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Record W2610020642 · doi:10.1111/febs.14088

Rev7, the regulatory subunit of Polζ, undergoes <scp>UV</scp>‐induced and Cul4‐dependent degradation

2017· article· en· W2610020642 on OpenAlexafffund
Audesh Bhat, Zhoushuai Qin, Guifen Wang, Wangyang Chen, Wei Xiao

Bibliographic record

VenueFEBS Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsProtein subunitDegradation (telecommunications)ChemistryCell biologyBiochemistryBiophysicsFood scienceBiologyGeneComputer science

Abstract

fetched live from OpenAlex

In eukaryotic cells, Rev7 interacts with Rev3 and functions as a regulatory subunit of Polζ, a translesion DNA synthesis (TLS) polymerase. In addition to its role in TLS, mammalian Rev7, also known as Mad2B/Mad2L2, participates in multiple cellular activities including cell cycle progression and double-strand break repair through its interaction with several proteins. Here we show that in mammalian cells, Rev7 undergoes ubiquitin/proteasome-mediated degradation upon UV irradiation in a time-dependent manner. We identified the Rev7 N-terminal destruction box as the degron and Cul4A/B as putative E3 ligases in this process. We also show that the nucleotide excision repair (NER) pathway protein HR23B physically interacts and colocalizes with Rev7 in the nuclear foci after UV irradiation and protects Rev7 from accelerated degradation. Furthermore, a similar Rev7 degradation profile was observed in cells treated with the UV-mimetic agent 4-nitroquinoline 1-oxide but not with cisplatin or camptothecin, suggesting a role of the NER pathway protein(s) in UV-induced Rev7 degradation. These data and the observation that cells deficient in Rev7 are sensitized to UV irradiation while excessive Rev7 protects cells from UV-induced DNA damage provide a new insight into the potential interplay between TLS and NER.

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 score0.049
Threshold uncertainty score0.342

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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