Thioredoxin-interacting Protein Promotes Islet Amyloid Polypeptide Expression through miR-124a and FoxA2
Bibliographic record
Abstract
Thioredoxin-interacting protein (TXNIP) is up-regulated by glucose and diabetes and plays a critical role in glucotoxicity, inflammation, and beta-cell apoptosis, whereas we have found that TXNIP deficiency protects against diabetes. Interestingly, human islet amyloid polypeptide (IAPP) is also induced by glucose, aggregates into insoluble amyloid fibrils found in islets of most individuals with type 2 diabetes and promotes inflammation and beta-cell cytotoxicity. However, so far no connection between TXNIP and IAPP signaling had been reported. Using TXNIP gain and loss of function experiments, INS-1 beta-cells and beta-cell-specific Txnip knock-out mice, we now found that TXNIP regulates IAPP expression. Promoter analyses and chromatin-immunoprecipitation assays further demonstrated that TXNIP increases IAPP expression at the transcriptional level, and we discovered that TXNIP-induced FoxA2 (forkhead box A2) transcription factor expression was conferring this effect by promoting FoxA2 enrichment at the proximal FoxA2 site in the IAPP promoter. Moreover, we found that TXNIP down-regulates miR-124a expression, a microRNA known to directly target FoxA2. Indeed, miR-124a overexpression led to decreased FoxA2 expression and IAPP promoter occupancy and to a significant reduction in IAPP mRNA and protein expression and also effectively inhibited TXNIP-induced IAPP expression. Thus, our studies have identified a novel TXNIP/miR-124a/FoxA2/IAPP signaling cascade linking the critical beta-cell signaling pathways of TXNIP and IAPP and thereby provide new mechanistic insight into an important aspect of transcriptional regulation and beta-cell biology.
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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".