Abstract
Background An imbalance in DNA methylation is a hallmark epigenetic alteration in cancer. The conversion of 5-methylcytosine (5-mC) to 5-hydroxymethyl cytosine (5-hmC), which causes the imbalance, results in aberrant gene expression. The precise functional role of 5-hydroxymethylcytosine in breast cancer remains elusive. In this study, we describe the landscape of 5-mC and 5-hmC and their association with breast cancer development.
Results We found a distinguishable global loss of 5-hmC in the localized and invasive types of breast cancer, which correlate strongly with TET expression. Genome-wide analysis revealed a unique 5-mC and 5-hmC signature in breast cancer. The differentially methylated regions (DMRs) were primarily concentrated in the proximal regulatory regions such as the promoters and UTRs, while the differentially hydroxymethylated regions (DhMRs) were densely packed in the distal regulatory regions such as the intergenic regions (>-5 kb from TSSs). Our results indicate 4809 DMRs and 4841 DhMRs associated with breast cancer. Validation of nine 5-hmC enriched loci in a distinct set of breast cancer and normal samples, positively correlated with their corresponding gene expression. The novel 5-hmC candidates such as TXNL1, CNIH3, and BNIPL implicate a pro-oncogenic role in breast cancer. Therefore, 5-hmC modified regions could be used as promising diagnostic and therapeutic markers for breast cancer.
Conclusion Global loss of 5-hmC is associated with down-regulation of the TET 1 and TET3 genes. Genome-wide profiling has revealed a profound imbalance in the region-specific distribution of 5-mC and 5-hmC in breast cancer. Predominant 5-hmC modifications are localized at distal gene regulatory sites. Novel 5-hmC candidates associated with breast cancer have been identified. Hence, these results provide new insights in the loci-specific accumulation of 5-mC and 5-hmC which are aberrantly methylated and demethylated in breast cancer.
Introduction
DNA methylation imbalance is one of the hallmark epigenetic events in cancer. Cytosine DNA methylation (5-methylcytosine or 5-mC) occurs at the gene promoter and is often associated with gene repression, while its oxidized form 5-hydroxymethylcytosine (5-hmC) relaxes the repression (1, 2). The oxidation of 5-mC to 5-hmC is catalyzed by the TET family of genes TET1, TET2, and TET3 (3–7). Based on tissue specificity, 5-hmC levels can vary between 0.1% and 1% of the human genome (8). The increase in 5-hmC is strongly associated with transcriptional activation (9). Effective binding of methylation readers such as MBD3 and MeCP2 preferentially to 5-hmC results in active transcriptional assembly and activity (10, 11). It turned out that 5-hmC is the stable epigenetic modification involved in the transcription machinery and not just serving as an intermediate in the demethylation process. The imbalance between 5-mC and 5-hmC is of recent interest as both are associated with gene expression and lead to carcinogenesis.
A global reduction of 5-hmC was evident in several cancers (8, 12–16). Studies on melanoma, pancreatic cancer, lung cancer, and prostate cancer suggest that aberrant 5-mC and 5-hmC levels may predispose to tumor progression (17–24). However, there is limited evidence for 5-mC and 5-hmC dynamics in breast cancer. We hypothesize an imbalance among the genomic 5-mC and 5-hmC levels that contributes to breast carcinogenesis. Previous reports affirm that 5-hmC levels depends on tissue-specific TET expression. Particularly, the genes TET1 and TET2 downregulation has been reported to alter the 5-hmC levels (25). A recent study on breast cancer showed the altered 5-hmC profiles and their association with lymph node metastases (26). In breast cancer, the locus-specific deposition of 5-hmC and its functional role in the control of gene expression are poorly understood. Emerging enrichment approaches can identify 5-mC and 5-hmC genomic regions with single-base resolution and describe the differentially methylated regions (DMRs) and differentially hydroxymethylated regions (DhMRs) in cancer (27, 28). Determining the 5-hmC modified genomic regions in breast cancer will be of promising diagnostic and therapeutic markers.
In this study we find that global methylation and hydroxymethylation levels were drastically reduced in breast cancer tissues. The global 5-hmC reduction was associated with the downregulation of the TET1 and TET3 genes. The genome-wide analysis revealed differentially methylated and differentially hydroxymethylated breast cancer loci. We also identified a strong correlation between the 5-hmC alterations and the gene expression changes. Altogether, the study provides a comprehensive genome-wide distribution of the 5-mC and 5-hmC and also the imbalance in the DNA methylation machinery that leads to breast cancer development.
Results
Loss of 5-hmC is Associated with TET 1 and TET3 Downregulation in Breast Cancer
Global levels of 5-hmC, 5-mC were first quantified in breast cancer [invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS)] paired normal (PN), and apparent normal (AN) tissues. We found a significant decrease in 5-hmC levels in IDC vs PN (FC = –2.58, p = 0.0003) and DCIS vs AN (FC = –2.08, p = 0.0324) (Figure 1a). Although global 5-mC levels were reduced in IDC vs PN (FC = –2.28, p = 0.0014), there was no significant difference between the DCIS vs AN group (p = 0.5467) (Figure 1b). The results imply that the “global loss of 5-hmC” is a characteristic epigenetic alteration of localized and invasive breast cancer. The differential expression of TET genes leads to the altered 5-hmC levels seen in breast cancer. Therefore, we quantified the TET gene expression levels and found that the genes TET2 (FC = +2.02, p = 0.0317) (Figure 1d), and TET3 (FC = +2.0, p = 0.0159) (Figure 1e) were upregulated in DCIS vs AN group. However, TET1 (FC = –2.08, p = 0.0266) (Figure 1c) and TET3 (FC = –2.0, p = 0.026) (Figure 1e) genes were downregulated in the IDC vs PN and IDC vs AN groups. Spearman’s rank test showed no significant correlation of TET genes with global 5-hmC levels in PN tissues (Figure 1f-h), while it showed a significant positive correlation of TET1 (r = 0.544, p = 0.05) (Figure 1i) and TET3 (r = 0.5662, p=0.0437) (Figure 1k) genes with the global loss of 5-hmC in breast cancer but not with the TET2 gene (Figure 1j). In addition to TET1, we report here that TET3 is also associated with a global loss of 5-hmC in breast cancer.
(a) Global levels of 5-hmC in IDC (n = 15), PN (n = 15), DCIS (n = 5), and AN (n = 5) tissues (b) Global levels of 5-mC in IDC (n = 15), PN (n = 15), DCIS (n = 5), and AN (n = 5) tissues (c) Gene expression analysis of TET1 among the IDC, PN, AN, and DCIS samples (d) Gene expression analysis of TET2 among the IDC, PN, AN, and DCIS samples (e) Gene expression analysis of TET3 among the IDC, PN, AN, and DCIS samples (f) Correlation analysis of global 5-hmC levels of PN samples with relative mRNA expression of TET1 gene (g) Correlation analysis of global 5-hmC levels of PN samples with relative mRNA expression of TET2 gene (h) Correlation analysis of global 5-hmC levels of PN samples with relative mRNA expression of TET3 gene (i) Correlation analysis of global 5-hmC levels of breast tumour samples with relative mRNA expression of TET1 gene (j) Correlation analysis of global 5-hmC levels of breast tumour samples with relative mRNA expression of TET2 gene (k) Correlation analysis of global 5-hmC levels of breast tumour samples with relative mRNA expression of TET3 gene. [Wilcoxon signed-rank test was applied to test the statistical significance of paired analysis, Mann–Whitney U test was used to evaluate unpaired or grouped analysis (***p< 0.0001; **p < 0.001, and *p < 0.05) (Abbreviations: PN-paired normal; IDC-invasive ductal carcinoma; AN-apparent normal breast tissues; DCIS-ductal carcinoma in situ)].
Relative Abundance of 5-hmC in Breast Cancer and their enrichment at distal regulatory sites
We performed the enrichment of genomic 5-mC and 5-hmC specific regions followed by high-throughput sequencing and achieved ∼14 million reads from 5-hmC-enriched libraries and ∼21 million reads from 5-mC-enriched libraries. Almost 99.7% of the 5-hmC reads and ∼98.94% of 5-mC reads were mapped effectively against the reference genome. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) showed a clear pattern of segregation from the tumor [IDC and DCIS] to normal samples [PN and AN] (SF 1a-d). MACS-2 peak calling of the mapped reads resulted in a total of 3.3 million 5-hmC-enriched peak sets and 4.7 million 5-mC-enriched peak sets. Differential peak calling analysis between breast cancer and normal groups identified 4809 differential methylated regions (DMRs) (p<0.01, FDR<0.05) (Figure 2a) and 4841 differential hydroxymethylated regions (DhMRs) (p<0.01, FDR<0.05) (Figure 2b) (Sf 1a-b). The distribution of peaks across the chromosomes showed a higher peak intensity of DhMR over DMR (Figure 2c-d) (window size: 1 × 10−6). A significantly higher peak intensity of DhMR indicates a potential difference in loci-specific 5-hmC levels between breast tumor and paired normal samples.
(a) Heatmap representing DMRs in breast cancer (b) Heatmap showing DhMRs in breast cancer (Z-score ranges from –2 (white) to +2 (red)) (c) Chromosomal distribution of differentially methylated regions (DMRs) in breast cancer (d) Chromosomal distribution of differentially hydroxymethylated regions (DhMRs) in breast cancer (e) Genomic features of DMRs and DhMRs in breast cancer (f) Relative peak count frequency of DhMR from Transcription Start Sites (TSSs) (g) Relative peak count frequency of DMR from TSSs
The peaks characterized by their genomic features indicated that both DMR and DhMR were moderately found in the gene body (28.97% and 37.31%, respectively), particularly in the intronic regions but not in the exons. The DMR profile was high in the promoter (28.57%) regions. However, only a mere 8.82% enrichment of DhMR was found in the promoter region compared to massive accumulation in the distal intergenic regions (43.11%) [(Figure 2e) (SF 2a-b and ST 1a-b)]. Therefore, we show here that the distribution in the gene body does not invariably differ between the two modifications, but DMR is typically enriched at the proximal regulatory sites (promoter), while DhMR at the distal regulatory sites (intergenic region). We found the enrichment of 5-mC and 5-hmC were significantly distinguishable between tumor and normal tissues. Further, the enriched peak sets of the DhMRs and DMRs were analyzed for the transcription factor binding sites. We found that the accumulation in the promoter regions of DMRs in the interval 1500 bp upstream and 1500 bp downstream of the TSSs (read count frequency>6.5 × 10−5) was higher than that of DhMRs (read count frequency <3 × 10−5) (SF 2c). While in DhMRs, the accumulation was observed in the distal intergenic regions upstream 5 kb from the TSSs (read count frequency>6.5 × 10−5) [(Figure 2f-g) (SF 2d)]. We also used LOLA-Web to identify the locus overlap between the 5-hmC sites and the regulatory sites in the distal intergenic regions (i.e., regions>5 kb from the TSSs). Tumor and PN peak sets of 5-hmC were tested against the ENCODE data set with the reference genome hg19 and normalized with preloaded Tiles1000.hg19.bed. Tumor-specific 5-hmC peak sets are significantly associated with the enhancer sites of the breast cancer cell line MCF-7 regions such as H3K4me1, H3K4me3, H3K14ac, and H3K9ac (log (p-value) > 300). Also, enhancer sites were overlapped with loci-specific 5-hmC levels of the candidate genes in the breast cancer cell lines such as MDA-MD-468, MDA-MB-231 and MCF-7 regions but not in the normal luminal cell line MCF-10A (SF 3a-f). Hence, the results suggest that DhMRs were widespread in the distal regulatory regions, while DMRs accumulated in the proximal regulatory regions of the breast cancer genome.
Locus-Specific Imbalance of DhMRs and DMRs in Breast Cancer
To determine the exact loci and the differential distribution of 5-hmC and 5-mC accumulation in breast cancer, we identified 35 hyper-hmC loci (Sf 2a), and 30 hypo-hmC loci (Sf 2b). The hyper-hmC loci included coding genes (GALC, BNIPL, TXNL1, CNIH3, etc.), lncRNA (LINC00535, LINC00662, and PTPRN2 lncRNA), and microRNA (MIR4278, MIR1204, MIR944, and MIR921). We found 26 coding genes (ZBTB16, SP8, THRB, HIC2, etc.,) and only four non-coding loci (MIR4417, MIR3612, LINC00911, and LINC00417) among the hypo-hmC-specific regions. A total of 57 hyper-mC loci inclusive of 53 coding genes (CCDC181, SIM2, ID4, etc.) and 4 non-coding genes (LINC01257, LOC728989, MIR5087, and MIR183) were obtained. The hypo-mC loci consisted of 24 coding genes (OPCML, MKI67, and SPOCK1) and 6 were non-coding (MIR548AR, LINC02347, MIR744, MIR3612, etc.) (Figure 3a) (Sf. 2c-d). Further, we validated five hyper-hmC and four hypo-hmC loci in breast cancer and normal tissues. The results confirmed the 5-hmC gain of TXNL1 (FC=4, p=0.0102) (Figure 3b-c), CNIH3 (FC=2, p=0.0242) (Figure 3d), while for BNIPL, A4GALT and CBLN4 no statistical significance was observed, although they follow the same trend (Figure 3e-g). On the other hand, hypo-hmC candidates CHODL showed a two-fold loss of 5-hmC (p = 0.0416) (Figure 3h). Other loci such as ZBTB16, HIC2, and SP8 showed a trend towards 5-hmC loss in tumors but did not show any statistical significance (Figure 3i-k). Our validation analysis confirmed that the gain of 5-hmC in TXNL1, BNIPL, CNIH3 and loss of 5-hmC in CHODL, ZBTB16, SP8, HIC2 in breast cancer samples. The gene function prediction by g: Profiler indicated 5-mC and 5-hmC genes were related to cell cycle regulation, cell cycle inhibition, and cell proliferative signals that could potentially affect breast cancer development and progression (Sf 3a-d). The gene ontology and KEGG pathway enrichment map of DhMRs and DMRs also revealed the association of breast cancer-specific 5-hmC and 5-mC with transcriptional machinery (SF 4a-d). Altered levels of 5-mC and 5-hmC eventually control and determine the progression of breast cancer development. Extensive functional analysis of the identified loci will elucidate the significance of the 5-mC and 5-hmC imbalance in breast cancer development.
(a) Ideogram representing DMRs (blue) and DhMRs (red) across all chromosomes and the candidate loci of hyper-mC, hypo-mC, hyper-hmC, and hypo-hmC groups (b) Integrative genome viewer representing the gain of 5-hmC in the distal regulatory region of TXNL1 in tumour, paired normal and apparent normal samples (c) Validation of 5-hmC levels of TXNL1 (d) Validation of 5-hmC levels of CNIH3 (e)Validation of 5-hmC levels of BNIPL (f) Validation of 5-hmC levels of A4GALT (g) Validation of 5-hmC levels of CBLN4 (h) Validation of 5-hmC levels of CHODL (i) Validation of 5-hmC levels of ZBTB16 (j)Validation of 5-hmC levels of HIC2 (k) Validation of 5-hmC levels of SP8. The p-value of <0.05 was considered statistically significant (***p < 0.0001; **p < 0.001, and *p < 0.05).
Association of 5-mC and 5-hmC modifications with Gene Expression
We investigated the aberrant levels of methylation and hydroxymethylation in the locus-specific aspect and its impact on the regulation of gene expression using TCGA-breast cancer data set (UALCAN) (Table 1). We found the hyper-methylation of CCDC181 (beta value > 0.5, p < 0.05), ID4 (beta value > 0.5, p < 0.05) and hypo-methylation of MKI67, OPCML, and SPOCK1 (beta value < 0.3, p < 0.05). Correspondingly, CCDC181 (normalized TPM count = –0.094, p < 0.1) (Figure 4a), OR4F29 (normalized TPM count = –0.005, p < 0.1) (Figure 4b), and ID4 (normalized TPM count = –58.839, p < 0.001) (Figure 4c), were downregulated in tumor samples (n = 1094) while, MKI67 (normalized TPM count = 9.94, p < 0.001) and SPOCK1 (normalized TPM count = 3.26, p < 0.001) were found to be over-expressed in tumor samples but not OPCML (Figure 4d-f). The inverse correlation confirmed that hypermethylation leads to the suppression of gene expression and hypomethylation leads to overexpression of the gene. Further, we found that hyper-hmC candidates, BNIPL (normalized TPM count = 5.344, p <0.05), CNIH3 (normalized TPM count= 0.778, p <0.005), and TXNL1 (normalized TPM count = 14.549, p < 0.001) to be upregulated and the hypo-hmC candidates such as ZBTB16 (normalized TPM count = –13.933, p < 0.001), HIC2 (normalized TPM count = –0.436, p < 0.001), CHODL (normalized TPM count = –1.093, p < 0.001), THRB (normalized TPM count = –13.792, p < 0.001) and RAPGEF2 (normalized TPM count = –9.296, p < 0.001) were significantly downregulated (Figure 4g-p). Several loci relax the repressive methylation marks by increasing the 5-hmC levels leading to gene activation. In this study, we found that three candidate genes, TXNL1, CNIH3, and BNIPL, showed an increase in 5-hmC associated with gene overexpression. The direct proportionality between 5-hmC and gene expression reinstate that gain of 5-hmC activates gene transcription. We further investigated the influence of gene expression and overall survival of the candidate genes with hyper-hmC specifications. The results indicated that overexpression of genes such as TXNL1 (p = 0.042) (SF 5a), CNIH3 (p = 0.26) (SF 5b) and BNIPL (p = 0.0001) (SF 5c) was associated with poor overall survival in breast cancer patients.
Hyper-mC/hmC and hypo-mC/hmC candidates for the validation and gene expression analysis.
(a-f) Gene expression analysis of hyper- and hypo-mC candidates (g-p); Gene expression analysis of hyper- and hypo-hmC candidates using UALCAN webtool (***p < 0.0001; **p < 0.001, and *p < 0.05).
Discussion
The present study illustrated the methylation and hydroxymethylation landscape of the breast cancer genome. Initial findings showed that the downregulation of the TET genes causes a global 5-hmC reduction in breast cancer. The genome-wide profiling revealed a higher 5-mC accumulation around the TSSs from –1.5k to +1.5k, while the 5-hmC accumulated in the intergenic regions (>–5 kb away from TSSs). Thus, DMRs have mainly been associated with proximal gene regulation and DhMRs with distal regulation. We found an intergenic and gene body gain of 5-hmC associated with gene overexpression and a loss of 5-hmC towards downregulation of the corresponding genes. The study results show that the imbalance between 5-mC and 5-hmC is a novel phenomenon orchestrating the epigenetic machinery of breast cancer.
Previous studies also reported global 5-mC and 5-hmC loss in various cancers, including breast cancer (17, 23, 29, 30). The tissue-specificity and the alterations of TET gene expression in advancing cancer stages determine the 5-hmC levels and the demethylation process (14, 18, 24, 31, 32). The cytoplasmic mislocalization of TET1 in ER/PR-negative subtypes of IDC and DCIS was directly proportional to the global reduction in 5-hmC levels (25). We show here, that the global reduction in the 5-hmC content in IDC is not only dependent on TET1, but also on TET3 genes. The CXXC domains of TET1 and TET3 enhance the DNA binding efficiency at DNA demethylation sites (33). The expression and nuclear import of TET1 and TET3 facilitate an active oxidation process. Our finding shows the downregulation of TET3 also contribute to 5hmC loss in breast cancer tissues.
Genome-wide profiling showed the effects of promoter methylation in breast cancer. The advancement in enrichment strategies and next-generation sequencing has resulted in the contemporary analysis of 5-mC and 5-hmC describing their genomic signatures in breast cancer (34–36). In our study, we found the enrichment of 5-mC and 5-hmC significantly distinguishable between tumor and normal tissues. Higher hydroxymethylation levels were observed in the promoter and UTRs of DCIS tissues. On the other hand, the invasive type showed a higher accumulation in the gene body and intergenic regions than in the promoter regions. Hence, we speculate that accumulation of 5hmC in the preliminary stage of breast cancer occurs mainly at the proximal regulatory regions, reducing the suppression caused by 5-mC. In the locally advanced breast tumors, the enrichment at the distal intergenic regions implicates an enhancer like activity of 5-hmC. Previous studies also reported that 5-hmC could potentially act as an enhancer or super-enhancer elements ∼5–10 kb and >20 kb away from the TSSs (37, 38). The overlapping histone markers from the ENCODE roadmap project and 5-hmC sites emphasized the association of active enhancer sites in the breast cancer genome. The positive association of histone activation and 5-hmC gain suggest a synergistically enhanced gene.
Several loci relax the repressive methylation marks by increasing the 5-hmC levels leading to gene activation. In our results, we found TXNL1 as a novel 5-hmC candidate gene in breast cancer with an increased 5-hmC level and corresponding gene overexpression. Survival analysis also indicated the overexpression of TXNL1 and BNIPL in breast cancer being significantly associated with poor overall survival. Previous studies reported that induction of oxidative stress led to the overexpression of TXNL1 associated with the downregulation of the DNA repair protein XRCC1, an accumulation of DNA damage, and BCL-2 regulation (39). Further functional analysis on 5-hmC gain at TXNL1 and other loci would be warranted to elucidate the mechanistic insight of 5-hmC in the transcriptional activation and breast cancer development.
The present study opens a new paradigm on the imbalance of 5-mC and 5-hmC in breast cancer. The study offers a detailed perspective on an epigenomic instability substantiated by the loss of 5-mC and 5-hmC in breast tumors. Global loss of 5-hmC is associated with TET1 and TET3 downregulation. Genome-wide profiling has revealed a profound imbalance in the region-specific distribution of 5-mC and 5-hmC in breast cancer. Predominant 5-hmC modifications localized at distal gene regulatory sites implicating a transcription enhancing function. The novel 5-hmC candidates identified in the study can be promising diagnostic and therapeutic markers for breast cancer.
Materials and Methods
Clinical Specimen
Breast cancer tissues of stages IIA–IV and paired normal (PN) tissue were obtained from the Tumor Bank, Cancer Institute (WIA), Chennai, India. Tissue samples were collected from the patient undergoing direct surgery for invasive ductal carcinoma (IDC) or ductal carcinoma in situ (DCIS). Tumor tissues (n = 15) were histopathologically confirmed to consist of >70% tumor cells, and paired non-cancerous tissue (n = 15) free of tumor cells was excised away from the tumor margin. Similarly, DCIS (n = 5) were obtained from patients undergoing a wide-excision biopsy, and absolute normal samples (n = 5) were collected from patients undergoing wide-excision biopsy for non-tumorous conditions like fibrosis or adenosis and histopathologically confirmed to be free of any tumor cells. An additional set of tumour samples (n = 30) and non-cancerous (n = 6) tissues were also collected for the validation study. Informed consent for participation and sampling was obtained from all patients. The study was approved by the Cancer Institute (WIA), Institutional Ethics Committee (IEC/2016/05).
Isolation of Genomic DNA
About 25 mg of tissue was homogenized and DNA was isolated using the Nucleospin Tissue DNA Kit (Macherey Nagel, GmbH) according to the manufacturer’s instructions. The isolated DNA was quantified with Nanodrop ND-2000 and stored at –20 ° C until further use.
Estimation of Global Levels of 5-hmC and 5-mC
Genomic DNA (100 ng) was used for the estimation of global 5-hmC and 5-mC levels by ELISA using the Quest 5-hmC ELISA kit and the 5-mC DNA ELISA kit (Zymo research Inc, USA) according to the manufacturer’s instructions.
RNA Isolation and TET Expression Assay
Briefly, tissues were homogenized and RNA was isolated using Nucleospin® RNA Isolation Kit (Macherey Nagel, GmbH). RNA was quantitated using Nanodrop ND-2000 and cDNA was synthesized from 500 ng of total RNA using a Quantitect® reverse transcription kit (Qiagen, USA). Gene expression analysis of TET 1, 2, and 3 was performed using TaqMan probes (Sf 4), TaqMan™ Universal Master Mix II, no UNG (Applied Biosystems, USA) and the Quant studio 12Kflex system (Applied Biosystems, USA).
Enrichment of 5-mC Modified DNA Regions and Library Preparation
Genomic DNA (1 μg) was fragmented to 300–600 bp after 25 cycles of 30 s of pulsed sonication in Bioruptor (Diagenode, Belgium). Fragmented DNA was end-repaired and adapter ligation was performed using the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs, USA) for Illumina. Furthermore, the fragments with a length of 400–500 bp (300 bp insert + 120 bp adapter) were size-selected using AMPure beads (Beckman Coulter, USA). Immunoprecipitation of methylated DNA was performed using the MagMeDIP kit (Diagenode, Belgium), and enriched DNA was purified using the I Pure kit (Diagenode, Belgium) according to the manufacturer’s protocol. Purified DNA was indexed and amplified using the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs, USA).
Enrichment of 5-hmC Modified DNA Regions and Library Preparation
For the enrichment of 5-hmC-modified DNA, a reduced representation hydroxymethylation profiling by RRHP kit (Zymo Research Inc, USA) was carried out. The genomic DNA (1 μg) was digested using the Msp1 enzyme and ligated with p5 and p7 adapters. The adapter-ligated fragments were glycosylated with UDP-glucose and T4-glycosyltransferase and digested again with Msp1 to cleave adapters from non-glycosylated fragments. The fragments were size selected 400–500 bp (300 bp insert + 120 bp adapter) using AMPure XP beads (Beckman Coulter, USA) and amplified as libraries using RRHP™ 5-hmC Library Prep Kit (Zymo Research. Inc, USA).
Sequencing and Data Analysis
Enriched libraries were sequenced by 150 × 2 paired ends to generate 25 million reads in the Illumina Nextseq 500. The FASTQ files were quality checked by FastQC. Trimmomatic was used to trim the adapter sequence and the low-quality reads (Phred > 30) were discarded. The processed fastq files were then aligned to the reference genome (hg19) using the BWA mem algorithm. Aligned files were then converted into sorted bam files using samtools. The peaks were called with MACS2 and the peak files were used to find overlapping peaks in multiple files, and their raw counts were extracted with the DiffBind R package.
DMR Analysis
DMR analyses were carried out using the DESeq2 R package and the likelihood ratio test (LRT) was used to determine DMR with the padj value cut-off of ≤0.01. Furthermore, the DMR regions were filtered based on the number of peaks called in biological replicate with at least 10 | 10 | 5 | 5 (T | PN | DCIS | AN). Filtered DMR was annotated using the ChIPseeker R package with the UCSC hg19 known gene sets. The Z-Score was calculated from normalized counts and the heatmap was plotted using the Complex Heat map R package. The sorted bam files were indexed and the coverage profile was calculated using the Deep Tools bam Coverage with RPKM normalization and a bin size of 20 bp. The resulted bigwig files were used to visualize peak regions with IGV.
DhMR Analysis
For the DhMR analysis, RPKM values were extracted from the DiffBind R package and a global rank-invariant set normalization was carried out using the rank-invariant function from the Lumi R package. The Kruskal–Wallis test was performed and the p-value was adjusted with FDR. Cut-off of padj ≤0.1 was set to define DhMR. In addition, the DhMR peaks were filtered with the same criteria as DMR. The chromosomal distribution of DMR and DhMR was analyzed with the Karyoplot R-Package with a p-value of 0.05. The ideogram was examined for the DMR and DhMR regions with an FDR of 0.05.
Pathway Enrichment and Gene Ontology Analysis
Pathway enrichment and gene ontology analysis were performed with the g: Profiler and Cluster Profiler R package. For the p-adjusted corrected method, FDR with a p-value cut-off of 0.05 was used. Dot blots were generated based on the criteria mentioned above both for DMRs and DhMRs.
Validation of Loci-Specific 5-hmC Enriched Regions Using qPCR Assays
Briefly, the tumor (n = 30) and normal tissue (n = 6) were homogenized and DNA was isolated using the Nucleospin Tissue DNA Kit (Macherey Nagel, GmBH) according to the manufacturer’s instructions. The genomic DNA was subjected to a 5-hmC-specific enrichment with EpiMark 5-hmC and 5-mC analysis kit (New England Biolabs, USA). The processed DNA samples were analyzed with qPCR using loci-specific primers (Sf 4). Gene expression, methylation analysis, and survival analysis were carried out with the UALCAN (40) web tool for 5-mC- and 5-hmC-specific candidate genes.
Statistical analysis
The correlation analysis was performed between the expression levels of TET enzymes and the global levels of 5-mC and 5-hmC distribution using GraphPad Prism v 7.0a (GraphPad Software, La Jolla, CA, USA). Spearman’s rank correlation test was performed with a confidence interval of 95% for generating the correlation plot. Linear regression lines were generated based on the R-value of the entities. Wilcoxon signed-rank test was used for the nonparametric paired analysis. Mann–Whitney U test was performed for the nonparametric unpaired analysis. The p-value of <0.05 is considered a significant outcome.
Funding
The study was fully funded by a financial grant sanctioned by the Science and Engineering Research Board, Department of Science and Technology (DST), Government of India (EMR/2015/001319), and the student fellowship was sanctioned by the DST-INSPIRE fellowship (IF190144), Government of India.
Ethical approval and consent to participate
This study was approved by the Ethics Committees of Cancer Institute (WIA), Regional Cancer Centre, Chennai – 600036 (IEC/2016/05). The study was conducted according to the principles expressed in the Declaration of Helsinki.
Author contributions
Deepa Ramasamy: Data curation, formal analysis, methodology, validation, visualization, writing—original draft; Arunagiri Kuha Deva Magendhra Rao: Formal analysis, methodology, investigation, writing—review and editing; Meenakumari Balaiah: Methodology; Arvinden Vittal Rangan: Methodology; Shirley Sundersingh: Resources; Sridevi Veluswami: Resources; Rajkumar Thangarajan: Writing—review and editing, supervision; Samson Mani: Conceptualization, funding acquisition, project administration, investigation, supervision, writing—review and editing.
Competing interests
The authors declare that they have no conflict of interest.
Consent for publication
All authors provide their consent for the publication of the manuscript
Data and materials availability
Raw sequencing data are available in Sequence Read Archive Hosted by National Centre for Biotechnology Information (NCBI) search database with accession number PRJNA769519
Supplementary Materials
Supplementary Figures
PCA and HCA of 5-mC and 5-hmC enriched regions of breast tumour and normal tissues
(a) Principal component analysis of 5-mC (b) Principal component analysis of 5-hmC (c) Hierarchical clustering analysis of 5-mC in PN, IDC, DCIS, and AN samples (d) Hierarchical clustering analysis of 5-hmC in PN, IDC, AN and DCIS samples.
Genome-wide distribution of DMR and DhMR in breast cancer
(a) Genome-wide distribution of DMRs (b) Genome-wide distribution of DhMRs (c) DMRs peak intensity of TFBS in the region of 1500 bp upstream and downstream of TSSs (d) DhMRs peak intensity of TFBS in the region of 1500 bp upstream and downstream of TSSs.
Locus Overlap analysis and ChIP seq analysis of DhMR
(a-b) Locus overlap analysis of DhMRs using LOLA webtool (c-f) Chip-seq analysis using UALCAN webtool.
KEGG pathway enrichment and GO term analysis of DMR and DhMR of breast cancer
(a) KEGG pathway enrichment map of DMRs (a) GO term pathway enrichment map of DMRs (c) KEGG pathway enrichment map of DhMRs (d) GO term pathway enrichment map of DhMRs.
Survival analysis of hyper-hmC candidates
(a)Survival analysis of TXNL1 (b) Survival analysis of CNIH3 (c) Survival analysis of BNIPL.
Supplementary Table
ST. 1 (1a) Genomic features of 5-mC (1b) Genomic features of 5-hmC (paired normal (PN), invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and apparent normal (AN) samples).
Supplementary Files
Sf 1: (1a) Significant differentially hydroxymethylated regions (DhMRs) of breast tumor and normal samples (1b) Significant differentially methylated regions (DMRs) of breast tumor and normal samples.
Sf 2: Significant hyper- and hypo-5-mC and 5-hmC regions (2a) Hyper-hmCgene list (2b) hypo-hmC gene list (2c) hyper-mC gene list (2d) hypo-mC gene list.
Sf 3: Gene specific analysis using gprofiler (3a) Hyper-hmC genes dataset (3b) Hypo-hmC genes dataset (3c) Hyper-mC genes dataset (3d) Hypo-mC genes dataset.
Sf 4: qPCR primers used in the study.
Acknowledgments
Not applicable
References
