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Abstract
Age is a major risk factor for severe coronavirus disease-2019 (COVID-19). Here, we interrogate the transcriptional features and cellular landscape of the aging human lung. By intersecting these age-associated changes with experimental data on SARS-CoV-2, we identify several factors that may contribute to the heightened severity of COVID-19 in older populations. The aging lung is transcriptionally characterized by increased cell adhesion and stress responses, with reduced mitochondria and cellular replication. Deconvolution analysis reveals that the proportions of alveolar type 2 cells, proliferating basal cells, goblet cells, and proliferating natural killer/T cells decrease with age, whereas alveolar fibroblasts, pericytes, airway smooth muscle cells, endothelial cells and IGSF21+ dendritic cells increase with age. Several age-associated genes directly interact with the SARS-CoV-2 proteome. Age-associated genes are also dysregulated by SARS-CoV-2 infection in vitro and in patients with severe COVID-19. These analyses illuminate avenues for further studies on the relationship between age and COVID-19.
Age is one of the strongest risk factors for severe illness from COVID-19. By integrating human lung transcriptomes with experimental data on SARS-CoV-2, the authors pinpoint specific age-associated factors that could contribute to the heightened severity of COVID-19 in older populations.
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Details
; Majety Medha 2 ; Chen, Sidi 3
1 Yale University School of Medicine, Department of Genetics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Systems Biology Institute, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Center for Cancer Systems Biology, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, M.D.-Ph.D. Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
2 Yale University School of Medicine, Department of Genetics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Systems Biology Institute, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Center for Cancer Systems Biology, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, The College, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
3 Yale University School of Medicine, Department of Genetics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Systems Biology Institute, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Center for Cancer Systems Biology, West Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, M.D.-Ph.D. Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Immunobiology Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Molecular Cell Biology, Genetics, and Development Program, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University, Combined Program in the Biological and Biomedical Sciences, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Yale Comprehensive Cancer Center, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Yale Stem Cell Center, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Yale Liver Center, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Yale Center for Biomedical Data Science, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale University School of Medicine, Yale Center for RNA Science and Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)




