SARS-CoV-2 Nsp13 helicase modulates miR-146a-mediated signaling pathways
Bibliographic record
Abstract
Despite the successful development of vaccines and antiviral therapeutics against SARS-CoV-2, its tendency to mutate rapidly has emphasized the need for continued research to better understand this virus's mechanism of pathogenesis and interactions with host signaling pathways. In this study, we sought to explore how the SARS-CoV-2 non-structural protein 13 (Nsp13) helicase, a highly conserved coronavirus protein that is essential for viral replication, influences host biological and cellular processes. Global transcriptomic analyses of Nsp13-transfected A549 cells identified changes in pathways involved in post-transcriptional gene silencing and translational repression by RNA, such as microRNAs (miRNAs). Upon further bioinformatic analyses, we identified miR-146a-mediated signaling pathways to be of interest as this miRNA has been previously linked to the regulation of host inflammation and innate immune responses. We found that miR-146a was induced in Nsp13-transfected cells and observed a corresponding decrease in the gene expression of two miR-146a targets, TRAF6 and IRAK1, which are important upstream regulators of NF-kB activation and IFN signaling. These results suggest that Nsp13-induced miR-146a signaling cascades, namely NF-kB activation and SMAD4 signaling, may provide valuable insight for the development of novel antiviral therapeutics against COVID-19 variants.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".