The age-dependent deposition of amyloid-β peptides, derived from amyloid precursor protein (APP), is a neuropathological hallmark of Alzheimer's disease (AD). Despite age being the greatest risk factor for AD, the molecular mechanisms linking ageing to APP processing are unknown. Here we show that asparagine endopeptidase (AEP), a pH-controlled cysteine proteinase, is activated during ageing and mediates APP proteolytic processing. AEP cleaves APP at N373 and N585 residues, selectively influencing the amyloidogenic fragmentation of APP. AEP is activated in normal mice in an age-dependent manner, and is strongly activated in 5XFAD transgenic mouse model and human AD brains. Deletion of AEP from 5XFAD or APP/PS1 mice decreases senile plaque formation, ameliorates synapse loss, elevates long-term potentiation and protects memory. Blockade of APP cleavage by AEP in mice alleviates pathological and behavioural deficits. Thus, AEP acts as a δ-secretase, contributing to the age-dependent pathogenic mechanisms in AD.

Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer's disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological traits of disease. In this proof-of-concept study, we performed a systems-based sub-proteomic analysis of membrane-enriched post-mortem brain samples from pathology-free control, AsymAD, and AD brains (n = 6 per group). Label-free mass spectrometry based on peptide ion intensity was used to quantify the 18 membrane-enriched fractions. Differential expression and weighted protein co-expression network analysis (WPCNA) were then used to identify and characterize modules of co-expressed proteins most significantly altered between the groups. We identified a total of 27 modules of co-expressed membrane-associated proteins. In contrast to the unfractionated proteome, these networks did not map strongly to cell-type specific markers. Instead, these modules were principally organized by their associations with a wide variety of membrane-bound compartments and organelles. Of these, the mitochondrion was associated with the greatest number of modules, followed by modules linked to the cell surface compartment. In addition, we resolved networks with strong associations to the endoplasmic reticulum, Golgi apparatus, and other membrane-bound organelles. A total of 14 of the 27 modules demonstrated significant correlations with clinical and pathological AD phenotypes. These results revealed that the proteins within individual compartments feature a heterogeneous array of AD-associated expression patterns, particularly during the preclinical stages of disease. In conclusion, this systems-based analysis of the membrane-associated AsymAD brain proteome yielded a unique network organization highly linked to cellular compartmentalization. Further study of this membrane-associated proteome may reveal novel insight into the complex pathways governing the earliest stages of disease.

Patients with Alzheimer's disease (AD) and Parkinson's disease (PD) often have overlap in clinical presentation and brain neuropathology suggesting that these two diseases share common underlying mechanisms. Currently, the molecular pathways linking AD and PD are incompletely understood. Utilizing Tandem Mass Tag (TMT) isobaric labeling and synchronous precursor selection-based MS3 (SPS-MS3) mass spectrometry, we performed an unbiased quantitative proteomic analysis of post-mortem human brain tissues (n=80) from four different groups defined as controls, AD, PD, and co-morbid AD/PD cases across two brain regions (frontal cortex and anterior cingulate gyrus). In total, we identified 11 840 protein groups representing 10 230 gene symbols, which map to ~65% of the protein coding genes in brain. The utility of including two reference standards in each TMT 10-plex assay to assess intra- and inter-batch variance is also described. Ultimately, this comprehensive human brain proteomic dataset serves as a valuable resource for various research endeavors including, but not limited to, the identification of disease-specific protein signatures and molecular pathways that are common in AD and PD.

Alzheimer's disease (AD) is characterized by an early, asymptomatic phase (AsymAD) in which individuals exhibit amyloid-beta (Aβ) plaque accumulation in the absence of clinically detectable cognitive decline. Here we report an unbiased multiplex quantitative proteomic and phosphoproteomic analysis using tandem mass tag (TMT) isobaric labeling of human post-mortem cortex (n = 27) across pathology-free controls, AsymAD and symptomatic AD individuals. With off-line high-pH fractionation and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Orbitrap Lumos mass spectrometer, we identified 11,378 protein groups across three TMT 11-plex batches. Immobilized metal affinity chromatography (IMAC) was used to enrich for phosphopeptides from the same TMT-labeled cases and 51,736 phosphopeptides were identified. Of these, 48,992 were quantified by TMT reporter ions representing 33,652 unique phosphosites. Two reference standards in each TMT 11-plex were included to assess intra- and inter-batch variance at the protein and peptide level. This comprehensive human brain proteome and phosphoproteome dataset will serve as a valuable resource for the identification of biochemical, cellular and signaling pathways altered during AD progression.

We have generated a controlled and manipulable resource that captures genetic risk for Alzheimer's disease: iPSC lines from 53 individuals coupled with RNA and proteomic profiling of both iPSC-derived neurons and brain tissue of the same individuals. Data collected for each person include genome sequencing, longitudinal cognitive scores, and quantitative neuropathology. The utility of this resource is exemplified here by analyses of neurons derived from these lines, revealing significant associations between specific Aβ and tau species and the levels of plaque and tangle deposition in the brain and, more importantly, with the trajectory of cognitive decline. Proteins and networks are identified that are associated with AD phenotypes in iPSC neurons, and relevant associations are validated in brain. The data presented establish this iPSC collection as a resource for investigating person-specific processes in the brain that can aid in identifying and validating molecular pathways underlying AD.

Alzheimer's disease (AD) pathology develops many years before the onset of cognitive symptoms. Two pathological processes-aggregation of the amyloid-β (Aβ) peptide into plaques and the microtubule protein tau into neurofibrillary tangles (NFTs)-are hallmarks of the disease. However, other pathological brain processes are thought to be key disease mediators of Aβ plaque and NFT pathology. How these additional pathologies evolve over the course of the disease is currently unknown. Here we show that proteomic measurements in autosomal dominant AD cerebrospinal fluid (CSF) linked to brain protein coexpression can be used to characterize the evolution of AD pathology over a timescale spanning six decades. SMOC1 and SPON1 proteins associated with Aβ plaques were elevated in AD CSF nearly 30 years before the onset of symptoms, followed by changes in synaptic proteins, metabolic proteins, axonal proteins, inflammatory proteins and finally decreases in neurosecretory proteins. The proteome discriminated mutation carriers from noncarriers before symptom onset as well or better than Aβ and tau measures. Our results highlight the multifaceted landscape of AD pathophysiology and its temporal evolution. Such knowledge will be critical for developing precision therapeutic interventions and biomarkers for AD beyond those associated with Aβ and tau.

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Proteomic studies have identified moesin (MSN), a protein containing a four-point-one, ezrin, radixin, moesin (FERM) domain, and the receptor CD44 as hub proteins found within a coexpression module strongly linked to Alzheimer's disease (AD) traits and microglia. These proteins are more abundant in Alzheimer's patient brains, and their levels are positively correlated with cognitive decline, amyloid plaque deposition, and neurofibrillary tangle burden. The MSN FERM domain interacts with the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) and the cytoplasmic tail of CD44. Inhibiting the MSN-CD44 interaction may help limit AD-associated neuronal damage. Here, we investigated the feasibility of developing inhibitors that target this protein-protein interaction. We have employed structural, mutational, and phage-display studies to examine how CD44 binds to the FERM domain of MSN. Interestingly, we have identified an allosteric site located close to the PIP2 binding pocket that influences CD44 binding. These findings suggest a mechanism in which PIP2 binding to the FERM domain stimulates CD44 binding through an allosteric effect, leading to the formation of a neighboring pocket capable of accommodating a receptor tail. Furthermore, high-throughput screening of a chemical library identified two compounds that disrupt the MSN-CD44 interaction. One compound series was further optimized for biochemical activity, specificity, and solubility. Our results suggest that the FERM domain holds potential as a drug development target. Small molecule preliminary leads generated from this study could serve as a foundation for additional medicinal chemistry efforts with the goal of controlling microglial activity in AD by modifying the MSN-CD44 interaction.

Polymorphic alleles in the apolipoprotein E (APOE) gene are the main genetic determinants of late-onset Alzheimer's disease (AD) risk. Individuals carrying the APOE E4 allele are at increased risk to develop AD compared to those carrying the more common E3 allele, whereas those carrying the E2 allele are at decreased risk for developing AD. How ApoE isoforms influence risk for AD remains unclear. To help fill this gap in knowledge, we performed a comparative unbiased mass spectrometry-based proteomic analysis of post-mortem brain cortical tissues from pathologically-defined AD or control cases of different APOE genotypes. Control cases (n = 10) were homozygous for the common E3 allele, whereas AD cases (n = 24) were equally distributed among E2/3, E3/3, and E4/4 genotypes. We used differential protein expression and co-expression analytical approaches to assess how changes in the brain proteome are related to APOE genotype. We observed similar levels of amyloid-β, but reduced levels of neurofibrillary tau, in E2/3 brains compared to E3/3 and E4/4 AD brains. Weighted co-expression network analysis revealed 33 modules of co-expressed proteins, 12 of which were significantly different by APOE genotype in AD. The modules that were significantly different by APOE genotype were associated with synaptic transmission and inflammation, among other biological processes. Deconvolution and analysis of brain cell type changes revealed that the E2 allele suppressed homeostatic and disease-associated cell type changes in astrocytes, microglia, oligodendroglia, and endothelia. The E2 allele-specific effect on brain cell type changes was validated in a separate cohort of 130 brains. Our systems-level proteomic analyses of AD brain reveal alterations in the brain proteome and brain cell types associated with allelic variants in APOE, and suggest further areas for investigation into the upstream mechanisms that drive ApoE-associated risk for AD.

Here, we report proteomic analyses of 129 human cortical tissues to define changes associated with the asymptomatic and symptomatic stages of Alzheimer's disease (AD). Network analysis revealed 16 modules of co-expressed proteins, 10 of which correlated with AD phenotypes. A subset of modules overlapped with RNA co-expression networks, including those associated with neurons and astroglial cell types, showing altered expression in AD, even in the asymptomatic stages. Overlap of RNA and protein networks was otherwise modest, with many modules specific to the proteome, including those linked to microtubule function and inflammation. Proteomic modules were validated in an independent cohort, demonstrating some module expression changes unique to AD and several observed in other neurodegenerative diseases. AD genetic risk loci were concentrated in glial-related modules in the proteome and transcriptome, consistent with their causal role in AD. This multi-network analysis reveals protein- and disease-specific pathways involved in the etiology, initiation, and progression of AD.

Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimer's disease (AD) remains elusive. To better characterize the complex nature of AD, we constructed multiscale causal networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene- and protein-expression. These probabilistic causal models enabled detection, prioritization and replication of high-confidence master regulators of AD-associated networks, including the top predicted regulator, VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was also achieved in this AD model, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF. Our findings support a causal role for VGF in protecting against AD pathogenesis and progression.

Alzheimer's disease (AD) lacks protein biomarkers reflective of its diverse underlying pathophysiology, hindering diagnostic and therapeutic advancements. Here, we used integrative proteomics to identify cerebrospinal fluid (CSF) biomarkers representing a wide spectrum of AD pathophysiology. Multiplex mass spectrometry identified ~3500 and ~12,000 proteins in AD CSF and brain, respectively. Network analysis of the brain proteome resolved 44 biologically diverse modules, 15 of which overlapped with the CSF proteome. CSF AD markers in these overlapping modules were collapsed into five protein panels representing distinct pathophysiological processes. Synaptic and metabolic panels were decreased in AD brain but increased in CSF, while glial-enriched myelination and immunity panels were increased in brain and CSF. The consistency and disease specificity of panel changes were confirmed in >500 additional CSF samples. These panels also identified biological subpopulations within asymptomatic AD. Overall, these results are a promising step toward a network-based biomarker tool for AD clinical applications.

One of the defining pathological features of Alzheimer's disease (AD) is the deposition of neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau in the brain. Aberrant activation of kinases in AD has been suggested to enhance phosphorylation and toxicity of tau, making the responsible tau kinases attractive therapeutic targets. The full complement of tau-interacting kinases in AD brain and their activity in disease remains incompletely defined. Here, immunoaffinity enrichment coupled with mass spectrometry (MS) identified TANK-binding kinase 1 (TBK1) as a tau-interacting partner in human AD cortical brain tissues. We validated this interaction in human AD, familial frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17) caused by mutations in MAPT (R406W & P301L) and corticobasal degeneration (CBD) postmortem brain tissues as well as human cell lines. Further, we document increased TBK1 activation in both AD and FTDP-17 and map TBK1 phosphorylation sites on tau based on in vitro kinase assays coupled to MS. Lastly, in a Drosophila tauopathy model, activating expression of a conserved TBK1 ortholog triggers tau hyperphosphorylation and enhanced neurodegeneration, whereas knockdown had the reciprocal effect, suppressing tau toxicity. Collectively, our findings suggest that increased TBK1 activation may promote tau hyperphosphorylation and neuronal loss in AD and related tauopathies.