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. 2022 Oct 9;115(2):205–217. doi: 10.1002/bdr2.2102

DNA methylation profile of lip tissue from congenital nonsyndromic cleft lip and palate patients by whole‐genome bisulfite sequencing

Bowen Zhang 1,2, Youmeng Zhang 3, Siyi Wu 1, Duan Ma 2,✉, Jing Ma 1,✉
PMCID: PMC10092010  PMID: 36210532

Abstract

Congenital nonsyndromic cleft lip and palate (NSCLP) is one of the most common malformations worldwide. DNA methylation has been implicated in many diseases. However, its involvement in lip tissue from NSCLP is not well understood. This study aimed to investigate the role of dysregulated DNA methylation in NSCLP. DNA methylation profile was determined in eight injured and five self‐normal lip tissue samples from children with NSCLP by whole‐genome bisulfite sequencing. A total of 2,711 differentially methylated regions (DMRs), corresponding to 1,231 genes were identified. Given the important role of promoter methylation in regulating gene expression, the promoter DMR‐related genes were considered. Bioinformatics analysis demonstrated that some of them showed potential associations with NSCLP. Therefore, the well‐known NSCLP susceptibility gene, GLI family zinc finger 2 (GLI2) with an unknown role in its DNA methylation in NSCLP, was selected for further analysis. The promoter hypomethylation and higher mRNA expression level of GLI2 were observed in injured lip tissues by verification in additional samples. Moreover, dual luciferase reporter assay indicated that promoter hypermethylation of GLI2 inhibited its transcription. Overall, this study suggested that abnormal DNA methylation in lip tissue may be correlated with the pathogenesis of congenital NSCLP.

Keywords: DNA methylation, GLI2, lip tissue, NSCLP

1. INTRODUCTION

Cleft lip and palate (CLP) is one of the most common congenital malformations and ranks third in birth defects worldwide with an overall incidence of approximately one in 700 live births (Watkins, Meyer, Strauss, & Aylsworth, 2014). It is characterized by unilateral or bilateral clefts of both the lip and palate due to the failure of maxillary and frontonasal fusion processes during embryonic development (Murray, 2002; Xu, Lie, Wilcox, Saugstad, & Taylor, 2019). Children born with CLP typically present with dysphagia, dysphonia, dysarthria, occlusal disharmony, and hypoplasia of the maxilla to varying degrees (Dixon, Marazita, Beaty, & Murray, 2011). Currently, surgical repair and orthodontic treatment are the main therapies for CLP. Postoperative patients can still face complications such as infection of the nasal base or nasal cavity wound and damage to the respiratory tract (Escher, Zavala, Lee, Roby, & Chinnadurai, 2021). It is therefore of great significance to explore the etiology and pathogenesis of CLP for its prevention and treatment. Based on whether it is combined with the abnormalities of other organs is combined, CLP is divided into syndromic CLP (SCLP) and nonsyndromic CLP (NSCLP). NSCLP accounts for 70% of all CLP cases (Dixon et al., 2011) and is broadly considered as a complex disorder affected by the interaction between genetic and environmental factors (Stanier & Moore, 2004). Genetic factors play a leading role in NSCLP and the heritability varies from 45% to 85% (Brito et al., 2011). At present, more than 30 NSCLP susceptibility genes have been identified, most of which are related to the FGF (D. Li et al., 2016; W. Li et al., 2019; Pauws & Stanier, 2007; Riley & Murray, 2007), BMP (Cela et al., 2016; Liu et al., 2005; Pauws & Stanier, 2007), TGF‐β (Blanco, Colombo, Pardo, & Suazo, 2017; Raju, Lakkakula, Murthy, Kannan, & Paul, 2017; Saleem, Rajendran, Moinak, Anna, & Pramod, 2012), and WNT (Chiquet et al., 2008; Mostowska et al., 2012) signaling pathways. Maxillofacial development is a tightly regulated event that requires the expression of many genes with a precise spatial‐temporal specificity (Alvizi et al., 2017). Thus, in addition to sequence variations, changes in gene expression are also involved in the manifestation of NSCLP. Among the factors that regulate gene expression in eukaryotic cells, DNA methylation appears to be particularly important and is one of the most common modifications at the transcriptional level (Anastasiadi, Esteve‐Codina, & Piferrer, 2018; Sharp, Stergiakouli, Sandy, & Relton, 2018; Song et al., 2019). Many studies have indicated that DNA methylation plays an essential role in the occurrence and development of diseases (Corso‐Díaz, Jaeger, Chaitankar, & Swaroop, 2018; Greenberg & Bourc'his, 2019; Weinberg et al., 2019). Although changes in the DNA methylation of few susceptible genes have been found in patients with CLP (Alvizi et al., 2017; Cáceres‐Rojas et al., 2020; Khan et al., 2019; Y. Li et al., 2019; Murthy, Gurramkonda, Karthik, & Lakkakula, 2014), the function of DNA methylation in CLP, especially in NSCLP remains largely unclear.

In this study, differential DNA methylation of several genes related with to CLP was identified between injured and self‐normal lip tissues from NSCLP children through whole‐genome bisulfite sequencing (WGBS) and bioinformatics analysis. GLI2 is a well‐known susceptibility gene of NSCLP, required in stem cell renewal and palatal fusion (Levi et al., 2011; Meng et al., 2019; Vieira et al., 2005). While it has not been reported to the involvement of its DNA methylation involved in NSCLP. We found that the DNA methylation level of the GLI2 promoter region negatively regulated its transcription by additional sample verification and in vitro experiments.

2. MATERIALS AND METHODS

2.1. Patients and samples

Written informed consent was obtained from all patients. This study was approved by the Institutional Research Ethics Committee of the Children's Hospital of Fudan University, Shanghai, China (2016‐121). Lip tissues were obtained from the patients with NSCLP at the Children's Hospital of Fudan University. WGBS was carried out using eight injured lip (IL) tissues and five self‐normal lip (SNL) tissues from NSCLP patients (Table S1). None of the patients with NSCLP were diagnosed with additional disorders and SNL tissues were used as controls. All lip samples were maintained in liquid nitrogen tanks immediately after surgery.

2.2. Whole‐genome bisulfite sequencing

Total tissue genomic DNA was extracted using the QIA amp DNA Mini Kit (Qiagen, Germany) according to the manufacturer's instructions. The DNA quality and concentration were evaluated based on the optical density 260/280 and 260/230 ratios using a NanoDrop ND‐2000 spectrophotometer (Thermo Fisher Scientific). DNA integrity was assessed by agarose gel electrophoresis. Then the genomic DNA was randomly sheared into 200–300 bp fragments for bisulfite treatment by an EZ DNA Methylation Gold Kit (Zymo Research). Finally, the complete DNA library was constructed for sequencing (Precisiongenes Inc., China). Raw reads were subjected to quality control to assess the suitability of the raw data for subsequent analysis. Trimmed data were obtained by removing the sequencing adapters and low‐quality fragments of raw data (≤15 base pairs) using Trimmomatic (http://www.usadellab.org/). The DNA methylation sites with low confidence were filtered (p value > .01 in all samples). The screened and corrected DNA methylation sites were used for subsequent analysis. The visual figure reflecting the methylation status at various positions among the whole genome was obtained by alignment analysis of human reference genomes, and CG methylation site analysis was performed by Bismark (http://www.bioinformatics.bbsrc.ac.uk/projects/bismark/).

2.3. Enrichment analysis of differentially methylated region‐related genes

Taking into spatial correlation, read depth of the sites, and variance among biological replicates, differentially methylated region (DMR) analysis was performed with stringent Bonferroni correction for multiple testing between comparison groups using decision support system (DSS). DMR‐related genes were obtained according to the human reference genome sequence and DMRs were compared with the whole gene body region (from transcription starting point to transcription termination point). Functional enrichment of the DMR‐related genes was then processed by Gene Ontology (GO) (http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg) analysis.

2.4. Weighted correlation network analysis analysis

Identification of modules was performed by weighted correlation network analysis using the R package (version 3.6.1). The topological overlap matrix was used to calculate the association degree between the genes and divide the genes into different modules (Ravasz, Somera, Mongru, Oltvai, & Barabási, 2002). We then use hierarchical clustering and dynamic tree cut method to identify gene clusters (Langfelder, Zhang, & Horvath, 2008; Zhang & Wong, 2022). The details are briefly described as follows: The module eigengene (ME) represented the gene expression profile of each module. Pearson's product–moment correlation was used to calculate the correlation between ME and clinical phenotype. Module membership (MM) represented the correlation between ME and all of the gene expression profiles. Gene significance (GS) represented the association between gene expression and clinical phenotype. The correlation between GS and MM of the module represented the association between a module and clinical phenotype.

2.5. Pyrosequencing

The sulfite conversion of qualified genomic DNA was performed with a CT transformation solution by an EZ Bisulfite conversion kit (Zymo Research). A total of 1 μl of the target DNA was used as a template, and the sequences containing selected differentially methylated sites were amplified using nested polymerase chain reaction (PCR), the products of which were used for direct pyrosequencing (PyroMark).

The primers used were designed as follows:

GLI2‐F1: AGATGATGAGAATGGTTTGTAGTTGTT,

GLI2‐F2: TATAAGAGAATTAGAATGAAGTTGGTTGT,

GLI2‐Seq: TAGGATTGTTATTTAGGAYGATGAG,

GLI2‐R1: AACCTTCAACACCCCAACCATATATAATCCCACTTTTAACTTCTTACTTCTC.

2.6. RNA extraction and quantitative PCR

Total RNA was extracted from lip tissue using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. After the evaluation of the quality and concentration, RNA was reverse transcribed to cDNA using a Prime Script RT Reagent Kit (Takara, Japan), and quantitative PCR (qPCR) was conducted using SYBR Premix Ex Taq™ (Takara) on a StepOnePlusTM Real‐Time PCR System (Thermo Fisher Scientific). The relative expression level of mRNA was normalized to glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) and was calculated by the relative quantification method (2−ΔΔCt). The primers used were designed as follows:

GLI2q‐F: CCCTCACCTCCATCAATGC,

GLI2q‐R: CTCACTGCTCTGCTTGTTCT,

GAPDHq‐F: GGGAAACTGTGGCGTGAT,

GAPDHq‐R: GAGTGGGTGTCGCTGTTGA.

2.7. Cell culture

Human embryonic kidney (HEK293T) cells were cultured in Dulbecco's modified Eagle's medium with 10% fetal bovine serum (Biological Industries, Israel) at 37 °C and 5% CO2. All cell culture dishes and plates were purchased from Hangzhou Xinyou Biotechnology Co., Ltd (China).

2.8. Construction of luciferase reporter plasmids and bisulfite sequencing PCR

The pGL3‐GLI2‐Basic (Promega) recombinant plasmid containing the promoter DMR of GLI2 was constructed (Promega). The fidelity of the recombinant plasmid was confirmed by Sanger sequencing using the designed primers as follows:

GLI2‐basic‐F: CTAGCTAGCATGGACTACAAGGACGACGATGACAAGATGCGGTGTGAATCTGGGTCGG,

GLI2‐basic‐R: CCCAAGCTTCTACTTGTCATCGTCGTCCTTGTAGTCGTCTCCATCTCAGCCGCT.

The pGL3‐GLI2‐Basic construct was methylated by M.SssI (New England BioLabs) for 4 hr at 37 °C. Bisulfite sequencing PCR (BSP) was used to confirm the methylation status. A bisulfite‐converted DNA fragment was amplified by PCR using the designed primers as follows:

GLI2‐bsp‐F: GYGTGTTAGTATGGATTATAAGGA,

GLI2‐bsp‐R: TTACTCTCCAACRATTCCATC.

The purified PCR products were cloned into a pGEM‐TEasy vector (Promega) for Sanger sequencing. The final sequencing data were analyzed on the QUMA website (http://quma.cdb.riken.jp/).

2.9. Dual‐luciferase reporter assays

HEK293T cells were seeded in 96‐well plates at 1 × 104 cells per well, and incubated overnight at 37 °C. The pRL‐Renilla control plasmid (Promega) was cotransfected into HEK293T cells with pGL3‐SV40, pGL3‐E1B, pGL3‐Basic, GLI2‐pGL3‐Basic (unmethylated), Meth‐GLI2‐pGL3‐Basic (methylated), respectively, using Viafect transfection agent (Promega). The pGL3‐SV40 and pGL3‐E1b containing strong promoter SV40 and weak promoter E1B sequences, respectively, were used as positive controls. Cells were then harvested 48 hr after transfection. Both firefly and Renilla luciferase activities were measured using a Dual‐Luciferase Reporter Assay System (Promega), and the firefly luciferase activities were normalized to Renilla luciferase activities.

2.10. Statistical analysis

All experiments were repeated three times. Statistical tests were performed using GraphPad Prism Software (version 7). In Figure 6c, the differences between the two groups were analyzed by the Mann–Whitney U test. In Figures 6a,b and 7c, the differences between the two groups were analyzed by two‐tailed unpaired Student's t test with Shapiro–Wilk normality test. A value of p < .05 was considered significant.

FIGURE 6.

FIGURE 6

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The validation of GLI2 promoter methylation and mRNA expression level in extended samples. (a) Seven methylation sites distribution on the promoter DMR of GLI2. The gray dot presented the site. (b) The comparison of the methylation level of the seven sites between IL and SNL from WGBS data. The No. 7 site with significant methylation difference between IL and SNL was highlighted in a red box. (c) Hypomethylation levels at No. 7 site in IL by pyrosequencing. (d) The high mRNA expression level of GLI2 in IL compared with SNL by qPCR. Values are mean ± SEM, *p < .05, ****p < .0001. DMR, differentially methylated region; IL, injured lip; qPCR, quantitative polymerase chain reaction; SNL, self‐normal lip; WGBS, whole‐genome bisulfite sequencing

FIGURE 7.

FIGURE 7

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Promoter hypermethylation inhibiting the transcription of GLI2. (a) Left: The position of the promoter DMR in GLI2 and the sequence of this DMR by MethPrimer online. Right: ChIP‐seq occupancy profiles of H3K36me3 and H3K4me3 from the upper to lower. (b) The methylation percentage of each CpG site was significantly increased in methylated pGL3‐Basic‐GLI2 according to BSP. (c) Dual‐luciferase reporter assay showed that hypermethylation of the DMR decreased the transcriptional activity of GLI2 in HEK293T cells. Values are mean ± SEM, **p < .01. ***p < .001. DMR, differentially methylated region

3. RESULTS

3.1. Preliminary construction of the DNA methylation profile of NSCLP

In total, eight IL and five SNL tissues from NSCLP patients were included for WGBS. Consequently, 2,711 DMRs were identified when considering CG content, including 1,225 hypermethylated and 1,486 hypomethylated DMRs (Table S2). Based on the average methylation level of DMRs given by DSS software, the heatmap and circos diagram revealed significant differential methylation status between IL and SNL samples (Figure 1a,b). DMR‐related genes were assessed according to the reference sequence in the human genome database. A total of 1,231 differentially methylated genes were found in IL samples compared with SNL samples, and 797 genes and 434 genes were hypomethylated and hypermethylated, respectively (Table S1). Methylation changes occurred in all regions of the DMR‐related genes, and the number of genes with abnormal intron methylation was the largest (Figure 2a). Moreover, there were more hypomethylated genes, and the regions where methylation levels varied greatly were mainly distributed among 2 kb upstream and downstream of the genes (Figure 2b). In the two regions, promoter methylation changes were more common (Figure 2a).

FIGURE 1.

FIGURE 1

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Differential DNA methylation profile in NSCLP. (a) Heatmap of DMRs identified from WGBS by hierarchical clustering analysis between IL and SNL samples from NSCLP patients. Methylation values were represented by red and blue shades, indicating methylations above and below the median methylation level across all samples, respectively. (b) The circos diagram of global methylation level. There were three circles in total from outside to inside: IL methylation level, differences in methylation level between IL and SNL, SNL methylation level. The methylation level was divided into seven levels from low to high (from light green to navy blue). DNA methylation difference was indicated by blue color for hypermethylation and red color for hypomethylation. IL, injured lip; NSCLP, nonsyndromic cleft lip and palate; SNL, self‐normal lip; WGBS, whole‐genome bisulfite sequencing

FIGURE 2.

FIGURE 2

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Distribution of DMRs in a genome‐wide scale. (a) The DNA methylation level of IL and SNL samples in the regions of gene body, downstream 2k bp and upstream 2k bp. The abscissa was the area coverage and the ordinate was the methylation level value. (b) DMRs distribution in different regions of the genome. The ordinate showed the number of DMRs, while the abscissa showed the different region of the genome. (c) The intersection of DMR‐related genes tested in lip tissue and publicly available database(blood). (d) All coincident DMR‐related genes in different sources. CGI, CpG island; DMR, differentially methylated region; IL, injured lip; SNL, self‐normal lip; utr, untranslated region

3.2. Potential function of DMR‐related genes

GO analysis showed that hypermethylated genes were mainly involved in “nervous system development” (119 genes, p = 5.40E−07), “neurogenesis” (85 genes, p = 1.42E−09), “generation of neurons” (80 genes, p = 4.17E−09), “synapse” (58 genes, p = 2.51E−06), and “neuron part” (74 genes, p = 2.30E−05) (Figure 3a), while hypomethylated genes were mainly related with the biological processes “system development” (257 genes, p = 2.65E−15), “nervous system development” (153 genes, p = 4.43E−14), “anatomical structure development” (280 genes, p = 9.00E‐13), and “cell periphery” (242 genes, p = 1.34E−05) (Figure 3b). KEGG enrichment analysis showed that “tryptophan metabolism” (4 genes, p = 2.14E−02), “phosphatidylinositol signaling system” (8 genes, p = 2.47E−02), and “fatty acid biosynthesis” (2 genes, p = 5.00E−02) were the most significantly enriched pathways of hypermethylated genes(Figure 3c) and “one carbon pool by folate” (4 genes, p = 1.47E−02), “cleft lip and palate” (11 genes, p = 3.34E−02) and “Hippo signaling pathway” (12 genes, p = 3.73E‐02) were the most significantly enriched pathways of hypomethylated genes (Figure 3d). Some of the enriched biological processes and pathways were related to the occurrence and development of NSCLP, which suggested the role of abnormal DNA methylation in NSCLP. This study focused on 299 genes with DMR anchored in the promoter region (Table S1). Through the GO and KEGG analyses, we found that these promoter DMR‐related genes were involved in biological processes and signaling pathways that mainly mediated embryonic development, such as “embryonic organ morphogenesis” (17 genes, p = 1.07E−08), “skeletal system development” (21 genes, p = 5.52E−08), “Hippo signaling pathway” (8 genes, p = 3.55E−03) and “signaling pathways regulating pluripotency of stem cells” (6 genes, p = 2.65E−02) (Figure 4a–d).

FIGURE 3.

FIGURE 3

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GO terms and KEGG pathway analysis of the DMR‐related genes. (a) Enriched GO terms of hypermethylated DMR‐related genes in IL samples compared with SNL samples. (b) Enriched GO terms of hypomethylated DMR‐related genes in IL samples compared with SNL. The ordinate was the enriched GO term and the abscissa was the number of DMR‐related genes in the term (*p value ≤ .05). (c) Enriched pathways of hypermethylated DMR‐related genes in IL compared with SNL. (d) Enriched pathways of hypomethylated DMR‐related genes in IL compared with SNL. Size and color of the bubble represented the amount of DMR‐related gene enriched in the pathway and enrichment significance, respectively. DMR, differentially methylated region; Gene Ontology; IL, injured lip; KEGG, Kyoto Encyclopedia of Genes and Genomes; SNL, self‐normal lip

FIGURE 4.

FIGURE 4

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GO terms and KEGG pathway analysis of the promoter DMR‐related genes DMRs in IL and SNL. (a) Enriched GO terms of promoter hypermethylated DMR‐related genes in IL compared with SNL. (b) Enriched GO terms of promoter hypomethylated DMR‐related genes in IL compared with SNL. The ordinate was the enriched GO term, and the abscissa was the number of DMR‐related genes in the term (*p value ≤ .05). (c) Enriched pathways of promoter hypermethylated DMR‐related genes in IL compared with SNL. (d) Enriched pathways of promoter hypomethylated DMR‐related genes in IL compared with SNL. Size and color of the bubble represented the amount of DMR‐related gene enriched in the pathway and enrichment significance, respectively. (e) The comparison of differentially expressed genes (DEGs) and promoter hyper/hypomethylated genes between IL and SNL samples. Left: The intersection of promoter DMR‐related genes and DEGs. Middle: Bar plots displaying the representative GO terms enriched in 20 coincident genes. Right: Candidate gene results. Shown are genes enriched in NSCLP‐related GO pathways. DMR, differentially methylated region; Gene Ontology; IL, injured lip; KEGG, Kyoto Encyclopedia of Genes and Genomes; NSCLP, nonsyndromic cleft lip and palate; SNL, self‐normal lip

Among these 299 genes, 5 hypermethylated genes (PIK3C2G, SOX2, AMOT, PITX2, and ) and 3 hypomethylated genes (UQCC2, GLI2, RPS29) were possible susceptibility genes of NSCLP (Table S2), but the function of their promoter methylation status in NSCLP has never been reported.

To further evaluate the importance of promoter DMR‐related genes in NSCLP, We have performed WCGNA analysis using the mRNA data of lip tissues from the patients with NSCLP in previous study of our group on SRA data database (PRJNA700692). WCGNA analysis for DEGs (differentially expressed genes) in lip tissues NSCLP patients identified 24 modules (Figure 5a–d), and 12 disease‐related modules (correlation coefficient [CC] > .2, p < .05) out of them were screened and contained 7,645 genes. A closer look at these 7,645 genes revealed that almost half of (142/299) promoter DMR‐related genes existed in it. GO analysis indicated that these overlapping genes enriched in pathways involved in NSCLP, such as “embryonic digit morphogenesis” and “chondrocyte differentiation.” Among these overlapping genes, GLI2, which contributes to the survival and differentiation of osteoblasts and chondrocytes, was selected for further research.

FIGURE 5.

FIGURE 5

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Conjoint analysis of hub gene modules associated with NSCLP and promoter DMR‐related genes. (a) Identification of soft‐threshold power by analyzing the scale‐free index (left) and the mean connectivity (right) in the WGCNA. (b) Dendrogram of all DEGs clustered based on a dissimilarity measure (1‐TOM). Clustering DEGs are shown in colors. (c) Numbers of hub genes in each module. (d) Heatmap showing the correlation between module eigengene (ME) and NSCLP. The CC and p values of each module in IL and SNL are presented in the center of the panels. Positive and negative associations are separately shown in red and blue, respectively. (e) The comparison of disease‐related genes and promoter DMR‐related genes. (f) Bar plots displaying the representative GO terms enriched in overlapping genes. The ordinate was the enriched pathway term, and the abscissa was −log10p value in each term. DMR, differentially methylated region; NSCLP, nonsyndromic cleft lip and palate; TOM, topological overlap matrix; WGCNA, weighted correlation network analysis

3.3. Validation of differences in promoter methylation and mRNA expression level of GLI2 in additional samples

According to the data of WGBS, there were seven methylated sites in the promoter DMR of GLI2, and only the methylation level of one site was significantly lower significantly in IL samples when compared with SNL samples (no. 7 site, p = .0164) (Figure 6a), which was validated by pyrosequencing using another 24 IL and 5 SNL tissues (p < .0001) (Figure 6b). Furthermore, compared with 12 SNL tissues, the mRNA expression level of GLI2 was increased in 27 IL samples using qPCR (p = .0490) (Figure 6c).

3.4. Hypermethylation in the promoter of GLI2 inhibits its transcription in vitro

The sequence of the GLI2 promoter DMR was identified (Figure 7a). The methylated and unmethylated GLI2 reporter plasmids containing above DMR were constructed and then transfected into HEK293T cells. The methylation efficiency was confirmed by BSP assay (Figure 7b). Dual‐luciferase reporter assay revealed that hypermethylation in the promoter decreased the transcriptional activity of GLI2 in HEK293T cells (Figure 7c).

4. DISCUSSION

Dysregulation of DNA methylation in regulatory regions may affect embryonic development by altering gene expression (Lin & Wu, 2020; Ma et al., 2020), which is associated with the occurrence of congenital defects. Development of the lip and palate requires a complex series of events including formation of the upper lip and the primary palate and the division of the oronasal space, and any disturbance in this process may lead to NSCLP (Mossey, Little, Munger, Dixon, & Shaw, 2009). Previous studies have suggested that differential DNA methylation in several genes was associated with NSCLP (Cáceres‐Rojas et al., 2020; Y. Li et al., 2019; Xu et al., 2019; Yang et al., 2021). However, whole blood samples were used for most studies, and even if tissue samples were used a very few number of studies, their controls were not self‐control. Moreover, genome‐wide methylation levels have rarely been studied, and the methylation profiles of certain genes have been widely studied. Finally, no distinction was made between NSCLP and nonsyndromic cleft lip when the case was included in previous studies. Given the above limitations, in this study, genome‐wide DNA methylation analysis was performed between injured and SNL tissue from NSCLP children. We found differential DNA methylation in various regions of numerous genes, most of which were related to the one‐carbon pool by the folate, Hippo, and phosphatidylinositol signaling pathways. Interestingly, because of their function in regulating the migration and differentiation of cranial neural crest cells, many biological processes and signaling pathways linked to the nervous system were enriched. It is well known that DNA methylation in the promoter of genes may alter transcription by affecting the binding of transcription factors (Baubec, Ivánek, Lienert, & Schübeler, 2013; Sasai & Defossez, 2009; Yin et al., 2017). According to the association with lip and palate development or NSCLP, five promoter hypermethylated genes (PIK3C2G, SOX2, AMOT, PITX2, and SLC25A5) and three promoter hypomethylated genes (UQCC2, GLI2, and RPS29) were filtered out. Notably, most of these gene methylation levels have never been reported to be related to NSCLP previously.

Then we focused on GLI2 because it is a well‐known susceptibility gene of NSCLP (Bear et al., 2014; Meng et al., 2019; Vieira et al., 2005), which showed a relatively low methylation level in IL tissue compared with the self‐control tissue. At the same time, GLI2 was also screened as a hub‐gene in NSCLP‐related modules by WCGNA on DEGs of NSCLP lip tissues. GLI2 belongs to the C2H2‐type zinc finger protein subclass of the Gli family (Liu, 2019). The Gli family members are mediators of Sonic hedgehog (Shh) signaling, which has been reported to play an indirect role in the etiology of CLP (Wang et al., 2019). DNA methylation of GLI2 has been found to be related to a variety of tumors and developmental‐related diseases (Carter et al., 2013; Cofer et al., 2016; Kolarova et al., 2015; Liu et al., 2021; Lu et al., 2016), and GLI2 activity can be regulated by its DNA methylation level (Abe & Tanaka, 2020; Vuong et al., 2020). However, there is only one report on DNA methylation dysregulation of GLI2 in NSCLP and no verification of the rudimentary sequencing results in a larger sample (Xu et al., 2018). In a larger number of samples, the hypomethylation of GLI2 was further verified and its increased mRNA expression level was observed in IL tissue. In vitro, dual‐luciferase reporter assay results also showed that hypermethylation in the promoter of GLI2 significantly inhibited gene transcription activity.

To the best of our knowledge, this study provides the first report of genome‐wide analysis of the DNA methylation profile in lip tissue of NSCLP using self‐control tissue. The hypomethylation of the GLI2 promoter involved in NSCLP was also revealed for the first time and the mechanism has also been partly revealed. These findings will help to provide a theoretical basis for the prevention and treatment of NSCLP.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

ETHICS STATEMENT

The permission was obtained from the patient and their parents to use their genetic test in this article.

Supporting information

TABLE S1 Supporting information

Click here for additional data file. (9.9KB, xlsx)

TABLE S2 Supporting information

Click here for additional data file. (328.6KB, xlsx)

ACKNOWLEDGMENT

This study was supported by grants from the National Key Research and Development Program of China (Nos. 2021YFC2701000 and 2016YFC1000500).

Zhang, B. , Zhang, Y. , Wu, S. , Ma, D. , & Ma, J. (2023). DNA methylation profile of lip tissue from congenital nonsyndromic cleft lip and palate patients by whole‐genome bisulfite sequencing. Birth Defects Research, 115(2), 205–217. 10.1002/bdr2.2102

Bowen Zhang and Youmeng Zhang contributed equally to this study.

Funding information The National Key Research and Development Program of China, Grant/Award Numbers: 2021YFC2701000, 2016YFC1000500

Contributor Information

Duan Ma, Email: duanma@fudan.edu.cn.

Jing Ma, Email: mj19815208@yeah.net.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in SRA database at https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA700692, https://www.ncbi.nlm.nih.gov/sra/PRJNA877826, reference numbers PRJNA700692, PRJNA877826.

REFERENCES

  1. Abe, Y. , & Tanaka, N. (2020). Fine‐tuning of GLI activity through arginine methylation: Its mechanisms and function. Cell, 9(9), 1973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alvizi, L. , Ke, X. , Brito, L. A. , Seselgyte, R. , Moore, G. E. , Stanier, P. , & Passos‐Bueno, M. R. (2017). Differential methylation is associated with non‐syndromic cleft lip and palate and contributes to penetrance effects. Scientific Reports, 7(1), 2441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anastasiadi, D. , Esteve‐Codina, A. , & Piferrer, F. (2018). Consistent inverse correlation between DNA methylation of the first intron and gene expression across tissues and species. Epigenetics & Chromatin, 11(1), 37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baubec, T. , Ivánek, R. , Lienert, F. , & Schübeler, D. (2013). Methylation‐dependent and ‐independent genomic targeting principles of the MBD protein family. Cell, 153(2), 480–492. [DOI] [PubMed] [Google Scholar]
  5. Bear, K. A. , Solomon, B. D. , Antonini, S. , Arnhold, I. J. P. , França, M. M. , Gerkes, E. H. , … Muenke, M. (2014). Pathogenic mutations in GLI2 cause a specific phenotype that is distinct from holoprosencephaly. Journal of Medical Genetics, 51(6), 413–418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Blanco, R. , Colombo, A. , Pardo, R. , & Suazo, J. (2017). Haplotype‐based gene‐gene interaction of bone morphogenetic protein 4 and interferon regulatory factor 6 in the etiology of non‐syndromic cleft lip with or without cleft palate in a Chilean population. European Journal of Oral Sciences, 125(2), 102–109. [DOI] [PubMed] [Google Scholar]
  7. Brito, L. A. , Cruz, L. A. , Rocha, K. M. , Barbara, L. K. , Silva, C. B. F. , Bueno, D. F. , … Passos‐Bueno, M. R. (2011). Genetic contribution for non‐syndromic cleft lip with or without cleft palate (NS CL/P) in different regions of Brazil and implications for association studies. American Journal of Medical Genetics. Part A, 155a(7), 1581–1587. [DOI] [PubMed] [Google Scholar]
  8. Cáceres‐Rojas, G. , Salamanca, C. , Krause, B. J. , Recabarren, A. S. , Recabarren, P. A. , Pantoja, R. , … Suazo, J. (2020). Nonsyndromic orofacial clefts in Chile: LINE‐1 methylation and MTHFR variants. Epigenomics, 12(20), 1783–1791. [DOI] [PubMed] [Google Scholar]
  9. Carter, T. C. , Kay, D. M. , Browne, M. L. , Liu, A. , Romitti, P. A. , Kuehn, D. , … Mills, J. L. (2013). Anorectal atresia and variants at predicted regulatory sites in candidate genes. Annals of Human Genetics, 77(1), 31–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cela, P. , Hampl, M. , Fu, K. K. , Kunova Bosakova, M. , Krejci, P. , Richman, J. M. , & Buchtova, M. (2016). MORN5 expression during craniofacial development and its interaction with the BMP and TGFβ pathways. Frontiers in Physiology, 7, 378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chiquet, B. T. , Blanton, S. H. , Burt, A. , Ma, D. , Stal, S. , Mulliken, J. B. , & Hecht, J. T. (2008). Variation in WNT genes is associated with non‐syndromic cleft lip with or without cleft palate. Human Molecular Genetics, 17(14), 2212–2218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cofer, Z. C. , Cui, S. , EauClaire, S. F. , Kim, C. , Tobias, J. W. , Hakonarson, H. , … Matthews, R. P. (2016). Methylation microarray studies highlight PDGFA expression as a factor in biliary atresia. PLoS One, 11(3), e0151521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Corso‐Díaz, X. , Jaeger, C. , Chaitankar, V. , & Swaroop, A. (2018). Epigenetic control of gene regulation during development and disease: A view from the retina. Progress in Retinal and Eye Research, 65, 1–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dixon, M. J. , Marazita, M. L. , Beaty, T. H. , & Murray, J. C. (2011). Cleft lip and palate: Understanding genetic and environmental influences. Nature Reviews. Genetics, 12(3), 167–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Escher, P. J. , Zavala, H. , Lee, D. , Roby, B. B. , & Chinnadurai, S. (2021). Malnutrition as a risk factor in cleft lip and palate surgery. Laryngoscope, 131, E2060–E2065. [DOI] [PubMed] [Google Scholar]
  16. Greenberg, M. V. C. , & Bourc'his, D. (2019). The diverse roles of DNA methylation in mammalian development and disease. Nature Reviews. Molecular Cell Biology, 20(10), 590–607. [DOI] [PubMed] [Google Scholar]
  17. Khan, M. F. J. , Little, J. , Aleotti, V. , Mossey, P. A. , Steegers‐Theunissen, R. P. M. , Autelitano, L. , … Rubini, M. (2019). LINE‐1 methylation in cleft lip tissues: Influence of infant MTHFR c.677C>T genotype. Oral Diseases, 25(6), 1668–1671. [DOI] [PubMed] [Google Scholar]
  18. Kolarova, J. , Tangen, I. , Bens, S. , Gillessen‐Kaesbach, G. , Gutwein, J. , Kautza, M. , … Caliebe, A. (2015). Array‐based DNA methylation analysis in individuals with developmental delay/intellectual disability and normal molecular karyotype. European Journal of Medical Genetics, 58(8), 419–425. [DOI] [PubMed] [Google Scholar]
  19. Langfelder, P. , Zhang, B. , & Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: The dynamic tree cut package for R. Bioinformatics, 24(5), 719–720. [DOI] [PubMed] [Google Scholar]
  20. Levi, B. , James, A. W. , Nelson, E. R. , Brugmann, S. A. , Sorkin, M. , Manu, A. , & Longaker, M. T. (2011). Role of Indian hedgehog signaling in palatal osteogenesis. Plastic and Reconstructive Surgery, 127(3), 1182–1190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li, D. , Zhang, H. , Ma, L. , Han, Y. , Xu, M. , Wang, Z. , … Pan, Y. (2016). Associations between microRNA binding site SNPs in FGFs and FGFRs and the risk of non‐syndromic orofacial cleft. Scientific Reports, 6, 31054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Li, W. , Wang, M. , Zhou, R. , Wang, S. , Zheng, H. , Liu, D. , … Beaty, T. H. (2019). Exploring the interaction between FGF genes and T‐box genes among chinese nonsyndromic cleft lip with or without cleft palate case‐parent trios. Environmental and Molecular Mutagenesis, 60(7), 602–606. [DOI] [PubMed] [Google Scholar]
  23. Li, Y. , Deng, Y. , Deng, C. , Xie, L. , Yu, L. , Liu, L. , … Dai, L. (2019). Association of long interspersed nucleotide element‐1 and interferon regulatory factor 6 methylation changes with nonsyndromic cleft lip with or without cleft palate. Oral Diseases, 25(1), 215–222. [DOI] [PubMed] [Google Scholar]
  24. Lin, Y. T. , & Wu, K. J. (2020). Epigenetic regulation of epithelial‐mesenchymal transition: Focusing on hypoxia and TGF‐β signaling. Journal of Biomedical Science, 27(1), 39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Liu, A. (2019). Proteostasis in the hedgehog signaling pathway. Seminars in Cell & Developmental Biology, 93, 153–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu, L. , Hu, K. , Feng, J. , Wang, H. , Fu, S. , Wang, B. , … Huang, H. (2021). The oncometabolite R‐2‐hydroxyglutarate dysregulates the differentiation of human mesenchymal stromal cells via inducing DNA hypermethylation. BMC Cancer, 21(1), 36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu, W. , Sun, X. , Braut, A. , Mishina, Y. , Behringer, R. R. , Mina, M. , & Martin, J. F. (2005). Distinct functions for bmp signaling in lip and palate fusion in mice. Development, 132(6), 1453–1461. [DOI] [PubMed] [Google Scholar]
  28. Lu, X. L. , Wang, L. , Chang, S. Y. , Shangguan, S. F. , Wang, Z. , Wu, L. H. , … Zhang, T. (2016). Sonic hedgehog signaling affected by promoter Hypermethylation induces aberrant Gli2 expression in Spina bifida. Molecular Neurobiology, 53(8), 5413–5424. [DOI] [PubMed] [Google Scholar]
  29. Ma, J. , Chen, S. , Hao, L. , Sheng, W. , Chen, W. C. , Ma, X. , … Huang, G. (2020). Hypermethylation‐mediated down‐regulation of lncRNA TBX5‐AS1:2 in tetralogy of Fallot inhibits cell proliferation by reducing TBX5 expression. Journal of Cellular and Molecular Medicine, 24(11), 6472–6484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Meng, P. , Zhao, H. , Huang, W. , Zhang, Y. , Zhong, W. , Zhang, M. , … Lin, J. (2019). Three GLI2 mutations combined potentially underlie non‐syndromic cleft lip with or without cleft palate in a Chinese pedigree. Molecular Genetics & Genomic Medicine, 7(9), e714. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mossey, P. A. , Little, J. , Munger, R. G. , Dixon, M. J. , & Shaw, W. C. (2009). Cleft lip and palate. Lancet, 374(9703), 1773–1785. [DOI] [PubMed] [Google Scholar]
  32. Mostowska, A. , Hozyasz, K. K. , Biedziak, B. , Wojcicki, P. , Lianeri, M. , & Jagodzinski, P. P. (2012). Genotype and haplotype analysis of WNT genes in non‐syndromic cleft lip with or without cleft palate. European Journal of Oral Sciences, 120(1), 1–8. [DOI] [PubMed] [Google Scholar]
  33. Murray, J. C. (2002). Gene/environment causes of cleft lip and/or palate. Clinical Genetics, 61(4), 248–256. [DOI] [PubMed] [Google Scholar]
  34. Murthy, J. , Gurramkonda, V. B. , Karthik, N. , & Lakkakula, B. V. K. S. (2014). MTHFR C677T and A1298C polymorphisms and risk of nonsyndromic orofacial clefts in a south Indian population. International Journal of Pediatric Otorhinolaryngology, 78(2), 339–342. [DOI] [PubMed] [Google Scholar]
  35. Pauws, E. , & Stanier, P. (2007). FGF signalling and SUMO modification: New players in the aetiology of cleft lip and/or palate. Trends in Genetics, 23(12), 631–640. [DOI] [PubMed] [Google Scholar]
  36. Raju, G. T. , Lakkakula, B. V. K. S. , Murthy, J. , Kannan, M. A. , & Paul, S. F. D. (2017). Transmission analysis of TGFB1 gene polymorphisms in non‐syndromic cleft lip with or without cleft palate. International Journal of Pediatric Otorhinolaryngology, 100, 14–17. [DOI] [PubMed] [Google Scholar]
  37. Ravasz, E. , Somera, A. L. , Mongru, D. A. , Oltvai, Z. N. , & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297(5586), 1551–1555. [DOI] [PubMed] [Google Scholar]
  38. Riley, B. M. , & Murray, J. C. (2007). Sequence evaluation of FGF and FGFR gene conserved non‐coding elements in non‐syndromic cleft lip and palate cases. American Journal of Medical Genetics. Part A, 143a(24), 3228–3234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Saleem, S. , Rajendran, R. , Moinak, B. , Anna, J. , & Pramod, B. J. (2012). Evidence for transforming growth factor‐beta 3 gene polymorphism in non‐syndromic cleft lip and palate patients from Indian sub‐continent. Medicina Oral, Patología Oral y Cirugía Bucal, 17(2), e197–e200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Sasai, N. , & Defossez, P. A. (2009). Many paths to one goal? The proteins that recognize methylated DNA in eukaryotes. The International Journal of Developmental Biology, 53(2–3), 323–334. [DOI] [PubMed] [Google Scholar]
  41. Sharp, G. C. , Stergiakouli, E. , Sandy, J. , & Relton, C. (2018). Epigenetics and orofacial clefts: A brief introduction. The Cleft Palate‐Craniofacial Journal, 55(6), 795–797. [DOI] [PubMed] [Google Scholar]
  42. Song, Y. , van den Berg, P. R. , Markoulaki, S. , Soldner, F. , Dall'Agnese, A. , Henninger, J. E. , … Jaenisch, R. (2019). Dynamic enhancer DNA methylation as basis for transcriptional and cellular heterogeneity of ESCs. Molecular Cell, 75(5), 905–920.e6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Stanier, P. , & Moore, G. E. (2004). Genetics of cleft lip and palate: Syndromic genes contribute to the incidence of non‐syndromic clefts. Human Molecular Genetics, 13(1), R73–R81. [DOI] [PubMed] [Google Scholar]
  44. Vieira, A. R. , Avila, J. R. , Daack‐Hirsch, S. , Dragan, E. , Félix, T. M. , Rahimov, F. , … Murray, J. C. (2005). Medical sequencing of candidate genes for nonsyndromic cleft lip and palate. PLoS Genetics, 1(6), e64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Vuong, T. A. , Jeong, H. J. , Lee, H. J. , Kim, B. G. , Leem, Y. E. , Cho, H. , & Kang, J. S. (2020). PRMT7 methylates and suppresses GLI2 binding to SUFU thereby promoting its activation. Cell Death and Differentiation, 27(1), 15–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wang, Q. , Kurosaka, H. , Kikuchi, M. , Nakaya, A. , Trainor, P. A. , & Yamashiro, T. (2019). Perturbed development of cranial neural crest cells in association with reduced sonic Hhedgehog signaling underlies the pathogenesis of retinoic‐acid‐induced cleft palate. Disease Models & Mechanisms, 12(10), dmm040279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Watkins, S. E. , Meyer, R. E. , Strauss, R. P. , & Aylsworth, A. S. (2014). Classification, epidemiology, and genetics of orofacial clefts. Clinics in Plastic Surgery, 41(2), 149–163. [DOI] [PubMed] [Google Scholar]
  48. Weinberg, D. N. , Papillon‐Cavanagh, S. , Chen, H. , Yue, Y. , Chen, X. , Rajagopalan, K. N. , … Lu, C. (2019). The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature, 573(7773), 281–286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Xu, X. Y. , Wei, X. W. , Ma, W. , Gu, H. , Liu, D. , & Yuan, Z. W. (2018). Genome‐wide screening of aberrant methylation loci for nonsyndromic cleft lip. Chinese Medical Journal, 131(17), 2055–2062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Xu, Z. , Lie, R. T. , Wilcox, A. J. , Saugstad, O. D. , & Taylor, J. A. (2019). A comparison of DNA methylation in newborn blood samples from infants with and without orofacial clefts. Clinical Epigenetics, 11(1), 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Yang, W. , Guo, Y. , Ni, W. , Tian, T. , Jin, L. , Liu, J. , … Wang, L. (2021). Hypermethylation of WNT3A gene and non‐syndromic cleft lip and/or palate in association with in utero exposure to lead: A mediation analysis. Ecotoxicology and Environmental Safety, 208, 111415. [DOI] [PubMed] [Google Scholar]
  52. Yin, Y. , Morgunova, E. , Jolma, A. , Kaasinen, E. , Sahu, B. , Khund‐Sayeed, S. , … Taipale, J. (2017). Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science, 356(6337), eaaj2239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Zhang, T. , & Wong, G. (2022). Gene expression data analysis using Hellinger correlation in weighted gene co‐expression networks (WGCNA). Computational and Structural Biotechnology Journal, 20, 3851–3863. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

TABLE S1 Supporting information

Click here for additional data file. (9.9KB, xlsx)

TABLE S2 Supporting information

Click here for additional data file. (328.6KB, xlsx)

Data Availability Statement

The data that support the findings of this study are openly available in SRA database at https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA700692, https://www.ncbi.nlm.nih.gov/sra/PRJNA877826, reference numbers PRJNA700692, PRJNA877826.

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