Network medicine informed multiomics 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. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Fig. 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 methylation (methyl) quantitative trait loci (meQTL) and protein quantitative trait loci (pQTL). In the final step, we focused on repurposing certain drugs (for example, gefitinib) that may be effective in treating Amyotrophic Lateral Sclerosis.

Fig. 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 (splicing) components, and DNA methylation (methyl) patterns.

Fig. 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 genes 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.

Fig. 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.