bioRxivBiophysicsPreprint
Polymer Model Unveils Quantitative Association of Chromatin Conformation and Gene Regulation
Abstract
The spatial organization of chromatin in the nucleus is of essence for regulating gene transcription. However, the mechanisms governing the intricate interplay of chromatin structure and gene transcription remain poorly understood. Hi-C experiments have unveiled a multiscale chromatin organization, significantly enriching our understanding of the structural control of gene expression. We introduce a computational framework to link chromatin structural modifications to gene regulation. This framework generates an ensemble of three-dimensional conformations of a given genomic locus using a bead-spring polymer model where the cHiC contact map is used as an input. We then correlate such chromatin conformations to the transcription level of the encoded genes using a Markov chain-based model, where binding/unbinding rates extracted from the molecular dynamics trajectories are used. By considering a specific example, we have demonstrated that the deletion of the CTCF binding domain between two consecutive TADs leads to a significant change in the enhancer-promoter interaction and gene transcription of the encoded genes, namely sox9 and kcnj2, responsible for limb development. Such a change in gene expression level is quantitatively consistent with experiments. Further insight from the polymer-based 3D conformation reveals that the higher gene expression level of the kcnj2 gene is caused by the specific enhancers of kcnj2, present in the sox9 TAD, which become accessible after boundary deletion. By quantifying the impact of these enhancers, our model can also be used to identify the functional enhancers. Together, the present computational framework not only advances our understanding of the relationship between the spatial architecture of the chromosome and the function of the cell but also provides invaluable insights into potential therapeutic interventions targeting aberrant gene regulation in pathological contexts.
- organism: human
- assay: Hi-C
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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