Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Stable expression of human growth hormone over 50 generations in transgenic insect larvae

Developments in insect transgenesis using transposons combined with available mass rearing technology for insects such as the Medfly, Ceratitis capitata, provide opportunity for the production of protein for industrial, agricultural and healthcare purposes on a very large scale. In this study, we report the germ-line transformation and expression of a cDNA encoding human growth hormone (hGH) in transgenic Drosophila using the Minos transposon. Production and secretion of a bioactive hGH into the haemolymph of transgenic larvae was demonstrated by immunoblot analysis, ELISA and a proliferation bioassay. Stable expression of hGH was observed over 50 generations. The results indicate that mass reared transgenic diptera with a rapid period of larval growth could provide cost effective production systems for the manufacture of therapeutic and other high value proteins.     

Structural signatures of enzyme binding pockets from order-independent surface alignment: a study of metalloendopeptidase and NAD binding proteins

Detecting similarities between local binding surfaces can facilitate identification of enzyme binding sites and prediction of enzyme functions, and aid in our understanding of enzyme mechanisms. Constructing a template of local surface characteristics for a specific enzyme function or binding activity is a challenging task, as the size and shape of the binding surfaces of a biochemical function often vary. Here we introduce the concept of signature binding pockets, which captures information on preserved and varied atomic positions at multiresolution levels. For proteins with complex enzyme binding and activity, multiple signatures arise naturally in our model, forming a signature basis set that characterizes this class of proteins. Both signatures and signature basis sets can be automatically constructed by a method called SOLAR (Signature Of Local Active Regions). This method is based on a sequence-order-independent alignment of computed binding surface pockets. SOLAR also provides a structure-based multiple sequence fragment alignment to facilitate the interpretation of computed signatures. By studying a family of evolutionarily related proteins, we show that for metzincin metalloendopeptidase, which has a broad spectrum of substrate binding, signature and basis set pockets can be used to discriminate metzincins from other enzymes, to predict the subclass of metzincins functions, and to identify specific binding surfaces. Studying unrelated proteins that have evolved to bind to the same NAD cofactor, we constructed signatures of NAD binding pockets and used them to predict NAD binding proteins and to locate NAD binding pockets. By measuring preservation ratio and location variation, our method can identify residues and atoms that are important for binding affinity and specificity. In both cases, we show that signatures and signature basis set reveal significant biological insight.     

A human cell surface receptor activated by free fatty acids and thiazolidinedione drugs

Fatty acids, which are essential nutritional components, are also involved in cardiovascular and metabolic diseases. Here we report a human cell surface receptor that we name free fatty acid receptor (FFAR), because it is specifically activated by medium to long-chain free fatty acids. The receptor belongs to the class of seven-transmembrane, G-protein coupled receptors (GPCRs) and also mediates responses to antidiabetic drugs of the thiazolidinedione type. It is expressed in skeletal muscle, heart, liver, and pancreatic beta-cells. Stimulation of FFAR increases the intracellular calcium concentration in cells expressing the receptor in a native (pancreatic beta-cell line) or in a recombinant form. In view of the nature of the activating substances, their physiological role in the body, and the tissue distribution of FFAR we suggest the term "nutrient sensing receptor" for receptors acting at the interface between dietary components and signalling molecules.