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HEAT INACTIVATION of THIAMINASE in WHOLE FISH by R
August 1966 COMMERCIAL FISHERIES REVIEW 11 HEAT INACTIVATION OF THIAMINASE IN WHOLE FISH By R. H. Gnaedinger and R. A. Krzeczkowskil,c ABSTRACT The time required at various temperatures to inactivate all of the thiam inase in several species of whole fish was studied. Some effects of pH and enzyme concentra tion on the time-temperature inactivation were also determined. Whole raw fish were ground! sealed in spec~ally-constructed m etal cans, heated a t various tempera tures .for. varIOUS length.s <;>f tune! and analyzed for residual thiaminase a ct ivity. Re sul~ md.lcate that a m~un .um tune -tempe.rature of 5 minutes a t 1800 F. is required t<;> mac.tlvate all the .thl~mma s e of who.le hsh. Enzyme concentrations, pH, a nd pos slbly 011 c ontent of flsh mfluence the tune required to destroy thiaminase. INTRODUCTION The heating conditions employed b y commercial mink-food producers and mink ranchers ;0 destroy thiaminase in whole fish are empiri cal. The conditions are not based on predeter nined time-temperature relations for the thermal inactivation of this antimetabolite. A com mon practice, for example, is to cook the fish at 1800 -2000 F. for 15 minutes (Bor gstrom 1962). Most of the specific data available on the time -temperature r e la tion is found in various research publications dealing with the occurrence of thiamina s e in fish , or with studies on the chemistry of the enzyme. Deutsch and Hasler (1943) used 15 m i nutes at 100 0 C . -
Control Engineering Perspective on Genome-Scale Metabolic Modeling
Control Engineering Perspective on Genome-Scale Metabolic Modeling by Andrew Louis Damiani A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama December 12, 2015 Key words: Scheffersomyces stipitis, Flux Balance Analysis, Genome-scale metabolic models, System Identification Framework, Model Validation, Phenotype Phase Plane Analysis Copyright 2015 by Andrew Damiani Approved by Jin Wang, Chair, Associate Professor of Chemical Engineering Q. Peter He, Associate Professor of Chemical Engineering, Tuskegee University Thomas W. Jeffries, Professor of Bacteriology, Emeritus; University of Wisconsin-Madison Allan E. David, Assistant Professor of Chemical Engineering Yoon Y. Lee, Professor of Chemical Engineering Abstract Fossil fuels impart major problems on the global economy and have detrimental effects to the environment, which has caused a world-wide initiative of producing renewable fuels. Lignocellulosic bioethanol for renewable energy has recently gained attention, because it can overcome the limitations that first generation biofuels impose. Nonetheless, in order to have this process commercialized, the biological conversion of pentose sugars, mainly xylose, needs to be improved. Scheffersomyces stipitis has a physiology that makes it a valuable candidate for lignocellulosic bioethanol production, and lately has provided genes for designing recombinant Saccharomyces cerevisiae. In this study, a system biology approach was taken to understand the relationship of the genotype to phenotype, whereby genome-scale metabolic models (GSMMs) are used in conjunction with constraint-based modeling. The major restriction of GSMMs is having an accurate methodology for validation and evaluation. This is due to the size and complexity of the models. -
Generated by SRI International Pathway Tools Version 25.0, Authors S
An online version of this diagram is available at BioCyc.org. Biosynthetic pathways are positioned in the left of the cytoplasm, degradative pathways on the right, and reactions not assigned to any pathway are in the far right of the cytoplasm. Transporters and membrane proteins are shown on the membrane. Periplasmic (where appropriate) and extracellular reactions and proteins may also be shown. Pathways are colored according to their cellular function. Gcf_000238675-HmpCyc: Bacillus smithii 7_3_47FAA Cellular Overview Connections between pathways are omitted for legibility. -
Effects of Dietary Thiaminase on Reproductive Traits in Three Populations of Atlantic Salmon Targeted for Reintroduction Into Lake Ontario
Western University Scholarship@Western Electronic Thesis and Dissertation Repository 1-22-2020 1:00 PM Effects of dietary thiaminase on reproductive traits in three populations of Atlantic salmon targeted for reintroduction into Lake Ontario Kimberly T. Mitchell The University of Western Ontario Supervisor Neff, Bryan D. The University of Western Ontario Graduate Program in Biology A thesis submitted in partial fulfillment of the equirr ements for the degree in Master of Science © Kimberly T. Mitchell 2020 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Aquaculture and Fisheries Commons, Biodiversity Commons, and the Terrestrial and Aquatic Ecology Commons Recommended Citation Mitchell, Kimberly T., "Effects of dietary thiaminase on reproductive traits in three populations of Atlantic salmon targeted for reintroduction into Lake Ontario" (2020). Electronic Thesis and Dissertation Repository. 6826. https://ir.lib.uwo.ca/etd/6826 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract The fitness of reintroduced salmonids in Lake Ontario can be reduced by high levels of thiaminase in exotic prey consumed at the adult stage. If sensitivity to dietary thiaminase differs among the three Atlantic salmon populations targeted for reintroduction into Lake Ontario, this could significantly influence their performance. I quantified the effects of experimental diets that contained high or low (control) levels of thiaminase on thiamine concentrations, survival, growth rate, and reproductive traits (sperm and egg quality) in Atlantic salmon from the three candidate source populations. -
The Structure of Allophanate Hydrolase from Granulibacter Bethesdensis Provides Insights Into Substrate Specificity in the Amidase Signature Family
Marquette University e-Publications@Marquette Biological Sciences Faculty Research and Publications Biological Sciences, Department of 2013 The Structure of Allophanate Hydrolase from Granulibacter bethesdensis Provides Insights into Substrate Specificity in the Amidase Signature Family Yi Lin Marquette University, [email protected] Martin St. Maurice Marquette University, [email protected] Follow this and additional works at: https://epublications.marquette.edu/bio_fac Part of the Biochemistry, Biophysics, and Structural Biology Commons Recommended Citation Lin, Yi and St. Maurice, Martin, "The Structure of Allophanate Hydrolase from Granulibacter bethesdensis Provides Insights into Substrate Specificity in the Amidase Signature Family" (2013). Biological Sciences Faculty Research and Publications. 138. https://epublications.marquette.edu/bio_fac/138 Marquette University e-Publications@Marquette Biological Sciences Faculty Research and Publications/College of Arts and Sciences This paper is NOT THE PUBLISHED VERSION; but the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below. Biochemistry, Vol. 54, No. 4 (January 29, 2013): 690-700. DOI. This article is © American Chemical Society Publications and permission has been granted for this version to appear in e- Publications@Marquette. American Chemical Society Publications does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from American Chemical Society Publications. The Structure of Allophanate Hydrolase from Granulibacter bethesdensis Provides Insights into Substrate Specificity in the Amidase Signature Family Yi Lin Department of Biological Sciences, Marquette University, Milwaukee, WI Martin St. Maurice Department of Biological Sciences, Marquette University, Milwaukee, WI Abstract Allophanate hydrolase (AH) catalyzes the hydrolysis of allophanate, an intermediate in atrazine degradation and urea catabolism pathways, to NH3 and CO2. -
Generated by SRI International Pathway Tools Version 25.0, Authors S
Authors: Pallavi Subhraveti Ron Caspi Quang Ong Peter D Karp An online version of this diagram is available at BioCyc.org. Biosynthetic pathways are positioned in the left of the cytoplasm, degradative pathways on the right, and reactions not assigned to any pathway are in the far right of the cytoplasm. Transporters and membrane proteins are shown on the membrane. Ingrid Keseler Periplasmic (where appropriate) and extracellular reactions and proteins may also be shown. Pathways are colored according to their cellular function. Gcf_000725805Cyc: Streptomyces xanthophaeus Cellular Overview Connections between pathways are omitted for legibility. -
CUMMINGS-DISSERTATION.Pdf (4.094Mb)
D-AMINOACYLASES AND DIPEPTIDASES WITHIN THE AMIDOHYDROLASE SUPERFAMILY: RELATIONSHIP BETWEEN ENZYME STRUCTURE AND SUBSTRATE SPECIFICITY A Dissertation by JENNIFER ANN CUMMINGS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2010 Major Subject: Chemistry D-AMINOACYLASES AND DIPEPTIDASES WITHIN THE AMIDOHYDROLASE SUPERFAMILY: RELATIONSHIP BETWEEN ENZYME STRUCTURE AND SUBSTRATE SPECIFICITY A Dissertation by JENNIFER ANN CUMMINGS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Approved by: Chair of Committee, Frank Raushel Committee Members, Paul Lindahl David Barondeau Gregory Reinhart Head of Department, David Russell December 2010 Major Subject: Chemistry iii ABSTRACT D-Aminoacylases and Dipeptidases within the Amidohydrolase Superfamily: Relationship Between Enzyme Structure and Substrate Specificity. (December 2010) Jennifer Ann Cummings, B.S., Southern Oregon University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Frank Raushel Approximately one third of the genes for the completely sequenced bacterial genomes have an unknown, uncertain, or incorrect functional annotation. Approximately 11,000 putative proteins identified from the fully-sequenced microbial genomes are members of the catalytically diverse Amidohydrolase Superfamily. Members of the Amidohydrolase Superfamily separate into 24 Clusters of Orthologous Groups (cogs). Cog3653 includes proteins annotated as N-acyl-D-amino acid deacetylases (DAAs), and proteins within cog2355 are homologues to the human renal dipeptidase. The substrate profiles of three DAAs (Bb3285, Gox1177 and Sco4986) and six microbial dipeptidase (Sco3058, Gox2272, Cc2746, LmoDP, Rsp0802 and Bh2271) were examined with N-acyl-L-, N-acyl-D-, L-Xaa-L-Xaa, L-Xaa-D-Xaa and D-Xaa-L-Xaa substrate libraries. -
The Microbiota-Produced N-Formyl Peptide Fmlf Promotes Obesity-Induced Glucose
Page 1 of 230 Diabetes Title: The microbiota-produced N-formyl peptide fMLF promotes obesity-induced glucose intolerance Joshua Wollam1, Matthew Riopel1, Yong-Jiang Xu1,2, Andrew M. F. Johnson1, Jachelle M. Ofrecio1, Wei Ying1, Dalila El Ouarrat1, Luisa S. Chan3, Andrew W. Han3, Nadir A. Mahmood3, Caitlin N. Ryan3, Yun Sok Lee1, Jeramie D. Watrous1,2, Mahendra D. Chordia4, Dongfeng Pan4, Mohit Jain1,2, Jerrold M. Olefsky1 * Affiliations: 1 Division of Endocrinology & Metabolism, Department of Medicine, University of California, San Diego, La Jolla, California, USA. 2 Department of Pharmacology, University of California, San Diego, La Jolla, California, USA. 3 Second Genome, Inc., South San Francisco, California, USA. 4 Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA. * Correspondence to: 858-534-2230, [email protected] Word Count: 4749 Figures: 6 Supplemental Figures: 11 Supplemental Tables: 5 1 Diabetes Publish Ahead of Print, published online April 22, 2019 Diabetes Page 2 of 230 ABSTRACT The composition of the gastrointestinal (GI) microbiota and associated metabolites changes dramatically with diet and the development of obesity. Although many correlations have been described, specific mechanistic links between these changes and glucose homeostasis remain to be defined. Here we show that blood and intestinal levels of the microbiota-produced N-formyl peptide, formyl-methionyl-leucyl-phenylalanine (fMLF), are elevated in high fat diet (HFD)- induced obese mice. Genetic or pharmacological inhibition of the N-formyl peptide receptor Fpr1 leads to increased insulin levels and improved glucose tolerance, dependent upon glucagon- like peptide-1 (GLP-1). Obese Fpr1-knockout (Fpr1-KO) mice also display an altered microbiome, exemplifying the dynamic relationship between host metabolism and microbiota. -
Supplementary Information
Supplementary information (a) (b) Figure S1. Resistant (a) and sensitive (b) gene scores plotted against subsystems involved in cell regulation. The small circles represent the individual hits and the large circles represent the mean of each subsystem. Each individual score signifies the mean of 12 trials – three biological and four technical. The p-value was calculated as a two-tailed t-test and significance was determined using the Benjamini-Hochberg procedure; false discovery rate was selected to be 0.1. Plots constructed using Pathway Tools, Omics Dashboard. Figure S2. Connectivity map displaying the predicted functional associations between the silver-resistant gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S3. Connectivity map displaying the predicted functional associations between the silver-sensitive gene hits; disconnected gene hits not shown. The thicknesses of the lines indicate the degree of confidence prediction for the given interaction, based on fusion, co-occurrence, experimental and co-expression data. Figure produced using STRING (version 10.5) and a medium confidence score (approximate probability) of 0.4. Figure S4. Metabolic overview of the pathways in Escherichia coli. The pathways involved in silver-resistance are coloured according to respective normalized score. Each individual score represents the mean of 12 trials – three biological and four technical. Amino acid – upward pointing triangle, carbohydrate – square, proteins – diamond, purines – vertical ellipse, cofactor – downward pointing triangle, tRNA – tee, and other – circle. -
Supplementary Table S1. the Key Enzymes for Autotrophic Growth in C
Electronic Supplementary Material (ESI) for Metallomics. This journal is © The Royal Society of Chemistry 2015 Supplementary Table S1. The key enzymes for autotrophic growth in C. metallidurans Name Rmet-number Spec. Predicted Mass, (kDa) Determined Mass, (kDa) Activity HOS Rmet_1522 to Rmet_1525 28.7 65.2, 25.4, 23.4, 52.8 = 166.8 62.4±1.8, 26.4±1.3, 24.0±0.6, 54.1±2.5, =235±20a HOP Rmet_1297, Rmet_1298, 67.0 69.0 + 38.6 = 108 148±24a CAX Rmet_1500, Rmet_1501 2.82 8 * (13.6 + 52.5) = 529 475±36a PRK Rmet_1512 0.994 8 * 32.4 = 259 256±11a aMean value of masses determined by S300 size exclusion chromatography and sucrose gradient centrifugation. HOS, NAD-reducing soluble hydrogenase; HOP, membrane-bound hydrogenase; CAX, ribulose-bis-phosphate carboxylase/oxygenase; PRK, phosphoribulokinase. Specific activity in U/mg protein. Supplementary Table S2. Genes expressed differently in AE104 compared to CH34 wild typea Operon Region Name Gene Q D Description UP Op1321r Rmet_4594 zntA 1.72 2.88 Q1LEH0 Heavy metal translocating P -type ATPase Op1322f Rmet_4595 czcI2 2.03 2.92 Q1LEG9 Putative uncharacterized protein Op1322f Rmet_4596 czcC2 23.34 5.36 Q1LEG8 Outer membrane efflux protein Op1322f Rmet_4597 czcB2' 15.36 9.68 Q1LEG7 Secretion protein HlyD Op0075f Rmet_0260 - 2.31 2.48 Q1LRT0 Putative transmembrane protein Op0075f Rmet_0261 coxB 2.08 2.49 Q1LRS9 Cytochrome c oxidase subunit 2 Adjacent to CMGI-7 Op0335f Rmet_1171 tnpA 7.03 21.74 Q9F8S6 Transposase (Transposase, IS4 family) CMGI-2 Op0362r Rmet_1251 tnp 4.08 0.61 Q1LNY9 Putative uncharacterized -
A Bayesian Model for Inference of Metabolic Divergence Among Microbial Communities
BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities Mahdi Shafiei1., Katherine A. Dunn2., Hugh Chipman3, Hong Gu1, Joseph P. Bielawski1,2* 1 Department of Mathematics & Statistics, Dalhousie University, Halifax, Nova Scotia, Canada, 2 Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada, 3 Department of Mathematics & Statistics, Acadia University, Wolfville, Nova Scotia, Canada Abstract Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. -
Modeling and Computational Prediction of Metabolic Channelling
MODELING AND COMPUTATIONAL PREDICTION OF METABOLIC CHANNELLING by Christopher Morran Sanford A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Molecular Genetics University of Toronto © Copyright by Christopher Morran Sanford 2009 Abstract MODELING AND COMPUTATIONAL PREDICTION OF METABOLIC CHANNELLING Master of Science 2009 Christopher Morran Sanford Graduate Department of Molecular Genetics University of Toronto Metabolic channelling occurs when two enzymes that act on a common substrate pass that intermediate directly from one active site to the next without allowing it to diffuse into the surrounding aqueous medium. In this study, properties of channelling are investigated through the use of computational models and cell simulation tools. The effects of enzyme kinetics and thermodynamics on channelling are explored with the emphasis on validating the hypothesized roles of metabolic channelling in living cells. These simulations identify situations in which channelling can induce acceleration of reaction velocities and reduction in the free concentration of intermediate metabolites. Databases of biological information, including metabolic, thermodynamic, toxicity, inhibitory, gene fusion and physical protein interaction data are used to predict examples of potentially channelled enzyme pairs. The predictions are used both to support the hypothesized evolutionary motivations for channelling, and to propose potential enzyme interactions that may be worthy of future investigation. ii Acknowledgements I wish to thank my supervisor Dr. John Parkinson for the guidance he has provided during my time spent in his lab, as well as for his extensive help in the writing of this thesis. I am grateful for the advice of my committee members, Prof.