COVID-19 and Genetic Variants of Protein Involved in the SARS-CoV-2 Entry into the Host Cells.
Andrea LatiniEmanuele AgoliniAntonio NovelliPaola BorgianiRosalinda GianniniPaolo GravinaAndrea SmarrazzoMario DauriMassimo AndreoniPaola RoglianiSergio BernardiniManuela Helmer-CitterichMichela BiancolellaGiuseppe NovelliPublished in: Genes (2020)
The recent global COVID-19 public health emergency is caused by SARS-CoV-2 infections and can manifest extremely variable clinical symptoms. Host human genetic variability could influence susceptibility and response to infection. It is known that ACE2 acts as a receptor for this pathogen, but the viral entry into the target cell also depends on other proteins. The aim of this study was to investigate the variability of genes coding for these proteins involved in the SARS-CoV-2 entry into the cells. We analyzed 131 COVID-19 patients by exome sequencing and examined the genetic variants of TMPRSS2, PCSK3, DPP4, and BSG genes. In total we identified seventeen variants. In PCSK3 gene, we observed a missense variant (c.893G>A) statistically more frequent compared to the EUR GnomAD reference population and a missense mutation (c.1906A>G) not found in the GnomAD database. In TMPRSS2 gene, we observed a significant difference in the frequency of c.331G>A, c.23G>T, and c.589G>A variant alleles in COVID-19 patients, compared to the corresponding allelic frequency in GnomAD. Genetic variants in these genes could influence the entry of the SARS-CoV-2. These data also support the hypothesis that host genetic variability may contribute to the variability in infection susceptibility and severity.
Keyphrases
- sars cov
- genome wide
- copy number
- public health
- genome wide identification
- induced apoptosis
- respiratory syndrome coronavirus
- dna methylation
- cell cycle arrest
- single cell
- endothelial cells
- genome wide analysis
- intellectual disability
- endoplasmic reticulum stress
- emergency department
- bioinformatics analysis
- healthcare
- angiotensin ii
- gene expression
- oxidative stress
- cell therapy
- machine learning
- electronic health record
- adverse drug
- autism spectrum disorder
- bone marrow
- cell proliferation
- drug induced