Defining native proteoform landscape for amyloid-beta in Alzheimers disease

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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.