Dataset Information

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure.

ABSTRACT:

Objectives

Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of Mn exposure is of interest. Here, global metabolomics is utilized to determine metabolites that differ between groups defined by Mn exposure status, with the goal being to help inform a potential metabolite biomarker of Mn exposure.

Methods

Mn exposed subjects were recruited from a Mn steel foundry and Mn unexposed subjects were recruited from crane operators at a metal recycling facility. Over the course of a work day, each subject wore a personal inhalable dust sampler (IOM), and provided an end of shift urine sample that underwent global metabolomics profiling. Both exposed and unexposed subjects were divided into a training set and demographically similar validation set. Using a two-sided adjusted t-test, relative abundances of all metabolites found were compared between Mn exposed and unexposed training sets, and those with a false discovery rates (FDR) <0.1 were further tested in the validation sets.

Results

Fifteen ions were found to be significantly different (FDR < 0.1) between the exposed and unexposed training sets, and nine of these ions remained significantly different between the exposed and unexposed validation set as well. When further dividing exposure status into 'lower exposure' and 'higher exposure', several of these nine ions exhibited an apparent exposure-response relationship.

Conclusions

This is the first time that metabolomics has been used to distinguish between Mn exposure status in an occupational cohort, though additional work should be done to replicate these findings with a larger cohort. With metabolite identification by name, empirical formula, or pathway, a better understanding of the relationship between Mn exposure and neurotoxic effects could be elucidated, and a potential metabolite biomarker of Mn exposure could be determined.

SUBMITTER: Baker MG

PROVIDER: S-EPMC6075188 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure.

Baker Marissa G MG Simpson Christopher D CD Lin Yvonne S YS Shireman Laura M LM Seixas Noah N

Annals of work exposures and health 20170501 4

<h4>Objectives</h4>Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of Mn exposure is of interest. Here, global metabolomics is utilized to determine metabolites that differ between groups defined by Mn exposure status, with the goal being to help inform a potential metabolite biomarker of Mn exposure.<h4>Methods</h4>Mn exposed subjects were recruited from a Mn steel foundry ...[more]

PMID: 28355443

Similar Datasets

Project description:BackgroundSubstantial research has investigated the adverse effects of traffic-related air pollutants (TRAP) on human health. Convincing associations between TRAP and respiratory and cardiovascular diseases are known, but the underlying biological mechanisms are not well established. High-resolution metabolomics (HRM) is a promising platform for untargeted characterization of molecular mechanisms between TRAP and health indexes.ObjectivesWe examined metabolic perturbations associated with short-term exposures to TRAP, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) among 180 participants of the Center for Health Discovery and Well-Being (CHDWB), a cohort of Emory University-affiliated employees.MethodsA cross-sectional study was conducted on baseline visits of 180 CHDWB participants enrolled during 2008-2012, in whom HRM profiling was determined in plasma samples using liquid chromatography-high-resolution mass spectrometry with positive and negative electrospray ionization (ESI) modes. Ambient pollution concentrations were measured at an ambient monitor near downtown Atlanta. Metabolic perturbations associated with TRAP exposures were assessed following an untargeted metabolome-wide association study (MWAS) framework using feature-specific Tobit regression models, followed by enriched pathway analysis and chemical annotation.ResultsSubjects were predominantly white (76.1%) and non-smokers (95.6%), and all had at least a high school education. In total, 7821 and 4123 metabolic features were extracted from the plasma samples by the negative and positive ESI runs, respectively. There are 3421 features significantly associated with at least one air pollutant by negative ion mode, and 1691 features by positive ion mode. Biological pathways enriched by features associated with the pollutants are primarily involved in nucleic acids damage/repair (e.g., pyrimidine metabolism), nutrient metabolism (e.g., fatty acid metabolism), and acute inflammation (e.g., histidine metabolism and tyrosine metabolism). NO2 and EC were associated most consistently with these pathways. We confirmed the chemical identity of 8 metabolic features in negative ESI and 2 features in positive ESI, including metabolites closely linked to oxidative stress and inflammation, such as histamine, tyrosine, tryptophan, and proline.ConclusionsWe identified a range of ambient pollutants, including components of TRAP, associated with differences in the metabolic phenotype among the cohort of 180 subjects. We found Tobit models to be a robust approach to handle missing data among the metabolic features. The results were encouraging of further use of HRM and MWAS approaches for characterizing molecular mechanisms underlying exposure to TRAP.

Project description:BackgroundHuman and animal exposure to bisphenol A (BPA) has been associated with adverse developmental and reproductive effects. The molecular mechanisms by which BPA exposure exerts its effects are not well-understood, even less known about its analogues bisphenol F (BPF). To address these knowledge gaps, we conducted an untargeted metabolome-wide association study (MWAS) to identify metabolic perturbations associated with BPA/BPF exposures in a pregnant African American cohort.MethodsFrom a subset of study participants enrolled in the Atlanta African American Maternal-Child cohort, we collected both urine samples, for targeted exposure assessment of BPA (N = 230) and BPF (N = 48), and serum samples, for high-resolution metabolomics (HRM) profiling (N = 230), during early pregnancy (8-14 weeks' gestation). Using an established untargeted HRM workflow consisting of MWAS modeling, pathway enrichment analysis, and chemical annotation and confirmation, we investigated the potential metabolic pathways and features associated with BPA/BPF exposures.ResultsThe geometric mean creatinine-adjusted concentrations of urinary BPA and BPF were 0.85 ± 2.58 and 0.70 ± 4.71 µg/g creatinine, respectively. After false positive discovery rate correction at 20 % level, 264 and 733 unique metabolic features were significantly associated with urinary BPA and BPF concentrations, representing 10 and 12 metabolic pathways, respectively. Three metabolic pathways, including steroid hormones biosynthesis, lysine and lipoate metabolism, were significantly associated with both BPA and BPF exposure. Using chemical standards, we have confirmed the chemical identity of 16 metabolites significantly associated with BPA or BPF exposure.ConclusionsOur findings support that exposure to BPA and BPF in pregnant women is associated with the perturbation of aromatic amino acid metabolism, xenobiotics metabolism, steroid biosynthesis, and other amino acid metabolism closely linked to stress responses, inflammation, neural development, reproduction, and weight regulation.

Dataset Information

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure.

Objectives

Methods

Results

Conclusions

Publications

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets