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. 2021 May 12;9:656865. doi: 10.3389/fcell.2021.656865

DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Non-syndromic Cleft Lip and Palate

Juan I Young 1,*, Susan Slifer 1, Jacqueline T Hecht 2,†, Susan H Blanton 1,†
PMCID: PMC8149607  PMID: 34055787

Abstract

Non-syndromic cleft lip with or without cleft palate (NSCLP) is the most common craniofacial birth defect. The etiology of NSCLP is complex with multiple genes and environmental factors playing causal roles. Although studies have identified numerous genetic markers associated with NSCLP, the role of epigenetic variation remains relatively unexplored. Because of their identical DNA sequences, monozygotic (MZ) twins discordant for NSCLP are an ideal model for examining the potential contribution of DNA methylation to non-syndromic orofacial clefting. In this study, we compared the patterns of whole genome DNA methylation in six MZ twin pairs discordant for NSCLP. Differentially methylated positions (DMPs) and regions (DMRs) were identified in NSCLP candidate genes, including differential methylation in MAFB and ZEB2 in two independent MZ twin pairs. In addition to DNA methylation differences in NSCLP candidate genes, we found common differential methylation in genes belonging to the Hippo signaling pathway, implicating this mechanosensory pathway in the etiology of NSCLP. The results of this novel approach using MZ twins discordant for NSCLP suggests that differential methylation is one mechanism contributing to NSCLP, meriting future studies on the role of DNA methylation in familial and sporadic NSCLP.

Keywords: methylation, NSCLP, non-syndromic cleft lip and cleft palate, twins, whole genome bisulfite sequencing

Introduction

Non-syndromic cleft lip with and without cleft palate (NSCLP) is a common birth defect occurring in 1/700-1000 livebirths, affecting 135,000 newborns worldwide. The severity varies based on laterality (bi- versus unilateral involvement) and extensiveness (involves only the lip, the primary palate and/or the secondary palate). The defect affects feeding initially and, in the long term, speech articulation, hearing, dental hygiene, dentition and overall well-being as there is always residual scarring. The cost of multiple surgical interventions exact a large emotional burden for patients and families, and high financial burden for families and society.

Despite many decades of research, only limited environmental agents and genetic causes of NSCLP are known (Chiquet et al., 2011; Letra et al., 2012, 2014; Cvjetkovic et al., 2015; Leslie et al., 2015, 2016a,b; Fish et al., 2017; Ludwig et al., 2017; Cox et al., 2018). While candidate gene and genome-wide studies of case-control and family-based datasets have identified causal/susceptibility loci and variants, the majority of the genetic causes remain elusive. Based on the analysis of multiplex families, in which there are multiple, but not necessarily first degree, affected relatives, there are likely extra-genic factors affecting penetrance and/or expression of the genetic liability. For example, MZ twins have a concordance rate of » 25–50% for NSCLP (compared to 1–3% in DZ twins) with a heritability estimate of approximately 70%, indicating that other factors play a role (Christensen, 1999; Grosen et al., 2011). It is likely that variation in multiple genes is etiologically necessary, with extra-genic factors contributing with a threshold effect.

Epigenetics is well-recognized as a critical mediator of the interplay between genes and the environment and influences phenotypic outcomes. DNA methylation, a major substrate of epigenetic information, regulates genome activity and provides an alternative mechanism for modulating cellular physiology compared to those arising solely from genetic changes (Esteller, 2008). Thus, a potential mechanism by which environmental factors could elicit altered tissue morphogenesis is variation in gene methylation. Support for this hypothesis comes from the observation that maternal use of the methyl-group donor folic acid may decrease the risk for NSCLP (Butali et al., 2013; Wallenstein et al., 2013).

Methylation differences within normal MZ twin pairs are well documented (Kaminsky et al., 2009; Czyz et al., 2012; Ollikainen et al., 2015). Importantly, methylation differences have been identified in MZ twin pairs discordant for a variety of congenital defects, including Beckwith-Wiedemann syndrome (OMIM 130650), congenital cataracts, congenital renal agenesis, cryptorchidism, Mayer-Rokitansky-Kuster-Hauser syndrome (OMIM 277000) and congenital heart disease (OMIM 217095), yielding insights into the underlying pathology (Weksberg et al., 2002; Rall et al., 2011; Tierling et al., 2011; Jin et al., 2014; Wei et al., 2015; Lu et al., 2017; Lyu et al., 2018). Given the relevance of DNA methylation in neural crest development, which plays a critical role in craniofacial development (Hu et al., 2014; Seelan et al., 2019), and the potential influence of epigenetics in the etiology of NSCLP, we compared genomic DNA methylation patterns between six pairs of monozygotic twins discordant for NSCLP.

Materials and Methods

Editorial Policies and Ethical Considerations

All DNA samples were collected and studied following informed and signed written consent from a parent or guardian. This study was approved by the University of Texas Health Science Center Committee for Protection of Human Subjects (HSC-MS-03-090) and the University of Miami Human Subject Research Office (IRB #20100505).

Dataset

Six monozygotic twin pairs discordant for NSCLP were ascertained and characterized as part of the larger genetic study of NSCLP that has been previously described (Chiquet et al., 2007; Morris et al., 2020). Briefly, participants were recruited at the craniofacial clinics affiliated with the UTHealth Science Center at Houston under IRB-approved protocols and written informed consent was obtained from parents and assents from the children. All participants were evaluated and syndromic forms of NSCLP were excluded. Study inclusion was irrespective of ethnicity and/or sex. Family history and a minimum of a three-generation pedigree were obtained from all families. The characteristics of the six pairs of MZ twins and their samples are listed in Table 1. All affected individuals were post-surgical repair. Saliva samples were collected using the Oragene® Saliva Collection Kit or the Oragene® Saliva Self−Collection Kit for Young Children (Sponge Kit) according to manufacturer’s protocol. Genomic DNA was isolated from saliva samples obtained from these male MZ twins discordant for NSCLP and from their available parents.

TABLE 1.

Description of twin pairs.

Pair* Sex# Status& Age (yrs)∧ Cleft type Ethnicity@
MZ1 M A 8 Left NHW
M U 8 –
MZ2 M A 1 Left H
M U 1 –
MZ3 M A 10 Left H
M U 10 –
MZ6 M A 8 Left H
M U 7 –
MZ7 M A 2 Left NHW
M U 2 –
MZ8 M A 15 Left NHW
M U 15 –
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*All samples were saliva. MZ, monozygotic; #M, male; &A, affected; U, unaffected;∧Age, Age at sampling; @NHW, Non-Hispanic White; H, Hispanic.

Candidate Gene Assembly

A list of candidate genes for NSCLP was assembled from a thorough review of the literature of orofacial clefting including human association and sequencing studies and animal models. Genes associated with non-syndromic cleft lip with or without cleft palate, cleft palate only, and syndromic forms of clefting were included (Supplementary Table 1).

Whole Genome Sample Preparation and Sequencing

gDNA samples were evaluated for concentration by Qubit® fluorometric assay or by using Picogreen®. The integrity of the gDNA was assessed by loading approximately 100 ng per sample on a 0.75% agarose gel and comparing size distribution to a size marker (Quick-Load® 1 kb Extend DNA Ladder, NEB). 0.6–1 μg of gDNA was fragmented using the Covaris L-series or E-series instrument and sheared to a final insert size of approximately 400 bp. The purified fragmented DNA was repaired by the phosphorylation of the 5′ end and the conversion of overhangs to blunt ends. One “A” nucleotide was added to each 3′ end of the blunt fragments. The indexing-specific paired-end adaptors were ligated to the ends of the DNA fragments and the adaptor-ligated library was PCR amplified. Quality and quantity of the purified ligated products were verified using the Caliper LabChip® GX. Libraries were pooled and sequenced according to manufacturer’s instructions via the Illumina HiSeq X platform. Multiplexing 6 whole human genomes per flow cell generated an average depth of 30× per sample, exceeding read depth recommendations for Whole genome bisulfite sequencing (WGBS) analysis (Ziller et al., 2015).

Comparison of the whole genome sequencing (WGS, Table 2) between the affected and unaffected co-twins did not identify a pathogenic point mutation, insertion/deletion, or structural alteration in any of the known CLP-associated genes listed in Supplementary Table 1.

TABLE 2.

Whole genome DNA sequencing statistics.

Average reads uniquely aligned to human genome* Average genome coverage Average discordant SNP calls Average discordant exonic/splicing SNP calls Discordant SNP calls in NSCLP candidate genes
9.19E + 10 (8.61E + 10–1.01E + 11) 29.12 (28.42–32.52) 196,253 (187,112–213,468) 14,920 (13,435–19,423) 0 (0–0)
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*Range in brackets.

Whole Genome Bisulfite Sequencing

Whole genome bisulfite sequencing gDNA library preparations were carried out using the TruSeq DNA Sample Prep Kit v2 (Illumina Inc.) combined with sodium bisulfite conversion. Briefly, 1 μg of gDNA spiked with 5 ng unmethylated λ DNA (Promega) was fragmented to 200–300 bp peak size (Covaris, Woburn, MA, United States). Fragment size was analyzed on a Bioanalyzer DNA 1000 Chip (Agilent). End repair, sample purification with AMPure beads (Agencourt Bioscience Corp.), adenylation of 3′ ends, and adaptor ligation were carried out following the Illumina standard protocol. Ligation DNA was purified with AMPure, analyzed on a Bioanalyzer and subjected to bisulfite conversion using the MethylCode Bisulfite Conversion Kit (ThermoFisher) according to manufacturer’s instructions. Adaptor-ligated DNA was amplified through five cycles of PCR using the Hifi Uracil + DNA polymerase (Kapa Biosystems, Woburn, MA, United States) according to manufacturer’s protocol. Library quality was monitored using the Bioanalyzer (Agilent) and the concentration of fragments carrying adapters at both ends determined by quantitative PCR using the Library Quantification Kit from KAPA Biosystem. Paired-end DNA sequencing (124 bp each) using the Illumina Hi-Seq 2500 was performed.

Generated sequencing reads (average read length 120.9) were trimmed using Trim Galore1 and aligned to the bisulfite-converted human reference genome (GRCh37/hg19) using Burrows–Wheeler alignment. Methylation states were called using Bismark v0.10.0 (Krueger and Andrews, 2011). Confirmed clonal reads and reads with low mapping quality score were removed (Johnson et al., 2012), as well as, reads aligning to regions of the reference genome identified as troublesome for high- throughput sequencing aligners [DAC Blacklisted Regions (DBR) and Duke Excluded Regions (DER)2].

After data preprocessing, »54.3% of the resulting reads were uniquely mapped to the human reference genome. The median bisulfite conversion efficiency of CpGs was determined to be ∼98%. CpGs not covered by at least 20 reads were excluded, which resulted in a minimum of two reads per strand across all twelve samples. Reads on opposite strands with an absolute strand difference in methylation > 20% were excluded, leaving on average 2.4 × 106 unique and high-confidence CpGs per sample. Most CpG sites retained were highly methylated (over 50%, Supplementary Figure 1).

Array-Based Methylation Assay

Array-based methylation profiling was performed using the MethylationEPIC BeadChip (Illumina, San Diego, CA, United States) assay. Briefly, 300 ng gDNA per sample were bisulfite converted using the EZ-96 DNA Methylation kit (Zymo Research, Irvine, CA, United States) and processed as described by Illumina. The MethylationEPIC BeadChip measures over 850,000 CpG loci across a diverse set of functionally relevant genomic regions that include CpG islands (CGI) and promoters as well as CGI shores, gene body and intergenic CpGs. BeadChip images were scanned and the data analyzed using the R software (version 3.2.43). Raw intensity files (.idat) were obtained and processed using RnBeads package (Assenov et al., 2014). For quality control, we selected probes that had a detection p-value < 0.01 for all samples. The detection p-value represents the quality of the probes compared to background noise. Because all samples were male, we did not filter probes associated with sex. Methylated and unmethylated signal intensities were quantile normalized for each probe type separately and both beta and M values were estimated. The beta values are the ratio of the methylated signal intensity to the sum of both methylated and unmethylated signals after background subtraction (beta values range from 0 to 1, corresponding to completely unmethylated and fully methylated sites, respectively). M values are logit transformation of the beta values, which is M = log (beta/1-beta). M values were used as outcome variables for subsequent analysis because they have been shown to have better statistical properties such as homoscedasticity (Du et al., 2017). For the analysis of individual CpGs, an absolute delta beta > 0.30 was used to detect differential methylation between twins.

Identification of Differentially Methylated Positions Within Twin Pairs

From the strand-overlapping sites with > 20 reads, we selected differentially methylated sites based on the following criteria: (1) intra-twin pair difference in methylation levels of at least 30% (delta methylation > | 0.3|) and (2) a coefficient of variation in methylation levels of the unaffected individuals of less than 0.25. This strategy of restricting the search on differential methylation to sites in which the variation in unaffected twins is <25% is based on the idea that sites that exhibit large inter-individual variation in methylation levels have less regulatory potential than those sites in which the methylation levels are more tightly preserved between unaffected individuals.

DMR Analysis

Differentially methylated regions (DMR) analysis used a Bayesian hierarchical model (Wu et al., 2015) to detect regional methylation differences with ≥ 3 contiguous sites each with a methylation difference > 20% within a 2 kb distance with unidirectional methylation changes and a region-wise average methylation difference > 25%.

DNA Bisulfite Conversion and Bisulfite Specific PCR

Flanking primers (methylation-unbiased nested PCR primers) were designed for a subset of differentially methylated CpG sites and quantitative levels of methylation for each CpG dinucleotide were evaluated using the BiQ Analyzer software. Bisulfite-PCR was followed by cloning and Sanger sequencing of multiple clones for short-range mapping of methylation levels in selected loci. Genomic DNA (0.25 μg) was bisulfite converted using the EpiTect Kit (Qiagen) following the manufacturer’s protocol. Converted DNA was amplified with primers designed in MethPrimer. Optimized PCR reactions were followed by cloning using the TOPO TA Kit (Invitrogen). Multiple clones were sequenced. Bisulfite-cloning validated 70% of the DMS (Supplementary Figure 2).

Pyrosequencing Validation

Locus-specific pyrosequencing was conducted to validate the methylation data at selected loci. Pyrosequencing assays were designed and implemented by EpigenDx (Worcester, MA, United States) following the manufacturer’s recommended protocol. The correlation between the WGBS and pyrosequencing data was evaluated using the Spearman rank order correlation test. Pyrosequencing validated 73% of the DMS. Interestingly, the majority of validated DMSs had contiguous CpGs that also showed differential methylation in the same direction and with a similar magnitude to the targeted CpG.

Results

DNA Methylation Profiles Are Different in Monozygotic Twins Discordant for NSCLP

Methylome analysis was performed on saliva samples from six sets of male MZ twins discordant for NSCLP using whole-genome bisulfite sequencing (WGBS). Globally, DNA methylation profiles generated by WGBS were highly correlated across all individuals, indicating inter-individual conservation in methylation levels. The correlation of methylation patterns of the co-twins was significantly higher compared to unrelated individuals, indicative of low within-pair DNA methylation variation (average r = 0.84 for twins and r = 0.75 for unrelated individuals at 30X coverage; Figure 1A and Supplementary Figure 3). Removal of areas of extreme variability across individuals (variably methylated regions, VMRs) (Feinberg et al., 2010) from the analysis of the 30X coverage increased the correlation for intra-twin comparisons (average r = 0.93), but decreased it for unrelated samples (average r = 0.65) (Figure 1B). This pattern of correlation change suggests that the polymorphic nature of these VMRs is affected to a larger extent by environmental rather than genetic influences.

FIGURE 1.

FIGURE 1

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MZ co-twins are highly correlated for DNA methylation but have site-specific differences. (A) Pearson’s correlation coefficients derived from comparing intra-twin, parent-child and parent to parent (unrelated) methylation levels at 100X (blue) or 30X (brown) genome coverage. (B) Correlation coefficients using only variable CpGs as identified by Feinberg et al. (2010) at 30X genome coverage. (C) Comparison between DNA methylation values for 16,920 individual CpG loci generated by MethylationEPIC microarray and whole-genome bisulfite sequencing. Methylation values generated by Rnbeads for the MethylationEPIC microarray were subtracted from methylation levels calculated from the sequencing data for the same sample (MZ1A to MZ3U). Averaged comparisons contrasting EPIC and WGBS data from unpaired (Unp.) samples is also included (black line). (D) Distribution of average absolute differences in DNA methylation within the MZ twins as well as comparisons of unrelated individuals. A significantly (P<1.2e-10 for WGBS and P<1.9E-12 for EPIC, Kolmogorov–Smirnov test) smaller number of CpG sites with a large average within-twin differences in methylation level was observed in within twin comparisons as contrasted with comparisons of unrelated individuals (unaffected-unaffected, NSCLP-unaffected (inter-twins) or NSCLP-NSCLP).

The WGBS data was validated globally by subjecting DNA from three of the MZ pairs to the MethylationEPIC microarray (Illumina) assay. This assay interrogates the DNA methylation status of ∼865,000 CpG sites, which account for only ∼3% of all human autosomal CpGs. The methylation values obtained from the microarray were correlated with those from WGBS. WGBS data (prefiltered as described in the Methods section) were further filtered to include only CpGs present in the MethylationEPIC microarray, resulting in a total of 16,920 loci for comparison (Figure 1C). The mean methylation level correlation between WGBS and the microarray determinations for the six samples was r = 0.90 and, on average, 97.63% of loci have delta methylation levels < | 0.2|, indicating that the methylation values derived from the sequencing and the array methods are consistent across commonly interrogated loci.

To identify CpGs displaying differential methylation in any of the discordant twin pairs, the differences in CpG methylation between any two paired samples were estimated. Figure 1D shows the distribution of the mean absolute differences in DNA methylation levels per individual CpG within twins, as well as between unrelated samples. The overall distribution of average intra-twin DNA methylation differences showed a significant skew to the left as compared to the distribution of the average unrelated pair differences, with fewer CpG sites having large average differences in DNA methylation (Figure 1D). However, there is still variability in DNA methylation levels at individual CpG sites within the pairs of MZ twins.

DNA Methylation Differences in MZ Twin Pairs

The differentially methylated CpG sites (DMSs) identified within twin pairs is presented in Supplementary Table 2. The number of DMS per MZ pair varies from 7 (MZ2) to 42(MZ1), for a total of 151 DMSs associated with 147 genes. No single CpG is differentially methylated in more than one twin pair. This lack of overlap in DMS per pair is also seen at the gene level; there are no shared genes containing DMSs in the different MZ pairs. Table 3 lists the five genes with the most differential methylation per twin pair.

TABLE 3.

Five most differentially methylated CpGs for each NSCLP twin pair.

Chr Position Gene 1 Gene 2
MZ1 16 84479557 TLDC1 ATP2C2
10 24754430 KIAA1217 ARHGAP21
10 24754448 KIAA1217 ARHGAP21
6 33518020 BAK1 ZBTB9
16 23296275 SCNN1B SCNN1G
MZ2 12 4141700 CCND2 PARP11
15 48849784 FBN1 DUT
8 28206128 PNOC ZNF395
12 4141713 CCND2 PARP11
6 129494272 LAMA2 ARHGAP18
MZ3 X 85175162 POF1B CHM
1 234913189 IRF2BP2 TOMM20
15 41104572 DNAJC17
15 41104527 DNAJC17
1 45280071 TCTEX1D4 PTCH2
MZ6 2 132153021 TUBA3D POTEE
5 10869280 DAP
7 50850334 GRB10 COBL
7 50850302 GRB10 COBL
22 30617445 LIF HORMAD2
MZ7 Y 15676869 TMSB4Y UTY
Y 15676854 TMSB4Y UTY
6 28663545 SCAND3 TRIM27
1 52616167 ZFYVE9 CC2D1B
1 52616219 ZFYVE9 CC2D1B
MZ8 8 71368648 NCOA2 TRAM1
16 2141571 NTHL1 PKD1
5 1331076 TERT CLPTM1L
7 150822700 AGAP3 GBX1
7 150822698 AGAP3 GBX1
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To evaluate epigenetic involvement of candidate NSCLP genes, we tested whether any of the identified DMSs in the discordant twins were located in known or suspected NSCLP genes (Supplementary Table 1). Evidence of aberrant methylation in single CpGs was observed in seven NSCLP genes, four in MZ1, two in MZ2 and one in MZ6 (Table 4). Note that the number of DMSs in candidate genes is more than expected by chance in MZ1 (p-value < 0.003) and in MZ2 (p-value < 0.0001).

TABLE 4.

Differentially methylated positions in candidate genes identified in three NSCLP twin pairs.

MZ pair Gene Chr Position Delta methylation Genomic feature* Distance to TSS (kb)# Candidate group&
MZ1 TP63 3 189509045 0.80 In (alt. promoter) 1.6 AGV
WNT7A 3 13787881 −0.78 IGR 133 AGV
MAFB 20 39373248 −0.84 IGR −55 AGV
GDF6 8 96365815 0.85 IGR 807 AGV
MZ2 MAF 16 79211133 0.63 IGR 400 AGV
FBN1 15 48849784 −0.78 In 88 SCLP
MZ6 SOX9 17 71010900 −0.59 IGR 890 SCLP
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*In, Intron; IGR, intergenic region; #TSS, transcriptional start site; Genome assembly, GRCh37/hg19; &AGV, associated genetic variant; SCLP, syndromic cleft lip and palate.

DNA methylation levels are strongly correlated among neighboring sites across the genome, and identification of clusters of differentially methylated positions rather than single CpGs usually predicts functional relevance (Eckhardt et al., 2006). Regional analysis is also less prone to be affected by the technical artifacts associated with individual sites. Thus, we performed systematic regional analyses of the WGBS data to identify differentially methylated regions (DMRs) in the MZ NSCLP discordant twins. Amongst the six MZ pairs, we identified a total of 768 DMRs, varying in numbers from 61 in MZ2 to 349 in MZ1 (Supplementary Table 2). Table 5 lists the 10 most differentially methylated DMRs per MZ twin pair. Candidate genes containing DMRs are listed in Table 6. While each twin pair had DMRs in one or more candidate genes, only twin pairs MZ1 MZ2, and MZ3 had an overrepresentation of candidate genes among all their respective DMRs (MZ1 p < 0.004, MZ2 p < 0.02, and MZ3 p < 0.0006). A gene-based overlap analysis detected only two candidate genes containing DMRs in more than a single MZ pair, MAFB (MZ6 and MZ8) and ZEB2 (MZ3 and MZ8). The DMRs in ZEB2 are in two different areas of intron 2 (AB011141) while the DMRs in MAFB are overlapping and located in the proximal upstream region.

TABLE 5.

Top ten differentially methylated regions in each NSCLP twin pair.

Chr Start End # CpGs Gene 1 Gene2
MZ1 10 133992228 133994340 25 DPYSL4 JAKMIP3
1 231475643 231476069 20 EXOC8 SPRTN
2 206723874 206723953 6 NRP2 INO80D
22 30309896 30310188 9 HORMAD2 MTMR3
2 95537267 95537391 11 TEKT4
17 46632154 46632211 7 HOXB3
4 185251292 185251345 7 ENPP6 IRF2
9 136405836 136405917 6 ADAMTSL2 FAM163B
10 69664114 69664588 7 SIRT1 HERC4
17 35285044 35285218 9 LHX1 MRM1
MZ2 12 4141672 4141751 5 CCND2 PARP11
19 38803473 38804151 5 YIF1B C19orf33
7 2832917 2832990 10 GNA12 AMZ1
17 14358178 14358230 4 HS3ST3B1 PMP22
17 57860285 57860352 7 VMP1 TUBD1
2 223481094 223481197 7 FARSB SGPP2
6 158619635 158619734 6 TULP4 GTF2H5
21 19274639 19274704 7 CHODL C21orf91
12 64216292 64216411 6 SRGAP1 TMEM5
2 131852087 131852175 6 FAM168B
MZ3 15 41104483 41104658 7 DNAJC17
1 45280056 45280131 8 TCTEX1D4 PTCH2
1 11178496 11178611 8 ANGPTL7 EXOSC10
22 19825520 19825636 6 C22orf29 TBX1
11 44359724 44359809 6 CD82 ALX4
16 85033868 85033924 4 ZDHHC7 CRISPLD2
7 137154759 137154861 5 PTN DGKI
12 69069932 69070032 5 NUP107 RAP1B
17 48520295 48520396 6 ACSF2 CHAD
17 77638489 77639656 6 RBFOX3 ENPP7
MZ6 6 169977358 169977440 12 THBS2 WDR27
19 2960100 2960479 11 TLE6 ZNF77
19 13129284 13129382 6 NFIX LYL1
6 28945493 28945566 10 TRIM27 ZNF311
16 15083564 15083666 9 PDXDC1 NTAN1
8 54442087 54442157 8 OPRK1 ATP6V1H
22 30617390 30617470 7 LIF HORMAD2
14 71721206 71721284 9 SIPA1L1 PCNX
12 3948535 3948614 9 EFCAB4B PARP11
6 24647358 24647446 8 KIAA0319
MZ7 6 28663014 28665327 29 SCAND3 TRIM27
Y 15676834 15676905 7 TMSB4Y UTY
6 25882373 25882434 8 SLC17A3 SLC17A2
3 169377635 169378355 11 MECOM ACTRT3
1 154908464 154908782 8 PMVK
5 15008177 15008861 7 FBXL7 ANKH
17 56634655 56634821 8 SEPT4 TEX14
X 2994672 2994725 7 ARSF MXRA5
9 138339180 138339399 8 PPP1R26 OLFM1
8 141220160 141220218 6 TRAPPC9 C8orf17
MZ8 10 135343183 135343281 16 CYP2E1 SYCE1
11 71210236 71210315 9 KRTAP5-7 NADSYN1
9 139546440 139546629 11 NOTCH1 EGFL7
22 46279171 46279335 9 WNT7B ATXN10
5 171541730 171541852 11 FBXW11 STK10
3 107416865 107416968 9 BBX CD47
2 68907054 68907187 7 ARHGAP25 PROKR1
7 5205136 5205268 7 WIPI2 RBAK
22 23605028 23605090 9 BCR IGLL1
1 64135554 64135639 6 ROR1 PGM1
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TABLE 6.

Differentially methylated regions identified in candidate genes.

Chr Start End# CpG Delta methylation Gene distance to TSS Genomic feature* Candidate group&
MZ11 2976318 297910310 0.50 PRDM16 −8 IGR MGI
1 55359014 553591535 −0.51 DHCR24 −6 IGR SCLP
1 93425751 934258035 0.50 RPL5 128 In SCLP
1 243451450 2434515444 −0.53 SDCCAG8 32 In MGI
2 114099657 1141003026 −0.59 PAX8 −63 IGR AGV
3 13787881 137879714 −0.78 WNT7A 134 IGR AGV
3 41282685 412832684 −0.52 CTNNB1 42 IGR AGV
3 129328813 1293291188 0.54 PLXND1 −3 IGR MGI
3 189509045 1895095354 0.58 TP63 160 In AGV
5 149861203 1498614154 −0.51 NDST1 −26 IGR MGI
8 96365815 963658694 0.85 GDF6 807 IGR SCLP
10 131555549 1315558496 −0.51 MGMT 290 In AGV
12 99652840 996528965 0.54 APAF1 614 In MGI
12 125008438 1250117835 0.55 NCOR2 30 In SCLP
13 106929698 1069302854 0.55 EFNB2 257 IGR MGI
14 37123643 371237005 −0.62 PAX9 −7 IGR MGI
14 53976736 539769235 0.64 BMP4 447 IGR AGV
15 26877653 268778594 0.54 GABRB3 140 In MGI
16 79361536 793616375 0.60 MAF 273 IGR AGV
17 42646731 426467965 −0.66 FZD2 12 IGR MGI
17 72038593 720406385 0.53 RPL38 −160 IGR MGI
18 46345407 463455535 0.52 SMAD7 132 In MGI
22 34207931 342113294 −0.50 LARGE 107 In AGV
MZ21 87668656 876687255 −0.63 HS2ST1 288 IGR MGI
1 164904923 1649049784 −0.54 PBX1 376 IGR AGV
3 57741006 577431715 0.50 SLMAP 0 Prom MGI
4 157863234 1578633284 −0.55 PDGFC 29 In AGV
6 44424513 444245704 −0.54 RUNX2 −872 IGR MGI
14 29568885 295695974 −0.56 FOXG1 334 IGR SCLP
MZ32 145216430 1452167095 −0.58 ZEB2 62 In MGI
5 176551269 1765513574 −0.52 NSD1 −10 IGR SCLP
9 17005412 170055224 0.58 BNC2 −135 IGR MGI
16 4000600 40006524 0.54 CREBBP/ADCY9 −70/166 IGR AGV
16 85033868 850339244 0.73 CRISPLD2 180 In AGV
17 44918054 449198024 −0.53 WNT3/WNT9B −23/−10 IGR AGV
22 19825520 198256366 −0.62 TBX1 81 In AGV
22 46438139 464382034 0.51 WNT7B −65 IGR AGV
MZ61 156095505 1560956085 0.63 LMNA 11 In MGI
2 43859116 438592215 0.52 THADA −36 IGR AGV
9 129250270 1292503326 −0.57 LMX1B −126 IGR SCLP
11 61143440 611435494 0.56 TMEM216 −16 IGR SCLP
15 28338841 283388985 0.50 OCA2 6 In MGI
20 39319752 393209396 −0.68 MAFB −2 Prom AGV
MZ713 22473353 224734475 0.51 FGF9 228 IGR MGI
2 158607860 1586080196 0.55 ACVR1 124 In MGI
21 43048389 430485686 0.50 RIPK4 139 IGR SCLP
MZ820 39320842 393210114 −0.59 MAFB −3 IGR AGV
17 8869579 88696508 −0.51 PIK3R5 −1 Prom AGV
16 68829893 688299584 0.51 CDH1 59 In AGV
2 145247233 1452472934 0.60 ZEB2 31 In MGI
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TSS, transcriptional start site; Genome assembly, GRCh37/hg19; *In, Intron; IGR, intergenic region; Prom, promoter; &AGV, associated genetic variant; SCLP, syndromic cleft lip and palate; MGI, Mouse Genome Informatics (orofacial cleft phenotype).

Enrichment of Identified DMRs in Shared Disease-Relevant Pathways

To identify enriched molecular pathways, EnrichR (Chen et al., 2013; Kuleshov et al., 2016) was used to analyze the DMR-gene set from each twin pair (Table 7). Across the six twin pairs, comparison of the top 10 significantly enriched Kegg pathways from all differentially methylated genes identified common enrichment of the “Hippo Signaling Pathway” in three pairs (MZ1, MZ3 and MZ8) and “Signaling pathways regulating pluripotency of stem cells” in two pairs (MZ1 and MZ3). Interestingly, pathway analysis using only our candidate genes identified enrichment in Hippo signaling in MZ1 (BMP4, FZD2, WNT7A, CTNNB1, GDF6, and SMAD7) and Wnt signaling in MZ3 (CREBBP, WNT9B, and WNT3). Additionally, we found that the combined set of genes identified as differentially methylated in any twin pair (set of 1,234 genes) was significantly enriched in the terms Osteoblast differentiation (p < 6.6E-8), and Neural crest migration (p < 4.1E-4) (Supplementary Figure 4).

TABLE 7.

Pathways enriched for differentially methylated genes*.

Index Name Adjusted p-value
MZ1
1 Signaling pathways regulating pluripotency of stem cells 0.009879
2 Thyroid hormone signaling pathway 0.01003
3 Longevity regulating pathway 0.0102
4 Transcriptional misregulation in cancer 0.01558
5 Small cell lung cancer 0.01709
6 Hippo signaling pathway 0.02574
7 Longevity regulating pathway 0.0261
MZ3
1 Wnt signaling pathway 0.0008854
2 Melanogenesis 0.001263
3 Pathways in cancer 0.001309
4 Insulin secretion 0.00152
5 Glucagon signaling pathway 0.002417
6 Pancreatic secretion 0.002557
7 Cushing syndrome 0.002559
8 Basal cell carcinoma 0.002761
9 Salivary secretion 0.01002
10 cAMP signaling pathway 0.01172
11 Aldosterone synthesis and secretion 0.01216
12 Gastric cancer 0.01276
13 Hippo signaling pathway 0.01431
14 Gastric acid secretion 0.02412
15 Thyroid hormone synthesis 0.02429
16 Oocyte meiosis 0.02514
17 Relaxin signaling pathway 0.02519
18 Vascular smooth muscle contraction 0.02559
19 Cell cycle 0.02588
20 p53 signaling pathway 0.02664
21 Estrogen signaling pathway 0.02739
22 Signaling pathways regulating pluripotency of stem cells 0.02791
23 Apelin signaling pathway 0.0287
24 Colorectal cancer 0.02993
25 Adrenergic signaling in cardiomyocytes 0.03086
MZ8
1 Hippo signaling pathway 0.03994
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*No significant enrichment in any Kegg pathways was identified in MZ2, MZ6, and MZ7.

Between-Group Analyses Identified Additional DMRs

Group level DNA methylation differences between NSCLP cases and controls were also examined. Unlike the within-pair discordant MZ twin design, between-group DNA methylation differences can be attributed to both genetic and epigenetic factors. This analysis identified significantly different methylation regions only in the genes KCNAB3, NWD1, IFLTD1, and TRBJ2-3. No DMRs in NSCLP candidate genes were identified in this analysis.

Discussion

This is the first comprehensive study of epigenetic differences in MZ twins discordant for NSCLP. This epigenome-wide study was performed using saliva DNA from six male MZ twin pairs discordant for NSCLP to determine if differences in DNA methylation could explain the discordant NSCLP phenotype. Results from this study provide novel and exciting findings which point to the importance of methylation in craniofacial development.

We found regional methylation differences in a number of genes in each twin pair (range 61–349). Less than 3% (37 out of 1,338) of these genes were differentially methylated in more than one twin pair. A single gene, TRIM27, was differentially methylated in three pairs. TRIM27 has not been previously associated with clefting or craniofacial development but has been shown to be differentially methylated in prostate and lung cancers (Ji et al., 2020; Wang et al., 2020). Interestingly, differential methylation of TRIM27 was reported in a study comparing cleft lip only to cleft palate only (Sharp et al., 2017).

When we restricted our analysis to NSCLP candidate genes, evidence of methylation differences was found, suggesting their possible contribution to NSCLP through epigenetic variation. Of the 49 candidate genes which exhibited differential methylation, only two, MAFB and ZEB2, were differentially methylated in more than one twin pair. Importantly, the changes were all in the same direction, suggesting that these two genes are epigenetically important in orofacial development. This provides additional support for the previously reported association between NSCLP and MAFB, which is expressed in palatal shelves (Beaty et al., 2010). ZEB2 is involved in embryonic development of facial features (Ghoumid et al., 2013; Funato and Nakamura, 2017), and Zeb2 mutant mice exhibit alterations in neural crest migration and midfacial clefting (Van de Putte et al., 2003; Miyoshi et al., 2006). The lack of significant gene overlap points to the underlying heterogeneity of NSCLP.

Interestingly, although the majority of the identified DMRs do not lie in genes previously associated with NSCLP, pathway enrichment analysis revealed overrepresentation of the Hippo pathway in three of the six twin pairs. This signaling pathway was initially discovered as a key regulator of tissue growth in flies and regulates organ size and tissue repair by controlling cell proliferation, survival, mobility, stemness, and differentiation. The Hippo pathway is tightly regulated by both intrinsic and extrinsic signals, such as mechanical force, cell–cell contact, polarity, energy status, stress, and many diffusible hormonal factors. Studies have shown evidence of crosstalk between the Hippo pathway and other key signaling pathways, such as Wnt signaling (Li et al., 2019), and has recently been associated with craniofacial development (Wang et al., 2016; Wang and Martin, 2017; Sun et al., 2018) and mouse secondary palate formation (Goodwin et al., 2020). The common enrichment in this pathway is not due to alterations in a small subset of similar genes in all pairs (see Supplementary Figure 5). Rather, there is pathway convergence through alterations in MZ pair-specific methylation in different genes. A comparison of our results with a recent orofacial clefting case-control report revealed a little overlap (Alvizi et al., 2017). Interestingly, this overlap includes the NSCLP candidate gene MAFB (Supplementary Table 3). The different findings between these studies provides further evidence for complex genetic and epigenetics involvement in the etiology of NSCLP.

It is well known that genetic variation in a single gene can cause syndromic cases of orofacial clefting. It is therefore possible that extreme epigenetic variation in a few key genes could produce a clefting phenotype. However, it is more likely that the effects of most DNA methylation changes are subtle, similar to some non-coding genetic variants, and that multiple changes act in concert to ultimately contribute to non-sydromic orofacial clefting, as discussed above. The observation of DMPs in genes previously implicated in NSCLP, such as TP63, TBX1, CRISPLD2, CREBBP, and BMP4, suggest that de novo epimutations, which could imitate genetic mutations (Martin et al., 2005), are likely involved in the pathogenesis of NSCLP. Since we found methylation changes in several NSCLP candidate genes in each affected twin, we propose that an “epigenetic load” mechanism is involved in the dysregulation of orofacial morphogenesis, rather than a single dysfunctional NSCLP gene. This is consistent with the accepted multifactorial inheritance of NSCLP (Carter, 1969; Hecht et al., 1991; Howe et al., 2018). Based on the important role of epigenetic mechanisms in regulating gene expression, it is likely that methylomic variation could mediate disease susceptibility by cumulative altered gene expression, similar to genetic variation in regulatory regions of the genome.

The use of saliva as a proxy for the lip and palatal tissues is justified by their common developmental origin (Patel and Hoffman, 2014). Salivary glands, lip, and palate all arise from neural crest cells, making cells from saliva a developmentally appropriate proxy for these tissues. In addition, saliva is a good source of high quality DNA for use in epigenomics (Thompson et al., 2013; Smith et al., 2015) and has been deemed more informative than blood for DNA methylation analysis (Lowe et al., 2013) in spite of the possibility of bacterial contamination resulting in non-human reads in human sequencing data (Samson et al., 2020).

Our sample size, while small, exceeds the number of twins in most epigenetic studies of birth defects with most being based on a single discordant twin pair (Weksberg et al., 2002; Rall et al., 2011; Tierling et al., 2011; Jin et al., 2014; Wei et al., 2015; Lu et al., 2017; Lyu et al., 2018). NSCLP has an estimated frequency of 1 in 700; thus, discordant MZ twin pairs occur infrequently but are rich source of information.

The stringent parameters used in this study, together with the validation of selected DMPs by either bisulfite sequencing, pyrosequencing, or methylation arrays, suggest that these are likely real differences rather than reflecting experimental noise. Amongst the identified methylation differences between co-twins, we propose that many of them underlie the discordance in NSCLP but recognize that there will be some that may reflect individual exposure to environmental factors unrelated to NSCLP, or perhaps non-craniofacial phenotypic differences. However, identification of DMRs in NSCLP candidate genes and the pathway analyses of the unbiased genes containing DMRs suggest that these DMRs play an etiologic role in NSCLP.

In summary, we show for the first time that MZ twins discordant for NSCLP have differential methylation. The findings from this novel study demonstrate that methylation may play a significant role in the etiology of NSCLP and, more importantly, differential methylation could account for the “reduced penetrance” seen in many multiplex families (Hecht et al., 1991; Hecht and Blanton, 1998). Since the discordant twins share age, sex and socioeconomic status and do not exhibit CLP-related genotype variability (as analyzed by WGS), our data highlights the role of non-shared environmental and stochastic factors in the etiology of epigenetic differences and resulting discordant NSCLP phenotypes. Importantly, we find methylation differences in genes previously implicated by NSCLP genetic studies. Moreover, our data suggest that although DNA methylation at some CpG sites is consistently altered in affected twins, most differences are individual-specific, revealing epigenetic heterogeneity between NSCLP individuals. This suggests that each affected individual/family may have their own unique epigenetic signature making etiologic identification complex. However, the differences converge at the level of potentially affected pathways including the Hippo signaling pathway. Our findings complement a recent NSCLP case-control study suggesting that epigenetic mechanisms underlie NSCLP (Alvizi et al., 2017). Future studies are aimed at defining epigenetic changes in sporadic cases, as well as in multiplex NSCLP families in which DNA methylation patterns can be traced through multiple generations.

Data Availability Statement

The data presented in the study are deposited in the Gene Expression Omnibus (GEO) repository, accession number GSE173211.

Ethics Statement

The studies involving human participants were reviewed and approved by this study was approved by the University of Texas Health Science Center Committee for Protection of Human Subjects (HSC-MS-03-090) and the University of Miami Human Subject Research Office (IRB #20100505). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

JY, JH, and SB: conceptualization, investigation, writing—original draft, and writing—review and editing. JY and SS: formal analysis and methodology. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Anastasia Vedenko, Ian Ergui, Sathesh Sivasankaran, and Derek Van Booven for their research and analytical assistance.

Funding. This work was supported by National Institute of Dental and Craniofacial Research Grant R01DE011931-17 (JH and SB).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2021.656865/full#supplementary-material

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Associated Data

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Supplementary Materials

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Data Availability Statement

The data presented in the study are deposited in the Gene Expression Omnibus (GEO) repository, accession number GSE173211.

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