Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease

Abstract

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.

Fig. 1. Study overview.

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 4,782 aptamers were tested for associations with 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-ε4-independent association and 17 proteins with an APOE-ε4-dependent association were defined. The APOE-ε4-dependent proteins were further expanded to first-degree PPI partners. All sets of proteins were subjected to functional enrichment analysis and bidirectional 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). This figure was created with BioRender.

Fig. 2. Proteins associated with incident LOAD in AGES (n = 5,127).

a,b, Volcano plots showing the protein association profile for incident LOAD with the HR for incident LOAD from the Cox PH models (x axis) and −log₁₀ of Benjamini–Hochberg FDRs (y axis) across two models: without APOE-ε4 adjustment (model 1) (a) and with APOE-ε4 adjustment (model 2) (b). c, Venn diagram for the overlap between models 1 and 2 for incident LOAD. d,e, Enrichment of top GO terms from GSEA analysis for incident LOAD (model 1) shown as a dot plot stratified by ontology (d) and gene-concept network (e). f,g, Comparison of effect sizes (HR) from Cox PH models 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 model 1 (f) and model 2 (g). Protein associations with Benjamini–Hochberg FDR < 0.05 are denoted in red. BP, biological process; CC, cellular component; MF, molecular function.

Fig. 3. Proteins with APOE-ε4-dependent association with incident LOAD in AGES (n = 5,127).

a, Spaghetti plot showing the statistical significance as Benjamini–Hochberg FDR 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 Benjamini–Hochberg FDR < 0.05 (dashed) and P < 0.05 (dot-dashed). The total number of significantly associated proteins (FDR < 0.05) for each model is shown above. b, Pairwise Pearson’s correlation among the 17 APOE-ε4-dependent proteins. c, Forest plot showing the effect (beta coefficient) of the APOE genotype on the 17 APOE-ε4-dependent proteins in AGES. The beta coefficients indicate the change in protein levels per ε4 allele count and are shown with 95% CIs. d,e, Forest plots showing the HR for incident LOAD per standard deviation increase in level for each of the 17 APOE-ε4-dependent proteins in AGES without APOE-ε4 adjustment (model 1) (d) and with APOE-ε4 adjustment (model 2) (e). The LOAD HRs are shown with 95% CIs. Proteins that change direction of effect between the two models are highlighted in red. f–h, Replication analyses for c–e were performed in the ACE cohort (n = 719) in the same manner as in the AGES cohort. FAM159B (in gray) was not measured in the ACE SOMAscan assay.

Fig. 4. Reverse MR analysis suggests a causal effect of LOAD on four proteins.

a, Comparison of HRs per SD increase of protein levels for incident LOAD with and without APOE-ε4 adjustment in the observational analysis (Cox PH) (n = 5,127) (upper), the effects of APOE-ε4 on protein levels in AGES (n = 5,332) and the effect of LOAD on protein levels from the reverse MR analysis (excluding the APOE locus) (lower), shown for the four APOE-ε4-dependent proteins that change direction of effect in both observational and causal analyses when APOE is accounted for. All effects are shown with 95% CIs. b,c, Visual summaries of the observed data. b, Mediation diagrams showing three possible hypotheses that could explain the relationship among 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) or 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 genotype leads to increased risk of LOAD by its effects in brain tissue. The same genotype results in a downregulation of serum levels of four proteins that are consequently 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. This figure was created with BioRender.

Fig. 5. Overlap between AD protein signatures in serum, brain and CSF.

a, Venn diagram showing the overlap of AD-associated proteins in serum, brain and CSF. b, 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-ε4 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 (two-sided Fisher’s test) of AD-associated proteins by tissue type (x axis) in the AGES serum protein modules (c), Emory CSF protein modules (d) and Emory brain protein modules (e) (y axis). Modules that are enriched for AD associations in more than one tissue are highlighted with red squares.

Extended Data Fig. 1. Functional enrichment analysis of APOE-ε4-dependent protein-protein interaction partners.

a) A scheme of the PPI partners selection, where first degree partners of the APOE-ε4-dependent proteins were extracted from the InWeb database. b-c) Enrichment of selected GO 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. BP, biological process; CC, cellular component; MF, molecular function.