Qing Lan, Roel Vermeulen, Mohammad Rahman, Yufei Dai, Wei Hu, Brooklynn McNeil Irving, Xiangping Lin, Batel Blechter, Dianzhi Ren Chaoyang, Huawei Duan, Jason Wong, Yong Niu, Jun Xu, Wei Fu Chaoyang, Kees Meliefste, Dean Hosgood, Meng Ye, Xiaowei Jia, Tao Meng, Ping Bin, Debra Silverman, Yuxin Zheng, Nathaniel Rothman, Douglas Walker
{"title":"O-077 EXPOSOME CHARACTERIZATION OF DIESEL ENGINE EXHAUST EXPOSURE","authors":"Qing Lan, Roel Vermeulen, Mohammad Rahman, Yufei Dai, Wei Hu, Brooklynn McNeil Irving, Xiangping Lin, Batel Blechter, Dianzhi Ren Chaoyang, Huawei Duan, Jason Wong, Yong Niu, Jun Xu, Wei Fu Chaoyang, Kees Meliefste, Dean Hosgood, Meng Ye, Xiaowei Jia, Tao Meng, Ping Bin, Debra Silverman, Yuxin Zheng, Nathaniel Rothman, Douglas Walker","doi":"10.1093/occmed/kqae023.0598","DOIUrl":null,"url":null,"abstract":"Introduction Exposure to diesel engine exhaust (DEE) is associated with increased lung cancer risk; however, underlying molecular mechanisms remain unclear. We apply an exposome approach to characterize early biological effects of occupational DEE exposure. Methods Plasma samples from 54 diesel engine factory workers and 55 non-exposed control workers were characterized using an integrated exposome platform that combines untargeted gas chromatography (GC-) and liquid chromatography (LC-) with high-resolution mass spectrometry (HRMS). Exposome profiles were evaluated by metabolome-wide association study (MWAS) for molecular features associated with DEE exposure and elemental carbon. Potential molecular mechanisms underlying DEE were further evaluated by integrating exposome profiles with plasma proteomics, urine aminopyrenes and mutagenicity, and buccal gene expression analysis. Results GC- and LC-HRMS untargeted analysis detected 68,285 metabolic features. Comparison of DEE-exposed and non-exposed workers identified 772 molecular features associated with exposure at a FDR <5%, including 102 detected using GC-HRMS and 670 detected using LC-HRMS. Molecular networking and annotation identified compounds consistent with DEE exposure, while metabolic pathway enrichment suggest alterations in oxidative stress and endothelial pathways. We conducted a secondary MWAS to link urinary mutagenicity, reflecting systemic exposure to genotoxic/carcinogenic agents, and associated with tumor development and identified 90 molecular features positively associated with urine mutagenicity at FDR<5%. Discussion Integration of exposome profiles with protein and genome-wide gene expression identified biological alterations consistent with many of the key characteristics of carcinogens. Conclusion Integrated exposome characterization of DEE exposure identified novel DEE biomarkers and biological response profiles in a high exposure setting.","PeriodicalId":19452,"journal":{"name":"Occupational medicine","volume":"16 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Occupational medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/occmed/kqae023.0598","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction Exposure to diesel engine exhaust (DEE) is associated with increased lung cancer risk; however, underlying molecular mechanisms remain unclear. We apply an exposome approach to characterize early biological effects of occupational DEE exposure. Methods Plasma samples from 54 diesel engine factory workers and 55 non-exposed control workers were characterized using an integrated exposome platform that combines untargeted gas chromatography (GC-) and liquid chromatography (LC-) with high-resolution mass spectrometry (HRMS). Exposome profiles were evaluated by metabolome-wide association study (MWAS) for molecular features associated with DEE exposure and elemental carbon. Potential molecular mechanisms underlying DEE were further evaluated by integrating exposome profiles with plasma proteomics, urine aminopyrenes and mutagenicity, and buccal gene expression analysis. Results GC- and LC-HRMS untargeted analysis detected 68,285 metabolic features. Comparison of DEE-exposed and non-exposed workers identified 772 molecular features associated with exposure at a FDR <5%, including 102 detected using GC-HRMS and 670 detected using LC-HRMS. Molecular networking and annotation identified compounds consistent with DEE exposure, while metabolic pathway enrichment suggest alterations in oxidative stress and endothelial pathways. We conducted a secondary MWAS to link urinary mutagenicity, reflecting systemic exposure to genotoxic/carcinogenic agents, and associated with tumor development and identified 90 molecular features positively associated with urine mutagenicity at FDR<5%. Discussion Integration of exposome profiles with protein and genome-wide gene expression identified biological alterations consistent with many of the key characteristics of carcinogens. Conclusion Integrated exposome characterization of DEE exposure identified novel DEE biomarkers and biological response profiles in a high exposure setting.