Crosstalk between the aryl hydrocarbon receptor and hypoxia inducible factor 1α pathways impairs downstream dioxin response in human islet models
Bibliographic record
Abstract
Abstract The incidence of type 2 diabetes (T2D) is increasing globally at a rate that cannot be explained solely by genetic predisposition, diet, or lifestyle. Epidemiology studies report positive associations between exposure to persistent organic pollutants, such as dioxins, and T2D. We previously showed that 2,3,7,8 tetrachlorodibenzo- p -dioxin (TCDD) activates the xenobiotic-sensitive aryl hydrocarbon receptor (AHR) in pancreatic islets. The AHR is known to crosstalk with the hypoxia inducible factor 1α (HIF1α) in hepatocytes but whether this crosstalk occurs in islet cells remains unknown. We assessed AHR-HIF1α pathway crosstalk by treating human donor islets and stem cell-derived islets (SC-islets) with TCDD +/- hypoxia and examined the changes in downstream targets of both AHR (e.g., CYP1A1 ) and HIF1α (e.g., HMOX1 ). SC-islets showed consistent crosstalk between AHR and HIF1α pathways; co-treatment of SC-islets with TCDD + hypoxia robustly suppressed the magnitude of CYP1A1 induction compared with TCDD treatment alone. In human islets, only 2 of 6 donors showing suppressed CYP1A1 induction following TCDD + hypoxia co-treatment. In both SC-islets and human donor islets we observed an unexpected hypoxia-mediated suppression of glucose-6-phosphate catalytic subunit 2 ( G6PC2 ) expression. Our study shows AHR-HIF1α crosstalk occurs in both SC-islets and primary human donor islets, but the response of human islets varied between donors. In both models, the HIF1α pathway dominated over the AHR pathway during TCDD + hypoxia co-treatment. Our study is the first to examine whether AHR-HIF1α crosstalk occurs in islet cells and presents novel data on the impact of hypoxia on G6PC2 gene expression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".