Research Article: Identification of gene expression predictors of occupational benzene exposure

Date Published: October 9, 2018

Publisher: Public Library of Science

Author(s): Courtney Schiffman, Cliona M. McHale, Alan E. Hubbard, Luoping Zhang, Reuben Thomas, Roel Vermeulen, Guilan Li, Min Shen, Stephen M. Rappaport, Songnian Yin, Qing Lan, Martyn T. Smith, Nathaniel Rothman, Shyamal D Peddada.

http://doi.org/10.1371/journal.pone.0205427

Abstract

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.

Partial Text

Benzene is a major industrial chemical and an extensive environmental contaminant present in traffic exhaust and cigarette smoke [1, 2]. It induces myelodysplastic syndrome and acute myeloid leukemia [3] and probably causes non-Hodgkin lymphoma [4] and other hematopoietic neoplasms [5–8]. In the U.S., occupational exposure levels are typically below 1 part per million (ppm) [9], the current permissible occupational exposure limit [10]. Development of biomarkers of exposure to benzene, particularly in people exposed below 1 ppm, would be a useful step towards improving risk assessment and minimizing adverse health effects.

In the current study, we validated our previous microarray findings in occupationally exposed subjects using the nCounter platform and used SuperLearner to refine pairs of genes whose expression could predict benzene exposure at low occupational levels (<1 ppm). Finding a small number of highly predictive genes could enable the development of sensitive gene expression assays, e.g. droplet digital PCR, that could be deployed inexpensively in large population studies in small quantities of blood. Using the nCounter platform, we validated the altered expression of 27 mRNAs in individuals occupationally exposed to <1 ppm benzene and identified 3 gene pairs that exclusively predict current benzene exposure. Our approach of using the cutting-edge digital counting method, nCounter, to validate differential gene expression, and of SL to identify predictive genes, has broad applicability in the field of environmental health. Future studies could explore whether the biomarkers are predictive of past benzene exposure, what roles (if any) they play in the toxicity and disease, and whether they can be modulated by factors such as diet to minimize risk.   Source: http://doi.org/10.1371/journal.pone.0205427

 

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