SYSTOMONAS--an integrated database for systems biology analysis of Pseudomonas

Pseudomonas aeruginosa: An Audacious Pathogen with an Adaptable Arsenal of Virulence Factors

Pseudomonas aeruginosa is a dominant pathogen in people with cystic fibrosis (CF) contributing to morbidity and mortality. Its tremendous ability to adapt greatly facilitates its capacity to cause chronic infections. The adaptability and flexibility of the pathogen are afforded by the extensive number of virulence factors it has at its disposal, providing P. aeruginosa with the facility to tailor its response against the different stressors in the environment. A deep understanding of these virulence mechanisms is crucial for the design of therapeutic strategies and vaccines against this multi-resistant pathogen. Therefore, this review describes the main virulence factors of P. aeruginosa and the adaptations it undergoes to persist in hostile environments such as the CF respiratory tract. The very large P. aeruginosa genome (5 to 7 MB) contributes considerably to its adaptive capacity; consequently, genomic studies have provided significant insights into elucidating P. aeruginosa evolution and its interactions with the host throughout the course of infection.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Metabolomic studies of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of P. aeruginosa have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by P. aeruginosa. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for Pseudomonas.

PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database

The Pseudomonas aeruginosaMetabolome Database (PAMDB, http://pseudomonas.umaryland.edu) is a searchable, richly annotated metabolite database specific to P. aeruginosa. P. aeruginosa is a soil organism and significant opportunistic pathogen that adapts to its environment through a versatile energy metabolism network. Furthermore, P. aeruginosa is a model organism for the study of biofilm formation, quorum sensing, and bioremediation processes, each of which are dependent on unique pathways and metabolites. The PAMDB is modelled on the Escherichia coli (ECMDB), yeast (YMDB) and human (HMDB) metabolome databases and contains >4370 metabolites and 938 pathways with links to over 1260 genes and proteins. The database information was compiled from electronic databases, journal articles and mass spectrometry (MS) metabolomic data obtained in our laboratories. For each metabolite entered, we provide detailed compound descriptions, names and synonyms, structural and physiochemical information, nuclear magnetic resonance (NMR) and MS spectra, enzymes and pathway information, as well as gene and protein sequences. The database allows extensive searching via chemical names, structure and molecular weight, together with gene, protein and pathway relationships. The PAMBD and its future iterations will provide a valuable resource to biologists, natural product chemists and clinicians in identifying active compounds, potential biomarkers and clinical diagnostics.

Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

Regulatory and metabolic networks for the adaptation of Pseudomonas aeruginosa biofilms to urinary tract-like conditions

Biofilms of the Gram-negative bacterium Pseudomonas aeruginosa are one of the major causes of complicated urinary tract infections with detrimental outcome. To develop novel therapeutic strategies the molecular adaption strategies of P. aeruginosa biofilms to the conditions of the urinary tract were investigated thoroughly at the systems level using transcriptome, proteome, metabolome and enzyme activity analyses. For this purpose biofilms were grown anaerobically in artificial urine medium (AUM). Obtained data were integrated bioinformatically into gene regulatory and metabolic networks. The dominating response at the transcriptome and proteome level was the adaptation to iron limitation via the broad Fur regulon including 19 sigma factors and up to 80 regulated target genes or operons. In agreement, reduction of the iron cofactor-dependent nitrate respiratory metabolism was detected. An adaptation of the central metabolism to lactate, citrate and amino acid as carbon sources with the induction of the glyoxylate bypass was observed, while other components of AUM like urea and creatinine were not used. Amino acid utilization pathways were found induced, while fatty acid biosynthesis was reduced. The high amounts of phosphate found in AUM explain the reduction of phosphate assimilation systems. Increased quorum sensing activity with the parallel reduction of chemotaxis and flagellum assembly underscored the importance of the biofilm life style. However, reduced formation of the extracellular polysaccharide alginate, typical for P. aeruginosa biofilms in lungs, indicated a different biofilm type for urinary tract infections. Furthermore, the obtained quorum sensing response results in an increased production of virulence factors like the extracellular lipase LipA and protease LasB and AprA explaining the harmful cause of these infections.

The Regulatory Network of Pseudomonas aeruginosa

Background

Pseudomonas aeruginosa is an important bacterial model due to its metabolic and pathogenic abilities, which allow it to interact and colonize a wide range of hosts, including plants and animals. In this work we compile and analyze the structure and organization of an experimentally supported regulatory network in this bacterium.

Results

The regulatory network consists of 690 genes and 1020 regulatory interactions between their products (12% of total genes: 54% sigma and 16% of transcription factors). This complex interplay makes the third largest regulatory network of those reported in bacteria. The entire network is enriched for activating interactions and, peculiarly, self-activation seems to occur more prominent for transcription factors (TFs), which contrasts with other biological networks where self-repression is dominant. The network contains a giant component of 650 genes organized into 11 hierarchies, encompassing important biological processes, such as, biofilms formation, production of exopolysaccharide alginate and several virulence factors, and of the so-called quorum sensing regulons.

Conclusions

The study of gene regulation in P. aeruginosa is biased towards pathogenesis and virulence processes, all of which are interconnected. The network shows power-law distribution -input degree -, and we identified the top ten global regulators, six two-element cycles, the longest paths have ten steps, six biological modules and the main motifs containing three and four elements. We think this work can provide insights for the design of further studies to cover the many gaps in knowledge of this important bacterial model, and for the design of systems strategies to combat this bacterium.

MGV: a generic graph viewer for comparative omics data

MOTIVATION

High-throughput transcriptomics, proteomics and metabolomics methods have revolutionized our knowledge of biological systems. To gain knowledge from comparative omics studies, strong data integration and visualization features are required. Knowledge gained from these studies is often available in the form of graphs, and their visualization is especially useful in a wide range of systems biology topics, including pathway analysis, interaction networks or gene models. Especially, it is necessary to compare biological models with measured data. This allows the identification of new models and new insights into existing ones.

RESULTS

We present MGV, a versatile generic graph viewer for multiomics data. MGV is integrated into Mayday (Battke et al., 2010). It extends Mayday's visual analytics capabilities by integrating a wide range of biological models, high-throughput data and meta information to display enriched graphs that combine data and models. A wide range of tools is available for visualization of nodes, data-aware graph layout as well as automatic and manual aggregation and refinement of the data. We show the usefulness of MGV applied to several problems, including differential expression of alternative transcripts, transcription factor interaction, cross-study clustering comparison and integration of transcriptomics and metabolomics data for pathway analysis.

AVAILABILITY

MGV is a open-source software implemented in Java and freely available as a part of Mayday at www.microarray-analysis.org/mayday.

CONTACT

symons@informatik.uni-tuebingen.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Regulatory and metabolic network of rhamnolipid biosynthesis: traditional and advanced engineering towards biotechnological production

During the last decade, the demand for economical and sustainable bioprocesses replacing petrochemical-derived products has significantly increased. Rhamnolipids are interesting biosurfactants that might possess a broad industrial application range. However, despite of 60 years of research in the area of rhamnolipid production, the economic feasibility of these glycolipids is pending. Although the biosynthesis and regulatory network are in a big part known, the actual incidents on the cellular and process level during bioreactor cultivation are not mastered. Traditional engineering by random and targeted genetic alteration, process design, and recombinant strategies did not succeed by now. For enhanced process development, there is an urgent need of in-depth information about the rhamnolipid production regulation during bioreactor cultivation to design knowledge-based genetic and process engineering strategies. Rhamnolipids are structurally comparable, simple secondary metabolites and thus have the potential to become instrumental in future secondary metabolite engineering by systems biotechnology. This review summarizes current knowledge about the regulatory and metabolic network of rhamnolipid synthesis and discusses traditional and advanced engineering strategies performed for rhamnolipid production improvement focusing on Pseudomonas aeruginosa. Finally, the opportunities of applying the systems biotechnology toolbox on the whole-cell biocatalyst and bioprocess level for further rhamnolipid production optimization are discussed.

Anaerobic adaptation in Pseudomonas aeruginosa: definition of the Anr and Dnr regulons

The anaerobic metabolism of the opportunistic pathogen Pseudomonas aeruginosa is important for growth and biofilm formation during persistent infections. The two Fnr-type transcription factors Anr and Dnr regulate different parts of the underlying network in response to oxygen tension and NO. Little is known about all members of the Anr and Dnr regulons and the mediated immediate response to oxygen depletion. Comprehensive transcriptome and bioinformatics analyses in combination with a limited proteome analyses were used for the investigation of the P. aeruginosa response to an immediate oxygen depletion and for definition of the corresponding Anr and Dnr regulons. We observed at first the activation of fermentative pathways for immediate energy generation followed by induction of alternative respiratory chains. A solid position weight matrix model was deduced from the experimentally identified Anr boxes and used for identification of 170 putative Anr boxes in potential P. aeruginosa promoter regions. The combination with the experimental data unambiguously identified 130 new members for the Anr and Dnr regulons. The basis for the understanding of two regulons of P. aeruginosa central to biofilm formation and infection is now defined.

Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes

Pseudomonas is a metabolically-diverse genus of bacteria known for its flexibility and leading free living to pathogenic lifestyles in a wide range of hosts. The Pseudomonas Genome Database (http://www.pseudomonas.com) integrates completely-sequenced Pseudomonas genome sequences and their annotations with genome-scale, high-precision computational predictions and manually curated annotation updates. The latest release implements an ability to view sequence polymorphisms in P. aeruginosa PAO1 versus other reference strains, incomplete genomes and single gene sequences. This aids analysis of phenotypic variation between closely related isolates and strains, as well as wider population genomics and evolutionary studies. The wide range of tools for comparing Pseudomonas annotations and sequences now includes a strain-specific access point for viewing high precision computational predictions including updated, more accurate, protein subcellular localization and genomic island predictions. Views link to genome-scale experimental data as well as comparative genomics analyses that incorporate robust genera-geared methods for predicting and clustering orthologs. These analyses can be exploited for identifying putative essential and core Pseudomonas genes or identifying large-scale evolutionary events. The Pseudomonas Genome Database aims to provide a continually updated, high quality source of genome annotations, specifically tailored for Pseudomonas researchers, but using an approach that may be implemented for other genera-level research communities.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

Shewanella knowledgebase: integration of the experimental data and computational predictions suggests a biological role for transcription of intergenic regions

Shewanellae are facultative γ-proteobacteria whose remarkable respiratory versatility has resulted in interest in their utility for bioremediation of heavy metals and radionuclides and for energy generation in microbial fuel cells. Extensive experimental efforts over the last several years and the availability of 21 sequenced Shewanella genomes made it possible to collect and integrate a wealth of information on the genus into one public resource providing new avenues for making biological discoveries and for developing a system level understanding of the cellular processes. The Shewanella knowledgebase was established in 2005 to provide a framework for integrated genome-based studies on Shewanella ecophysiology. The present version of the knowledgebase provides access to a diverse set of experimental and genomic data along with tools for curation of genome annotations and visualization and integration of genomic data with experimental data. As a demonstration of the utility of this resource, we examined a single microarray data set from Shewanella oneidensis MR-1 for new insights into regulatory processes. The integrated analysis of the data predicted a new type of bacterial transcriptional regulation involving co-transcription of the intergenic region with the downstream gene and suggested a biological role for co-transcription that likely prevents the binding of a regulator of the upstream gene to the regulator binding site located in the intergenic region.

Database URL:http://shewanella-knowledgebase.org:8080/Shewanella/ or http://spruce.ornl.gov:8080/Shewanella/

Reconstruction of metabolic pathways for the cattle genome

Background

Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement.

Results

An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly.

Conclusion

CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology.

A procedure for systematic identification of bacteriophage-host interactions of P. aeruginosa phages

Immediately after bacteriophage infection, phage early proteins establish optimal conditions for phage infection, often through a direct interaction with host-cell proteins. We implemented a yeast two-hybrid approach for Pseudomonas aeruginosa phages as a first step in the analysis of these - often uncharacterized - proteins. A 24-fold redundant prey library of P. aeruginosa PAO1 (7.32x10(6) independent clones), was screened against early proteins (gp1 to 9) of phiKMV, a P. aeruginosa-infecting member of the Podoviridae; interactions were verified using an independent in vitro assay. None resembles previously known bacteriophage-host interactions, as the three identified target malate synthase G, a regulator of a secretion system and a regulator of nitrogen assimilation. Although at least two-bacteriophage infections are non-essential to phiKMV infection, their disruption has an influence on infection efficiency. This methodology allows systematic analysis of phage proteins and is applicable as an interaction analysis tool for P. aeruginosa.

Pseudomonas Genome Database: facilitating user-friendly, comprehensive comparisons of microbial genomes

Pseudomonas aeruginosa is a well-studied opportunistic pathogen that is particularly known for its intrinsic antimicrobial resistance, diverse metabolic capacity, and its ability to cause life threatening infections in cystic fibrosis patients. The Pseudomonas Genome Database (http://www.pseudomonas.com) was originally developed as a resource for peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome. In order to facilitate cross-strain and cross-species genome comparisons with other Pseudomonas species of importance, we have now expanded the database capabilities to include all Pseudomonas species, and have developed or incorporated methods to facilitate high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. A choice of simple and more flexible user-friendly Boolean search features allows researchers to search and compare annotations or sequences within or between genomes. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. This database aims to continue to provide a high quality, annotated genome resource for the research community and is available under an open source license.

PRODORIC (release 2009): a database and tool platform for the analysis of gene regulation in prokaryotes

PRODORIC is a database that provides annotated information on the regulation of gene expression in prokaryotes. It integrates a large compilation of gene regulatory data including transcription factor binding sites, promoter structures and gene expression patterns. The whole dataset is manually curated and relies on published results extracted from the scientific literature. The current extended version of PRODORIC contains gene regulatory data for several new microorganisms. Major improvements were realized in the design of the web interface and the accessibility of the stored information. The database was further improved by the implementation of various new tools for the elucidation of gene regulatory interactions. Thus, the PRODORIC platform represents a framework for the interactive exploration, prediction and evaluation of gene regulatory networks in prokaryotes. PRODORIC is accessible at http://www.prodoric.de.

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions.

Results

We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected.

Conclusion

Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production.

A combined computational-experimental analyses of selected metabolic enzymes in Pseudomonas species

Comparative genomic analysis has revolutionized our ability to predict the metabolic subsystems that occur in newly sequenced genomes, and to explore the functional roles of the set of genes within each subsystem. These computational predictions can considerably reduce the volume of experimental studies required to assess basic metabolic properties of multiple bacterial species. However, experimental validations are still required to resolve the apparent inconsistencies in the predictions by multiple resources. Here, we present combined computational-experimental analyses on eight completely sequenced Pseudomonas species. Comparative pathway analyses reveal that several pathways within the Pseudomonas species show high plasticity and versatility. Potential bypasses in 11 metabolic pathways were identified. We further confirmed the presence of the enzyme O-acetyl homoserine (thiol) lyase (EC: 2.5.1.49) in P. syringae pv. tomato that revealed inconsistent annotations in KEGG and in the recently published SYSTOMONAS database. These analyses connect and integrate systematic data generation, computational data interpretation, and experimental validation and represent a synergistic and powerful means for conducting biological research.

Bacillus megaterium—from simple soil bacterium to industrial protein production host

Bacillus megaterium has been industrially employed for more than 50 years, as it possesses some very useful and unusual enzymes and a high capacity for the production of exoenzymes. It is also a desirable cloning host for the production of intact proteins, as it does not possess external alkaline proteases and can stably maintain a variety of plasmid vectors. Genetic tools for this species include transducing phages and several hundred mutants covering the processes of biosynthesis, catabolism, division, sporulation, germination, antibiotic resistance, and recombination. The seven plasmids of B. megaterium strain QM B1551 contain several unusual metabolic genes that may be useful in bioremediation. Recently, several recombinant shuttle vectors carrying different strong inducible promoters and various combinations of affinity tags for simple protein purification have been constructed. Leader sequences-mediated export of affinity-tagged proteins into the growth medium was made possible. These plasmids are commercially available. For a broader application of B. megaterium in industry, sporulation and protease-deficient as well as UV-sensitive mutants were constructed. The genome sequence of two different strains, plasmidless DSM319 and QM B1551 carrying seven natural plasmids, is now available. These sequences allow for a systems biotechnology optimization of the production host B. megaterium. Altogether, a "toolbox" of hundreds of genetically characterized strains, genetic methods, vectors, hosts, and genomic sequences make B. megaterium an ideal organism for industrial, environmental, and experimental applications.