Specific immune-regulatory transcriptional signatures reveal sex and age differences in SARS-CoV-2 infected patients
Bibliographic record
Abstract
ABSTRACT The coronavirus disease 2019 (COVID-19) fatality rate varies in different patient groups. However, the underlying mechanisms that explain this variation are poorly understood. Here, we reanalyzed and integrated public RNAseq datasets of nasopharyngeal swabs and peripheral blood leukocytes from patients with SARS-CoV-2, comparing transcription patterns according to sex, age, and viral load. We found that female and young patients infected by SARS-CoV-2 exhibited a similar transcriptomic pattern with a larger number of total (up- and downregulated) differentially expressed genes (DEGs) compared to males and elderly patients. The transcriptional analysis showed a sex-specific profile with a higher transcriptional modulation of immune response-associated genes in female and young subjects against SARS-CoV-2. The functional clustering was characterized by a highly correlated interferome network of cytokine/chemokine- and neutrophil-associated genes that were enriched both in nasopharyngeal cells and peripheral blood of COVID-19 patients. Females exhibited reduced transcriptional levels of key pro-inflammatory/neutrophil-related genes such as CXCL8 receptors ( CXCR1/CXCR2 ), IL-1β, S100A9, ITGAM , and DBNL compared to males, which correlate with a protective gene expression profile against inflammatory damage. Our data indicate specific immune-regulatory pathways associated with sex and age of patients infected with SARS-CoV-2. These results point out therapeutic targets to reduce morbidity and mortality of COVID-19.
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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 | 0.000 |
| 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".