The aryl hydrocarbon receptor regulates the expression of TIPARP and its cis long non-coding RNA, TIPARP-AS1
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor and member of the basic helix-loop-helix-PAS family. AHR is activated by numerous dietary and endogenous compounds that contribute to its regulation of genes in diverse signaling pathways including xenobiotic metabolism, vascular development, immune responses and cell cycle control. However, it is most widely studied for its role in mediating 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxicity. The AHR target gene and mono-ADP-ribosyltransferase, TCDD-inducible poly-ADP-ribose polymerase (TIPARP), was recently shown to be part of a novel negative feedback loop regulating AHR activity through mono-ADP-ribosylation. However, the molecular characterization of how AHR regulates TIPARP remains elusive. Here we show that activated AHR is recruited to the TIPARP promoter, through its binding to two genomic regions that each contain multiple AHR response elements (AHREs), AHR regulates the expression of both TIPARP but also TIPARP-AS1, a long non-coding RNA (lncRNA) which lies upstream of TIPARP exon 1 and is expressed in the opposite orientation. Reporter gene and deletion studies showed that the distal AHRE cluster predominantly regulated TIPARP expression while the proximal cluster regulated TIPARP-AS1. Moreover, time course and promoter activity assays suggest that TIPARP and TIPARP-AS1 work in concert to regulate AHR signaling. Collectively, these data show an added level of complexity in the AHR signaling cascade which involves lncRNAs, whose functions remain poorly understood.
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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 | 0.000 |
| 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".