Neonatal Metabolomic Profiles Related to Prenatal Arsenic

Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. ... Prenatal Diagnosis 2017 37 (13), 1...
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Neonatal Metabolomic Profiles Related to Prenatal Arsenic Exposure Jessica Evelyn Laine, Kathryn A Bailey, Andrew F. Olshan, Lisa Smeester, Zuzana Drobna, Miroslav Stýblo, Christelle Douillet, Gonzalo García-Vargas, Marisela RubioAndrade, Wimal W Pathmasiri, Susan L McRitchie, Susan J. Sumner, and Rebecca C Fry Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04374 • Publication Date (Web): 02 Dec 2016 Downloaded from http://pubs.acs.org on December 10, 2016

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Neonatal Metabolomic Profiles Related to

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Prenatal Arsenic Exposure

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Jessica E. Laine‡, Kathryn A. Bailey†, Andrew F. Olshan‡, Lisa Smeester†, Zuzana Drobná§, Miroslav Stýblo┴, Christelle Douillet┴, Gonzalo García-Vargas║, Marisela Rubio-Andrade║, Wimal Pathmasiriϕ, Susan McRitchieϕ, Susan J. Sumnerϕ and Rebecca C. Fry†*

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Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, 27599, USA † Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, 27599, USA. § Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, North Carolina, 27695, USA. ┴ Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, 27599, USA. ║ Facultad de Medicina, Universidad Juarez del Estado de Durango, Gómez Palacio, Durango, 35050, Mexico. ϕ RTI International, Research Triangle Park, North Carolina, 27709, USA.

Short title: Arsenic and the neonate metabolome

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ABSTRACT

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Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident

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at birth and later in life. An understanding of the relationship between prenatal iAs

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exposure and alterations in the neonatal metabolome could reveal critical molecular

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modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic

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resonance (NMR) spectroscopy-based metabolomic analysis was used to identify

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metabolites in neonate cord serum associated with prenatal iAs exposure in participants

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from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in Gómez

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Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites

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were identified as significantly associated with total urinary iAs and/or iAs metabolites,

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measured as %iAs, %monomethylated arsenicals (MMAs) and %dimethylated arsenicals

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(DMAs). A total of seventeen metabolites were identified as significantly associated with

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total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of

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changes in important biochemical pathways such as vitamin metabolism, the citric acid

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(TCA) cycle, and amino acid metabolism. These data highlight that maternal

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biotransformation of iAs and neonatal levels of iAs and its metabolites are associated

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with differences in neonate cord metabolomic profiles. The results demonstrate the

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potential utility of metabolites as biomarkers/indicators of in utero environmental

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exposure.

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INTRODUCTION

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in their drinking water that have been linked to chronic diseases such as precancerous

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skin lesions, cardiovascular disease, peripheral vascular disease, diabetes mellitus,

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hypertension, and cancers of the urinary bladder, skin, lung, and liver.1-3 Exposure to iAs

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during periods of increased susceptibility, including in utero and early childhood, is of

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particular concern. Early-life iAs exposure is associated with both short- and long-term

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adverse health effects including increased risk for preterm birth, low birth weight, infant

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mortality, childhood neurological impairments, susceptibility to infectious disease, and

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chronic disease development later in life, including cancer.4

Millions of individuals worldwide are exposed to levels of inorganic arsenic (iAs)

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A precise mechanistic basis for the relationship between prenatal iAs exposure

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and disease has not been elucidated and is likely multi-factorial. Potential mechanisms

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for iAs-associated diseases include the induction of oxidative stress, enzyme inhibition,

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interference with DNA repair, chromosomal aberrations, and perturbation of cell

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signaling pathways, and epigenetic instability.5 Specific to prenatal iAs exposure,

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previous studies have highlighted changes in fetal gene expression,6-8 fetal leukocyte

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DNA methylation,7-10 and increased protein signaling of inflammatory mediators in cord

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serum.11 In spite of these advances, much remains unknown about the biochemical

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mechanisms underlying the health effects associated with prenatal iAs exposure.

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One factor known to influence the susceptibility to iAs-associated diseases is an

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individual’s efficiency of iAs biotransformation/metabolism. Arsenic is biotransformed

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in humans to produce monomethylated arsenicals (MMAs) and dimethylated arsenicals

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(DMAs) that can exist in either a trivalent (+3) or pentavalent (+5) oxidation state.

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Specifically, six major arsenicals associated with iAs exposure have been detected in

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human urine, namely arsenite (iAsIII), arsenate (iAsV), monomethylarsonous acid

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(MMAIII), monomethylarsonic acid (MMAV), dimethylarsinous acid (DMAIII), and

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dimethylarsinic acid (DMAV).12, 13 High urinary proportions of MMAs/total arsenic and

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increased MMAs/DMAs are likely indicators of inefficient iAs biotransformation.

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Inefficient biotransformation (or methylation) of iAs, as indicated by higher proportions

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of MMAs in urine, has been associated with the development of several adverse

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outcomes in humans including urinary bladder cancer, non-melanoma skin cancers,

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carotid atherosclerosis, and chromosomal aberrations (reviewed in14). In recent work, we

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have shown that elevated levels and proportions of MMAs in maternal urine are

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associated with decreases in placental weight and fetal birth weight.15

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There are an increasing number of system-wide methodologies available to

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provide insight into mechanisms underlying environmentally-mediated disease, including

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metabolomics. Metabolomics involves the analysis of the low molecular weight

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metabolites in cells, tissues, and biological fluids, providing a profile of the biochemistry

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and end result of multiple enzymatic processes, or the “metabotype,” of an individual.

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Metabolomic profiling has been used to study the impacts of nutritional, pharmaceutical,

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and environmental toxicant exposures.16 Metabolomic profiling is an ideal tool for

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biomarker assessment as it is efficient and relatively noninvasive, and it can convey

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information

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identified in both maternal and fetal-health studies have been associated with fetal

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malformations, preterm delivery, premature rupture of membranes, gestational diabetes

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disease/physiologic

phenotypes.17,18

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Metabolomic

biomarkers

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mellitus, preeclampsia, low birth weight, and hypoxic-ischemic encephalopathy- all

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representing adverse outcomes that are relevant to iAs exposure.19

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Recent developments in metabolomic technologies and statistical methodologies

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have allowed for explorations for global changes in biological systems, predictive

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modeling of metabolomics alterations, and the use of metabolomics in clinical

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applications in regards to various exposures and/or disease states. Specifically, the use of

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nuclear magnetic resonance (NMR) spectroscopy to quantify and determine the

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metabotype of an individual or a population provides an excellent tool for detection of

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metabolites in ways that are non-biased, easily quantifiable, require little to no

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separation- all allowing for the identification of novel compounds that are less tractable

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to GC-MS or LC-MS analysis.20 Additionally, a NMR-based metabolomic approach is

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particularly beneficial for human samples that may not be easily attainable or are limited

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in supply, as it is a non-destructive method that enables preservation of the sample and

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analytes for subsequent analyses.21 NMR spectroscopy, combined with multivariable

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statistical analysis, can be a powerful tool to detect metabolic changes induced by very

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low doses of an exposure based on over- or underexpression of endogenous molecules.

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Previous work has demonstrated the validity of NMR methods for populations exposed to

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iAs.22

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We recently identified differences in the levels of 132 human urinary metabolites

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associated with iAs exposure in adults.23 Specifically, 59 metabolites that play a role in

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tricarboxylic acid cycle and amino acid metabolism in diabetics were identified. Building

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upon our understanding of the relationship between iAs exposure and the metabolome,

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the aim of the present study was to identify a potential metabolomics signature of in utero

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iAs exposure in cord serum using samples from the Biomarkers of Exposure to ARsenic

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(BEAR) prospective pregnancy cohort in Gómez Palacio, Mexico.15 Investigations of the

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metabolome of individuals exposed prenatally to iAs have not been previously

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investigated. The pregnant women in this cohort lived in areas with elevated iAs in their

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drinking water (up to 236 µg As/L, mean of 24.6 µg As/L).15 Several indicators of in

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utero iAs exposure were used, including the sum of iAsIII and iAsV in maternal urine (U-

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iAs), the sum of MMAIII and MMAV (U-MMAs), the sum of DMAIII and DMAV (U-

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DMAs), and the sum of U-iAs, U-MMAs, and U-DMAs as total As in maternal urine (U-

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tAs). As the efficiency of iAs metabolism has been linked to disease status, we set out to

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determine whether there was a relationship between metabolite patterns in cord serum

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and maternal iAs metabolism efficiency. Additionally, as a measurement of neonatal

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levels of iAs we analyzed total cord serum arsenic (C-tAs) and/or iAs metabolites in cord

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serum (C-iAs, C-MMAs, and/or C-DMAs). Using a quantitative metabolomics approach

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coupled with multiple regression analyses, we identified metabolites altered in cord

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serum that are significantly associated with prenatal iAs exposure and/or its methylated

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metabolites.

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MATERIALS AND METHODS

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Subcohort Selection

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The BEAR cohort has been described previously.15 Briefly, women were recruited

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prior to the time of delivery from the time frame of August 2011 to March 2012 at the

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General Hospital of Gómez Palacio. All procedures associated with this study were

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approved by the Institutional Review Boards of Universidad Juárez del Estado de

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Durango (UJED), Gómez Palacio, Durango, Mexico, and the University of North

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Carolina at Chapel Hill (UNC), Chapel Hill, North Carolina, U.S.A. In the present study,

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a subset of 50 cord serum samples were selected from the larger BEAR cohort that

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spanned a range of iAs exposure where drinking water had variable levels of iAs. These

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samples have been previously described as they were prioritized for other

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molecular/mechanistic investigations, including assessment of iAs-associated microRNA

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expression,24 DNA methylation,8 gene expression,8, 24 and protein expression.25

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Methods for the measurements of iAs levels in this cohort have been previously

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described.15 Briefly, the assessment of the levels of iAs in drinking water included

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samples from the study participants’ home of their primary source of drinking water was

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collected by the research team within four weeks of delivery. Maternal spot urine samples

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were collected at the time of delivery, immediately transferred to cryovials and placed in

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liquid nitrogen until and stored at -80oC until further processing.

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Arsenical levels in maternal urine have been used as indicators of prenatal iAs

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exposure.15,

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HG-AAS with cryotrapping (CT) as described previously.27-29 The LODs for U-iAs, U-

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MMAs, and U-DMAs were 0.2, 0.1, and 0.1 µg As/L, respectively. U-tAs was

Concentrations of U-iAs, U-MMAs, and U-DMAs were determined by

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determined by summing U-iAs, U-MMAs and U-DMAs. To account for differences in

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water intake/differential hydration, concentrations of U-iAs, U-MMAs, and U-DMAs in

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each urine sample were adjusted using the following equation: iAs x (mean SG-

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1)/(individual SG-1) as previously described.30 The efficiency of iAs biotransformation

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was determined by calculating the proportions of the individual arsenicals iAs, MMAs,

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and DMAs relative to total urinary As for each study subject. Metabolism efficiency for

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iAs in maternal urine was determined by calculating the percentage of the individual

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metabolites out of the total (U-%iAs, U-%MMAs, and U-%DMAs).

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Cord blood was collected immediately after newborn delivery using an

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anticoagulant-free vacutainer tube. Following clot formation, the tube was centrifuged at

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177.1 g-force and the serum was collected and stored at -80°C until aliquots were shipped

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on dry ice to UNC-Chapel Hill, NC, where they were immediately stored at -80oC.

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Concentrations of cord serum arsenic included the measurement of cord serum-analyzed

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iAs (C-iAs), MMAs (C-MMAs), and DMAs (C-DMAs) were determined using HG-CT-

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ICP-MS as described previously.28, 31 The LODs for C-iAs, C-MMAs, and C-DMAs were

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1.45, 0.06, and 0.12 pg As/mL, respectively. C-tAs was determined by summing C-iAs,

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C-MMAs and C-DMAs. The percentages of the individual metabolites were calculated

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relative to the total (C-%iAs, C-%MMAs, and C-%DMAs).

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A certified standard reference material, Arsenic Species in Frozen Human Urine

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(SRM 2669; National Institute of Standards and Technology) was used to assure accuracy

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of iAs speciation analysis. Here, aliquots of SRM were diluted in urine or serum from an

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unexposed subject and analyzed by HG-CT-AAS and HG-CT-ICP-MS, respectively. The

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concentrations of iAs species in SRM in urine (after deduction of background iAs)

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ranged from 88% to 105% of the certified values (105% for iAs, 88% for MMAs, 99%

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for DMAs); the concentrations of iAs species in SRM in serum ranged from 85% to 90%

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of the certified value (85% for iAs, 90% for MMAs, and 87% for DMAs).

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H NMR Spectroscopy

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The 50 cord serum samples were analyzed using nuclear magnetic resonance

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(NMR) spectroscopy to identify neonatal metabolites that were associated with maternal

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urinary arsenicals. Sample preparation, data acquisition, and data analysis followed

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procedures previously described.32-35 Each of the 50 samples were processed individually,

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whereby 150 µl of serum was prepared by adding 50 µl of 0.9% NaCl solution containing

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4mM sodium formate (Chemical Shift Indicator) and 0.2% NaN3 (to inhibit bacterial

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growth) in D2O. Each 200µl sample was then vortexed for 30 seconds and transferred

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into 3mm NMR tubes (Bruker Bio-Spin, Germany).

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H NMR spectra of serum samples were acquired on a Bruker Avance III 950

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MHz NMR spectrometer (located at the David H. Murdock Research Institute at

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Kannapolis, NC, USA) using a 5 mm cryogenically cooled ATMA inverse probe and

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ambient temperature of 25℃. A CPMG pulse sequence with presaturation (cpmgpr1d)

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was used for data acquisition. For each sample, 256 transients were collected into 32k

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data points using a spectral width of 19.5 kHz (20.5 ppm), 2 s relaxation delay, 400 µs

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fixed echo time, loop for T2 filter (l4)=80, and an acquisition time of 1.678 s per FID.

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The water resonance was suppressed using resonance irradiation during the relaxation

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delay. NMR spectra were processed using TopSpin software (Bruker, Germany). Spectra

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were zero filled, and Fourier-transformed after exponential multiplication with line

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broadening factor of 0.5. Phase and baseline of the spectra were manually corrected for

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each spectrum. Spectra were referenced internally to the formate signal. The quality of

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each NMR spectrum was assessed for the level of noise and alignment of identified

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markers. In addition, quality control (QC) pooled samples were prepared using small

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aliquots of each study sample by combining 12 µl aliquots into eppendorf tubes. Using a

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quality control (QC) procedure described by Chan and co-workers,36 pooled samples

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were prepared using aliquots of each study sample. Principal component analysis was

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performed to determine that sufficient separation of the data occurred. These QC samples

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were processed identically to each of the study samples.

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Metabolites were identified and the relative metabolite concentrations were

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determined prior to data analysis. This deconvolution of NMR peaks into metabolites and

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relative quantification of these identified metabolites are advantageous over traditional

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binning method in pattern recognition methods and identifying statistically significant

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changes of metabolites.37, 38 A targeted profiling approach was carried out to identify the

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metabolites. Metabolites were modeled using peak centers and splitting patterns of peaks

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(J-coupling) from the NMR spectra, signals were matched by the analyst to the 36

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metabolites identified using the library in Chenomx NMR Suite 8.1 Professional software

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(Chenomx, Edmonton, Alberta, Canada). This software contains an internal library

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adjustment for increments in chemical shift based on pH variation.38 Concentration

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determinations for metabolites were established using relative integration of the analyte

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to the formate internal standard. The library of concentrations was developed to account

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for differences in integral values as related to the relaxation time of the signal.38

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Statistical Analyses

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Spearman rank correlations between maternal urinary and cord serum arsenicals

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were calculated. Multivariable linear regression was used to determine the relationship

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between U-tAs, U-%iAs, U-%MMAs, and U-%DMAs C-tAs, C-%iAs, C-%MMAs, or

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C-%DMAs and the Chenomx concentration-matched metabolites using SAS 9.4 (SAS

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Institute Inc, Cary, NC). Potential confounders were identified a priori based on their

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known or potential association with both the exposure (iAs or its methylated metabolites)

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and the outcome (cord serum metabolite levels). All models were adjusted for maternal

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age (continuous), gender of the infant (male/female), and gestational age (continuous). To

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improve model fit U-tAs and C-tAs were was natural log transformed. Regression

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assumptions of linearity and the homogeneity of residuals were evaluated by examination

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of appropriate residual plots. A false discovery rate (FDR) correction was calculated for

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all p-values to account for multiple comparisons, with significance set at the acceptable

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level of q< 0.20.23, 39, 40 The identified cord serum metabolites that were significantly

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associated with U-tAs and/or the indicators for iAs biotransformation/metabolism

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efficiency indicators (U-%iAs, U-%MMAs, and U-%DMAs) and for cord serum total

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arsenic (C-tAs) and/or percentages of iAs in cord serum (C-%iAs, C-%MMAs, and/or C-

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%DMAs) were then analyzed for enriched pathways, using MetaboAnalyst.41

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Significance was set at p