Integrated analyses of multi-omics reveal global patterns of methylation and hydroxymethylation and screen the tumor suppressive roles of HADHB in colorectal cancer
Zhu et al. Clinical Epigenetics
Integrated analyses of multi-omics reveal global patterns of methylation and hydroxymethylation and screen the tumor suppressive roles of HADHB in colorectal cancer
Yimin Zhu 0 4
Hanlin Lu 0 1 3
Dandan Zhang 2
Meiyan Li 1
Xiaohui Sun 4
Ledong Wan 2
Dan Yu 2
Yiping Tian 2
Hongchuan Jin 7
Aifen Lin 6
Fei Gao 1 3
Maode Lai 2 5
0 Equal contributors
1 BGI-Shenzhen , Shenzhen 518083 , China
2 Key Laboratory of Disease Proteomics of Zhejiang Province and Department of Pathology, School of Medicine, Zhejiang University , Hangzhou 310058 , China
3 Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences , Shenzhen 518120 , China
4 Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University , Hangzhou 310058 , China
5 Department of Pathology, School of Medicine, Zhejiang University , 866 Yuhangtang Road, Zhejiang, Hangzhou 310058 , China
6 Human Tissue Bank/Medical Research Center, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University , Linhai, Zhejiang 317000 , China
7 Laboratory of Cancer Biology, Provincial Key Lab of Biotherapy in Zhejiang, Sir Runrun Shaw Hospital, Medical School of Zhejiang University , Hangzhou , China
Background: DNA methylation is an important epigenetic modification, associated with gene expression. 5Methylcytosine and 5-hydroxymethylcytosine are two epigenetic hallmarks that maintain the equilibrium of epigenetic reprogramming. Disequilibrium in genomic methylation leads to carcinogenesis. The purpose of this study was to elucidate the epigenetic mechanisms of DNA methylation and hydroxymethylation in the carcinogenesis of colorectal cancer. Methods: Genome-wide patterns of DNA methylation and hydroxymethylation in six paired colorectal tumor tissues and corresponding normal tissues were determined using immunoprecipitation and sequencing. Transcriptional expression was determined by RNA sequencing (RNA-Seq). Groupwise differential methylation regions (DMR), differential hydroxymethylation regions (DhMR), and differentially expressed gene (DEG) regions were identified. Epigenetic biomarkers were screened by integrating DMR, DhMR, and DEGs and confirmed using functional analysis. Results: We identified a genome-wide distinct hydroxymethylation pattern that could be used as an epigenetic biomarker for clearly differentiating colorectal tumor tissues from normal tissues. We identified 59,249 DMRs, 187,172 DhMRs, and 948 DEGs by comparing between tumors and normal tissues. After cross-matching genes containing DMRs or DhMRs with DEGs, we screened seven genes that were aberrantly regulated by DNA methylation in tumors. Furthermore, hypermethylation of the HADHB gene was persistently found to be correlated with downregulation of its transcription in colorectal cancer (CRC). These findings were confirmed in other patients of colorectal cancer. Tumor functional analysis indicated that HADHB reduced cancer cell migration and invasiveness. These findings suggested its possible role as a tumor suppressor gene (TSG). Conclusion: This study reveals the global patterns of methylation and hydroxymethylation in CRC. Several CRCassociated genes were screened with multi-omic analysis. Aberrant methylation and hydroxymethylation were found to be in the carcinogenesis of CRC.
DNA methylation; DNA hydroxymethylation; Sequencing; Colorectal cancer; Epigenetic
Cancer is a disease driven by the accumulation of genetic
] and the disruption of epigenetic regulation
]. Epigenetic modifications are associated with gene
]. DNA methylation, such as methylation of
cytosine to 5-methylcytosine (5-mC), is catalyzed de novo
and maintained by DNA methyltransferases (DNMTs) [
], and this methylation is preserved through cell division
]. The aberrant regulation of DNA methylation, such
as global hypomethylation or regional hypermethylation,
has consistently reported as an important epigenetic
hallmark of cancers, including colorectal cancer [
example, hypermethylation of CpG islands in the promoter
regions and in exon 1 represses or even silences the
transcriptional expression of tumor suppressor genes (TSG)
and promotes carcinogenesis.
In addition to DNA methylation, DNA
hydroxymethylation (5-hmC) is another important epigenetic hallmark
for cancers. 5-hmC is synthesized from 5-mC by
teneleven translocation (TET) proteins [
proteins further oxidize 5-hmC into 5-formylcytosine and
5-carboxylcytosine. An unmethylated cytosine is
restored by the removal of the carboxyl group from
5carboxylcytosine by the enzyme thymine-DNA
glycosylase (TDG). Therefore, 5-hmC is regarded as an
intermediate during active demethylation and is believed to
help maintain the equilibrium of epigenetic
]. Despite this, 5-hmC has been observed as
a stable epigenetic modification, especially in the cancer
genome, where reduced levels have been previously
Although there is a significant amount of data
regarding the global distribution of 5-mC in colorectal cancer,
there is a great need for examining both 5-mC and
5hmC simultaneously. Because of their resistance to
bisulfite conversion, 5mC and 5hmC cannot be
distinguished from each other using only bisulfite sequencing
]. In order to understand the role of DNA
demethylation, a series of techniques have been developed
to accurately differentiate cytosine methylation states,
including hMeDIP-seq, oxBS-seq, and TAB-seq [
]. Compared to enrichment steps, methods like
oxBSseq and TAB-seq require an immense amount of
sequencing and are very costly. In the present study, we
collected tumors and the corresponding adjacent normal
tissues from six colorectal cancer patients, then
determined the levels of genome-wide DNA methylation by
methylated DNA immune-precipitation sequencing
(MeDIP-seq) and hydroxymethylation by
hydroxymethylated DNA immunoprecipitation sequencing
(hMeDIPseq). Their transcriptional expression was determined
using RNA-seq. We found a distinct genome-wide
hydroxymethylation pattern that could be used as an
epigenetic biomarker for differentiating colorectal tumor
tissues from normal tissues. Furthermore,
hypermethylation of the hydroxyacyl-CoA dehydrogenase trifunctional
multi-enzyme complex subunit beta gene (HADHB) was
persistently found to be correlated with its
transcriptional downregulation in colorectal cancer (CRC). The
differences in methylation, hydroxymethylation, and
transcriptional expression of HADHB between
cancerous and normal tissues were confirmed in additional
colorectal cancer patients. To further validate these
findings, we performed functional analyses and found that
the overexpression of HADHB clearly reduced cancer
cell migration and invasiveness. These results suggest
that HADHB could play the role of a TSG. In brief, this
study provided valuable data for the screening of
epigenetic biomarkers and for elucidating the epigenetic
mechanisms of carcinogenesis in colorectal cancer.
Tissue collection and preparation
Colorectal tumor samples, as well as the corresponding
adjacent normal tissues (5 cm away from the edge of the
tumor), were surgically collected and then preserved in
liquid nitrogen. The genomic DNA and RNA of each
sample were extracted using Qiagen’s DNA and RNA
extraction kits, respectively. The study protocols were
approved by the research ethics committees of Zhejiang
University School of Medicine (2012-1-012) and
BGIShenzhen (NO. BGI-IRB 15060). All participants signed
the written informed consent form.
Library construction and data analysis of RNA-seq
The total RNA samples were first treated with DNase I
to degrade any possible DNA contamination. The
mRNA was then enriched using oligo (dT) magnetic
beads and mixed with a fragmentation buffer to be
fragmented into approximately 200-bp fragments.
Firststrand cDNA synthesis was performed using random
hexamers. Buffer, dNTPs, RNase H, and DNA
polymerase I were added to synthesize the second strand. The
double-stranded cDNA was purified with magnetic
beads. End preparation and 3′-end addition of the
nucleotide adenine (A) were performed. Finally, sequencing
adaptors were ligated to the fragments. The fragments
were enriched by PCR amplification. During the QC
step, the Agilent 2100 Bioanalyzer and ABI StepOnePlus
Real-Time PCR System were used to qualify and
quantify the DNA library. The library products were then
sequenced with the Illumina HiSeq 2000.
The levels of gene expression level and the
differentially expressed genes were analyzed using the method
described by Audic and Claverie [
]. Levels of gene
expression were calculated using the reads per kilobase
million (RPKM) method. In cases where more than one
transcript was found for a gene, the longest read was
used to calculate its expression level and coverage. The
RPKM values were then used directly to compare gene
expression differences between the tumor and the
normal samples. The significantly differentially expressed
genes (DEG) were determined at a threshold false
discovery rate (FDR) ≤ 0.05 and the absolute value of
log2ratio ≥ 0.585.
Library construction and data analysis of MeDIP-seq and hMeDIP-seq
Prior to immunoprecipitation, 5 μg of genomic DNA
was sonicated to a mean fragment size of 200 bp,
followed by end repair with the addition of
deoxyadenosine (dA) and adaptor ligation, according to the Illumina
Paired-End protocol. MeDIP-Seq and hMeDIP-Seq
libraries were constructed, as described in a previous
]. The libraries were sequenced using the
Illumina HiSeq analyzer, according to the manufacturer’s
instructions. After base calling, low-quality reads were
omitted, and the clean reads were aligned to the UCSC
human reference genome hg19 using SOAP2 (Version
2.21). Mismatches of no more than two bases were
allowed in the alignment.
Identification of DMR and DhMR between tumors and corresponding normal tissues
Identification of groupwise differential methylation
regions (DMR) and differential hydroxyl-methylation
regions (DhMR) was performed using a sliding windows
strategy along the entire genome, as described in our
previous study [
]. This strategy identified DMR and DhMR
between tumors and the corresponding normal tissues,
based on a threshold of P < 0.05 and at least five CG sites.
Functional enrichment analysis for DMRs and DhMR in promoters
Functional enrichment analysis was performed by Gene
Ontology (GO) and pathway analysis using the DAVID
(Database for Annotation, Visualization, and Integrated
Discovery) web server (http://david.abcc.ncifcrf.gov).
Genes with DMRs, DhMR in promoters, and DEG were
mapped to their respective human orthologs, and the
lists were submitted to DAVID for enrichment analysis
to determine any significant overrepresentation of GO
biological processes (GO-BP), molecular functions
(GOMF), and KEGG-pathway categories. For all analyses, the
known, full-length genes were set as the background,
and the P values (EASE score), indicating the
significance of the overlap between various gene sets, were
calculated using Benjamini-corrected modified Fisher’s
exact test. Only GO-BP, GO-MF, or KEGG-pathway
terms with P values less than 0.05 were considered
significant and listed as differentially expressed.
Quantitative PCR of HADHB expression
Total RNA was isolated from cells using TRIzol
(Invitrogen, USA). The concentration of RNAs was measured
and normalized using a spectrophotometer (Eppendorf,
Hamburg, Germany). Reverse transcription was
performed using a PrimeScript RT reagent kit (Perfect Real
Time) and real-time PCR was performed using the SYBR
Premix Ex Taq (Tli RNaseH Plus), both from TaKaRa
Biotechnology Co., Ltd. (Dalian, China). The following
PCR primers were used to amplify HADHB:
5′ACACTGTCACCATGGCTTGT -3′ (forward) and
5′CTGGCCAGAAGCAATCAAG -3′ (reverse). For GAPDH,
the following primers were used:
5′-ACCACAGTCCATGCCATCAC-3′ (forward) and 5′-TCCACCACCCTGTTGCTG
TA-3′ (reverse). GAPDH was used as the reference gene.
The Ct values of the samples were calculated, and the
relative levels of HADHB mRNA were analyzed by the
Cell culture and plasmid construction
Human colorectal carcinoma cell lines, HT29 and
HCT8, were obtained from the American Type Culture
Collection (ATCC). HT29 and HCT8 were maintained
in liquid nitrogen and incubated in 5% CO2 at 37 °C in a
PRMI1640 medium with 10% fetal bovine serum (FBS).
The pcDNA3.1 (+) vector was sliced using restriction
enzymes Xhol1 and Bamh1. First-strand cDNA was
synthesized using the HiScript® 1st Strand cDNA Synthesis Kit
(Vazyme biotech co., Ltd., Suzhou, China). The complete
coding sequence of HADHB was PCR-amplified with the
following primers: 5″–CTTGGTACCGAGCTCGGATC
(forward) and 5′–CCCTCTAGATGCATGCTCG A GTTATT
TTGGATAAGCTTCCACTATCAT–3″ (reverse). The PCR
product was then inserted into the linearized pcDNA3.1 to
perform recombination cloning using the ClonExpress II
one-step cloning kit (Vazyme Biotech Co., Ltd.). The
recombined products were verified by DNA sequencing and
transfected using Lipfectamine™2000, according to the
manufacturer’s instructions. The efficiency of overexpression
was validated using qRT-PCR and western blot analyses.
RNA interference analysis
Small interfering RNA (siRNA) against HADHB and a
negative control siRNA were purchased from Shanghai
Genepharma Co. Ltd. (Shanghai, China). The
antiHADHB siRNA sequence was
5`-GCACAGUGACAGCUGCAAATT-3`, which was not homologous to any
other human DNA sequence. HT29 and HCT8 cells
were cultured in six-well plates in antibiotic-free DMEM
for 48 h and transfected using the PowerFect™ siRNA
Transfection Reagent (SigmaGen) according to the
manufacturer’s instructions. The efficiency of knockdown
was determined by qRT-PCR and western blot analyses.
Western blot analysis
Cells were extracted using a RIPA lysis buffer and
prepared according to the standard procedure. Proteins
were extracted using 12% SDS-PAGE and transferred
onto nitrocellulose membranes. The membranes were
blocked with 5% skimmed milk in TBS-Tween 20 for
2 h before being incubated overnight with primary
antibodies at 4 °C. They were then incubated with secondary
antibodies at 20 °C for nearly 1 h. After extensive
washing in TBST, the protein level was measured using the
Odyssey system (Li-COR, Lincoln, NE, USA). The
loading was monitored by GAPDH. Primary antibodies were
directed against HADHB (Abcam, 1:1000) and GAPDH
(Santa Cruz, CA, USA, 1:5000).
Cell counting kit-8 (CCK-8) assay
Cells were plated in 96-well plates (1 × 103 cells/well)
with 100 μl of the medium. The absorbance at 450 nm
was measured to estimate the relative number of viable
cells after culturing with 10 μl of CCK-8 reagent, which
was purchased from Boster Biological Technology Co.,
Ltd. (Wuhan, China). The analysis was performed in
three replicate wells for each sample and repeated for
Cell migration and invasion assays
Transwell 24-well Boyden chambers (8-μm pores;
Costar, Corning, NK, USA) were used to measure cell
migration and invasion according to the manufacturer’s
protocol. For studying migration, cells (1 × 105) in 200 μl
of a serum-free medium were seeded on the upper
chamber, and 600 μl of a complete medium containing
10% FBS was added to the lower chamber as a
chemoattractant. After incubation at 37 °C for 20 h (HCT8
cells) and 96 h (HT29 cells), the non-migratory cells
were removed with cotton swabs. Cells on the lower
surface of the membrane were fixed in 4%
paraformaldehyde and stained with crystal violet solution. The
number of invading cells was counted in five randomly
selected fields using an inverted microscope equipped
with a digital camera at × 40 magnification.
For the cell invasion assay, 2 × 105 cells were seeded in
the upper chamber, which had been coated with 50 μL
Matrigel basement membrane matrix (BD Biosciences,
USA), followed by incubation for 16 h (HCT8 cells) and
96 h (HT29 cells).
Generation and characterization of CRC methylome and hydroxymethylome
Six patients with colorectal cancer, three with rectal
cancer, and three with colon cancer were recruited for this
study. The characteristics of these patients are
summarized in Additional file 1: Table S1, including gender,
age, and pathological types. Primary tumor tissue
samples and their adjacent normal tissues were collected
after surgery. We applied MeDIP-seq and hMeDIP-seq
technologies to examine whole-genome DNA
methylation and hydroxymethylation patterns, respectively, for
all 12 DNA samples. MeDIP-seq and hMeDIP-seq
technologies allow for the highly efficient enrichment of
methylated and hydroxymethylated DNA fragments [
using antibodies against methylated and
hydroxymethylated cytosines, respectively. On average, 120.1 and 117.9
million paired-end reads, 50 bp in length, were
generated from MeDIP-seq and hMeDIP-seq, respectively. Of
these reads, 116.0 (96.63%) and 114.2 (96.88%) million
clean reads were aligned to the human reference genome
hg19. After removing the ambiguously mapped reads,
we acquired 99.7 (83.03%) million and 104.7 (87.32%)
million uniquely aligned reads, reaching an average
depth coverage of 3.49 and 3.71 for DNA methylation
and hydroxymethylation, respectively (Additional file 1:
Table S2 and Additional file 2: Figure S1). To enable
pair-wise comparisons across different samples, we used
reads per million (RPM) as a measure of the methylation
and hydroxymethylation levels in a genomic region in
order to normalize the data.
We first characterized the global patterns of
methylome and hydroxymethylome by correlating their read
depths with the number of different genomic elements.
In general, both DNA methylation and
hydroxymethylation were positively correlated with the number of repeat
sequences, gene number, SNP number, and GC content,
both in the tumors and adjacent normal tissues (Fig. 1).
No significant correlation was found between
chromosome length and ratio of observed and expected number
of CpGs (CpG O/E), although similar patterns of
methylation and hydroxymethylation were observed in relation to
GC content. High levels of hydroxymethylation and
methylation were found in the regions of high GC content
of approximately 50 to 60% (Additional file 3: Figure S2).
Furthermore, uneven distribution of methylation and
hydroxymethylation was found in the features of
chromosomes in tumors and normal tissues, especially at
transcriptional start sites (TSS) and CpG islands (CGIs),
where there were lower levels. In contrast, CGI
shores (regions that flank CGIs with less CG density)
showed higher levels of methylation and
hydroxymethylation than other genomic elements. The
highest level of methylation, but not hydroxymethylation,
was observed in short interspersed elements (SINEs),
which are highly repetitive sequences (Additional file 4:
Figure S3). These findings indicated that the
distribution of both 5-mC and 5-hmC was closely dependent
on the characteristics of the genomic sequences
(Fig. 1, Additional file 1: Table S3), which were
consistent with previous studies [
Positive correlation between global methylation and hydroxymethylation levels
The whole genome was divided into 0.5-kb windows,
and the levels of 5-mC and 5-hmC were classified into
different groups, according to the RPMs of MeDIP
and hMeDIP, respectively. The correlations between
methylation and hydroxymethylation are shown in
Additional file 5: Figure S4. The levels of methylation
were positively correlated with those of
hydroxymethylation in tumors (Pearson’s correlation coefficient r =
0.9630, P = 2.843e−12) and normal tissues (Pearson’s
correlation coefficient r = 0.9686, P = 6.115e−13). These
results were consistent with previous results from mouse
], human brain [
], and pancreatic
]. As 5-hmC is believed to be an intermediate
compound in the oxidation reaction of 5-mC, this
finding suggests that methylated regions may be constantly
undergoing reprogramming, depending on the cell type.
For different cell populations, the hotspot of epigenomic
reprogramming may vary. For instance, in neurons and
stem cells, 5-mC usually co-localizes with
heterochromatin, whereas 5-hmC co-localizes with euchromatin
]. In the current CRC study, we found that, when
comparing tumors with normal tissues (slope of fitted
line = 0.234), the gradient response of
hydroxymethylation against methylation was lower in tumor tissues
(0.178) (Additional file 5: Figure S4). This result
suggested that tumor tissues in CRC display a global
reduction in 5-hmC compared to normal tissues like the
majority of cancers [
Distinct global pattern of hydroxymethylome, but not methylome, in CRC
In addition to the correlation coefficient difference
between 5-mC and 5-hmC, we also observed variations in
the average levels of the two types of DNA
modifications, when comparing tumors and normal tissues. For
instance, compared to normal tissues, tumor tissues
showed higher than average levels of DNA methylation
in TSSs, promoters, exons, transcriptional end sites
(TES), CpG islands, CGI shores, and SINEs, but lower
than average hydroxymethylation levels in exons,
introns, gene bodies, SINEs, TESs, enhancers, and CGI
shores (Additional file 4: Figure S3). Additionally, higher
inter-individual variations in tumors were suggested for
methylation and hydroxymethylation levels in nearby
TSSs and TES [
], as indicated by the comparisons of
standard deviations (SD) between tumors and normal
tissues (P < 0.001, paired t test) (Additional file 6: Figure S5).
These results suggested that the potential dysregulation of
epigenetic modifications could lead to large-scale latent
instability, which might cause carcinogenesis.
Based on these observations, we used principal
component analysis to infer the inter-group global patterns
of methylome and hydroxymethylome, using the RPM of
0.5-kb windows across the whole genome. The global
methylation pattern of tumor tissues and normal tissues
could not be clearly differentiated (Fig. 2a). In contrast, a
clear separation between the tumors and normal tissues
was observed in the principal component analysis (PCA)
of hydroxymethylation, indicating distinct patterns of
the hydroxymethylome in tumors, compared to
normal tissues (Fig. 2b). Therefore, a global change in
the hydroxymethylome can be considered as a key
characteristic of CRC. Gilat et al. also found that
global levels of 5-hmC could distinguish between colon
tumors and normal colon tissue adjacent to the
tumor based on the levels [
]. Bhattacharyya et al.
] reported a similar discovery in pancreatic cancer,
in which they found that the distribution pattern of
5-hmC samples were strikingly different from those of
Pair-wise comparison revealing extensive DhMRs and
DMRs in CRC
Next, we applied a sliding-window strategy to identify
differentially methylated regions (DMRs) and
differentially hydroxymethylated regions (DhMRs) in tumors
and normal corresponding normal tissues, in order to
reveal key genomic regions with significant DNA
methylation and hydroxymethylation changes during
carcinogenesis. Based on the threshold of P < 0.05 and at least
five CG sites, we obtained 59,249 DMRs and 187,172
DhMRs (Fig. 2c). The representative differential regions
of DMR (Fig. 2d, right) and DhMRs (Fig. 2d, left) were
presented. Most DMRs and DhMRs were more frequently
distributed (observed/expected ratio > 1) in promoter
regions, exons, enhancers, and repeat sequences, such as
LTRs, LINEs, and SINEs. Unlike DMRs, DhMRs were also
frequently distributed in TES regions (Additional file 7:
Figure S6). Aberrant methylations or hydroxymethylations
in promoter regions were less frequent than those in other
regions, consistent with previous observations in cancer
]. Despite this, aberrant DNA modifications within
promoters were most likely correlated with altered
gene expressions [
]. Therefore, we provided
further annotation to the genes with DMRs or DhMRs.
Because the gene promoter is the most important
regulatory element in the genome and the aberration
of methylation and hydroxymethylation in this region
may be associated with carcinogenesis, we focused on
the genes with DMRs and/or DhMRs in the
promoter. We obtained 1699 and 7864 genes containing
DMRs and DhMRs in the promoter, respectively. The
lists of these genes are presented in Additional file 1:
Table S4 and Table S5, respectively. KEGG analysis
was performed with the WebGestalt tool (http://
www.webgestalt.org). We found 49 significant
pathways enriched in genes containing DMRs and 170
containing DhMRs, respectively. The top five
methylation-enriched functional pathways were
neuroactive ligand-receptor interactions, tight junctions,
pathways in cancer, long-term depression, and
Chagas disease. Pathways with enriched DhMRs included
biosynthesis, African trypanosomiasis (sleeping sickness),
tyrosine metabolism, tryptophan metabolism, and
shigellosis (Additional file 1: Table S6).
CRC transcriptome profiling reveals epigenetics-regulated gene expression changes
We also performed RNA-seq to determine the
transcriptome-wide changes in CRC, compared to
adjacent normal tissues. We obtained 14.0 to 19.2
million reads per sample, of which 96.5 to 99.2% were
clean data. Most of these clean reads (91.4% - 94.1%)
could be uniquely aligned to the human reference
genome hg19, and 45.0–61.2% were mapped to
RefSeq genes (Additional file 1: Table S7). The
expression levels were measured in terms of reads per
kilobase per million (RPKM) and were used to further
analysis. Based on a strict threshold (FDR-adjusted
P < 0.05 and fold change > 1.5 in four or more
samples), 948 significant DEGs were identified between
the cancer and the normal samples (Additional file 1:
Table S8). From these 948 genes, 12 KEGG pathways
were found to be enriched, from which the following
top five categories were found to be relevant to
tumorigenesis: cell cycle, purine metabolism,
metabolic pathways, ribosome biogenesis in eukaryotes,
and ribosomes (Additional file 1: Table S6). We also
cross-matched these DEG KEGG pathways with those
previously identified to be enriched in DMR- and
DhMR-containing genes; we found that metabolic
pathways, purine metabolism, and axon guidance were
shared features in these groups (Additional file 8:
We then correlated the expression levels of all genes
with levels of methylation or hydroxymethlation in their
promoter regions (TSS ± 500 bp) and gene bodies
(Fig. 3a, b). Gene expression levels in tumors and
normal tissues were negatively correlated with promoter
methylation, but positively correlated with gene body
methylation, which has been reported in previous
genome-wide analyses [
]. A similar positive
correlation was observed for hydroxymethylation in the gene
body, although no clear correlation was observed in
promoter regions. When we classified the associated genes
into genes with high and low expression levels, the
highly expressed genes displayed significantly lower
levels of promoter methylation, but significantly higher
levels of hydroxymethylation in the gene body (Fig. 3c,
d). These results suggest that many DEGs could be
potentially regulated by promoter methylation or gene
Integrated analyses identifying DEGs aberrantly regulated by DNA methylation in tumors
In order to identify the DEGs that were aberrantly
regulated by DNA modifications in tumors, we
crossmatched the genes containing DMRs or DhMRs with
DEGs. Considering the role of 5-hmC as an intermediate
of demethylation, we reasoned that the genes with
hypermethylation and hypohydroxymethylation in
promoters would be most stably repressed within a cell
population, while genes with hypomethylation and
hyper-hydroxymethylation in promoters would have a
greater chance of being expressed. With this reasoning,
we identified seven genes that contained both DMRs
and DhMRs. These seven genes were HIGD1A,
AHCYL2, IL11RA, CHL1, SEMA6D, BIRC3, and
HADHB (Additional file 1: Table S9). Among these
genes, the methylation of HIGD1A and CHL1 has been
reported in common tumors. Expressions of SEMA6D,
IL11RA, and BIRC3 genes have been reported to be
associated with tumors; however, no association
between colorectal cancer and methylation has been
reported. There have been no reports on tumors or
methylation associated with HADHB or AHCYL2
genes. Thus, we are the first to report associations
between HADHB and AHCYL2 genes and tumors
and between the methylation of SEMA6D, IL11RA,
and BIRC3 genes and tumors.
HADHB as a potential tumor suppressor gene aberrantly repressed by promoter hypermethylation in CRC
Importantly, we identified one DEG, HADHB, which
showed hypermethylation and hypohydroxymethylation
with significantly downregulated expression in CRC
(Additional file 9: Figure S8). To confirm that the
expression level of the HADHB gene was associated with
its methylation and hydroxymethylation levels in a larger
population, we collected 15 additional pairs of samples
from colorectal tumors and their adjacent normal
tissues. The methylation and hydroxymethylation levels
were determined with MeDIP and hMeDIP, respectively,
followed by real-time PCR to determine the expression
level of HADHB (Additional file 6). We also collected the
expression data for HADHB from The Cancer Genome
Atlas (TCGA) (http://cancergenome.nih.gov/) and the
Gene Expression Omnibus (GEO)
(http://www.ncbi.nlm.nih.gov/geo/) databases. We found the expression levels of
HADHB in tumor tissues (0.18 ± 0.15) were
significantly lower than those in normal tissues (0.32 ± 0.24)
(P = 0.025) (Fig. 4a). This result was consistent with
the findings from data of GEO (Fig. 4b), TCGA
sequencing (Fig. 4c), and TCGA array (Fig. 4d).
Compared to normal tissues, tumor tissues had higher
levels of methylation (0.74 ± 0.19 vs 0.17 ± 0.068)
(Fig. 4e) and lower levels of hydroxymethylation (2.58
± 1.97 vs 3.48 ± 1.52) (Fig. 4f ). These results indicate
that lower levels of HADHB expression in tumor
tissues are associated with higher levels of methylation
and lower levels of hydroxymethylation. This is
consistent with the results of genome-wide sequencing
analyses of methylation and hydroxymethylation. The
HADHB gene may be a potential tumor suppressor gene,
whose expression is modified by methylation and reduced
To evaluate the potential contribution of the HADHB
gene to tumorigenesis, we further performed gene
knockdown and overexpression experiments in
colorectal cancer cell lines. After evaluating the expression
levels of HADHB in seven cell lines using reverse
transcriptase PCR (RT-PCR) and western blot analyses, we
selected the HCT-8 cell line for HADHB overexpression
and the HT-29 cell line for HADHB knockdown, in
which HADHB expression was the lowest and highest,
respectively (Fig. 5a). Our results showed that the
expression of HADHB efficiently decreased in the
HADHB-knockdown HT-29 cell line and increased in
the HADHB-overexpressed HT-8 cell line, compared
with that in the normal cell lines (Fig. 5b). In the
subsequent characterization of cell capacity, we found that
knockdown and overexpression of HADHB had no
effect on cell growth (Fig. 5c). Interestingly, the migration
and invasion of cells were significantly reduced in cells
overexpressing HADHB. In contrast, HADHB
knockdown caused enhanced migration and invasion (Fig. 5d).
Taken together, these functional experiments support
the hypothesis that HADHB is a potential tumor
suppressor gene, which can reduce tumor cell invasiveness
and migration, suggesting that silencing HADHB may
contribute to colorectal oncogenesis and progression.
DNA methylation has become a promising biological
marker of cancer risk, diagnosis, and prognosis. DNA
methylation in the promoter can repress or silence gene
expression. Therefore, 5-mC is usually considered the
“fifth base” of DNA. The discovery of
hydroxymethylation, often considered to be the sixth base of DNA, has
increased the complexity of methylation research. While
5-hmC is mostly believed to be an intermediate of the
demethylation process catalyzed by the TET enzyme,
many studies have shown that the TET enzyme and
5hmC act as regulatory factors. Therefore, genome-wide
analyses of DNA methylation and hydroxymethylation,
which are important epigenetic biomarkers, will help
reveal aberrantly regulated tumor suppressor genes that
may be involved in carcinogenesis.
Bisulfite treatment-based sequencing technologies
cannot distinguish between these two types of epigenetic
modifications. Methods like oxBS-seq and TAB-seq
require an immense amount of sequencing and are costly.
Instead, immunoprecipitation by methylation and
hydroxymethylation-specific antibodies, combined with
next-generation sequencing, can be used to determine
genome-wide methylation and hydroxymethylation. In
this study, we applied MeDIP-seq, hMeDIP-seq, and
RNA-seq for a thorough screening of the epigenome and
transcriptome of colorectal tumors.
Previous studies have revealed that aberrant
methylation and hydroxymethylation in cancers occur in either
specific or global genomic regions [
2, 8, 15
by comparing the distribution of methylation and
hydroxymethylation between tumors and normal tissues, this
study provides valuable data for screening epigenetic
biomarkers. Our results showed distinct
hydroxymethylome, but not methylome, global methylation patterns in
CRCs and their adjacent normal tissues, using PCA.
Specifically, divergent hypohydroxymethylation regions
were more often located in gene bodies, TESs,
enhancers, LTRs, LINEs, and SINEs. Overall, genomic
methylation correlated with hydroxymethylation.
However, these two modifications do not usually coexist on
the DNA [
]. 5-mC usually co-localizes in the
heterochromatin, whereas 5-hmC has been found to
colocalize in the euchromatin [
]. It can be inferred
that euchromatin-specific conversion of 5-mC to 5-hmC
is regulated by a combination of cell cycle-dependent
chromatin decompensation events and Tet enzyme
]. Our genome-wide study suggested that
5-hmC is a stable potential predictive biomarker for CRC.
Although we did not observe distinct global
methylation patterns, we did observe a higher frequency of
divergently methylated regions, when comparing tumors
and normal tissues, specifically in CGIs, promoters,
exons, enhancers, LTRs, LINEs, and SINEs. Previous
studies have confirmed that promoter hypermethylation
might cause reduced gene expression and contribute to
carcinogenesis, including colorectal cancer [
25, 39, 40
Based on genome-wide, pair-wise comparative analyses
of tumors and the corresponding normal tissues, 170
significant pathways were found to be enriched in 7864
genes with DhMRs, 49 pathways in 1699 genes with
DMRs, and 12 pathways in 948 DEGs. Among these
pathways, metabolic pathways, purine metabolism, and
axon guidance were overlapped. This suggests that these
pathways may be involved in the carcinogenesis of
By linking the divergent regions with gene expression,
we identified 26 DEGs with both DMRs and DhMRs.
Seven genes in which DEGs contained both DMRs and
DhMRs were identified (Additional file 1: Table S9):
HADHB, HIGD1A, AHCYL2, IL11RA, CHL1, SEMA6D,
and BIRC3. The methylation of five genes (AHCYL2,
IL11RA, SEMA6D, BIRC3, and HADHB) was associated
with tumors, and four genes (IL11RA, CHL1, SEMA6D,
and BIRC3) were associated with colorectal cancer.
HIGD1A, hypoxia-inducible gene domain 1A, is a
mitochondrial protein and a positive regulator of cytochrome
c oxidase, which is regulated by hypoxia-inducible
factor-1α (HIF1α) [
]. During glucose deprivation,
HIGD1A regulates oxygen consumption, ROS
production, and AMPK activity to modulate cell survival and
tumor growth [
]. The promoter of the HIGD1A gene
is differentially methylated in human cancers, preventing
its hypoxic induction. This protein is also a potential
marker of metabolic stress in vivo and is frequently
observed in diverse pathological states such as myocardial
infarction, hypoxic-ischemic encephalopathy (HIE), and
different cancers [
(adenosyl-homocysteinase like 2) is highly homologous to IRBIT, which
regulates ion-transporting proteins. It may also be a
potential regulator of NBCe1-B in mammalian cells .
However, its function remains unclear. CHL1 (cell
adhesion molecule), which encodes a member of the L1
family of neural cell adhesion molecules, is essential for
brain development and is involved in signal transduction
pathways. It has been found to play an important role in
carcinogenesis and cancer progression. He et al. had
found that CHL1 was downregulated in human breast
cancer and was associated with lower-grade tumors [
This downregulation is mediated by DNA methylation.
Therefore, CHL1 may be a putative tumor suppressor
gene in breast cancer and other common cancers [
]. Interleukin 11 receptor subunit alpha (IL11RA), a
stromal cell-derived cytokine, is overexpressed in
patients with human osteosarcoma and advanced breast
cancer with bone metastasis. Additionally, amplification
was detected at 9p13.3, where the IL11RA gene is
located. Some primary gastric adenocarcinoma samples
(19.1%) were found to have an increased copy number
of IL11RA [
]. Semaphorin 6D (SEMA6D) has been
previously implicated in immune responses, heart
development, and neurogenesis. SEMA6D has been reported
to be highly expressed in vascular epithelial cells in
gastric cancer; it was also positively correlated with the
expression of vascular endothelial growth factor receptor 2
(VEGFR2). Therefore, SEMA6D may be associated with
tumor angiogenesis [
]. The HADHB gene encodes
the beta subunit of a mitochondrial trifunctional protein
that catalyzes the last three steps of mitochondrial
betaoxidation of long-chain fatty acids. Mutations in the
HADHB gene have been associated with mitochondrial
trifunctional protein deficiency [
interacts with estrogen receptor alpha and affects
betaoxidation activity [
]. Hypermethylation in the HADHB
gene was found in hepatocellular carcinoma [
baculoviral IAP repeat, which contains the 3 apoptosis
inhibitor 2 (BIRC3) and encodes a member of the IAP
family of proteins, has multi-biological functions. It not
only regulates caspases and apoptosis, but also
modulates inflammatory signaling and immunity, mitogenic
kinase signaling and cell proliferation, and cell invasion
and metastasis. Overexpression of BIRC3 is associated
with glioma progression and aggression and chronic and
acute B cell lymphocytic leukemia [
]; it is also a
predictor of therapeutic resistance to treatment with
irradiation, doxorubicin, and temozolomide [
In this study, we used a two-stage strategy to confirm
hypermethylation and hypohydroxymethylation of the
promoter region of the HADHB gene, which exhibited
significantly decreased expression in CRC. The
functional studies indicated that HADHB might act as a
tumor suppressor gene. Therefore, our findings
implicated HADHB as a potential biomarker for the diagnosis
and treatment of CRC, especially for alleviating and
controlling cancer progression.
To summarize, this study characterized global patterns
of methylome and hydroxymethylome and found a
genome-wide distinct hydroxymethylation pattern that
could be used to differentiate between tumor tissues and
normal tissues. We screened 59,249 DMRs, 187,172
DhMRs, and 948 significant DEGs. After integrating
genome-wide expression with genome-wide patterns of
DNA methylation and hydroxymethylation, we identified
7 genes that were aberrantly regulated by DNA
methylation in tumors and were possibly associated with
carcinogenesis of colorectal cancer. We confirmed that
HADHB could be a novel tumor suppressor gene.
Additional file 1: Table S1. The basic characteristics of six patients of
colorectal cancer. Table S2. Summary of MeDIP-seq and hMeDIP-seq
data production. Table S3. Pearson's product-moment correlation for
the levels of DNA methylation and DNA hydroxymethylation with the
elements in chromosome. Table S4. Differentially methylated regions
(DMRs) in promoters. Table S5. Differentially hydroxymethylated regions
(DhMRs) in promoters. Table S6. The top 10 pathways enriched in DMRs,
DhMRs and DEG. Table S7. Summary of RNA-seq data production.
Table S8. Differentially expressed genes (DEGs) in 6 pairs of samples.
Table S9. The list of 7 genes of which DEGs contained both DMRs and
DhMRs. (XLSX 2853 kb)
Additional file 2: Figure S1. Coverage and depth of reads for
MeDIPseq and hMeDIP-seq. CpG sites coverage with different sequencing depth
are presented. MeDIP-seq data are shown at the top and hMeDIP-seq
data are shown at the bottom. Different colors indicate tumor and
normal samples. (PDF 192 kb)
Additional file 3: Figure S2. Methylation and hydroxymethylation levels
distribution against GC content(%) and CpG o/e ratio. Higher levels of
hydroxymethylation were found in the regions of GC content around
55% - 65% and CpG O/E ratio of 1.14-1.15. The regions with a GC content
around 45% - 55% had a higher methylation level. (PDF 271 kb)
Additional file 4: Figure S3. Methylation and hydroxymethylation levels
distribution in different types of genomic elements. Normal is shown by
green, and tumor is shown by red. TSS: transcriptional start sites; TES:
transcriptional end sites; CGI: CpG islands; LTR: long terminal repeat; SINE:
short interspersed nuclear elements; LINE: long interspersed nuclear
elements. *, P < 0.05, **, P < 0.01 and ***, P < 0.001. (PDF 388 kb)
Additional file 5: Figure S4. Correlation between 5-mC and 5-hmC in
normal (left) and tumor (right) samples. The whole genome was divided
into 0.5 kb windows and the levels of 5-mC and 5-hmC were classified
into different groups according to the RPMs of Medip and hMedip,
respectively. (PDF 172 kb)
Additional file 6: Figure S5. Mean profiles with standard deviation (SD)
over TSS and TES regions. 4-kb regions were divided in 80 bins from 5′ to
3′ end, and the mean RPM values (with SD) within each bin for each
modification type was determined (5-mC left, 5-hmC right). Additionally,
SD distribution are be shown at bottom left in each region. (PDF 217 kb)
Additional file 7: Figure S6. Distribution of DMRs and DhMRs in
different types of genomic elements. (PDF 162 kb)
Additional file 8: Figure S7. Gene functional pathways of DMR, DhMR
and DEG. Three pathway, metabolic pathways, purine metabolism and axon
guidance, overlapped pathways among DMR, DhMR and DEG. (PDF 138 kb)
Additional file 9: Figure S8. Visualization of DMRs (bottom) and DhMRs
(top) in HADHB. DMR and DhMR are denoted by the green box. (PDF 231 kb)
5-hmC: Hydroxyl-methylation; 5-mC: 5-Methylcytosine; CGI: CpG islands;
CRC: Colorectal cancer; DEG: Differentially expressed genes;
DhMR: Differential hydroxyl-methylation regions; DMR: Differential
methylation regions; DNMT: DNA methyltransferase; FDR: False discovery
rate; hMeDIP-seq: Hydroxyl-methylation by hydroxymethylated DNA
immunoprecipitation sequencing; LINE: Long interspersed nuclear elements;
LTR: Long terminal repeat; MeDIP-seq: Methylated DNA
immuneprecipitation sequencing; RPKM: Reads per kilobase million; SINE: Short
interspersed nuclear elements; TDG: Thymine-DNA glycosylase;
TES: Transcriptional end sites; TET: Ten-eleven translocation; TSG: Tumor
suppressor gene; TSS: Transcriptional start sites
This work was supported by the grants from the 111 Project (B13026), Major
program of Zhejiang Province (2012C13014-3), BGI-Shenzhen and the
Agricultural Science and Technology Innovation Program (ASTIP) of China,
Science and Technology Project of Shenzhen (JSGG20160427104724699),
Zhejiang Provincial Program for the Cultivation of High-level Innovative
Health talents, and the Fundamental Research Funds for Central Universities.
Availability of data and materials
MeDIP-seq, hMeDIP-seq, and RNA-seq have been deposited in Gene Expression
Omnibus (GEO) with accession number: GSE87096.
Ethics approval and consent to participate.
The study protocols were approved by the research ethics committees of
Zhejiang University School of Medicine (2012-1-012) and BGI-Shenzhen
(NO. BGI-IRB 15060). All participants provided the signed written informed
MDL and YMZ designed and supervised the study; FG, YMZ, DDZ, XHS, LDW,
DY, YPT, and HLL performed the experiments and analyzed the data. FG and
YMZ wrote parts of the manuscript. AFL, MYL, and HCJ collected samples
and data from CRC patients. MDL, FG, and YMZ discussed the results. All
authors read and approved the final manuscript.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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