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Record W4416852973 · doi:10.1038/s44318-025-00656-1

Ubiquitin pathway blockade reveals endogenous ADP-ribosylation marking PARP7 and AHR for degradation

2025· article· en· W4416852973 on OpenAlexaff
Andrii Gorelik, Nina Đukić, Rebecca Smith, Chatrin Chatrin, Osamu Suyari, Jason D. Matthews, Ivan Ahel

Bibliographic record

VenueThe EMBO Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity of Toronto
FundersBiotechnology and Biological Sciences Research CouncilCancer Research UKWellcome Trust
KeywordsUbiquitinUbiquitin ligaseAryl hydrocarbon receptorCrosstalkProtein degradationEndogenyUbiquitin-conjugating enzymeSignal transductionUbiquitin-Protein LigasesF-box protein

Abstract

fetched live from OpenAlex

ADP-ribosylation is an important protein post-translational modification catalysed by a family of PARP enzymes in humans and is involved in DNA damage and immunity among other processes. While poly-ADP-ribosylation has been established as a protein degradation signal in several cases, the role of mono-ADP-ribosylation in protein turnover has remained elusive and mostly relies on overexpression systems. Here, we describe a way to visualise high levels of endogenous ADP-ribosylation by inhibiting the ubiquitin pathway. By blocking ubiquitylation/proteasome, we found that ADP-ribosylation by at least three different PARPs (PARP7, PARP1 and TNKS) can be greatly induced. We discovered that specific activation of the aryl hydrocarbon receptor (AHR) pathway in combination with the ubiquitin pathway inhibition promotes quantitative ADP-ribosylation of PARP7 targets, including the mono-ADP-ribosyltransferase PARP7 itself and AHR. We found that DTX2 is the E3 ligase responsible for degrading ADP-ribosylated PARP7, AHR and other PARP7 substrates. This PARP7-DTX2 crosstalk establishes a mechanism to rapidly shut down AHR-mediated transcription by decreasing its protein levels. Taken together, our findings uncover a paradigm where mono-ADP-ribosylation acts as a degradation mark.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.316
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

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