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Record W2519805400 · doi:10.1371/journal.pbio.1002543

Sam68 Is Required for DNA Damage Responses via Regulating Poly(ADP-ribosyl)ation

2016· article· en· W2519805400 on OpenAlexfundno aff
Xin Sun, Kai Fu, Andrea Hodgson, Eric M. Wier, Matthew G. Wen, Olena Kamenyeva, Xue Xia, Lily Y. Koo, Fengyi Wan

Bibliographic record

VenuePLoS Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthMcGill UniversityNational Institute of General Medical SciencesUniversity of North TexasBeckman Research Institute, City of HopeJohns Hopkins UniversityAmerican Cancer Society
KeywordsPARP1DNA damageBiologyDNA repairPoly ADP ribose polymeraseDNACell biologyADP-ribosylationMolecular biologyPolymeraseBiochemistryEnzymeNAD+ kinase

Abstract

fetched live from OpenAlex

The rapid and robust synthesis of polymers of adenosine diphosphate (ADP)-ribose (PAR) chains, primarily catalyzed by poly(ADP-ribose) polymerase 1 (PARP1), is crucial for cellular responses to DNA damage. However, the precise mechanisms through which PARP1 is activated and PAR is robustly synthesized are not fully understood. Here, we identified Src-associated substrate during mitosis of 68 kDa (Sam68) as a novel signaling molecule in DNA damage responses (DDRs). In the absence of Sam68, DNA damage-triggered PAR production and PAR-dependent DNA repair signaling were dramatically diminished. With serial cellular and biochemical assays, we demonstrated that Sam68 is recruited to and significantly overlaps with PARP1 at DNA lesions and that the interaction between Sam68 and PARP1 is crucial for DNA damage-initiated and PARP1-conferred PAR production. Utilizing cell lines and knockout mice, we illustrated that Sam68-deleted cells and animals are hypersensitive to genotoxicity caused by DNA-damaging agents. Together, our findings suggest that Sam68 plays a crucial role in DDR via regulating DNA damage-initiated PAR production.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.072
GPT teacher head0.355
Teacher spread0.282 · 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

Citations41
Published2016
Admission routes1
Has abstractyes

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