Identification of gene expression predictors of occupational benzene exposure

• Published in: PloS one Vol. 13; no. 10; p. e0205427
• Main Authors: Schiffman, Courtney, McHale, Cliona M., Hubbard, Alan E., Zhang, Luoping, Thomas, Reuben, Vermeulen, Roel, Li, Guilan, Shen, Min, Rappaport, Stephen M., Yin, Songnian, Lan, Qing, Smith, Martyn T., Rothman, Nathaniel
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.10.2018; Public Library of Science (PLoS)
• Subjects: Adult; Aquaporins; Area Under Curve; Arsenic; Benzene; Benzene - toxicity; Biochemistry; Bioindicators; Biology and Life Sciences; Biomarkers; Biomarkers - metabolism; Blood; Cancer; Cigarettes; CLEC5A protein; Coenzyme A Ligases - genetics; Deoxyribonucleic acid; Disease control; DNA; DNA methylation; DNA microarrays; Eosinophil Major Basic Protein - genetics; Eosinophil Major Basic Protein - metabolism; Epidemiology; Epigenetics; Exposure; Factories; Female; Gene expression; Gene Expression - drug effects; Gene sequencing; Genes; Genomics; Humans; Hydrocarbons; Immunity; Immunity, Innate - genetics; Inflammation; Innate immunity; Lectins, C-Type - genetics; Lectins, C-Type - metabolism; Leukocytes (mononuclear); Leukocytes - cytology; Male; Medicine and Health Sciences; Mitochondrial DNA; NF-kappa B p50 Subunit - genetics; Nickel; Occupational Exposure; Occupational health; Oligonucleotide Array Sequence Analysis; Peripheral blood mononuclear cells; Physical Sciences; Proteoglycans - genetics; Proteoglycans - metabolism; Public health; Receptors, Cell Surface - genetics; Receptors, Cell Surface - metabolism; Research and Analysis Methods; RNA, Messenger - metabolism; ROC Curve; Sequence Analysis, RNA; Smoking; Subgroups; Systematic review; Toxicology; Young Adult; Beijing China; California; United States > US; Maryland; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0205427

Previously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells. In the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S. First, we used the nCounter platform to validate altered expression of 30 genes in 33 unexposed controls and 57 subjects exposed to benzene (<1 to ≥5 ppm). Second, we used SuperLearner (SL) to identify a minimal number of genes for which altered expression could predict <1 ppm benzene exposure, in 44 subjects with a mean air benzene level of 0.55±0.248 ppm (minimum 0.203ppm). nCounter and microarray expression levels were highly correlated (coefficients >0.7, p<0.05) for 26 microarray-selected genes. nCounter and mRNA-Seq levels were poorly correlated for 4 mRNA-Seq-selected genes. Using negative binomial regression with adjustment for covariates and multiple testing, we confirmed differential expression of 23 microarray-selected genes in the entire benzene-exposed group, and 27 genes in the <1 ppm-exposed subgroup, compared with the control group. Using SL, we identified 3 pairs of genes that could predict <1 ppm benzene exposure with cross-validated AUC estimates >0.9 (p<0.0001) and were not predictive of other exposures (nickel, arsenic, smoking, stress). The predictive gene pairs are PRG2/CLEC5A, NFKBI/CLEC5A, and ACSL1/CLEC5A. They play roles in innate immunity and inflammatory responses. Using nCounter and SL, we validated the altered expression of multiple mRNAs by benzene and identified gene pairs predictive of exposure to benzene at levels below the US occupational standard of 1ppm.