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Record W2133746023 · doi:10.1186/1710-1492-10-s1-a67

Genomewide DNA methylation dynamics upon diesel exhaust exposure in asthmatics

2014· article· en· W2133746023 on OpenAlexaffvenue
Ruiwei Jiang, Francesco Sava, Michael S. Kobor, Christopher Carlsten

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEpigeneticsDNA methylationDiesel exhaustInnate immune systemImmunologyBiologyGeneMethylationGeneticsGene expressionDiesel fuelImmune systemChemistry

Abstract

fetched live from OpenAlex

Particulate air pollution can induce epigenetic changes and regulate gene expression relevant to the pathophysiology of asthma and allergic diseases. Recently, epidemiologic data suggests that there are observable acute effects of air pollution on peripheral blood DNA methylation levels of genomewide Alu and LINE-1 repeat elements, as well as certain genes involved in oxidative stress response and innate immunity. In this study, we hypothesized that in a controlled exposure setting, diesel exhaust (as a model of particulate air pollution) can induce DNA methylation changes that are detectable on the genomewide level. We recruited 16 subjects with asthma, and/or airway hyper-responsiveness. They were exposed to both diesel exhaust (DE) and filtered air (FA) following a randomized crossover design. Peripheral blood mononuclear cells (PBMCs) were collected at baseline, 6 hours, and 30 hours post-exposure. Methylation at 415,382 CpG sites covering 39,136 genes was measured using the Illumina Infinium 450K bead chip methylation array. To detect effects of the diesel exposure, we conducted a principal component analysis (PCA) , resulting in principal components with common patterns of methylation variation across samples. Using this method we were able to pinpoint one principal component that was significantly associated with diesel exhaust exposure, from which we then selected a subset of probes that possessed that specific pattern of variation. Whole genome analysis using PCA followed by denoising revealed that principal component 22, which accounted for 0.5% of the total variance, was significantly associated with the treatment variable: [DE 6hr and 30hr] versus [DE 0hr, FA 0hr, 6hr, and 30hr] (Figure 1 ). Using loading cutoff of ±6 standard deviations, we found 89 CpG sites to possess the specific pattern of variation (Figure 2 ). These include genes whose expression is associated with exposure to either diesel exhaust or components of diesel exhaust as reported by literature: CASP7, ATCAY, ABCA1, JAK3, CYFIP2, and NOX2 [ 1 – 6 ]. Impact values of each sample in principal component 22. Samples were separated into DEP 6hr and 30hr, followed by FAP 0hr, 6hr and 30hr, and DEP 0hr. Distribution of probe loading values for principal component 22. The values located at ±3 and ±6 standard deviations are marked. These results suggest that short-term exposure to diesel exhaust in a controlled setting has minimal but detectable effects on a genomewide level in PBMCs. We are currently applying mixed effects modeling and intraclass correlation to our identified hits to further substantiate the association of these hit probes to the treatment variable.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 studies0.0000.000
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 head0.029
GPT teacher head0.321
Teacher spread0.291 · 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 designObservational
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

Citations3
Published2014
Admission routes2
Has abstractyes

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