bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel regulatory logic in the antibiotic resistance response ofEnterococcus faecalisagainst cell envelope targeting antibiotics
Authors: Morris Sali M. , Fritz Georg , Rogers Tim , Gebhard Susanne 
Published: November 2022

SummaryEnterococcal infections frequently show high levels of antibiotic resistance, including to cell envelope-acting antibiotics like daptomycin (DAP). While we have a good understanding of the resistance mechanisms, less is known about the control of such resistance genes in enterococci. Previous work unveiled a bacitracin resistance network, comprised of the sensory ABC transporter SapAB, the two-component system (TCS) SapRS and the resistance ABC transporter RapAB. Interestingly, components of this system have recently been implicated in DAP resistance, a role usually regulated by the TCS LiaFSR. We here explored the interplay between these two regulatory pathways. Our results show the regulation by SapR of an additional resistance operon,dltXABCD, and show that LiaFSR regulates the expression ofsapRS, placing SapRS target genes under dual control:dltXABCDexpression depends on both antibiotic-induced cellular damage (LiaFSR)andthe presence of a substrate drug for the sensory transporter (SapAB). We further show that this network protectsE. faecalisfrom antimicrobials produced by potential competitor bacteria, providing a potential rationale for the evolution of this regulatory strategy. The network structure described here can explain why clinical DAP resistance often emerges via mutations in regulatory components, which may ultimately lead to the discovery of new therapeutic targets.Graphical abstract
Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe
Authors: Draizen Eli J. , Mura Cameron , Bourne Philip E. 
Published: November 2022

AbstractModern proteins did not arise abruptly, as singular events, but rather over the course of at least 3.5 billion years of evolution. Can machine learning teach us how this occurred? The molecular evolutionary processes that yielded the intricate three-dimensional (3D) structures of proteins involve duplication, recombination and mutation of genetic elements, corresponding to short peptide fragments. Identifying and elucidating these ancestral fragments is crucial to deciphering the interrelationships amongst proteins, as well as how evolution acts upon protein sequences, structures & functions. Traditionally, structural fragments have been found using sequence-based and 3D structural alignment approaches, but that becomes challenging when proteins have undergone extensive permutations—allowing two proteins to share a common architecture, though their topologies may drastically differ (a phenomenon termed theUrfold). We have designed a new framework to identify compact, potentially-discontinuous peptide fragments by combining (i) deep generative models of protein superfamilies with (ii) layerwise relevance propagation (LRP) to identify atoms of great relevance in creating an embedding during an allsuperfamilies× alldomainsanalysis. Our approach recapitulates known relationships amongst the evolutionarily ancient smallβ-barrels (e.g. SH3 and OB folds) and amongst P-loop–containing proteins (e.g. Rossmann and P-loop NTPases), previously established via manual analysis. Because of the generality of our deep model’s approach, we anticipate that it can enable the discovery of new ancestral peptides. In a sense, our framework uses LRP as an ‘explainable AI’ approach, in conjunction with a recent deep generative model of protein structure (termedDeepUrfold), in order to leverage decades worth of structural biology knowledge to decipher the underlying molecular bases for protein structural relationships—including those which are exceedingly remote, yet discoverable via deep learning.
Label-free cleared tissue microscopy and machine learning for 3D histopathology of biomaterial implants
Authors: Ngo Tran B , DeStefano Sabrina , Liu Jiamin , Su Yijun , Shroff Hari , Vishwasrao Harshad D , Sadtler Kaitlyn 
Published: November 2022

ABSTRACTTissue clearing of whole intact organs has enhanced imaging by enabling the exploration of tissue structure at a subcellular level in three-dimensional space. Although clearing and imaging of the whole organ have been used to study tissue biology, the microenvironment in which cells evolve to adapt to biomaterial implants or allografts in the body is poorly understood. Obtaining high-resolution information from complex cell-biomaterial interactions with volumetric landscapes represents a key challenge in the fields of biomaterials and regenerative medicine. To provide a new approach to examine how tissue responds to biomaterial implants, we apply cleared tissue light-sheet microscopy and three-dimensional reconstruction to utilize the wealth of autofluorescence information for visualizing and contrasting anatomical structures. This study demonstrates the adaptability of the clearing and imaging technique to provide sub-cellular resolution (0.6μm isotropic) 3D maps of various tissue types, using samples from fully intact peritoneal organs to volumetric muscle loss injury specimens, with and without biomaterials implants. We further apply computational-driven image classification to analyze the autofluorescence spectrum at multiple emission wavelengths to categorize tissue types in the tissue-biomaterial microenvironment.
Continuous monitoring of cost-to-go for flexible reaching control and online decisions
Authors: De Comite Antoine , Lefèvre Philippe , Crevecoeur Frédéric 
Published: November 2022

AbstractHumans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time monitoring of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous monitoring of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.Author summaryThe way humans perform reaching movements is compatible with models considering that they result from the minimization of a task-related cost function. However, these models typically assume a cost function that does not change within movement, which is incompatible with experimental findings highlighting humans’ ability to adjust reaching control online and change target flexibly. We hypothesized that this later ability relied on the cost-to-go, which integrates task- and body-related information, being evaluated continuously during movement. We show that this model can optimally select and adjust control during movement in a way that reproduces human behavior in a set of tasks involving change in cost function and change in goal target. Our model predicted that decision-time to change target must be postponed when limb displacements and alternative rewards are smaller, which was borne out in our previous experimental dataset. To conclude, our model explains dynamic updates in reach control and suggests the cost-to-go as decision variable linking decision-making and motor control.
Root Walker: an automated pipeline for large scale quantification of early root growth responses at high spatial and temporal resolution
Authors: Pierre Platre Matthieu , Preyanka Mehta , Zachary Halvorson , Zhang Ling , Lukas Brent , Matias Gleason F. , Kian Faizi , Callum Goulding , Wolfgang Busch 
Published: November 2022

SummaryPlants are sessile organisms that constantly adapt to their changing environment. The root is exposed to numerous environmental signals ranging from nutrients and water to microbial molecular patterns. These signals can trigger distinct responses including the rapid increase or decrease of root growth. Consequently, using root growth as a readout for signal perception can help decipher which external cues are perceived by roots, and how these signals are integrated. To date, studies measuring root growth responses using large numbers of roots have been limited by a lack of high-throughput image acquisition, poor scalability of analytical methods, or low spatiotemporal resolution. Here, we developed the Root Walker pipeline, which uses automated microscopes to acquire time-series images of many roots exposed to controlled treatments with high-spatiotemporal-resolution, in conjunction with fast and automated image analysis software. We demonstrate the power of Root Walker by quantifying root growth rate responses at different time and throughput scales upon treatments with natural auxin, and upon treatment with two mitogen-associated protein kinase cascade inhibitors. We find a concentration-dependent root growth response to auxin and reveal the specificity of one MAPK inhibitor. We further demonstrate the ability of Root Walker for conducting genetic screens by performing a genome wide association study on 260 accessions under 2 weeks. This revealed known and unknown root growth regulators. Root Walker promises to be a useful toolkit for the plant science community, allowing large-scale screening of root growth dynamics for a variety of purposes, including genetic screens for root sensing and root growth response mechanisms.
Integrative single cell multiomics analysis of human retina indicates a role for hierarchical transcription factors collaboration in genetic effects on gene regulation
Authors: Wang Jun , Cheng Xuesen , Liang Qingnan , Owen Leah A. , Wang Meng , DeAngelis Margaret M. , Li Yumei , Chen Rui 
Published: November 2022

AbstractBackgroundSystematic characterization of how genetic variation modulates gene regulation in a cell type specific context is essential for understanding complex traits. To address this question, we profiled gene expression and chromatin state of cells from healthy retinae of 20 human donors with a single-cell multiomics approach, and performed genomic sequencing.ResultsWe mapped single-cell eQTL (sc-eQTLs), single-cell caQTL (sc-caQTL), single-cell allelic specific chromatin accessibility (sc-ASCA) and single-cell allelic specific expression (sc-ASE) in major retinal cell types. By integrating these results, we identified and characterized regulatory elements and genetic variants effective on gene regulation in individual cell types. Most of the sc-eQTLs and sc-caQTLs identified show cell type specific effects, while the cis-elements containing the genetic variants with cell type specific effects tend to be accessible in multiple cell types. Furthermore, the transcription factors with binding sites perturbed by genetic variants tend to have higher expression in the cell types, where the variants have effect, than the cell types where the variants do not have effect. Finally, we identified the enriched cell types, candidate causal variants and genes, and cell type specific regulatory mechanism underlying GWAS loci.ConclusionsOverall, genetic effects on gene regulation are highly context dependent. Our results suggest that among cell types sharing a similar lineage, cell type dependent genetic effect is primarily driven by trans-factors rather than cell type specific chromatin state of cis-elements. Our findings indicate a role for hierarchical transcription factors collaboration in cell type specific effects of genetic variants on gene regulation.
Integrative Teaching of Metabolic Modeling and Flux Analysis with Interactive Python Modules
Authors: Kaste Joshua A.M. , Green Antwan , Shachar-Hill Yair 
Published: November 2022

AbstractThe modeling of rates of biochemical reactions – fluxes – in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and Flux Balance Analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, Metabolic Control Analysis, l3C-Metabolic Flux Analysis and Flux Balance Analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners’ self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.
Structured surveys of Australian native possum excreta predict Buruli ulcer occurrence in humans
Authors: Vandelannoote Koen , Buultjens Andrew H. , Porter Jessica L. , Velink Anita , Wallace John R. , Blasdell Kim R. , Dunn Michael , Boyd Victoria , Fyfe Janet A. M. , Tay Ee Laine , Johnson Paul D. R. , Windecker Saras , Golding Nick , Stinear Timothy P. 
Published: November 2022

ABSTRACTBuruli ulcer (BU) is a neglected tropical disease caused by infection of subcutaneous tissue withMycobacterium ulcerans. BU is commonly reported across rural regions of Central and West Africa but has been increasing dramatically in temperate southeast Australia around the major metropolitan city of Melbourne. Previous research has shown that Australian native possums are reservoirs ofM. ulceransand that they shed the bacteria in their fecal material (excreta). Field surveys show that locales where possums harborM. ulceransoverlap with human cases of BU, raising the possibility of using possum excreta surveys to predict the risk of disease occurrence in humans. We thus established a highly structured 12-month possum excreta surveillance program across an area of 350 km2in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess ifM. ulceranssurveillance of possum excreta provided useful information for predicting future human BU case locations. Over two sampling campaigns in summer and winter, we collected 2282 possum excreta specimens of which 11% were PCR positive forM. ulcerans-specific DNA. Using the spatial scanning statistical toolSatScan, we observed non-random, co-correlated clustering of bothM. ulceranspositive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year’s human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread. This study highlights theOne Healthnature of BU by confirming a quantitative relationship between possum excreta shedding ofM. ulceransand humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for efficient targeting of public health responses to stop BU.
Cell type-specific assessment of cholesterol distribution in models of neurodevelopmental disorders
Authors: Czernecki Charlotte , Dixit Shirley , Riezman Isabelle , Innocenti Sabrina , Bornmann Caroline , Pfrieger Frank W. , Riezman Howard , Scheiffele Peter 
Published: November 2022

AbstractMost nervous system disorders manifest through alterations in neuronal signaling based on abnormalities in neuronal excitability, synaptic transmission, and cell survival. However, such neuronal phenotypes are frequently accompanied – or even caused – by metabolic dysfunctions in neuronal or non-neuronal cells. The tight packing and highly heterogenous properties of neural, glial and vascular cell types pose significant challenges to dissecting metabolic aspects of brain disorders. Perturbed cholesterol homeostasis has recently emerged as key parameter associated with sub-sets of neurodevelopmental disorders. However, approaches for tracking and visualizing endogenous cholesterol distribution in the brain have limited capability of resolving cell type-specific differences. We here develop tools for genetically-encoded sensors that report on cholesterol distribution in the mouse brain with cellular resolution. We apply these probes to examine sub-cellular cholesterol accumulation in two genetic mouse models of neurodevelopmental disorders,Npc1andPtchd1knock-out mice. While both genes encode proteins with sterol-sensing domains that have been implicated in cholesterol transport, we uncover highly selective and cell type-specific phenotypes in cholesterol homeostasis. The tools established in this work should facilitate probing sub-cellular cholesterol distribution in complex tissues like the mammalian brain and enable capturing cell type-specific alterations in cholesterol flow between cells in models of brain disorders.
Resolution of fibrosis in mdx dystrophic mouse after oral consumption of N-163 strain ofAureobasidium pullulansproduced biological response modifier β-glucan (BRMG)
Authors: Preethy Senthilkumar , Aoki Yoshitsugu , Minegishi Katsura , Iwasaki Masaru , Senthilkumar Rajappa , Abraham Samuel JK 
Published: November 2022

AbstractRecent advances in the management of Duchenne Muscular Dystrophy (DMD), such as exon skipping therapy, have reached a clinical stage, and although gene therapy is in clinical trials, the outcome at its best is still considered suboptimal to yield clinically discernible progress. In this study, we evaluated a novel N-163 strain ofAureobasidium pullulansproduced biological response modifier β-glucan (BRMG) for its potential as an adjuvant to slow down the progression of the disease by anti-inflammatory and anti-fibrotic effects. This N-163 β-glucan is a safe and orally consumable food supplement with similar effects that have been previously proven in pre-clinical studies of organ fibrosis, and their beneficial effects have been proven in DMD clinical studies through blood parameters as well. In this study, 45 mice in the three groups, 15 each in a group; Gr. 1 normal mice, Gr.2 mdx mice as vehicle, and Gr.3 mdx mice administered the N-163 strain produced β-glucan for 45 days. Blood biochemical parameters, body weight, muscle weight, inflammation score, and fibrosis score were evaluated using H&E and Masson’s trichrome staining. The N-163 β-glucan group showed a significant decrease in the plasma ALT, AST, and LDH levels (126 ± 69, 634 ± 371, 3335 ± 1258 U/l) compared with the vehicle group (177 ± 27 U/l, 912 ± 126 U/l, 4186 ± 398 U/l). Plasma TGF-β levels increased, and plasma IL-13 levels decreased in the N-163 group. The inflammation score of HE-stained muscle sections in the N-163 group (1.5 ± 0.8) was lower than that in the vehicle group (2.0 ± 0.8). The percentage of centrally nucleated fibres (CNF) evaluated by Masson’s trichrome staining was 0 in the normal group, while it increased to 80% in the vehicle group and 76.8% in the N-163 group. The N-163 β-glucan group (24.22 ± 4.80) showed a significant decrease in the fibrosis area (Masson’s trichrome-positive area). The N-163 β-glucan thus, demonstrated its anti-fibrotic effect in this study. Considering their safety and easy oral consumption, this BRMG could be worth large multicentre clinical studies as adjuvant in slowing down the progress of DMD.
Epigenetic variation in the Lombardy poplar along climatic gradients is independent of genetic structure and persists across clonal reproduction
Authors: Rodríguez Bárbara Díez , Galanti Dario , Nunn Adam , Peña-Ponton Cristian , Pérez-Bello Paloma , Sammarco Iris , Jandrasits Katharina , Becker Claude , De Paoli Emanuele , Verhoeven Koen J.F , Opgenoorth Lars , Heer Katrin 
Published: November 2022

Summary- Environmental changes can trigger phenotypic variation in plants through epigenetic mechanisms, but strong genetic influences make it difficult to isolate and study epigenetic effects. Clonal trees with low genetic variation, such as the Lombardy poplar(Populus nigracv. ‘Italica’ Duroi), offer a unique system to study epigenetic variation associated with the environment.- We collected cuttings (ramets) of Lombardy poplar along a wide geographical range in Europe. We performed whole-genome-bisulfite sequencing of 164 ramets grown in a common garden and of a subset of 35 of the original parental individuals. Using historical bioclimatic data, we tested the relationship between DNA methylation and climatic gradients.- We found that average methylation levels in TEs and promoter regions correlate with biologically relevant climatic variables. Furthermore, we observed that DNA methylation was transmitted to the next clonal generation, but a fraction of the methylome changed relatively fast when comparing the parental individuals with the clonal offspring.- Our results suggest that the poplar methylome is a dynamic layer of information that can be transmitted to the clonal offspring and potentially affect how poplars acclimate to new environmental conditions.
Reconceptualizing the retrieval state as an internal attention state
Authors: Long Nicole M. 
Published: November 2022

AbstractLarge-scale brain states or distributed patterns of brain activity modulate downstream processing and behavior. Sustained attention and memory retrieval states impact subsequent memory, yet how these states relate to one another is unclear. We hypothesize that the retrieval state reflects internal attention. The alternative is that the retrieval state reflects a controlled, episodic retrieval mode, specifically engaged when intentionally accessing events situated within a spatiotemporal context. To test our hypothesis, we developed a mnemonic state classifier independently trained to measure retrieval state evidence and applied this classifier to a spatial attention task. We find that retrieval state evidence increases during delay and response intervals when participants are maintaining spatial information. Critically, retrieval state evidence is positively related to the amount of maintained spatial location information and predicts target detection reaction times. Together, these findings strongly support the hypothesis that the retrieval state constitutes internal attention.