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Dissecting the Etiology of The Lewy Body Dementias

剖析路易体痴呆症的病因学

基本信息

批准号：
10346336
负责人：
Jose Bras
金额：
$ 238.32万
依托单位：
J. DAVID GLADSTONE INSTITUTES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10346336
关键词：
Adopted Age Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease related dementia Antibodies Artificial Intelligence Astrocytes Autopsy Brain Brain Pathology Brain region Cells Characteristics Clinical Complex Corpus striatum structure Data Dementia with Lewy Bodies Development Diagnosis Diagnostic Disease Etiology Family Funding Genes Genetic Genetic Determinism Genetic Diseases Hippocampus (Brain)Image Incidence Label Learning Lewy Bodies Lewy Body Dementia Mass Spectrum Analysis Measures Mining Modification Molecular Molecular Conformation Molecular Genetics Morphology Netherlands Neurites Neurodegenerative Disorders Neurons Outcome Measure Output Parkinson Disease Parkinson&apos s Dementia Pathologic Pathology Patient Care Patients Pattern Persons Pharmaceutical Preparations Population Post-Translational Protein Processing Reporting Sampling Scheme Senile Plaques Small Nuclear RNA Stratification Structure Substantia nigra structure Symptoms Synapses Syndrome Technology Test Result Testing Therapeutic Therapeutic Intervention Time Tissues Training Variant Western Blotting Work accurate diagnosis alpha synuclein alpha synuclein gene artificial intelligence algorithm base brain tissue clinical predictors cohort combinatorial deep learning deep learning algorithm design differential expression digital imaging digital pathology effective therapy endophenotype gene interaction genetic analysis genetic variant genome wide association study genome-wide high risk learning network machine learning algorithm machine learning model neuropathology novel screening supervised learning targeted biomarker therapeutic target tool transcriptomics

项目摘要

PROJECT SUMMARY Lewy Body dementia (LBD) is the second most common neurodegenerative disorder, afflicting 1 million people in the US. It is fatal, and its incidence is increasing as populations age. LBD includes dementia with Lewy bodies (DLB) and Parkinson disease (PD) with dementia (PDD), but it is not clear if DLB and PDD are distinct diseases with different underlying mechanisms or clinical syndromes on a single mechanistic spectrum. Their relationship to Alzheimer’s disease (AD) is also unclear. Answering these questions is important for patient care, as DLB patients may respond differently to drugs than PDD or PD patients, and the diseases may progress at different rates. We propose to investigate the molecular basis of DLB and PDD as a way to identify disease-specific therapeutic targets and biomarkers that will lead to effective treatments. We postulate that in each disease, alpha-synuclein (aSYN), a major component of Lewy bodies, may adopt different conformations or covalent modifications, and that these different forms of aSYN determine the neuron subtypes that degenerate and the clinical syndrome that ensues. Consistent with this hypothesis, our genetic analysis of DLB patients has shown that different aSYN gene (SNCA) variants are associated with DLB compared to PD. We also discovered that a unique family of antibodies against different regions and post-translational modifications of aSYN reveal different pathologies in the DLB brain. Our central hypothesis is that DLB and PDD are distinct diseases with distinct underlying mechanisms. To test this hypothesis, we will first analyse patient brain samples with digital pathology, combining new artificial intelligence (AI)-based approaches with classical hallmark pathology and some new indicators of pathology, including the antibodies specific to different forms of aSYN. We will determine if we can train deep learning (DL) algorithms to accurately diagnose DLB versus PDD and reveal the key features that form the basis for that diagnosis. Second, we will test if DLB and PDD can be distinguished genetically. We reported the first genome-wide association study (GWAS) for DLB and will now use a similar approach in PDD compare these findings to available AD and PD genetic data. To this end, we will also analyze PDD and DLB patients using GWA with well-defined AI-quantified pathological endophenotypes for the first time This work will uncover genetic determinants that predict clinical symptoms of DLB and PDD patients and the neuropathology and provide novel stratification schemes for these conditions. Third, we will use snRNA-seq and spatial transcriptomics on brain tissue to determine if we can distinguish DLB and PDD patients based on their respective transcriptomic signatures. We will further validate and integrate these results by testing if the genes differentially expressed in DLB and PDD patients can form the basis for new neuropathology labels and AI algorithms that distinguish DLB and PDD brains. Our studies will clarify the neuropathological, molecular and genetic differences and similarities between PDD and DLB, and identify the genetic determinants of LBD, paving the way for the development of targeted diagnostics and therapeutics.

项目总结