Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

Christoph Kaleta,Swetlana Nikolajewa,Anna Göhler,Stefan Schuster,Knut Jahreis,Reinhard Guthke

doi:10.1186/1752-0509-4-116

Abstract

BackgroundAlthough Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.ResultsWe used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.ConclusionsWe predicted a set of five novel TF - target gene interactions in E. coli. One of them, the regulation of lipA by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.

Highlights

Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved
The threshold for the acceptance of an interaction was determined by a comparison of the inferred network to known transcription factor (TF)-gene interactions contained within RegulonDB version 6.1 [10]
There are 316 known and predicted TFs denoted in RegulonDB

Summary

Introduction

Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. Mutual information based approaches are most useful for large-scale network inference. The prokaryote Escherichia coli is best suited as a model organism for genome-wide network inference studies due to the available and well-documented molecular biological knowledge and the remarkable amount of published target. In [5] the inferred interactions are restricted to cases where one partner is a transcription factor (TF) Another approach is to use active and gene-specific interventions, like knockouts, knockdowns or over expressions. A third way is to exploit time series data and use them to infer the direction of association from temporal patterns. In this context directed information (DTI, [7]) can be used. In this work we improved the computation of DTI and used it to infer regulatory networks on a genome scale

Methods

Results

Conclusion