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In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers., Competing Interests: Declaration of interests C.C. has received research support from GSK and EISAI. C.C. is a member of the advisory board of Circular Genomics and owns stocks in these companies. C.C. is on the advisory board of ADmit. J.L. reports speaker fees from Bayer Vital, Biogen, EISAI, TEVA, Zambon, Merck, and Roche; consulting fees from Axon Neuroscience, EISAI, and Biogen; author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers; and is inventor in a patent “Oral Phenylbutyrate for Treatment of Human 4-Repeat Tauopathies” (EP 23 156 122.6). He receives compensation for serving as chief medical officer for MODAG GmbH and is a beneficiary of the phantom share program of MODAG GmbH. E.M. reports research support received from NIA (U01AG059798), Anonymous Foundation, GHR, Alzheimer Association, Eli Lilly Eisai, and Hoffmann-La Roche and paid consulting for Eli Lilly, Alector, Alzamend, Sanofi, AstraZeneca, Hoffmann-La Roche, Grifols, and Merck., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches., Methods: We employed heparin-affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to enrich HBPs from plasma obtained from AD (n = 62) and control (n = 47) samples. These profiles were then correlated to Aβ, tau and phosphorylated tau (pTau) CSF biomarkers and plasma pTau181 from the same individuals, as well as a consensus brain proteome network to assess the overlap with AD brain pathophysiology., Results: Heparin enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundant proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude in abundance, were measured across 109 samples. Compared to the consensus AD brain protein co-expression network, we observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, highlighting the complex interplay between the two compartments. Elevated proteins in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate with Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and the APOE4 proteoform. Additionally, heparin-enriched proteins in plasma demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau181. A panel of five plasma proteins classified AD from control individuals with an area under the curve (AUC) of 0.85. When combined with plasma pTau181, the panel significantly improved the classification performance of pTau181 alone, increasing the AUC from 0.93 to 0.98. This suggests that the heparin-enriched plasma proteome captures additional variance in cognitive dementia beyond what is explained by pTau181., Conclusion: These findings support the utility of a heparin-affinity approach coupled with TMT-MS for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain., (© 2024. The Author(s).)

A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status., (© 2024. The Author(s).)

Objective: Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals., Methods: We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non-AD cases, including 495 (16.5%) self-identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data-driven multivariate Gaussian mixture of regressions., Results: Distinct effects of race were found in different groups. Total Tauand phospho181-Tau were lower among AA individuals in all groups (p < 0.0001), and Aβ 42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2-decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker-positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%)., Interpretation: Although the risk of dementia is higher, data-driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463-475., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Alzheimer's disease (AD) is a complex neurodegenerative disorder that develops over decades. AD brain proteomics reveals vast alterations in protein levels and numerous altered biologic pathways. Here, we compare AD brain proteome and network changes with the brain proteomes of amyloid β (Aβ)-depositing mice to identify conserved and divergent protein networks with the conserved networks identifying an Aβ amyloid responsome. Proteins in the most conserved network (M42) accumulate in plaques, cerebrovascular amyloid (CAA), and/or dystrophic neuronal processes, and overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), increases the accumulation of Aβ in plaques and CAA. M42 proteins bind amyloid fibrils in vitro, and MDK and PTN co-accumulate with cardiac transthyretin amyloid. M42 proteins appear intimately linked to amyloid deposition and can regulate amyloid deposition, suggesting that they are pathology modifiers and thus putative therapeutic targets. We posit that amyloid-scaffolded accumulation of numerous M42+ proteins is a central mechanism mediating downstream pathophysiology in AD., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) is currently defined by the aggregation of amyloid-β (Aβ) and tau proteins in the brain. Although biofluid biomarkers are available to measure Aβ and tau pathology, few markers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here, we characterized the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aβ and tau pathology in 300 individuals using two different proteomic technologies-tandem mass tag mass spectrometry and SomaScan. Integration of both data types allowed for generation of a robust protein coexpression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 ( APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen-associated protein kinase signaling, neddylation, and mitochondrial biology and overlapped with a previously described lipoprotein module in serum. Alterations of all three modules in blood were associated with dementia more than 20 years before diagnosis. Analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module-the network module most strongly correlated to cognitive function-were reduced by ATX treatment. Clustering of individuals based on their CSF proteomic profiles revealed heterogeneity of pathological changes not fully reflected by Aβ and tau.

Introduction: Cerebrovascular dysfunction is a pathological hallmark of Alzheimer's disease (AD). Nevertheless, detecting cerebrovascular changes within bulk tissues has limited our ability to characterize proteomic alterations from less abundant cell types., Methods: We conducted quantitative proteomics on bulk brain tissues and isolated cerebrovasculature from the same individuals, encompassing control (N = 28), progressive supranuclear palsy (PSP) (N = 18), and AD (N = 21) cases., Results: Protein co-expression network analysis identified unique cerebrovascular modules significantly correlated with amyloid plaques, cerebrovascular amyloid angiopathy (CAA), and/or tau pathology. The protein products within AD genetic risk loci were concentrated within cerebrovascular modules. The overlap between differentially abundant proteins in AD cerebrospinal fluid (CSF) and plasma with cerebrovascular network highlighted a significant increase of matrisome proteins, SMOC1 and SMOC2, in CSF, plasma, and brain., Discussion: These findings enhance our understanding of cerebrovascular deficits in AD, shedding light on potential biomarkers associated with CAA and vascular dysfunction in neurodegenerative diseases., (© 2024 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

There is an urgent need to improve the translational validity of Alzheimer's disease (AD) mouse models. Introducing genetic background diversity in AD mouse models has been proposed as a way to increase validity and enable the discovery of previously uncharacterized genetic contributions to AD susceptibility or resilience. However, the extent to which genetic background influences the mouse brain proteome and its perturbation in AD mouse models is unknown. In this study, we crossed the 5XFAD AD mouse model on a C57BL/6J (B6) inbred background with the DBA/2J (D2) inbred background and analyzed the effects of genetic background variation on the brain proteome in F1 progeny. Both genetic background and 5XFAD transgene insertion strongly affected protein variance in the hippocampus and cortex ( n = 3,368 proteins). Protein co-expression network analysis identified 16 modules of highly co-expressed proteins common across the hippocampus and cortex in 5XFAD and non-transgenic mice. Among the modules strongly influenced by genetic background were those related to small molecule metabolism and ion transport. Modules strongly influenced by the 5XFAD transgene were related to lysosome/stress responses and neuronal synapse/signaling. The modules with the strongest relationship to human disease-neuronal synapse/signaling and lysosome/stress response-were not significantly influenced by genetic background. However, other modules in 5XFAD that were related to human disease, such as GABA synaptic signaling and mitochondrial membrane modules, were influenced by genetic background. Most disease-related modules were more strongly correlated with AD genotype in the hippocampus compared with the cortex. Our findings suggest that the genetic diversity introduced by crossing B6 and D2 inbred backgrounds influences proteomic changes related to disease in the 5XFAD model, and that proteomic analysis of other genetic backgrounds in transgenic and knock-in AD mouse models is warranted to capture the full range of molecular heterogeneity in genetically diverse models of AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hurst, Dunn, Dammer, Duong, Shapley, Seyfried, Kaczorowski and Johnson.)

Cognitive impairment in the elderly features complex molecular pathophysiology extending beyond the hallmark pathologies of traditional disease classification. Molecular subtyping using large-scale -omic strategies can help resolve this biological heterogeneity. Using quantitative mass spectrometry, we measured ∼8000 proteins across >600 dorsolateral prefrontal cortex tissues with clinical diagnoses of no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. Unbiased classification of MCI and AD cases based on individual proteomic profiles resolved three classes with expression differences across numerous cell types and biological ontologies. Two classes displayed molecular signatures atypical of AD neurodegeneration, such as elevated synaptic and decreased inflammatory markers. In one class, these atypical proteomic features were associated with clinical and pathological hallmarks of cognitive resilience. We were able to replicate these classes and their clinicopathological phenotypes across two additional tissue cohorts. These results promise to better define the molecular heterogeneity of cognitive impairment and meaningfully impact its diagnostic and therapeutic precision., Competing Interests: Declaration of Competing Interest A.I.L, N.T.S., and D.M.D. are co-founders of Emtherapro Inc. The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) is a neurodegenerative disease with heterogenous pathophysiological changes that develop years before the onset of clinical symptoms. These preclinical changes have generated considerable interest in identifying markers for the pathophysiological mechanisms linked to AD and AD-related disorders (ADRD). On the basis of our prior work integrating cerebrospinal fluid (CSF) and brain proteome networks, we developed a reliable and high-throughput mass spectrometry-selected reaction monitoring assay that targets 48 key proteins altered in CSF. To test the diagnostic utility of these proteins and compare them with existing AD biomarkers, CSF collected at baseline visits was assayed from 706 participants recruited from the Alzheimer's Disease Neuroimaging Initiative. We found that the targeted CSF panel of 48 proteins (CSF 48 panel) performed at least as well as existing AD CSF biomarkers (Aβ 42 , tTau, and pTau 181 ) for predicting clinical diagnosis, FDG PET, hippocampal volume, and measures of cognitive and dementia severity. In addition, for each of those outcomes, the CSF 48 panel plus the existing AD CSF biomarkers significantly improved diagnostic performance. Furthermore, the CSF 48 panel plus existing AD CSF biomarkers significantly improved predictions for changes in FDG PET, hippocampal volume, and measures of cognitive decline and dementia severity compared with either measure alone. A potential reason for these improvements is that the CSF 48 panel reflects a range of altered biology observed in AD/ADRD. In conclusion, we show that the CSF 48 panel complements existing AD CSF biomarkers to improve diagnosis and predict future cognitive decline and dementia severity.

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., (© 2023. The Author(s).)

Background: Despite being twice as likely to get Alzheimer's disease (AD), African Americans have been grossly underrepresented in AD research. While emerging evidence indicates that African Americans with AD have lower cerebrospinal fluid (CSF) levels of Tau compared to Caucasians, other differences in AD CSF biomarkers have not been fully elucidated. Here, we performed unbiased proteomic profiling of CSF from African Americans and Caucasians with and without AD to identify both common and divergent AD CSF biomarkers., Methods: Multiplex tandem mass tag-based mass spectrometry (TMT-MS) quantified 1,840 proteins from 105 control and 98 AD patients of which 100 identified as Caucasian while 103 identified as African American. We used differential protein expression and co-expression approaches to assess how changes in the CSF proteome are related to race and AD. Co-expression network analysis organized the CSF proteome into 14 modules associated with brain cell-types and biological pathways. A targeted mass spectrometry method, selected reaction monitoring (SRM), with heavy labeled internal standards was used to measure a panel of CSF module proteins across a subset of African Americans and Caucasians with or without AD. A receiver operating characteristic (ROC) curve analysis assessed the performance of each protein biomarker in differentiating controls and AD by race., Results: Consistent with previous findings, the increase of Tau levels in AD was greater in Caucasians than in African Americans by both immunoassay and TMT-MS measurements. CSF modules which included 14-3-3 proteins (YWHAZ and YWHAG) demonstrated equivalent disease-related elevations in both African Americans and Caucasians with AD, whereas other modules demonstrated more profound disease changes within race. Modules enriched with proteins involved with glycolysis and neuronal/cytoskeletal proteins, including Tau, were more increased in Caucasians than in African Americans with AD. In contrast, a module enriched with synaptic proteins including VGF, SCG2, and NPTX2 was significantly lower in African Americans than Caucasians with AD. Following SRM and ROC analysis, VGF, SCG2, and NPTX2 were significantly better at classifying African Americans than Caucasians with AD., Conclusions: Our findings provide insight into additional protein biomarkers and pathways reflecting underlying brain pathology that are shared or differ by race., (© 2023. The Author(s).)

Proteomic studies for Alzheimer's disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson's disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.

Alzheimer's disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT-) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT-), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT- individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD., (© 2023. The Author(s).)

Large scale -omics datasets can provide new insights into normal and disease-related biology when analyzed through a systems biology framework. However, technical artefacts present in most -omics datasets due to variations in sample preparation, batching, platform settings, personnel, and other experimental procedures prevent useful analyses of such data without prior adjustment for these technical factors. Here, we demonstrate a tunable median polish of ratio (TAMPOR) approach for batch effect correction and agglomeration of multiple, multi-batch, site-specific cohorts into a single analyte abundance data matrix that is suitable for systems biology analyses. We illustrate the utility and versatility of TAMPOR through four distinct use cases where the method has been applied to different proteomic datasets, some of which contain a specific defect that must be addressed prior to analysis. We compare quality control metrics and sources of variance before and after application of TAMPOR to show that TAMPOR is effective at removing batch effects and other unwanted sources of variance in -omics data. We also show how TAMPOR can be used to harmonize -omics datasets even when the data are acquired using different analytical approaches. TAMPOR is a powerful and flexible approach for cleaning and harmonization of -omics data prior to downstream systems biology analysis.

Robust and accessible biomarkers that can capture the heterogeneity of Alzheimer's disease and its diverse pathological processes are urgently needed. Here, we undertook an investigation of Alzheimer's disease cerebrospinal fluid (CSF) and plasma from the same subjects (n=18 control, n=18 AD) using three different proteomic platforms-SomaLogic SomaScan, Olink proximity extension assay, and tandem mass tag-based mass spectrometry-to assess which protein markers in these two biofluids may serve as reliable biomarkers of AD pathophysiology observed from unbiased brain proteomics studies. Median correlation of overlapping protein measurements across platforms in CSF (r~0.7) and plasma (r~0.6) was good, with more variability in plasma. The SomaScan technology provided the most measurements in plasma. Surprisingly, many proteins altered in AD CSF were found to be altered in the opposite direction in plasma, including important members of AD brain co-expression modules. An exception was SMOC1, a key member of the brain matrisome module associated with amyloid-β deposition in AD, which was found to be elevated in both CSF and plasma. Protein co-expression analysis on greater than 7000 protein measurements in CSF and 9500 protein measurements in plasma across all proteomic platforms revealed strong changes in modules related to autophagy, ubiquitination, and sugar metabolism in CSF, and endocytosis and the matrisome in plasma. Cross-platform and cross-biofluid proteomics represents a promising approach for AD biomarker development., (© 2022. The Author(s).)

Development of disease-modifying therapies against Alzheimer's disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n = 50), AD-dementia (n = 230), non-AD dementias (n = 322) and cognitively unimpaired controls (n = 195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aβ+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aβ+) were primarily related to protein catabolism, energy metabolism and oxidative stress, whereas those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (area under the curve (AUC): 0.85-0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8-0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD., (© 2022. The Author(s).)

Mass spectrometry (MS)-based proteomics is a powerful tool to explore pathogenic changes of a disease in an unbiased manner and has been used extensively in Alzheimer disease (AD) research. Here, by performing a meta-analysis of high-quality proteomic studies, we address which pathological changes are observed consistently and therefore most likely are of great importance for AD pathogenesis. We retrieved datasets, comprising a total of 21,588 distinct proteins identified across 857 postmortem human samples, from ten studies using labeled or label-free MS approaches. Our meta-analysis findings showed significant alterations of 757 and 1,195 proteins in AD in the labeled and label-free datasets, respectively. Only 33 proteins, some of which were associated with synaptic signaling, had the same directional change across the individual studies. However, despite alterations in individual proteins being different between the labeled and the label-free datasets, several pathways related to synaptic signaling, oxidative phosphorylation, immune response and extracellular matrix were commonly dysregulated in AD. These pathways represent robust changes in the human AD brain and warrant further investigation., (© 2021. The Author(s).)

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., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Background: Alzheimer's disease (AD) is the most frequent and costly neurodegenerative disorder. Although diverse lines of evidence suggest that the amyloid precursor protein (APP) is involved in its causation, the precise mechanisms remain unknown and no treatments are available to prevent or halt the disease. A favorite hypothesis has been that APP contributes to AD pathogenesis through the cerebral accumulation of the amyloid-β peptide (Aβ), which is derived from APP through sequential proteolytic cleavage by BACE1 and γ-secretase. However, inhibitors of these enzymes have failed in clinical trials despite clear evidence for target engagement., Methods: To further elucidate the roles of APP and its metabolites in AD pathogenesis, we analyzed transgenic mice overexpressing wildtype human APP (hAPP) or hAPP carrying mutations that cause autosomal dominant familial AD (FAD), as well as App knock-in mice that do not overexpress hAPP but have two mouse App alleles with FAD mutations and a humanized Aβ sequence., Results: Although these lines of mice had marked differences in cortical and hippocampal levels of APP, APP C-terminal fragments, soluble Aβ, Aβ oligomers and age-dependent amyloid deposition, they all developed cognitive deficits as well as non-convulsive epileptiform activity, a type of network dysfunction that also occurs in a substantive proportion of humans with AD. Pharmacological inhibition of BACE1 effectively reduced levels of amyloidogenic APP C-terminal fragments (C99), soluble Aβ, Aβ oligomers, and amyloid deposits in transgenic mice expressing FAD-mutant hAPP, but did not improve their network dysfunction and behavioral abnormalities, even when initiated at early stages before amyloid deposits were detectable., Conclusions: hAPP transgenic and App knock-in mice develop similar pathophysiological alterations. APP and its metabolites contribute to AD-related functional alterations through complex combinatorial mechanisms that may be difficult to block with BACE inhibitors and, possibly, also with other anti-Aβ treatments.

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer's disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership - Alzheimer's Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline. Protein biomarkers of AD brain pathology, including β-amyloid and Tau, are reflected in cerebrospinal fluid (CSF), yet the identification of additional biomarkers linked to other brain pathophysiologies remains elusive. We recently reported a multiplex tandem-mass tag (TMT) CSF proteomic analysis of nearly 3000 proteins, following depletion of highly abundant proteins and off-line fractionation, across control and AD cases. Of these, over 500 proteins were significantly increased or decreased in AD, including markers reflecting diverse biological functions in brain. Here, we use a targeted mass spectrometry (MS) approach, termed parallel reaction monitoring (PRM), to quantify select CSF biomarkers without pre-depletion or fractionation to assess the reproducibility of our findings and the specificity of changes for AD versus other causes of cognitive impairment., Method: We nominated 41 proteins (94 peptides) from the TMT CSF discovery dataset, representing a variety of brain cell-types and biological functions, for label-free PRM analysis in a replication cohort of 88 individuals that included 20 normal controls, 37 clinically diagnosed AD cases and 31 cases with non-AD cognitive impairment. To control for technical variables, isotopically labeled synthetic heavy peptide standards were added into each of the 88 CSF tryptic digests. Furthermore, a peptide pool, representing an equivalent amount of peptide from all samples, was analyzed ( n  = 10) across each batch. Together, this approach enabled us to assess both the intra- and inter-sample differences in peptide signal response and retention time., Results: Despite differences in sample preparation, quantitative MS approaches and patient samples, 25 proteins, including Tau, had a consistent and significant change in AD in both the discovery and replication cohorts. Validated CSF markers with low coefficient of variation included the protein products for neuronal/synaptic (GDA, GAP43, SYN1, BASP1, YWHAB, YWHAZ, UCHL1, STMN1 and MAP1B), glial/inflammation (SMOC1, ITGAM, CHI3L1, SPP1, and CHIT1) and metabolic (PKM, ALDOA and FABP3) related genes. Logistical regression analyses revealed several proteins with high sensitivity and specificity for classifying AD cases from controls and other non-AD dementias. SMOC1, YWHAZ, ALDOA and MAP1B emerged as biomarker candidates that could best discriminate between individuals with AD and non-AD cognitive impairment as well as Tau/β-amyloid ratio. Notably, SMOC1 levels in postmortem brain are highly correlated with AD pathology even in the preclinical stage of disease, indicating that CSF SMOC1 levels reflect underlying brain pathology specific for AD., Conclusion: Collectively these findings highlight the utility of targeted MS approaches to quantify biomarkers associated with AD that could be used for monitoring disease progression, stratifying patients for clinical trials and measuring therapeutic response., Competing Interests: Competing interestsThe authors declare no competing financial interest., (© The Author(s) 2020.)

Our understanding of Alzheimer's disease (AD) pathophysiology remains incomplete. Here we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

Here, we report a method for the generation of complementary tryptic (CompTryp) isotope-labeled peptide standards for the relative and absolute quantification of proteins by mass spectrometry (MS). These standards can be digested in parallel with either trypsin (Tryp-C) or trypsin-N (Tryp-N), to generate peptides that significantly overlap in primary sequence having C- and N-terminal arginine and lysine residues, respectively. As a proof of concept, an isotope-labeled CompTryp standard was synthesized for Tau, a well-established biomarker in Alzheimer's disease (AD), which included both N- and C-terminal heavy isotope-labeled ( 15 N and 13 C) arginine residues and flanking amino acid sequences to monitor proteolytic digestion. Despite having the exact same mass, the N- and C-terminal heavy Tau peptides are distinguishable by retention time and MS/MS fragmentation profiles. The isotope-labeled Tau CompTryp standard was added to human cerebrospinal fluid (CSF) followed by parallel digestion with Tryp-N and Tryp-C. The native and isotope-labeled peptide pairs were quantified by parallel reaction monitoring (PRM) in a single assay. Notably, both tryptic peptides were effective at quantifying Tau in human CSF, and both showed a significant difference in CSF Tau levels between AD and controls. Treating these CompTryp Tau peptide measurements as independent replicates also improved the coefficient of variation and correlation with Tau immunoassays. More broadly, we propose that CompTryp standards can be generated for any protein of interest, providing an efficient method to improve the robustness and reproducibility for MS analysis of clinical and research samples.

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.

Background: The complicated cellular and biochemical changes that occur in brain during Alzheimer's disease are poorly understood. In a previous study we used an unbiased label-free quantitative mass spectrometry-based proteomic approach to analyze these changes at a systems level in post-mortem cortical tissue from patients with Alzheimer's disease (AD), asymptomatic Alzheimer's disease (AsymAD), and controls. We found modules of co-expressed proteins that correlated with AD phenotypes, some of which were enriched in proteins identified as risk factors for AD by genetic studies., Methods: The amount of information that can be obtained from such systems-level proteomic analyses is critically dependent upon the number of proteins that can be quantified across a cohort. We report here a new proteomic systems-level analysis of AD brain based on 6,533 proteins measured across AD, AsymAD, and controls using an analysis pipeline consisting of isobaric tandem mass tag (TMT) mass spectrometry and offline prefractionation., Results: Our new TMT pipeline allowed us to more than double the depth of brain proteome coverage. This increased depth of coverage greatly expanded the brain protein network to reveal new protein modules that correlated with disease and were unrelated to those identified in our previous network. Differential protein abundance analysis identified 350 proteins that had altered levels between AsymAD and AD not caused by changes in specific cell type abundance, potentially reflecting biochemical changes that are associated with cognitive decline in AD. RNA binding proteins emerged as a class of proteins altered between AsymAD and AD, and were enriched in network modules that correlated with AD pathology. We developed a proteogenomic approach to investigate RNA splicing events that may be altered by RNA binding protein changes in AD. The increased proteome depth afforded by our TMT pipeline allowed us to identify and quantify a large number of alternatively spliced protein isoforms in brain, including AD risk factors such as BIN1, PICALM, PTK2B, and FERMT2. Many of the new AD protein network modules were enriched in alternatively spliced proteins and correlated with molecular markers of AD pathology and cognition., Conclusions: Further analysis of the AD brain proteome will continue to yield new insights into the biological basis of AD.