Heterogeneity and randomness of DNA methylation patterns in human embryonic stem cells

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

OUP accepted manuscript

Motivation

Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.

Results

We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.

Availability and implementation

MeConcord is available at https://github.com/WangLabTHU/MeConcord.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bivalent Epigenetic Control of Oncofetal Gene Expression in Cancer

Multiple mechanisms of epigenetic control that include DNA methylation, histone modification, noncoding RNAs, and mitotic gene bookmarking play pivotal roles in stringent gene regulation during lineage commitment and maintenance. Experimental evidence indicates that bivalent chromatin domains, i.e., genome regions that are marked by both H3K4me3 (activating) and H3K27me3 (repressive) histone modifications, are a key property of pluripotent stem cells. Bivalency of developmental genes during the G₁ phase of the pluripotent stem cell cycle contributes to cell fate decisions. Recently, some cancer types have been shown to exhibit partial recapitulation of bivalent chromatin modifications that are lost along with pluripotency, suggesting a mechanism by which cancer cells reacquire properties that are characteristic of undifferentiated, multipotent cells. This bivalent epigenetic control of oncofetal gene expression in cancer cells may offer novel insights into the onset and progression of cancer and may provide specific and selective options for diagnosis as well as for therapeutic intervention.

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.

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/

Regionally specific and genome-wide analyses conclusively demonstrate the absence of CpG methylation in human mitochondrial DNA

Although CpG methylation clearly distributes genome-wide in vertebrate nuclear DNA, the state of methylation in the vertebrate mitochondrial genome has been unclear. Several recent reports using immunoprecipitation, mass spectrometry, and enzyme-linked immunosorbent assay methods concluded that human mitochondrial DNA (mtDNA) has much more than the 2 to 5% CpG methylation previously estimated. However, these methods do not provide information as to the sites or frequency of methylation at each CpG site. Here, we have used the more definitive bisulfite genomic sequencing method to examine CpG methylation in HCT116 human cells and primary human cells to independently answer these two questions. We found no evidence of CpG methylation at a biologically significant level in these regions of the human mitochondrial genome. Furthermore, unbiased next-generation sequencing of sodium bisulfite treated total DNA from HCT116 cells and analysis of genome-wide sodium bisulfite sequencing data sets from several other DNA sources confirmed this absence of CpG methylation in mtDNA. Based on our findings using regionally specific and genome-wide approaches with multiple human cell sources, we can definitively conclude that CpG methylation is absent in mtDNA. It is highly unlikely that CpG methylation plays any role in direct control of mitochondrial function.

Both CpG methylation and activation-induced deaminase are required for the fragility of the human bcl-2 major breakpoint region: implications for the timing of the breaks in the t(14;18) translocation

The t(14;18) chromosomal translocation typically involves breakage at the bcl-2 major breakpoint region (MBR) to cause human follicular lymphoma. A theory to explain the striking propensity of the MBR breaks at three CpG clusters within the 175-bp MBR region invoked activation-induced deaminase (AID). In a test of that theory, we used here minichromosomal substrates in human pre-B cell lines. Consistent with the essential elements of the theory, we found that the MBR breakage process is indeed highly dependent on DNA methylation at the CpG sites and highly dependent on the AID enzyme to create lesions at peak locations within the MBR. Interestingly, breakage of the phosphodiester bonds at the AID-initiated MBR lesions is RAG dependent, but, unexpectedly, most are also dependent on Artemis. We found that Artemis is capable of nicking small heteroduplex structures and is even able to nick single-base mismatches. This raises the possibility that activated Artemis, derived from the unjoined D to J(H) DNA ends at the IgH locus on chromosome 14, nicks AID-generated TG mismatches at methyl CpG sites, and this would explain why the breaks at the chromosome 18 MBR occur within the same time window as those on chromosome 14.