Crosstalk between the aryl hydrocarbon receptor and hypoxia-inducible factor 1α pathways in human islet models
Bibliographic record
Abstract
Background We previously showed that 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD – a persistent organic pollutant) activates the aryl hydrocarbon receptor (AHR) in pancreatic islets. The AHR is known to crosstalk with hypoxia-inducible factor 1α (HIF1α) in other cell types but AHR-HIF1α crosstalk has not been previously examined in islet cells. Islet cell function is sensitive to hypoxia; we hypothesize that AHR activation by environmental pollutant(s) will interfere with the HIF1α pathway response in islets, which may be detrimental to islet cell function and survival during periods of hypoxia.Methods We assessed AHR-HIF1α crosstalk by treating human donor islets and stem cell-derived islets (SC-islets) with 10 nM TCDD ± 1% O2 and measuring gene expression of downstream targets of AHR (e.g. CYP1A1) and HIF1α (e.g. HMOX1).Results In SC-islets, co-treatment with TCDD + hypoxia consistently suppressed CYP1A1 induction compared with TCDD treatment alone. In human islets, TCDD + hypoxia co-treatment suppressed CYP1A1 induction, but only in 2 of 6 donors. Both SC-islets and human donor islets displayed hypoxia-mediated suppression of glucose-6-phosphate catalytic subunit 2 (G6PC2) expression. Glucose-stimulated insulin secretion (GSIS) in human donor islets was impaired by hypoxia exposure, but unaffected by TCDD exposure.Conclusion Our study shows consistent AHR-HIF1α crosstalk in SC-islets and variable crosstalk in primary human islets, depending on the donor. In both cell models, hypoxia exposure interfered with activation of the AHR pathway by TCDD but there was no evidence that AHR activation interfered with the HIF1α pathway. In summary, our data show that co-exposure to an environmental pollutant and hypoxia results in molecular crosstalk in islets.
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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 | 0.001 | 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) | 0.002 | 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".