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Biology subjects

Saginc, G.

Publications and source records attributed to Saginc, G..

2 recordsLinked to original sources

Lack of evidence of ACE2 expression and replicative infection by SARS-CoV-2 in human endothelial cells

A striking feature of severe COVID-19 is thrombosis in large as well as small vessels of multiple organs. This has led to the assumption that SARS-CoV-2 virus directly infects and damages the vascular endothelium. However, endothelial expression of ACE2, the cellular receptor for SARS-CoV-2, has not been convincingly demonstrated. Interrogating human bulk and single-cell transcriptomic data, we found ACE2 expression in endothelial cells to be extremely low or absent in vivo and not upregulated by exposure to inflammatory agents in vitro. Also, the endothelial chromatin landscape at the ACE2 locus showed presence of repressive and absence of activation marks, suggesting that the gene is inactive in endothelial cells. Finally, we failed to achieve infection and replication of SARS-CoV-2 in cultured human endothelial cells, which were permissive to productive infection by coronavirus 229E that uses CD13 as the receptor. Our data suggest that SARS-Cov-2 is unlikely to infect endothelial cells directly; these findings are consistent with a scenario where endothelial injury is indirectly caused by the infection of neighbouring epithelial cells and/or due to systemic effects mediated by immune cells, platelets, complement activation, and/or proinflammatory cytokines.

pathology↗

Probability-based detection of phosphoproteomic uncertainty reveals rare signaling events driven by oncogenic kinase gene fusion

We describe a novel Bayesian method for estimating protein concentration and phosphorylation site occupancy ratios from mass spectrometry experiments. Our variance model assigns standard deviations to all quantitative ratios, even when only a single peptide is observed, increasing the number of quantifiable observations in a sample compared to conventional methods. We further demonstrate the application of this method using a dataset investigating the impact of the PRKAR1A-RET gene fusion in immortalized thyroid cells.

systems biology↗