Global analysis of DNA methylation in early-stage liver fibrosis
- Yoko Komatsu†1,
- Tsuyoshi Waku†2,
- Naoya Iwasaki†1,
- Wakana Ono1,
- Chie Yamaguchi1 and
- Junn Yanagisawa1, 2Email author
© Komatsu et al; licensee BioMed Central Ltd. 2012
Received: 11 October 2011
Accepted: 27 January 2012
Published: 27 January 2012
Liver fibrosis is caused by chemicals or viral infection. The progression of liver fibrosis results in hepatocellular carcinogenesis in later stages. Recent studies have revealed the importance of DNA hypermethylation in the progression of liver fibrosis to hepatocellular carcinoma (HCC). However, the importance of DNA methylation in the early-stage liver fibrosis remains unclear.
To address this issue, we used a pathological mouse model of early-stage liver fibrosis that was induced by treatment with carbon tetrachloride (CCl4) for 2 weeks and performed a genome-wide analysis of DNA methylation status. This global analysis of DNA methylation was performed using a combination of methyl-binding protein (MBP)-based high throughput sequencing (MBP-seq) and bioinformatic tools, IPA and Oncomine. To confirm functional aspect of MBP-seq data, we complementary used biochemical methods, such as bisulfite modification and in-vitro-methylation assays.
The genome-wide analysis revealed that DNA methylation status was reduced throughout the genome because of CCl4 treatment in the early-stage liver fibrosis. Bioinformatic and biochemical analyses revealed that a gene associated with fibrosis, secreted phosphoprotein 1 (Spp1), which induces inflammation, was hypomethylated and its expression was up-regulated. These results suggest that DNA hypomethylation of the genes responsible for fibrosis may precede the onset of liver fibrosis. Moreover, Spp1 is also known to enhance tumor development. Using the web-based database, we revealed that Spp1 expression is increased in HCC.
Our study suggests that hypomethylation is crucial for the onset of and in the progression of liver fibrosis to HCC. The elucidation of this change in methylation status from the onset of fibrosis and subsequent progression to HCC may lead to a new clinical diagnosis.
Fibrosis is one of the most severe systemic diseases and is characterized by excessive accumulation of fibrous connective tissues, such as collagen, induced by acute or chronic injury . Fibroproliferative diseases occur throughout the body, including in the lungs, kidneys, and liver. The progression of fibrosis leads to the failure of the physiological functions of tissues. Liver fibrosis, in particular, has been extensively investigated because its progression results in hepatocellular carcinoma (HCC), which is the fifth most common cancer worldwide [2, 3].
Currently, liver fibrosis is known to be a part of the dynamic process of continuous extracellular matrix (ECM) remodeling in chronic liver injury . In liver fibrosis, a liver injury activates the Kupffer cells--resident macrophages of the liver sinusoids--thereby inducing inflammation . This inflammatory response triggers the activation of hepatic stellate cells (HSCs), which play a key role in fibrogenesis by transdifferentiating into myofibroblasts . The proliferation of myofibroblasts and stimulation of ECM synthesis, ultimately results in liver fibrosis.
Recently, the progression of liver fibrosis has been reported to be associated with hypermethylation of DNA . HSC activation is inhibited by 5'-Azacytidine (5'-Aza), a DNA methylation inhibitor, resulting in the transdifferentiation of HSCs to myofibroblasts . Furthermore, in other tissues such as renal, several studies using heterozygous mice revealed that the DNA methyltransferase Dnmt1 and its inhibitor 5'-aza ameliorate renal fibrosis by inhibiting proliferation of myofibroblasts [8, 9]. These results indicate the pivotal role of DNA hypermethylation in the progression of both liver and renal fibrosis. However, the importance of DNA methylation in early-stage liver fibrosis remains unclear.
Here, we performed a global analysis of DNA methylation during the onset of liver fibrosis. A mouse model treated with carbon tetrachloride (CCl4) was used as a model of liver fibrosis. Analysis of the CCl4-treated mouse livers revealed symptoms of early-stage liver fibrosis. To analyze the genome-wide DNA methylation profile of this CCl4-induced early-stage liver fibrosis, we used a combination of methyl-binding protein (MBP)-based precipitation (MBP-IP) and high-throughput DNA sequencing (MBP-seq). This genome-wide analysis can reveal hypo- and hypermethylated sites (125 and 88, respectively) in the genomic DNA. Analyzing the MBP-seq data, we revealed that the DNA methylation status was reduced throughout the genome, and that the enhancer of secreted phosphoprotein 1 (Spp1), also known as osteopontin, was hypomethylated. Two bioinformatics tools, IPA and Oncomine, indicated that Spp1 is related to liver fibrosis and inflammation. Using biochemical methods, such as bisulfite modification and in-vitro-methylation assays, we confirmed that the hypomethylation of Spp1 enhancer up-regulates its mRNA. These results clearly indicate that hypomethylation of the genome may precede the onset of liver fibrosis. Moreover, Spp1 enhances tumor development. This suggests that hypomethylation during the early-stage liver fibrosis may be important in the development of the primary liver cancer HCC, which is an end-stage liver disease.
Five-week-old male C57BL/6 wild-type mice were purchased from CLEA Japan Inc. To induce liver fibrosis, 2 ml/kg CCl4 mixed with olive oil was intraperitoneally administered 3 times per week for 2 weeks. All animal husbandry and animal experiments were performed in accordance with the guidelines of the University of Tsukuba's Regulation of Animal Experiments Committee. Enzymatic activities of the serum proteins, alanine aminotranferease (Alt) and aspartate aminotransferase (Ast), were measured using the Fuji dri-chem 3000 analyzer (Fuji Film).
Mouse liver cell line, Hepa1-6 cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum (FBS). Buffalo Rat liver cell line, BRL-3 A were maintained in Ham's F12 medium with 10% FBS. 3uM of the 5-Aza deoxy derivative, 5-dAza-C, were treated with BRL-3 A for 36hr, and then harvested for indicated experiments.
After 2 weeks with or without CCl4 treatment, liver tissues were fixed with formalin. Formalin-paraffin livers were cut into 5 μm thick. Hematoxylin and Eosin (H&E) staining, Masson's trichrome (MT) staining and Sirius red staining were performed according to the manufacturer's protocol.
Methyl-binding protein (MBP)-based high throughput sequencing (MBP-seq)
The genomic DNA from each of 4 mouse livers treated with 2 ml/kg CCl4 for 2 weeks were pooled. The genomic DNA from 3 mouse livers treated with olive oil for 2 weeks was pooled and used as control. The genomic DNA purified from the CCl4-treated mouse liver specimens was randomly fragmented into 50-350 bp lengths as described in the SOLiD 5500xl fragment library protocol. The fragmented DNA was then subjected to the MethylMiner methylated DNA enrichment kit according to the manufacturer's protocol. These methylated fragments were eluted with 1000 mM NaCl and used to construct standard fragment libraries using a combination of adaptor ligation and nick translation (SOLiD Fragment Library Construction Kit, Invitrogen). Each DNA library was selected on the basis of size (inserts were approximately 500 bp) by AgencourtAMPure XP (Beckman) before PCR amplification, bead attachment, and emulsion PCR. Libraries were sequenced on a SOLiD 5500xl Analyzer (Applied Biosystems). The resulting tag sequences and quality files were mapped onto the mouse genome (NCBI Build 37, UCSC mm9) using Lifescope version 2.0 (Life Technologies), and peaks were detected using the Genomics Workbench version 4.7.2 (CLC Bio). Parameters for peak mapping and detection are described for details below. Gene annotation of sequence peaks was performed through the BioMart website version 0.7 (http://www.biomart.org). The chromosomal distribution of the MBP-seq peaks was described using R version 1.4 (http://www.r-project.org). UCSC Genome Browser (http://genome.ucsc.edu)  was used to obtain data on the epigenetic markers and to display epigenetic features around the target genome locus.
In silico functional analysis and network prediction by ingenuity pathway analysis (IPA)
The bioinformatics tool IPA version 1.0 (http://www.ingenuity.com) was used for in silico analysis of the MBP-seq data in the context of known functions and pathways using the Ingenuity Pathways Knowledge Base as a reference set, filtering for molecules and relationships associated with physiological and pathological processes of the liver. For the in silico functional analysis, a right-tailed Fisher's exact test was used to calculate the p-value determining the probability that the hepatotoxic function assigned to that data set was owing to chance alone.
Oncomine data analysis
The web-based human cancer microarray database Oncomine (https://www.oncomine.com) was used to analyze the mRNA expression of target genes associated with HCC identified in three studies [11–13]. Details of standardized normalization techniques and statistical calculations can be found on the Oncomine website (https://www.oncomine.com) . In brief, Student's t-test was performed to generate a p-value indicative of the significance of an observation. The lower the p-value, the more confidence in the difference between the groups. The Student's t-test statistic provided for the Oncomine visualizations reflects the magnitude of the difference between groups. Fold change is the magnitude of difference between the primary class and the other control classes, shown on a linear scale. An over-expression fold change is designated with a positive number.
MBP-based precipitation and quantitative PCR (qPCR) of methylated DNA using MBD-IP
The purified genomic DNA from the mouse livers was randomly fragmented into 50-350 bp lengths as described in the SOLiD 5500xl fragment library protocol. The fragmented DNA was then subjected to the MethylMiner methylated DNA enrichment kit, according to the manufacturer's protocol (Invitrogen). qPCR was then performed to amplify and quantify fragments representative of the methylated genome using the Thermal Cycle Dice TP800 (TaKaRa) and SYBR Premix Ex Taq (TaKaRa). The primer sequences and genome locus used for MBP-IP are summarized in Additional file 1.
Quantitative PCR (qPCR)
qPCR was performed as described previously . Tissues were homogenized in 1 ml of Sepazol and total RNA was extracted according to the manufacturer's instructions (Nacalai Tesque). cDNA was synthesized from total RNA using RevatraAce reverse transcriptase (TOYOBO) and oligodT primer. qPCR was performed to amplify and quantify fragments representative of the indicated mRNA expression using a Thermal Cycle Dice TP800 (TaKaRa) and SYBR Premix Ex Taq (TaKaRa). Cyclophilin and Gapdh was used as the normalization control (Additional file 2). The primer sequences for qPCR are summarized in Additional file 1.
The DNA fragments encoding Spp1 enhancer region (chr5:104846058-104846328) was amplified by PCR. The Spp1 enhancer fragment was methylated by the DNA methylase Sss1 (2U enzyme/10ug DNA), and then cloned into a pGL3-basic reporter plasmid. Each plasmid harboring methylated or unmethylated fragment was directly transfected into Hepa1-6 cells by Lipofectamine 2000 (Invitrogen), according to the manufacturer's instruction. Twenty-four hours after transfection, luciferase assays were performed by Dual-Luciferase Reporter 1000 Assay System (Promega), according to the manufacturer's instruction. phRG-TK (Promega) was used as a reference plasmid to normalize transfection efficiency.
Bisulfite modification assay
The purified genomic DNA from the mouse livers were denatured in 0.3M NaOH for 20min at 37°C. Then 3.6N sodium bisulfite and 10mM hydroquinone solution were added. Samples were incubated in 1min at 95°C then 12hr at 50°C. Salts were removed using the Wizard DNA Clean-Up System (Promega) and desulfonated in 0.3M NaOH at room temperature for 5 min. Then, the Spp1 enhancer region was amplified by PCR and cloned into pGEM-T Easy Vector System (Promega). The inserts were sequenced to identify the methylated and unmethylated sites. The primer sequences used for amplification are summarized in Additional file 1.
Sequence data of CCl4-treated and control samples from this study has been deposited to the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under accession no. SRA048978 and SRA048984, respectively.
Mouse model of CCl4-induced early-stage liver fibrosis
Genome-wide DNA methylation profile of the CCl4-treated livers
To investigate the effect of CCl4 treatment on DNA methylation status in early-stage liver fibrosis, we attempted to profile the genome-wide DNA methylation of the CCl4-treated liver tissues. The genomic DNA was fragmented, and the methylated DNA fragment was precipitated by MBP. The methylated DNA fraction was then subjected to high-throughput sequencing using a SOLiD 5500xl. These sequence reads were then mapped onto the mouse genome (NCBI37/mm9) using the MethylMiner methylated DNA enrichment kit and Lifescope. We obtained 7,612,236 reads from the CCl4 sample, and 24,584,122 reads from the control . To find the significant peak, these data were further analyzed using the ChIP-seq tool in Genomics Workbench with the mouse genome annotated using the BioMart website. The peak-finding algorithm included the following four steps: 1) Calculate the null distribution of the background sequencing signal; 2) Scan the mappings to identify candidate peaks with a higher read count than expected from the null distribution; 3) Merge overlapping candidate peaks; 4) Refine the set of candidate peaks based on the count and the spatial distribution of forward and backward reads within the peaks. The estimate for the null distribution of coverage and the calculation of the false discovery rate (FDR) were based on the window size and maximum FDR (%) parameters. The window size specifies the width of the window that is used to count reads during estimation of the null distribution as well as during subsequent scanning for candidate peaks. Maximum FDR indicates the maximum proportion of false positive peaks that are acceptable among the called peaks. In this study, when only MBP samples were used, each negative binomial distribution was fitted to the counts from the low coverage regions. This distribution was used as the null distribution to obtain the number of windows with a particular count of reads expected in the absence of significant binding. By comparing the number of windows with the specific count we expected to observe under the null distribution and the number we actually observed in our data, we can calculate FDR for a given read count and window size as the "fraction of windows with a read count expected under the null distribution/fraction of windows with the observed read count". In this study, we set window size and FDR to 300 bp and 1%, respectively. We identified 125 and 88 peaks in the CCl4-treated and control samples, and summarized all peaks with CpG annotation and several statistic values, including FDR, normalized difference, and Wilcoxon filter p-value, in Additional file 3.
Chromosomal distribution and genomic features of the MBP-seq peaks
In silico functional analysis of genes annotated by MBD-seq
Using IPA, we then re-analyzed the annotated genes in the promoters, which is important for gene transcription. Intriguingly, liver fibrosis was detected as the predominant hepatotoxic function in the control sample (p-value = 1.06 × 10-3-6.37 × 10-3) (Figure 3B). Moreover, none of the liver diseases detected in control samples were detected in the CCl4-treated samples. IPA re-analysis revealed that this fibrotic function in the control sample was associated with one gene--Spp1.
In IPA, the p-value is calculated using a right-tailed Fisher's exact test to assess the probability that the association between the focal molecules in the experiment and a given function is owing to random chance. Smaller the p-value, lesser is the probability of random association. In general, p-values < 0.05 indicate a statistically significant, nonrandom association. Therefore, these results suggest that CCl4-induced hypomethylation of a regulatory region, such as the promoter or enhancer of Spp1, may result in the onset of liver fibrosis and its progression to post-fibrotic diseases such as cirrhosis and HCC through an increase in Spp1 expression.
Epigenetic features and functional validations of the hypomethylated region upstream of Spp1
Functional significance of Spp1 in HCC
The DNA methylation status of well-known genes associated with fibrosis progression, such as Rasal1, Fli1, and Thy1, has been reported to increase along with fibrosis progression, which induces proliferation of fibroblasts and the production of collagen [9, 30, 31]. Furthermore, 5'-Aza, an inhibitor of DNA methylation, reportedly attenuates the progression of renal and liver fibrosis in vivo and in vitro [7, 8]. These reports indicate the importance of hypermethylation of the genome in the progression of liver fibrosis.
In contrast to these previous reports, our analysis revealed that DNA methylation status was significantly reduced in CCl4-induced early-stage liver fibrosis. Detailed analyses revealed that in early-stage liver fibrosis, the Spp1 enhancer was hypomethylated and Spp1 expression was up-regulated. Previous findings from genetically engineered Spp1-knockout mice have shown that Spp1 plays an important role in both progression and reduction of liver injury and fibrosis [32, 33]. Although the precise mechanism underlying the role of Spp1 in fibrosis remains unknown, Spp1 is a pivotal cytokine/chemokine generated by the Kupffer cells in response to liver damage  that induces inflammation , which is a contributing factor in liver fibrosis. Our results indicate that an epigenetic alteration, DNA hypomethylation of the Spp1 enhancer, may precede the up-regulation of Spp1 expression and induce the onset of CCl4-induced early-stage liver fibrosis. Spp1 is also a known enhancer of tumor development and metastasis . Using the Oncomine microarray database, we demonstrated that Spp1 expression level is increased in HCC. It has been documented in primary gastric cancers, that major phenotypic change in cancer-associated myofibroblasts is a global reduction in DNA methylation . This may also be indicated in liver, the importance of hypomethylation of the Spp1 enhancer in the progression of HCC.
Unlike Spp1, we found that almost DNA hypomethylation induced in early fibrosis was assigned in intergenic regions (Figure 2A). Recently, it has been reported that DNA hypomethylation causes genomic instability and alteration of gene transcription in several human cancers (i.e. colorectal and prostatic adenocarcinoma, breast cancer, intestinal type-gastric carcinoma, and HCC) [37, 38]. These results indicate that DNA hypomethylation in intergenic regions may trigger the progression of cancer through genomic instability of cancer-related genes in addition to transcriptional regulation of those genes in the onset of liver fibrosis. On the other hand, 5'-Aza treatment successfully reduces cancer in mammals, involving human, suggesting that carcinogenesis results from DNA hypermethylation as well as DNA hypomethylation [38–40]. Therefore, how these distinct DNA modifications commonly progress cancer is most important study for epigenetic effects on diseases in the future.
Although hypermethylation occurs during the progression of liver fibrosis, our results indicate the importance of hypomethylation in the onset of liver fibrosis. According to our results as well as other reports, it appears that DNA methylation status may change from hypo- to hypermethylation during the progression of liver fibrosis. Thus, hypomethylation in early-stage liver fibrosis may contribute to the onset and/or development of HCC.
α-Smooth muscle actin
- CCl4 :
Collagen, type I, alpha 2
Friend leukemia integration 1
RAS protein activator like 1
Secreted phosphoprotein 1
Thymocyte differentiation antigen 1
Tissue inhibitor of metalloproteinase 1
- Bataller R, Brenner DA: Liver fibrosis. The Journal of clinical investigation. 2005, 115 (2): 209-218.View ArticlePubMedPubMed CentralGoogle Scholar
- Iredale JP: Models of liver fibrosis: exploring the dynamic nature of inflammation and repair in a solid organ. The Journal of clinical investigation. 2007, 117 (3): 539-548. 10.1172/JCI30542.View ArticlePubMedPubMed CentralGoogle Scholar
- Llovet JM: Updated treatment approach to hepatocellular carcinoma. Journal of gastroenterology. 2005, 40 (3): 225-235. 10.1007/s00535-005-1566-3.View ArticlePubMedGoogle Scholar
- Friedman SL: Liver fibrosis -- from bench to bedside. Journal of hepatology. 2003, 38 (Suppl 1): S38-53.View ArticlePubMedGoogle Scholar
- Friedman SL: Mechanisms of disease: Mechanisms of hepatic fibrosis and therapeutic implications. Nature clinical practice. 2004, 1 (2): 98-105. 10.1038/ncpgasthep0055.PubMedGoogle Scholar
- Friedman SL: Mechanism of Hepatic Fibrogenesis. Gastroenterology. 2008, 134: 1655-1669. 10.1053/j.gastro.2008.03.003.View ArticlePubMedPubMed CentralGoogle Scholar
- Mann J, Oakley F, Akiboye F, Elsharkawy A, Thorne AW, Mann DA: Regulation of myofibroblast transdifferentiation by DNA methylation and MeCP2: implications for wound healing and fibrogenesis. Cell death and differentiation. 2007, 14 (2): 275-285. 10.1038/sj.cdd.4401979.View ArticlePubMedGoogle Scholar
- Bechtel W, McGoohan S, Zeisberg EM, Muller GA, Kalbacher H, Salant DJ, Muller CA, Kalluri R, Zeisberg M: Methylation determines fibroblast activation and fibrogenesis in the kidney. Nature medicine. 2010, 16 (5): 544-550. 10.1038/nm.2135.View ArticlePubMedPubMed CentralGoogle Scholar
- Ortiz A, Ucero AC, Egido J: Unravelling fibrosis: two newcomers and an old foe. Nephrol Dial Transplant. 2010, 25 (11): 3492-3495. 10.1093/ndt/gfq518.View ArticlePubMedGoogle Scholar
- Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D: The human genome browser at UCSC. Genome research. 2002, 12 (6): 996-1006.View ArticlePubMedPubMed CentralGoogle Scholar
- Mas VR, Maluf DG, Archer KJ, Yanek K, Kong X, Kulik L, Freise CE, Olthoff KM, Ghobrial RM, McIver P, et al: Genes involved in viral carcinogenesis and tumor initiation in hepatitis C virus-induced hepatocellular carcinoma. Molecular medicine (Cambridge, Mass. 2009, 15 (3-4): 85-94.Google Scholar
- Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, Lai KM, Ji J, Dudoit S, Ng IO, et al: Gene expression patterns in human liver cancers. Molecular biology of the cell. 2002, 13 (6): 1929-1939. 10.1091/mbc.02-02-0023..View ArticlePubMedPubMed CentralGoogle Scholar
- Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, et al: Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology (Baltimore, Md. 2007, 45 (4): 938-947. 10.1002/hep.21622.View ArticleGoogle Scholar
- Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia (New York, NY. 2004, 6 (1): 1-6.View ArticleGoogle Scholar
- Murayama A, Ohmori K, Fujimura A, Minami H, Yasuzawa-Tanaka K, Kuroda T, Oie S, Daitoku H, Okuwaki M, Nagata K, et al: Epigenetic control of rDNA loci in response to intracellular energy status. Cell. 2008, 133 (4): 627-639. 10.1016/j.cell.2008.03.030.View ArticlePubMedGoogle Scholar
- Fabre Y, Bueno MR, Rincon-Sanchez AR, Saldana-Cortes J, Vargas R, Armendariz-Borumda J: Mexican infants with extrahepatic biliary atresia display different fibrosis activity. Hepatology Research. 2004, 28: 79-86. 10.1016/j.hepres.2003.10.004.View ArticleGoogle Scholar
- Jiang L, Gonda TA, Gamble MV, Salas M, Seshan V, Tu S, Twaddell WS, Hegyi P, Lazar G, Steele I, et al: Global hypomethylation of genomic DNA in cancer-associated myofibroblasts. Cancer research. 2008, 68 (23): 9900-9908. 10.1158/0008-5472.CAN-08-1319.View ArticlePubMedPubMed CentralGoogle Scholar
- Bissell DM: Hepatic fibrosis as wound repair: a progress report. Journal of gastroenterology. 1998, 33 (2): 295-302. 10.1007/s005350050087.View ArticlePubMedGoogle Scholar
- Gallinari Paola, Stefania Di Marco, Jones Phillip, Pallaro Michele, Christian Steinkühler: HDACs, histone deacetylation and gene transcription: from molecular biology to cancer therapeutics. Cell Research. 2007, 17: 195-211.PubMedGoogle Scholar
- Heintzman ND, Hon GC, Hawkins RD, Kheradpour P, Stark A, Harp LF, Ye Z, Lee LK, Stuart RK, Ching CW, et al: Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature. 2009, 459 (7243): 108-112. 10.1038/nature07829.View ArticlePubMedPubMed CentralGoogle Scholar
- Euskirchen GM, Rozowsky JS, Wei CL, Lee WH, Zhang ZD, Hartman S, Emanuelsson O, Stolc V, Weissman S, Gerstein MB, et al: Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies. Genome research. 2007, 17 (6): 898-909. 10.1101/gr.5583007.View ArticlePubMedPubMed CentralGoogle Scholar
- Sabo PJ, Kuehn MS, Thurman R, Johnson BE, Johnson EM, Cao H, Yu M, Rosenzweig E, Goldy J, Haydock A, et al: Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nature methods. 2006, 3 (7): 511-518. 10.1038/nmeth890.View ArticlePubMedGoogle Scholar
- Bulger M, Groudine M: Enhancers: the abundance and function of regulatory sequences beyond promoters. Developmental biology. 2010, 339 (2): 250-257. 10.1016/j.ydbio.2009.11.035.View ArticlePubMedGoogle Scholar
- Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA, et al: Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nature genetics. 2007, 39 (3): 311-318. 10.1038/ng1966.View ArticlePubMedGoogle Scholar
- Pourcel C, Tiollais P, Farza H: Transcription of the S Gene in Transgenic Mice Is Associated with Hypomethylation at Specific Sites and with Dnase I Sensitivity. Journal of Virology. 1990, 64 (2): 931-935.PubMedPubMed CentralGoogle Scholar
- Kelley DE, Pollok BA, Atchison ML, Perry RP: The coupling between enhancer activity and hypomethylation of kappa immunoglobulin genes is developmentally regulated. Molecular and cellular biology. 1988, 8 (2): 930-937.View ArticlePubMedPubMed CentralGoogle Scholar
- Serandour AA, Avner S, Percevault F, Demay F, Bizot M, Lucchetti-Miganeh C, Barloy-Hubler F, Brown M, Lupien M, Metivier R, et al: Epigenetic switch involved in activation of pioneer factor FOXA1-dependent enhancers. Genome research. 2011, 21 (4): 555-565. 10.1101/gr.111534.110.View ArticlePubMedPubMed CentralGoogle Scholar
- Tokizane T, Shiina H, Igawa M, Enokida H, Urakami S, Kawakami T, Ogishima T, Okino ST, Li LC, Tanaka Y, et al: Cytochrome P450 1B1 is overexpressed and regulated by hypomethylation in prostate cancer. Clin Cancer Res. 2005, 11 (16): 5793-5801. 10.1158/1078-0432.CCR-04-2545.View ArticlePubMedGoogle Scholar
- Ramaiah SK, Rittling S: Pathophysiological role of osteopontin in hepatic inflammation, toxicity, and cancer. Toxicol Sci. 2008, 103 (1): 4-13.View ArticlePubMedGoogle Scholar
- Sanders YY, Pardo A, Selman M, Nuovo GJ, Tollefsbol TO, Siegal GP, Hagood JS: Thy-1 promoter hypermethylation: a novel epigenetic pathogenic mechanism in pulmonary fibrosis. American journal of respiratory cell and molecular biology. 2008, 39 (5): 610-618. 10.1165/rcmb.2007-0322OC.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Y, Fan PS, Kahaleh B: Association between enhanced type I collagen expression and epigenetic repression of the FLI1 gene in scleroderma fibroblasts. Arthritis and rheumatism. 2006, 54 (7): 2271-2279. 10.1002/art.21948.View ArticlePubMedGoogle Scholar
- Lorena D, Darby IA, Gadeau AP, Leen LL, Rittling S, Porto LC, Rosenbaum J, Desmouliere A: Osteopontin expression in normal and fibrotic liver. altered liver healing in osteopontin-deficient mice. Journal of hepatology. 2006, 44 (2): 383-390. 10.1016/j.jhep.2005.07.024.View ArticlePubMedGoogle Scholar
- Sahai A, Malladi P, Melin-Aldana H, Green RM, Whitington PF: Upregulation of osteopontin expression is involved in the development of nonalcoholic steatohepatitis in a dietary murine model. American journal of physiology. 2004, 287 (1): G264-273.PubMedGoogle Scholar
- Kawashima R, Mochida S, Matsui A, YouLuTu ZY, Ishikawa K, Toshima K, Yamanobe F, Inao M, Ikeda H, Ohno A, et al: Expression of osteopontin in Kupffer cells and hepatic macrophages and Stellate cells in rat liver after carbon tetrachloride intoxication: a possible factor for macrophage migration into hepatic necrotic areas. Biochemical and biophysical research communications. 1999, 256 (3): 527-531. 10.1006/bbrc.1999.0372.View ArticlePubMedGoogle Scholar
- Ramaiah SK, Rittling S: Role of osteopontin in regulating hepatic inflammatory responses and toxic liver injury. Expert opinion on drug metabolism & toxicology. 2007, 3 (4): 519-526. 10.1517/17425255.3.4.519.View ArticleGoogle Scholar
- Ariztia EV, Subbarao V, Solt DB, Rademaker AW, Iyer AP, Oltvai ZN: Osteopontin contributes to hepatocyte growth factor-induced tumor growth and metastasis formation. Experimental cell research. 2003, 288 (2): 257-267. 10.1016/S0014-4827(03)00118-6.View ArticlePubMedGoogle Scholar
- Eden Amir, Gaudet François, Waghmare Alpana, Jaenisch Rudlf: Chromosomal Instability and Tumors Promoted by DNA Hypomethylation. Science. 2003, 300 (18).
- Tischoff Iris, Tannpfel Andrea: DNA methylation in hepatocellular carcinoma. World Journal of Gastroenterology. 2008, 14 (11): 1741-1748. 10.3748/wjg.14.1741.View ArticlePubMedPubMed CentralGoogle Scholar
- Gaudet François, J Graeme Hodgson, Eden Amir, Laurie Jackson-Grusby, Dausman Jessica, Joe Gray, Leonhardt Heinrich, Jaenisch Rudolf: Induction of Tumors in Mice by Genomic Hypomethylation. Science. 2003, 300 (18).
- Adam Karpf, David Jones: Reactivating the expression of methylation silenced genes in human cancer. Oncogene. 2002, 21: 5496-5503. 10.1038/sj.onc.1205602.View ArticleGoogle Scholar
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