a better long-term stability because of the reduced number of negative charges. Not only that, but strategies to develop phosphonate and phosphate prodrugs are also well established, [57][58][59] which is necessary to be considered to enhance their overall druggability, especially as it relates to their oral bioavailability.</p><!><p>The four molecules were evaluated for their potential to inhibit FXIa hydrolysis of S-2366, a chromogenic tripeptide substrate, under the physiological conditions of pH 7.4 and 37 °C, as reported earlier. [60,61] Only the tetraphosphonate derivative 1 and phosphate derivative 4 inhibited FXIa in a dose-dependent fashion. Molecules 2 and 3 did not inhibit FXIa at the highest concentration tested of 100 μM. The inhibition of FXIa by molecules 1 and 4 could be fitted using the logistic equation 1, which resulted in an IC 50 value of 7.4 � 0.9 μM and efficacy of 68.0 � 3.7 % for inhibitor 1 (Figure 3, Table 1) and an IC 50 value of 59.4 � 17.5 μM and efficacy of 111 � 15.6 % for inhibitor 4 (Table 1). The lack of inhibition potential for phosphate derivatives 2 and 3 suggests a rather selective interaction between FXIa and inhibitors 1 and 4. Of note, molecules 2-4 also have two sulfonate groups in addition to the phosphate group, yet only molecule 4 inhibited human FXIa indicating that negative charges are not the only interacting groups and that other structural features are also important.</p><!><p>The inhibition profiles of benzyl tetraphosphonate 1 against thrombin, FIXa, and FXa were studied using the corresponding chromogenic substrate hydrolysis assays under physiological conditions, as described earlier. [54,55,60,61] In these assays, the inhibition potential was determined by spectrophotometric measurement of the residual protease activity in the presence of varying concentrations of inhibitor 1 (Figure 3). Furthermore, the molecule's activity against human FXIIIa was also studied using the bi-substrate, fluorescence-based trans-glutamination assay, as described earlier. [62] Based on the highest concentration tested of inhibitor 1 against the above enzymes, the calculated IC 50 values are estimated to be > 500 μM for thrombin, > 200 μM for FIXa, > 100 μM for FXa, and > 200 μM for FXIIIa (Table 2), suggesting selectivity indices of > 67.6-fold, 27-fold, > 14-fold, and > 27-fold, respectively. Overall, the above results indicate that benzyl tetraphosphonate 1 is a selective inhibitor for human FXIa, as determined in the corresponding in vitro assays.</p><!><p>Plasma clotting assays of APTT and prothrombin time (PT) are routinely used to investigate the anticoagulation potential of new procoagulant enzyme inhibitors under in vitro conditions. The former time measures the effect of potential anticoagulant on the intrinsic/contact pathway-driven clotting which involves FXIIa, FXIa, and FIXa. The latter time measures the effect of potential anticoagulant on the extrinsic pathway of coagulation which involves FVIIa. The effect of different concentrations of inhibitor 1 on APTT and PT of normal human plasma was measured (Table 3 and Figure 4), as described in earlier studies. [54,55,60,61] Results indicated that inhibitor 1 concentrationdependently prolonged APTT but not PT over the concentration range of 0-470 μM. Figure 4A shows the effect of anti-FXIa monoclonal antibody on the clotting times. The antibody selectively recognizes human FXI, and under our testing conditions, resulted in 1.5-fold increase in APTT at a concentration of 1.4 μg/mL. The antibody did not affect the PT at the highest concentration tested of 2.88 μg/mL. Likewise, Figure 4B shows the variation in APTT and PT in the presence of varying concentrations of inhibitor 1. A 1.5-fold increase in APTT required 311.3 μM of inhibitor 1. However, a 1.5-fold increase in the PT required > 750 μM of inhibitor 1. These results indicate, as expected, that inhibitor 1 is anticoagulant in normal human � 0.9 [b] > 500 [c] > 200 > 100 > 200</p><p>[a] The inhibition values were obtained following non-linear regression analysis of direct inhibition of human thrombin, factor Xa, and factor XIIIa in appropriate TrisÀ HCl buffers of pH 7. plasma and it does so by targeting proteins in the intrinsic pathway of coagulation, particularly FXIa. To confirm the involvement of FXIa in streaming the anticoagulant effect of inhibitor 1, we measured the effect of adding variable concentrations of inhibitor 1 on FXIa-induced clotting of FXIdeficient human plasma. Figure 4C shows that FXI-deficient human plasma clotted at 136.9 s, yet adding 2.4 nM or 4.8 nM of human FXIa accelerated its clotting so as to take place at 56.9 s or 42.4 s, respectively. However, the addition of 233-467 μM of inhibitor 1 significantly delayed the FXIa-induced clotting of FXI-deficient human plasma by 1.04-1.94-fold when clotting was induced by 2.4 nM FXIa or by 1.11-2.72-fold when clotting was induced by 4.8 nM FXIa. Overall, these results further establish that the anticoagulant activity of inhibitor 1 in human plasma is attributed to its effect on FXIa of the intrinsic coagulation pathway.</p><!><p>To understand the mechanistic basis of molecule 1 inhibition of human FXIa, Michaelis-Menten kinetics of S-2366 hydrolysis by the wild type full-length FXIa was performed in the presence of inhibitor 1 at pH 7.4 and 37 °C. Figure 5 shows the initial rate profiles in the presence of inhibitor 1 (0-100 μM). Each profile displays a characteristic rectangular hyperbolic trend, which could be fitted using equation 2 to give the apparent K M and V MAX (Table 4). The K M for S-2366 did not significantly change (0.41 � 0.04 mM-0.28 � 0.09 mM) in the presence or absence of inhibitor 1. Nevertheless, the V MAX decreased steadily from 116 � 4 mAU/min in the absence of inhibitor 1 to 45.7 � 4.2 mAU/min at 100 μM of inhibitor 1. Thus, the inhibitor appears to bring about structural changes in the active site of FXIa which do not affect the formation of Michaelis complex but lead to a significant disruption in FXIa catalytic activity. This indicates that molecule 1 is an allosteric inhibitor of human FXIa.</p><!><p>Although tetraphosphonate 1 inhibited the hydrolysis of chromogenic tripeptide substrate S-2366 by FXIa, yet we have aimed at establishing its physiological relevance by evaluating its effect on the physiological substrate of FXIa i. e. FIX. During coagulation, FXIa binds to and activates FIX (Figure 1) by successively cleaving two peptide bonds of Arg145-Ala146 and Arg180-Val181 so as to eventually generate FIXaβ. [50] Subsequently, FIXaβ along with factor VIIIa forms the intrinsic tenase complex, in the presence of calcium ions and phospholipids, to activate factor X to FXa which eventually amplifies thrombin generation. [63] To establish the physiological relevance of benzyl tetraphosphonate 1 inhibitory activity toward FXIa, we evaluated FXIa activation of FIX in the presence and absence of inhibitor 1 using SDS-PAGE (Figure 6A) and Western blotting (Figure 6B). The figures show that inhibitor 1 dose-dependently (0-1000 μM) inhibited the formation of the intermediate FIX i. e. FXIα as well as the fully activated FIX i. e. FIXaβ (heavy chain (FXIaβ-HC) and light chain (FXIaβ-LC)) as indicated by the absence of the corresponding bands. These results suggest that the inhibitory activity of tetraphosphonate 1 toward FXIa is physiologically relevant and it takes place at a concentration range (� 250 μM) similar to that used in the chromogenic substrate hydrolysis assays.</p><!><p>To understand the effect of benzyl tetraphosphonate 1 beyond the inhibition of FXIa catalytic activity, we investigated three intermolecular interactions involving FXIa and its zymogen FXI.</p><p>In this direction, it has been reported that FXIa can be inhibited by antithrombin, an endogenous serpin, in a reaction that is accelerated in the presence of heparin by a template-or bridging-based mechanism. [52] In this interaction, a denaturation-resistant complex between the FXIa active site and the reactive center loop of antithrombin is formed. In this complex, the light chain of FXIa (FXIa-LC) acylates antithrombin by forming a covalent bond. Figure 7A shows SDS-PAGE evidence of the 90 kDa FXIa-LC -antithrombin complex (lane 3) formation in the absence of inhibitor 1. In contrast, the presence of increasing concentrations of inhibitor 1 (50-1000 μM) inhibited the formation of such complex. In fact, the formation of the complex appears to be significantly diminished at the highest concentration tested of 1000 μM. At concentrations of 250 μM and 1000 μM, the band of 90 kDa complex on SDS-PAGE significantly diminished and that of FXIa-LC appeared again at 30 kDa (lanes 5 and 6). These results suggest that inhibitor 1 disrupts the formation of FXIa-antithrombin complex, potentially by competing with heparin for its anionbinding sites on the catalytic domain and/or apple 3 domain. Furthermore, it is well documented that the zymogen FXI is activated by the action of thrombin or FXIIa in processes that are accelerated in the presence of inorganic polyphosphates or dextran sulfate. [51,64,65] Specifically, dextran sulfate is a sulfated polysaccharide that appears to accelerate the activation reactions via template-dependent mechanism in the which the polysaccharide engages with the anion binding sites on both the substrate i. e. FXI as well as the activators i. e. thrombin or FXIIa. [64][65][66][67][68][69] Figure 7B shows that inhibitor 1 inhibited the activation of FXI by thrombin, as shown by the decreased intensity of FXIa-HC and FXIa-LC bands (lanes 6-10), over the concentration range of 2-1000 μM. Likewise, Figure 7C shows that inhibitor 1 inhibited the activation of FXI by FXIIa, as shown by the decreased intensity of FXIa-HC and FXIa-LC bands (lanes 7-9), over the concentration range of 50-1000 μM. Taken together, benzyl tetraphosphonate 1 has been found to inhibit the dextran sulfate-mediated activation of FXI by thrombin as well as by FXIIa at comparable concentrations to those determined in the above experiments. This suggests that inhibitor 1 potentially competes with dextran sulfate for anionbinding sites on the catalytic domain and/or apple 3 domain. Overall, the above results suggest that benzyl tetraphosphonate 1 is likely to bind to an allosteric site on FXI(a) rather than the active site. Similar to sulfated non-saccharide mimetics of heparins, the binding site is likely to be the anion-binding site in the catalytic domain.</p><!><p>To identify a plausible binding mode for benzyl tetraphosphonate 1, we performed molecular docking studies by considering the anion-binding site on the catalytic domain of FXIa. The rationale for considering this site is that its lysine and arginine residues have been implicated in FXI(a) interactions with the negatively charged functional groups of several macromolecules including the sulfate groups of heparins [52] and the phosphate groups of inorganic polyphosphates. [53] The anion binding site on the catalytic domain of FXIa has also been implicated in the action of SPGG and that of SCI, two small molecule heparin mimetics that act as allosteric inhibitors of human FXIa. [54][55][56] The docking studies of benzyl tetraphosphonate 1 onto the anion-binding site were carried out using Glide, [70] as described in the experimental part. Initial coordinates for FXIa catalytic domain were taken from the crystal structure of PDB ID: 2FDA. [71] The studies revealed that one phosphonate group on one end of the inhibitor structure interacts via electrostatic/hydrogen bond with K529 and T523 residues (Figure 8). The studies also revealed that the central urea carbonyl oxygen interacts via hydrogen bond with N524 residue and that another phosphonate group on the other end of inhibitor 1 interacts with K535 residue (Figure 8). In particular, the K529 residue (chymotrypsin number is K170) and the K535 (chymotrypsin number is K175) have been implicated in heparin-mediated inhibition of FXIa by antithrombin [52] as well as in polyphosphate-mediated activation of FXI. [51] Impor- tantly, these results highlight the essential requirement of at least two phosphonate groups for the inhibitor action toward FXIa. Although these results are to be experimentally confirmed via crystallography studies and/or mutagenesis studies, how-ever, the above computational exercise is important as the identified putative binding site can be used to guide subsequent efforts to optimize the potency and selectivity of inhibitor 1.</p><!><p>Oral as well as parenteral anticoagulants are widely used to prevent and/or treat thromboembolic diseases. [13][14][15] In addition to having potent and selective pharmacodynamic profile, ideal anticoagulants should have predictable pharmacokinetics and do not require continuous monitoring and/or dose adjustment. They should also be rapidly reversed via the use of affordable and effective antidotes. In contrast to the existing ones, ideal anticoagulants should be devoid of hepatotoxicity, osteoporosis, or thrombocytopenia. They should also be safe for use in compromised patients with a high risk of thrombosis as in pregnant patients and cancer patients. Importantly, they should induce no bleeding complications. [72][73][74] While a significant progress has been made towards satisfying the above criteria, bleeding risk continues to be a significant side effect of heparins and warfarin. Newer direct anticoagulants inhibiting thrombin or FXa are increasingly replacing heparins and warfarin, yet they appear to be still associated with significant risks of major bleeding. [17][18][19][20][21][22] Accordingly, patients who may benefit from anticoagulation therapy do not receive it or receive a lower dose which is often not effective. This is the case of patients of atrial fibrillation and chronic kidney diseases. [23][24][25][26][27] Thus, the search for safer anticoagulant drugs with little-to-none bleeding risk continues. Towards this goal, several FXI(a)-targeting agents are in development including small molecules (BMS-986177 and EP-7041), [35][36][37][38][39][40][41] monoclonal antibodies (AB023, MAA868, and Osocimab), [42][43][44][45] aptamers (FELIAP), [48,49] and sulfated glycosaminoglycan mimetics (SPGG and SCI). [54][55][56] Osocimab and BMS-986177, in particular, are currently in advanced clinical trials. Among the above molecules, SPGG and SCI were developed as small molecule allosteric inhibitors of FXIa. They have been projected to target the anion-binding site in the catalytic domain. [54][55][56] In fact, their structural features were designed in relevance to heparin, a heterogenous mixture of sulfated glycosaminoglycans that is known to allosterically inhibit FXIa by targeting the anion-binding sites in the catalytic domain and the apple 3 domain. Inorganic polyphosphates are also known to engage with FXI(a) anion-binding sites. However, the inorganic polyphosphates facilitate the autoactivation of the zymogen FXI as well as its activation in the presence of thrombin or FXIIa, and thus, leads to a procoagulant outcome.</p><p>Inspired by the anionic nature of heparin and inorganic polyphosphates, several aptamers have also been designed to potentially disrupt the inorganic polyphosphates' procoagulant role as well as the procoagulant properties of FXI(a). These include aptamers 12.7 and 11.16 (RNA aptamers which bind to FXI(a) anion binding site on the catalytic domain resulting in allosteric inhibition) [48] and FELIAP (DNA aptamer which binds to or near active site of FXIa). [49] In this study, we report on a small molecule (~1180.74 Da), benzyl tetraphosphonate 1, that directly inhibits the catalytic activity of FXIa with an IC 50 value of ~7.4 μM (K i value is estimated to be ~7.4 μM, [75] given the testing conditions), as determined in the corresponding chromogenic substrate hydrolysis assay. Importantly, the in vitro inhibition of FXIamediated hydrolysis of the chromogenic tripeptide substrate i. e. S-2366 has been found to translate into an in vitro inhibition of FXIa-mediated hydrolysis of its physiological substrate i. e. FIX. Thus, the activity of inhibitor 1 appears to be physiologically relevant. Furthermore, the inhibitor has demonstrated significant selectivity over other procoagulant serine proteases including thrombin, FIXa, and FXa as well as over the transglutaminase FXIIIa, as determined by the corresponding in vitro enzyme assays. Inhibitor 1 has also demonstrated a substantial anticoagulant effect by targeting FXIa in human plasma as demonstrated by its relatively selective effect on APTT as well as by its ability to abolish the FXIa-induced clotting of FXIdeficient human plasma.</p><p>Despite the direct effect of the inhibitor, its biological effects beyond the direct inhibition of FXIa activity are very interesting. In this arena, the molecule has been found to inhibit the dextran sulfate-mediated activation of the zymogen FXI by thrombin and FXIIa at concentrations comparable to those that inhibit FXIa hydrolysis of S-2366 as well as FIX. This has suggested that the benzyl tetra-phosphonate 1 may bind to a cluster of basic amino acids presented by both the enzyme FXIa and its zymogen FXI. This cluster is likely to be the anion binding sites, particularly the one in the catalytic domain. In fact, the ability of the inhibitor to disrupt FXIa-antithrombin complex formation in the presence of heparin lends support to the projection of the inhibitor targeting the anion-binding site on the catalytic domain. In line with these observations is also the fact that the molecule only affected the V MAX parameter of FXIa hydrolysis of the chromogenic substrate but not the K M parameter, as determined by Michaelis-Menten kinetics experiment. As a result, the above studies indicate that benzyl tetraphosphonate 1 is allosteric inhibitor of FXIa and it binds to similar anion-binding sites on both FXIa and FXI. In fact, molecular modeling studies show that the inhibitor can favorably bind to two key basic residues in this binding site. The two residues are K529 and K535 which have previously been implicated in heparin-mediated inhibition of FXIa by antithrombin [52] as well as in polyphosphate-mediated activation of FXI. [51] In contrast to the previous small molecule allosteric inhibitors of FXIa i. e. SPGG and SMI, which were associated with ~100 % inhibition of the enzyme activity, inhibitor 1 induces sub-maximal inhibition of FXIa at saturation, with an efficacy value of ~68 %. Such phenomenon is not possible for orthosteric inhibitors and can only be exhibited by allosteric inhibitors. This further supports the allosteric behavior of inhibitor 1 which, in turn, is important because it permits partial enzyme inhibition (modulation) rather than complete inhibition. Partial allosteric inhibition may translate into a lesser bleeding risk as previously proposed for partial allosteric inhibitors of thrombin. [76,77] Importantly, targeting allosteric sites on FXIa also better permits the realization of selective enzyme modulation, considering the fact that other coagulation proteases share significant active site similarity. Thus, the allosteric nature of inhibitor 1 will likely result in a better safety profile. Overall, the discovery of this inhibitor is extremely promising for the prospect of discovering more clinically relevant partial modulators of FXIa.</p><!><p>In this study, we have identified benzyl tetraphosphonate 1 as the first of its kind inhibitor of human FXIa. The inhibitor demonstrates substantial potency and selectivity toward FXIa and over other coagulation factors of thrombin, FIXa, FXa, and FXIIIa. The molecule not only inhibits the catalytic activity of FXIa, but it also inhibits the activation of its zymogen by thrombin and FXIIa. Importantly, the inhibitor represents an important step forward towards designing allosteric inhibitors of FXIa with submaximal efficacy. This is certainly important to modulate the catalytic activity of an enzyme belonging to a superfamily of largely conserved enzyme members so as to achieve a higher level of enzyme inhibition selectivity, and subsequently, a higher margin of safety. Furthermore, inhibitor 1 also exhibits a significant anticoagulant activity as demonstrated by extending the APTT of human plasma. Accordingly, we put forward this inhibitor as a lead molecule to develop a new generation of effective anticoagulants that are devoid of bleeding complications so as to be safely used in a wide range of patients populations, particularly those who are at a high risk of bleeding.</p><p>Future studies will focus on establishing the structureactivity relationship of inhibitor 1 to further enhance its potency and selectivity. The structural manipulation will focus on optimizing the number and the position of phosphonate groups as well as on the length and the substituents of the urea-based linker. The druggability of phosphonate derivatives can also be further advanced by preparing the corresponding prodrugs to achieve meaningful oral bioavailability. It is also worth to mention here that the toxicity profile of inhibitor 1 has been evaluated in three cell lines of breast (MCF-7), intestine (CaCo-2), and kidney (HEK-293) (unpublished data). Initial results suggest that 10 μM of the inhibitor does not significantly affect the proliferation of the above cell lines. Testing at higher concentrations will be performed and reported in due time.</p><p>Lastly, a recent report has suggested that drugs that target the contact activation enzymes including FXIa may serve as potential therapeutics for patients with COVID-19. [78] Earlier, targeting FXI(a)/FXIIa interface by pharmacological means has been shown to prevent coagulopathy, systemic inflammation, and mortality in experimental sepsis. [79,80] In nonhuman primates, inhibition of contact activation also prevented death from Staphylococcus aureus-induced systemic inflammatory response syndrome. [81,82] Overall, these studies suggest that the new class of allosteric FXIa inhibitors can be further developed as adjunct therapy for COVID-19 and similar microbial outbreaks that are typically associated with excessive coagulopathy and inflammation.</p><!><p>Phosphonate derivative (1) and phosphate derivatives (2-4) were purchased from Santa Cruz Biotechnology (Dallas, TX). Reagents for clotting assays including thromboplastin D, APTT reagent, and CaCl 2 solution were all from Fisher Scientific (Pittsburgh, PA). Chemicals used to prepare enzyme assay buffers were from Milipore-Sigma (Burlington, MA), Fisher Scientific, or Bio-Rad laboratories (Hercules, CA). N,N-dimethyl-casein, dansylcadaverine, and dithiothreitol for FXIIIa assay were also from Milipore-Sigma. All types of plasmas were purchased from George King Bio-Medical, Inc. (Overland Park, KS). Antithrombin, coagulation zymogens, and coagulation enzymes including thrombin, FIXa, FXa, FXIa, and FXIIIa were from Haematologic Technologies, Inc. (Essex Junction, VT). Chromogenic substrates: Spectrozyme TH, Spectrozyme FIXa, and Spectrozyme FXa were obtained from Biomedica-Diagnostics (Windsor, NS Canada). Factor XIa chromogenic substrate (S-2366; Lpyroglutamyl-L-prolyl-L-arginine p-nitroaniline hydrochloride) was obtained from Diapharma (West Chester, OH). These substrates have a nitro-anilino chromophore and they were designed based on the physiological substrate of the corresponding clotting factor to ensure its specificity. Heparin was from Milipore-Sigma. F11 (mouse monoclonal antibody from Abnova TM ) for plasma studies, dextran sulfate (MW ca > 500,000), and Coomassie Brilliant Blue for gel electrophoresis were also from Fisher Scientific. Human FXIIa (αform) and antibodies for western blot were from Enzyme Research Laboratories (South Bend, IN). The buffers used for enzyme assays were: a) 50 mM Tris-HCl buffer, pH 7.4, containing 100-150 mM, NaCl, 0.1 % PEG8000, and 0.02 % Tween 80 for human thrombin, FXa, and FXIa; b) 20 mM Tris-HCl buffer, pH 7.4, containing 100 mM NaCl, 2.5 mM CaCl 2 , 0.1 % PEG8000, 0.02 % Tween 80, and 33 % v/v ethylene glycol for human FIXa; and c) 50 mM TrisHCl buffer, pH 8.0, containing 10 mM CaCl 2 and 100 mM NaCl for human FXIIIa.</p><!><p>Direct inhibition of human FXIa was measured by the corresponding chromogenic substrate hydrolysis assay, as reported earlier [54,55,60,61] , at pH 7.4 and 37 °C. Each well of the 96-well microplate contained 85 μL of the buffer to which 5 μL of molecules 1-4 (or high pure water) and 5 μL of FXIa (0.765 nM) were sequentially added. Following 10-min incubation, 5 μL of FXIa substrate (345 μM) was rapidly added and the residual FXIa activity was measured from the initial rate of increase in absorbance at the wavelength of 405 nm. Stocks of the potential inhibitors were serially diluted. Relative residual FXIa activity at each concentration of the inhibitor was calculated from the ratio of FXIa activity in the presence and absence of the inhibitor. Logistic eq. 1 was used to fit the concentration dependence of residual FXIa activity so as to obtain the potency (IC 50 ) and efficacy (Î"Y%) of inhibition.</p><p>In this equation, Y is the ratio of residual FXIa activity in the presence of inhibitor to that in its absence, Y M and Y 0 are the maximum and minimum possible values of the fractional residual FXIa activity, IC 50 is the concentration of the inhibitor that leads to 50 % inhibition of enzyme activity, and HS is the Hill slope. Y M , Y 0 , IC 50 , and HS values are determined by nonlinear curve fitting of the data.</p><!><p>The inhibition potential of benzyl tetraphosphonate 1 against thrombin, FIXa, and FXa was also evaluated using the corresponding chromogenic substrate hydrolysis assays reported in our previous studies. [54,55,60,61] Briefly, to each well of a 96-well microplate containing 185 μL of 20-50 mM Tris-HCl buffer, pH 7.4, containing 100-150 mM NaCl, 0.1 % PEG8000, and 0.02 % Tween80 at either 25 °C (thrombin) or 37 °C (FIXa and FXa) was added 5 μL of 0-1 mM benzyl tetraphosphonate 1 (or high pure water) and 5 μL of the enzyme. The final concentrations of the enzymes were 6 nM (thrombin), 89 nM (FIXa), and 1.09 nM (FXa). Following 10-min incubation, 5 μL of Spectrozyme TH (final conc. 50 μM), Spectrozyme FIXa (850 μM), or Spectrozyme FXa (125 μM), was rapidly added and the residual enzyme activity was measured from the initial rate of increase in absorbance at the wavelength of 405 nm. Relative residual enzyme activity as a function of the concentration of the inhibitor was calculated. Likewise, to measure the effect of benzyl tetraphosphonate 1 on human FXIIIa, a bi-substrate, fluorescence-based trans-glutamination assay was performed as we reported previously. [62] Generally, 1 μL of molecule 1 was diluted with 87 μL of pH 7.4 buffer (50 mM Tris-HCl, 1 mM CaCl 2 , 100 mM NaCl, and 2 mg/mL N,N-dimethylcasein) and 5 μL dithiothreitol (20 mM) at 37 °C followed by the addition of 2 μL of human FXIIIa (0.3 μM) and incubation for 10 min. The activity of FXIIIa was monitored following the addition of 5 μL of dansylcadaverine (2 mM) by measuring the initial rate of increase in fluorescence emission (λ Ex. = 360 nm and λ Em. = 490 nm). As with the above enzymes, relative residual FXIIIa activity as a function of the concentration of the inhibitor was calculated. In all of the above enzyme experiments, data were plotted using equation 1 above to obtain the corresponding IC 50 values, only if 50 % or more of enzyme inhibition was obtained.</p><!><p>Clotting times (APTT and PT) were measured using the BBL Fibrosystem fibrometer (Becton-Dickinson, Sparles, MD), as reported in our previous studies. [54,55,60,61] For the APTT assay, 10 μL of benzyl tetraphosphonate 1 was mixed with 90 μL of citrated human plasma and 100 μL of prewarmed APTT reagent (0.2 % ellagic acid). After incubation for 4 min at 37 °C, clotting was initiated by adding 100 μL of prewarmed 25 mM CaCl 2 , and the time to clotting was recorded. For the PT assay, thromboplastin-D was prepared according to the manufacturer's directions by adding 4 mL of distilled water, and then, the resulting mixture was warmed to 37 °C. A 10 μL of benzyl tetraphosphonate 1 was then mixed with 90 μL of citrated human plasma and was subsequently incubated for 30 sec at 37 °C. Following the addition of 200 μL of prewarmed thromboplastin-D preparation, the time to clotting was recorded. In the two assays, seven concentrations of the inhibitor were used over the concentration range of 0-500 μM to establish a concentration vs effect curve. The concentrations vs clotting times data were fitted to a quadratic trend line, which was eventually used to determine the concentration of the inhibitor necessary to increase the clotting time by 1.5-fold. Clotting times in the absence of an anticoagulant was determined in a similar fashion using 10 μL of deionized water and was found to be 34.4 � 0.1 sec for APTT and 14.5 � 2.1 sec for PT. To establish the FXIa-dependent effect of the inhibitor in human plasma, the APTT assay was repeated using FXIdeficient human plasma to which human FXIa (0, 2.4, and 4.8 nM) was added in the absence and the presence of benzyl tetraphosphonate 1 (0, 233, 327, and 467 μM).</p><!><p>The initial rate of S-2366, a chromogenic tripeptide substrate, hydrolysis by purified human FXIa was obtained from the linear increase in absorbance at the wavelength of 405 nm corresponding to the consumption of < 10 % of the chromogenic substrate, as reported in our previous studies. [54][55][56] The initial rate was measured as a function of various concentrations of the substrate (0-2000 μM) in the presence of a fixed concentration of benzyl tetraphosphonate 1 in 20 mM TrisÀ HCl buffer, pH 7.4, containing 100-150 mM NaCl, 0.1 % PEG8000, and 0.02 % Tween80 at 37 °C.</p><p>The experiment was conducted at five concentrations of the inhibitor: 0, 5, 10, 25, 50, and 100 μM. The data was fitted using the standard MichaelisÀ Menten equation 2 to determine the K M (the affinity of the substrate to the active site of FXIa) and V MAX (the maximum hydrolysis reaction velocity).</p><!><p>The experiments were done as reported in our earlier studies. [54][55][56] FIX (6.2 μM) was incubated with FXIa (10 nM) in the presence of inhibitor 1 (0, 50, 250, and 1000 μM) in 50 mM HEPES supplemented with 5 mM CaCl 2 , at room temperature. Samples were incubated for 30 min. Following the 30-min incubation, the reactions were quenched using sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (SDS-PAGE) loading buffer containing dithiothreitol and electrophoresed on a 10 % SDS-polyacrylamide gel. Protein bands were visualized by staining with Coomassie Brilliant Blue.</p><p>For Western blot experiment, plasma FIX (200 nM) was incubated at room temperature with human FXIa (40 nM) in 50 mM HEPES buffer supplemented 5 mM CaCl 2 . After 30-min incubation, SDS-PAGE loading buffer containing dithiothreitol was added, fractionated on 10 % polyacrylamide-SDS gels, and then transferred to nitrocellulose membrane. The primary antibody was goat anti-human FIX polyclonal IgG, and the secondary antibody was horseradish peroxidase-conjugated anti-goat IgG. Detection was by chemiluminescence. The relative positions of FIX and FIXa bands were confirmed using Western blots of known standards for each protein.</p><!><p>The effect of inhibitor 1 on the complex formation between FXIa and antithrombin was performed in HEPES buffer supplemented with 5 mM of CaCl 2 , as reported earlier. [49] Briefly, FXIa (300 nM) was pre-incubated with inhibitor 1 (0, 50, 250, and 1000 μM) at 37 °C for 5 minutes, and then combined with 2 μM purified human antithrombin in the presence of 2 μM sodium heparin for a further 30 minutes. At the incubation time, samples were quenched using SDS-PAGE loading gel buffer containing dithiothreitol and subjected to electrophoresis on 10 % SDS-PAGE. Protein bands were visualized by staining with silver stain.</p><!><p>Thrombin-mediated FXI activation was analyzed by SDS-PAGE, as reported earlier. [49,[64][65][66][67][68] FXI (700 nM) was incubated with α-thrombin (70 nM) with or without dextran sulfate (10 μg/ml) in the presence of different concentrations of inhibitor 1 (2, 20, 50, 250, and 1000 μM) in HEPES buffer supplemented with 5 mM of CaCl 2 . Samples were incubated for 60 mins. After the incubation period, reactions were quenched using argatroban (2 μM) and polybrene (6 μg/ml). samples were placed into reducing sample buffer, size fractionated on 10 % polyacrylamide-SDS gel and stained with silver staining.</p><!><p>FXIIa-mediated activation of FXI activation was analyzed by SDS-PAGE, as reported earlier. [49,[64][65][66][67][68] FXI (700 nM) was incubated with α-FXIIa (200 nM) with or without dextran sulfate (2 μg/ml) in the presence of different concentrations of inhibitor 1 (50, 250, and 1000 μM) in HEPES buffer supplemented with 5 mM of CaCl 2 . Samples were incubated for 60 mins. After the incubation period, reactions were quenched using corn trypsin inhibitor (1 μM) and polybrene (6 μg/ml). samples were placed into reducing sample buffer, size fractionated on 10 % polyacrylamide-SDS gel and stained with silver staining.</p><!><p>The docking studies were carried out using Glide of Schrodinger Suite 2017-1. [70] Initial coordinates for FXIa were taken from the crystal structure of FXIa in complex with α-ketothiazole argininebased ligand (PDB: 2FDA). [71] The protein structure was prepared by removing the crystallographic water molecules and the crystal ligand, and by adding hydrogen atoms consistent with the physiologic pH of 7.0 using Maestro 11.1 of Schrodinger Suite. The protein molecule was then energy minimized with an RMSD cutoff value of 0.3 à for all heavy atoms. Initial coordinates for benzyl tetraphosphonate 1 were built and energy minimized using the Schrodinger Suite. The basic residue of K529, R530, R532, K535, and K539 in the catalytic domain and the surrounding area of these amino acids were specified as the ligand binding site. The grids for the target protein were generated using the OPLS3 forcefield. [83] The grid center was set to be the centroid of the above basic residues, with a cubic grid box of 10 à on each side. No constraints were used in the grid generations. The docking calculations were done using the default parameters under the stand precision mode. All the poses were subjected to post-docking minimization. The best-docked structure based on the docking score was selected for subsequent analysis. The chymotrypsin-based numbering for the presented amino acids is as follows: T523 = T164, N524 = N165, K529 = K170, R530 = R171, R532 = R173, K535 = K175, and K539 = K179.</p>

Chemistry Open

Emission and Absorption Tuning in MR-TADF B,N-Doped Heptacenes: Towards Ideal-Blue Hyperfluorescent OLEDs

Developing high-efficiency purely organic blue organic light-emitting diodes (OLEDs) that meet the stringent industry standards is a major current research challenge. Hyperfluorescent device approaches achieve in large measure the desired high performance by combining the advantages of a high-efficiency thermally activated delayed fluorescence (TADF) assistant dopant with a narrowband deep-blue multi-resonant TADF (MR-TADF) terminal emitter. However, this approach requires suitable spectral overlap to support Förster resonance energy transfer (FRET) between the two.Here we demonstrate colour tuning of a recently reported MR-TADF B,N-heptacene core through control of the boron substituents. While there is little impact on the intrinsic TADF properties -as both singlet and triplet energies decrease in tandem -this approach improves the emission colour coordinate as well as the spectral overlap for blue hyperfluorescence OLEDs (HF OLEDs). Crucially, the redshifted and more intense absorption allows us to pair this MR-TADF emitter with a high-performance TADF assistant dopant and achieve maximum external quantum efficiency (EQEmax) of 15% at colour coordinates of (0.15, 0.10). The efficiency values recorded for our device at a practical luminance of 100 cd m -2 are among the highest reported for HF TADF OLEDs with CIEy ≤ 0.1.

emission_and_absorption_tuning_in_mr-tadf_b,n-doped_heptacenes:_towards_ideal-blue_hyperfluorescent_

Introduction<!>Synthesis<!>Optical properties<!>Conclusions

<p>Organic light-emitting diode (OLED) display technology is evolving at a brisk rate, with high-end ultrahigh definition (UHD) 4K and 8K OLED displays already in the market. These high-resolution displays must meet stringent emission colour standards (BT.2020-2), 1 which are defined according to the Commission International de l'Éclairage (CIE) 1931 as (0.13, 0.05), (0.17, 0.80) and (0.71, 0.29) for blue, green, and red, respectively. 2 At present this colour requirement is met through the use of absorptive filters or microcavities to deliver saturated blue, green, and red emission. 3,4 Unfortunately this approach necessarily results in a reduced efficiency of the devices as unwanted emission wavelengths are rejected.</p><p>An alternative and attractive solution to the issue of colour purity is to develop materials with intrinsically narrowband emission.</p><p>Commercial OLED displays currently use organic triplet-triplet annihilation (TTA) emitters for blue pixels. By virtue of the operational exciton harvesting mechanism, the internal quantum efficiency of blue OLEDs is presently capped at 62.5%. Organic thermally activated delayed fluorescence (TADF) materials, by contrast, can harvest 100% of the electrically generated excitons to produce light and achieve higher device efficiencies. 5 Similarly, organometallic phosphorescent OLEDs can generate high-efficiency OLEDs, although with significant intrinsic limitations for blue-emitting device in terms of their stability. These limitations arise as photon energies approach metal-ligand bond dissociation energies, and as thermal population of metal-centred states (particularly for d6 metal complexes) leads to severe non-radiative decay.</p><p>The TADF mechanism involves the thermal upconversion of non-emissive triplet excitons into singlets via reverse intersystem crossing (RISC). A small energy gap between T1 and S1 (DEST) is necessary to achieve TADF, and this is ensured when there is spatial separation of the frontier orbitals, which is normally achieved in a highly twisted donor-acceptor (D-A) architecture. However, D-A TADF emitters typically emit from charge transfer (CT) states where there is a large reorganization in the excited state that leads to broad (70-100 nm) emission. 6,7 These broad emission bands interact poorly with the aforementioned colour purity filters, while also making it difficult to engineer deep-blue emitters [8][9][10] and suitably high-triplet hosts. 11 Multiresonant TADF (MR-TADF) compounds are an alternative class of TADF materials that are typically based on p-and n-doped nanographene (Figure S1). 12,13 Because of their rigid structure, these compounds show narrowband emission, typically with full width of half maxima (FWHM) of around 20-30 nm. Thus, MR-TADF emitters show potential to generate the required deep blue emission demanded by industry, and support the development of stable, efficient, pure blue narrowband emitting organic materials that are expected to revolutionize OLED displays. 14 MR-TADF compounds show TADF due to the alternating pattern of electron density between the ground and excited states that leads to small DEST, combined with upper-triplet crossings from thermally-populated Tn states back to the emissive S1 state. [15][16][17][18] The excited states thus possess a distinct short range charge transfer (SRCT) character, 13,19,20 and MR-TADF compounds are endowed with high singlet radiative decay (kr) rates of around 10 7 s -1 . 5,19 Despite these advantages, the RISC rates reported for MR-TADF materials typically lag ~100 times slower than those of leading D-A or D-A-D TADF materials. 13,21 This limitation frequently leads to inefficient triplet harvesting and low performance of the OLEDs at practical luminance. To circumvent this limitation, MR-TADFs have recently found a parallel application as terminal emitters in hyperfluorescent (HF) OLEDs. Indeed, truly ground-breaking device performances have been achieved when pairing a high-RISC D-A assistant dopant with narrowband MR-TADF terminal emitters. [22][23][24] This performance is in some cases supported by spontaneous alignment of the MR-TADF with the substrate, thus improving optical outcoupling of the device, 24 although the specific loss mechanisms that occur under electrical excitation, including charge trapping and Dexter transfer, 25 remain poorly understood.</p><p>High-performance HF OLEDs rely on efficient energy transfer between the D-A TADF assistant dopant that is responsible for exciton harvesting and the MR-TADF terminal emitter. Förster resonance energy transfer (FRET) is understood to be the main mechanism for this transfer, and FRET efficiency is proportional to the overlap between the emission spectrum of the D-A TADF assistant dopant and the absorption spectrum of the MR-TADF terminal emitter. As a result, the compatibility of many MR-TADF materials is severely limited by the low molar extinction coefficient of their lowest energy absorption bands -those that predominately overlap with the emission spectrum of the D-A TADF assistant dopant. Expanding the FRET compatibility of a specific terminal emitter requires increasing the spectral overlap between the assistant dopant and terminal emitter. This in turn requires development of deeper-blue D-A TADF assistant dopants, but such emitters remain persistently elusive -a circumstance that has stimulated the rapid development of the alternate hyperfluorescence approach. 26 Clearly then, synthetic control over both the emission and absorption spectra [27][28][29] of MR-TADF materials is crucial to enable and optimise their use in HF OLEDs with available D-A TADF co-hosts. We previously reported a linear B,N-doped ladder type heptacene that emits at 390 nm (near UV) with a FWHM of 31 nm (240 meV) in THF solution. 30 The material displayed weak TADF due in part to the large ΔEST of 0.31 eV, along with significant TTA contribution reflected in the extended ms-timescale of the emission decay. 30 Due to the near UV emission and subsequent paucity of suitable OLED hosts, the investigated device performance was poor. 31 In the present report we show how replacement of the hydroxyl groups for mesityl substituents in α-3BNMes (Figure 1) leads to a desired red-shifting of the emission towards an ideal-blue emission colour coordinate, as well as a red-shifting of the absorption that supports HF compatibility with available D-A TADF assistant dopants. Together, these changes in optical properties compared to α-3BNOH allow α-3BNMes to be used in high-performance deep-blue HF OLEDs that show 15% maximum external quantum efficiencies (EQEmax) and colour coordinates of (0.15, 0.10). These results represent highly competitive device performance metrics at this colour coordinate that are enabled by the finely-tuned emission and absorption spectra of α-3BNMes (Table S2).</p><!><p>α-3BNMes was synthesized in three steps (Scheme S1), where the key borylation step proceeds in 57% yield. The identity and purity of α-3BNMes were established from a combination of 1 H NMR spectroscopy (S2), high-resolution mass spectrometry (S4), HPLC (S5), GPC trace analyses (S7) and single crystal X-ray diffraction analysis (Figure 2). Like the parent compound (α-3BNOH), α-3BNMes shows high thermal stability, revealed by thermogravimetric analysis (TGA), with a decomposition temperature (Td), defined as the 5% weight loss of the material, at 503 °C (Figure S8). We investigated the structure of α-3BNMes by growing single crystals via slow evaporation of the compound in THF. Full datasets were collected from many different crystals, and we report here our best result. The data obtained are adequate to demonstrate connectivity and gross structure but do not merit discussion of bond lengths (Figure 2). Like the parent α-3BNOH, in α-3BNMes the heptacene core remains planar, and the aryl substituents attached to boron and nitrogen align nearly orthogonal to main acene core. The electron density distribution patterns of these orbitals is typical for MR-TADF compounds (Figure 3). 13 The HOMO density is mainly localized on the nitrogen and carbon atoms positioned ortho to them, while in the LUMO, electron density is mainly localized on the boron atoms and the carbons ortho to them. We applied spin component scaling second order approximate coupled-cluster (SCS-CC2) to accurately predict the nature and energies of the excited states and the ΔEST (Figure 3b and Table S1). 13,19,32 α-3BNMes shows a gratifyingly smaller ΔEST of 0.20 eV compared to that of α-3BNOH (ΔEST of 0.29 eV), but at the expense of a slightly smaller oscillator strength (f) for the transition to S1 of 0.08 compared to α-3BNOH (f of 0.09). 30 The predicted S1 energy is also stabilized to 3.40 eV compared to 3.69 eV in α-3BNOH. The difference density plot of the S1 state shows the characteristic alternating pattern of increasing and decreasing electron density on adjacent atoms that is characteristic of MR-TADF compounds. 32 The patterns revealed in the difference density plots for T1 and T2 differ slightly from that of S1. This difference in the nature of the excited singlet and triplet states will result in enhanced spin-orbit coupling and assist in RISC following El Sayed's rules. 33 The absorption spectrum was simulated by capturing transitions to the first five singlet excited states at the SCS-CC2/cc-pVDZ level of theory using DFT calculated ground state. Good agreement between the measured and simulated spectral shapes was obtained (Figure S9). Similar trends in the difference density pictures of the lowest-lying singlet excitations were obtained for α-3BNMes (Figure S10) compared to those of a-3BNOH , 30 explaining the near identical shapes of their absorption spectra (Figure 4).</p><!><p>Steady-state photophysical properties of both α-3BNMes and the previous α-3BNOH in dilute THF are shown in Figure 4. In the case of α-3BNMes, a bathochromic shift of 330 meV compared to α-3BNOH is observed in both the main π-π* absorption band at 3.50 eV (354 nm) and the quasi degenerate S1, S2</p><p>SRCT excited states at 2.94 eV (421 nm). By replacing the strongly mesomerically electron-donating hydroxyl groups with inductively electron-withdrawing mesityl substituents, both the HOMO and LUMO levels are stabilized although the LUMO to a greater extent. This results in a net stabilization of the excited states in α-3BNMes, and red-shifts both its absorption and emission spectra. The molar extinction coefficient for the high-energy band (S4) is also increased by a factor of two, although this still appears at very short wavelengths, and thus is unsuitable for FRET and HF OLED applications using available D-A TADF assistant dopants. Nonetheless, this demonstrates that significant control over the absorption spectrum is indeed possible. Promisingly, the HF device-relevant SRCT absorption bands are increased by a factor of 1.4 along with a useful red-shift to wavelengths beyond 400 nm in α-3BNMes. Although these device-relevant SRCT bands remain smaller and at higher energies than in v-DABNA (Figure S11, limiting HF compatibility with available D-A TADF co-hosts), they still represent a significant improvement compared to α-3BNOH. It remains unclear how the structure of v-DABNA leads to its lowest-energy absorbance band becoming dominant, compared to the S4 band in the heptacene systems.</p><p>The solution photoluminescence spectrum of α-3BNMes shows a narrow emission centred at lPL = 2.80 eV (442 nm), with FWHM of 190 meV (30 nm) and a small Stokes shift of 140 meV (20 nm). Gratifyingly, the replacement of the hydroxyl groups with the mesityl substituents leads to a yet narrower, deep-blue emission, shifting the CIE coordinates of the PL spectrum from (0.17, 0.01) to (0.15, 0.04).</p><p>These CIE 1931 values are considered ideal for blue OLEDs as defined by BT.2020-2. 1 Together, these THF solution results establish the absorption-and emission-tuning abilities of the boron substituents towards synthetic control of the singlet states of the B,N-heptacene core. We find that the ΔEST are very similar in both materials, indicating that mesityl substitution of the boron atoms results in similar changes to both the singlet and triplet state energies of α-3BNMes compared to α-3BNOH. Time-resolved photoluminescence decays of α-3BNMes doped at 1 wt% in PMMA matrix are shown in Figure 5b. Similar to previous reports of the photophysics of α-3BNOH 30 and many other reported MR-TADF materials, the delayed fluorescence from α-3BNMes is weak and long-lived compared to D-A TADF materials, 8,9,34,35 indicating only a moderate rate of rISC. When measured at lower temperatures, the delayed emission component (after 100 ns) is suppressed, evidencing the thermally activated mechanism of this emission. At longer delay times (beyond 0.1 ms) and at temperatures below 150 K the emission decay rate changes significantly, which along with a strong spectral red-shift identifies the phosphorescence regime from which the phosphorescence spectrum (Figures 5a, 5c) is extracted.</p><p>Exponential fitting of the room temperature decay reveals three different components: the prompt fluorescence has a lifetime, tp, of 10 ns, and there are two main components to the delayed emission with lifetimes of 9.08 μs and 7.06 ms (Figure S12a). The shorter of the delayed lifetimes, corresponding to a regime of dual emission, is attributed to combined monomer and a red-shifted aggregate emission (Figures 5c, S12c), most probably associated with excimer emission as described by Stavrou et al., 15 which is common in planar MR-TADF emitters. Notably, the contribution to the emission decay from this species is minimal and energetically is at the same positions as for α-3BNOH. 30 The longest delayed component is attributed to pure monomer emission with mono-excitonic origin, in contrast with α-3BNOH where it was found to have bi-excitonic TTA contribution (Figure S13). As in other recent studies of MR-TADF materials, 15,18 we find that changing the host matrix has only a modest influence on kRISC (Figure S12b). Finally, the photoluminescence quantum yield (PLQY) of α-3BNMes in 1% mCP doped film was determined to be 63%, thus nearly double that of α-3BNOH (35% in 1 wt% mCP), and is sufficiently high to support OLED applications. With increasing doping concentration, no significant differences were observed in the current densityvoltage-luminance (jVL) curvers, the spectra, or EQE as a function of current density in both host environments. Consequently, we do not anticipate qualitatively different behaviour in MR-TADF only devices with lower doping concentration, which were thus not investigated. More interestingly, no broadening of the electroluminescence (EL) spectrum was observed upon increasing concentration, indicating suppression of excimer formation even at high concentrations, a very common phenomenon of other MR-TADF emitters in films. Maximum EQEs of 1.7 % at ~ 100 cd m -2 were achieved in devices with each of the two hosts. We suggest that this poor performance is due to the weak TADF contribution of the material, leading to low efficiency combined with additional efficiency roll-off from the known instability of DPEPO, 36,37 especially at high current densities (Figure S14c). Nonetheless, the CIE coordinates are very attractive at (0.15, 0.08), which are the same as in the photoexcited film and similar to that determined in THF solution.</p><p>To compensate for the poor intrinsic exciton harvesting performance of α-3BNMes while still taking advantage of its ideal emission spectrum we then applied it as a terminal emitter in HF OLEDs in combination with a D-A(-D) TADF sensitizer. In order to ensure adequate spectral overlap necessary for the energy transfer, we employed 2,8-bis(2,7-di(tert-butyl)-9,9-dimethylacridin-10(9H)yl)dibenzo[b,d]thiophene (DtBuAc-DBT) as a TADF co-host, previously reported to give ~11% EQEmax and suitably high-energy blue emission with CIE coordinates of (0.16, 0.17). 8 This D-A-D TADF assistant dopant was co-evaporated at 25 vol% in the EML, alongside 1 vol% α-3BNMes and 74 vol% DPEPO. The device architecture was chosen to be the same as the one previously reported, 8 with the addition of a-3MesBN as the terminal emitter, using ITO The resulting HF OLEDs possess good efficiency, with an EQEmax of 15% and an EQE100 (at 100 cd/m 2 ) of 10.2%, with a FWHM of 290 meV (49 nm) and CIE of (0.15, 0.10), which is enabled by triplet harvesting by the D-A-D sensitizer, together with narrow deep blue emission from the α-3BNMes (Fig- ure 6). Although the EQEmax is improved at 15% in the HF OLED (compared to 11% for the DtBuAc-DBT device) the larger roll-off at higher current densities results to an EQE300 of 6.2% and 8.8% for the HF and TADF OLEDs, respectively. The increase in EQEmax is a compromise between detrimental factors (e.g., the moderate PLQY of α-3BNMes as the terminal emitter, competing unproductive Dexter energy transfer, in-situ charge trapping, etc.) and beneficial effects (e.g., α-3BNMes alignment/anisotropic emission, and good FRET overlap outcompeting DtBuAc-DBT non-radiative decay), although some of these factors are deceptively difficult to quantify by established experimental methods. 25 The overall HF OLED performance is nonetheless competitive with other recent leading reports at similar colour coordinates (Figure S16 and Table S2).</p><p>The EL spectrum of the HF OLEDs is nearly identical to the α-3BNMes spectrum, indicating efficient FRET with only a small contribution of the D-A-D TADF sensitizer at ~525 nm. This efficient FRET occurs despite a seemingly small FRET overlap (Figure S15) and highlights the importance and challenge of both emission and absorption spectral tuning for developing high-efficiency blue HF OLED material combinations. While it is incredibly challenging to further deepen the emission colour of available D-A-D TADF assistant dopants, the need to do so can be avoided in HF OLEDs by instead redshifting the absorption spectrum of the MR-TADF terminal emitters, as we demonstrate here.</p><!><p>We have achieved emission and absorption colour tuning in a deep blue non-triangulene type MR-TADF compound by altering the boron substituents in a B, N-doped heptacene. Compared to the parent UV emitter α-3BNOH, these changes imbue α-3BNMes with ideal CIE coordinates for blue OLEDs and suitable absorption spectrum for FRET compatibility with existing D-A TADF co-hosts. The resulting HF OLEDs achieved an EQEmax of 15% and deep-blue colour coordinates of (0.15, 0.10), compared to <1% efficiencies for the unassisted α-3BNOH. In light of slow progress towards the development of truly deep-blue D-A TADF emitters, we advance that controlling the absorption spectrum of terminal MR-TADF emitters to expand their compatibility in HF devices is a more fruitful approach.</p>

ChemRxiv

A New Four-Parameter Cubic Equation of State for Predicting Fluids Phase Behavior

A new four-parameter cubic equation of state (EoS) is generated by incorporating the critical compressibility factor (Zc) apart from the critical pressure (Pc) and temperature (Tc).One free parameter in the denominator of the attractive term and two parameters in the alpha function are adjusted using the experimental data of saturated liquid density, vapor pressure, and isobaric liquid heat capacity of 48 components including hydrocarbons and non-hydrocarbons.Applying this equation of state, saturated liquid density, saturated vapor density, and vapor pressure of pure components are accurately reproduced compared with experimental values. Furthermore, the predicted properties including derivatives of alpha function, such as enthalpy of vaporization, entropy of vaporization and isobaric heat capacity of liquid, also have decent accuracy. The global average absolute relative deviation (AAD) of saturated liquid density, saturated vapor density, saturated vapor pressure, enthalpy of vaporization, entropy of vaporization, and isobaric heat capacity of liquid in a wide reduced temperature (Tr) range of subcritical region reproduced by this work are 4.33%, 4.18%, 3.19%, 2.26%, 2.27%, and 5.82%, 2 respectively. Substantial improvement has been achieved for the isobaric liquid heat capacity calculation.

a_new_four-parameter_cubic_equation_of_state_for_predicting_fluids_phase_behavior

INTRODUCTION<!>THE NEW CUBIC EQUATION OF STATE<!>OBJECTIVE FUNCTION FOR IDENTIFYING OPTIMAL u, m AND n<!>RESULTS AND DISCUSSION

<p>Equation of state is of fundamental instrument for thermodynamic properties prediction of both pure substance and mixtures as well as simulating fluids phase behavior for practical application.</p><p>Cubic EoS is an outstanding member of the EoS family equipped with balanced feature of simplicity, accuracy, and computational speed. Therefore, cubic EoSs are widely applied in reservoir modelling, petrochemical industries, chemical design, and separation processes.</p><p>Since the time van der Waals proposed the first cubic equation of state, numerous equation of state have been deduced by either revising the repulsive term [1] or modifying the attractive term [2] in order to improve its predictive capability. Among them, Soave [3] (SRK EoS) and Peng and Robinson [4] (PR EoS) developed probably the most successful cubic equations of state for vapor liquid equilibrium calculation. However, both of these two generic cubic EoSs assumed a particular critical compressibility factor for all substances, 0.333 and 0.307, respectively, which violates the facts of component dependent critical compressibility. Therefore, the predicted densities differ significantly from their experimental values, so do some related properties. Many attempts have been made to counter this weakness. Equations of state proposed by Schmit and Wenzel [5], Patel and Teja [6], and Trebble and Bishnoi [7] introduced a component dependent empirical critical compressibility. While this parameter, in general, does not equal to the experimental value of critical compressibility of fluids, the new free parameter made the deviation of the thermodynamic properties to be adjustable, and subsequently improved the accuracy of the EoSs. On the other hand, the work conducted by Guennec and Privat [8] recently showed the volume translated-consistent Peng-Robinson (tc-PR) and the volume translated-consistent Redlich-Kwong (tc-RK) EoSs could noticeably eliminate the apparent discrepancy of the saturated densities reproduced by the original PR and RK EoSs.</p><p>Instead of using an empirical critical compressibility factor as those three parameter cubic EoSs, the present work integrate the experimental critical compressibility factor, and introduce a fourth parameter as a free one to be determined by minimizing the deviation of the saturated liquid density. Moreover, the Melhem [9] type of alpha function is found to be suitable for the attractive term of the new EoS,</p><!><p>The equation of state proposed in this work has the following form:</p><p>Where R is the universal gas constant, a(T) is function of temperature, while b, w, and u are temperature independent constant for particular component.</p><p>The arrangement of the cubic equation chosen here is not fresh, identical forms having been chosen earlier by Schmit and Wenzel [5], Adachi et al. [10], Jan et al. [11]. By imposing the van der Waals constraints at the critical point, the following set of equations are obtained:</p><p>where</p><p>In this particular work, the following Melhem [9] type of alpha function is employed for the proposed EoS:</p><p>Equations ( 1) -( 8) complete the description of the new cubic EoS.</p><p>48 pure components including hydrocarbons and non-hydrocarbons are selected to adjust the undetermined parameters in the new EoS. b data source from Perry's Chemical Engineers' Handbook, Eighth Edition. [13] c data source from Guennec and Privat. [8]</p><!><p>A proper objective function is indispensable for obtaining the optimal value of those undetermined parameters. The common form of the objective functions usually took the vapor pressure and saturated liquid density into consideration, and different weighting factor set (w1, w2) were used for authors, such as Dashtizadeh et al. [14], Nasrifar et al. [15], Bonyadi et al. [16] set (0.8, 0.2), while Haghtalab et al. [17] sets (0.7, 0.3), for vapor pressure term and saturated liquid density term, respectively. In contrast, in the literature of Guennec et al. [8], the properties, for instance enthalpy of vaporization and heat capacity, were incorporated into the objective function due to the enthalpy and heat capacity calculation involve the first and second derivatives of the alpha function with respect to temperature, while the vapor pressure and density calculation only involve the alpha function itself. Therefore, if the derivatives of alpha are not involved in the objective function, it may lead to parameters in the alpha function unsuitable for reproducing properties containing alpha derivatives.</p><p>In this work, objective function is also applied in order to acquire optimal value for those undetermined parameters in the new EoS, but the form of the objective functions used here are slightly different from those aforementioned, and details are described below. An interesting feature of the EoS proposed in this work is that the deviation of the saturated liquid density has weak correlation with the alpha value as long as the value belongs to the neighborhood of optimal alpha value, while strongly depend on the value of parameter u. Therefore, the procedure for identifying optimal u is decoupled from the procedure for identifying parameters in the alpha function.</p><p>In the present work, the following objective function one (OF1), eq 10, is chosen to optimize the value of u, while the discrete alpha value derived from the phase equilibrium condition when experimental value of pressure is used as input parameter at each data point. The calculation is confined by the principle of iso-fugacities of two phases in equilibrium condition, eq 9. The principle denotes as below:</p><p>For the present work, the Genetic Algorithm in MATLAB toolbox has been utilized to optimize the regulable parameters. With this technique, the optimal values of u, m and n can be acquired respectively.</p><p>The generalized form of u denotes as eq 12:</p><p>For the nonpolar and slightly polar substance pool, where methanol, ethanol, water, acetone, and ammonia were excluded, the following generalized form, eq 13 and 14, could be used for the parameters m and n in the alpha function. And the relationship between m and n was elaborated in literature by Forero G. et al. [18]. 𝑚 = 76.8570(𝜔𝑍 𝑐 2 ) 2 + 10.8880𝜔𝑧 𝑐 2 + 0.1486</p><p>In the case of polar substances (methanol, ethanol, water, acetone, and ammonia) included in</p><!><p>In order to assess the performance of the newly proposed cubic EoS, calculation of some thermodynamic properties, including saturated liquid density, saturated vapor density, vapor pressure, enthalpy of vaporization, entropy of vaporization, and isobaric heat capacity of liquid, of pure substances are carried out. The thermodynamic relations for using eq 1 are provided in Appendix. Results are compared with the Jan et al. [11] (JT) EoS, the Adachi et al. [19] (ALS)</p><p>EoS, the generalized tc-PR [8] EoS with Twu88 alpha function, and the generalized tc-RK [8] EoS with Twu88 alpha function. While addressing those substances listed in Table 2 with tc-PR and tc-RK EoS, the Twu91 alpha function is incorporated instead of Twu88 alpha function. The JT and ALS EoSs are selected as comparison is because they have the same form as the EoS proposed in this work, while developed with different methodology. The generalized tc-PR and tc-RK EoSs are involved due to their accuracy and the significance of original PR and RK EoSs.</p><p>Results are categorized in Tables 3-5. The calculated average absolute relative deviations (AAD)</p><p>for each component presented in Table 1 and the global average absolute relative deviation are reported.</p><p>In Table 3, the predicted saturated liquid density and saturated vapor density of 48 pure substances are compared with experimental data, as well as the predictions given by the JT, ALS, tc-PR and tc-RK EoSs. In regard to</p>

ChemRxiv

Evaluation of Pd→B Interactions in Diphosphinoborane Complexes and Impact on Inner‐Sphere Reductive Elimination

AbstractThe dative Pd→B interaction in a series of RDPBR’ Pd0 and PdII complexes (RDPBR’=(o‐PR2C6H4)2BR’, diphosphinoborane) was analyzed using XRD, 11B NMR spectroscopy and NBO/NLMO calculations. The borane acceptor discriminates between the oxidation state PdII and Pd0, stabilizing the latter. Reaction of lithium amides with [(RDPBR’)PdII(4‐NO2C6H4)I] chemoselectively yields the C−N coupling product. DFT modelling indicates no significant impact of PdII→B coordination on the inner‐sphere reductive elimination rate.

evaluation_of_pd→b_interactions_in_diphosphinoborane_complexes_and_impact_on_inner‐sphere_reductive_

<!>Introduction<!><!>Introduction<!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!><!>Syntheses and reactivity of [(DPB)Pd] complexes<!>Analyses of Pd→B interactions<!><!>Analyses of Pd→B interactions<!><!>Analyses of Pd→B interactions<!><!>Analyses of Pd→B interactions<!><!>Analyses of Pd→B interactions<!>Conclusions<!>General<!>Reactivity studies<!>Synthesis of [(PhDPBPh)PdCl2] (7)<!>Synthesis of [(PhDPBPh)PdBr2] (8)<!>Synthesis of [(PhDPBPh)PdCl]SbF6 (9)<!>Synthesis of [(PhDPBPh)Pd(C3H5)]SbF6 (10)<!>Synthesis of [(PhDPBPh)Pd] (6)<!>Synthesis of [(PhDPBPh)Pd(PMe3)] (11)<!>Conflict of interest<!>

<p>F. Ritter, L. John, T. Schindler, J. P. Schroers, S. Teeuwen, M. E. Tauchert, Chem. Eur. J. 2020, 26, 13436.</p><!><p>Z‐type acceptor ligands have attracted considerable attention over the past decade.1 Their coordination to transition metals grants access to complexes with unusual coordination geometries2 and electronic properties by formation of dative M→Z bonds. Group 13 acceptor ligands, with a special focus on boranes, have been particularly well studied. M→Z bonds can stabilize low oxidation states at the coordinated transition metal.3 Thus, facile access to complexes featuring transition metals with formally negative oxidations states is realized (Figure 1 a).4 This stabilization of low oxidation states appears to inhibit oxidative addition reactions.3b, 3e, 5 However, we demonstrated that this obstacle can be overcome for complex 1 by addition of catalytic amounts of acetate, which competes with Pd0 for the free coordination site at the borane, thus reversibly breaking the Pd0→B interaction (Figure 1 b).3b This concept allowed for the application of 1 in catalytic allylic amination, and most recently of 2 in the catalytic hydro‐/deutero‐dechlorination of aryl chlorides.3e Alternatively, bifunctional substrate activation across the M→Z interaction has been described.3a, 6 The aptitude of hydride,7 halide8 and carbon group9 migration between the Z‐type ligand and the coordinated transition metal has initiated further applications. Catalytic processes have concentrated on transformations in which the catalyst is not required to change its oxidation state quickly, but rather profits from an electronic fine‐tuning by electron‐withdrawing Z‐ligand coordination.10 Successful applications include CO2 hydrogenation11 and hydrosilylation,3d, 12 enyne cycloisomerization13 and alkyne hydroamination.14 Michaelis used the heterobimetallic TiIV/PdII complex (Figure 1 c), developed by Nagashima,15 for allylic amination of allyl chlorides with hindered secondary amines.5b, 16</p><!><p>M→Z interaction: stabilization of low oxidation states and impact on oxidative addition and reductive elimination.</p><!><p>Combined experimental and computational investigations indicated a rate enhancement of 103−–105 of the outer‐sphere reductive C−N bond elimination, due to the electron‐withdrawing PdII→TiIV interaction.5b, 17 This result agrees with previous investigations performed with Pd η3‐allyl and Ni η3‐allyl complexes, which showed favored reductive outer‐sphere reductive elimination in the presence of less electron‐donating spectator ligands.18</p><p>We speculated that the electron‐withdrawing properties of the borane functionality in diphosphinoborane (DPB) ligands enhances the rate of inner‐sphere reductive elimination from Pd complexes due to 1) overall reduced electron density at the PdII center and 2) increasing of the Pd→B interaction strength during reductive elimination. We determine how the oxidation state of Pd and co‐ligands affect the strength of the Pd→B interaction in DPB complexes. NBO/NLMO calculations and solid‐state structures are used to assess the strength of Pd→B interactions. The value of the 11B NMR chemical shift as a probe is discussed. The reductive elimination of N,N‐dimethyl‐4‐nitroaniline from [(PhDPBPh)PdII(4‐NO2‐C6H4)NMe2] (5) was studied and modelled with DFT calculations to investigate the assumed influence of the borane acceptor.</p><!><p>A series of [(PhDPBPh)PdII] complexes was synthesized to examine a possible correlation between the nature of ligands at Pd and the strength of the PdII→B interaction (Scheme 1).</p><!><p>Synthesis of [(PhDPBPh)PdII] complexes.</p><!><p>Complex [(PhDPBPh)PdIICl2] (7) was produced by reaction of PhDPBPh ligand with [(cod)PdCl2] in DCM and was isolated in 74 % yield (Scheme 1). Single crystals were grown from CH2Cl2/benzene and analyzed by X‐ray diffraction (Figure 2). A typical square‐pyramidal coordination around the palladium was observed around the PdII center. The chloride ligands are located in cis‐configuration at the basal position, and the borane adopts the apical position. The Pd,B distance of 2.762(3) Å is shorter than the sum of the van der Waals radii (3.28 Å),19 but elongated compared to the sum of the covalent radii (2.23 Å).20 A long Pd,C51 distance of 3.405(3) Å seems to rule out a η2‐(B,C) type coordination to the PdII center. A slightly increased pyramidalization at the boron atom is observed (ΣBα=355.4°) compared to complex [(iPrDPBPh)PdCl2] (ΣBα=359.9°).21</p><!><p>Left: thermal ellipsoid plot of the solid‐state structure of 7 at the 50 % probability level. Hydrogen atoms are omitted for clarity. Selected bond lengths (Å) and angles (°): Pd1−Cl1=2.3355(7), Pd1−Cl2=2.3628(7), Pd1−P1=2.2558(8), Pd1−P2=2.2932(8), Pd1−B1=2.762(3), Pd1−C51=3.405(3), P1‐Pd1‐P2=95.49(3), C51‐B1‐C61=118.3(3), C51‐B1‐C71=118.2(3), C71‐B1‐C61=118.8(3).22 Middle: Ball and stick display of [(PhDPBPh)PdCl]‐dimer (9) generated by symmetry. Right: thermal ellipsoid plot of the asymmetric unit of 9 at the 50 % probability level. Hydrogen atoms and crystal CH2Cl2 are omitted for clarity. Selected bond lengths (Å) and angles (°): Pd1−Cl1=2.3781(11), Pd1−Cl1†=2.3928(13), Pd1−P1=2.2638(13), Pd1−P2=2.3084(11), Pd1−B1=2.721(5), Pd1−C1=3.338(4), P1‐Pd1‐P2=95.38(5), C11‐B1‐C41=117.5(4), C1‐B1‐C11=119.4(4), C1‐B1‐C41=118.9(4).23.</p><!><p>The ligand backbone is twisted (dihedral angle C62‐C61‐C71‐C72: 35.6(3)°) to allow for a P‐Pd‐P angle of 95.49(3)°. This twist renders the two phosphine groups diastereotopic. The 31P NMR spectrum of 7 in CD2Cl2 displays two broad resonances of equal integral at δ=39.0 and 48.2 ppm. A series of 31P VT NMR spectra was recorded (Figure 3), covering a temperature range from −29.8 to 35.1 °C. The two singlet resonances coalesced into a single resonance (δ=48.2 ppm) at elevated temperatures. The rate constants of the dynamic process were determined by line‐shape analysis using Bruker's TopSpin software. An Arrhenius plot analysis gave an activation energy of Ea=9.3±0.5 kcal mol−1 with a pre‐exponential factor of A=(14±7) x 109.</p><!><p>31P VT NMR analysis of 7 in CD2Cl2. Left: recorded 31P NMR spectra. Middle: simulated 31P NMR spectra. Right: Arrhenius plot.</p><!><p>We suggest that the observed dynamic process in the 31P NMR spectrum of 7 is caused by an interconversion of 7 with its enantiomer ent ‐7 (Scheme 2).</p><!><p>Proposed interconversion between 7 and ent ‐7 by twisting of the DPB ligand.</p><!><p>In order to accommodate for the small P‐Pd‐P angle of 95.49(3)°, the σ‐symmetric PhDPBPh ligand is twisted. As a result, its B−Ph group points towards one of the two phosphine groups, rendering them chemically inequivalent. This assumption is in line with the observed two 31P NMR resonances at low temperatures. Twisting of the C62‐C61‐C71‐C72 dihedral angle converts 7 into its enantiomer ent ‐7, presumably via a σ‐symmetric transition in which the B−Ph group is orientated between the two chloro ligands.</p><p>Complex 8 was synthesized in the same fashion as 7 from [(cod)PdBr2] and was isolated in 67 % yield. The 31P NMR spectrum displays two broad resonances of equal intensity at δ=45.2 and 38.1 ppm (CD2Cl2), suggesting a similar dynamic process as in 7. Due to the poor solubility of both 7 and 8, no 11B NMR spectra could be obtained.</p><p>Cationic complex [(PhDPBPh)PdIICl]SbF6 (9) was produced in 51 % isolated yield by halide abstraction from 7 with AgSbF6 (Scheme 1). Single crystals were grown from CH2Cl2/hexane and analyzed by X‐ray diffraction (Figure 2). In the solid state a chloro‐bridged dimer [(PhDPBPh)PdII(μ‐Cl)]2(SbF6)2 is observed with an inversion center between the two PdII centers. Within the dimer, the PdII center is coordinated in a square‐pyramidal fashion with the borane located in the apical position. The Pd, B distance in complex 9 is 2.721(5) Å, which is slightly shorter than in [(PhDPBPh)PdIICl2] 7 (2.762(3) Å). However, pyramidalization of the borane is almost identical (ΣBα=355.8°). The absence of a relevant η2(B,C)→PdII interaction is suggested by the long Pd1,C1 distance of 3.338(4) Å. The Pd,B distance and lack of significant pyramidalization at the borane suggest a weak PdII→B interaction, which is in line with a broad resonance in the 11B NMR spectrum at δ=65 ppm (ω 1/2=1900±500 Hz).</p><p>The ligand backbone is twisted similarly to that in 7 (dihedral angle C42‐C41‐C11‐C12 of 33.5(5)° (9) vs. 35.6(3)° in 7), resulting in an almost parallel orientation of the B−Ph with the Pd1−Cl1 bond (dihedral angle C1‐B1‐Pd1‐Cl1 of 10.6(3)°). The 31P NMR spectrum of 9 displayed only a singlet resonance at δ=49.9 ppm which suggests a quick interconversion between the two diastereotopic phosphine donors in solution.</p><p>Cationic allyl complex [(PhDPBPh)PdII(η3‐C3H5)]SbF6 (10) was synthesized by reaction of AgSbF6 with zwitterionic allyl complex [{(o‐PPh2C6H4)2B(OAc)Ph}PdII(C3H5)] (4) (Scheme 1) and was isolated in 38 % yield by crystallization from CH2Cl2/hexane. Figure 4 depicts its solid‐state structure. The PdII center in complex 10 is located in a trigonal‐pyramidal environment in which the borane occupies the pseudo‐apical position and the C3H5‐ligand and the two phosphines are located in the trigonal‐planar positions. A weak PdII→B interaction is indicated by a Pd,B distance of 2.676(5) Å, which is in line with a minor pyramidalization at the borane center (ΣBα=354.7°) and a broad 11B NMR resonance at δ=62 ppm (ω 1/2=1200±100 Hz). A large Pd,C22 distance of 3.066(6) Å eliminates the possibility of a strong η2(B,C)→PdII interaction. The η3‐coordinated C3H5‐ligand is disordered. Using the borane as a reference point, a 39:61 mixture of the exo‐ and endo‐isomers is observed. A wider P‐Pd‐P angle of 102.86(5)° is realized by a decrease in the twisting of the ligand backbone (dihedral angle C18‐C17‐C28‐C33 of 24.04°). The observed disorder of the C3H5‐ligand is in good agreement with the observed NMR spectra. In the 31P NMR spectrum (CD2Cl2), two singlet resonances are observed in a 40:60 ratio (δ=28.1 and 26.9 ppm) and two sets of C3H5‐units are detected in the 1H NMR spectrum. DFT calculations (BP86/def‐SV(P)) based on the solid‐state structures of 10‐endo and 10‐exo indicate a small Gibbs free energy preference of ΔG=0.74 kcal mol−1 for 10‐endo, predicting a 29:71 ratio at 298 K.</p><!><p>Thermal ellipsoid plot of the solid‐state structure of 10 at the 50 % probability level. Hydrogen atoms and one molecule of CH2Cl2 are omitted for clarity. Selected bond lengths (Å) and angles (°): Pd1−B1=2.676(5), Pd1−C22=3.066(6), Pd1−P1=2.304(1), Pd1−P2=2.340(1), Pd1−C1=2.191(5), Pd1−C2a=2.186(12), Pd1−C2b=2.192(7), Pd1−C3=2.201(4), P1‐Pd1‐P2=102.86(5), P1‐Pd1‐B1=82.1(1), P2‐Pd1‐B1=75.1(1).24.</p><!><p>To explore the potential influence of the PdII→B interaction on reductive elimination proceeding via an inner‐sphere mechanism, complex [(PhDPBPh)PdII(4‐NO2‐C6H4)I] (5) was reacted with lithium amides. Complex 5 was reacted with LiNMe2 (1.1 equiv) at room temperature in [D8]THF (Scheme 3).25</p><!><p>Reductive elimination from 5 and independent synthesis of 11.</p><!><p>A conversion of 84 % was observed 31P NMR spectroscopically after 1 h. Two complexes were formed with singlet resonances at δ=31.1 (70 %) and 38.3 ppm (14 %). After a total of 4.5 h, all resonances in the 31P NMR spectrum disappeared in favor of the singlet at δ=31.1 ppm. 11B NMR spectroscopy suggested formation of a zero‐valent palladium complex by a broad resonance at δ=19 ppm (ω 1/2=400±100 Hz). The concurrent formation of the expected reductive elimination product N,N‐dimethyl‐4‐nitroaniline was confirmed by GC/MS analysis, using an independently prepared sample as a reference. The absence of an intermediate complex cis‐[(PhDPBPh)PdII(4‐NO2‐C6H4)NMe2] suggests that transmetalation is rate‐limiting in this transformation. The intermediate occurrence of the 31P NMR resonance at δ=38.3 ppm is possibly due to a reversible reaction of LiNMe2 with complex 6. In a control experiment complex [(PhDPBPh)Pd0(pyridine)] (1) was reacted with LiNCy2 and LiNMe2 in [D8]THF. In both cases ca. 7 % of a new complex at δ=38.5 (s) and 37.7 ppm (s) were observed.</p><p>Complex 6 decomposed within hours with simultaneous precipitation of palladium black. Addition of PMe3 as a stabilizing co‐ligand led to the formation of complex [(PhDPBPh)Pd0(PMe3)] 11. The 31P NMR spectrum of 11 showed a doublet at δ=35.3 and a triplet at −40.1 ppm (J=15.1 Hz) in a 2:1 ratio, which is consistent with the expected κ3P‐coordination. The broad resonance in the 11B NMR spectrum at δ=25 ppm (ω 1/2=400±100 Hz) suggested a strong Pd0→B interaction. Complex 11 could also be synthesized independently by reaction of PBP pincer 12 with PhLi and PMe3, or reaction of 1 with PMe3, thus confirming unambiguously the identity of 11 (Scheme 3).</p><p>Complex 5 reacted in a similar fashion with LiNCy2 (26 % 6 after 3 h) and LiNHtBu (14 % 6 after 5.5 h). However, the reaction proceeded slower with these sterically more demanding substrates. The reaction of complex 5 with LiNHtBu was monitored for 96 h by 31P NMR spectroscopy (46 % conversion towards 6) without any side products being observed (cf. Table S1). This is in line with the assumption of a rate‐determining transmetalation followed by a quick reductive elimination.</p><!><p>The solid‐state structures of Pd0/II DPB complexes were analyzed to identify factors which affect the strength of Pd→B interactions. In addition to the new Pd complexes presented in this work (6–10), the structurally characterized DPB complexes cis‐[(PhDPBPh)PdII(4‐NO2‐C6H4)I] (5),9d [(PhDPBPh)Pd0(pyridine)] (1),3b [(PhDPBMe)Pd0(PMe3)] (13)9d and [(CyDPBPh)Pd0] (3)3c (Figure 4) were included to cover a broad range of B‐/P‐substituents and co‐ligands at the Pd0/II center. The shorter Pd,B distances and higher degree of borane pyramidalization (Table 1) confirm a significantly stronger Pd,B interaction in Pd0 complexes, than in PdII complexes. Surprisingly, within a given oxidation state only a very moderate variation of the Pd→B bond strength is observed, regardless of substituents at the borane and phosphines, or the number and nature of co‐ligands (Pd0: ΣBα=338–346°, d(Pd0,B)=2.194(3) −2.243(2) Å vs. PdII: ΣBα=354–356°, d(PdII,B)=2.676(5) −2.762(2) Å). Remarkably, even the generation of cationic PdII complexes (9 and 10) has no significant impact on the strength of PdII→B interactions. The oxidation state at Pd is unambiguously the dominant factor for the strength of the Pd,B bond.</p><!><p>Experimental and computational analysis of the Pd→B interactions.[a]</p><p></p><p>7</p><p>8</p><p>9[e]</p><p>10‐endo</p><p>5</p><p>1</p><p>13</p><p>3</p><p>6</p><p>d(Pd,B) [Å] (XRD/DFT)</p><p>2.762(3)</p><p>2.740</p><p>–</p><p>−2.654</p><p>2.721(5)</p><p>2.554</p><p>2.676(5)</p><p>2.731</p><p>2.7402(4)</p><p>2.781</p><p>2.194(3)</p><p>2.193</p><p>2.278(3)</p><p>2.360</p><p>2.243(2)</p><p>2.264</p><p>–</p><p>−2.253</p><p>(Pd,Cipso) [Å] (XRD/DFT)</p><p>3.405(3)</p><p>3.256</p><p>–</p><p>−3.292</p><p>3.338(4)</p><p>3.112</p><p>3.066(6)</p><p>3.259</p><p>3.346(4)</p><p>3.440</p><p>2.463(3)</p><p>2.865</p><p>2.815(2)</p><p>2.685</p><p>3.079(2)</p><p>3.054</p><p>–</p><p>−2.768</p><p>ΣBα [°] (xrd/dft)</p><p>355/355</p><p>–/352</p><p>356/355</p><p>355/355</p><p>354/351</p><p>346/346</p><p>338/341</p><p>341/343</p><p>–/349</p><p>11B NMR (δ, ω 1/2)</p><p>–</p><p>–</p><p>65 ppm</p><p>1900 Hz</p><p>67 ppm</p><p>1400 Hz</p><p>63 ppm</p><p>3000 Hz</p><p>20 ppm</p><p>400 Hz</p><p>25 ppm</p><p>500 Hz</p><p>22 ppm</p><p>800 Hz</p><p>19 ppm</p><p>400 Hz</p><p>E 2(Pd,B)[b] [kcal/mol]</p><p>11.46</p><p>10.42</p><p>11.41</p><p>8.04</p><p>8.72</p><p>23.46</p><p>19.53</p><p>46.83</p><p>42.12</p><p>NLMO %B[c]/Pd[c]</p><p>6.6/91.9</p><p>6.3/92.2</p><p>5.4/92.9</p><p>3.7/93.9</p><p>4.7/93.4</p><p>16.0/78.7</p><p>15.0/81.5</p><p>15.5/81.7</p><p>14.3/83.0</p><p>occ. B[d]</p><p>0.391</p><p>0.387</p><p>0.400</p><p>0.360</p><p>0.353</p><p>0.618</p><p>0.621</p><p>0.498</p><p>0.519</p><p>occ. Pd[d]</p><p>1.859</p><p>1.865</p><p>1.870</p><p>1.887</p><p>1.879</p><p>1.666</p><p>1.702</p><p>1.686</p><p>1.704</p><p>B‐hybrid % (s/p)</p><p>7.6/92.4</p><p>7.2/2.7</p><p>7.2/92.7</p><p>6.7/93.3</p><p>6.4/93.6</p><p>11.6/88.4</p><p>13.9/86.1</p><p>12.8/87.2</p><p>10.7/89.3</p><p>WBI (Pd,B)</p><p>0.2164</p><p>0.2063</p><p>0.2119</p><p>0.1738</p><p>0.1801</p><p>0.4207</p><p>0.3634</p><p>0.5032</p><p>0.4604</p><p>WBI (Pd,Cipso)</p><p>0.0079</p><p>0.0079</p><p>0.0208</p><p>0.0093</p><p>0.0062</p><p>0.0697</p><p>0.0171</p><p>0.0103</p><p>0.0325</p><p>[a] Structure optimization: Turbomole 7.0.1, BP86/def‐SV(P); NBO analysis: Gaussian 09/NBO 6.0, BP86/6‐31G(d), MWB10 (P,Cl), MWB28 (Pd, Br), MWB46 (I). [b] NBO stabilizing energy E2 associated with the Pd→B interaction. [c] Contribution of the donor/acceptor NBO to the NLMO. [d] Occupancy of the donor/acceptor NBO. [e] Calculated structure parameters of 9 are based on the monomer.</p><!><p>The Pd→B interactions were further analyzed using QM calculations. Complexes 1, 3, 5–11 and 13 were geometrically optimized using Turbomole 7.0.1 (BP86/def‐SV(P)). A good agreement was observed between the optimized structures and their corresponding solid‐state structures (Table 1). Complexes 6 and 8 were constructed based on the solid‐state structure of complexes 1 and 7. The Pd→B interactions were further analyzed using NBO/NLMO calculations. In all cases, an NBO donor/acceptor interaction was found between an occupied d‐orbital at Pd and an unoccupied p‐orbital at B (Figure 5). For all examined complexes no relevant η2(B,C)‐coordination was found in the NBO calculations. The Wiberg bond index for Pd,Cipso was below 0.02, with the exception of Pd0 complexes 1 (0.0697) and 6 (0.0325). Reactivity studies of [(DPB)Pd]‐complexes presented in this paper thus appear to be unaffected from significant η2(B,C)‐coordination.</p><!><p>Graphical representation of the NLMOs associated with the Pd→B interactions in [(PhDPBPh)Pd(0/II)] complexes.</p><!><p>The NBO stabilizing energy of this Pd→B interaction varied depending on the Pd oxidation state. For PdII→B interactions, a narrow range of NBO stabilizing energies between 8.04 and 11.46 kcal mol−1 was observed. Surprisingly, generation of cationic complexes (9, 10‐endo), exchange of chloro‐ligands by bromide (8) or iodide/aryl (5) had very little effect. In the case of Pd0→B interactions, significantly higher NBO stabilizing energies of 19.53–46.83 kcal mol−1 were found. Regardless of the oxidation state at Pd an approximately linear correlation between the Pd,B distance and the NBO stabilizing energy (E 2) associated with the Pd,B interaction was observed (Figure 6) for 16 valence electron (VE) complexes 1, 5, 7, 8, 10 and 13. The Pd,B distance appears to be dictated by the Pd,B bond strength, and not by constraints imposed by the chelating ligand. Substitution of PPh2‐groups (6) by PCy2‐groups (3) had only a minor effect. The E 2 values for the Pd0→B interaction in the 14 VE complexes 3 (46.83 kcal mol−1) and 6 (42.12 kcal mol−1) significantly deviate from this correlation and are almost twice as much as for 16 VE complexes 1 (23.46 kcal mol−1) and 13 (19.53 kcal mol−1). Neither the 11B NMR chemical shift, Pd,B distance or pyramidalization at B indicate a change of the Pd0→B interaction strength in this magnitude between the 14 VE and the 16 VE complexes (Table 1). This discrepancy might be explained by the difficulty to compare the 2nd order perturbation interaction energies from NBO analysis from 14 VE with 16 VE complexes.</p><!><p>Left: correlation between solid state Pd,B distances and δ(11B). Right: correlation between calculated Pd,B distances and NBO stabilizing energies.</p><!><p>The 11B NMR resonances are shifted linearly towards higher field with an increasing Pd,B distance for Pd0 complexes, regardless of the valence electron count at the Pd center (Figure 6). Complex [(PhDPBPh)Pd0(PPh3)] (2) reported by Kameo and Bourissou3e also fits perfectly into this correlation (d(Pd,B)=2.294(2) Å, δ(11B) 27 ppm). In contrast, the 11B NMR resonance shifts linearly towards lower field with an increasing Pd,B distance in case of PdII complexes. 11B NMR spectroscopy therefore can be used as a tool to assess the strength of Pd→B interactions within a given ligand system, provided that the oxidation state at the Pd center is taken into account. However, given the difficulty to determine the precise δ(11B) of [(DPB)PdII] complexes (poor solubility and ω 1/2 >1000 Hz ), a certain error for weak PdII→B interactions needs to be factored in.26</p><p>Quantum chemical calculations (DFT) were used to model the inner‐sphere reductive elimination of N,N‐dimethyl‐4‐nitroaniline from complex 14‐B (Scheme 4). C−N bond formation is predicted to proceed via an inner sphere reductive elimination with a low activation barrier of ΔG ≠=+7.90 kcal mol−1 (transition state 15‐B), yielding Pd0 complex 6 and N,N‐dimethyl‐4‐nitroaniline (overall ΔG=−58.75 kcal mol−1). In order to understand how the PdII→B interaction affects the reductive elimination, the reaction was also modeled for bis[(2‐diphenylphosphino)phenyl]ether (DPEphos) complex 14‐O and diphosphinoamine complex 14‐N. DPEphos is well established as an effective ligand in palladium catalyzed Buchwald–Hartwig‐type coupling reactions,27 and commands very similar structural features to PhDPBPh (Table 2). However, DPEphos cannot mimic the potential steric effect of the B−Ph group on the coordinated reactive ligands. For this reason, the diphosphinoamine ligand (o‐PPh2C6H4)2NPh28 has also been included in the theoretical considerations, as its N‐Ph bridgehead gives a good model of the B‐Ph group in 14‐B. Elimination of N,N‐dimethyl‐4‐nitroaniline from complexes 14‐O and 14‐N gave very similar Gibbs free reaction energies of ΔG=−38.52 kcal mol−1 and ΔG=−38.63 kcal mol−1, respectively. No Pd0/II→E interactions were observed in complexes featuring DPEphos and the diphosphinoamine ligand (Table 2, WBI(Pd,E)=0.005, E=O, N). Given the high structural similarity of complexes 6, 16‐O and 16‐N the increase of ΔG by ca. 20 kcal mol−1 in case of the PhDPBPh ligand is a good approximation for the increase of the Pd0→B interaction strength in 6 compared to the PdII→B interaction strength in complex 14‐B. When switching from PhDBPPh to DPEphos, a small decrease of ΔΔG ≠=0.41 kcal mol−1 was found for the reductive elimination barrier (Scheme 4). This was surprising, as a more facile reductive elimination was expected from 14‐B than from 14‐O, due to 1) an electronic effect by Pd→B coordination and 2) increased steric bulk of the DPB ligand imposed by the B‐Ph group. In case of diphosphinoamine complex 14‐N the reductive elimination barrier decreased to ΔG ≠=5.54 kcal mol−1 (ΔΔG ≠=2.46 kcal mol−1), possibly as a result of the increased steric pressure imposed by the N‐Ph group (Table 2). Reductive elimination from 14‐E (E=B, O, N) proceeds via structurally early transition‐state 15‐E (Figure 7).</p><!><p>Reductive elimination of N,N‐dimethyl‐4‐nitroaniline from PEP complexes 14‐B, 14‐O and 14‐N.</p><p>Computational analysis of C−N bond formation from complexes 14‐B, 14‐O and 14‐N.[a]</p><p>E=B, O, N</p><p>14‐B</p><p>15‐B</p><p>6</p><p>14‐O</p><p>15‐O</p><p>16‐O</p><p>14‐N</p><p>15‐N</p><p>16‐N</p><p>d(Pd,E) [Å]</p><p>2.845</p><p>2.947</p><p>2.253</p><p>3.343</p><p>3.349</p><p>2.955</p><p>3.360</p><p>3.381</p><p>3.023</p><p>d(C,N) [Å]</p><p>2.904</p><p>2.084</p><p>–</p><p>2.816</p><p>2.077</p><p>–</p><p>2.801</p><p>2.068</p><p>–</p><p>d(Pd,C) [Å]</p><p>2.042</p><p>2.059</p><p>–</p><p>2.036</p><p>2.051</p><p>–</p><p>2.033</p><p>2.051</p><p>–</p><p>d(Pd,N) [Å]</p><p>2.102</p><p>2.108</p><p>–</p><p>2.091</p><p>2.102</p><p>–</p><p>2.089</p><p>2.100</p><p>–</p><p>∢(P,Pd,P) [°]</p><p>101.2</p><p>101.0</p><p>147.1</p><p>100.4</p><p>102.0</p><p>136.4</p><p>97.5</p><p>98.8</p><p>132.9</p><p>q(Pd) [b]</p><p>+0.376</p><p>+0.330</p><p>+0.055</p><p>+0.318</p><p>+0.275</p><p>−0.162</p><p>+0.320</p><p>+0.276</p><p>−0.123</p><p>q(E)[b]</p><p>+0.722</p><p>+0.735</p><p>+0.527</p><p>−0.498</p><p>−0.496</p><p>−0.485</p><p>−0.448</p><p>−0.448</p><p>−0.444</p><p>WBI(Pd,E)[c]</p><p>0.193</p><p>0.162</p><p>0.460</p><p>0.005</p><p>0.005</p><p>0.005</p><p>0.005</p><p>0.005</p><p>0.005</p><p>ΣBα [°]</p><p>355.4</p><p>354.6</p><p>348.8</p><p>–</p><p>–</p><p>–</p><p>–</p><p>–</p><p>–</p><p>[a] Structure optimization: Turbomole 7.0.1, BP86/def‐SV(P); NBO analysis: Gaussian 09/NBO 6.0, BP86/6‐31G(d), MWB10 (P), MWB28 (Pd). [b] Natural population analysis (NPA) charge. [c] Wiberg bond index.</p><p>Calculated intermediates of reductive elimination from 14‐B (top), 14‐O (middle) and 14‐N (bottom). For clarity the H atoms are omitted, and only the Cipso atoms of the Ph‐groups at B and P are shown. Red: NPA charges, blue: bond distances.</p><!><p>Unexpectedly, the Pd→B interaction is slightly weakened in transition‐state 15‐B, compared to starting complex 14‐B, as indicated by a slightly elongated Pd,B distance (2.947 Å) in 15‐B compared to 14‐B (2.906 Å). Similarly, the Wiberg bond index for the Pd→B interaction is reduced to 0.162 in 15‐B (14‐B: 0.176), and the NPA charge at the borane remains unchanged (14‐B: +0.737 vs. 15‐B: +0.735). The increase of the Pd→B interaction strength occurs after the reductive elimination, explaining why the inner‐sphere reductive elimination of the C−N bond does not kinetically profit from the substantial increase of the Pd→B strength in the course of the reaction.</p><p>To rule out effects originating from restraints imposed by a chelating ligand frame work, the reductive elimination of N,N‐dimethyl‐4‐nitroaniline was also modeled using cis‐[(PMe3)2PdII(4‐NO2C6H4)NMe2] (17, ΔG=37.47 kcal mol−1) and its BH3 adduct [(PMe3)2(BH3)PdII(4‐NO2C6H4)NMe2] (17‐B, ΔG=49.19 kcal mol−1) as substrates (cf. Scheme S1). Again, a more favorable transition state was found for the acceptor free complex 17 (ΔG ≠=+7.35 kcal mol−1), than for the borane adduct 17‐B (ΔG ≠=+8.55 kcal mol−1).</p><!><p>The strength of Pd→B interactions in [(DPB)Pd] complexes depends primarily on the oxidation state of Pd. In contrast, modifications of the DPB ligand or co‐ligands have only a minor effect. 11B NMR spectroscopy has been established as a useful tool to assess the strength of Pd→B interactions in solution. Reaction of lithium amides with [(PhDPBPh)PdII(4‐NO2C6H4)I] (5) chemoselectively yields the C‐N coupling product and [(PhDPBPh)Pd0] (6). Inner‐sphere reductive C−N bond elimination was modelled with DFT methods for the PhDPBPh ligand. In contrast to reports on acceptor promoted outer‐sphere reductive C−N bond elimination,5b, 17 no significant effect of the borane acceptor on the inner‐sphere reductive elimination rate was found. This is explained by the fact that the strengthening of the Pd→B bond occurs after the reductive elimination.</p><!><p>All manipulations were performed under an argon atmosphere using standard Schlenk line and glovebox techniques. Glassware was oven dried at 120 °C overnight and dried with a heat gun under vacuum prior to use. Tetrahydrofuran was dried by an MBraun solvent purification system. Benzene and n‐hexane were dried over sodium, distilled under argon prior to use and stored over activated molecular sieves (4 Å).</p><p>CD2Cl2 and C6D6 were degassed employing the freeze‐pump‐thaw technique and stored over activated molecular sieves (4 Å). [D8]THF was dried over activated molecular sieves (3 Å), distilled under an argon atmosphere and degassed employing the freeze‐pump‐thaw technique. PhDPBPh, [(PhDPBPhOAc)Pd(C3H5)] (4), [(PhDPBPh)Pd(4‐NO2C6H4)I] (5) and [{(o‐PPh2C6H4)2BPh}PdI] (12) were synthesized according to published procedures.3b, 9d</p><p>NMR‐experiments were performed in Wilmad® quick pressure valve NMR tubes. 1H, 11B{1H}, 13C{1H}, 19F{1H}, and 31P{1H} NMR spectra were recorded on a Bruker Avance II (400.1 MHz, probe: BBO) or a Bruker Avance (400.3 MHz, probe: ATM BBFO) spectrometer. 1H and 13C{1H} NMR spectra were referenced to residual solvent resonances as implemented in MesReNova 10.0.2. Infrared spectra were recorded on an Avatar 360 FT‐IR E.S.P. device by Nicolet. CHN combustion analysis were carried out on an Elementar EL device by Elementar Analysesysteme GmbH.</p><p>Deposition Number(s) 1987620 (7), 1987625 (9) and 1987626 (10) contain(s) the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service www.ccdc.cam.ac.uk/structures.</p><!><p>A solution of the respective lithium amide (5.7 μmol, 1.1 equiv) in [D8]THF (0.25 mL) was added dropwise over a period of 4 min to a stirred solution of nitroarene complex 5 (5.0 mg, 5.2 μmol, 1.0 equiv) in [D8]THF (0.25 mL). The resulting mixture was stirred for another 5 min and then transferred into an NMR tube. Reductive elimination was monitored by 31P NMR spectroscopy.</p><!><p>CH2Cl2 (8 mL) was added to a mixture of PhDPBPh (400 mg, 0.665 mmol, 1.0 equiv) and [(cod)PdCl2] (187 mg, 0.665 mmol, 1.0 equiv). The mixture was stirred for 30 min at room temperature. Yellow crystals (380 mg, 0.482 mmol, 74 %) were formed by overlaying the solution n‐pentane (16 mL). Single crystals suitable for X‐ray diffraction were grown from a solution of [(cod)PdCl2] (9.7 mg, 34 μmol, 1.0 equiv) and PhDPBPh (21.2 mg, 34.7 μmol, 1.0 equiv) in CD2Cl2 (0.7 mL) overlaid with benzene (0.3 mL). 11B and 13C NMR data have not been collected due to poor solubility. 1H NMR (400.13 MHz, CD2Cl2, 25 °C): δ 7.81–7.76 (m, 2 H), 7.55 (tdd, J=7.3, 3.0, 1.1 Hz, 3 H), 7.50–7.46 (m, 3 H), 7.46–7.38 (m, 6 H), 7.35–7.14 (m, 13 H), 6.97–6.78 (m, 5 H), 5.32 (s, 2 H, CH2Cl2). 31P{1H} NMR (161.98 MHz, CD2Cl2, 26 °C): δ 44.5 (s, w1/2=570 Hz). IR (KBr): ν˜ =3643‐3284 (w), 3049 (w), 1587 (w), 1497 (m), 1433 (vs., sh), 1223 (s), 1158 (vw), 1128 (w), 1093 (vs.), 987 (w), 889 (vw), 864 (vw), 754 (s), 744 (s), 733 (m), 688 (vs.), 667 (w), 611 (m), 600 (s), 542 (m), 523 (vs.), 505 (m) cm−1. Elemental analysis calcd (%) for C42H33BCl2P2Pd⋅CH2Cl2: C 59.18, H 4.04, found: C 59.61, H 4.33.</p><!><p>The PhDPBPh ligand (200 mg, 0.328 mmol, 1.0 equiv) and [(cod)PdBr2] (122.7 mg, 0.328 mmol, 1.0 equiv) were solved in DCM (10 mL) and stirred at r.t. for 30 min. The solution was overlaid with n‐hexane (20 mL) yielding title compound 8 as orange crystals (192.0 mg, 0.219 mmol, 67 %). 11B and 13C NMR data have not been collected due to poor solubility. 1H NMR (400.30 MHz, CD2Cl2): δ 7.85–7.76 (m, 3 H), 7.59–7.19 (m, 30 H). 31P{1H} NMR (162.04 MHz, CD2Cl2): δ 45.2 (bs, 1P, w1/2=450 Hz), 38.1 (bs, 1P, w1/2=450 Hz). IR (KBr): ν˜ =3424 (s), 3048 (m), 1621 (w), 1587 (w), 1478 (m), 1455 (w), 1432 (s), 1311 (w), 1237 (w), 1220 (s), 1205 (m), 1187 (m), 1153 (w), 1126 (m), 1092 (s), 1027 (w), 1000 (m), 887 (w), 863 (w), 753 (s), 741 (s), 713 (m), 699 (s), 690 (s), 667 (m), 610 (s), 600 (s), 539 (s), 522 (s), 505 (s), 465 (m) cm−1. Elemental analysis calcd (%) for C42H33BBr2P2Pd⋅0.25CH2Cl2: C 56.51; H 3.76, found: C 56.72, H 3.83.</p><!><p>Complex 7 (200 mg, 254 μmol, 1.0 equiv) and AgSbF6 (87.2 mg, 254 μmol, 1.0 equiv) were stirred in DCM (15 mL) for 40 minutes. The suspension was filtered through a syringe filter (0.2 μm, PTFE membrane). The clear solution was overlaid with n‐hexane (30 mL) yielding the title compound 9 as long colorless needles (128 mg 130 μmol, 51 %). 1H NMR (400.30 MHz, CD2Cl2): δ 7.97–7.92 (m, 2 H), 7.80 (tdd, J=7.5, 2.8, 0.9 Hz, 2 H), 7.69 (dd, J=7.6, 2.6 Hz, 2 H), 7.65 (t, J=7.5 Hz, 2 H), 7.55 (tt, J=7.4, 1.4 Hz, 1 H), 7.47–7.34 (m, 6 H), 7.27–7.16 (m, 10 H), 7.00 (dt, J=7.6, 2.4 Hz, 4 H), 6.83 (dd, J=12.4, 7.9 Hz, 4 H). 11B{1H} NMR (128.43 MHz, CD2Cl2): δ=65 (bs, w1/2=1900±300 Hz). 13C{1H} NMR (100.67 MHz, CD2Cl2): δ=δ 141.79, 135.43 (d, J=8.5 Hz), 134.88 (d, J=11.1 ‐Hz), 134.25, 133.69 (d, J=19.5 Hz), 133.22 (d, J=17.4 Hz), 132.49 (d, J=3.7 Hz), 129.67 (d, J=8.9 Hz), 129.33–128.82 (m), 128.10, 127.13, 126.74, 126.16. 31P{1H} NMR (162.04 MHz, CD2Cl2): δ 49.9 (s, w1/2=30 Hz). IR (KBr): ν˜ =3441 (s), 3058 (w), 1588 (w), 1482 (w), 1435 (s), 1230 (m), 1200 (w), 1125 (w), 1034 (m), 1001 (w), 867 (vw), 752 (s), 702 (s), 692 (s), 659 (vs.), 614 (m), 538 (s), 517 (s), 697 (w) cm−1. Elemental analysis calcd (%) for C42H33BClF6P2PdSb⋅0.25 C6H14: C 51.75, H 3.64, found: C 51.77, H 3.785.</p><!><p>Allyl complex 4 (120 mg, 143 μmol, 1.0 equiv) and AgSbF6 (49.0 mg, 143 μmol, 1.0 equiv) were solved in CH2Cl2 (7 mL) and stirred at r.t. for 20 min. The suspension was filtered through a syringe filter (0.2 μm, PTFE membrane). The clear solution was overlaid with n‐hexane (10 mL). The obtained crystals showed insufficient purity and were crystallized again under the same conditions yielding 10 as slightly yellow crystals (50.2 mg, 53.8 μmol, 38 %). 1H NMR (400.30 MHz, CD2Cl2): δ 7.72–7.59 (m, 4 H), 7.58–7.53 (m, 2 H), 7.53–7.44 (m, 13 H), 7.43–7.29 (m, 6 H), 7.23–7.15 (m, 2 H), 7.05–6.87 (m, 5.5 H), 6.78–6.67 (bs, 2 H), 5.88–5.70 (bs, 0.7 H), 3.77–3.61 (bs, 1.3 H), 3.59–3.33 (bs, 1.3 H), 3.03–2.85 (bs, 0.9 H), 2.49–2.29 (bs, 1.2 H) (fractional integrals are a result from signal splitting caused by a dynamic process). 11B{1H} NMR (128.38 MHz, CD2Cl2): δ 64 (bs, w1/2=1550±50 Hz). 13C{1H} NMR (100.67 MHz, CD2Cl2): δ 141.1, 140.2, 136.1, 135.5, 135.3, 135.0, 134.4, 134.3, 134.0, 133.2 (t, J=5.8 Hz), 132.3, 132.2, 132.1, 131.6, 131.5, 131.2, 131.0, 129.6 (t, J=5.3 Hz), 129.3, 128.9, 123.1, 80.4, 80.2. 31P{1H} NMR (162.04 MHz, CD2Cl2): δ 28.1 (s, 0.6P), 26.9 (s, 0.4P). IR (KBr): ν˜ =3430 (s), 3000 (m), 1588 (m), 1480 (m), 1458 (w), 1434 (s), 1268 (m), 1227 (s), 1127 (m), 1095 (m), 1031 (w), 999 (w), 950 (vw), 875 (w), 772 (w), 754 (m), 742 (m), 733 (m), 695 (s), 659 (vs.), 609 (s), 537 (m), 521 (s), 478 (w), 430 (w) cm−1. Elemental analysis calcd (%) for C46H40BCl2F6P2PdSb: C 51.22, H 3.74, found: C 51.04, H, 3.86.</p><!><p>A solution of LiNMe2⋅THF (0.7 mg, 6 μmol, 1.1 equiv) in [D8]THF (0.25 mL) was added over a period of 3 min to a solution of complex 5 (5.0 mg, 5 μmol, 1 equiv) in [D8]THF (0.25 mL). The combined solutions were transferred to an NMR tube and NMR spectra were recorded after 1.5 and 4.5 h. 11B{1H} NMR (128.38 MHz, [D8]THF): δ 19 (bs, w1/2=550 Hz±50 Hz). 31P{1H} NMR (162.04 MHz, [D8]THF): δ 30.93 (s).</p><!><p>A solution of PhLi (3.2 mg, 38 μmol, 1.2 equiv) in THF (0.5 mL) was slowly added to a solution of complex 12 (25 mg, 33 μmol, 1.0 equiv) in THF (0.5 mL). After stirring for 10 min at r.t. a solution of PMe3 in toluene (1.0 m, 50 μL, 50 μmol, 1.5 equiv) was added. The precipitate was removed by filtration and the solution was concentrated in vacuo. The resulting solid was washed with pentane and dried in vacuo (20.7 mg, 26.1 μmol, 79 %). 1H NMR (400.13 MHz, C6D6): δ 8.34 (d, 2 H, J=7.8 Hz), 7.69–7.58 (m, 4 H), 7.44–7.37 (m, 2 H), 7.36–7.28 (m, 4 H, Ar‐H), 7.12 (t, 2 H, J=6.7 Hz), 7.09–7.05 (m, 13 H), 6.85 (m, 2 H), 6.68 (pt, 4 H, J=7.8 Hz), 0.64 (d, 2 J P‐H=5.0 Hz, 9 H, PMe3). 11B{1H} NMR (128.38 MHz, C6D6): δ 25 (bs, w1/2=740 Hz ±50 Hz). 13C{1H} NMR (100.62 MHz, C6D6): δ 168.7 (bs), 143.2 (d, J=16.3 Hz), 143.0 (d, J=16.3 Hz), 141.5 (td, J=15.2, 2.0 Hz), 138.9 (t, J=13.5 Hz), 135.8 (t, J=6.4 Hz), 135.7 (t, J=2.7 Hz), 133.5 (t, J=7.7 Hz), 133.0 (dt, J=16.7, 5.0 Hz), 132.3 (s), 132.3 (s), 132.4 (t, J=6.7 Hz), 129.5 (s), 129.0 (s), 128.6 (s), 127.2 (s), 126.1 (t, J=2.8 Hz), 125.2 (s), 18.1 (dt, J=11.8, 2.2 Hz, PMe3). 31P{1H} NMR (162.04 MHz, C6D6): δ 35.44 (d, 2 J P‐P=14.1 Hz, 2P, ArPPh2), −40.13 (t, 2 J P‐P=14.2 Hz, 1P, PMe3).</p><!><p>The authors declare no conflict of interest.</p><!><p>As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.</p><p>Supplementary</p><p>Click here for additional data file.</p>

PubMed Open Access

COMPUTATIONAL PREDICTION OF THE SUPRAMOLECULAR SELF-ASSEMBLING PROPERTIES OF ORGANIC MOLECULES: FLEXIBILITY vs RIGIDITY A PREPRINT

Two families of organic molecules with different backbones have been considered. The first family, composed by a substituted central phenyl is considered as flexible. The second one, based on a macrolactam-like unit, is considered as rigid. They have however a common feature, three amide moieties (as substituents for the phenyl and within the cycle for the macrolactam-like molecule) that allow hydrogen bonding when molecules are stacked. In this study we propose a computational protocol to unravel the ability of the different families to self-assemble into organic nanotubes. Starting from the monomer and going towards larger assemblies like dimers, trimers, and pentamers we applied different theoretical protocols to rationalize the behavior of the different assemblies. Both structures and thermodynamics were investigated to give a complete picture of the process. Thanks to the combination of a quantum mechanics approach and molecular dynamics simulations along with the use of tailored tools (non covalent interaction visualization) and techniques (umbrella sampling), we have been able to differentiate the two families and highlight the best candidate for self-assembling purposes.

computational_prediction_of_the_supramolecular_self-assembling_properties_of_organic_molecules:_flex

Introduction<!>Single point calculations<!>Non-covalent interactions<!>Classical Molecular Dynamics<!>Umbrella sampling<!>Monomers<!>Qualitative approach<!>Quantitative approach: Umbrella Sampling<!>Pentamers<!>Aggregation mode<!>One long simulation<!>Many short simulations<!>Conclusion<!>Supporting Information

<p>Supramolecular chemistry is a key concept for many edifices or mechanisms that are essential for life [1].</p><p>So it is for some applications going from material sciences to medicine through information storage [2,3,4,5,6]. It has been defined by Lehn, one of its founders, as the chemistry of intermolecular bonds and interactions [7]. Supramolecular chemistry involves a wide variety of weak interactions of different strength.</p><p>Metal-ligand interactions are the strongest one with an interaction energy of about 80 kcal.mol −1 . Ionic, ion-dipole and dipole-dipole interactions are slightly less stabilizing with an interaction energy ranging from 15 to 50 kcal.mol −1 . Aromatic interactions, encompassing π-π, π-cation and π-hydrogen bond, lead to a bounding of 3 kcal.mol −1 . Finally, hydrogen bonds and van der Waals forces are the less energetic ones with interactions energies of 1-10 kcal.mol −1 and lower than 1 kcal.mol −1 , respectively. As the previously mentioned interactions are non-covalent, it means that the interactions can be reversible and thus confer a kind of flexibility to the system. Nevertheless, increasing the number or combining weak interactions can lead to really stable assemblies and one can cite for example the structure of ice, DNA [8] or the synthetic supramolecular polymer, namely the nylon [9]. In both cases, hydrogen bonds (H-Bonds) are at the origin of the large stability of the supramolecular assemblies (SMA). The association and dissociation processes within large SMA is ensured by the inter-and intramolecular H-Bonds, the best example being the secondary and tertiary structures of proteins that are dictated by those weak interactions [10,11,12]. In particular, H-Bonds based on an amide pattern (N-H ••• O=) are frequently found. The low to medium strength of this type of H-Bond, around 2 kcal.mol−1, along with its flexibility, allow SMA to assemble and disassemble easily. One of the specificity of the amide-based H-Bonds is their directionality. It has been used to build SMA that are characterized by a stacking of the molecules leading to a one-dimensional columnar SMA [13,14]. Many studies have reported SMA structures where the building block was a substituted benzene with various number of amide moieties for the formation of organogels or liquid crystals [15,16,17,18]. The number and the orientation of the amide moieties have been shown to be crucial for the effectiveness of the formation of the stacking [13].</p><p>Based on the idea of an amide-substituted benzene for the formation of columnar stacking, we have designed four benzenetricarboxamide molecules (B4s family, Figure 1). Amide moieties are perpendicular to the plane of the benzene. A difference in the orientation of the three amide moieties was observed for B4s family, while two of them were oriented in the same direction, the last one was found antiparallel (see Figure S3 in ESI †).</p><p>The remaining positions of the central benzene were substituted by -CH 3 (B4, B4c, B4p) or -I (B4pI) groups to maintain the amide moieties perpendicular to the plane of the molecule. In addition to the substitution of the central benzene, amides were also substituted by -C 6 H 13 alkane chain (B4), -C 6 H 11 cyclic group (B4c), and CH 2 -Ph moiety for both B4p and B4pI. A different backbone, based on a macrolactam-like unit, has been considered (B9s family, Figure 1). It also encompassed three amide moieties that are conjugated to an alcene to avoid their rotation. Two substitution patterns have been added, a -CH 3 for B9m and a -CH 2 OCOCH 3 for B9. The global idea is to compare those two apparently C3-symmetric backbones for an efficient formation of columnar stacking involving amide-based H-Bonds.</p><p>To address this challenge we propose a computational approach. Recent studies have already shown that a fully theoretical approach, based on molecular dynamics simulations and quantum chemical calculations, was able to unravel hydrogen bond interactions between organic molecules within supramolecular assemblies [19].</p><p>Hence, the multiscale approach that will be developed in this study will address for the different small organic molecules (See Figure 1) their self-assembling property and the stability of the possible columnar stackings, considered as organic nanotubes. The multiscale approach will involve calculations based on quantum mechanics but also classical molecular dynamics simulations combined with tailored tools to characterize the H-Bonds. Umbrella sampling is particularly relevant for the analysis of supramolecular interactions as it allows the calculation of a binding energy through the derivation of a potential of mean force [20,21,22,23].</p><p>Non-covalent interactions (NCI) visualization is also a valuable tool that can illustrate the underlying weak interactions such as hydrogen bonds or van der Waals interactions within SMA [24,25].</p><!><p>All the calculations have been performed using Gaussian16 package [26] within the density functional theory (DFT) framework. We used the ωB97X-D range-separated hybrid exchange correlation functional (XCF) combined with the 6-311+G(d,p) atomic basis set [27]. This XCF is known as one of the most efficient to consider structures and energies of assemblies involving hydrogen bond interactions [28]. The solvent (water) was modeled using an implicit solvation model, namely the polarizable continuum model (PCM) [29]. In order to describe large assemblies, we relied on a QM/QM' ONIOM approach. Within this model it is necessary to define 2 subsystems. The model system, composed of the central phenyl and the amide groups for B4s and the macrolactam-like unit for B9s, will be described at both the high level of theory (ωB97X-D/6-311+G(d,p)) and the low level (HF/3-21G(d)) of theory. The real system, encompassing the model system and the lateral groups of both families, will only be treated at the low level of theory. A charge embedding framework was also added to the hybrid QM/QM' ONIOM scheme [30].</p><!><p>It has been possible to visualize non-covalent interactions (NCI) through the use of NCIPLOT code [31]. NCI analysis gives rise to an index that is based on the calculated electronic density and its reduced gradient, represented as a two-dimensional plot. For a given system, there will be a drastic change in the reduced density gradient (RDG) between the atoms that are interacting, leading to density critical points. The latter can be represented on the molecular structure as an isosurface to indicate the region where a weak interaction is occuring. Nevertheless, both attractive (H-Bonds, van der Waals) and repulsive (steric repulsion) interactions can be spotted thanks to this index. By looking at the second derivative of the density and to the sign of its eigenvalue it is possible to distinguish attractive and repulsive interactions. Hence the density and the sign of the eigenvalue of the density second derivative give information about the strength and the type of interaction respectively and one can visualize them via isosurfaces. The electronic density used to compute the NCI index is the one calculated at the same level of theory as the one presented in Section 2.1.1.</p><!><p>Molecular dynamics (MD) simulations were run with the generalized AMBER force field (GAFF) [32] within GROMACS 2018.3 package [33]. As GAFF is not directly implemented in GROMACS, we used acpypi script [34,35,36] to convert the files from AMBER to GROMACS formalism. For each molecule, the atomic charges were derived following the parametrization procedure in GAFF, that is to say using HF/6-31G(d) RESP charges. The validity of the force field was checked by comparing the structures obtained after an optimization process in vacuum with GAFF and with DFT at the ωB97X-D/6-311+G(d,p) level. Results are provided in the Supporting Information section.</p><p>During the simulations, the system is composed by n organic molecules (n being equal to 1, 2, 3, 5, and 10 and the organic molecules being either from B4s or B9s families). The general philosophy of our molecular dynamics simulations is represented schematically on Figure 2. The size of the simulation box and the number of water molecules depend on the system under investigation. All those information for each system under investigation are gathered in Supporting Information.</p><p>To describe the electrostatic interactions, periodic boundary conditions were imposed along with a cut-off of 10 Å and the use of the Particle Mesh Ewald (PME) method [37,38]. Following a steepest descents minimization, each system was equilibrated in two steps. For the first step, a simulation in the canonical ensemble (NVT) during 100 ps was carried out. The temperature was set to 310 K using the Berendsen weak coupling method [39]. Organic molecules and solvent were coupled to separate temperature coupling baths.</p><p>For the following second step, simulation under constant pressure (NPT) was performed. To maintain an isotropic pressure of 1 bar, we relied on the previously mentioned weak-coupling Berendsen method. The production phase was then carried out in the same NPT ensemble. Temperature was controlled thanks to the Nosé-Hoover thermostat while the isotropic character of the pressure was maintained via the Parinello-Rahman barostat. Combining this thermostat and barostat ensures the presence of a true NPT ensemble. Simulation time, if not explicitly precised, was set to 10 ns.</p><!><p>Within the umbrella sampling (US) approach we have considered dimers, trimers and pentamers. The systems were placed in a rectangular box that can allow a pulling simulation (e.g a simulation box that is too small will lead to an interaction with the periodic images). In particular, the z length had to be large enough in order to satisfy the minimum image convention. The solvent molecules (water), were described through the TIP3P model. The first step consisted in an NPT equilibration of 100 ps, as it was described above. For the proper pulling simulation, restraints were applied to one of the monomers for dimers, to a dimer for trimers and to a tetramer in the case of the pentamer. The molecules that were restrained were thus considered as immobile references. The molecule that was not restrained was then pulled away from the immobile one, along the z-axis over 500 ps at a rate of 0.1 nm.ps −1 with a spring constant of 250 kJ.mol −1 nm −2 . The final COM (center of mass) distance between the two considered assemblies that was obtained was 4 nm. Snapshots were extracted from this pulling simulation in order to be as many starting points for the different umbrella sampling windows. For COM distances under 1 nm, a separation of 0.05 nm was considered between each window and then, for COM distances above 1 nm and up to 2.5 nm, the spacing between the windows was 0.1 nm. An example of the corresponding histogram is provided in Supporting Information. It allows a smoother and more accurate description of the interaction at small COM distances. This approach leads to around 25 windows.</p><p>For each window, a 10 ns simulation was performed, resulting in a total simulation time of 250 ns for the US approach, for each assembly of each molecule. The analysis of the results was done using the Weight Histogram Analyzis Method (WHAM), implemented in the GROMACS 2018.3 package [40]. A schematic representation of the umbrella sampling approach is provided in Figure 3.</p><p>3 Results and discussion</p><!><p>Key structural parameters (see Figure 1 for their definitions) have been selected to study the different monomers.</p><p>For both B4s and B9s families, three dihedral angles were defined (α, β , and γ), illustrating the relative position of the amide groups with respect to the plane of the molecule. We followed their evolution along the 10 ns MD simulations. The average values are reported in Table 1.</p><p>Table 1: Average values of the selected dihedral angles (in degrees) defined on Figure 1 along with the RMSD value (in Å) for each of the molecules. Standard deviations are provided in parentheses.</p><p>-90 ( 13) 91 ( 14) 88 ( 13) 2.33 (0.48) B4c</p><p>-90 ( 13) 89 ( 14) 89 ( 13) 2.08 (0.48) B4p</p><p>-89 ( 13) 90 ( 13) 90 ( 13) 3.07 (0.56) B4pI</p><p>-88 ( 14) 90 ( 12) 89 (13) 3.12 (0.51) B9 95 ( 7) 95 ( 6) 96 (8) 0.44 (0.12) B9m 83 ( 7) 83 ( 7) 84 (8) 0.80 (0.13)</p><p>For the B4s family, as previously mentioned, all the amide moieties are not oriented in the same direction. As illustrated by α that is negative, there is one amide moiety that is antiparallel to the two other ones for B4s.</p><p>This feature will thus induce a difference for the stacking behavior of the next steps. with perfectly perpendicular amide groups. One has to notice that for the RMSD calculation, the hydrogen atoms were not considered. The results are gathered in Figure 4 and Table 1. this family (0.44 and 0.80 Å for B9 and B9m respectively). Fluctuations are also small with deviations equal to 0.12 and 0.13 Å for B9 and B9m respectively. On the other hand, for B4s molecules, the average RMSD is ranging from 2.08 Å to 3.12 Å for B4c and B4pI respectively, indicating quite large structural modifications during the simulation. Standard deviations confirm this trend with values around 0.50 Å for the four molecules of B4s family. Studying the monomers of the different families has highlighted the fact that one family can be considered as rigid (B9s) while the other one appears to be more flexible (B4s). This feature may have an impact if one wants to build larger supramolecular assemblies as it will ease, or not, the formation of hydrogen bonds. The next sections are dedicated to the comparison of the two families for the formation and the stabilities of different SMA.</p><!><p>To study the possible self-assembling behavior of the different molecules, we decided to consider the smallest and thus simplest supramolecular entity, namely a dimer. The interaction of the two monomers is ensured by the amide moieties. Indeed it is possible to form a hydrogen bond between the N-H part of the amide of one molecule and the C=O bond of another amide. As previously observed during the study of the monomers, the amide groups are perpendicular to the plan of the molecule and it is thus possible to stack monomers via a network of three hydrogen bonds. The dimers we built are represented on Figure 5. 2.</p><p>By looking at the average value of d along the MD simulations it is already possible to have a qualitative idea of the stability of the different assemblies. If all the dimers involving monomers from the B4s family are stable, it is not the case for the B9s one. Indeed, the d values that are reported in Table 2 illustrate the fact that the two monomers involved within a dimer are remaining close to each other. If we go deeper in the analysis, we observe that the average value of d, ranging from 3.77 Å to 4.23 Å for B4s, is close to the crystalline characteristic value (4.8 Å) for such a stacking involving amide-based H-Bonds [41,42,43]. One has to notice that the standard deviations presented in Table 2, around 0.2-0.3 Å, illustrate the fact that the dimers are not "broken" along the simulation. The same conclusion can be drawn for B9m with an average value of 3.82 Å.</p><p>As the dimer involving B9 monomers were not stable, it was not relevant to measure d along the simulation.</p><p>One has to notice that if h 1 , h 2 , and h 3 are equivalent for B9s family, they are not for B4s dimers. Because of the asymmetry in the orientation of the amide moieties, only h 2 and h 3 are equivalent while h 1 is unique. 3) -a For the calculation of the average value and the associated standard deviation, only the areas highlighted on Figure 6 are considered. The same procedure is used for the other molecules, averages are made when the bonds are effective during the corresponding time.</p><p>Looking at those different values gives insights into the interactions involved in the stability of the dimers. We have represented on Figure 6 the evolution of h i distances for B4. It is clear from Figure 6(a) that the hydrogen bond h 1 is always effective during the 10 ns simulation. Moreover, with an average value of 1.99±0.24 Å this hydrogen bond is within the range (1.5-3.5 Å) of a hydrogen bond with a medium strength (4 to 15 kcal.mol−1). The same conclusion can be drawn for all the dimers of the B4s</p><p>family. The analysis is quite trickier for h 2 and h 3 . As represented on Figure 6b and c, those H-Bonds are not always effective. The area that are colored represent the moment where h 2 and h 3 distances are within the 1.5-3.5 Å range. The time corresponding to the colored area are given in Table 2. If h 2 is effective during almost the 10 ns simulation (8.4 ns), it is not the case for h 3 , that is only effective transiently for 3.1 ns.</p><p>Nevertheless, the dimer of B4 is stable and the stacking is maintained during all the simulation (d distance gives this information). By looking closer to the structure along the trajectory it has been possible to highlight a particular structure (Figure 5(c)). During the simulation the two monomers do not stay aligned. There is a tilt of one monomer with respect to the other one, inducing an hybrid hydrogen bonding. The keto part of one amide points between two amino moieties of two other amides of the other monomer. One expected H-bond is thus presents (h 2 or h 3 ), even if it is longer than a perfectly aligned H-bond, while the other one is new. We have thus introduced two new H-bonds, namely h' and h" (Figure 5(c)). Their evolutions are represented on Figure 6d and e. One can observe that either h' or h" is effective during the simulation. Their average values along with their effective time are gathered in Table 2. It is possible to say that the H-Bonds complement each other. When h 2 is not effective, one can observe that h' is. When h 3 is not effective, h" is taking over. We decided to combine those four H-Bonds on the same graphics (Figure 6(e)). It appears that there is always at least one but most of the time two hydrogen bonds (h 2 , h 3 , h', or h") that is present within the dimer, ensuring its stability along with h 1 . The Boltzmann population ratio for the normal (with h 1 , h 2 and h 3 ) and the hybrid (with h 1 and h 2 /h" or h 3 /h') dimer (see Figure S4 in ESI †) is always around 40/60 for all the dimers of the B4s family. Indicating that the hybrid dimer is more favorable than the normal one. For the other dimers of the B4s family, the same behavior is observed with a compensation of h 2 and h 3 by h' and h" to ensure the global stability of the dimers. For B9m dimer, h 1 , h 2 , and h 3 are effective during all the trajectory but no hybrid H-Bond can be observed as there is no sufficient flexibility for the amide moieties.</p><p>To summarize the findings about dimers, we have been able to highlight (i) the (non-)stability of B4s family and B9m (B9), (ii) the effectiveness of the hydrogen bond network to build supramolecular assemblies and (iii) an hybrid H-bond pattern allowed by the flexibility and the possibility for B4s dimers to orient their amide moieties.</p><!><p>To go further in assessing the stability of the different dimers, we have undertaken US simulations. It allows us to retrieve the binding free energy, ∆G, along a reaction coordinate, x, that represents the preferential direction for the stacking pattern. Using around 25 sampling windows along this axis, one can construct a one-dimensional potential of mean force (PMF) profile for each system under study, leading to a binding energy, E bind . US simulations for larger assemblies, namely a trimer and a pentamer, were also performed.</p><p>For each PMF profile, the minimum energy is associated to a particular distance, d com , that can be roughly compared to the d distance discussed in the previous section as it is the distance between the center of masses of the different assemblies. All those values, E bind and d com , for each system and for each molecule, are gathered in Table 3. For the following discussion, the comparison and evolution of E bind will be made on the absolute value. The question that motivated the new simulations involving trimers and pentamers is the following: How is the binding energy evolving when the supramolecular assembly is getting larger? More specifically, is it getting harder to add a monomer to a dimer, a tetramer? The answer to this question will help us to understand the self-supramolecular assembling behavior of B4s and B9s into larger assemblies. The same umbrella sampling approach presented before was used to study first the interaction within a trimer. Two subsystems were considered, a dimer and a monomer and we were looking for the binding energy of the monomer with the dimer. For the pentamer, the binding energy is computed for the interaction of a tetramer and a monomer.</p><p>The PMF profile for trimers and pentamers, for each molecule, is provided on three possible different behavior are observed, a slight increase of E bind for B4 (+4) and B4pI (+2), a decrease for B4c (-7) and no evolution for B4. One can nevertheless notice that the formation of trimers still remain favorable in all the cases. For B9 and B9m, E bind is slightly increasing (+2) and decreasing (-6) respectively.</p><p>In conclusion, adding a monomer to an already formed dimer is more favorable for B4s than for B9s. Trimers of B4s appear even more stable than dimers of B9s. Going further and considering pentamers for both families leads to a unique conclusion. The binding energy is always decreasing when compared to the energies obtained for dimers and trimers. Nevertheless, the values obtained for B4s (15, 12, 8, and 22 kcal.mol −1 for B4, B4c, B4p, and B4pI respectvively) are still higher than the ones obtained for B9s (7 and 4 kcal.mol −1 for B9 and B9m respectively), indicating that adding a monomer to a small oligomer of B9s family will be less favorable than for the B4s family.</p><!><p>Classical molecular dynamics simulations involving pentamers have been performed for each of the molecules.</p><p>Two behaviors were observed for the two families. For B9s, almost no H-bond interactions were maintained throughout the trajectory leading to a non-stability for both of the assemblies. Though, dimers were observed ponctually (see Figure S8 and S9 in ESI †). The conclusions that were drawn in the previous sections are thus confirmed with (1) a low but still possible stability for dimers and (2) an unfavorable binding energy for systems involving a large number of monomers. For B4s family the conclusions are different with respect to B9s. Indeed, for the entire B4s family, the pentamers appeared as stable along the trajectory (Figure 8 for B4p and Figure S5, S6, and S7 in ESI † for B4, B4c, B4pI respectively). The H-Bond network is at the origin of this stability. As for the dimers, h 1 is always effective and can thus be considered as the backbone of the entire supramolecular assembly. Within the pentamer, amide moieties are no longer perpendicular to the plane of the molecules, leading on average to a tilt of 15 • (8(a)). The fact that the amide moieties are quite flexible also allows the formation of the previously mentioned hybrid bonds. During the simulation, there is an alternation of h 2 /h' and h 3 /h" bonds (Figure 8(c)) which implies that the monomers are stacked in staggered rows. The interactions between each pair of monomers and the stability of this interaction is provided by the formation of one expected H-Bond (h 1 ) and an hybrid scheme composed of two H-Bonds (h 2 /h' or h 3 /h"). This finding confirms the importance of the slight flexibility of the amide moieties for the global stability of SMA. It also highlights the fact that the stability of the SMA is dynamic as there is a constant compensation of the H-bonds.</p><!><p>Once the strength of the different supramolecular assemblies (dimers to pentamers) have been considered from both a qualitative and quantitative point of view, we decided to have a look at the formation of those assemblies. In the previous sections, we reported data about already formed supramolecular assemblies, in this section, we will study the aggregation and association process, aiming at answering the question: How do we go from monomers to larger assemblies? Different simulations with similar starting configurations were set up as follows to address different problems:</p><p>-Non-interacting monomers (no H-Bond between them) placed in the center of the simulation box for a long (100 ns) simulation time.</p><p>-Non-interacting monomers (no H-Bond between them) placed in the center of the simulation box for 100 short (1 ns) simulation times.</p><p>The first type of simulation will allow us to know if SMA can self-assemble spontaneously and if this SMA will be stable along a long simulation time. The second type of simulation will provide information on the frequency of formation of such SMA and more precisely the frequency of formation of H-Bonds. For simplicity, results and detailed analysis on B4p are presented here and in Supporting Information for the others.</p><!><p>We used PACKMOL [44] to generate a starting configuration encompassing 10 monomers that are loosely compacted, meaning they are relatively close to each other but with no hydrogen bonds or particular interaction between them. They were then placed in the center of a simulation box for a 100 ns long simulation time. To ensure that the monomers are not interacting at the beginning of the simulation and more particularly that no hydrogen bond is effective for the starting configuration, we represented the radial distribution function (RDF) for the oxygen and hydrogen atoms involved in those interactions (see Figure 9 g(r) On Figure 10 we have provided a representative structure of a SMA of B4p, involving 8 monomers, that has been formed during the simulation. One has to notice that the two remaining monomers are involved in a dimer that is not interacting with the octamer. To illustrate the interactions between the different monomers we also provided interaction surfaces extracted from the NCI analysis for each pair of monomers. Various If we go deeper in the analysis of the different interactions, it is possible to observe that all the interactions are not formed simultaneously but sequentially. Indeed the first interaction that is effective within the SMA is the h 1 bond, with a formation at the nanosecond timescale (see Figure 11). We then have the formation of the previously mentioned interactions (h 2 and h') at a slightly higher timescale (few nanoseconds), followed by N-H ••• π interactions and O 1 -H 2 /H 3 after tens of nanosecond. If h 1 is the first interaction being observed, it is also the most stable one as it is effective during almost the entire trajectory (Figure 11(b)). For other molecules of the B4s family, there is always the formation of a SMA involving at least five monomers (B4 and B4pI). B4p, with 8 monomers involved is the most efficient one while B4c forms a seven-members SMA (see Figure S10, S11, and S12 in ESI †). The same kind of interactions are present within all the SMA with a predominance for the h 1 bond, completed with the previously mentioned interactions.</p><p>On the opposite, when considering B9 and B9m molecules, no large SMA were detected and only poorly stable dimers were observed. As a conclusion one can say that the self-assembling process is (1) efficient for B4s family but not for the B9s one, (2) quite fast, of the order of the nanosecond, (3) can be a long process as the interactions are added sequentially, and (4) dynamic in the sense that some interactions (N-H</p><!><p>We observed in the previous section that, when a H-Bond is formed, it is then quite stable along the trajectory.</p><p>The analysis we propose to perform in this section aims at retrieving the frequency of formation of the H-Bond.</p><p>To do so, we generated 100 different starting configurations encompassing 10 monomers close enough to each other but with no H-Bond between them and performed a 1 ns simulation. We then extracted the final structure of each simulation and counted the H-Bonds between all the monomers. One has to notice that no distinction was made between all the possible H-Bonds (9 in total). On Figure 12 we have represented, for the 100 simulations and for B4p and B9m only, the total number of H-Bonds that have been observed between each pair of dimers. The results for B4, B4c, B4pI and B9 are also provided in ESI † (Figure S13 and S14). ..,10) for B4p (bottom left, green) and B9m (up right, gray) for a total of 100 simulations. The structures that are considered for the count are the final ones obtained at the end of the 1 ns simulation. The size of the dots is proportionnal to the number of H-Bonds that have been detected. For the largest dots, we provided the corresponding exact number of interactions.</p><p>One can observe immediately on Figure 12 that the number of H-Bonds present within B4p aggregate is larger than the ones of B9m. Due to the particular arrangement of the molecules, some preferential interactions are observed. For example, for B4p, the formation of H-Bond between monomer 1 and monomer 2 is almost systematic. Indeed, the count reveals 143 H-bonds between those 2 monomers for a maximum of 300.</p><p>Nevertheless, one can also observe that monomer 1 is also involved in interactions with monomer 5 (52) and in a lesser extent with monomer 6 (31) and 7 (18). So does monomer 2 with monomer 7 (17), 8 (43) and 10 (29). For B4p, there are six main interactions involving M1-M2, M3-M4, M4-M10, M10-M7 M5-M8, and M6-M9. For information, all the monomers are staying close to each other, as a loose aggregate, during all the simulations. This can be explained by the fact that other interactions (C-H ••• π and N-H ••• π) are formed and thus enforce the stability of the entire supramolecular assembly. For B9m, no systematic interaction was observed with a maximum count observed for a M6-M9 interaction. This result may also be due to the fact that B9m monomers are not staying "packed" during the simulation. The loose aggregate is thus not even stable for B9m molecules.</p><!><p>By defining a complete theoretical protocol based mainly on molecular dynamics simulations and aiming at studying the ability of organic molecules to form supramolecular assemblies and their resulting stabilities, we have been able to provide some hints for an effective self-supramolecular assembling process. A total of 6 molecules (B4, B4c, B4p, B4pI, B9, and B9m) divided into 2 families (B4s and B9s), bearing a different backbone but with three amide moieties in common have been considered. The study of the monomers allowed us to validate our molecular dynamics approach and also to understand the properties of the molecules when they are isolated. Considering the dimers has permitted to define the hydrogen bonds network. Expected hydrogen bonds (h 1 , h 2 , and h 3 ) were always observed for both families. Nevertheless, they were not always effective all together and an hybrid scheme (h' and h") has been highlighted. This hybrid H-bond network was observed only for one of the two families, namely B4s. The calculation of the binding energies clearly showed that dimers and even trimers or pentamers of B4s family were more favorable than those of the B9s. Nevertheless, the evolution of the binding energy going from dimers to pentamers indicates that small oligomers (e.g trimers) may be more stabilized than larger SMA (e.g pentamers). Looking at the the formation of the assemblies starting from a loose aggregate allowed us to observe that not only H-bonds can ensure the stability of the aggregate but also N-H • • • π or C-H • • • π in a lesser extent. Finally, the most important point to consider for the self-assembling process is the dynamical behavior of the stability. If SMA of B4s family are more stable than those of B9s it is because they have a relative flexibility. One can also notice that the stability is not something that is frozen but that is also dynamical. We have shown within this study that considering relatively flexible molecules, instead of rigid ones, is a better strategy for the conception of supramolecular assemblies.</p><!><p>See supporting information for: Validation of GAFF parameters; General parameters for molecular dynamics simulations; Parameters for the umbrella sampling approach; Particular structure of B4s and B9s families; Hybrid hydrogen bond network; Pentamers for B4, B4c, B4pI, B9, and B9m; Interaction maps for B4, B4c, B4pI and B9.</p>

ChemRxiv

Cell Surface Heparan Sulfate Released by Heparanase Promotes Melanoma Cell Migration and Angiogenesis

Heparan sulfate proteoglycans are essential components of the cell-surface and extracellular matrix which provide structural integrity and act as storage depots for growth factors and chemokines, through their heparan sulfate (HS) side chains. Heparanase is the only mammalian endoglycosidase known that cleaves HS, thus contributing to matrix degradation and cell invasion. The enzyme acts as an endo-\xce\xb2-D-glucuronidase resulting in HS fragments of discrete molecular weight size. Cell-surface HS is known to inhibit or stimulate tumorigenesis depending upon size and composition. We hypothesized that heparanase contributes to melanoma metastasis by generating bioactive HS from the cell-surface to facilitate biological activities of tumor cells as well as tumor microenvironment. We removed cell-surface HS from melanoma (B16B15b) by HPSE treatment and resulting fragments were isolated. Purified cell-surface HS stimulated in vitro B16B15b cell migration but not proliferation, and importantly, enhanced in vivo angiogenesis. Furthermore, melanoma cell-surface HS did not affect in vitro endothelioma cell (b.End3) migration. Our results provide direct evidence that, in addition to remodeling extracellular matrix and releasing growth factors and chemokines, HPSE contributes to aggressive phenotype of melanoma by releasing bioactive cell-surface HS fragments which can stimulate melanoma cell migration in vitro and angiogenesis in vivo.

cell_surface_heparan_sulfate_released_by_heparanase_promotes_melanoma_cell_migration_and_angiogenesi

Introduction<!>Materials<!>Cells and Tissue Culture Conditions<!>HPSE Isolation and Activity<!>Flow Cytometric Analysis<!>Isolation of cell surface HS<!>Wound Healing Assay<!>Proliferation Assay<!>In Vivo Angiogenic Assay<!>Removal and Isolation of Cell-Surface HS by HPSE Treatment<!>Effects of HPSE-Degraded HS on Endothelioma In Vitro<!>HPSE-Degraded Cell Surface HS Modulates Melanoma Cell Migration<!>HPSE-Degraded Cell-Surface HS does not Influence Melanoma Cell Proliferation<!>HPSE-Degraded Cell-Surface HS Promotes In Vivo Angiogenesis<!>Discussion<!>Dose-dependent reduction of cell surface HSGAG with HPSE<!>

<p>Enzymatic remodeling of heparan sulfate proteoglycans (HSPG) within the tumor microenvironment is emerging as an important mechanism for the dynamic regulation of tumorigenesis [1-3]. HSPG are a family of glycoproteins that are distinguished by the covalent attachment of one or more HS chains to their protein core. HS are directly involved with the angiogenic process by acting as co-receptors with angiogenic growth factors [3]. As the interface between tumor cells and host cells, HS mediate cellular interactions. HS also influence tumor metastasis to sites such as the brain by arbitrating interactions between cancer cells, platelets, endothelial cells, and host organ cells. Intact HS prevent metastasis by acting as a physical barrier in the extracellular matrix (ECM). Enzymes that cleave HS may release fragments that can either support or inhibit tumorigenesis [4, 5].</p><p>HPSE is the only mammalian endo-β-D-glucuronidase which cleaves HS at specific intrachain sites, resulting in fragments of appreciable size (10–20 sugar units) [7]. Numerous in vitro and in vivo studies have asserted a role for HPSE in tumor invasion and metastasis [6-12]. In addition, HPSE activity is upregulated in human cancers; a number of studies using clinical samples demonstrated a correlation between tumor malignancy and HPSE levels [13-18]. In vivo animal studies have also indicated that changes in the fine structure of tumor-cell-surface insoluble HS or soluble HS in the ECM have profound effects on tumor-cell growth kinetics, angiogenesis and metastasis formation [4, 5, 19-22]. Depending upon HS composition and/or sequence involved in the process of tumorigenesis, they can either act as pro-tumorigenic, or anti-tumorigenic agents [4, 5, 19].</p><p>Soluble or ECM HS can also differentially regulate fibroblast growth factor-2 (FGF2) binding and signaling leading to modification of angiogenesis of brain-metastatic melanoma cells depending upon its concentration [19]. Amongst additional signaling molecules, some of HS-binding growth factors important for angiogenesis and tumor development are vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), transforming growth factor-β (TGF-β), and platelet-derived growth factor (PDGF) [23]. VEGF or vascular permeability factor (VPF) [24] was initially described as a highly specific mitogen for endothelial cells [25] and as a potent inducing agent for angiogenesis and vasculogenesis in a variety of physiological and pathological conditions [26]. Inhibition of VEGF signaling by VEGF antagonists, antisense VEGF, or dominant negative VEGF receptor (VEGFR) impaired tumorigenesis in vivo [27, 28]. The VEGF family of proteins includes VEGF A through E and placenta growth factor [26]. VEGF A is the most studied and one of its splice variants, VEGF165, is the most potent and widely expressed isoform known [29]. VEGF165 is secreted in the ECM as a disulfide-linked homodimer with two identical heparin-binding sites. HS, by binding to VEGF, regulate the diffusion, half-life, and affinity of VEGF165 to its cognate receptors [3, 30].</p><p>We have shown previously that HPSE treatment or HPSE-generated bovine kidney HS products increase FGF2 binding and signaling in melanoma cells in vitro and FGF2-medited angiogenesis in vivo. In this report, we have extended our study using cell surface HS from murine brain-metastatic melanoma cells (B16B15b) to investigate their effect on melanoma biology in respect to VEGF signaling. We sought to assess the role of HPSE-degraded cell-surface HS in VEGF-mediated activity in brain-metastatic melanoma cells since VEGF is known to be essential for brain metastasis in melanoma [28]. We hypothesized that HPSE contributes to melanoma metastasis by generating bioactive HS from the cell-surface that stimulate biological activities associated with metastatic cascade. We also examined if these fragments could differentially affect VEGF165-mediated biological activities of melanoma and endothelioma. We demonstrate that the isolated cell-surface HS stimulate in vitro migration but not proliferation of melanoma cells (B16B15b). Furthermore, they also promote in vivo angiogenesis by Matrigel™ plug assays. Interestingly, VEGF165 does not affect melanoma migration or angiogenesis alone or together with the cell-surface HS in these experiments. Finally, melanoma cell-surface HS do not stimulate in vitro migration of murine brain endothelioma cells (b.End3). Our results suggest that, in addition to remodeling the ECM and releasing ECM-based growth factors and chemokines, HPSE can contribute to aggressive phenotype of melanoma by releasing bioactive cell-surface HS which in turn stimulate melanoma cell migration and angiogenesis.</p><!><p>Heparan sulfate (HS) from bovine kidney was purchased from Sigma Chemical Company (St. Louis, MO). Heparin-lyase III (heparitinase, EC 4.2.2.8) from Flavobacterium heparinium was obtained from Seikagaku (Seikagaku America, Falmouth, MA). DMEM and Ham's F-12 nutrient medium and trypsin-EDTA were purchased from Gibco (Grand Island, New York, NY), and FBS from Hyclone Laboratories (Logan, UT). Reduced-growth factor Matrigel™ was obtained from BD Biosciences Discovery Labware (Bedford, MA). All other chemicals used were reagent grade or better.</p><!><p>Early-passage, Mycoplasma-negative, murine melanoma (B16B15b) cells with high metastatic capabilities [12, 31] were maintained as monolayer cultures in a 1:1 (v/v) mixture of Dulbecco's modified Eagle's medium/F-12 (DMEM/F12) supplemented with 5% (v/v) fetal bovine serum. Murine brain endothelioma cells (b.End3) [32] were passaged in DMEM/F12 supplemented with 4 mM L-glutamine adjusted to contain 1.5 g/L sodium bicarbonate and 4.5 g/L glucose supplemented with 10% (v/v) fetal bovine serum. Cells were maintained at 37°C in a humidified 5% CO2/95% air (v/v) atmosphere and passaged using 2 mM EDTA (B16B15b) or trypsin-EDTA (b.End3) before reaching confluence.</p><p>For enzymatic treatment with HPSE, cells were washed 3 times in DMEM/F-12 containing 0.1% (w/v) BSA, penicillin (100 U/ml) and streptomycin (100 μg/ml), then incubated with indicated concentrations (0-10 μg/ml) of recombinant HPSE in 50 mM HEPES-buffered DMEM/F-12 (pH 6.8) containing penicillin (100 U/ml) and streptomycin (100 μg/ml) for 18 h at 37°C in a shaker incubator at 50 rpm.</p><!><p>Recombinant human HPSE was purified as previously described [19, 33]. Briefly, Sf9 insect cells, transfected with baculovirus transfer vectors containing HPSE subunits, were grown in SF900II serum-free medium (Gibco BRL, Grand Island, NY) for high-titer stocks. Tni cells cultured in suspension using ExCell405 serum-free medium (JRH Bioscience, Lenexa, KS) were infected with high-titer stock for 48 h, and cells were subsequently removed by centrifugation. The supernatant was then tested for HPSE activity, filtered through a 0.45 μm filter, and loaded on a HiTrap heparin column (Amersham Biosciences, Piscataway, NJ). The column was subsequently washed in TBS, and then eluted using a 100 ml gradient of 0.15-1.0 M NaCl in 25 mM Tris-HCl (pH 7.5). Collected fractions (1 ml) were screened for HPSE activity (Heparan Degrading Enzyme Assay Kit; Takara Mirus, Madison, WI) [19, 33]. HPSE eluted at 0.67 M NaCl, as expected [19, 33].</p><!><p>Degradation of cell-surface HS was confirmed by flow cytometric analyses. Briefly, B16B15b metastatic melanoma cells were treated with different concentrations of HPSE (0-10 μg/ml) for 18 h at 37°C using a shaker incubator. The medium was removed at the end of treatment and cells were collected by PBS-EDTA. Cells (5 × 105) were then incubated with HS mAb 10E4 (Seikagaku) followed by incubations with super sensitive biotin-goat anti-mouse IgM (BioGenex, San Ramon, CA) and PE streptavidin (Molecular Probes, Eugene, OR) respectively. Cells were then fixed in 200 μl cold 1% (v/v) paraformaldehyde and stored at 4°C until analysis. Samples were analyzed for cell-surface HS staining using FACScan Flowcytometer. Data were analyzed with WinMDI. Appropriate control samples without the primary and/or the secondary antibody (anti-IgM) were run to subtract background staining.</p><!><p>B16B15b metastatic melanoma cells were treated with or without HPSE (10 μg/ml) overnight at 37°C using a shaker incubator. Conditioned medium was collected and pH was adjusted to 6.0. The medium was then centrifuged at 5000 rpm for 10 minutes, filtered through 0.22 μm filters and boiled for 10 minutes. The medium was incubated with DEAE-Sephacel (Sigma) for 18 h and subsequently poured onto column. HS fragments bound to DEAE were washed with 0.1M sodium phosphate and 0.15M NaCl, pH 6.0. The fragments were gradient eluted with 0.5M, 1.0M, and 2.0M NaCl in 0.1M sodium phosphate buffer pH 6.0. The fragments were then concentrated and buffer-exchanged into ultra-pure water by application to a Centricon filter (Millipore Corporation, Bedford, MA). Finally, isolated fragments were treated with pronase (Sigma; 58) to remove protein core from isolated HS.</p><p>HPSE-degraded HS used in biological experiments was analyzed by separating HS on a Criterion 4-20% TBE gel (Bio-Rad Laboratories, Hercules, CA) for 20 min at 60 mAmp. Bands were visualized with alcian blue 8GX (Sigma-Aldrich) followed by silver staining (Pierce Endogen, Rockford, IL) [34]. Densitometric analyses were performed using a Versadoc imaging system (Bio-Rad Laboratories) to determine profiles leading edge. Non-treated commercial HS from bovine kidney were electrophoresed at various concentrations to obtain quantitative analysis.</p><!><p>Migratory properties of melanoma cells were analyzed by a standard wound healing assay. Briefly, cells were plated in 12-well plates at a high density and allowed to grow to confluence. Cells were washed 3 times in DMEM/F-12 containing 0.1% (w/v) BSA, penicillin (100 U/ml) and streptomycin (100 μg/ml) and then incubated with the same medium for one hour. Using a sterile 100 μl tip, a single scratch was made through the middle of each well. The medium was subsequently removed and the wells were rinsed three times with DMEM/F-12 containing 0.1% (w/v) BSA and penicillin/streptomycin to remove the detached cells. Cell-surface HS (1 ng/ml) or recombinant VEGF165 (10-50 ng/ml) were added to cells in DMEM/F-12 containing 0.1% (w/v) BSA, 4mM Hepes, penicillin (100 U/ml), and streptomycin (100 μg/ml) for 8 h at 37°C in a humidified 5% CO2/95% air (v/v) atmosphere. Photomicrographs were taken at 0 hour (T0) and at the end of the experiment (T8) using identical conditions to calculate percent relative gap closure. Relative gap closure was measured as [1-(T8/T0)]. Migration assays for endothelioma were incubated for 24 h (T24).</p><!><p>Proliferation of melanoma cells were assayed by using alamarBlue™ (BioSource International, Camarillo, CA, USA) [35], a non-toxic dye which monitors the reducing environment of the proliferating cell, as per manufacturer's instructions. Briefly, 1 × 104 cells/ml were plated into 24-well plates and incubated for 24 h. At the start of the proliferation assay, cells were washed 3 times in DMEM/F-12 containing 0.1% (w/v) BSA and penicillin (100 U/ml) and streptomycin (100 μg/ml), and then incubated with the same medium for one hour. Indicated concentrations of cell-surface HS or recombinant VEGF165 were added to cells in triplicates in DMEM/F-12 containing 0.1% (w/v) BSA, 4mM Hepes, penicillin (100 U/ml), and streptomycin (100 μg/ml) at 37°C in a humidified 5% CO2/95% air (v/v) atmosphere. For indicated time points alamarBlue™ (10%, v/v) was added per well and were incubated for 4 h at 37°C in a humidified 5% CO2/95% air (v/v) atmosphere. Cell proliferation was measured by monitoring the fluorescence of alamarBlue™ supplemented cell culture media at excitation and emission wavelengths of 540 and 630 nm respectively. The greater is the percentage of reduction (fluorescent count), the higher is the proliferative activity.</p><!><p>B16B15b cells were released with PBS-EDTA, washed two times in DMEM/F-12, and resuspended at 1×107 cells/ml in 50% (v/v) reduced-growth factor Matrigel™ (Becton Dickinson, Labware, Bedford, MA) in DMEM/F-12 at 4°C. HS fragments and VEGF165 were added accordingly. Cells (2 × 106) were injected using a 25-gauge needle to the left and right abdominal subcutaneous tissue of female C57BL6 (Harlan Teklan, Madison, WI) mice (n=6-9). B16B15b cells, in reduced-growth factor Matrigel along with HPSE-degraded melanoma cell-surface HS or without, as mock control, were injected into the right (with VEGF) and left (without VEGF) abdominal subcutaneous tissue of female C57BL6 mice (n = 6-9). Animals were divided in three groups in a split-plot arrangement. Group A received HPSE-treated HS with VEGF (right) or without VEGF (left). Group B received HPSE-treated HS that were further treated with Hep III to cleave them into inactive disaccharide fragments with VEGF (right) or without VEGF (left). Group C received melanoma cells in mock-buffer with VEGF (right) or without VEGF (left). Mice were sacrificed on the 10th day; tumors were excised, fixed in 10% (v/v) formalin, and embedded in paraffin. Tumor sections (7 μm thick) were then stained with haematoxylene and eosin (H&E) and examined under the microscope. Blood vessel density was assessed by counting vessels within the tumor region in five sections in each tumor. Tumor sections were photographed using Olympus DP70 camera, Olympus BX45 microscope and saved in JPEG format using DP Manager (Olympus America Inc., Center Valley, PA). Tumor areas were measured by counting pixels on ImageJ software (NIH). Pixel counts were converted to mm2 to present the number of vessels per unit area. Statistical analyses were done using SAS (Version 9.1.3) in an analysis of variance in a split-plot arrangement of treatments. Main plot effects included Group and Animal Id within Group; subplot effects included Side and Group*Side interaction. Pairwise comparisons of main effects were conducted with Tukey's HSD test. When appropriate, interaction effect comparisons were performed with t-tests of least-square means. All comparisons were considered significant at p<0.05. Prior to analysis, the data were natural log-transformed to stabilize variance terms.</p><!><p>Highly brain-metastatic B16B15b melanoma cells were chosen as a source of HS since they possess high HSPG expression on the cell-surface [36]. The extent of HS degradation by HPSE was assessed by detection of cell-surface HS on FACS analysis. Detectable reduction in HS expression levels was seen with as low as 5.0 ng/ml HPSE compared to no HPSE treatment (data not shown). To optimize isolation of cell surface HS, when cells were treated with higher HPSE concentrations (100-10,000 ng/ml), a dose-dependent decrease in cell-surface HS expression was observed (Fig. 1A).</p><p>Conditioned medium following HPSE treatment (10 μg/ml) from B16B15b melanoma cells was collected and HS were isolated by ion-exchange column chromatography. Assessment of HPSE-mediated HS degradation was determined by gel electrophoresis of isolated fragments (Fig. 1B). Since the isolated HS were a heterogeneous mixture of oligosaccharides due to HPSE digestion, it migrated as a broad band during gel electrophoresis (Fig. 1B). The leading edge of HS profiles was determined after densitometric analysis and concentration was determined by generating a curve with known standards (Fig. 1B).</p><!><p>Angiogenesis is an important step in solid tumor growth beyond a certain dimension (0.2-2.0 mm or about 105-106 cells) that requires formation of new blood vessels from the preexisting vascular network [37]. Endothelial cells migrate and proliferate during angiogenesis and are influenced by the tumor microenvironment including heparin/HS-binding growth factors secreted by the tumors such as FGF2 and VEGF [38]. Therefore, we decided to study effects of HPSE-degraded HS in an endothelial system. We investigated changes in migratory properties in murine brain endothelioma cellline b.End3, since migration is a critical event in angiogenesis [38].</p><p>To study how exogenous addition of HS will influence endothelioma (b.End3) biological activity, we added HPSE-digested melanoma cell surface HS to serum-free endothelioma medium. HS did not have any effect on endothelioma cell migration (Fig. 2). We also used VEGF165, a known mitogenic factor for endothelial cells, which stimulated endothelioma cell migration compared to no VEGF treatment (p< 0.05, Fig. 2). Moreover, addition of melanoma cell surface HS to VEGF165 treatment did not augment this response (Fig. 2).</p><!><p>To directly test whether the B16B15b cell-surface HS were biologically active, we tested their effects on melanoma cell migration. B16B15b cells possess an aggressive migratory behavior, express VEGF receptors, and respond to VEGF. Interestingly, when HS (1 ng/ml) were added externally, there was a 30% up to a 2-fold increase in cell migration compared to no treatment (p< 0.05, Fig. 3). Addition of VEGF165 (0-100 ng/ml) did not augment this effect compared to no VEGF165 control (data not shown). Interestingly, VEGF165 did not effect migration even when added along with HS compared to HS alone (Fig. 3). One possibility could be that melanoma cells tested secrete autocrine growth factors [39] even after serum starvation, and thus do not respond to exogenously added stimuli. This was unexpected since VEGF165 is known to require HS to exert its biological effects. However, changes in cell migratory properties following addition of melanoma HS isolated by HPSE-digestion suggested that the HPSE-degraded HS fragments are bio-active and possess tumor stimulatory activity.</p><!><p>We next explored effects of B16B15b cell-surface HS on melanoma cell proliferation to test if similar conditions used in our wound healing assays also affect cell proliferation. Melanoma cell proliferation was assayed by alamarBlue™ (see "Experimental Procedures"), cell proliferation was monitored every 24 h for 72 h. The basal cell proliferation rate is high in these cells; we did not observe any change in proliferative properties of the cells either by HS or by VEGF treatment over a period of 72 hours (Fig. 4). Thus, exogenous addition of melanoma cell-surface HS isolated by HPSE treatment influences melanoma cell migration without affecting proliferation.</p><!><p>To investigate effects of HPSE-derived cell-surface HS on in vivo angiogenesis, Matrigel plug assays were performed. B16B15b cells, in reduced-growth factor Matrigel along with HPSE-degraded melanoma cell-surface HS (1 ng/ml) or without, as mock control, were injected into the right (with 50 ng/ml VEGF) and left (without VEGF) abdominal subcutaneous tissue of female C57BL6 mice (n = 6-9). Mice were sacrificed on the 10th day post-injection and Matrigel plugs were removed. Sections (7 μm thick) were then H&E-stained to examine for blood vessel formation. Blood vessel density was assessed by counting vessels within the Matrigel plug region in five sections in each plug.</p><p>HPSE-treated cell surface HS induced a significant increase in intratumor blood vessel formation (Fig. 5A and 5B) in animals in group A compared to mock (group C) or Hep III treatment of HPSE-derived cell-surface HS (group B) (p < 0.0001). Interestingly, the increased numbers of tumor vessel formation were VEGF-independent. Presence of VEGF did not affect angiogenesis in all three groups (p = 0.2-0.8). Notably, the absence of blood vessels inside the tumors led to areas of necrosis due to lack of nutrients and oxygen (Fig. 5A). Importantly, the Matrigel plugs in all groups seemed to be approximately the same size and this observation was confirmed following excision and weighing of the plugs (not shown). Therefore, the differences in vascularity between the mock control and/or Hep III control vs. the HPSE-generated cell-surface HS tumors are not due to differences in tumor size, rather it is due to the tumor promoting effects of cell-surface HS fragments on tumor microenvironment.</p><!><p>In the present study, we have investigated roles of HPSE-degraded cell-surface HS in melanoma tumorigenesis and possible effects on host endothelial system. Our findings suggest that melanoma cell-surface HS isolated by HPSE treatment promotes 1) melanoma migration, and 2) angiogenesis independent of VEGF activity. These results also provide evidence that, in addition to remodeling the ECM and releasing growth factors and chemokines, HPSE contributes to the aggressive phenotype of melanoma by releasing bioactive HS which stimulate melanoma tumorigenesis.</p><p>HSPG are recognized as key cell-surface/ECM active biological modulators [3-5, 23, 40]. Their degradation at the level of HS chains by glycosidases has significant regulatory consequences in cancer metastasis [41]. HS present on tumor cells also contain bioactive sequences that may affect tumor-cell phenotype in relation to cell growth and metastasis [5, 20, 21, 40]. It has been established that growth factor binding to HS which leads to mitogenic activity takes place only when definite structural features are present within the HS chains, such as, sulfation at specific positions within a disaccharide (N, 2-O, 3-O, 6-O) by the enzymes mediating HS synthesis within the Golgi apparatus [41]. On the other hand, it has also been shown that besides the modification that occurs in the Golgi during its synthesis and expression, HS can also be structurally and functionally modulated within the extracellular compartment. The two families of mammalian enzymes currently known to modify HS are the endosulfatases (Hsulf-1 and -2) which remove 6-O sulfation on the HS [20, 21] and HPSE, which cleaves HS into small, biologically active fragments, [4, 7, 8, 9, 36, 42-44]. Recently HPSE has also been shown to promote shedding of cell-surface syndecan-1 and modify tumorigenesis [45, 46].</p><p>Elevated levels of HPSE are known to be associated with brain-metastatic melanoma [10-12]. The enzymatic activity of HPSE is characterized by specific intrachain HS cleavage of glycosidic bonds with a hydrolase (but not eliminase) type of action that facilitates the release of several protein modulators of cell function, including migration, adhesion, inflammation, angiogenesis, embryogenesis, and metastatic invasion [2, 4, 7, 47]. When over expressed, HPSE increases tumor cell invasiveness in vitro and in vivo settings [10, 12]; conversely, a downregulation of HPSE by anti-sense or siRNA methodologies decreases tumorigenesis [10-12]. However, at higher concentration HPSE can also inhibit tumorigenesis possibly by extensive remodeling of cell-surface HS that interferes with growth factor binding and signaling leading to subsequent inhibition of biological effects [19, 48, 49].</p><p>Extensive evidence suggests that cellular function and phenotype are highly influenced by the composition and size of HS chains on HSPG [4, 5, 23, 40]. A cell can respond to its microenvironment in markedly different ways by dynamically regulating HS structure on its cell-surface as insoluble HS and in the ECM as soluble HS [4, 5, 19, 23, 40]. HSPG and HS chains are present on the surface of all eukaryotic cells, including tumor cells. This is valid also for cells that are important for tumor survival, e.g., endothelial-cell junction surrounding a growing tumor, where HS can participate in the process of angiogenesis. This led us to believe that proangiogenic activity of HPSE could partly be due to generation of bio-active fragments by its enzymatic activity. We found that these fragments are indeed active and probably mediate their effects through melanoma autocrine/paracrine factors.</p><p>Highly brain-metastatic B16B15b melanoma cells were chosen as a source of HS since they express large amount of it on the cell-surface [36]. The extent of HS degradation on B16B15b by HPSE was assessed by detecting cell-surface HS on FACS analysis. We were able to remove melanoma (B16B15b) cell-surface HS with HPSE treatment in a dose-dependent manner as indicated by the leftward shift in the profile on the X axis (Fig. 1A). The associated protein core due to shed syndecan-1 by direct action of HPSE [45, 46] and other possible protein contamination were removed by pronase digestion during the isolation process. As expected, HPSE digestion generated HS fragments of about 10 kDa (Fig. 1B).</p><p>To directly demonstrate whether the B16B15b cell-surface HS were biologically active, we tested their effects on melanoma and endothelioma biological activity. We reasoned that because VEGF is a HS-binding growth factor and is essential for brain-metastasis formation [28], these fragments would participate in VEGF-mediated activities. Isoforms of VEGF can bind VEGF receptors in the absence of HS, but this interaction is enhanced by cellular or exogenous heparin/HS suggesting that heparin/HS on HSPG regulate the interaction of VEGF to VEGF receptor and subsequent biological activity [50-54]. Interestingly, in our melanoma cell system, the presence of VEGF did not influence the biological activities of the HPSE-degraded melanoma cell-surface HS including migration (Fig. 3) and proliferation (Fig. 4) of melanoma in vitro, and angiogenesis in vivo by Matrigel™ plug assay (Fig. 5). We also investigated effects on melanoma migration by adding FGF2, platelet-derived growth factor (PDGF) and interleukin-8 (IL-8) but no difference in cell migration was observed compared to no growth factor control (data not shown). A possibility could be that VEGF bound to endogenous cell-surface HS and/or because the melanoma cells already have autocrine production of VEGF, we did not observe an added effect with exogenously added growth factor [39]. A recent report by Robinson et al. demonstrated that VEGF requires highly sulfated sites on the HS for binding [55]. According to this study, these sites were exposed by enzymatic action of K5-lyase on HS and it retained significant VEGF165 affinity; in contrast, cleavage of HS by heparinases or HPSE severely reduced VEGF165 binding [55]. This may explain the reason we failed to observe any biological effect with exogenously added VEGF along with HPSE-derived HS in melanoma cell activity. However, melanoma cell-surface HS were able to stimulate melanoma migration (Fig. 3) and angiogenesis (Fig. 5) compared to the controls suggesting that these fragments are tumorigenic although they do not affect melanoma cell proliferation (Fig. 4). The enhanced angiogenesis by these fragments were possibly due to signaling by some other heparin-binding growth factor(s) which could either stimulate tumorigenesis or alternatively, could abolish tumor inhibitory signal. In addition, there were no significant differences observed in tumor weight in all treatment groups, hence the increased vascularity seen with the HPSE-generated cell-surface HS tumors were due to its effects on the tumor microenvironment. These findings are further strengthened by the fact that, following Hep III-mediated digestion into smaller fragments, HPSE-degraded HS lose their proangiogenic properties (Fig. 5).</p><p>The HPSE-degraded HS did not have any effects on in vitro b.End3 endothelioma cell migration (Fig. 2), or signaling (data not shown) which could be due to tissue-specific HS structural differences present between the systems [56]. Even though, the same sets of disaccharides are present in most tissues, their relative content varies quantitatively in terms of sulfation or epimerization pattern [56]. Attempts to remove endothelioma cell-surface HS by enzymatic degradation of Hep III or HPSE did not alter response to growth factor in this system (data not shown). This could be due to the fact that removal of cell-surface HS was incomplete and remaining quantities of cell-surface HS were sufficient to arbitrate growth factor-mediated signaling which is known to occur [57]. Nonetheless, the in vivo induction of angiogenesis by the melanoma cell HS fragments could also be due to availability of additional support to the endothelial cells directly or indirectly from the tumor cells.</p><p>Remodeling of the ECM and BM is vital for a normal embryonic development, wound healing and tumorigenesis. During tumor progression, this turnover is highly controlled and involves the coordinated action of proteases and endoglycosidases [1, 2]. This process not only contributes to angiogenesis and tumor invasion by altering the integrity of the BM/ECM, but also results in the release of HS-binding molecules such as chemokines and proangiogenic growth factors, initiating numerous downstream signaling cascades. While large families of proteases (matrix metalloproteases, aspartic, cysteine, and serine proteases) mediate the cleavage of protein components of the BM/ECM, cleavage of the HS side chains is performed by a limited set of enzymes, notably HPSE [1, 2]. Characterization of these HPSE-degraded melanoma cell-surface HS would potentially be useful. Devising a method that would isolate individual oligosaccharides is required to test for pro-angiogenic/pro-tumorigenic properties of the fragments. Moreover, the design of novel agents targeted against these HS fragments can be an important addition to developing polysaccharide based anti-tumor therapy in melanoma.</p><!><p>A. Murine B16B15b melanoma cells were treated with 0-10,000 ng/ml HPSE. Cell surface HS level was detected by flowcytometry using mAb10E4. HPSE removes cell surface HSGAG in a dose-dependent manner, (green) 0 ng/ml HPSE, (blue) 100 ng/ml HPSE, (purple) 1,000 ng/ml HPSE, (light blue) 10,000 ng/ml HPSE. Appropriate controls were run to account for background staining, (black) no primary antibody control, (solid red) no primary and no secondary antibody control.</p><p>B. HPSE-degraded cell surface HSGAG profile on silver stain. As expected, HPSE digestion generated HS fragments of about 10 kDa. Various concentrations of untreated HS was run to generate a standard curve for determination of concentration of HSGAG after densitometric analysis. Numbers on the right hand of figure refer to M.W. standards (kDa).</p><!><p>Endothelioma cell migration is not influenced by HPSE-degraded HS but VEGF stimulates wound healing. To study how exogenous addition of HS will influence endothelioma (b.End3) biological activity, we added HPSE-digested melanoma cell surface HS to serum-free endothelioma medium. HS treatment did not stimulate endothelioma cell migration while VEGF did (p< 0.05), as expected. HS, when added along with VEGF, did not augment VEGF response.</p><p>HPSE-degraded cell surface HS modulate melanoma cell migration. When HS (1 ng/ml) were added externally to melanoma cells in wound healing assays, there was increased migration compared to control (p< 0.05). Addition of VEGF (50 ng/ml) did not affect migration with or without HS.</p><p>HPSE-degraded cell surface HSGAG does not influence melanoma cell proliferation. Proliferation of melanoma cells were assayed by alamarBlue™, a non-toxic dye that monitors the reducing environment of the proliferating cell. Melanoma cell proliferation was monitored every 24 h for 72 h. Exogenous addition of VEGF (50 ng/ml) or melanoma cell surface HS (1 ng/ml) isolated by HPSE treatment did not influence cell proliferation.</p><p>HPSE-degraded cell surface HSGAG promotes angiogenesis in vivo. Blood vessel density was assessed by counting vessels within the tumor region in five different sections in each tumor. Tumor sections were photographed using Olympus DP70 camera, Olympus BX45 microscope and saved as JEPG format using DP Manager (Olympus). Tumor areas were measured by counting pixels on ImageJ software (NIH). Pixel counts were converted to mm2 to present the number of vessels per unit area. Statistical analyses were done using SAS (Version 9.1.3) in an analysis of variance in a split-plot arrangement of treatments.</p><p>A. Representative tumor sections from each treatment group (H & E). HPSE-treated cell surface HSGAG induced a significant increase in intratumor blood vessel (arrow) formation in animals compared to mock or HepIII treatment of HPSE-treated cell surface HSGAG (p<0.0001). Hep III treatment renders the HPSE-degraded fragments inactive, hence abolishes their biological activity. Presence of VEGF did not affect angiogenesis in all three groups (p=0.2-0.8). Notably, inside the tumors, absence of blood vessels, thereby lack of nutrition and oxygen led to areas of necrosis.</p><p>B. Bar graph representation of mean blood vessel density with standard deviation plotted on a log scale. Bars with the same letter are not significantly different from each other.</p>

PubMed Author Manuscript

Time-Resolved Raman Spectroscopy of Polaron Formation in a Polymer Photocatalyst

Polymer photocatalysts are a synthetically diverse class of materials that can be used for the production of solar fuels such as H2, but the underlying mechanisms by which they operate are poorly understood. Time-resolved vibrational spectroscopy provides a powerful structure-specific probe of photogenerated species. Here we report the use of time-resolved resonance Raman (TR3) spectroscopy to study the formation of polaron pairs and electron polarons in one of the most active linear polymer photocatalysts for H2 production, poly(dibenzo[b,d]thiophene sulfone), P10. We identify that polaron-pair formation prior to thermalization of the initially generated excited states is an important pathway for the generation of long-lived photoelectrons.

time-resolved_raman_spectroscopy_of_polaron_formation_in_a_polymer_photocatalyst

<!>Kerr-Gated Time-Resolved Resonant Raman Experiment of P10 (Structure Upper Left)<!><!>Author Contributions<!>

<p>The development of scalable photocatalysts that can split water efficiently by using solar energy would transform the energy landscape, providing a way to generate hydrogen sustainably. Historically, research has focused on inorganic semiconductors, but in the past 12 years there has been a rapid increase in the study of organic photocatalysts for water splitting1 following studies in 2009 which showed that graphitic carbon nitride was an effective hydrogen evolution photocatalyst.2 More recently, a wider variety of classes of organic photocatalysts have been reported including polymeric networks such as conjugated microporous polymers (CMPs),3 covalent triazine-based frameworks (CTFs),4−6 covalent organic frameworks (COFs),7−9 and linear conjugated polymers.10,11 Among these, the linear homopolymer of dibenzo[b,d]thiophene sulfone (P10, Scheme 1) was shown to be one of the most active for hydrogen evolution, both when using a sacrificial electron donor12 and in a z-scheme water splitting system.13 P10 can also promote oxygen evolution14 and CO2 reduction,15 all under visible light irradiation.</p><!><p>The laser pump pulse (400 nm) generates photoexcited states that can be subsequently interrogated by the probe pulse (630 nm) which is delayed with respect to the pump (Δt). The wavelength of this probe pulse was selected to be resonant with an electronic transition associated with a transient species observed in the transient absorption spectra of P10. The probe pulse generates both Raman scatter and photoluminescence (PL). An ∼2 ps duration 800 nm high-energy laser pulse induced a transient optical anisotropy which lasts for approximately the duration of the gating pulse in the CS2 Kerr medium. During this period the gate is "opened", and the polarization of the incident linearly polarized light is rotated by 90° with respect to its original orientation and passed through the crossed exit polarizer into the spectrometer while the unrotated light is rejected. Raman scattering is a fast process that occurs on a subpicosecond time scale. By synchronizing the timing of the Raman probe laser pulse with the gate pulse, it is possible to selectively transmit Raman scattered photons while rejecting the majority of the much longer lived (ns or greater) PL.</p><!><p>To facilitate the design of polymer photocatalysts and to truly exploit the synthetic control available, it is important to understand their underlying photophysics and mechanisms. In contrast to inorganic semiconductors, where the photogeneration of free charges occurs with a high efficiency, the poor dielectric screening of charges in organic absorbers means that polaron yields are often low; this is a central issue for this class of materials. Understanding why particular organic photocatalysts can efficiently generate separated charges following photon absorption is important. A body of literature exists on the underlying mechanisms of ultrafast polaron formation in organic photovoltaic (OPV) materials16−19 with proposed mechanisms including formation via hot and relaxed exciton dissociation and direct polaron-pair photogeneration. However, it is not clear if such models are also directly applicable to polymer photocatalysts where the additional presence of metal catalysts (e.g., for H2 and O2 evolution) and water may play an important role.</p><p>Transient absorption (TA) UV–vis spectroscopy is an established technique that has been widely applied to study electron–hole dynamics of inorganic and organic solar fuel materials.20,21 TA studies of P1012−14,22,23 report initial formation of a broad positive absorption at >700 nm assigned to a singlet excitonic state that decays on the picosecond time scale.12 In the presence of a sacrificial electron donor (commonly triethylamine (TEA) in a methanol/water solvent, 1:1:1 vol), a long-lived band at 630 nm has been assigned to an electron polaron (P10(e–)), proposed to form by quenching of the excitonic state by TEA on a time scale between 1 and 100 ps.23,12 In the absence of a sacrificial electron donor, a 630 nm TA band is still observed, and this has been proposed to be due to a polaron pair, a spatially separated weakly interacting electron and hole, with similar spectral characteristic to the P10(e–).12,13 The P10(e–) is very stable as it is retained on the polymer chain for ∼100 μs, despite the presence of residual Pd in the structure from the polymer synthesis which acts as a hydrogen evolution catalyst.23 For P10, fast polaron formation and trapping of the electron on the polymer leads to a high level of photocatalytic activity, but the chemical nature and mechanism of polaron formation are unclear.</p><p>Interpretation and assignment of TA features can be challenging due to the number of broad, often overlapped UV–vis bands. Time-resolved resonance Raman (TR3) spectroscopy directly probes the vibrational modes of short-lived intermediates, enabling assignment of nonequilibrium structures. Raman modes are sensitive to both the local structure and the intermolecular ordering of polymers, making TR3 a potentially useful way to study the mechanism and site of polaron formation.24 Time-resolved Raman spectroscopy has been used to study exciton conformational changes and polaron formation in OPV materials but has not been previously applied to study polymer photocatalysts.24−27 Here we apply TR3 to study the mechanisms of P10 polaron formation.</p><p>The ground state Raman spectrum (600–1400 cm–1) of P10 powder (633 nm Raman probe) shows peaks at 1411, 1340, 1301, 1269, and 1145 cm–1 (Figure 1a) and a strong band at 1596 cm–1 (Figure S1) that is outside the spectral window used for the TR3 experiment. These bands are assigned to ring/carbon backbone modes except for 1145 cm–1, which has contributions from the sulfone mode through comparison to Raman spectra predicted by density functional theory (DFT) calculations (Supporting Information, Figure S2 and Table S1); see section 1.4 of the Supporting Information for details about the computational method employed. TA experiments performed on P10 aggregates, formed from a toluene suspension, are shown in Figure S4. Toluene is used as an inert, nonpolar solvent to generate a thin layer of P10 for our TA experiment. In agreement with past reports of P10 in polar solvents,12 the TA spectra of the aggregates recorded following 400 nm excitation show a weak transient band between 620 and 660 nm, assignable to either a P10 polaron pair or the P10(e–) polaron. This assignment is also supported by the species associated spectra generated through target analysis fitting of the TA data, based on the kinetic model derived within this Letter as a result of the TR3 data, Figure S5, and the accompanying text. The TR3 spectra of P10 powder following 400 nm excitation recorded by using a 630 nm Raman probe that is resonant with the proposed P10 polaron are shown in Figure 1b–e. In common with many organic photocatalysts P10 is photoluminescent following excitation at energies greater than the optical gap (2.61 eV, λ < 475 nm; see Figure S3).12 Here we make use of an optically pumped Kerr gate to remove the majority of the photoluminescence (PL) background that otherwise masks the weak Raman scatter from the photogenerated transients (Scheme 1).28−30 2 ps after excitation of P10 the TR3 spectrum shows bleaching (a decrease in scattering intensity) of the ground state Raman modes of P10, and only broad excited state Raman bands are present, which are assigned to vibrationally hot photogenerated state(s) (Figure 1b). Within 5 ps these begin to cool, and transient Raman bands are observed. These are centered at 713, 847, and 988 cm–1 (weak) and in the region of 1210 and 1110 cm–1 (partially overlapped with the ground state bleaches). The new transient features persist for longer than 1 ns (Figure 1e).</p><p>(a) Ground state Raman spectrum (633 nm probe) of P10 powder. (b–e) TR3 spectra of P10 recorded at the time indicated after 400 nm excitation of P10 powder by using a 630 nm Raman probe.</p><p>Two experiments were performed to test the assignment of the transient Raman bands to either P10(e–) or the polaron pair. First, we recorded TR3 spectra in the presence of a sacrificial electron donor (TEA/methanol/water, 1:1:1) that will increase the yield and lifetime of the P10(e–) polaron. For a discussion of the implications of P10(e–) accumulation on the TR3 experiment, please refer to the text accompanying Figure S6. The TR3 spectra show transient bands at 719, 849, 1138, 1262, and 1331 cm–1, in good agreement with the TR3 data recorded in the absence of the sacrificial electron donor (Figure 1 and Figure S6). The TR3 bands at 1262 and 1331 cm–1 were not visible in the absence of the electron donor (Figure 1) due to the overlap with the P10 ground state bleach. Second, as P10(e–) accumulates under steady state illumination,23 we have also recorded the Raman spectra under 365 nm LED illumination using a conventional (steady state) microscope both on resonance (633 nm) and off resonance (532 nm) with the known UV–vis absorption maximum of P10(e–) (Figure 2a, b). The Raman spectrum of the photogenerated P10(e–) by using a 633 nm probe wavelength shows excellent agreement with the proposed P10(e–) TR3 spectrum (Figure 2c), while the spectrum recorded with a 532 nm probe under identical conditions shows no bands that can be assigned to a photogenerated species.</p><p>(a) UV–vis spectrum of P10(e–) generated by CW 365 nm illumination of P10 in the presence of TEA/methanol/water (1:1:1). The red and blue arrows indicate the Raman probe wavelengths used in (b). (b) Raman difference spectrum (using the sample in the dark as a background) of P10 in the presence of TEA/methanol/water (1:1:1) on and off resonance with the observed feature in the UV–vis spectrum. (c, d) Comparison between the steady state (d) and TR3 Raman data (c, 100 ps) which show the presence of the same species.</p><p>Preresonance31 and resonance32 Raman spectra for P10, P10(e–), and one-electron-oxidized P10 (P10(h+)) of the monomer and oligomers of different length have been predicted by DFT using the ωB97XD exchange-correlation functional33 and the cc-pVDZ basis set.34,35 See Figure 3 for a comparison between the experimental spectra and predicted (pre)resonance spectra for a P10 hexamer and Figures S7–S10 for all predicted (pre)resonance spectra for the different oligomer lengths and those predicted using another functional. All DFT predicted spectra discussed herein and within the Supporting Information have been scaled by the same factor, obtained by aligning the intense predicted P10 peak to that of experiment. For all oligomer lengths the predicted P10 preresonance Raman spectrum is dominated by a single intense transition with good agreement to its experimental counterpart (see Figure 3). The predicted resonance Raman spectra for P10(e–) and P10(h+) show an increased number of intense peaks below 1600 cm–1 which differ depending on the excited state the probe wavelength is on resonance with, complicating our ability to distinguish between the two species. However, for oligomers it is apparent that the calculations in the case of P10(e–) consistently reproduce the experimentally observed red-shift of the strong 1596 cm–1 peak of the P10 polaron species. This is not the case for its P10(h+) counterparts for which the intense peak is predicted to be unshifted relative to that of P10. Resonance Raman spectra predicted with a different range-separated exchange-correlation functional, CAM-B3LYP (see Figure S9) suggest that this red-shift of the most intense peak in the Raman spectra is representative of P10(e–) oligomers and the lack of such a shift typical of their P10(h+) counterparts. In Figure 3, the unpaired electron density is shown for the P10(e–) and P10(h+) hexamer species. For both species, the polaron is localized on the central P10 moieties, with the calculations of P10(e–) showing increased electron density on the thiophene ring.</p><p>Comparison of the experimental spectra of P10 and the P10 polaron to the predicted (pre)resonance (ωB97XD/cc-pVDZ) spectra of a P10 hexamer in different charge states. All predicted spectra have been scaled by a factor of 0.943, obtained by aligning the intense peak in the predicted neutral spectrum to that of the P10 experimental spectrum. The predicted resonance Raman spectra shown for P10(e–) and P10(h+) are calculated for the intermediate excited state with a predicted vertical absorption closest to 630 nm. Similar spectra for other intermediate excited states can be found in Figure S10. The inset is a schematic of the polaron localization for the two charged species.</p><p>These results indicate that by using the TR3 experiment we are measuring the spectrum of either the P10(e–) polaron or the vibrational modes associated with electron localization within a polaron pair. We now turn to the rate and mechanism of polaron formation in the absence and presence of a sacrificial electron donor (Figure 4). An advantage of the TR3 experiment is that contributions from other off-resonance intermediates are minimal, simplifying the analysis of the transient data. The kinetics of the 847 cm–1 Raman band are studied, but all TR3 bands in this spectral region show similar kinetics. Following excitation of P10 in the absence of a sacrificial electron donor the decay can be well fitted to a biexponential function with an initial rise in intensity, which is close to the instrument response function (τ ∼ 3 ps), and a subsequent slower decay (101 ps) to form a population that persists until the longest time scales studied (3.4 ns); full fitting parameters are in Table S5. The slow decay is not due to electron transfer from the P10 to residual Pd left during polymer synthesis, as this is known to occur on the microsecond and slower time scale;23 instead, it is assigned to recombination of the polaron pair. The fast rise in intensity of the 847 cm–1 band is in line with the observed rate of vibrational cooling (Figure S11), from which we estimate that the transient species reach thermal equilibrium by ∼10 ps. After 10 ps, we see no further increase in intensity of the 847 cm–1 Raman mode (Figure 4). To probe the lifetime of the excitonic state, PL between 634 and 697 nm is measured by blocking the Raman probe beam during the Kerr gated experiment (Figure S12). The PL at 657 nm has an amplitude-weighted average lifetime of 314 ps (Table S6), demonstrating that the excitonic state of P10 persists at time scales beyond those where we observe polaron formation. This leads to the conclusion that polaron formation does not occur at significant levels from the thermalized exciton state, despite this species persisting for several hundred picoseconds. Instead, the thermalized exciton decays to the ground state, resulting in the slow recovery of the negative P10 band in the TR3 spectrum at 1265 cm–1 (Figure S13). In the time-resolved experiment P10 is excited with photon energies ca. 0.5 eV greater than the P10 optical gap;12 this excess energy is important in enabling P10(e–)/polaron pair formation, with hot exciton dissociation being the dominant pathway for P10 in the absence of a sacrificial electron donor. Target analysis of the TA data (Figure S5 and accompanying text) shows that the TA data can be well fitted to the TR3-derived model with ∼95% of the polaron-pair population being generated with a lifetime of ∼4.5 ps directly from the hot excitonic state.</p><p>(a) Kinetics of P10 polaron Raman band at 847 cm–1 following 400 nm excitation. P10 powder (no electron donor) data are shown with red circles, and data with a MeOH/TEA/H2O sacrificial electron donor are shown with black squares. The parameters of the fit lines are described in the text and Table S5. The data are normalized (to 0, 1) to allow easy comparison to the Raman kinetics of P10 in the absence of the sacrificial electron donor. The kinetics of the 847 cm–1 bands are baseline corrected by taking the difference in Raman scatter at 847 and 928 cm–1, where no transient bands are present.</p><p>In the presence of the sacrificial electron donor the P10(e–) Raman band (847 cm–1, Figure 4) shows both a fast initial rise (τ < 2 ps) and a second slower growth (τ ∼ 20 ps). The similar fast lifetime component in the presence and absence of an electron donor shows that polaron formation occurs both directly from the hot excitonic state (<2 ps) that is present for up to 10 ps (Figure S11) and via reductive quenching of the thermalized exciton by the amine electron donor (∼20 ps), with both processes contributing similar amounts to the overall amplitude (Table S5). We see only minimal decay of the 847 cm–1 band in the presence of the sacrificial electron donor, suggesting that hole transfer is occurring following the fast polaron-pair formation. The conclusion that reductive quenching of the relaxed excitonic state can occur is supported by PL measurements at 657 nm which show a decreased amplitude weighted lifetime (67 ps) with a fast decay component (τ ∼ 13 ps) in the presence of the electron donor mix (Table S6 and Figure S13). A significant PL population persists to >100 ps, showing that a proportion of the exciton population is inaccessible to the scavenger. Past studies have correlated the yield of P10(e–) to the driving force for hole transfer from the polymer exciton to a sacrificial electron donor,12 and we confirm the presence of this pathway. However, the observation that a similar fast rise in the 847 cm–1 band is existent in both the presence and absence of the electron donor suggests that fast exciton dissociation to form polaron pairs with subsequent electron transfer from the sacrificial electron donor is also occurring, and this is a major contributing factor to the high level of measured photocatalytic activity of P10 for hydrogen evolution.</p><p>More widely, hot exciton dissociation is expected to be of particular importance for other particulate polymer photocatalysts which exist as aggregates ranging from several hundred nanometers to micrometers.12 Most photons will be absorbed away from the polymer/solvent (water) interface. In addition to preventing access to the sacrificial electron donor, the absence of the high-dielectric environment presents a large barrier to dissociation, with past calculations36 of binding energies of ∼1.2 eV for excitons within the polymer matrix of a similar linear polymer, as compared to only ∼0.17 eV near the polymer/water interface. In the absence of charge separation occurring prior to thermalization driven by the excess energy of the hot exciton, most excitons formed away from the polymer/interface would be lost via parasitical de-excitation.</p><p>In conclusion, we have shown that Kerr gated TR3 spectroscopy enables the study of ultrafast polaron electron formation in P10, a highly active hydrogen evolution photocatalyst under sacrificial conditions. More widely, we propose that it is a valuable technique for the study of photogenerated transients of polymer photocatalysts and photoelectrodes and could contribute to the effective design, for example, of Z-scheme composite materials for overall water splitting. A combination of experimental Raman spectroscopy and DFT calculations supports the assignment of the TR3 spectra, and the calculations indicate that the electron is predominantly localized on a single P10 moiety, in particular around the thiophene ring, which is beneficial given the proposed role of the sulfone groups in enabling water molecules to localize providing a more polar environment.12 Past models have focused on P10(e–) formation through hole transfer from the excitonic state to the sacrificial amine.12 Here we also show that hole transfer following polaron-pair formation from hot states is also an important pathway for forming long-lived P10(e–). It is known from OPV research that the distribution of excess energy following singlet exciton formation can have a critical role in facilitating charge separation,18 and our work also demonstrates the importance of polaron pair formation prior to thermalization for this polymer photocatalyst, P10.</p><p>Experimental procedures, details of the DFT calculations of P10 with different chain lengths, transient absorption data and additional TR3 data (PDF)</p><p>DFT optimized geometries of all relevant structures (ZIP)</p><p>jz1c03073_si_001.pdf</p><p>jz1c03073_si_002.zip</p><!><p>V.L.P., K.H.S., and A.W.P. contributed equally to this work.</p><!><p>The authors declare no competing financial interest.</p>

PubMed Open Access

Visible-Light Induced Singlet Nucleophilic Carbenes: Rapid and Mild Access to Fluorinated Tertiary Alcohol Derivatives

Singlet nucleophilic carbenes (SNCs) that contain only one heteroatom donor remain underexplored and underutilized in chemical synthesis. To discover new synthetic strategies that harness these SNCs as reactive intermediates, aromatic or aliphatic siloxy carbenes represent excellent model substrates as they can be readily generated photochemically from stable acyl silane precursors. We herein report the discovery that photochemically generated siloxy carbenes undergo 1,2-carbonyl addition to trifluoromethyl ketones, followed by a silyl transfer process to afford benzoin-type products. This new transformation is a rare example of the use of ketones as trapping reagents for SNC intermediates and delivers an efficient, user-friendly and scalable process to access fluorinated tertiary alcohol derivatives driven by only light, circumventing the use of catalysts or additives.

visible-light_induced_singlet_nucleophilic_carbenes:_rapid_and_mild_access_to_fluorinated_tertiary_a

<p>Carbenes are neutral reactive intermediates that possess six valence electrons, four of which occupy two sigma-bonding orbitals while the remaining two occupy either one or both of the non-bonding orbitals. 1 Carbenes are important reactive intermediates in organic synthesis, where triplet carbenes (unpaired non-bonding electrons) exhibit reactivity akin to radical intermediates and singlet carbenes (paired non-bonding electrons) can display either electrophilic or nucleophilic reactivity. 2 Electrophilic singlet carbenes, typically generated from diazo precursors, have been widely exploited in organic synthesis in processes including C-H insertion and cyclopropanation reactions. 3 Highly stabilized singlet nucleophilic carbenes (exemplified by NHC-based organocatalysts and ligands) have also been extensively utilized in synthesis with chiral NHC derivatives employed to catalyse a variety of chemical reactions with high chemo-and enantioselectivity. 4 Comparatively, the application of singlet nucleophilic carbenes (SNCs) containing only one heteroatom substituent in chemical synthesis remains limited. Examples of such SNCs include cyclic oxacarbenes generated photochemically from cyclobutanone, 5 aryl alkoxy carbenes, 6 aryl amino carbenes 7 and siloxy carbenes. 8 When considering the reaction of singlet carbenes with ketones, electrophilic carbenes undergo an O-insertion process whereby the oxygen atom of the carbonyl group donates electrons into vacant 2p orbital of the carbene to generate a carbonyl ylide (see Figure 1). The ylide is available to react in subsequent processes including dipolar cycloadditions to generate chemotypes including oxiranes and furans. 9 While the reactivity of nucleophilic carbenes with aldehydes, esters and acyl fluorides is well-established to generate species including Breslow, acyl azolium and diazolium enolate intermediates, 4e-h the reaction of nucleophilic carbenes with ketones remains relatively unexplored. To date, few reports have described the reaction of stabilized singlet nucleophilic carbenes (e.g. NHCs) with ketones. 10 For nucleophilic carbenes containing one heteroatom donor, there exists only two single examples from 1971 (Figure 1), where Brook and co-workers reported that a siloxy carbene reacted with acetone to afford an oxirane or with cyclohexanone to afford a silyl enol ether derivative (Figure 1). To gain new insight into this underexplored mode of reactivity for carbene intermediates, we set out to conduct a detailed investigation into the reaction of SNC intermediates with ketones. While oxacarbenes, aryl alkoxy carbenes, and aryl amino carbenes would all be suitable SNCs for such a study, we focused on siloxy carbenes as model substrates for studying partially stabilized SNCs as these intermediates can be readily generated from stable acyl silane precursors via irradiation with visible light, circumventing the inclusion of catalysts or additives in the reaction mixture. 1d,12 We envisaged that if successful, the photochemical addition of singlet nucleophilic carbene intermediates to ketones would deliver a new metal-free approach to the synthesis of tertiary alcohol derivatives, an important chemotype in organic synthesis and pharmaceutical sciences. 13 Our initial studies involved irradiation of benzoyltrimethylsilane (1a) with blue LEDs (427 nm, 40W) in the presence of two equivalents of various ketones including acetone, cyclobutanone, cyclohexanone, acetophenone and 2,2,2-trifluoroacetophenone in dichloromethane. Only the reaction with 2,2,2-trifluoroacetophenone led to quantifiable amounts of product, whereby after only 2 hours, complete consumption of the acyl silane was observed and trifluoromethyl alcohol derivative 3a was isolated in 82% yield. A solvent screen was subsequently performed which revealed n-hexane to be the optimal solvent for this reaction (affording 3a in near quantitative yield), however THF, diethyl ether and chloroform also proved suitable. The inclusion of 4Å molecular sieves also improved the reaction by limiting hydrolysis of the acyl silane to the corresponding aldehyde which can occur when the carbene reacts with adventitious water in the reaction mixture. Furthermore, experimental controls revealed that in the absence of light no reaction was observed.</p><p>We subsequently investigated the UV/Vis absorption profile of both the individual reaction components and the reaction mixture (see supporting information). The trifluoromethyl ketone exhibits absorption at wavelengths below 300 nm whereas acyl silane 1a absorbs light in the visible region (max = 420 nm) facilitating selective excitation of the carbene precursor by irradiation at 427 nm. Due to the absence of spectral shifts in the absorbance profile of the mixture, there was also no evidence for the formation of an electron-donor acceptor (EDA) complex between the two reagents.</p><p>A key consideration when employing siloxy carbene intermediates is that following nucleophilic carbene addition to a electrophile such as a ketone, an oxonium ion is formed that contains two potential sites of subsequent reaction. Following carbene addition to the trifluoromethyl ketones, the zwitterionic species formed can react in two possible manners, either via oxyanion addition to the base of the oxonium ion to afford an oxirane (for an example see Figure 1), or the oxyanion can abstract the silyl group from the oxonium ion to regenerate the carbonyl system, the latter of which is operating during the reaction described herein.</p><p>The structure of the ketone product was confirmed by 13 C NMR analysis with key spectral characteristics including a resonance at  = 192 ppm assigned to the new ketone motif, a quartet at  = 121 ppm (J = 286 Hz) assigned to the CF3 group, and a quartet at  = 83 ppm (J = 26 Hz) assigned to the new tetrasubstituted carbon center formed which is bonded to the CF3, silyl ether and ketone functional groups. A shift of the CF3 resonance from  = -69 to -74 ppm from the 2,2,2-trifluoroacetophenone starting material to the product can also be observed following analysis by 19 F NMR.</p><p>With high-yielding conditions in hand, the photochemical reaction process was explored using different acylsilane and trifluoromethyl ketone substrates (Scheme 1). Initially, variations in the silyl group were explored with the trimethyl, triethyl and tert-butyldimethyl silyl analogues all affording the product in high yield (3b-d), however the tert-butyldimethyl silyl analogue required a longer reaction time (6h) to achieve full conversion and was isolated in a reduced yield (88%). The corresponding triisopropyl silyl analogue failed to react under the standard conditions.</p><p>Variations in the aryl component of 1 were then explored with 3and 4-monosubstituted aroyl silanes containing methyl, methoxy, chloro or fluoro substituents all performing well (Scheme 1, 3e-h, 3k-m, 78-99%). Di-and tri-substituted aroyl silanes also afforded the corresponding fluorinated tertiary alcohol derivatives 3i and 3j in excellent yield. Trifluoroacetophenone derivatives containing additional substituents in either the 3-or 4-aryl position were then subjected to reaction with acyl silane 1a under the standard photochemical conditions to afford the corresponding benzoin-type adducts in exceptional yield (Scheme 1, 3n-3t, 87-99% yield).</p><p>To further explore the reactivity of photochemically generated nucleophilic carbene intermediates with ketones, a series of aroyl silanes 1 were reacted with an aliphatic ketone in the form of 1,1,1trifluoroacetone 4 (Scheme 2). This reaction also proceeded with high efficiency, affording to corresponding ketones 5a-f in near quantitative yield. Again, diverse substitution patterns on the aromatic ring of the aroyl silanes were tolerated (Scheme 2). To this point, attempts to react the carbene intermediate generated from aliphatic acyl silanes with 2a or 4 via direct irradiation (using 370, 390 and 427 nm LEDs) or triplet energy transfer (employing 2 mol% of [Ir(dF(CF3)ppy)2(dtbbpy)]PF6 at 440 nm) proved unsuccessful. 8g,h To further probe this photochemical process a series of additional experiments were conducted (Figure 2). Aroyl silanes were reacted with 2-chloro-2,2-difluoroacetophenone to demonstrate that other halogenated ketone derivatives are applicable in this reaction process (Figure 2a). Subsequently, a competition experiment between aroyl silanes containing either electron withdrawing or electron donating substituents was conducted by irradiating a solution containing 0.25 mmol each of aroyl silanes 3g and 3i in the presence of 0.25 mmol of 2,2,2-trifluoroacetophenone for 2 hours at 427 nm. Scheme 2. Photochemical reaction of siloxy carbenes with trifluoroacetone. Reaction conditions: acyl silane (0.25 mmol), 1,1,1-trifluoroacetone (0.50 mmol), n-hexane (1.0 mL), 4Å MS (~250 mg) irradiated at 427 nm for 2h.</p><p>The conversion to the product was analyzed using 1 H NMR which revealed that the carbene generated from the 4-chlorophenyl acyl silane trapped 68% of the available ketone with the remaining 32% trapped by the carbene generated from the electron rich acyl silane. This result infers that either formation of the carbene from acyl silane 3i is more facile or that the carbene generated from 3i is in fact more reactive.</p><p>According to the Beer-Lambert law, light transmittance decreases exponentially as distance from the photon source increases. It is well established that the use of flow chemical technology (where reactions are conducted in microchannels or microtubing) can significantly increase the efficiency of photochemical processes leading to shorter reaction times and decreased by-product formation. 14 Thus, the opportunity to increase the efficiency and scalability of the process described herein was explored in flow by pumping a 0.25M solution of the reagents in n-hexane through a 4 mL reactor coil irradiated by 427 nm LEDs (40 W).</p><p>Initially, optimization of the reaction time was conducted with 99% and 100% conversion achieved at 15-and 20-minute reaction (residence) times, respectively. Attempts to reduce the residence time to 10 minutes led to a decreased conversion of 96%, and irradiation of the 4 mL reactor coil with two 427 nm LEDs for 10 mins failed to increase the conversion. Finally, a reaction time of 5 minutes employing two photoreactors resulted in 75% conversion of the acyl silane to the product. With optimized flow conditions in hand, a 0.25M solution of anhydrous n-hexane containing 7 mmol of acyl silane and two equivalents of the 2,2,2-trifluoroacetophenone was pumped in a continuous fashion through the 4 mL reactor coil employing a residence time of 15 minutes. Following the completion of this process, evaporation of the volatile components and recrystallisation from n-hexanes afforded 2.32 g of 3a in 94% isolated yield (Figure 2c).</p><p>A crystal structure of 1a confirmed the structure of the tertiary alcohol derivative generated from the photochemical bond-forming process (Figure 2f) and the fluorinated silyl ether products could be further manipulated through cleavage of the silyl group to afford the tertiary alcohol, and reduction to produce the diol (Figure 2d).</p><p>Finally, the difference in reactivity between an electrophilic carbene and nucleophilic carbene intermediate with the same trifluoromethyl ketone is depicted mechanistically in Figure 2e. In 2017, Jiang and co-workers reported that the electrophilic Pd-carbene intermediate generated from an aryl alkyl tosyl hydrazone underwent an O-insertion process with the carbonyl group of 2,2,2-trifluoroacetophenone to afford a carbonyl ylide which further reacted to generate the oxirane product. 15 For the new transformation described herein, reaction of the photochemically generated nucleophilic carbene intermediate occurs at the carbon-atom end of the trifluorocarbonyl group which gives rise to a zwitterionic intermediate affording the benzoin-type product after silyl transfer.</p><p>In summary, we have discovered a new process for the formation of tertiary alcohol derivatives employing fluorinated ketone derivatives as trapping reagents in the presence of photochemically generated nucleophilic carbene intermediates. To note, the formation of related products has been achieved employing reactions that proceed via acyl anion intermediates generated from the addition of an NHC catalysts to an aldehyde or a cyanide reagent to acyl phosphonates. 16 Advantageously, the protocol described herein proceeds In addition, this new transformation represents a unique example of SNCs reacting with ketones and importantly, providing additional insights into the properties and reactivity of the relatively underexplored class of singlet nucleophilic carbene intermediates that contain one-heteroatom donor.</p>

ChemRxiv

Saikosaponin d protects against acetaminophen-induced hepatotoxicity by inhibiting NF\xce\xbaB and STAT3 signaling

Overdose of acetaminophen (APAP) can cause acute liver injury that is sometimes fatal, requiring efficient pharmacological intervention. The traditional Chinese herb Bupleurum falcatum has been widely used for the treatment of several liver diseases in eastern Asian countries, and saikosaponin d (SSd) is one of its major pharmacologically-active components. However, the efficacy of Bupleurum falcatum or SSd on APAP toxicity remains unclear. C57BL/6 mice were administered SSd intraperitoneally once daily for five days, followed by APAP challenge. Biochemical and pathological analysis revealed that mice treated with SSd were protected against APAP-induced hepatotoxicity. SSd markedly suppressed phosphorylation of nuclear factor kappa B (NF-kB) and signal transducer and activator of transcription 3 (STAT3) and reversed the APAP-induced increases in the target genes of NF-kB, such as pro-inflammatory cytokine Il6 and Ccl2, and those of STAT3, such as Socs3, Fga, Fgb and Fgg. SSd also enhanced the expression of the anti-inflammatory cytokine Il10 mRNA. Collectively, these results demonstrate that SSd protects mice from APAP-induced hepatotoxicity mainly through down-regulating NF-kB- and STAT3-mediated inflammatory signaling. This study unveils one of the possible mechanisms of hepatoprotection caused by Bupleurum falcatum and/or SSd.

saikosaponin_d_protects_against_acetaminophen-induced_hepatotoxicity_by_inhibiting_nf\xce\xbab_and_s

1. Introduction<!>2.1. Chemicals and reagents<!>2.2. Animals and drug administration<!>2.3. Biochemical and histological analyses<!>2.4. Quantitative polymerase chain reaction (qPCR) analysis<!>2.5. Western blot analysis<!>2.6. Statistical analysis<!>3.1. SSd concentration, biochemical and toxicological reactions<!>3.2. Influence of SSd on APAP detoxification, oxidative stress, and peroxisome proliferator-activated receptor \xce\xb1 (PPAR\xce\xb1) signaling in the liver<!>3. 3. Influence of SSd on hepatocyte apoptotic signaling induced by APAP challenge<!>3.4. SSd suppresses APAP-induced increases in the expression of STAT3 target genes and pro-inflammatory cytokines<!>3.5. SSd inhibits APAP-induced activation of STAT3 and NF-kB<!>4. Discussion

<p>Acetaminophen (APAP), also known as paracetamol and N-acetyl-p-aminophenol, is an over-the-counter analgesic and antipyretic agent that is widely used in the world. Although safe at therapeutic doses, APAP overdose can cause acute liver failure [1]. Over the past four decades, numerous studies have focused on the molecular mechanism of APAP toxicity and therapeutic strategies to intervene in APAP-induced hepatotoxicity [2-5]. Natural products or their components are promising candidates because of their abundance, diversity, long history of use, and safety.</p><p>Bupleurum falcatum is a popular prescribed herb for the treatment of various liver diseases in eastern Asian countries. Saikosaponin d (SSd, Fig. 1A) is considered one of the major active components isolated and identified from this herb [6]. In Sprague-Dawley rats, SSd can decrease transforming growth factor β1 in the liver and attenuate the development of hepatic fibrosis and carcinogenesis induced by dimethylnitrosamine [7]. Supplementation with SSd alone or in combination with curcumin, significantly reduced carbon tetrachloride (CCl4)-induced inflammation and fibrogenesis [8]. In cell culture models, SSd exhibited potent cytotoprotection and anti-proliferation activity against hepatocellular carcinoma cells [9,10]. However, there have been no studies to evaluate the protective effect of SSd against hepatotoxicity induced by APAP.</p><p>SSd was found to modulate inflammatory response. Early studies showed that SSd can activate the phagocytosis of macrophages, modulate T lymphocyte function, and up-regulate interleukin (IL)-2/IL-4 production in thymocytes [11]. It can also elevate corticotropin-releasing factor mRNA levels in the hypothalamus and increase serum corticotropic hormone levels, which are involved in the pro-inflammatory processes. SSd can decrease apoptosis in both p53-postive HepG2 and p53-negative Hep3B cells, as indicated by reduced activation of nuclear factor kappa B (NF-κB) and attenuated expression of Bcl-xl [12]. Protection against CCl4-induced inflammation and fibrogenesis by SSd was correlated with down-regulation of the pro-inflammatory cytokines tumor necrosis factor-α (TNFα), IL-1β, and IL-6, and up-regulation of the anti-inflammatory cytokine IL-10 [8]. Despite the risk of APAP-induced toxicity and the wide application of Bupleurum falcatum for liver diseases in clinic, there are no data on the effect of Bupleurum falcatum or SSd on APAP-induced hepatotoxicity as well as the underlying mechanism. In this study, APAP was injected to SSd-pretreated C57/B6 mice and changes in liver phenotypes and gene expression were examined.</p><!><p>Saikosaponin d (SSd, Fig. 1A), APAP, glutathione (GSH) assay kit, and chlorpropamide were purchased from Sigma–Aldrich (Sigma-Aldrich, St. Louis, MO). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) assay kits were from Catachem (Bridgeport, CT). Antibodies against NFκB subunit p65 and signal transducer and activator of transcription 3 (STAT3) and their phosphorylated form, p-p65 and p-STAT3, and GAPDH were purchased from Cell Signaling Technologies (Danvers, MA). HPLC grade solvents such as acetonitrile and formic acid were purchased from Fisher Scientific (Hampton, NH). All the other chemicals were of the highest grade from commercial source.</p><!><p>Male 6- to 7-week-old C57BL6 mice (Jackson Laboratories, Bar Harbor, ME) were maintained in the NCI animal facility under a standard 12 h light/12 h dark cycle with free access to food and water. All procedures were performed in accordance with Institute of Laboratory Animal Resource Guidelines and the animal study protocols approved by the National Cancer Institute Animal Care and Use Committee. Mice were randomly divided into four groups, vehicle/control, SSd/control, vehicle/APAP, and SSd/APAP, and killed 4 h or 24 h after single APAP injection.</p><p>For APAP injection, a typical single dose of 200 mg/kg/day was used as described elsewhere [3,13,14]. Considering the published pharmacodynamic and pharmacokinetic information of SSd [6,7], 2 mg/kg once daily was used as the dosing regimen. SSd powder was dissolved in a saline solution supplemented with 0.1% Tween 20 and was administered by intraperitoneal injection at a dose of 2 mg/kg/day once daily for five days. Saline solution containing 0.1% Tween 20 without SSd was administered as a vehicle. APAP was dissolved in warm saline solution (20 mg/mL) and was injected intraperitoneally 30 minutes after the last SSd injection. Saline was injected to mice in the control groups.</p><p>Blood was taken from retro-orbital space of the mice in the SSd/control group 1 h after the SSd injection on day 1, 3, and 5 in order to determine circulating SSd concentration. Twenty-four hour urine samples were also collected after APAP administration to measure APAP and its metabolites. Mice were killed at 4 h and 24 h after APAP challenge, following which serum and liver were collected. The liver was fixed in 10% neutral buffered formalin, after briefly washing with phosphate buffered saline. The remaining liver tissue was flash frozen in liquid nitrogen and stored at -80°C for further analysis.</p><!><p>To measure serum SSd levels, an aliquot of 5 μL serum supernatant was subjected to a Waters ACQUITY ultra-performance liquid chromatography (UPLC) system coupled with a XEVO triple-quadrupole tandem mass spectrometer (Waters, Corp., Milford. MA). The MRM transition 779.5→617.4 in ESI- was monitored after fragmentation analysis (Fig. 1A) and chlorpropamide (277→111) was used as an internal standard. For quantification, a calibration curve constructed using authentic standard had the r2 value above 0.99. The urinary profile was acquired by subjecting an aliquot of 5 μL of urine sample to UPLC coupled to a quadrupole time-of-flight mass spectroscopy (UPLC-ESI-QTOFMS) (Waters, Corp.) and analyzing as previously reported [15].</p><p>Serum AST and ALT activities were measured following the manufacturer's instruction with VetSpecTM kits (Catachem, Bridgeport, CT). For hepatic GSH measurement, 50 mg of frozen liver tissues were homogenized in 0.5 mL 5% 5-sulfosalicylic acid using Precellys 24 (Bertin Technologies). After centrifugation at 10,000g for 10 minutes, the supernatant were measured according to the protocol, and results were normalized by the tissue mass and expressed as nmol/mg.</p><p>Formalin-fixed liver tissues were subjected to dehydration in serial concentrations of ethanol and xylene and embedded in paraffin. Four-micrometer serial sections were cut and stained with hematoxylin and eosin, followed by histological examination with an Olympus BX41 light microscope.</p><!><p>Total RNA was extracted from approximately 20 mg frozen liver tissues, using TRIzol reagent (Invitrogen, Carlsbad, CA). cDNA was generated from 1 μg mRNA with a SuperScript II Reverse Transcriptase kit and random oligonucleotides (Invitrogen, Carlsbad, CA). The primer sequences listed in Supplementary Table 1 were designed using qPrimer Depot and their specificity tested by melting curve profiles. qPCR reactions contained 1 μL cDNA, 150 nM of each primer and 5 μL of SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) in a total volume of 10 μL. qPCR was carried out on an ABI-Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA). Measured mRNA abundance was normalized to 18S rRNA and expressed as fold change relative to the vehicle control group.</p><!><p>Liver tissues were homogenized using RIPA buffer (1:10, g/v) in which cocktail inhibitors and mercaptoethanol were freshly added. Protein concentrations were measured using the BCA protein assay kit (Thermo Scientific, Waltham, MA) and normalized to 5 mg/mL. After being mixed with a two-fold dilution of loading buffer (1:1, v/v), 60 μg of protein was loaded on the gel. After electrophoresis, protein was transferred to a PVDF membrane and blocked with 5% bovine serum albumin or skim milk in Tris-buffered saline containing 0.1% Tween 20 for at least 1 h. Membranes were incubated overnight with primary antibodies against p65, p-p65, STAT3, p-STAT3, or GAPDH. Secondary antibodies were incubated for 1 h, and the blotted membranes were prepared with ECL substrate (Thermo Scientific). Band intensities were quantified by densitometry. The ratio p-STAT3/total STAT3 and p-p65/p65 were calculated and normalized by those in vehicle/control group.</p><!><p>All the experimental values were expressed as mean ± SD. Statistical analysis was performed by two-tailed nonparametric Mann-Whitney test for unpaired data using GraphPad Prism 5 (GraphPad Software Inc., San Diego, CA). 95% was set as confidence intervals and difference was considered as significant if the p value was less than 0.05.</p><!><p>Injection of 2 mg/kg/day of SSd yielding serum concentrations between 0.3-0.6 μg/mL (Fig. 1B). This level was relatively steady during the administration period. In the SSd/APAP group, more APAP and less 3-cysteinylacetaminophen (Cys-APAP) were excreted in urine compared with vehicle/APAP mice (Fig. 1C).</p><p>Serum ALT and AST are reliable indicators of acute hepatic injury. Typically, a time-dependent increase of these markers occurs between 2 to 6 h post-APAP challenge [14]. At 4 h after 200 mg/kg APAP administration, serum ALT was significantly increased with no change in AST (Fig. 2A and B), while at 24 h after APAP treatment, both ALT and AST were increased in the vehicle/APAP group. In contrast, neither enzyme activity increased in mice pretreated with SSd for five days before APAP treatment (Fig. 2C and D).</p><p>To confirm the changes in serum AST/ALT levels, liver histology was assessed. In the vehicle control and SSd/control groups, the liver tissues were histologically normal, while a dose of 200 mg/kg APAP resulted in large patchy necrosis around the central veins. No overt liver damage was observed in SSd/APAP group (Fig. 3). These results demonstrate a clear protective effect of SSd pre-administration on APAP-induced liver injury.</p><!><p>In the SSd/APAP group, more APAP and less 3-cysteinylacetaminophen (Cys-APAP) were excreted in urine compared with vehicle/APAP mice (Fig. 1C), suggesting that SSd affects APAP metabolism. APAP is metabolized to the toxic intermediate metabolite N-acetyl-p-benzoquinone imine (NAPQI) in the liver through cytochrome P450 (CYP) 2E1, and to minor extent CYP3A11 (in mice) and CYP1A2. However, expression of Cyp2e1 or Cyp3a11 mRNAs was not changed in any of the groups (Supplementary Fig. S2A and B). NAPQI is catabolized by conjugation with GSH, causing consumption of GSH and enhanced oxidative stress and ensuing mitochondrial dysfunction. Depletion of hepatic GSH usually precedes the increase of AST and ALT activity [14]. Four hours after APAP treatment, a small decrease was seen in both the vehicle/APAP and SSd/APAP groups, but was not significant compared with the vehicle/control. However, 24 h after APAP challenge, GSH levels in the SSd/APAP group were higher than that in the vehicle/APAP group (Supplementary Fig. S1A and B). Expression of superoxide dismutases 1 and 2 (Sod1 and Sod2) mRNAs were analyzed and found to not differ among the four groups, similar to the small variation of GSH as noted above (Supplementary Fig. S2A). Thus, the protective mechanism by traditional antioxidants was apparently not involved in this study.</p><p>Activation of peroxisome proliferator-activated receptor α (PPARα) was reported to have an important role in protecting against APAP-induced liver injury through induction of uncoupling protein 2 (UCP2) [3]. Induction of its target genes, such as Acox1, Ehhadh, Cpt1, and Cpt2 were also associated with the protective effect of Schisandra Spehanthera extract against APAP [2]. These PPARα target genes were analyzed and were not significantly altered by SSd (Supplementary Fig. S2C). Thus, PPARα activation was not involved in the protection by SSd, probably due to the lower dose of APAP compared with the previous reports that used doses in excess of 400 mg/kg.</p><!><p>Apoptosis is an important step in APAP hepatotoxicity [18,19]. Since SSd was reported to inhibit apoptosis, typical genes involved in apoptosis were analyzed. Anti-apoptotic gene Bcl-2 mRNA was higher and pro-apoptotic Bax mRNA was lower in the SSd/APAP group compared with those in the vehicle/APAP group, while Bcl-xl and Bim mRNAs were not changed in any of those groups (Fig. 4A).</p><!><p>STAT3 was reported to be involved in APAP-induced hepatotoxicity, where IL-22, a STAT3-activating cytokine, was shown to attenuate APAP-induced liver injury [20]. Thus, expression of mRNAs encoded by the STAT3 target genes Socs3, Fga, Fgb and Fgg were assessed. These mRNAs were significantly up-regulated in the vehicle/APAP group, and the induction by APAP challenge was reversed by SSd pretreatment (Fig. 4B).</p><p>Prevailing evidence indicates that inflammation is induced following production of the toxic metabolite from APAP. Therefore, mRNAs encoded by genes involved in inflammation, including Il-10, Il-6, Tlr4, Ddit3, c-Jun, c-Fos, Ccl2, Icam1, Ripk3 and Tnfα, were analyzed. Among these, Il-6 and Ccl2 mRNAs were markedly increased by APAP injection, and these inductions were reversed by SSd pretreatment. APAP-induced moderate increases in Ddit3, c-Jun, c-Fos, and Icam1 and similar attenuation by SSd treatment were also observed. Additionally, the mRNA encoding the anti-inflammatory cytokine IL-10 was up-regulated in the SSd/APAP group but unchanged in the vehicle/APAP group (Fig. 5).</p><p>Collectively, these results show that attenuation of APAP-induced up-regulation of STAT3 target genes and pro-inflammatory cytokines, such as Il-6 and Ccl2, is associated with amelioration of hepatotoxicity caused by SSd administration.</p><!><p>Western blotting was used to assess the activation of STAT3 and NFκB, a master regulator of genes involved in inflammation. The expression levels of phosphorylated STAT3 were increased sharply (5.5±0.93 fold) in the vehicle/APAP group and was markedly attenuated in the SSd/APAP group (2.2±1.19 fold) (Fig. 6). Similarly, the active form p-p65 were also increased moderately (2.08±0.10 fold) in the vehicle/APAP group and decreased in the SSd/APAP group (0.53±0.04 fold) (Fig. 6). Therefore, these results indicate that the protective effect of SSd against APAP hepatotoxicity is mainly derived from inhibition of NF-κB and STAT3 activation.</p><!><p>After oral ingestion of APAP, about 90% is directly conjugated with glucuronic acid or sulfate. The remaining 5-10% is metabolized largely by CYP2E1 to NAPQI [15,21]. This reactive metabolite can bind covalently with cellular proteins, leading to irreversible toxicity. After activation of APAP to NAPQI, subsequent propagation and amplification of reactive oxygen species (ROS) and stress-induced signal transduction pathways lead to cell death [21]. Protection against APAP-induced hepatotoxicity by GSH, N-acetylcysteine, taurine, hypotaurine and other antioxidants was reported [4,5,22-24]. The unchanged expression of Cyp2e1, Cyp3a11, Sod1, and Sod2 mRNAs, as well as the slight variation of GSH after SSd administration, indicates the above traditional mechanisms were likely not directly involved in the protective effect of SSd.</p><p>The relationship between PPARα, fatty acid β-oxidation and APAP-induced hepatotoxicity was established [14,15], and Ucp2, a PPARα target gene, was recently found to mediate protective effects of PPARα activators [3]. In Cyp2e1-null mice, PPARα activation was much more significant and more persistent than in wild-type mice following a toxic APAP dose, leading to prolonged up-regulation of PPARα target genes (Cpt1, Cpt2, Acot1, and Cyp4a10) involved in fatty acid β-oxidation [14,15]. Inhibition of fatty acid β-oxidation leads to an increase in serum palmitoylcarnitine and other acylcarnitines following high-dose APAP treatment [14]. The Chinese traditional herb Schisandrae Sphenanthera extract was recently shown to protect against APAP-induced hepatotoxicity as revealed by the recovery of fatty acid β-oxidation and lowering serum acylcarnitines [2]. In the present study, none of the typical PPARα target genes or the serum acylcarnitines were altered by SSd. This difference could be due to the lower dose of 200 mg/kg applied in the present study. Thus, protection against APAP hepatotoxicity by SSd does not involve PPARα activation.</p><p>Attenuation of IL-6 was observed with increased liver injury induced by APAP in Kupffer cell-depleted mice [25]. In IL-6 knockout mice dosed with APAP, fewer regenerating hepatocytes were present, and thus IL-6 appears to play a role in liver regeneration [26]. Additionally, IL-6 can protect against liver injury by up-regulating the hepatic cytoprotective heat shock proteins [27]. However, considering the differential activation of NFκB between the APAP group and SSd/APAP group, IL-6, the downstream product of NFκB, likely promotes toxicity rather than regeneration in this context.</p><p>IL-10 was reported to be a protective factor in limiting formation of TNF-α and IL-1 [25]. In Kupffer cells, binding of IL-10 to its receptor leads to prolonged activation of STAT3, thereby inhibiting the inflammatory response. In IL-10 knockout mice, more severe liver injury was associated with increased formation of pro-inflammatory cytokines and inducible nitric oxide synthase expression after APAP administration [28]. In the present study, IL-10 was unchanged in the vehicle/APAP group, but increased in the SSd/APAP group. Thus, modulation of IL-10 by SSd may offer a protective role against APAP toxicity.</p><p>Recently, prophylactic injection of IL-22, a STAT3-activating cytokine, significantly reduced hepatocyte damage due to APAP, suggesting a protective role of STAT3 [20]. However, forced expression of STAT3 target gene Socs3 in T cells was reported to exacerbate APAP-induced hepatotoxicity [29]. In the present study, expression of Socs3, and the other identified STAT3 target genes Fga, Fgb and Fgg was increased in the vehicle/APAP group and these increases were reversed in SSd/APAP group. In accordance with this result, STAT3 was activated in the APAP group but attenuated in SSd/APAP group. Since IL-6 is the result of NFκB activation and an activator of STAT3, the above modifications could be downstream responses to NFκB activation.</p><p>In the progression of drug-induced hepatotoxicity and liver disease, it is generally accepted that inflammation plays a critical role [21,30,31]. Among the inflammatory pathways, STAT3 and NFκB were most often reported [31,32], both of which are involved in APAP-induced hepatotoxicity [29]. In the present study, both were activated as revealed by western blot analysis. Additionally, their typical downstream response factor IL-6 and target gene Socs3 were up-regulated. This indicates the NFκB and STAT3 signal transduction pathways are directly involved in hepatotoxicity induced by APAP. Additionally, APAP-induced enhancement of expression of c-Jun and c-Fos was corrected/reversed/attenuated by SSd. This is similar to the protective mechanism of the PPARα activators Wy-14643 and fenofibrate where anti-inflammation mediated by c-Jun and c-Fos is involved [3]. In contrast, the pro-apopototic genes Bim and Bax were only moderately modified in the present study. These data suggest that inflammation is related to the toxic events induced by APAP and that the protective mechanism of SSd is associated with modification of NFκB-IL6-STAT3 signaling.</p><p>In conclusion, SSd, the active component of traditional Chinese herb Bupleurum falcatum, can protect against APAP-induced hepatotoxicity via inhibiting of NFκB and STAT3 signaling. These data suggest new insights to understand the role of inflammation underlying APAP hepatotoxicity and clinical application of Bupleurum falcatumas, a hepatoprotectant in eastern Asian countries.</p>

PubMed Author Manuscript

Biochemical and Structural Characterization of Germicidin Synthase: Analysis of a Type III Polyketide Synthase that Employs Acyl-ACP as a Starter Unit Donor

Germicidin synthase (Gcs) from Streptomyces coelicolor is a type III polyketide synthase (PKS) with broad substrate flexibility for acyl groups linked through a thioester bond to either coenzyme A (CoA) or acyl carrier protein (ACP). Germicidin synthesis was reconstituted in vitro by coupling Gcs with fatty acid biosynthesis. Since Gcs has broad substrate flexibility, we directly compared the kinetic properties of Gcs with both acyl-ACP and acyl-CoA. The catalytic efficiency of Gcs for acyl-ACP was 10-fold higher than for acyl-CoA suggesting a strong preference towards carrier protein starter unit transfer. The 2.9 \xc3\x85 germicidin synthase crystal structure revealed canonical type III PKS architecture along with an unusual helical bundle of unknown function that appears to extend the dimerization interface. A pair of arginine residues adjacent to the active site affect catalytic activity but not ACP binding. This investigation provides new and surprising information about the interactions between type III PKSs and ACPs that will facilitate the construction of engineered systems for production of novel polyketides.

biochemical_and_structural_characterization_of_germicidin_synthase:_analysis_of_a_type_iii_polyketid

INTRODUCTION<!>Materials<!>Bacterial Strains and Protein Purification<!>Size Exclusion Chromatography<!>X-ray Crystallography<!>Reconstituting the Fatty Acid Biosynthesis Pathway with Gcs<!>Synthesis of 3-Oxo-4-methyl-pentyl-CoA<!>Acylation of AcpP<!>Enzyme Kinetics<!>Binding Kinetics<!>Relative Enzyme Activity Assay<!>Germicidin Biosynthesis Pathway<!>Enzyme Kinetics<!>X-ray Crystal Structure of Gcs<!>Identifying Putative Residues for Gcs\xe2\x80\xa2AcpP Binding<!>Effects of Surface Mutations on Gcs\xe2\x80\xa2AcpP Binding and Activity<!>CONCLUSION

<p>Polyketides are ubiquitous secondary metabolites produced by bacteria, fungi, plants, and animals,1–4 and constitute one of the largest sources for natural product-based pharmaceuticals (antibiotics, antiparasitics, antifungals, anticancer drugs, and immunosuppressants) and other commercial products (food additives, pigments and nutraceuticals).5–7 Most polyketides are synthesized by three broad classes of polyketide synthases (PKSs), types I, II, and III, that share a common mechanism of sequential decarboxylative condensations of a wide range of acyl-coenzyme A (CoA) substrates.3,8,9 Polyketide structural diversity is dictated by selectivity of starter and extender units, the number of condensation reactions, and the manner of off-loading or ring closure of the fully elaborated polyketide chains.10 Type I PKSs are multifunctional proteins made up of modules sub-divisible into multiple discrete catalytic domains, all of which ultimately control the size, regio- and stereochemical characteristics of the polyketide scaffolds. Type II PKS are composed of dissociable enzyme complexes where each catalytic domain is expressed from an individual gene.9 Type III PKSs differ in that a single active site is used iteratively for starter unit loading, each Claisen condensation step, and the final off-loading/cyclization of the polyketide chain. Found as homodimers of a single ~40 kDa polypeptide, conventional type III PKSs iteratively condense malonyl-CoA derivatives with acyl-CoA esters3 rather than utilizing substrates that are covalently linked to acyl carrier proteins (ACP), which is a general feature of type I and II PKSs.8 Plant type III PKSs tend to utilize a larger variety of acyl-CoAs (e.g. cinnamoyl-CoAs) while the more divergent bacterial type III PKSs usually capture acyl-CoA substrates from primary metabolism.10</p><p>Manipulation of secondary metabolic pathways has enabled the generation of libraries of unnatural compounds.11,12 For example, 50 macrolides were prepared by systematically modifying modular components of the type I PKS, 6-deoxyerythronolide B synthase.13 However, since type III PKSs contain only a single catalytic site, exchange of functional domains is not a viable strategy to introduce new functional properties. Rather, differences in the active site cavity are responsible for the diversity among type III PKSs.3 Until recently, in vitro synthesis of novel polyketides has been dependent upon the substrate flexibility of the type III PKSs.14–16In vivo combinatorial biosynthesis was shown to be another promising route.17–19 Expanding the active site pocket by amino acid substitutions of a type III PKS enabled the synthesis of more complex molecules.20,21 This characteristic makes type III PKSs excellent candidates for enzyme engineering approaches to expand the structural diversity of small molecules derived from these systems.</p><p>Although early work indicated that type III PKSs use acyl-CoA starter units exclusively, numerous exceptions have been reported since. Recently, two type III PKSs, germicidin synthase (Gcs) and SCO7671 from Streptomyces coelicolor, were shown in vitro to accept both unnatural acyl-CoAs and acyl-ACPs as starter unit substrates.22 Similarly, ArsB and ArsC, both type III PKSs from Azotobacter vinelandii, obtain substrates directly from the ACP domains of the type I fatty acid synthase (FAS), ArsA, to give phenolic lipids.23 BpsA from Bacillus subtilis is speculated to directly accept the acyl moiety of acyl-ACP that is synthesized by the type II FAS system.24 FtpA from Myxococcus xanthus was also shown to use acyl-ACPs, which involves initial activation and transfer by a fatty acid-AMP ligase.25 In addition, the architecture of Steely1, a type I FAS/type III PKS hybrid from the social amoeba, Dictyostelium discoideum, whose type III PKS domain presumably accepts a thioester directly from its ACP domain.26 The ability of type III PKSs to utilize acyl-CoA and/or acyl-ACP starter units represents a new opportunity to manipulate these systems to expand natural product chemical diversity by engineering artificial hybrid PKS systems. However, our ability to develop rational approaches to engineer this remarkable class of biosynthetic enzymes requires a deeper understanding of the molecular basis for starter unit selectivity and processing. Therefore, following our initial observation of the unique molecular recognition of Gcs for an acyl-ACP starter unit donor,22 we were motivated to further investigate the biochemical and structural details of its catalytic function and protein-protein interactions.</p><p>Bioinformatic analysis of the S. coelicolor genome resulted in identification and characterization of Gcs as a type III PKS responsible for producing germicidin A, B and C (Scheme 1).27,28 This investigation provided evidence that the proposed pathway for these natural products includes incorporation of branched acyl-chain starter units. The proposed β-ketoacyl thioester-linked ACPs are known intermediates in Streptomyces fatty acid biosynthesis formed by FabH- or FabF-catalyzed condensation of malonyl-ACP with 2-methylbutyryl-, isovaleryl-, isobutyryl, or n-butyryl-CoA.29 Homologs of Gcs (Figure S1) can be found in numerous Streptomyces species including S. lividans (99% identity), S. sviceus (88% identity), S. scabiei (87% identity), S. zinciresistens (84% identity) and S. viridochromogenes (87% identity), the latter being a confirmed producer of germicidins.30 The recently described S. coelicoflavus strain that produces surugapyrone A (germicidin D) likely contains a Gcs homolog as well.31</p><p>In this study, we sought to determine the preference of S. coelicolor Gcs for acyl-CoAs or acyl-ACPs starter unit substrates. Furthermore, the fatty acid pathway, the putative source of acyl-ACPs, was reconstituted biochemically and coupled with Gcs to generate germicidin A in vitro. Additional insights were gained by solving the crystal structure of Gcs to assess potential surface residues involved in the molecular recognition of the type II FAS ACP acyl-chain starter unit donor. Protein-protein interaction studies were conducted to analyze the binding characteristics of the Gcs•ACP complex, and to investigate the role of the surface residues. Cumulatively, these data provide new insights into a type III PKS enzyme capable of using both acyl-CoAs and acyl-ACPs for assembly of a range of bioactive and structurally diverse pyrone natural products.</p><!><p>Malonyl-CoA, methylmalonyl-CoA, isobutyryl-CoA and acetoacetyl-CoA were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ethylmalonyl-CoA was synthesized according to published procedures.32 Aliquots of acyl-CoAs were maintained at −80 °C with 0.05% formic acid. 4-Hydroxy-3,6-dimethyl-2H-pyran-2-one was purchased from Alpha Aesar for use as an authentic standard. 3-Ethyl-4-hydroxy-6-methyl-2H-pyran-2-one was prepared from dehydroacetic acid (Sigma) as previously described.33</p><!><p>E. coli BL21(DE3) strains transformed with either pDHS9758, pDHS10019, and pSG3045, containing Sco2389 (acyl-carrier protein, AcpP), Sco7221 (germicidin synthase, Gcs), and Sco4744 (acyl-carrier protein synthase, AcpS), respectively, were constructed as previously described.22E. coli BL21(DE3) strains harboring plasmids pLH14 and pLH16, containing S. glaucescens β-ketoacyl-(acyl carrier protein) synthase III (KAS III, FabH) and malonyl CoA:acyl carrier protein malonyltransferase (FabD), respectively, were kind gifts from the laboratory of Kevin Reynolds.29 Mutants of Gcs were constructed by mutagenesis using the QuikChange site-directed mutagenesis kit from Stratagene (La Jolla, CA, USA) to replace an arginine with alanine at positions Arg276, Arg277, Arg280 or Arg317 using pDHS10019 as a template. Primers used for mutagenesis are provided in Table S1. Protein expression and purification was performed as previously described.22 Approximately 50 mg Gcs could be purified from a 1 L culture. Protein concentrations were determined using absorbance at 280 nm and calculated extinction coefficient (1 A280 = 0.62 mg/mL).</p><!><p>Nickel-NTA purified His6-tagged Gcs protein was loaded onto a 120 mL HiLoad 16/60 Superdex 200 (GE Healthcare Bio-Sciences Corp, Piscataway, NJ, USA) column equilibrated with storage buffer (20 mM HEPES, pH 7.4, 150 mM NaCl, 10% glycerol, 0.5 mM TCEP). Fractions were combined, concentrated, frozen, and stored at −80 °C. Calibration of the column was performed with molecular weight markers from Sigma. Gcs eluted as a single peak at 74 mL, which is consistent with a dimeric complex in solution (Figure S2).</p><!><p>Gcs crystals were grown in 3–5 days at 4 °C using the hanging-drop vapor-diffusion method from an equal mixture of protein solution (10 mg/mL, freshly dialyzed into 20 mM HEPES, pH 7.5, 1 mM TCEP) and reservoir solution (21–25% isopropanol, 300 – 400 mM ammonium acetate, 0.1 M Tris buffer pH 8.5–9.0). To protect crystals from isopropanol evaporation during mounting, a volume of glycerol equal to twice the starting drop volume was added to the drop before the crystals were harvested in loops and cryo-protected in liquid N2. Attempts to obtain co-crystals with a CoA substrate (hexanoyl- or acetoacetyl-CoA) were unsuccessful. Attempts to soak in hexanoyl- or acetoacetyl-CoA into Gcs crystals after they had formed resulted in dissolution of the crystals within seconds.</p><p>Diffraction data were collected at 100 K on GM/CA-CAT beamline 23ID-D at the Advanced Photon Source (Argonne National Laboratory, Argonne, IL, USA). Data were processed in cubic point group 432 (space group P4132 or P4332) using the HKL2000 suite.34 The structure was solved by molecular replacement. The BALBES server35 identified a solution in space group P4132 using a homology model based on the structure of S. coelicolor tetrahydroxynaphthalene synthase (PDB code 1U0M).36 A single polypeptide occupied the asymmetric unit, which had extremely high solvent content (80% v/v, Vm = 6.1 Å3/Da). An identical molecular replacement solution was obtained from the same probe structure using PHASER.37,38 The expected physiological dimer forms on a crystallographic 2-fold axis. Initial refinement steps were performed with REFMAC539 and manual modeling was completed using COOT.40 Final rounds of refinement were performed with PHENIX41 using a translation-libration-screw (TLS) model of molecular motion with 4 TLS groups identified by the TLSMD server (Table 1).42</p><!><p>In order to synthesize germicidin A, the Gcs reaction was coupled with the fatty acid biosynthesis pathway to access the proposed branched 3-oxo-4-methyl-pentyl-ACP intermediate. In a total of 2.0 mL, the reaction consisted of 50 mM HEPES, pH 7, 150 mM NaCl, 10 mM MgSO4, 2 mM TCEP, 100 μM CoA, 400 μM malonyl-CoA, 200 μM ethylmalonyl-CoA, 200 μM isobutyryl-CoA, 10 μM AcpP, 2.5 μM FabD, 2.5 μM FabH, 2.5 μM Gcs, and 2.5 μM AcpS. The reaction proceeded at room temperature overnight followed by extraction with two equal volumes of ethyl acetate. The resulting organic layer was dried under vacuum. The dried residue was re-dissolved in acetonitrile and fractionated on a semi-preparative C18 column using an isocratic gradient (25% acetonitrile in water with 0.1% formic acid). Approximately 15 μg of purified product was characterized by capillary 1H NMR and LC-ESIMS.</p><!><p>A 1 mL reaction mixture containing of 50 mM HEPES, pH 7, 150 mM NaCl, 1 mM TCEP, 1 mM malonyl-CoA, 1 mM isobutyryl-CoA and 25 μM FabH was prepared. The reaction proceeded at 25 °C for three hours followed by the addition of 10 μL of formic acid. 3-oxo-4-methyl-pentyl-CoA was purified on a semi-preparative C18 column using a binary gradient starting at 95% solvent A (5 mM NH4OAc, pH 5.4) and 5% solvent B (MeOH, 0.1% formic acid) for five minutes followed by a linear gradient over 30 minutes ending with 50% solvent B. Pooled elution fractions were concentrated using rotary evaporation to remove methanol and finally dried by lyophilization. Product formation was verified by LC-MS as described in the Supporting Information. Stock solutions were quantified at 260 nm using a calibration curve of acetoacetyl-CoA.</p><!><p>A 250 μM mixture of apo- and holo-AcpP, 10 μM AcpS, 0.5 mM 3-oxo-4-methylpentyl-CoA, 10 mM MgSO4, HEPES buffer (50 mM HEPES, pH 7.0, 150 mM NaCl), in a total volume of 2 mL was incubated at 25 °C for 3 hours (of note, Sfp and Svp were both ineffective).43,44 The reaction mixture was loaded onto a 5 mL His-Trap column (GE Healthcare), washed with 5 column volumes of HEPES buffer, and finally eluted with 5 column volumes of HEPES buffer with 0.3 M imidazole. The protein was concentrated using a 3K MWCO centrifuge column (Pall Life Sciences, Ann Arbor, MI, USA) followed by buffer exchange on a PD-10 column (10% glycerol, 0.1 M NaCl, 50 mM HEPES, pH 6.8; GE Healthcare). Protein concentration was determined by using the Bradford protein assay (Bio-Rad, Hercules, CA, USA) with BSA as the standard. 3-Oxo-4-methyl-pentyl-AcpP was analyzed with electrospray mass spectrometry by using a ThermoFinnigan LTQ linear ion trap instrument (capillary temperature, 250 °C; capillary voltage, 32 V; tube lens, 95 V). Mass spectra were deconvoluted by using ProMassN for Xcalibur (Novatia, Monmouth Junction, NJ, USA).</p><!><p>The kinetic constants of Gcs, Km and kcat, were determined by varying starter unit acyl-CoA concentrations while using a fixed extender unit concentration of either methylmalonyl-CoA or ethylmalonyl-CoA. The enzyme reaction solution consisted of 100 mM Tris-HCl, pH 7.8, 150 mM NaCl, 1.0 mM methylmalonyl-CoA or ethylmalonyl-CoA, and various concentrations of 3-oxo-4-methyl-pentyl-CoA in a total volume of 40 μL. The reaction solution was pre-warmed at 30 °C for five minutes before adding 0.2 μM Gcs. The reaction proceeded for one minute before being quenched with the addition of 10 μL of 6.05 M HCl in methanol and frozen at −80 °C until analysis. Reactions with acetoacetyl-CoA used 3 μM Gcs and the reaction time was extended to five minutes. Reactions were analyzed by injecting 25 μL onto a Beckman HPLC with a XBridge C18 column (5 μm particle size, 4.6 × 250 mm) using an isocratic gradient of 25% or 30% acetonitrile in water with 0.1% formic acid. Authentic standards were used to quantify product formation at 290 nm. Reactions with varying concentrations of 3-oxo-4-methyl-pentyl-AcpP (a mixture of 33% apo-, 31% holo-, 36% 3-oxo-4-methyl-pentyl-AcpP as determined by MS) were conducted in the same fashion. Steady-state parameters were determined by fitting to the equation, ν = [E]kcat[S]/([S]+Km) using GraphPad Prism 5.0 (GraphPad Software Inc., La Jolla, CA, USA).</p><!><p>All biolayer interferometry measurements were made using an Octet RED instrument (ForteBio, Menlo Park, CA, USA) using streptavidin (SA) biosensors. Assays were performed in 96-well black microplates at 25 °C and 1000 rpm. All volumes were 200 L. AcpP was biotinylated using EZ-Link NHS-LC-LC-biotin (succinimidyl-6-[biotinamido]-6-hexanamidohexanoate) (Thermo Scientific Pierce, Rockford, IL, USA) at a 5:1 molar ratio of biotin to protein for 30 min at 25 °C followed by dialysis into phosphate buffered saline (10 mM Phosphate, pH 7.4, 150 mM NaCl) using a 3K MWCO Slide-A-Lyzer (Thermo Scientific Pierce). All Gcs proteins were similarly dialyzed. Biotinylated AcpP (50 μg/mL) was loaded onto the sensors for 600 s. After a baseline in 1× PBS kinetics buffer (ForteBio) was established, tethered AcpP was exposed to Gcs protein at concentrations between 0.3 and 10 μM. Association was monitored for 1800 s followed by dissociation in 1× PBS kinetics buffer for 1800 s. A reference sensor (only tethered AcpP) was subtracted from each data set. Shift data were analyzed with ForteBio Analysis software (version 7.0). Kinetic parameters (kon and koff) and affinity (KD) were determined from a global non-linear regression of association and dissociation binding kinetics using a 1:1 Langmuir binding model.</p><!><p>The relative activities of Gcs wild-type and each Gcs mutant were determined. Reactions preceded similarly as described above using 0.2 μM Gcs wild-type or mutant, 1.0 mM ethylmalonyl-CoA and either 100 μM 3-oxo-4-methyl-pentyl-CoA or 5 μM 3-oxo-4-methyl-pentyl-AcpP. In the case of the R276/317A double mutant, for which no activity was detected, the reaction time was subsequently extended to two hours (with wild-type enzyme as a control) to confirm the absence of product (see main text).</p><!><p>Starter units for the germicidin pathways were predicted to originate from fatty acid biosynthesis with Gcs utilizing S. coelicolor endogenous fatty acid intermediates as reaction substrates.22,27 A previous study revealed that a S. coelicolor ΔScFabH∷EcFabH mutant produced substantially less germicidin metabolites than the wild-type strain27 because E. coli FabH preferentially accepts straight-chain acyl-CoAs.45 The fatty acid acyl carrier protein, AcpP, is adjacent to FabH within the S. coelicolor type II FAS operon. Thus, we reasoned that AcpP was the most likely acyl carrier protein starter unit donor for Gcs. The proposed pathway (Scheme 1) begins with AcpS, which converts AcpP from the apo into the holo form. FabD employs malonyl-CoA to convert holo-AcpP into malonyl-AcpP and FabH follows by catalyzing the condensation of acyl-CoAs (e.g. 2-methylbutyryl-CoA, isobutyryl-CoA, and acetyl-CoA) with malonyl-AcpP to generate the corresponding β-ketoacyl-AcpPs. For example, we reasoned that 3-oxo-4-methyl-pentyl-AcpP would form in a FabH-catalyzed reaction of isobutyryl-CoA with malonyl-AcpP and serve as the starter unit for germicidin biosynthesis.29 We tested this model by reconstituting the fatty acid-coupled germicidin pathway in vitro. The reaction mixture consisted of FabD, FabH, AcpS, apo/holo-AcpP, and Gcs. When co-administered with ethylmalonyl-CoA, germicidin A is the predicted product. The mass of the major peak formed was consistent with germicidin A and the 1H NMR confirmed the assignment based on comparison with published data (Figure S3).30</p><p>Surprisingly, in control experiments, where one of the enzymes was omitted from the reaction, we observed that just FabH and Gcs were sufficient to form germicidin A in vitro (Figure S3). We hypothesized that the condensation of isobutyryl-CoA with malonyl-CoA was catalyzed by FabH to form 3-oxo-4-methyl-pentyl-CoA. This acyl-CoA substrate could then be converted by Gcs directly to germicidin A. A reaction consisting of only FabH with isobutyryl-CoA and malonyl-CoA was tested for the production of 3-oxo-4-methyl-pentyl-CoA. Indeed, this reaction produced both free coenzyme A along with a new product bearing a molecular mass matching the predicted 3-oxo-4-methyl-pentyl-CoA (LC-ESIMS: [M−H]− = 878.25 m/z, and [M+H]+ = 880.35; calculated 878.16 and 880.17, respectively) (Figure S4). Upon purification, Gcs was able to convert the new FabH product along with methylmalonyl-CoA or ethylmalonyl-CoA into germicidin D and germicidin A, respectively. The ability of FabH to act independent of malonyl-AcpP was surprising, but not without precedent in bacterial anabolism. FabH from E. coli can catalyze formation of acetoacetyl-CoA from malonyl-CoA and acetyl-CoA.46 Additionally, an acetoacetyl-CoA synthase from a soil-isolated Streptomyces sp. strain, which shares homology to KAS III was recently described.47</p><!><p>The experiments above demonstrated that two parallel paths to germicidin products are possible, involving acyl-AcpP and acyl-CoA starter units. In order to assess which route might be favored, we determined the kinetic parameters for both types of reactions (Scheme 2). Many bacterial type III PKS enzymes use only malonyl-CoA as both starter and extender units. For example, 1,3,6,8-tetrahydroxynaphthalene (THN) synthase from S. griseus,48 PhlD from Pseudomonas fluorescens,49 DpgA from Amycolatopsis orientalis,50 use five, four and three molecules of malonyl-CoA, respectively. Other bacterial type III PKSs including ArsB and ArsC from Azotobacter vinelandii and SrsA from S. griseus use long chain acyl-thioesters as a starter unit.23,51,52 Like SrsA, Gcs can use additional extender units such as methylmalonyl- and ethylmalonyl-CoA.22,27 Furthermore, we have previously demonstrated that Gcs and SCO7661 are able to catalyze reactions using a range of unnatural acyl-CoA and acyl-ACP-tethered starter units.22</p><p>Our initial in vitro experiments with Gcs used acetoacetyl-CoA and 3-oxo-4-methyl-pentyl-CoA as starter units to determine substrate preference and whether the extender unit choice (methylmalonyl-CoA or ethylmalonyl-CoA), plays a significant role in product profile (Figure S5a and Table 2). When using acetoacetyl-CoA, Gcs displayed typical saturation kinetics with methylmalonyl-CoA as the extender unit but not with ethylmalonyl-CoA, for which the apparent Km was greater than 1 mM. In contrast, enzymatic activity was substantially higher for 3-oxo-4-methyl-pentyl-CoA when comparing specificity constants (kcat/Km). With the branched acyl-CoA starter unit, Gcs did not display any extender unit preference (Figure S5b and Table 2). The kinetic parameters of Gcs using 3-oxo-4-methyl-pentyl-CoA were comparable to other bacterial type III PKS enzymes that employ a long chain acyl-CoA starter unit (ArsB from Azotobacter vinelandii for n-behenyl-CoA: kcat = 0.931 min−1, Km = 4.86 μM, compared to Gcs for 3-oxo-4-methyl-pentyl-CoA with saturating amounts of methylmalonyl-CoA: kcat = 12.97 min−1, Km = 12.12 μM).52</p><p>Gcs was then characterized using 3-oxo-4-methyl-pentyl-AcpP as a substrate to generate either germicidin A or germicidin D (Figure S5c and Table 2). The catalytic efficiency using 3-oxo-4-methylpentyl-AcpP along with either methylmalonyl-CoA or ethylmalonyl-CoA was an order of magnitude higher compared to the corresponding acyl-CoA analog. For comparison, the apparent Km value of Staphylococcus aureus FabH for malonyl-AcpP was 1.76 ± 0.40 μM53 and the Km value of Gcs for 3-oxo-4-methyl-pentyl-AcpP was 1.6 ± 0.2 μM. This data suggests that Gcs can preferentially select acyl-AcpP over acyl-CoA to produce germicidins.</p><!><p>To gain fundamental information about type III PKS•ACP interactions, we determined the crystal structure of recombinant His6-Gcs at 2.9 Å resolution (Figure 1). All previously reported type III PKS structures are dimeric, and size exclusion chromatography indicated that two Gcs molecules associated to form a homodimer in solution (Figure S2). The asymmetric unit of Gcs crystals contains a single protein molecule. However, the presumed physiologically relevant dimer is formed by a crystallographic 2-fold contact that buries 2300 Å2 of protein surface area per monomer (~13.6% of the monomer surface area).</p><p>Similar to other type III PKS structures,1,54 Gcs contains a Cys-His-Asn catalytic triad within a deep active site cavity that is accessible to the surrounding solvent. Apart from a few exterior loops, there are few major differences in the conserved αβαβα-fold or dimer interface compared with S. coelicolor THNS.36 Curiously, Gcs contains a long insertion between residues 61 and 100, which has not been observed in other type III PKSs (Figure S1). Examination of sequence alignments fails to shed light on the function of this insertion. A structural role is implied because the large insertion protrudes from the core of Gcs and folds into a four-helix bundle formed by two helices from each monomer. The shape of the helical bundle resembles a "basket" hanging from the catalytic core of the protein (Figure 1a). Electron density is especially poor for the basket, likely because it extends into the large solvent channels of the crystal and makes no contacts with other molecules within the lattice. As the basket is not in proximity to the active site entrance (Figure 1a), we do not expect that it interacts with the carrier protein. It is possible that this insertion reinforces the dimer interface. Attempts to produce Gcs mutants in which the basket insert was removed (GcsΔ63–96, GcsΔ63–97, and GcsΔ63–98) resulted only in insoluble protein (data not shown).</p><!><p>Based upon sequence alignments, bacterial type III PKSs, and not plant type III PKSs, were predicted to accept starter units from ACPs.1 Direct biochemical evidence was obtained when Gcs was shown to accept acyl groups carried by either CoA or ACP.22 Although the bacterial and plant enzymes have strong structural similarities, only bacterial PKSs have highly conserved residues aligned with a cationic/hydrophobic patch determined to be crucial for FabH•ACP binding.46 Similarly, other FAS complexes (ACP with FabD, FabG, and FabI) have cationic/hydrophobic patches in close proximity to their active sites that when mutated affect ACP binding.55–58</p><p>Based on the Gcs crystal structure, a cationic patch including four arginine residues (Arg276, Arg277, Arg280 and Arg317) was identified adjacent to the type III catalytic triad active site (Figure 1b). Moreover, Gcs position Arg317 aligns with the E. coli FabH Arg249 residue responsible for KAS III•ACP binding (Figure S1a).46 The arginine residues in the cationic patch were each replaced with alanine to determine the importance of these position(s) for promoting Gcs•AcpP molecular recognition. Perturbations to these interactions were predicted to affect binding and/or catalysis by affecting protein-protein contacts and/or pantetheinate arm-protein interactions. For example, a decrease in protein-protein interactions may also result in attenuated catalysis while changes to pantetheinate binding could result in attenuated catalysis and may or may not affect protein-protein interactions.</p><!><p>Two assays were employed to evaluate the effects of surface residue mutations on the Gcs•AcpP specificity. The first assay was a direct measurement of AcpP binding to Gcs using biolayer interferometry.59 The second assay was a measurement of catalytic activity of Gcs mutants with either acyl-AcpP or acyl-CoA. We reasoned that any mutation that interferes with the Gcs•AcpP interaction could also affect AcpP-dependent Gcs catalytic activity.</p><p>Binding of Gcs to the AcpP immobilized onto the streptavidin biosensor led to a measured increase in biolayer thickness in real time to provide the association rate constant (kon), the dissociation rate constant (koff) and the dissociation constant (KD) (Table 3). The curve fits for the wild-type and mutant forms of Gcs closely followed a 1:1 binding model (Figure S6). However, none of these mutants significantly disrupted the specific binding to AcpP. Overall, wild-type Gcs has greater affinity for ACP than do type II FAS enzymes, based on reported KDs; for instance, S. coelicolor AcpP was reported to bind to FabD with a KD of 1.9 ± 0.3 μM,58 whereas a KD of 0.2 μM was observed for the Gcs•AcpP interaction.</p><p>The mutants and wild-type Gcs were compared in their ability to produce germicidin A. Two of the mutants (R276A and R317A) had significantly impaired activity with the acyl-CoA substrate analog when compared to the wild-type Gcs protein (Table 4). Similarly, these two mutants had somewhat reduced activity with acyl-AcpP substrates (Table 4). Therefore, these residues are not likely to be responsible for the observed Gcs selectivity for AcpP. In contrast, a cationic patch residue of FabH, Arg249 (sequence aligned with Gcs Arg317), was shown to affect both binding and catalysis of malonyl-AcpP substrate while not affecting catalysis with malonyl-CoA,46 a result that is inconsistent with structures of FabH co-crystallized with CoA and related analogs where the pantetheinate arm is in direct contact with Arg249.60,61 When the pantetheinate arm was modeled directly into the Gcs structure using the coordinates from a FabH structure (PDB: 2GYO),61 the model suggests that Arg276 and Arg317 both make direct contact with the phosphoryl groups (Figure 1b). Consistent with this hypothesis, the double mutant (R276/317A) had no detectable activity for either acyl-CoA or acyl-AcpP, implying that phosphopantetheine interaction with either Arg276 or Arg317 is essential to Gcs activity. Our results suggest that the major role of residues Arg276 and Arg317 involves pantetheinate binding, and that AcpP selectivity resides elsewhere on Gcs.</p><!><p>PKSs are responsible for making a vast range of natural products with diverse biological activities. Harnessing the biosynthesis of polyketides has the potential to open up new sources of valuable small molecules for pharmaceutical development. Re-engineering of PKSs demands an in-depth understanding of the multiple proteins involved and how they interact for functional catalysis. In this study we investigated the Gcs type III PKS, which selectively employs acyl-ACPs as starter unit donor, a role previously limited to type I and II PKSs. Towards this goal, we set out to characterize the specificity of the protein-protein interaction, and to initiate efforts to probe the key features that govern ACP recognition by Gcs. We first reconstituted germicidin synthesis by coupling Gcs with the endogenous S. coelicolor fatty acid pathway. This study revealed that Gcs functions with a 10-fold higher activity towards acyl-AcpP compared to the corresponding acyl-CoA, which suggests the predominance of this pathway in vivo.27 The Gcs crystal structure revealed canonical type III PKS architecture except that the dimer interface was extended by a 40-residue insertion. Similar to type II FAS enzymes, Gcs has a cationic patch surrounding the entrance to its catalytic pocket, a feature considered pivotal for FAS•ACP docking.1,46,55–58 However, upon changing the putative surface arginine residues to alanine, the Gcs mutant proteins retained high affinity for AcpP. Differences between the Gcs mutants were revealed when comparing relative activities, with two alanine variants showing attenuated catalytic function against both acyl-AcpP and acyl-CoA and the double mutant having no activity. Based on homology modeling, the two surface arginine residues are likely to directly interact with the phosphate group of pantetheinate, and the synergistic effect of the two partially inactivating mutations implies that at least one of these arginines is essential. However, the amino acids that affect Gcs•AcpP binding remain to be identified. This work has highlighted the role of cationic residues surrounding the active site of a bacterial type III PKS capable of accepting acyl-ACPs. Further study into the type III PKS•AcpP interaction may lead to engineered type I/III PKS hybrid proteins capable of creating novel polyketide structures.</p>

PubMed Author Manuscript

The use of HPLC-Q-TOF-MS for comprehensive screening of drugs and psychoactive substances in hair samples and several “legal highs” products

AbstractNon-targeted screening of drugs present in herbal products, known as “legal high” drugs and in hair as a biological matrix commonly used in toxicological investigations was accomplished with the use of high pressure liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS). In total, 25 and 14 therapeutical drugs and psychoactive substances/metabolites were detected in investigated hair samples and herbal products, respectively. We demonstrate that the HPLC-Q-TOF methodology seems to be a powerful tool in the qualitative analysis applied in identification of these designer drugs, thus enabling a laboratory to stay-up-to-date with the drugs that are being sold as legal high products on black market.Graphical abstract

the_use_of_hplc-q-tof-ms_for_comprehensive_screening_of_drugs_and_psychoactive_substances_in_hair_sa

Introduction<!><!>Results and discussion<!><!>Results and discussion<!>Conclusions<!>Chemicals<!>Herbal products collection and preparation<!><!>Instrumentation

<p>The identification of large number of new continuously and increasingly appearing designer drugs on drug market is currently a major priority for forensic laboratories [1, 2]. Due to the novelty of incoming psychoactive products known as "legal highs" or "herbal highs", at present there is limited research in the published literature concerning the pharmacokinetics, pharmacological or toxicological effects of these drugs [3]. Control of production and distribution of new emerging legal highs is strongly dependent on implementation of new screening methods and analytical solutions. The identification of psychoactive substances is challenging due to their fast transience on the drug scene.</p><p>Recently, many analytical techniques have been applied in comprehensive drug screening in biological and non-biological specimens. Application of routine toxicological methods based on the use of immunochemical assays is limited mainly due to their insufficient sensitivity and limited coverage. Their use is also hampered by the risk of obtaining false positive results, what can lead to serious medical and social consequences [4, 5]. Therefore, high performance liquid chromatography coupled with high resolution TOF (or Q-TOF) mass spectrometry is mostly applied. HPLC-Q-TOF-MS technique enables tentative identification of unknown compound based on prediction of chemical formula from accurate ion mass measurement and characteristic isotopic pattern [6]. Moreover, the volume of sample required for analysis is very small [7]. Therefore, it is stated as a very powerful tool for identification of species and can be very useful when reference standards are not available. Moreover, it provides excellent full-scan sensitivity, that makes it suitable for wide-scope screening in forensic investigations. The successful application of Q-TOF-MS technique in screening of many different families of drugs has been reported by many researchers [5, 6, 8–10].</p><p>The availability of the legal high products on the black market, in various forms of preparations like powders, pills, and teas is increasing tremendously. These products are easily available in herbal shops. Moreover, they are intaken as an alternative for federally illegal amphetamines or opioids [4, 11].</p><p>In the last decades, hair analysis has become a well established strategy to investigate retrospectively drug abuse histories [4]. Hair, as a human matrix, exhibit a lot of highlights in drug of abuse analysis compared to other biological samples (blood or urine) [12]. Firstly, sampling step for hair is non-invasive, simple, and painless for the patient. Secondly, hair sample does not require any special storage and transport conditions due to slow process of hair destruction in comparison to another biological samples [13]. Besides, drugs can stay in this matrix for a long time (even months). However, hair samples have got some limitations for analysts, just to mention time-consuming analytical procedures and high correlation to melanin concentration dramatically affecting results [14]. In short, what is the most important, hair allows to retrospective detection of chronic exposure to drugs or poisons up to years back. Hair analysis consists of few principal steps: sampling, storage and transport, decontamination, extraction of features from biological matrix, instrumental analysis, and finally data interpretation. The decontamination phase involves of one or washings of the sample to eliminate possible external contamination. The extraction of the analytes from the hair can be achieved by various methods, which differ according to the nature of the analytes themselves and the identification technique to be employed [15].</p><p>The purpose of this paper was to investigate the capability of high pressure liquid chromatography coupled with quadrupole time-of-flight mass spectrometry for rapid screening of representative multiclass drugs including antidepressants, non-narcotic, antibiotics, or illegal drugs such as opioids and amphetamines in herbal products and hair samples taken as the one of the most commonly used as biological matrices. The high sensitivity obtained in full-scan MS mode allows to the retrospective detection of unknown compounds. Our aim was to present the overall concept of application of Q-TOF technique in two areas of toxicological screening: firstly, detection of chronic intake of therapeutical drugs and illegal substances in 13 hair samples obtained from volunteers, former addicts and secondly to evaluate the presence of federally controlled active substances in four commercially illegal highs products investigated under this study.</p><!><p>Steps in the identification of unknown compound with the use of METLIN database. Example was presented for talbutal detected in herbal product</p><p>Steps in the identification of unknown compound with the use of MassBank database. Example was presented for mephedrone detected in herbal product</p><p>Steps in the identification of methoxetamine in hair samples by HPLC-Q-TOF-MS/MS: a MS/MS spectrum of ion at m/z = 248.1646; b generated chemical formula at m/z = 248.1646 –(C15H21NO2) with software MassHunter Workstation QualitativeAnalysis. B.03.01, measured mass: m/z = 248.1646; calculated mass [M+H]: 248.1645; c proposed fragmentation of methoxetamine</p><p>Summary of drug detected in herbal products and their theoretical and experimental masses</p><p>Summary of drug detected in hair samples and their theoretical and experimental masses</p><!><p>The Controlled Substances Act (CSA) regulates import, possession, use, and distribution of certain substances. The legislation includes five schedules, that are describing the characteristics of each substance. Talbutal, as intermediate-acting barbiturate, belongs to substances controlled in Schedule III, which use can lead to moderate and psychical dependence. Synthetic cannabinoid compounds, such as UR-144, AKB-48, JWH-016, JWH 369 and synthetic stimulants, like mephedrone have been controlled by United States Drug Enforcement Agency under Schedule I (drugs with high potential of abuse) [16]. As can be seen in Table 1 federally controlled compounds were detected in commercially and easily available legal herbal products. After legislation and registration processes the use of these controlled substances is unexpectedly not waned, what confirms and enforces of the necessity of qualitative identification of this type of products in order to enable forensic laboratories to be "on time" with current drugs and its possible replacements being sold as designer products.</p><p>In the 13 hair samples, 21 therapeutic and illegal drugs were identified. This included mainly: sedative hypnotic drugs (zopiclone), antitussives (dextromethorphan, dimethyltryptamine), antidepressants (fluoxetine, doxepin), antihistaminics (hydroxyzine), non-narcotic analgesics (methadone, fentanyl, paracetamol), antibiotics (sulfamethoxazole, trimethoprim), antipsychotics (perazine), adulterants (hydroxyperazine), narcotic analgesics (α-tramadol), medications for treatment of cardiovascular diseases (metropolol), nootropics (noopept), stimulants and psychedelics (6-APB, 6-APBD, α-methyltryptamine) and illegal drugs such as opioids (methadone), amphetamines (amphetamine), cannabinoids (UR-144), cathinones (methoxetamine). The results are summarized in Table 2.</p><!><p>Summary of analyzed hair samples with undetected substances</p><!><p>Few reasons can be given to explain this phenomena. Firstly, low incorporation rate of drugs in hair, which can be affected by washing-out by shampooing and hair pigmentation (sample III). It is well known, that the incorporation of drugs in the hair depends on melanin content in the matrix and is regulated by the pharmacological principles of the substance distribution. The incorporation and binding of drugs in the hair are much greater in pigmented versus non-pigmented hair, so no detection of these drugs in gray hair is explicable [13]. The reason can also lie in irregular intake of drugs (sample IX), insufficient stability of features in hair, a long-term medical treatment in case of some drugs and finally low concentration of drug in hair sample which is not sufficient for Q-TOF-MS detection (sample VIII). In case of sample IX, hair were collected from tip (distal) section of hair as well. This additional analysis was performed in order to verify how cutting/not-cutting of hair for 5 years (as was declared in questionnaire) will affect results. This effort allowed to detect 6-APB (this drug was not detected in hair sample taken from posterior vertex of the head), what confirms hypothesis that this drug was intaken in earlier period of life.</p><!><p>In this study, a HPLC method coupled with Q-TOF-MS for the toxicological screening and identification of 39 drugs and metabolites in herbal products and hair samples was developed. The proposed HPLC-Q-TOF method based both on accurate mass, isotopic pattern recognition and fragmentation spectra obtained in Targeted MS/MS mode has been successfully applied to hair samples from 13 abusers with known therapeutic and psychoactive drug intake at the life time and 4 herbal legal high products. Positive and negative ion mode was applied in order to increase sensitivity for basic and acidic analytes. The sample preparation including basic incubation followed by liquid–liquid extraction with ethyl acetate was suitable for variety of substances present in investigated hair samples. Despite the large advantages of HPLC-Q-TOF-MS technique in comprehensive forensic investigations, a disagreement between substances mentioned in questionnaires and detected in hair in some cases was observed.</p><p>The developed HPLC-Q-TOF-MS method provides to be applicable in comprehensive forensic investigations, depending on the aim of the research: (1) introducing a legislation and restriction according to new federally uncontrolled substances detected in so called "legal highs", (2) studies on mechanism of action, diffusion among selected populations of drug abusers as well as metabolism of novel psychoactive substances based on their detection in biological specimens, such as hair samples.</p><p>Further investigations to improve results obtained in qualitative screening should mainly focus on performance of semi-quantitative determination of detected drugs.</p><!><p>Acetonitrile, methanol, ammonium formate (LC–MS grade) were purchased from Sigma-Aldrich (St. Louis, USA). Formic acid (FA) was purchased from Merck (Darmstadt, Germany). Acetone and hexane (analytical grade) were purchased from POCH (Gliwice, Poland). Sodium hydroxide, ethyl acetate, and hydrochloric acid (analytical grade) were obtained from POCH (Gliwice, Poland). Nylon (PA) ProFill™ 25 mm bright blue (0.2 μm pore size) syringe filters Whatman Puradisc™ 13 mm PTFE (0.2 μm pore size) syringe filters were purchased from Sigma-Aldrich (St. Louis, USA). Ultrapure H2O was prepared using HLP5 system from Hydrolab (Wiślina, Poland).</p><!><p>The research collaborator collected four samples of legal high products available and sold on the drug market over the Internet under the names of "Tajfun", "The recidivist", "R.I.P.", and "The Bandit". For toxicological screening 50 mg of dried herbal material was used. Subsequently, 5 cm3 of solvent mixture ACN:MeOH (1:1) was added and the content was vortex mixed for 10 min. The mixture was sealed with aluminum for protection from light and left for 72 h in darkness. After incubation content was mixed for 2 min and consecutively filtered through syringe filters (0.2 μm pore size). Prior to analysis solution was diluted 1:100 with acetonitrile/water mixture (3:7). Subsequently it was transferred into autosampler vial. 10 mm3 were injected for Q-TOF-MS analysis.</p><!><p>Workflow for identification of xenobiotics in hair sample</p><!><p>The HPLC-Q-TOF-MS was performed with the use of an Agilent 1290 LC system equipped with a binary pump, an online degasser, an autosampler and a thermostated column compartment coupled with a 6540 Q-TOF-MS with a Dual ESI source (Agilent Technologies, Santa Clara, CA, USA). An Agilent ZORBAX XDB-C-8, 150 × 4.6 mm, 3.5 μm column was used for RP-HPLC separation of extracts obtained from hair samples and herbal products with gradient elution program from 10 to 100 % B during 20 min followed by 100 % B maintained for 5 min. The mobile phase flow rate was 0.5 cm3/min and injection volume in this case was 10 mm3. The mobile phase consisted of water containing 0.01 % formic acid (component A) and methanol containing 0.01 % formic acid (component B). The column temperature throughout the separation process was kept at 40 °C. During all analyses, the samples were kept in an autosampler at 4 °C.</p><p>The ESI source was operated both in positive and negative ion mode with the following conditions: the fragmentor voltage was set at 120 V, nebulizer gas was set at 35 psig, capillary voltage was set at 3500 V, and drying gas flow rate and temperature were set at 10 dm3/min and 300 °C, respectively. For MS/MS measurements collision energy ramp ranging from 15 to 40 eV to promote fragmentation was used. The data were acquired in centroid and profile mode using High Resolution mode (4 GHz). The mass range was set at 50-1000 m/z in MS and MS/MS mode. The data were processed with the MassHunter Workstation QualitativeAnalysis.B.03.01 Software. The Q-TOF-MS was calibrated on a daily basis.</p>

PubMed Open Access

Deficiencies in acetyl-CoA carboxylase and fatty acid synthase 1 differentially affect eggshell formation and blood meal digestion in Aedes aegypti

To better understand the mechanism of de novo lipid biosynthesis in blood fed Ae. aegypti mosquitoes, we quantitated acetyl-CoA carboxylase (ACC) and fatty acid synthase 1 (FAS1) transcript levels in blood fed mosquitoes, and used RNAi methods to generate ACC and FAS1 deficient mosquitoes. Using the ketogenic amino acid 14C-leucine as a metabolic precursor of 14C-acetyl-CoA, we found that 14C-triacylglycerol and 14C-phospholipid levels were significantly reduced in both ACC and FAS1 deficient mosquitoes, confirming that ACC and FAS1 are required for de novo lipid biosynthesis after blood feeding. Surprisingly however, we also found that ACC deficient mosquitoes, but not FAS1 deficient mosquitoes, produced defective oocytes, which lacked an intact eggshell and gave rise to inviable eggs. This severe phenotype was restricted to the 1st gonotrophic cycle, suggesting that the eggshell defect was due to ACC deficiencies in the follicular epithelial cells, which are replaced after each gonotrophic cycle. Consistent with lower amounts of de novo lipid biosynthesis, both ACC and FAS1 deficient mosquitoes produced significantly fewer eggs than control mosquitoes in both the 1st and 2nd gonotrophic cycles. Lastly, FAS1 deficient mosquitoes, but not ACC deficient mosquitoes, showed delayed blood meal digestion, suggesting that a feedback control mechanism may coordinate rates of fat body lipid biosynthesis and midgut digestion during feeding. We propose that decreased ACC and FAS1 enzyme levels lead to reduced lipid biosynthesis and lower fecundity, whereas altered levels of the regulatory metabolites acetyl-CoA and malonyl-CoA account for the observed defects in eggshell formation and blood meal digestion, respectively.

deficiencies_in_acetyl-coa_carboxylase_and_fatty_acid_synthase_1_differentially_affect_eggshell_form

1. INTRODUCTION<!>2.1. Mosquitoes rearing<!>2.2. Bioinformatic analyses<!>2.3. Expression Analysis<!>2.4. RNA interference<!>2.5. 14C- labeling and quantification<!>2.6. Measuring mosquito fecundity and egg viability<!>2.7. Oocyte staining<!>2.8. BSA protein quantification<!>2.9. Statistical Analysis<!>3.1 ACC and FAS1 transcript levels are regulated in the fat body after blood feeding<!>3.2. ACC and FAS1 are required for lipid biosynthesis in sugar and blood fed mosquitoes<!>3.3. Blood fed ACC deficient mosquitoes fail to synthesize an intact eggshell<!>3.4. Delayed blood meal digestion and reduced fecundity in FAS1-deficient mosquitoes<!>4. DISCUSSION<!>Figure S1

<p>Anautogenous mosquito species, including the Dengue vector mosquito, Aedes aegypti, are strictly dependent on a blood meal in order to complete a gonotrophic cycle. In addition to providing metabolic energy to the female mosquito, and amino acids for protein synthesis, blood meal nutrients also provide reduced carbon for fatty acid biosynthesis. Lipids make up approximately 35 percent the dry weight of Ae. aegypti eggs, the majority of which is derived from fat body stores and transported to the ovaries through the hemolymph (Troy et al., 1975; Ziegler and Ibrahim, 2001). Using blood meals containing 14C-labeled amino acids or protein, it was shown that ~65% of blood meal carbon is fully oxidized or excreted, ~15% is converted to maternal and egg lipids, ~10% is found in maternal and egg proteins, and the remaining ~10% is divided amongst glycogen and other metabolites (Zhou et al., 2004a). Most of the accumulated lipid in developing oocytes in the first gonotrophic cycle comes from pre-existing maternal stores in the fat body, which were acquired from larval food and adult nectar meals prior to blood feeding (Briegel et al., 2002; Zhou et al., 2004b). Based on studies showing that urban adult female Ae. aegypti rarely feed on nectar following the first gonotrophic cycles (Edman et al., 1992; Harrington et al., 2001; Scott et al., 1997), egg lipids in subsequent gonotrophic cycles are primarily derived from fatty acids synthesized from blood meal carbon and from the blood meal itself.</p><p>Two key lipid biosynthetic enzymes in eukaryotes are the rate-limiting enzyme acetyl-CoA carboxylase (ACC), and the multifunctional fatty acid synthase (FAS). ACC carboxylates acetyl-CoA to generate malonyl-CoA in a biotin-dependent manner, which is then covalently attached to FAS at the beginning of each elongation cycle to synthesize palmitate, a C16 saturated fatty acid (Wakil et al., 1983). As shown in figure 1, the source of acetyl-CoA for the ACC reaction in adult female mosquitoes is meals consisting of nectar (hexose sugars) and blood (ketogenic amino acids). Palmitate is the building block for stored fats in the form of triacylglycerol (TAG), a neutral lipid used as an energy reserve, and phospholipid (PL), which is an abundant membrane lipid. Acetyl-CoA is a major source of acetate in protein acetyltransferase reactions, which modulate gene expression through chromatin remodeling (Kim and Yang, 2011), whereas malonyl-CoA is an allosteric effector of the mitochondrial transporter protein carnitine acyltransferase I (Saggerson, 2008). It can be seen from the pathway diagram in figure 1 that RNAi-mediated enzyme deficiencies in ACC or FAS1 in blood fed mosquitoes should lead to inhibition of fat storage and membrane biogenesis owing to decreased palmitate synthesis. Moreover, an ACC deficiency in blood fed mosquitoes will increase acetyl-CoA levels due to substrate accumulation and result in decreased levels of malonyl-CoA. However, a deficiency in FAS1 will cause a build-up of malonyl-CoA when ACC is fully active and acetyl-CoA levels are high.</p><p>Molecular studies investigating the regulation of genes involved in lipid metabolism during diapause in Culex pipiens mosquitoes have recently been reported (Sim and Denlinger, 2009). However, little is known about the expression and biochemical function of ACC and FAS1 in blood feeding Ae. aegypti mosquitoes. Since lipid biosynthesis from blood meal carbon appears to be critical to oocyte maturation in Ae. aegypti (Zhou et al., 2004a; Zhou et al., 2004b), we undertook a series of molecular genetic and biochemical experiments to investigate the function of the Ae. aegypti ACC and FAS1 genes. Our data show that biochemical deficiencies in ACC and FAS1 not only inhibit de novo lipid biosynthesis in blood fed mosquitoes, but also impact eggshell formation and blood digestion.</p><!><p>Ae. aegypti (NIH-Rockefeller strain) were maintained on 10% sucrose and reared at 25°C, 80% relative humidity, and a 16 h light:8 h dark cycle as previously described (Isoe et al., 2009). Blood feeding in the gene expression and metabolic labeling studies used an artificial feeder containing bovine blood purchased from Pel-Freez Arkansas LLC (Rogers, AR), whereas all other experiments were carried out using an artificial feeder containing human blood donated by the American Red Cross (Tucson, AZ). Blood was supplemented with fresh ATP (5.0 mM final concentration) prior to feeding, and a dissecting microscope was used to identify fully engorged females for use in the metabolic studies.</p><!><p>The Ae. aegypti genome and EST sequence databases were queried by a BLAST search using the human ACC1 (AAC50135), ACC2 (NP_001084), and FAS (NP_004095) gene sequences to identify the corresponding ACC and FAS genes in mosquitoes. A summary of the bioinformatic analyses is shown in supplemental table S1 where it can be seen that a single Ae. aegypti ACC gene was identified (XP_001651879), along with six FAS-related genes. Based on percent identities and representation in EST databases, the FAS1 (XP_001658180) and FAS2 (XP_001659008) genes were chosen for further analysis as described in section 3.1.</p><!><p>Quantitative real-time reverse transcriptase polymerase chain reaction (QRT-PCR) was carried out using FastStart Universal SYBR Green Master Mix (Applied Biosystems) using a 7300 Real-Time PCR System (Applied Biosystems). The primer sequences used for these QRTPCR analyses are listed in Table S2. Tissues were collected from three separate cohorts of mosquitoes at 10 discreet time points (0, 3, 6, 12, 24, 36, 48, 72, 96, 120 h post blood meal). cDNA was synthesized using 1.0 μg total RNA isolated from pools of midgut, fat body, and ovary tissue samples as described by Isoe et al (Isoe et al., 2011), and the resulting cDNA was diluted 8-fold. All mRNA transcripts were normalized using the ribosomal protein S7 as an internal control. ACC protein levels were analyzed by Western blotting as described (Isoe et al., 2011). Anti ACC rabbit polyclonal antibody was obtained from Cell Signaling Technology (Danvers, MA), and anti α-tubulin mouse monoclonal antibody was obtained from Developmental Studies Hybridoma Bank (Iowa City, IA). The ACC and α-tubulin primary antibodies were each diluted 1:1,000. The secondary antibodies were diluted 1:10,000 and were either IRDye 800CW goat anti-rabbit secondary antibody (LI-COR Biosciences, Lincoln, NE) or IRDye 800CW goat anti-mouse secondary antibody (LI-COR Biosciences). Immunoreactive proteins were detected with an Odyssey Infrared Imaging System (LI-COR Biosciences).</p><!><p>Protocols for dsRNA design, synthesis, and injection have been described by Isoe et al (Isoe et al., 2011). The primer sequences for dsRNA production are listed in Table S1. The control dsRNA used in these experiments was derived from the firefly luciferase (Fluc) gene (Isoe et al., 2009). Adult females were injected 2–3 days post-eclosion with of 1.0 μg using a Nanoject II microinjector (Drummond Scientific Company, Broomall, PA) and a MM33 micromanipulator (Märzhäuser Wetzlar, Germany), which was operated hands-free by an electric foot peddle. After dsRNA injection, mosquitoes were fed on only water or blood to ensure that de novo fatty acid synthesis was derived from blood meal proteins. Efficiency of RNAi-mediated knock down was confirmed by quantitative reverse transcriptase polymerase chain reaction (QRT-PCR) and Western blotting as previously described (Isoe et al., 2009). These data are included in supplemental figure S1.</p><!><p>14C-Leucine was used for radioactive labeling of lipids based on results from Zhou and Miesfeld (Zhou, 2009). In the metabolic labeling experiments described here, 20–50 μCi of 14C-Leu, or 50 μCi of 14C-labeled glucose, were added to 1.0 ml of bovine blood supplemented with 5mM ATP. Each group of dsRNA injected mosquitoes was allowed to feed on the labeled blood for 30 minutes before being moved to the growth chamber. At 48 hours post blood meal (PBM) in the whole body experiments, or 24 and 48 hours PBM in the tissue labeling experiments, mosquito samples were collected and stored in 300 μl chloroform:methanol (2:1 v/v). A standard Folch lipid extraction method was used to isolate lipids from the tissue samples as described by Zhou et al. (Zhou et al., 2004a). The organic phase containing total lipids from the tissue extract was saved and transferred to a silicic column prepared by packing 200 mg of 100-mesh silicic acid (Sigma Chemical Co, MO) in a Pasteur pipette column containing a glass wool plug. The column was washed eight times with 1 ml chloroform and the eluant collected in glass test tubes to extract the triacylglycerol (TAG) content. This was repeated with a methanol wash to collect the phospholipid (PL) fraction. The TAG and PL fractions were then dried completely under nitrogen gas and resuspended in either chloroform (TAG) or methanol (PL). Each sample was mixed with a scintillation cocktail and quantitated by liquid scintillation.</p><!><p>For each experiment, dsRNA-injected mosquitoes were dissected at 48 hours PBM in 1X PBS (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4, pH of 7.4), and visualized under a dissecting microscope for phenotypic analysis, including visualization of blood digestion in the midgut and follicle size. An additional cohort of mosquitoes was saved for fecundity analysis. At 48 hours PBM, mosquitoes were transferred to individual scintillation vials with 5 ml water and oviposition paper and allowed to lay eggs over the next 72 hrs (up to 5 days PBM). In some experiments, mosquitoes were transferred to clean scintillation vials at 96 hr PBM fecundity was recorded for a second gonotrophic cycle. Mosquitoes were blood fed in the vials and again transferred to clean vials containing oviposition paper at 48 hours PBM. Mosquitoes were allowed to lay eggs as described above. Eggs from individual mosquitoes were counted and recorded for both gonotrophic cycles. Stored eggs were also allowed to hatch to determine egg viability as described by Isoe et al (Isoe et al., 2009).</p><!><p>Light microscopy was used to examine dissected ovaries that were fixed in 2.5% glutaraldehyde in 0.1 M PIPES buffer (pH 7.4) for 60 min at 4°C and washed in 0.1 M PIPES buffer before imbedding in LX112 epoxy resin and staining with toluidine blue and basic fuchsin. Neutral red (Sigma)and rhodamine B (Sigma) were used to stain oocytes isolated from ovaries of dsRNA Fluc or dsRNA ACC injected mosquitoes at 72 or 96 h PBM. Individual oocytes were separated from the ovaries, transferred to eppendorf tubes, and immediately stained with neutral red (final concentration in H2O of 0.5% w:v) or rhodamine B (final concentration of 1 mM in H2O) for 10 min on a rocking shaker. The stained oocytes were rinsed with ddH2O, and the representative oocytes were photographed under a light microscope.</p><!><p>Anti-bovine serum albumin (BSA) polyclonal antibody was obtained from Gallus Immunotech (Cary, NC), and BSA western blotting to quantitate blood meal digestion was done as described (Isoe et al., 2011). Briefly, 0.2 midgut equivalents from dsRNA injected mosquitoes were loaded into each well and resolved by 12% acrylamide SDS PAGE prior to transfer to a nitrocellulose membrane (Odyssey Nitrocellulose, LI-COR Biosciences, Lincoln, Nebraska). The nitrocellulose membranes were blocked in a 4% nonfat milk solution before incubation with the primary and secondary antibodies. The BSA primary antibody (Gallus Immunotech, Cary, NC) was diluted 1:1000 and the secondary anti-chicken antibody (LI-COR) diluted 1:10,000.</p><!><p>Time course data presented in figure 2 were analyzed by one-way ANOVA, and all other statistical analyses used unpaired student's T-test (GraphPad Software Inc, San Diego, CA). Asterisks indicate significant differences (*P < 0.05; ** P < 0.01; *** P < 0.001).</p><!><p>Bioinformatic analysis of the Ae. aegypti genome identified a single ACC gene (XP_001651879), and six FAS-related genes, of which FAS1 (XP_001658180) had the highest sequence identity to human FAS (50%), whereas FAS2 (XP_001659008) had the highest number of EST hits in the Ae. aegypti EST database (Table S1). As shown in figure 2, western blotting and QRT-PCR analysis were used to determine if ACC, FAS1, and FAS2 gene expression is regulated by blood feeding in fat body and ovary tissue. As shown in figure 2A, ACC protein levels were found to be maximal in the fat body at 48 hr PBM, and then declined to baseline levels by 96 hr PBM. ACC protein in ovary tissue was not detectable by Western blotting (data not shown). Figure 2B shows results from QRT-PCR studies of ACC expression in fat body and ovary tissue, where it can be seen that ACC transcript levels in the fat body were significantly higher at 48 hr PBM compared to unfed mosquitoes (P < 0.001), whereas 72 hr PBM was the peak expression time point in ovary tissue (P < 0.05). As shown in figure 2C, FAS1 transcript levels also peaked at 48 hr PBM in fat body tissue (P < 0.001), and at 72 hr PBM in ovary tissue (P < 0.01). In contrast to ACC and FAS1, FAS2 transcripts in fat body and ovary tissues were not significantly different at any time points PBM as compared to unfed mosquitoes, suggesting that FAS2 expression is not regulated by blood feeding. In addition, FAS2 transcript levels were 10–20 times lower than FAS1 transcripts in the same RNA samples, which was consistent with our finding that RNAi-mediated knock down of FAS2 expression had no effect on blood meal metabolism (AA and RLM, unpublished data).</p><p>Based on these expression data, the ACC and FAS1 genes were chosen for detailed biochemical and molecular genetic analyses as described in the following sections.</p><!><p>Based on the known enzymatic function of ACC in fatty acid biosynthesis in other organisms (Brownsey et al., 2006), and the fact that the Ae. aegypti genome encodes only a single ACC gene (EAT42106.1), we reasoned that RNAi-mediated knock down of ACC expression should inhibit fatty acid biosynthesis after blood feeding. Using an optimized dsRNA injection protocol we developed for Ae. aegypti (Isoe et al., 2011; Isoe et al., 2009), we injected adult female mosquitoes with 1.0 μg of ACC dsRNA three days prior to blood feeding. As shown in figure S1, ACC transcript and protein levels were reduced by >80% in ACC dsRNA injected mosquitoes based on QRT-PCR analysis and western blotting, respectively. The level of TAG and PL biosynthesis in ACC dsRNA injected blood fed mosquitoes was measured using 14C-glucose or 14C-Leucine as metabolic sources of 14C-acetyl-CoA. The results of these metabolic labeling studies are shown in figure 3A where it can be seen that the amount of 14C-labeled TAG and PL was significantly lower in whole body lipid extracts of ACC dsRNA injected mosquitoes as compared to Fluc dsRNA injected mosquitoes (P<0.05). As shown in Figure 3B, we also examined the effect of FAS1 knock down on the accumulation of 14C-labeled TAG and PL in mosquitoes fed blood containing 14C-leucine (see FAS1 knock down efficiencies in figure S1). In these experiments, the mean level of 14C-labeled TAG was reduced by ~60% in FAS1 dsRNA injected mosquitoes compared to Fluc dsRNA injected mosquitoes (P<0.001), whereas 14C-labeled PL was reduced by ~45% (P<0.001). These data confirm that ACC and FAS1 are required for fatty acid biosynthesis in blood fed mosquitoes, and are consistent with the observed >80% reduction in ACC and FAS1 transcript levels in dsRNA injected mosquitoes based on QRT-PCR (data not shown).</p><p>To characterize tissue-specific functions of FAS1 on lipid biosynthesis, we quantitated 14C-labeled TAG and PL accumulation in fat body and ovary tissues of blood fed mosquitoes at 24 hr and 48 hr PBM using 14C-leucine as the metabolic label. As shown in Figure 4A, the level of 14C-labeled TAG in the control Fluc dsRNA injected mosquitoes was significantly higher than in FAS1 dsRNA injected mosquitoes in both fat body and ovary tissues at 24 hr and 48 hr PBM, indicating that FAS1 has a central role in TAG synthesis after blood feeding. A similar pattern of decreased 14C-labeled PL accumulation in FAS1 dsRNA injected mosquitoes, compared to Fluc dsRNA injected mosquitoes, is seen in figure 4B, with the exception of the 48 hr PBM time point in fat body samples, which were not significantly different. Since lipids are transported from the fat body to the ovaries by lipoproteins during the gonotrophic cycle (Cheon et al., 2006), it is likely that some portion of the 14C-labeled TAG and PL accumulated in the ovaries at 48 hr PBM was derived from fatty acids synthesized in the fat body.</p><!><p>Female Ae. aegypti mosquitoes depend primarily on maternal stores of fat body lipids to complete the 1st gonotrophic cycle, whereas lipids derived from blood meal proteins are used for the subsequent gonotrophic cycle (Briegel et al., 2002; Cheon et al., 2006; Zhou et al., 2004b). Based on this observation, we expected to find that ACC deficient female mosquitoes would have only a minimal reduction in egg production during the 1st gonotrophic cycle, whereas egg production in the 2nd gonotrophic cycle would be more severe due to decreased lipid biosynthesis after the first blood feeding. However, as shown in figure 5A, mosquitoes injected with ACC dsRNA two days prior to feeding oviposited defective eggs that failed to tan, and were found to be inviable (data not shown). In all cases, the untanned eggs from ACC deficient mosquitoes were curved and elongated. This was not the case with FAS1 deficient mosquitoes, which oviposited viable eggs that tanned normally and were indistinguishable from eggs oviposited by Fluc dsRNA control mosquitoes (figure 5B).</p><p>To determine if an ACC deficiency in the 1st gonotrophic cycle affected egg production during the 2nd gonotrophic cycle, we followed batches of ACC dsRNA injected mosquitoes through two blood feedings. As shown in figure 6A, ACC deficient mosquitoes oviposited 50% fewer eggs in the 1st gonotrophic cycle than Fluc dsRNA injected mosquitoes, and moreover, >95% of these eggs failed to tan and were inviable. However, during the 2nd gonotrophic cycle, all of the eggs from ACC dsRNA injected mosquitoes tanned normally and were viable, even though there was a significant reduction in the total number of eggs compared to the Fluc dsRNA control mosquitoes (P≤0.001). We extended these experiments to determine if the defective egg phenotype in the 1st gonotrophic cycle was dependent on the time of ACC dsRNA injection relative to blood feeding. For these experiments, we injected female mosquitoes at 1, 2, or 3 days prior to blood feeding, 1 hr after feeding, or at 24 hr after feeding (figure 6B). The results of this time course experiment revealed that ACC dsRNA injection prior to blood feeding resulted in >85% defective eggs, whereas, the majority of eggs were normal when the ACC dsRNA was injected at any time after blood feeding. The mean number of normal and defective eggs oviposited by the ACC dsRNA injected mosquitoes under all conditions was ~30–55/mosquito, as compared to ~100 eggs/mosquito in Fluc dsRNA injected mosquitoes (figure 6A).</p><p>To better understand what might be causing the defective egg phenotype in ACC dsRNA injected mosquitoes, we examined oocytes at 72 hr and 96 hr PBM in females withheld from oviposition substrate. As shown in figure 7, the density of vitellogenin granules in Fluc and ACC dsRNA injected mosquitoes at 72 hr PBM was similar based on toluidine blue staining, however oocytes from ACC dsRNA injected mosquitoes lacked tubercles, which are normally seen on Ae. aegypti eggs (Linley, 1989). The porosity of the oocytes was tested using two different dyes, neutral red and rhodamine B, which should be excluded from the oocytes if the eggshell is intact. It can be seen that oocytes isolated from ACC deficient mosquitoes at 72 hr and 96 hr PBM (oviposition substrate was not available) accumulated both dyes to a much greater extent than oocytes from the control Fluc dsRNA injected mosquitoes, indicating that the eggshell is porous. Moreover, oocytes from ACC deficient mosquitoes were misshapen similar to the oviposited eggs (see figure 5B).</p><p>Taken together, these data suggest that defective eggshells are due to ACC deficiencies in the follicular epithelial cells, since this ovarian cell type is responsible for eggshell production during oocyte maturation (Raikhel and Lea, 1991). In contrast, the decreased total number of oviposited normal (tanned) and defective (white) eggs in ACC deficient mosquitoes (see figure 6), is more likely the result of ACC deficiencies in the fat body, which results in lower amounts of de novo lipid biosynthesis and reduced egg production.</p><!><p>To determine the effect of a FAS1 deficiency on egg production, we injected female mosquitoes three days prior to blood feeding and counted the number of oviposited eggs after the 1st and 2nd gonotrophic cycles (96 hr PBM). The data in figure 8 show ~20% fewer oviposited eggs/female after the 1st gonotrophic cycle in the FAS1 dsRNA injected mosquitoes compared to the Fluc controls (P<0.05). The decrease in egg production is more pronounced in the 2nd gonotrophic cycle, in which ~50% fewer eggs are oviposited by the FAS1 dsRNA injected mosquitoes compared to the Fluc controls (P<0.001). These results are consistent with metabolic labeling studies in blood fed Ae. aegypti showing that egg development in the 2nd gonotrophic cycle is dependent on de novo lipid biosynthesis in the 1st gonotrophic cycle (Zhou et al., 2004b)</p><p>While the reduced fecundity in the 1st gonotrophic cycle of FAS1 deficient mosquitoes could be due to decreased de novo lipid biosynthesis alone, we observed that dissected midguts isolated from dsFAS1, dsACC, and dsFluc RNA injected mosquitoes were quite different, suggesting that a FAS1 deficiency may affect midgut digestion. As seen in figure 9A, midguts from ACC dsRNA injected mosquitoes at 48 hr PBM were similar in size to that of Fluc dsRNA injected mosquitoes, and considerably smaller than midguts from FAS1 dsRNA injected mosquitoes. In order to quantitate this apparent difference in digestion rates, we measured the amount of undigested bovine serum albumin (BSA) in the midguts of individual dsRNA injected mosquitoes at 24 hr PBM using a BSA Western blot assay (Isoe et al., 2009). Figure 9B is a BSA Western blot showing representative midgut protein samples from four individual blood fed Fluc, ACC, and FAS1 dsRNA injected mosquitoes, and figure 9C contains data collected from three independent biological experiments. It can be seen from these data that BSA digestion at 24 hr PBM in Fluc and ACC dsRNA injected mosquitoes was similar, however BSA digestion in FAS1 dsRNA injected mosquitoes was significantly reduced (P<0.01).</p><!><p>We found that inhibiting fatty acid biosynthesis in Ae. aegypti mosquitoes by RNAi-mediated knock down of the ACC and FAS1 gene expression reduced conversion of blood meal derived carbon into TAG and PL. Indeed, fecundity was significantly reduced in both ACC and FAS1 deficient mosquitoes during the 1st and 2nd gonotrophic cycles, indicating that de novo fatty acid biosynthesis contributes to egg production. In addition, data collected from metabolic labeling studies using the ketogenic amino acid 14C-leucine as a source of 14C-acetyl-CoA, provided experimental evidence that fatty acids synthesized in the fat body of blood fed mosquitoes are transported to the ovaries. This can be seen in figure 4, which shows a decrease in TAG and PL in the fat body from 24 hr to 48 hr, which is coincident with an increase in TAG and PL levels in the ovaries over this same time period. An apparent redistribution of lipids from the fat body to the ovaries was seen in both the Fluc and FAS1 dsRNA injected mosquitoes, although the total lipid content in the FAS1 deficient mosquitoes was significantly lower than in the control mosquitoes owing to decreased fatty acid synthesis.</p><p>A significant reduction in de novo lipid biosynthesis and egg production in blood fed ACC and FAS1 deficient mosquitoes was predicted based on what is known about the fatty acid biosynthetic pathway in insects (Arrese and Soulages, 2010). However, we were surprised to find that decreased ACC expression led to defects in eggshell formation, and moreover, that a deficiency in FAS1 caused a delay in blood meal digestion. One explanation for the observed defect in eggshell formation in ACC deficient mosquitoes is that de novo fatty acid biosynthesis in ovary tissues is required to generate lipid components of the eggshell (Urbanski et al, 2010), i.e., fat body lipid biosynthesis is not the only source of lipids for egg production. However, this would not explain why FAS1 dsRNA injected mosquitoes produced normal eggs, given that ACC and FAS are both required to synthesize fatty acids and FAS1 transcripts are present at high levels in ovary tissue (figure 2).</p><p>A second explanation is that increased levels of acetyl-CoA and decreased levels of malonyl-CoA, both of which would occur in ACC deficient mosquitoes (see figure 1), could alter metabolic flux through biosynthetic pathways involved in eggshell formation. For example, acetyl-CoA is not only the primary substrate for the citrate cycle, but is also a major source of acetyl groups in protein acetyltransferase reactions (Kim and Yang, 2011), some of which modulate metabolic flux through gene regulation (Jeninga et al., 2010). Therefore, ACC deficient mosquitoes may have an altered transcriptome due to increased acetylation of gene regulatory proteins in response to elevated acetyl-CoA, which could affect eggshell formation independent of decreased lipid biosynthesis. Similarly, it is possible that decreased malonyl-CoA levels in ACC deficient mosquitoes specifically inhibits fatty acid elongation reactions, which might be required to produce specialized eggshell lipids using maternal stores of palmitate as a precursor.</p><p>What explains the delay in blood meal digestion in FAS1 deficient mosquitoes? The primary site of fatty acid biosynthesis in insects is the fat body (Arrese and Soulages, 2010), and indeed we observed high levels of TAG and PL biosynthesis in the fat body tissue of blood fed mosquitoes. Moreover, since ACC and FAS1 expression in the midgut is very low in mosquitoes maintained on water prior to blood feeding (AA and AM, unpublished), it seems likely that the observed delay in blood meal digestion in FAS1 deficient mosquitoes results from indirect physiological affects. For example, fatty acid biosynthesis in the fat body within 24 hr PBM may provide TAG for β-oxidation in the midgut to support energy needs during digestion. Since FAS1 deficient mosquitoes accumulate ~70% less 14C-TAG than control mosquitoes at 24 hr PBM (see figure 3), the observed delay in digestion could result from insufficient metabolic energy in midgut epithelial cells. Another possibility is that midgut digestion rates may be regulated by a feedback signaling mechanism initiated in the fat body which serves to coordinate blood meal digestion in multiple tissues. Indeed, such a fat body signal may be a fatty acid derivative, or some intermediate metabolite in the fatty acid synthesis pathway.</p><p>Future studies will be necessary to uncover the mechanisms behind ACC and FAS1 control of eggshell formation and digestion, respectively. As we learn more about the metabolic processes unique to blood feeding mosquitoes, it may be possible to develop mosquito specific control methods to slow the spread of mosquito borne diseases.</p><!><p>RNAi-mediated knock down of ACC and FAS1 expression during the 1st and 2nd gonotrophic cycles. A) QRT-PCR analysis of ACC transcript levels at 48 hr PBM in mosquitoes injected with 1.0 μg of ACC or Fluc dsRNA 3 days prior to blood feeding. ACC transcript levels were normalized to S7 ribosomal protein transcript levels in the same RNA samples. Asterisks indicate a significant difference in the level of ACC transcripts between dsFluc and dsACC RNA injected mosquitoes (*P <0.05). B) Western blot of ACC protein levels in uninjected, dsFluc, and dsACC injected mosquitoes at 48 hr PBM during the 1st and 2nd gonotrophic cycles. Mosquitoes were injected with 1.0 μg of dsRNA at 3 days prior to the first blood feeding. C) QRT-PCR analysis of FAS1 transcript levels in dsFluc and dsFAS1 injected mosquitoes at 48 hr PBM during the 1st and 2nd gonotrophic cycles. Mosquitoes were injected with 1.0 μg of dsRNA at 3 days prior to the first blood feeding. FAS1 transcript levels were normalized to S7 ribosomal protein transcript levels in the same RNA samples. Asterisks indicate a significant difference in the level of FAS1 transcripts between dsFluc and dsFAS1 RNA injected mosquitoes (** < 0.01).</p>

PubMed Author Manuscript

Exfoliated black phosphorous-mediated CuAAC chemistry for organic and macromolecular synthesis under white LED and near-IR irradiation

The development of long-wavelength photoinduced copper-catalyzed azide-alkyne click (CuAAC) reaction routes is attractive for organic and polymer chemistry. In this study, we present a novel synthetic methodology for the photoinduced CuAAC reaction utilizing exfoliated two-dimensional (2D) few-layer black phosphorus nanosheets (BPNs) as photocatalysts under white LED and near-IR (NIR) light irradiation. Upon irradiation, BPNs generated excited electrons and holes on its conduction (CB) and valence band (VB), respectively. The excited electrons thus formed were then transferred to the Cu II ions to produce active Cu I catalysts. The ability of BPNs to initiate the CuAAC reaction was investigated by studying the reaction between various low molar mass alkyne and azide derivatives under both white LED and NIR light irradiation. Due to its deeper penetration of NIR light, the possibility of synthesizing different macromolecular structures such as functional polymers, cross-linked networks and block copolymer has also been demonstrated. The structural and molecular properties of the intermediates and final products were evaluated by spectral and chromatographic analyses.

exfoliated_black_phosphorous-mediated_cuaac_chemistry_for_organic_and_macromolecular_synthesis_under

Introduction<!>Results and Discussion<!>Conclusion<!>Experimental Materials<!>Synthesis of black phosphorus crystals and preparation of its nanosheets<!>Preparation of azide and alkyne derivatives<!>Synthesis of (azidomethyl)anthracene (Az-2)<!>Synthesis of acetylene-terminated poly(ε-caprolactone) (PCL-Alk)<!>Synthesis of organic molecules<!>Synthesis of polystyrene-b-poly(ε-caprolactone) (PS-b-PCL)<!>Synthesis of cross-linked polymer<!>Supporting Information

<p>For the last decade, click chemistry has been recognized as an indispensable part of synthetic chemistry due to its easiness of application, efficiency to produce the targeted products with very high yields and little or no byproducts under a variety of conditions, and high interconnected group tolerance. Since the introduction of click chemistry by Sharpless [1,2] and Mendal [3], many studies have been dedicated to better understanding of the concept and expanding its scope to be applied in various fields of chemistry including bioconjugation [4], drug discovery [5], materials science [6][7][8][9] and so on [10]. The development of the use of light in click chemistry has set a milestone as a new and effective method for the synthesis of macromolecules [11]. Initiation of this reaction photocatalytically provides many advantages for the synthetic methodologies including bioconjugation, labeling, surface functionalization, dendrimer synthesis, polymer synthesis, and polymer modification by adding spatial and temporal control [12,13].</p><p>In recent years, heterogeneous photocatalysts have been performed in many photosynthetic reactions since they provide a more reasonable and easy way to synthesize the targeted products compared to the classical homogenous photocatalysts. In this respect, 2D materials offer great potential due to converting the inexhaustible energy of sunlight into chemical and electrical energy along with having a less environmental impact. After the discovery of the photocatalytic effect of 2D materials under UV light [14,15] the heterogeneous photocatalysts have been successfully applied in both small-and large-scale synthesis such as organic reactions [16,17], free radical polymerization (FRP) [18][19][20], controlled radical polymerization (CRP) [21,22], CuAAC chemistry [23][24][25], and thiol-ene chemistry [26,27]. However, most of the conventional 2D materials have a wide bandgap that requires UV light irradiation for their activation. Since 94% of the rays from the sun are not sufficient to activate these conventional semiconductor materials, many strategies have been proposed to design photocatalysts that can harvest in a wide spectrum of sunlight, especially in the NIR region [28,29]. In particular, the development of new photocatalyst systems that absorb the incident light from the sun at much longer wavelengths have aroused widespread interest [30][31][32][33]. However, the most of the NIR photocatalysts applied exhibit relatively low catalytic efficiency due to their low absorption characteristics and require complicated synthetic procedures. In this respect, it is worth to mention that elemental 2D materials with a proper bandgap and charge mobilities have been shown to act as photocatalysts in several reactions [34,35]. Exfoliated black phosphorus (BP), the most stable allotrope of phosphorus, has been shown as a highly efficient photocatalyst possessing superior features in many respects [36,37]. BP, a vital semiconductor 2D material with excellent physicochemical properties such as high carrier mobility, tunable optical absorption, and novel electronic band structure, fills the gap between graphene and wide bandgap semiconductors [35,38]. Furthermore, BP shows a layer thickness tunable bandgap ranging between 0.3 and 2.1 eV. Therefore, BPNs can efficiently be applied as a photoredox catalyst with broadband solar absorption [34,[38][39][40].</p><p>The use of 2D materials for the photoinitiated electron transfer reactions with Cu II catalysts for the photoinduced atom transfer radical polymerization (ATRP) and CuAAC reactions prompted us to develop a new photoredox system that works under NIR irradiation for the CuAAC reaction. In this work, we report a new synthetic strategy to the photochemical reduction of Cu II to Cu I for the CuAAC reaction using BPNs as the photo-initiator under NIR light.</p><!><p>The detailed preparation and characterization of the initial BP crystals and BPNs were previously reported [40]. BPNs were tested as NIR photoinitiator for the CuAAC reactions of low molar mass compounds and polymers possessing antagonist azide and alkyne functionalities (Figure 1).</p><p>The optical absorption spectra of BPNs, copper(I) chloride (Cu I Cl, 0.05 mmol) and copper(II) chloride (Cu II Cl 2, Initially, the model reaction between benzyl azide (Az-1) and phenylacetylene (Alk-3) in the presence of copper(II) chloride/ N,N,N',N',N''-pentamethyldiethylenetriamine (Cu II Cl 2 / PMDETA) and exfoliated BPNs under the white LED irradiation was performed (Figure 3). The reaction was followed by 1 H NMR spectroscopy during the click process. The decrease of the acetylene proton at 4.42 ppm and appearance of the new signal at 8.67 ppm corresponding to the triazole moiety con-firmed successful click reaction under white LED exposure conditions after 4 h (Figure 3a). Kinetic studies conducted by 1 H NMR analysis confirmed that the click reaction between benzyl azide and phenylacetylene resulted in almost complete conversion within 4 h white LED irradiation (Figure 3b). In this connection, it should be pointed out that the reaction proceeds also in dark almost at the same rate (Supporting Information File 1, Figure S4). This is an expected observation because there is no back reaction to reform Cu(II). Similar observations were reported by the other photoinduced CuAAC reactions [41].</p><p>In order to demonstrate the functional group tolerance, the extent of the reaction was investigated on various alkyne groups using benzyl azide under both white LED and NIR light irradiation. The results presented in Table 1 revealed that NIR-lighttriggered click reactions produced the corresponding products with slightly higher yields favored by the higher penetration of NIR light to the reaction media containing heterogeneously dispersed BPNs. Compared with propargyl alcohol (Alk-1) and propargyl acrylate (Alk-4), the rate of clicking slightly decreased in the case of propargylamine (Alk-2), but still gave high yields. Therefore, it can be concluded that Alk-2 and Alk-1 exhibit relatively lower efficiency probably due to the additional coordination of the Cu I catalyst. Notably, the reaction with Alk-4 gave higher yields with both light sources.</p><p>In the light of previous studies, a photoinduced electron transfer mechanism presented in Scheme 1 can be proposed. Upon the light irradiation, BPNs absorb the light and generate a single electron which was transferred from the conduction band to the Cu II complex to form Cu I capable of catalyzing the click reaction in a conventional manner.</p><p>Scheme 1: Proposed mechanism for photoinduced CuAAC reaction using exfoliated BPNs.</p><p>The applicability of the described click reaction to synthetic polymer chemistry was also demonstrated. For this purpose, polymer functionalization by using alkyne functional poly(εcaprolactone) (PCL-Alk) and 9-(azidomethyl)anthracene (Az-2) as click components was investigated. The detailed 1 H NMR spectrum of the resulting anthracene functional polymer (PCL-Anth) exhibited the characteristic signals of triazole and benzylic protons at 5.5 ppm and 8.70 ppm, respectively (Figure 4a). The obtained polymer has similar absorption characteristic to bare anthracene (Figure 4b). The fluorescence spectrum of diluted solution of PCL-Anth in THF excited at λ exc = 350 nm showed the characteristic emission bands of the excited (singlet) anthracene at 595, 655, and 725 nm (Figure 4c). These observations clearly confirmed the successful chain-end functionalization.</p><p>In addition, block copolymer formation via NIR activated CuAAC process between the polymers having antagonist click components, namely, polystyrene azide (PS-Az) and PCL-Alk, was investigated. At the end of irradiation in the presence of exfoliated BPNs and Cu II Cl 2 /PMDETA, polystyrene-b-poly(εcaprolactone) (PS-b-PCL) is selectively formed (Scheme 2).</p><p>Figure 5a displays the GPC traces of precursors PS-Az, PCL-Alk, and the block copolymer PS-b-PCL. As it can be seen, the trace of Ps-b-PCL block copolymer was clearly shifted to higher molecular weight region without contamination of the precursor polymers. The 1 H NMR spectrum of the block copolymer displayed the characteristic peaks of both macromolecular segments. Additionally, the methylene protons adjacent to the triazole ring at 7.48 ppm were noted (Figure 5b). These results indicated that structurally diverse polymers formed by different polymerization mechanisms can readily be linked just by a simple NIR-induced CuAAC reaction.</p><p>The macromolecular scope was further extended to the preparation of cross-linked materials. Thus, the formulations containing bisphenol A di(3-azido-2-hydroxypropan-1-ol) ether (Az-3), and 1-(prop-2-yn-1-yloxy)-2,2-bis((prop-2-yn-1-yloxy)methyl)butane (Alk-5) as multifunctional click components were irradiated in the presence of BPNs and Cu II ligand under NIR light. The gelation was completed after 24 h (Scheme 3).</p><p>The photocuring process was also followed by differential scanning calorimetry (DSC). The DSC thermogram shows two exo- thermic peaks at 220.38 and 241.74 °C, corresponding to the photo click cure reaction in two stages (Figure 6a). Since a complete reaction of all the azide groups could not occur during the dynamic ramping of temperature, the residual azide groups decomposed at higher temperature. The IR spectrum of the cross-linked polymer further demonstrates the formation of a triazole ring by the decrease of the azide peak at 2100 cm −1 (Figure 6b).</p><p>Representative TEM images recorded at different magnifications of the resulting cross-linked polymer are shown in Figure 7. From the TEM images, it can be concluded that the process leads to the formation of BPNs-embedded cross-linked polymers. The darker regions circled with yellow dashed line in Figure 6a were attributed to the BPNs while the other relatively lighter regions were ascribed to the cross-linked polymer. To further prove the existence of BPNs in the cross-linked structure, a high-angle annular dark-field scanning TEM (HAADF-STEM) image and the associated elemental mapping images for C, N, and P were recorded and depicted in Figure 7c and 7d.</p><p>The elemental mapping images adequately demonstrated the presence and the distribution of P atoms that are attributed to BPNs in the cross-linked polymer in addition to C and N atoms (Figure 7d). In contrast to the cross-linked polymer, the distribution of BPNs in the block copolymer structure could not be visualized by TEM, HAADF-STEM, and elemental mapping images (Supporting Information File 1, Figures S5 and S6). This behavior is expected since BPNs are immobilized between the interconnected chains in the cross-linked structure.</p><!><p>In conclusion, we have demonstrated the use of BPNs as an efficient photoinitiator for the photoinduced CuAAC reactions under white LED and NIR light irradiation. The described method is applicable to organic and macromolecular syntheses. NIR irradiation appeared to be more efficient compared to the while LED due to the higher penetration in the dispersed media.</p><p>In macromolecular syntheses, polymer chain-end functionalization, block copolymer formation of structurally different polymers and cross-linking polymerization can successfully be achieved by using suitably selected click components. This new method would dramatically extend the applications of photoin-duced CuAAC reactions, particularly when the components are light sensitive at short wavelength region and spatial control is required. Characterizations 1 H NMR spectra were recorded at room temperature at 500 MHz on an Agilent VNMRS 500 spectrometer. Gel permeation chromatography (GPC) measurements were performed on a TOSOH EcoSEC GPC system equipped with an auto sampler system, a temperature-controlled pump, a column oven, a refractive index (RI) detector, a purge and degasser unit and a TSKgel superhZ2000, 4.6 mm ID × 15 cm × 2cm column.</p><!><p>Tetrahydrofuran was used as an eluent at a flow rate of 1.0 mL/min at 40 °C. The refractive index detector was calibrated with polystyrene standards having narrow molecular-weight distributions. The data were analyzed using Eco-SEC analysis software. A Hitachi HT7700 (TEM) with EXALENS (120 kV) working at a high-resolution (HR) mode was used to obtain transmission electron microscopy (TEM) images, high-angle annular dark field (HAADF) scanning transmission microscope (STEM) images and the associated EDS elemental mapping images.</p><!><p>Black phosphorus (BP) was prepared using a modified lowpressure chemical vapor transport method [40,42,43]. For the synthesis, 500 mg of red phosphorus, 20 mg of Sn and 10 mg of SnI 4 were placed into a quartz ampoule with the dimensions of 20 cm length and 1.5 cm width. The air was evacuated by vacuum, and the ampoule was left to dry at least for 30 min under vacuum. The sealed ampoule was placed horizontally in a muffle furnace. The applied heating program was as follows:</p><p>firstly, the temperature raised to 893 K in 5 h and kept at this temperature for 5 h. Next, the temperature was lowered to 758 K in the span of 6 h and the temperature was kept at this temperature for 2 h. Finally, the oven was cooled to 393 K in 5 h, and it was left for natural cooling afterwards. After the heating process, the ampoule was cracked in dry toluene and the crystalline BP was separated. In order to remove surface impurities, the BP crystals were transferred into absolute ethanol and sonicated for 30 minutes. The sonicated crystals were carefully transferred to a Schlenk tube and dried under vacuum. The Schlenk tube was filled with argon and crushed under inert atmosphere. The produced BP crystals were stored under vacuum.</p><p>BP nanosheets were prepared by the liquid phase exfoliation of BP crystals. A specific amount of BP was dispersed thoroughly in DMSO by a sonication bath (200 W) for 10 h at 6 °C. The resulting BP nanosheets dispersion was kept under an inert atmosphere for the further use.</p><!><p>Synthesis of benzyl azide (Az-1)</p><p>A literature procedure was used [44]. Product was obtained pale yellow oil, yield 96%.</p><!><p>A literature procedure was used [45]. 9-Hydroxymethylanthracene (7.40 mmol, 1 equiv) was added to DCM (50 mL) and cooled to 0 °C. Then, SOCl 2 (1.5 equiv) was slowly introduced to the reaction media and allowed to warm up to room temperature while being stirred for 1 h. The solvent was removed under vacuum and the residue redissolved in DMF (10 mL). Following dissolution of the compound, NaN 3 (1.5 equiv) was added, and the reaction was stirred at 50 °C. After 1 h, the reaction mixture was allowed to cool down, diluted with water and extracted with EtOAc. The combined organic phases were washed with brine, dried with anhydrous MgSO 4 , filtered, and concentrated under vacuum. Brownish yellow crystalline solid, yield = 93%. Synthesis of bisphenol A di(3-azido-2-hydroxypropan-1-ol) ether (Az-3)</p><p>Diazido monomer, bisphenol A di(3-azido-2-hydroxypropan-1ol) (Az-3) was synthesized according to a described method [46]. Az-3 was obtained as light yellowish viscous oil and was directly used without further purification, yield 98%. 1 Synthesis of 1-(prop-2-yn-1-yloxy)-2,2-bis((prop-2yn-1yloxy)methyl)butane (Alk-5)</p><p>A literature procedure was followed [47]. The crude obtained product was then purified using column chromatography to give a clear oil, yield 70%. 1 Synthesis of ω-azido terminated polystyrene (PS-Az)</p><p>ω-Bromo functional polystyrene was synthesized by ATRP according to a reported procedure [48]. In a flask equipped with a magnetic stirrer, PS-Br (1 equiv) and sodium azide (5 equiv) were dissolved in 5 mL DMF. The reaction mixture was stirred at room temperature 24 h, then precipitated in 10-fold excess of methanol, filtered and dried in vacuum to yield PS-N 3 . Yield 95% (M n,GPC : 1589 g•mol −1 , M w /M n : 1.13). FTIR: 2096 cm −1 .</p><!><p>Acetylene-terminated PCL-Alk was synthesized according to a modified procedure [49]. To a Schlenk tube, 3-butyn-1-ol was dissolved in ε-caprolactone and heated to 110 °C under nitrogen. After the reaction mixture warmed up homogeneously, one drop of tin octoate was added to the reaction media and the solution was stirred for 3 hours. The obtained polymer was dissolved in chloroform and precipitated in methanol:water (2:1) to yield poly(ε-caprolactone). White solid, (85%) M n,GPC : 1576 g•mol −1 , M w /M n : 1.2. FTIR: 2102 cm −1 .</p><!><p>For the first step of the reaction an appropriate amount of black phosphorus was exfoliated in DMSO-d 6. In a typical experiment, exfoliated BP in DMSO-d 6 (0.5 mL) and azide compound (1 mmol, 1 equiv) were added to a NMR tube containing Cu (II) Cl 2 (0.05 equiv), PMDETA (0.1 equiv). After 5 min, alkyne derivative (1 mmol, 1 equiv) was added slowly to the NMR tube. The reaction tube was irradiated by using a Philips 150 W PAR38E E27 halogen pressure glass type bulb with strong IR-A (NIR) emission. The light intensity inside the reaction tube was ≈200 mW•cm −2 . The light bulb was attached to the top of a photoreactor setup equipped with a large air cooling fan and the reaction temperature was kept constant at room temperature (24−25 °C). 1 H NMR spectra were recorded 4 h later.</p><p>Synthesis of anthracene functional poly(ε-caprolactone) (PCL-Anth)</p><p>The same process as in the block copolymerization was applied. Az-2 (19.27 mg, 1 equiv), PCL-Alk (1 equiv), CuCl 2 (1 equiv) and PMDETA (1 equiv) were placed in a Schlenk tube. The tube was degassed by three freeze pump-thaw cycles. Then the tube was irradiated under NIR light for 48 h. After the given time, the mixture was diluted with THF and the copper complex was removed by passing through a neutral alumina column. Excess amount of THF was evaporated by a rotary evaporator. After precipitation of the mixture to cold methanol, the polymer was collected by filtration and dried under vacuum overnight. 1 H NMR was demonstrated in Figure 4.</p><!><p>Firstly, under dark conditions BP was exfoliated in dry DMF by a sonic bath for 8 h at 10 °C. Subsequently, the solution was transferred into a centrifuge at 2500 rpm for 15 min. Terminally, this exfoliated BPNs with PS-Az (200 mg, 1 equiv), Cu II Cl 2 (1 equiv), PMDETA (1 equiv) and PCL-Alk (1 equiv) were placed in a Schlenk tube. The tube was degassed by three freez-pump thaw cycles. Then the tube was irradiated with NIR light 48 h. At the end of the reaction, the mixture diluted THF and the copper complex was removed by passing it through a neutral alumina column. Excess amount of THF was evaporated by a rotary evaporator. After precipitation of the mixture to cold methanol, the polymer was collected by filtration and dried under vacuum overnight. M n , GPC : 3510 g•mol −1 , M w /M n : 1.10.</p><!><p>Az-3 and Alk-5 was mixed in equal ratio (1 equiv) with Cu II Cl 2 (0.05 equiv) and PMDETA (0.1 equiv) in a small transparent vial and 300 µL BPNs in DMF was added to the vial, then irradiated 4 h. After the gelation was completed, the gel was placed in DCM for 24 h hours, then filtered and dried 24 h in a vacuum oven.</p><!><p>Supporting Information File 1</p>

Beilstein

Luminore CopperTouch Surface Coating Effectively Inactivates SARS-CoV-2, Ebola Virus, and Marburg Virus In Vitro

ABSTRACTWe investigated the ability of Luminore CopperTouch copper and copper-nickel surfaces to inactivate filoviruses and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The copper and copper-nickel surfaces inactivated 99.9% of Ebola and Marburg viruses after 30 min, and the copper surfaces inactivated 99% of SARS-CoV-2 in 2 h. These data reveal that Ebola virus, Marburg virus, and SARS-CoV-2 are inactivated by exposure to copper ions, validating Luminore CopperTouch as an efficacious tool for infection control.

luminore_coppertouch_surface_coating_effectively_inactivates_sars-cov-2,_ebola_virus,_and_marburg_vi

INTRODUCTION<!>Exposure of filoviruses to copper and copper-nickel sprayed surfaces.<!><!>Exposure of SARS-CoV-2 to copper-sprayed surfaces.<!><!>Exposure of SARS-CoV-2 to copper-sprayed surfaces.

<p>Emerging viruses continue to pose a major threat to public health worldwide, as demonstrated by two ongoing outbreaks. Viruses from the Filoviridae family have caused several outbreaks and epidemics in the past. Ebola virus caused an outbreak of Ebola virus disease in the West African nations Liberia, Sierra Leone, Guinea, Nigeria, Senegal, and Mali in 2014 to 2015, with a current outbreak in the Democratic Republic of the Congo (1, 2). More recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged from a seafood market in Wuhan, China, and has since spread quickly around the world.</p><p>Many infectious microorganisms can survive for days or weeks after landing on a surface, presenting a tremendous challenge for infection control. In one study, Zaire Ebola virus and Lake Victoria Marburg virus were shown to survive in liquid medium at titers above detectable limits for up to 46 days, although the number of viable particles decreased over that time (3). When dried on solid surfaces, such as rubber, glass, or plastic, Ebola virus survives at detectable levels for 5.9 (4) to 14  (3) days. SARS-CoV-2 can survive on plastic or stainless steel surfaces for up to 72 h and on copper for 4 h (5).</p><p>Because of the threat of fomite transmission of these viruses, attention has turned to the feasibility of self-sanitizing surfaces. Copper exhibits a self-sanitizing characteristic, but its weight and bulk have limited its use. To circumvent these challenges, antimicrobial copper coatings have been developed. Luminore CopperTouch is an EPA (Environmental Protection Agency)-registered antimicrobial copper surface. Copper and copper-nickel formulations are currently registered and available for application in hospitals, doctor's offices, airports, buses, trains, and other heavily trafficked areas. These antimicrobial coatings greatly expand the possibility of using copper within the transportation and health care settings, particularly for high-touch surfaces, to reduce the spread of infection.</p><p>Several studies have demonstrated the effect of copper in reducing the bacterial burden in hospitals and health care facilities. Salgado et al. (6) reported a significant reduction in hospital-acquired infections and/or methicillin-resistant Staphylococcus aureus or vancomycin-resistant Enterococcus colonization for patients who received treatment in intensive care unit (ICU) rooms with copper alloy surfaces compared with those treated in standard ICU rooms. Viruses, including influenza A virus (7) and norovirus (8, 9), both of which are inactivated by copper, are susceptible to copper surfaces as well. Solid copper surfaces have also been shown to inactivate SARS-CoV-2 (5).</p><!><p>Luminore is a polymetal alloy that is 85% copper, and the copper-nickel alloy is a proprietary blend containing 62.5% copper. For surfaces, 1-mm-thick 1- by 1-cm coupons were sprayed with the substance. All surfaces were disinfected with CaviCide followed by 70% ethanol. Stainless steel type 304 was used as the experimental control because it is biologically inert and has no known antimicrobial effect. The coupons of nonmetal surfaces were polyvinyl chloride (PVC). The sham metal surface was spray-painted stainless steel (Krylon fine paint).</p><p>All work with filoviruses was conducted by approved personnel under approved protocols at the Galveston National Laboratory, a biosafety level four (BSL-4) laboratory registered with the Centers for Disease Control and Prevention Select Agent Program, the University of Texas Medical Branch in Galveston, TX, in compliance with all regulations therein. Zaire Ebola virus (EBOV; stock) and Angola Marburg virus (MARV; stock) were utilized. The viral load consisted of 105 plaque-forming units (PFU) and viral suspensions maintained in growth medium solution (Dulbecco's modified Eagle's medium with 1× l-glutamine, 1× pen/strep, 1% MEM vitamins, and 10% fetal bovine serum). Vero E6 cells were grown to 70% to 80% confluence in 6- or 12-well plates. Ten 1-μl drops (MARV) or one 10-μl drop (MARV and EBOV) of viral suspension was dispensed on copper, copper-nickel, or sham coupons (as described above) or on uncoated metallic or plastic surfaces as controls, with a subsequent 30-min incubation at room temperature. The drops were then collected with 100 μl of fresh dilution medium. Medium was pipetted on the dried virus spot and spread around the coupon to ensure maximum collection of deposited virus. This was then titrated on Vero E6 cells using a standard plaque assay technique (0.5% agarose overlay). Inoculated Vero E6 cells were incubated at 37 ± 2°C with 5% CO2 for 7 (MARV) or 10 (EBOV) days. The titrations were subsequently fixed with formalin. To enumerate the plaques, the wells were stained with neutral red or crystal violet solutions using standard procedures. To minimize the extent of work performed in the BSL-4 laboratory and the production of waste, experiments were conducted in duplicate.</p><p>We observed a 99.9% reduction in viral titer on copper surfaces for MARV and EBOV relative to the sham surfaces after 30 min, approaching the limits of assay detection (Fig. 1). To determine how rapidly copper surfaces can inactivate viral particles, we measured EBOV titers using plaque assays on Vero E6 cells after viral suspensions had been exposed to surfaces for 1, 15, or 30 min at room temperature (Fig. 2). At 1 and 15 min, the viral titers decreased by ∼0.5 log-fold relative to the sham surface. After 30 min, the copper surface had reduced the viral load by ∼1.5 log-fold, corresponding to a 97% reduction in viral titers. The copper-nickel surface displayed a decrease of 2.3 log-fold, corresponding to a reduction of >99% in viral titers. These data suggest that viral inactivation occurs within 15 to 30 min and that the viral load is reduced by nearly 99% within that time.</p><!><p>Ebola virus (Zaire) and Marburg virus (Angola) are inactivated on copper and copper-nickel surfaces. EBOV (10-μl drop) (top) and MARV (10-μl drop or 10 × 1-μl drops) (bottom) viruses were exposed to the indicated surfaces for 30 min and were then used to infect Vero E6 cells for titration. The average from two experiments is shown, with error bars representing the range. Dotted lines, limit of detection.</p><p>Ebola virus is inactivated within 30 min of exposure to copper and copper-nickel surfaces. Suspensions of Zaire Ebola virus (EBOV) were exposed to each surface for the indicated amount of time, and the exposed viruses were used to infect Vero E6 cells for titration. Copper, copper-nickel, and sham surfaces were compared for up to 30 min. The average from two experiments is shown, with error bars representing the range.</p><!><p>SARS-CoV-2 (USA-WA1/2020) was obtained from the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA). All experiments with SARS-CoV-2 were approved and conducted by certified personnel in approved BSL-3 facilities at the University of Texas Medical Branch. Vero CCL-81 cells were grown to 85% to 95% confluence in 96-well plates. One 10-μl drop of SARS-CoV-2 stock (5 × 105 50% tissue culture infective dose [TCID50]/ml) was added to copper-coated or uncoated metallic or plastic surfaces as controls and incubated for the indicated duration at room temperature. These experiments were conducted in triplicate. The drops were collected with 90 μl of fresh dilution medium after 2, 4, or 8 h and titrated using a TCID50 assay. Regardless of the incubation time, the copper-coated surfaces reduced the viral titers by >2-fold, approaching the limit of detection (Fig. 3). This value corresponds to a reduction of >99% in viral titers. Exposure to copper surfaces for 2, 5, or 30 min did not significantly lower viral titers (data not shown).</p><!><p>SARS-CoV-2 is inactivated within 2 h of exposure to copper-coated surfaces. One 10-μl drop of SARS-CoV-2 was added to the indicated surfaces for the denoted amount of time. The samples were collected with medium and were used to immediately infect Vero CCL-81 cells for calculating the viral titer via TCID50. Data shown represent the average titers for experiments performed in triplicate. The error bars display the standard distribution. Dotted line, limit of detection.</p><!><p>The mechanism by which copper inactivates microorganisms is not completely understood. Pastor et al. (10) demonstrated that copper can donate and accept single electrons, which produces reactive oxygen species and free radicals, causing cell death. The ability of copper to inhibit bacteria involves the rapid degradation of genomic and plasmid DNA. One study further showed that DNA degrades rapidly on copper surfaces (11). Thus, it is plausible to hypothesize that the genomes of these viruses are disrupted by the free radicals produced on contact with copper ions.</p><p>Research on the antiviral and antibacterial properties of copper alloys and other metals (e.g., silver, iron) has increased tremendously over the past 5 years. Sehmi et al. (12) developed a method for encapsulating silicone and polyurethane with copper, and both exhibited antibacterial activity. By comparing thick (100-μm) and thin (25-μm) rolled copper plates, researchers have noted that thin-rolled sheets are rougher in texture than thick-rolled sheets. However, the data comparing their relative effectiveness are contradictory. Whereas Zeiger et al. (13) demonstrated that rough deposited copper surfaces have more antibacterial activity than smooth surfaces, Yousuf et al. (14) showed that thin-rolled sheets have more potent activity and attributed this effect to the increased surface area of the rough surfaces. More recently, a hybrid coating containing silver, copper, and zinc cations was found to significantly reduce viral titers for HIV-1, human herpesvirus 1, dengue virus type 2, and influenza H1N1 virus (15). One of the likely reasons for the early inactivation of EBOV compared to SARS-CoV-2 has to do with the different structures of the viruses. EBOV is long and filamentous, whereas SARS-CoV-2 particles are spherical with spike proteins; therefore, EBOV has more surface area in contact with the copper surface, leading to early inactivation. The coronavirus spike proteins also increase the distance of the viral capsid (the active site for copper) from the contact surface of the copper surface, increasing the inactivation time. These new data may further support the use of copper-coated surfaces in hospitals and other public places as an additional infection control measure during ongoing and future epidemics.</p>

PubMed Open Access

Quinone binding in respiratory complex I: Going through the eye of a needle. The squeeze-in mechanism of passing the narrow entrance of the quinone site

At the joint between the membrane and hydrophilic arms of the enzyme, the structure of the respiratory complex I reveals a tunnel-like Q-chamber for ubiquinone binding and reduction. The narrow entrance of the quinone chamber located in ND1 subunit forms a bottleneck (eye of a needle) which in all resolved structures was shown to be too small for a bulky quinone to pass through, and it was suggested that a conformational change is required to open the channel. The closed bottleneck appears to be a well-established feature of all structures reported so-far, both for the so-called open and closed states of the enzyme, with no indication of a stable open state of the bottleneck. We propose a squeeze-in mechanism of the bottleneck passage, where dynamic thermal conformational fluctuations allow quinone to get in and out. Here, using molecular dynamics simulations of the bacterial enzyme, we have identified collective conformational changes that open the quinone chamber bottleneck. The model predicts a significant reduction—due to a need for a rare opening of the bottleneck—of the effective bi-molecular rate constant, in line with the available kinetic data. We discuss possible reasons for such a tight control of the quinone passage into the binding chamber and mechanistic consequences for the quinone two-electron reduction.Graphic abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s43630-021-00113-y.

quinone_binding_in_respiratory_complex_i:_going_through_the_eye_of_a_needle._the_squeeze-in_mechanis

Introduction<!>The Bottleneck of Q-chamber<!><!>The Bottleneck of Q-chamber<!><!>The bottleneck is closed in all resolved structures<!>Barrier simulations<!><!>Barrier simulations<!>Bottleneck opening in ND1 subunit<!>MD simulations<!>Principal component analysis (PCA) of MD trajectories<!><!>1. Bottleneck opening is related to soft collective modes of ND1<!>Bottleneck participation spectra<!><!>Bottleneck participation spectra<!>The structure of the open bottleneck state<!>Stressed conditions of ND1 subunit in the enzyme structure<!>Bottleneck in ND1 subunit in the context of the enzyme structure. Coarse-grained simulations<!><!>Bottleneck in ND1 subunit in the context of the enzyme structure. Coarse-grained simulations<!>The bottleneck is too narrow for a free passage. The eye of a needle<!>Quantitative estimates of timescales and barriers<!>The squeeze-in model and the kinetic analysis<!>

<p>NADH:ubiquinone oxidoreductase, or respiratory complex I, is a key proton-pumping enzyme of the energy-generating machinery in the cell [1, 2]. Recent structural studies [3–13] of the enzyme have revealed molecular details that suggest possible molecular mechanisms of its redox-driven proton pumping. Complex I is an L-shaped structure with a hydrophilic domain where electron transport takes place and a membrane domain that performs proton translocation [4, 14]. In the hydrophilic domain, NADH transfers 2 electrons to flavin mononucleotide (FMN), which then transfers electrons via a chain of seven iron sulfur (FeS) clusters to a quinone molecule (Q) reducing it to a quinol [15]. The transfer of two electrons to quinone [16] is a key exergonic step, which is believed to drive local conformational changes [2, 11, 17] that transmit to the membrane domain of the complex and help to drive the proton pumping [18, 19]. The new structures have also opened a new intriguing question about the mechanism of quinone binding to the enzyme, which is addressed in this paper.</p><p>In all organisms, from bacteria to human, the structure of the core part of the enzyme reveals an almost 30 angstrom tunnel-like chamber for ubiquinone binding (Q-chamber) that leads from the N-edge of the membrane up to N2 FeS cluster. Presumably, the quinone molecule migrates from the membrane into the binding chamber, diffuses up to N2 cluster, receives two electrons and migrates back – tail first, from the narrow binding cavity to the membrane [8, 18, 20–25].</p><p>In all organisms, the core part of the enzyme is very similar to bacterial enzyme; here, the entrance to Q-chamber is formed by a specific crossing of TM1, AH1, and TM6 in Nqo8 (mtND1/H E.coli) [4] and forms a narrow bottleneck that restricts the access to the Q-tunnel. The bottleneck was identified in the structure early on [4, 26], and it was speculated that conformational changes are needed to open it; however, the molecular mechanism of quinone passing the narrow bottleneck remains to be obscure, see Ref. [27] Recently [28], we characterized the entrance bottleneck more rigorously using Molecular Dynamics (MD) simulations to quantify the energy barrier formed by the narrow bottleneck. Computer simulations of quinone passage through the bottleneck suggest that in all structures available, from bacterial to human (including most recent structures [3, 11–13], see below), this bottleneck is too narrow for the quinone or quinol to pass and that a conformational change is indeed required to open the channel. Moreover, in yeast Y. lipolytica [8] structure, the quinone is seen bound in the cavity, with a half of the isoprenoid tail crossing the bottleneck. However, here too the bottleneck (taken as in the reported pdb structure) was found to be too narrow for the head group, or even for the isoprenoid tail, to freely move through the narrow entrance of the quinone chamber, indicating the quinone molecule is stuck in the position seen in the structure and suggesting that dynamic or static opening of the bottleneck is needed to allow movement of the quinone. Thus, the question arises as to how the bulky substrate is going through a narrow passage, as if through the eye of a needle?</p><p>Previously we concluded that the apparent bottleneck closed structure could be explained by two possibilities: in one, the closed structure is an artifact of the crystallization packing forces, cryo-EM low temperature, or other specific conditions occurring in the structural data acquisition that affect this flexible part of the enzyme, assuming that a functional open bottleneck structure exists in the natural membrane environment of the enzyme, yet unseen in the available structures. Another possibility is that the stable open bottleneck state in enzyme does not actually exist, and only rare thermal fluctuations of the enzyme structure would open the bottleneck and allow admission of the quinone molecule to the quinone chamber, with tightly controlled overall passage of the quinone to the binding site by some intricate mechanism.</p><p>Most recent data [3, 11] indicate that in all available structures (twenty-three analyzed so far) including both so-called "open" and "closed" states [3, 11] (not to be confused with open and closed bottleneck of this paper), involving well-resolved structural changes of other parts of the enzyme seen both in X-ray and cryo-EM, the bottleneck is about the same and is in the closed state as we identified it. Thus, the new data suggest that the stable open bottleneck state (or quasi-stable state with significant thermal population to be captured in the plunge-freezing of cryo-EM) does not seem to exist. Therefore, it now appears more likely that the bottleneck opening occurs dynamically in the course of thermal fluctuations, suggesting a specific intricate mechanism of the passage of the quinone through the bottleneck.</p><p>Here, using molecular dynamics simulations (both all-atomic and coarse grained) of the bacterial enzyme we have identified collective conformational changes (Principal Component Analysis, PCA, modes) that dynamically open the quinone chamber bottleneck. The changes involve mostly TM1 helix, which straightens up, AH1 helix, which moves to open the structure, and the loop between them; but in general, the changes involve rearrangement of a larger part of the enzyme. We propose a specific "squeeze-in" mechanism of the bottleneck passage, where the rare conformational fluctuations along the identified PCA mode allow quinone in and out.</p><p>The simulations indicate that the most flexible part of the enzyme Nqo8 subunit involves structural elements that form the bottleneck; this suggests that if external forces—e.g., from the bound adjacent subunits—were applied, the entrance into Q-chamber would be affected most, possibly to be squeezed and locked in a closed state. This could explain the closed bottleneck state in all the resolved structures. The model predicts a significant reduction—due to a need for a rare opening of the bottleneck—of the effective bi-molecular binding rate constant, which is in line with the available kinetic data. We discuss possible reasons for such a tight control of the passing of the quinone into the binding chamber, which remain to be obscure at present.</p><!><p>Previously [28], five structures were analyzed: bacterial T. thermophilus [4], yeast Y. lipolytica [8], and three mammalian structures, ovine [5], mice [9], and human [10, 29]. As the core part of the enzyme is almost identical to the 14 subunits of the bacterial complex, and the results are qualitatively similar for all structures analyzed, here we consider first the bacterial structure. The entrance of the quinone chamber is formed in ND1 subunit (Nqo8/H subunit of bacterial enzyme); the elastic properties of this subunit, considered within the enzyme context, define the static and dynamic properties of the bottleneck.</p><!><p>A Left. The entrance into the Q-binding cavity is shown in red. Gray surface refers to subunit ND1, green lines—subunit A, and cyan spheres represent the quinone (from MD simulations in Ref. [28]). B Right. 10 residues that form the entrance of complex I Q-cavity in bacterial enzyme; the bottleneck is in the opening in the middle of the structure</p><!><p>The bottleneck is localized at the narrow crossing framed by three helices: TM1, AH1, and TM6 [4] of ND1/Nqo8 subunit (see below); the properties of the bottleneck is the focus of the MD simulations in this paper.</p><!><p>Overlap of all 23 chain-H structures. The bottleneck is formed by the crossing of three helices: TM1, AH1, and TM6 of ND1/H/Nqo8 subunit. The crystal structure of bacterial (T. thermophilus) is shown in red and appears to be the most open among all structures analyzed. The shown structures include different organisms and different so-called "open" and "closed" states of the enzyme [3, 11]</p><!><p>We showed previously that taken as in the pdb structure, the bottleneck is essentially closed. We demonstrate this by calculating the barrier of crossing the bottleneck structure. This is done in the following manner. (Additional probes were explored in Ref. [28]).</p><!><p>The MD simulation details are given in SI and are the same as in Ref. [28] Briefly, the protein was incorporated into POPC membrane. The quinone was placed near the entrance of Complex I, and after equilibration, was pulled into quinone cavity. We also pulled quinone out of the quinone cavity. The energy and the pulling force were measured along the pulling trajectory. Both ubiquinone and menaquinone with various tail lengths were simulated, see details in Ref. [28]. To improve statistics, focused MD simulations on a restricted system that involved only the residues of the bottleneck (Fig. 1) were used. (For additional simulation details, see MD Methods in SI.)</p><!><p>The average work along the pulling trajectory of ubiquinone (reduced, Q3H2, with 3 isoprenoid units) through the bottleneck of the Q-chamber in Y. lipolytica enzyme. The values under dotted lines give corresponding energy barriers for the headgroup and the two isoprenoid units of the tail passage. Lower panels show JA work along the pulling trajectory. A All atoms are strongly restrained as in pdb structure. B Backbone atoms are weakly restrained by 50 kJ mol−1 Å−2, and side-chain atoms by 10 kJ mol−1 Å−2 (a hydrogen bond, for comparison, is about 20 kJ/mol)</p><!><p>Here the goal was to probe the bottleneck passage in the structures given directly by the reported X-ray or cryo-EM pdb and to see if conformational changes are needed for the passage. Therefore, the backbone atoms were restrained to the positions given by the pdb structures, and different strengths of restraint were probed. Asp of the bottleneck was protonated in our simulations. Typical results are shown in the following figure.</p><p>Figure 3 shows a typical example of the average work of bottleneck crossing by the reduced quinone, Q3H2, in yeast Y. lipolytica enzyme. (Most simulations were done with 3-isoprenoid unit tail ubiquinone, half-inserted in the Q-chamber; due to repetition of the tail structure, the Q10 results could be inferred from the data.) Here the cryo-EM structure gives the initial position of the quinol (reduced) in the Q-cavity. We calculate the barrier to move the quinone in the structure captured by cryo-EM. The dotted lines in the figure correspond to so-called "first passage" work, i.e. work required to find and enter the bottleneck by the headgroup of QH2 and moving the methyl groups of the tail through the narrow entrance. In this case, two methyl groups were passing the bottleneck. The reduced form of QH2 requires some 20 kJ/mol more energy to pass the bottleneck barrier. The found average work is obviously too high to pass the bottleneck without conformational opening of the structure.</p><p>As can be seen, the barriers are too high for the bottleneck passage both for the headgroup and for the tail. The weaker restraints on the structure were also probed, Ref. [28], but the barriers still remained too high to allow a suitable timescale of passage. A reasonable barrier that would give a ms timescale should produce a barrier no higher than some 30 kJ/mol, see Discussion.</p><p>The results for all other enzymes, including bacterial, mt ovine, and human enzymes, yield the same qualitative conclusion—the bottleneck is too narrow for the headgroup and even for the tail passage (yeast), as the barrier to cross the bottleneck is too high.</p><p>We conclude that as seen in the resolved structures, the size of the entrance is prohibitively small for the quinone molecule to pass either in oxidized or in the reduced form. There are slight variations in all structures examined, but in all structures the bottleneck is too narrow to be operational to admit quinone to the binding site. There are two possibilities: one is that quinone may never get out of the quinone cavity and works as an electron shuttle, another is that dynamic conformational changes open the bottleneck and allow quinone in and out. After exploring details of conformational dynamics below, we will discuss both possibilities in the last section of the paper.</p><!><p>We now turn to exploring possible conformational openings of the bottleneck in ND1 subunit (without quinone, assuming binding by an empty enzyme). The idea is to first use the most accurate all-atomic force field and explore the elastic properties of isolated ND1 subunit itself; we then extend simulations to include other subunits adjacent to ND1 using a less accurate coarse-grained force field.</p><!><p>In MD simulations, we use Gromacs [31] simulation package with CHARMM36 forcefield [32]. The initial coordinates of ND1 (Nqo8) subunit were extracted from the whole structure of T. thermophilus [4], and ND1 was simulated as described in MD Methods of SI. The Principal Component Analysis detailed below was applied to analyze the trajectories.</p><!><p>The PCA method is described in Ref. [33]. Here, we briefly summarize our approach.</p><p>The PCA normal modes are collective coordinates \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$Q_{1} ,Q_{2} , \ldots$$\end{document}Q1,Q2,… that are linear combinations of the usual (mass-weighted) Cartesian displacements of the protein atoms \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$x_{i}$$\end{document}xi from their average positions:\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$Q_{\lambda } = \sum {x_{i} S_{i\lambda } } ,$$\end{document}Qλ=∑xiSiλ,where \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$i = (a,\sigma )$$\end{document}i=(a,σ), a – atom/site number, \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\sigma = x,y,z$$\end{document}σ=x,y,z. The expansion coefficients \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$S_{i\lambda }$$\end{document}Siλ are found by diagonalization of the correlation matrix \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$M_{ij} = < x_{i} x_{j} >$$\end{document}Mij=<xixj>, where averaging < … > is assumed along the MD trajectory. After diagonalization, the diagonal elements of the correlation matrix give PC variances – the averaged values of squared amplitudes of PCA modes:\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$(diagM)_{\lambda \mu } = \delta_{\lambda \mu } < Q_{\lambda }^{2} > .$$\end{document}(diagM)λμ=δλμ<Qλ2>.</p><p>The expansion coefficients \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$S_{i\lambda }$$\end{document}Siλ are eigenvectors of the correlation matix. The square values \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$P_{i\lambda } = \left( {S_{i\lambda } } ight)^{2}$$\end{document}Piλ=Siλ2 can be considered as "probabilities" (after proper normalization) and \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$S_{i\lambda }$$\end{document}Siλ as "amplitudes" for a given eigenvector. Atom participation value is defined by summing \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$P_{i\lambda }$$\end{document}Piλ for a given atom a over x, y, z components. The PCA modes are similar and formally equivalent to familiar normal modes of an artificial harmonic system with the same displacement correlation matrix.</p><p>Each normal mode can be thought as describing coherent motion of all coordinates x involved in it:\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$egin{gathered} x_{i} (t) = S_{i\lambda } \cdot Q_{\lambda } \hfill \ Q_{\lambda } = Q_{\lambda 0} \cos \left( {\omega_{\lambda } t} ight) \hfill \ \end{gathered}$$\end{document}xi(t)=Siλ·QλQλ=Qλ0cosωλt</p><p>The picture of coherent motion is only qualitative because there is significant dumping of the oscillations and the low-frequency modes are mostly in the over-dumped regime. However, the picture of normal modes is a convenient way to think about the collective or correlated motions of a big system.</p><p>The frequency of each mode can be found from\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\omega_{\lambda } = \sqrt {rac{{k_{\lambda } }}{{M_{\lambda } }}} .$$\end{document}ωλ=kλMλ.</p><p>Here the force constant \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$k_{\lambda }$$\end{document}kλ and the effective mass of a mode can be found from the energy relations. From the averaged potential energy,\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$k_{\lambda } = rac{{k_{B} T}}{{ < Q_{\lambda }^{2} > }},$$\end{document}kλ=kBT<Qλ2>,and from kinetic energy (\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$m_{i}$$\end{document}mi are atomic masses):\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$M_{\lambda } = \sum {m_{i} S_{i\lambda }^{2} } .$$\end{document}Mλ=∑miSiλ2.</p><p>When the mass-weighted coordinates are used, the effective masses are all the same and can be normalized to unity. The timescales of the low-frequency modes (periods \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$T_{\lambda } = 2\pi /\omega_{\lambda }$$\end{document}Tλ=2π/ωλ) can be large and therefore difficult to access via direct MD. One can use these modes for probing possible large amplitude motions of the protein by artificially moving along one or several low-frequency modes, guiding direct MD, and thus more efficiently sample phase space. Also, it was suggested [34] that the functional or essential conformational changes of proteins most likely occur along with the low-frequency collective coordinates, as a low energy path for large change.</p><!><p>The helix structure representation of H-subunit (ND1) of complex I and the entrance into Q-binding cavity. A Left, closed structure; B Right, open bottleneck structure. The opening occurs in deformation along the lowest frequency collective PCA mode (Q0), see Movie S1 in SI. The red color represents atoms and residues that contribute most to PCA mode Q0. The un-bending deformation of TM1 helix and the increased size of the bottleneck are two most prominent features of Q0. Details of several other modes are discussed in the text, and further details are given in SI</p><!><p>It is seen in Fig. 4 and Movie S1 in SI that the deformation along with the first PCA mode Q0 mainly involves helices TM1, AH1, and TM2 and the two loops connecting them. The un-bending deformation of TM1 helix and the increased size of the bottleneck are the two most prominent features of the softest deformation mode of ND1. This mode also involves the opening of the so-called E-channel in H/ND1-subunit, see S1 in SI. The timescale of this collective motion is 0.3 ns, Table S1; this is a slow motion, about thousand times slower than the usual molecular vibrations (e.g., CC bond). Details of several other modes are discussed later in the text, and further details are given in SI. Generally, we find that many low-frequency modes involve the motion of TM1 and AH1 helices, but their contribution is scaled by their amplitudes—the higher the frequency the lower the amplitude, and so is the contribution.</p><p>As the structural elements of the bottleneck—TM1, AH1, and TM6—correspond to the softest mode, it is clear that if external forces were applied to deform ND1 subunit, the deformation would firstly affect the bottleneck structure. Thus, the closure of the bottleneck can be expected if one assumes compressing, de-solvation forces acting on the protein in the structure-resolution conditions or low temperature, which is particularly clearly seen in Movie S1 in SI.</p><!><p>To evaluate quantitatively the involvement of the bottleneck residues in a given collective mode, we calculate the so-called participation value; namely, for the bottleneck residues, A29, F28, P59, D62, A63, S66, I239, A242, L243, and M246, shown in Fig. 1, we calculate the total participation probability of all atoms involved as \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$${I}_{\lambda }=\sum_{a\epsilon { ext{bottleneck}}}{P}_{a\lambda }$$\end{document}Iλ=∑aϵbottleneckPaλ. The resulting values then are scaled according to thermal amplitudes of the modes (eigenvalues of the PCA) and normalized to unity. Each mode thus is assigned the total bottleneck participation value, by which its contribution to the bottleneck conformations can be evaluated. For a group of modes, these data provide a spectrum of bottleneck participation. Alternatively, we directly calculate the variation of the size of the bottleneck (measured by its shortest dimension) for each mode, assuming the average thermal amplitudes of the modes.</p><!><p>Left: Bottleneck involvement in the first 40 modes. The higher modes are increasingly stiffer in character with less relevance to large-scale structural changes. Right: Bottleneck size increments for the first 40 modes</p><!><p>It is of interest to characterize the qualitative differences between different modes. If we neglect the difference in amplitudes and only focus on how coordinates are mixed in a collective mode (see Fig. S1), Modes 0, 4 and 5 show a higher bottleneck involvement than the rest of the first 10 low-frequency modes. Those modes are shown in animations in SI, Movies S1a-b. These examples clearly illustrate the different characters of the collective motions involved; surprisingly, many of them involve some elements of the bottleneck. We need to remember, however, that participation of different modes is scaled by their thermal amplitudes, and thus in practice we can focus only on a few first low-frequency modes, with the first mode clearly dominating, Fig. 5, left panel.</p><p>Indeed, the first mode Q0 shown in Fig. 4 already tells most of the story; namely, it is TM1 straightening and the related upside AH1 motion that are mostly responsible for the opening of the bottleneck. For a system involving tens of thousands of coordinates, to find only a few functionally relevant collective coordinates is quite remarkable. In the following section, we will explore how adjacent subunits affect this conclusion.</p><!><p>An open bottleneck structure under Q0 deformation, see Fig. 4 and animation S1 in SI, can be envisioned as a possible open state of the bottleneck. On average, the changes are relatively small, but they involve rearrangement of essentially the whole subunit ND1 (as is particularly clearly seen in all-atom animation of Q0 in SI). As this subunit is in contact with other parts of the enzyme, including membrane subunit A (ND3), some changes in the equilibrium structure of the whole membrane part are expected in the open functional state. It is interesting that deformation under Q0 mode also opens the so-called E-channel in the ND1 subunit, see SI, Movie. S1C. In simulations, the open state (maximum amplitude of Q0 quasi-harmonic deformation) occurs only in the course of thermal fluctuations, i.e. it is not a stable state. This is in line with most recent structural data where no open state was detected, see Fig. 2; thus, the bottleneck open state occurs as a rare thermal fluctuation.</p><!><p>By design, in this section our simulations involved isolated ND1 subunit with no specific external forces acting on it (except for hydrophobic solvent—to model the inner part of the membrane, external pressure from the surrounding environment, including adjacent subunits, and some restriction of the terminal residues. This is still not an accurate representation of the enzyme context, which is examined in greater detail in coarse-grained simulations later.) This is done in part to explore the effect of the stress conditions [35] on the subunit when it is part of the whole enzyme structure; the stress conditions are due to inter-subunit forces, proper membrane solvation [35], etc. To evaluate the effect of boundary conditions imposed by the neighboring subunits, here we artificially remove the external forces that keep ND1 subunit in the constrained configuration in the enzyme structure and monitor changes occurring in structural evolution.</p><p>In MD simulations of an isolated ND1, the overall global structural changes are already seen in the trajectories of the order of 100 ns (SI Fig. S5); but most prominently the change occurs already in the first 1–3 ns of the trajectories, which indicates the release of the stressed conditions of ND1 subunit in the enzyme structure. The SI provides detailed data that illustrate the overall evolution of the entire ND1 subunit upon release of the restraints. The main qualitative result is shown in SI Fig. S6. It turns out that already in the first few nanoseconds of the trajectory the helices forming the bottleneck are moving, with TM1 straightening up, and AH1 moving up to open the bottleneck. The further evolution results in structures with an overall greater opening of the bottleneck due to movement of the helices involved.</p><p>A clear tendency in the expansion of the structure to open the bottleneck is also seen in the low-frequency modes at different time-segments of a long trajectory, see SI Movies S2–S4, with corresponding data in SI Table S1–S4.</p><p>Having these insights, we next explore in greater detail how the ND1-surrounding enzyme structure affects the results described in this section.</p><!><p>Here we put ND1 subunit in the context of the entire enzyme structure. The question is how the adjacent subunits of ND1 are affecting its fluctuations. As simulations with all-atomic force-field are limited in timescales, here we apply less accurate but more efficient coarse-grained (CG) simulations using Martini force field [36, 37]. These simulations can be expected to yield a reasonable qualitative picture. The simulated system includes all subunits that can directly affect the motion of ND1 structure and include subunits ND1/Nqo8, Nqo6, 4, 9, and A, simulated in the membrane and solvent environment. (One should keep in mind that the bottleneck opening can in principle involve the whole structure of the protein; however, the available structure of the enzyme with the bound quinone, does not show such clear global conformational changes. We, therefore, focused on the local changes—ND1 and surrounding subunits). The simulation details are given in SI MD methods.</p><p>Here we first compared the all-atomic simulations of an isolated ND1 subunit with the same simulations using Martini coarse-grained force field. Numerical comparison was done by calculating the overlap of the lowest frequency PCA modes of both force-fields. Not surprisingly, quantitatively the PCA modes are quite different in the two force fields (the overlap is low); however, the qualitative comparison is still possible. Namely, the bottleneck opening in the low-frequency modes is clearly seen in both force-fields simulations and involves the same elements: TM1, AH1, and the loop between then among others. However, the amplitudes of motion of different elements are regulated by the details of the force fields, which are quite different in the two cases. In particular, the coarse-grained Martini involves the phenomenological pair-wise rubber band restraints on the sites (5 kJ/mol/Å [2]) within each subunit, which in the long-term simulation keeps the overall structure as it appears in the initial pdb; but at the same time there is no direct analogy to such terms in the all-atomic force field. We explored different possibilities in the variation of this parameter, recognizing that in any case the results should be taken only as qualitative indication of possible dynamic behavior of the system. We then explored PCA modes of a multi-subunit system.</p><!><p>CG PCA lowest-frequency mode of the structure of ND1 (B), and (A) with additional subunits: A (yellow), 6 (cyan), 4 (green), and 9 (magenta), all shown in (A). Red color intensity corresponds to elements with high participation in the Q0 PCA mode. The open structure resulting from the full amplitude Q0 PCA mode is shown</p><!><p>Overall, these results indicate that thermal fluctuations indeed mostly affect the bottleneck structure, even in the enzyme context, providing the needed opening states along the dynamic trajectory. In the following, we discuss the mechanism of the bottleneck passage that involves these rare fluctuations.</p><!><p>Here and previously [28], we showed that in five published structures of complex I—bacterial T. thermophilus, yeast Y. lipolytica, and three mammal, mice, ovine, and human, the bottleneck at the entrance of the quinone chamber is too narrow for a quinol or quinone to pass through it. Most recent additional structures of different conformational states show that the bottleneck in all available structures (twenty-three analyzed so far) is about the same and, therefore, impossible for the quinone to get in or out of the binding cavity unless driven by a conformational changes that presumably occur in the course of thermal fluctuations of the enzyme. This is confirmed by MD simulations of the barrier formed by the narrow entrance in an intact pdb structures. Moreover, the bottleneck appears to be too narrow even for a passage of the isoprenoid tail of ubiquinone in the case of the yeast enzyme, although one quinone molecule is seen as stuck half-way to the binding site in the yeast structure. The shuttle model, where one quinone molecule never gets out the binding cavity appears to be unlikely, as we discussed previously [28].</p><p>The conclusion is that fluctuations of the structure not reflected in the pdb structures has to be included in the complete picture. In addition, minor deformations may also result from artificial, out of the membrane conditions [38], both in X-ray or cryo-EM, as the lack of proper membrane solvation of the enzyme [38] can deform the molecule in such a way that the intrinsically narrow entrance path becomes even smaller, impossible for actual passage of quinone and render enzyme appear to be non-functional. To open the entrance of the quinone chamber, some conformational changes are needed; however, the nature of these changes—given their collective character—is not trivial.</p><p>Here, using PCA modes of the MD trajectories of the bacterial enzyme, we have identified collective conformational changes that open the quinone chamber bottleneck. The main qualitative result is shown in Fig. 4, and in animation S1 in SI. The changes involve mostly TM1 helix, which straightens up, AH1 helix, which moves up to open the structure, and the loop between them; but in general, the changes involve rearrangement of a larger part of the enzyme. The simulations allow to reconstruct to some extent the elusive structure of ND1 in which the bottleneck is open. It is now clear (given most recent structural data) that the open state is unstable, producing a very low population, which is not readily captured in plunge-freezing of cryo-EM.</p><!><p>The barrier crossing rate by a quinone molecule at the bottleneck can be estimated from the transition state theory; namely, the rate of a single barrier crossing—be it a headgroup, or one of the methyl groups of the isoprenoid tail, is given by the following expression:\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$k pprox rac{{D_{q} }}{{L_{0}^{2} }}10^{{ - rac{{V_{b} }}{\ln (10)RT}}}$$\end{document}k≈DqL0210-Vbln(10)RTwhere \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$D_{q}$$\end{document}Dq is the diffusion coefficient of quinone in the membrane environment, \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$D_{q} \sim 10^{ - 7} - 10^{ - 8} { ext{cm}}^{{2}} { ext{/s}}$$\end{document}Dq∼10-7-10-8cm2/s [21], and \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$L_{0}$$\end{document}L0 is a characteristic length of the barrier width [39]. The first factor (assuming barrier width 1–3 Å) is of the order of 108 s−1. The exponential factor should be greater than 10–5; as ln10RT = 6 kJ/mol, \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$V_{b} pprox 30\;{ ext{kJ/mol}}$$\end{document}Vb≈30kJ/mol or less. All our energy barriers are in gross excess of this critical value, thus in the functional state the bottleneck structure should be opened.</p><p>Although it is not clear to what extent the structure is opened in the transition state, it appears unlikely that the structure would be open only for a free tail passage while blocking the headgroup. This is because the methyl groups of isoprenoid tail present almost the same challenge of passing the narrow bottleneck as the headgroup, as our calculations suggest. The effective diffusion constant for isoprenoid tail movement through the bottleneck is modified by the same exponential factor discussed above and is too small to be operational in all structures examined. Thus, taken as is in pdb structures, the bottleneck is too narrow to be operational even for the shuttle model [40].</p><!><p>It is clear that conformational changes are needed to open the bottleneck for the diffusion-like motion of the quinone through the bottleneck; presumably, they occur in thermal fluctuations along the low-frequency PCA modes described above. These fluctuations would allow individual random-walk steps to occur in the overall diffusion-like motion. In such a squeeze-in mechanism, of getting through the eye of a needle, the overall entrance remains to be relatively narrow, which allows for tight control of the entrance of the binding cavity. This could provide desirable selectivity of admission of quinone vs quinol, or vs other bulky molecules by the quinone cavity.</p><p>The available kinetic data [25] appear to support this conclusion. Consider the efficiency parameter defined as \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\kappa_{BM} = k_{cat} /K_{m}$$\end{document}κBM=kcat/Km for quinone binding and reduction, assuming Michaelis–Menten kinetics. Under the condition of \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$k_{cat} > > k_{dis}$$\end{document}kcat>>kdis, the efficiency parameter is the second-order rate constant that can be estimated as follows. (Alternative case \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$k_{cat} < < k_{dis}$$\end{document}kcat<<kdis, i.e. the opposite to what we assumed in the above, is an unlikey scenario [28]) In 3D diffusional model [28, 41]\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\kappa_{BM} = 4\pi r_{0} D_{q} (N_{A} /10^{3} )P_{r}$$\end{document}κBM=4πr0Dq(NA/103)Pr</p><p>Here the diffusion of quinone head-group from the membrane internal medium to the entrance of Q-tunnel is envisioned as a 3D process, see Fig. 1A. It is assumed that the binding site is a 3D sphere of radius r0 (the rate is half of the above for a half-sphere), diffusion coefficient (for center of mass) of substrate is Dq, the Avogadro number is introduced for conventional units M−1 s−1, and CGS units are assumed for r0 and Dq. The factor Pr describes the probability that a substrate arriving at the binding site via diffusion will have a right orientation to for binding, and/or that the binding site is open. The more intricate binding site configuration is, the smaller the probability Pr; this could be combined with the effective capture radius, r0. However, the remaining part of probability Pr is the probability that the binding site is open.</p><p>An equivalent expression for 2D diffusion [42, 43] in the membrane plane gives qualitatively similar results. Here,\documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\kappa_{BM} = rac{{\pi D_{q} d_{m} (N_{A} /10^{3} )P_{r} }}{{\ln (R_{0} /r_{0} )}}$$\end{document}κBM=πDqdm(NA/103)Prln(R0/r0)</p><p>where dm is the membrane width (of the order of 50 Å), and R0 is the typical distance between substrate molecules in the membrane (for [Q] = 10 mM, R0 = 10 nm). The factor in denominator is never too large, and realistically is in the range of 5–7 for realistic r0 = 1 Å, or somewhat less, assuming order of magnitude values. Given these values, the two expressions give qualitatively similar results for r0 = 1 Å, and the expression is not sensitive to this parameter.</p><p>The diffusion coefficient Dq is assumed to be in the range [21, 41] of 10–7 to 10–8 cm2/s for the substrates of our interest, but can be modified by the barrier at the bottleneck, as discussed earlier.</p><p>The above expressions predict rates that are in line with the available kinetic data. For example, for NADH oxidation reaction of Ref. [25] both theory and experiment give kcat/Km = \documentclass[12pt]{minimal} sepackage{amsmath} sepackage{wasysym} sepackage{amsfonts} sepackage{amssymb} sepackage{amsbsy} sepackage{mathrsfs} sepackage{upgreek} \setlength{\oddsidemargin}{-69pt} egin{document}$$\kappa_{BM} \sim$$\end{document}κBM∼ 107, for r0 = 1 Å and Pr = 1 (no need to open the binding site). Similar values are obtained for AOX of Ref. [25].</p><p>However, for Q-reduction kcat/Km in Ref. [25] is much smaller and, depending on the length of isoprenoid tail, is in the range of 104 to 105. This significant reduction can be readily explained by an additional small factor Pr in the range of 10–2 to 10–3 or even smaller (effective reduction of the capture radius, r0, would not produce such an effect). This could be rationalized by the difficulty of passing through a narrow entrance of the Q-channel and be interpreted as a small probability of the bottleneck open state.</p><p>At the same time, the difficulty of quinone accessing the reduction catalytic site and the relatively fast reduction of the FeS chain by NADH (that provide electrons for quinone reduction) should result in a (partially) reduced state of FeS clusters in the chain, which then can serve as a buffer of electrons reducing quinone. In such conditions, the redox potential of the FeS chain would be in equilibrium with that of NADH pool, i.e. around -320 mV, and thus reduction of quinone to produce semiquinone state appears to be quite possible, despite that the redox potential of the last FeS cluster in the chain N2, which reduces quinone, by itself is much more positive [24].</p><!><p>Supplementary file1 (PDF 889 KB)</p><p>Supplementary file2 (MP4 14819 KB)</p><p>Supplementary file3 (MP4 14819 KB)</p><p>Supplementary file4 (MP4 14819 KB)</p><p>Supplementary file5 (MP4 14819 KB)</p><p>Supplementary file6 (MP4 14819 KB)</p><p>Supplementary file7 (MP4 14819 KB)</p><p>Supplementary file8 (MP4 14819 KB)</p><p>Supplementary file9 (MP4 14819 KB)</p><p>Supplementary file10 (MP4 14819 KB)</p><p>Supplementary file11 (MP4 14819 KB)</p><p>Supplementary file12 (MP4 14819 KB)</p><p>Pushing the limits of flash photolysis to unravel the secrets of biological electron and proton transfer- a topical issue in honour of Klaus Brettel.</p>

PubMed Open Access

The crystalline state as a dynamic system: IR microspectroscopy under electrochemical control for a [NiFe] hydrogenase

Controlled formation of catalytically-relevant states within crystals of complex metalloenzymes represents a significant challenge to structure-function studies. Here we show how electrochemical control over single crystals of [NiFe] hydrogenase 1 (Hyd1) from Escherichia coli makes it possible to navigate through the full array of active site states previously observed in solution. Electrochemical control is combined with synchrotron infrared microspectroscopy, which enables us to measure high signal-to-noise IR spectra in situ from a small area of crystal. The output reports on active site speciation via the vibrational stretching band positions of the endogenous CO and CN À ligands at the hydrogenase active site. Variation of pH further demonstrates how equilibria between catalytically-relevant protonation states can be deliberately perturbed in the crystals, generating a map of electrochemical potential and pH conditions which lead to enrichment of specific states. Comparison of in crystallo redox titrations with measurements in solution or of electrode-immobilised Hyd1 confirms the integrity of the proton transfer and redox environment around the active site of the enzyme in crystals. Slowed proton-transfer equilibria in the hydrogenase in crystallo reveals transitions which are only usually observable by ultrafast methods in solution. This study therefore demonstrates the possibilities of electrochemical control over single metalloenzyme crystals in stabilising specific states for further study, and extends mechanistic understanding of proton transfer during the [NiFe] hydrogenase catalytic cycle.

the_crystalline_state_as_a_dynamic_system:_ir_microspectroscopy_under_electrochemical_control_for_a_

Introduction<!>Purication of Hyd1<!>Single-crystal IR microspectroscopic-electrochemical experiments<!>Solution infrared spectroscopic-electrochemical measurements<!>Initial characterisation and electrochemical reduction of crystalline Hyd1<!>Potential-controlled single-crystal redox titration of Hyd1, pH 8.0, monitored by IR microspectroscopic electrochemistry<!>The transition from Ni a -C to Ni a -SI<!>Conclusions

<p>Obtaining crystals of redox enzymes in intermediate states relevant to catalysis is a high-prole, yet challenging target. Methods for controlling the redox state of protein crystals include the titration of crystal medium with reductant or oxidant until a desired solution potential is reached, 1,2 exposure of crystals to substrate/inhibitor, 3,4 or crystallisation of protein which has been pre-equilibrated to a desired redox state. 5,6 However, these methods oen lack precision in generating pure enzyme states. There is also growing interest in triggering catalytic steps in enzyme crystals which can be coupled with time-resolved serial synchrotron or XFEL crystallography, and to date, such methods have typically relied on photo-triggers for reactivity. [7][8][9] Methods for studying crystalline and lyophilised enzyme using gas exchange have also been reported. 10 Verication of protein redox states in crystallo presents a further challenge, and to this end, a number of synchrotron macromolecular crystallography beamlines have introduced microspectroscopic methods for secondary characterisation of protein crystals, including UV-visible and Raman spectroscopy. [11][12][13] We have previously demonstrated the possibility of electrochemical control over single crystals of hydrogenase I from Escherichia coli (Hyd1) coupled with synchrotron infrared (IR) microspectroscopy for simultaneous reporting on the active site speciation. 14 Vibrational absorption bands of the integral CO and CN À ligands at the active site of hydrogenases make this spectroscopic method ideal for elucidating the redox and coordination state of the active site. By applying steps in electrode potential, we were able to achieve uniform and reversible manipulation of Hyd1 in crystallo from the most oxidised to the most reduced levels. We now show how ne potential control over Hyd1 crystals can be used to generate specic redox levels, enabling us to control and examine transitions between catalytically-relevant redox and protonation states.</p><p>Hydrogenases are a broad group of enzymes responsible for bidirectional heterolytic activation of dihydrogen (H 2 # H + + H À / 2H + + 2e À ) at di-iron or nickel-iron bimetallic active sites. 15,16 They have attracted attention for wide-ranging applications in biotechnology: energy applications in microbial H 2 production and bioanodes for H 2 /O 2 fuel cells, through to H 2driven biocatalytic cascades. [17][18][19][20][21] The active sites of most hydrogenases are 'wired' to a bacterial membrane or to their natural redox partner via a chain of FeS clusters in the protein.</p><p>In the [NiFe] enzymes, the active site Ni atom is ligated by two terminal cysteine thiolates, with two additional cysteines bridging to the Fe atom. The Fe is further coordinated by one CO and two CN À ligands (Scheme 1). During catalysis, the Ni formally cycles through Ni I/II/III , whereas the Fe remains formally Fe II , presumably stabilised by buffering of electron density from the combination of pi-acceptor and sigma-donor properties of the coordinated CO and CN À ligands. 16 The CO and CN À stretching bands in the mid-IR (n CO and n CN respectively) respond sensitively in wavenumber position to changes in electron density at the active site, and even to protonation and changes in hydrogen-bonding in the vicinity of the active site. 22 Since protons (H + ) are the product/substrate of hydrogenases, the activity and speciation of these enzymes are greatly pH-dependent. 23,24 It is possible, electrochemically, to step through the range of catalytically-relevant redox levels of hydrogenase by controlling the electron transfer and protoncoupled electron transfer (PCET) steps, as shown in Scheme 1.</p><p>A catalytic cycle for [NiFe] hydrogenases has been proposed by combining insight from spectroscopic, computational, structural and activity studies. 16 Viewed in the direction of H 2 oxidation (Scheme S1 †), it is generally accepted that H 2 binds at the Ni II redox level, 'Ni a -SI', the most oxidised catalytic state (subscript 'a' denoting an 'active' catalytic species), however, any Michaelis complex with H 2 has evaded detection to date. Heterolytic cleavage of H 2 leaves a hydridic H in a bridging position between the Ni and Fe, 25 and a proton on a nearby base, the identity of which remains hotly debated. [26][27][28] The resulting state is generally termed Ni a -R, though several Ni a -R sub-states exist, likely reecting sequential proton movement away from the active site. 22 An electron must next be transferred from the active site to the FeS cluster relay chain to form the Ni a -C state which still contains a bridging hydride, but is formally Ni III . [29][30][31] From the Ni a -C state, the bridging hydride is lost as a proton to a nearby base (not necessarily the same base that accepts the initial proton from H 2 cleavage at the Ni a -R level 32 ) leaving the Ni reduced formally by two electrons to Ni I (generally termed 'Ni a -L', noting that there are also multiple Ni a -L sub-states, again likely reecting differential proton location in the region of the active site). Since the transition from Ni a -C to Ni a -L simply requires relocation of electron density and a proton, these two states have been described as tautomeric and exist at the same redox level (Scheme 1). Although Ni a -L was rst observed as a low temperature photo-product of Ni a -C, evidence for Ni a -L as a catalytic intermediate has accumulated from a series of steady state and transient spectroscopic studies which support earlier theoretical mechanistic proposals. 22,[33][34][35][36][37] Finally, the Ni must be oxidised back to Ni II via electron transfer to the FeS cluster chain, to re-generate the Ni a -SI state ready to bind the next molecule of H 2 .</p><p>Hyd1 belongs to the group of so-called O 2 -tolerant hydrogenases, along with membrane bound hydrogenase (MBH) enzymes from Ralstonia eutropha (Cupriavidus necator), Aquifex aeolicus, and Hydrogenovibrio marinus. 15 Hydrogenases within this group differ principally in the structure and potential of the electron-relay FeS cluster proximal to their active site, and the unusually high potential of this cluster has been linked to their O 2 -tolerance. [38][39][40][41][42] Like other [NiFe] hydrogenases, Hyd1 is isolated in a mixture of oxidised inactive states that require reductive activation. 43,44 Scheme 1 Skeletal structure of the active site redox states for [NiFe] hydrogenases ordered by redox level. Dashed arrows represent the H 2 binding and activation step during catalytic H 2 oxidation. States are colour-coded to match data throughout this work. Catalytically active states are labelled "Ni a -X", where X ¼ SI, C, L or R. n CO band positions refer to Hyd1, pH 5.9.</p><p>The predominant state is Ni-B, which has a bridging OH À ligand 16,45 (Scheme 1), and is reversibly re-formed following oxidation, particularly at low H 2 . 46 Despite a wealth of spectroscopic, structural, and biophysical studies on hydrogenases from diverse organisms, many details of the mechanism of H 2 activation remain uncertain. Individual proton and electron transfer events, how they are temporally linked to the catalytic mechanism and/or formation and reactivation of inactive states, the identities of proton donors and acceptors, and the identity of key catalytic intermediates are all questions that remain unanswered for both the [NiFe] and [FeFe] hydrogenases.</p><p>One of the challenges is how to unify the understanding gained from measurements made on different physical sample types. Spectroscopy of hydrogenases is typically performed in solution, and solution IR spectroelectrochemical 'redox titrations' are well established for hydrogenases, with use of smallmolecule redox mediators to facilitate diffusion-controlled electron transfer. 16 Frozen samples are required for nuclear resonance vibrational spectroscopy and most EPR measurements, while crystalline material is required for structure determination. Electrochemistry on lms of electrodeimmobilised protein (Protein Film Electrochemistry, PFE) has been used widely in studying hydrogenases, [47][48][49] and we have previously introduced a complementary IR spectroelectrochemical approach termed Protein Film Infrared Electrochemistry (PFIRE) which provides chemical/structural insight to complement information from PFE alone. 32,50,51 Here, we compare the potential-dependence of IR-detected equilibrium active site states observed for Hyd1 under electrochemical control in solution, on an electrode (PFIRE) and in single crystals. Signicantly, we now show that, within single crystals of Hyd1, it is possible to achieve control over the full manifold of states observed in solution for this enzyme. A related report by Morra et al. demonstrates electrochemical manipulation of [FeFe] hydrogenase I from Clostridium pasteurianum using similar methods, 78 and these studies present the possibility for using electrochemical control over single protein crystals to establish samples in the solid state for further structural study.</p><!><p>Hyd1 was prepared aerobically according to a published procedure. 52 For PFIRE and solution-based experiments, no further purication was required, however for crystallisation it was essential to remove any aggregated protein and bound cytochrome-b subunit via size exclusion chromatography (SEC) followed by hydroxyapatite chromatography, as described previously. 26 Fractions containing highly pure Hyd1 (HyaAB) were identied by SDS-PAGE, pooled and buffer exchanged into SEC buffer (20 mM Tris, pH 7.2, 150 mM NaCl, 0.02% (w/v) DDM detergent, 1 mM dithiothreitol) by repeated spin concentration and dilution (using Vivaspin 20 mL, 50 kDa molecular weight cut-off centrifugal concentrators until a 1500-2000 fold dilution of the phosphate buffer used during hydroxyapatite chromatography was achieved). For crystal growth, protein samples were concentrated to 5 mg mL À1 , as judged by Bradford assay. 53 Crystals of Hyd1 were acquired according to previously established protocols, 26 using the sitting drop vapour diffusion technique, where 1.5 mL of protein solution was mixed with an equal amount of crystallisation buffer (either 100 mM Bis-Tris, pH 5.5-5.9, 200 mM Li 2 SO 4 , 150 mM NaCl, PEG 3350 (19-21% w/v) or 100 mM Tris$HCl, pH 8.0, 200 mM Li 2 SO 4 , 150 mM NaCl, 19-21% PEG 3350) followed by streak seeding with old smaller crystals of Hyd1. Incubation under an anaerobic atmosphere (<0.3 ppm O 2 ) at 20 C resulted in crystals appearing within 24 hours.</p><!><p>An adaptation of our previously-reported cell design 14,54 was used for single-crystal microspectroscopic electrochemistry, and is described in more detail in the ESI (Fig. S1 †). The microspectroscopicelectrochemical cell contained a miniature Ag/AgCl reference electrode (3 M KCl, 2 mm diameter, eDAQ), a graphite ring counter electrode (cut from a graphite tube, Goodfellow), and a glassy carbon working electrode (4 mm diameter, Alfa Aesar). The working electrode was polished to high reectivity (ca. 10-20% in the mid-IR) using increasingly ne grades of silicon carbide paper (2500 and 4000 grit, Kemet). The polished electrodes were washed by ultrasonication in ultrahigh purity water (MilliQ, 18 MU cm) prior to cell assembly. The reference electrode was removed during the polishing process to avoid damage and contamination.</p><p>A solution containing the redox mediators 2,6-dichloroindophenol, phenazine methosulfate, indigo carmine, anthroquinone-2-sulfonate, and methyl viologen, each at 1 mM concentration (Table S1 †) was prepared in N 2 -degassed crystal stabilisation buffer (for experiments conducted at pH 5.9 this was 100 mM Bis-Tris, pH 5.9, 200 mM Li 2 SO 4 , 150 mM NaCl, 22% v/v PEG 3350, whereas for experiments conducted at pH 8.0 the buffer used was 100 mM Tris, pH 8.0, 200 mM MgCl 2 , 150 mM NaCl, 22% PEG 3350). A 3 mL aliquot of the mediator solution was added to each well of a crystallisation plate containing Hyd1 crystals (crystals were stored in $3 mL mother liquor, and the size and number of crystals varied between wells). Gentle pipetting suspended the crystals without damaging crystal integrity. The resulting 6 mL mixture containing redox mediators and Hyd1 crystals was then deposited onto the glassy carbon working electrode of the microspectroscopic-electrochemical cell. An additional 12 mL of redox mediator solution in crystal stabilisation buffer was then pipetted onto the Ag/AgCl reference electrode and graphite counter electrode such that the cell was lled with approximately 18 mL of ca. 0.66 mM mediator solution. A CaF 2 window (UV grade, 30 mm diameter, 1 mm thickness, Crystran) was sealed onto the cell surface using a PTFE gasket (Harrick, 25 mm thick) and silicone sealant (Dowsil, SE 9187L Silicone RTV) to maintain an anaerobic environment within the cell. Assembly of the IR microspectroscopic-electrochemical cell, crystal handling, and mediator solution preparation, were carried out in a N 2 -lled glovebox (Plas-Labs Inc., 815 PGB series, <20 ppm O 2 ). The addition of redox mediators facilitates diffusion-controlled transfer of electrons through the electrolyte to enable electron transfer between the working electrode and the crystalline protein (a representative cyclic voltammogram of the mediator solution is shown in Fig. S2 †). Solvent channels within the Hyd1 crystal have radii between 5.4-6.6 Å (calculated using pdb 6FPO and MAP_CHANNELS 55 ) and are thus large enough to allow diffusion of redox mediators throughout the crystal (Fig. S3 †).</p><p>IR microspectroscopic-electrochemical experiments were carried out on the MIRIAM beamline B22 at Diamond Light Source, UK, using a Vertex 80V Fourier transform IR spectrometer coupled to a Hyperion 3000 IR microscope (Bruker) with a high-sensitivity photovoltaic mercury cadmium telluride (MCT) 50 mm pitch detector cooled to 77 K using liquid N 2 . A transection geometry was used to obtain IR spectra (i.e. the microscope is used in reection mode and detected light that passes through the cell twice), using a 36Â objective and 15 Â 15 mm 2 knife-edge aperture in the detection beampath. Each spectrum was recorded as an average of 1024 interferograms working at 80 kHz scanner velocity and at 4 cm À1 resolution (ca. 160 s measurement time). Data acquisition was performed using Bruker OPUS soware (version 7.5). Electrochemical measurements were acquired using an AutoLab 128N potentiostat (Metrohm) controlled by Nova soware (version 1.10). The miniature Ag/AgCl reference electrode was calibrated against a saturated calomel reference electrode (SCE, BAS), and potentials quoted in the text are adjusted to mV vs. the standard hydrogen electrode (SHE) using the conversion E (mV vs. SHE) ¼ E (mV vs. SCE) + 241 mV at 25 C. 56 Baseline correction, and all subsequent data analysis was carried out using OriginPro soware (OriginLab Corp., version 9.1). Baseline correction was applied using an interpolated spline function, and careful comparison with 2 nd derivative and difference spectra was used to avoid distortion of peak shapes. Baseline corrected spectra are presented in the main text, and representative raw spectra are shown in the ESI. †</p><!><p>Electrochemically-controlled IR redox titrations of solution phase Hyd1 were recorded using our previously-reported methods. 57,58 Briey, a 3D carbon particle network electrode 59 containing Hyd1 trapped within a mixture of the polymer electrolyte Naon (Sigma, titrated to pH 6 in phosphate buffer) and carbon black particles (XC72R, DUPONT) was prepared on the surface of an ATR-IR accessory (GladiATR, Pike Technologies) housed in an anaerobic, dry glovebox (Glove Box Technologies). The 3D network electrode was sealed into an electrochemical cell containing a carbon rod working electrode connection, saturated calomel reference electrode, and a Pt wire counter electrode. A closed loop of N 2 -purged electrolyte was pumped through the cell to prevent build-up of any trace H 2 produced by Hyd1. For more details see ESI. † IR spectra were recorded using an Agilent 680-IR spectrometer controlled by ResPro 4 soware, as an average of 1024 interferograms (ca. 360 s measurement time). Electrochemical control was provided by an Autolab 128N potentiostat (Metrohm), and potentials (E) are reported relative to SHE using the conversion E (mV vs. SHE) ¼ E (mV vs. SCE) + 241 mV at 25 C. 56 Infrared spectroscopic-electrochemical measurements of electrode-adsorbed Hyd1 (PFIRE)</p><p>The IR spectroscopic data collected from electrode-adsorbed Hyd1 are reproduced using data from Hidalgo et al. 51 The PFIRE method is briey described in the ESI. † In order to aid comparison to our single crystal method, we report the absorbance of individual Ni a -R sub-states separately in this manuscript, whereas they were summed to give a 'total' Ni a -R absorbance in Hidalgo et al. We have also reassigned some of the Ni a -L absorbances relative to the original manuscript such that both the Ni a -L I,II,III and Ni a -R I,II,III sub-states are labelled in order of decreasing wavenumber of the active site CO stretch, n CO , in line with other literature. 35,36,[60][61][62][63][64][65][66]</p><!><p>Fig. 1A shows a visible image, at 36Â magnication, of a single Hyd1 crystal lying on the working electrode surface (ne scratches are also visible in the glassy carbon surface, and another crystal can be seen to the le of the image, roughly vertically oriented). The 15 Â 15 mm 2 area used to record IR spectra is shown with a black square. Prior to electrochemical manipulation of the crystal, an IR spectrum was recorded at the open circuit potential (OCP) imposed by the oxidised mediator cocktail (typical OCP values were +209 to +274 mV vs. SHE). Fig. 1B shows a representative IR spectrum of crystalline Hyd1 recorded at pH 5.9 and an OCP of +209 mV before any electrochemical manipulation, showing the CN À and CO stretching regions, n CN and n CO , respectively (for raw data see Fig. S4 †). Crystals prepared from aerobically puried 'as-isolated' Hyd1 contain a mixture of oxidised inactive states as is common for [NiFe] hydrogenases, 16 predominantly the Ni-B state (Fig. 1B, n CO 1943 cm À1 ) with minor contributions from another oxidised species with n CO 1937 cm À1 . The identity of this minor component is unknown, but similar species have been observed in other hydrogenases and attributed to readily-activated species at the same redox level as Ni a -SI. 16,22 Very intense absorbances are observed from crystalline Hyd1 due to the high effective protein concentration within the crystals (ca. 8 mM of active site, see ESI †). Furthermore, the transection geometry of the microspectroscopic-electrochemical cell means that the effective IR pathlength through the sample is of the order of 30-50 mm, approximately double the crystal thickness. In the case of the crystal sample shown in Fig. 1A we can estimate the molar extinction coefficient of the Ni-B n CO band as approximately 4000 M À1 cm À1 , in good agreement with the extinction coefficient reported for this state of the active site in the large subunit of Ralstonia eutropha MBH in solution. 67 Crystalline Hyd1 was subjected to electrochemical reduction by applying a potential of À597 mV for a minimum of 1 hour, until no further changes in n CO and n CN bands were observed over a period of 10 minutes. Electrochemical reduction of crystalline Hyd1 is somewhat analogous to reductive activation used in PFE and PFIRE measurements on [NiFe] hydrogenase, 32,68 and as shown in Fig. 1C 2 shows a series of spectra of a single Hyd1 crystal recorded as a function of applied potential at pH 5.9, as both baselinecorrected spectra (Fig. 2A) and a 2D 'heatmap' plot (Fig. 2B). Fig. 2 focusses on the n CO region only, data including the n CN region are shown in Fig. S6, † and raw data are shown in Fig. S7. † These spectroscopic data were recorded following electrochemical reduction at À597 mV, as a series of small steps (25-100 mV per step) towards more positive potentials were applied between À597 mV and +203 mV. Aer each step the potential was held until spectroscopic equilibration was achieved, as judged by no further changes to IR spectra (or a minimum of 8.5 minutes; corresponding chronoamperometry data shown in Fig. S8 †). The changes in Hyd1 active site speciation during this in crystallo oxidative redox titration can be seen from the potentialdependent shi in n CO bands in Fig. 2. These correlate well with the ladder of redox states shown in Scheme 1. The Ni a -R species (1922 and 1914 cm À1 ) dominate at the most reducing potentials, converting to the Ni a -C (1951 cm À1 ) and Ni a -L (1877 and 1866 cm À1 ) states at intermediate potentials, and then forming the most oxidised catalytically active state Ni a -SI (1929 cm À1 , maximum intensity at À122 mV). At the most oxidising potentials the oxidised, inactive state Ni-B dominates (1943 cm À1 ). Signicantly, all previously established active site states for Hyd1 are observed in crystallo, including multiple sub-states of Ni a -R and Ni a -L. Fitting of the n CO bands to Gaussian band proles and extracting the tted peak absorbances allows plotting of titration curves of each active site state as a function of potential, as shown in Fig. 3A (representative spectral peak tting shown in Fig. S9 †).</p><p>In order to correlate states observed in Hyd1 crystals with the potential dependence of states observed in more conventional spectroscopic-electrochemical studies, Fig. 3 compares equilibrium potential-controlled redox titrations of the Hyd1 active site in single crystals (Fig. 3A), of Hyd1 in solution (Fig. 3B) and of electrode-adsorbed Hyd1 (Fig. 3C, recorded under an Ar atmosphere, reproduced using data from Hidalgo et al.). 51 The assignments of the n CO and n CN bands for each active site redox species in the crystalline state are consistent with those observed in both solution and electrode-adsorbed IR spectra of Hyd1 (Table S2 †). At pH 5.9, there is little or no catalytic H + reduction by Hyd1 (ref. 24 and 51) and as such the data in Fig. 3, recorded under an inert atmosphere, reect essentially non-turnover behaviour of the Hyd1 active site. The titration curves measured from crystalline, solution, and electrode-adsorbed samples are remarkably similar: the potentials at which the maximum intensity for each redox species is observed are consistent throughout. This result conrms the integrity of the dynamic behaviour around the active site of Hyd1 in single crystals, thus showing that observations made in the crystalline state of Hyd1 are mechanistically relevant to the enzyme in solution, and to PFE studies of enzyme activity.</p><!><p>Hydrogenases catalyse H 2 activation via a series of exquisitelytimed electron transfer and proton-coupled electron transfer steps (Scheme S1 †). Studies of hydrogenase activity and spectroscopic properties have been reported at a range of pH in order to establish details of the proton inventory during catalysis. 35,36,69 Crystals of Hyd1 are stable over a relatively wide pH range, 32 and by pre-soaking crystals in pH-adjusted crystal additional species with n CO at 1938 cm À1 is evident at potentials above +100 mV, and accounts for the apparent loss of Ni-B (1942 cm À1 ) at these potentials in Fig. 4B. The potential dependence of this 1938 cm À1 species (Fig. S13 †) is similar to the potential of the [Fe 4 S 3 ] 5+/4+ proximal cluster transition 39,70 and could be related to formation of the superoxidised proximal cluster (Table S4 †). Further investigation of this behaviour is a target for future studies.</p><p>pH-dependent behaviour of the active site and surroundings Fig. 5A compares IR spectra in the n CO region at both pH 5.9 and pH 8.0, extracted from redox titrations at À222 mV (pH 5.9, Fig. 3A) and À299 mV (pH 8.0, Fig. 4), potentials at which the intensities of the Ni a -C and Ni a -L states are maximal. We observe three main differences in both the titration data and spectra at pH 5.9 in comparison to pH 8.0:</p><p>(1) The relative populations of Ni a -C and total Ni a -L, and of the individual Ni a -L II/III sub-states, are pH dependent.</p><p>(2) The equilibrium midpoint potential for transitions between each redox level shis to more negative potentials at pH 8.0.</p><p>(3) The n CO peak positions for all redox states shi to lower wavenumbers at pH 8.0 relative to pH 5.9 (Fig. 5A and Table S3 †).</p><p>The redox titration data reported in Fig. 3 (at pH 5.9) and Fig. 4 (at pH 8.0) explicitly show contributions from two Ni a -R sub-states present in Hyd1, Ni a -R II and Ni a -R III . The peak positions at each pH are provided in Table S3. † For simplicity in Fig. 3 (n CO at 1877 cm À1 at pH 5.9) and Ni a -L III (n CO at 1866 cm À1 at pH 5.9). We have previously demonstrated a pH-dependent tautomeric equilibrium between the Ni a -C and Ni a -L species in Hyd1 (Scheme 1). 23 Here we observe the same shi in equilibrium towards Ni a -L at higher pH (Fig. 5B), showing that Ni a -C/Ni a -L tautomerism is maintained in the crystalline state. This observation is critical, as it suggests that crystallisation does not perturb proton transfer equilibria in the vicinity of the [NiFe] active site, in addition to the unperturbed electron transfer redox equilibria demonstrated by the equilibrium redox titrations in Fig. 3 and 4.</p><p>In addition to providing evidence of Ni a -C/Ni a -L tautomerisation in the crystalline state, it is clear from Fig. 5 that the relative proportions of each Ni a -L sub-state also vary with pH, consistent with the behaviour of electrode-adsorbed Hyd1 (Fig. S14 †) where the population of Ni a -L II remains roughly constant above pH 6. 71,72 The mechanistic role of the Ni a -L substates as sequential intermediates in proton transfer to/from the [NiFe] active site has been demonstrated in phototriggered potential jump measurements on soluble hydrogenase 1 (SH1) from P. furiosus, 36 and cryogenic photolysis of the [NiFe] hydrogenase from D. vulgaris Miyazaki F. 33,34 The most common representation of 'Ni a -L' invokes protonation of a terminal cysteine-S ligand to Ni at the active site. Evidence of cysteine-S protonation in the Ni a -L I sub-state has been reported S3. † For IR spectra of the n CN and n CO regions across the full potential range of À600 to +200 mV see Fig. S10. † (B) The speciation curves illustrate how the absorbance of the n CO peaks of Hyd1 active site species vary with potential at pH 8.0. in the D. vulgaris Miyazaki F [NiFe] hydrogenase, where H/D labelling suggested the presence of an S-H stretching vibration in Ni a -L I . 37 We have previously noted that the Ni a -L I sub-state does not accumulate signicantly, if at all, in O 2 -tolerant [NiFe] hydrogenases such as Hyd1, 22 and this behaviour is maintained in the crystalline state. Computational modelling studies of the active site suggest that deprotonation of a terminal cysteine thiol ligand to Ni causes n CO to shi to lower energy by ca. 30 cm À1 . 73 This shi upon deprotonation matches the difference in n CO observed between Ni a -L I and Ni a -L II/III for a range of [NiFe] hydrogenases, 22,72 leading us to postulate that a terminal cysteine thiol is not present in either of the Ni a -L II or Ni a -L III sub-states. The high Hyd1 concentration (8 mM) within single crystals allows us to test this hypothesis further through direct observation of the S-H stretching region (ca. 2450-2600 cm À1 ). Difference spectra are particularly sensitive to changes in cysteine-S protonation between individual redox states. 37 Potential-induced single crystal difference spectra (Fig. S15, † calculated from raw in crystallo microspectroscopy data) suggest that there is no change in cysteine-S protonation between the Ni a -R II/III , Ni a -C, Ni a -L II/III , and Ni a -SI redox states in Hyd1. Therefore we nd no evidence of S-H bond formation in the Ni a -L II or Ni a -L III , and Ni a -R II or Ni a -R III sub-states of Hyd1 (Fig. S15 †). Whilst the apparent lack of an S-H resonance in crystalline Hyd1 does not conclusively rule out cysteine thiol formation in Ni a -L II/III or Ni a -R II/III , the high S/N and intensity of spectra recorded from concentrated, crystalline Hyd1 would provide the ideal scenario for detecting any low-intensity S-H resonances.</p><p>It is generally accepted that a glutamate residue (E28 in Hyd1 numbering) close to the active site is critical for proton transfer during Ni a -L formation from Ni a -C, 32,36,37,74 and the primary proton acceptor during this transition in P. furiosus SH1 has been shown to have a pK a of approximately 7. 35 It is therefore possible that deprotonation of E28 is required for enrichment of Ni a -L III at pH 8.0. However the Ni a -L II sub-state has a considerably lower apparent pK a $ 5 (Fig. S14 †), implying that deprotonation of E28 is not required for Ni a -L II formation from Ni a -C in E. coli Hyd1.</p><p>The spectra of Hyd1 in Fig. 5A show an apparent peak shi and broadening of the Ni a -C n CO band upon change of pH. Peak tting of these data (Table S3 †) suggests that the Ni a -C peak actually contains contributions from two distinct n CO resonances for Ni a -C, at 1951 cm À1 and 1947 cm À1 , with the lower wavenumber species enriched at pH 8.0. This is consistent with the observations of Greene et al., who noted a pH equilibrium between two forms of Ni a -C in P. furiosus SH1 with an apparent pK a of 6.8. 36 The mechanistic relevance of this is not clear, although Greene et al. have postulated that protonation/deprotonation of glutamate E28 could account for the pH-dependent shi in the n CO position of Ni a -C. 36 In Fig. 5A we also observe a pH-dependent shi in the n CO band of Ni a -SI (Table S3 †).</p><!><p>We have previously shown that redox transitions involving chemical steps such as proton transfer appear to be retarded in the crystalline state. 14 By continuously recording IR spectra during equilibration aer each potential step in an electrochemical redox titration, we can monitor these kinetic aspects of equilibration in the crystals. Fig. 6A shows a series of difference spectra of a Hyd1 crystal at pH 5.9, following equilibration aer a small oxidative potential step from À197 mV to À172 mV, i.e. a positive potential step from where Ni a -C and the Ni a -L states are maximal (Fig. 3A). The difference spectra are presented as À172 mV minus À197 mV, and the raw experimental data and baseline corrected spectra are shown in Fig. S16 and S17. † The corresponding change in absorbance of the Previous studies have shown the involvement of the Ni a -L substates as an on-pathway intermediate between Ni a -C and Ni a -SI during catalysis. 23,33,36 Fast kinetic methods, capable of probing redox chemistry of the [NiFe] active site with sub-turnover frequency time resolution, were necessary to conclusively conrm the catalytic competence of the Ni a -L states. Here we are able to access similar information without the need for fast time resolution. The electrochemical control afforded by the microspectroscopicelectrochemical cell, in combination with solution redox mediators, provides a source or sink of electrons that are available to the crystalline protein on a timescale that is clearly faster than some chemical steps in crystallo. This is evident due to the relatively fast equilibration of the Ni a -C and Ni a -R states (which differ only in active site redox state rather than protonation in Hyd1, Scheme 1), relative to Ni a -L and Ni a -SI (which additionally require a proton transfer step) in Fig. 6. We have previously noted that O 2 -tolerant [NiFe] hydrogenases do not accumulate either the Ni a -R I or Ni a -L I sub-states, and instead favour the Ni a -R II/III and Ni a -L II/III substates. 22 We nd no evidence for cysteine-S protonation in the Ni a -R II/III and Ni a -L II/III sub-states in crystallo, and the faster rate of the Ni a -R / Ni a -C transition implied by the data in Fig. 6 and S17 is consistent with this transition involving only electron transfer in the case of Hyd1. The fact that the Ni a -L to Ni a -SI transition is apparently rate limiting during this potential step is consistent with involvement of both proton and electron transfer.</p><p>We have previously discussed possible mechanistic implications of the unusual high-potential [4Fe3S] 5+/4+/3+ cluster proximal to the active site found in O 2 -tolerant [NiFe] hydrogenases, in particular concerning whether proton-coupled electron transfer between Ni a -L and Ni a -SI occurs via a concerted or stepwise mechanism. 22 From the work of the groups of Hirota and Dyer it is known that onwards formation of Ni a -SI from Ni a -C, via Ni a -L, requires the proximal iron-sulfur cluster to be capable of receiving an electron, i.e. to be in an oxidised state. 33,68 The potential of both the [4Fe3S] 4+/3+ and [4Fe3S] 5+/4+ transitions of the proximal cluster in Hyd1 are relatively high, +3 mV and +230 mV respectively at pH 6 (see Table S4 †), 38,39,70 and so the proximal cluster of Hyd1 will largely be in the [4Fe3S] 3+ state at equilibrium at the potentials applied in Fig. 6. In the O 2 -sensitive hydrogenases the proximal cluster is a standard cubane [4Fe4S] 2+/+ cluster and its potential is closer to the potential of the H + /H 2 couple at neutral pH. 38 The electron transfer necessary for the Ni a -R / Ni a -C and Ni a -C / Ni a -SI transitions is therefore hindered in Hyd1 relative to O 2 -sensitive [NiFe] hydrogenases, and we suggest that this may be responsible for the fact that the Ni a -R I and Ni a -L I sub-states do not accumulate in Hyd1. In combination with our earlier hypothesis that cysteine-S protonation is not present in either of the Ni a -L II and Ni a -L III sub-states of Hyd1, we tentatively suggest two possible proton-coupled electron transfer mechanisms for the conversion between Ni a -L and Ni a -SI. In the rst mechanism, concerted proton and electron transfer occurs during Ni a -SI formation as previously reported by Dyer and co-workers. 35 In the second mechanism, likely prevalent in O 2 -tolerant hydrogenases such as Hyd1, proton and electron transfer is stepwise due to electron transfer between the active site and proximal cluster becoming limiting in the presence of the unusual high potential [4Fe3S] cluster. Proton transfer is relatively unaffected, as the active site structure and surrounding amino acids are highly conserved, and so H + can leave the active site ahead of electron transfer during the Ni a -L / Ni a -SI transition. Scope for crystal structures of well-dened states Prolonged exposure to the mediator cocktail and application of potential have no effect on the ability of Hyd1 crystals to diffract X-rays (Fig. S19 and Table S5 †), suggesting electrochemical manipulation of Hyd1 crystals offers the exciting prospect of producing molecular models for intermediates of catalysis that have so far been inaccessible to structure determination. The exquisite control of electrochemical potential afforded by the electrode allows crystals to be precisely poised under conditions that favour formation of only the intermediate of interest, for example the most reducing potentials applied allowed accumulation of pure Ni a -R. This advantage contrasts strongly with the reduction of [NiFe] hydrogenase crystals by H 2 which generates complex mixtures of states that are less suitable for structure determination. 10,25 Manipulation of pH offers a further dimension to the control over speciation of the crystalline enzyme. Such control offers a more rational approach to obtaining structures for catalytic intermediates than has previously been possible and eliminates the need for low activity variants, 75 inhibitors, 76 or transition-state analogues 77 that have been mainstays of classical (pre-XFEL) time-resolved structure determination from single crystals. Furthermore, our technique offers the possibility of nally linking the spectral ngerprints of each intermediate to a dened conguration of the active site and spectral changes occurring during turnover with specic atomic motions.</p><!><p>Working with single crystals of Hyd1, we have described a method that allows complete control over the redox state of crystalline proteins. The measurements reported conrm that protein crystals can be viewed as a dynamic system where all known states and intermediates are reliably accessible. High protein concentrations in the crystalline state allow us to record spectra with high signal/noise ratios, facilitating assignment of the n CO bands of each active site state. The active site n CO and n CN band positions and redox chemistry in single crystals of Hyd1 are consistent with previously reported behaviour, providing compelling evidence that crystallisation does not change the immediate environment or chemical properties of the active site relative to solution-phase protein. All known states of the Hyd1 active site can be generated under ne potential control, and detailed redox titrations recorded from single crystals match those for Hyd1 in solution or adsorbed on an electrode. These single crystal measurements thus bridge the gap between structural, spectroscopic, and activity-based biophysical methods, and provide conrmation that the behaviour of proteins in a range of physical states are comparable. The pH of the crystals is also readily manipulated, and all aspects of proton transfer, including Ni a -C/Ni a -L tautomerism are retained in the crystalline state. Detailed electrochemicallycontrolled redox titrations of the Hyd1 active site demonstrate the importance of single crystal microspectroscopy as a complementary method to protein crystallography, and could be used for spectroscopic characterisation post-X-ray diffraction to provide conrmation of the redox state solved. This aspect is particularly important given the oen complex mixtures of redox states that are present over wide potential windows. Likewise, the ability to generate pure redox states across a narrow potential window, as demonstrated here, allows infrared microspectroscopic-electrochemical methods to deliver a roadmap for how to enrich and prepare individual redox states in crystallo for downstream structure determination. Crystal structures can thus be directly relevant to redox states probed during the catalytic cycle. In addition to electrochemical navigation of the redox states, through careful control over pH it is possible to access different sub-states (e.g. of Ni a -R and Ni a -L), providing an extra dimension of control for future crystallographic studies.</p><p>Of further signicance, through electrochemical control over single crystals we are able to access retarded reaction steps that are otherwise hidden in steady-state catalytic studies. The use of both positive and negative potential steps reveals details of proton-coupled electron transfer to and from the active site, and has allowed us to hypothesise sequential, rather than concerted, proton and electron transfer during the Ni a -L / Ni a -SI transition in O 2 -tolerant [NiFe] hydrogenases such as Hyd1. This is in contrast to concerted proton and electron transfer observed in O 2 -sensitive [NiFe] hydrogenases.</p><p>The method reported here, already extended to crystals of C. pasteurianum [FeFe] hydrogenase I, 78 is likely to have general relevance in structure-function studies of complex redox metalloenzymes.</p>

Royal Society of Chemistry (RSC)

Direct in situ observation of the electron-driven synthesis of Ag filaments on α-Ag2WO4 crystals

In this letter, we report, for the first time, the real-time in situ nucleation and growth of Ag filaments on a-Ag 2 WO 4 crystals driven by an accelerated electron beam from an electronic microscope under high vacuum. We employed several techniques to characterise the material in depth. By using these techniques combined with first-principles modelling based on density functional theory, a mechanism for the Ag filament formation followed by a subsequent growth process from the nano-to micro-scale was proposed. In general, we have shown that an accelerated electron beam from an electronic microscope under high vacuum enables in situ visualisation of Ag filaments with subnanometer resolution and offers great potential for addressing many fundamental issues in materials science, chemistry, physics and other fields of science.

direct_in_situ_observation_of_the_electron-driven_synthesis_of_ag_filaments_on_α-ag2wo4_crystals

I<!>Results<!>Discussion<!>Methods

<p>n science, discovering new ways of thinking about facts is more important than obtaining these facts 1 . The investigation of nanocrystal growth is a rich research field that impacts both fundamental and applied science because controlling nanoscale sizes and morphologies can directly affect functional applications 2 . By observing nanocrystal structures microscopically, insight into the growth mechanism can be used to design a method to control nucleation, which is one of the most challenging processes in nanoscience and nanotechnology 3 . With ever-increasing temporal and spatial resolution allowing for atomic scale nanomaterial characterisation, the advent of highly sophisticated electron-and photon-based spectroscopies and scanning probe microscopies is primarily responsible for the developments in this field. In particular, measurements using a transmission electron microscopy (TEM) heating holder for in situ analysis provided an in-depth understanding of the crystal growth process, and further application of this method has attracted considerable interest [4][5][6] . Recently, de Jonge and Ross 7 reviewed in situ liquid TEM characterisation because it facilitates the study of step-by-step nanoscale evolution 8,9 . In this manuscript, for the first time, we report the real-time in situ formation and growth of Ag filaments on a-Ag 2 WO 4 crystals using an accelerated electron beam under high vacuum.</p><p>Noble metal nanoparticle preparation is an interdisciplinary subject that is attracting intense research and development due to both the fundamental and applied scientific value of nanometer scale metals [10][11][12] . In particular, the synthesis and surface chemistry of Ag nanoparticles have been extensively reported 13 . Very recently, Xia et al. 14 outlined the current developments in the shape-controlled synthesis of Ag nanocrystals, and Li et al. 15 reviewed research focused on Ag nanowire preparation using a soft solution method as well as applications using the Ag nanowires. Recently, we have obtained silver tungstate (a-Ag 2 WO 4 ) crystals using various methods (coprecipitation, sonochemistry and hydrothermal treatment), and their corresponding photoluminescence properties have been studied 16 . An overview of results reported from the various growth experiments reveals the absence of research on nanocrystal growth by electron irradiation.</p><p>In this research, we used an electron beam to grow Ag nanofilaments from a-Ag 2 WO 4 crystals. X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), selected-area electron diffraction (SAD), TEM and high-resolution microscopy (HRTEM) have been employed to study these materials. Through the use of these complementary techniques as well as first-principles calculations based on density functional theory (DFT), we determined the electronic structure of the a-Ag 2 WO 4 bulk. We investigated the formation of Ag nanofilament, which was followed by a subsequent growth process from the nano-to micro-scale. This observation facilitated an in-depth investigation of the physicochemical property behaviour and possible applications in various fields (i.e., sensors, catalysis, optical devices and bio systems).</p><!><p>TEM images obtained at 5 s intervals, which show the interesting growth of the Ag filaments from the a-Ag 2 WO 4 matrix, are displayed in Figures 1(a-h) (indicated by blue arrows). The red arrows indicate another region that illustrates particle absorption by the matrix. To the best of our knowledge, this phenomenon (growth and re-absorption of Ag) is new for this material and occurs in all of the regions that the electron beam irradiated. Importantly, when the TEM microscope is used, the growth process is faster and more intense than when the SEM microscope is used due to the higher electron energy and dosage associated with TEM. As shown in the earlier stage of growth for another series of images (Supplementary Movie S1), an initial superficial Ag particle is formed from the a-Ag 2 WO 4 matrix which follows the growth of the filaments over time. In addition, to thoroughly investigate these nucleation and growth processes, the different regions of the sample where the Ag filaments are present were analysed using TEM and HRTEM.</p><p>Figures 2a-c show a time-resolved series of FE-SEM images obtained under high vacuum (1 3 10 25 Pa) and the crystal morphology simulation during the growth of Ag filaments stimulated by the electron beam on the a-Ag 2 WO 4 surface. The a-Ag 2 WO 4 crystal has a hexagonal rod-like elongated shape (see Fig. 2a), and the corresponding morphology was modelled using the crystallographic data listed in the Supplementary Information (see Table SI1). Figure 2a shows a FE-SEM image of the a-Ag 2 WO 4 crystals that were acquired after a rapid approach and focus adjustment (time zero). This image reveals that after receiving a small electron dose, the a-Ag 2 WO 4 crystal surface contains a small amount of Ag nanoparticles. Figure 2b confirms that after 6 min of exposure to a 30 kV electron beam, the metallic Ag nanoparticles on the a-Ag 2 WO 4 crystal surface begin to grow. A reasonable amount of electrons induces the appearance of several defects in the surface which produces a continuous axial flow of metallic Ag particles. FE-SEM analyses revealed that this axial Ag growth process is highly reproducible. Figure 2c shows that increasing the exposure time to 10 min produces two effects: 1) Ag nanoparticles change to filamentary Ag; and 2) the nucleation of new Ag nanoparticles on the crystal surface. Figures 2a-c indicate that the Ag filament formation process from unstable a-Ag 2 WO 4 crystals is a reduction process converting the [AgO 2 ], [AgO 4 ], [AgO 6 ] or [AgO 7 ] clusters into Ag 0 . Therefore, this redox process promotes the transformation of a-Ag 2 WO 4 crystals with an ordered structure to a disordered structure.</p><p>Recently, using the chemical reduction method, Tsuji et al. 17 monitored the rapid transformation of various Ag nanostructures from Ag 1 ions to Ag 0 in solution by time-dependent surface plasmon resonance. In another example, using in situ TEM images, Yasuda et al. 18 observed indium oxide reduction at 820uC for metallic indium and intermetallic species (PdIn 3 ). In the present work, using energetic electrons and in absence of an external heat treatment, we observed the formation of Ag nanorods in the solid state. This process does not follow an epitaxial growth mechanism.</p><p>Figures 3a-d show low-magnification and HRTEM images of the Ag-Ag 2 WO 4 interface. The beam effect on the original a-Ag 2 WO 4 crystal allows the Ag to form particles and/or filaments and induce amorphisation of the a-Ag 2 WO 4 crystals. To fully understand these structural and chemical changes in the a-Ag 2 WO 4 crystals, X-ray energy dispersive spectroscopy (EDS) analysis was performed at several points along the filamentary grown region (crosses inside the yellow circle in Fig 3c ), and these results are presented in Figures 3e-h. The region adjacent to the Ag filament (Region 1) is composed of tungsten oxide with a small amount of Ag. The small amount of Ag in this region is expected once the Ag atoms migrate to form the filaments. The C and Cu contributions observed in the EDS analysis are from the Lacey Cu grid. At the interface (Region 2) where the matrix and filament are superposed, the primary elements are W, Ag and O. In this region, we observe contributions from the metallic Ag particle/filament and amorphous matrix. Therefore, the relative intensity of Ag increased compared to region 1. In the filament (Regions 3 and 4), the EDS analysis indicates that the filament is primarily composed of Ag atoms with a small amount of W and O. No significant change in the chemical composition was observed along the filament. The EDS and HRTEM results confirm that the crystalline Ag with a minor amount of W and O atoms are segregated indicating that a small portion of the W and O atoms from the matrix was pulled into the filament during the fast Ag filament growth.</p><p>Figure 4 displays the structure modification effect in the a-Ag 2 WO 4 crystal induced by the electron beam. The early characterisation step indicated (see Fig. 4a) that the Ag filaments have not begun to grow from the a-Ag 2 WO 4 matrix. The corresponding electron diffraction pattern (SAD; see Fig. 4b) shows a set of rings indicating the polycrystalline nature of the a-Ag 2 WO 4 crystals (indexed as the orthorhombic structure). These results are in agreement with the XRD results (see Fig. S1. in Support Information). However, after focusing the electron beam on the sample for a few seconds, the Ag nanofilaments begin to grow in several regions of the a-Ag 2 WO 4 crystal (see Fig. 4c). In addition, the regions adjacent to the filamentary growth, denoted by the red circle, tend to disrupt, which was confirmed by the amorphous diffraction pattern (see Fig. 4d) and is in agreement with the high-resolution images of the matrix after filamentary growth (Figs. 3b,d). This result indicates that the Ag mass transport modifies the original structure of the compound, which no longer present long-range order.</p><!><p>This behaviour can be explained by the structural and electronic information recently published by our group 16 structure). The corresponding electronic structure dictates its stability and activity 16 .</p><p>In addition, this core-shell arrangement has a core that is composed of internal [WO 6 ], [AgO 6 ] and [AgO 7 ] clusters, whereas the external portion is formed by [AgO 4 ] and [AgO 2 ] clusters. When an electron beam irradiates this material, these distorted clusters produce a lattice distortion that is propagated along the material, altering the electronic distribution along these polar cluster networks. Because the absorption of one electron is typically a quantum phenomenon, first-principles calculations were performed to verify the polar nanodomains in a-Ag 2 WO 4 at the atomic level. The details of our calculations are reported in the Supplementary Information (see Figs. S2-S5).</p><p>The theoretical results indicate that the [AgO 4 ] clusters are the most positively charged, whereas the [AgO 2 ] clusters are the most negatively charged. Therefore, it is feasible that electron absorption occurs at the [AgO 4 ] cluster. Our method utilises irradiation with electrons, which generates a redox environment. We propose the following mechanism: upon electron irradiation, the external and positively charged [AgO 4 ] clusters become polarised to form highly reactive moieties that can be rapidly used to reduce the adjacent angular [AgO 2 ] clusters undergoing a disproportionation rearrangement. This procedure produces [AgO 6 ] clusters and metallic Ag, which flows to the surface and results in local amorphisation of the a-Ag 2 WO 4 crystal. Based on these theoretical results, a reasonable mechanism has been proposed to explain the experimental results (supporting information) obtained in this work.</p><p>For the first time, we have demonstrated that metallic Ag nanoparticles/nanofilaments grow in situ from a-Ag 2 WO 4 crystals. In this experiment, an external stimulus, such as an accelerated electron beam from FE-SEM/TEM measurements, is capable of initiating the nucleation and growth of Ag filaments. XRD, FE-SEM, SAD, TEM and HRTEM techniques have been used to extensively characterise the material. To complement these experimental measurements, structural and electronic properties have been estimated using first-principles calculations.</p><p>Based on these results, we have identified that the driving force for this redox process can be attributed to order-disorder effects of the constituent clusters of a-Ag 2 WO 4 in the short-, intermediate-and long-range structures. These interlinking patterns are responsible for the physical/chemical properties of the material. In addition, we have proposed a possible formation mechanism where: irradiated electrons are absorbed by the external and higher charged tetrahedral [AgO 4 ] clusters, followed by a subsequent disproportionation reaction with the angular [AgO 2 ] clusters. This procedure produces [AgO 6 ] clusters and metallic Ag that migrate to the surface, resulting in the local amorphisation of a-Ag 2 WO 4 . We have confirmed that this material is an ideal platform with outstanding potential for use in biological, plasmonics and catalytic applications, and these studies are currently in progress.</p><!><p>Synthesis. The a-Ag 2 WO 4 crystals were prepared at 90uC in 1 min by the injection of precursors ions into hot aqueous solutions. A typical a-Ag 2 WO 4 crystal synthesis procedure is described below: First, 1 3 10 23 mols of tungstate sodium dihydrate (Na 2 WO 4 .2H 2 O) (99.995% purity, Sigma-Aldrich) and 2 3 10 23 mols of silver nitrate (AgNO 3 ) (99.8% purity, Sigma-Aldrich) were dissolved separately in 50 mL of deionised water. The first solution was transferred to a 250 mL glass flask and heated to 90uC under constant stirring for 10 min. Then, the second solution that contained 50 mL of AgNO 3 at room temperature was pumped by a syringe and injected into the hot aqueous solutions (90uC), and a suspension was rapidly formed with a temperature reduction to 70uC. The following suspension was immersed in a beaker with 50 mL of deionised water at 5uC. These a-Ag 2 WO 4 crystals were obtained as a fine white powder precipitated at the bottom of the glass flask. The resulting suspensions were washed several times with deionised water to remove any remaining sodium ions. Finally, these white powder precipitates were collected and dried with acetone at room temperature for 4 h.</p><p>Characterisations. The a-Ag 2 WO 4 crystals were characterised by their XRD patterns using a D/Max-2500PC diffractometer (Rigaku, Japan) with Cu-Ka radiation (l 5 1.5406 A ˚) in the 2h range from 10u to 110u with a scanning velocity of 1u/min and a step of 0.02u. The shape and size of the a-Ag 2 WO 4 crystals were observed by FE-SEM through a Carl Zeiss microscope (Model Supra 35) operated at 30 kV and by TEM with a CM200 model microscope (Philips) operated at 200 kV. The a-Ag 2 WO 4 microcrystals were characterised using SAD and HRTEM. The samples used to obtain the TEM images were prepared by drying droplets of the as-prepared samples from an acetone dispersion sonicated for 10 min and deposited on the Cu grids.</p>

Scientific Reports - Nature

Design, synthesis and structure-activity relationship of rhenium 2-arylbenzothiazoles as \xce\xb2-amyloid plaque binding agents

To continue our efforts toward the development of 99mTc PiB analogs, we have synthesized twenty-four neutral and lipophilic Re (as a surrogate of 99mTc) 2-arylbenzothiazoles, and explored their structure-activity relationship for binding to A\xce\xb21\xe2\x80\x9340fibrils. These Re complexes were designed and synthesized via the integrated approach, so their 99mTc analogs would have a greater chance of crossing the blood-brain barrier. While the lipophilicities (logPC18= 1.59\xe2\x80\x933.53) of these Re 2-arylbenzothiazoles were all within suitable range, their binding affinities (Ki= 30\xe2\x80\x93617 nM) to A\xce\xb21\xe2\x80\x9340 fibrils varied widely depending on the selection and integration of the tetradentate chelator into the 2-phenylbenzothiazole pharmacophore. For potential clinical applications, further refinement to obtain Re 2-arylbenzothiazoles with better binding affinities (< 10 nM) will likely be needed. The integrated approach reported here to generate compact, neutral and lipophilic Re 2-arylbenzothiazoles could be applied to other potent pharmacophores as well to convert other current A\xce\xb2 PET tracers to their 99mTc analogs for more widespread application via the use of SPECT scanners.

design,_synthesis_and_structure-activity_relationship_of_rhenium_2-arylbenzothiazoles_as_\xce\xb2-am

<p>Alzheimer's disease (AD) is a progressive and fatal neurodegenerative disorder characterized by irreversible memory impairment, continuous cognitive decline and behavioral disturbances. AD causes about two thirds of dementia in the elderly1. It is estimated that by the year of 2050, there will be 13.2 million cases of AD in the US2. At present, there is no medical treatment that cures or prevents AD. The production and accumulation of β-amyloid peptides (Aβ) is believed to be pivotal to the pathogenesis and progression of AD3, and therefore, research on the treatment of AD has focused on the anti-amyloid therapies4. It is well documented that the formation of Aβ plaques precedes the appearance of clinical symptoms5. In order to achieve the best therapeutic outcome, it may be necessary to identify potential subjects for therapy before neurons are damaged by Aβ aggregates. Therefore, the development of a noninvasive imaging method capable of quantifying the deposition of Aβ plaques could provide a useful tool for identifying preclinical cases of AD as candidates for early intervention and to follow the effectiveness of anti-amyloid therapy in individual patients6.</p><p>Toward this end, the development of Aβ plaque-targeting radiotracers for use with positron emission tomography (PET) and single photon emission tomography (SPECT) has been an active research topic in the past two decades7–8. PET and SPECT are effective nuclear imaging modalities to detect probes that bind saturable binding sites because their high sensitivity is suitable for extremely low tracer concentrations. Both modalities are now commonly coupled with computed tomography (CT) to generate hybrid images that provide the benefits of both structural and functional/molecular information. Currently, there are several 11C- and 18F-labeled Aβ PET tracers that have been successfully applied in clinical research studies of AD (Figure 1). Among these imaging agents, 2-(4-[11C]methylaminophenyl)-6-hydroxybenzothiazole (Pittsburgh Compound B, PiB)9 has a high signal-to-noise ratio and has been adopted to perform AD-related research studies worldwide. Unfortunately, due to its short half-life (20 min), the 11C label on PiB limits its use to major academic PET facilities with on-site cyclotrons and sophisticated radiochemistry laboratories. Promising radiotracers labeled with the longer half-life (110 min) radioisotope 18F have been developed. Among them, Florbetapir10 has recently been approved by the US Food and Drug Administration (FDA) for clinical use for ruling out AD. Manufacturers of other 18F-labeled tracers such as Florbetaben11, Flutemetamol12 and NAV469413 are expected to seek FDA approval in the next few years. These 18F-labeled PET tracers could increase the availability of Aβ imaging to all PET facilities, but this still represents a minority of modern hospitals with PET scanners. Many more hospitals have the capacity to perform SPECT imaging. Aβ imaging agents labeled with SPECT radionuclides, particularly inexpensive and readily available 99mTc will have more widespread clinical applicability especially in developing countries.</p><p>With the success in the development of the 2-arylbenzothiazole (2-ABT) based PET radiotracers, PiB and Flutemetamol, we were also interested in the development of 99mTc-labeled 2-ABTs for more widespread application using SPECT scanners. In contrast to most attempts by other investigators on the development of 99mTc-labeled 2-ABTs using the pendant approach14–16, we have previously demonstrated the feasibility of design and synthesis of three neutral and lipophilic Re (as a surrogate of 99mTc) 2-ABTs (compounds# 6, 12 and 21 in Table 1) with moderate Aβ binding affinity (30–87 nM) using the integrated approach to minimize their overall molecular weight (<550 daltons)17. Compound 6 was prepared using a thiol-triamine SN3 tetradentate chelator (Scheme 1A), while semi-rigid thiol-diamine-phenol (SN2O) and thiol-diamine-thiol (SN2S) chelators (Scheme 1B, X= O and S) were used for the preparation of 12 and 21, respectively. Besides the SN3, SN2O and SN2S chelators, the semi-rigid thiol-diamine-thioether (SN2Sether)18 chelator is also commonly used for the design and synthesis of neutral and lipophilic Tc/Re complexes. While SN3, SN2X, SN2Sether chelators all form stable Tc/Re complexes, the preparation of Tc/Re 2-ABTs by the use of different tetradentate chelators or the same chelator integrated at different positions of the pharmacophore might generate Tc/Re 2-ABTs with different binding affinity, lipophilicity and in vivo pharmacokinetics. The aim of this present work was to systematically explore the potential of utilizing SN3, SN2X, and SN2Sether chelators to generate compact, neutral, and lipophilic Re 2-ABTs, and to investigate the structure-activity relationship for their lipophilicity and binding affinity to aggregated Aβ.</p><p>As shown in Table 1, besides 6, 12 and 21 that were reported earlier, we synthesized an additional twenty-one Re 2-ABTs with chelators integrated at different positions of 2-ABT pharmacophore for comparison. Our previous results19–20 indicated that substitution on the 2-ABT pharmacophore with an electron-donating group or a halogen significantly increases the binding affinity. For example, the Ki for an amino, N-methylamino, N,N-dimethylamino, fluoro, bromo, or iodo substitution at the 4'-position of 2-ABT were 37.0, 11.0, 4.0, 43.8, 8.8, and 2.6 nM, respectively. The definition of substitution positions is depicted on the structure of PiB shown in Fig. 1. The integration of a tetradentate chelator into the benzothiazole ring provides an electron-donating group at the 6-position (compounds 1–3), whereas the integration of a tetradentate chelator into the phenyl ring provides an electron-donating group at the 4'-position (compounds 4–6) or both the 3'- and 4'-position (compounds 7–24). To further enhance their binding affinity, we also synthesized analogs with a fluoro or methoxy substitution at the 6-position when a tetradentate chelator is integrated into the phenyl ring, or at the 4'-position when a tetradentate chelator is integrated into the benzothiazole ring. The choice of the small halogen fluorine and a methoxy group is to limit the overall size increase. In addition, substitution with an aromatic fluoro or methoxy group will not significantly change the overall lipophilicity.</p><p>The synthetic steps for the preparation of Re 2-ABT 1–24 are depicted in Schemes 2–9. The SN3 chelator used for the preparation of 1–6 (Schemes 2 and 3) was modified from the thiol-triamide21–22 chelator as shown in Scheme 1A. Complexation of the original thiol-triamide chelator with [Tc(V)O]3+ or [Re(V)O]3+ led to metal complexes with one negative charge due to the loss of four protons from three amide N-H groups and one thiol S-H group. It is well documented that charged Tc complexes do not cross the blood-brain barrier (BBB). In addition, Tc complexes derived from tetradentate chelators containing amide groups (such as monoamide-monoamine-dithiol, MAMA) showed less brain uptake when compared to those derived from their corresponding amino chelators (such as diaminedithiol, DADT)23–24. Therefore, we modified the original thiol-triamide chelator by replacing the three amide groups with three amino groups. In order to obtain neutral Tc/Re complexes, we also added one small methyl group to one of the aliphatic amino groups. After such modification (see Scheme 1A), all three protons were lost after complexation with [Re(V)O]3+, and the overall charge of 2-ABT 1–5 was balanced, which is in agreement with our previously reported results from the synthesis of 617.</p><p>Re 2-ABTs 7–9 (Scheme 4), 10–15 (Schemes 5 and 6), and 16–21 (Schemes 7 and 8) were synthesized using modified semi-rigid SN2X chelators (Scheme 1B, X= NH, O, and S, respectively) integrated into the 3'- and 4'-position of phenyl ring. This design was to further reduce their overall molecular weight to 500 daltons or less as suggested by the Rule of Five25, and in hopes that their corresponding 99mTc analogs would show rapid and high brain entry. These semi-rigid SN2X chelating systems were modified from previously reported thiol-diamide-X systems (X= NX, O and S)26. As shown in Scheme 1B, the complexation of [Re(V)O]3+ with thiol-diamide-X chelating systems led to Re complexes with one negative charge. Similar to our modification to the thiol-triamide chelator shown in Scheme 1A, we also replaced the two amide groups with two amino groups in order to produce neutral Re complexes and increase brain uptake of their 99mTc analogs. As expected, after complexation with [Re(V)O]3+, only three protons were lost. The aliphatic amino N-H group that has relatively higher pKa value was not deprotonated after Re complexation reaction, and therefore, the overall charge was balanced. These results were consistent with our previously reported results for the synthesis of 12 and 2117.</p><p>Syntheses of 22–24 are shown in Scheme 9 using the previously reported semi-rigid SN2Sether chelator18. The methyl thioether group was chosen to keep the overall molecular weight at minimum. After complexation with [Re(V)O]3+, as expected, neutral Re 2-ABT 22–24 were obtained resulting from the loss of three protons (two aromatic N-H and one thiol S-H groups). The final step in the preparation of Re 2-ABT 1–24 involved a two-stage reaction, deprotection of p-methoxybenzyl (PMB)/methoxymethyl (MOM) protecting groups to restore the chelating core followed by Re complexation reaction using Re(V)O(PPh3)2Cl327. In spite of potential existence of cis- and anti-isomers, similar to our previous results for the syntheses of 6, 12 and 2117, only one single isomer was isolated for each of these additional twenty-one Re 2-ABTs reported here, and their identities were confirmed by NMR Spectroscopy28.</p><p>Re 2-ABT 1–24 are moderately lipophilic with logPC18 (PC18: estimation of Poct by a reverse-phase HPLC method19) in the range of 1.59–3.53 (Table 1). Among them, 10–15 derived from the SN2O chelator displayed the lowest lipophilicity (logPC18= 1.59–1.90), whereas 22–24 derived from the SN2Sether chelator had the highest lipophilicity (logPC18= 3.25–3.53). Replacing the phenol of the SN2O chelator in 10–15 with a thiol resulted in Re SN2S derivatives 16–21 with an average increase of 0.75 in their logPC18 values (10–12 vs 19–21; 13–15 vs 16–18). The Re 2-ABTs derived from the same tetradentate chelator (SN2O or SN2S) but with different integration patterns into the phenyl ring (the amino group substituted at the 3'- or 4'-position) had similar lipophilicity (10–12 vs 13–15; 16–18 vs 19–21). Compared with 1–6 derived from the SN3 chelating system with only one lateral amino group of the chelator integrated into the 2-ABT backbone, 7–9 derived from the semi-rigid SN3 chelator with two amino groups integrated into the phenyl ring had lower lipophilicity due to the presence of an amino N-H proton at the 3'-position and one less ethylene moiety in the overall structure. If comparing the Re 2-ABTs with the same tetradentate chelator but with different substitution (H, F and OMe) at the 6- or 4'-position, the methoxy-substituted 2-ABTs had the lowest logPC18 values with an average of 0.10 and 0.25 lower than those of their respective un-substituted and fluoro-substituted Re 2-ABTs.</p><p>Re 2-ABTs 1–24 bind Aβ1–40 fibrils with moderate to poor affinity (Ki= 30–617 nM, Table 1) as determined by previously published in vitro competition binding assays19,29 using [3H]BTA-1 as the radioactive control compound. In general, compared to the un-substituted Re 2-ABTs, substitution with a fluoro or a methoxy group at the 6- or 4'-position enhanced their binding affinity to Aβ1–40 fibrils. The integration of an SN3 chelator into the phenyl ring (in 4–6) rather than the benzothiazole ring (in 1–3) resulted in Re 2-ABTs with better binding affinities. Replacing the free rotating Re-SN3 complex in 4–6 with a semi-rigid Re-SN3 complex in 7–9 further enhance the binding affinity. As discussed above, different integration patterns of the same tetradentate chelator (SN2O or SN2S) had little effects on the overall lipophilicity of the resulted Re 2-ABTs (10–12 vs 13–15; 16–18 vs 19–21). However, the binding affinities of these 2-ABTs were strongly influenced by the integration patterns of the tetradentate chelator. When comparing the 2-ABTs 10–15 with the semi-rigid SN2O chelator, 10–12 (Ki= 30–109 nM) with an amino group of the chelator substituted at the 4'-position had 2.6- to 4.7-fold better binding affinity than their corresponding analogs 13–15 (Ki= 140–280 nM) with the amino group substituted at the 3'-position. Similar results were obtained when comparing the binding affinity of Re 2-ABTs 16–21 with the semi-rigid SN2S chelator. The binding affinities of 19–21 (Ki= 31–43 nM) with an amino group of the SN2S chelator substituted at the 4'-position were 3.1- to 6.9-fold better than those of their corresponding analogs 16–18 (Ki= 93–264 nM) with the amino group substituted at the 3'-position. These results are in agreement with the fact that most of the promising PET tracers derived from the 2-ABT pharmacophore including [11C]PiB, [11C]AZD2184, and [18F]Flutemetamol (see Fig. 1) have an amino group substituted at the 4'-position.</p><p>In summary, we have synthesized twenty-four neutral and compact Re 2-ABTs, and measured their lipophilicity and binding affinity to aggregated Aβ. These Re 2-ABTs were designed and prepared via the integrated approach, so their 99mTc analogs would have a greater chance of crossing the BBB, and bind to Aβ plaques deposited in the brain parenchyma. While the lipophilicities of these 2-ABTs were within suitable range (logPC18 = 1–4), their binding affinities (Ki= 30–617 nM) to Aβ1–40 fibrils varied widely depending on the selection of the chelators, and the ways the chelators were integrated into the 2-ABT pharmacophore. Based on the binding affinity data, we have identified two promising semi-rigid chelators, SN2O and SN2S. The Re 2-ABTs 12 and 20 derived from the SN2O and SN2S chelators, respectively, had fairly good binding affinity (Ki ~ 30 nM) to Aβ1–40 fibrils. However, before translation into their 99mTc analogs and for potential clinical application, further modification to obtain Re 2-ABTs with even better binding affinity will likely be needed since most of the clinical Aβ imaging agents have binding affinities less than 10 nM. This might be achievable by optimizing the substitution at the 6-position of the 2-ABT pharmacophore with other potent electron-donating groups, and/or by the 3D-QSAR analysis as recently reported by Kim30 and Yang31. The integrated approach reported here to generate compact, neutral and lipophilic Re 2-arylbenzothiazoles could be applied to generate Re complexes of other potent pharmacophores, including stilbene and benzoxazole. Once potent Re complexes are obtained, their 99mTc analogs will have great potential to extend current Aβ imaging practices from PET to SPECT.</p><p>This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.</p>

PubMed Author Manuscript

Hybrid SSF/SHF Processing of SO2 Pretreated Wheat Straw—Tuning Co-fermentation by Yeast Inoculum Size and Hydrolysis Time

Wheat straw is one of the main agricultural residues of interest for bioethanol production. This work examines conversion of steam-pretreated wheat straw (using SO2 as a catalyst) in a hybrid process consisting of a short enzymatic prehydrolysis step and a subsequent simultaneous saccharification and fermentation (SSF) step with a xylose-fermenting strain of Saccharomyces cerevisiae. A successful process requires a balanced design of reaction time and temperature in the prehydrolysis step and yeast inoculum size and temperature in the SSF step. The pretreated material obtained after steam pretreatment at 210 °C for 5 min using 2.5 % SO2 (based on moisture content) showed a very good enzymatic digestibility at 45 °C but clearly lower at 30 °C. Furthermore, the pretreatment liquid was found to be rather inhibitory to the yeast, partly due to a furfural content of more than 3 g/L. The effect of varying the yeast inoculum size in this medium was assessed, and at a yeast inoculum size of 4 g/L, a complete conversion of glucose and a 90 % conversion of xylose were obtained within 50 h. An ethanol yield (based on the glucan and xylan in the pretreated material) of 0.39 g/g was achieved for a process with this yeast inoculum size in a hybrid process (10 % water-insoluble solid (WIS)) with 4 h prehydrolysis time and a total process time of 96 h. The obtained xylose conversion was 95 %. A longer prehydrolysis time or a lower yeast inoculum size resulted in incomplete xylose conversion.

hybrid_ssf/shf_processing_of_so2_pretreated_wheat_straw—tuning_co-fermentation_by_yeast_inoculum_siz

Introduction<!><!>Enzymatic Hydrolysis<!>Cell Cultivation<!>Shake Flask Fermentations<!>Hybrid SSF/SHF Experiments<!><!>Pretreatment and Enzymatic Digestibility<!><!>Hybrid SSF<!>Conclusion

<p>Wheat straw is one of the main agricultural feedstocks for lignocellulosic ethanol production, and has—among others—been used in demonstration-scale facilities in Kalundborg, Denmark (Dong) [1], Salamanca, Spain (Abengoa) [2], and Straubing, Germany (Clariant) [3], and in the commercial-scale facility in Crescentino, Italy (Beta Renewables) [4]. Wheat straw contains around 35 % glucan and 20 % xylan together with minor amounts of arabinan (e.g., [5]), and the main challenge in fermentation of wheat straw hydrolysates is thus to accomplish an efficient conversion of both glucose and xylose. Furthermore, this needs to be done in the presence of various inhibitors in the hydrolysate, most of which are generated or liberated in the pretreatment process (e.g., [6], [7]). Saccharomyces cerevisiae (Baker's yeast) is a proven robust organism in the traditional sucrose or starch-based ethanol industry and has been genetically engineered for efficient fermentation of lignocellulose-derived sugar mixtures (recently reviewed in [8]). A number of recombinant strains are now available, also commercially. This enables the use of engineered S. cerevisiae for conversion of xylose-rich hydrolysates. Fermentation inhibitors derived from lignocellulose pretreatment, in particular acid catalyzed pretreatments, include furans—primarily the 2-furaldehyde (furfural) and 5-hydroxy-2-furaldehyde (5-hydroximethylfurfural or HMF)—carboxylic acids—primarily acetic acid—and phenolic compounds [9]. The inhibitory mechanisms and effects of these compounds, in particular the furan compounds, are well elucidated in numerous studies, reviewed by, e.g., [10]. Of particular significance is the fact that furans—and a number of other aldehydes—can be converted by the yeast [11]. Under anaerobic conditions, this takes place mainly by reduction, facilitated by various dehydrogenases [12–15]. Once the reduction has been completed, the inhibition of the glycolytic flux is substantially reduced.</p><p>The uptake and conversion of xylose in recombinant xylose-fermenting yeasts are influenced by other sugars, primarily glucose. This is partly an effect of competition in the sugar uptake systems, since xylose is primarily transported into the yeast cell by hexose transporters, which have a higher affinity for glucose [16]. The expression levels of these transporters are furthermore affected by glucose concentration levels [17]. In some cases, xylose consumption is aided by simultaneous glucose consumption, compared with the rate of xylose consumption only. This effect may be even more pronounced in the presence of inhibitors. The relative concentration of glucose and xylose in the medium is strongly affected by the chosen process option. In a true separate hydrolysis and fermentation (SHF) process design, i.e., in a process where the enzymatic hydrolysis of the material is completely made before fermentation, the initial glucose to xylose concentration ratio from wheat straw will be in the order of 2:1—reflecting the carbohydrate composition of the raw material. In a simultaneous saccharification and fermentation process (SSF), i.e., in a process where the enzymatic hydrolysis takes place concomitant with the fermentation of released sugars, the ratio between the sugars will be rather different. Xylose as well as xylooligomers will be present in the liquid at the onset of an SSF as a result of the pretreatment, whereas glucose is both gradually released from glucan polymers and simultaneously consumed by the fermenting organism. By controlling enzyme dosage, temperature, and yeast concentration, the time profile of glucose and xylose concentrations may be tuned to facilitate co-consumption [18]. Process design is currently moving away from the base cases, i.e., SHF and SSF, toward hybrid processes. These may involve, as the case in the Crescentino plant, an initial high-temperature enzymatic hydrolysis, called prehydrolysis or viscosity reduction, followed by a lower-temperature SSF of the entire slurry [19]. In this way, a high rate of enzymatic hydrolysis—giving efficient liquefaction—is achieved initially, and by shifting to an SSF in which sugars are consumed, end-product inhibition of the cellulases is decreased.</p><p>The wheat straw structure is not highly rigid, which gives several possibilities with respect to pretreatment. A mild, autocatalytic, steam or hot water pretreatment using inherent acetyl groups is one option [20]. This will remove a substantial part of the hemicellulose from the fiber fraction, but results in a large fraction of oligomeric hemicellulose in the liquid fraction. These oligomers require hemicellulase components in the enzyme mixture used for hydrolysis. Alkaline pretreatment methods act by solubilizing lignin. Ammonia fiber expansion (AFEX) is often used on agricultural residues and gives effects both in terms of structural disruption and changed crystallinity of the material (see, e.g., [21]). A more recent development is extractive ammonia pretreatment, in which lignin is (partly) separated from the fiber in the ammonia stream after pretreatment [22]. The alkaline methods typically leave hemicellulose in the remaining fiber fraction. Alternatively, an acidic catalyst can be used in the steam pretreatment, in which case hemicellulose is more completely removed from the fiber fraction. Options include sulfuric acid, which is the most widely reported, but also weaker acids such as phosphoric acid or organic acids have been used [23]. The gaseous compound sulfur dioxide, SO2, is a further option. For a number of lignocellulosic raw materials, such as corn stover [24], aspen [25], sugarcane bagasse [26], and quinoa straw [27], this has resulted in a good overall yield of fermentable sugars, while minimizing the formation of degradation by-products. Conditions for high total recovery of both xylan—from the pretreatment—and glucose after enzymatic digestion of wheat straw using SO2 pretreatment have been described [28], but fermentation of such hydrolyzates has not yet been assessed.</p><p>The balanced design of a hybrid co-fermentation process for wheat straw hydrolysates requires the following: (a) a sufficient prehydrolysis time to allow enzymatic conversion of the solid fraction; (b) a suitable ratio of monosaccharides (glucose and xylose) to allow efficient xylose conversion during the fermentation; and (c) a sufficient yeast concentration in the broth to complete the conversion of the sugars. The feedstock composition and pretreatment have a strong influence on needed time for hydrolysis and the levels of inhibitors. The yeast concentration affects the volumetric consumption of sugars not only directly but also indirectly through the bioconversion of inhibitors. The volumetric rate will likely be higher when more yeast is inoculated, but not necessarily in a linear manner due to exposure time effects. Since there is a cost for producing yeast biomass, the yeast concentration should not be larger than necessary. In the present study, the design of hybrid saccharification and co-fermentation (SSCF) processes for SO2-pretreated wheat straw using an industrial xylose-fermenting strain of S. cerevisiae is outlined based on digestibility of the material and the fermentation performance in the pretreatment liquid at different yeast inoculum sizes.</p><!><p>Composition of pretreated wheat slurry</p><p>The solid composition is based on weight percentage of the WIS content, and the soluble components are reported in grams per liquid of liquid. The WIS content of the pretreated material was measured to 12.1 wt-%</p><p>n.d. not detected, i.e., below detection limit</p><!><p>Enzymatic hydrolysis at low water-insoluble contents was performed in order to establish the maximum digestibility of the pretreated material. This was done by diluting the slurry with sterile water to 2 % WIS in capped bottles (100 mL final volume). The pH of the diluted slurry was then set to pH 5, and the bottles were placed in a temperature-controlled (45 °C) rotary shaker before adding an enzyme load of 0.1 g enzyme solution per gram of WIS (same as in SSF/SHF experiments). A hydrolysis experiment was also performed in a bioreactor at 30 °C in order to see how well the material was digested at the lower temperature.</p><!><p>The recombinant xylose-fermenting strain S. cerevisiae C5LT 1202 was used in all experiments. Strain C5LT 1202 is rationally engineered using the C5LT gene package technology [32] with the xylose reductase and xylitol dehydrogenase pathway in addition to auxiliary genes encoding xylose metabolism. Yeast cell mass was produced by an initial preculture in a shake flask, followed by aerobic cultivation on glucose, first in batch mode and finally in fed-batch mode.</p><p>The yeast was inoculated (from agar plate) in 300-mL shake flasks (liquid volume of 100 mL) containing 20 g L−1 glucose, 7.5 g L−1 (NH4)2SO4, 3.5 g L−1 KH2PO4, 0.74 g L−1 MgSO2∙7H2O, trace metals, and vitamins [33]. The cells were grown for 24 h at 30 °C and a starting pH of 5.5 in a rotary shaker at 180 rpm. Subsequently, the aerobic batch cultivation was performed in a 2.5-L bioreactor (Biostat A, B.Braun Biotech International, Melsungen, Germany) at 30 °C. The working volume was 0.7 L, and the medium contained 20.0 g L−1 glucose, 20.0 g L−1 (NH4)2SO4, 10.0 g L−1 KH2PO4, 2.0 g L−1 MgSO4, 27.0 mL L−1 trace metal solution, and 2.7 mL L−1 vitamin solution. The cultivation was initiated by adding 40.0 mL of the preculture to the bioreactor. The pH was maintained at 5.0 throughout the cultivation, by automatic addition of 3 M NaOH. Aeration was maintained at 1.2 L min−1, and the stirrer speed was kept at 800 rpm. When the glucose in the batch phase was depleted, as indicated by a sharp drop in carbon dioxide evolution rate (CER), the glucose feed was started and 1.0 L of feed (50 g/L glucose) was fed to the reactor. The feed rate was set initially to 0.04 L h−1 and increased linearly to 0.10 L h−1 during the 16-h fed-batch cultivation. The aeration during the fed-batch phase was maintained at 1.5 L min−1, and the stirrer speed was kept at 800 rpm.</p><p>After cultivation, the cells were harvested by centrifugation in 700-mL flasks using a HERMLE Z 513K centrifuge (HERMLE Labortechnik, Wehingen, Germany). The pellets were resuspended in 9 g L−1 NaCl solution to obtain a cell suspension with a cell mass concentration of 60 g dry weight per liter. The time between cell harvest and initiation of the following SSCF/shake flask fermentation was no longer than 3 h.</p><!><p>One hundred-milliliter shake flask fermentation was performed in duplicates under anaerobic conditions in a 300-mL Erlenmeyer flask. The medium used for fermentation was the clarified liquid fraction obtained by separating the solids (with high-pressure filtration) from the pretreated hydrolyzate. Glucose (50 g/L) and xylose (30 g/L) were added to the pretreated hydrolysate liquid to mimic the expected concentrations reached after enzymatic hydrolysis of the fibers. The hydrolysate was supplemented with 0.5 g L−1 NH4H2PO4, 0.025 g L−1 MgSO4·7H2O, and 1.0 g L−1 yeast extract. During fermentation, the temperature was kept constant at 30 °C, while the pH was set initially to 5.3 and then left uncontrolled (pH did not drop below 5.0 in any fermentation). The precultivated yeast was added to start experiments at initial yeast concentrations of 1, 2, 4, and 8 g dry weight per liter. Liquid samples (2.5 mL) were withdrawn repeatedly during 48 h and analyzed for sugars and metabolites.</p><!><p>All experiments were carried out under anaerobic conditions using 2.5-L bioreactors (Biostat A, B.Braun Biotech International, Melsungen, Germany) with an initial WIS content of 10 % and a final working broth weight of 1.0 kg. The experiments were run for a total of 96 h. During the initial enzymatic hydrolysis phase, a temperature of 45 °C was maintained and when yeast was added (i.e., at the start of the SSF phase), the temperature was lowered and kept at 30 °C. The enzyme solution used was Cellic CTec 2 (Novozymes A/S, Bagsvaerd, Denmark) at a dose of 0.1 g enzyme solution per gram of WIS (corresponding to approximately 10 filter paper units (FPU)/g WIS). The pH was maintained at 5.0 throughout fermentation by automatic addition of 4 M NaOH. The wheat straw slurry was supplemented with 0.5 g L−1 NH4H2PO4, 0.025 g L−1 MgSO4·7H2O, and 1.0 g L−1 yeast extract at the start of the fermentation phase (same concentrations as for the shake flask fermentations). The initial yeast concentration was 2 and 4 g dry weight per liter. All experiments were carried out in duplicates.</p><!><p>a Enzymatic hydrolysis at low (2 % WIS) solid loading and 45 °C. Diamonds represent glucose concentrations and squares represent xylose concentrations. The dotted line corresponds to the theoretical maximum for the respective sugars. b Enzymatic hydrolysis at 10 % WIS loading at 30 °C (solid lines) and 45 °C (dotted lines). Diamonds represent glucose concentrations and squares represent xylose concentrations</p><!><p>Enzymatic hydrolysis was also performed in bioreactors at a solid loading of 10 % WIS (Fig. 1b). The hydrolysis at 45 °C was not fully complete after 24 h (as the case for 2 % WIS), but the degree of hydrolysis reached was about 77 %. The optimal fermentation temperature for strain C5LT 1202 is 30 °C, thus SSF should be done at this temperature. At this lower temperature, the rate of hydrolysis was strongly decreased and the degree of conversion reached at 24 h was only 37 % (Fig. 1b).</p><!><p>Shake flask fermentation of hydrolysis liquid with a yeast inoculum size of 1 g (a, e), 2 g (b, f), 4 g (c, g), and 8 g (d, h) dry weight per liter of yeast. Diamonds represent glucose, squares represent xylose, and triangles represent ethanol in a–d. Squares represent furfural, stars represent glycerol, and circles represent xylitol</p><p>Specific uptake and production rates during the shake flask fermentation with different yeast inoculum sizes (Figs. 3 and 4). a Glucose uptake rate, b xylose uptake rate, c furfural uptake rate, and d ethanol production rate. The yeast inoculum size, going from 1 g/L up to 8 g/L, is indicated by the bar patterns; black is 1 g/L, white is 2 g/L, hashed is 4 g/L, and gray is 8 g/L. *The lower specific uptake rate of furfural at the highest yeast loading is an artifact of slow sampling, i.e., all furfural is depleted before the 2-h sample</p><p>Specific xylose uptake rate shown as a function of the xylose concentration in the media throughout the time course of shake flask fermentation (Fig. 2). The different symbols indicate different yeast inoculum sizes (diamonds 1 g/L, filled triangles 2 g/L, squares 4 g/L, and unfilled triangles 8 g/L). The dashed circle indicates time-points where glucose was still present in the media, i.e., early samples before all glucose was consumed</p><p>Concentration profiles for hybrid SSF/SHF experiments. In a, b, a 24-h prehydrolysis (45 °C) was performed before lowering the temperature to 30 °C and adding 2 g/L of yeast to start the fermentation. In c, d, a 4-h prehydrolysis (45 °C) was performed before lowering the temperature to 30 °C and adding 4 g/L of yeast to start the fermentation. Symbols used in a, c: Diamonds represent glucose, squares represent xylose, and triangles represent ethanol. Symbols used in b, d: Diamonds represent furfural, squares represent xylitol, and triangles represent glycerol</p><!><p>To ensure a complete xylose consumption, the time for EH should be decreased and/or the yeast inoculum size should be increased. In order to ensure that the specific glucose consumption would not fall due to decreased performance following furfural conversion, the yeast inoculum size was doubled. The initial free glucose was also reduced by 50 % from shortening the high-temperature EH to only 4 h (Fig. 5b). In this case, the hybrid SSF resulted in 95 % conversion of xylose, and the glucose concentration at the end was also negligible. A final ethanol concentration of 35 g/L corresponding to a technical yield of 0.39 g/g was reached, 17 % higher than the previous case. Further fine tuning may give a somewhat increased yield with a slightly lower yeast inoculum size.</p><!><p>The present work illustrates the importance of yeast inoculum size for detoxification of pretreated wheat straw and how this needs to be taken into account when designing hybrid SSCF processes. Depending on the yeast strain used, one can anticipate at least three more or less sequential processes: in situ detoxification, glucose conversion, and finally xylose conversion. Increasing the initial time for high-temperature enzymatic hydrolysis in a hybrid process will speed up the hydrolysis of glucan in the fiber. However, it will also increase the concentration ratio between glucose and xylose in the liquid, which in turn will delay the onset of xylose fermentation. Furaldehyde inhibitors present in the medium will delay the start of glucose fermentation, and a sufficient yeast inoculum must be chosen with this in mind.</p>

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