Arsenic Exposure from Drinking Water and Urinary Metabolomics: Associations and Long-Term Reproducibility in Bangladesh Adults

Seventy-five years of impactful environmental and occupational health research at the Nelson Institute of Environmental Medicine at New York University

Founded in 1947 as the Institute of Industrial Medicine, the Nelson Institute and Department of Environmental Medicine at New York University (NYU) Grossman School of Medicine (NYUGSOM) was supported by a National Institute of Environmental Health Science (NIEHS) Center Grant for over 56 years. Nelson Institute researchers generated 75 years of impactful research in environmental and occupational health, radiation effects, toxicology, and cancer. Environmental health research is continuing at NYUGSOM in its departments of medicine and population health. The objective of this historical commentary is to highlight the major achievements of the Nelson Institute and the department in the context of its history at facilities in Sterling Forest, Tuxedo, NY and Manhattan, NY. Aspects of our discussion include leadership, physical facilities, and research in many areas, including air pollution, health effects of environmental radiation exposures, inhalation toxicology methodology, carcinogenesis by chemicals, metals, and hormones, cancer chemoprevention, human microbiome, ecotoxicology, epidemiology, biostatistics, and community health concerns. The research of the institute and department benefited from unique facilities, strong leadership focused on team-based science, and outstanding investigators, students, and staff. A major lasting contribution has been the training of hundreds of graduate students and postdoctoral fellows, many of whom have been and are training the next generation of environmental and occupational health researchers at various institutions.

LC-MS based untargeted metabolomics studies of the metabolic response of Ginkgo biloba extract on arsenism patients

Arsenic is an environmentally ubiquitous toxic metalloid. Chronic exposure to arsenic may lead to arsenicosis, while no specific therapeutic strategies are available for the arsenism patients. And Ginkgo biloba extract (GBE) exhibited protective effect in our previous study. However, the mechanisms by which GBE protects the arsenism patients remain poorly understood. A liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics analysis was used to study metabolic response in arsenism patients upon GBE intervention. In total, 39 coal-burning type of arsenism patients and 50 healthy residents were enrolled from Guizhou province of China. The intervention group (n = 39) were arsenism patients orally administered with GBE (three times per day) for continuous 90 days. Plasma samples from 50 healthy controls (HC) and 39 arsenism patients before and after GBE intervention were collected and analyzed by established LC-MS method. Statistical analysis was performed by MetaboAnalyst 5.0 to identify differential metabolites. Multivariate analysis revealed a separation in arsenism patients between before (BG) and after GBE intervention (AG) group. It was observed that 35 differential metabolites were identified between BG and AG group, and 30 of them were completely or partially reversed by GBE intervention, with 14 differential metabolites significantly up-regulated and 16 differential metabolites considerably down-regulated. These metabolites were involved in promoting immune response and anti-inflammatory functions, and alleviating oxidative stress. Taken together, these findings indicate that the GBE intervention could probably exert its protective effects by reversing disordered metabolites modulating these functions in arsenism patients, and provide insights into further exploration of mechanistic studies.

Assessment of drinking water quality and health risk using water quality index and multiple computational models: a case study of Yangtze River in suburban areas of Wuhan, central China, from 2016 to 2021

Water quality, increasingly recognized for its significant impact on health, is garnering heightened attention. Previous studies were limited by the number of water quality indicators and the duration of analysis. This study assessed the drinking water quality and its associated health risk in suburban areas of Wuhan, a city in central China, from 2016 to 2021. We collected 368 finished water samples and 1090 tap water samples and tested these for 37 different indicators. The water quality was evaluated using the water quality index, with trends over time analyzed via the Mann-Kendall test. Furthermore, an artificial neural network model was employed for future water quality prediction. Our findings indicated that the water quality in rural Wuhan was generally good and had an improvement from 2016 to 2021. The qualification and excellent rates were 98.91% and 86.81% for finished water, and 97.89% and 78.07% for tap water, respectively. The drinking water quality was predicted to maintain satisfactory in 2022 and 2023. Additionally, principal component analysis revealed that the primary sanitary issues in the water were poor sensory properties, elevated metal contents, high levels of dissolved solids, and microbial contamination. These issues were likely attributable to domestic and industrial waste discharge and aging water pipelines. The health risks associated with the long-term consumption of this water have been steadily decreasing over the years, underscoring the effectiveness of Wuhan's ongoing water management efforts.

Analysis of Relationships between Metabolic Changes and Selected Nutrient Intake in Women Environmentally Exposed to Arsenic

Nutrients involved in the metabolism of inorganic arsenic (iAs) may play a crucial role in mitigating the adverse health effects associated with such exposure. Consequently, the objective of this study was to analyze the association between the intake levels of nutrients involved in iAs metabolism and alterations in the metabolic profile during arsenic exposure. The study cohort comprised environmentally exposed women: WL (lower total urinary arsenic (As), n = 73) and WH (higher As, n = 73). The analysis included urinary untargeted metabolomics (conducted via liquid chromatography-mass spectrometry) and the assessment of nutrient intake involved in iAs metabolism, specifically methionine, vitamins B2, B6, and B12, folate, and zinc (based on 3-day dietary records of food and beverages). In the WL group, the intake of all analyzed nutrients exhibited a negative correlation with 5 metabolites (argininosuccinic acid, 5-hydroxy-L-tryptophan, 11-trans-LTE4, mevalonic acid, aminoadipic acid), while in the WH group, it correlated with 10 metabolites (5-hydroxy-L-tryptophan, dihyroxy-1H-indole glucuronide I, 11-trans-LTE4, isovalerylglucuronide, 18-oxocortisol, 3-hydroxydecanedioic acid, S-3-oxodecanoyl cysteamine, L-arginine, p-cresol glucuronide, thromboxane B2). Furthermore, nutrient intake demonstrated a positive association with 3 metabolites in the WL group (inosine, deoxyuridine, glutamine) and the WH group (inosine, N-acetyl-L-aspartic acid, tetrahydrodeoxycorticosterone). Altering the intake of nutrients involved in iAs metabolism could be a pivotal factor in reducing the negative impact of arsenic exposure on the human body. This study underscores the significance of maintaining adequate nutrient intake, particularly in populations exposed to arsenic.

Arsenic exposure caused male infertility indicated by testis and sperm metabolic dysfunction in SD rats

Arsenic containment is one of the most severe environmental problems. It has been reported that arsenic exposure could cause male reproductive damage. However, the evidence chain from sodium arsenite (NaAsO2) exposure to adverse male fertility outcomes has not been completed by molecular events. In this study, adult male rats were exposed to NaAsO2 for eight weeks via drinking water for verifying their reproductive capacity by checking the phenotypes of testis damage, sperm quality, and female pregnancy rate. H&E staining indicated testicular cells had atrophied, and necrosis was observed under transmission electron microscopy. Sperm viability tended to decrease, and sperm malformation increased. Notably, metabolites in the testes and sperm showed substantial disruption, especially sperm metabolites. The pregnancy rate tests showed that arsenic decreased male rats' reproduction, with some adverse outcomes of the increased numbers of unpregnant females. However, the fetal crown-rump length remained unaltered, indicating that the pregnancy rate was impacted by arsenic exposure but not fetal growth. On arsenic toxicometabolomics analysis, docosahexaenoic acid (DHA) in sperm was the clearest metabolic sign to correlate with the unpregnant rate. In summary, arsenic exposure can cause male infertility via the injured sperm, which results in decreased female pregnancy. The DHA information may imply the dietary intervention for improving sperm quality. Although the fetal growth of the successful pregnancy has not been affected, the changes in epigenetic phenotypes carried by sperms still need to be verified.

Chronic Arsenic Exposure Perturbs Gut Microbiota and Bile Acid Homeostasis in Mice

Arsenic exposure can perturb gut microbiota and their metabolic functions. We exposed C57BL/6 mice to 1 ppm arsenic in drinking water and investigated whether arsenic exposure affects the homeostasis of bile acids, a group of key microbiome-regulated signaling molecules of microbiome-host interactions. We found that arsenic exposure differentially changed major unconjugated primary bile acids and consistently decreased secondary bile acids in the serum and liver. The relative abundance of Bacteroidetes and Firmicutes was associated with the bile acid level in serum. This study demonstrates that arsenic-induced gut microbiota dysbiosis may play a role in arsenic-perturbed bile acid homeostasis.

Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms

BACKGROUND

Long-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no validated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites associated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning algorithms.

METHODS

In total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe subgroups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Machine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk.

RESULTS

A total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely associated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970-0.979 on calibration set (n = 132) and 0.818-0.848 on validation set (n = 33).

CONCLUSION

Altered metabolites and disrupted pathways are prevalent in arsenicosis patients; The disrupted metabolism of one carbon and dysfunction of antioxidant defense system may partially be causes of the systematic multi-organ damage and carcinogenesis in arsenicosis patients; Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of arsenicosis.

Metabolomics-based study of potential biomarkers of sepsis

The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.

Metabolic Changes and Their Associations with Selected Nutrients Intake in the Group of Workers Exposed to Arsenic

Arsenic (As) exposure causes numerous adverse health effects, which can be reduced by the nutrients involved in the metabolism of iAs (inorganic As). This study was carried out on two groups of copper-smelting workers: WN, workers with a urinary total arsenic (tAs) concentration within the norm (n = 75), and WH, workers with a urinary tAs concentration above the norm (n = 41). This study aimed to analyze the association between the intake level of the nutrients involved in iAs metabolism and the signal intensity of the metabolites that were affected by iAs exposure. An untargeted metabolomics analysis was carried out on urine samples using liquid chromatography-mass spectrometry, and the intake of the nutrients was analyzed based on 3-day dietary records. Compared with the WN group, five pathways (the metabolism of amino acids, carbohydrates, glycans, vitamins, and nucleotides) with twenty-five putatively annotated metabolites were found to be increased in the WH group. In the WN group, the intake of nutrients (methionine; vitamins B2, B6, and B12; folate; and zinc) was negatively associated with six metabolites (cytosine, D-glucuronic acid, N-acetyl-D-glucosamine, pyroglutamic acid, uridine, and urocanic acid), whereas in the WH group, it was associated with five metabolites (D-glucuronic acid, L-glutamic acid, N-acetyl-D-glucosamine, N-acetylneuraminic acid, and uridine). Furthermore, in the WH group, positive associations between methionine, folate, and zinc intake and the signal intensity of succinic acid and 3-mercaptolactic acid were observed. These results highlight the need to educate the participants about the intake level of the nutrients involved in iAs metabolism and may contribute to further considerations with respect to the formulation of dietary recommendations for people exposed to iAs.

Metabolome-Wide Association Study of Multiple Plasma Metals with Serum Metabolomic Profile among Middle-to-Older-Aged Chinese Adults

Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine (P for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 (P for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.

Arsenolipids reduce butyrate levels and influence human gut microbiota in a donor-dependent way

Arsenolipids are organic arsenic species with variable toxicity. Accurate assessment of the risks derived from arsenic-contaminated seafood intake requires studying the interplay between arsenolipids and the human gut microbiota. This research used the in vitro mucosal simulator of the human intestinal microbial ecosystem (M-SHIME) to assess the effect of defined chemical standards of arsenolipids (AsFA 362 and AsHC 332) on a simulated healthy human gut microbiota (n = 4). Microbial-derived metabolites were quantified by gas chromatography and microbiota structure was characterized by 16S rRNA gene sequencing. A specific reduction in butyrate production (control=5.28 ± 0.3 mM; AsFAs=4.56 ± 0.4 mM; AsHC 332=4.4 ± 0.6 mM, n = 4 donors), concomitant with a reduction in the abundance of Lachnospiraceae UCG-004 group and the Faecalibacterium genus was observed, albeit in a donor-dependent manner. Furthermore, an increase in Escherichia/Shigella, Proteobacteria and Fusobacterium abundance was observed after arsenolipid treatments, depending on individual microbiota background. These alterations in microbial functionality and microbial community structure suggest a detrimental effect of arsenolipids intake towards the commensal gut microbiome, and consequently, on human health.

Metabolomics as a valid analytical technique in environmental exposure research: application and progress

BACKGROUND

In recent years, studies have shown that exposure to environmental pollutants (e.g., radiation, heavy metal substances, air pollutants, organic pollutants) is a leading cause of human non-communicable diseases. The key to disease prevention is to clarify the harmful mechanisms and toxic effects of environmental pollutants on the body. Metabolomics is a high-sensitivity, high-throughput omics technology that can obtain detailed metabolite information of an organism. It is a crucial tool for gaining a comprehensive understanding of the pathway network regulation mechanism of the organism. Its application is widespread in many research fields such as environmental exposure assessment, medicine, systems biology, and biomarker discovery.

AIM OF REVIEW

Recent findings show that metabolomics can be used to obtain molecular snapshots of organisms after environmental exposure, to help understand the interaction between environmental exposure and organisms, and to identify potential biomarkers and biological mechanisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the application of metabolomics to understand the biological effects of radiation, heavy metals, air pollution, and persistent organic pollutants exposure, and examines some potential biomarkers and toxicity mechanisms.

Metabolic characteristics related to the hazardous effects of environmental arsenic on humans: A metabolomic review

Arsenic (As) is a toxic metalloid exist ubiquitously in environment. Epidemiological studies and laboratory animal studies have verified that As damages multiple organs or tissues in the body and is associated with a variety of diseases. Changes in metabolites usually indicate disturbances in metabolic pathways and specific metabolites are considered as biomarkers of diseases or drugs/toxins or environmental effects. Metabolomics is the quantitative measurement of the dynamic multi-parameter metabolic responses of biological systems due to pathophysiological or genetic changes. Current years, some metabolomic studies on the hazardous effect of environmental As on humans have been reported. In this paper, we first overviewed the metabolomics studies of environmental As exposure in humans since 2011, emphasizing on the data mining process of metabolic characteristics related to the hazardous effects of environmental As on humans. Then, the relationship between metabolic characteristics and the toxic mechanism of environmental As exposure in humans were discussed, and finally, the prospects of metabolomics studies on populations exposed to environmental As were put forward. Our paper may shed light on the study of mechanisms, prevention and individualized treatment of As poisoning.

TNF-α derived from arsenite-induced microglia activation mediated neuronal necroptosis

Arsenic, an identified environmental toxicant, poses threats to the health of human beings through contaminated water and food. Recently, increasing reports focused on arsenic-induced nerve damage, however, the underlying mechanism remains elusive. Microglia are important immune cells in the nervous system, which produce a large number of inflammatory factors including TNF-α when activated. Recent reports indicated that TNF-α is involved in the process of necroptosis, a new type of programmed cell death discovered recently. Although there were evidences suggested that arsenic could induce both microglia activation and TNF-α production in the nervous system, the mechanism of arsenic-induced neurotoxicity due to microglia activation is rarely studied. In addition, the role of microglia-derived TNF-α in response to arsenic exposure in necroptosis has not been documented before. In this study, we found that arsenite induced microglial activation through p38 MAPK signaling pathway, leading to the production of TNF-α. Microglia-derived TNF-α further induced necroptosis in the neuronal cells. Our findings suggested that necroptosis induced by microglia-derived TNF-α upon arsenite exposure partially played a role in arsenic-induced cell death which underlie the fundamental event of arsenic-related neurotoxicity.

A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics

Humans are exposed to an ever-increasing number of environmental toxicants, some of which have gradually been elucidated to be important risk factors for metabolic diseases, such as diabetes and obesity. These metabolism-sensitive diseases typically occur when key metabolic and signaling pathways were disrupted, which can be influenced by the exposure to contaminants such as endocrine disrupting chemicals (EDCs), along with genetic and lifestyle factors. This promotes the concept and research on environmental metabolism disrupting chemicals (MDCs). In addition, identifying endogenous biochemical markers of effect linked to disease states is becoming an important tool to screen the biological targets following environmental contaminant exposure, as well as to provide an overview of toxicity risk assessment. As such, the current review aims to contribute to the further understanding of exposome and human health and disease by characterizing environmental exposure and effect metabolic biomarkers. We summarized MDC-associated metabolic biomarkers in laboratory animal and human cohort studies using high throughput targeted and nontargeted metabolomics techniques. Contaminants including heavy metals and organohalogen compounds, especially EDCs, have been repetitively associated with metabolic disorders, whereas emerging contaminants such as perfluoroalkyl substances and microplastics have also been found to disrupt metabolism. In addition, we found major limitations in the effective identification of metabolic biomarkers especially in human studies, toxicological research on the mixed effect of environmental exposure has also been insufficient compared to the research on single chemicals. Thus, it is timely to call for research efforts dedicated to the study of combined effect and metabolic alterations for the better assessment of exposomic toxicology and health risks. Moreover, advanced computational and prediction tools, further validation of metabolic biomarkers, as well as systematic and integrative investigations are also needed in order to reliably identify novel biomarkers and elucidate toxicity mechanisms, and to further utilize exposome and metabolome profiling in public health and safety management.

An atlas of metallome and metabolome interactions and associations with incident diabetes in the Strong Heart Family Study

BACKGROUND

Chronic exposure to certain metals plays a role in disease development. Integrating untargeted metabolomics with urinary metallome data may contribute to better understanding the pathophysiology of diseases and complex molecular interactions related to environmental metal exposures. To discover novel associations between urinary metal biomarkers and metabolism networks, we conducted an integrative metallome-metabolome analysis using a panel of urinary metals and untargeted blood metabolomic data from the Strong Heart Family Study (SHFS).

METHODS

The SHFS is a prospective family-based cohort study comprised of American Indian men and women recruited in 2001-2003. This nested case-control analysis of 145 participants of which 50 developed incident diabetes at follow up in 2006-2009, included participants with urinary metal and untargeted metabolomic data. Concentrations of 8 creatinine-adjusted urine metals/metalloids [antimony (Sb), cadmium (Cd), lead (Pb), molybdenum (Mo), selenium (Se), tungsten (W), uranium (U) and zinc (Zn)], and 4 arsenic species [inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (AsB)] were measured. Global metabolomics was performed on plasma samples using high-resolution Orbitrap mass spectrometry. We performed an integrative network analysis using xMWAS and a metabolic pathway analysis using Mummichog.

RESULTS

8,810 metabolic features and 12 metal species were included in the integrative network analysis. Most metal species were associated with distinct subsets of metabolites, forming single-metal-multiple-metabolite clusters (|r|>0.28, p-value < 0.001). DMA (clustering with W), iAs (clustering with U), together with Mo and Se showed modest interactions through associations with common metabolites. Pathway enrichment analysis of associated metabolites (|r|>0.17, p-value < 0.1) showed effects in amino acid metabolism (AsB, Sb, Se and U), fatty acid and lipid metabolism (iAs, Mo, W, Sb, Pb, Cd and Zn). In stratified analyses among participants who went on to develop diabetes, iAs and U clustered together through shared metabolites, and both were associated with the phosphatidylinositol phosphate metabolism pathway; metals were also associated with metabolites in energy metabolism (iAs, MMA, DMA, U, W) and xenobiotic degradation and metabolism (DMA, Pb) pathways.

CONCLUSION

In this integrative analysis of multiple metals and untargeted metabolomics, results show common associations with fatty acid, energy and amino acid metabolism pathways. Results for individual metabolite associations differed for different metals, indicating that larger populations will be needed to confirm the metal-metal interactions detected here, such as the strong interaction of uranium and inorganic arsenic. Understanding the biochemical networks underlying metabolic homeostasis and their association with exposure to multiple metals may help identify novel biomarkers, pathways of disease, potential signatures of environmental metal exposure.

Exposure to legacy and novel perfluoroalkyl substance disturbs the metabolic homeostasis in pregnant women and fetuses: A metabolome-wide association study

BACKGROUND

Perfluoroalkyl substances (PFASs) exist extensively and several of these have been verified to be toxic. Prenatal exposure to PFASs has attracted much attention. Metabolome-wide association analyses can be used to explore the toxicity mechanisms of PFASs by identifying associated biomarkers.

OBJECTIVES

To evaluate associations between the metabolites in maternal and cord serum and internal exposure to several common PFASs.

METHODS

Paired maternal and cord serum samples were collected from 84 pregnant women who gave birth between 2015 and 2016. Seven legacy and two novel PFASs were measured. A nontarget metabolomic method and an iterative metabolite annotation based on metabolic pathways were applied to characterize the metabolic profiles. Linear regression adjusted with the false discovery rate and covariates was used to indicate the associations.

RESULTS

A total of 279 features in maternal serum and 338 features in cord serum were identified as metabolites associated with PFAS exposure. Perfluorooctanoic acid (PFOA) and perfluorohexane sulfonic acid (PFHxS) were two PFASs associated with more metabolites, while the two novel chlorinated polyfluorinated ether sulfonic acids (Cl-PFESAs) showed less relevance to the metabolome. With pathway enrichment analysis, we found that three fatty acid metabolisms and retinol metabolism were correlated with PFAS exposure in maternal blood, and that sterol metabolism showed the correlation in both maternal serum and cord serum.

CONCLUSIONS

We identified metabolites and pathways in pregnant women and fetuses associated with the exposure to several PFAS, indicating a promising application for metabolome-wide association studies. Additional research is needed to confirm causation.

Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR)

With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQS_RS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (¹H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).

Lipid Metabolism Alterations in a Rat Model of Chronic and Intergenerational Exposure to Arsenic

Chronic exposure to arsenic (As), whether directly through the consumption of contaminated drinking water or indirectly through the daily intake of As-contaminated food, is a health threat for more than 150 million people worldwide. Epidemiological studies found an association between chronic consumption of As and several pathologies, the most common being cancer-related disorders. However, As consumption has also been associated with metabolic disorders that could lead to diverse pathologies, such as type 2 diabetes mellitus, nonalcoholic fatty liver disease, and obesity. Here, we used ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization/quadrupole time-of-flight mass spectrometry (ESI-QToF) to assess the effect of chronic intergenerational As exposure on the lipid metabolism profiles of serum from 4-month-old Wistar rats exposed to As prenatally and also during early life in drinking water (3 ppm). Significant differences in the levels of certain identified lysophospholipids, phosphatidylcholines, and triglycerides were found between the exposed rats and the control groups, as well as between the sexes. Significantly increased lipid oxidation determined by the malondialdehyde (MDA) method was found in exposed rats compared with controls. Chronic intergenerational As exposure alters the rat lipidome, increases lipid oxidation, and dysregulates metabolic pathways, the factors associated with the chronic inflammation present in different diseases associated with chronic exposure to As (i.e., keratosis, Bowen's disease, and kidney, liver, bladder, and lung cancer).

Application of metabolomics to characterize environmental pollutant toxicity and disease risks

The increased incidence of non-communicable human diseases may be attributed, at least partially, to exposures to toxic chemicals such as persistent organic pollutants (POPs), air pollutants and heavy metals. Given the high mortality and morbidity of pollutant exposure associated diseases, a better understanding of the related mechanisms of toxicity and impacts on the endogenous host metabolism are needed. The metabolome represents the collection of the intermediates and end products of cellular processes, and is the most proximal reporter of the body's response to environmental exposures and pathological processes. Metabolomics is a powerful tool for studying how organisms interact with their environment and how these interactions shape diseases related to pollutant exposure. This mini review discusses potential biological mechanisms that link pollutant exposure to metabolic disturbances and chronic human diseases, with a focus on recent studies that demonstrate the application of metabolomics as a tool to elucidate biochemical modes of actions of various environmental pollutants. In addition, classes of metabolites that have been shown to be modulated by multiple environmental pollutants will be discussed with an emphasis on their use as potential early biomarkers of disease risks. Taken together, metabolomics is a useful and versatile tool for characterizing the disease risks and mechanisms associated with various environmental pollutants.

Chronic Arsenic Exposure Induces Oxidative Stress and Perturbs Serum Lysolipids and Fecal Unsaturated Fatty Acid Metabolism

Chronic arsenic exposure from drinking water is a global public health issue, which is associated with numerous human diseases and influences millions of people worldwide. The effects of arsenic exposure to the metabolic networks remain elusive. Here, we exposed female C57BL/6J mice to 1 ppm inorganic arsenic in drinking water for 3 months to investigate how arsenic exposure perturbs serum and fecal metabolic profiles. We found decreased levels of serum compounds with antioxidative activities in arsenic-treated mice, in accordance with elevated oxidative stress indicated by higher urinary 8-oxo-2'-deoxyguanosine (8-oxo-dG) levels. Moreover, the levels of multiple lysophosphatidylcholines (lysoPCs) were significantly increased in the sera of arsenic-exposed mice, including lysoPC (O-18:0), lysoPC (20:3), lysoPC (18:1), and lysoPC (22:6). Arsenic exposure perturbed the levels of several key polyunsaturated fatty acids (PUFAs) in the fecal samples in concert with alterations in related microbial pathways. Additionally, changes in the abundances of many functional metabolites, together with decreased levels of amino acids, were found in the fecal samples of arsenic-treated mice. By delineating the impact of arsenic exposure on the metabolic profiles, the findings may provide new biomarkers and mechanistic insights into arsenic-associated diseases.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

Toxicity of environmentally-relevant concentrations of arsenic on developing T lymphocyte

Arsenic is a ubiquitous environmental contaminant that exists in many inorganic and organic forms. In particular, arsenite is known to induce immunotoxicity in humans and animals. There are still major gaps in our understanding of the mechanism(s) of the immunotoxicity induced by arsenic at environmentally-relevant concentrations. T cells are an essential part of the immune system required for host resistance to infections and protection from cancer. Developing T cells in the thymus have been shown to be particularly prone to arsenite-induced toxicity at low concentrations. Suppression of DNA repair proteins and oxidative stress have been identified as a mechanism of genotoxicity that occurs at low to moderate concentrations. Inhibition of the IL-7 signaling pathway was thought to be responsible for the non-genotoxicity induced by low to moderate doses of arsenic. Interestingly, T cells at different stages of their development had distinct sensitivities to arsenite, which was regulated by arsenite exporters. The current evidence strongly suggests that low to moderate doses of arsenic induces toxic effects in the developing T cells and accumulates to highest levels in the early cells that are least capable to pump out arsenic, which may be the mechanism of the high arsenic sensitivity. Therefore, quantification of the exposure levels should be encouraged in future arsenic toxicity studies.