Abstract
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.
Highlights
In the past years, the large-scale generation of high-throughput biological data has enabled the construction of complex interaction networks that provide a new framework for gaining a systems level understanding of disease mechanisms.[9]
Network-based methods rely on a unique network topology while there are compelling evidences suggesting that different cellular phenotypes, such as healthy and disease states, are characterized by fairly different gene regulatory networks (GRNs) topologies,[41,42] leaving these methods unable to identify differential regulatory mechanisms leading to a disease pathology
To obtain contextualized GRNs, our method prunes literature interaction maps compiled in each case, including interactions occurring in different tissues and organisms, and at the same time infers the mode of action of interactions having unspecified effects
Summary
The large-scale generation of high-throughput biological data has enabled the construction of complex interaction networks that provide a new framework for gaining a systems level understanding of disease mechanisms.[9]. The Connectivity Map (CMap)[30,31] constitutes a widely used database of gene expression profiles from cultured human cancer cells perturbed with chemicals and genetic reagents It has been successfully applied for predicting drug effects and mode of action in different human diseases.[32,33,34,35] following this approach disregards the underlying gene regulatory mechanisms. Gene signature-based methods, such as the CMap,[30,31] have some important shortcomings for the selection of the right subset of genes composing a signature.[43] Recently, new methods have been proposed to improve gene signature analysis assuming independence between the expression of different genes, leading to more reliable drug–drug[43,44] and drug–disease connections.[29,45] a detailed analysis of gene–gene interaction networks in a specific phenotype is generally neglected in these approaches. Strategies for predicting multitarget drugs are scarce and there is a lack of a robust set of design tools to routinely apply these multitarget approaches.[7]
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