Serum proteomics reveals APOE dependent and independent protein signatures in Alzheimer's disease

Abstract

The current demand for early intervention, prevention, and treatment of late onset Alzheimer's disease (LOAD) warrants deeper understanding of the underlying molecular processes which could contribute to biomarker and drug target discovery. Utilizing high-throughput proteomic measurements in serum from a prospective population-based cohort of older adults (n = 5,294), we identified 303 unique proteins associated with incident LOAD (median follow-up 12.8 years). Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status. These proteins were implicated in neuronal processes and overlapped with protein signatures of LOAD in brain and cerebrospinal fluid. We found 17 proteins which LOAD-association was strongly dependent on APOE-ε4 carrier status. Most of them showed consistent associations with LOAD in cerebrospinal fluid and a third had brain-specific gene expression. Remarkably, four proteins in this group (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated as a consequence of LOAD as determined in a bi-directional Mendelian randomization analysis, reflecting a potential response to the disease onset. Accordingly, the direct association of these proteins to LOAD was reversed upon APOE-ε4 genotype adjustment, a finding which we replicate in an external cohort (n = 719). Our findings provide an insight into the dysregulated pathways that may lead to the development and early detection of LOAD, including those both independent and dependent on APOE-ε4. Importantly, many of the LOAD-associated proteins we find in the circulation have been found to be expressed - and have a direct link with AD - in brain tissue. Thus, the proteins identified here, and their upstream modulating pathways, provide a new source of circulating biomarker and therapeutic target candidates for LOAD.

Keywords: APOE; Alzheimer’s disease; Brain; CSF; Cross-sectional study; Longitudinal study; Mendelian randomization; Network; Proteomics.

Figure 1. Study overview

Flowchart of the current study. a) Overview of the AGES cohort and study participants. Prevalent non-AD dementia cases were excluded from the analysis. b) Overview of the aptamers tested and their associations with LOAD. Serum measurements of 4782 aptamers were associated to prevalent and incident LOAD status, using logistic and Cox proportional hazards regression models, respectively. From the proteins associated with incident LOAD, sets of 140 proteins with an APOE-independent associations and 17 proteins with an APOE-dependent association were defined. The APOE-dependent proteins were further expanded to first degree protein-protein interaction (PPI) partners. All sets of proteins were subjected to functional enrichment analysis and bi-directional Mendelian Randomization (MR) analysis. c) Overview of the replication cohorts used in the study which include proteins measured in the circulation (ACE) as well as in brain and CSF (Emory).

Figure 2. Proteins associated with incident LOAD in AGES (n = 5127).

a-b) Volcano plots showing the protein association profile for incident LOAD from the Cox PH a)without APOE-ε4 adjustment (model 1) and b) with APOE-ε4 adjustment (model 2). c) Venn diagram for the overlap between models 1 and 2 for incident LOAD. d-e) Enrichment of top Gene Ontology terms from GSEA analysis for incident LOAD (model 1) shown as d) dotplot, stratified by ontology and e) gene-concept network. f-g) Comparison of effect sizes (HR) for incident LOAD between the AGES and the ACE (n = 719) cohorts for all proteins reaching nominal significance (P < 0.05) in the Cox PH in ACE for f) model 1 and g) model 2.

Figure 3. Proteins with APOE-ε4 dependent association to incident LOAD in AGES (n = 5127).

a) Spaghetti plot showing the statistical significance of protein associations with incident LOAD across the three Cox PH models, highlighting a set of 17 unique proteins (green) whose association with incident LOAD is attenuated upon APOE-ε4 adjustment. The horizontal lines indicate FDR < 0.05 (dashed) and P < 0.05 (dot-dashed). b) Pairwise Pearson’s correlation between the 17 APOE-dependent proteins. c) Forest plot showing the linear associations between APOE genotype and the 17 APOE-dependent proteins. The beta coefficient shows the change in protein levels per ε4 allele count. d-e) Forest plots showing the associations between the 17 APOE-dependent proteins and incident LOAD d)without APOE-ε4 adjustment (model 1) and e) with APOE-ε4 adjustment (model 2). LOAD-HR indicates risk per SD increase of protein levels. Proteins that change direction of effect between the two models are highlighted in red.

Figure 4. Functional enrichment analysis of APOE-dependent protein-protein interaction partners.

a) A scheme of the PPI partners selection, where first degree partners of the APOE-dependent proteins were extracted from the InWeb database. b-c) Enrichment of selected Gene Ontology terms for the PPI partner proteins shown as b) dotplot and c) gene-concept network. d-e)Enrichment of top seven unique Wikipathways shown as d) dotplot and e)gene-concept network.

Figure 5. Reverse Mendelian randomization analysis.

a) Comparison of hazard ratios for incident LOAD with and without APOE-ε4 adjustment in the observational analysis (Cox PH), the effects of APOE-ε4 on protein levels and reverse MR odds ratios (excluding the APOE locus) for the four APOE-dependent proteins that change direction of effect in both observational and causal analyses when APOE is accounted for. b-c)Visual summaries of the observed data. b) Mediation diagrams showing 3 possible hypotheses that could explain the relationship between APOE-ε4, LOAD and the four proteins shown in a). Our analyses do not support the hypothesis that LOAD mediates the effect of APOE-ε4 on proteins (Hypothesis 1) nor the other way around (Hypothesis 2). However, our results from both the observational and causal analyses support the hypothesis that two mechanisms are at play that affect the same proteins in the opposite direction (Hypothesis 3). c) The APOE-ε4 mutation leads to increased risk of LOAD via its effects in brain tissue. The same mutation results in a downregulation of serum levels of four proteins that are themselves negatively associated with incident LOAD. Additionally, other non-APOE LOAD risk variants lead to upregulation of the same proteins in the reverse MR analysis, possibly reflecting a response to LOAD or its genetic liability.

Figure 6. Overlap between AD protein signatures in serum, brain and CSF.

a) A Venn diagram showing the overlap of AD-associated proteins in serum, brain and CSF. b) A comparison of the effect sizes for AD associated proteins that overlap between serum and brain (top) and serum and CSF (bottom). The proteins are stratified based on the APOE-dependence in AGES for incident LOAD. The effect size in AGES is shown for incident LOAD model 1 (Cox PH), except for proteins that were uniquely identified using the shorter 10-year follow-up (Cox PH) or prevalent LOAD (logistic regression), in which case the respective effect size from the significant association is shown. c-e) Heatmap showing the enrichment (Fisher’s test) of AD-associated proteins by tissue type (x-axis) in the AGES serum protein modules, d) Emory CSF protein modules and e) Emory brain protein modules (y-axis).

Figure 7. Graphical summary of the results.