Dissecting the Etiology of The Lewy Body Dementias

项目总结 路易体痴呆(LBD)是第二种最常见的神经退行性疾病，困扰着100万人 在美国。它是致命的，随着人口老龄化，其发病率正在增加。LBD包括路易体痴呆 (DLB)和帕金森病(PD)合并痴呆(PDD)，但目前尚不清楚DLB和PDD是否是不同的疾病 在单一的机制谱上具有不同的潜在机制或临床症状。他们的关系 阿尔茨海默病(AD)的发病机制也不清楚。回答这些问题对病人护理很重要，因为DLB 患者对药物的反应可能与PDD或PD患者不同，疾病的进展也可能不同 费率。我们建议研究DLB和PDD的分子基础作为鉴定疾病特异性的一种方法。 将导致有效治疗的治疗靶点和生物标记物。我们假设在每种疾病中， α-突触核蛋白(ASYN)是路易小体的主要成分，可以采用不同的构象或共价形式 这些不同形式的aSYN决定了退化的神经元亚型和 随之而来的临床综合征。与这一假设一致，我们对DLB患者的基因分析表明 与帕金森病相比，不同的aSYN基因(SNCA)变异与DLB相关。我们还发现，一个 针对不同区域的独特抗体家族和aSYN的翻译后修饰显示不同 大脑中的病理变化。我们的中心假设是DLB和PDD是不同的疾病，具有不同的 潜在的机制。为了验证这一假设，我们首先将用数字病理学分析患者的大脑样本， 将基于人工智能(AI)的新方法与经典标志病理学和一些新的 病理指标，包括针对不同形式的aSYN的抗体。我们会确定我们是否可以 培训深度学习(DL)算法以准确诊断DLB与PDD，并揭示以下关键特征 构成了这一诊断的基础。其次，我们将测试DLB和PDD是否可以从基因上区分开来。我们 报道了第一个针对DLB的全基因组关联研究，现在将在PDD中使用类似的方法 将这些发现与现有的阿尔茨海默病和帕金森病基因数据进行比较。为此，我们还将分析PDD和DLB 首次使用具有明确定义的AI量化病理内表型的GWA的患者这项工作将 揭示预测DLB和PDD患者临床症状和神经病理的基因决定因素 并为这些条件提供了新的分层方案。第三，我们将使用SnRNA-seq和space 在脑组织上进行转录转录以确定我们是否可以根据他们的情况区分DLB和PDD患者 各自的转录签名。我们将进一步验证和整合这些结果，测试基因是否 DLB和PDD患者的差异表达可形成新的神经病理标记和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，分子和 PDD和DLB的遗传异同，并确定LBD的遗传决定因素，铺平 发展有针对性的诊断和治疗的方法。

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.