Identification of Streptococcus agalactiae virulence genes in the neonatal rat sepsis model using signature-tagged mutagenesis

Group B streptococcal (GBS) infections are the most common cause of bacterial sepsis in the immediate newborn period. Apart from the capsule, the factors required for survival of GBS in the host are not well defined. In this study, signature-tagged transposon mutagenesis (STM) was used to identify genes required for growth and survival of GBS in a neonatal rat sepsis infection model. Approximately 1600 transposon mutants were screened in pools of 80 mutants, and approximately 120 mutants defective for survival in the animal host were identified. We successfully cloned and sequenced DNA flanking the transposon insertions from 92 of the mutants. Fifty per cent of the mutants had transposon insertions in genes with homologues in the public databases, whereas the remaining 50% had transposon insertions in genes with unknown function. A significant proportion of the avirulent mutants had transposon insertions in genes encoding transport-associated or regulatory proteins or in genes involved in cell surface metabolism, emphasizing the significance of these functions for in vivo survival of GBS. Overall, STM analysis revealed GBS genomic loci that encode a wide variety of functional gene classes, underscoring the diversity of bacterial processes required for the infection process. Currently, the function of the genes identified during the screening can only be inferred by homology to previously described genes. However, a number of the genes identified in this study have been shown to correlate with virulence in other pathogens. A virulence of a subset of mutants identified during the screening was confirmed by performing competitive index assays and lethal dose assays. This represents the first report of a genome-wide scan for virulence factors in GBS. The identified genes will further our understanding of the pathogenesis of GBS infections and may represent targets for intervention or lead to the development of novel therapies.     

Genomic mining type III secretion system effectors in Pseudomonas syringae yields new picks for all TTSS prospectors

Many bacterial pathogens of plants and animals use a type III secretion system (TTSS) to deliver virulence effector proteins into host cells. Because effectors are heterogeneous in sequence and function, there has not been a systematic way to identify the genes encoding them in pathogen genomes, and our current inventories are probably incomplete. A pre-closure draft sequence of Pseudomonas syringae pv. tomato DC3000, a pathogen of tomato and Arabidopsis, has recently supported five complementary studies which, collectively, identify 36 TTSS-secreted proteins and many more candidate effectors in this strain. These studies demonstrate the advantages of combining experimental and computational approaches, and they yield new insights into TTSS effectors and virulence regulation in P. syringae, potential effector targeting signals in all TTSS-dependent pathogens, and strategies for finding TTSS effectors in other bacteria that have sequenced genomes.     

A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case

We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity.     

The social evolution of bacterial pathogenesis

Many of the genes responsible for the virulence of bacterial pathogens are carried by mobile genetic elements that can be transferred horizontally between different bacterial lineages. Horizontal transfer of virulence-factor genes has played a profound role in the evolution of bacterial pathogens, but it is poorly understood why these genes are so often mobile. Here, I present a hypothetical selective mechanism maintaining virulence-factor genes on horizontally transmissible genetic elements. For virulence factors that are secreted extracellularly, selection within hosts may favour mutant 'cheater' strains of the pathogen that do not produce the virulence factor themselves but still benefit from factors produced by other members of the pathogen population within a host. Using simple mathematical models, I show that if this occurs then selection for infectious transmission between hosts favours pathogen strains that can reintroduce functional copies of virulence-factor genes into cheaters via horizontal transfer, forcing them to produce the virulence factor. Horizontal gene transfer is thus a novel mechanism for the evolution of cooperation. I discuss predictions of this hypothesis that can be tested empirically and its implications for the evolution of pathogen virulence.     

基于生物信息学的致病菌特有基因预测及分析

目的:利用生物信息学方法对致病菌特有基因进行大规模预测,同时探讨致病菌特有基因与致病菌毒力之间的关系。方法:构建致病性细菌蛋白质序列数据库和非致病性细菌蛋白质序列数据库,利用同源性比对的方法(BlastP工具)对致病菌特有基因进行预测;同时从文献中提取与致病菌毒力紧密相关的毒力因子,构建具有代表性的毒力因子分析库,对预测的致病菌特有基因进行比较分析。结果:在致病菌780310个基因中,预测了致病菌特有基因79166个,约占致病菌总基因的10.15%;预测的致病菌特有基因包含了构建的毒力因子分析库中的大部分毒力基因。结论:预测的致病菌特有基因与致病菌毒力紧密相关,大大减少了进一步在致病菌基因组中鉴定毒力基因时整个基因组的数据量。     

PseudoPipe: an automated pseudogene identification pipeline

MOTIVATION: Mammalian genomes contain many 'genomic fossils' i.e. pseudogenes. These are disabled copies of functional genes that have been retained in the genome by gene duplication or retrotransposition events. Pseudogenes are important resources in understanding the evolutionary history of genes and genomes. RESULTS: We have developed a homology-based computational pipeline ('PseudoPipe') that can search a mammalian genome and identify pseudogene sequences in a comprehensive and consistent manner. The key steps in the pipeline involve using BLAST to rapidly cross-reference potential "parent" proteins against the intergenic regions of the genome and then processing the resulting "raw hits" -- i.e. eliminating redundant ones, clustering together neighbors, and associating and aligning clusters with a unique parent. Finally, pseudogenes are classified based on a combination of criteria including homology, intron-exon structure, and existence of stop codons and frameshifts.     

Characterization of the ELPhiS prophage from Salmonella enterica serovar Enteritidis strain LK5

Phages are a primary driving force behind the evolution of bacterial pathogens by transferring a variety of virulence genes into their hosts. Similar to other bacterial genomes, the Salmonella enterica serovar Enteritidis LK5 genome contains several regions that are homologous to phages. Although genomic analysis demonstrated the presence of prophages, it was unable to confirm which phage elements within the genome were viable. Genetic markers were used to tag one of the prophages in the genome to allow monitoring of phage induction. Commonly used laboratory strains of Salmonella were resistant to phage infection, and therefore a rapid screen was developed to identify susceptible hosts. This approach showed that a genetically tagged prophage, ELPhiS (Enteritidis lysogenic phage S), was capable of infecting Salmonella serovars that are diverse in host range and virulence and has the potential to laterally transfer genes between these serovars via lysogenic conversion. The rapid screen approach is adaptable to any system with a large collection of isolates and may be used to test the viability of prophages found by sequencing the genomes of various bacterial pathogens.     

Proteomic Detection of Non-Annotated Protein-Coding Genes in Pseudomonas fluorescens Pf0-1

Genome sequences are annotated by computational prediction of coding sequences, followed by similarity searches such as BLAST, which provide a layer of possible functional information. While the existence of processes such as alternative splicing complicates matters for eukaryote genomes, the view of bacterial genomes as a linear series of closely spaced genes leads to the assumption that computational annotations that predict such arrangements completely describe the coding capacity of bacterial genomes. We undertook a proteomic study to identify proteins expressed by Pseudomonas fluorescens Pf0-1 from genes that were not predicted during the genome annotation. Mapping peptides to the Pf0-1 genome sequence identified sixteen non-annotated protein-coding regions, of which nine were antisense to predicted genes, six were intergenic, and one read in the same direction as an annotated gene but in a different frame. The expression of all but one of the newly discovered genes was verified by RT-PCR. Few clues as to the function of the new genes were gleaned from informatic analyses, but potential orthologs in other Pseudomonas genomes were identified for eight of the new genes. The 16 newly identified genes improve the quality of the Pf0-1 genome annotation, and the detection of antisense protein-coding genes indicates the under-appreciated complexity of bacterial genome organization.     

The group B streptococcal sialic acid O-acetyltransferase is encoded by neuD, a conserved component of bacterial sialic acid biosynthetic gene clusters

Nearly two dozen microbial pathogens have surface polysaccharides or lipo-oligosaccharides that contain sialic acid (Sia), and several Sia-dependent virulence mechanisms are known to enhance bacterial survival or result in host tissue injury. Some pathogens are also known to O-acetylate their Sias, although the role of this modification in pathogenesis remains unclear. We report that neuD, a gene located within the Group B Streptococcus (GBS) Sia biosynthetic gene cluster, encodes a Sia O-acetyltransferase that is itself required for capsular polysaccharide (CPS) sialylation. Homology modeling and site-directed mutagenesis identified Lys-123 as a critical residue for Sia O-acetyltransferase activity. Moreover, a single nucleotide polymorphism in neuD can determine whether GBS displays a "high" or "low" Sia O-acetylation phenotype. Complementation analysis revealed that Escherichia coli K1 NeuD also functions as a Sia O-acetyltransferase in GBS. In fact, NeuD homologs are commonly found within Sia biosynthetic gene clusters. A bioinformatic approach identified 18 bacterial species with a Sia biosynthetic gene cluster that included neuD. Included in this list are the sialylated human pathogens Legionella pneumophila, Vibrio parahemeolyticus, Pseudomonas aeruginosa, and Campylobacter jejuni, as well as an additional 12 bacterial species never before analyzed for Sia expression. Phylogenetic analysis shows that NeuD homologs of sialylated pathogens share a common evolutionary lineage distinct from the poly-Sia O-acetyltransferase of E. coli K1. These studies define a molecular genetic approach for the selective elimination of GBS Sia O-acetylation without concurrent loss of sialylation, a key to further studies addressing the role(s) of this modification in bacterial virulence.     

Mining virulence genes using metagenomics

When a bacterial genome is compared to the metagenome of an environment it inhabits, most genes recruit at high sequence identity. In free-living bacteria (for instance marine bacteria compared against the ocean metagenome) certain genomic regions are totally absent in recruitment plots, representing therefore genes unique to individual bacterial isolates. We show that these Metagenomic Islands (MIs) are also visible in bacteria living in human hosts when their genomes are compared to sequences from the human microbiome, despite the compartmentalized structure of human-related environments such as the gut. From an applied point of view, MIs of human pathogens (e.g. those identified in enterohaemorragic Escherichia coli against the gut metagenome or in pathogenic Neisseria meningitidis against the oral metagenome) include virulence genes that appear to be absent in related strains or species present in the microbiome of healthy individuals. We propose that this strategy (i.e. recruitment analysis of pathogenic bacteria against the metagenome of healthy subjects) can be used to detect pathogenicity regions in species where the genes involved in virulence are poorly characterized. Using this approach, we detect well-known pathogenicity islands and identify new potential virulence genes in several human pathogens.     

Computational and comparative analyses of 150 full-length cDNA sequences from the oomycete plant pathogen Phytophthora infestans

Phytophthora infestans is a devastating phytopathogenic oomycete that causes late blight on tomato and potato. Recent genome sequencing efforts of P. infestans and other Phytophthora species are generating vast amounts of sequence data providing opportunities to unlock the complex nature of pathogenesis. However, accurate annotation of Phytophthora genomes will be a significant challenge. Most of the information about gene structure in these species was gathered from a handful of genes resulting in significant limitations for development of ab initio gene-calling programs. In this study, we collected a total of 150 bioinformatically determined near full-length cDNA (FLcDNA) sequences of P. infestans that were predicted to contain full open reading frame sequences. We performed detailed computational analyses of these FLcDNA sequences to obtain a snapshot of P. infestans gene structure, gauge the degree of sequence conservation between P. infestans genes and those of Phytophthora sojae and Phytophthora ramorum, and identify patterns of gene conservation between P. infestans and various eukaryotes, particularly fungi, for which genome-wide translated protein sequences are available. These analyses helped us to define the structural characteristics of P. infestans genes using a validated data set. We also determined the degree of sequence conservation within the genus Phytophthora and identified a set of fast evolving genes. Finally, we identified a set of genes that are shared between Phytophthora and fungal phytopathogens but absent in animal fungal pathogens. These results confirm that plant pathogenic oomycetes and fungi share virulence components, and suggest that eukaryotic microbial pathogens that share similar lifestyles also share a similar set of genes independently of their phylogenetic relatedness.     

Virulence signatures: microarray-based approaches to discovery and analysis

Rapid, accurate, and sensitive detection of biothreat agents requires a broad-spectrum assay capable of discriminating between closely related microbial or viral pathogens. Moreover, in cases where a biological agent release has been identified, forensic analysis demands detailed genetic signature data for accurate strain identification and attribution. To date, nucleic acid sequences have provided the most robust and phylogentically illuminating signature information. Nucleic acid signature sequences are not often linked to genomic or extrachromosomal determinants of virulence, a link that would further facilitate discrimination between pathogens and closely related species. Inextricably coupling genetic determinants of virulence with highly informative nucleic acid signatures would provide a robust means of identifying human, livestock, and agricultural pathogens. By means of example, we present here an overview of two general applications of microarray-based methods for: (1) the identification of candidate virulence factors; and (2) the analysis of genetic polymorphisms that are coupled to Bacillus anthracis virulence factors using an accurate, low cost solid-phase mini-sequencing assay. We show that microarray-based analysis of gene expression can identify potential virulence associated genes for use as candidate signature targets, and, further, that microarray-based single nucleotide polymorphism assays provide a robust platform for the detection and identification of signature sequences in a manner independent of the genetic background in which the signature is embedded. We discuss the strategy as a general approach or pipeline for the discovery of virulence-linked nucleic acid signatures for biothreat agents.     

Computational Prediction of Genomic Functional Cores Specific to Different Microbes

Computational and experimental attempts tried to characterize a universial core of genes representing the minimal set of functional needs for an organism. Based on the increasing number of available complete genomes, comparative genomics has concluded that the universal core contains < 50 genes. In contrast, experiments suggest a much larger set of essential genes (certainly more than several hundreds, even under the most restrictive hypotheses) that is dependent on the biological complexity and environmental specificity of the organism. Highly biased genes, which are generally also the most expressed in translationally biased organisms, tend to be over represented in the class of genes deemed to be essential for any given bacterial species. This association is far from perfect; nevertheless, it allows us to propose a new computational method to detect, to a certain extent, ubiquitous genes, nonorthologous genes, environment-specific genes, genes involved in the stress response, and genes with no identified function but highly likely to be essential for the cell. Most of these groups of genes cannot be identified with previously attempted computational and experimental approaches. The large variety of life-styles and the unusually detectable functional signals characterizing translationally biased organisms suggest using them as reference organisms to infer essentiality in other microbial species. The case of small parasitic genomes is discussed. Data issued by the analysis are compared with previous computational and experimental studies. Results are discussed both on methodological and biological grounds. Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users. [Reviewing Editor: Martin Kreitman]     

Reduced set of virulence genes allows high accuracy prediction of bacterial pathogenicity in humans

Although there have been great advances in understanding bacterial pathogenesis, there is still a lack of integrative information about what makes a bacterium a human pathogen. The advent of high-throughput sequencing technologies has dramatically increased the amount of completed bacterial genomes, for both known human pathogenic and non-pathogenic strains; this information is now available to investigate genetic features that determine pathogenic phenotypes in bacteria. In this work we determined presence/absence patterns of [Formula: see text] different virulence-related genes among more than [Formula: see text] finished bacterial genomes from both human pathogenic and non-pathogenic strains, belonging to different taxonomic groups (i.e: Actinobacteria, Gammaproteobacteria, Firmicutes, etc.). An accuracy of 95% using a cross-fold validation scheme with in-fold feature selection is obtained when classifying human pathogens and non-pathogens. A reduced subset of highly informative genes ([Formula: see text]) is presented and applied to an external validation set. The statistical model was implemented in the BacFier v1.0 software (freely available at [Formula: see text]), that displays not only the prediction (pathogen/non-pathogen) and an associated probability for pathogenicity, but also the presence/absence vector for the analyzed genes, so it is possible to decipher the subset of virulence genes responsible for the classification on the analyzed genome. Furthermore, we discuss the biological relevance for bacterial pathogenesis of the core set of genes, corresponding to eight functional categories, all with evident and documented association with the phenotypes of interest. Also, we analyze which functional categories of virulence genes were more distinctive for pathogenicity in each taxonomic group, which seems to be a completely new kind of information and could lead to important evolutionary conclusions.     

Campylobacter fetus subspecies: Comparative genomics and prediction of potential virulence targets

The genus Campylobacter contains pathogens causing a wide range of diseases, targeting both humans and animals. Among them, the Campylobacter fetus subspecies fetus and venerealis deserve special attention, as they are the etiological agents of human bacterial gastroenteritis and bovine genital campylobacteriosis, respectively. We compare the whole genomes of both subspecies to get insights into genomic architecture, phylogenetic relationships, genome conservation and core virulence factors. Pan-genomic approach was applied to identify the core- and pan-genome for both C. fetus subspecies and members of the genus. The C. fetus subspecies conserved (76%) proteome were then analyzed for their subcellular localization and protein functions in biological processes. Furthermore, with pathogenomic strategies, unique candidate regions in the genomes and several potential core-virulence factors were identified. The potential candidate factors identified for attenuation and/or subunit vaccine development against C. fetus subspecies contain: nucleoside diphosphate kinase (Ndk), type IV secretion systems (T4SS), outer membrane proteins (OMP), substrate binding proteins CjaA and CjaC, surface array proteins, sap gene, and cytolethal distending toxin (CDT). Significantly, many of those genes were found in genomic regions with signals of horizontal gene transfer and, therefore, predicted as putative pathogenicity islands. We found CRISPR loci and dam genes in an island specific for C. fetus subsp. fetus, and T4SS and sap genes in an island specific for C. fetus subsp. venerealis. The genomic variations and potential core and unique virulence factors characterized in this study would lead to better insight into the species virulence and to more efficient use of the candidates for antibiotic, drug and vaccine development.     

T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis

With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http://www.milser.co.in/research.htm and http://www.srmbioinformatics.edu.in/ forum.htm.