Role of meprins to protect ileal mucosa of Crohn's disease patients from colonization by adherent-invasive E. coli

Ileal lesions in Crohn's disease (CD) patients are colonized by pathogenic adherent-invasive Escherichia coli (AIEC) able to adhere to and invade intestinal epithelial cells (IEC), and to survive within macrophages. The interaction of AIEC with IEC depends on bacterial factors mainly type 1 pili, flagella, and outer membrane proteins. In humans, proteases can act as host defence mechanisms to counteract bacterial colonization. The protease meprin, composed of multimeric complexes of the two subunits alpha and beta, is abundantly expressed in IECs. Decreased levels of this protease correlate with the severity of the inflammation in patients with inflammatory bowel disease. The aim of the present study was to analyze the ability of meprin to modulate the interaction of AIEC with IECs. In patients with ileal CD we observed decreased levels of meprins, in particular that of meprin β. Dose-dependent inhibition of the abilities of AIEC strain LF82 to adhere to and invade intestinal epithelial T84 cells was observed when bacteria were pre-treated with both exogenous meprin α and meprin β. Dose-dependent proteolytic degradation of type 1 pili was observed in the presence of active meprins, but not with heat-inactivated meprins, and pretreatment of AIEC bacteria with meprins impaired their ability to bind mannosylated host receptors and led to decreased secretion of the pro-inflammatory cytokine IL-8 by infected T84 cells. Thus, decreased levels of protective meprins as observed in CD patients may contribute to increased AIEC colonization

Iterative sorting reveals CD133+ and CD133- melanoma cells as phenotypically distinct populations

Background: The heterogeneity and tumourigenicity of metastatic melanoma is attributed to a cancer stem cell model, with CD133 considered to be a cancer stem cell marker in melanoma as well as other tumours, but its role has remained controversial. Methods: We iteratively sorted CD133+ and CD133- cells from 3 metastatic melanoma cell lines, and observed tumourigenicity and phenotypic characteristics over 7 generations of serial xeno-transplantation in NOD/SCID mice. Results: We demonstrate that iterative sorting is required to make highly pure populations of CD133+ and CD133- cells from metastatic melanoma, and that these two populations have distinct characteristics not related to the cancer stem cell phenotype. In vitro, gene set enrichment analysis indicated CD133+ cells were related to a proliferative phenotype, whereas CD133- cells were of an invasive phenotype. However, in vivo, serial transplantation of CD133+ and CD133- tumours over 7 generations showed that both populations were equally able to initiate and propagate tumours. Despite this, both populations remained phenotypically distinct, with CD133- cells only able to express CD133 in vivo and not in vitro. Loss of CD133 from the surface of a CD133+ cell was observed in vitro and in vivo, however CD133- cells derived from CD133+ retained the CD133+ phenotype, even in the presence of signals from the tumour microenvironment. Conclusion: We show for the first time the necessity of iterative sorting to isolate pure marker-positive and marker-negative populations for comparative studies, and present evidence that despite CD133+ and CD133- cells being equally tumourigenic, they display distinct phenotypic differences, suggesting CD133 may define a distinct lineage in melanoma

Constraint solving in uncertain and dynamic environments - a survey

International audienceThis article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 (Ninth International Conference on Principles and Practice of Constraint Programming) in Kinsale, Ireland. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments

The Metalloprotease Meprinβ Processes E-Cadherin and Weakens Intercellular Adhesion

BACKGROUND: Meprin (EC 3.4.24.18), an astacin-like metalloprotease, is expressed in the epithelium of the intestine and kidney tubules and has been related to cancer, but the mechanistic links are unknown. METHODOLOGY/PRINCIPAL FINDINGS: We used MDCK and Caco-2 cells stably transfected with meprin alpha and or meprin beta to establish models of renal and intestinal epithelial cells expressing this protease at physiological levels. In both models E-cadherin was cleaved, producing a cell-associated 97-kDa E-cadherin fragment, which was enhanced upon activation of the meprin zymogen and reduced in the presence of a meprin inhibitor. The cleavage site was localized in the extracellular domain adjacent to the plasma membrane. In vitro assays with purified components showed that the 97-kDa fragment was specifically generated by meprin beta, but not by ADAM-10 or MMP-7. Concomitantly with E-cadherin cleavage and degradation of the E-cadherin cytoplasmic tail, the plaque proteins beta-catenin and plakoglobin were processed by an intracellular protease, whereas alpha-catenin, which does not bind directly to E-cadherin, remained intact. Using confocal microscopy, we observed a partial colocalization of meprin beta and E-cadherin at lateral membranes of incompletely polarized cells at preconfluent or early confluent stages. Meprin beta-expressing cells displayed a reduced strength of cell-cell contacts and a significantly lower tendency to form multicellular aggregates. CONCLUSIONS/SIGNIFICANCE: By identifying E-cadherin as a substrate for meprin beta in a cellular context, this study reveals a novel biological role of this protease in epithelial cells. Our results suggest a crucial role for meprin beta in the control of adhesiveness via cleavage of E-cadherin with potential implications in a wide range of biological processes including epithelial barrier function and cancer progression

Metalloprotease Meprinβ in Rat Kidney: Glomerular Localization and Differential Expression in Glomerulonephritis

Meprin (EC 3.4.24.18) is an oligomeric metalloendopeptidase found in microvillar membranes of kidney proximal tubular epithelial cells. Here, we present the first report on the expression of meprinβ in rat glomerular epithelial cells and suggest a potential involvement in experimental glomerular disease. We detected meprinβ in glomeruli of immunostained rat kidney sections on the protein level and by quantitative RT-PCR of laser-capture microdissected glomeruli on the mRNA level. Using immuno-gold staining we identified the membrane of podocyte foot processes as the main site of meprinβ expression. The glomerular meprinβ expression pattern was altered in anti-Thy 1.1 and passive Heymann nephritis (PHN). In addition, the meprinβ staining pattern in the latter was reminiscent of immunostaining with the sheep anti-Fx1A antiserum, commonly used in PHN induction. Using Western blot and immunoprecipitation assays we demonstrated that meprinβ is recognized by Fx1A antiserum and may therefore represent an auto-antigen in PHN. In anti-Thy 1.1 glomerulonephritis we observed a striking redistribution of meprinβ in tubular epithelial cells from the apical to the basolateral side and the cytosol. This might point to an involvement of meprinβ in this form of glomerulonephritis

MetWAMer: eukaryotic translation initiation site prediction

<p>Abstract</p> <p>Background</p> <p>Translation initiation site (TIS) identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for TIS prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations.</p> <p>Results</p> <p>MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron. Also, our program implements clustering capabilities through use of the <it>k</it>-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5'-complete coverage.</p> <p>Conclusion</p> <p>We demonstrate that improvements in statistically-based models for TIS prediction can be achieved by taking the class of each potential start-methionine into account pending certain testing conditions, and that our perceptron-based model is suitable for the TIS identification task. MetWAMer represents a well-documented, extensible, and freely available software system that can be readily re-trained for differing target applications and/or extended with existing and novel TIS prediction methods, to support further research efforts in this area.</p

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

<p>Abstract</p> <p>Background</p> <p>The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix^®technology provides both a quantitative fluorescence signal and a decision (<it>detection call</it>: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients.</p> <p>Results</p> <p>After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM).</p> <p>Conclusion</p> <p>This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.</p

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BACKGROUND: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques. RESULTS: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers. CONCLUSION: The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles

Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants

<p>Abstract</p> <p>Background</p> <p>One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness).</p> <p>Results</p> <p>Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in <it>Arabidopsis </it>according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration.</p> <p>Conclusion</p> <p>Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change.</p

Multi-Platform Next-Generation Sequencing of the Domestic Turkey (Meleagris gallopavo): Genome Assembly and Analysis

The combined application of next-generation sequencing platforms has provided an economical approach to unlocking the potential of the turkey genome