bluuebunny/crossref_metadata_2025_split huggingface dataset:
Charting cellular differentiation trajectories with Ricci flow
Authors: Baptista Anthony , MacArthur Ben D. , Banerji Christopher R. S. 
Published: July 2023

AbstractComplex biological processes, such as cellular differentiation, require an intricate rewiring of intra-cellular signalling networks. Previous characterisations of these networks revealed that promiscuity in signalling, quantified by a raised network entropy, underlies a less differentiated and malignant cell state. A theoretical connection between entropy and Ricci curvature has led to applications of discrete curvatures to characterise biological signalling networks at distinct time points during differentiation and malignancy. However, understanding and predicting the dynamics of biological network rewiring remains an open problem. Here we construct a framework to apply discrete Ricci curvature and Ricci flow to the problem of biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, both theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as has been previously suggested, and provide complementary rather than interchangeable information. We next employ discrete normalised Ricci flow, to derive network rewiring trajectories from transcriptomes of stem cells to differentiated cells, which accurately predict true intermediate time points of gene expression time courses. In summary, we present a differential geometry toolkit for investigation of dynamic network rewiring during cellular differentiation and cancer.
Dermonecrosis caused by spitting cobra snakebite results from toxin potentiation and is prevented by the repurposed drug varespladib
Authors: Bartlett Keirah E. , Hall Steven R. , Rasmussen Sean A. , Crittenden Edouard , Dawson Charlotte A. , Albulescu Laura-Oana , Laprade William , Harrison Robert A. , Saviola Anthony J. , Modahl Cassandra M. , Jenkins Timothy P. , Wilkinson Mark C. , Gutiérrez José María , Casewell Nicholas R. 
Published: July 2023

AbstractSnakebite envenoming is a neglected tropical disease that causes substantial mortality and morbidity globally. The venom of African spitting cobras often causes permanent injury via tissue-destructive dermonecrosis at the bite site, which is ineffectively treated by current antivenoms. To address this therapeutic gap, we identified the aetiological venom toxins responsible for causing local dermonecrosis. While cytotoxic three-finger toxins were primarily responsible for causing spitting cobra cytotoxicity in cultured keratinocytes, their potentiation by phospholipases A2toxins was essential to cause dermonecrosisin vivo. This evidence of probable toxin synergism suggests that a single toxin-family inhibiting drug could prevent local envenoming. We show that local injection with the repurposed phospholipase A2-inhibiting drug varespladib significantly prevents local tissue damage caused by several spitting cobra venoms in murine models of envenoming. Our findings therefore provide a new therapeutic strategy to more effectively prevent life-changing morbidity caused by snakebite in rural Africa.Significance StatementSpitting cobra venoms cause extensive local tissue damage surrounding the site of a snakebite. This damage cannot be effectively prevented with current antivenom treatments, and patients are often left with life-changing wounds. In this study we used cellular and mouse experiments to determine which toxins in African spitting cobra venom are responsible for causing tissue damage, revealing that a combination of two different types of toxins are required to cause pathologyin vivo. We then showed that the repurposed drug, varespladib, which targets one of these toxin types, effectively prevents skin and muscle damage in mouse models of envenoming. Collectively these findings suggest that varespladib could be an effective new type of therapy for preventing snakebite morbidity in Africa.
PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements
Authors: Parizi Farzaneh M. , Marzella Dario F. , Ramakrishnan Gayatri , ‘t Hoen Peter A. C. , Karimi-Jafari Mohammad Hossein , Xue Li C 
Published: July 2023

AbstractT-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation athttps://github.com/X-lab-3D/PANDORA.
Efficient protein structure archiving using ProteStAr
Authors: Deorowicz Sebastian , Gudyś Adam 
Published: July 2023

AbstractMotivationThe introduction of Deep Minds’ Alpha Fold 2 enabled prediction of protein structures at unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data which hinders the effective use of predicted structures in large-scale analyses.ResultsHere, we present ProteStAr, a compressor dedicated to CIF/PDB as well as, supplementary PAE files. Its main contribution is a novel approach to predict atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates which are the largest component of the protein structure files. By default, the compression is lossless, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e., BinaryCIF, Foldcomp, PDC, our approach offers superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of multicore architecture of current central processing units and operates with speeds about 1 GB/s. The presence of C++ API further increases the usability of the presented method.Availability and implementationThe source code of ProteStAr is available athttps://github.com/refresh-bio/protestar.
Identifying Clusters in Graph Representations of Genomes
Authors: Herencsárová Eva , Brejová Broňa 
Published: July 2023

AbstractIn many bioinformatics applications the task is to identify biologically significant locations in an individual genome. In our work, we are interested in finding high-density clusters of such biologically meaningful locations in a graph representation of a pangenome, which is a collection of related genomes. Different formulations of finding such clusters were previously studied for sequences. In this work, we study an extension of this problem for graphs, which we formalize as finding a set of vertex-disjoint paths with a maximum score in a weighted directed graph. We provide a linear-time algorithm for a special class of graphs corresponding to elastic-degenerate strings, one of pangenome representations. We also provide a fixed-parameter tractable algorithm for directed acyclic graphs with a special path decomposition of a limited width.
FocA: A deep learning tool for reliable, near-real-time imaging focus analysis in automated cell assay pipelines
Authors: Winchell Jeff , Comolet Gabriel , Buckley-Herd Geoff , Hutson Dillion , Bose Neeloy , Paull Daniel , Migliori Bianca 
Published: July 2023

AbstractThe increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array®. The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 seconds per 96-well plate, and achieves this result even in heavily downsampled data (∼30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.
Voyager: exploratory single-cell genomics data analysis with geospatial statistics
Authors: Moses Lambda , Einarsson Pétur Helgi , Jackson Kayla , Luebbert Laura , Booeshaghi A. Sina , Antonsson Sindri , Bray Nicolas , Melsted Páll , Pachter Lior 
Published: July 2023

AbstractExploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.
BuDDI:Bulk Deconvolution with Domain Invarianceto predict cell-type-specific perturbations from bulk
Authors: Davidson Natalie R. , Zhang Fan , Greene Casey S. 
Published: July 2023

AbstractWhile single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses.We evaluated BuDDI’s performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.
Predicting gene expression changes upon epigenomic drug treatment
Authors: Agrawal Piyush , Gopalan Vishaka , Hannenhalli Sridhar 
Published: July 2023

AbstractBackgroundTumors are characterized by global changes in epigenetic changes such as DNA methylation and histone modifications that are functionally linked to tumor progression. Accordingly, several drugs targeting the epigenome have been proposed for cancer therapy, notably, histone deacetylase inhibitors (HDACi) such asVorinostatisand DNA methyltransferase inhibitors (DNMTi) such asZebularine. However, a fundamental challenge with such approaches is the lack of genomic specificity, i.e., the transcriptional changes at different genomic loci can be highly variable thus making it difficult to predict the consequences on the global transcriptome and drug response. For instance, treatment with DNMTi may upregulate the expression of not only a tumor suppressor but also an oncogene leading to unintended adverse effect.MethodsGiven the pre-treatment transcriptome and epigenomic profile of a sample, we assessed the extent of predictability of locus-specific changes in gene expression upon treatment with HDACi using machine learning.ResultsWe found that in two cell lines (HCT116 treated with Largazole at 8 doses and RH4 treated with Entinostat at 1µM) where the appropriate data (pre-treatment transcriptome and epigenome as well as post-treatment transcriptome) is available, our model distinguished the post-treatment up versus downregulated genes with high accuracy (up to ROC of 0.89). Furthermore, a model trained on one cell line is applicable to another cell line suggesting generalizability of the model.ConclusionsHere we present a first assessment of the predictability of genome-wide transcriptomic changes upon treatment with HDACi. Lack of appropriate omics data from clinical trials of epigenetic drugs currently hampers the assessment of applicability of our approach in clinical setting.
The relationships between experimental task and questionnaire measures of reward/punishment sensitivity in attention deficit hyperactivity disorder (ADHD): protocol for a scoping review
Authors: Oguchi Mana , Furukawa Emi , Nagahama Naano , Perera Kokila Dilhani , Tripp Gail 
Published: July 2023

AbstractIntroductionOne of the purported underlying causal mechanisms of attention deficit hyperactivity disorder (ADHD) is altered motivational processes. Questionnaires have been used to identify the characteristics of reward and punishment sensitivity in individuals with ADHD. However, these questionnaires were initially developed to measure individual traits related to anxiety (inhibitory) and impulsivity (approach) tendencies or differences in pleasure-seeking. These reward and punishment sensitivity questionnaires are useful but might not capture all relevant aspects of altered motivational processes in ADHD. The proposed scoping review aims to: 1) examine which aspects of hypothesized altered reward and punishment sensitivity correspond to constructs measured by existing questionnaires, 2) characterize the relationships between ADHD symptomatology and reward and punishment sensitivity as measured by existing questionnaires, and 3) evaluate the consistency between the altered reward and punishment sensitivity as measured by existing questionnaires and experimental task performance.Methods and analysisThis scoping review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews and the Joanna Briggs Methodology for Scoping Reviews. Published English language literature will be searched in three electronic databases, with no restriction on the year of publication. Two researchers will independently screen all identified titles/abstracts and review the method sections of the identified papers to confirm their eligibility before proceeding to full-text review and data extraction. Methods, results, and conclusions will be tabulated by research questions. A narrative review, and summary conclusions will be presented. The evidence will be summarized as descriptive data in the Excel table.Ethics and disseminationThis study reviews existing publications with ethical approval in place. Therefore, ethical approval is not required. Review results will be disseminated through academic conferences and peer-reviewed manuscripts. Scoping review results will also inform future research to measure and identify altered motivational processes in ADHD.Strengths and limitations of this studyThis scoping review is the first study to identify which aspects of ADHD reinforcement sensitivity have been measured by existing reward and punishment scales and comprehensively review studies reporting relationships between experimental task results and reward and punishment scales in ADHD. (strengths)This study will be conducted according to PRISMA guidelines for systematic reviews. (strength)Results will be summarized separately for children/adolescents and adults. (strength)This review includes only published peer-reviewed English language studies. (limitation)
The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis
Authors: Smeland Olav B. , Kutrolli Gleda , Bahrami Shahram , Fominykh Vera , Parker Nadine , Hindley Guy F. L. , Rødevand Linn , Jaholkowski Piotr , Tesfaye Markos , Parekh Pravesh , Elvsåshagen Torbjørn , Grotzinger Andrew D. ,   The International Multiple Sclerosis Genetics Consortium (IMSGC),   The International Headache Genetics Consortium (IHGC), Steen Nils Eiel , der Meer Dennis van , O’Connell Kevin S. , Djurovic Srdjan , Dale Anders M. , Shadrin Alexey A. , Frei Oleksandr , Andreassen Ole A. 
Published: July 2023

AbstractWhile neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.
Mucosal and Systemic Immune Correlates of Viral Control following SARS-CoV-2 Infection Challenge in Seronegative Adults
Authors: Wagstaffe Helen R. , Thwaites Ryan S. , Reynaldi Arnold , Sidhu Jasmin K. , McKendry Richard , Ascough Stephanie , Papargyris Loukas , Collins Ashley M. , Xu Jiayun , Lemm Nana-Marie , Siggins Matthew K. , Chain Benny M. , Killingley Ben , Kalinova Mariya , Mann Alex , Catchpole Andrew , Davenport Miles P. , Openshaw Peter J.M. , Chiu Christopher 
Published: July 2023

AbstractHuman infection challenge permits characterisation of the associated immune response in unparalleled depth, enabling evaluation of early pre-symptomatic immune changes and the dynamic immune factors important for viral clearance. Here, 34 healthy young adult volunteers, seronegative to SARS-CoV-2, were inoculated with a D614G-containing pre-Alpha SARS-CoV-2 strain. Nasal and systemic soluble mediator and antibody responses, and peripheral blood T cell and B cell responses were measured by MesoScale Discovery and flow cytometry just before and up to 1 year after intra-nasal inoculation. In the 18 (53%) participants who became infected, both nasal and systemic mediator responses were dominated by interferons (IFN) but with divergent kinetics. T cell activation and proliferation in blood peaked at day 10 in CD4+T cells and day 14 in CD8+T cells, returning to baseline by day 28. Following infection, antigen-specific T cells were largely CD38+Ki67+and displayed central and effector memory phenotypes. T cells contracted after viral clearance with expanded antigen-specific memory T cell populations persisting past day 28. Both mucosal and systemic antibodies became detectable around day 10 but nasal antibodies plateaued after day 14 while circulating antibodies continued to rise. Using piecewise linear regression modelling, viral load related closely to the induction of type I IFN responses, moreover, CD8+T cell responses and early IgA responses were strongly associated with viral clearance. Detailed analysis of innate and adaptive immune responses to primary SARS-CoV-2 infection following human challenge thus revealed the relationship between immune kinetics and viral load as factors associated with resolution of infection.