A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data

Gennaro Gambardella, Ivana Peluso, Sandro Montefusco, Mukesh Bansal, Diego L. Medina, Neil D. Lawrence, Diego Bernardo
BMC Bioinformatics, 16(279), 2015.

Abstract

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. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest can be found at http://dmi.tigem.it.

Cite this Paper

BibTeX
@Article{Gambardella-reverse15, title = {A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data}, author = {Gambardella, Gennaro and Peluso, Ivana and Montefusco, Sandro and Bansal, Mukesh and Medina, Diego L. and Lawrence, Neil D. and Bernardo, Diego}, journal = {BMC Bioinformatics}, year = {2015}, volume = {16}, number = {279}, doi = {10.1186/s12859-015-0700-3}, pdf = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-015-0700-3.pdf}, url = {http://inverseprobability.com/publications/gambardella-reverse15.html}, abstract = {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. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest can be found at http://dmi.tigem.it.} }
Endnote
%0 Journal Article %T A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data %A Gennaro Gambardella %A Ivana Peluso %A Sandro Montefusco %A Mukesh Bansal %A Diego L. Medina %A Neil D. Lawrence %A Diego Bernardo %J BMC Bioinformatics %D 2015 %F Gambardella-reverse15 %R 10.1186/s12859-015-0700-3 %U http://inverseprobability.com/publications/gambardella-reverse15.html %V 16 %N 279 %X 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. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest can be found at http://dmi.tigem.it.
RIS
TY - JOUR TI - A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data AU - Gennaro Gambardella AU - Ivana Peluso AU - Sandro Montefusco AU - Mukesh Bansal AU - Diego L. Medina AU - Neil D. Lawrence AU - Diego Bernardo DA - 2015/09/03 ID - Gambardella-reverse15 VL - 16 IS - 279 DO - 10.1186/s12859-015-0700-3 L1 - https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-015-0700-3.pdf UR - http://inverseprobability.com/publications/gambardella-reverse15.html AB - 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. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest can be found at http://dmi.tigem.it. ER -
APA
Gambardella, G., Peluso, I., Montefusco, S., Bansal, M., Medina, D.L., Lawrence, N.D. & Bernardo, D.. (2015). A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data. BMC Bioinformatics 16(279) doi:10.1186/s12859-015-0700-3 Available from http://inverseprobability.com/publications/gambardella-reverse15.html.

Related Material