The aryl hydrocarbon receptor reduces LC3II expression and controls endoplasmic reticulum stress
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor whose physiological function is poorly understood. The AhR is highly expressed in barrier organs such as the skin, intestine, and lung. The lungs are continuously exposed to environmental pollutants such as cigarette smoke (CS) that can induce cell death mechanisms such as apoptosis, autophagy, and endoplasmic reticulum (ER) stress. CS also contains toxicants that are AhR ligands. We have previously shown that the AhR protects against apoptosis, but whether the AhR also protects against autophagy or ER stress is not known. Using cigarette smoke extract (CSE) as our in vitro surrogate of environmental tobacco exposure, we first assessed the conversion of LC3I to LC3II, a classic feature of both autophagic and ER stress-mediated cell death pathways. LC3II was elevated in CSE-exposed lung structural cells [mouse lung fibroblasts (MLFs), MLE12 and A549 cells] when AhR was absent. However, this heightened LC3II expression could not be explained by increased expression of key autophagy genes ( Gabarapl1, Becn1, Map1lc3b), upregulation of upstream autophagic machinery (Atg5–12, Atg3), or impaired autophagic flux, suggesting that LC3II may be autophagy independent. This was further supported by the absence of autophagosomes in Ahr−/− lung cells. However, Ahr−/− lung cells had widespread ER dilation, elevated expression of the ER stress markers CHOP and GADD34, and an accumulation of ubiquitinated proteins. These findings collectively illustrate a novel role for the AhR in attenuating ER stress by a mechanism that may be autophagy independent.
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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) | 0.001 | 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".