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Polychlorinated biphenyl exposure and DNA methylation in the Anniston Community Health Survey

ABSTRACT Anniston, Alabama was home to a major polychlorinated biphenyl (PCB) production facility from 1929 until 1971. The Anniston Community Health Survey I and II (ACHS-I 2005–2007, ACHS-II 2013–2014) were conducted to explore the effects of PCB exposures. In this report we examined associations between PCB exposure and DNA methylation in whole blood using EPIC arrays (ACHS-I, n = 518; ACHS-II, n = 299). For both cohorts, 35 PCBs were measured in serum. We modelled methylation versus PCB wet-weight concentrations for: the sum of 35 PCBs, mono-ortho substituted PCBs, di-ortho substituted PCBs, tri/tetra-ortho substituted PCBs, oestrogenic PCBs, and antiestrogenic PCBs. Using robust multivariable linear regression, we adjusted for age, race, sex, smoking, total lipids, and six blood cell-type percentages. We carried out a two-stage analysis; discovery in ACHS-I followed by replication in ACHS-II. In ACHS-I, we identified 28 associations (17 unique CpGs) at p ≤ 6.70E-08 and 369 associations (286 unique CpGs) at FDR p ≤ 5.00E-02. A large proportion of the genes have been observed to interact with PCBs or dioxins in model studies. Among the 28 genome-wide significant CpG/PCB associations, 14 displayed replicated directional effects in ACHS-II; however, only one in ACHS-II was statistically significant at p ≤ 1.70E-04. While we identified many novel CpGs significantly associated with PCB exposures in ACHS-I, the differential methylation was modest and the effect was attenuated seven years later in ACHS-II, suggesting a lack of persistence of the associations between PCB exposures and altered DNA methylation in blood cells.


Introduction
From 1929 until 1971, Anniston, Alabama was home to the major polychlorinated biphenyl (PCB) manufacturing facility in the United States. Releases of PCBs from the Anniston production facility resulted in substantial environmental contamination. Since PCBs are relatively resistant to biological and chemical decomposition and are highly lipophilic, they easily bioaccumulate in the food chain. In 2005, the Anniston Community Health Survey was established to assess the health effects of general population PCB exposure (ACHS-I) with a follow-up study conducted in 2014 (ACHS-II) [1,2]. Previous ACHS-I studies reported associations between PCB exposure and race [1], hypertension [3], blood pressure [4], leukocyte telomere length [5], liver disease [6], metabolic syndrome [7], serum lipid levels [8], and diabetes [9]. Several previous population studies have found associations between PCB or dioxin exposures and altered blood DNA methylation levels in Dutch men [10], Greenlandic Inuits [11], Koreans [12], Faroe Islanders [13] and Japanese [14].
Some PCBs are ligands for the aryl hydrocarbon receptor (AHR), and it is hypothesized that most, but not all, of the PCB-associated adverse health outcomes are related to persistent activation of AHR [15]. In some tissues, the aryl hydrocarbon receptor repressor (AHRR) is upregulated following AHR activation, and AHRR protein acts as an AHR negative regulator [16]. Numerous recent epigenome-wide association studies (EWAS) of tobacco smoke exposure have demonstrated that in smokers the AHRR CpG cg05575921 is strongly hypomethylated. Also, among smokers, AHRR gene expression is upregulated, particularly in CD14+ monocytes [17][18][19]. We and others have hypothesized that this altered state could be caused by the presence of polyaromatic hydrocarbons (PAH) in tobacco smoke that activate AHR and lead to AHRR hypomethylation and persistent upregulation of AHRR mRNA. PCB exposures, as AHR ligands, could result in similar effects. So, in addition to examining epigenome-wide associations with PCB exposures, we also assessed whether PCBs were associated with the methylation of the AHRR smoking biomarker.
Here we carry out an EWAS examining the associations between DNA methylation levels using the Illumina EPIC array and the sum of 35 measured PCBs (Σ35PCBs), as well as various exposure groupings: mono-ortho substituted PCBs, di-ortho substituted PCBs, tri/tetra-ortho substituted PCBs, and PCBs with putative oestrogenic or antiestrogenic activity [9,[20][21][22].

Study population
ACHS-I was conducted between 2005 and 2007, and details of the study design have been reported [1,23]. In ACHS-I, we recruited 765 participants using random-stratified sampling, and residents in the area closest to the PCB manufacturing facility (west Anniston) were over-sampled (two-thirds of eligible participants). The residents were interviewed and had blood samples taken for serum level measurement of 35 PCB congeners, serum lipids, and DNA isolation. We measured DNA methylation in a subset (n = 518) of the ACHS-I cohort members using DNA from stored frozen blood clot samples [5].
ACHS-II was conducted between in 2014 as a follow-up study [2]. For ACHS-II, surviving participants from ACHS-I were contacted and 359 eligible individuals were enrolled. The same 35 PCBs measured in ACHS-I were determined again in ACHS-II. DNA methylation was measured in a subset (n = 299) of the ACHS-II cohort. Both studies were approved by the Institutional Review Boards at the Center of Disease Control and the University of Alabama at Birmingham. See Table 1 for study demographics.

Statistical analysis
Bioconductor's ChAMP package v2.12.0 [26][27][28] was used to normalize and batch correct methylation data. We excluded any samples which failed array QC standards. We excluded from analysis all CpG probes on the X and Y chromosomes, probes containing a SNP with a minor allele frequency ≥ 1% in the CpG site, and probes failing QC standards. We also removed an additional 43,254 probes reported  PCB28  PCB156  PCB28  PCB99  PCB177  PCB66  PCB99  PCB66  PCB44  PCB157  PCB66  PCB138-158  PCB178  PCB74  PCB153  PCB74  PCB49  PCB167  PCB74  PCB146  PCB183  PCB99  PCB105  PCB52  PCB170  PCB105  PCB153  PCB187  PCB118  PCB66  PCB172  PCB118  PCB170  PCB195  PCB156  PCB74  PCB177  PCB156  PCB172  PCB196-203  PCB167  PCB87  PCB178  PCB157  PCB180  PCB199  PCB99  PCB180  PCB167  PCB194  PCB206  PCB101  PCB183  PCB189  PCB209  PCB105  PCB187  PCB110  PCB189  PCB118  PCB194  PCB128  PCB195  PCB138-158  PCB196-203  PCB146  PCB199  PCB149  PCB206  PCB151  PCB209  PCB153 PCB values (wet-weight substituted, ng/g) below the limit of detection were imputed by dividing the limit of detection for the assay by the square root of 2. to hybridize to one or more non-target sites in the genome [29]. There were 706,625 CpG probes remaining after exclusions. Figure 1 outlines our analysis strategy. In brief, associations between PCB exposures and methylation in ACHS-I were analysed using robust multivariable linear regression (M-estimation) with the rlm function in Modern Applied Statistics with S (MASS v.7.3-51.1) [30]. We log 10 -transformed PCB measurements, consistent with previous ACHS-I analyses. All models were adjusted for age, race, sex, smoking status (yes/no), log 10transformed total lipids, bisulphite-conversion batch, and white blood cell percentages. Other covariates e.g., body mass index and alcohol use, were tested in our models, but did not contribute to model fit. The six white blood cell types (CD4 and CD8 T-cells, B-cells, monocytes, natural killer cells, and granulocytes) were estimated using the Houseman et al. method [31] based on the Reinius reference panel [32]. For CpGs significant at Bonferroni threshold p ≤ 6.70E-08, differential methylation (ΔM) was calculated between the highest and lowest PCB exposure quartiles for CpG sites in ACHS-I. Benjamini-Hochberg false discovery rate (FDR) significant CpGs at p ≤ 5.00E-02 (n = 283) in ACHS-I were then analysed in ACHS-II.
Potential effect measure modification by race and sex for ACHS-I CpGs with an absolute ΔM ≥ 1.00% was evaluated in ACHS-I. We identified effect modification by including an interaction term in our regression models. For CpGs with nominally significant interaction terms (p ≤ 5.00E-02), we generated stratified ΔM results. We conducted interaction analyses using SAS v9.4 PROC ROBUSTREG (SAS Institute Inc.). The ACHS-II had a smaller sample size, which resulted in our having lower statistical power to detect true effect measure modification. As a result, we did not conduct stratification analyses for ACHS-II CpGs.
Differential methylated regions (DMRs) in ACHS-I were identified using Bioconductor's DMRcate v.1.18.0 [33], comparing the highest versus lowest tertiles for each exposure. For each DMR we calculated FDR p-values. The model was adjusted for age, race, sex, smoking status (yes/no), log10transformed total lipids, bisulphite-conversion batch, and white blood cell percentages. Again, because of the smaller sample size in ACHS-II, we did not perform DMR analysis in ACHS-II.

CpG/DMR functional annotations and associations in the comparative toxicogenomics database
To find functional annotations of FDR CpGs and DMRs, we used The Genomic Regions Enrichment of Annotations Tool (GREAT) method [34]. We also retrieved curated chemical-gene association data from the 2019 Comparative Toxicogenomics Database (CTD) [ In ACHS-I, analyzed PCB exposures and methylation using robust linear regression.
In ACHS-II, for those CpGs significant at p≤6.7E-08 in ACHS-I, calculated differential methylation in a replication analysis.
In ACHS-I, calculated methylation differences between highest and lowest quartiles for PCB exposures (only for p≤6.70E-08 CpGs).
In ACHS-I, examined possible effect modification by sex and race for p≤6.70E-08 CpGs and identified DMRs. Figure 1. Overview of data analyses strategy. Robust linear regression models were adjusted for age, race, sex, smoking status, total serum lipids, and percentages for six different white blood cell types. Seven different PCBs groupings were used in regression: 1) Σ35PCBs, 2) mono-ortho substituted PCBs, 3) di-ortho substituted PCBs, 4) tri/tetra-ortho substituted PCBs, 5) oestrogenic PCBs group 1, 6) oestrogenic PCBs group 2, 7) antiestrogenic PCBs. Stratified and DMR analyses were not conducted in ACHS-II.

Demographics
Demographics for individuals available for DNA methylation analysis in ACHS-I and ACHS-II are found in Table 1. Participants were mostly female (ACHS-I female, 71.2% and ACHS-II female, 74.9%) and were heavier (ACHS-I BMI, 31.1 ± 0.3; ACHS-II BMI, 31.6 ± 0.5) than the U.S. general population as measured in the National Health and Nutrition Examination Survey 2015-2016 [37]. African-American participants were significantly more likely to live closer to the PCB production facility (ACHS-I African-Americans' residential distance from plant, 2.5 km ± 0.1 and whites', 4.3 km ± 0.1; t-test p = 2.51E-27).

Serum PCB levels by age and race
Serum PCB levels in the ACHS-I were previously reported to be 1.5-to 3.0-fold higher than the general population [1]. Figure  . We also compared the correlation of Σ35PCBs between ACHS-I and ACHS-II for the 245 subjects with exposure data in both cohorts ( Figure S1, r 2 = 0.86, p = 1.30E-107). The average measured Σ35PCBs in ACHS-II was reduced approximately 10%, but differences by age and race varied considerably as they did in ACHS-I.
Cell type analysis in ACHS-I As described in the Method section, DNA methylation profiles were used to estimate distributions of six blood cell types, and the percentages were used as covariates in the linear regression models. Because PCB exposures have been associated with cell type shifts and other immune system effects [38], we hypothesized that exposure might alter methylation-based estimated cell type composition. We tested if any PCB exposures were associated with shifts in estimated immune cell composition in ACHS-I and observed no significant results.

Smoking and methylation in ACHS-I
We examined if tobacco smoke exposure in the ACHS-I population was associated with altered DNA methylation at the AHRR CpG cg05575921, a site which has been confirmed to be a biomarker of tobacco smoke exposure in numerous studies [17][18][19][39][40][41][42]. The multivariable robust linear regression model that tested smoking versus methylation at AHRR cg05575921 was highly significant (smoking regression coefficient p = 7.27E-269, r 2 = 0.58; adjusted for age, sex, race, lipids, cell types and batch). The differential methylation of AHRR cg05575921 between non-smokers and smokers was −18.3% (Wilcoxon rank-sum pairwise test, p = 1.62E-64). There was no association between the smoking biomarker AHRR cg05575921 and PCB levels in non-smokers (p = 9.44E-01).
To test the reproducibility of the ACHS-I results, and to see if the observed effects persisted in 2014, we did a replication analysis on the 369 FDR significant (p ≤ 5.00E-02) ACHS-I CpG associations in the ACHS-II. Thirty-three were nominally significant (p ≤ 5.00E-02) in ACHS-II. Of the 28 associations at p ≤ 6.70E-08 in ACHS-I, 14     ACHS-II (based on the directionality of the PCB regression coefficient); however, only one association was statistically significant at Bonferroni p ≤ 1.70E-04 (the Bonferroni threshold for ACHS-II): PRSS23 cg00475490 versus tri/tetra-ortho substituted PCBs, p = 1.33E-04 (Table 3). Of the associations with directional replication both the regression coefficient and the significance were reduced. Absolute ΔM in ACHS-II ranged from 0.01% to 3.12%, with four CpG sites with absolute ΔM ≥ 1.00%.

Race and sex stratified analysis in ACHS-I
For ACHS-I Bonferroni significant CpGs with an absolute ΔM ≥ 1.00%, we tested for race and sex specific effects by running multivariable robust linear regression models with an interaction term (either sex or race by exposure) for each of the seven PCB exposure groups and the CpG. For the exposure*CpG interaction terms with p ≤ 1.00E-02, we generated stratified ΔM results. We observed potential effect measure modification by sex for AC068134.2 cg21566642 and tri/tetra-ortho substituted PCBs, with a much larger effect in males (ΔM males

Look up analysis
Leung et al. identified 241 CpG sites that were associated with PCB105 levels in a Faroe Island study [13]. We looked up the significance levels of these CpGs sites within our Σ35PCBs results and observed 23 CpGs in our analysis were nominally significant (p ≤ 5.00E-02) but none of these were significant after FDR correction (Table S2).

DMR analysis in ACHS-I
To determine if there were groups of CpGs (i.e. DMRs) that differed by PCB exposure, we grouped methylation data into PCB exposure tertiles and used the DMRcate analysis method. We identified 185 DMR/PCB exposure associations at FDR p ≤ 5.00E-02 (Table S3). Of the 115 unique FDR DMRs, only one DMR contained an ACHS-I Bonferroni significant individual CpG, GIMAP8 cg14251777. The 56 DMR/PCB associations with Roadmap project. ENCODE transcription factor ChIP-seq for small MAF proteins (binding partners of NRF2). c. Small MAF/NRF2 binding motif located within ChIP-seq peak.

Functional analysis and Comparative Toxicogenomics Database
The GREAT functional analysis tool was used to find possible functional enrichment of molecular signatures of the FDR significant CpGs or DMRs [45]. We observed enrichment of five related immunological molecular signatures (Table S5) in the Molecular Signatures Database [46].
We also searched the 2019 Comparative Toxicogenomics Database (CTD) [47] to determine if CpG (Table 3 and S1) and DMR genes ( Table 4 and S2) had been reported in the literature to interact with either PCB or dioxin exposure in experimental studies. For individual CpGs, 13 of the 17 (76%) unique genome-wide significant genes and 199 (70%) of the 286 FDR significant unique genes were listed as interacting with PCB or dioxin exposures. For ACHS-I 37 unique DMRs in Table 4, 19 (51%) of the genes were associated with either PCB or dioxin exposure based on retrieved curated chemical-gene association.  The differential methylation value (ΔM) for the DMR CpG with the largest absolute differential value. Differential methylation was calculated by subtracting methylation in lowest tertile from the highest. c The average differential methylation value across the entire DMR. Differential methylation was calculated by subtracting methylation in lowest quartile from the highest. d Genes associated with PCB or dioxin exposure based on the 2019 Comparative Toxicogenomics Database.

Discussion
The subgroup of participants in ACHS-I and II that were analysed for DNA methylation levels in whole blood DNA had very similar PCB exposure distributions relative to age, race and sex as have been reported [1,48]. Our initial hypothesis for these analyses was that methylation in the AHRR gene, known to be regulated by AHR, might be strongly affected by PCB exposure. Interestingly, while smoking had the expected profound impact on AHRR methylation levels in ACHS-I (cg05575921 p = 7.27E-269), as has been reported many times [17][18][19][39][40][41][42], we detected no association between PCBs (potential AHR ligands) and AHRR methylation in non-smokers (p = 9.40E-01). Many studies of PCBs and dioxins clearly implicate AHR as mediating the adverse outcomes associated with exposure in model systems and humans [15]. Some PCBs and dioxins interact with AHR in terms of the planar orientation of their two phenyl groups. In dioxin, the two phenyl groups are in a fixed coplanar orientation, which makes it a strong ligand for AHR. The phenyl groups in non-ortho and mono-ortho substituted PCBs can assume a coplanar configuration, allowing these PCBs to have dioxin-like properties including being an AHR ligand. In di-ortho and tri/tetra-ortho substituted PCBs, the chlorines in the ortho positions force the two phenyl groups out of coplanar conformation, leading to nondioxin-like properties [49]. It should be noted that the most dioxin-like PCBs (non-ortho substituted) were not measured in ACHS-I. Based on our original hypothesis, we expected to observe the most significant changes in methylation among the PCB exposure group that was the most dioxinlike i.e., the mono-ortho substituted PCBs. However, in ACHS-I, exposure to the least dioxinlike PCBs (tri/tetra-ortho substituted) produced the largest number of significant associations (229 CpGs, 82% of all associations). This finding suggests that most of the effects of PCB exposure on whole blood DNA methylation that we observe in this study are not likely to be mediated through the AHR pathway. However, only nine of the 35 PCBs in these analyses were mono-ortho substituted, and these PCBs are weak AHR ligands.
Other studies have reported associations between PCB or dioxin exposures and altered DNA methylation, although most examined global measures of methylation or specific candidate genes. Rusiecki et al. observed that in the Arctic Monitoring and Assessment Program, higher serum levels of 11 PCBs (99, 105, 118, 128, 138,  153, 156, 170, 180, 183, 187) in 71 Greenlandic Inuits were related to global hypomethylation [11]. Two studies found that TCDD [50] and PCB153 [51] exposure were predominantly associated with hypomethylation in sperm DNA. In a study of 368 cancer-free Koreans, researchers found a nonmonotonic association between PCB105, PCB138, and PCB153 and MGMT promoter hypomethylation [52]. In a Swedish cross-sectional cohort of 524 elderly men and women, two POPs (PCB126 and OCDD) were connected to global DNA hypermethylation [53]. The research from van den Dungen et al. found in a cohort of Dutch men that serum levels of dioxins and the sum of seven PCBs (28,52,101,118,138,180) were significantly associated with primarily hypomethylated DMRs in multiple genes with an average change of 7.4% between the highest and lowest exposed subjects [10].
Some studies have observed possible sex-specific effects. In a subset of a prospective Japanese birth cohort (the Hokkaido Birth Cohort Study on Environment and Children's Health, n = 169), cord blood methylation levels for H19 and LINE1 were hypermethylated and related to PCB170, PCB 178, PCB180, and PCB183 exposure with the suggestion that this association might be more pronounced in females than males [14]. In the only study utilizing the 450K array, Leung et al. [13] examined a prospective birth cohort of 73 Faroe islanders with high levels of PCB exposure at birth and found sex-specific methylation associations with PCB105 levels. Our look-up analysis of the 195 CpGs reported to be FDR significant in the Leung et al. cord blood study identified 23 CpGs with nominal p-value 5.00E-02, but none reached FDR-corrected significance level. The lack of overlap between a cord blood study with in utero exposures with the present study of older adults is not surprising. Among our most significant CpGs, we tested for sex-specific effects in ACHS-I and observed potential sex-related effect modification for AC068134.2 cg21566642 and GALNT2 cg04991747; however, the size of the ACHS-I cohort (n = 518) limited the statistical power to detect true effect-measure modification.
Among the ACHS-I Bonferroni significant CpGs, the most differentially methylated was PRSS23 cg00475490 (ΔM −5.02 ± 0.60 in diortho substituted PCBs), and it showed a strong hypomethylation trend from low to high exposure when examined by quartiles. PRSS23 cg00475490 was the only CpG that displayed persistent and significant alteration in ACHS-II. PRSS23 was first identified as an ovarian protease [54] and has been shown to be upregulated by oestrogen receptor alpha and stimulate growth in the MCF-7 breast cancer cell line [55]. In the Comparative Toxicogenomics Database, it is also associated with dioxin exposures in model systems. Among blood cells, it is expressed only in natural killer cells ( Figure 5). The presence of a functional NRF2/MAF binding site in PRSS23 enhancer may indicate that this gene is regulated by NRF2 (NFE2L2), the master transcription factor regulator of the response to oxidative stress, which also modulates inflammatory and metabolic pathways [56]. GALNT2 has been associated with triglyceride levels [57] and may be associated with Type 2 diabetes [58]. The deubiquitinase, OTULIN, was also highly significant in ACHS-I, is an essential negative regulator of the inflammatory transcript factor nuclear factor κB and plays a role in immune homoeostasis [59]. Using genomic location of the FDR significant CpGs and the GREAT functional annotation program we identified a group of immunological molecular signatures. It is established that PCBs can have effects on the immune system [60][61][62], and our data suggest that PCB exposures may be perturbing the epigenetic profile of immune system genes.
Our study observed 28 CpG/PCB associations (17 unique genes) at genome-wide significance, 369 associations in 286 CpGs at FDR significance, and 115 genes with significant DMRs. Of these genes a large proportion [14/17 (76%) Bonferroni significant genes; 199/286 (70%) FDR significant genes; and 78/115 (68%) FDR significant DMR genes] had a reported interaction with PCB or dioxin in various model system studies reported in the Comparative Toxicogenomics Database. However, surprisingly none of the canonical AHR pathway genes were affected, e.g. genes in the CYP, GST and UGT families.
Two factors that were limitations of our study were the relatively limited genome-wide coverage of the EPIC array (~3% of all genomic CpGs) and the relatively small sample sizes (ACHS-I n = 518 and ACHS-II n = 299) in terms of multiple comparisons correction. The main strength of our study was the opportunity to examine effects in ACHS-I and then later in ACHS-II. We observed half of the ACHS-I genome-wide associations displayed similar directional effect in ACHS-II and only one CpG (PRSS23 cg00475490) among ACHS-I top hits, displayed a persistent, significant association in ACHS-II. The factors that may have contributed to the limited replication in ACHS-II include the smaller sample size (n = 299), the reduced exposure levels (~10% lower), and potential sampling bias (survival bias). In both ACHS-I and ACHS-II individuals recruited long after the occurrence of very high exposures could be more resilient to adverse health effects. From a public health perspective, our observation of reduced PCB effects on methylation after seven years can be considered a reassuring outcome as opposed to a lack of replication. In future research, we will explore methylation associations with dioxins, furans and non-ortho PCBs that were only measured in ACHS-II. We will also examine the associations among methylation, POPs, and adverse health outcomes, e.g. hypertension, liver disease, diabetes, and metabolic syndrome in both cohorts.
We have conducted the first EWAS exploring the associations between PCB exposure and DNA methylation levels in adults and identified numerous highly significant CpGs and DMRs associated with exposure. Our study design has allowed us to examine effects of PCB exposure on DNA methylation at two points in time, seven years apart. It is unclear how biological important these altered methylation levels are, or if CpGs such as PRSS23 cg00475490 are useful biomarkers of exposure. However, it is encouraging that PCB effects on methylation generally appear to be attenuated in ACHS-II after seven years.