Gene regulatory network modeling of disease-associated DNA methylation perturbations

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PROJECT SUMMARY Somatic mutations in DNMT3A and TET2 are common in the hematopoietic lineages of elderly individuals, estimated to affect more than 10% of adults over the age of 65. These mutations increase the risk for age-related comorbidities, including severe infection, atherosclerotic cardiovascular disease, osteoporosis, chronic kidney disease and hematologic malignancies, nearly doubling the mortality rate of affected individuals. DNMT3A and TET2 encode enzymes essential for remodeling DNA methylation during cellular differentiation. Animal studies suggest that mutations in these genes drive aberrant activation of immune cells, such as macrophages, which may underlie the disease associations. We recently developed a human pluripotent stem cell (hPSC)-derived macrophage model, where the differentiation-dependent effects of DNMT3A or TET2 perturbation can be precisely delineated. We discovered that DNMT3A- and TET2- perturbations impaired DNA methylation remodeling at thousands of regulatory loci, altering enhancer activities and expression of genes important for macrophage function. Our study highlighted the need for engineering approaches, and mathematical modeling in particular, to unravel the complex effects of DNMT3A and TET2 perturbations on cellular function and disease risk. Here, we pair novel computational modeling approaches with unique experimental resources to mechanistically connect site-specific changes in DNA methylation to aberrant immune responses and disease risk. Aim 1 builds deep neural network models (and requisite training data resources) to predict the effects of DNA methylation on chromatin binding of 100+ transcription factors (TFs), the “readers” of DNA methylation patterns that ultimately recruit RNA polymerase and co-activators to drive gene transcription. In Aim 2, we predict genome-scale TF-binding patterns from chromatin accessibility, transcriptional activity and DNA methylation data in our contexts of interest: DNMT3A- or TET2-perturbed human macrophages in response to viral and bacterial infection-induced immune activation. To discover links between existing and novel disease associations, we will intersect the TFBS predictions with curated sets of age-related disease risk variants, to nominate TFs and contexts where DNMT3A- or TET2-perturbation and downstream alterations in TF binding might mediate disease risk. In Aim 3, we will construct gene regulatory network (GRN) models of DNMT3A- and TET2-perturbed human macrophage to identify TFs driving differential gene expression responses to infection, hypotheses that (1) we will experimentally test and (2) could eventually lead to therapies that mitigate the negative, pathogenic consequences of common DNMT3A and TET2 mutations. Furthermore, we build significant generalizable resources (models, modeling methodologies and training data) that will enable future discoveries in new cell types and disease contexts where alterations in DNA methylation drive phenotypes.