Microbial Disruption of Autophagy Alters Expression of the RISC Component AGO2, a Critical Regulator of the miRNA Silencing Pathway
Bibliographic record
Abstract
BACKGROUND: Autophagy is implicated in Crohn's disease (CD) pathogenesis. Recent evidence suggests autophagy regulates the microRNA (miRNA)-induced silencing complex (miRISC). Therefore, autophagy may play a novel role in CD by regulating expression of miRISC, thereby altering miRNA silencing. As microbes associated with CD can alter autophagy, we hypothesized that microbial disruption of autophagy affects the critical miRISC component AGO2. METHODS: AGO2 expression was assessed in epithelial and immune cells, and intestinal organoids with disrupted autophagy. Microarray technology was used to determine the expression of downstream miRNAs in cells with defective autophagy. RESULTS: Increased AGO2 was detected in autophagy-deficient ATG5-/- and ATG16-/- mouse embryonic fibroblast cells (MEFs) in comparison with wild-type MEFs. Chemical agents and VacA toxin, which disrupt autophagy, increased AGO2 expression in MEFs, epithelial cells lines, and human monocytes, respectively. Increased AGO2 was also detected in ATG7-/- intestinal organoids, in comparison with wild-type organoids. Five miRNAs were differentially expressed in autophagy-deficient MEFs. Pathway enrichment analysis of the differentially expressed miRNAs implicated signaling pathways previously associated with CD. CONCLUSIONS: Taken together, our results suggest that autophagy is involved in the regulation of the critical miRISC component AGO2 in epithelial and immune cells and primary intestinal epithelial cells. We propose a mechanism by which autophagy alters miRNA expression, which likely impacts the regulation of CD-associated pathways. Furthermore, as enteric microbial products can manipulate autophagy and AGO2, our findings suggest a novel mechanism by which enteric microbes could influence miRNA to promote disease.
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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".