ORIGINAL RESEARCH ARTICLES Human Epigenome Data Reveal Increased CpG Methylation in Alternatively Spliced Sites and Putative Exonic Splicing Enhancers Christina Anastasiadou, 1 Andigoni Malousi, 1 Nicos Maglaveras, 1 and Sofia Kouidou 2 The role of gene body methylation, which represents a major part of methylation in DNA, remains mostly unknown. Evidence based on the CpG distribution associates its presence with nucleosome positioning and alternative splicing. Recently, it was also shown that cytosine methylation influences splicing. However, to date, there is no methylation-based data on the association of methylation with alternative splicing and the distri- bution in exonic splicing enhancers (ESEs). We presently report that, based on the computational analysis of the Human Epigenome Project data, CpG hypermethylation (>80%) is frequent in alternatively spliced sites (par- ticularly in noncanonical) but not in alternate promoters. The methylation frequency increases in sequences containing multiple putative ESEs. However, significant differences in the extent of methylation are observed among different ESEs. Specifically, moderate levels of methylation, ranging from 20% to 80%, are frequent in SRp55-binding elements, which are associated with response to extracellular conditions, but not in SF2/ASF, primarily responsible for alternative splicing, or in CpG islands. Finally, methylation is more frequent in the presence of AT repeats and CpGs separated by 10 nucleotides and lower in adjacent CpGs, probably indicating its dependence on helical formations and on the presence of nucleosome positioning-related sequences. In conclusion, our results show the regulation of methylation in ESEs and support its involvement in alternative splicing. Introduction T he participation of DNA methylation in the regu- lation of gene expression was proposed several years ago, on account of the dramatic changes observed during X chromosome inactivation (Constancia et al., 1998), ontogen- esis, and differentiation (Razin and Riggs, 1980). Never- theless, although the role of methylation in CpG islands (CIs) and first introns is presently well understood, the impact of methylation on the binding of splicing factors and splicing regulatory elements and in CpG-dense gene body sequences (Malousi et al., 2008) remains unknown. Recently, it was verified that DNA methylation is one of the principle mechanisms that exerts a regulatory role on nucleosomal packaging and positioning in gene promoters and thus modulates DNA accessibility to transcription factors and gene expression (Choi and Kim, 2009). This process nega- tively affects transcription and is known to lead to gene si- lencing ( Jones and Laird, 1999). Modifications of DNA packaging and nucleosome positioning by DNA methylation could also affect other aspects of the regulation of gene ex- pression, such as splicing (Robertson, 2002). This is particu- larly probable in view of the fact that spliceosome assembly is a cotranscriptional process (Pandya-Jones and Black, 2009). During the preparation of this work, it was also shown that the factors that control CpG methylation at selected Hox genes affect splicing probably by Pol II stalling (Tao et al., 2010). Nucleosomal packaging and its dependence on confor- mational changes introduced by the presence of specific di- nucleotides such as TG have been extensively investigated (Adams et al., 1987; Liu et al., 2008; Caserta et al., 2009). Some dinucleotides reducing the conformational strain (AA/TT, TA, etc.) were found to facilitate nucleosome formation (Caserta et al., 2009), and the TATAAACGCC repeat se- quence was identified as the optimal oligonucleotide se- quence associated with nucleosome formation (Widlund et al., 1999). The CpG dinucleotide that is included in the optimal repeat sequence is considered responsible for intro- ducing particular conformation characteristics (conforma- tional distortion) in model oligonucleotide sequences (Arnott et al., 1983; Svozil et al., 2008) and has been investigated with respect to the nucleosome assembly (Adams et al., 1987; Schwartz et al., 2009). 1 Laboratory of Medical Informatics and 2 Department of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. DNA AND CELL BIOLOGY Volume 30, Number 5, 2011 ª Mary Ann Liebert, Inc. Pp. 267–275 DOI: 10.1089/dna.2010.1094 267
Transcript
ORIGINAL RESEARCH ARTICLES
Human Epigenome Data Reveal IncreasedCpG Methylation in Alternatively Spliced Sites
and Putative Exonic Splicing Enhancers
Christina Anastasiadou,1 Andigoni Malousi,1 Nicos Maglaveras,1 and Sofia Kouidou2
The role of gene body methylation, which represents a major part of methylation in DNA, remains mostlyunknown. Evidence based on the CpG distribution associates its presence with nucleosome positioning andalternative splicing. Recently, it was also shown that cytosine methylation influences splicing. However, to date,there is no methylation-based data on the association of methylation with alternative splicing and the distri-bution in exonic splicing enhancers (ESEs). We presently report that, based on the computational analysis of theHuman Epigenome Project data, CpG hypermethylation (>80%) is frequent in alternatively spliced sites (par-ticularly in noncanonical) but not in alternate promoters. The methylation frequency increases in sequencescontaining multiple putative ESEs. However, significant differences in the extent of methylation are observedamong different ESEs. Specifically, moderate levels of methylation, ranging from 20% to 80%, are frequent inSRp55-binding elements, which are associated with response to extracellular conditions, but not in SF2/ASF,primarily responsible for alternative splicing, or in CpG islands. Finally, methylation is more frequent in thepresence of AT repeats and CpGs separated by 10 nucleotides and lower in adjacent CpGs, probably indicatingits dependence on helical formations and on the presence of nucleosome positioning-related sequences. Inconclusion, our results show the regulation of methylation in ESEs and support its involvement in alternativesplicing.
Introduction
The participation of DNA methylation in the regu-lation of gene expression was proposed several years
ago, on account of the dramatic changes observed during Xchromosome inactivation (Constancia et al., 1998), ontogen-esis, and differentiation (Razin and Riggs, 1980). Never-theless, although the role of methylation in CpG islands (CIs)and first introns is presently well understood, the impact ofmethylation on the binding of splicing factors and splicingregulatory elements and in CpG-dense gene body sequences(Malousi et al., 2008) remains unknown. Recently, it wasverified that DNA methylation is one of the principlemechanisms that exerts a regulatory role on nucleosomalpackaging and positioning in gene promoters and thusmodulates DNA accessibility to transcription factors andgene expression (Choi and Kim, 2009). This process nega-tively affects transcription and is known to lead to gene si-lencing ( Jones and Laird, 1999). Modifications of DNApackaging and nucleosome positioning by DNA methylationcould also affect other aspects of the regulation of gene ex-pression, such as splicing (Robertson, 2002). This is particu-
larly probable in view of the fact that spliceosome assemblyis a cotranscriptional process (Pandya-Jones and Black, 2009).During the preparation of this work, it was also shown thatthe factors that control CpG methylation at selected Hoxgenes affect splicing probably by Pol II stalling (Tao et al.,2010).
Nucleosomal packaging and its dependence on confor-mational changes introduced by the presence of specific di-nucleotides such as TG have been extensively investigated(Adams et al., 1987; Liu et al., 2008; Caserta et al., 2009). Somedinucleotides reducing the conformational strain (AA/TT,TA, etc.) were found to facilitate nucleosome formation(Caserta et al., 2009), and the TATAAACGCC repeat se-quence was identified as the optimal oligonucleotide se-quence associated with nucleosome formation (Widlundet al., 1999). The CpG dinucleotide that is included in theoptimal repeat sequence is considered responsible for intro-ducing particular conformation characteristics (conforma-tional distortion) in model oligonucleotide sequences (Arnottet al., 1983; Svozil et al., 2008) and has been investigated withrespect to the nucleosome assembly (Adams et al., 1987;Schwartz et al., 2009).
1Laboratory of Medical Informatics and 2Department of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki,Thessaloniki, Greece.
DNA AND CELL BIOLOGYVolume 30, Number 5, 2011ª Mary Ann Liebert, Inc.Pp. 267–275DOI: 10.1089/dna.2010.1094
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CpG methylation is also known to have a strong impacton the conformational characteristics of the DNA sequence(Fang et al., 1995). Moreover, evidence for the strong impactof DNA methylation on splicing comes from the fact thatmethyl-binding protein mutants (MeCP2) affect gene splic-ing (Moretti and Zoghbi, 2006). DNA methylation mostlikely introduces an additional parameter influencing nu-cleosome positioning and binding of splicing regulatoryfactors (SR proteins) recognizing CpG-containing sites ofexonic splicing enhancers (ESEs). SR proteins are Serine-Arginine–rich proteins, which bind to specific RNA domainsand are critical factors for the splicing process. Studies usingartificial splicing models such as minigenes reveal that splicesite selection depends on the concerted action of these pro-teins (Goncalves et al., 2009). Among these, SF2/ASF hasbeen mostly reported to act as a splicing enhancer, whereasothers (SRp20) act as silencers. In addition, SF2/ASF plays aregulatory role in mRNA export (Li and Manley, 2005).
Several recent studies address the issue of nonpromoterDNA methylation and its possible involvement in tran-scriptional regulation and nucleosomal positioning. None ofthese systems refer to the human epigenome of differentiatedcells. The complete epigenome analysis of Arabidopsis thaliana(Chodavarapu et al., 2010) provides valuable informationconcerning the plant epigenome, and the study by Lister et al.(2009) involves the analysis of human stem cells. Althoughthese cells are of human origin, it is clearly shown by theseauthors that their epigenetic state is distinctly different fromthat of mature differentiated cells from different tissues, be-cause they exhibit epigenetic characteristics such as a highfrequency of non-CpG methylation, which has been previ-ously reported in lung carcinoma cells (Kouidou et al., 2005).
Nevertheless, to date, there is no information regarding thepresence of methylation in splicing regulation and nucleosomeformation inducing sequences in cells from human tissues.The methylation data in nonpromoter sequences availablethrough the Epigenome Project (Rakyan et al., 2004; Eckhardtet al., 2006) can provide useful information for evaluating therole of gene body and nonpromoter methylation.
In this study, we investigated the extent of DNA methyl-ation in CpGs for sequences that contain ESEs and thosewhich can give potential alternative splicing events or can beidentified as part of CIs. Moreover, we evaluated the se-quence specificity of methylation in short sequence motifs(di- and trinucleotides), some of which are considered criticalfor nucleosome positioning.
Materials and Methods
The source data used in this study are a compilation ofmethylation sites at specific loci of the human chromosomes6, 20, and 22, which are available from the Human Epigen-ome Project (HEP) (Eckhardt et al., 2006) (released June 26,2006), as well as the corresponding genomic sequences fromthe Human Genome Project (HGP) (Build 34, hg16, Jul.2003).We examined 33,352 methylated sites from multiple tissuetypes, and for each one of these sites, we extracted themethylation levels as well as the adjacent genomic sequencesfrom HEP and HGP, respectively. Methylation sites with noavailable methylation levels or with known data not ana-lyzed were ignored. The methylation level was used as acriterion for the classification of methylated sites. Sites with
methylation levels greater or equal to 80% are characterizedas hypermethylated, whereas those with methylation levelslower or equal to 20% are classified as hypomethylated.From the set of 33,352 methylated sites, 9012 CpG sites werefound to be hypermethylated and a total of 15,296 sites arehypomethylated. The remaining CpG sites are categorized toa third class containing intermediate methylation levels.
Putative ESEs responsive to the human SR proteins SF2/ASF, SC35, SRp40, and SRp55 were identified in the studiedsequences using ESEfinder (Cartegni et al., 2003). The defaultthresholds for the computational detection of the ESE con-sensus motifs were used in this analysis. For the comparativeanalyses of methylation and alternative splicing, we per-formed searches for different types of alternative splicingevents in the studied human genomic regions (NCBI36/hg18assembly) using the UCSC Table browser (Karolchik et al.,2004). The exon coordinates in chromosomes 6, 20, and 22were extracted using the Martview interface of Biomart forthe same human assembly (Haider et al., 2009).
In addition, we identified CIs using two types of analyses.First, we extracted all actual CIs in the studied chromosomesusing UCSC Table browser for the same human genomeassembly and visualized their distribution. Then, to comparethe actual CIs with those neighboring the analyzed CpGmethylation sites, we identified CIs in the 400-nt regioncentered at the hypermethylated and hypomethylated cyto-sines using the Takai and Jones algorithm (Takai and Jones,2003). The search criteria follow the definition of the CIsperformed by Gardiner-Garden and Frommer (1987) (i.e.,200 nt sequence length with >50% GþC content and ob-served CpC/expected CpG ratio [ObsCpG/ExpCpG] �0.6).
The expressed sequence tags (ESTs) were extracted usingthe UCSC Table browser of the human genome (hg19, Feb2009) for chromosomes 6, 20, and 22. ESTs were classifiedinto CpG rich (number of CpGs/EST length: >0.1) and CpGpoor (no CpGs in whole EST length) and the putative ESEswere identified for each dataset using ESEfinder.
Chi-square tests were used to estimate the statistical sig-nificance for all bivariate tabular analyses and Pearsonproduct–moment correlation coefficients provided a measureof the linear relationship between the distributions of specificfeatures in different chromosomes. Matlab and Perl scriptswere built to delineate the source dataset, to extract the ge-nomic sequences, and to analyze their features.
Results
CpG methylation in experimentally detectedalternative splice sites
The association of methylation with alternative splicingwas examined by defining the level of CpG methylationclose to sites where alternative splicing events have beenreported. The frequency of alternative splicing sites nearhyper- and hypomethylated CpGs (methylation frequencies�80% and frequencies �20%, respectively) was evaluatedin� 200-nt sequences. It is evident from Table 1 that thisfrequency is significantly higher for hypermethylated, com-pared with hypomethylated sequences. It is, however, in-teresting to note that such differences are not observed withregard to the extent of methylation in alternative promot-ers (Table 1). Actually, when alternative promoters areexcluded from the statistical correlation, the association of
268 ANASTASIADOU ET AL.
hypermethylated CpGs with alternative splicing events isstrengthened. Particularly evident is also the association ofhypermethylation with atypical splice sites, that is, strangesplice sites and atypical intron splice ends (AT/AC).
ESE density in proximity with CpGs
To classify the methylation data for the 33,352 CpGs inchromosomes 6, 20, and 22 in different genetic regulatoryregions, we first evaluated the frequency of the identifiedESEs in� 50-nt sequences adjacent to each CpG, as a measureof their splicing regulatory complexity (Table 2A). It is evi-dent from Table 2A that the average regulatory complexityobtained by the computational analysis of the� 50-nt se-quences differs considerably among chromosomes. Specifi-cally, in chromosome 20, there is a higher average ESE
frequency for each CpG-adjacent sequence compared withchromosomes 6 and 22. Given that the ESEs’ length usuallyranges from 5 to 7 nt, we also investigated the number of ESEsin shorter sequences of� 30 nt length adjacent to CpGs. De-creasing the size of the analyzed sequences by 40% introducesconsiderable increase of the ESEs density, indicating that ESEsare clustered adjacent to the CpGs. This finding is probablyunrelated to the presence of multiple ESEs in a single CpG,because the frequency of ESEs containing two CpGs is verylow [e.g., 3/238 ESE hexamers identified according to Rescue-ESE (Fairbrother et al., 2004)]. It is, however, probable that theincrease of CpG density could be related to the presence ofmore dense or overlapping ESEs. The most considerable in-crease is observed in chromosome 20.
To further verify that the splicing regulatory complexity inCpG-proximal sequences is higher than in the remaining
Table 1. Alternative Splicing Events Reported in 400-nt Sequences Centered at the Analyzed
CpGs, Relative to Their Level of Methylation
Alternative splicing event Hypermethylated (9012) Hypomethylated (15,296) p-Value
The corresponding ESE frequencies in 100 nt centered at CpGs are shown within parentheses.ESEs, exonic splicing enhancers.
Table 2B. Exonic Splicing Enhancers in CpG-Dense and CpG-Poor Expressed Sequence
Tags for Chromosomes 6, 20, and 22
CHR6 CHR20 CHR22
Total ESTs 446,330 199,686 191,500
CpG-rich ESTs 8576 3565 4072Average length 452.4 384.5 437.5Average number (standard deviation) of ESEs per EST 149.2 (53.9) 124.5 (69) 143.5 (62.9)
CpG-poor ESTs 5912 3308 502Average length 452.7 383.1 436.5Average number (standard deviation) of ESEs per EST 52.8 (17.2) 50.4 (23) 67.8 (21.6)
ESTs, expressed sequence tags.
SPLICING AND CPG METHYLATION 269
sequences, we also evaluated the frequency of ESEs in CpG-dense and CpG-poor ESTs for the same chromosomes (Table2B). It is evident from Table 2B that, although there is con-siderable variation among the studied chromosomes, thenumber of ESTs is higher in CpG-rich ESTs, compared withCpG-poor ESTs, whereas the corresponding average ESTlength remains the same in the three chromosomes. Theseresults reinforce the previous evidence that intragenic CpGsmight act as an additional regulatory factor in expressedsequences of high regulatory complexity.
Moreover, we investigated the spatial relationship ofCpGs with respect to splicing regulatory factors, by evalu-ating the ESE positioning relative to the CpG sequence. Asshown in Figure 1, it is evident that there is a stringentlydefined spatial relationship between the CpG and ESE dis-tribution and that the ESEs are clustered around CpGs.
Provided that the average ESE size is six nucleotides, it isevident that ESEs are evenly distributed around these sites oftentative epigenetic modification.
Comparison of the ESE content and exonic distributionin the analyzed CpG-containing sequences
To further verify that the analyzed CpGs represent auseful sample for estimating the impact of methylation withrespect to the regulation of splicing and ESEs, we evaluatedthe ESE frequency relative to the analyzed CpG density inchromosomal increments of 3.5�106 nt length (Fig. 2). Thescatter plots shown in Figure 2 reveal a linear correlationbetween the CpG and ESE densities that is mostly observedin areas of moderate CpG density in all chromosomes. Onthe contrary, in sequences with very high CpG densities
FIG. 1. Correlation of theESE and CpG densities. Thex-axis represents the averagedistance of the putative ESEsfrom a certain CpG and they-axis shows the number ofCpGs at the correspondingdistances for chromosomes6, 20, and 22. ESEs, exonicsplicing enhancers.
[>400 CpGs, probably corresponding to CIs and mini-CIs(Malousi et al., 2008)], the correlation coefficient decreases(r¼ 0.971 for less than 400 CpGs to r¼ 0.901 for higher than400 CpGs in chromosome 6). The same analysis in chromo-some 20 shows that the correlation coefficient is also reducedat high CpGs densities (r¼ 0.983 for less than 400 CpGs tor¼ 0.824 for higher than 400 CpGs densities). In chromosome20, the density of the analyzed CpGs is generally lowercompared with that in chromosomes 6 and 22.
The ESE frequency with respect to the CpG density was alsoinvestigated for chromosomal increments of 1�106 nt length.The results indicate a similar average correlation between theCpG and ESE densities for all chromosomes (r¼ 0.965, r¼ 0.969,and r¼ 0.984 for chromosomes 6, 20, and 22, respectively).
Methylation at different CpG-containing putative ESEs
Following the classification of CpG sequences as hyper-methylated and hypomethylated CpGs and intermediate
methylation CpGs, we then investigated the total ESE fre-quency and that of the different splicing regulatory elementscategories identified by ESEfinder. The data in Table 3 showthe frequency of CpG methylation with respect to the num-ber of putative ESE elements identified in� 50-nt sequences,adjacent to each CpG. This analysis reveals that, as expected,regardless of the chromosome tested, methylation is frequentin putative ESEs, which are exonic sequences. However, italso shows that the extent of methylation increases relative tothe putative ESE density surrounding the CpG dinucleotide,and as different putative ESE types are frequently clusteredor overlapping, methylation appears to be related to theputative ESE complexity.
In addition, the results in Tables 4A and 4B show that themethylation frequency in the different SR protein-bindingESEs varies according to the ESE type (SF2/ASF, SRp40,SRp55, and SC35) and that its frequency is invariable, re-gardless of the chromosome analyzed. Thus, the percentageof CpGs that show intermediate methylation levels
FIG. 2. Linear regression analysis of the ESE density relative to the number of analyzed CpGs (diamonds for >400 CpGsand triangles for �400 CpGs). Each chromosome is divided in 3.5�106 nt length increments. (A) Chromosome 6; (B) chro-mosome 20; (C) chromosome 22.
Table 3. Exonic Splicing Enhancer Frequency Relative to the Extent of CpG Methylation
in� 50-nt Nucleotide Sequences, Adjacent to Each CpG
The ESE frequency was estimated in hyper- (O) and hypomethylated (U) CpG sequences.aESE frequency in 100-nt sequences centered at analyzed CpGs, relative to the total CpGs in each chromosome (10,683, 5486, and 17,183 for
(20%–80%) is higher in SRp55-binding elements comparedwith all the remaining SR protein-binding elements, and themethylation frequency in this putative ESE type shows slightvariation among the three different chromosomes analyzed(Fig. 3). On the contrary, intermediate methylation is lessfrequent in SF2/ASF-binding motifs (Fig. 3). These resultsreveal that methylation is not only more frequent in the se-quences corresponding to putative ESEs but also mostprobably ESE-type dependent.
Statistical analysis of the above results concerning thefrequency of intermediate methylation reveals that it is sig-nificantly different in SF2/ASF- and SRp55-binding se-quences compared with methylation in the remaining ESEs(SC35 and SRp40) (Table 4B). On the contrary, there is a
stronger correlation between the levels of methylation inSC35- and SRp40-binding sequences.
Methylation in CIs
Using the same approach, we also evaluated the frequencyof hyper- and hypomethylated CpGs in computationallyidentified CIs (Table 5). As expected, most of the CpGs in CIswere either hyper- or hypomethylated and intermediatemethylation was infrequent in CIs compared with ESEs. CICpGs represent the majority of all CpGs, at least for chro-mosomes 6 and 22 (85.5% and 80.86%, respectively). It is alsonoteworthy that in CIs there is a higher variation of themethylation frequency than in ESEs and that intermediatelevels of methylation are more frequent in CIs in chromo-some 20 (ratio: 0.325) compared with chromosomes 6 and 22(ratio: 0.143 and 0.195, respectively).
CpG methylation adjacent to differentdinucleotide repeats
As the presence of specific repetitive CpG sequence ele-ments is supposed to influence significantly DNA windingand nucleosome positioning and could potentially influence
Total 136,977 10,736 12,397 32,818 22,827 7229 24,156 110,163 26,814
Within parentheses, the number of hypermethylated CpGs in each ESE category versus total number of hyper- and hypomethylated ESEs(O and U) in each chromosome, respectively, is given.
FIG. 3. Relative frequency ofmethylation in different ESEtypes for all chromosomes. Thefrequency of hypermethylatedand hypomethylated CpGs andCpGs with intermediate methyl-ation levels was estimated foreach type of ESE relative to thetotal number of each ESE type inall three chromosomes. Data wereobtained from Table 4.
splicing, we determined the levels of methylation in CpGswith respect to their distance from the next CpG dinucleo-tide. It is evident from Figure 4A that sequences with higherCpG density are frequently hypomethylated. However,methylation is slightly more frequent in CpGs that are di-vided by 10 nucleotides from the following CpG, comparedwith adjacent CpGs or those separated by <10 nucleotides.These data could indicate that although local helical distor-tions introduced by successive methylated CpGs might notfavor methylation, this limitation is less stringent in 12-ntCpG repeats, possibly because of the formation of specific,alternative helical structures.
In addition, we investigated the level of methylation inCpGs adjacent to other repetitive sequence elements, some ofwhich contribute to nucleosome positioning (Fig. 4B). TheTA-repeat sequence appears to contribute considerably toCpG methylation, whereas other dinucleotide repeats, forexample, TC, do not introduce similar preferences with re-spect to the methylation levels. Again, this analysis showsthat the nucleotide distance separating these dinucleotiderepeats is evidently a critical parameter in the process ofmethylation.
Discussion
Evidence regarding the involvement of methylation insplicing is given in very recent publications (Laurent et al.,2010; Tao et al., 2010). Moreover, clinical data regarding p53splicing-deregulating mutants in cancer-related syndromes(Kouidou et al., 2009) and cellular deregulation in the agingcell (Bork et al., 2010) clearly show the critical role of meth-ylation changes in human disease, the use of stem cells andits association with splicing. However, the mechanism bywhich epigenetic processes might be involved in splicingremained unresolved.
Our results, which are based on the analysis of a large setof methylation data selected from three chromosomes andprovided by the HEP, reveal that CpG hypermethylation isstrongly associated with alternative and noncanonical splic-ing events and with atypical splice sites, that is, strangesplice sites and atypical intron splice ends (AT/AC), whichare related to the minor spliceosome (Levine and Durbin,2001). The above data could provide an explanation for theinfluence of MeCP2 in the moderation of the splicing process(Moretti and Zoghbi, 2006).
Table 5. Methylation in CpGs of CpG Islands in Chromosomes 6, 20, and 22
Total CpGs (O þ U þ Int) O þ U [(O þ U)/total CpGs] CI (O þ U) [CI (O þ U)/(O þ U)] CIint [CIint/CI]
The 400-nt sequences centered at CpGs were analyzed for CpG islands.CI(O þ U), hyper- (O) and hypomethylated (U) CpGs in CIs; CIint, intermediate methylation (Int) in CI; CI, CpG islands.
FIG. 4. (A) The frequency ratio[m(C) >80]/[m(C) <20] of hy-permethylated cytosines (m(C) >80)and hypomethylated cytosines(m(C) <20) in repetitive CpGsseparated by different number ofnucleotides in the� 50-nt regionadjacent to the methylated CpG.(B) The corresponding frequencyratio in other repetitive dinucleotidemotifs.
In addition, our results show the strong association ofcomplex splicing regulatory sites (containing a large numberof ESEs) with CpGs, as well as the nonrandom distribution ofmethylation among different putative splicing regulatoryelements. SF2/ASF-binding sites, which are involved inconstitutive/alternative pre-mRNA splicing selection (Kra-lovicova and Vorechovsky, 2007; Sanford et al., 2008), areshown to be mostly hyper- or hypomethylated, similar toCpGs in CIs. On the contrary, methylation in SRp55-bindingmotifs, which are involved in the regulation of cellular re-sponse to intra- and extracellular conditions (Tran andRoesser, 2003; Filippov et al., 2008), mostly assumes inter-mediate values.
Finally, it is recently becoming evident that the presence ofCpG sequences in minigenes contributes to the efficiency oftheir translation (Bauer et al., 2010) and that these sequencesare associated with nucleosomal positioning (Widlund et al.,1999). In view of the high frequency of methylation in TArepeats, which also strongly favor nucleosome positioning(Adams et al., 1987; Caserta et al., 2009) present in theTATAAACGCC sequence (Widlund et al., 1999), it is prob-able that nucleosome positioning and assembly is associatedwith the presence of methylated cytosine and DNA-bindingproteins might play a positive role in this process.
Conclusion
In conclusion, our results show that there is a correlationbetween the distribution of DNA methylation and the pres-ence of sequence motifs affecting splicing and nucleosomepositioning. These data support an additional role for DNAmethylation, namely, that of a molecular switch contributingto the regulation of the splicing process, probably related tosplicing regulatory factor binding and nucleosome posi-tioning. Further studies on these processes will improve ourunderstanding of the mechanisms controlling splicing anddifferentiation.
Disclosure Statement
No competing financial interests exist.
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Address correspondence to:Prof. Sofia Kouidou
Department of Biological ChemistrySchool of Medicine
Received for publication June 02, 2010; received in revisedform November 01, 2010; accepted November 14, 2010.
SPLICING AND CPG METHYLATION 275
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