Corticosteroids reduce pathologic interferon responses by downregulating STAT1 in patients with high-risk COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
We do not yet understand exactly how corticosteroids attenuate hyperinflammatory responses and alleviate high-risk coronavirus disease 2019 (COVID-19). We aimed to reveal the molecular mechanisms of hyperinflammation in COVID-19 and the anti-inflammatory effects of corticosteroids in patients with high-risk COVID-19. We performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from three independent COVID-19 cohorts: cohort 1 was used for comparative analysis of high-risk and low-risk COVID-19 (47 PBMC samples from 28 patients), cohort 2 for longitudinal analysis during COVID-19 (57 PBMC samples from 15 patients), and cohort 3 for investigating the effects of corticosteroid treatment in patients with high-risk COVID-19 (55 PBMC samples from 13 patients). PBMC samples from healthy donors (12 PBMC samples from 12 donors) were also included. Cohort 1 revealed a significant increase in the proportion of monocytes expressing the long noncoding RNAs NEAT1 and MALAT1 in high-risk patients. Cohort 2 showed that genes encoding inflammatory chemokines and their receptors were upregulated during aggravation, whereas genes related to angiogenesis were upregulated during improvement. Cohort 3 demonstrated downregulation of interferon-stimulated genes (ISGs), including STAT1, in monocytes after corticosteroid treatment. In particular, unphosphorylated STAT-dependent ISGs enriched in monocytes from lupus patients were selectively downregulated by corticosteroid treatment in patients with high-risk COVID-19. Corticosteroid treatment suppresses pathologic interferon responses in monocytes by downregulating STAT1 in patients with high-risk COVID-19. Our study provides insights into the mechanisms underlying COVID-19 aggravation and improvement and the effects of corticosteroid treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it