Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19

SUMMARY We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in SARS-CoV-2, by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by ICU admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.

High Prevalence of Post-Traumatic Stress Disorder and Psychological Distress Among Healthcare Workers in COVID-19 Field Hospitals: A Cross-Sectional Study from Vietnam.

Objective: To evaluate the prevalence of post-traumatic stress disorder (PTSD) and other psychological disturbances in the Vietnamese healthcare workers (HCWs) at COVID-19 field hospitals. Methods: A cross-sectional study was conducted using the Impact of Event Scale-Revised (IES-R) to measure PTSD and the Depression Anxiety Stress scale (DASS) to measure other psychological disturbances. The anxiety about COVID-19 was evaluated by the fear of COVID-19 (FOC) scale. A self-developed questionnaire was used to assess work conditions and HCW's major concerns and preparedness. Ordinal logistic regression was used to identify factors associated with the severity of PTSD. A structural modeling equation (SEM) model was fitted to examine the correlation between PTSD and other psychological disturbances. Results: A total of 542 HCWs participated in this study. The prevalence of PTSD was 21.2%, most cases were mild. In the ordinal logistic regression analysis, a history of mental illness, poor preparedness, working in a condition with poor resources, a greater number of concerns, and greater fear of COVID-19 were independently associated with higher severity of PTSD. The prevalence of depression, anxiety, and stress was 46.8%, 38.3%, and 60.2, respectively. In the SEM model, PTSD and psychological disturbances had a strong correlation (standardized covariance 0.86). Conclusion: The prevalence of PTSD and other psychological disturbances was alarmingly high among HCWs who worked at COVID-19 field hospitals. The reported associated factors can be useful for policymakers and health authorities in the preparation for future pandemics.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.

MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.