Benchmark Dose Modeling Estimates of the Concentrations of Inorganic Arsenic That Induce Changes to the Neonatal Transcriptome, Proteome, and Epigenome in a Pregnancy Cohort

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Benchmark Dose of Melamine Exposure for a Renal Injury Marker Mediated by Oxidative Stress: Examples in Patients with Urolithiasis and Occupational Workers

Establishing a safe exposure level from epidemiological studies while providing direct hazard characterization in humans often faces uncertainty in causality, especially cross-sectional data. With advances in molecular epidemiology, it is reasonable to integrate identified intermediate biomarkers into health risk assessment. In this study, by considering the mediation of the oxidative stress marker malondialdehyde (MDA), we explored the exposure threshold of melamine on the early renal injury marker N-acetyl-β-D glucosaminidase (NAG). The benchmark dose (BMD) was derived from model averaging of the composite direct effect of melamine exposure and the indirect effect through the mediation of MDA on NAG levels. As illustrative examples, we analyzed 309 adult patients with calcium urolithiasis and 80 occupational workers for the corresponding exposure thresholds. The derived threshold was subpopulation-dependent, with the one-sided lower bound BMDL10 for the patients with urolithiasis with (without) the mediator MDA for the patients with kidney stones and the occupational workers being 0.88 (0.96) μg/kg_bw/day and 22.82 (18.09) μg/kg_bw/day, respectively. The derived threshold levels, considering the oxidative stress marker MDA, were consistent with those without adjusting for the mediation effect. However, the study outcomes were further supported by the suggested mechanism pathway. The threshold for the patients with urolithiasis was up to two orders lower than the current tolerable daily intake level of 200 μg/kg_bw/day recommended by the WHO (EFSA).

Investigation of Peroxisome Proliferator-Activated Receptor Genes as Requirements for Visual Startle Response Hyperactivity in Larval Zebrafish Exposed to Structurally Similar Per- and Polyfluoroalkyl Substances (PFAS)

BACKGROUND

Per- and polyfluoroalkyl Substances (PFAS) are synthetic chemicals widely detected in humans and the environment. Exposure to perfluorooctanesulfonic acid (PFOS) or perfluorohexanesulfonic acid (PFHxS) was previously shown to cause dark-phase hyperactivity in larval zebrafish.

OBJECTIVES

The objective of this study was to elucidate the mechanism by which PFOS or PFHxS exposure caused hyperactivity in larval zebrafish.

METHODS

Swimming behavior was assessed in 5-d postfertilization (dpf) larvae following developmental (1-4 dpf) or acute (5 dpf) exposure to 0.43 - 7.86 μ M PFOS, 7.87 - 120 μ M PFHxS, or 0.4% dimethyl sulfoxide (DMSO). After developmental exposure and chemical washout at 4 dpf, behavior was also assessed at 5-8 dpf. RNA sequencing was used to identify differences in global gene expression to perform transcriptomic benchmark concentration-response (BMC T ) modeling, and predict upstream regulators in PFOS- or PFHxS-exposed larvae. CRISPR/Cas9-based gene editing was used to knockdown peroxisome proliferator-activated receptors (ppars) pparaa/ab, pparda/db, or pparg at day 0. Knockdown crispants were exposed to 7.86 μ M PFOS or 0.4% DMSO from 1-4 dpf and behavior was assessed at 5 dpf. Coexposure with the ppard antagonist GSK3787 and PFOS was also performed.

RESULTS

Transient dark-phase hyperactivity occurred following developmental or acute exposure to PFOS or PFHxS, relative to the DMSO control. In contrast, visual startle response (VSR) hyperactivity only occurred following developmental exposure and was irreversible up to 8 dpf. Similar global transcriptomic profiles, BMC T estimates, and enriched functions were observed in PFOS- and PFHxS-exposed larvae, and ppars were identified as putative upstream regulators. Knockdown of pparda/db, but not pparaa/ab or pparg, blunted PFOS-dependent VSR hyperactivity to control levels. This finding was confirmed via antagonism of ppard in PFOS-exposed larvae.

DISCUSSION

This work identifies a novel adverse outcome pathway for VSR hyperactivity in larval zebrafish. We demonstrate that developmental, but not acute, exposure to PFOS triggered persistent VSR hyperactivity that required ppard function. https://doi.org/10.1289/EHP13667.

Early microRNA responses in rodent liver mediated by furan exposure establish dose thresholds for later adverse outcomes

To reduce reliance on long-term in vivo studies, short-term data linking early molecular-based measurements to later adverse health effects is needed. Although transcriptional-based benchmark dose (BMDT) modeling has been used to estimate potencies and stratify chemicals based on potential to induce later-life effects, dose-responsive epigenetic alterations have not been routinely considered. Here, we evaluated the utility of microRNA (miRNA) profiling in mouse liver and blood, as well as in mouse primary hepatocytes in vitro, to indicate mechanisms of liver perturbation due to short-term exposure of the known rodent liver hepatotoxicant and carcinogen, furan. Benchmark dose modeling of miRNA measurements (BMDmiR) were compared to the referent transcriptional (BMDT) and apical (BMDA) estimates. These analyses indicate a robust dose response for 34 miRNAs to furan and involvement of p53-linked pathways in furan-mediated hepatotoxicity, supporting mRNA and apical measurements. Liver-sourced miRNAs were also altered in the blood and primary hepatocytes. Overall, these results indicate mechanistic involvement of miRNA in furan carcinogenicity and provide evidence of their potential utility as accessible biomarkers of exposure and disease.

Toenail arsenic species and metallome profiles associated with breast, cervical, prostate, and skin cancer prevalence in the Atlantic Partnership for Tomorrow's Health cohort

Introduction

Chronic exposure to arsenic through drinking water has been linked to several cancers. The metabolism of arsenic is thought to play a key role in arsenic-related carcinogenesis as metabolites of varying toxicity are produced and either stored in or excreted from the body. Atlantic Canada has the highest age-standardized incidence rates of all cancers in the country. This may be due to its high levels of environmental arsenic and the prevalence of unregulated private wells for water consumption. Here, we aimed to characterize the profiles of arsenic species and metallome in the toenails of four cancer groups, compare them to healthy participants (N = 338), and assess potential associations between the profiles with cancer prevalence.

Methods

This study employed a case-control design. Toenail samples and questionnaire data from cases (breast, cervical, prostate, and skin cancers) and controls were sourced from the Atlantic Partnership for Tomorrow's Health (PATH) cohort study. The levels of arsenic species were measured using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) paired with High Performance Liquid Chromatography (HPLC) and total concentrations of metallome (23 metals) were determined by ICP-MS separately. Multivariate analyses were conducted to compare cases with controls within each cancer group.

Results

Arsenic speciation profiles varied by cancer type and were significantly different between cases and controls in the breast (p = 0.0330), cervical (p = 0.0228), and skin (p = 0.0228) cancer groups. In addition, the profiles of metallome (nine metals) were significantly differentiated in the prostate (p = 0.0244) and skin (p = 0.0321) cancer groups, with higher zinc concentrations among cases compared to controls.

Conclusion

History of cancer diagnosis was associated with specific profiles of arsenic species and metallome. Our results indicate that arsenic methylation and zinc levels, as measured in toenails, may be an important biomarker for cancer prevalence. Further research is needed to use toenails as a prognostic measure of arsenic-and other metal-induced cancer.

Electrochemical detection of arsenic (III) hazardous chemicals using cubic CsPbBr3 single crystals: Structural insights from DFT study

Long-term exposure to the highly toxic heavy metal arsenic can harm ecological systems and pose serious health risks to humans. Arsenic pollutant in water and the food chain must be addressed, and active prompt detection of As(III) is essential. The development of an effective detection method for As(III) ions is urgently needed to slow the alarming growth of arsenic pollution in the environment and safeguard the well-being of future generations. This study presents the results of our exhaustive investigation into cubic CsPbBr₃ single crystals, the glassy carbon (GC) electrode modification with CsPbBr₃ single crystals prepared by direct solvent evaporation, as well as our observations of the material's remarkable electrocatalytic properties and exceptional anti-interference sensing of As(III) ions in neutral pH media. The developed CsPbBr₃/GC is exceptionally useful for the ultra-sensitive and specific identification of arsenic in water, exhibiting a detection limit of 0.381 μmol/L, a rapid response across a defined range of 0.1-25 μmol/L, and an ultra-sensitivity of 0.296 μA/μmolL^-1. CsPbBr₃/GCE (prepared without a specific reagent) is superior to other modified electrodes used as sensors in electrocatalytic activity, detection limit, analytical sensitivity, and stability response.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights

BACKGROUND

Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples.

CONCLUSIONS

Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Metal mixtures modeling identifies birth weight-associated gene networks in the placentas of children born extremely preterm

Prenatal exposure to toxic metals is linked to numerous adverse birth and later-in-life outcomes. These outcomes are tied to disrupted biological processes in fetal-derived tissues including the placenta and umbilical cord yet the precise pathways are understudied in these target tissues. We set out to examine the relationship between metal concentrations in umbilical cord and altered gene expression networks in placental tissue. These novel relationships were investigated in a subset of the Extremely Low Gestational Age Newborn (ELGAN) cohort (n = 226). Prenatal exposure to 11 metals/metalloids was measured using inductively coupled plasma tandem-mass spectrometry (ICP-MS/MS) in cord tissue, ensuring passage through the placental barrier. RNA-sequencing was used to quantify >37,000 mRNA transcripts. Differentially expressed genes (DEGs) were identified with respect to each metal. Weighted gene co-expression analysis identified gene networks modulated by metals. Two innovative mixtures modeling techniques, namely principal components analysis and quantile-based g-computation, were employed to identify genes/gene networks associated with multi-metal exposure. Individually, lead was associated with the strongest genomic response of 191 DEGs. Joint lead and cadmium exposure was related to 657 DEGs, including DNA Methyl Transferase 1 (DNMT1). These genes were enriched for the Eukaryotic Initiation Factor 2 (EIF2) pathway. Four gene networks, each containing genes within a Nuclear Factor kappa-light-chain-enhancer of Activated B Cells (NF-kB)-mediated network, were significantly increased in average expression level in relation to increases in all metal concentrations. All four of these metal mixture-associated gene networks were negatively correlated with important predictors of neonatal health including birth weight, placenta weight, and fetal growth. Bringing together novel methodologies from epidemiological mixtures analyses and toxicogenomics, applied to a unique cohort of extremely preterm children, the present study highlighted critical genes and pathways in the placenta dysregulated by prenatal metal mixtures. These represent potential mechanisms underlying the developmental origins of metal-induced disease.

Influence of differential arsenic exposure on cellular redox homeostasis of exposed rural women of West Bengal

The metalloid arsenic (As) induces oxidative stress is a well-known fact. However, the extent of variation of oxidative stress according to different exposure levels of As in groundwater and the mechanism responsible for As mediated oxidative stress is yet to be elucidated in a human population of West Bengal. In the present study, we have investigated the impact of low level (> 10 ≤ 50 µg/L) and high-level groundwater As (> 50 µg/L) on cellular redox status, DNA damage, and repair mechanisms in chronically exposed rural women of West Bengal. Prediction models of ordinary least square regression of nail As, forced vital capacity (FVC) %, and that of forced expiratory volume during the first one second (FEV1) % deciphered that accumulation of As in nails may predict hemoglobin deficiency. Moreover, consumption of As-laced water tends to decrease FEV1% and FVC%. A strong positive correlation was observed between water and nail As level and reactive oxygen species (ROS) generation. ROS, perturbed nuclear factor erythroid 2-related factor 2(Nrf2)/ Kelch-like ECH-associated protein 1 (Keap1) redox regulation, compromised antioxidant defense machinery-superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione transferase (GST), induced DNA damage, and suppressed DNA repair proteins-poly ADP ribose polymerase1(PARP1)/ X-ray repair cross-complementing protein 1(XRCC1)/ 8-oxoguanine glycosylase (OGG1) in a dose-dependent manner. All the low and high As areas had very high cancer risk values for the exposed population. It has been predicted that if the As level in the drinking water of a 40-year adult increases by 2 ug/L, the likelihood of the cancer risk will increase by 10%, keeping the body weight and amount of water intake constant. Thus, long-term exposure to either low or high As is seriously affecting the lives of asymptomatic women who are vulnerable to developing carcinogenic changes after a period of latency.

Computational Approach to Link Chemicals in Anthropogenic Smoke Particulate Matter with Toxicity

A weighted chemical coexpression network analysis (WCCNA) was utilized to identify chemicals co-modulated to variable burning of anthropogenic materials and to link chemicals to biological responses (lung toxicity and mutagenicity). Polyaromatic hydrocarbons (PAHs) were co-modulated with increased concentrations in flaming smoke particulate matter (PM) from the burning of plastic-containing materials and showed significant association with increased neutrophil influx, cytokine levels, and mutagenicity. Inorganic elements were co-modulated with increased concentrations in flaming plywood and cardboard smoke PM and showed significant association with increased protein and albumin levels. This study shows the potential for using a computational network analysis to identify and prioritize hazardous chemical components within complex environmental mixtures and provides guidance on key chemical tracers required for intervention research to protect public health from the exposure.

Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring

The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.

The placenta epigenome-brain axis: placental epigenomic and transcriptomic responses that preprogram cognitive impairment

Aim: The placenta-brain axis reflects a developmental linkage where disrupted placental function is associated with impaired neurodevelopment later in life. Placental gene expression and the expression of epigenetic modifiers such as miRNAs may be tied to these impairments and are understudied. Materials & methods: The expression levels of mRNAs (n = 37,268) and their targeting miRNAs (n = 2083) were assessed within placentas collected from the ELGAN study cohort (n = 386). The ELGAN adolescents were assessed for neurocognitive function at age 10 and the association with placental mRNA/miRNAs was determined. Results: Placental mRNAs related to inflammatory and apoptotic processes are under miRNA control and associated with cognitive impairment at age 10. Conclusion: Findings highlight key placenta epigenome-brain relationships that support the developmental origins of health and disease hypothesis.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Nitric oxide-related gene and microRNA expression in peripheral blood in pregnancy vary by self-reported race

Adverse pregnancy outcomes disproportionately affect non-Hispanic (NH) Black patients in the United States. Structural racism has been associated with increased psychosocial distress and inflammation and may trigger oxidative stress. Thus, the nitric oxide (NO) pathway (involved in the regulation of inflammation and oxidative stress) may partly explain the underlying disparities in obstetric outcomes.Cohort study of 154 pregnant patients with high-risk obstetric histories; n = 212 mRNAs and n = 108 microRNAs (miRNAs) in the NO pathway were evaluated in circulating white blood cells. NO pathway mRNA and miRNA transcript counts were compared by self-reported race; NH Black patients were compared with women of other races/ethnicities. Finally, miRNA-mRNA expression levels were correlated.Twenty-two genes (q < 0.10) were differentially expressed in self-identified NH Black individuals. Superoxide dismutase 1 (SOD1), interleukin-8 (IL-8), dynein light chain LC8-type 1 (DYNLL1), glutathione peroxidase 4 (GPX4), and glutathione peroxidase 1 (GPX1) were the five most differentially expressed genes among NH Black patients compared to other patients. There were 63 significantly correlated miRNA-mRNA pairs (q < 0.10) demonstrating potential miRNA regulation of associated target mRNA expression. Ten miRNAs that were identified as members of significant miRNA-mRNA pairs were also differentially expressed among NH Black patients (q < 0.10).These findings support an association between NO pathway and inflammation and infection-related mRNA and miRNA expression in blood drawn during pregnancy and patient race/ethnicity. These findings may reflect key differences in the biology of inflammatory gene dysregulation that occurs in response to the stress of systemic racism and that underlies disparities in pregnancy outcomes.

'Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map

BACKGROUND

Systematic evidence maps are increasingly used to develop chemical risk assessments. These maps can provide an overview of available studies and relevant study information to be used for various research objectives and applications. Environmental epidemiological studies that examine the impact of chemical exposures on various 'omic profiles in human populations provide relevant mechanistic information and can be used for benchmark dose modeling to derive potential human health reference values.

OBJECTIVES

To create a systematic evidence map of environmental epidemiological studies examining environmental contaminant exposures with 'omics in order to characterize the extent of available studies for future research needs.

METHODS

Systematic review methods were used to search and screen the literature and included the use of machine learning methods to facilitate screening studies. The Populations, Exposures, Comparators and Outcomes (PECO) criteria were developed to identify and screen relevant studies. Studies that met the PECO criteria after full-text review were summarized with information such as study population, study design, sample size, exposure measurement, and 'omics analysis.

RESULTS

Over 10,000 studies were identified from scientific databases. Screening processes were used to identify 84 studies considered PECO-relevant after full-text review. Various contaminants (e.g. phthalate, benzene, arsenic, etc.) were investigated in epidemiological studies that used one or more of the four 'omics of interest: epigenomics, transcriptomics, proteomics, and metabolomics . The epidemiological study designs that were used to explore single or integrated 'omic research questions with contaminant exposures were cohort studies, controlled trials, cross-sectional, and case-control studies. An interactive web-based systematic evidence map was created to display more study-related information.

CONCLUSIONS

This systematic evidence map is a novel tool to visually characterize the available environmental epidemiological studies investigating contaminants and biological effects using 'omics technology and serves as a resource for investigators and allows for a range of applications in chemical research and risk assessment needs.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic vs. epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (i.e., cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (i.e., miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R^2 (predicted vs. observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization's International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

Development of the genomic inflammatory index (GII) to assess key maternal antecedents associated with placental inflammation

INTRODUCTION

Placental inflammation is associated with a variety of adverse health outcomes, including poor pregnancy outcomes as well as later in life health. The current clinical methodologies for evaluating placental histology for inflammation are limited in their sensitivity. The objective of this study was to develop a genomic inflammatory index (GII) that can be utilized as a biomarker to effectively quantify and evaluate placental inflammation.

METHODS

RNA-sequencing of n = 386 placentas from the Extremely Low Gestational Age Newborn (ELGAN) cohort was conducted. Transcriptional data for a biologically-targeted set of 14 genes, selected for their established role in pro-inflammatory signaling pathways, were aggregated to construct the GII. Multiple linear regression models were used to examine relationships between 47 perinatal factors and the GII.

RESULTS

The GII demonstrated a nine-fold difference across subjects and displayed positive trends with other indicators of placental inflammation. Significant differences in the GII were observed for race where women who self-identified as Black displayed higher levels of placental inflammation than those who self-identified as White women (p < 0.001). Additionally, married Black women showed reduced placental inflammation compared to those who were unmarried (beta value: 0.828, p-value: 0.032). Placentas from women who were treated with steroids during the delivery of the infant displayed higher GII levels than those who were not (p = 0.023).

DISCUSSION

Overall, the GII demonstrated an association between various perinatal factors and placental inflammation. It is anticipated that the GII will provide a novel genomics tool for quantifying placental inflammation, allowing for further investigation of causes, and ultimately the prevention, of inflammation in the placenta.

Predictive modeling of biological responses in the rat liver using in vitro Tox21 bioactivity: Benefits from high-throughput toxicokinetics

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Mid-pregnancy maternal blood nitric oxide-related gene and miRNA expression are associated with preterm birth

Aim: The nitric oxide (NO) pathway modulates inflammation and may influence birth timing. Patients & methods: Case-control analysis of 136 pregnant women with RNA obtained <28 weeks; n = 212 mRNAs and n = 108 miRNAs in the NO pathway were evaluated. NO-pathway mRNA and miRNA transcript counts in women delivering preterm versus at term were compared, miRNA-mRNA expression levels correlated and prediction models generated. Results: Fourteen genes were differentially expressed in women delivering <37 weeks; 13/14 were also differentially expressed in those delivering <34 weeks (q <0.10) versus term births. Multiple miRNA-mRNA pairs were correlated. Models with gene expression better predicted prematurity than models with only clinical or nongenomic predictors. Conclusion: Maternal blood NO pathway-related mRNA and miRNA expression is associated with prematurity.

Long-term spatial tracking of cells affected by environmental insults

Background

Harsh environments surrounding fetuses and children can induce cellular damage in the developing brain, increasing the risk of intellectual disability and other neurodevelopmental disorders such as schizophrenia. However, the mechanisms by which early damage leads to disease manifestation in later life remain largely unknown. Previously, we demonstrated that the activation of heat shock (HS) signaling can be utilized as a unique reporter to label the cells that undergo specific molecular/cellular changes upon exposure to environmental insults throughout the body. Since the activation of HS signaling is an acute and transient event, this approach was not intended for long-term tracing of affected cells after the activation has diminished. In the present study, we generated new reporter transgenic mouse lines as a novel tool to achieve systemic and long-term tracking of affected cells and their progeny.

Methods

The reporter transgenic mouse system was designed so that the activation of HS signaling through HS response element (HSE) drives flippase (FLPo)-flippase recognition target (FRT) recombination-mediated permanent expression of the red fluorescent protein (RFP), tdTomato. With a priority on consistent and efficient assessment of the reporter system, we focused on intraperitoneal (i.p.) injection models of high-dose, short prenatal exposure to alcohol (ethanol) and sodium arsenite (ethanol at 4.0 g/kg/day and sodium arsenite at 5.0 mg/kg/day, at embryonic day (E) 12 and 13). Long-term reporter expression was examined in the brain of reporter mice that were prenatally exposed to these insults. Electrophysiological properties were compared between RFP⁺ and RFP⁻ cortical neurons in animals prenatally exposed to arsenite.

Results

We detected RFP⁺ neurons and glia in the brains of postnatal mice that had been prenatally exposed to alcohol or sodium arsenite. In animals prenatally exposed to sodium arsenite, we also detected reduced excitability in RFP⁺ cortical neurons.

Conclusion

The reporter transgenic mice allowed us to trace the cells that once responded to prenatal environmental stress and the progeny derived from these cells long after the exposure in postnatal animals. Tracing of these cells indicates that the impact of prenatal exposure on neural progenitor cells can lead to functional abnormalities in their progeny cells in the postnatal brain. Further studies using more clinically relevant exposure models are warranted to explore this mechanism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-020-09339-w.

Identifying the Transcriptional Response of Cancer and Inflammation-Related Genes in Lung Cells in Relation to Ambient Air Chemical Mixtures in Houston, Texas

Atmospheric pollution represents a complex mixture of air chemicals that continually interact and transform, making it difficult to accurately evaluate associated toxicity responses representative of real-world exposure. This study leveraged data from a previously published article and reevaluated lung cell transcriptional response induced by outdoor atmospheric pollution mixtures using field-based exposure conditions in the industrialized Houston Ship Channel. The tested hypothesis was that individual and co-occurring chemicals in the atmosphere relate to altered expression of critical genes involved in inflammation and cancer-related processes in lung cells. Human lung cells were exposed at an air-liquid interface to ambient air mixtures for 4 h, with experiments replicated across 5 days. Real-time monitoring of primary and secondary gas-phase pollutants, as well as other atmospheric conditions, was simultaneously conducted. Transcriptional analysis of exposed cells identified critical genes showing differential expression associated with both individual and chemical mixtures. The individual pollutant identified with the largest amount of associated transcriptional response was benzene. Tumor necrosis factor (TNF) and interferon regulatory factor 1 (IRFN1) were identified as key upstream transcription factor regulators of the cellular response to benzene. This study is among the first to measure lung cell transcriptional responses in relation to real-world, gas-phase air mixtures.

Inorganic arsenic and its methylated metabolites as endocrine disruptors in the placenta: Mechanisms underpinning glucocorticoid receptor (GR) pathway perturbations

Exposure to inorganic arsenic (iAs) is a significant public health concern with individuals around the globe exposed to harmful levels through contaminated drinking water. Exposure to iAs during pregnancy is of particular concern and has been associated with pregnancy complications and adverse child health later in life. Effects of in utero exposure may be mediated through alterations in key signaling pathways in the placenta that regulate fetal growth and development. A pathway of interest is the glucocorticoid receptor (GR)- signaling pathway, which is known to regulate fetal and placental development. While prior research has shown that iAs alters GR-associated gene expression in trophoblasts, the mechanisms that underlie these perturbations remain unknown. In the present study, we set out to elucidate the molecular mechanisms that underpin observed alterations in GR-associated gene expression. We also aimed to determine whether the methylated metabolites of iAs, namely monomethyl‑arsenic (MMA) and dimethyl‑arsenic (DMA), also influence GR-associated signaling in the placenta. The data indicate that iAs alters GR activation in a dose-dependent manner, reduces nuclear translocation, and reduces DNA binding. Additionally, the results demonstrate that MMA and DMA alter the expression of eight GR-associated genes, modulate GR activation, and alter DNA binding. These data are significant as they highlight the role of iAs as an endocrine disruptor and for the first time explore the effects of MMA and DMA on endocrine signaling in the placenta.

A role for microRNAs in the epigenetic control of sexually dimorphic gene expression in the human placenta

Aim: The contribution of miRNAs as epigenetic regulators of sexually dimorphic gene expression in the placenta is unknown. Materials & methods: 382 placentas from the extremely low gestational age newborns (ELGAN) cohort were evaluated for expression levels of 37,268 mRNAs and 2,102 miRNAs using genome-wide RNA-sequencing. Differential expression analysis was used to identify differences in the expression based on the sex of the fetus. Results: Sexually dimorphic expression was observed for 128 mRNAs and 59 miRNAs. A set of 25 miRNA master regulators was identified that likely contribute to the sexual dimorphic mRNA expression. Conclusion: These data highlight sex-dependent miRNA and mRNA patterning in the placenta and provide insight into a potential mechanism for observed sex differences in outcomes.

Placental genomic and epigenomic signatures associated with infant birth weight highlight mechanisms involved in collagen and growth factor signaling

Birth weight (BW) represents an important clinical and toxicological measure, indicative of the overall health of the newborn as well as potential risk for later-in-life outcomes. BW can be influenced by endogenous and exogenous factors and is known to be heavily impacted in utero by the health and function of the placenta. An aspect that remains understudied is the influence of genomic and epigenomic programming within the placenta on infant BW. To address this gap, we set out to test the hypothesis that genes involved in critical placental cell signaling are associated with infant BW, and are likely regulated, in part, through epigenetic mechanisms based on microRNA (miRNA) mediation. This study leveraged a robust dataset based on 390 infants born at low gestational age (ranged 23-27 weeks) to evaluate genome-wide expression profiles of both mRNAs and miRNAs in placenta tissues and relate these to infant BW. A total of 254 mRNAs and 268 miRNAs were identified as associated with BW, the majority of which showed consistent associations across placentas derived from both males and females. BW-associated mRNAs were found to be enriched for important biological pathways, including glycoprotein VI (the major receptor for collagen), human growth, and hepatocyte growth factor signaling, a portion of which were predicted to be regulated by BW-associated miRNAs. These miRNA-regulated pathways highlight key mechanisms potentially linking endogenous/exogenous factors to changes in birth outcomes that may be deleterious to infant and later-in-life health.

A Network of Sputum MicroRNAs Is Associated with Neutrophilic Airway Inflammation in Asthma

Rationale: MicroRNAs are potent regulators of biologic systems that are critical to tissue homeostasis. Individual microRNAs have been identified in airway samples. However, a systems analysis of the microRNA-mRNA networks present in the sputum that contribute to airway inflammation in asthma has not been published.Objectives: Identify microRNA and mRNA networks in the sputum of patients with asthma.Methods: We conducted a genome-wide analysis of microRNA and mRNA in the sputum from patients with asthma and correlated expression with clinical phenotypes. Weighted gene correlation network analysis was implemented to identify microRNA networks (modules) that significantly correlate with clinical features of asthma and mRNA expression networks. MicroRNA expression in peripheral blood neutrophils and lymphocytes and in situ hybridization of the sputum were used to identify the cellular sources of microRNAs. MicroRNA expression obtained before and after ozone exposure was also used to identify changes associated with neutrophil counts in the airway.Measurements and Main Results: Six microRNA modules were associated with clinical features of asthma. A single module (nely) was associated with a history of hospitalizations, lung function impairment, and numbers of neutrophils and lymphocytes in the sputum. Of the 12 microRNAs in the nely module, hsa-miR-223-3p was the highest expressed microRNA in neutrophils and was associated with increased neutrophil counts in the sputum in response to ozone exposure. Multiple microRNAs in the nely module correlated with two mRNA modules enriched for TLR (Toll-like receptor) and T-helper cell type 17 (Th17) signaling and endoplasmic reticulum stress. hsa-miR-223-3p was a key regulator of the TLR and Th17 pathways in the sputum of subjects with asthma.Conclusions: This study of sputum microRNA and mRNA expression from patients with asthma demonstrates the existence of microRNA networks and genes that are associated with features of asthma severity. Among these, hsa-miR-223-3p, a neutrophil-derived microRNA, regulates TLR/Th17 signaling and endoplasmic reticulum stress.

Early microRNA indicators of PPARα pathway activation in the liver

MicroRNAs (miRNAs) are short non-coding RNA species that play key roles in post-transcriptional regulation of gene expression. MiRNAs also serve as a promising source of early biomarkers for different environmental exposures and health effects, although there is limited information linking miRNA changes to specific target pathways. In this study, we measured liver miRNAs in male B6C3F1 mice exposed to a known chemical activator of the peroxisome proliferator-activated receptor alpha (PPARα) pathway, di(2-ethylhexyl) phthalate (DEHP), for 7 and 28 days at concentrations of 0, 750, 1500, 3000, or 6000 ppm in feed. At the highest dose tested, DEHP altered 61 miRNAs after 7 days and 171 miRNAs after 28 days of exposure, with 48 overlapping miRNAs between timepoints. Analysis of these 48 common miRNAs indicated enrichment in PPARα–related targets and other pathways related to liver injury and cancer. Four of the 10 miRNAs exhibiting a clear dose trend were linked to the PPARα pathway: mmu-miRs-125a-5p, -182−5p, -20a−5p, and -378a−3p. mmu-miRs-182−5p and -378a−3p were subsequently measured using digital drop PCR across a dose range for DEHP and two related phthalates with weaker PPARα activity, di-n-octyl phthalate and n-butyl benzyl phthalate, following 7-day exposures. Analysis of mmu-miRs-182−5p and -378a−3p by transcriptional benchmark dose analysis correctly identified DEHP as having the greatest potency. However, benchmark dose estimates for DEHP based on these miRNAs (average 163; range 126−202 mg/kg-day) were higher on average than values for PPARα target genes (average 74; range 29−183 mg/kg-day). These findings identify putative miRNA biomarkers of PPARα pathway activity and suggest that early miRNA changes may be used to stratify chemical potency.

Genome-Wide DNA Methylation Patterns in Children Exposed to Nonpharmacologically Treated Prenatal Depressive Symptoms: Results From 2 Independent Cohorts

BACKGROUND

Maternal depressive symptoms are a common phenomenon during pregnancy and are related to negative outcomes for child development and health. Modifications in child DNA methylation are discussed as an underlying mechanism for the association between prenatal depressive symptoms and alterations in child outcomes. However, formerly reported genome-wide associations have yet to be replicated.

METHODS

In an epigenome-wide association study (EWAS), alterations of DNA methylation related to maternal prenatal depressive symptoms were investigated in buccal cell samples from 174 children (n = 52 exposed to prenatal depressive symptoms; 6-9 years old) of the German longitudinal study FRAMES-FRANCES. Whole blood samples from the independent, age-comparable ARIES subsample of the ARIES/ALSPAC study (n = 641; n = 159 exposed to prenatal depressive symptoms; 7-8 years old) were examined as a confirmation sample. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. DNA methylation was analyzed with the Infinium Human Methylation 450k BeadChip. Modifications in single CpGs, regions, and biological pathways were investigated. Results were adjusted for age and birth outcomes as well as postnatal and current maternal depressive symptoms. Analyses were performed for the whole sample as well as separated for sex.

RESULTS

The EWAS yielded no differentially methylated CpG or region as well as no accordance between samples withstanding correction for multiple testing. In pathway analyses, no overlapping functional domain was found to be enriched for either sample. A comparison of current and former findings suggests some overlapping methylation modifications from infancy to childhood. Results suggest that there might be sex-specific differential methylation, which should be further investigated in additional studies.

CONCLUSIONS

The current, mainly nonsignificant, results challenge the assumption of consistent modifications of DNA methylation in children exposed to prenatal depressive symptoms. Despite the relatively small sample size used in this study, this lack of significant results may reflect diverse issues of environmental epigenetic studies, which need to be addressed in future research.

Transcriptome responses in blood reveal distinct biological pathways associated with arsenic exposure through drinking water in rural settings of Punjab, Pakistan

BACKGROUND

Groundwater Arsenic (As) contamination is a global public health concern responsible for various health implications and a neglected area of environmental health research in Pakistan. Because of interindividual differences in genetic predisposition, As-related health issues may not be equally distributed among the As-exposed population. However, till date, no studies have been conducted including multiple SNPs involved in As metabolism and disease risk using a linear mixed effect model approach to analyze peripheral blood transcriptomics results.

OBJECTIVES

In order to detect early responses on the gene expression level and to evaluate the impact of selected SNPs inferring disease risks associated with As exposure, we designed a systematic study to investigate blood transcriptomics profiles of 57 differentially exposed rural subjects living in drinking water As-contaminated settings of Lahore and Kasur districts in Punjab Province in southeast Pakistan. Exposure among the subjects was correlated with individual transcriptome responses applying urinary As profiles as the main biomarker for risk stratification.

METHODS

We performed whole genome gene expression analysis in blood of subjects using microarrays. Linear effect mixed models were applied for evaluating the combined impact of SNPs hypothetically increasing the risk for As exposure-induced health effects (GSTM1, GSTT1, As3MT, DNMT1, MTHFR, ERCC2 and EGFR).

RESULTS

Our findings confirmed important signaling, growth factor, cancer and other disease related pathways known to be associated with increased As exposure levels. In addition, upon implementing our integrative SNPs-based genetic risk factor, pathways associated with an increased risk of NAFLD and diabetes appeared significantly enhanced by down-regulation of genes NDUFV3, IKBKB, IL6R, ADIPOR1, PPARA, OGT and FOXO1.

CONCLUSION

We report the first comprehensive study applying state-of-the-art bioinformatics approaches to address multiple SNP-based inter-individual variability in adverse molecular responses among subjects exposed to drinking water As contamination in Pakistan thereby providing strong evidence of various gene expression targets associated with development of known As-related diseases.

Highly Sensitive and Selective Detection of Arsenic Using Electrogenerated Nanotextured Gold Assemblage

Arsenic is considered as a toxic heavy metal which is highly detrimental to ecological systems, and long-term exposure to it is highly dangerous to life as it can cause serious health effects. Timely detection of traces of active arsenic (As³⁺) is very crucial, and the development of simple, cost-effective methods is imperative to address the presence of arsenic in water and food chain. Herein, we present an extensive study on chemical-free electrogenerated nanotextured gold assemblage for the detection of ultralow levels of As³⁺ in water up to 0.08 ppb concentration. The gold nanotextured electrode (Au/GNE) is developed on simple Au foil via electrochemical oxidation-reduction sweeps in a metal-ion-free electrolyte solution. The ultrafine nanoscale morphological attributes of Au/GNE substrate are studied by scanning electron microscopy. Square wave anodic stripping voltammetry (ASV) response for different concentrations of arsenites is determined and directly correlated with As³⁺ detection regarding the type of electrolyte solution, deposition potential, and deposition time. The average of three standard curves are linear from 0.1 ppb up to 9 ppb (n = 15) with a linear regression coefficient R ² = 0.9932. Under optimized conditions, a superior sensitivity of 39.54 μA ppb^-1 cm^-2 is observed with a lower detection limit of 0.1 ppb (1.3 nM) (based on the visual analysis of calibration curve) and 0.08 ppb (1.06 nM) (based on the standard deviation of linear regression). Furthermore, the electrochemical Au/GNE is also applicable for arsenic detection in a complex system containing Cu²⁺, Ni²⁺, Fe²⁺, Pb²⁺, Hg²⁺, and other ions for the selective and sensitive analysis. Au/GNE substrate also possesses remarkable reproducibility and high stability for arsenic detection during repeated analysis and thus can be employed for prolonged applications and reiterating analyses. This electrochemically generated nanotextured electrode is also applicable for As³⁺ detection and analysis in a real water sample under optimized conditions. Therefore, fabrication conditions and analytical and electroanalytical performances justify that because of its low cost, easy preparation method and assembly, high reproducibility, and robustness, nanosensor Au/GNE can be scaled up for further applications.

Arsenic exposure of child populations in Northern Argentina

Chronic exposure to inorganic arsenic (As) is associated with numerous adverse effects. Argentina is one of the countries affected by arsenicism; however, there are few studies that evaluate inorganic As exposure and its effects on child population. The aim of this study is to evaluate exposure to As through water and food in child populations living in the provinces of Santiago del Estero and Chaco (n = 101), and to determine the impact of this exposure analysing biomarkers of exposure (urine and hair As contents) and effect [8-oxo-7,8-dihydro-2'-deoxyguanosine (8-OHdG)]. The populations selected live in three areas with different levels of As in the drinking water (Santa Teresa de Carballo, 0.925 mg/L; Taco Pozo, 0.210 mg/L; Jumi Pozo, 0.016 mg/L). The As intakes through water and food are especially high in the areas with the greatest As exposure (Santa Teresa de Carballo, 1575 ± 8 μg/day; Taco Pozo, 386 ± 8 μg/day; Jumi Pozo, 39 ± 1 μg/day). The total As contents in most of the samples of hair (0.11-13.11 mg/kg) and urine (31-4258 μg/g creatinine) are higher than the reference values (hair: 1 mg/kg; urine: 50 μg/g creatinine). The increase in the level of As exposure alters the profile of metabolites in urine, with a decrease of dimethylarsinic acid (10%) and an increase in the percentages of monomethylarsonic acid (4%) and inorganic As (6%). The results also show high values of 8-OHdG (3.7-37.8 μg/g creatinine), a oxidative DNA damage marker, in the two areas with greater As exposure.

Endocrine active metals, prenatal stress and enhanced neurobehavioral disruption

Metals, including lead (Pb), methylmercury (MeHg) and arsenic (As), are long-known developmental neurotoxicants. More recently, environmental context has been recognized to modulate metals toxicity, including nutritional state and stress exposure. Modulation of metal toxicity by stress exposure can occur through shared targeting of endocrine systems, such as the hypothalamic-pituitary-adrenal axis (HPA). Our previous rodent research has identified that prenatal stress (PS) modulates neurotoxicity of two endocrine active metals (EAMs), Pb and MeHg, by altering HPA and CNS systems disrupting behavior. Here, we review this research and further test the hypothesis that prenatal stress modulates metals neurotoxicity by expanding to test the effect of developmental As ± PS exposure. Serum corticosterone and behavior was assessed in offspring of dams exposed to As ± PS. PS increased female offspring serum corticosterone at birth, while developmental As exposure decreased adult serum corticosterone in both sexes. As + PS induced reductions in locomotor activity in females and reduced response rates on a Fixed Interval schedule of reinforcement in males, with the latter suggesting unique learning deficits only in the combined exposure. As-exposed males showed increased time in the open arms of an elevated plus maze and decreased novel object recognition whereas females did not. These data further confirm the hypothesis that combined exposure to chemical (EAMs) and non-chemical (PS) stressors results in enhanced neurobehavioral toxicity. Given that humans are exposed to multiple environmental risk factors that alter endocrine function in development, such models are critical for risk assessment and public health protection, particularly for children.