Persistent alterations of gene expression profiling of human peripheral blood mononuclear cells from smokers

Perinatal Lead Exposure Promotes Sex-Specific Epigenetic Programming of Disease-Relevant Pathways in Mouse Heart

Environmental contaminants such as the metal lead (Pb) are associated with cardiovascular disease, but the underlying molecular mechanisms are poorly understood. In particular, little is known about how exposure to Pb during early development impacts the cardiac epigenome at any point across the life course and potential differences between sexes. In a mouse model of human-relevant perinatal exposures, we utilized RNA-seq and Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) to investigate the effects of Pb exposure during gestation and lactation on gene expression and DNA methylation, respectively, in the hearts of male and female mice at weaning. For ERRBS, we identified differentially methylated CpGs (DMCs) or differentially methylated 1000 bp regions (DMRs) based on a minimum absolute change in methylation of 10% and an FDR < 0.05. For gene expression data, an FDR < 0.05 was considered significant. No individual genes met the FDR cutoff for gene expression; however, we found that Pb exposure leads to significant changes in the expression of gene pathways relevant to cardiovascular development and disease. We further found that Pb promotes sex-specific changes in DNA methylation at hundreds of gene loci (280 DMCs and 99 DMRs in males, 189 DMCs and 121 DMRs in females), and pathway analysis revealed that these CpGs and regions collectively function in embryonic development. In males, differential methylation also occurred at genes related to immune function and metabolism. We then investigated whether genes exhibiting differential methylation at weaning were also differentially methylated in hearts from a cohort of Pb-exposed mice at adulthood. We found that a single gene, Galnt2, showed differential methylation in both sexes and time points. In a human cohort investigating the influence of prenatal Pb exposure on the epigenome, we also observed an inverse association between first trimester Pb concentrations and adolescent blood leukocyte DNA methylation at a locus in GALNT2, suggesting that this gene may represent a biomarker of Pb exposure across species. Together, these data, across two time points in mice and in a human birth cohort study, collectively demonstrate that Pb exposure promotes sex-specific programming of the cardiac epigenome, and provide potential mechanistic insight into how Pb causes cardiovascular disease.

Identification of gene expression predictors of occupational benzene exposure

BACKGROUND

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.

OBJECTIVES

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Gene expression profiles associated with cigarette smoking and moist snuff consumption

Background

Among the different tobacco products that are available on the US market, cigarette smoking is shown to be the most harmful and the effects of cigarette smoking have been well studied. US epidemiological studies indicate that non-combustible tobacco products are less harmful than smoking and yet very limited biological and mechanistic information is available on the effects of these alternative tobacco products. For the first time, we characterized gene expression profiling in PBMCs from moist snuff consumers (MSC), compared with that from consumers of cigarettes (SMK) and non-tobacco consumers (NTC).

Results

Microarray analysis identified 100 differentially expressed genes (DEGs) between the SMK and NTC groups and 46 DEGs between SMK and MSC groups. However, we found no significant differences in gene expression between MSC and NTC. Both hierarchical clustering and principle component analysis revealed that MSC and NTC expression profiles were more similar than to SMK. Random forest classification identified a subset of DEGs which predicted SMK from either NTC or MSC with high accuracy (AUC 0.98).

Conclusions

PMBC gene expression profiles of NTC and MSC are similar to each other, while SMK exhibit distinct profiles with alterations in immune related pathways. In addition to discovering several biomarkers, these studies support further understanding of the biological effects of different tobacco products.

Trial registration

ClinicalTrials.gov. Identifier: NCT01923402. Date of Registration: August 14, 2013. Study was retrospectively registered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3565-1) contains supplementary material, which is available to authorized users.

Menthol Smokers: Metabolomic Profiling and Smoking Behavior

BACKGROUND

The use of menthol in cigarettes and marketing is under consideration for regulation by the FDA. However, the effects of menthol on smoking behavior and carcinogen exposure have been inconclusive. We previously reported metabolomic profiling for cigarette smokers, and novelly identified a menthol-glucuronide (MG) as the most significant metabolite directly related to smoking. Here, MG is studied in relation to smoking behavior and metabolomic profiles.

METHODS

This is a cross-sectional study of 105 smokers who smoked two cigarettes in the laboratory one hour apart. Blood nicotine, MG, and exhaled carbon monoxide (CO) boosts were determined (the difference before and after smoking). Spearman correlation, χ2, and ANCOVA adjusted for gender, race, and cotinine levels for menthol smokers assessed the relationship of MG boost, smoking behavior, and metabolic profiles. Multivariate metabolite characterization using supervised partial least squares-discriminant analysis (PLS-DA) was carried out for the classification of metabolomics profiles.

RESULTS

MG boost was positively correlated with CO boost, nicotine boost, average puff volume, puff duration, and total smoke exposure. Classification using PLS-DA, MG was the top metabolite discriminating metabolome of menthol versus nonmenthol smokers. Among menthol smokers, 42 metabolites were significantly correlated with MG boost, which linked to cellular functions, such as of cell death, survival, and movement.

CONCLUSIONS

Plasma MG boost is a new smoking behavior biomarker that may provide novel information over self-reported use of menthol cigarettes by integrating different smoking measures for understanding smoking behavior and harm of menthol cigarettes.

IMPACT

These results provide insight into the biological effect of menthol smoking. Cancer Epidemiol Biomarkers Prev; 26(1); 51-60. ©2016 AACR.