bioRxivGenomicsPreprint
WGBS of Differentiating Adipocytes Reveals Variations in DMRs and Context-Dependent Gene Expression
Binduma YadaviD, Dalwinder SinghiD, Shrikant Subhash MantriiD, Vikas RishiiD
Abstract
Obesity, characterised by the accumulation of excess fat, is a complex condition resulting from the combination of genetic and epigenetic factors. Recent studies have found correspondence between DNA methylation and cell differentiation, suggesting a role of the former in cell fate determination. There is a lack of comprehensive understanding concerning the underpinnings of preadipocyte differentiation, specifically when cells are undergoing terminal differentiation (TD). To gain insight into dynamic genome-wide methylation, 3T3 L1 preadipocyte cells were differentiated by a hormone cocktail. The genomic DNA was isolated from undifferentiated cells and 4 hrs (4H), 2 days (2D) post-differentiated cells, and 15 days (15D) TD cells. We employed whole-genome bisulfite sequencing (WGBS) to ascertain global genomic DNA methylation alterations at single base resolution as preadipocyte cells differentiate. The genome-wide distribution of DNA methylation showed similar overall patterns in pre- and post- and terminally differentiated adipocytes, according to WGBS analysis. DNA methylation decreases at 4H after differentiation initiation, followed by methylation gain as cells approach TD. Studies revealed novel differentially methylated regions (DMRs) associated with adipogenesis. DMR analysis suggested that though DNA methylation is global, noticeable changes are observed at specific sites known as hotspots. Hotspots are genomic regions rich in transcription factor (TF) binding sites and exhibit methylation-dependent TF binding. Subsequent analysis indicated hotspots as part of DMRs. The gene expression profile of key adipogenic genes in differentiating adipocytes is context-dependent, as we found a direct and inverse relationship between promoter DNA methylation and gene expression.
- organism: mouse
- assay: whole-genome sequencing
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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