Identification of Novel Biomarkers for Priority Serotypes of Shiga Toxin-Producing 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 (STEC) isolates to subpopulations. Such a scheme should take the high genomic plasticity of 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.

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.

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.

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.

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.