Environment, diet and CpG island methylation: epigenetic signals in gastrointestinal neoplasia.
ABSTRACT The epithelial surfaces of the mammalian alimentary tract are characterised by very high rates of cell proliferation and DNA synthesis, and in humans they are highly susceptible to cancer. The role of somatic mutations as drivers of carcinogenesis in the alimentary tract is well established, but the importance of gene silencing by epigenetic mechanisms is increasingly recognised. Methylation of CpG islands is an important component of the epigenetic code that regulates gene expression during development and normal cellular differentiation, and a number of genes are well known to become abnormally methylated during the development of tumours of the oesophagus, stomach and colorectum. Aberrant patterns of DNA methylation develop as a result of pathological processes such as chronic inflammation, and in response to various dietary factors, including imbalances in the supply of methyl donors, particularly folates, and exposure to DNA methyltransferase inhibitors, which include polyphenols and possibly isothiocyanates from plant foods. However the importance of these environmental interactions in human health and disease remains to be established. Recent moves to modify the exposure of human populations to folate, by mandatory supplementation of cereal foods, emphasise the importance of understanding the susceptibility of the human epigenome to dietary and other environmental effects.
- SourceAvailable from: Carlos Guerrero-Bosagna[Show abstract] [Hide abstract]
ABSTRACT: We assessed the impact of high serum folate concentration on erythrocyte S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH) concentrations, SAM/SAH ratio, CpG methylation levels across the promoter region of the extracellular superoxide dismutase (ec-SOD) gene, and ec-SOD activity in healthy men. Serum folate levels were measured in 111 subjects who were categorized in quintiles according to their folate status. Subjects located at the lowest, middle, and upper quintiles were selected for assessment of SAM and SAH by high-performance liquid chromatography, C677T genotype of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, ec-SOD methylation of CpG sites in lymphocytes genomic DNA by bisulfate treatment, and ec-SOD activity by a chemical assay. Sixteen subjects were in the lowest serum folate quintile (<23.6 nmol/L), 17 in the middle (>34-<42 nmol/L), and 14 in the highest (>45nmol/L). SAM concentration was higher in the upper than in the middle and lowest quintiles (5.57 +/- 1.58, 2.52 +/- 0.97, 2.29 +/- 1.2 micromol/L; P < 0.0001). SAH concentration was higher in the upper compared with the lowest quintile (0.76 +/- 0.24 versus 0.52 +/- 0.23 micromol/L, P < 0.001). There were no differences in the SAM/SAH ratio, ec-SOD activity, methylation status of CpG sites of the ec-SOD gene, and TMTHFR C677T genotype between groups. Serum folate concentrations in the highest quintile among healthy humans are associated with increased erythrocyte SAM and SAH concentrations, but not with SAM/SAH ratio or with methylation levels of CpG sites across the promoter region of the ec-SOD gene. Further research is required to determine if these findings are beneficial or harmful.Nutrition 11/2008; 24(11-12):1103-9. DOI:10.1016/j.nut.2008.05.018 · 3.05 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Epigenetics research is one of the emerging research fields in biomedical research. During the last few decades, a collection of useful tools (both to design the experiments and to analyze the results) and databases are developed. This review chapter discusses basic tools which are used to detect CpG islands and the Transcription Start Site (TSS) and discusses experimental design and analysis, mainly of DNA-methylation experiments. During the last years, an enormous amount of experimental data had been generated and published. Therefore, we describe some epigenetic databases, with a special focus on DNA methylation and cancer. Some general cancer databases are discussed as well, as they might reveal the link between the results from epigenetic experiments and their biological influence on the development or progression of cancer. Next, some novel computational approaches in epigenetics are discussed, for instance used to predict the methylation state of a promoter in certain circumstances. To show a possible data analysis strategy of an epigenetic dataset in cancer research, there is a showcase where a DNA-methylation dataset, generated on colorectal cancer samples, is analyzed. This demonstrates how a DNA-methylation dataset might look like and the different steps in a possible analysis strategy and how to interpret the results.Advances in genetics 01/2010; 71:259-95. DOI:10.1016/B978-0-12-380864-6.00009-2 · 5.41 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Detecting aberrant DNA methylation as diagnostic or prognostic biomarkers for cancer has been a topic of considerable interest recently. However, current classifiers based on absolute methylation values detected from a cohort of samples are typically difficult to be transferable to other cohorts of samples. Here, focusing on relative methylation levels, we employed a modified rank-based method to extract reversal pairs of CpG sites whose relative methylation level orderings differ between disease samples and normal controls for cancer diagnosis. The reversal pairs identified for five cancer types respectively show excellent prediction performance with the accuracy above 95%. Furthermore, when evaluating the reversal pairs identified for one cancer type in an independent cohorts of samples, we found that they could distinguish different subtypes of this cancer or different malignant stages including early stage of this cancer from normal controls. The identified reversal pairs also appear to be specific to cancer type. In conclusion, the reversal pairs detected by the rank-based method could be used for accurate cancer diagnosis, which are transferable to independent cohorts of samples. Copyright © 2014 Elsevier B.V. All rights reserved.Gene 11/2014; 555(2). DOI:10.1016/j.gene.2014.11.004 · 2.08 Impact Factor