A myeloid program associated with COVID-19 severity is decreased by therapeutic blockade of IL-6 signaling
Bibliographic record
Abstract
Summary Altered myeloid inflammation and lymphopenia are hallmarks of severe infections, including SARS-CoV-2. Here, we identified a gene program, defined by correlation with EN-RAGE ( S100A12 ) gene expression, which was up-regulated in patient airway and blood myeloid cells. The EN-RAGE program was expressed in 7 cohorts and observed in patients with both COVID-19 and acute respiratory distress syndrome (ARDS) from other causes. This program was associated with greater clinical severity and predicted future mechanical ventilation and death. EN-RAGE + myeloid cells express features consistent with suppressor cell functionality, with low HLA-DR and high PD-L1 surface expression and higher expression of T cell-suppressive genes. Sustained EN-RAGE signature expression in airway and blood myeloid cells correlated with clinical severity and increasing expression of T cell dysfunction markers, such as PD-1. IL-6 upregulated many of the severity-associated genes in the EN-RAGE gene program in vitro , along with potential mediators of T cell suppression, such as IL-10. Blockade of IL-6 signaling by tocilizumab in a placebo-controlled clinical trial led to rapid normalization of ENRAGE and T cell gene expression. This identifies IL-6 as a key driver of myeloid dysregulation associated with worse clinical outcomes in COVID-19 patients and provides insights into shared pathophysiological mechanisms in non-COVID-19 ARDS.
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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".