Browsing by Author "Wang, Quan"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease
    (BMC, 2022-01-10) Fang, Jiansong; Zhang, Pengyue; Wang, Quan; Chiang, Chien‑Wei; Zhou, Yadi; Hou, Yuan; Xu, Jielin; Chen, Rui; Zhang, Bin; Lewis, Stephen J.; Leverenz, James B.; Pieper, Andrew A.; Li, Bingshan; Li, Lang; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, School of Medicine
    Background: Genome-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. Methods: To 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. Results: 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 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. Conclusions: In 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.
  • Thumbnail Image
    Item
    Progress and challenges of network meta-analysis
    (Wiley, 2021) Tian, Jinhui; Gao, Ya; Zhang, Junhua; Yang, Zhirong; Dong, Shengjie; Zhang, Tiansong; Sun, Feng; Wu, Shanshan; Wu, Jiarui; Wang, Junfeng; Yao, Liang; Ge, Long; Li, Lun; Shi, Chunhu; Wang, Quan; Li, Jiang; Zhao, Ye; Xiao, Yue; Yang, Fengwen; Fan, Jinchun; Bao, Shisan; Song, Fujian; Biochemistry and Molecular Biology, School of Medicine
    In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.
  • Thumbnail Image
    Item
    SUMO1 degrader induces ER stress and ROS accumulation through deSUMOylation of TCF4 and inhibition of its transcription of StarD7 in colon cancer
    (Wiley, 2023) Zhao, Yin Quan; Jin, Hong Ri; Kim, Daeho; Jung, Sung Han; Liu, Sheng; Wan, Jun; Lo, Ho-Yin; Fu, Xue Qi; Wang, Quan; Hao, Chunhai; Bellail, Anita C.; Pathology and Laboratory Medicine, School of Medicine
    Small molecule degraders of small ubiquitin-related modifier 1 (SUMO1) induce SUMO1 degradation in colon cancer cells and inhibits the cancer cell growth; however, it is unclear how SUMO1 degradation leads to the anticancer activity of the degraders. Genome-wide CRISPR-Cas9 knockout screens has identified StAR-related lipid transfer domain containing 7 (StarD7) as a critical gene for the degrader’s anticancer activity. Here, we show that both StarD7 mRNA and protein are overexpressed in human colon cancer and its knockout significantly reduces colon cancer cell growth and xenograft progression. The treatment with the SUMO1 degrader lead compound HB007 reduces StarD7 mRNA and protein levels and increases endoplasmic reticulum (ER) stress and reactive oxygen species (ROS) production in colon cancer cells and three-dimensional (3D) organoids. The study further provides a novel mechanism of the compound anticancer activity that SUMO1 degrader-induced decrease of StarD7 occur through degradation of SUMO1, deSUMOylation and degradation of T cell-specific transcription 4 (TCF4) and thereby inhibition of its transcription of StarD7 in colon cancer cells, 3D organoids and patient-derived xenografts (PDX).