The aryl hydrocarbon receptor in β-cells mediates the effects of TCDD on glucose homeostasis in mice
Bibliographic record
Abstract
Abstract Chronic exposure to persistent organic pollutants (POPs) is associated with increased incidence of type 2 diabetes, hyperglycemia, and poor insulin secretion in humans. Dioxins and dioxin-like compounds are a broad class of POPs that exert cellular toxicity through activation of the aryl hydrocarbon receptor (AhR). We previously showed that a single high-dose injection of 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD, aka dioxin; 20 µg/kg) in vivo reduced fasting and glucose-stimulated plasma insulin levels for up to 6 weeks in male and female mice. TCDD-exposed male mice were also modestly hypoglycemic and had increased insulin sensitivity, whereas TCDD-exposed females were transiently glucose intolerant; whether these effects are driven by AhR activation in β-cells requires investigation. Here we exposed female and male β-cell specific AhR knockout (βAhr KO ) mice and littermate Ins1-Cre genotype controls (βAhr WT ) to a single high dose of 20 µg/kg TCDD and tracked the mice for 6 weeks. We found that deleting AhR from β-cells increased insulin secretion ex vivo in female mouse islets and promoted modest weight gain in male mice under baseline conditions. Importantly, high-dose TCDD exposure impaired glucose homeostasis and β-cell function in βAhr WT mice, but these phenotypes were largely abolished in TCDD-exposed βAhr KO mice. Our study demonstrates that AhR signaling in β-cell is important for regulating baseline β-cell function in female mice and energy homeostasis in male mice. We also show that β-cell AhR signaling largely mediates the effects of TCDD on glucose homeostasis in both female and male mice, suggesting that the effects of TCDD on β-cell function/health are driving metabolic phenotypes in peripheral tissues.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".