Comprehensive profiling of zebrafish hepatic proximal promoter CpG island methylation and its modification during chemical carcinogenesis
© Mirbahai et al; licensee BioMed Central Ltd. 2011
Received: 30 July 2010
Accepted: 4 January 2011
Published: 4 January 2011
DNA methylation is an epigenetic mechanism associated with regulation of gene expression and it is modulated during chemical carcinogenesis. The zebrafish is increasingly employed as a human disease model; however there is a lack of information on DNA methylation in zebrafish and during fish tumorigenesis.
A novel CpG island tiling array containing 44,000 probes, in combination with immunoprecipitation of methylated DNA, was used to achieve the first comprehensive methylation profiling of normal adult zebrafish liver. DNA methylation alterations were detected in zebrafish liver tumors induced by the environmental carcinogen 7, 12-dimethylbenz(a)anthracene. Genes significantly hypomethylated in tumors were associated particularly with proliferation, glycolysis, transcription, cell cycle, apoptosis, growth and metastasis. Hypermethylated genes included those associated with anti-angiogenesis and cellular adhesion. Of 49 genes that were altered in expression within tumors, and which also had appropriate CpG islands and were co-represented on the tiling array, approximately 45% showed significant changes in both gene expression and methylation.
The functional pathways containing differentially methylated genes in zebrafish hepatocellular carcinoma have also been reported to be aberrantly methylated during tumorigenesis in humans. These findings increase the confidence in the use of zebrafish as a model for human cancer in addition to providing the first comprehensive mapping of DNA methylation in the normal adult zebrafish liver.
Tumorigenesis by chemical carcinogens is a multistep process  with accumulation of both mutations and epigenetic aberrations in regulatory regions of genes and disruption of cellular signaling pathways [2, 3]. In particular, DNA methylation at CpG dinucleotides is an important component of epigenetic gene expression regulation , resulting in modulation of protein-DNA interactions [5, 6]. Aberrant methylation of CpG islands (CGI) in the promoter and exonic regions [7, 8], and changes in gene expression, have been associated with tumorigenesis [7, 9, 10]. Global hypomethylation occurs in most human tumors [3, 4, 7] leading to potential activation of imprinted genes, parasite sequences and oncogenes and increased chromosome instability . In addition, hypermethylation of genes associated with negative regulation of tumorigenesis, such as tumor suppressor genes (TSG), DNA repair genes, and anti-angiogenic genes, is a common and key characteristic of neoplastic cells [4, 10–13]. Furthermore, a range of rodent carcinogens alter methylation status contributing to the carcinogenic mechanisms .
Fish have been used as models to study tumors induced by environmental carcinogens. For example, rainbow trout (Oncorhynchus mykiss) , zebrafish (Danio rerio) , guppy (Poecilia reticulata) , platyfish (Xiphophorus sp.)  and medaka (Oryzias latipes)  have been employed in carcinogen bioassays. Zebrafish is a particularly well established model for investigating embryogenesis, organogenesis, environmental carcinogenesis and for modeling human diseases such as cancer [15, 19–23]. Chemically-induced tumors in zebrafish and tumors in humans are histopathologically very similar [20, 24] and orthologous TSGs and oncogenes in human and fish have been identified . Recent studies comparing hepatic gene expression in human and zebrafish tumors demonstrated conservation of gene expression profiles at different stages of tumor aggressiveness between these two phylogenetically distant species . However, the contribution of altered methylation to such changes is unknown.
Although there are reports on global levels of methylation during embryogenesis or in adults [25–27], effect of temperature on global levels of methylation  methylation profiling in zebrafish embryo  and role of chromatin mediated gene regulation during embryogenesis , to our knowledge no study has been published on the DNA methylation patterns or methylation changes associated with carcinogenesis in adult zebrafish. Therefore, the aims of the study were to achieve a comprehensive mapping of zebrafish hepatic proximal promoter CGI methylation in both normal liver and in chemically induced hepatocellular carcinoma (HCC) tumors. We aimed to determine if there was a linkage between methylation and the observed changes in the zebrafish HCC gene expression, and to compare biological pathways represented by altered gene methylation in zebrafish HCC with pathways commonly altered in human HCC.
To our knowledge this paper is the first to describe methyl-DNA immunoprecipitation (MeDIP) in combination with a CGI zebrafish tiling array for establishing normal and HCC methylation profiles in the liver of any adult fish species. This microarray, in combination with well-established methylation immunoprecipitation, serves as a powerful tool for elucidating comprehensive methylation profiles. To further validate the data derived from the tiling array, bisulfite sequencing PCR was used.
Global measurement of DNA methylation
Unbiased enrichment of methylated DNA using methyl-DNA immunoprecipitation (MeDIP)
To optimize and test the specificity of the immunoprecipitation method, CGI regions of 14 genes (15 regions) located 1.5 kb upstream to 1 kb downstream of transcriptional start sites (TSS) were screened for methylation of cytosine using bisulfite sequencing PCR (Additional file 1, Table S1). Following bisulfite treatment and sequencing of the 14 genes, CGIs located at the promoter region of the "no-tail" (ntla) gene were found to be completely methylated while the remaining 13 genes, including glutathione S- transferase P1 (gstp1), were completely un-methylated (Additional file 2, Figures S1 and S2). The no-tail gene and the glutathione S-transferase P1 gene were thus used as fully methylated (positive control) and un-methylated (negative control) DNA regions for validating the immunoprecipitation enrichment method.
DNA was immunoprecipitated and bisulfite-treated. Equal amounts of immunoprecipitated, bisulfite-treated DNA were amplified for the two genes of interest. The gel images achieved indicated a clear enrichment of methylated ntla gene in comparison to un-methylated glutathione S-transferase P1 using 5-methyl cytosine antibody (the primers used and the regions amplified are shown in Additional file 3, Table S1 and the gel electrophoresis image is shown in Additional file 4, Figure S1).
Design of CGI (1.5 kb upstream to 1 kb downstream of TSS) zebrafish tiling array and comprehensive mapping of adult zebrafish liver
Methylation analysis of zebrafish hepatocellular carcinoma and comparison to gene expression
Identified genes with altered methylation in zebrafish HCC.
A. Hypomethylated Genes
Kruppel-like factor 12b
31117162 - 31117483
Insulin-like growth factor binding protein 5a
49194032 - 49194791
Insulin-like growth factor binding protein 2a
49290381 - 49290816
Insulin-like growth factor binding protein 1b
178184 - 178306
Insulin-like growth factor-binding protein 2A precursor
22745784 - 22746301
Estrogen related receptor delta fragment
48225445 - 48225631
Hypothetical protein LOC550398
51038733 - 51038845
Similar to SH2 domain containing 3C
14303115 - 14303263
28544220 - 28544532
4698036 - 4698168
23684991 - 23685203
Cell cycle, metastasis, adhesion, cell growth, stress
14274343 - 14274635
BCL2-associated athanogene 5
17324708 - 17324877
34165085 - 34165237
Angiopoietin-1 receptor precursor
625275 - 625436
Ras homolog gene family, member Ua
25136705 - 25136882
Menage a trois homolog 1
31141636 - 31142019
Serine/threonine and tyrosine protein kinase
461194 - 461772
DNA binding and regulation of transcription
DNA (cytosine-5-)-methyltransferase 6
34759411 - 34759539
Leucine zipper protein 2 precursor
35625841 - 35625986
724191 - 724473
Histone deacetylase 4
46375505 - 46375895
Homeobox protein Hox-B5a
20707021 - 20707754
Pancreas transcription factor 1 subunit alpha
27672474 - 27672641
Hypothetical protein LOC692291
12689887 - 12690054
Metastasis associated 1 family, member 2
17243535 - 17244290
Homeobox protein Hox-B4a
20721064 - 20721722
Lysine-specific demethylase 4A
4575451 - 4575994
RAB11 family interacting protein 4 (class II) a
13380260 - 13380369
B. Hypermethylated Genes
Hematopoietically-expressed homeobox protein hhex
43558447 - 43558684
Hypothetical protein LOC678612
27963202 - 27963305
Novel protocadherin protein fragment
55112983 - 55113251
ATP-binding cassette, sub-family A, member 5
38940033 - 38940251
Novel protein similar to nuclear factor, interleukin 3 regulated
21400978 - 21401243
Novel protein fragment
55112983 - 55113251
C5a anaphylatoxin chemotactic receptor
45692130 - 45692420
Angiogenesis and oxidative stress protection
Vascular endothelial zinc finger 1
35492470 - 35493037
Coronin, actin binding protein 2ba
33649014 - 33649398
IPA identified significant networks, top functions and canonical pathways associated with the differentially methylated genes for each comparison analyzed. Significantly over represented (FDR <5%) categories of functions for both hypo- and hyper-methylated genes in zebrafish HCC are shown in Additional file 7, tables S1 and S2.
Comparison of DNA methylation levels with gene expression levels.
Matrix metalloproteinase 14 (membrane-inserted) alpha
Tubulin, alpha 8 like 4
E2F transcription factor 6
Cell division protein kinase 8 (probe 54)
Cell division protein kinase 8 (probes 21, 31)
Hepatoma-derived growth factor-related protein 2
RAB2A, member RAS oncogene family
LIM domain containing preferred translocation partner in lipoma
Insulin-like growth factor binding protein 5a
Mitogen-activated protein kinase 1
Cancer susceptibility candidate gene 3 protein homolog
Proliferating cell nuclear antigen
Ret proto-oncogene (probes 16, 19)
Ret proto-oncogene (probes 20, 24)
Integrin-linked kinase (probes 8, 1)
Integrin-linked kinase (probes 6)
Novel protein similar to vertebrate threonyl-tRNA synthetase
Ribonuclease inhibitor 1
Calpain 2, (m/II) large subunit, like
Confirmation of the CGI tiling array data using bisulfite sequencing PCR
Based on the data obtained from the methylation tiling array, six genes from 3 different categories of hypomethylated, hypermethylated and genes showing no significant change between tumor and healthy tissue based on microarray analysis were selected for validation of the array data by bisulfite sequencing PCR (three tumor samples and three control samples). Bisulfite sequencing PCR provided a detailed analysis of the methylation status of individual CpGs within the amplified regions . The glutathione S-transferase P1 CGI, as shown in Additional file 2, Figure S1, is un-methylated in both tumor and control samples. Following treatment with CpG methyltransferase (SssI) in the presence of SAM, it became fully methylated (Additional file 2, Figure S3). This artificially methylated gene was used as a positive control to confirm our semi-quantitative measurements of DNA methylation.
To achieve a global non-biased enrichment of the methylated fragments of genomic DNA, methyl-DNA immunoprecipitation (MeDIP) combined with the novel zebrafish CpG island tiling array was used. This method enabled us to perform the first gene-specific large scale analysis and establishment of a comprehensive methylation map at CGIs of the regions 1.5 kb upstream to 1 kb downstream of the TSS in both healthy zebrafish liver and in hepatocellular carcinoma.
Regulation of transcription is a complex procedure. It is partly accomplished by formation of nucleosomes  and by modulating the binding of regulatory factors and the transcription complex to transcriptional response elements, both directly and indirectly . Epigenetic mechanisms such as chromatin modifications, RNA interference (RNAi) and DNA methylation are key modulators of transcription through different mechanisms, such as prohibiting transcription factor access to their binding sites and affecting the formation of nucleosomes [12, 35, 36].
The methylation profiles of cancer cells are extensively distorted . Hypomethylation in tumors is associated with transcriptional activation of previously suppressed genes and is a hallmark of tumorigenesis [1, 11], where active genes are un-methylated with hyperacetylated histones . As shown in Table 1A, GO terms associated with DNA binding and transcription regulation were significantly over-represented amongst genes hypomethylated in HCC samples. Genes including histone deacetylase 4, DNA (cytosine-5-)-methyltransferase 6 and lysine-specific demethylase 4A showed significant decreases in their methylation levels in HCC samples in comparison to healthy liver.
Comparison of the normal hepatic gene methylation pattern between human liver, presented by Archer et al , and zebrafish liver in this study showed a low correlation (r2 = 0.187) implying lack of conservation of methylation patterns between human and zebrafish. However, our comparisons showed that, in zebrafish and human HCC tumors, similar gene families and genes involved in shared pathways are altered in terms of methylation. For example, zebrafish HCC samples showed methylation changes in genes involved in proliferation, cell cycle, metastasis, apoptosis, energy production, adhesion, stress, DNA binding and regulation of transcription (Table 1A, B and Additional file 6, Tables S2 and S3), similar to the biological processes that contain genes with altered methylation in human HCC . Several other genes from families such as ABCA, CHST, DHX, KCTD, MEGF, MYO, NPY, RNF and TBCID were found to be hypermethylated in both zebrafish and human HCC . Ingenuity Pathway Analysis indicated that the genes with altered methylation in zebrafish hepatocellular carcinoma were associated with biological functions such as cell death, cell morphology, inflammatory response, DNA repair and replication and induced molecules involved in cancer formation such as c-jun, shc and pka. These functions and molecules are commonly altered in human cancers. We have associated changes in methylation of these particular genes with changes in gene expression during tumorigenesis. The changes in methylation levels of these particular genes and pathways could be directly or indirectly linked to their altered expression levels during tumorigenesis.
In our study, GO terms associated with cell proliferation were significantly over-represented in the list of hypomethylated genes in tumors which is particularly relevant since an imbalance between regulation of cellular proliferation pathways and cell death by apoptosis can promote the development of tumors . Anti-apoptotic genes, such as BCL-2, and their regulators, are often over expressed in human tumors [40, 41]. Our results showed a significant decrease in the methylation of a positive regulator of the Bcl-2 protein, bcl-2 associated athanogene 5 (baga5) gene . Changes in anti-apoptotic pathways in zebrafish HCC were concurrent with changes in pathways of proliferation. Insulin like growth factors (IGF) and insulin like growth factor binding proteins (IGFBPs) play important roles in organizing cell proliferation, apoptosis and differentiation and are commonly deregulated in human tumors . In zebrafish HCC, genes for several insulin growth factor binding proteins (IGFBPs) such as igfbp2b were significantly hypomethylated. The promoter region of the human IGFBP-2 gene is rich in CpGs and lacks a TATA box . It is therefore plausible that methylation plays an important role in regulating the expression of this gene. Multiple complex IGF-dependent and independent biological functions influenced by the tissue type and pathological status have been identified for IGFBPs [42, 43]. An increased level of IGFBP-2 protein has been reported in liver tissues and serum during human malignancy [42, 43] with a positive correlation to the malignancy status of the tumor . In contrast to its normal role as a negative regulator of growth, increased levels of IGFBPs in tumors have been linked to enhanced proliferation, partly as a response to androgens and hypoxia-inducible factor-1 (HIF-1) protein. Thus, anaerobic conditions as well as IGF, can result in increased amount of HIF-1 . A lack of vascular supply at the early stages of tumorigenesis in highly proliferating tumor cells results in hypoxia . Under hypoxic circumstances glycolysis becomes the dominant pathway for energy production in tumors and glycolytic enzymes are induced. Associated with this, the HIF-1 protein, expressed in an anaerobic environment, initiates the transcription of several genes involved in stress and glycolysis as well as IGFBP2[43, 44]. This is in accord with our finding that GO terms related to glycolysis and hypoxia pathways were more prevalent in the list of hypomethylated genes in HCC samples. Glycolytic enzymes such as enolase 2 (ENO2) and hexokinase 2 showed significant decreases in methylation of their genes in HCC samples, indicating a potential increase in expression. Increased expression of enolases such as ENO1 and 2, as a response to hypoxia and HIF-1, has been reported in human HCC . As well as its function in energy production, ENO1 has been associated with enhanced proliferation in HCC . Therefore, based on the functions of genes whose methylation was significantly altered in HCC samples it appears that there is a link between induction of IGF, IGFBPs, HIF, anti-apoptotic and glycolytic pathways [43, 44, 46]. This is similar to findings previously reported on gene expression in human HCC implying that differential methylation is at least partially causative of differential gene expression in HCC.
In this paper we described the development of a zebrafish CGI tiling array and showed that in combination with the MeDIP technique, it can be used to detect and profile the methylation status of specific genes. We used this method to provide a comprehensive profiling of zebrafish liver methylation as well as establishing the methylation changes observed in HCC.
The methylation alterations detected can help to explain some of the changes in gene expression in HCC. There are striking similarities between the pathways disrupted by aberrant gene expression and methylation, both within and between human and zebrafish HCC. Achieving a better understanding of genetic and epigenetic regulation in the zebrafish will increase the confidence regarding the use of zebrafish as a convenient model for human disease.
All chemicals were obtained from Sigma-Aldrich, Poole, Dorset, UK, unless otherwise stated.
Measurement of genome wide DNA methylation by HPLC
An AKTA Explorer 10 with P900 pump, automatic UV detector (Amersham Biosciences), APEX ODS C18 column, 250 × 4.6 mm i.d., 5 μm particle size (Waters HPLC Ltd, UK, Phenomenex, UK); and grade column (Phenomenex, UK) were used. Following purification of DNA using Qiagen DNA/RNA purification kit (Qiagen, Ltd, West Sussex, UK), percentage of global DNA methylation was quantified according to the method of Ramsahoye .
Experimental procedure and sample preparation
Methyl-DNA immunoprecipitation in combination with a CpG island tiling array was used for profiling methylation patterns in un-treated zebrafish liver and hepatocellular carcinoma (HCC) samples. The chemical treatment procedure and doses used for generating zebrafish tumors are described by Zhan et al . The exposures were carried out at the National University of Singapore and the experimental procedure was approved by its Institutional Animal Care and Use Committee. Briefly, three-week-old zebrafish fry were treated with 0.75 ppm 7, 12-dimethylbenz[α]anthracene (DMBA) or dimethyl sulfoxide (DMSO, vehicle control) for 24 h and the treatment was repeated once 2 weeks later for another 24 hours with 1.25 ppm DMBA or DMSO. Treated fish were rinsed three times in fresh water and transferred into new tanks for maintenance. Fish were sampled 6-10 months after DMBA exposure. The tumor samples used for the present study were all larger than 3 mm in diameter. Liver tumors were sampled for histopathological diagnosis (histopathology images of HCC and healthy zebrafish liver are presented in Additional file 10, Figure S1 and histopathology procedures and criteria used for recognizing HCC are explained in detail in a previous publication 23). Briefly, criteria for classification of liver neoplasms were based on nuclear factors described by Goodman et al . In this study, healthy male livers (n = 4) from the vehicle control exposure (n = 4), hepatocellular carcinoma from the DMBA exposure (n = 4, male) and positive and negative controls artificially generated from the vehicle control exposure (detailed below) were used for MeDIP and immunoprecipitated DNA was labeled with fluorescent Cy5-dCTP. A non-immunoprecipitated genomic DNA pool was used as a reference and was labeled with fluorescent Cy3-dCTP.
Design of the 4 × 44 k format CpG island (1.5 kb downstream to 1 kb upstream of TSS) zebrafish tiling array
Probes were designed by Genotypic (Genotypic Technology, Bangalore, India) and were synthesized and printed by Agilent (Agilent technologies, Berkshire, UK). The criteria described by Gardiner-Garden and Frommer  for prediction of CpG islands in vertebrates were used to predict CpG islands in zebrafish, with minor modifications. The zebrafish genome sequence was derived from the Ensembl data base (version 56, genome build zv8, genome build date April 2009). For each gene, the region 1.5 kb upstream to 1 kb downstream of predicted transcription start sites (TSS) was found, using the 5' end of each transcript as the putative TSS. Repetitive sequences were masked and the Emboss CpG prediction tool (European Molecular Biology Laboratory-EBI) identified 9,192 genes containing 14,507 CpG islands. From the preliminary list of genes (9,192), 60-mer probes were designed with an average spacing of 25 bp where possible. Due to the requirements for probe specificity, probes could not be constructed for all predicted CGIs. Probes with multiple BLAST hits on the zebrafish genome and with identical 55 bp alignments were removed. Finally, 43,960 probes were designed for 7,903 CGIs in 6,024 genes. Slides were printed containing 45,220 features; 1,227 Agilent control features, 33 blank features and 43,960 probes. The array design was named Agilent Birmingham D.rerio 025794 45220v1 and is available from ArrayExpress under accession A-MEXP-1813. Lists of genes with predicted CGIs and the detailed criteria used for prediction of CGIs are presented in Additional file 5, Table S1. Probes were functionally annotated from the zebrafish genome and additional gene ontology (GO) terms were found using Blast2GO .
The "Core Analysis" function included in Ingenuity Pathway Analysis (IPA) (Ingenuity Systems Inc, USA)  was used to aid interpretation of the hypo- and hyper-methylated (fold change > 1.5) genes found by comparison of zebrafish HCC with healthy liver. A Benjamini and Hochberg multiple testing correction was employed to determine significant enrichment of annotation with biological functions and canonical pathways among these gene sets.
Positive and Negative Controls
CpG methyltransferase (New England Biolabs, U.S.A) was used to generate artificially methylated DNA from zebrafish liver genomic DNA using S-adenosyl methionine according to the manufacturer's protocol. Briefly, 10×NEBuffer 2 (2 μl), S-adenosyl methionine (160 μM), genomic DNA (1 μg) and Sss I methylase (40 U/μl; New England Biolabs, U.S.A) were mixed in a total volume of 20 μl. The sample was incubated at 37°C for 4 h followed by incubation at 65°C for 20 min for inactivation of the enzyme.
A negative control for the array was generated by PCR amplification of sonicated zebrafish liver genomic DNA. DNA samples were amplified using GenomePlex Complete Whole Genome Amplification (WGA) Kit (Sigma-Aldrich, Poole, Dorset, U.K). PCR amplification removes DNA methylation, resulting in an artificially un-methylated DNA. Positive and negative control samples were purified (Qiagen) prior to use in methyl-DNA immunoprecipitation.
Methyl-DNA immunoprecipitation (MeDIP)
DNA from tumor, control, SAM-treated DNA (positive control) and amplified DNA (negative control) were dissolved in TE buffer (6 μg in 300 μl) and fragmented to 200 bp-1 Kb using a sonicator (SONICS Vibra Cell, 100 watt, 3 × 10 s with 35 s intervals on ice with 20% amplitude). Sizes of the generated fragments were checked on a 1% agarose gel.
Methylated fragments of DNA were separated from the un-methylated fragments using MagMeDIP kit (Diagenode, Belgium) according to the manufacturer's instructions. DNA sample (1 μg) was used in each immunoprecipitation. To avoid amplification bias, five aliquots of each sample were immunoprecipitated and combined. Samples were re-purified by phenol/chloroform/isoamyl alcohol extraction and precipitated with ethanol and glycogen. DNA pellets were air dried and stored at -80°C until subsequent use for labeling.
Sample labeling and hybridization to CGI (1.5 kb downstream to 1 kb upstream of TSS) zebrafish tiling array
The Agilent genomic DNA enzymatic labeling kit (Agilent technologies, Berkshire, UK) and the protocol provided were used for labeling the immunoprecipitated DNA with the fluorophore Cy-5 and zebrafish genomic DNA with the fluorophore Cy-3. The specific activities of the labeled samples were checked using a NanoDrop spectrophotometer. Cy3-labeled genomic DNA samples were pooled. For each hybridization, equal amounts (80 pmol incorporated) of Cy-3 labeled genomic DNA and Cy-5 labeled immunoprecipitated DNA were mixed. Microarray hybridization was performed using an Agilent Oligo aCGH/Chip on chip hybridization kit according to the manufacturer's protocol. Briefly, the hybridization mixes were loaded onto 4 × 44 K format slides (Agilent Birmingham D.rerio 025794 45220v1) hybridized overnight, washed, stabilized and dried. The dried slides were scanned using an Axon 4000B laser scanner (Molecular Devices, Wokingham, UK).
MIAME-compliant raw microarray data were submitted to ArrayExpress at EMBL-EBI and can be found under accession E-MTAB-209. GeneSpring v7.2 (Agilent) was used for analyzing the data. Data flagged as present in at least 4 of the 10 samples were used for analyses. The Cy-5 test signal was divided by the control signal (genomic pool) and each microarray was normalized to the mean of the control group. Data with low raw intensity (less than 25) or standard deviation greater than 1.4 between biological replicates were removed from the analyses. Lists of hypo- and hyper- methylated genes were generated using 1.5 fold change cut-offs and parametric Welch t-tests between tumor and healthy groups (p-value <0.05) on the normalized data.
DNA methylation analysis using bisulfite sequencing PCR
The MethPrimer data base (MethPrimer - Li Lab, UCSF) was used to design BSP primers (primer sequences and annealing temperatures used are listed in Additional file 1, Table S1). An artificially methylated positive control was used for assessing complete conversion of un-methylated cytosine to uracil after treatment with bisulfite and methylation of all CpG dinucleotides. The EZ DNA Methylation kit (Cambridge Biosciences, UK) was used for bisulfite conversion according to manufacturer's protocol. Briefly, for each sample, 500 ng of genomic DNA was bisulfite treated then amplified using Zymo Taq DNA polymerase (Cambridge Biosciences, UK). The PCR products were analyzed via DNA gel electrophoresis followed by sequencing using an ABI3730 DNA analyzer.
This work was funded by Natural Environment Research Council and Biochemical Research Council of Singapore.
- Counts JL, Goodman JI: Hypomethylation of DNA: a nongenotoxic mechanism involved in tumor promotion. Toxicology Letters. 1995, 82/83: 663-672. 10.1016/0378-4274(95)03512-5.View ArticleGoogle Scholar
- Tischoff I, Tannapfel A: DNA methylation in hepatocellular carcinoma. World Journal of Gastroenterology. 2008, 14 (11): 1741-1748. 10.3748/wjg.14.1741.PubMed CentralPubMedView ArticleGoogle Scholar
- Gronbaek K, Hother C, Jones PA: Epigenetic changes in cancer. APMIS. 2007, 115: 1039-1059. 10.1111/j.1600-0463.2007.apm_636.xml.x.PubMedView ArticleGoogle Scholar
- Rauch T, Wang Z, Zhang X, Zhong X, Wu X, Lau SK, Kernstine KH, Riggs AD, Pfeifer GP: Homeobox gene methylation in lung cancer studied by genome-wide analysis with microarray-based methylation CpG island recovery assay. PNAS. 2007, 104 (13): 5527-5532. 10.1073/pnas.0701059104.PubMed CentralPubMedView ArticleGoogle Scholar
- Jones PA, Takai D: The role of DNA methylation in mammalian epigenetics. Science. 2001, 293: 1068-1070. 10.1126/science.1063852.PubMedView ArticleGoogle Scholar
- Pomraning KR, Smith KM, Freitag M: Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods. 2009, 47: 142-150. 10.1016/j.ymeth.2008.09.022.PubMedView ArticleGoogle Scholar
- Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D: Chromosome-wide and promoter-specific analyses identify sites of different DNA methylation in normal and transformed human cells. Nature Genetics. 2005, 37 (8): 853-862. 10.1038/ng1598.PubMedView ArticleGoogle Scholar
- Takai D, Jones PA: Comprehensive analysis of CpG islands in human chromosome 21 and 22. PNAS. 2002, 99 (6): 3740-3745. 10.1073/pnas.052410099.PubMed CentralPubMedView ArticleGoogle Scholar
- Egger G, Liang G, Apqricio A, Jones PA: Epigenetics in human disease and prospects for epigenetic therapy. Nature. 2004, 429: 457-460. 10.1038/nature02625.PubMedView ArticleGoogle Scholar
- Rauch T, Li H, Wu X, Pfeifer GP: MIRA-assisted microarray analysis, a new technology for the determination of DNA methylation patterns, identifies frequent methylation of homeodomain-containing genes in lung cancer cells. Cancer Research. 2006, 66 (16): 7939-7947. 10.1158/0008-5472.CAN-06-1888.PubMedView ArticleGoogle Scholar
- Lopez J, Percharde M, Coley HM, Webb A, Crook T: The context and potential of epigenetics in oncology. British Journal of Cancer. 2009, 100: 571-577. 10.1038/sj.bjc.6604930.PubMed CentralPubMedView ArticleGoogle Scholar
- Momparler RL, Bovenzi V: DNA methylation and Cancer. Journal of Cellular Physiology. 2000, 183: 145-154. 10.1002/(SICI)1097-4652(200005)183:2<145::AID-JCP1>3.0.CO;2-V.PubMedView ArticleGoogle Scholar
- Baylin SB, Herman JG: DNA hypermethylation in tumourigenesis. Epigenetics joins genetics. Trends in Genetics. 2000, 16 (4): 168-174. 10.1016/S0168-9525(99)01971-X.PubMedView ArticleGoogle Scholar
- Watson RE, Goodman JI: Epigenetics and DNA methylation come of age in toxicology. Toxicological sciences. 2002, 67: 11-16.PubMedView ArticleGoogle Scholar
- Bailey GS, Williams DE, Hendricks JD: Fish models for environmental carcinogenesis: The rainbow trout. Environmental Health Perspectives. 1996, 104 (1): 5-21. 10.2307/3432693.PubMed CentralPubMedView ArticleGoogle Scholar
- Khudoley W: Use of aquarium fish, Danio rerio and Poecilia reticulata, as test species for evaluation of nitrosamine carcinogenicity. National Cancer Institute Monograph. 1984, 65: 65-70.PubMedGoogle Scholar
- Hawkins WE, Walker W, Overstreet RM, Lytle TF, Lytle JS: Dose-related carcinogenic effects of water-borne benzo[a]pyrene on livers of two small fish species. Ecotoxical Enviro Safety. 1988, 16: 219-231. 10.1016/0147-6513(88)90052-8.View ArticleGoogle Scholar
- Schwab M, Scholl E: Neoplastic pigment cells induced by N-methyl-N-nitrosourea (MNU) in Xiphophorus and epigenetic control of their terminal differentiation. Differentiation. 1981, 19: 77-83. 10.1111/j.1432-0436.1981.tb01132.x.View ArticleGoogle Scholar
- Wardle FC, Odom DT, Bell GW, Yuan B, Danford TW, Wiellete EL, Herbolsheimer E, Sive HL, Young RA, Smith JC: Zebrafish promoter microarray identify actively transcribed embryonic genes. Genome Biology. 2006, 7 (8): R71-10.1186/gb-2006-7-8-r71.PubMed CentralPubMedView ArticleGoogle Scholar
- Berghmans S, Jette C, Langenau D, Hsu K, Stewart R, Look T, Kanki JP: Making waves in cancer research: new models in the zebrafish. BioTechniques. 2005, 39: 227-237. 10.2144/05392RV02.PubMedView ArticleGoogle Scholar
- Zon LL: Zebrafish: A new model for human disease. Genome Research. 2010, 9: 99-100.Google Scholar
- Lam SH, Gong Z: Modeling liver cancer using zebrafish. Cell Cycle. 2006, 5 (6): 573-577. 10.4161/cc.5.6.2550.PubMedView ArticleGoogle Scholar
- Lam S, Wu Y, Vega VB, Miller LD, Spitsbergen J, Tong Y, Zhan H, Govindarajan KR, Lee S, Mathavan S, Krishna Murthy KR, Buhler DR, Liu ET, Gong Z: Conservation of gene expression signature between zebrafish and human liver tumors and tumor progression. Nature Biotechnology. 2006, 24 (1): 73-75. 10.1038/nbt1169.PubMedView ArticleGoogle Scholar
- Amatruda JF, Shepard JL, Stern HM, Zon LI: Zebrafish as a cancer model system. Cancer cell. 2002, 1: 229-231. 10.1016/S1535-6108(02)00052-1.PubMedView ArticleGoogle Scholar
- MacLeod D, Clark V, Bird A: Absence of genome wide change in DNA methylation during development of the zebrafish (Danio rerio). Nat Genet. 1999, 23: 139-140. 10.1038/13767.PubMedView ArticleGoogle Scholar
- Mhanni AA, McGowan RA: Global changes in genomic methylation levels during early development of the zebrafish embryo. Dev Genes Evol. 2004, 214: 412-417. 10.1007/s00427-004-0418-0.PubMedView ArticleGoogle Scholar
- Jabbari K, Bernardi G: Cytosine methylation and CpG, TpG (CpA) and TpA frequencies. Gene. 2004, 333: 143-149. 10.1016/j.gene.2004.02.043.PubMedView ArticleGoogle Scholar
- Varriale A, Bernardi G: DNA methylation and body temperature in fishes. Gene. 2006, 385: 111-121. 10.1016/j.gene.2006.05.031.PubMedView ArticleGoogle Scholar
- Fenga S, Cokusb SJ, Zhangd X, Chenb P, Bostickb M, Golle MG, Hetzelb J, Jaine J, Straussf SH, Halperne ME, Ukomadug C, Sadlerh KC, Pradhani S, Pellegrinib M, Jacobsena SE: Conservation and divergence of methylation patterning in plants and animals. PNAS. 2010, 107: 8689-8694. 10.1073/pnas.1002720107.View ArticleGoogle Scholar
- Suzuki MM, Bird A: DNA methylation landscapes: provocative insights from epigenomics. Nature. 2008, 9: 465-475.Google Scholar
- Lewin J, Schmitt AO, Adorjan P, Hildmann T, Piepenbrock C: Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates. Bioinformatics. 2004, 20: 1-8. 10.1093/bioinformatics/bth346.View ArticleGoogle Scholar
- Mill J, Yazdanpanah S, Guckel E, Ziegler S, Kaminsky Z, Petronis A: Whole genome amplification of sodium bisulfite-treated DNA allows the accurate estimate of methylated cytosine density in limited DNA resources. BioTechniques. 2006, 41: 603-607. 10.2144/000112266.PubMedView ArticleGoogle Scholar
- Paul CL, Clark SJ: Cytosine methylation quantification by automated genomic sequencing and GENESCAN analysis. Journal of BioTechniques. 1996, 21 (1): 126-133.Google Scholar
- Rein T, DePamphilis ML, Zorbas H: Identifying 5-methylcytosine and related modifications in DNA genomes. Nucleic Acids Research. 1998, 26: 2255-2264. 10.1093/nar/26.10.2255.PubMed CentralPubMedView ArticleGoogle Scholar
- Zilberman D, Gehring M, Tran RK, Ballinger T, Henikoff S: Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nature Genetics. 2007, 39 (1): 61-69. 10.1038/ng1929.PubMedView ArticleGoogle Scholar
- Cui P, Zhang L, Lin Q, Ding F, Xin C, Fang X, Hu S, Yu J: A novel mechanism of epigenetic regulation: Nucleosome-space occupancy. Biochemical and Biophysical Research Communications. 2010, 391: 884-889. 10.1016/j.bbrc.2009.11.157.PubMedView ArticleGoogle Scholar
- Archer KJ, Mas VR, Maluf DG, Fisher RA: High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma. Mol Genet Genomics. 2010, 283: 341-34938. 10.1007/s00438-010-0522-y.PubMed CentralPubMedView ArticleGoogle Scholar
- Gao W, Kondo Y, Shen L, Shimizu Y, Sano T, Yamao K, Natsume A, Goto Y, Ito M, Murakami H, Osada H, Zhang J, Issa JJ, Sekido Y: Variable DNA methylation patterns associated with progression of disease in hepatocellular carcinomas. Carcinogenesis. 2008, 29 (10): 1901-1910. 10.1093/carcin/bgn170.PubMedView ArticleGoogle Scholar
- Sakinah SS, Handayani ST, Hawariah LA: Zerumbone induced apoptosis in liver cancer cells via modulation of Bax/Bcl-2 ratio. Cancer Cell International. 2007, 7 (4):
- Yang L, McBurney D, Tang SC, Carlson SG, Horton WE: A novel role for bcl-2 associated-athanogene-1 (BAG-1) in regulation of the endoplasmic reticulum stress response in mammalian chondrocytes. Journal of Cellular Biochemistry. 2007, 102: 786-800. 10.1002/jcb.21328.PubMedView ArticleGoogle Scholar
- Yang J, Liu X, Bhalla K, Kim CN, Ibrado AM, Cai J, Peng TI, Jones DP, Wang X: Prevention of apoptosis by Bcl-2: release of cytochrome c from mitochondria blocked. Science. 1997, 275: 1129-1132. 10.1126/science.275.5303.1129.PubMedView ArticleGoogle Scholar
- Inman BA, Harel F, Audet JF, Meyer F, Douville P, Fradet Y, Lacombe L: Insulin-like growth factor binding protein 2: An androgen-dependent predictor of prostate cancer survival. European Urology. 2005, 47: 695-702. 10.1016/j.eururo.2004.12.015.PubMedView ArticleGoogle Scholar
- Hoeflich A, Reisinger R, Lahm H, Kiess W, Blum WF, Kolb HJ, Weber MM, Wolf E: Insulin-like growth factor-binding protein 2 in tumourigenesis: protector or promoter?. Cancer Research. 2001, 61: 8601-8610.PubMedGoogle Scholar
- Kelly C, Smallbone K, Brady M: Tumor glycolysis: The many faces of HIF. Journal of Theoretical Biology. 2008, 254: 508-513. 10.1016/j.jtbi.2008.05.025.PubMedView ArticleGoogle Scholar
- Hamaguchi T, Norio I, Ryouichi T, Yoshihiko H, Takanobu M, Michihisa I, Yoshihiro T, Kazuhiko S, Motonari T, Takao T, Masaaki O: Glycolysis module activated by hypoxia-inducible factor 1α is related to the aggressive phenotype of hepatocellular carcinoma. International Journal of Oncology. 2008, 33: 725-731.PubMedGoogle Scholar
- Feldser D, Agani F, Iyer NV, Pak B, Ferreira G, Semenza GL: Reciprocal positive regulation of hypoxia-inducible factor 1α and insulin-like growth factor 2. Cancer Research. 1999, 59: 3915-3918.PubMedGoogle Scholar
- Ramsahoye BH: Measurement of genome wide DNA methylation by reversed-phase high-performance liquid chromatography. Methods. 2002, 27: 156-161. 10.1016/S1046-2023(02)00069-5.PubMedView ArticleGoogle Scholar
- Zhan H, Spitsbergen JM, Qing W, Wu YL, Paul TA, Casey JW, Her GM, Gong Z: Transgenic expression of walleye dermal sarcoma virus rv-cyclin gene in zebrafish and its suppressive effect on liver tumor development after carcinogen treatment. Marine Biotechnology. 2010, 12: 640-649. 10.1007/s10126-009-9251-9.PubMed CentralPubMedView ArticleGoogle Scholar
- Counts JL, Goodman JI: Principles underlying dose selection for, and extrapolation from, the carcinogen bioassay: Dose influences mechanism. Regulatory Toxicology and pharmacology. 1995, 21: 418-421. 10.1006/rtph.1995.1056.PubMedView ArticleGoogle Scholar
- Gardiner-Garden M, Frommer M: CpG islands in vertebrate genomes. Journal of Molecular Biology. 1987, 196 (2): 261-282. 10.1016/0022-2836(87)90689-9.PubMedView ArticleGoogle Scholar
- Gotz S, Garcia-Gomez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talon M, Dopazo J, Conesa A: High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Research. 2008, 36 (10): 3420-3435. 10.1093/nar/gkn176.PubMed CentralPubMedView ArticleGoogle Scholar
- Ingenuity Pathway Analysis software web link. [http://www.ingenuity.com/]
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.