Network-based Translation of GWAS Findings to Pathobiology and Drug Repurposing for Alzheimer’s Disease

Abstract

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS to identify high-confidence AD risk genes (ARGs) that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. To address this critical problem in the field, we have developed a genotype-informed, network-based methodology that interrogates pathogenesis to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and preclinical AD 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. Through 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 pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.895, 95% confidence interval [CI] 0.841-0.951, P = 3.97 x 10-4) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor 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 0.861-0.983, P = 0.0146), 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. In summary, we present an integrated, network-based methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

“…This methodology allows to identify several candidate repurposable drugs for Ebola virus 11,14 . Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs 13,[22][23][24][25][26][27] and drug combinations 28 for multiple human diseases. For example, network-based drug-disease proximity sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies 13,23,27,28 .…”

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

“…In this section, we use sildenafil and pioglitazone as two examples. In our recent studies, we found that both sildenafil and pioglitazone were associated with a reduced risk of AD using network proximity analysis and retrospective case-control validation [14]. Mechanistically, in vitro assays showed that both drugs were able to downregulate cyclin-dependent kinase 5 (CDK5) and glycogen synthase kinase 3 beta (GSK3B) in human microglia cells.…”

“…The large amount of multiomics data and recent advances in network-based methodologies for drug repurposing today present unprecedented opportunities for accelerating target identification for drug discovery for AD. This potential has been demonstrated in other complex diseases as well, such as cancer [11], cardiovascular disease [12], and schizophrenia [13], and is beginning to be exploited in AD [6,14]. Drug repurposing offers a rapid and costeffective solution for drug discovery for complex disease, such as the current global pandemic of coronavirus disease 2019 (COVID-19) [15,16] and AD [6].…”

“…1a). Our recent efforts using network-based methodologies and AD omics data have led to the discovery of two drugs that show efficacy in network models in AD: sildenafil [6] and pioglitazone [14]. Network analysis provides potential mechanisms for these drugs and facilitates experimental validation.…”

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

“…We found that one of the agents identified-pioglitazone (an approved anti-diabetes drug)-is significantly associated with decreased risk of AD in a retrospective case-control study. 203 However, several limitations exist for current GWAS-based drug repurposing. First, given that many genome-wide significant loci are in noncoding regions, only a small portion of druggable genes could be mapped by GWAS analysis 204 and the closest-based GWAS genes cannot represent AD-risk genes by regulatory roles of noncoding variants.…”