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.
Understanding the mechanism behind and treatment of genome abnormalities in the population that cause rare genetic disease in the individual represents a major challenge from both basic science and clinical perspectives. We will study the impact of genetic variation found in the Niemann Pick C1 (NPC1) gene, that, like APOE alleles driving late-onset Alzheimers disease (LOAD), is responsible for management of cholesterol homeostasis. We will use Gaussian process regression machine learning (GPR-ML) based spatial covariance (SCV) tools to define the mechanism of action of proteostasis components found in the Hsp70 axis to therapeutically manage the NPC1 fold leading to improved to cholesterol homeostasis in the individual to provide basic and clinical insight into disease management.