Metabolic programs define dysfunctional immune responses in severe COVID-19 patients
Author(s) -
Elizabeth A. Thompson,
Katherine Cascino,
Alvaro A. Ordoñez,
Weiqiang Zhou,
Ajay Vaghasia,
Anne HamacherBrady,
Nathan Brady,
ImHong Sun,
Rulin Wang,
Avi Z. Rosenberg,
Michael J. Delannoy,
Richard E. Rothman,
Katherine Fenstermacher,
Lauren Sauer,
Kathryn ShawSaliba,
Evan M. Bloch,
Andrew D. Redd,
Aaron A.R. Tobian,
Maureen Horton,
Kellie N. Smith,
Andrew Pekosz,
Franco R. D’Alessio,
Srinivasan Yegnasubramanian,
Hongkai Ji,
Andrea L. Cox,
Jonathan D. Powell
Publication year - 2021
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2021.108863
Subject(s) - immune system , vdac1 , phenotype , disease , immunology , apoptosis , biology , ards , population , medicine , gene , genetics , lung , environmental health , escherichia coli , bacterial outer membrane
Summary It is unclear why some SARS-CoV-2 patients readily resolve infection while others develop severe disease. By interrogating metabolic programs of immune cells in severe and recovered coronavirus disease 2019 (COVID-19) patients compared with other viral infections, we identify a unique population of T cells. These T cells express increased Voltage-Dependent Anion Channel 1 (VDAC1), accompanied by gene programs and functional characteristics linked to mitochondrial dysfunction and apoptosis. The percentage of these cells increases in elderly patients and correlates with lymphopenia. Importantly, T cell apoptosis is inhibited in vitro by targeting the oligomerization of VDAC1 or blocking caspase activity. We also observe an expansion of myeloid-derived suppressor cells with unique metabolic phenotypes specific to COVID-19, and their presence distinguishes severe from mild disease. Overall, the identification of these metabolic phenotypes provides insight into the dysfunctional immune response in acutely ill COVID-19 patients and provides a means to predict and track disease severity and/or design metabolic therapeutic regimens.
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