bioRxivMicrobiologyPreprint
Epigenetic liquid biopsies reveal elevated vascular endothelial cell turnover and erythropoiesis in asymptomatic COVID-19 patients
R. Ben Ami, N. Loyfer, E. Cohn, G. Fialkoff, I. Sharkia, N. Bogot, D. Kochan, G. Kalak, A. Jarjoui, C. Chen-Shuali, H. Azulai, H. Barhoum, N. Arish, M. M. Greenberger, D. Velleman, R. Kurd, E. B. Chetrit, D. Bohm, T. Wolak, A. Quteineh, G. Cann, B. Glaser, N. Friedman, T. Kaplan, R. Shemer, , Y. Dor
Abstract
The full spectrum of tissues affected by SARS-CoV-2 infection is crucial for deciphering the heterogenous clinical course of COVID-19. Here, we analyzed DNA methylation and histone modification patterns in circulating chromatin to assess cell type-specific turnover in severe and asymptomatic COVID-19 patients, in relation to clinical outcome. Patients with severe COVID-19 had a massive elevation of circulating cell-free DNA (cfDNA) levels, which originated in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts, suggesting increased cell death or turnover in these tissues. The immune response to infection was reflected by elevated B cell and monocyte/macrophage cfDNA levels, and by evidence of an interferon response in cells prior to cfDNA release. Strikingly, monocyte/macrophage cfDNA levels (but not monocyte counts), as well as lung epithelium cfDNA and vascular endothelial cfDNA, predicted clinical deterioration and duration of hospitalization. Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage. Surprisingly, these patients showed elevated levels of vascular endothelial cell and erythroblast cfDNA, suggesting that sub-clinical vascular and erythrocyte turnover are universal features of COVID-19, independent of disease severity. Epigenetic liquid biopsies provide non-invasive means of monitoring COVID-19 patients, and reveal sub-clinical vascular damage and red blood cell turnover.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- research focus: spatial domain detection
- approach: statistical model
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