bioRxivImmunology
T-cell cellular stress and reticulocyte signatures, but not loss of naive T lymphocytes, characterize severe COVID-19 in older adults
M. Jergovic, M. Watanabe, R. Bhat, C. P. Coplen, S. A. Sonar, R. Wong, Y. Castaneda, L. Davidson, M. Kala, R. C. Wilson, H. L. Twigg, K. Knox, H. E. Erickson, C. C. Weinkauf, C. Bime, B. A. Bixby, S. Parthasarathy, J. M. Mosier, B. J. LaFleur, D. Bhattacharya, J. Z. Nikolich
Abstract
In children and younger adults up to 39 years of age, SARS-CoV-2 usually elicits mild symptoms that resemble the common cold. Disease severity increases with age starting at 30 and reaches astounding mortality rates that are ~330 fold higher in persons above 85 years of age compared to those 18-39 years old. To understand age-specific immune pathobiology of COVID-19 we have analyzed soluble mediators, cellular phenotypes, and transcriptome from over 80 COVID-19 patients of varying ages and disease severity, carefully controlling for age as a variable. We found that reticulocyte numbers and peripheral blood transcriptional signatures robustly correlated with disease severity. By contrast, decreased numbers and proportion of naive T-cells, reported previously as a COVID-19 severity risk factor, were found to be general features of aging and not of COVID-19 severity, as they readily occurred in older participants experiencing only mild or no disease at all. Single-cell transcriptional signatures across age and severity groups showed that severe but not moderate/mild COVID-19 causes cell stress response in different T-cell populations, and some of that stress was unique to old severe participants, suggesting that in severe disease of older adults, these defenders of the organism may be disabled from performing immune protection. These findings shed new light on interactions between age and disease severity in COVID-19.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: statistical model
- approach: experimental assay
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