Characterization of TCDD-inducible poly-ADP-ribose polymerase (TIPARP/ARTD14) catalytic activity
Bibliographic record
Abstract
Here, we report the biochemical characterization of the mono-ADP-ribosyltransferase 2,3,7,8-tetrachlorodibenzo-p-dioxin poly-ADP-ribose polymerase (TIPARP/ARTD14/PARP7), which is known to repress aryl hydrocarbon receptor (AHR)-dependent transcription. We found that the nuclear localization of TIPARP was dependent on a short N-terminal sequence and its zinc finger domain. Deletion and in vitro ADP-ribosylation studies identified amino acids 400–657 as the minimum catalytically active region, which retained its ability to mono-ADP-ribosylate AHR. However, the ability of TIPARP to ADP-ribosylate and repress AHR in cells was dependent on both its catalytic activity and zinc finger domain. The catalytic activity of TIPARP was resistant to meta-iodobenzylguanidine but sensitive to iodoacetamide and hydroxylamine, implicating cysteines and acidic side chains as ADP-ribosylated target residues. Mass spectrometry identified multiple ADP-ribosylated peptides in TIPARP and AHR. Electron transfer dissociation analysis of the TIPARP peptide 33ITPLKTCFK41 revealed cysteine 39 as a site for mono-ADP-ribosylation. Mutation of cysteine 39 to alanine resulted in a small, but significant, reduction in TIPARP autoribosylation activity, suggesting that additional amino acid residues are modified, but loss of cysteine 39 did not prevent its ability to repress AHR. Our findings characterize the subcellular localization and mono-ADP-ribosyltransferase activity of TIPARP, identify cysteine as a mono-ADP-ribosylated residue targeted by this enzyme, and confirm the TIPARP-dependent mono-ADP-ribosylation of other protein targets, such as AHR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".