Shuttle vectors allow for an efficient transfer of recombinant DNA into yeast cells and are widely used in fundamental research and biotechnology. While available shuttle vectors are applicable in many experimental settings, their use in quantitative biology is hampered by insufficient copy number control. Moreover, they often have practical constraints, such as limited modularity and few unique restriction sites. We constructed the pRG shuttle vector series, consisting of single-and multi-copy integrative, centromeric and episomal plasmids with marker genes for the selection in all commonly used auxotrophic yeast strains. The vectors feature a modular design and a large number of unique restriction sites, enabling an efficient exchange of every vector part and expansion of the series. Integration into the host genome is achieved using a double-crossover recombination mechanism, resulting in stable single-and multi-copy modifications. As centromeric and episomal plasmids give rise to a heterogeneous cell population, an analysis of their copy number distribution and loss behaviour was performed. Overall, the shuttle vector series supports the efficient cloning of genes and their maintenance in yeast cells with improved copy number control.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Human tuberculosis is caused by members of the Mycobacterium tuberculosis complex (MTBC) that vary in virulence and transmissibility. While genome-wide association studies have uncovered several mutations conferring drug resistance, much less is known about the factors underlying other bacterial phenotypes. Variation in the outcome of tuberculosis infection and diseases has been attributed primarily to patient and environmental factors, but recent evidence indicates an additional role for the genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the effect of genetic variation on the strain-specific metabolic adaptive capacity and vulnerability. To define the functionality of single-nucleotide polymorphisms (SNPs) systematically, we developed a constraint-based approach that integrates metabolomic and genomic data. Our model-based predictions correctly classify SNP effects in pyruvate kinase and suggest a genetic basis for strain-specific inherent baseline susceptibility to the antibiotic para-aminosalicylic acid. Our method is broadly applicable across microbial life, opening possibilities for the development of more selective treatment strategies.