Prediction of disease–gene–drug relationships following a differential network analysis

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple g...

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Bibliographic Details
Published in:Cell death & disease 2016-01, Vol.7 (1), p.e2040-e2040
Main Authors: Zickenrott, S, Angarica, V E, Upadhyaya, B B, del Sol, A
Format: Article
Language:English
Subjects:
45/61
631/208/2489/144
631/92/360
631/92/436/434
692/700/565
Antibodies
Biochemistry
Biomedical and Life Sciences
Cell Biology
Cell Culture
Chemical compounds
Computational Biology - methods
Databases, Genetic - utilization
Disease
Gene expression
Gene Regulatory Networks - genetics
Genotype & phenotype
Humans
Immunology
Life Sciences
Original
original-article
Pharmaceutical sciences
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Summary:Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.
ISSN:2041-4889
2041-4889
DOI:10.1038/cddis.2015.393