Sex-biased expression of the TLR7 gene in severe COVID-19 patients: Insights from transcriptomics and epigenomics
Bibliographic record
Abstract
There is abundant epidemiological data indicating that the incidence of severe cases of coronavirus disease (COVID-19) is significantly higher in males than females worldwide. Moreover, genetic variation at the X-chromosome linked TLR7 gene has been associated with COVID-19 severity. It has been suggested that the sex-biased incidence of COVID-19 might be related to the fact that TLR7 escapes X-chromosome inactivation during early embryogenesis in females, thus encoding a doble dose of its gene product compared to males. We analyzed TLR7 expression in two acute phase cohorts of COVID-19 patients that used two different technological platforms, one of them in a multi-tissue context including saliva, nasal, and blood samples, and a third cohort that included different post-infection timepoints of long-COVID-19 patients. We additionally explored methylation patterns of TLR7 using epigenomic data from an independent cohort of COVID-19 patients stratified by severity and sex. In line with genome-wide association studies, we provide supportive evidence indicating that TLR7 has altered CpG methylation patterns and it is consistently downregulated in males compared to females in the most severe cases of COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".