CLINICAL GENETICS

Personalized DNA methylomics

DNA methylation dynamics likely influence the pathobiology of disease, but few studies have been performed on the temporal and spatial regulation of the DNA methylome. Chen et al. now report the DNA methylome and transcriptome of a male individual at time points over 3 years. They find that, whereas changes in the transcriptome are associated with viral infection (an acute event), changes in the DNA methylome are associated with elevated glucose levels and might thus regulate chronic conditions.

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The authors profiled the transcriptome (using RNA sequencing) of peripheral blood mononuclear cells (PBMCs) from a 54-year-old male at 57 time points over 3 years. During this time the individual had six viral infections (which lasted days) and two periods of elevated glucose levels (which lasted months). Taking these events into account, the authors profiled the DNA methylome (by whole-genome bisulfite sequencing (WGBS)) of the same PBMC samples at 28 time points spanning periods of health, infection and high glucose levels.

Initially, the DNA methylome was assessed for differentially methylated regions (DMRs) between adjacent time points, revealing that the highest numbers of DMRs occurred between the onset of glucose elevation and the time points immediately preceding this. By comparing the methylomes of glucose-normal samples and glucose-elevated samples (the 110 days before glucose elevation were classed as ‘glucose-elevated’), the authors showed that the highest number of DMRs occurred at 80 and 90 days before glucose elevation and were located 50–500 kb from transcription start sites (TSSs).

To understand the link between DNA methylation and glucose elevation further the authors assessed changes in the methylome at gene promoters across the whole time course. Genes that had differentially methylated promoters over time were enriched for genes associated with glucose-related and diabetes mellitus-related pathways, whereas transcriptome analysis revealed that changes in gene expression levels over time were enriched for genes associated with immune-related pathways. Thus, the authors speculated that changes in the methylome and transcriptome might be associated with chronic and acute health conditions, respectively.

In support of this hypothesis, the highest percentage of differentially methylated genes (including those associated with glucose-related and diabetes mellitus-related pathways) occurred at 80 and 90 days before glucose elevation, in line with the highest number of DMRs occurring at these times. By contrast, changes in gene expression during periods of viral infection were associated with immune-related pathways. Furthermore, cytosines that were differentially methylated between high and low glucose events were more enriched at promoter regions, TSSs and transcription factor binding sites than cytosines that were differentially methylated between viral infection and non-viral infection states; these data suggest that changes in glucose level influence gene expression through promoter DNA methylation more than viral infections do.

Finally, as the authors had previously identified which alleles were specific to either maternal or paternal chromosomes, they could assess allele-specific methylation (ASM) patterns over the time course. ASM patterns were stable over the 26 time points assessed; this enabled the authors to combine the WGBS reads from all samples and characterize ASM patterns at an unprecedented depth. This characterization indicated a higher density of allelic DMRs (aDMRs) on chromosomes 4, 17 and 19 and showed that the number of DMRs correlated with the number of genes in each chromosome; thus, aDMRs might help to regulate gene expression.

“changes in the methylome and transcriptome might be associated with chronic and acute health conditions, respectively”

This study indicates that, at least for this individual, the DNA methylome and transcriptome are associated with different health-related events and that personalized DNA methylomics might predict disease.

References

Original article

  1. Chen, R. et al. Longitudinal personal DNA methylome dynamics in a human with a chronic condition. Nat. Med. https://doi.org/10.1038/s41591-018-0237-x (2018)

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Further reading

  1. Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018)

    CAS  Article  Google Scholar 

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Correspondence to Katharine H. Wrighton.

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Wrighton, K.H. Personalized DNA methylomics. Nat Rev Genet 20, 4–5 (2019). https://doi.org/10.1038/s41576-018-0076-0

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