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Results: 51 to 58 of 58

Publication Record


Discovery of an operon that participates in agmatine metabolism and regulates biofilm formation in Pseudomonas aeruginosa.
Williams BJ, Du RH, Calcutt MW, Abdolrasulnia R, Christman BW, Blackwell TS
(2010) Mol Microbiol 76: 104-19
MeSH Terms: Agmatine, Base Sequence, Biofilms, Cell Fractionation, Gene Expression Profiling, Gene Order, Hydrolases, Metabolic Networks and Pathways, Models, Biological, Molecular Sequence Data, Mutagenesis, Site-Directed, Operon, Periplasm, Promoter Regions, Genetic, Protein Sorting Signals, Protein Transport, Pseudomonas aeruginosa
Show Abstract · Added February 19, 2015
Agmatine is the decarboxylation product of arginine and a number of bacteria have devoted enzymatic pathways for its metabolism. Pseudomonas aeruginosa harbours the aguBA operon that metabolizes agmatine to putrescine, which can be subsequently converted into other polyamines or shunted into the TCA cycle for energy production. We discovered an alternate agmatine operon in the P. aeruginosa strain PA14 named agu2ABCA' that contains two genes for agmatine deiminases (agu2A and agu2A'). This operon was found to be present in 25% of clinical P. aeruginosa isolates. Agu2A' contains a twin-arginine translocation signal at its N-terminus and site-directed mutagenesis and cell fractionation experiments confirmed this protein is secreted to the periplasm. Analysis of the agu2ABCA' promoter demonstrates that agmatine induces expression of the operon during the stationary phase of growth and during biofilm growth and agu2ABCA' provides only weak complementation of aguBA, which is induced during log phase. Biofilm assays of mutants of all three agmatine deiminase genes in PA14 revealed that deletion of agu2ABCA', specifically its secreted product Agu2A', reduces biofilm production of PA14 following addition of exogenous agmatine. Together, these findings reveal a novel role for the agu2ABCA' operon in the biofilm development of P. aeruginosa.
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17 MeSH Terms
Quantification of protein expression changes in the aging left ventricle of Rattus norvegicus.
Grant JE, Bradshaw AD, Schwacke JH, Baicu CF, Zile MR, Schey KL
(2009) J Proteome Res 8: 4252-63
MeSH Terms: Aging, Animals, Blotting, Western, Databases, Protein, Heart Ventricles, Isotope Labeling, Male, Metabolic Networks and Pathways, Myocardium, Proteome, Proteomics, Rats, Signal Transduction, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Show Abstract · Added May 27, 2014
As the heart ages, electrophysiological and biochemical changes can occur, and the ventricle in many cases loses distensibility, impairing diastolic function. How the proteomic signature of the aged ventricle is unique in comparison to young hearts is still under active investigation. We have undertaken a quantitative proteomics study of aging left ventricles (LVs) utilizing the isobaric Tagging for Relative and Absolute Quantification (iTRAQ) methodology. Differential protein expression was observed for 117 proteins including proteins involved in cell signaling, the immune response, structural proteins, and proteins mediating responses to oxidative stress. For many of these proteins, this is the first report of an association with the aged myocardium. Additionally, two proteins of unknown function were identified. This work serves as the basis for making future comparisons of the aged left ventricle proteome to that of left ventricles obtained from other models of disease and heart failure.
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14 MeSH Terms
Introduction: Human metabolites in safety testing (MIST) issue.
Guengerich FP
(2009) Chem Res Toxicol 22: 237-8
MeSH Terms: Animals, Chemistry, Pharmaceutical, Drug Evaluation, Preclinical, Drug-Related Side Effects and Adverse Reactions, Guidelines as Topic, Humans, Metabolic Networks and Pathways, Mice, Mice, Transgenic, Models, Animal, Pharmaceutical Preparations, Toxicity Tests
Added March 26, 2014
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12 MeSH Terms
Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control.
Young JD, Henne KL, Morgan JA, Konopka AE, Ramkrishna D
(2008) Biotechnol Bioeng 100: 542-59
MeSH Terms: Acetates, Anaerobiosis, Cells, Cultured, Computer Simulation, Escherichia coli, Metabolic Networks and Pathways, Models, Biological, Operon
Show Abstract · Added January 23, 2015
Cybernetic modeling strives to uncover the inbuilt regulatory programs of biological systems and leverage them toward computational prediction of metabolic dynamics. Because of its focus on incorporating the global aims of metabolism, cybernetic modeling provides a systems-oriented approach for describing regulatory inputs and inferring the impact of regulation within biochemical networks. Combining cybernetic control laws with concepts from metabolic pathway analysis has culminated in a systematic strategy for constructing cybernetic models, which was previously lacking. The newly devised framework relies upon the simultaneous application of local controls that maximize the net flux through each elementary flux mode and global controls that modulate the activities of these modes to optimize the overall nutritional state of the cell. The modeling concepts are illustrated using a simple linear pathway and a larger network representing anaerobic E. coli central metabolism. The E. coli model successfully describes the metabolic shift that occurs upon deleting the pta-ackA operon that is responsible for fermentative acetate production. The model also furnishes predictions that are consistent with experimental results obtained from additional knockout strains as well as strains expressing heterologous genes. Because of the stabilizing influence of the included control variables, the resulting cybernetic models are more robust and reliable than their predecessors in simulating the network response to imposed genetic and environmental perturbations.
(c) 2008 Wiley Periodicals, Inc.
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8 MeSH Terms
Alteration of Akt activity increases chemotherapeutic drug and hormonal resistance in breast cancer yet confers an achilles heel by sensitization to targeted therapy.
McCubrey JA, Sokolosky ML, Lehmann BD, Taylor JR, Navolanic PM, Chappell WH, Abrams SL, Stadelman KM, Wong EW, Misaghian N, Horn S, Bäsecke J, Libra M, Stivala F, Ligresti G, Tafuri A, Milella M, Zarzycki M, Dzugaj A, Chiarini F, Evangelisti C, Martelli AM, Terrian DM, Franklin RA, Steelman LS
(2008) Adv Enzyme Regul 48: 113-35
MeSH Terms: Antineoplastic Agents, Hormonal, Antineoplastic Combined Chemotherapy Protocols, Apoptosis, Breast Neoplasms, Cell Cycle, Cell Transformation, Neoplastic, Cellular Senescence, Doxorubicin, Drug Resistance, Neoplasm, Extracellular Signal-Regulated MAP Kinases, Female, Humans, Metabolic Networks and Pathways, PTEN Phosphohydrolase, Phosphatidylinositol 3-Kinases, Protein Kinases, Proto-Oncogene Proteins c-akt, Signal Transduction, TOR Serine-Threonine Kinases, Tumor Cells, Cultured, raf Kinases
Added January 20, 2015
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21 MeSH Terms
Multiple pathways involved in the biosynthesis of anandamide.
Liu J, Wang L, Harvey-White J, Huang BX, Kim HY, Luquet S, Palmiter RD, Krystal G, Rai R, Mahadevan A, Razdan RK, Kunos G
(2008) Neuropharmacology 54: 1-7
MeSH Terms: Animals, Arachidonic Acids, Cell Line, Transformed, Chromatography, High Pressure Liquid, Chromatography, Thin Layer, Drug Interactions, Endocannabinoids, Glycerophosphates, Hydrolases, Hydrolysis, Inositol Polyphosphate 5-Phosphatases, Lipopolysaccharides, Macrophages, Metabolic Networks and Pathways, Mice, Mice, Knockout, Neomycin, Phosphatidylinositol-3,4,5-Trisphosphate 5-Phosphatases, Phospholipase D, Phosphoric Monoester Hydrolases, Polyunsaturated Alkamides, Protein Synthesis Inhibitors, Protein Tyrosine Phosphatase, Non-Receptor Type 22, RNA, Small Interfering, Transfection, Type C Phospholipases
Show Abstract · Added April 10, 2014
Endocannabinoids, including anandamide (arachidonoyl ethanolamide) have been implicated in the regulation of a growing number of physiological and pathological processes. Anandamide can be generated from its membrane phospholipid precursor N-arachidonoyl phosphatidylethanolamine (NAPE) through hydrolysis by a phospholipase D (NAPE-PLD). Recent evidence indicates, however, the existence of two additional, parallel pathways. One involves the sequential deacylation of NAPE by alpha,beta-hydrolase 4 (Abhd4) and the subsequent cleavage of glycerophosphate to yield anandamide, and the other one proceeds through phospholipase C-mediated hydrolysis of NAPE to yield phosphoanandamide, which is then dephosphorylated by phosphatases, including the tyrosine phosphatase PTPN22 and the inositol 5' phosphatase SHIP1. Conversion of synthetic NAPE to AEA by brain homogenates from wild-type and NAPE-PLD(-/-) mice can proceed through both the PLC/phosphatase and Abdh4 pathways, with the former being dominant at shorter (<10 min) and the latter at longer (60 min) incubations. In macrophages, the endotoxin-induced synthesis of anandamide proceeds uniquely through the phospholipase C/phosphatase pathway.
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26 MeSH Terms
Transcriptomics, proteomics and interactomics: unique approaches to track the insights of bioremediation.
Singh OV, Nagaraj NS
(2006) Brief Funct Genomic Proteomic 4: 355-62
MeSH Terms: Biodegradation, Environmental, Computational Biology, Electrophoresis, Gel, Two-Dimensional, Environmental Microbiology, Genomics, Mass Spectrometry, Metabolic Networks and Pathways, Models, Theoretical, Oligonucleotide Array Sequence Analysis, Proteomics
Show Abstract · Added June 14, 2013
Microbial mediated bioremediation has a great potential to effectively restore contaminated environment, but the lack of information about factors regulating the growth and metabolism of various microbial communities in polluted environment often limits its implementation. Newly seeded techniques such as transcriptomics, proteomics and interactomics offer remarkable promise as tools to address longstanding questions regarding the molecular mechanisms involved in the control of mineralization pathways. During mineralization, transcript structures and their expression have been studied using high-throughput transcriptomic techniques with microarrays. Generally however, transcripts have no ability to operate any physiological response; rather, they must be translated into proteins with significant functional impact. These proteins can be identified by proteomic techniques using powerful two-dimensional polyacrylamide gel electrophoresis (2-DE). Towards the establishment of functional proteomics, the current advances in mass spectrometry (MS) and protein microarrays play a central role in the proteomics approach. Exploring the differential expression of a wide variety of proteins and screening of the entire genome for proteins that interact with particular mineralization regulatory factors would help us to gain insights into bioremediation.
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10 MeSH Terms
Multivariable difference gel electrophoresis and mass spectrometry: a case study on transforming growth factor-beta and ERBB2 signaling.
Friedman DB, Wang SE, Whitwell CW, Caprioli RM, Arteaga CL
(2007) Mol Cell Proteomics 6: 150-69
MeSH Terms: Amino Acid Sequence, Cathepsin D, Cluster Analysis, Electrophoresis, Gel, Two-Dimensional, Genes, Tumor Suppressor, Humans, Hydrogen-Ion Concentration, Mass Spectrometry, Metabolic Networks and Pathways, Molecular Sequence Data, Multivariate Analysis, Principal Component Analysis, Proteome, Receptor, ErbB-2, Reproducibility of Results, Serpins, Signal Transduction, Time Factors, Transforming Growth Factor beta1
Show Abstract · Added March 5, 2014
Multivariable DIGE/MS was used to investigate proteins altered in expression and/or post-translational modification in response to activation of transforming growth factor (TGF)-beta receptors in MCF10A mammary epithelial cells overexpressing the HER2/Neu (ErbB2) oncogene. Proteome changes were monitored in response to exogenous TGF-beta over time (0, 8, 24, and 40 h), and proteins were resolved using medium range (pH 4-7) and narrow range (pH 5.3-6.5) isoelectric focusing combined with up to 2 mg of protein to allow inspection of lower abundance proteins. Triplicate samples were prepared independently and analyzed together across multiple DIGE gels using a pooled sample internal standard to quantify expression changes with statistical confidence. Unsupervised principle component analysis and hierarchical clustering of the individual DIGE proteome expression maps provided independent confirmation of distinct expression patterns from the individual experiments and demonstrated high reproducibility between replicate samples. Fifty-nine proteins (including some isoforms) that exhibited significant kinetic expression changes were identified using mass spectrometry and database interrogation and were mapped to existing biological networks involved in TGF-beta signaling. Several proteins with a potential role in breast cancer, such as maspin and cathepsin D, were identified as novel molecules associated with TGF-beta signaling.
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19 MeSH Terms