5-hydroxymethylcytosine marks promoters in colon that resist DNA hypermethylation in cancer
- Santiago Uribe-Lewis1,
- Rory Stark1,
- Thomas Carroll1,
- Mark J Dunning1,
- Martin Bachman1,
- Yoko Ito1,
- Lovorka Stojic1,
- Silvia Halim1,
- Sarah L Vowler1,
- Andy G Lynch1,
- Benjamin Delatte2,
- Eric J de Bony2,
- Laurence Colin2,
- Matthieu Defrance2,
- Felix Krueger3,
- Ana-Luisa Silva4,
- Rogier ten Hoopen4,
- Ashraf EK Ibrahim4,
- François Fuks2 and
- Adele Murrell1, 5Email author
© Uribe-Lewis et al.; licensee BioMed Central. 2015
Received: 20 December 2014
Accepted: 4 February 2015
Published: 1 April 2015
The discovery of cytosine hydroxymethylation (5hmC) as a mechanism that potentially controls DNA methylation changes typical of neoplasia prompted us to investigate its behaviour in colon cancer. 5hmC is globally reduced in proliferating cells such as colon tumours and the gut crypt progenitors, from which tumours can arise.
Here, we show that colorectal tumours and cancer cells express Ten-Eleven-Translocation (TET) transcripts at levels similar to normal tissues. Genome-wide analyses show that promoters marked by 5hmC in normal tissue, and those identified as TET2 targets in colorectal cancer cells, are resistant to methylation gain in cancer. In vitro studies of TET2 in cancer cells confirm that these promoters are resistant to methylation gain independently of sustained TET2 expression. We also find that a considerable number of the methylation gain-resistant promoters marked by 5hmC in normal colon overlap with those that are marked with poised bivalent histone modifications in embryonic stem cells.
Together our results indicate that promoters that acquire 5hmC upon normal colon differentiation are innately resistant to neoplastic hypermethylation by mechanisms that do not require high levels of 5hmC in tumours. Our study highlights the potential of cytosine modifications as biomarkers of cancerous cell proliferation.
Cancer is a complex disease characterised by genetic and epigenetic aberrations. DNA methylation, an epigenetic mark catalysed by de novo DNA methyltransferases (DNMT) , can modulate gene activity and its distribution across the genome is grossly disrupted in neoplasia . The gain of methylation that frequently associates with the silencing of tumour suppressor genes can occur through the targeting of methylating complexes [3-5] but may also result from a failure to protect an unmethylated state . Global losses, prominent across large expanses of the genome and thought to modulate genome function through higher order chromatin architectures [7-9], may occur through passive DNA demethylation caused by a failure to maintain DNA methylation during DNA replication . The precise nature of the processes that govern DNA methylation changes in cancer are nevertheless still poorly defined and the recent discovery of active DNA demethylation mechanisms [11-16] bring about an additional level of complexity to our understanding of how such changes occur.
Methylation in DNA can be actively removed through oxidative demethylation by the TET family of alpha-glutarate-dependent oxygenases (TET1, TET2 and TET3) [15,17]. Further oxidation of 5hmC generates 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC)  that are readily recognised by DNA repair processes . The interconversion of cytosine modifications is now understood to be involved in the control of epigenetic plasticity and gene expression programmes .
Global reduction in 5hmC has been observed in all cancers studied to date [21-27], including colon cancer [21,28,29]. However, many but not all neoplasias show changes in expression levels of TETs [24-26,30-35]. Reduced levels of 5hmC in myelodysplasia and leukaemia frequently associate with mutations in TET2 [30,36,37] but changes in 5hmC levels are also thought to result from inhibition of TET activity by the onco-metabolite 2-hydroxyglutarate which accumulates through mutations of isocitrate dehydrogenases (IDH1/2) [38,39]. Importantly, reduced 5hmC does not always correlate with presence of IDH mutation [22,34] and IDH mutations are largely mutually exclusive to TET2 mutations in leukaemia . In colon cancer on the other hand, mutations in TETs and IDHs are very rare [31,40]. Thus reduction of 5hmC appears to be a universal feature of tumourigenesis but factors implicated in regulating cytosine hydroxylation show tumour-type-specific aberrations.
The aim of this study was to provide an insight into the potential role of oxidative demethylation in the progressive changes in DNA methylation that occur in colon tumourigenesis. Molecular characterisation of the behaviour of 5hmC, 5mC and TETs in colon cancer tissues and cancer cells shows that changes in 5hmC levels in proliferating cells do not correlate with TET transcripts levels or with identifiable mutations in their catalytic domains. Importantly, we show that presence of 5hmC at promoters in normal tissues associates with resistance to methylation gain in colon cancer.
Results and discussion
Two distinct classes of 5hmC enrichment profiles are observed at active genes in normal human colon
From the profiling of 5hmC content across genes we identified two types of enrichments at gene promoters (Figure 1c). A ‘narrow’ type was observed after ranking 5hmC read content inside a window of -1 kb to +0.5 kb of the TSS and a ‘broad’ type after ranking by 5hmC read content in the gene body (from TSS to the TTS). We identified 2,156 unique ‘narrow’ and 2,199 unique ‘broad’ promoters (listed in Additional file 1).
The ‘narrow’ and ‘broad’ profiles were distinct in terms of promoter CpG content (Figure 1d) and in distribution of 5hmC around promoter CpG islands (Figure 1e). Promoters with the ‘narrow’ profile were enriched for intermediate CpG content promoters (ICP) whereas the ‘broad’ promoters where mostly high in CpG content (Figure 1d). Both promoter types showed that 5hmC is enriched within the shores of promoter CpG islands, more so within the upstream shore, and a higher overall content of 5hmC for the ‘narrow’ type (Figure 1e). Note, however, that the enrichment of 5hmC in the downstream shore of ACTN2 is lower than that for AGAP1. The levels measured over the islands represent an average of the population for each type of promoter, and thus individual loci may not necessarily display the full enrichment profile across the associated promoter CpG island. Additional file 2 shows further examples to illustrate this. Interestingly, comparison of the 5hmC profiles with Illumina expression array data from four normal cases showed that ‘narrow’ promoter genes are less active than the ‘broad’ type (Figure 1f), in accordance with previous correlations made for higher 5hmC content at promoters and reduced gene activity in mouse and human ES cells [41,42]. Biological processes also typified the 5hmC promoters; gene ontology categories indicative of gut function were enriched for the ‘narrow’ type whereas cell differentiation and development where enriched for the ‘broad’ type (Additional file 2).
Together these data show that the content and distribution of 5hmC within promoters and gene bodies correlates with gene activities involved in normal gut epithelial function and differentiation.
5hmC enrichment is similar to 5mC enrichment at genic regions
Next we examined DNA methylation content with respect to the 5hmC profiles by comparing our hmeDIP-seq data to published meDIP-seq data for normal colon tissue  (Additional file 3). We generated heatmaps for 5hmC and 5mC enrichment profiles from -3 kb to +20 kb around the TSS (Additional file 3a). Ten clusters were generated based on the distribution of 5hmC and 5mC within this window. Overall we found that where 5hmC-specific enrichment is observed, the enrichment profiles are similar for 5mC (Additional file 3a). The exception was cluster 2 where there was more DNA methylation near the TSS than 5 hmC. Further comparison of 5hmC and 5mC profiles closer to the TSS (-3 kb to +3 kb) of all loci suggest that the differences in enrichment patterns for 5hmC and 5mC occur near the TSS and upstream promoter region (Additional file 3b). This suggests that several gene promoters may have DNA methylation without 5 hmC.
The heatmaps also identified the ‘narrow’ promoters as typified by clusters 3 and 8 whereas the ‘broad’ promoters fell within clusters 5, 6, 7 and 9 (Additional file 3c). With the exception of clusters 2 and 3 that showed an enrichment for LCP promoters, most of the 5hmC/5mC clusters fell with promoters of an intermediate or high CpG content (Additional file 3e).
We then compared the meDIP-seq methylation clusters to the methylation levels assessed by the Infinium27k arrays in 17 normal samples from our patient cohort (Additional file 3e). For the loci plotted in the heatmap the maximal distance of the Infinium probes to the TSS is 1499 bp. The highest methylation levels for these probes were around the promoters grouped within clusters 1 and 2, which correspond to the meDIP-seq data where the highest methylation enrichment was observed (Additional file 3a and e). Similarly clusters 4 to 9 which all reported low amounts of DNA methylation around the TSS by meDIP-seq also had lower levels of DNA methylation at the corresponding Infinium probes (Additional file 3a and e).
Thus in our normal colon tissues, the Infinium arrays concur with meDIP-seq enrichment patterns proximal to the TSS of genes.
Reduced levels of 5hmC in colon tumours do not correlate with changes in TET transcript levels
Mutation at the Fe2 and a-KG binding pockets could account for a lack of TET activity  but these were specifically excluded in our sample set through targeted exonic sequencing (Additional file 5a and Additional methods). We identified non-synonymous mutations elsewhere in the catalytic domains of TETs but their presence did not correlate with the changes in global 5hmC levels (Additional file 5b). Reduction of 5hmC in tumours may also be due to inhibition of TETs by metabolites that accumulate through mutation of IDH1/2, Fumarate hydratase (FH) or Succinate dehydrogenase (SDH) [39,45]. In our study IDH1/2 mutations were excluded in a subset of samples (not shown) and recent larger studies have shown IDH1/2, FH or SDH mutation is rare or absent in colon cancer [31,40].
We do not have TET protein data associated with our sample set and therefore we cannot exclude that the global reduction in 5hmC could be due to post-transcriptional events with an impact on variations in the stability or activity of TETs. However, the detection of mRNA at levels similar to the normal tissues suggests that the reduced levels of 5hmC that we uncover in all our colon tumours is unlikely to be due to an absence or mutation of TETs or an inhibition by currently recognised onco-metabolites.
5hmC is reduced across the genome of tumours with a small effect on gene transcription
We profiled 5hmC in four matching adenocarcinomas. The hmeDIP-seq read content in tumours showed an overall similar distribution to the normal tissue but with markedly reduced 5hmC levels across the genome as assessed by 5hmC content within repetitive elements (Additional file 6) and within genes (Additional file 7a and b). The reduced level of 5hmC in tumours compared to normal was confirmed at selected loci by a glycosylase-restriction enzyme sensitive assay (gluc-MS-qPCR - Additional file 7c) indicating that genes continue to be marked by a reduced amount of 5hmC in tumours.
Illumina expression array data generated from four normals and 14 tumours showed a small but statistically significant reduction in gene activity for genes with ‘broad’ 5hmC promoters (Additional file 7d). Thus, although 5hmC associates with active gene transcription, the reduction of 5hmC in tumours were accompanied by very small expression level changes. These results indicate that genes that acquire 5hmC in normal colon are transcriptionally active in tumours and suggest that low levels of 5hmC do not hinder transcription.
Loci marked by 5hmC in normal have an innate resistance to DNA hypermethylation in cancer
To ascertain whether promoters normally marked by 5hmC undergo DNA methylation changes in colon cancer, we assessed DNA methylation in 17 tumours matched to the normal tissues using Infinium methylation arrays. The Infinium27k arrays are a robust platform for quantitative measurement of the DNA methylation status of 27,578 CpG sites located at the promoter regions of 14,495 protein-coding genes [43,46]. Infinium technology is based on bisulfite conversion that does not distinguish between 5mC and 5hmC. However, 5hmC only makes up a small percentage of modified cytosines in normal colon and an even smaller percentage in colon cancer tissue. Based on the median levels of 5hmC detected by LCMS (Figure 2), only about 2.4% of 5mC reported in the Infinium data is likely to be undistinguishable from 5hmC in normal cells, and about 0.7% in tumours.
Importantly, the reciprocal pattern of high/low 5hmC in normal with loss/gain of methylation in adenocarcinoma was already present at the adenoma stages (Additional file 8a and b) and observed at CpG islands and island shores (Additional file 8c). This reciprocal pattern was also present at previously identified colon cancer-specific small regions of differential DNA methylation (sDMRs)  (Additional file 8d) and clearly observed and verified in a number of colon cancer relevant gene promoters (Figure 3e and Additional file 9).
Together these results indicate that gene promoters marked with 5hmC in normal rarely become hypermethylated when 5hmC is reduced in tumours. Indeed these promoters have a tendency to lose DNA methylation in cancer. We also identified 117 promoters where 5hmC was still detected in adenocarcinomas, albeit at very low levels, and found that these where three times more likely to have lost methylation rather than gain (27% vs. 8.5%, respectively) (Additional file 10). These results may suggest that DNA demethylation at a subset of proximal promoters could be mediated via hydroxymethylation and/or that the presence of 5hmC helps to repel DNA methylating complexes as previously suggested [48,49].
There is strong evidence from cell labelling experiments that colon cancer can originate from the stem cell/progenitor compartment . Our data, and that of others , showing that global 5hmC levels are low in the stem cell compartment and in cancer tissues may suggest that 5hmC is not lost in colon cancer. Rather, 5hmC may not accumulate due to an aberrant progenitor-like proliferative state. One explanation for why the loci that would accumulate 5hmC upon terminal differentiation are seemingly more resistant to gain of DNA methylation in cancer, in contrast with loci that do not accumulate 5hmC, could be that the TETs in cancer cells are bound to their target promoters to prevent de novo DNA methylation.
TET2 marks promoters in cancer cells that resist DNA methylation gain in primary tumours but is not required to maintain a demethylated state
In order to examine whether TETs are bound to DNA in cancer cells we turned to the colorectal cancer cell line HCT116. This cell line shows low global levels of 5hmC and TET2 and TET3 transcript levels comparable to that observed in normal and adenocarcinoma tissue (Additional file 11a to c). Despite the extremely low global content of 5hmC in these cells, lower than that seen in the primary tissues, TET2 and TET3 proteins can be detected in the nuclear fraction (Additional file 11d) albeit a sizeable amount of TET2 is present in the cytoplasm (Additional file 11d and e). A similar subcellular distribution of TET2 is observed in normal colon crypts and tumours by immunohistochemistry (Additional file 12).
Survival outcomes estimated from publicly available colorectal cancer datasets [52,53] further indicate that TET2 expression levels do not significantly associate with patient survival, which is consistent with the small effect that we see in these in vitro TET2 studies. TET2 therefore seems to play a moderate role in controlling cytosine modifications during gut tumourigenesis.
Promoters with high levels of 5hmC in normal colon overlap with bivalently marked promoters in human embryonic stem cells that do not become methylated in colon cancer
If tumours arise from intestinal cells in the crypt and if 5hmC is a mark of terminally differentiated cells, then how do we explain the resistance of 5hmC promoters to methylation gain in tumours prior to their accumulating 5hmC in normal tissue? TET2 depletion only has a moderate effect on DNA methylation in cancer cells, suggesting that the protective mechanism is unlikely to be due to continuous TET2 binding at target promoters. Although TET2 may not be involved in maintaining the unmethylated state of its target promoters, we cannot exclude that other proteins within a TET-complex may be involved. However there may be alternative explanations, one of which is that 5hmC promoters are epigenetically marked during early development to make them intrinsically unlikely to develop characteristics such as H3K27me3 in the soma that predispose to DNA methylation gain.
Precedents for early epigenetic marking include genomic imprinting and X-inactivation, but may also include the recently described instructive process for gain of methylation in cancer which occurs at promoters containing histone H3K4 and H3K27 tri-methylation (so-called bivalent promoters) in human embryonic stem cells (hESC) [54-58]. ESCs unlike most other proliferating cells already have high levels of 5hmC. In mouse ESCs Tet1 is found either at the TSS of bivalent promoters together with silencing complexes independent of 5hmC or downstream of the TSS together with 5hmC and the PRC2 complex [59,60]. In human ESC 5hmC has been found more at active gene promoters and enhancers than at poised (bivalent) enhancers .
DNA methylation change is a prominent feature of cancer and in recent years, low levels of 5hmC have been reported as a hallmark of several cancers. We confirm that 5hmC is strongly reduced in colon cancer cells relative to the normal tissues. However, we also find that the TETs are present in cancer tissues, albeit at the transcript level, and with no evidence for mutations that could account for the decreased levels of 5hmC. We have recently shown that there is a delay in the generation of 5hmC on newly synthesised DNA  that can be responsible for the low levels of 5hmC in proliferating cells in the presence of TETs.
Genome-wide mapping shows that gene promoters marked by 5hmC seem distinctly resistant to DNA methylation gain, and slightly prone to DNA methylation loss. One explanation for this finding may be that the TETs continue to maintain the DNA methylation levels at promoters in the proliferating tumour cells. However, the marginal changes in DNA methylation at a significant number of gene promoters induced by the TET2 knockdowns were hardly observed at promoters we have identified as TET2 targets.
Correlative observations indicate that promoters with 5hmC in normal colon have a substantial overlap with bivalent promoters in hESCs, and may suggest that 5hmC is a mark of loci that have undergone a counteractive process that prevents the acquisition of hypermethylation-predisposing characteristics. Although we have potentially eliminated a maintenance role for TET2 in keeping the target promoters free of DNA methylation in colon cancer, this does not preclude the TETs from having an initiating function that marks genes for activation during early development. This initiating process could counter the instructive hypermethylation in cancer process by active removal of methylated DNA , by inhibiting de novo DNA methylation  and/or by attracting regulatory complexes to chromatin .
Finally, it has been suggested that 5hmC levels can be used as diagnostic criteria to distinguish between benign nevi and malignant melanomas . Our study highlights the potential of cytosine modifications as biomarkers of cancerous cell proliferation, but questions whether colon cancer is suited to the recently described potential therapeutic avenue to restore TET activity .
Materials and methods
Research was conducted under the principles of the World Medical Association Helsinki agreement. Ethical approval was obtained from the Cambridgeshire Local Research Ethics Committee (LREC references 04/Q0108/125 and 06/Q0108/307). Forty-seven normal samples (composed of 16 samples taken more than 20 cm away from tumours (normal away (NA)) and 31 samples taken close to tumours (normal close (NC)), 36 adenoma (Ad) and 31 adenocarcinoma (T). Samples for tissue microarrays are described .
Anti-5hmC rabbit polyclonal (Active Motif, 39791), anti-TET1 (SantaCruz sc-163443), anti-TET2 for western and IHC (Abcam ab94580), anti-TET2 for ChIP-seq (Santa Cruz sc-136926), anti-TET3 (Abnova), anti-Lamin B1 (Abcam ab16048), anti-beta Tubulin (Sigma T0198).
Illumina libraries were prepared before the pull-down using 1 to 3 micrograms of sonicated genomic DNA (Bioruptor). Libraries were prepared using a ‘with-bead’ procedure  or with the TruSeq DNA sample preparation kit (Illumina) following manufacturer’s instructions. Adaptor modified genomic DNA was then immunoprecipitated following . Input and pull-down material was whole genome amplified as previously described  except that samples were amplified with 10 PCR cycles, ran on 2% EX agarose gels (LifeTechnologies), size selection of 300 to 500 bp fragments with the MinElute gel extraction kit (Qiagen), further amplified with seven PCR cycles and purified with AMPURE XP beads. This procedure was also done for amplification with TruSeq reagents. Libraries were qualified and quantified by Bioanalyzer and submitted for sequencing by the CI Genomics core facility.
Bioinformatic analysis - hmeDIP-seq
Illumina sequencing reads were aligned against the hg18 genome assembly using BWA. Mean read coverage around TSS was calculated using ‘GenomicRanges’ and ‘Rsamtools’ (Bioconductor). Read coverage was normalised per million mapped reads, subtracted from input and mean TSS coverage plotted. Feature Enrichment analysis was performed by using Rsamtools to count reads within feature locations obtained from Ensembl (hg18, May 2009). Promoter CpG content classification was as described in . For gene ontology, functional enrichment of selected gene sets was assessed by fisher exact tests with and without graph correction using the ‘TopGO’ Bioconductor package . False discovery rates were calculated by Benjamini Hochberg correction using R . For genome wide repeats analysis, repeat scores were obtained by alignment of hmeDIP-seq reads to a repeat genome obtained by concatenating repeat locations annotated in Ensembl. ‘DiffBind’  package was used to quantitatively compare 5hmC within peaks in normal and tumours. Heatmaps for comparison to meDIP-seq used ‘SeqMiner’  and data kindly provided by C. Bock .
Normal (n = 4), adenoma (n = 7) and adenocarcinoma (tumour, n = 14) mRNA were profiled using Illumina HumanWG6-V2 chips. The raw data were summarised using BeadStudio version 3.1.7, without background correction and imported into R using the ‘beadarray’ package  in Bioconductor. After quality control, arrays were background corrected using a normal-exponential model and then quantile normalised . Illumina probes were annotated using the illuminaHumanv2.db Bioconductor package and poorly annotated probes were excluded prior to differential expression analysis . A linear modelling approach was used to estimate the expression of each probe in normal, adenoma, and adenocarcinoma groups. Differential expression statistics were generated following empirical Bayes’ shrinkage of variances . Illumina expression arrays validation used the 96.96 Biomark Dynamic Array platform (Fluidigm) and Taqman assays (ABI) following manufacturer’s instructions (Fluidigm). Fold change between normal and tumour was calculated by the delta Ct method using B2M, HPRT1, SDHA or PSMC4 as normaliser loci (Additional file 14 and list of assays in Additional file 1). Expression values for loci identified as TET2 targets by ChIP-seq in HCT116 cells were obtained from GSE36133.
1 μg of genomic DNA was incubated with 5 U of DNA Degradase Plus (Zymo Research) at 37°C for 3 h and filtered through Amicon 10 kDa centrifugal filter units (Millipore). The concentrations of 2′-deoxycytidine, 5-methyl-2′-deoxycytidine and 5-hydroxymethyl-2′-deoxycytidine in the filtrate were determined using an AB Sciex Triple Quad 6500 mass spectrometer fitted with an Agilent Infinity 1290 LC system and an Acquity UPLC HSS T3 column. The global levels of mC and hmC were expressed as percentages over total 2′-deoxycytidines.
CRC TMA IF
After epitope retrieval by boiling in an EDTA solution the slide was rinsed in PBS and blocked. Primary (anti-5hmC and anti-5mC) and secondary (Alexa647 anti-rabbit and Alexa448 anti-mouse (Invitrogen)) antibodies were sequentially applied for 1 h each with 3× washes of PBS/0.1% Tween 20 in between. After a final 3× washes the slide was mounted with DAPI and scanned onto the Ariol system for analysis.
For Figure 2, 1 μg total RNA was treated with 1U DNaseI (Promega 9PMIM610) and cDNA prepared with SuperscriptIII reverse transcriptase (Invitrogen) and random primers. Targets were quantified with 1× Fast Sybr (ABI) and 1× Quantitect assays (Qiagen) by the standard curve method using serial dilutions of cDNA template from Jeg3 cells and normalised to B2M. For supplementary Figure 3, 1 μg RNA was reverse transcribed using Quantitect reverse transcription kit (Qiagen) following manufacturer’s instructions. Real-time PCR used the 96.96 Biomark Dynamic Array platform (Fluidigm) and Taqman assays (ABI) following manufacturer’s instructions (Fluidigm). Fold change between normal and tumour was calculated by the delta Ct method using B2M as the normaliser. All expression assays are listed in Additional file 1.
Infinium27k and 450 k
Bisulfite-converted DNA (EZ DNA Methylation-Gold, Zymo Research) was analysed using Illumina Infinium HumanMethylation27 BeadChips in the Cambridge Genomic Services, Cambridge University, UK. Data were analysed using BeadStudio (Illumina, Inc.) and R. The locus methylation was calculated as the log ratio of the Unmethylated and Methylated channels, and a standard error of the log-ratio was estimated. Models were fitted with limma  using weights derived from the standard errors. Separate analysis were performed for loci measured in the red channel and loci measured in the green channel. Infinium450k for HCT116 cells in Figure 4 used beta values from GSE29290 for Infinium ID annotations obtained using the ‘IlluminaHumanMethylation450k.db’ package (Bioconductor) in R. For the 450 k analysis of TET2 shRNA knockdowns, raw Infinium data were filtered by removing low quality data using a detection P value threshold of 0.05. Cross-reactive probes (that is, targeting several genomic locations) and probes containing SNPs were filtered out using the extended annotation provided by Price et al.  (see  for a detailed description). Probes associated to X and Y chromosomes were removed from the analysis. Beta-values were computed using the formula Beta-value = M/[U + M] where M and U are the raw ‘methylated’ and ‘unmethylated’ signals, respectively. Beta values were corrected for type I and type II bias using the peak-based correction [79,80].
ChIP-seq for TET2 was performed as previously described .
Stable knockdown of TET2 in HCT116 used MISSION shRNA Lentiviral Transduction Particles (SHCLNV-NM_017628, Sigma) following manufacturer’s instructions and selection with 2 ug/mL Puromycin (A11138-03, Life Technologies). For TET2C 10MOI of TRCN0000418976 and for TET2 + 3 5MOI each of TRCN0000418976 and TRCN0000246258 were used. shRNA control used 10MOI of MISSION pLKO.1-puro Non-Target shRNA Control Transduction Particles (SHC016V-1EA, Sigma).
Additional methods can be found in Additional file 15.
The authors would like to acknowledge the support of The University of Cambridge, Cancer Research UK (CRUK SEB-Institute Group Award A ref10182; CRUK Senior fellowship C10112/A11388 to AEKI) and Hutchison Whampoa Limited. The Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre. FF is a ULB Professor funded by grants from the F.N.R.S. and Télévie, the IUAP P7/03 programme, the ARC (AUWB-2010-2015 ULB-No 7), the WB Health program and the Fonds Gaston Ithier. The authors would like to thank Will Howat and the CI histopathology core, James Hadfield and the CI Genomics core, Jane Gray for technical help at the CI Equipment Park, Reiner Schulz and Richard Grenfell at the CI Flow Cytometry core, Michael Williams at the CI PK/PD core facility for help with mass spectrometry, Sarah Dawson and Deepak Parashar at the CI Bioinformatics core, Dana Tsui and Helen Ross-Adams for technical advice.
- Goll MG, Bestor TH. Eukaryotic cytosine methyltransferases. Annu Rev Biochem. 2005;74:481–514.View ArticlePubMedGoogle Scholar
- Baylin SB, Jones PA. A decade of exploring the cancer epigenome - biological and translational implications. Nat Rev Cancer. 2011;11:726–34.View ArticlePubMed CentralPubMedGoogle Scholar
- Di Croce L, Raker VA, Corsaro M, Fazi F, Fanelli M, Faretta M, et al. Methyltransferase recruitment and DNA hypermethylation of target promoters by an oncogenic transcription factor. Science. 2002;295:1079–82.View ArticlePubMedGoogle Scholar
- Vire E, Brenner C, Deplus R, Blanchon L, Fraga M, Didelot C, et al. The Polycomb group protein EZH2 directly controls DNA methylation. Nature. 2006;439:871–4.View ArticlePubMedGoogle Scholar
- Brenner C, Deplus R, Didelot C, Loriot A, Vire E, De Smet C, et al. Myc represses transcription through recruitment of DNA methyltransferase corepressor. EMBO J. 2005;24:336–46.View ArticlePubMed CentralPubMedGoogle Scholar
- Williams K, Christensen J, Helin K. DNA methylation: TET proteins-guardians of CpG islands? EMBO Rep. 2012;13:28–35.View ArticlePubMed CentralGoogle Scholar
- Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462:315–22.View ArticlePubMed CentralPubMedGoogle Scholar
- Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, et al. Increased methylation variation in epigenetic domains across cancer types. Nat Genet. 2011;43:768–75.View ArticlePubMed CentralPubMedGoogle Scholar
- Berman BP, Weisenberger DJ, Aman JF, Hinoue T, Ramjan Z, Liu Y, et al. Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains. Nat Genet. 2012;44:40–6.View ArticlePubMed CentralGoogle Scholar
- Hon GC, Hawkins RD, Caballero OL, Lo C, Lister R, Pelizzola M, et al. Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res. 2012;22:246–58.View ArticlePubMed CentralPubMedGoogle Scholar
- Barreto G, Schafer A, Marhold J, Stach D, Swaminathan SK, Handa V, et al. Gadd45a promotes epigenetic gene activation by repair-mediated DNA demethylation. Nature. 2007;445:671–5.View ArticlePubMedGoogle Scholar
- Bhutani N, Brady JJ, Damian M, Sacco A, Corbel SY, Blau HM. Reprogramming towards pluripotency requires AID-dependent DNA demethylation. Nature. 2010;463:1042–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Hajkova P, Jeffries SJ, Lee C, Miller N, Jackson SP, Surani MA. Genome-wide reprogramming in the mouse germ line entails the base excision repair pathway. Science. 2010;329:78–82.View ArticlePubMedGoogle Scholar
- Popp C, Dean W, Feng S, Cokus SJ, Andrews S, Pellegrini M, et al. Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency. Nature. 2010;463:1101–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Ito S, D’Alessio AC, Taranova OV, Hong K, Sowers LC, Zhang Y. Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature. 2010;466:1129–33.View ArticlePubMed CentralPubMedGoogle Scholar
- Cortellino S, Xu J, Sannai M, Moore R, Caretti E, Cigliano A, et al. Thymine DNA glycosylase is essential for active DNA demethylation by linked deamination-base excision repair. Cell. 2011;146:67–79.View ArticlePubMed CentralPubMedGoogle Scholar
- Tahiliani M, Koh KP, Shen Y, Pastor WA, Bandukwala H, Brudno Y, et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science. 2009;324:930–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Ito S, Shen L, Dai Q, Wu SC, Collins LB, Swenberg JA, et al. Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science. 2011;333:1300–3.View ArticlePubMed CentralPubMedGoogle Scholar
- He YF, Li BZ, Li Z, Liu P, Wang Y, Tang Q, et al. Tet-mediated formation of 5-carboxylcytosine and its excision by TDG in mammalian DNA. Science. 2011;333:1303–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Shen L, Wu H, Diep D, Yamaguchi S, D’Alessio AC, Fung HL, et al. Genome-wide analysis reveals TET- and TDG-dependent 5-methylcytosine oxidation dynamics. Cell. 2013;153:692–706.View ArticlePubMed CentralPubMedGoogle Scholar
- Haffner MC, Chaux A, Meeker AK, Esopi DM, Gerber J, Pellakuru LG, et al. Global 5-hydroxymethylcytosine content is significantly reduced in tissue stem/progenitor cell compartments and in human cancers. Oncotarget. 2011;2:627–37.PubMed CentralPubMedGoogle Scholar
- Jin SG, Jiang Y, Qiu R, Rauch TA, Wang Y, Schackert G, et al. 5-Hydroxymethylcytosine is strongly depleted in human cancers but its levels do not correlate with IDH1 mutations. Cancer Res. 2011;71:7360–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Kraus TF, Globisch D, Wagner M, Eigenbrod S, Widmann D, Munzel M, et al. Low values of 5-hydroxymethylcytosine (5hmC), the “sixth base”, are associated with anaplasia in human brain tumors. Int J Cancer. 2012;131:1577–90.View ArticlePubMedGoogle Scholar
- Yang H, Liu Y, Bai F, Zhang JY, Ma SH, Liu J, et al. Tumor development is associated with decrease of TET gene expression and 5-methylcytosine hydroxylation. Oncogene. 2013;32:663–9.View ArticlePubMed CentralPubMedGoogle Scholar
- Liu C, Liu L, Chen X, Shen J, Shan J, Xu Y, et al. Decrease of 5-hydroxymethylcytosine is associated with progression of hepatocellular carcinoma through downregulation of TET1. PLoS One. 2013;8:e62828.View ArticlePubMed CentralPubMedGoogle Scholar
- Gambichler T, Sand M, Skrygan M. Loss of 5-hydroxymethylcytosine and ten-eleven translocation 2 protein expression in malignant melanoma. Melanoma Res. 2013;23:218–20.View ArticlePubMedGoogle Scholar
- Bhattacharyya S, Yu Y, Suzuki M, Campbell N, Mazdo J, Vasanthakumar A, et al. Genome-wide hydroxymethylation tested using the HELP-GT assay shows redistribution in cancer. Nucleic Acids Res. 2013;41:e157.View ArticlePubMed CentralPubMedGoogle Scholar
- Li W, Liu M. Distribution of 5-hydroxymethylcytosine in different human tissues. J Nucleic Acids. 2011;2011:870726.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang LT, Zhang LJ, Zhang JJ, Ye XX, Xie AM, Chen LY, et al. Quantification of the sixth DNA base 5-hydroxymethylcytosine in colorectal cancer tissue and C-26 cell line. Bioanalysis. 2013;5:839–45.View ArticlePubMedGoogle Scholar
- Ko M, Huang Y, Jankowska AM, Pape UJ, Tahiliani M, Bandukwala HS, et al. Impaired hydroxylation of 5-methylcytosine in myeloid cancers with mutant TET2. Nature. 2010;468:839–43.View ArticlePubMed CentralPubMedGoogle Scholar
- Seshagiri S, Stawiski EW, Durinck S, Modrusan Z, Storm EE, Conboy CB, et al. Recurrent R-spondin fusions in colon cancer. Nature. 2012;488:660–4.View ArticlePubMed CentralPubMedGoogle Scholar
- Lian CG, Xu Y, Ceol C, Wu F, Larson A, Dresser K, et al. Loss of 5-hydroxymethylcytosine is an epigenetic hallmark of melanoma. Cell. 2012;150:1135–46.View ArticlePubMed CentralPubMedGoogle Scholar
- Kudo Y, Tateishi K, Yamamoto K, Yamamoto S, Asaoka Y, Ijichi H, et al. Loss of 5-hydroxymethylcytosine is accompanied with malignant cellular transformation. Cancer Sci. 2012;103:670–6.View ArticlePubMedGoogle Scholar
- Muller T, Gessi M, Waha A, Isselstein LJ, Luxen D, Freihoff D, et al. Nuclear exclusion of TET1 is associated with loss of 5-hydroxymethylcytosine in IDH1 wild-type gliomas. Am J Pathol. 2012;181:675–83.View ArticlePubMedGoogle Scholar
- Song SJ, Ito K, Ala U, Kats L, Webster K, Sun SM, et al. The oncogenic microRNA miR-22 targets the TET2 tumor suppressor to promote hematopoietic stem cell self-renewal and transformation. Cell Stem Cell. 2013;13:87–101.View ArticlePubMed CentralPubMedGoogle Scholar
- Konstandin N, Bultmann S, Szwagierczak A, Dufour A, Ksienzyk B, Schneider F, et al. Genomic 5-hydroxymethylcytosine levels correlate with TET2 mutations and a distinct global gene expression pattern in secondary acute myeloid leukemia. Leukemia. 2011;25:1649–52.View ArticlePubMedGoogle Scholar
- Perez C, Martinez-Calle N, Martin-Subero JI, Segura V, Delabesse E, Fernandez-Mercado M, et al. TET2 mutations are associated with specific 5-methylcytosine and 5-hydroxymethylcytosine profiles in patients with chronic myelomonocytic leukemia. PLoS One. 2012;7:e31605.View ArticlePubMed CentralPubMedGoogle Scholar
- Figueroa ME, Abdel-Wahab O, Lu C, Ward PS, Patel J, Shih A, et al. Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer Cell. 2010;18:553–67.View ArticlePubMed CentralPubMedGoogle Scholar
- Xu W, Yang H, Liu Y, Yang Y, Wang P, Kim SH, et al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of alpha-ketoglutarate-dependent dioxygenases. Cancer Cell. 2011;19:17–30.View ArticlePubMed CentralPubMedGoogle Scholar
- Atlas TCG. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–7.View ArticleGoogle Scholar
- Tan L, Xiong L, Xu W, Wu F, Huang N, Xu Y, et al. Genome-wide comparison of DNA hydroxymethylation in mouse embryonic stem cells and neural progenitor cells by a new comparative hMeDIP-seq method. Nucleic Acids Res. 2013;41:e84.View ArticlePubMed CentralPubMedGoogle Scholar
- Kim M, Park YK, Kang TW, Lee SH, Rhee YH, Park JL, et al. Dynamic changes in DNA methylation and hydroxymethylation when hES cells undergo differentiation toward a neuronal lineage. Hum Mol Genet. 2014;23:657–67.View ArticlePubMedGoogle Scholar
- Bock C, Tomazou EM, Brinkman AB, Muller F, Simmer F, Gu H, et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28:1106–14.View ArticlePubMed CentralPubMedGoogle Scholar
- Batlle E, Bacani J, Begthel H, Jonkheer S, Gregorieff A, van de Born M, et al. EphB receptor activity suppresses colorectal cancer progression. Nature. 2005;435:1126–30.View ArticlePubMedGoogle Scholar
- Xiao M, Yang H, Xu W, Ma S, Lin H, Zhu H, et al. Inhibition of alpha-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Dev. 2012;26:1326–38.View ArticlePubMed CentralPubMedGoogle Scholar
- Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, et al. Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics. 2009;1:177–200.View ArticlePubMedGoogle Scholar
- Sproul D, Kitchen RR, Nestor CE, Dixon JM, Sims AH, Harrison DJ, et al. Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns. Genome Biol. 2012;13:R84.View ArticlePubMed CentralPubMedGoogle Scholar
- Valinluck V, Sowers LC. Endogenous cytosine damage products alter the site selectivity of human DNA maintenance methyltransferase DNMT1. Cancer Res. 2007;67:946–50.View ArticlePubMedGoogle Scholar
- Branco MR, Ficz G, Reik W. Uncovering the role of 5-hydroxymethylcytosine in the epigenome. Nat Rev Genet. 2012;13:7–13.Google Scholar
- Visvader JE. Cells of origin in cancer. Nature. 2011;469:314–22.View ArticlePubMedGoogle Scholar
- Jin C, Lu Y, Jelinek J, Liang S, Estecio MR, Barton MC, et al. TET1 is a maintenance DNA demethylase that prevents methylation spreading in differentiated cells. Nucleic Acids Res. 2014;42:6956–71.View ArticlePubMed CentralPubMedGoogle Scholar
- Jorissen RN, Gibbs P, Christie M, Prakash S, Lipton L, Desai J, et al. Metastasis-associated gene expression changes predict poor outcomes in patients with dukes stage B and C colorectal cancer. Clin Cancer Res. 2009;15:7642–51.View ArticlePubMed CentralPubMedGoogle Scholar
- Smith JJ, Deane NG, Wu F, Merchant NB, Zhang B, Jiang A, et al. Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer. Gastroenterology. 2010;138:958–68.View ArticlePubMed CentralPubMedGoogle Scholar
- Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007;39:237–42.View ArticlePubMed CentralPubMedGoogle Scholar
- Widschwendter M, Fiegl H, Egle D, Mueller-Holzner E, Spizzo G, Marth C, et al. Epigenetic stem cell signature in cancer. Nat Genet. 2007;39:157–8.View ArticlePubMedGoogle Scholar
- Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J, et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat Genet. 2007;39:232–6.View ArticlePubMedGoogle Scholar
- Easwaran H, Johnstone SE, Van Neste L, Ohm J, Mosbruger T, Wang Q, et al. A DNA hypermethylation module for the stem/progenitor cell signature of cancer. Genome Res. 2012;22:837–49.View ArticlePubMed CentralPubMedGoogle Scholar
- Hinoue T, Weisenberger DJ, Lange CP, Shen H, Byun HM, Van Den Berg D, et al. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 2012;22:271–82.View ArticlePubMed CentralPubMedGoogle Scholar
- Williams K, Christensen J, Pedersen MT, Johansen JV, Cloos PA, Rappsilber J, et al. TET1 and hydroxymethylcytosine in transcription and DNA methylation fidelity. Nature. 2011;473:343–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Neri F, Incarnato D, Krepelova A, Rapelli S, Pagnani A, Zecchina R, et al. Genome-wide analysis identifies a functional association of Tet1 and Polycomb repressive complex 2 in mouse embryonic stem cells. Genome Biol. 2013;14:R91.View ArticlePubMed CentralPubMedGoogle Scholar
- Stroud H, Feng S, Morey Kinney S, Pradhan S, Jacobsen SE. 5-Hydroxymethylcytosine is associated with enhancers and gene bodies in human embryonic stem cells. Genome Biol. 2011;12:R54.View ArticlePubMed CentralPubMedGoogle Scholar
- Bachman M, Uribe-Lewis S, Yang X, Williams M, Murrell A, Balasubramanian S. 5-Hydroxymethylcytosine is a predominantly stable DNA modification. Nat Chem. 2014;6:1049–55.View ArticlePubMedGoogle Scholar
- Deplus R, Delatte B, Schwinn MK, Defrance M, Mendez J, Murphy N, et al. TET2 and TET3 regulate GlcNAcylation and H3K4 methylation through OGT and SET1/COMPASS. EMBO J. 2013;32:645–55.View ArticlePubMed CentralPubMedGoogle Scholar
- Uchiyama R, Uhara H, Uchiyama A, Ogawa E, Takazawa Y, Ashida A, et al. 5-Hydroxymethylcytosine as a useful marker to differentiate between malignant melanomas and benign melanocytic nevi. J Dermatol Sci. 2014;73:161–3.View ArticlePubMedGoogle Scholar
- Ibrahim AE, Arends MJ, Silva AL, Wyllie AH, Greger L, Ito Y, et al. Sequential DNA methylation changes are associated with DNMT3B overexpression in colorectal neoplastic progression. Gut. 2011;60:499–508.View ArticlePubMedGoogle Scholar
- Fisher S, Barry A, Abreu J, Minie B, Nolan J, Delorey TM, et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 2011;12:R1.View ArticlePubMed CentralPubMedGoogle Scholar
- Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, Rebhan M, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007;39:457–66.View ArticlePubMedGoogle Scholar
- Schmidt D, Wilson MD, Spyrou C, Brown GD, Hadfield J, Odom DT. ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions. Methods. 2009;48:240–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Alexa A, Rahnenfuhrer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22:1600–7.View ArticlePubMedGoogle Scholar
- R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2008. [http://www.R-project.org]
- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012;481:389–93.PubMed CentralPubMedGoogle Scholar
- Ye T, Krebs AR, Choukrallah MA, Keime C, Plewniak F, Davidson I, et al. seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 2011;39:e35.View ArticlePubMed CentralPubMedGoogle Scholar
- Dunning MJ, Smith ML, Ritchie ME, Tavare S. beadarray: R classes and methods for Illumina bead-based data. Bioinformatics. 2007;23:2183–4.View ArticlePubMedGoogle Scholar
- Shi W, Oshlack A, Smyth GK. Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. Nucleic Acids Res. 2010;38:e204.View ArticlePubMed CentralPubMedGoogle Scholar
- Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JF, Ritchie ME, Lynch AG, et al. A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res. 2010;38:e17.View ArticlePubMed CentralPubMedGoogle Scholar
- Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.Google Scholar
- Smyth GK. Limma: linear models for microarray data. In: Gentleman VC R, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer; 2005. p. 397–420.View ArticleGoogle Scholar
- Price ME, Cotton AM, Lam LL, Farre P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin. 2013;6:4.View ArticlePubMed CentralPubMedGoogle Scholar
- Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G. Fuks F A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2013;15:929–41.View ArticlePubMed CentralPubMedGoogle Scholar
- Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F. Evaluation of the Infinium Methylation 450 K technology. Epigenomics. 2011;3:771–84.View ArticlePubMedGoogle Scholar
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