Mapping DNA’s hidden switches: A methylation atlas microbiologystudy

A new study has been published in Nature Communications, presenting the first comprehensive atlas of allele-specific DNA methylation across 39 primary human cell types. The study was led by Ph.D. student Jonathan Rosenski under the guidance of Prof. Tommy Kaplan from the School of Computer Science and Engineering and Prof. Yuval Dor from the Faculty of Medicine at the Hebrew University of Jerusalem and Hadassah Medical Center.

Using machine learning algorithms and deep whole-genome bisulfite sequencing on freshly isolated and purified cell populations, the study unveils a detailed landscape of genetic and epigenetic regulation that could reshape our understanding of gene expression and disease.

A key focus of the research is the success in identifying differences between the two alleles and, in some cases, demonstrating that these differences result from genomic imprinting—meaning that it is not the sequence (genetics) that matters, but rather whether the allele is inherited from the mother or the father. These findings could reshape our understanding of gene expression and disease.

Key findings include:

• Scope of bimodal methylation: Identification of 325,000 genomic regions—approximately 6% of the genome and 11% of CpG sites—that exhibit a bimodal pattern of fully methylated and fully unmethylated molecules.
• Allele-specific insights: In 34,000 of these regions, genetic variations (SNPs) correlate with the methylation patterns, confirming allele-specific methylation and indicating the extent of genetic influence on DNA methylation.
• Novel imprinting discoveries: Detection of 460 regions with parental allele-specific methylation, including hundreds of previously unknown imprinted regions.
• Tissue-specific variability: Evidence that both sequence-dependent and parental allele-specific methylation are frequently unique to specific tissues or cell types, revealing previously unappreciated diversity in epigenetic regulation across the human body.
• Implications for pathogenesis of genetic diseases: Validation of tissue-specific, maternal allele-specific methylation of the CHD7 gene suggests a potential mechanism for the paternal bias observed in CHARGE syndrome inheritance.

This research leverages the power of whole-genome bisulfite sequencing to characterize DNA methylation patterns at an unprecedented resolution.

By analyzing sorted samples representing a wide range of healthy human cell types, and using advanced machine learning algorithms and genetic information to disentangle the methylation patterns of the two parental copies of DNA, the team precisely identified hundreds of “imprinted” regions—where the maternal allele is methylated and silenced while the paternal allele is active, or vice versa.

“Genomic imprinting is set early during development, and the common dogma was that it is then maintained throughout life across all cell types. Yet, our atlas not only confirms most previously known imprinted regions, but we also identified many novel regions showing parental imprinting in a cell-type-specific manner,” explained Prof. Kaplan.

“These findings open new avenues for investigating how parental methylation influences gene regulation and the development of certain diseases.”

Prof. Dor added, “The discovery of tissue-specific imprinting, such as that observed in CHD7, highlights the dynamic nature of epigenetic regulation. This could have implications for understanding some autosomal dominant diseases (i.e. when a mutation inherited from one parent, but not the other parent, is sufficient to cause disease) and for developing innovative diagnostic tools.”

The atlas represents a valuable resource for the scientific community, offering a platform for further computational and molecular analyses of allele-specific methylation. Its insights may lead to novel strategies for diagnosing imprinting-related disorders and exploring therapeutic interventions based on tissue-specific epigenetic profiles.

Researchers and clinicians are encouraged to explore this extensive dataset to uncover new regulatory mechanisms underlying imprinted gene expression and to better understand the complex interplay between genetic variation and epigenetic modifications.