Using Genetic Diversity to Manage Neurological Disease

利用遗传多样性来治疗神经系统疾病

• 批准号: 10321554
• 负责人: William Edward Balch
• 金额: $ 44.38万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10321554
• 关键词: Address; Aging; Alleles; Alzheimer' s Disease; Amino Acid Sequence; Automobile Driving; Autophagocytosis; Basic Science; Biochemical; Biological Sciences; Birth; Brain; Buffers; Cells; Cessation of life; Characteristics; Chemicals; Cholesterol; Cholesterol Homeostasis; Clinical; Disease; Disease Management; Disease Progression; Environment; Etiology; GRP gene; Gaussian model; Genes; Genetic; Genetic Diseases; Genetic Variation; Genome; Genotype; Goals; Heat-Shock Proteins 70; Heat-Shock Response; Human; Individual; Inherited; Knowledge; Late Onset Alzheimer Disease; Lead; Learning; Light; Lipids; Lysosomes; Machine Learning; Modernization; Molecular; Molecular Chaperones; Molecular Mechanisms of Action; Neurodegenerative Disorders; Niemann-Pick Diseases; Pathology; Pathway interactions; Patients; Phenotype; Population; Process; Proteins; Rare Diseases; Resolution; Role; Signal Pathway; Specificity; Stress; Structure; System; Target Populations; Therapeutic; Therapeutic Intervention; Time; Ubiquitin; Uncertainty; United States National Library of Medicine; Variant; base; biochemical tools; biophysical tools; cell type; cholesterol control; druggable target; early onset; fitness; genomic variation; human model; improved; insight; loss of function; multicatalytic endopeptidase complex; nervous system disorder; novel; polypeptide; precision medicine; prevent; prognostic; protein folding; protein function; protein misfolding; proteostasis; rare genetic disorder; rare variant; response; small molecule; tool; trafficking

Project Summary/Abstract Understanding and treating genome abnormalities that lead to rare genetic neurodegenerative diseases such as Niemann-Pick C1 globally managing cholesterol homeostasis, or APOE alleles impacting cholesterol homeostasis in the brain triggering late-onset Alzheimer’s disease (LOAD), present a major challenge from both basic science and clinical perspectives. We have developed a Gaussian process regression (GPR) based machine learning (ML) approach that captures for the first time genomic variation in the population to understand the spatial covariance (SCV) relationships contributing to sequence-to-function-to-structure relationships in the individual. Genetic disease is fundamentally a problem of understanding the impact of altered folding intermediates found in response to variation in the protein fold and how they are managed by proteostasis. Proteostasis encompasses a broad range of chaperone and degradative components that manage the synthesis, folding/stability and function of the protein fold in response to inherited and environmental stress and aging. The general premise of this proposal is to develop a deep genome-based understanding of proteostasis that will teach us how to manage genetic diseases triggered by folding stress. The rationale for this proposal is that sparse genetic diversity found in the population, when used as a collective through application of GRP-ML defined SCV relationships, can provide us on a residue-by-residue basis insight into the folding intermediates that contribute to disease for the entire polypeptide sequence. The objective of this proposal is to understand the role of proteostasis in managing this genetic diversity for the benefit of therapeutic intervention. We hypothesize that management of the polypeptide fold of disease-causing variant proteins found in the population by targeting the function of the multivalent Hsp40 and Hsp70 co-chaperone/chaperone branch (the Hsp70 axis) of the proteostasis network will enable precision correction of misfolding phenotypes found in neurodegenerative disease. Our approach will study the impact of variation in the Niemann Pick C1 (NPC1) gene. NPC1 is an inherited, autosomal recessive, disorder characterized by the abnormal accumulation of unesterified cholesterol and other lipids in late endosomal (LE) and lysosome (Ly) compartments of all cell types. The primary effect of NPC1 variation results in early onset neurodegenerative disease in response to loss of cholesterol homeostasis. In Aim 1 we will explore the ability of small molecules to allosterically regulate the activity of components of the Hsp70 axis to retune the synthesis, folding/stability, trafficking and/or function of NPC1 variants. In Aim 2 we will explore the molecular mechanism of action (MoA) of the Hsp70 axis components that are responsible for enabling NPC1 variant correction. Completion of both aims will generate a comprehensive assessment of the role of Hsp70 axis in NPC1 disease progression and will be used as a guide for advancement of a precision medicine approach to reduce or prevent the onset of neurodegenerative disease triggered by genomic variation in NPC1 population.

项目概要/摘要 了解和治疗导致罕见遗传性神经退行性疾病的基因组异常，例如 Niemann-Pick C1 全局管理胆固醇稳态，或影响胆固醇的 APOE 等位基因 大脑稳态引发迟发性阿尔茨海默氏病 (LOAD)，这对两者提出了重大挑战 我们开发了基于高斯过程回归（GPR）的方法。 机器学习 (ML) 方法首次捕获群体中的基因组变异以了解 空间协方差（SCV）关系有助于序列到功能到结构的关系 遗传疾病从根本上来说是一个了解折叠影响的问题。 响应蛋白质折叠变化的中间体以及它们如何通过蛋白质稳态进行管理。 蛋白质稳态控制合成的广泛伴侣和降解成分，包括 蛋白质折叠响应遗传和环境压力和衰老的折叠/稳定性和功能。 该提案的总体前提是对蛋白质稳态进行基于基因组的深入理解，这将 教我们如何管理由折叠压力引发的遗传疾病。该提案的基本原理是： 当通过应用 GRP-ML 作为一个集体使用时，在种群中发现稀疏的遗传多样性 定义的 SCV 关系，可以让我们在逐个残基的基础上深入了解折叠中间体 该提案的目的是了解导致整个多肽序列疾病的因素。 蛋白质稳态在管理这种遗传多样性以促进治疗干预方面的作用。 促进对人群中发现的致病变异蛋白的多肽折叠的管理 通过靶向多价 Hsp40 和 Hsp70 共伴侣/伴侣分支（Hsp70 轴）的功能 蛋白质稳态网络的研究将能够精确纠正神经退行性疾病中发现的错误折叠表型 我们的方法将研究 Niemann Pick C1 (NPC1) 基因变异的影响。 遗传性常染色体隐性遗传性疾病，其特征是未酯化胆固醇异常积累 以及所有细胞类型的晚期内体 (LE) 和溶酶体 (Ly) 区室中的其他脂质的主要作用。 NPC1 变异会导致早发性神经退行性疾病，以应对胆固醇稳态的丧失。 在目标 1 中，我们将探索小分子变构调节细胞组分活性的能力。 Hsp70 轴重新调整 NPC1 变体的合成、折叠/稳定性、运输和/或功能。 将探索 Hsp70 轴成分的分子作用机制 (MoA)，这些成分负责 实现 NPC1 变异校正。这两个目标的完成将产生对 NPC1 变异的全面评估。 Hsp70 轴在 NPC1 疾病进展中的作用，并将用作提高精确度的指南 减少或预防基因组变异引发的神经退行性疾病发作的医学方法 在 NPC1 人群中。