Human microRNA‐30 inhibits influenza virus infection by suppressing the expression of SOCS1, SOCS3, and NEDD4
Bibliographic record
Abstract
Influenza A virus (IAV) has evolved multiple mechanisms to compromise type I interferon (IFN) responses. The antiviral function of IFN is mainly exerted by activating the JAK/STAT signalling and subsequently inducing IFN-stimulated gene (ISG) production. However, the mechanism by which IAV combat the type I IFN signalling pathway is not fully elucidated. In this study, we explored the roles of human microRNAs modulated by IAV infection in type I IFN responses. We demonstrated that microRNA-30 (miR-30) family members were downregulated by IAV infection. Our data showed that the forced expression of miR-30 family members inhibited IAV proliferation, while miR-30 family member inhibitors promoted IAV proliferation. Mechanistically, we found that miR-30 family members targeted and reduced SOCS1 and SOCS3 expression, and thus relieved their inhibiting effects on IFN/JAK/STAT signalling pathway. In addition, miR-30 family members inhibited the expression of NEDD4, a negative regulator of IFITM3, which is important for host defence against influenza viruses. Our findings suggest that IAV utilises a novel strategy to restrain host type I IFN-mediated antiviral immune responses by decreasing the expression of miR-30 family members, and add a new way to understand the mechanism of immune escape caused by influenza viruses.
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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) | 0.001 | 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".