A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function

Abstract

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

Keywords: Alzheimer’s disease; C. elegans; genetics; post-GWAS; systems biology.

Figure 1.. Integrative systems biology approach for LOAD risk gene identification and functional screening.

(A) Candidate risk genes are identified from LOAD GWAS summary statistics, using functional genomics data from large-scale brain eQTL and chromatin interaction studies. (B) Relevance of candidate risk genes to LOAD biology is assessed by correlation of expression patterns with clinical and neuropathological traits, and connectivity within co-expression networks built from LOAD cohort brain RNA-seq data. (C) Prioritized candidate risk genes are screened for in vivo effects on memory function through the use of associative memory assays in C. elegans.

Figure 2.. Data from eQTL and chromatin interaction studies implicates potential causal genes in LOAD GWAS loci.

(A) Enrichment signal for sub-threshold LOAD GWAS SNPs in neuronal open chromatin becomes evident following the removal of GWS loci and nearby SNPs (+/− 1 Mb), becoming similar in magnitude to that of microglia. Each point on the curves represents the difference in fold of the proportion of SNPs with a p-value below the cutoff in the ATAC-seq peaks versus all SNPs present in the GWAS summary statistics. (B) Numbers of candidate risk genes unique to, and shared by, the two gene-mapping methods. (C) Distribution of candidate risk genes by gene type and significance threshold. (D-E) Heatmap of expression patterns of candidate risk genes from (D) GWS LOAD-associated loci and (E) sub-GWS LOAD-associated loci across cell types in the human frontal cortex. Color scale represents relative expression across cell types (red = higher, blue = lower). Abbreviations: Endo, endothelial; Micro, microglia; Oligo, oligodendrocytes; Exc, excitatory neurons; Inh, inhibitory neurons; Astro, astrocytes; OPC, oligodendrocyte precursor cells. (F) Example LOAD GWAS locus (CELF1/SPI1), highlighting challenges in the identification of causal genes. Top to bottom – Manhattan plot of -log10(p-value) association statistics from Jansen et al., with the top SNP rs10437655 highlighted in purple and remaining variants colored according to LD (r²) with the lead SNP; Genome browser track showing all coding genes present in the locus. Gene names colored in green or blue are candidate risk genes nominated by QTL evidence or SNP-promoter interaction evidence, respectively. Gene names colored in red are candidate risk genes nominated by both kinds of evidence; Track showing the location of significant GWAS SNPs (P<1×10⁻⁵), and SNPs in LD (r²>0.6); Tracks indicating the positions of enhancer elements identified in different human brain cell types; Track illustrating the significant chromatin interactions between LOAD GWAS SNPs and gene promoters in the locus; Track illustrating the significant eQTL associations between LOAD GWAS SNPs and genes in the locus. See also Figure S1 and Tables S1–S3.

Figure 3.. Neuronal co-expression network modules are enriched for candidate LOAD risk genes and are associated with dementia severity in LOAD.

(A) Co-expression network analysis of RNA-seq data from the parahippocampal gyrus identifies 32 distinct co-expression modules. Correlations between module eigengenes and traits are shown in the heatmap, with Pearson’s correlation and FDR-corrected P-values indicated for each significant association (FDR < 0.05). Colored bars indicate significant enrichment for cell type gene expression signatures (Fisher’s exact test, FDR < 0.05) for each module. (B) Overlap of protein-coding candidate risk genes whose expression is significantly associated with one or more clinical and neuropathological traits. (Pearson’s correlation, FDR < 0.05) (C) Significance of enrichment of protein-coding LOAD candidate risk genes in each module. Bar color corresponds to cell type gene expression signature enrichment, using the same color schema shown in (A). Grey bars = modules with no cell type enrichment. Bars extending above the line represent FDR < 0.05. (D-F) Violin plots of module scores across cell types in human brain snRNA-seq for the three candidate risk gene-enriched modules that were also enriched for neuronal gene expression signatures. Enrichment of cell type expression was assessed by Fisher’s exact test. ^#FDR < 0.05. (G-I). Bar plots of the top 10 enriched Gene Ontology biological process terms are shown for the candidate risk gene-enriched neuronal modules M9 (G), M10 (H), and M16 (I). The dotted vertical line in (G) corresponds to the significance threshold of FDR < 0.05 which was not met by any of the enriched terms for module M9. (J-L) Expression of the module eigengene decreases significantly with increased dementia severity for both module M9 (J) and module M16 (L), but not for module M10 (K). Pearson’s correlation and FDR-corrected P-values are indicated. Differences in the expression of the module eigengene at each CDR score with respect to cognitive baseline (CDR=0) was also assessed by t test. *P < 0.05, **P < 0.01, ***P < 0.001. (M) Gene expression correlation with CDR is significantly correlated with network connectivity as measured by kME. Pearson’s correlation and FDR-corrected P-value are indicated. Core network candidate risk genes, according to max kME, are shown in teal. The top 20 high-priority risk gene candidates, as determined by correlation with CDR and network centrality, are highlighted in orange. (N) Bar plot of significantly enriched (FDR < 0.05) Gene Ontology biological process terms are shown for the core network genes. From the top 30 significantly enriched terms, 10 non-redundant terms are shown. See also Figures S2 and S3 and Tables S4–S7.

Figure 4.. Neuronal knockdown of LOAD risk gene orthologs alters memory function in C. elegans.

(A-G) 1 hour and 2 hour post-conditioning learning indices of worms treated with whole-life RNAi for LOAD candidate risk gene orthologs. Grouping of the tested orthologs was random and does not represent candidate prioritization. n ≥ 4 (n: technical replicates). Statistical significance determined by One-way ANOVA, with Dunnett’s post hoc test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. See also Figure S4 and Tables S8 and S9.