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Longitudinal personal DNA methylome dynamics in a human with a chronic condition


Epigenomics regulates gene expression and is as important as genomics in precision personal health, as it is heavily influenced by environment and lifestyle. We profiled whole-genome DNA methylation and the corresponding transcriptome of peripheral blood mononuclear cells collected from a human volunteer over a period of 36 months, generating 28 methylome and 57 transcriptome datasets. We found that DNA methylomic changes are associated with infrequent glucose level alteration, whereas the transcriptome underwent dynamic changes during events such as viral infections. Most DNA meta-methylome changes occurred 80–90 days before clinically detectable glucose elevation. Analysis of the deep personal methylome dataset revealed an unprecedented number of allelic differentially methylated regions that remain stable longitudinally and are preferentially associated with allele-specific gene regulation. Our results revealed that changes in different types of ‘omics’ data associate with different physiological aspects of this individual: DNA methylation with chronic conditions and transcriptome with acute events.

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Data availability

The GEO accession number for all of the MethylC-seq and RNA-seq datasets generated in this study is GSE111405. For RNA-seq data from day 0 to day 400 (published previously2), the GEO accession number is GSE33029.

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This work is supported by the following grants from the National Institutes of Health: 5U54DK10255603 and 5P50HG00773503 (M.S.); by grants 91631111, 31571327 and 31771426 from Chinese National Natural Science Foundation (D.X.); as well as by funding from Stanford University. M.S. is a cofounder and member of the scientific advisory board of Personalis and Q-bio.

Author information

M.S., D.X. and R.C. conceived and designed the study. R.C. performed the experiments and generated the data with the help of K.K. and J.L.-P.-T. R.C., L.X., K.T. and M.D. analyzed the data. M.S., D.X., R.C., L.X. and K.T. wrote the paper. All authors read, edited and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Dan Xie or Michael Snyder.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–9

  2. Reporting Summary

  3. Supplementary Table 1

    Health parameters of the participant

  4. Supplementary Table 2

    The DMR between adjacent time points using Bsmooth algorithm

  5. Supplementary Table 3

    The DMR between glucose level elevated state and normal state using Bsmooth algorithm

  6. Supplementary Table 4

    List of results from functional enrichment analysis of ADV1-specific DEGs and ADV2-specific DEGs

  7. Supplementary Table 5

    List of all aDMRs and their associated genes

  8. Supplementary Table 6

    RNA-seq read counts of each sample

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Further reading

Fig. 1: Overview of methylome and transcriptome data during time series.
Fig. 2: The dynamic pattern of the whole-genome methylome.
Fig. 3: Methylomic changes were associated with glucose alterations.
Fig. 4: Changes in gene expression during infection.
Fig. 5: Annotation of the differentially methylated sites related to glucose elevation and DEGs during viral infection.
Fig. 6: Allele-specific methylation regions profile.