RLM: Fast and simplified extraction of Read-Level Methylation metrics from bisulfite sequencing data

Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin that reflect nucleosome organisation

High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome.

Dynamic antagonism between key repressive pathways maintains the placental epigenome

DNA and Histone 3 Lysine 27 methylation typically function as repressive modifications and operate within distinct genomic compartments. In mammals, the majority of the genome is kept in a DNA methylated state, whereas the Polycomb repressive complexes regulate the unmethylated CpG-rich promoters of developmental genes. In contrast to this general framework, the extra-embryonic lineages display non-canonical, globally intermediate DNA methylation levels, including disruption of local Polycomb domains. Here, to better understand this unusual landscape's molecular properties, we genetically and chemically perturbed major epigenetic pathways in mouse trophoblast stem cells. We find that the extra-embryonic epigenome reflects ongoing and dynamic de novo methyltransferase recruitment, which is continuously antagonized by Polycomb to maintain intermediate, locally disordered methylation. Despite its disorganized molecular appearance, our data point to a highly controlled equilibrium between counteracting repressors within extra-embryonic cells, one that can seemingly persist indefinitely without bistable features typically seen for embryonic forms of epigenetic regulation.

mHapTk: a comprehensive toolkit for the analysis of DNA methylation haplotypes

SUMMARY

Bisulfite sequencing remains the gold standard technique to detect DNA methylation profiles at single-nucleotide resolution. The DNA methylation status of CpG sites on the same fragment represents a discrete methylation haplotype (mHap). The mHap-level metrics were demonstrated to be promising cancer biomarkers and explain more gene expression variation than average methylation. However, most existing tools focus on average methylation and neglect mHap patterns. Here, we present mHapTk, a comprehensive python toolkit for the analysis of DNA mHap. It calculates eight mHap-level summary statistics in predefined regions or across individual CpG in a genome-wide manner. It identifies methylation haplotype blocks, in which methylations of pairwise CpGs are tightly correlated. Furthermore, mHap patterns can be visualized with the built-in functions in mHapTk or external tools such as IGV and deepTools.

AVAILABILITY AND IMPLEMENTATION

https://jiantaoshi.github.io/mhaptk/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Acute lymphoblastic leukemia displays a distinct highly methylated genome

DNA methylation is tightly regulated during development and is stably maintained in healthy cells. In contrast, cancer cells are commonly characterized by a global loss of DNA methylation co-occurring with CpG island hypermethylation. In acute lymphoblastic leukemia (ALL), the commonest childhood cancer, perturbations of CpG methylation have been reported to be associated with genetic disease subtype and outcome, but data from large cohorts at a genome-wide scale are lacking. Here, we performed whole-genome bisulfite sequencing across ALL subtypes, leukemia cell lines and healthy hematopoietic cells, and show that unlike most cancers, ALL samples exhibit CpG island hypermethylation but minimal global loss of methylation. This was most pronounced in T cell ALL and accompanied by an exceptionally broad range of hypermethylation of CpG islands between patients, which is influenced by TET2 and DNMT3B. These findings demonstrate that ALL is characterized by an unusually highly methylated genome and provide further insights into the non-canonical regulation of methylation in cancer.