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Network and pathway expansion of genetic disease associations identifies successful drug targets.

ABSTRACT: Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

SUBMITTER: MacNamara A

PROVIDER: S-EPMC7708424 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Network and pathway expansion of genetic disease associations identifies successful drug targets.

MacNamara Aidan A Nakic Nikolina N Amin Al Olama Ali A Guo Cong C Sieber Karsten B KB Hurle Mark R MR Gutteridge Alex A

Scientific reports 20201201 1

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic association ...[more]

PMID: 33262371

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Project description:Rationale: With increasing incidence and prevalence of inflammatory bowel disease (IBD), it has become one of the major public health threats, and there is an urgent need to develop new therapeutic agents. Although the pathogenesis of IBD is still unclear, previous research has provided evidence for complex interplays between genetic, immune, microbial, and environmental factors. Here, we constructed a gene-microbiota interaction-based framework to discover IBD biomarkers and therapeutics. Methods: We identified candidate biomarkers for IBD by analyzing the publicly available transcriptomic and microbiome data from IBD cohorts. Animal models of IBD and diarrhea were established. The inflammation-correlated microbial and genetic variants in gene knockout mice were identified by 16S rRNA sequences and PCR array. We performed bioinformatic analysis of microbiome functional prediction and drug repurposing. Our validation experiments with cells and animals confirmed anti-inflammatory properties of a drug candidate. Results: We identified the DNA-sensing enzyme cyclic GMP-AMP synthase (cGAS) as a potential biomarker for IBD in both patients and murine models. cGAS knockout mice were less susceptible to DSS-induced colitis. cGAS-associated gut microbiota and host genetic factors relating to IBD pathogenesis were also identified. Using a computational drug repurposing approach, we predicted 43 candidate drugs with high potency to reverse colitis-associated gene expression and validated that brefeldin-a mitigates inflammatory response in colitis mouse model and colon cancer cell lines. Conclusions: By integrating computational screening, microbiota interference, gene knockout techniques, and in vitro and in vivo validation, we built a framework for predicting biomarkers and host-microbe interaction targets and identifying repurposing drugs for IBD, which may be tested further for clinical application. This approach may also be a tool for repurposing drugs for treating other diseases.

Project description:BackgroundGenome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.MethodsTo address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.ResultsThrough this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.ConclusionsIn summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

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Network and pathway expansion of genetic disease associations identifies successful drug targets.

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Network and pathway expansion of genetic disease associations identifies successful drug targets.

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