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The human genome is folded into 3D structures, including compartments and TADs (Topologically Associating Domains). These 3D structures are critical for many cellular processes. For example, TADs define the regulatory region of gene expression. Disrupting TAD organization can change gene expression and cause human diseases including cancer. Our lab aims to explore the molecular mechanisms that fold the genome, and identify novel diagnostic and therapeutical targets to manage cancer.
The mechanisms that fold and regulate the genome
The loop extrusion model was proposed to explain how the TADs and loops are formed. In this model, CTCF and cohesin are two key players. CTCF is a transcription factor and is enriched at TAD boudaries as TAD insulators, while cohesin is the key molecule that can bind to chromatin and extrude chromatin loops. This activity stops at CTCF binding sites. Although both genetic and single molecular assays provide solid supporting evidence, the mechanistic detail is largely unclear. Our lab incorporates biochemical assays with genomic analyses to explore how cohesin folds the genome and how this process is regulated.
"A" compartments contain active chromatin with open conformation, whereas in "B" compartments, chromatin is closed and inactive. "A/B" compartments have distinct epigenetic modifications. Based on these differences, the current microphase separation model was proposed. It is believed that chromatin blocks with similar properties tend to cluster together to form A/B compartments. However, direct supporting evidence and mechanistic detail are both lacking. We are using biophysical and genomic methods to examine how the A/B compartments are formed.
The interplay between histone modifications and genome folding
Epigenetic modifications determine chromatin states and affect chromatin structures. We aim to understand the relationship between chromatin states and 3D structures.
Biological and clinical relevance of genome folding
To explore the biological and clinical relevance of 3D genome organization in cancer, we are integrating our mechanism results with omics and machine learning analyses. We aim to draw a comprehensive picture of 3D nuclear dynamics in cancer, which will aid to improve diagnostics and support the development of new therapeutic approaches.
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