Defining native proteoform landscape for amyloid-beta in Alzheimers disease

摘要 不同的淀粉样β蛋白(Aβ)构象，如多肽、寡聚体(AβOs)和纤维，长期以来一直是靶标 研究阿尔茨海默病(AD)的病因、诊断和治疗。βOS的时空扩展 是AD进展不足的理论；然而，由于显著的多分散性，还没有达成共识 达到AβO结构元素或大小分布导致强烈的神经毒性。事实上，有报道称 提示一些Aβ物种可能通过疱疹病毒科的一种机制在中枢神经系统中发挥保护作用 感染促进β淀粉样变性。证据还表明，β存在于不同的修饰蛋白形式或 与辅因(如金属)联系在一起。Aβ单体的多样化可能会导致不同的速率 一种β寡聚，以一种方式导致不同的AβO群体，从而触发突触功能障碍。我们的 研究表明，通过翻译后修饰(PTM)和非翻译后修饰(PTM)实现Aβ的化学多样化 共价相互作用(例如，金属)可能导致数以百计的天然单体Aβ蛋白形式。我们 提出这些单体的组成变化与阶段有关。 以及AD发病和进展过程中的大脑区域，类似于为斑块和缠结建立的阶段 布拉克和布拉克。我们团队首创的一种新的自顶向下质谱学(NTDMS)方法 通过提供对天然Aβ蛋白形式的敏感测量，为我们提供了测试这一假说的动力 几乎存在于任何大小的AβO中。该检测读取AβPtm-Status并鉴定结合的辅助因子， 包括金属，在单个检测事件中。目标1将描述天然Aβ蛋白形式的空间模式 痴呆症患者和对照动物模型。数据挖掘将描述与β相关的签名 共价PTM或非共价相互作用，将签名与病理协变量相关联。目标2将 利用数据挖掘来定义与细胞表型(例如突触结合)相关联的蛋白质形式签名 和神经炎症)。目标3将描述Aβ蛋白形式相对于DISTINCT的时间变异性 动物模型的神经病理学特征。与神经科学家合作，在目标4中，我们将创建一个 蛋白质病蛋白质形式知识库，以链接蛋白质形式数据子集的方式聚合蛋白质形式数据 与疾病相关的表型(例如，Aβ病理)或其他临床数据的蛋白质形式。总的来说，我们的工作将 提供关于时空信号导致痴呆的基本见解，并将为许多Aβ研究提供信息 跟踪，包括与体内靶向β成像探针或诊断性或 治疗性抗体。

Abstract Distinct amyloid-beta (Aβ) conformers such as peptides, oligomers (AβOs), and fibrils have long been targets studied for the cause, diagnosis and treatment of Alzheimer’s disease (AD). Spatiotemporal spreading of AβOs is theorized to underly AD progression; however, because of significant polydispersity, no consensus has been reached into which AβO structural elements or size distribution leads to potent neurotoxicity. Indeed, reports suggest some Aβ species may play a protective role in the CNS, through a mechanism in which herpesviridae infection promotes Aβ amyloidosis. Evidence also suggests Aβ exists in diverse modified proteoforms or associate with cofactors (e.g., metals). The diversification of Aβ monomers may contribute to different rates of Aβ oligomerization, in a manner that results in distinct AβOs populations that trigger synaptic dysfunction. Our work suggests that chemical diversification of Aβ through post-translational modifications (PTMs) and non- covalent interactions (e.g., metals) leads to potentially hundreds of native monomeric Aβ proteoforms. We propose that the compositional makeup of these monomers varies in a manner that is associated with stages and brain regions during AD onset and progression, analogous to the stages established for plaques and tangles by Braak and Braak. A new native Top-down mass spectrometry (nTDMS) procedure pioneered by our team has provided us momentum to test this hypothesis by providing a sensitive measure of the native Aβ proteoforms that exist in AβOs of virtually any size. The assay reads the Aβ PTM-status and characterizes bound co-factors, including metals, in a single detection event. Aim 1 will describe the spatial pattern of native Aβ proteoforms in demented patients and animal models relative to controls. Data mining will describe signatures of Aβ related by covalent PTMs or non-covalent interactions, correlating the signatures to pathological co-variables. Aim 2 will utilize data mining to define proteoform signatures that associate with cellular phenotypes (e.g., synapse binding and neuroinflammation). Aim 3 will describe the temporal variability of Aβ proteoforms relative to distinct neuropathological features in animal models. Partnering with neuroscientists, in Aim 4 we will create a Proteinopathy Proteoform Knowledgebase that aggregates proteoform data in a manner that links subsets of proteoforms to disease relevant phenotypes (e.g., Aβ pathologies) or other clinical data. Overall, our work will provide fundamental insights on spatiotemporal signaling leading to dementia, and will inform many Aβ research tracks, including hypothesis testing in relation to in vivo targeting of Aβ imaging probes or diagnostic or therapeutic antibodies.