Evidence of a Large Novel Gene Pool Associated with Prokaryotic Genomic Islands

Microbial genes that are “novel” (no detectable homologs in other species) have become of increasing interest as environmental sampling suggests that there are many more such novel genes in yet-to-be-cultured microorganisms. By analyzing known microbial genomic islands and prophages, we developed criteria for systematic identification of putative genomic islands (clusters of genes of probable horizontal origin in a prokaryotic genome) in 63 prokaryotic genomes, and then characterized the distribution of novel genes and other features. All but a few of the genomes examined contained significantly higher proportions of novel genes in their predicted genomic islands compared with the rest of their genome (Paired t test = 4.43E-14 to 1.27E-18, depending on method). Moreover, the reverse observation (i.e., higher proportions of novel genes outside of islands) never reached statistical significance in any organism examined. We show that this higher proportion of novel genes in predicted genomic islands is not due to less accurate gene prediction in genomic island regions, but likely reflects a genuine increase in novel genes in these regions for both bacteria and archaea. This represents the first comprehensive analysis of novel genes in prokaryotic genomic islands and provides clues regarding the origin of novel genes. Our collective results imply that there are different gene pools associated with recently horizontally transmitted genomic regions versus regions that are primarily vertically inherited. Moreover, there are more novel genes within the gene pool associated with genomic islands. Since genomic islands are frequently associated with a particular microbial adaptation, such as antibiotic resistance, pathogen virulence, or metal resistance, this suggests that microbes may have access to a larger “arsenal” of novel genes for adaptation than previously thought

Pseudomonas aeruginosa Genome Database and PseudoCAP: facilitating community-based, continually updated, genome annotation

Using the Pseudomonas aeruginosa Genome Project as a test case, we have developed a database and submission system to facilitate a community-based approach to continually updated genome annotation (http://www.pseudomonas.com). Researchers submit proposed annotation updates through one of three web-based form options which are then subjected to review, and if accepted, entered into both the database and log file of updates with author acknowledgement. In addition, a coordinator continually reviews literature for suitable updates, as we have found such reviews to be the most efficient. Both the annotations database and updates-log database have Boolean search capability with the ability to sort results and download all data or search results as tab-delimited files. To complement this peer-reviewed genome annotation, we also provide a linked GBrowse view which displays alternate annotations. Additional tools and analyses are also integrated, including PseudoCyc, and knockout mutant information. We propose that this database system, with its focus on facilitating flexible queries of the data and providing access to both peer-reviewed annotations as well as alternate annotation information, may be a suitable model for other genome projects wishing to use a continually updated, community-based annotation approach. The source code is freely available under GNU General Public Licence

Thiol-dependent mycobacterial responses to oxidative and nitrosative stress

Mycothiol (MSH), produced in actinomycetes including mycobacteria, is functionally analogous to glutathione (GSH) in other organisms, replacing G S H as the main systemic protectant against oxidative stress. In this work, we investigated two possible control points in the regulation of MSH in response to oxidative stress: 1) transcriptional upregulation of MSH biosynthesis mediated by Rv0485 & Rv0818 (putative transcriptional regulators located directly upstream of some MSH biosynthesis genes); and 2) maintenance of the MSH:MS=SM redox balance upon oxidative stress. To monitor the changes in redox state and total MSH levels in Mycobacterium smegmatis mc²155 and M. bovis BCG cultures upon exposure to diamide (a thiol-specific oxidative agent), H₂O₂, or gaseous nitric oxide, we performed mycothiol assays and developed a novel, modified mycothiol assay to detect MSH oxidized as MS=SM. We found that diamide and H₂O₂-induced oxidative stress in M. bovis BCG induces partial depletion of MSH to the oxidized form MS=SM, while treatment with gNO does not. M. smegmatis, an environmental saprophyte, displays a greater tolerance to these oxidative stresses than M. bovis BCG, as reflected by the lesser magnitudes in changes in redox state and total MSH levels upon treatment. We also investigated gene expression of Rv0485 and Rv0818 in M. bovis BCG upon exposure to diamide and upon infection of J774A.1 murine macrophages, using quantitative real-time reverse-transcriptase PCR. We found that although expressions of Rv0485 and Rv0818 were unchanged in diamide-treated bacteria, they were increased about 8-fold in bacteria harvested 6 and 18 hours after macrophage infections, hi addition, we conducted protein-protein binding assays to investigate if Rv0818 protein binds to the SigH RNA polymerase subunit specifically under oxidizing conditions in vitro, as would be expected if Rv0818 is involved in the transcriptional regulation of msh biosynthesis genes upon oxidative stress. As an addendum to this thesis, we looked at two potential GSH-dependent genes in mycobacteria, ggtA & ggtB, which code for putative gammaglutamyltranspeptidases and might have a role in the recently described phenomenon of mycobacterial sensitivity to GSH and GSNO.Medicine, Faculty ofMedicine, Department ofExperimental Medicine, Division ofGraduat

Proportion of Novel Genes in Genomic Islands (Red Bars) versus the Rest of the Genome (Blue Bars) according to a SUPERFAMILY-Based Analysis

<p>Proportions of novel genes are calculated as a percentage of all genes within islands or outside of islands, respectively, for each genome (listed on the x axis). A paired <i>t</i> test indicates that significantly higher proportions of genes in islands (red bars) versus outside islands (non-islands; purple bars) do not have a SUPERFAMILY prediction (potential novel genes; <i>p</i> = 4.43E-14).</p

Proportion of Novel Genes in Genomic Islands (Red Bars) versus the Rest of the Genome (Blue Bars) according to a COG-Based Analysis

<p>Proportions of novel genes are calculated as a percentage of all genes within islands or outside of islands, respectively, for each genome (listed on the x axis). A paired <i>t</i> test indicates that significantly more genes in islands versus non-islands do not have a COG classification (<i>p</i> = 1.20E-18). This phenomenon is uniform across prokaryotic lineages and domains. Similar results are also observed if different datasets are analyzed, or different methods for identifying novel genes are used (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010062#pgen-0010062-t003" target="_blank">Table 3</a>).</p