Integrative Systems Biology Approaches to Identify and Prioritize Disease and Drug Candidate Genes
Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Methods in molecular biology (Clifton, N.J.)
(Impact Factor: 1.29).
01/2011; 700:241-59. DOI: 10.1007/978-1-61737-954-3_16
Although a number of computational approaches have been developed to integrate data from multiple sources for the purpose of predicting or prioritizing candidate disease genes, relatively few of them focus on identifying or ranking drug targets. To address this deficit, we have developed an approach to specifically identify and prioritize disease and drug candidate genes. In this chapter, we demonstrate the applicability of integrative systems-biology-based approaches to identify potential drug targets and candidate genes by employing information extracted from public databases. We illustrate the method in detail using examples of two neurodegenerative diseases (Alzheimer's and Parkinson's) and one neuropsychiatric disease (Schizophrenia).
Available from: Anna M Method
- "Heat map was created using GeneSpring. The differentially expressed genes were subjected to Gene Ontology functional enrichment analyses using the ToppCluster server . See Table S4 in File S1 for a list of primers used in these studies. "
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ABSTRACT: SOX17 is a key transcriptional regulator that can act by regulating other transcription factors including HNF1β and FOXA2, which are known to regulate postnatal β cell function. Given this, we investigated the role of SOX17 in the developing and postnatal pancreas and found a novel role for SOX17 in regulating insulin secretion. Deletion of the Sox17 gene in the pancreas (Sox17-paLOF) had no observable impact on pancreas development. However, Sox17-paLOF mice had higher islet proinsulin protein content, abnormal trafficking of proinsulin, and dilated secretory organelles suggesting that Sox17-paLOF adult mice are prediabetic. Consistant with this, Sox17-paLOF mice were more susceptible to aged-related and high fat diet-induced hyperglycemia and diabetes. Overexpression of Sox17 in mature β cells using Ins2-rtTA driver mice resulted in precocious secretion of proinsulin. Transcriptionally, SOX17 appears to broadly regulate secretory networks since a 24-hour pulse of SOX17 expression resulted in global transcriptional changes in factors that regulate hormone transport and secretion. Lastly, transient SOX17 overexpression was able to reverse the insulin secretory defects observed in MODY4 animals and restored euglycemia. Together, these data demonstrate a critical new role for SOX17 in regulating insulin trafficking and secretion and that modulation of Sox17-regulated pathways might be used therapeutically to improve cell function in the context of diabetes.
PLoS ONE 08/2014; 9(8):e104675. DOI:10.1371/journal.pone.0104675 · 3.23 Impact Factor
Available from: Tamsyn M Crowley
- "Another study identified potential drug targets for three neurological disorders - Alzheimer's disease, Parkinson's disease and Schizophrenia. This study involved the prediction of candidate genes using the ToppGene and ToppNet prediction systems [24,35]. The repositioning tools could be used as an initial screening tool for potential drugs which can be used for further evaluation . "
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Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.
We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level.
Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets.
By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.
BMC Medical Genomics 05/2014; 7(Suppl 1). DOI:10.1186/1755-8794-7-S1-S8 · 2.87 Impact Factor
Available from: Jiny Nair
- "Despite decades of genetic research on CAD, it is striking to note that the identified genetic loci explain only a small proportion of the disease heritability , suggesting that there may still be many other genes involved in CAD that remain unknown to date. Network based approach is being extensively used for the prediction of putative candidate genes, prioritizing drug targets  and in the construction of gene regulatory networks  thus utilizing the data from gene expression or genome wide linkage and association studies. This approach primarily focuses on the inter-relationship between the various components using protein-protein interaction (PPI) network and assist in the identification of the best discerning molecules associated with the disease. "
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ABSTRACT: Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.
PLoS ONE 04/2014; 9(4):e94328. DOI:10.1371/journal.pone.0094328 · 3.23 Impact Factor
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