Covalent interaction of L-2-amino-4-oxo-5-chloropentanoic acid with rat renal phosphate-dependent glutaminase. Evidence for a specific glutamate binding site and of subunit heterogeneity

Rat renal phosphate-dependent glutaminase is rapidly inactivated by incubating with L-2-amino-4-oxo-5-chloropentanoic scid. Concentrations of phosphate, which increase the glutaminase activity, decrease the rate of inactivation by chloroketone. In addition, inactivation is not blocked by glutamine. Instead, glutamate was shown to specifically reduce the rate of chloroketone inactivation. Upon sodium lauryl sulfate-polyacrylamide gel electrophoresis, the purified glutaminase preparation exhibits at least five protein staining bands which range in molecular weight from 57,000 to 75,000. Studies with 14C-labeled chloroketone indicate that this reagent reacts with each of these peptides. The mean stoichiometry of binding was calculated to be 1.3 mol/mol of enzyme. Therefore, these results indicate that the glutaminase may contain a specific site for binding glutamate and that the purified enzyme consists of a series of related peptides which may have resulted from partial proteolysis.     

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)