The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure

Baker, Marissa G.; Simpson, Christopher D.; Lin, Yvonne S.; Shireman, Laura M.; Seixas, Noah S.

doi:10.1093/annweh/wxw032

Cited by 27 publications

(16 citation statements)

References 42 publications

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“…subjects recruited from a Mn steel foundry (Baker et al, 2017). This study showed that 9 mass spectral features classified individuals according to Mn exposure status in an occupational cohort, thereby establishing the utility of metabolomics for evaluation of Mn exposures in humans.…”

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confidence: 75%

“…This study showed that 9 mass spectral features classified individuals according to Mn exposure status in an occupational cohort, thereby establishing the utility of metabolomics for evaluation of Mn exposures in humans. This use did not attempt to distinguish adaptive and toxic responses to Mn, however, and the authors recognized challenges due to the bottleneck in identification of the metabolites and metabolic pathways in untargeted metabolomics analyses with mass spectrometry (Baker et al, 2017). In the current study, we used high-resolution metabolomics (HRM) platform which enables measurement of metabolites in most metabolic pathways (Walker et al, 2016).…”

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confidence: 99%

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Metabolomic Responses to Manganese Dose in SH-SY5Y Human Neuroblastoma Cells

Fernandes

Chandler

Liu

et al. 2019

Toxicological Sciences

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Manganese (Mn)-associated neurotoxicity has been well recognized. However, Mn is also an essential nutrient to maintain physiological function. Our previous study of human neuroblastoma SH-SY5Y cells showed that Mn treatment comparable to physiological and toxicological concentrations in human brain resulted in different mitochondrial responses, yet cellular metabolic responses associated with such different outcomes remain uncharacterized. Herein, SH-SY5Y cells were examined for metabolic responses discriminated by physiological and toxicological levels of Mn using high-resolution metabolomics (HRM). Before performing HRM, we examined Mn dose (from 0 to100 lM) and time effects on cell death. Although we did not observe any immediate cell death after 5 h exposure to any of the Mn concentrations assessed (0-100 lM), cell loss was present after a 24-h recovery period in cultures treated with Mn ! 50 lM. Exposure to Mn for 5 h resulted in a wide range of changes in cellular metabolism including amino acids (AA), neurotransmitters, energy, and fatty acids metabolism. Adaptive responses at 10 lM showed increases in neuroprotective AA metabolites (creatine, phosphocreatine, phosphoserine). A 5-h exposure to 100 mM Mn, a time before any cell death occurred, resulted in decreases in energy and fatty acid metabolites (hexose-1,6 bisphosphate, acyl carnitines). The results show that adjustments in AA metabolism occur in response to Mn that does not cause cell death while disruption in energy and fatty acid metabolism occur in response to Mn that results in subsequent cell death. The present study establishes utility for metabolomics analyses to discriminate adaptive and toxic molecular responses in a human in vitro cellular model that could be exploited in evaluation of Mn toxicity.

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confidence: 75%

mentioning

confidence: 99%

Metabolomic Responses to Manganese Dose in SH-SY5Y Human Neuroblastoma Cells

Fernandes

Chandler

Liu

et al. 2019

Toxicological Sciences

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“…Seventeen of the retained studies had a cross-sectional design (21%) [73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89], and twelve were experimental (15%) (i.e., three randomized trials on metabolomic profile alterations [90,91] or clinical health outcomes [92] following administration of the experimental exposure; two open label cross-over trials exploring metabolomic and clinical effects of an experimental (juice) [93,94] or environmental (airborne and grass pollen) intervention [95], and seven experimental non-randomized studies). Five studies were case-control (6%) [94,[96][97][98][99] and three case-control studies nested in cohorts (4%) [100][101][102].…”

Section: Description Of the Selected Articlesmentioning

confidence: 99%

A Scoping Review on the Characteristics of Human Exposome Studies

2019

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Embraced as a breaking through methodological framework, the exposome is accompanied by novel exposure assessment methods and data processing tools or models. However, systematic mapping of the landscape of exposome studies, including their characteristics, components, tools and language has not been done so far. We conducted a scoping review to answer the question: "Which main domains of the human exposome have been included in the literature and which metrics of exposure(s)/ outcome(s) have been used?" We performed a comprehensive search of human studies containing the word "exposom*" and published up to March 8, 2019. We screened 1133 records and 82 studies were included in the analysis. Most studies took place in Europe. Data analysis showed the non-systematic use of the exposome term. Most studies had a longitudinal design (n = 30, 37%), were conducted on adults (n = 40, 51%), and had a clearly defined health outcome in methodology (n = 48, 61%). Omics tools, such as metabolomics were used in 38 studies (49%), while environment-wide association analysis was used in 9 studies (11%). Thirty-seven (48%) studies included all three exposome domains (general external, specific external and internal) while 33 (42%) studies included two. Despite the large number of environmental components that comprise each of the exposome domains, only a subset has been currently studied. An optimized consideration of the components from all exposome domains, as well as the standardization of the exposure and outcome assessment methods is warranted to advance the utility of the human exposome concept.

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“…Xenobiotics can be processed by enzymatic machinery, and metabolomics also allows quantification of these metabolites. Therefore, metabolomics can simultaneously analyze both exogenous chemicals and their metabolites, and changes to the endogenous metabolome, to allow assessment of broadly defined exposures and their biological impact [ 20 – 23 ]. One such example was a recent study of occupational exposure to trichloroethylene (TCE) [ 24 ].…”

Section: Application Of High-dimensional Biology To the Environmentalmentioning

confidence: 99%

Beyond genomics: understanding exposotypes through metabolomics

Rattray

Deziel

Wallach³

et al. 2018

Hum Genomics

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BackgroundOver the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.Main textMetabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.ConclusionsComparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

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The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure

Cited by 27 publications

References 42 publications

Metabolomic Responses to Manganese Dose in SH-SY5Y Human Neuroblastoma Cells

Metabolomic Responses to Manganese Dose in SH-SY5Y Human Neuroblastoma Cells

A Scoping Review on the Characteristics of Human Exposome Studies

Beyond genomics: understanding exposotypes through metabolomics

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