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Stawiski, E. W.

Publications and source records attributed to Stawiski, E. W..

2 recordsLinked to original sources

Human ACE2 receptor polymorphisms predict SARS-CoV-2 susceptibility

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease (COVID-19) that has resulted in a global pandemic. It is a highly contagious positive strand RNA virus and its clinical presentation includes severe to critical respiratory disease that appears to be fatal in [~]3-5% of the cases. The viral spike (S) coat protein engages the human angiotensin-converting enzyme2 (ACE2) cell surface protein to invade the host cell. The SARS-CoV-2 S-protein has acquired mutations that increase its affinity to human ACE2 by [~]10-15-fold compared to SARS-CoV S-protein, making it highly infectious. In this study, we assessed if ACE2 polymorphisms might alter host susceptibility to SARS-CoV-2 by affecting the ACE2 S-protein interaction. Our comprehensive analysis of several large genomic datasets that included over 290,000 samples representing >400 population groups identified multiple ACE2 protein-altering variants, some of which mapped to the S-protein-interacting ACE2 surface. Using recently reported structural data and a recent S-protein-interacting synthetic mutant map of ACE2, we have identified natural ACE2 variants that are predicted to alter the virus-host interaction and thereby potentially alter host susceptibility. In particular, human ACE2 variants S19P, I21V, E23K, K26R, T27A, N64K, T92I, Q102P and H378R are predicted to increase susceptibility. The T92I variant, part of a consensus NxS/T N-glycosylation motif, confirmed the role of N90 glycosylation in immunity from non-human CoVs. Other ACE2 variants K31R, N33I, H34R, E35K, E37K, D38V, Y50F, N51S, M62V, K68E, F72V, Y83H, G326E, G352V, D355N, Q388L and D509Y are putative protective variants predicted to show decreased binding to SARS-CoV-2 S-protein. Overall, ACE2 variants are rare, consistent with the lack of selection pressure given the recent history of SARS-CoV epidemics, however, are likely to play an important role in altering susceptibility to CoVs.

genetics↗

SCINA: Semi-Supervised Analysis of Single Cells in silico

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. We reversed this paradigm and developed SCINA, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation-maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. Notably, SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out mouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

bioinformatics↗