Gene regulatory network modeling of disease-associated DNA methylation perturbations

疾病相关 DNA 甲基化扰动的基因调控网络建模

• 批准号: 10730859
• 负责人: Minji Byun
• 金额: $ 81.04万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10730859
• 关键词: ATAC-seq; Adult; Affect; Animals; Atherosclerosis; Automobile Driving; Awareness; Bacterial Infections; Binding; Binding Sites; Cardiovascular Diseases; Cell physiology; Cells; ChIP-seq; Chemicals; Chromatin; Chronic Kidney Failure; Communities; Complex; Computer Models; DNA; DNA Methylation; DNA Methylation Regulation; DNA-Directed RNA Polymerase; DNMT3a; Data; Data Set; Defect; Disease; Disease model; Elderly; Engineering; Enhancers; Enzymes; Future; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Diseases; Genetic Engineering; Genetic Risk; Genetic Transcription; Genomic DNA; Genomics; Hematologic Neoplasms; Hematopoietic; Human; Human Engineering; Immune; Immune System Diseases; Immune response; Impairment; Individual; Infection; Investigation; Link; Macrophage; Mediating; Methodology; Methods; Methylation; Modeling; Modification; Molecular; Mutate; Mutation; Neural Network Simulation; Osteoporosis; Pathogenicity; Pathway interactions; Pattern; Phenotype; Reader; Resources; Risk; Site; Somatic Mutation; Stimulus; Testing; Tissue-Specific Gene Expression; Training; Virus Diseases; age related; cell type; comorbidity; data resource; deep neural network; disorder risk; gene regulatory network; genetic analysis; genome-wide; human disease; human old age (65+); human pluripotent stem cell; immune activation; improved; in silico; interest; knock-down; mathematical model; methylation pattern; mortality; network models; novel; recruit; response; risk variant; therapeutic target; trait; transcription factor; user-friendly

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.

项目摘要 DNMT 3A和TET 2的体细胞突变在老年个体的造血谱系中是常见的， 估计影响超过10%的65岁以上的成年人。这些突变增加了 年龄相关的合并症，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恶性肿瘤，几乎使受影响个体的死亡率增加一倍。 DNMT 3A和TET 2编码在细胞分化期间重塑DNA甲基化所必需的酶。 动物研究表明，这些基因的突变驱动免疫细胞的异常激活，如 巨噬细胞，这可能是疾病关联的基础。我们最近开发了一种人类多能干细胞 细胞（hPSC）衍生的巨噬细胞模型，其中DNMT 3A或TET 2的分化依赖性作用 可以精确地描绘扰动。我们发现DNMT 3A和TET 2的扰动损害了 在数千个调控位点进行DNA甲基化重塑，改变增强子活性和表达， 对巨噬细胞功能重要的基因。我们的研究强调了工程方法的必要性， 特别是数学建模，以揭示DNMT 3A和TET 2扰动对 细胞功能和疾病风险。 在这里，我们将新颖的计算建模方法与独特的实验资源配对， 将DNA甲基化的位点特异性变化与异常免疫反应和疾病联系起来 风险Aim 1建立深度神经网络模型（和必要的训练数据资源）来预测 DNA甲基化对100多个转录因子（TF）染色质结合的影响，这些转录因子是DNA甲基化的“读者” 最终招募RNA聚合酶和共激活因子来驱动基因转录的模式。在目标2中， 从染色质可及性、转录活性和DNA预测基因组规模的TF结合模式 在我们感兴趣的背景下的甲基化数据：响应于DNA甲基化的DNMT 3A-或TET 2-扰动的人巨噬细胞。 病毒和细菌感染诱导的免疫激活。发现现有疾病和新疾病之间的联系 关联，我们将TFBS预测与年龄相关疾病风险变异的策划集交叉， 指定TF和其中DNMT 3A-或TET 2-扰动和TF结合的下游改变的背景 可能介导疾病风险。目的3：构建DNMT 3A基因调控网络模型。 和TET 2干扰的人巨噬细胞，以鉴定驱动差异基因表达应答的TF， 感染，假设（1）我们将通过实验测试和（2）最终可能导致缓解感染的疗法 常见DNMT 3A和TET 2突变的负面致病后果。此外，我们建立 重要的可推广资源（模型，建模方法和培训数据），将使未来 在新的细胞类型和疾病背景下发现DNA甲基化的改变驱动表型。