Large-Scale Multi-omic Analysis of COVID-19 Severity

• Published in: Cell systems Vol. 12; no. 1; pp. 23 - 40.e7
• Main Authors: Overmyer, Katherine A., Shishkova, Evgenia, Miller, Ian J., Balnis, Joseph, Bernstein, Matthew N., Peters-Clarke, Trenton M., Meyer, Jesse G., Quan, Qiuwen, Muehlbauer, Laura K., Trujillo, Edna A., He, Yuchen, Chopra, Amit, Chieng, Hau C., Tiwari, Anupama, Judson, Marc A., Paulson, Brett, Brademan, Dain R., Zhu, Yunyun, Serrano, Lia R., Linke, Vanessa, Drake, Lisa A., Adam, Alejandro P., Schwartz, Bradford S., Singer, Harold A., Swanson, Scott, Mosher, Deane F., Stewart, Ron, Coon, Joshua J., Jaitovich, Ariel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.01.2021; The Authors. Published by Elsevier Inc
• Subjects: Aged; Aged, 80 and over; ARDS; Cohort Studies; COVID-19; COVID-19 - blood; COVID-19 - genetics; COVID-19 - therapy; Female; Gelsolin - blood; Gelsolin - genetics; Humans; ICU; Inflammation Mediators - blood; Machine Learning; Male; mass spectrometry; Middle Aged; multi-omics; Neutrophils - metabolism; outcomes; Principal Component Analysis - methods; RNA sequencing; Sequence Analysis, RNA - methods; severity; Severity of Illness Index; COVID-19; severity; RNA sequencing; outcomes; multi-omics; mass spectrometry; ICU; machine learning; ARDS

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational...