Gene regulatory network modeling of disease-associated DNA methylation perturbations

项目总结 DNMT3A和TET2的体细胞突变在老年人的造血谱系中很常见， 估计影响超过10%的65岁以上的成年人。这些突变增加了罹患 与年龄有关的并发症，包括严重感染、动脉粥样硬化性心血管疾病、骨质疏松症、 慢性肾脏疾病和血液系统恶性肿瘤，几乎使受影响个人的死亡率翻了一番。 DNMT3A和TET2编码在细胞分化过程中重塑DNA甲基化所必需的酶。 动物研究表明，这些基因的突变会导致免疫细胞的异常激活，例如 巨噬细胞，这可能是疾病联系的基础。我们最近开发出了一种人类多能干细胞 细胞(HPSC)来源的巨噬细胞模型，其中DNMT3A或TET2的分化依赖效应 微扰可以精确地描绘出来。我们发现DNMT3A-和TET2-扰动受损 数千个调控位点的DNA甲基化重塑，改变增强子活性和表达 巨噬细胞功能的重要基因。我们的研究强调了工程方法的必要性，以及 尤其是数学建模，以揭示DNMT3A和TET2扰动对 细胞功能和疾病风险。 在这里，我们将新的计算建模方法与独特的实验资源相结合，以 DNA甲基化位点特异性变化与异常免疫反应和疾病的机械性联系 风险。目标1建立深度神经网络模型(和必要的训练数据资源)以预测 DNA甲基化对DNA甲基化“阅读器”--100+转录因子染色质结合的影响 最终招募RNA聚合酶和共激活剂来驱动基因转录的模式。在目标2中，我们 从染色质可及性、转录活性和DNA预测基因组规模的转铁蛋白结合模式 感兴趣环境中的甲基化数据：DNMT3A或TET2干扰人巨噬细胞对 病毒和细菌感染引起的免疫激活。发现现有疾病和新疾病之间的联系 关联，我们将TFBS的预测与精心策划的年龄相关疾病风险变量集相交，以 提名TF和Tf结合中DNMT3A-或TET2-扰动和下游变化的上下文 可能会影响疾病风险。在目标3中，我们将构建DNMT3A-的基因调控网络(GRN)模型。 和TET2干扰的人巨噬细胞识别驱动差异基因表达反应的转录因子 感染，假设(1)我们将进行实验测试，(2)最终可能导致缓解 常见DNMT3A和TET2突变的阴性致病后果。此外，我们还建立了 重要的可推广资源(模型、建模方法和培训数据)，将支持未来 在DNA甲基化改变驱动表型的新细胞类型和疾病背景下的发现。

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.