CHARACTERIZATION OF F107 FIMBRIAE OF ESCHERICHIA-COLI 107/86, WHICH CAUSES EDEMA DISEASE IN PIGS, AND NUCLEOTIDE-SEQUENCE OF THE F107 MAJOR FIMBRIAL SUBUNIT GENE, FEDA

F107 fimbriae were isolated and purified from edema disease strain 107/86 of Escherichia coli. Plasmid pIH120 was constructed, which contains the gene cluster that codes for adhesive F107 fimbriae. The major fimbrial subunit gene, fedA, was sequenced. An open reading frame that codes for a protein with 170 amino acids, including a 21-amino-acid signal peptide, was found. The protein without the signal sequence has a calculated molecular mass of 15,099 Da. Construction of a nonsense mutation in the open reading frame of fedA abolished both fimbrial expression and the capacity to adhere to isolated porcine intestinal villi. In a screening of 28 reference edema disease strains and isolates from clinically ill piglets, fedA was detected in 24 cases (85.7%). In 20 (83.3%) of these 24 strains, fedA was found in association with Shiga-like toxin II variant genes, coding for the toxin that is characteristic for edema disease strains of E. coli. The fimbrial subunit gene was not detected in enterotoxigenic E. coli strains. Because of the capacity of E. coli HB101(pIH120) transformants to adhere to isolated porcine intestinal villi, the high prevalence of fedA in edema disease strains, and the high correlation with the Shiga-like toxin II variant toxin-encoding genes, we suggest that F107 fimbriae are an important virulence factor in edema disease strains of E. coli

Mechanisms of Action of Currently Prescribed and Newly Developed Antiepileptic Drugs

Clinically available antiepileptic drugs (AEDs) decrease membrane excitability by interacting with neurotransmitter receptors or ion channels. AEDs developed before 1980 appear to act on sodium (Na) channels, -y-aminobutyric acid A (GABA A ) receptors, or calcium (Ca) channels. Benzodiazepines and barbiturates enhance GABA A -receptor-mediated inhibition. Phenytoin, car-bamazepine and, possibly, valproate (VPA) decrease high-frequency repetitive firing of action potentials by enhancing Na channel inactivation. Ethosuximide and VPA reduce a low threshold (T-type) Ca-channel current. The mechanisms of action of recently developed AEDs are less clear. Lamotrigine may decrease sustained high-frequency repetitive firing of voltage-dependent Na action potentials, and gabapentin (GBP) appears to bind to a specific binding site in the CNS with a restricted regional distribution. However, the identity of the binding site and the mechanism of action of GBP remain uncertain. The antiepileptic effect of felbamate may involve interaction at the strychnine-insensitive glycine site of the Af-methyl-D-aspartate receptor, but the mechanism of action is not yet proven.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65554/1/j.1528-1157.1994.tb05955.x.pd

An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

<p>Abstract</p> <p>Background</p> <p>Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.</p> <p>Results</p> <p>We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs.</p> <p>Conclusions</p> <p>The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the <it>F</it>₁score over Resnik, the next best method, on our <it>Saccharomyces cerevisiae </it>PPI dataset and 2 times on our <it>Homo sapiens </it>PPI dataset using cellular component, biological process and molecular function GO annotations.</p

The bZIP Transcription Factor Rca1p Is a Central Regulator of a Novel CO2 Sensing Pathway in Yeast

Like many organisms the fungal pathogen Candida albicans senses changes in the environmental CO2 concentration. This response involves two major proteins: adenylyl cyclase and carbonic anhydrase (CA). Here, we demonstrate that CA expression is tightly controlled by the availability of CO2 and identify the bZIP transcription factor Rca1p as the first CO2 regulator of CA expression in yeast. We show that Rca1p upregulates CA expression during contact with mammalian phagocytes and demonstrate that serine 124 is critical for Rca1p signaling, which occurs independently of adenylyl cyclase. ChIP-chip analysis and the identification of Rca1p orthologs in the model yeast Saccharomyces cerevisiae (Cst6p) point to the broad significance of this novel pathway in fungi. By using advanced microscopy we visualize for the first time the impact of CO2 build-up on gene expression in entire fungal populations with an exceptional level of detail. Our results present the bZIP protein Rca1p as the first fungal regulator of carbonic anhydrase, and reveal the existence of an adenylyl cyclase independent CO2 sensing pathway in yeast. Rca1p appears to regulate cellular metabolism in response to CO2 availability in environments as diverse as the phagosome, yeast communities or liquid culture

Resveratrol Targeting of Carcinogen-Induced Brain Endothelial Cell Inflammation Biomarkers MMP-9 and COX-2 is Sirt1-Independent

The occurrence of a functional relationship between the release of metalloproteinases (MMPs) and the expression of cyclooxygenase (COX)-2, two inducible pro-inflammatory biomarkers with important pro-angiogenic effects, has recently been inferred. While brain endothelial cells play an essential role as structural and functional components of the blood-brain barrier (BBB), increased BBB breakdown is thought to be linked to neuroinflammation. Chemopreventive mechanisms targeting both MMPs and COX-2 however remain poorly investigated. In this study, we evaluated the pharmacological targeting of Sirt1 by the diet-derived and antiinflammatory polyphenol resveratrol. Total RNA, cell lysates, and conditioned culture media from human brain microvascular endothelial cells (HBMEC) were analyzed using qRT-PCR, immunoblotting, and zymography respectively. Tissue scan microarray analysis of grade I–IV brain tumours cDNA revealed increased gene expression of Sirt-1 from grade I–III but surprisingly not in grade IV brain tumours. HBMEC were treated with a combination of resveratrol and phorbol 12-myristate 13-acetate (PMA), a carcinogen known to increase MMP-9 and COX-2 through NF-κB. We found that resveratrol efficiently reversed the PMA-induced MMP-9 secretion and COX-2 expression. Gene silencing of Sirt1, a critical modulator of angiogenesis and putative target of resveratrol, did not lead to significant reversal of MMP-9 and COX-2 inhibition. Decreased resveratrol inhibitory potential of carcinogen-induced IκB phosphorylation in siSirt1-transfected HBMEC was however observed. Our results suggest that resveratrol may prevent BBB disruption during neuroinflammation by inhibiting MMP-9 and COX-2 and act as a pharmacological NF-κB signal transduction inhibitor independent of Sirt1

A transversal approach to predict gene product networks from ontology-based similarity

<p>Abstract</p> <p>Background</p> <p>Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.</p> <p>Results</p> <p>The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity.</p> <p>Conclusion</p> <p>Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression.</p