Identification of Novel Biomarkers for Priority Serotypes of Shiga Toxin-Producing Escherichia coli and the Development of Multiplex PCR for Their Detection.

It would be desirable to have an unambiguous scheme for the typing of Shiga toxin-producing Escherichia coli (STEC) isolates to subpopulations. Such a scheme should take the high genomic plasticity of E. coli into account and utilize the stratification of STEC into subgroups, based on serotype or phylogeny. Therefore, our goal was to identify specific marker combinations for improved classification of STEC subtypes. We developed and evaluated two bioinformatic pipelines for genomic marker identification from larger sets of bacterial genome sequences. Pipeline A performed all-against-all BLASTp analyses of gene products predicted in STEC genome test sets against a set of control genomes. Pipeline B identified STEC marker genes by comparing the STEC core proteome and the "pan proteome" of a non-STEC control group. Both pipelines defined an overlapping, but not identical set of discriminative markers for different STEC subgroups. Differential marker prediction resulted from differences in genome assembly, ORF finding and inclusion cut-offs in both workflows. Based on the output of the pipelines, we defined new specific markers for STEC serogroups and phylogenetic groups frequently associated with outbreaks and cases of foodborne illnesses. These included STEC serogroups O157, O26, O45, O103, O111, O121, and O145, Shiga toxin-positive enteroaggregative E. coli O104:H4, and HUS-associated sequence type (ST)306. We evaluated these STEC marker genes for their presence in whole genome sequence data sets. Based on the identified discriminative markers, we developed a multiplex PCR (mPCR) approach for detection and typing of the targeted STEC. The specificity of the mPCR primer pairs was verified using well-defined clinical STEC isolates as well as isolates from the ECOR, DEC, and HUSEC collections. The application of the STEC mPCR for food analysis was tested with inoculated milk. In summary, we evaluated two different strategies to screen large genome sequence data sets for discriminative markers and implemented novel marker genes found in this genome-wide approach into a DNA-based typing tool for STEC that can be used for the characterization of STEC from clinical and food samples.

Development and validation of a multi-locus DNA metabarcoding method to identify endangered species in complex samples.

DNA metabarcoding provides great potential for species identification in complex samples such as food supplements and traditional medicines. Such a method would aid Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) enforcement officers to combat wildlife crime by preventing illegal trade of endangered plant and animal species. The objective of this research was to develop a multi-locus DNA metabarcoding method for forensic wildlife species identification and to evaluate the applicability and reproducibility of this approach across different laboratories. A DNA metabarcoding method was developed that makes use of 12 DNA barcode markers that have demonstrated universal applicability across a wide range of plant and animal taxa and that facilitate the identification of species in samples containing degraded DNA. The DNA metabarcoding method was developed based on Illumina MiSeq amplicon sequencing of well-defined experimental mixtures, for which a bioinformatics pipeline with user-friendly web-interface was developed. The performance of the DNA metabarcoding method was assessed in an international validation trial by 16 laboratories, in which the method was found to be highly reproducible and sensitive enough to identify species present in a mixture at 1% dry weight content. The advanced multi-locus DNA metabarcoding method assessed in this study provides reliable and detailed data on the composition of complex food products, including information on the presence of CITES-listed species. The method can provide improved resolution for species identification, while verifying species with multiple DNA barcodes contributes to an enhanced quality assurance.

Pseudomonas aeruginosa Genome Evolution in Patients and under the Hospital Environment.

Pseudomonas aeruginosa is a Gram-negative environmental species and an opportunistic microorganism, establishing itself in vulnerable patients, such as those with cystic fibrosis (CF) or those hospitalized in intensive care units (ICU). It has become a major cause of nosocomial infections worldwide and a serious threat to Public Health because of overuse and misuse of antibiotics that have selected highly resistant strains against which very few therapeutic options exist. Herein is illustrated the intraclonal evolution of the genome of sequential isolates collected in a single CF patient from the early phase of pulmonary colonization to the fatal outcome. We also examined at the whole genome scale a pair of genotypically-related strains made of a drug susceptible, environmental isolate recovered from an ICU sink and of its multidrug resistant counterpart found to infect an ICU patient. Multiple genetic changes accumulated in the CF isolates over the disease time course including SNPs, deletion events and reduction of whole genome size. The strain isolated from the ICU patient displayed an increase in the genome size of 4.8% with major genetic rearrangements as compared to the initial environmental strain. The annotated genomes are given in free access in an interactive web application WallGene  designed to facilitate large-scale comparative analysis and thus allowing investigators to explore homologies and syntenies between P. aeruginosa strains, here PAO1 and the five clinical strains described.

Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information.

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed.

Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks.

Genetic Network Analyzer (GNA) is a tool for the qualitative modeling and simulation of gene regulatory networks, based on so-called piecewise-linear differential equation models. We describe the use of this tool in the context of the modeling of bacterial regulatory networks, notably the network of global regulators controlling the adaptation of Escherichia coli to carbon starvation conditions. We show how the modeler, by means of GNA, can define a regulatory network, build a model of the network, determine the steady states of the system, perform a qualitative simulation of the network dynamics, and analyze the simulation results using model-checking tools. The example illustrates the interest of qualitative approaches for the analysis of the dynamics of bacterial regulatory networks.

Genepi: a blackboard framework for genome annotation.

BACKGROUND: Genome annotation can be viewed as an incremental, cooperative, data-driven, knowledge-based process that involves multiple methods to predict gene locations and structures. This process might have to be executed more than once and might be subjected to several revisions as the biological (new data) or methodological (new methods) knowledge evolves. In this context, although a lot of annotation platforms already exist, there is still a strong need for computer systems which take in charge, not only the primary annotation, but also the update and advance of the associated knowledge. In this paper, we propose to adopt a blackboard architecture for designing such a system RESULTS: We have implemented a blackboard framework (called Genepi) for developing automatic annotation systems. The system is not bound to any specific annotation strategy. Instead, the user will specify a blackboard structure in a configuration file and the system will instantiate and run this particular annotation strategy. The characteristics of this framework are presented and discussed. Specific adaptations to the classical blackboard architecture have been required, such as the description of the activation patterns of the knowledge sources by using an extended set of Allen's temporal relations. Although the system is robust enough to be used on real-size applications, it is of primary use to bioinformatics researchers who want to experiment with blackboard architectures. CONCLUSION: In the context of genome annotation, blackboards have several interesting features related to the way methodological and biological knowledge can be updated. They can readily handle the cooperative (several methods are implied) and opportunistic (the flow of execution depends on the state of our knowledge) aspects of the annotation process.

Tree pattern matching in phylogenetic trees: automatic search for orthologs or paralogs in homologous gene sequence databases.

MOTIVATION: Comparative sequence analysis is widely used to study genome function and evolution. This approach first requires the identification of homologous genes and then the interpretation of their homology relationships (orthology or paralogy). To provide help in this complex task, we developed three databases of homologous genes containing sequences, multiple alignments and phylogenetic trees: HOBACGEN, HOVERGEN and HOGENOM. In this paper, we present two new tools for automating the search for orthologs or paralogs in these databases. RESULTS: First, we have developed and implemented an algorithm to infer speciation and duplication events by comparison of gene and species trees (tree reconciliation). Second, we have developed a general method to search in our databases the gene families for which the tree topology matches a peculiar tree pattern. This algorithm of unordered tree pattern matching has been implemented in the FamFetch graphical interface. With the help of a graphical editor, the user can specify the topology of the tree pattern, and set constraints on its nodes and leaves. Then, this pattern is compared with all the phylogenetic trees of the database, to retrieve the families in which one or several occurrences of this pattern are found. By specifying ad hoc patterns, it is therefore possible to identify orthologs in our databases.

Integration of data and methods for genome analysis.

The development of genomic and post-genomic technologies has created an explosion in the quantity, diversity and availability of both biological data and methods of analysis. Biologists are currently facing the problem of using all these resources to convert raw data into new valuable knowledge. This review presents software platforms designed to handle data and/or methods in the context of genome analysis.