AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer's drug discovery

Abstract

Background: Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor.

Methods: In this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer's Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD.

Results: Via AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~ 3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000 AD clinical trials noting drug's mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization.

Conclusions: Network visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.

Keywords: Alzheimer’s disease; Clinical trial; Drug repurposing; Genomics; Mechanism-of-action; Multi-omics; Network medicine; Systems pharmacology.

Fig. 1

The architecture of AlzGPS. a AlzGPS was built on three main data entities (genes, drugs, and omics layers) and their relationships. The multi-omics data (genomics, transcriptomics (bulk and single cell/single nucleus), and proteomics) in AlzGPS help identify likely causal genes/targets that are associated with Alzheimer’s disease (AD) and disease modules in the context of human protein-protein interactome. Via network proximity measure between drug-target networks and disease modules in the human protein-protein interactome, drugs can be prioritized for their potential to alter the genes in the module for potential treatment of AD. b Detailed statistics of the entities and relations in AlzGPS. EGO, brain-specific neighborhood network (ego network); LCC, largest connected component network; MOA, mechanism-of-action network

Fig. 2

Web interface overview. a The home page provides access to searching, listing entries, and viewing brain-specific gene/target networks. User will be redirected to the interactive explorer (b), in which all information is then dynamically loaded and added to the same web page. Each data entity has its own basic information page under the “DATA TABLE” tab. Additional information regarding the relations (e.g., proximity results) can be loaded by clicking the corresponding button in the “DETAIL” section. c An example brain-specific neighborhood network using APOE. d An example largest connected component network using data set “V2”

Fig. 3

Case study—target identification. Four genes, MAPT (a), INPP5D (b), APOE (c), and BACE1 (d), are used as examples to show the gene page. On the gene page, we show a summary of several statistics of the gene in AlzGPS, including the number of drugs that can target it, number of data sets of omics in which the target/protein coding gene is differentially expressed, number of genetic records, and the brain expression specificity. Detailed information can be loaded by clicking corresponding buttons. Examples of detailed differential expression results and genetic records are shown for these four genes. In addition, a brain-specific neighborhood network is available that centers around the gene-of-interest and shows the targetability of its neighborhood

Fig. 4

Case study—drug repurposing. Sildenafil and pioglitazone are used as examples to demonstrate how to use AlzGPS for drug repurposing. a Basic information for sildenafil. b Network proximity results for sildenafil. c Literature evidence for sildenafil. d Inferred mechanism-of-action for sildenafil targeting the “V1 AD-seed” data set, which contains 144 high-quality literature-based Alzheimer’s disease (AD) endophenotype genes. e Basic information for pioglitazone. f Network proximity results for pioglitazone. g Five studies were found that were related to treating AD with pioglitazone. h Inferred mechanism-of-action for pioglitazone targeting the “V4 AD-inferred-GWAS-risk-genes” data set which contains 103 high-confidence AD risk genes identified using genome-wide association studies