Computational and Structural Biotechnology JournalGenetic & Genomic MedicinePeer-reviewed journal article
Exploiting Molecular Basis of Age and Gender Differences in Outcomes of SARS-CoV-2 Infections.
Daniele MercatelliiD, Elisabetta Pedace, Pierangelo VeltriiD, Federico Manuel GiorgiiD, Pietro Hiram GuzziiD
Abstract
MotivationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease, 2019; COVID-19) is associated with adverse outcomes in patients. It has been observed that lethality seems to be related to the age of patients. Moreover, it has been demonstrated that ageing causes some modifications at a molecular level. ObjectiveThe study aims to shed out light on a possible link between the increased COVID-19 lethality and the molecular changes that occur in elderly people. MethodsWe considered public datasets on ageing-related genes and their expression at tissue level. We selected interactors that are known to be related to ageing process. Then, we performed a network-based analysis to identify interactors significantly related to both SARS-CoV-2 and ageing. Finally, we investigated changes on the expression level of coding genes at tissue, gender and age level. ResultsWe observed a significant intersection between some SARS-CoV-2 interactors and ageing-related genes suggesting that those genes are particularly affected by COVID-19 infection. Our analysis evidenced that virus infection particularly affects ageing molecular mechanisms centred around proteins EEF2, NPM1, HMGA1, HMGA2, APEX1, CHEK1, PRKDC, and GPX4. We found that HMGA1, and NPM1 have a different expression in lung of males, while HMGA1, APEX1, CHEK1, EEF2, and NPM1 present changes in expression in males due to aging effects. ConclusionOur study generated a mechanistic framework to explaining the correlation between COVID-19 incidence in elderly patients and molecular mechanisms of ageing. This will provide testable hypotheses for future investigation and pharmacological solutions tailored on specific age ranges.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 44
- FWCI
- 3.01
- Field percentile
- top 7%
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