Regulog Analysis: Detection of Conserved Regulatory Networks Across Bacteria: Application to Staphylococcus aureus

Abstract

A transcriptional regulatory network encompasses sets of genes (regulons) whose expression states are directly altered in response to an activating signal, mediated by trans-acting regulatory proteins and cis-acting regulatory sequences. Enumeration of these network components is an essential step toward the creation of a framework for systems-based analysis of biological processes. Profile-based methods for the detection of cis-regulatory elements are often applied to predict regulon members, but they suffer from poor specificity. In this report we describe Regulogger, a novel computational method that uses comparative genomics to eliminate spurious members of predicted gene regulons. Regulogger produces regulogs, sets of coregulated genes for which the regulatory sequence has been conserved across multiple organisms. The quantitative method assigns a confidence score to each predicted regulog member on the basis of the degree of conservation of protein sequence and regulatory mechanisms. When applied to a reference collection of regulons from Escherichia coli, Regulogger increased the specificity of predictions up to 25-fold over methods that use cis-element detection in isolation. The enhanced specificity was observed across a wide range of biologically meaningful parameter combinations, indicating a robust and broad utility for the method. The power of computational pattern discovery methods coupled with Regulogger to unravel transcriptional networks was demonstrated in an analysis of the genome of Staphylococcus aureus. A total of 125 regulogs were found in this organism, including both well-defined functional groups and a subset with unknown functions.

Prediction and Analysis of Gene Regulatory Networks in Prokaryotic Genomes

“…This is particulary important for the interspecies transfer of gene regulatory networks (Babu et al, 2006;Baumbach, 2010) but also for the scanning of new regulon members (Pérez et al, 2007). The utilization of pattern matching methods in combination with phylogenetic conservation is also called regulog analysis (Alkema et al, 2004). During a regulog analysis the relativ conservation score RCS is defined by the fraction of orthologs, that share the same potential TFBS.…”

Impact of Transcription Units rearrangement on the evolution of the regulatory network of gamma-proteobacteria

“…This rewiring has been compared to changing the "software" of the transcriptional regulation machinery [20], as opposed to changing the "hardware". The latter would mean changing the set of TFs or regulated genes -a process observed by Babu et al [7] and Lozada-Chavez et al [16] -between phylogenetically distant organisms.…”

“…Both works showed that the set of regulatory genes -even global transcription factors -clearly varies from one group of organisms to another. On the other hand, several studies have shown that, within a group of closely related organisms, the conservation of a TF and a subset of the structural genes it regulates are not sufficient to assume that regulatory links between them are conserved between such two organisms [10][11][12][13][14][15][16][17][18].…”

“…Several approaches have been developed to overcome the lack of comparative information on regulatory interactions that is necessary in works aimed at studying the evolution of regulatory networks. Most of these approaches combine the use of sequence profiles of the sites recognized by a given Transcription Factor (TF) in a model organism (for instance E. coli) with orthology information between the genes of closely related organisms (based on the model organism) to identify putative orthologous regulatory interactions in organisms other than the model [5,[10][11][12][13][14][15][16][17][18].…”

General Trends in the Evolution of Prokaryotic Transcriptional Regulatory Networks

“…In order to gain a better understanding of the transcriptional regulatory network in other organisms, computational methods to extrapolate this information from model organisms to poorly studied organisms by exploiting the wealth of information in the form of publicly available completely sequenced genomes have been developed [18][19][20][21][22][23][24][25][26][27][28][29]. Such methods can be broadly grouped into two classes: (i) Orthology based methods: This approach exploits the basic principle that orthologous transcription factors regulate orthologous target genes in the different genomes.…”