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Record W2766941439 · doi:10.1139/bcb-2017-0149

MMS19 localizes to mitochondria and protects the mitochondrial genome from oxidative damage

2017· article· en· W2766941439 on OpenAlexvenueno aff
Rui Wu, Qunsong Tan, Kaifeng Niu, Yuqi Zhu, Di Wei, Yongliang Zhao, Hongbo Fang

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMitochondrial DNAMitochondrionBiologyGene knockdownCell biologyOxidative phosphorylationOxidative stressInner mitochondrial membraneATP–ADP translocaseCytoplasmMolecular biologyGeneticsGeneBiochemistry

Abstract

fetched live from OpenAlex

MMS19 localizes to the cytoplasmic and nuclear compartments involved in transcription and nucleotide excision repair (NER). However, whether MMS19 localizes to mitochondria, where it plays a role in maintaining mitochondrial genome stability, remains unknown. In this study, we provide the first evidence that MMS19 is localized in the inner membrane of mitochondria and participates in mtDNA oxidative damage repair. MMS19 knockdown led to mitochondrial dysfunctions including decreased mtDNA copy number, diminished mtDNA repair capacity, and elevated levels of mtDNA common deletion after oxidative stress. Immunoprecipitation - mass spectrometry analysis identified that MMS19 interacts with ANT2, a protein associated with mitochondrial ATP metabolism. ANT2 knockdown also resulted in a decreased mtDNA repair capacity after oxidative damage. Our findings suggest that MMS19 plays an essential role in maintaining mitochondrial genome stability.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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 routes1
Has abstractyes

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