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ABSTRACT: Pseudomonas syringae pv. tomato DC3000 encodes fifteen sigma factors. The majority are members of the extracytoplasmic function class of sigma factors, including five that belong to iron starvation sub-group. In this study we identified the genes controlled by three iron starvation sigma factors. Their regulons are composed of a small number of genes likely to be involved with iron uptake.
Applied and environmental microbiology 11/2012; · 3.69 Impact Factor
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ABSTRACT: The diversity of regulatory systems encoded by bacteria provides an indication of the variety of stresses and interactions that these organisms encounter in nature. We have been investigating how the plant pathogen Pseudomonas syringae pv. tomato DC3000 responds to iron limitation and have focused on the iron starvation (IS) sigma factors to identify regulon members and to explore the mechanistic details of genetic control for this class of regulators. In the study described in this report, we used chromatin immunoprecipitation paired with high-throughput sequencing (ChIP-Seq) to screen the genome for locations associated with binding of the P. syringae IS sigma factor PSPTO_1203. We used multiple methods to demonstrate differential regulation of two genes identified in the ChIP-Seq screen and characterize the promoter elements that facilitate PSPTO_1203-dependent regulation. The genes regulated by PSPTO_1203 encode a TonB-dependent transducer (PSPTO_1206) and a cytoplasmic membrane protein (PSPTO_2145), which is located in the P. syringae pyoverdine cluster. Additionally, we identified siderophores that induce the activity of PSPTO_1203 and used this information to investigate the functional components of the signal transduction cascade.
Journal of bacteriology 08/2011; 193(20):5775-83. · 3.94 Impact Factor
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ABSTRACT: Non-coding RNAs (ncRNAs) are important components of many regulatory pathways and have key roles in regulating diverse functions. In the Pseudomonads, the two-component system, GacA/S, directly regulates at least two well-characterized ncRNAs, RsmZ and RsmY, which act by sequestration of translation repressor proteins to control expression of various exoproducts. Pseudomonas fluorescens CHA0 possesses a third ncRNA, RsmX, which also participates in this regulatory pathway. In this study we confirmed expression of five rsmX ncRNAs in Pseudomonas syringae pv. tomato DC3000, and determined the distribution of the members of the rsmX ncRNA family by screening available genomic sequences of the Pseudomonads. Variable numbers of the rsmX family exist in Pseudomonas genomes, with up to five paralogs in Pseudomonas syringae strains. In Pseudomonas syringae pv. tomato DC3000, the rsmX genes are 112 to 120 nucleotides in size and are predicted by structural analysis to contain multiple exposed GGA motifs, which is consistent with structural features of the Rsm ncRNAs. We also found that these rsmX ncRNA genes share a conserved upstream region suggesting that their expression is dependent upon the global response regulator, GacA.
RNA biology 09/2010; 7(5):508-16. · 5.56 Impact Factor
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ABSTRACT: In this paper, we describe the context sensitivity problem encountered in partitioning a heterogeneous biological sequence into statistically homogeneous segments. After showing signatures of the problem in the bacterial genomes of Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are segmented using two entropic segmentation schemes, we clarify the contextual origins of these signatures through mean-field analyses of the segmentation schemes. Finally, we explain why we believe all sequence segmentation schems are plagued by the context sensitivity problem.
05/2009;
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ABSTRACT: In this paper, we extend a previously developed recursive entropic segmentation scheme for applications to biological sequences. Instead of Bernoulli chains, we model the statistically stationary segments in a biological sequence as Markov chains, and define a generalized Jensen-Shannon divergence for distinguishing between two Markov chains. We then undertake a mean-field analysis, based on which we identify pitfalls associated with the recursive Jensen-Shannon segmentation scheme. Following this, we explain the need for segmentation optimization, and describe two local optimization schemes for improving the positions of domain walls discovered at each recursion stage. We also develop a new termination criterion for recursive Jensen-Shannon segmentation based on the strength of statistical fluctuations up to a minimum statistically reliable segment length, avoiding the need for unrealistic null and alternative segment models of the target sequence. Finally, we compare the extended scheme against the original scheme by recursively segmenting the Escherichia coli K-12 MG1655 genome.
05/2009;
