The relationship between cytokine and neutrophil gene network distinguishes SARS-CoV-2–infected patients by sex and age
Bibliographic record
Abstract
The fact that the COVID-19 fatality rate varies by sex and age is poorly understood. Notably, the outcome of SARS-CoV-2 infections mostly depends on the control of cytokine storm and the increasingly recognized pathological role of uncontrolled neutrophil activation. Here, we used an integrative approach with publicly available RNA-Seq data sets of nasopharyngeal swabs and peripheral blood leukocytes from patients with SARS-CoV-2, according to sex and age. Female and young patients infected by SARS-CoV-2 exhibited a larger number of differentially expressed genes (DEGs) compared with male and elderly patients, indicating a stronger immune modulation. Among them, we found an association between upregulated cytokine/chemokine- and downregulated neutrophil-related DEGs. This was correlated with a closer relationship between female and young subjects, while the relationship between male and elderly patients was closer still. The association between these cytokine/chemokines and neutrophil DEGs is marked by a strongly correlated interferome network. Here, female patients exhibited reduced transcriptional levels of key proinflammatory/neutrophil-related genes, such as CXCL8 receptors (CXCR1 and CXCR2), IL-1β, S100A9, ITGAM, and DBNL, compared with male patients. These genes are well known to be protective against inflammatory damage. Therefore, our work suggests specific immune-regulatory pathways associated with sex and age of patients infected with SARS-CoV-2 and provides a possible association between inverse modulation of cytokine/chemokine and neutrophil transcriptional signatures.
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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".