Colossal, gigantic, mammoth, immense; none of these adjectives appropriately describes the amount of research carried out or curated by the National Institute of Health Roadmap Epigenomics Mapping Consortium, whose final goal is to produce a public resource of human epigenomic data to drive forward basic biology and disease-oriented research. As part of this research, scientists from the lab of Aleksandar Milosavljevic have constructed maps of allelic differences in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors.
DNA methylation levels at critical regions in the human genome can differ between homologous chromosomes due to differences in the underlying sequence; where polymorphisms create allelic methylation “imbalances”. Analyses of “sequence-dependent allele-specific methylation” (or SD-ASM) previously measured average methylation levels across many cells and, has left us craving the essential information apparent at the single-cell and single-chromosome level. However, the deep whole-genome bisulfite sequencing (WGBS) to detect DNA methylation, alongside integrated information on other epigenomic marks and gene transcription employed, in this new study represents a great approach to tackle this frontier of epigenomics.
Here is just a snippet of what analysis of their “titanic” dataset revealed:
- Extensive sequence-dependent CpG methylation differences at thousands of heterozygous regulatory sequences (single nucleotide polymorphisms, or SNPs) known to be bound by transcription factors
- Histone modifications exhibit much lower levels of allelic imbalances
- Random transitions between fully methylated and unmethylated states of DNA, corresponding to “off” and “on” switches, occur at these sequences within individual chromosomal DNA molecules
- The intermediate levels of methylation known to exist at these regulatory sequences reflect the differences in methylation patterns observed between the two chromosomes and explain SD-ASM
- The levels of DNA methylation on each allele corresponds to the ability of transcription factors (TFs) to bind to a said region
- To do this, the authors used 377 TFs assessed for binding affinity using the high-throughput systematic evolution of ligands by exponential enrichment (SELEX) method and Coefficient of Constraint analysis
- While one allele may have a DNA methylation profile that permits transcription factor binding, sequence differences leading to an altered DNA methylation profile may inhibit transcription factor binding to the other allele
- Interestingly, this mode of gene regulation tends to occur at rare disease-associated genetic variants
- The study estimates that around 200 detrimental rare genetic variants display SD-ASM
- The association of housekeeping genes (HKGs) with CpG islands endows HKG’s methylomes with a heightened resistance to changes in the underlying genetic code that may influence DNA methylation
- However, the methylomes of tissue/cell specific-genes not associated with CpG islands display higher sensitivity to changes in the underlying genetic code that promote alterations to DNA methylation
- This may promote positive evolutionary changes by altering the TF profile of a regulatory sequence that controls gene expression
Overall, the authors believe that their analysis provides a unifying model that links sequence-dependent allelic differences in the epigenome, “on/off” switching at gene regulatory sequences, the resistance of essential HKGs to the effects of random mutations, and disease-associated genetic variation.
Senior author Aleksandar Milosavljevic shares, “Our findings may add another layer of complexity that so far has not been taken into account in certain intricate human diseases. If we add this layer of complexity, we might be able to better understand how dosage-sensitive genes may contribute to human diseases that have so far been hard to tackle, such as neuropsychiatric disorders. This work is meant to provide insights into a new important layer of biological complexity and hopefully create the basis for subsequent research into specific diseases.”
For more on this fascinating new study and to see the gargantuan data set for yourself, see Science, September 2018.