Alzheimer disease (AD), the most common cause of dementia, is a heterogeneous neurodegenerative disease characterized by neuronal loss, neuroinflammation, and memory decline.6 Clinical sequelae reduce quality of life and cost the healthcare system up to $244 billion annually in the US, with additional impact on caregivers’ emotional distress.7 Proteomic studies have been instrumental in identifying biomarkers and pathways implicated in AD, but most have been limited to single tissues and only differentiate between sporadic AD cases and controls. Deep molecular characterization of controls, sporadic AD cases and genetically defined AD subtypes, such as individuals carrying pathogenic mutations in amyloid-beta precursor protein (APP) and presenilin genes (PSEN1 and PSEN2) or high-effect risk variants in triggering receptor expressed on myeloid cells 2 (TREM2),1,2,8 is critical for fully understanding the biology of this heterogeneous disease and for identifying novel molecular biomarkers and therapeutic targets. Here, we present the results of multi-tissue, high-throughput deep proteomic profiling. We identified proteins associated with AD status that replicated in external datasets. We not only identified proteomic profiles for sporadic AD, but also for individuals with AD-risk variants in TREM2 and pathogenic variants in APP and PSEN1/2. These proteomic profiles enabled the creation of tissue-specific prediction models and the identification of causal proteins and pathways for sporadic and genetically defined AD subtypes.
To elucidate the downstream effects of genes and the functional mechanisms associated with AD, we generated high-throughput, deep proteomic profiles using SOMAscan targeting 1,305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma (Fig. 1).5 These neurologically relevant tissues were obtained from well-characterized individuals with comprehensive clinical information about AD pathology and cognition in the Knight ADRC3 and DIAN.3,4,9,10 After stringent quality control (QC) and data cleaning, a total of 1,092 proteins from 360 brain tissues remained. These brain proteomic data include 24 individuals carrying autosomal dominant AD (ADAD) mutations in APP and PSEN1/2, 290 individuals with autopsy-confirmed AD, 21 TREM2 variant carriers, and 25 cognitively normal individuals with no significant brain pathology (Table 1). CSF data contained 713 proteins from 176 individuals with a clinical diagnosis of AD, 47 TREM2 variant carriers, and 494 cognitively normal individuals. Plasma data contain 931 proteins from 105 individuals with a clinical diagnosis of AD, 131 TREM2 variant carriers, and 254 cognitively normal individuals (Fig. 1).
AD status was defined based on neuropathological examination for those samples with brain autopsy and clinical examination for those with CSF and plasma tissue. In this study we identified proteins with different levels in clinical AD cases vs. controls and not based on biomarker levels or the ATN framework, which combines the amyloid-β pathway (A), tau-mediated pathophysiology (T), and neurodegeneration (N), because one of the goals of this study was to compare the performance of the prediction models generated in this study with these well-accepted and validated CSF biomarkers (Aβ and p-tau181).
To validate and replicate proteins that were associated with AD, TREM2 risk variant carriers or ADAD mutation carrier status, we followed two approaches: first, for sporadic AD and TREM2 risk variant carriers, we identified the common set of proteins dysregulated in the three tissues (brain, CSF, and plasma). For ADAD, only high-throughput proteomic screening was performed on brain tissue. Those proteins that were associated with ADAD status in brain were analyzed in CSF from 289 ADAD mutation carriers and 184 non carriers from the DIAN study. Second, for sporadic AD, we used seven publicly available datasets to replicate our findings (Supplementary Table 1). For brain, we downloaded the mass-spectrometry data for following 6 studies: the Adult Changes In Thought (ACT), Banner Sun Health Research Institute (BANNER), Baltimore longitudinal study of aging (BLSA), Mayo Clinic (MAYO), Mount Sinai Brain Bank (MSBB), the Religious Orders Study and the Memory and Aging Project (ROSMAP). We then performed differential abundance analysis jointly for a total of 10,078 proteins measured in 415 AD patients and 194 controls, called hereafter MassSpec Joint. For CSF, we obtained and analyzed Alzheimer’s Disease Neuroimaging Initiative (ADNI) multiple reaction monitoring (MRM) proteomic data containing 320 proteins in 263 samples. We also used results based on BioFinder OLINK data from Whelan et al.11 and Emory-ADRC mass-spectrometry data from Higginbotham et al.12 For plasma, we downloaded and performed differential analysis on the AddNeuroMed SOMAscan 1.1K proteomic data that was processed and deposited by Sattlenecker et al.13 We were not able to use public datasets to replicate the proteins dysregulated in TREM2 or ADAD mutation carriers because there were not enough carriers in public datasets. Finally, we used the replicated proteins to generate prediction models and run pathway analyses. We combined the results from this study with our recent pQTL, colocalization, and Mendelian randomization findings to identify causal proteins.5
Multi-tissue proteomic signatures of AD
Sporadic AD cases
To identify multi-tissue proteomic signatures for clinical AD, we performed differential analysis with a subgroup of sporadic AD patients and healthy individuals in each of the three tissues, independently. Specifically, we performed a surrogate variable analysis (SVA)14 to remove batch effects and other unmeasured heterogeneity in all three proteomic datasets. We then performed regression analysis of log-transformed protein abundance levels as a dependent variable and sporadic AD status as an independent variable while considering age, sex, and SVA as covariates.
Brain proteomic profiles for sporadic AD
In the brain, 12 proteins showed significant association for AD status after Bonferroni correction (Fig. 2a, Supplementary Table 2). We chose the Bonferroni-corrected threshold as it is more conservative than false discovery rate (FDR). All 12 proteins were nominally significant (P < 0.05) with other AD-related phenotypes including age at onset and AD neuropathology characteristics such as Braak scores and CDR at death (Supplementary Table 2, Supplementary Fig. 1-2). As we had proteomic data from CSF and plasma, we determined which proteins are also associated with AD risk or onset in these other two tissues. Given low overlap (Fig. 1) in individuals who have proteomic data across tissues, this was used as an internal validation. By leveraging across-tissue data, any tissue-specific signal will not replicate. One caveat of using the multi-tissue data is that not all proteins passed QC across all three tissues. Among the 12 proteins associated with AD status in brain, only 6 were found in both CSF and plasma. Of these, 5 proteins (SMOC1, HGF, FSTL1, UBC9, and NET1) were associated with AD status or age at onset in both CSF and plasma data (P < 0.05, Supplementary Table 2), which represents an enrichment of 333 fold (P = 5.8×10-13) to what would be expected by chance.
To externally replicate these findings, we used the merged mass-spectrometry brain data (MassSpec Joint) that includes 10,078 proteins from 415 AD patients and 194 controls, and performed association analyses with AD status. As the proteomic data available in these studies were generated using a different platform, we were not able to test all 12 proteins that were significant in our discovery data. Of the nine proteins that were present in these datasets, 8 replicated (Midkine, SMOC1, CgA, HGF, NRX1B, UBC9, NET1, and SAP) with P < 0.05 and in the same direction of effect. This represents an enrichment of 35 fold to what would be expected by chance (P = 1.3×10-12). In addition, to confirm that our results were not false positives due to the joint analysis that included all 6 studies, we performed additional analyses in each study (Johnson et al,15 Higginbotham et al,12 and Wingo et al.16). Individual study analyses also provided enrichments of 25-34 fold (Supplementary Table 1). We also found a significant correlation in the effect size for the association of the proteins with AD status between our discovery results and the merged replication results (MassSpec Joint) (P < 3.6×10-3; Supplementary Fig. 3a). Together, these results indicate that our identified brain proteomic signature replicates in external independent samples and is extremely robust across orthogonal proteomics platforms.
CSF proteomic profiles for sporadic AD
In CSF, 117 proteins were associated with clinical AD status after Bonferroni correction (Fig. 2a, Supplementary Table 3). Of these 117 proteins, 78 passed QC in brain and plasma tissues, and 27 proteins (including ERK-1 and LRRK2) replicated in both tissues (138-fold enrichment, P = 3.3×10-50). An additional 44 proteins replicated in brain and 16 in plasma. To externally replicate our identified proteins in CSF, we downloaded and analyzed Alzheimer’s Disease Neuroimaging Initiative (ADNI) multiple reaction monitoring (MRM) proteomic data containing 320 proteins in 263 samples. In addition, we obtained results based on BioFinder OLINK data of 201 proteins in 576 samples presented by Whelan et al.,11 and from the mass-spectrometry-based Emory-ADRC study that includes 2,875 proteins in just 40 samples presented by Higginbotham et al.12 Of the 117 CSF proteins identified in our study, 90 were present in these external datasets. Of these, 39 proteins (including 14-3-3, Calcineurin, SMOC1, GFAP, SPP1, and Peroxiredoxin-1) replicated in the same direction (14- to 34-fold enrichments, P ≤ 4.4×10-5). It is important to mention that the major overlap in the number of proteins with our data is the Emory-ADRC study, which only includes 40 samples. Therefore, the power to replicate the initial findings is limited. We expect that a larger number of proteins will replicate in larger studies.
Several studies have demonstrated that up to 30% of cognitively normal elderly individuals could be pre-symptomatic for AD17 and that other neurodegenerative diseases can masquerade, clinically, as AD dementia.18 Therefore, clinically defined case-control status may not be the best phenotype for novel biomarker discovery.19 It has been proposed that biomarker-based categorization provides a more powerful approach to identify proteins altered in AD. CSF Aβ42 and p-tau levels are one of the best fluid biomarkers identified to date for distinguishing pathology-free controls from AD dementia and several studies have demonstrated that CSF p-tau/Aβ42 ratio is a marker not only for AD status but also for predicting AD progression from normal to dementia within 5 years.3 As we had access to CSF p-tau/Aβ42 for most samples with CSF (689 out of 720), we also performed a regression analysis of protein levels considering p-tau/Aβ42 ratio as a predictor. We found 92 proteins that were significant for p-tau/Aβ42 ratio at Bonferroni-corrected threshold. Of the 117 proteins associated with clinical AD status, 74 were significant for CSF p-tau/Aβ42 at Bonferroni-corrected threshold and the remaining were nominally significant. In fact, we found a very strong correlation (R2=0.86 and P < 1.0×10-16; Supplementary Fig. 4) of the effect across all 713 QCed proteins between the two analyses. This indicates that using case-control status for the Knight ADRC is highly accurate and leads to the similar results as using biomarker-defined case-control status
Plasma proteomic profiles for sporadic AD
In plasma, 26 proteins were associated with sporadic AD status after Bonferroni correction (Fig. 2a, Supplementary Table 4). Similar to previous analyses, we leveraged the multi-tissue data to replicate these findings. Of the 26 plasma proteins associated with AD status, 16 passed QC in brain and CSF and seven proteins (including ERK-1, CDON, and SHC1) replicated (175-fold enrichment, P= 6.8×10-15). To externally replicate our findings, we downloaded the AddNeuroMed SOMAscan 1.1K proteomic data that was processed and deposited by Sattlenecker et al13 and performed differential analysis in 320 individuals with AD and 194 controls. Out of 26 proteins, we were able to test 19 in this dataset and 9 proteins (including CAMK2D and HMG-1) replicated (18.9-fold enrichment, p = 2.8×10-10).
In summary, we have identified 8, 39, and 9 proteins that are associated with AD status and replicated in several independent cohorts using orthogonal technologies in brain, CSF and plasma, respectively. These proteins likely represent only a subset of proteins that could be associated with AD status, as not all proteins identified in our study were assayed in the replication datasets and most of the replication datasets had smaller sample sizes than our discovery data, providing limited power. We also leveraged multi-tissue data to replicate the single-tissue findings. Sometimes, it may not be possible to use external datasets for replication, therefore we performed an enrichment test to determine whether the proteins that showed an internal cross-tissue replication would also replicate in other studies. Our analyses indicate that proteins identified in each tissue and supported by the two remaining tissues were more likely to replicate in external independent datasets (15- to 40-fold enrichments, P ≤ 3.63×10-3, Supplementary Table 5), suggesting that multi-tissue proteomic data may be used as a viable replication strategy.
TREM2 risk variant carriers
Our group and other, identified several rare coding variants in TREM2 that increase risk of AD by almost two fold, making TREM2 the second strongest genetic risk factor for sporadic AD after APOE.1,20-23 Multiple TREM2 risk variants have been identified, but it has been proposed that all TREM2 AD-risk variants cause a partial loss of function24. Given the low frequency of these variants, performing separate analysis for each specific variant would not provide enough statistical power. For these reasons, we combined all TREM2 variant carriers in these analyses. We generated proteomic data from 21, 47, and 131 TREM2 variant carriers in brain, CSF, and plasma, respectively (Table 1). To identify multi-tissue proteomic signatures of individuals carrying AD-risk variants in TREM2, we compared the protein levels of TREM2 variant carriers with both cognitively normal individuals and individuals who were diagnosed with AD dementia, but did not carry any TREM2 or autosomal dominant variant. This is the first time a proteomic profile for TREM2 variant carriers has been generated.
In the brain, 9 proteins (including α-Synuclein) showed differential abundance levels in TREM2 variant carriers compared to cognitively normal individuals at Bonferroni-corrected threshold (Fig. 3a; Supplementary Table 6). In addition, 23 proteins (including LRRK2) were associated with AD status after multi-test correction for TREM2 risk variant carriers vs. AD (Supplementary Table 7). From the genetic data available for the replication datasets, we found 4 TREM2 variant carriers in Mayo, 7 in MSBB, and 8 in ROSMAP. This low number did not provide any statistical power to support a replication analysis. As we demonstrated, our multi-tissue study design is a viable alternative approach to identify proteins that would replicate in external datasets, and we leveraged our data to identify those proteins that replicate across tissues. Out of these 27 unique TREM2-associated proteins (combining 9 and 23 proteins), 11 passed QC in both CSF and plasma, and 5 (ALT, α -Synuclein, MIS, LRRK2, and PAFAH beta subunit) replicated in both tissues. This represents a 74-fold enrichment (p=7.53×10-9) to what would be expected by chance.
In CSF, our analyses identified a total of 38 unique proteins, among which 31 were associated with TREM2 risk variant carriers vs. cognitively normal individuals and 10 for the TREM2 vs. AD, after multiple test correction (Supplementary Tables 8-9). Out of these 38 proteins, 20 passed QC in the other tissues, and 7 (14-3-3E, 14-3-3 protein zeta/delta, Somatostatin-28, SMOC1, Ubiquitin+1, QORL1 and calcineurin) replicated across tissues (Supplementary Tables 8-9). This represents a 73-fold enrichment (p=7.19×10-12) to what would be expected by chance.
In the plasma proteomic data, we identified a total of 69 proteins, among which 65 and 7 showed differential abundance levels in TREM2 variant carriers compared to cognitively normal individuals and to individuals who were diagnosed with AD dementia, respectively (Supplementary Tables 10-11). Among the 41 proteins that passed QC in the brain and CSF, 21 proteins (including bone proteoglycan II, PAPP-A, ERK-1, suPAR and VCAM-1) replicated, which represents a 122-fold enrichment (p=5.47×10-38) to what would be expected by chance.
Autosomal dominant AD status
Although most AD cases are considered sporadic and manifest after the age of 65,24 around 1-3% of AD cases show an autosomal dominant (ADAD) inheritance pattern, often with onset before age 65.25 Pathogenic variants in APP, PSEN1 and PSEN2 have been identified as the cause of ADAD.9 We generated proteomic data from the parietal cortex of 24 ADAD gene variant carriers (19 individuals with PSEN1, 1 with PSEN2, and 4 with APP variants) recruited from the DIAN and the Knight ADRC studies. We identified 109 proteins with differential abundance in ADAD mutation carriers compared to cognitively normal individuals with no significant brain pathology, at Bonferroni corrected threshold (Supplementary Fig. 5). In order to validate these findings, we analyzed whether these 109 proteins were also associated with ADAD status in CSF from 289 carriers and 184 non-carriers from the DIAN study. Due to the limited amount of CSF samples for these subjects, we were unable to perform proteomic discovery in sporadic AD or TREM2 variant carriers. From those 109 proteins identified in brain, 106 passed QC in CSF proteomic data and 17 were associated with ADAD in CSF and in the same direction (Fig. 4, Supplementary Table 12), which represents a 6.4-fold enrichment (p=1.36×10-9) to what would be expected by chance.
As presented earlier, we identified 12 proteins associated with sporadic AD status in brain tissue (Supplementary Table 2). We also sought to determine if the proteins associated with sporadic AD status showed similar differential abundance in ADAD mutation carriers. We found that most of the proteins associated with sporadic AD brains displayed even stronger effect size when comparing ADAD mutation carriers to controls (Supplementary Table 13). The proteins associated with sporadic AD status showed 39% higher effect sizes in ADAD brain samples on average (P = 3.8×10-5; Fig. 4). For example, SMOC1 showed a significant association AD vs. control (Effect = 0.04: P=3.1×10-6) but also for ADAD vs. CO (Effect = 0.13; P=2.3×10-6). As presented earlier, SMOC1 has also been found to be associated in sporadic AD status in both CSF (P=8.4×10-29) and plasma (P=0.002), suggesting that it could be used to create a new prediction model for AD, independent of Aβ and tau.
Tissue-specific Prediction Models
Our analyses identified tissue-specific proteomic signatures for sporadic AD and TREM2 risk-variant carriers. Here, we used the proteins that replicated in external datasets (for AD status) or across tissues (for TREM2 variant carriers and ADAD) to create prediction models. To assess the specificity and selectivity of our prediction models we computed receiver operator characteristic (ROC) curve and area under the curve (AUC) using the R package pROC. Age at measurement and sex were included as covariates. We also performed analysis by adding APOE e4 status as a covariate. In sporadic AD cases, these prediction models were examined for both the discovery and replication datasets.
In brain tissue, our prediction model based on the 8 proteins that replicated in our analysis (Supplementary Tables 1, 14) led to an AUC of 0.84 in the discovery and an AUC of 0.99 in the replication cohort (Fig. 2b). In CSF, we found 39 proteins associated with AD status that replicated in external datasets (Supplementary Table 3). A prediction model including these proteins led to an AUC of 0.90 in the replication and of 0.89 in the discovery cohort (Fig. 2b). As the number of proteins is too large to generate a prediction model that could be translated to the clinic, we performed the stepwise model selection to identify the minimum set of proteins that capture the same information as the 39 identified in our study. We found a panel of 12 proteins that provided accuracy in distinguishing clinically defined AD patients from controls almost as high as all 39 proteins and led to an AUC of 0.88 in the discovery and 0.999 in replication data. We compared our prediction model to CSF p-tau/Aβ42, known and validated biomarkers. In our dataset the CSF p-tau/Aβ42 ratio led to an AUC of 0.81, which is significantly lower than our prediction model (P = 2.4×10-6). Using the same approach for plasma, the 9 proteins identified and replicated in an external dataset (Supplementary Table 4, 14) led to an AUC of 0.79 in both discovery and replication datasets, which was not statistically different from the AUC with CSF p-tau/Aβ42 ratio (AUC=0.82; P>0.05). The prediction model based on each externally replicated protein is similar between the discovery and replication data (Supplementary Fig. 6).
We also created prediction models that could distinguish TREM2 variant carriers from non-carriers in both sporadic AD cases and controls. Therefore, we included the proteins that were differentially abundant between TREM2 risk variant carriers when compared not only to AD cases but also to controls. Due to a lack of external datasets, we included only those proteins that replicated across tissues, as explained above. In CSF, the prediction model that included 7 proteins (Supplementary Tables 8-9) resulted in an AUC of 0.79 when comparing TREM2 risk variant carriers to controls. The same proteins showed an AUC of 0.84 for TREM2 risk variant carriers compared to AD cases (Fig. 3b). CSF p-tau/Aβ42 levels have been shown to be a very good biomarker to distinguish AD cases vs controls, but no previous studies examined how CSF p-tau/Aβ42 ratio provides prediction forTREM2 variant carriers. In this study, CSF p-tau/Aβ42 showed an AUC of 0.74 for TREM2 variant carriers vs AD cases and AUC of 0.53 for TREM2 risk variant carriers vs cognitively normal individuals. Both AUC values are significantly lower than those from our TREM2-associated prediction model with 7 proteins (P < 1.6×10-5; Fig. 3b).
In plasma, the 21 proteins included in the model (Supplementary Tables 10-11) led to an AUC of 0.93 in differentiating TREM2 risk variant carriers from controls, while the CSF p-tau/Aβ42 ratio led to a significantly lower AUC of 0.69 (P = 1.1×10-3). Similarly, in differentiating TREM2 risk carriers from other AD cases, the same 21 proteins led to an AUC of 0.90, which is significantly higher (P = 1.5×10-4) than the AUC with the CSF p-tau/Aβ42 ratio (AUC=0.63). As the number of proteins is large, we performed a stepwise model selection and found a subset of 9 proteins that provided AUCs of 0.89 and 0.88 to discriminate TREM2 variant carriers from cognitively normal individuals and from individuals with AD dementia, respectively (Fig. 3b). The prediction models including age, sex and APOE e4 status as covariates provided similar performance (Supplementary Fig. 7).
We also leveraged the 17 proteins that were found to be associated with ADAD status and in the same direction in brain and CSF (Supplementary Table 12) to create potential prediction models for distinguishing ADAD mutation carriers from non-carriers. In brain data, the model with these 17 proteins provided an AUC of 1, which is significantly higher than the model based on age alone (AUC = 0.76; P = 9.9×10-3). In CSF data, the same 17 proteins provided a higher AUC value than the model with age alone (AUC = 0.87 vs 0.53, P < 2.2×10-16; Fig. 4).
Finally, we wanted to determine if the proteins identified in our analyses were enriched in common functional pathways. Functional enrichment analysis was performed with Enrichr.26 As expected, the AD pathway was significant in CSF in both the sporadic AD (FDR = 1.9×10-3) and TREM2 variant-specific analyses (FDR = 5.8×10-3, Supplementary Table 15). The proteins that are part of this pathway that were identified in our analyses include APOE, calcineurin (PPP3R1 and PPP3CA), and MAPK3 (Fig. 5, Supplementary Fig. 8). APOE is the strongest and most common genetic risk factor for AD,27 and individuals with the APOE e4 allele have lower CSF Aβ42 levels27 and lower Aβ42 clearance.28,29 Genetic variants in calcineurin have been associated with higher CSF p-tau levels and earlier age at onset.30 MAPK3 has also been reported to be involved in AD pathology,31-33 likely by affecting tau phosphorylation. In any biomarker discovery study, it is often difficult to determine whether the proteins identified are part of a causal pathway or just a product of the disease. Several facts strongly suggest that many of the proteins identified in this study are, in fact, causal. As mentioned, APOE is known to be part of the causal AD pathway, and calcineurin and MAPK3 have recently been reported as part of the causal AD pathway by pQTL and Mendelian randomization analyses5 .
Several proteins that are part of the Parkinson disease pathway, including α-synuclein, LRRK2, granulin, and UCHL1, were also found to be dysregulated in CSF and plasma for the sporadic AD and TREM2 analyses (FDR < 3.4×10-3, Supplementary Table 15). On autopsy, around 30% of the AD cases, including autosomal dominant AD, present with Lewy bodies, which are deposits of α-synuclein.34 Those reports, together with our analyses, indicate that PD pathology shares similarities with AD pathology. Similar to α-synuclein, LRRK2 also showed a strong association with autosomal dominant AD (P = 7.7×10-4) and TREM2 (P = 9.3×10-6). The GRN gene, which encodes the granulin protein, was initially associated with frontotemporal dementia,35,36 but recent, large GWAS have also found GRN in both AD37 and PD.38
Granulin, implicated in wound healing39 as a part of the innate immune response pathway, was also found to be enriched in the proteomic analyses for sporadic AD in CSF (FDR = 6.9×10-9 ) and plasma (FDR = 2.1×10-3), as well as the CSF TREM2-specific analyses (FDR = 1.1×10-3). Other dysregulated proteins identified in our analyses that are also part of this pathway include SHC1, MAPK3, ITGB1, and SPP1, among others. SPP1 has recently been implicated in microglia activation and the AD pathway.40 Similar to SPP1, ITBG1 is a microglia gene and has been shown to be differentially expressed in the hippocampus and peripheral blood mononuclear cells (PBMC) of AD cases,41 important in microglia activation,42 and part of the causal pathway in network analyses.43 Recent studies have also demonstrated that meningeal lymphatics affect microglia and AD risk.44 Our analyses also found several endothelial-specific proteins (ERK-1, SHC1, and BCAM).
The 17 proteins that were associated with ADAD status in both brain and CSF in the same direction, were also enriched for proteins part of the Alzheimer disease pathway (p<1×10-4) and
the cellular response to chemical stimulus pathway (go:0.0070887; p=0.034), which includes, among others, MIF, a pro-inflammatory cytokine involved on involved in the innate immune response; LILRB; and CD22 also part of the immune response pathway. IDE is involved in the cellular breakdown of insulin and has been reported to be involved in the degradation and clearance of naturally secreted amyloid beta-protein by neurons and microglia.
In summary, the proteins dysregulated in our analyses are not randomly distributed across functional groups; they are enriched in specific pathways known to be implicated in AD and other pathways (PD, immune response) that may be instrumental to AD pathophysiology and may represent new therapeutic targets. Indeed, our analyses indicate that the proteins identified here are not only dysregulated in AD but also play a causal role.