Differential effects of age, sex and dexamethasone therapy on ACE2/TMPRSS2 expression and susceptibility to SARS-CoV-2 infection
Bibliographic record
Abstract
ACE2 and TMPRSS2 are crucial for SARS-CoV-2 entry into the cell. Although ACE2 facilitates viral entry, its loss leads to promoting the devastating clinical symptoms of COVID-19 disease. Thus, enhanced ACE2/TMPRSS2 expression is likely to increase predisposition of target cells to SARS-CoV-2 infection. However, little evidence existed about the biological kinetics of these two enzymes and whether dexamethasone treatment modulates their expression. Here, we show that the expression of ACE2 at the protein and mRNA levels was significantly higher in the lung and heart tissues of neonatal compared to adult mice. However, the expression of TMPRSS2 was developmentally regulated. Our results may introduce a novel concept for the reduced susceptibility of the young to SARS-CoV-2 infection. Moreover, ACE2 expression but not TMPRSS2 was upregulated in adult female lungs compared to their male counterparts. Interestingly, the ACE2 and TMPRSS2 expressions were upregulated by dexamethasone treatment in the lung and heart tissues in both neonatal and adult mice. Furthermore, our findings provide a novel mechanism for the observed differential therapeutic effects of dexamethasone in COVID-19 patients. As such, dexamethasone exhibits different therapeutic effects depending on the disease stage. This was supported by increased ACE2/TMPRSS2 expression and subsequently enhanced infection of normal human bronchial epithelial cells (NHBE) and Vero E6 cells with SARS-CoV-2 once pre-treated with dexamethasone. Therefore, our results suggest that individuals who take dexamethasone for other clinical conditions may become more prone to SARS-CoV-2 infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".