Application of Gaussia luciferase in bicistronic and non-conventional secretion reporter constructs

Background: Secreted luciferases are highly useful bioluminescent reporters for cell-based assays and drug discovery. A variety of secreted luciferases from marine organisms have been described that harbor an N-terminal signal peptide for release along the classical secretory pathway. Here, we have characterized the secretion of Gaussia luciferase in more detail. / Results: We describe three basic mechanisms by which GLUC can be released from cells: first, classical secretion by virtue of the N-terminal signal peptide; second, internal signal peptide-mediated secretion and third, non-conventional secretion in the absence of an N-terminal signal peptide. Non-conventional release of dNGLUC is not stress-induced, does not require autophagy and can be enhanced by growth factor stimulation. Furthermore, we have identified the golgi-associated, gamma adaptin ear containing, ARF binding protein 1 (GGA1) as a suppressor of release of dNGLUC. / Conclusions: Due to its secretion via multiple secretion pathways GLUC can find multiple applications as a research tool to study classical and non-conventional secretion. As GLUC can also be released from a reporter construct by internal signal peptide-mediated secretion it can be incorporated in a novel bicistronic secretion system

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

<p>Abstract</p> <p>Background</p> <p>Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.</p> <p>Results</p> <p>Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.</p> <p>Conclusion</p> <p>Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.</p