Dissecting the Etiology of The Lewy Body Dementias

项目摘要 路易体痴呆症（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，其发病率随着人口老龄化而增加。LBD包括路易体痴呆 (DLB)帕金森病（PD）伴痴呆（PDD），但DLB和PDD是否是不同的疾病尚不清楚 在单一的机制谱上具有不同的潜在机制或临床综合征。他们的关系 阿尔茨海默病（AD）的发病机制也不清楚。回答这些问题对患者护理很重要，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时间进展， rates.我们建议研究DLB和PDD的分子基础，作为鉴定疾病特异性 治疗靶点和生物标志物将导致有效的治疗。我们假设在每种疾病中， α-突触核蛋白（aSYN）是路易体的主要成分，可以采用不同的构象或共价结合。 这些不同形式的aSYN决定了退化的神经元亚型， 临床上的综合症。与这一假设相一致，我们对DLB患者的遗传分析表明， 与PD相比，不同的aSYN基因（SNCA）变体与DLB相关。我们还发现， 针对不同区域的抗体的独特家族和aSYN的翻译后修饰揭示了不同的 DLB大脑中的病理学。我们的中心假设是DLB和PDD是不同的疾病， 基本机制。为了验证这一假设，我们将首先用数字病理学分析患者的大脑样本， 将新的基于人工智能（AI）的方法与经典的标志性病理学和一些新的 病理学指标，包括对不同形式的aSYN特异性的抗体。我们将决定我们是否可以 训练深度学习（DL）算法，以准确诊断DLB与PDD，并揭示 这是诊断的基础。其次，我们将测试DLB和PDD是否可以在遗传上区分。我们 报道了第一个DLB的全基因组关联研究（GWAS），现在将在PDD中使用类似的方法 将这些发现与现有的AD和PD遗传数据进行比较。为此，我们还将分析PDD和DLB 首次使用GWA的患者具有明确的AI定量病理内表型，这项工作将 揭示预测DLB和PDD患者临床症状和神经病理学的遗传决定因素 并为这些病症提供新的分层方案。第三，我们将使用snRNA-seq和空间 转录组学，以确定我们是否可以区分DLB和PDD患者，根据他们的 各自的转录组特征我们将进一步验证和整合这些结果，通过测试基因是否 在DLB和PDD患者中的差异表达可以形成新的神经病理学标记和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的遗传决定因素， 发展靶向诊断和治疗的方法。

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.