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Benzene is a recognized hematotoxin and leukemogen; however, its mechanism of action in humans remain unclear. To provide insight into the processes underlying benzene hematotoxicity, we performed high-resolution metabolomic profiling of plasma collected from a cross-sectional study of 33 healthy workers exposed to benzene (median 8-h time-weighted average exposure; 20 ppma), and 25 unexposed controls in Shanghai, China. Metabolic features associated with benzene were identified using a metabolome-wide association study (MWAS) that tested for the relationship between feature intensity and benzene exposure. MWAS identified 478 mass spectral features associated with benzene exposure at false discovery rate < 20%. Comparison to a list of 13 known benzene metabolites and metabolites predicted using a multi-component biotransformation algorithm showed five metabolites were detected, which included the known metabolites phenol and benzene diolepoxide. Metabolic pathway enrichment identified 41 pathways associated with benzene exposure, with altered pathways including carnitine shuttle, fatty acid metabolism, sulfur amino acid metabolism, glycolysis, gluconeogenesis and branched chain amino acid metabolism. These results suggest disruption to fatty acid uptake, energy metabolism and increased oxidative stress, and point towards pathways related to mitochondrial dysfunction, which has previously been linked to benzene exposure in animal models and human studies. Taken together, these results suggest benzene exposure is associated with disruption of mitochondrial pathways, and provide promising, systems biology biomarkers for risk assessment of benzene-induced hematotoxicity in humans.

Chronic arsenic exposure via drinking water has become a worldwide public health concern. In humans, inorganic arsenic (iAs) is metabolized to monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) mainly mediated by arsenic (+3 oxidation state) methyltransferase (As3MT). We reported recently that N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) was involved in arsenic metabolism, and examined its interactive effect with As3MT on arsenic metabolism in vitro. To further evaluate the interactive effect of N6AMT1 and As3MT on arsenic biomethylation in humans, we conducted a human population-based study including 289 subjects living in rural villages in Inner Mongolia, China, and assessed their urinary arsenic metabolites profiles in relation to genetic polymorphisms and haplotypes of N6AMT1 and As3MT. Five N6AMT1 single nucleotide polymorphisms (SNPs; rs1003671, rs7282257, rs2065266, rs2738966, rs2248501) and the N6AMT1 haplotype 2_GGCCAT were significantly associated with the percentage of iAs (% iAs) in urine (e.g., for rs7282257, mean was 9.62% for TT, 6.73% for AA). Rs1003671 was also in a significant relationship with urinary MMA and DMA (the mean of %MMA was 24.95% for GA, 31.69% for GG; the mean of % DMA was 69.21% for GA, 59.82% for GG). The combined effect of N6AMT1 haplotype 2_GGCCAT and As3MT haplotype 2_GCAC showed consistence with the additive significance of each haplotype on % iAs: the mean was 5.47% and 9.36% for carriers with both and null haplotypes, respectively. Overall, we showed that N6AMT1 genetic polymorphisms were associated with arsenic biomethylation in the Chinese population, and its interaction with As3MT was observed in specific haplotype combinations.

Exposure to Formaldehyde (FA) results in many pathophysiological symptoms, however the underlying mechanisms are not well understood. Given the complicated modulatory role of intestinal microbiota on human health, we hypothesized that interactions between FA and the gut microbiome may account for FA's toxicity. Balb/c mice were allocated randomly to three groups: a control group, a methanol group (0.1 and 0.3 ng/mL MeOH subgroups), and an FA group (1 and 3 ng/mL FA subgroups). Groups of either three or six mice were used for the control or experiment. We applied high-throughput sequencing of 16S ribosomal RNA (rRNA) gene approaches and investigated possible alterations in the composition of mouse gut microbiota induced by FA. Changes in bacterial genera induced by FA exposure were identified. By analyzing KEGG metabolic pathways predicted by PICRUSt software, we also explored the potential metabolic changes, such as alpha-Linolenic acid metabolism and pathways in cancer, associated with FA exposure in mice. To the best of our knowledge, this preliminary study is the first to identify changes in the mouse gut microbiome after FA exposure, and to analyze the relevant potential metabolisms. The limitation of this study: this study is relatively small and needs to be further confirmed through a larger study.

Arsenic is a human toxin and carcinogen commonly found as a contaminant in drinking water. Arsenite (As(III)) is the most toxic inorganic form, but recent evidence indicates that the metabolite monomethylarsonous acid (MMA(III)) is even more toxic. We have used a chemical genomics approach to identify the genes that modulate the cellular toxicity of MMA(III) and As(III) in the yeast Saccharomyces cerevisiae. Functional profiling using homozygous deletion mutants provided evidence of the requirement of highly conserved biological processes in the response against both arsenicals including tubulin folding, DNA double-strand break repair, and chromatin modification. At the equitoxic doses of 150 microM MMA(III) and 300 microM As(III), genes related to glutathione metabolism were essential only for resistance to the former, suggesting a higher potency of MMA(III) to disrupt glutathione metabolism than As(III). Treatments with MMA(III) induced a significant increase in glutathione levels in the wild-type strain, which correlated to the requirement of genes from the sulfur and methionine metabolic pathways and was consistent with the induction of oxidative stress. Based on the relative sensitivity of deletion strains deficient in GSH metabolism and tubulin folding processes, oxidative stress appeared to be the primary mechanism of MMA(III) toxicity whereas secondary to tubulin disruption in the case of As(III). Many of the identified yeast genes have orthologs in humans that could potentially modulate arsenic toxicity in a similar manner as their yeast counterparts.

Although the toxicity of benzene has been linked to its metabolism, the dose-related production of metabolites is not well understood in humans, particularly at low levels of exposure. We investigated unmetabolized benzene in urine (UBz) and all major urinary metabolites [phenol (PH), E,E-muconic acid (MA), hydroquinone (HQ) and catechol (CA)] as well as the minor metabolite, S-phenylmercapturic acid (SPMA), in 250 benzene-exposed workers and 139 control workers in Tianjin, China. Median levels of benzene exposure were approximately 1.2 p.p.m. for exposed workers (interquartile range: 0.53-3.34 p.p.m.) and 0.004 p.p.m. for control workers (interquartile range: 0.002-0.007 p.p.m.). (Exposures of control workers to benzene were predicted from levels of benzene in their urine.) Metabolite production was investigated among groups of 30 workers aggregated by their benzene exposures. We found that the urine concentration of each metabolite was consistently elevated when the group's median benzene exposure was at or above the following air concentrations: 0.2 p.p.m. for MA and SPMA, 0.5 p.p.m. for PH and HQ, and 2 p.p.m. for CA. Dose-related production of the four major metabolites and total metabolites (micromol/l/p.p.m. benzene) declined between 2.5 and 26-fold as group median benzene exposures increased between 0.027 and 15.4 p.p.m. Reductions in metabolite production were most pronounced for CA and PH

We used natural spline (NS) models to investigate nonlinear relationships between levels of benzene metabolites (E,E-muconic acid, S-phenylmercapturic acid, phenol, hydroquinone, and catechol) and benzene exposure among 386 exposed and control workers in Tianjin, China. After adjusting for background levels (estimated from the 60 control subjects with the lowest benzene exposures), expected mean trends of all metabolite levels increased with benzene air concentrations from 0.03 to 88.9 ppm. Molar fractions for phenol, hydroquinone, and E,E-muconic acid changed continuously with increasing air concentrations, suggesting that competing CYP-mediated metabolic pathways favored E,E-muconic acid and hydroquinone below 20 ppm and favored phenol above 20 ppm. Mean trends of dose-specific levels (μmol/L/ppm benzene) of E,E-muconic acid, phenol, hydroquinone, and catechol all decreased with increasing benzene exposure, with an overall 9-fold reduction of total metabolites. Surprisingly, about 90% of the reductions in dose-specific levels occurred below about 3 ppm for each major metabolite. Using generalized linear models with NS–smoothing functions (GLM + NS models), we detected significant effects upon metabolite levels of gender, age, and smoking status. Metabolite levels were about 20% higher in females and decreased between 1% and 2% per year of life. In addition, levels of hydroquinone and catechol were greater in smoking subjects. Overall, our results indicate that benzene metabolism is highly nonlinear with increasing benzene exposure above 0.03 ppm, and that current human toxicokinetic models do not accurately predict benzene metabolism below 3 ppm. Our results also suggest that GLM + NS models are ideal for evaluating nonlinear relationships between environmental exposures and levels of human biomarkers. (Cancer Epidemiol Biomarkers Prev 2006;15(11):2246–52)