Ralstonia solanacearum genes induced during growth in tomato: an inside view of bacterial wilt

The phytopathogen Ralstonia solanacearum has over 5000 genes, many of which probably facilitate bacterial wilt disease development. Using in vivo expression technology (IVET), we screened a library of 133 200 R. solanacearum strain K60 promoter fusions and isolated approximately 900 fusions expressed during bacterial growth in tomato plants. Sequence analysis of 307 fusions revealed 153 unique in planta-expressed (ipx) genes. These genes included seven previously identified virulence genes (pehR, vsrB, vsrD, rpoS, hrcC, pme and gspK) as well as seven additional putative virulence factors. A significant number of ipx genes may reflect adaptation to the host xylem environment; 19.6%ipx genes are predicted to encode proteins with metabolic and/or transport functions, and 9.8%ipx genes encode proteins possibly involved in stress responses. Many ipx genes (18%) encode putative transmembrane proteins. A majority of ipx genes isolated encode proteins of unknown function, and 13% were unique to R. solanacearum. The ipx genes were variably induced in planta; beta-glucuronidase reporter gene expression analysis of a subset of 44 ipx fusions revealed that in planta expression levels were between two- and 37-fold higher than in culture. The expression of many ipx genes was subject to known R. solanacearum virulence regulators. Of 32 fusions tested, 28 were affected by at least one virulence regulator; several fusions were controlled by multiple regulators. Two ipx fusion strains isolated in this screen were reduced in virulence on tomato, indicating that gene(s) important for bacterial wilt pathogenesis were interrupted by the IVET insertion; mutations in other ipx genes are necessary to determine their roles in virulence and in planta growth. Collectively, this profile of ipx genes suggests that in its host, R. solanacearum confronts and overcomes a stressful and nutrient-poor environment.     

Computational bacterial genome-wide analysis of phylogenetic profiles reveals potential virulence genes of Streptococcus agalactiae

The phylogenetic profile of a gene is a reflection of its evolutionary history and can be defined as the differential presence or absence of a gene in a set of reference genomes. It has been employed to facilitate the prediction of gene functions. However, the hypothesis that the application of this concept can also facilitate the discovery of bacterial virulence factors has not been fully examined. In this paper, we test this hypothesis and report a computational pipeline designed to identify previously unknown bacterial virulence genes using group B streptococcus (GBS) as an example. Phylogenetic profiles of all GBS genes across 467 bacterial reference genomes were determined by candidate-against-all BLAST searches,which were then used to identify candidate virulence genes by machine learning models. Evaluation experiments with known GBS virulence genes suggested good functional and model consistency in cross-validation analyses (areas under ROC curve, 0.80 and 0.98 respectively). Inspection of the top-10 genes in each of the 15 virulence functional groups revealed at least 15 (of 119) homologous genes implicated in virulence in other human pathogens but previously unrecognized as potential virulence genes in GBS. Among these highly-ranked genes, many encode hypothetical proteins with possible roles in GBS virulence. Thus, our approach has led to the identification of a set of genes potentially affecting the virulence potential of GBS, which are potential candidates for further in vitro and in vivo investigations. This computational pipeline can also be extended to in silico analysis of virulence determinants of other bacterial pathogens.     

Bioinformatics Annotation of the Hypothetical Proteins Found by Omics Techniques Can Help to Disclose Additional Virulence Factors

The advent of genomics should have facilitated the identification of microbial virulence factors, a key objective for vaccine design. When the bacterial pathogen infects the host it expresses a set of genes, a number of them being virulence factors. Among the genes identified by techniques as microarrays, in vivo expression technology, signature-tagged mutagenesis and differential fluorescence induction there are many related to cellular stress, basal metabolism, etc., which cannot be directly involved in virulence, or at least cannot be considered useful candidates to be deleted for designing a live attenuated vaccine. Among the genes disclosed by these methodologies there are a number of hypothetical or unknown proteins. As they can hide some true virulence factors, we have reannotated all of these hypothetical proteins from several respiratory pathogens by a careful and in-depth analysis of each one. Although some of the re-annotations match with functions that can be related to microbial virulence, the identification of virulence factors remains difficult.     

Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1,000 rapidly responding genes and new linkages to glucose,trehalose-6-phosphate,iron, and sulfate metabolism

The genomic response to low levels of nitrate was studied in Arabidopsis using the Affymetrix ATH1 chip containing more than 22,500 probe sets. Arabidopsis plants were grown hydroponically in sterile liquid culture on ammonium as the sole source of nitrogen for 10 d, then treated with 250 microm nitrate for 20 min. The response to nitrate was much stronger in roots (1,176 genes showing increased or decreased mRNA levels) than in shoots (183 responding genes). In addition to known nitrate-responsive genes (e.g. those encoding nitrate transporters, nitrate reductase, nitrite reductase, ferredoxin reductase, and enzymes in the pentose phosphate pathway), genes encoding novel metabolic and potential regulatory proteins were found. These genes encode enzymes in glycolysis (glucose-6-phosphate isomerase and phosphoglycerate mutase), in trehalose-6-P metabolism (trehalose-6-P synthase and trehalose-6-P phosphatase), in iron transport/metabolism (nicotianamine synthase), and in sulfate uptake/reduction. In many cases, only a few select genes out of several in small gene families were induced by nitrate. These results show that the effect of nitrate on gene expression is substantial (affecting almost 10% of the genes with detectable mRNA levels) yet selective and affects many genes involved in carbon and nutrient metabolism.     

Enhanced functional and structural domain assignments using remote similarity detection procedures for proteins encoded in the genome of<Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> H37Rv

The sequencing of theMycobacterium tuberculosis (MTB) H37Rv genome has facilitated deeper insights into the biology of MTB, yet the functions of many MTB proteins are unknown. We have used sensitive profile-based search procedures to assign functional and structural domains to infer functions of gene products encoded in MTB. These domain assignments have been made using a compendium of sequence and structural domain families. Functions are predicted for 78% of the encoded gene products. For 69% of these, functions can be inferred by domain assignments. The functions for the rest are deduced from their homology to proteins of known function. Superfamily relationships between families of unknown and known structures have increased structural information by ∼ 11%. Remote similarity detection methods have enabled domain assignments for 1325 ‘hypothetical proteins’. The most populated families in MTB are involved in lipid metabolism, entry and survival of the bacillus in host. Interestingly, for 353 proteins, which we refer to as MTB-specific, no homologues have been identified. Numerous, previously unannotated, hypothetical proteins have been assigned domains and some of these could perhaps be the possible chemotherapeutic targets. MTB-specific proteins might include factors responsible for virulence. Importantly, these assignments could be valuable for experimental endeavors. The detailed results are publicly available at http://hodgkin.mbu.iisc.ernet.in/∼dots. An erratum to this article is available at .