<scp>miRNA</scp> repertoires of cystic fibrosis <i>ex vivo</i> models highlight <scp>miR‐181a</scp> and <scp>miR</scp>‐101 that regulate <scp>WISP1</scp> expression
Bibliographic record
Abstract
Cystic fibrosis (CF), a genetic disorder, is characterized by chronic lung disease. Small non-coding RNAs are key regulators of gene expression and participate in various processes, which are dysregulated in CF; however, they remain poorly studied. Here, we determined the complete microRNAs (miRNAs) expression pattern in three CF ex vivo models. The miRNA profiles of air-liquid interface cultures of airway epithelia (bronchi, nasal cells, and nasal polyps) samples from patients with CF and non-CF controls were obtained by deep sequencing. Compared with non-CF controls, several miRNAs were deregulated in CF samples; for instance, miR-181a-5p and the miR-449 family were upregulated. Moreover, mature miRNAs often showed variations (i.e. isomiRs) relative to their reference sequence, such as miR-101, suggesting that miRNAs consist of heterogeneous repertoires of multiple isoforms with different effects on gene expression. Analysis of miR-181a-5p and miR-101-3p roles indicated that they regulate the expression of WISP1, a key component of cell proliferation/migration programs. We showed that miR-101 and miR-181a-5p participated in aberrant recapitulation of wound healing programs by controlling WISP1 mRNA and protein level. Our miRNA expression data bring new insights into CF physiopathology and define new potential therapeutic targets in CF. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".