“Hot standards” for the thermoacidophilic archaeon Sulfolobus solfataricus

Within the archaea, the thermoacidophilic crenarchaeote Sulfolobus solfataricus has become an important model organism for physiology and biochemistry, comparative and functional genomics, as well as, more recently also for systems biology approaches. Within the Sulfolobus Systems Biology (“SulfoSYS”)-project the effect of changing growth temperatures on a metabolic network is investigated at the systems level by integrating genomic, transcriptomic, proteomic, metabolomic and enzymatic information for production of a silicon cell-model. The network under investigation is the central carbohydrate metabolism. The generation of high-quality quantitative data, which is critical for the investigation of biological systems and the successful integration of the different datasets, derived for example from high-throughput approaches (e.g., transcriptome or proteome analyses), requires the application and compliance of uniform standard protocols, e.g., for growth and handling of the organism as well as the “–omics” approaches. Here, we report on the establishment and implementation of standard operating procedures for the different wet-lab and in silico techniques that are applied within the SulfoSYS-project and that we believe can be useful for future projects on Sulfolobus or (hyper)thermophiles in general. Beside established techniques, it includes new methodologies like strain surveillance, the improved identification of membrane proteins and the application of crenarchaeal metabolomics

Transcriptional differences between triploid and diploid Chinook salmon (Oncorhynchus tshawytscha) during live Vibrio anguillarum challenge

Understanding how organisms function at the level of gene expression is becoming increasingly important for both ecological and evolutionary studies. It is evident that the diversity and complexity of organisms are not dependent solely on their number of genes, but also the variability in gene expression and gene interactions. Furthermore, slight differences in transcription control can fundamentally affect the fitness of the organism in a variable environment or during development. In this study, triploid and diploid Chinook salmon (Oncorhynchus tshawytscha) were used to examine the effects of polyploidy on specific and genome-wide gene expression response using quantitative real-time PCR (qRT-PCR) and microarray technology after an immune challenge with the pathogen Vibrio anguillarum. Although triploid and diploid fish had significant differences in mortality, qRT-PCR revealed no differences in cytokine gene expression response (interleukin-8, interleukin-1, interleukin-8 receptor and tumor necrosis factor), whereas differences were observed in constitutively expressed genes, (immunoglobulin (Ig) M, major histocompatibility complex (MHC) -II and beta-actin) upon live Vibrio anguillarum exposure. Genome-wide microarray analysis revealed that, overall, triploid gene expression is similar to diploids, consistent with their similar phenotypes. This pattern, however, can subtly be altered under stress (for example, handling, V. anguillarum challenge) as we have observed at some housekeeping genes. Our results are the first report of dosage effect on gene transcription in a vertebrate, and they support the observation that diploid and triploid salmon are generally phenotypically indistinguishable, except under stress, when triploids show reduced performance

A proposed framework for the description of plant metabolomics experiments and their results

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data