@article{2fc1da0ac6224f78882dd3d471308f31,
title = "Cellular and plasma proteomic determinants of COVID-19 and non-COVID-19 pulmonary diseases relative to healthy aging",
abstract = "We examine the cellular and soluble determinants of coronavirus disease 2019 (COVID-19) relative to aging by performing mass cytometry in parallel with clinical blood testing and plasma proteomic profiling of ~4,700 proteins from 71 individuals with pulmonary disease and 148 healthy donors (25–80 years old). Distinct cell populations were associated with age (GZMK+CD8+ T cells and CD25low CD4+ T cells) and with COVID-19 (TBET−EOMES− CD4+ T cells, HLA-DR+CD38+ CD8+ T cells and CD27+CD38+ B cells). A unique population of TBET+EOMES+ CD4+ T cells was associated with individuals with COVID-19 who experienced moderate, rather than severe or lethal, disease. Disease severity correlated with blood creatinine and urea nitrogen levels. Proteomics revealed a major impact of age on the disease-associated plasma signatures and highlighted the divergent contribution of hepatocyte and muscle secretomes to COVID-19 plasma proteins. Aging plasma was enriched in matrisome proteins and heart/aorta smooth muscle cell-specific proteins. These findings reveal age-specific and disease-specific changes associated with COVID-19, and potential soluble mediators of the physiological impact of COVID-19.",
author = "Laura Arthur and Ekaterina Esaulova and Mogilenko, {Denis A.} and Petr Tsurinov and Samantha Burdess and Anwesha Laha and Rachel Presti and Brian Goetz and Watson, {Mark A.} and Goss, {Charles W.} and Gurnett, {Christina A.} and Mudd, {Philip A.} and Courtney Beers and O{\textquoteright}Halloran, {Jane A.} and Artyomov, {Maxim N.}",
note = "Funding Information: Data for gene expression analysis in different tissues were acquired from the open database GTEx. The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH), and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this paper were obtained from the GTEx portal in January 2021. Data included RNA-seq performed with TruSeq library construction protocol (non-stranded, polyA+ selection) for 980 donors, 52 tissue subtypes, 17,382 samples and 56,200 genes. To compare samples between each other, original GTEx-acquired read counts were converted to trimmed mean of M values, and then we calculated the median value for each gene for each tissue. To curate tissue-specific gene lists, for each gene, we calculated z-scores on median tissue values across all tissues, and tissues with values higher than three sigmas were accepted as specific for that gene. We mapped 52 resulting lists of tissue-subtype-specific genes on SomaScan, and lists were 4–640 genes long. We only used tissue-subtype-specific lists that had more than 15 genes. Downstream analysis was performed according to the GSEA described above. Funding Information: This research was supported by a grant from Aging Biology Foundation to the laboratory of M.N.A. This study utilized samples obtained from the Washington University School of Medicine{\textquoteright}s COVID-19 biorepository supported by the Barnes Jewish Hospital Foundation, the Siteman Cancer Center (grant P30 CA091842 from the NCI of NIH) and the Washington University Institute of Clinical and Translational Sciences (grant UL1TR002345 from the National Center for Advancing Translational Sciences of the NIH). The content is solely the responsibility of the authors and does not necessarily represent the views of the NIH. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.",
year = "2021",
month = jun,
doi = "10.1038/s43587-021-00067-x",
language = "English",
volume = "1",
pages = "535--549",
journal = "Nature Aging",
issn = "2662-8465",
number = "6",
}