A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data

作者：Gambardella Gennaro; Peluso Ivana; Montefusco Sandro; Bansal Mukesh; Medina Diego L; Lawrence Neil; di Bernardo Diego^*

来源：BMC Bioinformatics, 2015, 16(1): 279.

DOI：10.1186/s12859-015-0700-3

Background: Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods.
Results: We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors.
Conclusions: DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery.

• 出版日期2015-9-3