Enzymatic characterization of the recombinant Arabidopsis thaliana nitrilase subfamily encoded by the NIT2/NIT1/NIT3-gene cluster

Three of the nitrilase isoenzymes of Arabidopsis thaliana (L.) Heynh. are located on chromosome III in tandem and these genes (NIT2/NIT1/NIT3 in the 5′→3′ direction) encode highly similar polypeptides. Copy DNAs encompassing the entire coding sequences for all three nitrilases were expressed in Escherichia coli as fusion proteins containing a C-terminal hexahistidine extension. All three nitrilases were obtained as enzymatically active proteins, and their characteristics were determined, including a detailed comparative analysis of their substrate preferences. All three nitrilases converted indole-3-acetonitrile (IAN) to indole-3-acetic acid (IAA), albeit, compared to the most effective substrates found, phenylpropionitrile (PPN), allylcyanide, (phenylthio)acetonitrile and (methylthio)acetonitrile, with low affinity and velocity. The preferred substrates are either naturally occurring substrates, which may originate from glucosinolate breakdown, or they are close relatives of these. Thus, a major function of NIT1, NIT2 and NIT3 is assigned to be the conversion to carboxylic acids of nitriles from glucosinolate turnover or degradation. While all nitrilases exhibit a similar pH optimum around neutral, and NIT1 and NIT3 exhibit a similar temperature optimum around 30 °C independent of the substrate analyzed (IAN, PPN), NIT2 showed a remarkably different temperature optimum for IAN (15 °C) and PPN (35–40 °C). A potential role for NIT2 in breaking seed dormancy in A. thaliana by low temperatures (stratification), however, was ruled out, although NIT2 was the predominantly expressed nitrilase isoform in developing embryos and in germinating seeds, as judged from an analysis of β-glucuronidase reporter gene expression under the control of the promoters of the four isogenes. It is possible that NIT2 is involved in supplying IAA during seed development rather than during stratification. Received: 13 May 2000 / Accepted: 14 August 2000     

A generalized discriminant rule when training population and test population differ on their descriptive parameters

Standard discriminant analysis methods make the assumption that both the labeled sample used to estimate the discriminant rule and the nonlabeled sample on which this rule is applied arise from the same population. In this work, we consider the case where the two populations are slightly different. In the multinormal context, we establish that both populations are linked through linear mapping. Estimation of the nonlabeled sample discriminant rule is then obtained by estimating parameters of this linear relationship. Several models describing this relationship are proposed and associated estimated parameters are given. An experimental illustration is also provided in which sex of birds that differ morphometrically over their geographical range is to be deterrmined and a comparison with the standard allocation rule is performed. Extension to a partially labeled sample is also discussed.     

Determinants of human B cell migration across brain endothelial cells

Circulating B cells enter the CNS as part of normal immune surveillance and in pathologic states, including the common and disabling illness multiple sclerosis. However, little is known about the molecular mechanisms that mediate human B cell interaction with the specialized brain endothelial cells comprising the blood-brain barrier (BBB). We studied the molecular mechanisms that regulate the migration of normal human B cells purified ex vivo, across human adult brain-derived endothelial cells (HBECs). We found that B cells migrated across HBECs more efficiently than T cells from the same individuals. B cell migration was significantly inhibited by blocking Abs to the adhesion molecules ICAM-1 and VLA-4, but not VCAM-1, similar to the results previously reported for T cells. Blockade of the chemokines monocyte chemoattractant protein-1 and IL-8, but not RANTES or IFN-gamma-inducible protein-10, significantly inhibited B cell migration, and these results were correlated with the chemokine receptor expression of B cells measured by flow cytometry and by RNase protection assay. Tissue inhibitor of metalloproteinase-1, a natural inhibitor of matrix metalloproteinases, significantly decreased B cell migration across the HBECs. A comprehensive RT-PCR comparative analysis of all known matrix metalloproteinases and tissue inhibitors of metalloproteinases in human B and T cells revealed distinct profiles of expression of these molecules in the different cell subsets. Our results provide insights into the molecular mechanisms that underlie human B cell migration across the BBB. Furthermore, they identify potential common, and unique, therapeutic targets for limiting CNS B cell infiltration and predict how therapies currently developed to target T cell migration, such as anti-VLA-4 Abs, may impact on B cell trafficking.     

Calibration of a complex activated sludge model for the full-scale wastewater treatment plant

In this study, the results of the calibration of the complex activated sludge model implemented in BioWin software for the full-scale wastewater treatment plant are presented. Within the calibration of the model, sensitivity analysis of its parameters and the fractions of carbonaceous substrate were performed. In the steady-state and dynamic calibrations, a successful agreement between the measured and simulated values of the output variables was achieved. Sensitivity analysis revealed that upon the calculations of normalized sensitivity coefficient (S _i,j ) 17 (steady-state) or 19 (dynamic conditions) kinetic and stoichiometric parameters are sensitive. Most of them are associated with growth and decay of ordinary heterotrophic organisms and phosphorus accumulating organisms. The rankings of ten most sensitive parameters established on the basis of the calculations of the mean square sensitivity measure (δ _j ^msqr) indicate that irrespective of the fact, whether the steady-state or dynamic calibration was performed, there is an agreement in the sensitivity of parameters.     

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.