bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel inhibitors against COVID-19 main protease suppressed viral infection
Authors: Ramachandran Vijayan , Liu Yanyun , He Qianying , Tang Andrew , Ronaldson Patrick , Schenten Dominik , Chang Rui 
Published: November 2022

SummarySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19, can cause severe disease with high mortality rates, especially among older and vulnerable populations. Despite the recent success of vaccines and approval of first-generation anti-viral inhibitor against SARS-CoV-2, an expanded arsenal of anti-viral compounds that limit viral replication and ameliorate disease severity is still urgently needed in light of the continued emergence of viral variants of concern (VOC). The main protease (Mpro) of SARS-CoV-2 is the major non-structural protein required for the processing of viral polypeptides encoded by the open reading frame 1 (ORF1) and ultimately replication. Structural conservation of Mpro among SARS-CoV-2 variants make this protein an attractive target for the anti-viral inhibition by small molecules. Here, we developed a structure-basedin-silicoscreening of approximately 11 million compounds in ZINC15 database inhibiting Mpro, which prioritized 9 lead compounds for the subsequentin vitrovalidation in SARS-CoV-2 replication assays using both Vero and Calu-3 cells. We validated three of these compounds significantly inhibited SARS-CoV-2 replication in the micromolar range. In summary, our study identified novel small-molecules significantly suppressed infection and replication of SARS-CoV-2 in human cells.
Exploring of a novel monitoring tool for injury risk when sidestepping: Using unsupervised learning to characterize movement patterns
Authors: David Sina , Barton Gabor J. 
Published: November 2022

AbstractThe monitoring of athletes is crucial to prevent injuries, identify fatigue or support return-to-play decisions. The purpose of this study was to explore the ability of Kohonen neural network self-organizing maps (SOM) to objectively visualize and characterize different movement patterns during sidestepping and to detect those patterns that are associated with signs of injury risk or fatigue. The marker trajectories of 631 pre-planned sidestepping trials were used to train a SOM. Out of 61913 input vectors, the SOM identified 1250 unique body postures, determined by the 3D marker positions. Visualizing the movement trajectories and adding the latent parameter time, allows for the investigation of different movement patterns. Additionally, the SOM can be used to identify zones with increased injury risk, by adding more latent parameters more directly linked to injuries which opens the option to monitor athletes and give feedback. The results highlight the ability of unsupervised learning to visualize movement patterns and to give further insight into an individual athlete’s status without the necessity to reduce the complexity of the data describing the movement.
The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19
Authors: Demers Jeffery , Fagan William F. , Potluri Sriya , Calabrese Justin M. 
Published: November 2022

AbstractThe severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between ‘clinical’ testing, which targets symptomatic individuals, and ‘non-clinical’ testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.
Neutralization of zoonotic retroviruses by human antibodies: genotype-specific epitopes within the receptor-binding domain from simian foamy virus
Authors: Dynesen Lasse Toftdal , Fernandez Ignacio , Coquin Youna , Delaplace Manon , Montange Thomas , Njouom Richard , Bilonga-Ndongo Chanceline , Rey Felix A. , Gessain Antoine , Backovic Marija , Buseyne Florence 
Published: November 2022

AbstractInfection with viruses of animal origin pose a significant threat to human populations. Simian foamy viruses (SFVs) are frequently transmitted to humans, in which they establish a life-long infection, with the persistence of replication-competent virus. However, zoonotic SFVs do not induce severe disease nor are they transmitted between humans. Thus, SFVs represent a model of zoonotic retroviruses that lead to a chronic infection successfully controlled by the human immune system. We previously showed that infected humans develop potent neutralizing antibodies (nAbs). Within the viral envelope (Env), the surface protein (SU) carries a variable region that defines two genotypes, overlaps with the receptor binding domain (RBD), and is the exclusive target of nAbs. However, its antigenic determinants are not understood. Here, we characterized nAbs present in plasma samples from SFV-infected individuals living in Central Africa. Neutralization assays were carried out in the presence of recombinant SU that compete with SU at the surface of viral vector particles. We defined the regions targeted by the nAbs using mutant SU proteins modified at the glycosylation sites, RBD functional subregions, and genotype-specific sequences that present properties of B-cell epitopes. We observed that nAbs target conformational epitopes. We identified three major epitopic regions: the loops at the apex of the RBD, which likely mediate interactions between Env protomers to form Env trimers, a loop located in the vicinity of the heparan binding site, and a region proximal to the highly conserved glycosylation site N8. We provide information on how nAbs specific for each of the two viral genotypes target different epitopes. Two common immune escape mechanisms, sequence variation and glycan shielding, were not observed. We propose a model according to which the neutralization mechanisms rely on the nAbs to block the Env conformational change and/or interfere with binding to susceptible cells. As the SFV RBD is structurally different from known retroviral RBDs, our data provide fundamental knowledge on the structural basis for the inhibition of viruses by nAbs.
Transcriptome Data Analysis of Primary Cardiomyopathies Reveals Perturbations in Arachidonic Acid Metabolism
Authors: Chauhan Pankaj Kumar , Sowdhamini R. 
Published: November 2022

AbstractCardiomyopathies are complex heart diseases with significant prevalence around the world. Among these, primary forms are the major contributor to heart failure and sudden cardiac death. As the high-energy demanding engine, the heart utilizes fatty acids, glucose, amino acid, lactate and ketone bodies as energy to meet its requirement. However, continuous myocardial stress and cardiomyopathies drive towards metabolic impairment that advances heart failure (HF) pathogenesis. So far, metabolic profile correlation across different cardiomyopathies remains poorly understood. In this study, we systematically explore metabolic differences amongst primary cardiomyopathies. By assessing the metabolic gene expression of all primary cardiomyopathies, we highlight the significantly shared and distinct metabolic pathways that may represent specialized adaptations to unique cellular demands. We utilized publicly available RNA-seq datasets to profile global changes in the above diseases and performed Gene set analysis (GSA). Our analysis demonstrates genes in arachidonic acid metabolism (AA) as significantly perturbated across cardiomyopathy. In particular, arachidonic acid metabolism genePLA2G2Ainteracts with fibroblast marker genes and can potentially influence fibrosis during cardiomyopathy.
Learning predictive models of tissue cellular neighborhoods from cell phenotypes with graph pooling
Authors: Hu Yuxuan , Rong Jiazhen , Xie Runzhi , Xu Yafei , Peng Jacqueline , Gao Lin , Tan Kai 
Published: November 2022

AbstractIt remains poorly understood how different cell types organize and coordinate with each other to support tissue functions. We describe CytoCommunity for identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from cell phenotype space to TCN space by a graph neural network model without using additional gene or protein expression features and is thus applicable to tissue imaging data with a small number of measured features. By leveraging graph pooling, CytoCommunity enablesde novoidentification of condition-specific TCNs under the supervision of image labels. Using various types of single-cell-resolution spatial proteomics and transcriptomics images, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. To further evaluate the ability of CytoCommunity for discovering condition-specific TCNs by supervised learning, we apply it to colorectal and breast cancer tissue images with clinical outcome information. Our analysis reveals novel granulocyte- and cancer associated fibroblast-enriched TCNs specific to high-risk tumors as well as altered tumor-immune and tumor-stromal interactions within and between TCNs compared to low-risk tumors. CytoCommunity represents the first computational tool for end-to-end unsupervised and supervised analyses of single-cell spatial maps and enables direct discovery of conditional-specific cell-cell communication patterns across variable spatial scales.
Whole-body gene expression atlas of an adult metazoan
Authors: Ghaddar Abbas , Armingol Erick , Huynh Chau , Gevirtzman Louis , Lewis Nathan E. , Waterston Robert , O’Rourke Eyleen J. 
Published: November 2022

SummaryAnimals are integrated organ systems composed of interacting cells whose structure and function are in turn defined by their active genes. Understanding what distinguishes physiological and disease states therefore requires systemic knowledge of the gene activities that define the distinct cells that make up an animal. Towards this goal, this study reports the first single-cell resolution transcriptional atlas of a fertile multicellular organism:Caenorhabditis elegans. The scRNA-Seq compendium of wild-type young adultC. eleganscomprises 159 distinct cell types with 18,033 genes expressed across cell types. Fewer than 300 of these genes are housekeeping genes as evidenced by their consistent expression across cell types and conditions, and by their basic and essential functions; 170 of these housekeeping genes are conserved across phyla. The 362 transcription factors with available ChIP-Seq data are linked to patterns of gene expression of different cell types. To identify potential interactions between cell types, we used thein silicotool cell2cell to predict molecular patterns reflecting both known and uncharacterized intercellular interactions across theC. elegansbody. Finally, we present WormSeq (wormseq.org), a web interface that, among other functions, enables users to query gene expression across cell types, identify cell-type specific and potential housekeeping genes, analyze candidate ligand-receptors mediating communication between cells, and study promiscuous and cell-specific transcription factors. The datasets, analyses, and tools presented here will enable the generation of testable hypotheses about the cell and organ-specific function of genes in diverse biological contexts.
Two RhoGEF isoforms with distinct localisation act in concert to control asymmetric cell division
Authors: Montembault Emilie , Deduyer Irène , Claverie Marie-Charlotte , Bouit Lou , Tourasse Nicolas , Dupuy Denis , McCusker Derek , Royou Anne 
Published: November 2022

SummaryCytokinesis is essential for the partitioning of cellular contents into daughter cells. It relies on the formation of an acto-myosin contractile ring, whose constriction induces the ingression of the cleavage furrow between the segregated chromatids. Rho1 GTPase and its RhoGEF (Pbl) are essential for this process as they drive the assembly and constriction of the contractile ring. However, how Rho1 is regulated to sustain efficient furrow ingression while maintaining correct furrow position remains poorly defined. Here, we show that during asymmetric division of Drosophila neuroblasts, Rho1 is controlled by two Pbl isoforms with distinct localisation. Spindle midzone- and furrow-enriched Pbl-A focuses Rho1 at the furrow to sustain efficient ingression, while Pbl-B pan-plasma membrane localization promotes the broadening of Rho1 activity and the subsequent enrichment of cortical myosin. This enlarged zone of Rho1 activity becomes essential to adjust furrow position during ingression, thereby preserving correct daughter cell size asymmetry. Our work highlights how the use of isoforms with distinct localisation patterns provides robustness to an essential process.
Identification of Potential DNA Gyrase Inhibitors: Virtual Screening, Extra-Precision Docking and Molecular Dynamics Simulation Study
Authors: Kumar Avinash , Prasun Chakrawarti , Rathi Ekta , Nair Maya S. , Kini Suvarna G 
Published: November 2022

AbstractDNA gyrase brings negative supercoils into DNA and loosens up certain positive supercoils that collect during replication and transcription and is a notable antibacterial target. To fight against the menace of antibiotic-resistant bacterial infections, we have employed various computational tools like high throughput virtual screening (HTVS), standard precision (SP), extra precision (XP), and molecular dynamics (MD) simulation studies to identify some potential DNA gyrase inhibitors. A focused library of 5968 anti-bacterial compounds was screened using the HTVS docking protocol of the glide module of Maestro. The top 200 docked compounds were further filtered using SP and XP docking protocols and their free binding energies were calculated using MM-GBSA studies. Binding and stability of the top two compounds which showed better docking score than the co-crystallized ligand (clorobiocin) of DNA gyrase (PDB ID: 1KZN) was further probed by MD simulation of 100 ns using GROMACS. MD simulation study suggested that the compounds AM1 and AM5 form a stable complex with DNA gyrase with a good number of hydrogen bonds. XP docking study showed that interaction with the crucial amino acids for compounds AM1 and AM5 was like the co-crystallized ligand. These compounds were also predicted to be drug-like molecules with good water solubility and excellent absorption profiles. Based on the above studies, herein we report compounds AM1 (1R,3S)-1-(2-((3-(ammoniomethyl)phenyl)amino)-2-oxoethyl)-3-carbamoylpiperidin-1-ium and AM5 (1’S,2s,4R)-4-ammonio-6-ethyl-1’-methylspiro[chromane-2,4’-piperidin]-1’-ium as potential DNA gyrase inhibitors which can be further developed as a potential drug against the menace of antibiotic resistance.
Mathematical Modeling of Impacts of Patient Differences on Renin-Angiotensin System and Applications to COVID-19 Lung Fibrosis Outcomes
Authors: Islam Mohammad Aminul , Ford Versypt Ashlee N. 
Published: November 2022

AbstractPatient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating the effects of these factors. Recent evidence shows patient-specific alterations of RAS homeostasis concentrations with premorbidity and the expression level of angiotensin-converting enzyme 2 (ACE2) during COVID-19. However, conflicting evidence suggests decreases, increases, or no changes in RAS peptides after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced RAS alterations are still unknown. Here, a multiscale computational model was developed to quantify the systemic contribution of heterogeneous factors of RAS during COVID-19. Three submodels were connected—an agent-based model for in-host COVID-19 response in the lung tissue, a RAS dynamics model, and a fibrosis dynamics model to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicated cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2. In contrast, there were no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. The model explained possible mechanisms for conflicting evidence of patient-group-specific changes in RAS peptides in previously published studies. Simulated results were consistent with reported RAS peptide values for SARS-CoV-2-negative and SARS-CoV-2-positive patients. RAS peptides decreased for all virtual patient groups with aging in both sexes. In contrast, large variations in the magnitude of reduction were observed between male and female virtual patients in the older and middle-aged groups. The patient-specific variations in homeostasis RAS peptide concentrations of SARS-CoV-2-negative patients also affected the dynamics of RAS during infection. The model results also showed that feedback between RAS signaling and renin dynamics could restore ANGI homeostasis concentration but failed to restore homeostasis values of RAS peptides downstream of ANGI. In addition, the results showed that ACE2 variations with age and sex significantly altered the concentrations of RAS peptides and led to collagen deposition with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.
Carcinogenicity and testicular toxicity of 2-bromopropane in a 26-week inhalation study using the rasH2 mouse model
Authors: Goto Yuko , Saito Arata , Takanobu Kenji , Senoh Hideki , Saito Misae , Umeda Yumi , Yamano Shotaro 
Published: November 2022

Abstract2-Bromopropane (2-BP) is a colorless liquid at room temperature and is used in closed systems in factories, mainly as an intermediate for medicines, pesticides, and other chemicals. However, the carcinogenicity of 2-BP is still unknown. The CByB6F1-Tg(HRAS)2Jic (rasH2) transgenic mouse model has been established as an alternative to long-term studies (1.5years-lifetime) to detect carcinogenicity in as short a time as six months. We performed a 26-week inhalation exposure study of 2-BP using the rasH2 mouse model. Male and female rasH2 mice were exposed to 0, 67, 200, or 600 ppm of 2-BP for 6 hours/day, 5 days/week for 26 weeks. All tissues and blood were collected and subjected to biological and histopathological analyses. The results showed a concentration-dependent increase in lung tumor development in male and female rasH2 mice exposed by inhalation to 2-BP, which was significant by Peto’s trend test. Furthermore, in male rasH2 mice, 2-BP was found to be a testicular toxin. This study is the first to demonstrate that 2-BP is carcinogenic in male and female mice and a testicular toxin in male mice in the rasH2 mouse model.
Upgrading Voxel-wise Encoding Model via Integrated Integration over Features and Brain Networks
Authors: Li Yuanning , Yang Huzheng , Gu Shi 
Published: November 2022

AbstractA central goal of cognitive neuroscience is to build computational models that predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to borrow the representation power of deep neural networks (DNN) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, each DNN instance is often specified for certain computer vision tasks which may not lead to optimal brain correspondence. On the other hand, these voxel-wise encoding models focus on predicting single voxels independently, while brain activity often demonstrates rich and dynamic structures at the population and network levels during cognitive tasks. These two important properties suggest that we can improve the prevalent voxel-wise encoding models by integrating features from DNN models and by integrating cortical network information into the models. In this work, we propose a new unified framework that addresses these two aspects through DNN feature-level ensemble learning and brain atlas-level model integration. Our proposed approach leads to superior performance over previous DNN-based encoding models in predicting whole-brain neural activity during naturalistic video perception. Furthermore, our unified framework also facilitates the investigation of the brain’s neural representation mechanism by accurately predicting the neural response corresponding to complex visual concepts.