The repeated failures of amyloid-targeting therapies have challenged our narrow understanding of Alzheimer’s disease (AD) pathogenesis and inspired wide-ranging investigations into the underlying mechanisms of disease. Increasing evidence indicates that AD develops from an intricate web of biochemical and cellular processes that extend far beyond amyloid and tau accumulation. This growing recognition surrounding the diversity of AD pathophysiology underscores the need for holistic systems-based approaches to explore AD pathogenesis. Here we describe how network-based proteomics has emerged as a powerful tool and how its application to the AD brain has provided an informative framework for the complex protein pathophysiology underlying the disease. Furthermore, we outline how the AD brain network proteome can be leveraged to advance additional scientific and translational efforts, including the discovery of novel protein biomarkers of disease. Fig. 1UNBIASED NETWORK-BASED PROTEOMICS TO CHARACTERIZE THE COMPLEX BIOCHEMICAL AND CELLULAR ENDOPHENOTYPES OF ALZHEIMER’S DISEASE (AD).: Traditional “bottom-up” mass spectrometry (MS)-based techniques, such as label-free quantitation (LFQ) or isobaric labeling, are used for protein identification and quantification in a large cohort of postmortem brain tissues from control, asymptomatic AD (AsymAD), and AD cases. Following fractionation, the deep discovery proteomic data generated from these large cohorts are organized into biologically meaningful groups, or modules, of proteins with sophisticated analytical methods, such as weighted gene correlation network analysis (WGCNA). The co-expression modules are assessed for enrichment with specific cell types, organelles, biological pathways, and genetic risk factors generated from genome-wide association studies (GWAS). In addition, abundance changes at the module level can be correlated with disease status, clinical features, and neuropathological measures. Using this framework, six core and highly conserved modules across AsymAD and AD cohorts with reproducible links to specific cell types, organelles, and biological functions have been identified. Three of the modules are consistently increased in AD brain network proteome: inflammatory, myelination, and RNA binding/splicing, while the remaining three are consistently decreased: synaptic, mitochondrial, and cytoskeleton.Fig. 2LOCAL PROTEOMICS APPROACHES TO DEFINE THE CELLULAR BASIS OF ALZHEIMER’S DISEASE.: Summary of approaches: laser capture microdissection (LCM) is a method that can procure subpopulations of cells or very small regions of interest under direct microscopic visualization. LCM (pink panel) has been used to micro-dissect neurons from both fresh frozen human brain and formalin-fixed paraffin embedded tissue sections for proteomic analyses. Prior to dissection, immunohistochemical or histological staining of fixed tissue sections is performed to identify a specific cell type or population of cells in a region of interest without compromising protein quality. For acute isolation of brain cell types (green panel), a fresh brain sample needs to be processed to yield a single-cell suspension which is then subjected to magnetically-activated cell sorting (MACS) or fluorescent-activated cell sorting (FACS). For MACS, the desired cell type is labeled with a 50 nm magnetic microbead conjugated to an antibody specific to cell-surface receptor. After incubation, the sample is placed on a magnet to drain unbound cells and retain desired cell type within the column. Once the column is removed from the magnet, the bound cells are released and collected for downstream analyses. Analogous to MACS, in FACS, the single-cell suspension is incubated with a fluorophore-conjugated antibody specific to a cell-surface receptor. Subsequently, the desired cell type is sorted based on their size and fluorescent signal directly into a buffer amenable for downstream proteomic analyses. In vivo biorthogonal amino acid tagging (BONCAT) of proteins (purple panel) is achieved by expressing MetRS* under a cell type-specific promoter in a mouse (Camk2a-Cre-MetRS*). MetRS* harbors a mutation (L247G) in the amino acid binding site which preferentially tags nascent proteins with an azide-tagged methionine analog, azidonorleucine (Anl). After treating the mice with tamoxifen (Tmx) to facilitate Cre-mediated recombination, Camk2a cells express MetRS* and nascent proteins are tagged with Anl. The azide residue of Anl is amenable to copper-catalyzed azide-alkyne cycloaddition or “click” chemistry. Following protein extraction, Anl-tagged proteins residues are “clicked” with a PEG-biotin-alkyne and then purified using avidin beads or avidin resin for subsequent MS analyses. Proximity labeling (orange panel) is achieved by various enzymes that biotinylate proximal endogenous proteins. The BioID approach uses the Escherichia coli biotin ligase, BirA*, with a catalytic site mutation (R118G). The mutation destabilizes the enzyme, facilitating active biotin molecules (biotinoyl-5′-AMP) to dissociate and bind to primary amines of exposed lysine residues on adjacent proteins. APEX catalyzes the oxidation of biotin-phenol to the short-lived (<1 ms) biotin-phenoxyl radical in the presence of hydrogen peroxide, which then reacts with electron-rich amino acids, such as tyrosine, in neighboring proteins. TurboID was developed by taking advantage of yeast display-based directed evolution. TurboID retains the promiscuous biotinylation property of BioID, but rapidly labels proteins in 10 min compared to the 18-24 h by BioID. Subsequent to biotin labeling and protein extraction from a sample, proteins can be affinity captured by streptavidin beads or matrices for downstream proteomic analyses by MS.Fig. 3CONCEPTUAL FRAMEWORK FOR TRANSLATING BRAIN PROTEIN NETWORKS INTO CLINICAL BIOMARKERS.: In this framework, multidimensional discovery-driven proteomics data collected from local cell type-specific approaches and antemortem biofluids will be integrated with the AD brain network proteome to identify systems-based panels of promising CSF and/or plasma biomarkers. Target prioritization will rest on the significance, magnitude, reproducibility, and ease of detection of the candidate biomarker in disease. Prioritized targets will also require links to disease mechanisms, informed in part by localized and cell type-specific proteomics. These network-based biomarker panels will then be validated using targeted quantitation approaches, including mass spectrometry (MS) and immunoassays. Both validation methods offer highly sensitive and accurate quantitation, though MS may offer certain advantages, such as highly selective target detection using unique peptides and the ability to cost-effectively analyze large panels of proteins in an initial verification phase prior to more expensive validation efforts. Validated biomarker panels representing a wide range of pathophysiologies could serve a variety of clinical uses, including preclinical profiling, disease monitoring, measuring therapeutic response, and confirming target engagement.

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