bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of High Pressure NMR Spectroscopy to Rapidly Identify Proteins with Internal Ligand-Binding Voids
Authors: Gagné Donald , Azad Roksana , Aramini James M. , Xu Xingjian , Isiorho Eta A. , Edupuganti Uthama R. , Williams Justin , Marcelino Leandro Pimentel , Akasaka Kazuyuki , Gardner Kevin H. 
Published: August 2020

AbstractSmall molecule binding within internal cavities provides a way to control protein function and structure, as exhibited in numerous natural and artificial settings. Unfortunately, most ways to identify suitable cavities require high-resolution structuresa prioriand may miss potential cryptic sites. Here we address this limitation via high-pressure solution NMR spectroscopy, taking advantage of the distinctive nonlinear pressure-induced chemical shift changes observed in proteins containing internal cavities and voids. We developed a method to rapidly characterize such nonlinearity among backbone1H and15N amide signals without needing to have sequence-specific chemical shift assignments, taking advantage of routinely available15N-labeled samples, instrumentation, and 2D1H/15N HSQC experiments. From such data, we find a strong correlation in the site-to-site variability in such nonlinearity with the total void volume within proteins, providing insights useful for prioritizing domains for ligand binding and indicating mode-of-action among such protein/ligand systems. We suggest that this approach provides a rapid and useful way to rapidly assess otherwise hidden dynamic architectures of protein that reflect fundamental properties associated with ligand binding and control.Significance StatementMany proteins can be regulated by internally binding small molecule ligands, but it is often not cleara prioriwhich proteins are controllable in such a way. Here we describe a rapid method to address this challenge, using solution NMR spectroscopy to monitor the response of proteins to the application of high pressure. While the locations of NMR signals from most proteins respond to high pressure with linear chemical shift changes, proteins containing internal cavities that can bind small molecule ligands respond with easily identified non-linear changes. We demonstrate this approach on several proteins and protein/ligand complexes, suggesting that it has general utility.
Non-targeted Screening of Commercial CBD Products in the United States Reveals Common Contamination and Adulteration
Authors: Orsburn Ben 
Published: August 2020

AbstractThe production of hemp and products derived from these plants that contain zero to trace amounts of the psychoactive cannabinoid tetrahydrocannabidiol (THC) is a rapidly growing new market in the United States. The most common products today contain relatively high concentrations of the compound cannabidiol (CBD). Recent studies have investigated commercial CBD products using targeted assays and have found varying degrees of misrepresentation and contamination of these products. To expand on previous studies, we demonstrate the application of non-targeted screening by high resolution accurate mass spectrometry to more comprehensively identify potential adulterants and contaminants. We find evidence to support previous conclusions that CBD products are commonly misrepresented in terms of cannabinoid concentrations present. Specifically, we observe a wide variation in relative THC concentrations across the product tested, with some products containing 10-fold more relative signal than others. In addition, we find that several products appear to be purposely adulterated with over the counter drugs such as caffeine and melatonin. We also observe multiple small molecule contaminants that are typically linked to improper production or packaging methods in food or pharmaceutical production. Finally, we present high resolution accurate mass spectrometry data and tandem MS/MS fragments supporting the presence of trace amounts of fluorofentanyl in a single mail order CBD product. We conclude that the CBD industry would benefit from more robust testing regulations and that the cannabis testing industry, in general, would benefit from the use of non-targeted screening technologies.
Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli
Authors: Nara Sanjeev , Lizarazu Mikel , Richter Craig G , Dima Diana C , Cichy Radoslaw M , Bourguignon Mathieu , Molinaro Nicola 
Published: August 2020

AbstractPredictive processing has been proposed as a fundamental cognitive mechanism to account for how the brain interacts with the external environment via its sensory systems. In vision, contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). In line with previous findings, we observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.
Neural signatures of attentional engagement during narratives and its consequences for event memory
Authors: Song Hayoung , Finn Emily S. , Rosenberg Monica D. 
Published: August 2020

AbstractAs we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation, and test whether neural signatures of engagement predict later recall. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. During fMRI, we observed highly synchronized activity in the default mode network when people were, on average, more engaged in the same narratives. Models based on time-varying whole-brain functional connectivity predicted evolving states of engagement across participants and even across different datasets. The same functional connections also predicted post-scan event recall, suggesting that engagement during encoding impacts subsequent memory. Finally, group-average engagement was related to fluctuations of an independent functional connectivity index of sustained attention. Together, our findings characterize the neural signatures of engagement dynamics and elucidate relationships between narrative engagement, sustained attention, and event memory.
Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis andBrassica rapacultivars
Authors: Calderwood Alexander , Hepworth Jo , Woodhouse Shannon , Bilham Lorelei , Jones D. Marc , Tudor Eleri , Ali Mubarak , Dean Caroline , Wells Rachel , Irwin Judith A. , Morris Richard J. 
Published: August 2020

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model speciesArabidopsis thalianaand the closely related cropBrassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis andB. rapacan be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis andB. rapa, and between twoB. rapaaccessions reveals different modes of regulation of the key floral integratorSOC1, and that the floral transition in theB. rapaaccessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cyclingB. rapaunder long days and highlights the importance of registration methods for the comparison of developmental gene expression data.
Co-administration of macropinocytosis inhibitory nanoparticles (MiNP) for enhanced nanoparticle circulation time and target tissue accumulation following subcutaneous injection
Authors: Stack Trevor , Liu Yu-gang , Frey Molly , Bobbala Sharan , Vincent Michael , Scott Evan 
Published: August 2020

AbstractA signficant barrier to the application of nanoparticles for precision medicine is the mononuclear phagocyte system (MPS), a diverse population of phagocytic cells primarily located within the liver, spleen and lymph nodes. The majority of nanoparticles are indiscriminantly cleared by the MPS via macropinocytosis before reaching their intended targets, resulting in side effects and decreased efficacy. This work demonstrates that the biodistribution and desired tissue accumulation of targeted nanoparticles can be significantly enhanced by co-injection with polymeric micelles containing the actin depolymerizing agentlatrunculin A. These macropinocytosis inhibitory nanoparticles (MiNP) were found to selectively inhibit non-specific uptake of a second “effector” nanoparticlein vitrowithout impeding receptor-mediated endocytosis. In tumor bearing mice, co-injection with MiNP in a single multi-nanoparticle formulation significantly increased the accumulation of folate-receptor targeted nanoparticles within tumors. Furthermore, subcutaneous co-administration with MiNP allowed effector nanoparticles to achieve serum levels that rivaled a standard intravenous injection. This effect was only observed if the effector nanoparticles were injected within 24 h following MiNP administration, indicating a temporary avoidance of MPS cells. Co-injection with MiNP therefore allows reversible evasion of the MPS for targeted nanoparticles and presents a previously unexplored method of modulating and improving nanoparticle biodstribution following subcutaneous administration.Abstract FigureTOC Text: Polymeric macropinocytosis inhibiting nanoparticles reduce non-specific uptake of an “effector” nanoparticle by cells of the mononuclear phagocyte system. This macropinocytosis specific inhibition allows for greater accumulation and uptake of targeted nanoparticles in tissues of interest thereby increasing their efficacy and reducing side effects.
Epitopes targeted by T cells in convalescent COVID-19 patients
Authors: Quadeer Ahmed A. , Ahmed Syed Faraz , McKay Matthew R. 
Published: August 2020

AbstractKnowledge of the epitopes of SARS-CoV-2 that are targeted by T cells in convalescent patients is important for understanding T cell immunity against COVID-19. This information can aid the design, development and assessment of COVID-19 vaccines, and inform novel diagnostic technologies. Here we provide a unified description and meta-analysis of emerging data of SARS-CoV-2 T cell epitopes compiled from 15 independent studies of cohorts of convalescent COVID-19 patients. Our analysis demonstrates the broad diversity of T cell epitopes that have been collectively recorded for SARS-CoV-2, while also identifying a selected set of immunoprevalent epitopes that induced consistent T cell responses in multiple cohorts and in a large fraction of tested patients. The landscape of SARS-CoV-2 T cell epitopes that we describe can help guide future immunological studies, including those related to vaccines and diagnostics. A web-based platform has been developed to help complement these efforts.
3D Deconvolution Processing for STEM Cryo-Tomography
Authors: Waugh Barnali , Wolf Sharon G. , Fass Deborah , Branlund Eric , Kam Zvi , Sedat John , Elbaum Michael 
Published: August 2020

AbstractThe complex environment of biological cells and tissues has motivated development of three dimensional imaging in both light and electron microscopies. To this end, one of the primary tools in fluorescence microscopy is that of computational deconvolution. Wide-field fluorescence images are often corrupted by haze due to out-of-focus light, i.e., to cross-talk between different object planes as represented in the 3D image. Using prior understanding of the image formation mechanism, it is possible to suppress the cross-talk and reassign the unfocused light to its proper sourcepost facto. Electron tomography based on tilted projections also exhibits a cross-talk between distant planes due to the discrete angular sampling and limited tilt range. By use of a suitably synthesized 3D point spread function, we show here that deconvolution leads to similar improvements in volume data reconstructed from cryo-scanning transmission electron tomography (CSTET), namely a dramatic in-plane noise reduction and improved representation of features in the axial dimension. Contrast enhancement is demonstrated first with colloidal gold particles, and then in representative cryo-tomograms of intact cells. Deconvolution of CSTET data collected from the periphery of an intact nucleus revealed partially condensed, extended structures in interphase chromatin.Significance statementElectron tomography is used to reveal the structure of cells in three dimensions. The combination with cryogenic fixation provides a snapshot in time of the living state. However, cryo-tomography normally requires very thin specimens due to image formation by conventional phase contrast transmission electron microscopy (TEM). The thickness constraint can be relaxed considerably by scanning TEM (STEM), yet three-dimensional (3D) reconstruction is still subject to artifacts inherent in the collection of data by tilted projections. We show here that deconvolution algorithms developed for fluorescence microscopy can suppress these artifacts, resulting in significant contrast enhancement. The method is demonstrated by cellular tomography of complex membrane structures, and by segmentation of chromatin into distinct, contiguous domains of heterochromatin and euchromatin at high and low density, respectively.
Mechanistic Studies of the Stabilization of Insulin Helical Structure by Coomassie Brilliant Blue
Authors: Dolui Sandip , Pariary Ranit , Saha Achintya , Ratha Bhisma N , Harikishore Amaravadhi , Saha Susmita , Swarnakar Snehasikta , Bhunia Anirban , Maiti Nakul C 
Published: August 2020

AbstractHuman insulin (HI) is an essential protein hormone and its biological activity mostly depends on folded and active conformation in the monomeric state. The present investigation established that Coomassie Brilliant Blue G-250 (CBBG), a small multicyclic hydroxyl compound can reversibly bind to the hormonal protein dimer and maintained most of α-helical folds crucial for biological function of the enzyme. The solution-state 1D NMR and isothermal calorimetric analysis showed a sub-micromolar binding affinity of the molecule to HI. 2D NOESY NMR established that the HI dimer undergoes residue level local conformational change upon binding to CBBG. The chemical shift perturbation and the NOE parameters of active protons of amino acid residues throughout the polypeptides further suggested that CBBG upon binding the protein stabilize α-helixes of both the A and B subunits of the hormonal protein. The changes in Gibb’s free energy (∆G) of the binding was of ~−11.1 kcal/mol and suggested a thermodynamically favourable process. The changes in enthalpy (∆H) and entropy term (T∆S) were −57.2 kcal/mol and −46.1 kcal/mol, respectively. The negative changes in entropy and the NOE transfer effectiveness of several residues in the presence of CBBG molecules indicated that the binding was an enthalpy driven favourable equilibrium process. The NMR-based atomic resolution data and molecular docking studies confirmed that the CBBG binds to HI at the dimeric stage and prevents the availability of the crucial residue segments that partake directly in further oligomerization and subsequent fibrillation. Extended computational analysis based on chemical shift perturbation of protons of active residues further established receptor-ligand based pharmacophore model comprised of 5 hydrophobic and a hydrogen bond acceptor features that can anchor the residues at the A and B chains of HI and inhibit the partial unfolding and hydrophobic collapse to nucleate the fibrillation. Taken together, the results demonstrated that CBBG and their close analogues might be useful to develop a formulation that will maintain the active and functional form of the hormonal protein for a significantly longer time.TOC
Scanning ion conductance microscopy reveals differences in the ionic environments of gram positive and negative bacteria
Authors: Cremin Kelsey , Jones Bryn , Teahan James , Meloni Gabriel N. , Perry David , Zerfass Christian , Asally Munehiro , Soyer Orkun S. , Unwin Patrick R. 
Published: August 2020

AbstractThis paper reports on the use of scanning ion conductance microscopy (SICM) to locally map the ionic properties and charge environment of two live bacterial strains: the gramnegativeEscherichia coliand the gram-positiveBacillus subtilis. SICM results find heterogeneities across the bacterial surface, and significant differences among the grampositive and -negative bacteria. The bioelectrical environment of theB. subtiliswas found to be considerably more negatively charged compared toE. coli. SICM measurements, fitted to a simplified finite element method (FEM) model, revealed surface charge values of −80 to −140 mC m−2for the gram-negativeE. coli. The gram-positiveB. subtilisshow a much higher conductivity around the cell wall, and surface charge values between −350 and −450 mC m−2were found using the same simplified model. SICM was also able to detect regions of high negative charge nearB. subtilis, not detected in the topographical SICM response and attributed to extracellular polymeric substance. To further explore how theB. subtiliscell wall structure can influence the SICM current response, a more comprehensive FEM model, accounting for the physical properties of the gram-positive cell wall, was developed. The new model provides a more realistic description of the cell wall and allowed investigation of the relation between its key properties and SICM currents, building foundations to further investigate and improve understanding of the gram-positive cellular microenvironment.Abstract Figure
DNA methylation in cockroaches is essential in early embryo development and reduces gene expression noise
Authors: Ventos-Alfonso Alba , Ylla Guillem , Montañes Jose-Carlos , Belles Xavier 
Published: August 2020

AbstractThe influence of DNA methylation on gene behavior, and its consequent phenotypic effects appear to be very important, but the details are not well understood. Insects offer a diversity of DNA methylation modes, making them an excellent lineage for comparative analyses. However, functional studies have tended to focus on quite specialized holometabolan species, such as wasps, bees, beetles, and flies. Here we have studied DNA methylation in a hemimetabolan insect, the cockroachBlattella germanica, a model of early-branching insects. In this cockroach, one of the main genes responsible for DNA methylation,DNA methyltransferase 1(DNMT1), is expressed in early embryogenesis. In our experiments,DNMT1interference by RNAi reduces DNA methylation and impairs blastoderm formation. Using Reduced Representation Bisulfite Sequencing (RRBS) and transcriptomic analyses, we observed that hypermethylated genes are associated with metabolism and are highly expressed, whereas hypomethylated genes are related to signaling and have low expression levels. Moreover, the expression change in hypermethylated genes is greter than that in hypomethylated genes, whereas hypermethylated genes have less expression variability than hypomethylated genes. The latter observation has also been reported for humans and inArabidopsisplants. A reduction in expression noise may therefore be one of the few universal effects of DNA methylation.
Predicting Chemical Shifts with Graph Neural Networks
Authors: Yang Ziyue , Chakraborty Maghesree , White Andrew D 
Published: August 2020

AbstractInferring molecular structure from NMR measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state of the art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules.