Defining native proteoform landscape for amyloid-beta in Alzheimers disease

摘要 不同的淀粉样蛋白β（Aβ）构象，如肽，寡聚体（Aβ O）和原纤维长期以来一直是靶点 研究阿尔茨海默病（AD）的病因、诊断和治疗。AβOs的时空分布 理论上是AD进展的基础;然而，由于显著的多分散性， AβO的结构成分或大小分布会导致强神经毒性。事实上，报告 提示某些Aβ种类可能通过疱疹病毒科的一种机制在CNS中发挥保护作用， 感染促进Aβ淀粉样变性。证据还表明，Aβ存在于各种修饰的蛋白质形式中， 与辅因子相关联（例如，金属）。Aβ单体的多样性可能导致不同的速率， Aβ寡聚化，以一种导致不同AβOs群体的方式触发突触功能障碍。我们 这项工作表明，Aβ通过翻译后修饰（PTM）和非翻译修饰（NMT）的化学多样性。 共价相互作用（例如，金属）可能导致数百种天然单体Aβ蛋白质型。我们 我建议这些单体的组成以与阶段相关的方式变化 与斑块和缠结的阶段类似， Braak和Braak。我们团队开创的一种新的自顶向下质谱（nTDMS）程序 通过提供一种灵敏的天然Aβ蛋白形式的测量方法， 存在于几乎任何大小的Aβ O中该检测试剂盒读取Aβ PTM状态并表征结合的辅因子， 包括金属在内的所有物质。目的1将描述天然Aβ蛋白形式的空间模式， 痴呆患者和动物模型相对于对照。数据挖掘将通过以下方式描述Aβ的特征： 共价PTM或非共价相互作用，将特征与病理学协变量相关联。目标2将 利用数据挖掘来定义与细胞表型相关联的蛋白形式特征（例如，突触结合 和神经炎症）。目的3将描述Aβ蛋白形式相对于不同的 动物模型中的神经病理学特征。与神经科学家合作，在目标4中，我们将创建一个 蛋白质病变蛋白质形态知识库，以链接以下子集的方式聚集蛋白质形态数据： 蛋白质型与疾病相关表型（例如，Aβ病理学）或其他临床数据。总的来说，我们的工作将 提供有关导致痴呆的时空信号传导的基本见解，并将为许多Aβ研究提供信息。 跟踪，包括与Aβ成像探针或诊断或 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.