Differential Expression of microRNAs in Peripheral Blood Mononuclear Cells Identifies Autophagy and TGF-Beta-Related Signatures Aberrantly Expressed in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: MicroRNAs [miRNAs] have emerged as important regulators in inflammatory bowel disease [IBD]. This study investigated differential expression of miRNAs across clinical phenotypes in a well-characterized cohort of IBD patients and healthy controls [HCs]. METHODS: A cohort of Crohn's disease [CD] and ulcerative colitis [UC] patients and HCs was prospectively accrued. Total RNA was extracted from peripheral blood mononuclear cells for all subjects. miRNA expression was measured using NanoString technologies. The subjects were stratified according to disease activity and location. Statistical significance was assessed per miRNA across outcomes and corrected for multiple testing. miRNA regulation of transcription of important results was confirmed in vitro by a dual luciferase reporter assay and autophagy function was evaluated using immunofluorescence imaging of LC3 puncta in HeLa cells. RESULTS: In total, 120 subjects were enrolled. Seventy-four miRNAs were differentially expressed across CD, UC and HCs. Comparing quiescent CD [CDq] with HCs we found ten miRNAs upregulated in CDq. When comparing colonic CD [CCD] to UC, seven miRNAs were upregulated in CCD. The most differentially expressed miRNA in CCD vs UC was miR-874-3p, and we showed its possible utility as a biomarker of differential diagnosis. We showed miR-874-3p targets ATG16L1 and reduces its expression in vitro. An miR-874-3p mimic dysregulates autophagy by a reduction of LC3 in vitro. CONCLUSIONS: We identified unique miRNA signatures expressed in distinct IBD phenotypes. These associations highlight pathways dysregulated by aberrant miRNA expression, revealing possible mechanisms underlying the pathophysiology of IBD, but also suggest a cluster of miRNAs as readily accessible biomarkers to aid in differential diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".