MicroRNA-9-1 Attenuates Influenza A Virus Replication via Targeting Tankyrase 1
Bibliographic record
Abstract
An unstable influenza genome leads to the virus resistance to antiviral drugs that target viral proteins. Thus, identification of host factors essential for virus replication may pave the way to develop novel antiviral therapies. In this study, we investigated the roles of the poly(ADP-ribose) polymerase enzyme, tankyrase 1 (TNKS1), and the endogenous small noncoding RNA, miR-9-1, in influenza A virus (IAV) infection. Increased expression of TNKS1 was observed in IAV-infected human lung epithelial cells and mouse lungs. TNKS1 knockdown by RNA interference repressed influenza viral replication. A screen using TNKS1 3'-untranslation region (3'-UTR) reporter assays and predicted microRNAs identified that miR-9-1 targeted TNKS1. Overexpression of miR-9-1 reduced influenza viral replication in lung epithelial cells as measured by viral mRNA and protein levels as well as virus production. miR-9-1 induced type I interferon production and enhanced the phosphorylation of STAT1 in cell culture. The ectopic expression of miR-9-1 in the lungs of mice by using an adenoviral viral vector enhanced type I interferon response, inhibited viral replication, and reduced susceptibility to IAV infection. Our results indicate that miR-9-1 is an anti-influenza microRNA that targets TNKS1 and enhances cellular antiviral state.
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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) | 0.001 | 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".