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Published on: August 15, 2019
MTOR gene variants are associated with severe COVID-19 outcomes: A multicenter study
Bruna Ramos Tosta1, Ingrid Marins de Almeida1, Laiane da Cruz Pena1
1Laboratório de Imunofarmacologia e Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Brazil.
Genetic variants in the mammalian target of rapamycin (mTOR) pathway are linked to severe COVID-19 outcomes. Specific MTOR variants increase the risk of severe disease and death, suggesting a role in COVID-19 pathogenesis.
Area of Science:
- Immunology
- Genetics
- Infectious Diseases
Background:
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can trigger a cytokine storm, a key factor in severe Coronavirus Disease 2019 (COVID-19) pathogenesis.
- The mammalian target of rapamycin (mTOR) pathway regulates innate immune responses, including cytokine production, and is often dysregulated in severe COVID-19.
- Individual genetic variations may influence the severity of the immune response to SARS-CoV-2.
Purpose of the Study:
- To investigate the association between specific genetic variants in the MTOR gene and COVID-19 outcomes.
- To determine if MTOR variants contribute to the severity of COVID-19 infection.
Main Methods:
- Genotyping of MTOR variants (rs1057079 and rs2536) in individuals with severe (n=285) and mild (n=207) COVID-19.
- Logistic regression analysis and Kaplan-Meier survival curves were used to assess the association between variants and outcomes.
- Measurement of plasma levels of tumor necrosis factor (TNF) and interleukin-6 (IL-6), and application of a genotyping risk score.
Main Results:
- The T allele of MTOR rs1057079 was associated with an increased likelihood of severe COVID-19.
- The T allele of MTOR rs2536 was linked to higher IL-6 levels and increased COVID-19 mortality.
- A cumulative risk was observed when these MTOR variants were inherited together.
Conclusions:
- MTOR gene variants may play a pathogenetic role in severe COVID-19.
- These findings could aid in predicting severe outcomes of COVID-19 infection.
- Identifying at-risk individuals may allow for more effective allocation of healthcare resources.
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