bioRxivGenomicsPreprint
Developmentally dynamic changes in DNA methylation in the human pancreas
Ailsa MacCalman, Elisa De FrancoiD, Alice FrankliniD, Christine S. FlaxmaniD, Sarah J. RichardsoniD, Kathryn MurralliD, Joe BurrageiD, Barts Pancreas Tissue Bank (BPTB), Emma WalkeriD, Noel G. MorganiD, Andrew Tym HattersleyiD, Emma Louise DempsteriD, Eilis J. HannoniD, Aaron R. JeffriesiD, Nick D. L. OwensiD, Jonathan S. MilliD
Abstract
Development of the human pancreas requires the precise temporal control of gene expression via epigenetic mechanisms and the binding of key transcription factors. We quantified genome-wide patterns of DNA methylation in human fetal pancreatic samples from donors aged 6 to 21 post-conception weeks. We found dramatic changes in DNA methylation across pancreas development, with >21% of sites characterized as developmental differentially methylated positions (dDMPs) including many annotated to genes associated with monogenic diabetes. An analysis of DNA methylation in postnatal pancreas tissue showed that the dramatic temporal changes in DNA methylation occurring in the developing pancreas are largely limited to the prenatal period. Significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small proportion of sites showing sex-specific DNA methylation trajectories across pancreas development. Pancreas dDMPs were not distributed equally across the genome, and were depleted in regulatory domains characterized by open chromatin and the binding of known pancreatic development transcription factors. Finally, we compared our pancreas dDMPs to previous findings from the human brain, identifying evidence for tissue-specific developmental changes in DNA methylation. To our knowledge, this represents the most extensive exploration of DNA methylation patterns during human fetal pancreas development, confirming the prenatal period as a time of major epigenomic plasticity.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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