A microRNA network regulates expression and biosynthesis of wild-type and ΔF508 mutant cystic fibrosis transmembrane conductance regulator
Bibliographic record
Abstract
Production of functional proteins requires multiple steps, including gene transcription and posttranslational processing. MicroRNAs (miRNAs) can regulate individual stages of these processes. Despite the importance of the cystic fibrosis transmembrane conductance regulator (CFTR) channel for epithelial anion transport, how its expression is regulated remains uncertain. We discovered that miRNA-138 regulates CFTR expression through its interactions with the transcriptional regulatory protein SIN3A. Treating airway epithelia with an miR-138 mimic increased CFTR mRNA and also enhanced CFTR abundance and transepithelial Cl(-) permeability independent of elevated mRNA levels. An miR-138 anti-miR had the opposite effects. Importantly, miR-138 altered the expression of many genes encoding proteins that associate with CFTR and may influence its biosynthesis. The most common CFTR mutation, ΔF508, causes protein misfolding, protein degradation, and cystic fibrosis. Remarkably, manipulating the miR-138 regulatory network also improved biosynthesis of CFTR-ΔF508 and restored Cl(-) transport to cystic fibrosis airway epithelia. This miRNA-regulated network directs gene expression from the chromosome to the cell membrane, indicating that an individual miRNA can control a cellular process more broadly than recognized previously. This discovery also provides therapeutic avenues for restoring CFTR function to cells affected by the most common cystic fibrosis mutation.
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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".