Prenatal Lead (Pb) Exposure and Peripheral Blood DNA Methylation (5mC) and Hydroxymethylation (5hmC) in Mexican Adolescents from the ELEMENT Birth Cohort
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 6 Linguagem: Inglês
10.1289/ehp8507
ISSN1552-9924
AutoresChristine A. Rygiel, Jaclyn M. Goodrich, Maritsa Solano-González, Adriana Mercado‐García, Howard Hu, Martha María Téllez‐Rojo, Karen E. Peterson, Dana C. Dolinoy,
Tópico(s)Child Nutrition and Water Access
ResumoVol. 129, No. 6 ResearchOpen AccessPrenatal Lead (Pb) Exposure and Peripheral Blood DNA Methylation (5mC) and Hydroxymethylation (5hmC) in Mexican Adolescents from the ELEMENT Birth Cohort Christine A. Rygiel, Jaclyn M. Goodrich, Maritsa Solano-González, Adriana Mercado-García, Howard Hu, Martha M. Téllez-Rojo, Karen E. Peterson, and Dana C. Dolinoy Christine A. Rygiel Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author , Jaclyn M. Goodrich Address correspondence to Jaclyn M. Goodrich, Environmental Health Sciences, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109 USA. Telephone: (734) 647-4564, Email: E-mail Address: [email protected] Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author , Maritsa Solano-González National Institute of Public Health, Cuernavaca, Mexico Search for more papers by this author , Adriana Mercado-García National Institute of Public Health, Cuernavaca, Mexico Search for more papers by this author , Howard Hu Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Martha M. Téllez-Rojo National Institute of Public Health, Cuernavaca, Mexico Search for more papers by this author , Karen E. Peterson Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author , and Dana C. Dolinoy Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author Published:21 June 2021CID: 067002https://doi.org/10.1289/EHP8507AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Gestational lead (Pb) exposure can adversely affect offspring health through multiple mechanisms, including epigenomic alterations via DNA methylation (5mC) and hydroxymethylation (5hmC), an intermediate in oxidative demethylation. Most current methods do not distinguish between 5mC and 5hmC, limiting insights into their individual roles.Objective:Our study sought to identify the association of trimester-specific (T1, T2, T3) prenatal Pb exposure with 5mC and 5hmC levels at multiple cytosine-phosphate-guanine sites within gene regions previously associated with prenatal Pb (HCN2, NINJ2, RAB5A, TPPP) in whole blood leukocytes of children ages 11–18 years of age.Methods:Participants from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) birth cohorts were selected (n=144) for pyrosequencing analysis following oxidative or standard sodium bisulfite treatment. This workflow directly quantifies total methylation (5mC+5hmC) and 5mC only; 5hmC is estimated by subtraction.Results:Participants were 51% male, and mean maternal blood lead levels (BLL) were 6.43±5.16μg/dL in Trimester 1 (T1), 5.66±5.21μg/dL in Trimester 2 (T2), and 5.86±4.34μg/dL in Trimester 3 (T3). In addition, 5hmC levels were calculated for HCN2 (mean±standard deviation(SD), 2.08±4.18%), NINJ2 (G/C: 2.01±5.95; GG: 0.90±3.97), RAB5A (0.66±0.80%), and TPPP (1.11±6.67%). Furthermore, 5mC levels were measured in HCN2 (81.3±9.63%), NINJ2 (heterozygotes: 38.6±7.39%; GG homozygotes: 67.3±9.83%), RAB5A (1.41±1.21%), and TPPP (92.5±8.03%). Several significant associations between BLLs and 5mC/5hmC were identified: T1 BLLs with 5mC in HCN2 (β=−0.37, p=0.03) and 5hmC in NINJ2 (β=0.49, p=0.003); T2 BLLs with 5mC in HCN2 (β=0.37, p=0.03) and 5hmC in NINJ2 (β=0.27, p=0.008); and T3 BLLs with 5mC in HCN2 (β=0.50, p=0.01) and NINJ2 (β=−0.35, p=0.004) and 5hmC in NINJ2 (β=0.45, p<0.001). NINJ2 5mC was negatively correlated with gene expression (Pearson r=−0.5, p-value=0.005), whereas 5hmC was positively correlated (r=0.4, p-value=0.04).Discussion:These findings suggest there is variable 5hmC in human whole blood and that prenatal Pb exposure is associated with gene-specific 5mC and 5hmC levels at adolescence, providing evidence to consider 5hmC as a regulatory mechanism that is responsive to environmental exposures. https://doi.org/10.1289/EHP8507IntroductionThe Developmental Origins of Health and Disease (DOHaD) hypothesis (Barker 2007) postulates that in utero exposures, including exposure to environmental contaminants such as lead (Pb), can permanently modify an organism’s molecular biology, physiology, and metabolism, potentially leading to myriad effects on cognition, growth, maturation, and metabolic risk (Bellinger et al. 2016). Pb is a widely abundant environmental pollutant known to be a potent neurotoxicant, even at low levels. Pregnancy is a vulnerable time period for Pb exposure because both current and past maternal Pb exposure may affect the developing fetus. Animal and human studies have provided evidence for the impact of early-life Pb exposure on the epigenome (Faulk et al. 2013; Montrose et al. 2020; Goodrich et al. 2015; Rygiel et al. 2020; Wu et al. 2017; Dou et al. 2019).Epigenetic modifications are mitotically heritable molecular changes that regulate gene expression without altering the underlying DNA sequence. DNA 5-methylcytosine (5mC) is the addition of a methyl group covalently bound to the 5′-carbon of Cytosine; in mammals this typically occurs on a Cytosine adjacent to a Guanine, referred to as a CpG site (Illingworth et al. 2010; Ohlsson and Kanduri 2002). During demethylation, 5mC is oxidized into 5-hydroxymethylcytosine (5hmC), an intermediate of oxidative demethylation that can remain as a stable modification (Sadakierska-Chudy et al. 2015; Bachman et al. 2014). 5hmC perturbations in the brain have been reported in several early-life neurological disorders and later-life neurodegenerative disorders in both human and animal studies (Zhao et al. 2017; Wang et al. 2013; Zhubi et al. 2014; Chouliaras et al. 2013). Levels of 5hmC vary with tissue, where brain contains the highest levels and blood contains the lowest levels, but 5hmC is still detectable in blood at modest levels (Kochmanski et al. 2018a, 2018b; Globisch et al. 2010; Li and Liu 2011; Nestor et al. 2012). Recent studies suggest that the functional roles of 5hmC are distinct from 5mC (López et al. 2017). 5mC and 5hmC undergo dynamic changes during early gestation that disseminate through mitosis to new cells and developing organs, potentially persisting throughout the life span. These modifications are important for gene regulation, including in the nervous system, and have implications for learning and memory from early development, adolescence, and into adulthood (Jobe and Zhao 2017; Spiers et al. 2017; Zhao et al. 2017; Vogel Ciernia and LaSalle 2016). Most current methods for quantifying DNA methylation, including the gold standard sodium bisulfite sequencing, collectively measure 5mC and 5hmC without distinguishing between the two. This approach has limited our ability to identify whether environmental exposures alter 5mC, 5hmC, or both and what the implications are for early life neurodevelopment and long-term health.Pregnancy is a key starting point to investigate the association between environmental contaminants and epigenetic modifications leading to health outcomes in offspring. An important mechanism by which developmental exposures can affect long-term disease risk is through the disruption of normal epigenetic processes, thereby affecting gene regulation and subsequent chronic outcomes (Dolinoy and Jirtle 2008). A study prenatally exposing dams, which are genetically invariant mice 93% identical to C57BL/6J strain, to physiologically relevant doses of Pb (2.1 ppm, 16 ppm, and 32 ppm in water) 2 wk prior to mating through lactation until weaning at postnatal day 21 assessed DNA total methylation via pyrosequencing at four Intracisternal A particle (IAP) elements in the brain (Montrose et al. 2017). IAPs are a class of murine retrotransposons that are environmentally responsive (Waterland and Jirtle 2004). Prenatal Pb exposure reduced DNA methylation at three of the IAPs in the brain with dose–dependent and sex-specific effects in comparison with control mice. A study exposing rodents to 0.2% Pb acetate in water postnatally reported hypomethylation and initial transient 8-hydroxy-2′-deoxyguanosine (oxo8dG) accumulation in the cerebral cortex, a neurodegeneration biomarker (Bolin et al. 2006). A U.S. prospective human pregnancy cohort, Project Viva, with low Pb exposure (averaging 1.22±0.63μg/dL in erythrocytes) conducted an epigenome-wide analysis on 268 umbilical cord blood samples to evaluate the association between maternal Pb exposure and DNA methylation and identified sex-specific differentially methylated CpG sites (Wu et al. 2017). Using a similar epigenome-wide approach but with a moderately to highly exposed population, we identified differentially methylated genes in 89 umbilical cord blood DNA samples from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) cohort that varied by trimester of exposure (Rygiel et al. 2020). Although these and several other studies provide evidence for associations between Pb exposure and DNA methylation profiles in humans and animals, only total methylation was assessed.Only one study has considered intermediates (5hmC) in adverse environmental associations with prenatal Pb, which may have implications for understanding the association between prenatal exposures and adverse health outcomes. That study provided some evidence for associations between prenatal Pb and 5hmC in umbilical cord blood at the regional level through a modified epigenome-wide method called hMeDIP-450K chip (Sen et al. 2015). The investigators identified both sex-independent and sex-specific differentially methylated and hydroxymethylated regions, where sex-dependent associations were more common in 5mC in comparison with 5hmC. There is a mechanistic basis by which Pb may affect 5hmC levels. Pb-induced oxidative stress results in the accumulation of α-ketoglutarate (α-KG) (Tretter and Adam-Vizi 2005), a cofactor for ten-eleven translocation (TET) enzymes, which are involved in the oxidation of 5mC to 5hmC (Chia et al. 2011; Coulter et al. 2013). Thus, Pb may increase activity of TET enzymes and increase 5hmC across the genome (Chia et al. 2011).We hypothesized that prenatal Pb exposure would alter epigenetic programming of 5mC and 5hmC within genes involved in neurological function and that this can be detected in samples from adolescents. We leveraged the ELEMENT longitudinal birth cohort with rich data on prenatal Pb exposure and offspring follow-up through adolescence, including whole blood leukocyte DNA and prenatal Pb exposure biomarkers, to investigate whether Pb is associated with programming of neurocognitive-related genes between the ages of 11–18 y in offspring whole blood leukocytes. We used oxidative-bisulfite (oxBS) pyrosequencing to profile both 5mC and 5hmC in adolescent blood leukocyte DNA at four neurocognitive-related genes—HCN2, NINJ2, RAB5A, and TPPP—that were associated with prenatal Pb exposure in ELEMENT in our previous epigenome-wide study of total methylation in umbilical cord blood samples (Rygiel et al. 2020).MethodsStudy PopulationParticipants are from the second and third cohorts of the ELEMENT project, a series of longitudinal birth cohorts originally designed to investigate the influence of Pb exposure—in utero and in childhood —on child growth and neurodevelopment. Mothers were recruited between 1997 and 2000 (second cohort) and 2001–2005 (third cohort) from the Mexican Social Security Institute hospital in Mexico City. Eligibility and exclusion criteria are as previously described (Perng et al. 2019; Hu et al. 2006). Data collected included sex, gestational age, household socioeconomic status, anthropometry, and other environmental exposures at multiple follow-up visits from participants’ infancy through adolescence. For the current study, 144 of the ELEMENT participants who were followed-up through the 2015 study visit and who provided an adolescent blood sample for DNA isolation were selected (Figure 1). These participants with prenatal and adolescent Pb measures and/or previous cord blood DNA methylation analysis were prioritized for the adolescent epigenetic analysis (Rygiel et al. 2020).Figure 1. Data collection timeline from the ELEMENT birth cohort in Mexico City, Mexico, between 1997–2000 and 2001–2005 for pregnancies and through adolescence into 2018. Maternal whole blood samples were collected during the first trimester (T1), second trimester (T2), and third trimester (T3) and analyzed for blood Pb concentrations. Maternal bone Pb was measured 1 month postpartum as an indicator of cumulative exposure over the course of the gestational period. Adolescent whole blood samples were collected in offspring at a follow-up visit occurring once between the ages of 11 and 18 y for Pb measures and DNA 5mC and 5hmC profiling. Covariate data including demographics and anthropometry were obtained at each stage with sample collection. Note: BLL, blood lead levels.Prior to participation, all mothers were informed about the study; those who agreed to participate read and signed a letter of informed consent about the original study. Children also provided informed assent, and their mothers provided informed consent prior to participation in the adolescent follow-up visits. The research protocol and all amendments to the study were approved by the ethics committees of the National Institutes of Public Health of Mexico, participating hospitals, and the internal review boards at participating institutions, including the University of Michigan.Pb Exposure Assessment and Genomic DNA IsolationCohort 2 maternal venous blood lead levels (BLLs) from each trimester were measured using inductively coupled plasma mass-spectrometry (ICP-MS; Thermo Finnigan) at the University of California, Santa Cruz, as described previously (Lamadrid-Figueroa et al. 2006). Cohort 3 trimester-specific maternal BLLs were measured using graphite furnace atomic absorption spectrometry (instrument model 3000; PerkinElmer) at the Trace Metal Laboratory of the American British Cowdry Hospital. Perinatal maternal bone Pb levels were also measured in the left patella (trabecular bone) and mid-shaft of the left tibia (cortical bone) 1 month postpartum as an indicator of cumulative Pb exposure during pregnancy using a spot-source Cd109 K-shell X-ray fluorescence (K-XRF) instrument. The technical specifications and validation of this instrument are described in detail elsewhere (Aro et al. 1994). Tibia and patella bond Pb measures were dropped if their associated uncertainty levels were greater than 10μg/g (n=1) and 15μg/g (n=0), respectively. Next, any negative tibia (n=27) and patella (n=20) bone Pb measurements were recoded as positive values with random numbers in a uniform distribution between 0 and the limit of detection. Whole blood samples were collected during the 2015 follow-up visit conducted when the children were between the ages of 11 and 18 y old, and blood was stored frozen at −80∘C. DNA was isolated from blood leukocytes using Qiagen kits and standard protocols for blood DNA isolation. Nucleic acid yield and purity were assessed first using a NanoDrop spectrophotometer (ThermoFisher Scientific), and double-stranded DNA was also quantified via a Qubit fluorometer. All DNA samples were stored at −80∘C.Candidate Gene SelectionCpG sites that mapped back to four genes relevant to neurological function were selected as candidates from an epigenome-wide study of prenatal Pb exposure conducted with a subset of ELEMENT children in umbilical cord blood (Rygiel et al. 2020) and adolescent whole blood DNA (unpublished): HCN2 (cg06657917), NINJ2 (cg19692784, cg14911689, cg05578102), RAB5A (cg17138393), and TPPP (cg25353752; probe IDs from the Infinium MethylationEPIC BeadChip). RAB5A DNA methylation in umbilical cord blood (Rygiel et al. 2020) and TPPP DNA methylation (unpublished) in adolescent whole blood were inversely associated with T1 maternal BLLs and were selected for the current targeted analysis. HCN2 and NINJ2 were selected because T1 BLLs were associated with greater than 5% change in methylation (unpublished; hypermethylated in HCN2 and hypomethylated in NINJ2) in both umbilical cord blood and adolescent whole blood leukocytes, and these genes have been previously shown to be involved in early-life neurological and/or cognitive development, for which mutations or disruptions in function have been associated with neuronal activity (Zhong et al. 2018), and neurite growth and regeneration (Araki and Milbrandt 2000). EXT1 and LRFN1 were additionally reported as significantly associated with prenatal Pb exposure biomarkers in the original umbilical cord blood epigenome-wide study but were not included in this study because a) we were unable to design primers to amplify the desired region in EXT1 and b) LRFN1 was not associated with first trimester BLLs, which was the exposure time period of most interest.DNA 5mC and 5hmC QuantificationPyrosequencing primers for HCN2, NINJ2, RAB5A, and TPPP were designed using the PyroMark Assay Design Software 2.0 Methylation Analysis (CpG) Assay (Table S1). Primers were designed to target a specific CpG site within a region reported to be differentially methylated by first trimester Pb exposure in umbilical cord blood and/or adolescent whole blood DNA (see “Candidate Gene Selection” section). ELEMENT genomic DNA samples were oxidative and/or bisulfite treated according to the NuGen TrueMethyl oxBS Module protocol (Cat. No. 0414-32) and Zymo EZ DNA Methylation kit (Cat. No. D5003). Briefly, 1μg of input genomic DNA was dissolved in nuclease-free water to 50μL, and each genomic DNA sample was divided into two aliquots (Booth et al. 2013). Each aliquot underwent independent, parallel treatments and were either oxidative bisulfite–converted with the NuGen TrueMethyl oxBS Module or Zymo EZ DNA Methylation kit. The yield and purity of treated samples were quantified using a NanoDrop spectrophotometer.The target loci within HCN2, NINJ2, RAB5A, and TPPP were amplified in both bisulfite and oxidative bisulfite–converted samples. Polymerase chain reaction (PCR) products were verified using the QIAxcel automated DNA electrophoresis. DNA methylation levels were quantified using the PyroMark Q96 ID instrument (Qiagen). Targeted pyrosequencing captured 6 CpG sites within a CpG island of HCN2, 8 within a DNase hypersensitivity region of NINJ2, 11 within a DNase hypersensitivity region in the first exon of RAB5A, and 4 within the third exon of TPPP (Table S2). The sixth CpG site covered by the NINJ2 assay was dropped because 90% of samples failed at this location, leaving seven CpG sites for NINJ2 from downstream analysis. Sequencing the bisulfite converted samples quantifies the total level of 5mC+5hmC, whereas sequencing the oxidative bisulfite–treated samples quantifies total levels of 5mC. Thus, 5hmC levels were quantified by subtracting the results from the oxidative bisulfite–converted samples (5mC) from the bisulfite-converted sample (5mC+5hmC). It should be noted that because 5hmC is based on a calculation, the difference can sometimes be negative at sites with zero or low levels of methylation as a consequence of random noise (Field et al. 2015; Stewart et al. 2015). A threshold for calling 5hmC was set to −13.85%, which corresponds to the 95th percentile of all negative 5hmC values observed across all samples and sites. It should be noted that fewer than 2.0% of all CpG sites were less than −13.85%, for which those values were dropped from subsequent analyses. For quality control, plates were run with Qiagen EpiTect bisulfite-converted unmethylated (0%) and methylated (100%) human methylation standards (Cat. No. 59665 and No. 59655). OxBS triplicates of eight samples were included for quality control. Standard deviations of triplicate measures following OxBS treatment of NINJ2 (variably methylated) and TPPP (highly methylated) for 5mC averaged 2.11 and 2.28, respectively, at each CpG site captured. Measures of 5mC were precise, with <10% coefficient of variation, whereas the measures of methylation using the standard bisulfite method were more precise, with <5% coefficient of variation. DNA samples were randomized across experimental batches that consisted of four plates for each gene. Paired oxBS and BS conversion samples were always in the same batch. All data that failed internal quality control checks within the PyroMark software were excluded from analysis (HCN2: 5mC n=3, 5hmC n=7; NINJ2: 5mC n=7, 5hmC n=9; RAB5A: 5mC n=1, 5hmC n=1; and TPPP: 5mC n=2, 5hmC n=3).Estimates of cell-type composition [CD4+ and CD8+ T lymphocytes, B cells, natural killer (NK) cells, monocytes, granulocytes] for each sample were performed using an established method based on adult cell type–specific differentially methylated regions using data from the Infinium EPIC array for each sample (Houseman et al. 2012).A methylation quantitative trait locus (MeQTL) was identified within NINJ2 and was found to be correlated with a single-nucleotide polymorphism (SNP) (C/G; rs34038797) within the pyrosequenced region (Gaunt et al. 2016). Genotyping was performed on genomic DNA from all 144 samples using the PyroMark Q96 ID instrument (Qiagen). Pyrosequencing SNP primers for NINJ2 were designed using the PyroMark Assay Design Software 2.0 Genotyping (SNP) Assay (dbSNP, https://www.ncbi.nlm.nih.gov/snp/) (Table S1). Primers were designed to target the rs34038797 SNP.Gene Expression Analysis via RNA SequencingFor a subset of participants (n=70), next-generation sequencing of RNA (RNA-seq) was conducted to obtain relative expression data for all genes, and we used data from the four genes that are the focus of this paper. Following collection of whole blood into EDTA-containing tubes, white blood cells were extracted by centrifugation, preserved in RNALater, and stored frozen (−80∘C) until further processing. RNA was isolated via the All-Prep kit (Qiagen). Quality and quantity were assessed via a Bioanalyzer Tapestation (Agilent). Libraries were prepared with Universal Plus mRNA-Seq with Human globin AnyDeplete (NuGEN Technologies, Inc.), which removes globin transcripts that are highly abundant in blood samples. Library preparation and sequencing were performed at the University of Michigan Advanced Genomics Core. Paired-end 50 cycle sequencing on an Illumina HiSeq 4000 was performed. Quality of the raw reads data for each sample was checked using FastQC (version 0.11.3, Babraham Bioinformatics). The Tuxedo Suite software package was used for alignment (Trapnell et al. 2009, 2013; Langmead et al. 2009). Briefly, reads were aligned to the reference mRNA transcriptome (hg19) using TopHat (version 2.0.13) and Bowtie2 (version 2.2.1., both from John Hopkins University, Center for Computational Biology) followed by a second round of post-alignment quality control in FastQC, which allows at most three mismatched values. One sample was dropped due to low alignment rates. All samples used in downstream analysis (n=69) had at least 16.8 million good quality aligned reads with alignment rates averaging 60%. Prior to analysis, read counts were normalized by the trimmed mean of M-values method (Robinson and Oshlack 2010).Statistical AnalysisAll statistical analyses were performed in R (version 3.6.1; R Foundation for Statistical Computing). Summary statistics were first calculated. Pb variables analyzed include maternal BLLs at each trimester (T1, T2, T3), bone Pb levels in maternal patella and tibia, and BLLs measured at the follow-up visit, which were all treated as continuous variables. We performed a Wilcox signed-rank test to compare the current subset of ELEMENT with the entire ELEMENT population from the same cohorts (2 and 3). Univariate analyses between Pb exposure variables and covariates [e.g., Pearson correlation coefficient analysis: cell-type proportions, maternal age at birth, adolescent age at time of sample collection, height-for-age z-score, body mass index (BMI)-for-age z-score, weight, education; t-test: adolescent sex; analysis of variance (ANOVA): socioeconomic status] were performed (Table S3A–C). We also estimated Pearson correlations between DNA methylation and cell-type proportions [T lymphocytes (CD4T, CD8T), B cells, NK cells, monocytes, granulocytes], where cell-types were not correlated with either DNA 5mC or 5hmC (Table S3D). Potential confounding variables that were significantly associated with both trimester-specific Pb–adolescent age at follow-up and adolescent BLL were identified for inclusion in final statistical models of site-specific DNA methylation data. Although not associated with Pb, sex was also included in statistical models because of its biological effect on DNA methylation. Experimental plate (i.e., batch) was also added as a covariate because of its effect on the technical measurement of DNA methylation. A random intercept for each participant was included in all models to account for autocorrelation from matched 5mC and 5hmC percentages for each individual. Last, in models of NINJ2 methylation, C/C individuals had 0% regional methylation as expected, because the SNP converts the CpG site to a CpC site, resulting in the inability to methylate the locus and subsequent regional methylation changes. Thus, C/C individuals (n=34) were dropped from analysis and genotype (G/G or G/C) was included as a covariate.A mixed-effects model was run, treating 5mC and 5hmC values as repeated measures of a single outcome variable—DNA methylation, given that 5mC and 5hmC are both measured at each CpG site, and the values for these two marks are dependent on each other both biologically and statistically (Kochmanski et al. 2019). To determine whether Pb exposure modifies the balance between the DNA modification categories (5mC and 5hmC), an interaction term between prenatal Pb measures and a dichotomous variable signifying whether the outcome measure was 5hmC or 5mC were included in the statistical model. Because there was evidence for an interaction between Pb and type of DNA methylation, we next used separate mixed-effects regression models treating CpG sites as repeated measures to estimate associations between prenatal Pb and 5mC and associations between prenatal Pb and 5hmC, separately. To assess potential sex-specific effects on the association between prenatal Pb and DNA 5mC and 5hmC, identical models were run as described above but stratified by sex. The lme4 and lmerTest packages within the statistical program R were used for these analyses, and a p-value<0.05 was considered significant. Finally, RNAseq normalized read counts of HCN2, NINJ2, RAB5A, and TPPP were log-transformed and compared with 5mC% and 5hmC% from pyrosequencing. We used Pearson’s product-moment correlation to quantify the strength of the relationship between these expression data 5mC or 5hmC. p-Values<0.05 were considered significant. The ggplot2 R package was used to plot 5mC and 5hmC results.Due to outliers in many of the Pb biomarkers, a sensitivity analysis was performed. In this analysis, we reran the models excluding outliers for BLLs or bone Pb measures. Outliers were defined as ±3D from the mean. We excluded 4, 5, 1, 3, and 5 outliers that were all 3 SD greater than the means for T1 BLL, T2 BLL, T3 BLL, tibia bone and patella bone Pb, respectively. We compared results with and without the outliers.ResultsPopulation Parameters and Phenotypic DataAmong the 144 children in the analytic sample, 73 (51%) were male (Table 1). The mean gestational age at birth was 39.1 (SD=1.15) wk, with the minimum being 36.0 wk and the maximum being 42.0 wk. The mean age of the offspring was 14.0 (SD=1.96) y, ranging from 11.1 y to 17.7 years of age. At the adolescent follow-up visit, mean weight was 53.2 (SD=12.2) kg. The height for age z-score and the BMI for age z-score were −0.23 (SD=0.88) and 0.51 (SD=1.25), respectively. Among the 144 mothers, the mean age at offspring birth was 26.7 (SD=5.42) y. About 77% of the mothers were from households of low-middle, middle, or middle-high socioeconomic status. Mean maternal education was 11.0 (SD=2.90) y, with the minimum being 3.0 y and the maximum being 21.0 y. We compared mother–offspring pairs included in this analysis with all ELEMENT mother–offspring pairs from cohorts 2 and 3 and found that demographic characteristics and Pb biomarker concentrations were not statistically different between the subset and entire ELEMENT population, with the exception of gestational age and adolescent age at follow-up (Table 1).Table 1 Characteristics of ELEMENT Mexican mother–offspring pairs with adolescent blood leukocyte DNA methylation data compared to all ELEMENT mother–infant pairs.Table 1 has four main columns, namely, Characteristics, Number, Element Subset, and All element. The Element Subset column is sub divided into four columns, namely, Mean plus or minus standard deviation or uppercase n (percentage), Median, Minimum, and Maximum. The All element column is sub divided into five columns, namely, Number, Mean plus or minus standard deviation or uppercase n (percentage), Minimum, Maximum, and uppercase p.CharacteristicsNo.ELEMENT SubsetbAll ELEMENTcMean±SD or n (%)MedianMinMaxNo.Mean±SD or n (%)MinMaxp-ValueaMot
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