tetano
Editor, Senior Moderator
J Genet
. 2020;99:80.
Age-related gene expression alterations by SARS-CoV-2 infection contribute to poor prognosis in elderly
Upasana Bhattacharyya[SUP] 1 [/SUP], B K Thelma
Affiliations
Abstract
The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide and with notable heterogeneity in its clinical presentation. Probability of contracting this highly contagious infection is similar across age groups but disease severity and fatality among aged patients with or without comorbidities are reportedly higher. Previous studies suggest that age associated transcriptional changes in lung and immune system results in a proinflammatory state and increased susceptibility to infectious lung diseases. Similarly, SARS-CoV-2 infection could augment ageing-related gene expression alterations resulting in severe outcomes in elderly patients. To identify genes that can potentially increase covid-19 disease severity in ageing people, we compared age associated gene expression changes with disease-associated expression changes in lung/BALF and whole blood obtained from publicly available data. We observed (i) a significant overlap of gene expression profiles of patients' BALF and blood with lung and blood of the healthy group, respectively; (ii) a more pronounced overlap in blood compared to lung; and (iii) a similar overlap between host genes interacting with SARS-CoV-2 and ageing blood transcriptome. Pathway enrichment analysis of overlapping gene sets suggest that infection alters expression of genes already dysregulated in the elderly, which together may lead to poor prognosis. eQTLs in these genes may also confer poor outcome in young patients worsening with age and comorbidities. Further, the pronounced overlap observed in blood may explain clinical symptoms including blood clots, strokes, heart attack, multi-organ failure etc. in severe cases. This model based on a limited patient dataset seems robust and holds promise for testing larger tissue specific datasets from patients with varied severity and across populations.
. 2020;99:80.
Age-related gene expression alterations by SARS-CoV-2 infection contribute to poor prognosis in elderly
Upasana Bhattacharyya[SUP] 1 [/SUP], B K Thelma
Affiliations
- PMID: 33168795
Abstract
The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people worldwide and with notable heterogeneity in its clinical presentation. Probability of contracting this highly contagious infection is similar across age groups but disease severity and fatality among aged patients with or without comorbidities are reportedly higher. Previous studies suggest that age associated transcriptional changes in lung and immune system results in a proinflammatory state and increased susceptibility to infectious lung diseases. Similarly, SARS-CoV-2 infection could augment ageing-related gene expression alterations resulting in severe outcomes in elderly patients. To identify genes that can potentially increase covid-19 disease severity in ageing people, we compared age associated gene expression changes with disease-associated expression changes in lung/BALF and whole blood obtained from publicly available data. We observed (i) a significant overlap of gene expression profiles of patients' BALF and blood with lung and blood of the healthy group, respectively; (ii) a more pronounced overlap in blood compared to lung; and (iii) a similar overlap between host genes interacting with SARS-CoV-2 and ageing blood transcriptome. Pathway enrichment analysis of overlapping gene sets suggest that infection alters expression of genes already dysregulated in the elderly, which together may lead to poor prognosis. eQTLs in these genes may also confer poor outcome in young patients worsening with age and comorbidities. Further, the pronounced overlap observed in blood may explain clinical symptoms including blood clots, strokes, heart attack, multi-organ failure etc. in severe cases. This model based on a limited patient dataset seems robust and holds promise for testing larger tissue specific datasets from patients with varied severity and across populations.