Metabolomics and gene expression profiles in association with air pollution exposure mixtures among young adults with asthma
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Résumé
BACKGROUND AND AIM: Air pollution exposure has been shown to increase severity of various disease outcomes including asthma control, however, the underlying biological mechanisms are not well established. In this study, we aim to leverage transcriptomics and metabolomics to identify biological mechanisms of air pollutants exposure. METHODS: In this cross-sectional study, 102 young adults with childhood asthma history, who were participants of Southern California Children’s Health Study, were enrolled in 2012. Whole blood gene expression data was measured with Illumina HumanHT-12 v4 Expression BeadChip, with 20,869 expression signatures included in the analysis. Serum untargeted metabolomics were analyzed using the Metabolon UPLC-MS/MS, and 937 metabolites were confirmed for all samples. Participants’ regional (NO2, O3, PM10, PM2.5) and near-roadway air pollution exposure were based on nearby central monitoring and modelling during one-month and one-year before the study visit. Multi-omics network analysis (R package ‘xMWAS’) was conducted to identify subnetworks that link metabolomics and transcriptomics to specific air pollutants exposure. Joint-pathway analysis based on MetaboAnalyst (McGill University) was performed to identify pathways associated with air pollutants in each subnetwork. Key covariates such as SES, ethnicity, sex, and smoking were adjusted in all analyses. RESULTS:Network analysis found that 357 gene markers, 92 metabolites, and one-year and one-month exposures to 8 air pollutants were clustered into 9 subnetworks. For the subnetwork including PM10 and one-month O3, gene expression markers were enriched in pathways for insulin secretion, antigen processing and presentation. Another subnetwork including PM2.5 and NO2 exposures was inked to altered metabolism of amino acids such as arginine, serine, and aspartic acid. One-year O3 exposure was clustered with metabolites and genes involved in glycerophospholipid metabolism and N-Glycan biosynthesis. CONCLUSIONS:This study demonstrates that exposure to various air pollutants may induce changes in gene expression and metabolomics in individuals with asthma, potentially affecting disease prognosis. KEYWORDS: Air Pollution, Metabolomics, Transcriptomics, Network Analysis, Pathway Analysis
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
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score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle