Archaebacterial class I and class II aldolases from extreme halophiles

Both, class I (Schiff-base forming) and class II (metal requiring) fructose biphosphate aldolases were found to be distributed among halophilic archaebacteria. The aldolase activity fromHalobacterium halobium, H. salinarium, H. cutirubrum, H. mediterranei andH. volcanii exhibited properties of a bacterial class II aldolase as it was metal-dependent for activity and therefore inhibited by EDTA. In contrast, aldolase fromH. saccharovorum, Halobacterium R-113, H. vallismortis andHalobacterium CH-1 formed a Schiff-base intermediate with the substrate and therefore resembled to eukaryotic class I type. The type of aldolase did not vary by changes in the growth medium.     

The pH-dependence of class B and class C beta-lactamases.

The classification by structure allots beta-lactamases to (at present) three classes, A, B and C. The pH-dependence of the kinetic parameters for class B and class C have been determined. They differ from each other and from class A beta-lactamases. The class B enzyme was beta-lactamase II from Bacillus cereus 569/H/9. The plots of kcat against pH for the hydrolysis of benzylpenicillin by Zn(II)-requiring beta-lactamase II and Co(II)-requiring beta-lactamase II were not symmetrical, but those of kcat/Km were. A similar feature was observed for the hydrolysis of both benzylpenicillin and cephalosporin C by a class C beta-lactamase from Pseudomonas aeruginosa. The results have been interpreted by a scheme in which two ionic forms of an intermediate can give product, but do so at differing rates.     

Comparison of microarray designs for class comparison and class discovery

MOTIVATION: Two-color microarray experiments in which an aliquot derived from a common RNA sample is placed on each array are called reference designs. Traditionally, microarray experiments have used reference designs, but designs without a reference have recently been proposed as alternatives. RESULTS: We develop a statistical model that distinguishes the different levels of variation typically present in cancer data, including biological variation among RNA samples, experimental error and variation attributable to phenotype. Within the context of this model, we examine the reference design and two designs which do not use a reference, the balanced block design and the loop design, focusing particularly on efficiency of estimates and the performance of cluster analysis. We calculate the relative efficiency of designs when there are a fixed number of arrays available, and when there are a fixed number of samples available. Monte Carlo simulation is used to compare the designs when the objective is class discovery based on cluster analysis of the samples. The number of discrepancies between the estimated clusters and the true clusters were significantly smaller for the reference design than for the loop design. The efficiency of the reference design relative to the loop and block designs depends on the relation between inter- and intra-sample variance. These results suggest that if cluster analysis is a major goal of the experiment, then a reference design is preferable. If identification of differentially expressed genes is the main concern, then design selection may involve a consideration of several factors.     

Feedforward neural network models for handling class overlap and class imbalance

This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-controlled neural networks (VCNNs), which are developed to handle class overlap by minimizing an error function involving the class-specific variance (CSV) computed at their outputs. Another special case of the proposed error function can be used for training class-balancing neural networks (CBNNs), which are developed to handle class imbalance by relying on class-specific correction (CSC). VCNNs and CBNNs are compared with conventional feedforward neural networks (FFNNs), quantum neural networks (QNNs), and resampling techniques. The properties of VCNNs and CBNNs are illustrated by experiments on artificial data. Various experiments involving real-world data reveal the advantages offered by VCNNs and CBNNs in the presence of class overlap and class imbalance.     

HLA class I and class II polymorphism in three Sicilian populations

Two human leukocyte antigen (HLA) class I loci (HLA-A and HLA-B) and one class II locus (HLA-DR) were typed at the DNA level in the Sicilian population. Study participants were of Sicilian origin (183 for class I loci and 260 for class II loci) and live in three towns, chosen on the basis of geographic position and different historical events. These towns are Sciacca (southwest Sicily, located at sea level, conquered by Arabs in A.D. 814), Piana degli Albanesi (northwest Sicily, 720 m above sea level, has maintained religious, cultural, and linguistic peculiarities traced to Albanian settlement in 1488), and Troina (northeast Sicily, 1120 m above sea level, known as the first settlement of Normans). The assumptions underlying the study of genetic structure, based on HLA allele polymorphism, are that these three towns are located in areas that can be distinguished according to historical criteria and that they are likely to have contributed to cultural and probably genetic differences. As such, the high frequency of some alleles in Sciacca and Troina seems to be correlated with Greek, Phoenician, North African, and Arab influence. In accordance with different human settlements in Sicily, we found that the HLA allele frequencies support the existence of genetic differentiation between the western and eastern sides of Sicily. This separation is attributed to Greek colonization in the east and to Phoenician-Carthaginian-Arab influence in the west. Moreover, the comparisons of all allele frequencies between Mediterranean and AfrIcan populations show the same trend, highlighting in some cases European origin and in other cases non-European origin.     

An engineered class I transfer RNA with a class II tertiary fold

Structure-based engineering of the tertiary fold of Escherichia coli tRNA(Gln)2 has enabled conversion of this transfer RNA to a class II structure while retaining recognition properties of a class I glutamine tRNA. The new tRNA possesses the 20-nt variable stem-loop of Thermus thermophilus tRNA(Ser). Enlargement of the D-loop appears essential to maintaining a stable tertiary structure in this species, while rearrangement of a base triple in the augmented D-stem is critical for efficient glutaminylation. These data provide new insight into structural determinants distinguishing the class I and class II tRNA folds, and demonstrate a marked sensitivity of glutaminyl-tRNA synthetase to alteration of tRNA tertiary structure.     

Classifying antibodies using flow cytometry data: class prediction and class discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.     

Enzyme-catalyzed mechanism of isoniazid activation in class I and class III peroxidases

There is an urgent need to understand the mechanism of activation of the frontline anti-tuberculosis drug isoniazid by the Mycobacterium tuberculosis catalase-peroxidase. To address this, a combination of NMR spectroscopic, biochemical, and computational methods have been used to obtain a model of the frontline anti-tuberculosis drug isoniazid bound to the active site of the class III peroxidase, horseradish peroxidase C. This information has been used in combination with the new crystal structure of the M. tuberculosis catalase-peroxidase to predict the mode of INH binding across the class I heme peroxidase family. An enzyme-catalyzed mechanism for INH activation is proposed that brings together structural, functional, and spectroscopic data from a variety of sources. Collectively, the information not only provides a molecular basis for understanding INH activation by the M. tuberculosis catalase-peroxidase but also establishes a new conceptual framework for testing hypotheses regarding the enzyme-catalyzed turnover of this compound in a number of heme peroxidases.     

Efficient inhibition of class A and class D beta-lactamases by Michaelis complexes

A 6-alkylidiene penam sulfone, SA-1-204, is an efficient inhibitor of both SHV-1 and OXA-1 beta-lactamases with K(I) = 42 +/- 4 nm and 1.0 +/- 0.1 microm, respectively. To gain insight into the reaction chemistry of SA-1-204, the reactions between this inhibitor and SHV-1 and OXA-1 were studied by Raman spectroscopy in single crystals and in solution. Raman signatures characteristic of the unreacted beta-lactam ring show that in both phases the inhibitor binds as a noncovalent Michaelis-like complex. This complex is present as the major population for periods of up to an hour. On longer time scales, the Raman data show that beta-lactam ring opening eventually leads to a complex mixture of reaction products. However, the data clearly demonstrate that the key species for inhibition on the time scale of bacterial half-lives is the noncovalent complex preceding acylation.     

Regulation of MHC class II gene expression by the class II transactivator

MHC class II molecules are pivotal for the adaptive immune system, because they guide the development and activation of CD4+ T helper cells. Fulfilling these functions requires that the genes encoding MHC class II molecules are transcribed according to a strict cell-type-specific and quantitatively modulated pattern. This complex gene-expression profile is controlled almost exclusively by a single master regulatory factor, which is known as the class II transactivator. As we discuss here, differential activation of the three independent promoters that drive expression of the gene encoding the class II transactivator ultimately determines the exquisitely regulated pattern of MHC class II gene expression.     

MHC class I multimers

T lymphocytes play a key role in the immune response to both foreign and self peptide antigens, which they recognize in combination with MHC molecules. In the past it has been difficult to analyse objectively the specificity, frequency and intensity of T cell responses. The recent application of fluorescent-labelled MHC class I multimers, however, has provided a powerful experimental approach to the direct visualisation of antigen-specific T cells. As a result, our perspective of how T cells respond to both viruses and other antigens in vivo has been greatly enhanced.     

Latent class model diagnosis

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.