global hypermethylation that characterizes cancer cells in our experiments. To confirm the results obtained in cell lines, we checked the possible existence of undermethylation in regions under selective pressure by a different approach. It is well established in scientific literature that the 5methylcytosine present in some CpG sites is subject to mutational pressure by spontaneous deamination to thymine [31]. A fraction of CpG sites in the genome are clustered into CpG islands that are thought to be mainly unmethylated [32]. Since 5-Methylcytosine is the initial molecule in the deamination reaction that generates thymine, CpG sequences within CpG islands, which are not methylated, would be less likely to mutate. Tomso et al. found a general underrepresentation of polymor- phisms in CpG islands, strongly supporting the idea that decreased methylation in CpG islands leads to decreased variation at island CpGs [33]. Using the same way of reasoning, we predicted that, if CGIs in regions under selective pressure were undermethylated, they would show less polymorphisms than the CGIs in the remaining genome. Independently of the method used to define the regions under selective pressure, we found that CGIs inside regions under selective pressure contain less SNPs than the CGIs in the remaining genome. When we compared CGIs in regions with two signatures of selective pressure to CGIs in regions with one signature only, we found that CGIs showing both 5SLR and CE signatures contained less SNPs than CGIs showing CE signature only. On the contrary, when we compared CGIs showing both HIR and CE signatures to CGIs showing CE signature only, we found no differences in SNP content. A possible explanation is that the selective pressure that acted on HIRs was very recent. Its effect could be evident in cell CGIs methylation but not (or not yet) in genetic variation. CGIs can be located inside the genes or outside them. CGIs located inside genes can be divided, according their position, in CGIs in 59 region, CGIs in the 39 regions and CGIs in internal exons or introns. CGIs located near 59 region of genes are known to influence gene expression but also CGIs located outside these regions can be involved in important biological processes [3] [34] [35]. We decided to analyze the methylation of CGIs, categorized by their position, to assess if the CGIs undermethylation that we have found in regions under selective pressure was a general phenomenon or it was restricted to CGIs with a specific localization. We were able to analyze only CE CGIs because, after classification, the number of HIR CGIs and 5LSR CGIs in intragenic and 39 regions was too low to perform a reliable statistical analysis. We found that, at least for CE, the CGIs undermethylation in regions under selective pressure specifically involved CGIs located at the 59 and in the intergenic regions. For the 59 regions, the finding was quite expected because of their well established role in gene regulation. The functional role of intergenic CGIs is less clear. There is a growing evidence of the role of CGIs methylation in the regulation of microRNAs [36]. In particular, it has been demonstrated that 80% of the promoters of ‘‘intergenic’’ microRNAs contain CGIs. In addition, these regulatory regions show signals of evolutionary conservation [37]. We also cannot exclude that some CGIs categorized as intergenic, may be related to yet unidentified genes. Bock et al. developed a computational epigenetics approach to discriminate between CpG islands that are prone to methylation from those that remain unmethylated on the basis of a set of 1,184 DNA attributes [12] . One of these attributes was the evolutionary conservation that the authors found to be uncorre- lated with CpG island methylation. It should be noted that in this study (published in 2006) only CGIs on chromosome 21 were CpG Islands Methylation and Selective Pressure analyzed. Further, the methods to evaluate evolutionary conser- vation and for the statistical analysis are not the same that we used. Our study has some limit. The most important one is the estimation of CGIs methylation. For each CGI we have data only on a limited number of CpGs, and from their methylation values we estimated the total CGI methylation. It should be noted that the dataset that we used is the largest genome-wide dataset available and that, in any case, this could be considered a systematic error that could cause a general noise only. Another limit is that we analyzed the DNA methylation only. Epigenetic control of transcription involves a complex network of signals, including transcription factors, noncoding RNAs, DNA methylation, and histone modifications [38]. In this study we looked only to a part of these mechanisms. Further studies are needed to analyze the other component of this machinery. Another possible limit concerns the method used to define regions under selective pressure. Other methods have been described and our choice could not be exhaustive. A final caveat concerns possible cell-culture induced DNA methylation. It is well established that in vitro culture can cause changes in epigenetic marking of the genome [39] [40], probably due to the adaptation of the cells to the in vitro conditions. Therefore it should be underlined that, concerning DNA methylation, cell lines could be not representative of their relative primary tissues. In conclusion, in this paper we demonstrated, in several cell lines, that CpG islands in regions showing signatures of selective pressure are undermethylated in comparison with the other regions of the genome. Additionally, by analyzing SNP frequency in CpG islands, we demonstrated that CpG islands in regions under selective pressure show lower genetic variation among individuals. Materials and Methods Data and evolutionary scores All the data and the scores that we used were downloaded from annotation tracks in the UCSC Genome Browser [41] . A brief description is provided below. Further and more detailed information about the dataset used can be found at http:// genome.ucsc.edu/. CpG island coordinates. CGIs genomic coordinates were obtained from the UCSC GB CpgIslandExt track. In this track CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and 21 for others) and identifying maximally scoring segments. In this dataset, to define a CpG island the following criteria were used: i) to have a GC content of 50% or greater, ii) to have a length greater than 200 bp, and iii) to show a ratio greater than 0.6 of observed number of CG dinucleotides to the expected number, calculated on the basis of the number of Gs and Cs in the segment under analysis. DNA methylation data. Methylation profiles from each cell sample were downloaded from the UCSC GB HAIB Methyl RRBS Track. These tables report the percentage of DNA molecules that show cytosine methylation at specific CpG dinucleotides in several cell lines. To obtain these data, researchers belonging the ENCODE Consortium assayed DNA methylation at CpG sites with a modified version of Reduced Representation Bisulfite Sequencing [20]. We used data from 25 cell lines, which were the first ones to come out from the moratorium period (expiration of moratorium period = 2011-04- 13). The data set contains, for each cell line at least two replicas each containing, on average, about 1.5 million of CpG PLoS ONE | www.plosone.org 7 August 2011 | Volume 6 | Issue 8 | e23156