Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Figure 1.. A flowchart describing the network-based multi-omics interaction workflow to infer drug targets and repurposable treatments for Amyotrophic Lateral Sclerosis (ALS).

First, we employed an advanced machine learning technique to analyze the intricate network formed by protein-protein interactions (PPIs). This network was segmented into several smaller, interconnected clusters. We then found that these clusters could serve as predictors for protein roles as per the annotations in the Gene Ontology (GO) database. Moving forward, we identified potential genes associated with Amyotrophic Lateral Sclerosis. These genes share functional characteristics with previously known genes regulated by various genomic elements, such as methyl quantitative trait loci (meQTLs) and protein quantitative trait loci (pQTLs). In the final step, we focused on repurposing certain drugs (for example, gefitinib) that may be effective in treating Amyotrophic Lateral Sclerosis.

Figure 2.. Gene regulatory landscape of ALS GWAS loci.

Overview of genetic loci linked to Amyotrophic Lateral Sclerosis (ALS) identified through genome-wide association studies, distributed among various chromosomes and analyzed in relation to five genomic regulatory factors: expression quantitative trait loci (eQTL), histone modifications, protein interactions, spliceosome components, and DNA methylation patterns.

Figure 3.. Network-based visualization of 105 predicted ALS-associated genes.

Prioritized ALS-associated genes are colored with various evidence. Yellow genes are the ones identified by GisGeNET with an association score ≥ 0.1. Blue genes are the ones enriched in KEGG pathway. Purple geens are the ones detailed in Diseases JansenLab database with a Z-score ≥ 3. Red Genes are the ones identified by enrichment analysis from other ALS-relavant literature.

Figure 4.. Network Proximity-predicted drugs for six existing gene sets from literatures and the predicted ALS-associated genes.

Z-score between −4 to 0 is depicted by the gradient from red to blue. Drugs are categorized by colors according to the primary codes of the Anatomical Therapeutic Chemical (ATC) classification system. Three candidate drugs and their target genes using our drug-target network analysis. Blue genes are predicted ALS PPI nodes only, green genes are druggable targets that are in direct proximity with predicted ALS PPI nodes, yellow genes are both an ALS PPI node and a druggable target.