PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.22.307421v1?rss=1

Authors: Liu, G., Li, Y., Zhang, T., Li, M., Li, S., Liu, S., Xu, M., Zhen, S., Shi, W., Li, W.

Abstract:

Type 2 diabetes, characterized by malfunction of pancreatic {beta} cells, is affected by multiple cues including sex differences. Nevertheless, mechanisms of sex differences in type 2 diabetes susceptibility and pathogenesis remain unclear. Using single-cell RNA sequencing (scRNA-seq) technology, we showed that sexual dimorphism of transcriptome exists in mouse {beta} cells. Our analysis further revealed the existence of sex-dependent type 2 diabetes altered genes in high fat diet induced T2D model, suggesting divergences in pathological mechanisms of type 2 diabetes between sexes. Our results indicated that sex should be taken into consideration when treating diabetes, which was further validated by the sex-matched and sex-mismatched islet transplantation in mice. Compared to sex-matched transplants, sex-mismatched transplants showed downregulation of genes involved in the longevity regulating pathway in {beta} cells and led to impaired glucose tolerance in diabetic mice. Taken together, our findings could advance current understanding of type 2 diabetes pathogenesis with sexually dimorphic perspectives and provide new insights to the development of precision medicine.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.22.307967v1?rss=1

Authors: Marchal, C., Singh, N., Corso-Diaz, X., Swaroop, A.

Abstract:

Three-dimensional (3D) conformation of the chromatin is crucial to stringently regulate gene expression patterns and DNA replication in a cell-type specific manner. HiC is a key technique for measuring 3D chromatin interactions genome wide. Estimating and predicting the resolution of a library is an essential step in any HiC experimental design. Here, we present the mathematical concepts to estimate the resolution of a library and predict whether deeper sequencing would enhance the resolution. We have developed HiCRes, a docker pipeline, by applying these concepts to human and mouse HiC libraries.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.305847v1?rss=1

Authors: Hasegawa, K., Yamaguchi, Y., Pengjam, Y.

Abstract:

Pyruvic acid therapy is used for various diseases, but the therapeutic effect decreases at high doses. The molecular mechanism of high-dose pyruvate is not well understood. The purpose of this study was to identify the effects of high dose pyruvate addition on skeletal muscle using C2C12. The gene expression profile for the GSE5497 dataset was taken from the Gene Expression Omnibus database. GEO2R was used to identify specifically expressed genes (DEGs). Functional analysis and pathway enrichment analysis of DEG were performed using the DAVID database. The protein-protein interaction (PPI) network was built in the STRING database and visualized using Cytoscape. GO analysis showed that up-regulated DEG was primarily involved in angiogenesis, cell adhesion, and inflammatory response. We also showed that down-regulated DEG is involved in the regulation of muscle contraction, skeletal muscle fiber development. In addition, the upregulated KEGG pathway of DEG included Rheumatoid arthritis, Chemokine signaling pathway, and Cytokine-cytokine receptor interaction. Downregulated DEG included Calcium signaling pathway, hypertrophic cardiomyopathy (HCM), Dilated cardiomyopathy, Neuroactive ligand-receptor interaction, and Cardiac muscle contraction. Further, analysis of two modules selected from the PPI network showed that high-dose pyruvate exposure to C2C12 was primarily associated with muscle contraction, muscle organ morphogenesis, leukocyte chemotaxis, and chemokine activity. In conclusion, High-dose pyruvate treatment of C2C12 was found to be associated with an increased inflammatory response and decreased skeletal muscle formation. However, further studies are still needed to verify the function of these molecules at high doses of pyruvate.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.305516v1?rss=1

Authors: Nikolic, A., Signal, D., Ellestad, K., Johnston, M., Gillmor, A., Morrissy, S., Chan, J. A., Neri, P., Bahlis, N., Gallo, M.

Abstract:

The single-cell assay for transposase accessible chromatin (scATAC) is an invaluable asset to profile the epigenomic landscape of heterogeneous cells populations in complex tissue and organ systems. However, the lack of tools that enable the use of scATAC data to discriminate between malignant and non-malignant cells has prevented the widespread application of this technique to clinical tumor samples. Here we describe Copy-scAT, a new computational tool that uses scATAC data to infer both large-scale and focal copy number alterations. Copy-scAT can call both clonal and subclonal copy number changes, allowing identification of cancer cells and cell populations that putatively constitute the tumor microenvironment. Copy-scAT therefore enables downstream chromatin accessibility studies that focus on malignant or non-malignant cell populations in clinical samples that are profiled by scATAC.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.307074v1?rss=1

Authors: Cristian, G. I.

Abstract:

Studying viral antibody neutralization data is a complex task and knowledge relating to the effectiveness of a particular antibody to particular strains of viruses cannot easily be extrapolated to other new, related strains. We have developed ANNAVP, a software that uses neural networks to model viral protein data. ANNAVP uses supervised or unsupervised learning and viral protein sequence data to form correlations between different strains and to predict the effectiveness of neutralizing agents against them.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.306332v1?rss=1

Authors: Yuan, Y., Bar-Joseph, Z.

Abstract:

Motivation: Time-course gene expression data has been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offers several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, this data also raises new computational challenges. Results: Using a novel encoding for scRNA-Seq expression data we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as a 3D tensor and train convolutional and recurrent neural networks (CNN and RNN) for predicting interactions. We tested our Time-course Deep Learning (TDL) models on five different time series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time series scRNA-Seq data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.305698v1?rss=1

Authors: Sahoo, D., Katkar, G. D., Khandelwal, S., Behroozikhah, M., Claire, A., Castillo, V., Tindle, C., Fuller, M., Taheri, S., Rogers, T. F., Beutler, N., Ramirez, S., Rawlings, S. A., Pretorius, V., Smith, D., Burton, D. R., Alexander, L. C., Duran, J. M., Crotty, S., Dan, J. M., Das, S., Ghosh, P.

Abstract:

We sought to define the host immune response, a.k.a, the cytokine storm that has been implicated in fatal COVID-19 using an AI-based approach. Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a seed gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. Surprisingly, this 166-gene signature was conserved in all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViPand severe-ViPsignatures, respectively. The ViPsignatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determines severity/fatality. Precise therapeutic goals were formulated and subsequently validated in high-dose SARS-CoV-2-challenged hamsters using neutralizing antibodies that abrogate SARS-CoV-2/ACE2 engagement. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine tracked with disease severity. Thus, the ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.304071v1?rss=1

Authors: Narasimhan, R. L., Throm, A. A., Koshy, J. J., Saldanha, K. M. R., Chandranpillai, H., Lal, R. D., Kumravat, M., Kumar K M, A., Batra, A., Zhong, F., Liu, J.

Abstract:

Inflammatory bowel disease (IBD) is a complex, chronic inflammatory disease of the gastrointestinal tract with subtypes Crohn's disease (CD) and ulcerative colitis (UC). While evidence indicates IBD is characterized by alterations in the composition and abundance of the intestinal microbiome, the challenge remains to specify bacterial species and their metabolites associated with IBD pathogenesis. By the integration of microbiome multi-omics data and computational methods, we provide analyses and methods for the first time to identify microbiome species and their metabolites that are associated with the human intestine mucosal immune response in patients with CD and UC at a systems level. First, we identified seven gut bacterial species and seventeen metabolites that are significantly associated with Th17 cellular differentiation and immunity in patients with active CD by comparing with those obtained in inactive CD and non-IBD controls. The seven species are Ruminococcus gnavus, Escherichia coli, Lachnospiraceae bacterium, Clostridium hathewayi, Bacteroides faecis, Bacteroides vulgatus, and Akkermansia muciniphila, and a few associated metabolites include the secondary bile acid lithocholate and three short-chain fatty acids (SCFAs): propionate, butyrate, and caproate. We next systematically characterized potential mechanistic relationships between the Th17-involved metabolites and bacterial species and further performed differential abundance analysis for both microbiome species and their metabolites in CD and UC relative to non-IBD controls with their metagenomic and metabolomic data. Based on the deconvolution of immune cell compositions from host intestinal bulk RNA-seq, we investigated changes in immune cell composition and abundance in CD and UC in comparison to non-IBD controls. Finally, we further extended our species and metabolite associations with immune cells from Th17 and Th2 cells to B cells, plasma B cells, CD4+ T cells, and CD8+ T cells. While a set of associations of immune cells with bacterial species and metabolites was supported by published evidence, the new findings in this work will help to furthering our understanding of immune responses and pathogenesis in IBD.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.304519v1?rss=1

Authors: Lau, K. A., Goncalves da Silva, A., Ballard, S. A., Theis, T., Gray, J., Rawlinson, W. D.

Abstract:

The adoption of whole genome sequencing (WGS) data over the past decade for pathogen surveillance, and decision-making for infectious diseases has rapidly transformed the landscape of clinical microbiology and public health. However, for successful transition to routine use of these techniques, it is crucial to ensure the WGS data generated meet defined quality standards for pathogen identification, typing, antimicrobial resistance detection and surveillance. Further, the ongoing development of these standards will ensure that the bioinformatic processes are capable of accurately identifying and characterising organisms of interest, and thereby facilitate the integration of WGS into routine clinical and public health laboratory setting. A pilot proficiency testing (PT) program for WGS of infectious agents was developed to facilitate widely applicable standardisation and benchmarking standards for WGS across a range of laboratories. The PT participating laboratories were required to generate WGS data from two bacterial isolates, and submit the raw data for independent bioinformatics analysis, as well as analyse the data with their own processes and answer relevant questions about the data. Overall, laboratories used a diverse range of bioinformatics tools and could generate and analyse high-quality data, either meeting or exceeding the minimum requirements. This pilot has provided valuable insight into the current state of genomics in clinical microbiology and public health laboratories across Australia. It will provide a baseline guide for the standardisation of WGS and enable the development of a PT program that allows an ongoing performance benchmark for accreditation of WGS-based test processes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.21.306720v1?rss=1

Authors: Dolan, M. E., Hill, D. P., Mukherjee, G., McAndrews, M. S., Chesler, E. J., Blake, J. A.

Abstract:

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

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