Characterisation of a 34 kD protein from potato cyst nematodes, using monoclonal antibodies with potential for species diagnosis

Two monoclonal antibodies, which differentially recognise the two species of potato cyst nematodes (PCN), Globodera pallida and G. rostochiensis, are described. They have been shown to have potential for quantification of these two species, recognising proteins of the same molecular weight (34 kD) in both species. Further investigation showed these proteins to have isoelectric points at pH values of 5.7 in G. pallida and 5.9 in G. rostochiensis, in common with the proteins used by Fleming & Marks (1983) to differentiate the species of PCN. They are likely to be structurally very similar, with the same physiological function (and therefore similar concentrations) in the two species. In cross-reactivity tests with a wide range of soil nematode species, the antibodies reacted strongly only with species of the genus Globodera, and thereby confirmed their potential as the basis of a quantitative immunoassay likely to be useful in management of PCN populations.     

Defined media optimization for growth of recombinant Escherichia coli X90

An optimized, defined minimal medium was developed to support balanced growth of Escherichia coli X90 harboring a recombinant plasmid. Foreign protein expression was repressed in these studies. A pulse injection technique was used to identify the growth responses to nutrients in a chemostat. Once the nutrients essential for growth had been identified, the yield coefficients for individual medium components. These yield coefficients were used to develop an optimized, glucose-limited defined minimal medium that supports balanced cell growth in chemostat culture. The biomass and substrate concentrations follow the Monod chemostat model. The maximum specific growth rate determined in a washout experiment is 0.87 h(-1) for this strain in the optimized medium. the glucose yield factor is 0.42 g DCW/g glucose and the maintenance coefficient is zero in the glucose-limited chemostat culture. (c) 1993 John Wiley & Sons, Inc.     

Novel members of the mitogen-activated protein kinase activator family in Xenopus laevis.

Mitogen-activated protein (MAP) kinases comprise an evolutionarily conserved family of proteins that includes at least three vertebrate protein kinases (p42, p44, and p55 MAPK) and five yeast protein kinases (SPK1, MPK1, HOG1, FUS3, and KSS1). Members of this family are activated by a variety of extracellular agents that influence cellular proliferation and differentiation. In Saccharomyces cerevisiae, there are multiple physiologically distinct MAP kinase activation pathways composed of structurally related kinases. The recently cloned vertebrate MAP kinase activators are structurally related to MAP kinase activators in these yeast pathways. These similarities suggest that homologous kinase cascades are utilized for signal transduction in many, if not all, eukaryotes. We have identified additional members of the MAP kinase activator family in Xenopus laevis by a polymerase chain reaction-based analysis of embryonic cDNAs. One of the clones identified (XMEK2) encodes a unique predicted protein kinase that is similar to the previously reported activator (MAPKK) in X. laevis. XMEK2, a highly expressed maternal mRNA, is developmentally regulated during embryogenesis and expressed in brain and muscle. Expression of XMEK2 in yeast cells suppressed the growth defect associated with loss of the yeast MAP kinase activator homologs, MKK1 and MKK2. Partial sequence of a second cDNA clone (XMEK3) identified yet another potential MAP kinase activator. The pattern of expression of XMEK3 is distinct from that of p42 MAPK and XMEK2. The high degree of amino acid sequence similarity of XMEK2, XMEK3, and MAPKK suggests that these three are related members of an amphibian family of protein kinases involved in the activation of MAP kinase. Discovery of this family suggests that multiple MAP kinase activation pathways similar to those in yeast cells exist in vertebrates.     

Development of predictive models of laboratory animal growth using artificial neural networks

Traditional regression analysis of body weight growth curvesencounters problems .when the data are extremely variable. Whiletransformations are often employed to meet the criteria of theanalysis, some transformations are inadequate for normalizingthe data. Regression analysis also requires presuppositionsregarding the model to be fit and the techniques to be usedin the analysis. An alternative approach using artificial neuralnetworks is presented which may be suitable for developing predictivemodels of growth. Neural networks are simulators of the processesthat occur in the biological brain during the learning process.They are trained on the data, developing the necessary algorithmswithin their internal architecture, and produce a predictivemodel based on the learned facts. A dataset of Sprague–Dawleyrat (Rattus norvegicus) weights is analyzed by both traditionalregression analysis and neural network training. Predictionsof body weight are made from both models. While both methodsproduce models that adequately predict the body weights, theneural network model is superior in that it combines accuracyand precision, being less influenced by longitudinal variabilityin the data. Thus, the neural network provides another toolfor researchers to analyze growth curve data.