Molecular basis for the development of diagnostic methods and specific treatment modalities for idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with unknown aetiology and poor prognosis. IPF is characterized by alveolar epithelial damage that leads tissue remodelling and ultimately to the loss of normal lung architecture and function. Treatment has been focused on anti-inflammatory therapies, but due to their poor efficacy new therapeutic modalities are being sought. There is a need for early diagnosis and also for differential diagnostic markers for IPF and other interstitial lung diseases. The study utilized patient material obtained from bronchoalveolar lavage (BAL), diagnostic biopsies or lung transplantation. Human pulmonary fibroblast cell cultures were propagated and asbestos-induced pulmonary fibrosis in mice was used as an experimental animal model of IPF. The possible markers for IPF were scanned by immunohistochemistry, RT-PCR, ELISA and western blot. Matrix metalloproteinases (MMPs) are proteolytic enzymes that participate in tissue remodelling. Microarray studies have introduced potential markers that could serve as additional tools for the assessment of IPF and one of the most promising was MMP 7. MMP-7 protein levels were measured in the BAL fluid of patients with idiopathic interstitial lung diseases or idiopathic cough. MMP-7 was however similarly elevated in the BAL fluid of all these disorders and thus cannot be used as a differential diagnostic marker for IPF. Activation of transforming growth factor (TGF)-ß is considered to be a key element in the progression of IPF. Bone morphogenetic proteins (BMP) are negative regulators of intracellular TGF-ß signalling and BMP-4 signalling is in turn negatively regulated by gremlin. Gremlin was found to be highly upregulated in the IPF lungs and IPF fibroblasts. Gremlin was detected in the thickened IPF parenchyma and endothelium of small capillaries, whereas in non-specific interstitial pneumonia it localized predominantly in the alveolar epithelium. Parenchymal gremlin immunoreactivity might indicate IPF-type interstitial pneumonia. Gremlin mRNA levels were higher in patients with end-stage fibrosis suggesting that gremlin might be a marker for more advanced disease. Characterization of the fibroblastic foci in the IPF lungs showed that immunoreactivity to platelet-derived growth factor (PDGF) receptor-α and PDGF receptor-β was elevated in IPF parenchyma, but the fibroblastic foci showed only minor immunoreactivity to the PDGF receptors or the antioxidant peroxiredoxin II. Ki67 positive cells were also observed predominantly outside the fibroblastic foci, suggesting that the fibroblastic foci may not be composed of actively proliferating cells. When inhibition of profibrotic PDGF-signalling by imatinib mesylate was assessed, imatinib mesylate reduced asbestos-induced pulmonary fibrosis in mice as well as human pulmonary fibroblast migration in vitro but it had no effect on the lung inflammation.

On a Special Co-cycle Basis of Graphs

In this paper we consider the problems of computing a minimum co-cycle basis and a minimum weakly fundamental co-cycle basis of a directed graph G. A co-cycle in G corresponds to a vertex partition (S,V ∖ S) and a { − 1,0,1} edge incidence vector is associated with each co-cycle. The vector space over ℚ generated by these vectors is the co-cycle space of G. Alternately, the co-cycle space is the orthogonal complement of the cycle space of G. The minimum co-cycle basis problem asks for a set of co-cycles that span the co-cycle space of G and whose sum of weights is minimum. Weakly fundamental co-cycle bases are a special class of co-cycle bases, these form a natural superclass of strictly fundamental co-cycle bases and it is known that computing a minimum weight strictly fundamental co-cycle basis is NP-hard. We show that the co-cycle basis corresponding to the cuts of a Gomory-Hu tree of the underlying undirected graph of G is a minimum co-cycle basis of G and it is also weakly fundamental.

Stiff-String Basis Functions for Vibration Analysis of High Speed Rotating Beams

A new rotating beam finite element is developed in which the basis functions are obtained by the exact solution of the governing static homogenous differential equation of a stiff string, which results from an approximation in the rotating beam equation. These shape functions depend on rotation speed and element position along the beam and account for the centrifugal stiffening effect. Using this new element and the Hermite cubic finite element, a convergence study of natural frequencies is performed, and it is found that the new element converges much more rapidly than the conventional Hermite cubic element for the first two modes at higher rotation speeds. The new element is also applied for uniform and tapered rotating beams to determine the natural frequencies, and the results compare very well with the published results given in the literature.

Rotorcraft parameter estimation using radial basis function neural network

Increased emphasis on rotorcraft performance and perational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.

Heat transfer in rarefied couette flow on the basis of discrete ordinate method

The method of discrete ordinates, in conjunction with the modified "half-range" quadrature, is applied to the study of heat transfer in rarefied gas flows. Analytic expressions for the reduced distribution function, the macroscopic temperature profile and the heat flux are obtained in the general n-th approximation. The results for temperature profile and heat flux are in sufficiently good accord both with the results of the previous investigators and with the experimental data.

Molecular Basis for the Role of Staphylococcus aureus Penicillin Binding Protein 4 in Antimicrobial Resistance

Penicillin binding proteins (PBPs) are membrane-associated proteins that catalyze the final step of murein biosynthesis. These proteins function as either transpeptidases or carboxypeptidases and in a few cases demonstrate transglycosylase activity. Both transpeptidase and carboxypeptidase activities of PBPs occur at the D-Ala-D-Ala terminus of a murein precursor containing a disaccharide pentapeptide comprising N-acetyl-glucosamine and N-acetyl-muramic acid-L-Ala-D-Glu-L-Lys-D-Ala-D-Ala. beta-Lactam antibiotics inhibit these enzymes by competing with the pentapeptide precursor for binding to the active site of the enzyme. Here we describe the crystal structure, biochemical characteristics, and expression profile of PBP4, a low-molecular-mass PBP from Staphylococcus aureus strain COL. The crystal structures of PBP4-antibiotic complexes reported here were determined by molecular replacement, using the atomic coordinates deposited by the New York Structural Genomics Consortium. While the pbp4 gene is not essential for the viability of S. aureus, the knockout phenotype of this gene is characterized by a marked reduction in cross-linked muropeptide and increased vancomycin resistance. Unlike other PBPs, we note that expression of PBP4 was not substantially altered under different experimental conditions, nor did it change across representative hospital- or community-associated strains of S. aureus that were examined. In vitro data on purified recombinant S. aureus PBP4 suggest that it is a beta-lactamase and is not trapped as an acyl intermediate with beta-lactam antibiotics. Put together, the expression analysis and biochemical features of PBP4 provide a framework for understanding the function of this protein in S. aureus and its role in antimicrobial resistance.

Protein sequence design on the basis of topology optimization techniques -Using continuous modeling of discrete amino acid types

The notion of optimization is inherent in protein design. A long linear chain of twenty types of amino acid residues are known to fold to a 3-D conformation that minimizes the combined inter-residue energy interactions. There are two distinct protein design problems, viz. predicting the folded structure from a given sequence of amino acid monomers (folding problem) and determining a sequence for a given folded structure (inverse folding problem). These two problems have much similarity to engineering structural analysis and structural optimization problems respectively. In the folding problem, a protein chain with a given sequence folds to a conformation, called a native state, which has a unique global minimum energy value when compared to all other unfolded conformations. This involves a search in the conformation space. This is somewhat akin to the principle of minimum potential energy that determines the deformed static equilibrium configuration of an elastic structure of given topology, shape, and size that is subjected to certain boundary conditions. In the inverse-folding problem, one has to design a sequence with some objectives (having a specific feature of the folded structure, docking with another protein, etc.) and constraints (sequence being fixed in some portion, a particular composition of amino acid types, etc.) while obtaining a sequence that would fold to the desired conformation satisfying the criteria of folding. This requires a search in the sequence space. This is similar to structural optimization in the design-variable space wherein a certain feature of structural response is optimized subject to some constraints while satisfying the governing static or dynamic equilibrium equations. Based on this similarity, in this work we apply the topology optimization methods to protein design, discuss modeling issues and present some initial results.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

A structural basis for the role of nucleotide specifying residues in regulating the Rv1625c adenylyl cyclase oligomerization of the from M-tuberculosis

The Rv1625c Class III adenylyl cyclase from Mycobacterium tuberculosis is a homodimeric enzyme with two catalytic centers at the dimer interface, and shows sequence similarity with the mammalian adenylyl and guanylyl cyclases. Mutation of the substrate-specifying residues in the catalytic domain of Rv1625c, either independently or together, to those present in guanylyl cyclases not only failed to confer guanylyl cyclase activity to the protein, but also severely abrogated the adenylyl cyclase activity of the enzyme. Biochemical analysis revealed alterations in the behavior of the mutants on ion-exchange chromatography, indicating differences in the surface-exposed charge upon mutation of substrate-specifying residues. The mutant proteins showed alterations in oligomeric status as compared to the wild-type enzyme, and differing abilities to heterodimerize with the wild-type protein. The crystal structure of a mutant has been solved to a resolution of 2.7 angstrom. On the basis of the structure, and additional biochemical studies, we provide possible reasons for the altered properties of the mutant proteins, as well as highlight unique structural features of the Rv1625c adenylyl cyclase. (c) 2005 Elsevier Ltd. All rights reserved.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Classification of Protein Kinases on the Basis of Both Kinase and Non-Kinase Regions

Background: Protein phosphorylation is a generic way to regulate signal transduction pathways in all kingdoms of life. In many organisms, it is achieved by the large family of Ser/Thr/Tyr protein kinases which are traditionally classified into groups and subfamilies on the basis of the amino acid sequence of their catalytic domains. Many protein kinases are multidomain in nature but the diversity of the accessory domains and their organization are usually not taken into account while classifying kinases into groups or subfamilies. Methodology: Here, we present an approach which considers amino acid sequences of complete gene products, in order to suggest refinements in sets of pre-classified sequences. The strategy is based on alignment-free similarity scores and iterative Area Under the Curve (AUC) computation. Similarity scores are computed by detecting common patterns between two sequences and scoring them using a substitution matrix, with a consistent normalization scheme. This allows us to handle full-length sequences, and implicitly takes into account domain diversity and domain shuffling. We quantitatively validate our approach on a subset of 212 human protein kinases. We then employ it on the complete repertoire of human protein kinases and suggest few qualitative refinements in the subfamily assignment stored in the KinG database, which is based on catalytic domains only. Based on our new measure, we delineate 37 cases of potential hybrid kinases: sequences for which classical classification based entirely on catalytic domains is inconsistent with the full-length similarity scores computed here, which implicitly consider multi-domain nature and regions outside the catalytic kinase domain. We also provide some examples of hybrid kinases of the protozoan parasite Entamoeba histolytica. Conclusions: The implicit consideration of multi-domain architectures is a valuable inclusion to complement other classification schemes. The proposed algorithm may also be employed to classify other families of enzymes with multidomain architecture.

Microcycle conidiation and its genetic basis in Neurospora crassa

Some wild isolates of Neurospora show microcycle conidiation in liquid culture under continuous agitation. Macroconidia from agar-grown mycelial cultures germinated in liquid and the germlings spontaneously produced conidia with no intervening mycelial phase. Three types of microcycle conidiation were seen among progeny of N. crassa Vickramam A x N. crassa a wild-type: (1) multinucleate blastoconidia produced by apical budding and septation, (2) multinucleate arthroconidia produced by holothallic septation and disarticulation of cells, and (3) uninucleate microconidia produced directly from conidiogenous cells of the germlings. Two genes were identified which control specific patterns of microcycle conidiogenesis. A single gene mcb in linkage group VR near al-3 (3.2% recombination) controls blastoconidiation. This gene is epistatic to gene mcm located in linkage group IIL, very near ro-7 (1.4%). mcm controls both microconidiation and arthroconidiation depending on temperature. Strains of genotype mcm produce microconidia almost exclusively at 18-22 degrees C, but arthroconidia with few or no microconidia at 30 degrees C. Because they result in rapid and synchronized conidiation in liquid culture, the two genes should be useful for studies of developmental gene regulation. mcm makes it possible to obtain large quantities of pure microconidia rapidly for experimentation.