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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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Environmental Science & Technology

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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

ACS Paragon Plus Environment

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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

about

disease/physiologic

phenotypes.17,18


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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1

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

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