|Title||Characterizing regulation of metabolism in Geobacter sulfurreducens through genome-wide expression data and sequence analysis.|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Mahadevan R, Yan B, Postier B, Nevin KP, Woodard TL, O'Neil R, Coppi MV, Methé BA, Krushkal J|
|Date Published||2008 Mar|
|Keywords||Gene Expression Regulation, Bacterial, Genome, Bacterial, Geobacter, Models, Genetic, Oligonucleotide Array Sequence Analysis, Sequence Analysis, DNA, Transcription, Genetic|
Geobacteraceae are a family of metal reducing bacteria with important applications in bioremediation and electricity generation. G. sulfurreducens is a representative of Geobacteraceae that has been extensively studied with the goal of extending the understanding of this family of organisms for optimizing their practical applications. Here, we have analyzed gene expression data from 10 experiments involving environmental and genetic perturbations and have identified putative transcription factor binding sites (TFBS) involved in regulating key aspects of metabolism. Specifically, we considered data from both a subset of 10 microarray experiments (7 of 10) and all 10 experiments. The expression data from these two sets were independently clustered, and the upstream regions of genes and operons from the clusters in both sets were used to identify TFBS using the AlignACE program. This analysis resulted in the identification of motifs upstream of several genes involved in central metabolism, sulfate assimilation, and energy metabolism, as well as genes potentially encoding acetate permease. Further, similar TFBS were identified from the analysis of both sets, suggesting that these TFBS are significant in the regulation of metabolism in G. sulfurreducens. In addition, we have utilized microarray data to derive condition specific constraints on the capacity of key enzymes in central metabolism. We have incorporated these constraints into the metabolic model of G. sulfurreducens and simulated Fe(II)-limited growth. The resulting prediction was consistent with data, suggesting that regulatory constraints are important for simulating growth phenotypes in nonoptimal environments.