Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics

Abstract

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.complex systems ͉ time series ͉ gene interaction

Kinetic profiling of the c-Myc transcriptome and bioinformatic analysis of repressed gene promoters

“…13 We have previously reported a cell culture model system in which c-Myc activity can be modulated from essentially null to only slightly above physiological with excellent temporal resolution. 14,15 This contrasts with the great majority of other studies that examine the consequences of c-Myc overexpression in tumor cells. We have previously shown that many genes affected by c-Myc overexpression are not regulated during its normal physiological range of expression.…”

“…GSEA is a program that computes the statistical significance of the affected by c-Myc overexpression are not regulated during its normal physiological range of expression. 14,15 The relative proportions of repressed genes in our data set that are regulated transcriptionally and posttranscriptionally thus remains to be determined.…”

“…We have previously shown that many genes affected by c-Myc overexpression are not regulated during its normal physiological range of expression. 14,15 In this communication, we report expression profiling of c-Myc target genes using a high resolution time course of c-Myc induction. To our knowledge this is the most extensive in vivo kinetic analysis of c-Myc activity and allowed us not only to determine c-Myc regulated patterns of gene expression with high confidence, but also to identify rapidresponse genes likely to be direct targets.…”

Networks From Gene Expression Time Series: Characterization of Correlation Patterns

“…This task is complex because typical highthroughput genomics experiments are performed with high number of probesets (10 3 − 10 4 genes) and a limited number of observations (< 10 2 time points). In this paper we develop a deepest analysis of our previous work [Remondini et al, 2005] in which we characterized some of the main features of a gene-gene interaction network reconstructed from temporal correlation of gene expression time series. One first advancement is based on the comparison of the reconstructed network with networks obtained from randomly generated data, in order to characterize which features retrieve real biological information, and which are instead due to the characteristics of the network reconstruction method.…”

“…10 5 probesets for human microarrays), as well as their complexity, has slowed down reliable modelling. An emerging and powerful approach to tackle the complexity in functional genomics experiments, is the so called perturbation method, that consists in perturbing the system with external tunable stimuli and following the changes in the gene interaction-network properties as a function of time and perturbation magnitude [Remondini et al, 2005;Ideker et al, 2001]. An experimental example of this strategy is the dataset obtained from measurements in which a single, but very important gene was conditionally switched on or off.…”