Symposium Session Summaries
Bibliographic record
Abstract
The expression of functional proteins requires multiple steps including gene transcription and post-translational processing.MicroRNAs (miRNA) can regulate individual stages of these processes.We hypothesized that events in CFTR biogenesis are regulated in part by miRNA.We profiled miRNA expression in human airway epithelia to identify candidates for additional studies.MiRNA-138, which has highly conserved target sequences in the transcriptional regulatory gene product SIN3A, emerged as a candidate of interest.SIN3A is known to interact with the DNA binding protein CTCF and serve in the recruitment of transcriptional regulatory proteins to the promoter regions of many genes, including CFTR.We found that miRNA-138 regulates CFTR expression through its interactions with SIN3A.The treatment of human airway epithelia with a miRNA-138 mimic decreased SIN3A and increased CFTR mRNA and also increased CFTR abundance and transepithelial Cl -permeability independently of elevated mRNA levels.A miRNA-138 anti-miR had the opposite effects.The most common CFTR mutation, ΔF508, causes protein misfolding, degradation, and cystic fibrosis.Manipulating the miRNA-138 regulatory network through treatment with a miRNA-138 mimic or siRNA inhibition of SIN3A also improved biosynthesis of CFTR-ΔF508 and restored anion transport to primary human cystic fibrosis airway epithelia.We used microarray analysis following treatment of epithelia with the miRNA-138 mimic or SIN3A siRNA to identify genes with differential expression in response to these interventions.These treatments altered the expression of many genes encoding proteins that can associate with CFTR and might influence its biosynthesis.We intersected these gene sets with a curated list of gene products known to associate with CFTR.Interestingly, 34.5% (125/362) were in our curated CFTR Associated Gene Network, representing a significant enrichment over random expectations.These 125 genes function in multiple cellular compartments and many positively influence CFTR protein expression or stability.Further pathway and GO term analysis of 773 differentially expressed genes using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed a significant enrichment of gene sets in pathways that include heat shock, unfolded protein response, chaperones, protein ubiquitination and proteosomal catabolic processes, and negative regulation of apoptosis.These results further support the conclusion that miRNA-138 enhances CFTR biogenesis and influences the gene expression at multiple steps along its biosynthetic pathway.This novel miRNA-regulated network influences CFTR expression from the chromosome to the cell surface.Thus, an individual miRNA can control a cellular process broader than previously recognized.Ongoing studies aim to further identify and characterize the individual gene products in this miRNA-138/SIN3A regulated gene network.This discovery provides new 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.628 | 0.412 |
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".