CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction

Abstract

Background: Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways.

Results: We have developed CyTRANSFINDER, a new Cytoscape plugin able to infer three-component signal transduction pathways based on user defined regulatory patterns and including miRNAs, TFs and genes. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. To show the potential of the plugin we have applied it in a study of two miRNAs that are particularly relevant in human melanoma progression, miR-146a and miR-214.

Conclusions: CyTRANSFINDER supports the reconstruction of small signal transduction pathways among groups of genes. Results obtained from its use in a real case study have been analyzed and validated through both literature data and preliminary wet-lab experiments, showing the potential of this tool when performing exploratory analysis.

Keywords: Cytoscape; Data fusion; Network analysis; Network modules; Pathway analysis; Signal transduction pathways; microRNA.

Fig. 1

CyTRANSFINDER overview. a The main plugin control panel. It allows the user to set the source and destination list of genes and the specific pattern of regulators to search. b Shows the graphical output of the plugin that consists of a network connecting source genes with destination genes. Nodes of this network represent genes, TFs and miRNAs. c This panel allows to define a set of parameters related to the integration of miRNAs into the generated STPs. They can be used to control the size of the generated networks. d This panel allows the user to export the results in the form of a text file including all identified circuits or to delete the current experiment and start with a new one. e The Cytoscape node and edge tables. They can be used to access detailed information on the nodes and arcs of the identified STPs

Fig. 2

CyTRANSFINDER software architecture. CyTRANSFINDER processes three inputs: (1) the source list of genes (SRL), (2) the destination list of genes (DGL), and (3) the STP pattern (STPP) to be reconstructed. Its fusion engine connects to several on-line repositories to collect regulatory information used to infer STPs connecting source and destination genes according to the selected STP pattern. The identified STPs are then purged to remove duplicated nodes and arcs and the result is returned to the user as a Cytoscape network or exported in the form of a tab separated text file

Fig. 3

CyTRANSFINDER built-in STPPs. The figure presents the five default STPPs embedded in the plugin. i) Direct miRNA STPP is the simplest pattern: a source gene hosts a intragenic mirna miRNA or is located close to the region of an intergenic miRNA, which targets one of the destination genes. ii) Indirect miRNA STPP, is pretty similar to the Direct miRNA STPP, but it involves a TF as miRNA mediator for the regulation of the destination genes. iii) the miRNA sourced version of (ii). iv) Double miRNA indirect STPP is the most complex pattern. It involves two levels of regulation; the first indirect regulation is modeled on top of an Indirect miRNA STPP, which regulates a Direct miRNA STPP that targets the destination genes. v) The miRNA sourced version of (iv)

Fig. 4

CyTRANSFINDER data fusion algorithm. A pseudocode description of the main steps carried out by the plugin to integrate different data sources and to construct the final STP network

Fig. 5

miR-146a Indirect s. miRNA STPs involving TFAP2C. Subnetwork of the Indirect s. miRNA STPs using human miR-146a as source intergenic miRNA, miR-146a targets according to TargetScan 5.1 as destination genes and involving TFAP2C as a hub transcription factor. Results are computed using miRNA targets confirmed in at least one source database

Fig. 6

miR-146a overexpression leads to reduced TFAP2C mRNA levels. Quantitative-Real Time PCR (qRT-PCR) evaluation of TFAP2C mRNA was performed in melanoma cells upon miR-146a overexpression, compared to controls (pre-146a vs pre-Cntrl). Three independent preparations of melanoma cells RNA were used and results were pooled together. **P <0.01

Fig. 7

miR-146a Double Indirect s. miRNA STPs. Network of the Double Indirect s. miRNA STPs using human miR-146a as source intergenic miRNA, miR-146a targets according to TargetScan 5.1 as destination genes and miRNA targets confirmed in at least two of the source databases

Fig. 8

miR-214 Double Indirect miRNA STPs. Sub-Network of a selection of 101 interesting Double Indirect miRNA STPs using human DNM3 as source gene, a signature of 73 genes published in [33] as destination genes, and involving TFAP2C and CREB1 transcription factors. Results are computed using miRNA targets confirmed in at least two databases