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Record W2156130847 · doi:10.1038/srep00275

Particulate matter Air Pollution induces hypermethylation of the p16 promoter Via a mitochondrial ROS-JNK-DNMT1 pathway

2012· article· en· W2156130847 on OpenAlexaff
Saul Soberanes, Ángel González, Daniela Urich, Sergio E. Chiarella, Kathryn A. Radigan, Álvaro Osornio-Vargas, Joy Joseph, Balaraman Kalyanaraman, Karen M. Ridge, Navdeep S. Chandel, Gökhan M. Mutlu, Andrea De Vizcaya‐Ruíz, G. R. Scott Budinger

Bibliographic record

VenueScientific Reports · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Alberta
FundersNational Center for Research ResourcesNational Institute of Environmental Health SciencesNational Institutes of HealthNorthwestern UniversityNational Heart, Lung, and Blood InstituteAmerican Lung Association
KeywordsParticulatesDNA methylationMitochondrial DNACell biologyBiologyGeneticsGene expressionEcologyGene

Abstract

fetched live from OpenAlex

Exposure of human populations to chronically elevated levels of ambient particulate matter air pollution < 2.5 μm in diameter (PM(2.5)) has been associated with an increase in lung cancer incidence. Over 70% of lung cancer cell lines exhibit promoter methylation of the tumor suppressor p16, an epigenetic modification that reduces its expression. We exposed mice to concentrated ambient PM(2.5) via inhalation, 8 hours daily for 3 weeks and exposed primary murine alveolar epithelial cells to daily doses of fine urban PM (5 µg/cm(2)). In both mice and alveolar epithelial cells, PM exposure increased ROS production, expression of the DNA methyltransferase 1 (DNMT1), and methylation of the p16 promoter. In alveolar epithelial cells, increased transcription of DNMT1 and methylation of the p16 promoter were inhibited by a mitochondrially targeted antioxidant and a JNK inhibitor. These findings provide a potential mechanism by which PM exposure increases the risk of lung cancer.

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 score0.073
Threshold uncertainty score0.361

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations96
Published2012
Admission routes1
Has abstractyes

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