Deciphering the Genomics of Gene Network Regulation of T Cell and Fibroblast States in Autoimmune Inflammation

破译自身免疫炎症中 T 细胞和成纤维细胞状态的基因网络调控的基因组学

• 批准号: 10472615
• 负责人: Christina S Leslie
• 金额: $ 128万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-20 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472615
• 关键词: 3-Dimensional; Affect; Alleles; Antigen-Presenting Cells; Autoimmune; CAST/EiJ Mouse; Case Study; Cell model; Cell physiology; Cells; Clinical; Complex; Cultured Cells; Data; Degenerative Disorder; Development; Disease; Disease model; Distant; Enhancers; Etiology; Fibroblasts; Functional disorder; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Goals; Hereditary Disease; Heterogeneity; Human; Hybrids; Immune; Immunology; In Situ; Inflammation; Inflammatory; Joints; Knowledge; Laboratories; Learning; Link; Machine Learning; Metabolic Diseases; Modeling; Molecular; Mus; Nature; Network-based; Organizational Models; Pathology; Patients; Phenotype; Polygenic Traits; Process; Public Health; Regulation; Regulator Genes; Regulatory Element; Rheumatoid Arthritis; Sampling; Specific qualifier value; System; T cell regulation; T-Lymphocyte; Tissues; Training; Transcriptional Regulation; Variant; arthropathies; autoimmune inflammation; cell community; cell type; connectome; disease phenotype; empowered; epigenetic regulation; epigenome; experimental analysis; experimental study; functional genomics; gene network; gene regulatory network; genetic variant; genomic locus; genomic variation; human data; human disease; intercellular communication; joint inflammation; learning strategy; machine learning framework; machine learning model; mouse genetics; multiple omics; network models; novel; predictive modeling; programs; sedentary; transcription factor; transcriptome; transcriptomics; transfer learning

Abstract Natural genetic variation impacts most human diseases, yet predicting how regulatory variants control gene expression and ultimately disease phenotypes poses considerable challenges. First, the polygenic inheritance influencing most conditions requires consideration of a vast number of genes and regulatory elements. This task is challenged by the complexity of gene regulation, where 3D regulatory interactions can link enhancers and genes over large genomic distances. Second, multiple interacting cell types are often dysregulated in disease pathology. This necessitates an understanding of how the collective variants associating with a disease affect each cell type involved in the disease process and subsequently how these dysregulated cellular phenotypes crossregulate and drive subsequent cellular states. In this IGVF project, we will use rheumatoid arthritis (RA), a human autoimmune inflammatory disease, as a case study to develop robust machine learning models of gene regulation to decipher the impact of genomic variation on multiple cellular drivers of pathology—namely, inflammatory T cell and fibroblast subsets found in affected joint tissue. The choice of RA is motivated by its public health importance, specified target tissue, access to clinical samples, considerable knowledge of disease-associated gene loci, and our team’s complementary expertise in machine learning, RA pathophysiology, immunology and inflammation, and single-cell functional genomics. We will develop an advanced machine learning framework to model the effects of allelic variation on gene regulatory networks based on the analysis of epigenomes, transcriptomes, and connectomes of mouse activated T cells and synovial fibroblasts and extend these models to RA patient joint tissue and primary cells. We will train allele-specific gene regulatory models (GRMs) that account for long-range regulatory interactions by integrating single-cell transcriptome and epigenome (sc-multiome) data with bulk 3D interactome analyses. A notable feature of our approach is that we leverage the genetic diversity of evolutionarily distant F1 hybrid mice to provide robust training data for these models, and then apply these advances to the human context through transfer learning. Highly parallelized Perturb-seq experiments in primary synovial fibroblasts from RA patients with single-cell multiomic readouts will then be used to evaluate and refine regulatory models and to train network models that connect gene expression programs to phenotype. Finally, we will combine spatial and single-cell transcriptomics conducted on samples from RA inflamed joints to model the organization and interactions between T cells and sedentary tissue-organizing fibroblasts within local cellular communities. The predictive GRMs that will be generated from our study along with the experimental systems for human disease will be readily transferrable to other polygenic disorders which must consider complex regulatory genomic networks for various interacting cell types in affected tissues.

抽象的 自然遗传变异影响大多数人类疾病，但可以预测调控变异如何控制基因 表达和最终的疾病表型提出了相当大的挑战，首先，多基因遗传。 大多数情况需要考虑大量基因和调控元件。 任务受到基因调控复杂性的挑战，其中 3D 调控相互作用可以连接增强子 其次，多种相互作用的细胞类型通常失调。 这需要了解集体变异如何与疾病病理学相关。 疾病会影响疾病过程中涉及的每种细胞类型，以及随后这些细胞类型如何失调 细胞表型交叉调节并驱动随后的细胞状态 在这个 IGVF 项目中，我们将使用。 类风湿性关节炎（RA）是一种人类自身免疫性炎症疾病，作为案例研究来开发强有力的治疗方法 基因调控的机器学习模型，用于破译基因组变异对多种细胞的影响 病理学的驱动因素，即在受影响的关节组织中发现的炎症 T 细胞和成纤维细胞亚群。 选择 RA 的动机是其对公共卫生的重要性、特定的目标组织、临床样本的获取、 对疾病相关基因位点的丰富知识，以及我们团队在机器方面的互补专业知识 学习、RA 病理生理学、免疫学和炎症以及单细胞功能基因组学。 我们将开发一个先进的机器学习框架来模拟等位基因变异对基因的影响 基于小鼠表观基因组、转录组和连接组分析的调控网络 激活的 T 细胞和滑膜成纤维细胞，并将这些模型扩展到 RA 患者关节组织和原代细胞。 我们将训练等位基因特异性基因调控模型（GRM），以解释远程调控相互作用 通过将单细胞转录组和表观基因组 (sc-multiome) 数据与批量 3D 相互作用组分析相结合。 我们方法的一个显着特点是我们利用了进化距离较远的 F1 杂种的遗传多样性 小鼠为这些模型提供强大的训练数据，然后将这些进步应用到人类环境中 通过迁移学习在 RA 原代滑膜成纤维细胞中进行高度并行的 Perturb-seq 实验。 然后，具有单细胞多组学读数的患者将用于评估和完善监管模型，并 训练将基因表达程序与表型连接起来的网络模型最后，我们将在空间上进行组合。 对 RA 发炎关节的样本进行单细胞转录组学以模拟组织和 T 细胞和局部细胞群内固定组织组织成纤维细胞之间的相互作用。 我们的研究以及人类实验系统将生成预测 GRM 疾病很容易转移到其他多基因疾病，必须考虑复杂的监管 用于与受影响组织中的各种细胞类型相互作用的基因组网络。