Defining native proteoform landscape for amyloid-beta in Alzheimers disease

Northwestern University, Evanston IL

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Abstract

Abstract Distinct amyloid-beta (A?) conformers such as peptides, oligomers (A?Os), and fibrils have long been targets studied for the cause, diagnosis and treatment of Alzheimer?s disease (AD). Spatiotemporal spreading of A?Os is theorized to underly AD progression; however, because of significant polydispersity, no consensus has been reached into which A?O structural elements or size distribution leads to potent neurotoxicity. Indeed, reports suggest some A? species may play a protective role in the CNS, through a mechanism in which herpesviridae infection promotes A? amyloidosis. Evidence also suggests A? exists in diverse modified proteoforms or associate with cofactors (e.g., metals). The diversification of A? monomers may contribute to different rates of A? oligomerization, in a manner that results in distinct A?Os populations that trigger synaptic dysfunction. Our work suggests that chemical diversification of A? through post-translational modifications (PTMs) and non- covalent interactions (e.g., metals) leads to potentially hundreds of native monomeric A? 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 A? proteoforms that exist in A?Os of virtually any size. The assay reads the A? PTM-status and characterizes bound co-factors, including metals, in a single detection event. Aim 1 will describe the spatial pattern of native A? proteoforms in demented patients and animal models relative to controls. Data mining will describe signatures of A? 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 A? 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., A? pathologies) or other clinical data. Overall, our work will provide fundamental insights on spatiotemporal signaling leading to dementia, and will inform many A? research tracks, including hypothesis testing in relation to in vivo targeting of A? imaging probes or diagnostic or therapeutic antibodies.