Whole-genome expression profiling defines the HrpL regulon of Pseudomonas syringae pv. tomato DC3000, allows de novo reconstruction of the Hrp cis clement, and identifies novel coregulated genes.

Pseudomonas syringae pv. tomato DC3000 is a model pathogen of tomato and Arabidopsis that uses a hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS) to deliver virulence effector proteins into host cells. Expression of the Hrp system and many effector genes is activated by the HrpL alternative sigma factor. Here, an open reading frame-specific whole-genome microarray was constructed for DC3000 and used to comprehensively identify genes that are differentially expressed in wild-type and deltahrpL strains. Among the genes whose differential regulation was statistically significant, 119 were upregulated and 76 were downregulated in the wild-type compared with the deltahrpL strain. Hierarchical clustering revealed a subset of eight genes that were upregulated particularly rapidly. Gibbs sampling of regions upstream of HrpL-activated operons revealed the Hrp promoter as the only identifiable regulatory motif and supported an iterative refinement involving real-time polymerase chain reaction testing of additional HrpL-activated genes and refinements in a hidden Markov model that can be used to predict Hrp promoters in P. syringae strains. This iterative bioinformatic-experimental approach to a comprehensive analysis of the HrpL regulon revealed a mix of genes controlled by HrpL, including those encoding most type III effectors, twin-arginine transport (TAT) substrates, other regulatory proteins, and proteins involved in the synthesis or metabolism of phytohormones, phytotoxins, and myo-inositol. This analysis provides an extensively verified, robust method for predicting Hrp promoters in P. syringae genomes, and it supports subsequent identification of effectors and other factors that likely are important to the host-specific virulence of P. syringae.

The distinctive signatures of promoter regions and operon junctions across prokaryotes.

Here we show that regions upstream of first transcribed genes have oligonucleotide signatures that distinguish them from regions upstream of genes in the middle of operons. Databases of experimentally confirmed transcription units do not exist for most genomes. Thus, to expand the analyses into genomes with no experimentally confirmed data, we used genes conserved adjacent in evolutionarily distant genomes as representatives of genes inside operons. Likewise, we used divergently transcribed genes as representative examples of first transcribed genes. In model organisms, the trinucleotide signatures of regions upstream of these representative genes allow for operon predictions with accuracies close to those obtained with known operon data (0.8). Signature-based operon predictions have more similar phylogenetic profiles and higher proportions of genes in the same pathways than predicted transcription unit boundaries (TUBs). These results confirm that we are separating genes with related functions, as expected for operons, from genes not necessarily related, as expected for genes in different transcription units. We also test the quality of the predictions using microarray data in six genomes and show that the signature-predicted operons tend to have high correlations of expression. Oligonucleotide signatures should expand the number of tools available to identify operons even in poorly characterized genomes.