Expression of inhibitor of apoptosis protein Livin in renal cell carcinoma and non-tumorous adult kidney

The antiapoptotic Livin/ML-IAP gene has recently gained much attention as a potential new target for cancer therapy. Reports indicating that livin is expressed almost exclusively in tumours, but not in the corresponding normal tissue, suggested that the targeted inhibition of livin may present a novel tumour-specific therapeutic strategy. Here, we compared the expression of livin in renal cell carcinoma and in non-tumorous adult kidney tissue by quantitative real-time reverse transcription-PCR, immunoblotting, and immunohistochemistry. We found that livin expression was significantly increased in tumours (P=0.0077), but was also clearly detectable in non-tumorous adult kidney. Transcripts encoding Livin isoforms α and β were found in both renal cell carcinoma and normal tissue, without obvious qualitative differences. Livin protein in renal cell carcinoma samples exhibited cytoplasmic and/or nuclear staining. In non-tumorous kidney tissue, Livin protein expression was only detectable in specific cell types and restricted to the cytoplasm. Thus, whereas the relative overexpression of livin in renal cell carcinoma indicates that it may still represent a therapeutic target to increase the apoptotic sensitivity of kidney cancer cells, this strategy is likely to be not tumour-specific

OrthoSelect: a protocol for selecting orthologous groups in phylogenomics

Background: Phylogenetic studies using expressed sequence tags (EST) are becoming a standard approach to answer evolutionary questions. Such studies are usually based on large sets of newly generated, unannotated, and error-prone EST sequences from different species. A first crucial step in EST-based phylogeny reconstruction is to identify groups of orthologous sequences. From these data sets, appropriate target genes are selected, and redundant sequences are eliminated to obtain suitable sequence sets as input data for tree-reconstruction software. Generating such data sets manually can be very time consuming. Thus, software tools are needed that carry out these steps automatically. Results: We developed a flexible and user-friendly software pipeline, running on desktop machines or computer clusters, that constructs data sets for phylogenomic analyses. It automatically searches assembled EST sequences against databases of orthologous groups (OG), assigns ESTs to these predefined OGs, translates the sequences into proteins, eliminates redundant sequences assigned to the same OG, creates multiple sequence alignments of identified orthologous sequences and offers the possibility to further process this alignment in a last step by excluding potentially homoplastic sites and selecting sufficiently conserved parts. Our software pipeline can be used as it is, but it can also be adapted by integrating additional external programs. This makes the pipeline useful for non-bioinformaticians as well as to bioinformatic experts. The software pipeline is especially designed for ESTs, but it can also handle protein sequences. Conclusion: OrthoSelect is a tool that produces orthologous gene alignments from assembled ESTs. Our tests show that OrthoSelect detects orthologs in EST libraries with high accuracy. In the absence of a gold standard for orthology prediction, we compared predictions by OrthoSelect to a manually created and published phylogenomic data set. Our tool was not only able to rebuild the data set with a specificity of 98%, but it detected four percent more orthologous sequences. Furthermore, the results OrthoSelect produces are in absolut agreement with the results of other programs, but our tool offers a significant speedup and additional functionality, e.g. handling of ESTs, computing sequence alignments, and refining them. To our knowledge, there is currently no fully automated and freely available tool for this purpose. Thus, OrthoSelect is a valuable tool for researchers in the field of phylogenomics who deal with large quantities of EST sequences. OrthoSelect is written in Perl and runs on Linux/Mac OS X

Doing synthetic biology with photosynthetic microorganisms

The use of photosynthetic microbes as synthetic biology hosts for the sustainable production of commodity chemicals and even fuels has received increasing attention over the last decade. The number of studies published, tools implemented, and resources made available for microalgae have increased beyond expectations during the last few years. However, the tools available for genetic engineering in these organisms still lag those available for the more commonly used heterotrophic host organisms. In this mini-review, we provide an overview of the photosynthetic microbes most commonly used in synthetic biology studies, namely cyanobacteria, chlorophytes, eustigmatophytes and diatoms. We provide basic information on the techniques and tools available for each model group of organisms, we outline the state-of-the-art, and we list the synthetic biology tools that have been successfully used. We specifically focus on the latest CRISPR developments, as we believe that precision editing and advanced genetic engineering tools will be pivotal to the advancement of the field. Finally, we discuss the relative strengths and weaknesses of each group of organisms and examine the challenges that need to be overcome to achieve their synthetic biology potential.Peer reviewe