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Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium

Lara Rajeev, Eric G Luning, Paramvir S Dehal, Morgan N Price, Adam P Arkin and Aindrila Mukhopadhyay*

Author Affiliations

Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA

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Genome Biology 2011, 12:R99  doi:10.1186/gb-2011-12-10-r99

Published: 12 October 2011



Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems.


We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study.


The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.