HIF-dependent induction of alveolar miR-147b dampens SARS-CoV-2 immune evasion 3751
Bibliographic record
Abstract
Abstract Description Acute respiratory distress syndrome (ARDS) causes morbidity and mortality during SARS-CoV-2 infections. Nevertheless, most patients with SARS-CoV-2 infection recover seamlessly without developing ARDS. Here, we hypothesized a functional role of microRNAs (miRNAs) in endogenous lung protection during SARS-CoV-2-associated ARDS. Screening studies identified miR-147b (hsa-miR-147b-3p or mmu-miR-147-3p) as the lead candidate during murine and human SARS-CoV-2 infections. Functional molecular studies implicate hypoxia-inducible factor 1A (HIF1A) in miR-147b induction. Subsequent loss-and-gain-of-function studies revealed a protective role of alveolar-expressed miR-147b during murine SARS-CoV-2-associated ARDS. Proof-of-principle studies in patients also implicate this pathway during SARS-CoV-2 infection. Finally, we identified SARS-CoV-2 ORF8 as a direct miR-147b target, and silent mutation of the viral miR-147b-binding site within ORF-8 abolished the observed protection. Together, our findings identify a previously unrecognized role of miR-147b in attenuating SARS-CoV-2-associated lung disease by targeting the viral genome. Funding Sources R01HL154720, R01DK122796, R01HL133900, R01HL155950, R01HL169519, T32GM135118; Department of Defense Grant W81XWH2110032; Parker B. Francis Fellowship; American Lung Association Catalyst Award CA-622265. Topic Categories Viral Immunology (VIR)
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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) | 0.004 | 0.001 |
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".