Back

Sex differences in ACE2, TMPRSS2, and HLA-DQA2 expression in gray matter: Implications for post-COVID-19 neurological symptoms

Kesler, S. R.; Schutz, A. D.; Franco-Rocha, O. Y.; Lewis, K.

2024-11-04 infectious diseases
10.1101/2024.11.04.24316706 medRxiv
Show abstract

COVID-19 has been associated with sex differences in terms of mortality and morbidity. Viral entry proteins including those regulated by ACE2 and TMPRSS2 may play a role, but few studies have been conducted to date and none have examined sex differences in brain expression. Additionally, HLA-DQA2 expression has emerged as a potential moderator of COVID-19 outcomes. Using non-invasive imaging transcriptomics, we measured ACE2, TMPRSS2, and HLA-DQA2 mRNA expression in gray matter volumes using MRI scans obtained from 1,045 healthy adults aged 21-35 years (44% male) imaged prior to the COVID-19 pandemic. ACE2 (t = 9.24, p < 0.001, d = 0.576), TMPRSS2 (t = 24.66, p < 0.001, d = 1.54), and HLA-DQA2 (t = 3.70, p < 0.001, d = 0.231) expression was significantly higher in males compared to females. Bayesian network analysis indicated significant (p < 0.05) positive causal paths from ACE2 to HLA-DQA2 (B = 0.282), ACE2 to TMPRSS2 (B = 0.357), and TMPRSS2 to HLA-DQA1 (B = 0.139) and a negative causal path from sex (males = -1, females = 1) to TMPRSS2 (B = -0.607). Our results have important implications for neurological symptoms associated with COVID-19 and long COVID including complex interactions between viral entry proteins and immune responses, sex-related disparities in symptom reporting and diagnosis, assessment of neurological problems after COVID-19, and potential COVID-19 related syndemics. However, further research is needed to determine gene expression patterns by sex and COVID-19 outcomes, to evaluate additional genes that may influence neurologic status, and studies that include objective assessments of neurologic outcomes.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.