CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources

The Yin and Yang of pathogens and probiotics: interplay between Salmonella enterica sv. Typhimurium and Bifidobacterium infantis during co-infection

Probiotic bacteria have been proposed as an alternative to antibiotics for the control of antimicrobial resistant enteric pathogens. The mechanistic details of this approach remain unclear, in part because pathogen reduction appears to be both strain and ecology dependent. Here we tested the ability of five probiotic strains, including some from common probiotic genera Lactobacillus and Bifidobacterium, to reduce binding of Salmonella enterica sv. Typhimurium to epithelial cells in vitro. Bifidobacterium longum subsp. infantis emerged as a promising strain; however, S. Typhimurium infection outcome in epithelial cells was dependent on inoculation order, with B. infantis unable to rescue host cells from preceding or concurrent infection. We further investigated the complex mechanisms underlying this interaction between B. infantis, S. Typhimurium, and epithelial cells using a multi-omics approach that included gene expression and altered metabolism via metabolomics. Incubation with B. infantis repressed apoptotic pathways and induced anti-inflammatory cascades in epithelial cells. In contrast, co-incubation with B. infantis increased in S. Typhimurium the expression of virulence factors, induced anaerobic metabolism, and repressed components of arginine metabolism as well as altering the metabolic profile. Concurrent application of the probiotic and pathogen notably generated metabolic profiles more similar to that of the probiotic alone than to the pathogen, indicating a central role for metabolism in modulating probiotic-pathogen-host interactions. Together these data imply crosstalk via small molecules between the epithelial cells, pathogen and probiotic that consistently demonstrated unique molecular mechanisms specific probiotic/pathogen the individual associations.

The AraC-Type Transcriptional Regulator GliR (PA3027) Activates Genes of Glycerolipid Metabolism in Pseudomonas aeruginosa

Pseudomonas aeruginosa encodes a large set of transcriptional regulators (TRs) that modulate and manage cellular metabolism to survive in variable environmental conditions including that of the human body. The AraC family regulators are an abundant group of TRs in bacteria, mostly acting as gene expression activators, controlling diverse cellular functions (e.g., carbon metabolism, stress response, and virulence). The PA3027 protein from P. aeruginosa has been classified in silico as a putative AraC-type TR. Transcriptional profiling of P. aeruginosa PAO1161 overexpressing PA3027 revealed a spectacular increase in the mRNA levels of PA3026-PA3024 (divergent to PA3027), PA3464, and PA3342 genes encoding proteins potentially involved in glycerolipid metabolism. Concomitantly, chromatin immunoprecipitation-sequencing (ChIP-seq) analysis revealed that at least 22 regions are bound by PA3027 in the PAO1161 genome. These encompass promoter regions of PA3026, PA3464, and PA3342, showing the major increase in expression in response to PA3027 excess. In Vitro DNA binding assay confirmed interactions of PA3027 with these regions. Furthermore, promoter-reporter assays in a heterologous host showed the PA3027-dependent activation of the promoter of the PA3026-PA3024 operon. Two motifs representing the preferred binding sites for PA3027, one localized upstream and one overlapping with the -35 promoter sequence, were identified in PA3026p and our data indicate that both motifs are required for full activation of this promoter by PA3027. Overall, the presented data show that PA3027 acts as a transcriptional regulator in P. aeruginosa, activating genes likely engaged in glycerolipid metabolism. The GliR name, from a glycerolipid metabolism regulator, is proposed for PA3027 of P. aeruginosa.

The MarR-Type Regulator PA3458 Is Involved in Osmoadaptation Control in Pseudomonas aeruginosa

Pseudomonas aeruginosa is a facultative human pathogen, causing acute and chronic infections that are especially dangerous for immunocompromised patients. The eradication of P. aeruginosa is difficult due to its intrinsic antibiotic resistance mechanisms, high adaptability, and genetic plasticity. The bacterium possesses multilevel regulatory systems engaging a huge repertoire of transcriptional regulators (TRs). Among these, the MarR family encompasses a number of proteins, mainly acting as repressors, which are involved in response to various environmental signals. In this work, we aimed to decipher the role of PA3458, a putative MarR-type TR from P. aeruginosa. Transcriptional profiling of P. aeruginosa PAO1161 overexpressing PA3458 showed changes in the mRNA level of 133 genes; among them, 100 were down-regulated, suggesting the repressor function of PA3458. Concomitantly, ChIP-seq analysis identified more than 300 PA3458 binding sites in P. aeruginosa. The PA3458 regulon encompasses genes involved in stress response, including the PA3459–PA3461 operon, which is divergent to PA3458. This operon encodes an asparagine synthase, a GNAT-family acetyltransferase, and a glutamyl aminopeptidase engaged in the production of N-acetylglutaminylglutamine amide (NAGGN), which is a potent bacterial osmoprotectant. We showed that PA3458-mediated control of PA3459–PA3461 expression is required for the adaptation of P. aeruginosa growth in high osmolarity. Overall, our data indicate that PA3458 plays a role in osmoadaptation control in P. aeruginosa.

Exploring the Secretomes of Microbes and Microbial Communities Using Filamentous Phage Display

Microbial surface and secreted proteins (the secretome) contain a large number of proteins that interact with other microbes, host and/or environment. These proteins are exported by the coordinated activities of the protein secretion machinery present in the cell. A group of bacteriophage, called filamentous phage, have the ability to hijack bacterial protein secretion machinery in order to amplify and assemble via a secretion-like process. This ability has been harnessed in the use of filamentous phage of Escherichia coli in biotechnology applications, including screening large libraries of variants for binding to “bait” of interest, from tissues in vivo to pure proteins or even inorganic substrates. In this review we discuss the roles of secretome proteins in pathogenic and non-pathogenic bacteria and corresponding secretion pathways. We describe the basics of phage display technology and its variants applied to discovery of bacterial proteins that are implicated in colonization of host tissues and pathogenesis, as well as vaccine candidates through filamentous phage display library screening. Secretome selection aided by next-generation sequence analysis was successfully applied for selective display of the secretome at a microbial community scale, the latter revealing the richness of secretome functions of interest and surprising versatility in filamentous phage display of secretome proteins from large number of Gram-negative as well as Gram-positive bacteria and archaea.

PSORTdb: expanding the bacteria and archaea protein subcellular localization database to better reflect diversity in cell envelope structures

Protein subcellular localization (SCL) is important for understanding protein function, genome annotation, and has practical applications such as identification of potential vaccine components or diagnostic/drug targets. PSORTdb (http://db.psort.org) comprises manually curated SCLs for proteins which have been experimentally verified (ePSORTdb), as well as pre-computed SCL predictions for deduced proteomes from bacterial and archaeal complete genomes available from NCBI (cPSORTdb). We now report PSORTdb 3.0. It features improvements increasing user-friendliness, and further expands both ePSORTdb and cPSORTdb with a focus on improving protein SCL data in cases where it is most difficult—proteins associated with non-classical Gram-positive/Gram-negative/Gram-variable cell envelopes. ePSORTdb data curation was expanded, including adding in additional cell envelope localizations, and incorporating markers for cPSORTdb to automatically computationally identify if new genomes to be analysed fall into certain atypical cell envelope categories (i.e. Deinococcus-Thermus, Thermotogae, Corynebacteriales/Corynebacterineae, including Mycobacteria). The number of predicted proteins in cPSORTdb has increased from 3 700 000 when PSORTdb 2.0 was released to over 13 000 000 currently. PSORTdb 3.0 will be of wider use to researchers studying a greater diversity of monoderm or diderm microbes, including medically, agriculturally and industrially important species that have non-classical outer membranes or other cell envelope features.

Expediting topology data gathering for the TOPDB database

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52 000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database.

Reverse vaccinology in the 21st century: improvements over the original design

Reverse vaccinology (RV), the first application of genomic technologies in vaccine research, represented a major revolution in the process of discovering novel vaccines. By determining their entire antigenic repertoire, researchers could identify protective targets and design efficacious vaccines for pathogens where conventional approaches had failed. Bexsero, the first vaccine developed using RV, has recently received positive opinion from the European Medicines Agency. The use of RV initiated a cascade of changes that affected the entire vaccine development process, shifting the focus from the identification of a list of vaccine candidates to the definition of a set of high throughput screens to reduce the need for costly and labor intensive tests in animal models. It is now clear that a deep understanding of the epidemiology of vaccine candidates, and their regulation and role in host-pathogen interactions, must become an integral component of the screening workflow. Far from being outdated by technological advancements, RV still represents a paradigm of how high-throughput technologies and scientific insight can be integrated into biotechnology research.

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such 'ecosystems biology' approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.

Surface proteome analysis and characterization of surface cell antigen (Sca) or autotransporter family of Rickettsia typhi

Surface proteins of the obligate intracellular bacterium Rickettsia typhi, the agent of murine or endemic typhus fever, comprise an important interface for host-pathogen interactions including adherence, invasion and survival in the host cytoplasm. In this report, we present analyses of the surface exposed proteins of R. typhi based on a suite of predictive algorithms complemented by experimental surface-labeling with thiol-cleavable sulfo-NHS-SS-biotin and identification of labeled peptides by LC MS/MS. Further, we focus on proteins belonging to the surface cell antigen (Sca) autotransporter (AT) family which are known to be involved in rickettsial infection of mammalian cells. Each species of Rickettsia has a different complement of sca genes in various states; R. typhi, has genes sca1 thru sca5. In silico analyses indicate divergence of the Sca paralogs across the four Rickettsia groups and concur with previous evidence of positive selection. Transcripts for each sca were detected during infection of L929 cells and four of the five Sca proteins were detected in the surface proteome analysis. We observed that each R. typhi Sca protein is expressed during in vitro infections and selected Sca proteins were expressed during in vivo infections. Using biotin-affinity pull down assays, negative staining electron microscopy, and flow cytometry, we demonstrate that the Sca proteins in R. typhi are localized to the surface of the bacteria. All Scas were detected during infection of L929 cells by immunogold electron microscopy. Immunofluorescence assays demonstrate that Scas 1–3 and 5 are expressed in the spleens of infected Sprague-Dawley rats and Scas 3, 4 and 5 are expressed in cat fleas (Ctenocephalides felis). Sca proteins may be crucial in the recognition and invasion of different host cell types. In short, continuous expression of all Scas may ensure that rickettsiae are primed i) to infect mammalian cells should the flea bite a host, ii) to remain infectious when extracellular and iii) to infect the flea midgut when ingested with a blood meal. Each Sca protein may be important for survival of R. typhi and the lack of host restricted expression may indicate a strategy of preparedness for infection of a new host.

Rickettsia typhi, a member of the typhus group (TG) rickettsia, is the agent of murine or endemic typhus fever – a disease exhibiting mild to severe flu-like symptoms resulting in significant morbidity. It is maintained in a flearodent transmission cycle in urban and suburban environments. The obligate intracellular lifestyle of rickettsiae makes genetic manipulation difficult and impedes progress towards identification of virulence factors. All five Scas were detected on the surface of R.. typhi using a combination of a biotin-labeled affinity assay, negative stain electron microscopy and flow cytometry. Sca proteins are members of the autotransporter (AT) family or type V secretion system (TVSS). We employed detailed bioinformatic analyses and evaluated their transcript abundance in an in vitro infection model where sca transcripts are detected at varying levels over the course of a 5 day in vitro infection. We also observe expression of selected Sca proteins during infection of fleas and rats. Our study provides a proteomic analysis of the bacterial surface and an initial characterization of the Sca family as it exists in R. typhi.

Subcellular localization of extracytoplasmic proteins in monoderm bacteria: rational secretomics-based strategy for genomic and proteomic analyses

Genome-scale prediction of subcellular localization (SCL) is not only useful for inferring protein function but also for supporting proteomic data. In line with the secretome concept, a rational and original analytical strategy mimicking the secretion steps that determine ultimate SCL was developed for Gram-positive (monoderm) bacteria. Based on the biology of protein secretion, a flowchart and decision trees were designed considering (i) membrane targeting, (ii) protein secretion systems, (iii) membrane retention, and (iv) cell-wall retention by domains or post-translocational modifications, as well as (v) incorporation to cell-surface supramolecular structures. Using Listeria monocytogenes as a case study, results were compared with known data set from SCL predictors and experimental proteomics. While in good agreement with experimental extracytoplasmic fractions, the secretomics-based method outperforms other genomic analyses, which were simply not intended to be as inclusive. Compared to all other localization predictors, this method does not only supply a static snapshot of protein SCL but also offers the full picture of the secretion process dynamics: (i) the protein routing is detailed, (ii) the number of distinct SCL and protein categories is comprehensive, (iii) the description of protein type and topology is provided, (iv) the SCL is unambiguously differentiated from the protein category, and (v) the multiple SCL and protein category are fully considered. In that sense, the secretomics-based method is much more than a SCL predictor. Besides a major step forward in genomics and proteomics of protein secretion, the secretomics-based method appears as a strategy of choice to generate in silico hypotheses for experimental testing.

The conserved polarity factor podJ1 impacts multiple cell envelope-associated functions in Sinorhizobium meliloti

Although diminutive in size, bacteria possess highly diverse and spatially confined cellular structures. Two related alphaproteobacteria, Sinorhizobium meliloti and Caulobacter crescentus, serve as models for investigating the genetic basis of morphological variations. S. meliloti, a symbiont of leguminous plants, synthesizes multiple flagella and no prosthecae, whereas C. crescentus, a freshwater bacterium, has a single polar flagellum and stalk. The podJ gene, originally identified in C. crescentus for its role in polar organelle development, is split into two adjacent open reading frames, podJ1 and podJ2, in S. meliloti. Deletion of podJ1 interferes with flagellar motility, exopolysaccharide production, cell envelope integrity, cell division and normal morphology, but not symbiosis. As in C. crescentus, the S. meliloti PodJ1 protein appears to act as a polarity beacon and localizes to the newer cell pole. Microarray analysis indicates that podJ1 affects the expression of at least 129 genes, the majority of which correspond to observed mutant phenotypes. Together, phenotypic characterization, microarray analysis and suppressor identification suggest that PodJ1 controls a core set of conserved elements, including flagellar and pili genes, the signalling proteins PleC and DivK, and the transcriptional activator TacA, while alternative downstream targets have evolved to suit the distinct lifestyles of individual species.

Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation

Background

Outer membrane proteins (OMPs) of Pasteurella multocida have various functions related to virulence and pathogenesis and represent important targets for vaccine development. Various bioinformatic algorithms can predict outer membrane localization and discriminate OMPs by structure or function. The designation of a confident prediction framework by integrating different predictors followed by consensus prediction, results integration and manual confirmation will improve the prediction of the outer membrane proteome.

Results

In the present study, we used 10 different predictors classified into three groups (subcellular localization, transmembrane β-barrel protein and lipoprotein predictors) to identify putative OMPs from two available P. multocida genomes: those of avian strain Pm70 and porcine non-toxigenic strain 3480. Predicted proteins in each group were filtered by optimized criteria for consensus prediction: at least two positive predictions for the subcellular localization predictors, three for the transmembrane β-barrel protein predictors and one for the lipoprotein predictors. The consensus predicted proteins were integrated from each group into a single list of proteins. We further incorporated a manual confirmation step including a public database search against PubMed and sequence analyses, e.g. sequence and structural homology, conserved motifs/domains, functional prediction, and protein-protein interactions to enhance the confidence of prediction. As a result, we were able to confidently predict 98 putative OMPs from the avian strain genome and 107 OMPs from the porcine strain genome with 83% overlap between the two genomes.

Conclusions

The bioinformatic framework developed in this study has increased the number of putative OMPs identified in P. multocida and allowed these OMPs to be identified with a higher degree of confidence. Our approach can be applied to investigate the outer membrane proteomes of other Gram-negative bacteria.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/

SORGOdb: Superoxide Reductase Gene Ontology curated DataBase

BACKGROUND

Superoxide reductases (SOR) catalyse the reduction of superoxide anions to hydrogen peroxide and are involved in the oxidative stress defences of anaerobic and facultative anaerobic organisms. Genes encoding SOR were discovered recently and suffer from annotation problems. These genes, named sor, are short and the transfer of annotations from previously characterized neelaredoxin, desulfoferrodoxin, superoxide reductase and rubredoxin oxidase has been heterogeneous. Consequently, many sor remain anonymous or mis-annotated.

DESCRIPTION

SORGOdb is an exhaustive database of SOR that proposes a new classification based on domain architecture. SORGOdb supplies a simple user-friendly web-based database for retrieving and exploring relevant information about the proposed SOR families. The database can be queried using an organism name, a locus tag or phylogenetic criteria, and also offers sequence similarity searches using BlastP. Genes encoding SOR have been re-annotated in all available genome sequences (prokaryotic and eukaryotic (complete and in draft) genomes, updated in May 2010).

CONCLUSIONS

SORGOdb contains 325 non-redundant and curated SOR, from 274 organisms. It proposes a new classification of SOR into seven different classes and allows biologists to explore and analyze sor in order to establish correlations between the class of SOR and organism phenotypes. SORGOdb is freely available at http://sorgo.genouest.org/index.php.

PSORTdb--an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea

The subcellular localization (SCL) of a microbial protein provides clues about its function, its suitability as a drug, vaccine or diagnostic target and aids experimental design. The first version of PSORTdb provided a valuable resource comprising a data set of proteins of known SCL (ePSORTdb) as well as pre-computed SCL predictions for proteomes derived from complete bacterial genomes (cPSORTdb). PSORTdb 2.0 (http://db.psort.org) extends user-friendly functionalities, significantly expands ePSORTdb and now contains pre-computed SCL predictions for all prokaryotes—including Archaea and Bacteria with atypical cell wall/membrane structures. cPSORTdb uses the latest version of the SCL predictor PSORTb (version 3.0), with higher genome prediction coverage and functional improvements over PSORTb 2.0, which has been the most precise bacterial SCL predictor available. PSORTdb 2.0 is the first microbial protein SCL database reported to have an automatic updating mechanism to regularly generate SCL predictions for deduced proteomes of newly sequenced prokaryotic organisms. This updating approach uses a novel sequence analysis we developed that detects whether the microbe being analyzed has an outer membrane. This identification of membrane structure permits appropriate SCL prediction in an auto-updated fashion and allows PSORTdb to serve as a practical resource for genome annotation and prokaryotic research.

Identification of new homologs of PD-(D/E)XK nucleases by support vector machines trained on data derived from profile-profile alignments

PD-(D/E)XK nucleases, initially represented by only Type II restriction enzymes, now comprise a large and extremely diverse superfamily of proteins. They participate in many different nucleic acids transactions including DNA degradation, recombination, repair and RNA processing. Different PD-(D/E)XK families, although sharing a structurally conserved core, typically display little or no detectable sequence similarity except for the active site motifs. This makes the identification of new superfamily members using standard homology search techniques challenging. To tackle this problem, we developed a method for the detection of PD-(D/E)XK families based on the binary classification of profile–profile alignments using support vector machines (SVMs). Using a number of both superfamily-specific and general features, SVMs were trained to identify true positive alignments of PD-(D/E)XK representatives. With this method we identified several PFAM families of uncharacterized proteins as putative new members of the PD-(D/E)XK superfamily. In addition, we assigned several unclassified restriction enzymes to the PD-(D/E)XK type. Results show that the new method is able to make confident assignments even for alignments that have statistically insignificant scores. We also implemented the method as a freely accessible web server at http://www.ibt.lt/bioinformatics/software/pdexk/.