Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning.

Marco A De Bastiani,Bianca Pfaffenseller,Fabio Klamt

doi:10.3389/fphar.2018.00697

Abstract

Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning.

Highlights

Customary approaches to drug development focus on identification of a new treatment target, followed by a search for a compound capable of modulating that target and lastly a validation process
We suggest that gene co-expression networks centered on master regulator transcription factors may be used to identify promising candidates for drug repositioning through a module-oriented adaptation of classical Connectivity Maps
Previous literature have observed that, given differential gene expression profiles from two independent studies, there was virtually no statistical significance in the overlap between them and these signatures performed poorly in classifying samples from the other study (Michiels et al, 2005; Lim et al, 2009; Padi and Quackenbush, 2015). This observation fits well with the idea of transcription factors acting as master regulators, supporting an approach of exploring the controllers of expression profiles, rather than evaluating all differentially expressed genes between two phenotypes of interest

Summary

Introduction

Customary approaches to drug development focus on identification of a new treatment target, followed by a search for a compound capable of modulating that target and lastly a validation process. We suggest that gene co-expression networks centered on master regulator transcription factors may be used to identify promising candidates for drug repositioning through a module-oriented adaptation of classical Connectivity Maps. This observation fits well with the idea of transcription factors acting as master regulators, supporting an approach of exploring the controllers of expression profiles, rather than evaluating all differentially expressed genes between two phenotypes of interest.

Results

Conclusion