Housekeeping genes for quantitative expression studies in the three-spined stickleback Gasterosteus aculeatus

Background During the last years the quantification of immune response under immunological challenges, e.g. parasitation, has been a major focus of research. In this context, the expression of immune response genes in teleost fish has been surveyed for scientific and commercial purposes. Despite the fact that it was shown in teleostei and other taxa that the gene for beta-actin is not the most stably expressed housekeeping gene (HKG), depending on the tissue and experimental treatment, the gene has been us Results To establish a reliable method for the measurement of immune gene expression in Gasterosteus aculeatus, sequences from the now available genome database and an EST library of the same species were used to select oligonucleotide primers for HKG, in order to perform quantitative reverse-transcription (RT) PCR. The expression stability of ten candidate reference genes was evaluated in three different tissues, and in five parasite treatment groups, using the three algorithms BestKeeper, geNorm and N Conclusion As they were the most stably expressed genes in all tissues examined, we suggest using the genes for the L13a ribosomal binding protein and ubiquitin as alternative or additional reference genes in expression analysis in Gasterosteus aculeatus.

Synthetic biology for the directed evolution of protein biocatalysts:navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the ‘search space’ of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (K (d)) and catalytic (k (cat)) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving k (cat) (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the ‘best’ amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust