Dissecting the Etiology of The Lewy Body Dementias

剖析路易体痴呆症的病因学

• 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
• 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' s Disease; Alzheimer' s disease brain; Alzheimer' 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' 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

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.

项目摘要 刘易体内痴呆（LBD）是第二常见的神经退行性疾病，遭受了100万人 在美国。它是致命的，随着人口年龄的增长，其发病率正在增加。 LBD包括Lewy身体的痴呆症 （DLB）和带痴呆（PDD）的帕金森病（PD），但尚不清楚DLB和PDD是否是不同的疾病 在单个机械光谱上具有不同的潜在机制或临床综合征。他们的关系 阿尔茨海默氏病（AD）也不清楚。回答这些问题对于患者护理很重要，因为DLB 患者对药物的反应可能与PDD或PD患者不同，并且疾病可能在不同的情况下进展 费率。我们建议研究DLB和PDD的分子基础，以鉴定疾病特异性 将导致有效治疗的治疗靶标和生物标志物。我们假设在每种疾病中 路易尸体的主要组成部分Alpha-synclein（Asyn）可能会采用不同的考虑或共价 修改，并且这些不同形式的ASYN决定了堕落的神经元亚型 随之而来的临床综合征。与这一假设一致，我们对DLB患者的遗传分析已显示 与PD相比，不同的ASYN基因（SNCA）变体与DLB相关。我们还发现 针对不同区域和ASYN翻译后修饰的独特抗体家族揭示了不同的 DLB大脑中的病理。我们的中心假设是DLB和PDD是不同的疾病 基本机制。为了检验该假设，我们将首先分析具有数字病理学的患者脑样本， 结合新的人工智能（AI）的方法与古典标志病理学和一些新的方法 病理的指标，包括针对不同形式的ASYN的抗体。我们将确定是否可以 训练深度学习（DL）算法准确诊断DLB对PDD，并揭示了关键特征 构成该诊断的基础。其次，我们将测试是否可以一般区分DLB和PDD。我们 报道了DLB的首次全基因组协会研究（GWAS），现在将在PDD中使用类似的方法 将这些发现与可用的AD和PD遗传数据进行比较。为此，我们还将分析PDD和DLB 使用GWA使用明确定义的AI量化的患者内表单型的患者首次将 发现预测DLB和PDD患者临床症状的遗传决定素和神经病理学 并为这些条件提供新的分层方案。第三，我们将使用snRNA-seq和空间 在脑组织上的转录组学，以确定我们是否可以根据他们的DLB和PDD患者来区分 转录组签名。我们将通过测试基因来进一步验证和整合这些结果 在DLB和PDD患者中表达的不同表达可以构成新的神经病理标签和AI的基础 区分DLB和PDD大脑的算法。我们的研究将阐明神经病理学，分子和 PDD和DLB之间的遗传差异和相似性，并确定LBD的遗传决定剂，铺路 开发目标诊断和治疗的方式。