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: 10305241
• 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/10305241
• 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; Psychological Transfer; 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; genetic variant; genomic locus; genomic variation; human data; human disease; intercellular communication; joint inflammation; learning strategy; mouse genetics; multiple omics; network models; novel; predictive modeling; programs; sedentary; transcription factor; transcriptome; transcriptomics

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.

摘要 自然遗传变异影响大多数人类疾病，但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。一是多基因遗传 影响大多数病症需要考虑大量的基因和调节元件。这 基因调控的复杂性挑战了这项任务，其中3D调控相互作用可以连接增强子 和大基因组距离上的基因。第二，多种相互作用的细胞类型通常在细胞周期中失调。 疾病病理学这就需要了解如何集体变量关联一个 疾病影响疾病过程中涉及的每种细胞类型，以及随后这些细胞如何失调 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），一种人类自身免疫性炎症性疾病，作为一个案例研究，以开发强大的 基因调控的机器学习模型，以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性T细胞和成纤维细胞亚群。的 选择RA的动机是其公共卫生重要性、特定的靶组织、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 活化的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），以解释长程调控相互作用 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。 我们的方法的一个显着特点是，我们利用进化上遥远的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，然后将这些进展应用于人类环境 通过迁移学习。在来自RA的原代滑膜成纤维细胞中的高度平行化的Perturb-seq实验 具有单细胞多组学读数的患者将被用于评估和改进调节模型， 训练将基因表达程序与表型联系起来的网络模型。最后，我们将联合收割机空间 和单细胞转录组学进行的样本从RA发炎关节模型的组织和 在局部细胞群落中，T细胞和静止的组织组织成纤维细胞之间的相互作用。 预测GRM将产生从我们的研究沿着与人类的实验系统 疾病将很容易转移到其他多基因疾病，必须考虑复杂的调节 受影响组织中各种相互作用的细胞类型的基因组网络。