(712b) Physics-Based Modeling of Chromosomal Organization Impacted By Multiple Epigenetic Factors

Dysregulation of chromatin organization contributes to age-related diseases, such as neurological disorders and cancers, which collectively account for millions of deaths and billions of dollars each year. Aberrations in 3D genomic structure disrupt gene regulation, thereby impairing cell function and altering cell identity. Instabilities in DNA folding arise from years of progressive drift in the epigenome­–the patterns of chemical modifications to DNA and histones that govern binding by effector proteins. Interactions between effector proteins stabilize the structure of the genome, slow epigenetic drift, and maintain cell identity. By characterizing these interactions, we may reveal failure modes that can be targeted to enhance epigenetic stability. While the role of epigenetics in aging is apparent, the specifics of effector protein interactions remain poorly characterized. This is due to the lack of mechanistic understanding of effector protein contributions to chromosomal organization and due to the experimental challenge of systematically manipulating the epigenome in vivo. Polymer theory and simulation offer opportunities to efficiently model DNA folding under the influence of interacting effector proteins, enabling fundamental research of chromosomal organization resulting from multiple epigenetic factors.

In this study, we apply a custom-built, physics-based copolymer simulator to model DNA folding from patterns of epigenetic modifications and characteristic effector protein interactions. We leverage the wormlike chain model to capture the semiflexible properties of DNA. Epigenetic modifications along our polymer are fixed based on experimental chromatin immunoprecipitation sequencing (ChIP-seq) data from literature. We sample binding patterns of effector proteins based on their affinities for different chemical modifications and their concentrations in the nuclear environment. Using a field-theoretic approach, we calculate the energy associated with effector protein interactions based on their local densities. Individual components are modeled with experimentally measured interaction parameters, and we modulate these parameters to represent cooperative, noncooperative, and anticooperative interactions between different effector proteins. Through a Monte Carlo algorithm, we sample thermodynamically favorable DNA configurations under variable degrees of effector protein cooperativity. We validate our model using theoretical polymer chain statistics and experimental contact maps.

Our physics-based approach enables us to elucidate causal relationships between environmental factors and DNA organization. We aim to predict chromatin structures for variable effector protein interaction strengths to identify conditions that cause the chromosome to adopt aberrant configurations associated with age-related disease. We also simulate how effector protein interactions affect susceptibility to epigenetic drift. Overall, the study applies polymer theory and simulation to advance mechanistic understanding into biophysical factors contributing to epigenetic diseases.