Influenza A virus infection dysregulates the expression of microRNA-22 and its targets; CD147 and HDAC4, in epithelium of asthmatics
Bibliographic record
Abstract
BACKGROUND: Specific microRNAs (miRNAs) play essential roles in airway remodeling in asthma. Infection with influenza A virus (IAV) may also magnify pre-existing airway remodeling leading to asthma exacerbation. However, these events remain to be fully defined. We investigated the expression of miRNAs with diverse functions including proliferation (miR-20a), differentiation (miR-22) or innate/adaptive immune responses (miR-132) in primary bronchial epithelial cells (pBECs) of asthmatics following infection with the H1N1 strain of IAV. METHODS: pBECs from subjects (n = 5) with severe asthma and non-asthmatics were cultured as submerged monolayers or at the air-liquid-interface (ALI) conditions and incubated with IAV H1N1 (MOI 5) for up to 24 h. Isolated miRNAs were subjected to Taqman miRNAs assays. We confirmed miRNA targets using a specific mimic and antagomir. Taqman mRNAs assays and immunoblotting were used to assess expression of target genes and proteins, respectively. RESULTS: At baseline, these miRNAs were expressed at the same level in pBECs of asthmatics and non-asthmatics. After 24 h of infection, miR-22 expression increased significantly which was associated with the suppression of CD147 mRNA and HDAC4 mRNA and protein expression in pBECs from non-asthmatics, cultured in ALI. In contrast, miR-22 remained unchanged while CD147 expression increased and HDAC4 remained unaffected in cells from asthmatics. IAV H1N1 mediated increases in SP1 and c-Myc transcription factors may underpin the induction of CD147 in asthmatics. CONCLUSION: The different profile of miR-22 expression in differentiated epithelial cells from non-asthmatics may indicate a self-defense mechanism against aberrant epithelial responses through suppressing CD147 and HDAC4, which is compromised in epithelial cells of asthmatics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".