Genome-wide quantitative assessment of variation in DNA methylation patterns

Characterization and machine learning prediction of allele-specific DNA methylation

A large collection of Single Nucleotide Polymorphisms (SNPs) has been identified in the human genome. Currently, the epigenetic influences of SNPs on their neighboring CpG sites remain elusive. A growing body of evidence suggests that locus-specific information, including genomic features and local epigenetic state, may play important roles in the epigenetic readout of SNPs. In this study, we made use of mouse methylomes with known SNPs to develop statistical models for the prediction of SNP associated allele-specific DNA methylation (ASM). ASM has been classified into parent-of-origin dependent ASM (P-ASM) and sequence-dependent ASM (S-ASM), which comprises scattered-S-ASM (sS-ASM) and clustered-S-ASM (cS-ASM). We found that P-ASM and cS-ASM CpG sites are both enriched in CpG rich regions, promoters and exons, while sS-ASM CpG sites are enriched in simple repeat and regions with high frequent SNP occurrence. Using Lasso-grouped Logistic Regression (LGLR), we selected 21 out of 282 genomic and methylation related features that are powerful in distinguishing cS-ASM CpG sites and trained the classifiers with machine learning techniques. Based on 5-fold cross-validation, the logistic regression classifier was found to be the best for cS-ASM prediction with an ACC of 0.77, an AUC of 0.84 and an MCC of 0.54. Lastly, we applied the logistic regression classifier on human brain methylome and predicted 608 genes associated with cS-ASM. Gene ontology term enrichment analysis indicated that these cS-ASM associated genes are significantly enriched in the category coding for transcripts with alternative splicing forms. In summary, this study provided an analytical procedure for cS-ASM prediction and shed new light on the understanding of different types of ASM events.

Inter-locus as well as intra-locus heterogeneity in LINE-1 promoter methylation in common human cancers suggests selective demethylation pressure at specific CpGs

Background

Hypomethylation of long interspersed element (LINE)-1 has been observed in tumorigenesis when using degenerate assays, which provide an average across all repeats. However, it is unknown whether individual LINE-1 loci or different CpGs within one specific LINE-1 promoter are equally affected by methylation changes. Conceivably, studying methylation changes at specific LINE-1 may be more informative than global assays for cancer diagnostics. Therefore, with the aim of mapping methylation at individual LINE-1 loci at single-CpG resolution and exploring the diagnostic potential of individual LINE-1 locus methylation, we analyzed methylation at 11 loci by pyrosequencing, next-generation bisulfite sequencing as well as global LINE-1 methylation in bladder, colon, pancreas, prostate, and stomach cancers compared to paired normal tissues and in blood samples from some of the patients compared to healthy donors.

Results

Most (72/80) tumor samples harbored significant methylation changes at at least one locus. Notably, our data revealed not only the expected hypomethylation but also hypermethylation at some loci. Specific CpGs within the LINE-1 consensus sequence appeared preferentially hypomethylated suggesting that these could act as seeds for hypomethylation. In silico analysis revealed that these CpG sites more likely faced the histones in the nucleosome. Multivariate logistic regression analysis did not reveal a significant clinical advantage of locus-specific methylation markers over global methylation markers in distinguishing tumors from normal tissues.

Conclusions

Methylation changes at individual LINE-1 loci are heterogeneous, whereas specific CpGs within the consensus sequence appear to be more prone to hypomethylation. With a broader selection of loci, locus-specific LINE-1 methylation could become a tool for tumor detection.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0051-y) contains supplementary material, which is available to authorized users.

Nonparametric Bayesian clustering to detect bipolar methylated genomic loci

Background

With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events. Among these methylation patterns, bipolar patterns are important as they may originate from allele-specific methylation (ASM) or cell-specific methylation (CSM).

Results

Utilizing nonparametric Bayesian clustering followed by hypothesis testing, we have developed a novel statistical approach to identify bipolar methylated genomic regions in bisulfite sequencing data. Simulation studies demonstrate that the proposed method achieves good performance in terms of specificity and sensitivity. We used the method to analyze data from mouse brain and human blood methylomes. The bipolar methylated segments detected are found highly consistent with the differentially methylated regions identified by using purified cell subsets.

Conclusions

Bipolar DNA methylation often indicates epigenetic heterogeneity caused by ASM or CSM. With allele-specific events filtered out or appropriately taken into account, our proposed approach sheds light on the identification of cell-specific genes/pathways under strong epigenetic control in a heterogeneous cell population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0439-2) contains supplementary material, which is available to authorized users.

Deciphering the heterogeneity in DNA methylation patterns during stem cell differentiation and reprogramming

BACKGROUND

Human induced pluripotent stem cells (iPSCs) have a wide range of applications throughout the fields of basic research, disease modeling and drug screening. Epigenetic instable iPSCs with aberrant DNA methylation may divide and differentiate into cancer cells. Unfortunately, little effort has been taken to compare the epigenetic variation in iPSCs with that in differentiated cells. Here, we developed an analytical procedure to decipher the DNA methylation heterogeneity of mixed cells and further exploited it to quantitatively assess the DNA methylation variation in the methylomes of adipose-derived stem cells (ADS), mature adipocytes differentiated from ADS cells (ADS-adipose) and iPSCs reprogrammed from ADS cells (ADS-iPSCs).

RESULTS

We observed that the degree of DNA methylation variation varies across distinct genomic regions with promoter and 5'UTR regions exhibiting low methylation variation and Satellite showing high methylation variation. Compared with differentiated cells, ADS-iPSCs possess globally decreased methylation variation, in particular in repetitive elements. Interestingly, DNA methylation variation decreases in promoter regions during differentiation but increases during reprogramming. Methylation variation in promoter regions is negatively correlated with gene expression. In addition, genes showing a bipolar methylation pattern, with both completely methylated and completely unmethylated reads, are related to the carbohydrate metabolic process, cellular development, cellular growth, proliferation, etc.

CONCLUSIONS

This study delivers a way to detect cell-subset specific methylation genes in a mixed cell population and provides a better understanding of methylation dynamics during stem cell differentiation and reprogramming.

Tumor LINE-1 methylation level and microsatellite instability in relation to colorectal cancer prognosis

BACKGROUND

Hypomethylation in long interspersed nucleotide element-1 (LINE-1) and high-degree microsatellite instability (MSI-high) in colorectal cancer (CRC) have been associated with inferior and superior survival, respectively; however, it remains uncertain whether the prognostic association of LINE-1 hypomethylation differs by MSI status. We hypothesized that the adverse prognostic association of LINE-1 hypomethylation might be stronger in MSI-high CRCs than in microsatellite stable (MSS) CRCs.

METHODS

Utilizing 1211 CRCs in the Nurses' Health Study and the Health Professionals Follow-up Study, we examined patient survival according to LINE-1 hypomethylation status in strata of MSI status. A Cox proportional hazards model was used to compute multivariable CRC-specific mortality hazard ratios (HRs) for a 10% decrease in LINE-1 methylation level (range = 23.1-93.1%), adjusting for potential confounders, including CpG island methylator phenotype, and KRAS, BRAF, and PIK3CA mutations. Statistical tests (log-rank test, chi-square test, and likelihood ratio test) were two-sided.

RESULTS

In MSI-high cancers, the association of LINE-1 hypomethylation with higher mortality (HR = 2.45, 95% confidence interval [CI] = 1.64 to 3.66, P < .001) was stronger than that in MSS cancers (HR = 1.10, 95% CI = 0.98 to 1.24, P = .11) (P interaction < .001, between LINE-1 and MSI statuses). In MSI-high cases with CRC family history, the association of LINE-1 hypomethylation with higher mortality (HR = 5.13, 95% CI = 1.99 to 13.2; P < .001) was stronger than that in MSI-high cases without CRC family history (HR = 1.62, 95% CI = 0.89 to 2.94, P = .11) (P interaction = .02, between LINE-1 and CRC family history statuses).

CONCLUSIONS

The association of LINE-1 hypomethylation with inferior survival is stronger in MSI-high CRCs than in MSS CRCs. Tumor LINE-1 methylation level may be a useful prognostic biomarker to identify aggressive carcinomas among MSI-high CRCs.

The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation

The faithful transmission of DNA methylation patterns through cell divisions is essential for the daughter cells to retain a proper cell identity. To achieve a comprehensive assessment of methylation fidelity, we implemented a genome-scale hairpin bisulfite sequencing approach to generate methylation data for DNA double strands simultaneously. We show here that methylation fidelity increases globally during differentiation of mouse embryonic stem cells (mESCs), and is particularly high in the promoter regions of actively expressed genes and positively correlated with active histone modification marks and binding of transcription factors. The majority of intermediately (40%–60%) methylated CpG dinucleotides are hemi-methylated and have low methylation fidelity, particularly in the differentiating mESCs. While 5-hmC and 5-mC tend to coexist, there is no significant correlation between 5-hmC levels and methylation fidelity. Our findings may shed new light on our understanding of the origins of methylation variations and the mechanisms underlying DNA methylation transmission.

Quantitative identification of differentially methylated loci based on relative entropy for matched case–control data

Aim: As an important epigenetic modification, DNA methylation plays a critical role in regulating multiple biological processes and diseases. Many efforts have been devoted to identifying differentially methylated loci (DML) between cases and controls. Materials & methods: However, most present methods are statistical and are limited in handling methylation data with characteristics of high heterogeneity and non-normal distribution. Here, a quantitative method, quantitative DML (QDML), based on modified relative entropy is introduced to face these challenges, which can identify DML, hypermethylated loci and hypomethylated loci simultaneously. QDML, compared with some statistical methods, does not require a presupposed distribution of methylation data. Furthermore, QDML is more powerful in handling highly heterogeneous data, owing to the difference in sensitivity on every matched sample pair in case–control groups rather than the overall difference of all samples. Results: Simulation studies and real-data application show that QDML has a higher accuracy and a lower false-positive rate when identifying DML than statistical methods. Conclusion: QDML is developed to identify DML based on relative entropy that can quantify the difference in methylation status between cases and controls. Its applications are not limited to methylation data and can be extended to other case–control studies.

HERV-K and LINE-1 DNA Methylation and Reexpression in Urothelial Carcinoma

Changes in DNA methylation frequently accompany cancer development. One prominent change is an apparently genome-wide decrease in methylcytosine that is often ascribed to DNA hypomethylation at retroelements comprising nearly half the genome. DNA hypomethylation may allow reactivation of retroelements, enabling retrotransposition, and causing gene expression disturbances favoring tumor development. However, neither the extent of hypomethylation nor of retroelement reactivation are precisely known. We therefore assessed DNA methylation and expression of three major classes of retroelements (LINE-1, HERV-K, and AluY) in human urinary bladder cancer tissues and cell lines by pyrosequencing and quantitative reverse transcription–polymerase chain reaction, respectively. We found substantial global LINE-1 DNA hypomethylation in bladder cancer going along with a shift toward full-length LINE-1 expression. Thus, pronounced differences in LINE-1 expression were observed, which may be promoted, among others, by LINE-1 hypomethylation. Significant DNA hypomethylation was found at the HERV-K_22q11.23 proviral long terminal repeat (LTR) in bladder cancer tissues but without reactivation of its expression. DNA methylation of HERVK17, essentially absent from normal urothelial cells, was elevated in cell lines from invasive bladder cancers. Accordingly, the faint expression of HERVK17 in normal urothelial cells disappeared in such cancer cell lines. Of 16 additional HERV-Ks, expression of 7 could be detected in the bladder, albeit generally at low levels. Unlike in prostate cancers, none of these showed significant expression changes in bladder cancer. In contrast, expression of the AluYb8 but not of the AluYa5 family was significantly increased in bladder cancer tissues. Collectively, our findings demonstrate a remarkable specificity of changes in expression and DNA methylation of retroelements in bladder cancer with a significantly different pattern from that in prostate cancer.

DNA methylation profiling in human B cells reveals immune regulatory elements and epigenetic plasticity at Alu elements during B-cell activation

Memory is a hallmark of adaptive immunity, wherein lymphocytes mount a superior response to a previously encountered antigen. It has been speculated that epigenetic alterations in memory lymphocytes contribute to their functional distinction from their naive counterparts. However, the nature and extent of epigenetic alterations in memory compartments remain poorly characterized. Here we profile the DNA methylome and the transcriptome of B-lymphocyte subsets representing stages of the humoral immune response before and after antigen exposure in vivo from multiple humans. A significant percentage of activation-induced losses of DNA methylation mapped to transcription factor binding sites. An additional class of demethylated loci mapped to Alu elements across the genome and accompanied repression of DNA methyltransferase 3A. The activation-dependent DNA methylation changes were largely retained in the progeny of activated B cells, generating a similar epigenetic signature in downstream memory B cells and plasma cells with distinct transcriptional programs. These findings provide insights into the methylation dynamics of the genome during cellular differentiation in an immune response.

DMEAS: DNA methylation entropy analysis software

SUMMARY

DMEAS is the first user-friendly tool dedicated to analyze the distribution of DNA methylation patterns for the quantification of epigenetic heterogeneity. It supports the analysis of both locus-specific and genome-wide bisulfite sequencing data. DMEAS progressively scans the mapping results of bisulfite sequencing reads to extract DNA methylation patterns for contiguous CpG dinucleotides. It determines the DNA methylation level and calculates methylation entropy for genomic segments to enable the quantitative assessment of DNA methylation variations observed in cell populations.

AVAILABILITY AND IMPLEMENTATION

DMEAS program, user guide and all the testing data are freely available from http://sourceforge.net/projects/dmeas/files/

Epigenetic regulation of cancer stem cell gene expression

The concept of cancer as a stem cell disease has slowly gained ground over the last decade. A 'stem-like' state essentially necessitates that some cells in the developing tumor express the properties of remaining quiescent, self-renewing and regenerating tumors through establishment of aberrant cellular hierarchies. Alternatively, such capacities may also be reacquired through a de-differentiation process. The abnormal cellular differentiation patterns involved during either process during carcinogenesis are likely to be driven through a combination of genetic events and epigenetic regulation. The role(s) of the latter is increasingly being appreciated in acquiring the requisite genomic specificity and flexibility required for phenotypic plasticity, specifically in a context wherein genome sequences are not altered for differentiation to ensue. In this chapter, the recent advances in elucidating epigenetic mechanisms that govern the self-renewal, differentiation and regenerative potentials of cancer stem cells will be presented.

Cellular redox, epigenetics and diseases

Since the Central dogma of Molecular Biology was proposed about 40 years ago; our understanding of the intricacies of gene regulation has undergone tectonic shifts almost every decade. It is now widely accepted that the complexity of an organism is not directed by the sheer number of genes it carries but how they are decoded by a myriad of regulatory modules. Over the years, it has emerged that the organizations chromatins and its remodeling; splicing and polyadenylation of pre-mRNAs, stability and localization of mRNAs and modulation of their expression by non-coding and miRNAs play pivotal roles in metazoan gene expression. Nevertheless, in spite of tremendous progress in our understanding of all these mechanisms of gene regulation, the way these events are coordinated leading towards a highly defined proteome of a given cell type remains enigmatic. In that context, the structures of many metazoan genes cannot fully explain their pattern of expression in different tissues, especially during embryonic development and progression of various diseases. Further, numerous studies done during the past quarter of a century suggested that the heritable states of transcriptional activation or repression of a gene can be influenced by the covalent modifications of constituent bases and associated histones; its chromosomal context and long-range interactions between various chromosomal elements (Holliday 1987; Turner 1998; Lyon 1993). However, molecular dissection of these phenomena is largely unknown and is an exciting topic of research under the sub-discipline epigenetics (Gasser et al. 1998).

Analysing and interpreting DNA methylation data

DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.

Constitutive promoter methylation of BRCA1 and RAD51C in patients with familial ovarian cancer and early-onset sporadic breast cancer

Genetic defects in breast cancer (BC) susceptibility genes, most importantly BRCA1 and BRCA2, account for ∼40% of hereditary BC and ovarian cancer (OC). Little is known about the contribution of constitutive (soma-wide) epimutations to the remaining cases. We developed bisulfite pyrosequencing assays to screen >600 affected BRCA1/BRCA2 mutation-negative patients from the German Consortium for Hereditary Breast and Ovarian Cancer for constitutive hypermethylation of ATM, BRCA1, BRCA2, RAD51C, PTEN and TP53 in blood cells. In a second step, patients with ≥6% promoter methylation were analyzed by bisulfite plasmid sequencing to demonstrate the presence of hypermethylated alleles (epimutations), indicative of epigenetic gene silencing. Altogether we identified nine (1.4%) patients with constitutive BRCA1 and three (0.5%) with RAD51C hypermethylation. Epimutations were found in both sporadic cases, in particular in 2 (5.5%) of 37 patients with early-onset BC, and familial cases, in particular 4 (10%) of 39 patients with OC. Hypermethylation was always confined to one of the two parental alleles in a subset (12–40%) of the analyzed cells. Because epimutations occurred in cell types from different embryonal layers, they most likely originated in single cells during early somatic development. We propose that analogous to germline genetic mutations constitutive epimutations may serve as the first hit of tumor development. Because the role of constitutive epimutations in cancer development is likely to be largely underestimated, future strategies for effective testing of susceptibility to BC and OC should include an epimutation screen.

Individual variation and longitudinal pattern of genome-wide DNA methylation from birth to the first two years of life

Prenatal development and early childhood are critical periods for establishing the tissue-specific epigenome, and may have a profound impact on health and disease in later life. However, epigenomic profiles at birth and in early childhood remain largely unexplored. The focus of this report is to examine the individual variation and longitudinal pattern of genome-wide DNA methylation levels from birth through the first two years of life in 105 Black children (59 males and 46 females) enrolled at the Boston Medical Center. We performed epigenomic mapping of cord blood at birth and venous blood samples from the same set of children within the first two years of life using Illumina Infinium Humanmethylation27 BeadChip. We observed a wide range of inter-individual variations in genome-wide methylation at each time point including lower levels at CpG islands, TSS200, 5'UTR and 1st Exon locations, but significantly higher levels in CpG shores, shelves, TSS1500, gene body and 3'UTR. We identified CpG sites with significant intra-individual longitudinal changes in the first two years of life throughout the genome. Specifically, we identified 159 CpG sites in males and 149 CpG sites in females with significant longitudinal changes defined by both statistical significance and magnitude of changes. These significant CpG sites appeared to be located within genes with important biological functions including immunity and inflammation. Further studies are needed to replicate our findings, including analysis by specific cell types, and link those individual variations and longitudinal changes with specific health outcomes in early childhood and later life.

The influence of cis-regulatory elements on DNA methylation fidelity

It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation (“methylome”), i.e., predominant hypomethylation and localized hypermethylation, within “CpG islands” (CGIs). Moreover, although cancer cells have reduced methylation “fidelity” and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical Bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high- methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable “random” methylation (582 loci). These results support a “stochastic model” of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10⁻⁵) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having “random” methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs.

Demethylation of the FCER1G promoter leads to FcεRI overexpression on monocytes of patients with atopic dermatitis

BACKGROUND

Overexpression of the high-affinity receptor for immunoglobulin E on atopic monocytes and dendritic cells is known to contribute to the pathogenesis of atopic dermatitis (AD). However, it remains unclear what is the underlying mechanism of FcεRI deregulation. It has been speculated that epigenetic deregulation may play a role.

METHODS

Global DNA methylation levels of monocytes from 10 AD patients and 10 healthy controls were measured using a global DNA methylation kit. Bisulfite sequencing was performed to determine the methylation status of the FCER1G promoter region. FcεRIγ mRNA and FcεRI protein levels were detected by real-time RT-PCR, Western blotting, and flow cytometry, respectively. Patch methylation and the demethylating agent, 5-azacytidine, were used to determine the functional significance of methylation changes on FcεRI expression.

RESULTS

Monocytes from AD patients show a global hypomethylation, as well as a locus-specific hypomethylation at FCER1G promoter, as compared to healthy controls. Furthermore, this hypomethylation of FCER1G is inversely correlated with its expression. Patch methylation in combination with luciferase reporter assay confirmed the direct relationship between methylation and expression. Moreover, treating healthy monocytes with 5-azacytidine caused a reduction in methylation levels and an induction in FcεRIγ transcription and surface expression of FcεRI.

CONCLUSION

Demethylation of specific regulatory elements within the FCER1G locus contributes to FcεRI overexpression on monocytes from patients with AD.

Nuclear receptor HNF4α binding sequences are widespread in Alu repeats

Background

Alu repeats, which account for ~10% of the human genome, were originally considered to be junk DNA. Recent studies, however, suggest that they may contain transcription factor binding sites and hence possibly play a role in regulating gene expression.

Results

Here, we show that binding sites for a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors, hepatocyte nuclear factor 4alpha (HNF4α, NR2A1), are highly prevalent in Alu repeats. We employ high throughput protein binding microarrays (PBMs) to show that HNF4α binds > 66 unique sequences in Alu repeats that are present in ~1.2 million locations in the human genome. We use chromatin immunoprecipitation (ChIP) to demonstrate that HNF4α binds Alu elements in the promoters of target genes (ABCC3, APOA4, APOM, ATPIF1, CANX, FEMT1A, GSTM4, IL32, IP6K2, PRLR, PRODH2, SOCS2, TTR) and luciferase assays to show that at least some of those Alu elements can modulate HNF4α-mediated transactivation in vivo (APOM, PRODH2, TTR, APOA4). HNF4α-Alu elements are enriched in promoters of genes involved in RNA processing and a sizeable fraction are in regions of accessible chromatin. Comparative genomics analysis suggests that there may have been a gain in HNF4α binding sites in Alu elements during evolution and that non Alu repeats, such as Tiggers, also contain HNF4α sites.

Conclusions

Our findings suggest that HNF4α, in addition to regulating gene expression via high affinity binding sites, may also modulate transcription via low affinity sites in Alu repeats.

Epigenetics and colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. It results from an accumulation of genetic and epigenetic changes in colon epithelial cells, which transforms them into adenocarcinomas. Over the past decade, major advances have been made in understanding cancer epigenetics, particularly regarding aberrant DNA methylation. Assessment of the colon cancer epigenome has revealed that virtually all CRCs have aberrantly methylated genes and that the average CRC methylome has hundreds to thousands of abnormally methylated genes. As with gene mutations in the cancer genome, a subset of these methylated genes, called driver genes, is presumed to have a functional role in CRC. The assessment of methylated genes in CRCs has also revealed a unique molecular subgroup of CRCs called CpG island methylator phenotype (CIMP) cancers; these tumors have a particularly high frequency of methylated genes. These advances in our understanding of aberrant methylation in CRC have led to epigenetic alterations being developed as clinical biomarkers for diagnostic, prognostic and therapeutic applications. Progress in this field suggests that these epigenetic alterations will be commonly used in the near future to direct the prevention and treatment of CRC.

Methylation of L1Hs promoters is lower on the inactive X, has a tendency of being higher on autosomes in smaller genomes and shows inter-individual variability at some loci

LINE-1 repeats account for ∼17% of the human genome. Little is known about their individual methylation patterns, because their repetitive, almost identical sequences make them difficult to be individually targeted. Here, we used bisulfite conversion to study methylation at individual LINE-1 repeats. The loci studied included 39 X-linked loci and 5 autosomal loci. On the X chromosome in women, we found statistically significant less methylation at almost all L1Hs compared with men. Methylation at L1P and L1M did not correlate with the inactivation status of the host DNA, while the majority of L1Hs that were possible to be studied lie in inactivated regions. To investigate whether the male–female differences at L1Hs on the X are linked to the inactivation process itself rather than to a mere influence of gender, we analyzed six of the L1Hs loci on the X chromosome in Turners and Klinefelters which have female and male phenotype, respectively, but with reversed number of X chromosomes. We could confirm that all samples with two X chromosomes are hypomethylated at the L1Hs loci. Therefore, the inactive X is hypomethylated at L1Hs; the latter could play an exclusive role in the X chromosome inactivation process. At autosomal L1Hs, methylation levels showed a correlation tendency between methylation level and genome size, with higher methylation observed at most loci in individuals with one X chromosome and the lowest in XXY individuals. In summary, loci-specific LINE-1 methylation levels show considerable plasticity and depend on genomic position and constitution.

Long interspersed nuclear element-1 hypomethylation in cancer: biology and clinical applications

Epigenetic changes in long interspersed nuclear element-1s (LINE-1s or L1s) occur early during the process of carcinogenesis. A lower methylation level (hypomethylation) of LINE-1 is common in most cancers, and the methylation level is further decreased in more advanced cancers. Consequently, several previous studies have suggested the use of LINE-1 hypomethylation levels in cancer screening, risk assessment, tumor staging, and prognostic prediction. Epigenomic changes are complex, and global hypomethylation influences LINE-1s in a generalized fashion. However, the methylation levels of some loci are dependent on their locations. The consequences of LINE-1 hypomethylation are genomic instability and alteration of gene expression. There are several mechanisms that promote both of these consequences in cis. Therefore, the methylation levels of different sets of LINE-1s may represent certain phenotypes. Furthermore, the methylation levels of specific sets of LINE-1s may indicate carcinogenesis-dependent hypomethylation. LINE-1 methylation pattern analysis can classify LINE-1s into one of three classes based on the number of methylated CpG dinucleotides. These classes include hypermethylation, partial methylation, and hypomethylation. The number of partial and hypermethylated loci, but not hypomethylated LINE-1s, is different among normal cell types. Consequently, the number of hypomethylated loci is a more promising marker than methylation level in the detection of cancer DNA. Further genome-wide studies to measure the methylation level of each LINE-1 locus may improve PCR-based methylation analysis to allow for a more specific and sensitive detection of cancer DNA or for an analysis of certain cancer phenotypes.