Systems-Level Approach to Neuronopathic Lysosomal Storage Disorders

神经病性溶酶体贮积症的系统级方法

• 批准号: 10721768
• 负责人: JONATHAN D COOPER
• 金额: $ 160.46万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721768
• 关键词: Affect; Applications Grants; Artificial Intelligence; Astrocytes; Atlases; Automobile Driving; Behavioral; Biochemical; Biochemical Pathway; Biogenesis; Biology; Brain; CLN2 gene; Candidate Disease Gene; Catalogs; Cell Nucleus; Cells; Cellular Metabolic Process; Cellular Stress; Central Nervous System; Central Nervous System Diseases; Cessation of life; Communication; Complex; Data; Disease; Disease Progression; Disease model; Effector Cell; Enzymes; Foundations; Functional disorder; Future; Genes; Genetic; Genetic Transcription; Goals; Growth; Health; Human; Individual; Induced pluripotent stem cell derived neurons; Interphase Cell; Investigation; Knowledge; Link; Lipids; Lysosomes; Macrophage; Mediating; Membrane; Metabolic; Metabolic Pathway; Methodology; Microglia; Mucopolysaccharidosis III B; Mutation; Nerve Degeneration; Neurodegenerative Disorders; Neurons; Nuclear; Organ; Outcome; Output; Pathogenesis; Pathogenicity; Pathologic; Pathway Analysis; Pathway interactions; Patients; Phenotype; Post-Translational Protein Processing; Proteins; Proteomics; Recycling; Regulation; Role; Route; Signal Pathway; Signal Transduction; Site; Stress; System; Systems Biology; Systems Integration; Test Result; Testing; Therapeutic; Tissues; Training; Transgenic Mice; Translational Research; Work; brain health; candidate identification; cell type; cellular pathology; combat; disease phenotype; enzyme deficiency; exhaust; human model; individual response; innovation; knowledge of results; lipidomics; lysosomal proteins; metabolomics; mouse model; novel; novel therapeutics; premature; prevent; protein protein interaction; response; therapeutic development; therapeutic target; tool; transcription factor

Neuronopathic lysosomal storage disorders (LSDs) are a group of fatal neurodegenerative diseases caused by genetic defects in components of the lysosome, which is the major site within the cell for the degradation and recycling of exhausted cellular components. Defective lysosomes accumulate undegraded storage material, which has classically been thought to be toxic for the cell and the driver of the pathogenic cascade of the dis- ease. Recent advances in the investigation of the lysosome, however, have shown that the storage burden it- self can be uncoupled from the most dramatic effects of the disease on the affected organs. Moreover, there are emerging roles of the lysosome as a central player in the regulation of cell metabolism which are distinct from its classical role as a cellular degradation site. The central hypothesis of this proposal is that are the changes in the communication between the lysosome and the rest of the cell, rather than the storage itself, that drive disease pathogenesis. Determining exactly which components of the lysosomal communication network mediate disease propagation, in which cell type, and how, is important because it could unveil the next generation of therapeutic targets. We propose to use innovative genetic tools and artificial intelligence-driven analytical pipelines to mechanistically investigate changes in lysosomal content and communication in the central nervous system of mouse models of two distinct LSDs. In Aim 1 we will catalogue LSD-associated changes in lysosomal content and composition and determine their relationship with LSD-specific cellular features based on the perturbation of components of the broad lysosomal gene metabolic network. In Aim 2 we will investigate the changes in the signaling network that mediates communication of the lysosome with the nucleus and determine the effectors of the cell’s response to lysosomal stress. In Aim 3 we will place the study of lysosomal content, signaling, and dysfunction in the context of specific cell types (neurons, astroglia, macrophages) to determine their role in the initiation and propagation of disease. Results from this study will provide the first atlas of changes in lysosomal content and signaling components in LSDs and will pioneer the integrative study of the lysosome as a multi-level network of causally associated components and pathways. Knowledge resulting from this study could lay the foundation for future translational investigation of treatments for neuronopathic LSDs.

神经病性溶酶体贮积症（LSD）是一组由溶酶体成分的遗传缺陷引起的致命性神经退行性疾病，溶酶体是细胞内降解和回收耗尽的细胞成分的主要部位。有缺陷的溶酶体会积累未降解的储存物质，传统上认为这些物质对细胞有毒，并且是疾病致病级联的驱动因素。然而，溶酶体研究的最新进展表明，储存负担本身可以与疾病对受影响器官最显着的影响无关。此外，溶酶体作为细胞代谢调节的核心角色正在发挥作用，这与其作为细胞降解位点的经典作用不同。该提议的中心假设是，溶酶体与细胞其他部分之间通讯的变化，而不是储存本身，推动了疾病的发病机制。准确确定溶酶体通讯网络的哪些组成部分介导疾病传播、在哪种细胞类型中以及如何传播非常重要，因为它可以揭示下一代治疗靶点。我们建议使用创新的遗传工具和人工智能驱动的分析流程来机械地研究两种不同LSD小鼠模型中枢神经系统溶酶体含量和通讯的变化。在目标 1 中，我们将记录 LSD 相关的溶酶体含量和组成变化，并根据广泛的溶酶体基因代谢网络的成分扰动确定它们与 LSD 特异性细胞特征的关系。在目标 2 中，我们将研究介导溶酶体与细胞核通讯的信号网络的变化，并确定细胞对溶酶体应激反应的效应器。在目标 3 中，我们将在特定细胞类型（神经元、星形胶质细胞、巨噬细胞）的背景下研究溶酶体内容、信号传导和功能障碍，以确定它们在疾病发生和传播中的作用。这项研究的结果将提供第一个关于 LSD 中溶酶体含量和信号成分变化的图谱，并将开创将溶酶体作为因果相关成分和途径的多层次网络的综合研究。这项研究得出的知识可以为未来神经病性LSD治疗的转化研究奠定基础。