Dynamic relationship between the aryl hydrocarbon receptor and long noncoding RNA balances cellular and toxicological responses
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AhR) is a cytosolic transcription factor activated by endogenous ligands and xenobiotic chemicals. Once the AhR is activated, it translocates to the nucleus, dimerizes with the AhR nuclear translator (ARNT) and binds to xenobiotic response elements (XRE) to promote gene transcription, notably the cytochrome P450 CYP1A1. The AhR not only mediates the toxic effects of environmental chemicals, but also has numerous putative physiological functions. This dichotomy in AhR biology may be related to reciprocal regulation of long non-coding RNA (lncRNA). lncRNA are defined as transcripts more than 200 nucleotides in length that do not encode a protein but are implicated in many physiological processes such as cell differentiation, cell proliferation, and apoptosis. lncRNA are also linked to disease pathogenesis, particularly the development of cancer. Recent studies have revealed that AhR activation by environmental chemicals affects the expression and function of lncRNA. In this article, we provide an overview of AhR signaling pathways activated by diverse ligands and highlight key differences in the putative biological versus toxicological response of AhR activation. We also detail the functions of lncRNA and provide current data on their regulation by the AhR. Finally, we outline how overlap in function between AhR and lncRNA may be one way in which AhR can be both a regulator of endogenous functions but also a mediator of toxicological responses to environmental chemicals. Overall, more research is still needed to fully understand the dynamic interplay between the AhR and lncRNA.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".