Background The coronavirus disease-19 (COVID-19) pandemic has cost lives and economic hardships globally. Various studies have found a number of different factors, such as hyperinflammation and exhausted/suppressed T cell responses to the etiological SARS coronavirus-2 (SARS-CoV-2), being associated with severe COVID-19. However, sieving the causative from associative factors of respiratory dysfunction has remained rudimentary. Methods We postulated that the host responses causative of respiratory dysfunction would track most closely with disease progression and resolution and thus be differentiated from other factors that are statistically associated with but not causative of severe COVID-19. To track the temporal dynamics of the host responses involved, we examined the changes in gene expression in whole blood of 6 severe and 4 non-severe COVID-19 patients across 15 different timepoints spanning the nadir of respiratory function. Findings We found that neutrophil activation but not type I interferon signaling transcripts tracked most closely with disease progression and resolution. Moreover, transcripts encoding for protein phosphorylation, particularly the serine-threonine kinases, many of which have known T cell proliferation and activation functions, were increased after and may thus contribute to the upswing of respiratory function. Notably, these associative genes were targeted by dexamethasone, but not methylprednisolone, which is consistent with efficacy outcomes in clinical trials. Interpretation Our findings suggest neutrophil activation as a critical factor of respiratory dysfunction in COVID-19. Drugs that target this pathway could be potentially repurposed for the treatment of severe COVID-19. Funding This study was sponsored in part by a generous gift from The Hour Glass. EEO and JGL are funded by the National Medical Research Council of Singapore, through the Clinician Scientist Awards awarded by the National Research Foundation of Singapore.

Genetic interaction networks are a powerful approach for functional genomics, and the synthetic lethal interactions that comprise these networks offer a compelling strategy for identifying candidate cancer targets. As the number of published shRNA and CRISPR perturbation screens in cancer cell lines expands, there is an opportunity for integrative analysis that goes further than pairwise synthetic lethality and discovers genetic vulnerabilities of related sets of cell lines. We re-analyze over 100 high-quality, genomescale shRNA screens in human cancer cell lines and derive a quantitative fitness score for each gene that accurately reflects genotype-specific gene essentiality. We identify pairs of genes with correlated essentiality profiles and merge them into a cancer coessentiality network, where shared patterns of genetic vulnerability in cell lines give rise to clusters of functionally related genes in the network. Network clustering discriminates among all three defined subtypes of breast cancer cell lines (basal, luminal,, and further identifies novel subsets of Her2+ and ovarian cancer cells. We demonstrate the utility of the network as a platform for both hypothesis-driven and data-driven discovery of context-specific essential genes and their associated biomarkers.

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Opening and processing gene expression data files in Excel runs into the inadvertent risk of converting gene names to dates. As pathway analysis tools rely on gene symbols to query against pathway databases, the genes that are converted to dates will not be recognized, potentially causing voids in pathway analysis. Molecular pathways related to cell division, exocytosis, cilium assembly, protein ubiquitination and nitric oxide biosynthesis were found to be most affected by Excel auto-conversion. A plausible solution is hence to update these genes and dates to the newly approved gene names as recommended by the HUGO Gene Nomenclature Committee (HGNC), which are resilient to Excel auto-conversion. Herein, we developed a web tool with Streamlit that can convert old gene names and dates back into the new gene names recommended by HGNC. The web app is named Gene Updater, which is open source and can be either hosted locally or at https://share.streamlit.io/kuanrongchan/date-to-gene-converter/main/date_gene_tool.py. Additionally, as Mar-01 and Mar-02 can each be potentially mapped to 2 different gene names, users can assign the date terms to the appropriate gene names within the Gene Updater web tool. This user-friendly web tool ensures that the accuracy and integrity of gene expression data is preserved by minimizing errors in labelling gene names due to Excel auto-conversions.

Gene expression profiling has helped tremendously in the understanding of biological processes and diseases. However, interpreting processed data to gain insights into biological mechanisms remain challenging, especially to the non-bioinformaticians, as many of these data visualization and pathway analysis tools require extensive data formatting. To circumvent these challenges, we developed STAGEs (Static and Temporal Analysis of Gene Expression studies) that provides an interactive visualisation of omics analysis outputs. Users can directly upload data created from Excel spreadsheets and use STAGEs to render volcano plots, differentially expressed genes stacked bar charts, pathway enrichment analysis by Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or customized gene sets, clustergrams and correlation matrices. Moreover, STAGEs takes care of Excel gene to date misconversions, ensuring that every gene is considered for pathway analysis. Output data tables and graphs can be exported, and users can easily customize individual graphs using widgets such as sliders, drop-down menus, text boxes and radio buttons. Collectively, STAGEs is an integrative platform for data analysis, data visualisation and pathway analysis, and is freely available at https://kuanrongchan-stages-stages-vpgh46.streamlitapp.com/. In addition, developers can customise or modify the web tool locally based on our existing codes, which is publicly available at https://github.com/kuanrongchan/STAGES.