Vasohibin-1 is identified as a master-regulator of endothelial cell apoptosis using gene network analysis

Abstract

BackgroundApoptosis is a critical process in endothelial cell (EC) biology and pathology, which has been extensively studied at protein level. Numerous gene expression studies of EC apoptosis have also been performed, however few attempts have been made to use gene expression data to identify the molecular relationships and master regulators that underlie EC apoptosis. Therefore, we sought to understand these relationships by generating a Bayesian gene regulatory network (GRN) model.ResultsECs were induced to undergo apoptosis using serum withdrawal and followed over a time course in triplicate, using microarrays. When generating the GRN, this EC time course data was supplemented by a library of microarray data from EC treated with siRNAs targeting over 350 signalling molecules.The GRN model proposed Vasohibin-1 (VASH1) as one of the candidate master-regulators of EC apoptosis with numerous downstream mRNAs. To evaluate the role played by VASH1 in EC, we used siRNA to reduce the expression of VASH1. Of 10 mRNAs downstream of VASH1 in the GRN that were examined, 7 were significantly up- or down-regulated in the direction predicted by the GRN.Further supporting an important biological role of VASH1 in EC, targeted reduction of VASH1 mRNA abundance conferred resistance to serum withdrawal-induced EC death.ConclusionWe have utilised Bayesian GRN modelling to identify a novel candidate master regulator of EC apoptosis. This study demonstrates how GRN technology can complement traditional methods to hypothesise the regulatory relationships that underlie important biological processes.

The recurrent architecture of tumour initiation, progression and drug sensitivity

“…The Algorithm for the Accurate Reconstruction of Cellular Networks (ARACNe) 59 was the first of its kind to be experimentally validated in mammalian cells 10,39,57,74 and has been among the most frequently used to infer regulatory targets for MR analysis. However, various alternative approaches have also been popular, from Bayesian networks 75 to co-expression network integration with chromatin immunoprecipitation–sequencing (ChIP–seq) assays 47 . To achieve high accuracy, all these methods require relatively large collections ( n ≥100) of gene expression profiles representing a specific cellular context, under conditions that induce broad gene expression variability, such as from genetically heterogeneous tumour samples 10 or from concomitant genetic and small molecule perturbations 9 .…”

Vasohibin-1 Is a Poor Prognostic Factor of Ovarian Carcinoma

“…VASH1 is a VEGF-related factor and its relationship with cancer cells and apotheosis has been demonstrated (Hosaka et al 2009;Affara et al 2013). Its correlations with angiogenesis and other markers are also drawing attention.…”

The role of master regulators in gene regulatory networks

“…Values of g larger than expected lead to the rejection of the null hypothesis (i.e., random processes). The exact g-distribution is given by: (4) α = N/2 and r is the largest integer less than 1/x. So if the observed value of g is g , then there is a p-value P (g > g ) to evaluate the null hypothesis.…”

“…7, art. 070011 (2015) / E. Hernández-Lemus et al [5], but also on specific cellular phenotypes [4,6,7].…”

“…Due to the multiplicity of mTOR signaling interactions, this molecule acts as a master regulator on a variety of phenotypes. More specific master regulatory activity may be exemplified by cases such as the one of VASH1 that has been identified as a master-regulator of endothelial cell apoptosis [4]; PAX5 is known to be a master regulator of B-cell development also involved in neoplastic processes in leukemogenesis [6] and the yeast protein Gcn4Pp (that contains a conserved domain cd12193 present in human JUN proto-oncogene) that is triggered by starvation and stress signals [7] and is an MR in the phenotypic response to such stimuli. Due to the complex mechanisms behind transcriptional regulation in eukaryotes, identification of MRs is mostly based on the (inferred or empirical) relationships between them and their downstream RNA targets in the GRN.…”