Preventing occludin tight-junction disruption via inhibition of microRNA-193b-5p attenuates viral load and influenza-induced lung injury
Bibliographic record
Abstract
Virus-induced lung injury is associated with loss of pulmonary epithelial-endothelial tight junction integrity. While the alveolar-capillary membrane may be an indirect target of injury, viruses may interact directly and/or indirectly with miRs to augment their replication potential and evade the host antiviral defense system. Here, we expose how the influenza virus (H1N1) capitalizes on host-derived interferon-induced, microRNA (miR)-193b-5p to target occludin and compromise antiviral defenses. Lung biopsies from patients infected with H1N1 revealed increased miR-193b-5p levels, marked reduction in occludin protein, and disruption of the alveolar-capillary barrier. In C57BL/6 mice, the expression of miR-193b-5p increased, and occludin decreased, 5-6 days post-infection with influenza (PR8). Inhibition of miR-193b-5p in primary human bronchial, pulmonary microvascular, and nasal epithelial cells enhanced antiviral responses. miR-193b-deficient mice were resistant to PR8. Knockdown of occludin, both in vitro and in vivo, and overexpression of miR-193b-5p reconstituted susceptibility to viral infection. miR-193b-5p inhibitor mitigated loss of occludin, improved viral clearance, reduced lung edema, and augmented survival in infected mice. Our results elucidate how the innate immune system may be exploited by the influenza virus and how strategies that prevent loss of occludin and preserve tight junction function may limit susceptibility to virus-induced lung injury.
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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".