Computational Genomics of Hyperthermophiles
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Oxidative Pentose Phosphate Pathway Enzyme 6-Phosphogluconate Dehydrogenase Plays a Key Role in Breast Cancer Metabolism
biology Article Oxidative Pentose Phosphate Pathway Enzyme 6-Phosphogluconate Dehydrogenase Plays a Key Role in Breast Cancer Metabolism Ibrahim H. Polat 1,2,3 ,Míriam Tarrado-Castellarnau 1,4 , Rohit Bharat 1, Jordi Perarnau 1 , Adrian Benito 1,5 , Roldán Cortés 1, Philippe Sabatier 2 and Marta Cascante 1,4,* 1 Department of Biochemistry and Molecular Biomedicine and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; [email protected] (I.H.P.); [email protected] (M.T.-C.); [email protected] (R.B.); [email protected] (J.P.); [email protected] (A.B.); [email protected] (R.C.) 2 Equipe Environnement et Prédiction de la Santé des Populations, Laboratoire TIMC (UMR 5525), CHU de Grenoble, Université Grenoble Alpes, 38700 CEDEX La Tronche, France; [email protected] 3 Department of Medicine, Hematology/Oncology, Goethe-University Frankfurt, 60590 Frankfurt, Germany 4 Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), 28001 Madrid, Spain 5 Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London W12 0NN, UK * Correspondence: [email protected] Simple Summary: Cancer cells alter their metabolism to maintain their high need for energy, produce Citation: Polat, I.H.; enough macromolecules for biosynthesis, and preserve their redox status. The investigation of Tarrado-Castellarnau, M.; Bharat, R.; cancer cell-specific metabolic alterations has vital importance to identify targets to be exploited for Perarnau, J.; Benito, A.; Cortés, R.; therapeutic development. The pentose phosphate pathway (PPP) is often highly activated in tumor Sabatier, P.; Cascante, M. -
Comparison of the Effects on Mrna and Mirna Stability Arian Aryani and Bernd Denecke*
Aryani and Denecke BMC Research Notes (2015) 8:164 DOI 10.1186/s13104-015-1114-z RESEARCH ARTICLE Open Access In vitro application of ribonucleases: comparison of the effects on mRNA and miRNA stability Arian Aryani and Bernd Denecke* Abstract Background: MicroRNA has become important in a wide range of research interests. Due to the increasing number of known microRNAs, these molecules are likely to be increasingly seen as a new class of biomarkers. This is driven by the fact that microRNAs are relatively stable when circulating in the plasma. Despite extensive analysis of mechanisms involved in microRNA processing, relatively little is known about the in vitro decay of microRNAs under defined conditions or about the relative stabilities of mRNAs and microRNAs. Methods: In this in vitro study, equal amounts of total RNA of identical RNA pools were treated with different ribonucleases under defined conditions. Degradation of total RNA was assessed using microfluidic analysis mainly based on ribosomal RNA. To evaluate the influence of the specific RNases on the different classes of RNA (ribosomal RNA, mRNA, miRNA) ribosomal RNA as well as a pattern of specific mRNAs and miRNAs was quantified using RT-qPCR assays. By comparison to the untreated control sample the ribonuclease-specific degradation grade depending on the RNA class was determined. Results: In the present in vitro study we have investigated the stabilities of mRNA and microRNA with respect to the influence of ribonucleases used in laboratory practice. Total RNA was treated with specific ribonucleases and the decay of different kinds of RNA was analysed by RT-qPCR and miniaturized gel electrophoresis. -
Bacteria Belonging to Pseudomonas Typographi Sp. Nov. from the Bark Beetle Ips Typographus Have Genomic Potential to Aid in the Host Ecology
insects Article Bacteria Belonging to Pseudomonas typographi sp. nov. from the Bark Beetle Ips typographus Have Genomic Potential to Aid in the Host Ecology Ezequiel Peral-Aranega 1,2 , Zaki Saati-Santamaría 1,2 , Miroslav Kolaˇrik 3,4, Raúl Rivas 1,2,5 and Paula García-Fraile 1,2,4,5,* 1 Microbiology and Genetics Department, University of Salamanca, 37007 Salamanca, Spain; [email protected] (E.P.-A.); [email protected] (Z.S.-S.); [email protected] (R.R.) 2 Spanish-Portuguese Institute for Agricultural Research (CIALE), 37185 Salamanca, Spain 3 Department of Botany, Faculty of Science, Charles University, Benátská 2, 128 01 Prague, Czech Republic; [email protected] 4 Laboratory of Fungal Genetics and Metabolism, Institute of Microbiology of the Academy of Sciences of the Czech Republic, 142 20 Prague, Czech Republic 5 Associated Research Unit of Plant-Microorganism Interaction, University of Salamanca-IRNASA-CSIC, 37008 Salamanca, Spain * Correspondence: [email protected] Received: 4 July 2020; Accepted: 1 September 2020; Published: 3 September 2020 Simple Summary: European Bark Beetle (Ips typographus) is a pest that affects dead and weakened spruce trees. Under certain environmental conditions, it has massive outbreaks, resulting in attacks of healthy trees, becoming a forest pest. It has been proposed that the bark beetle’s microbiome plays a key role in the insect’s ecology, providing nutrients, inhibiting pathogens, and degrading tree defense compounds, among other probable traits. During a study of bacterial associates from I. typographus, we isolated three strains identified as Pseudomonas from different beetle life stages. In this work, we aimed to reveal the taxonomic status of these bacterial strains and to sequence and annotate their genomes to mine possible traits related to a role within the bark beetle holobiont. -
Enzymes Handling/Processing
Enzymes Handling/Processing 1 Identification of Petitioned Substance 2 3 This Technical Report addresses enzymes used in used in food processing (handling), which are 4 traditionally derived from various biological sources that include microorganisms (i.e., fungi and 5 bacteria), plants, and animals. Approximately 19 enzyme types are used in organic food processing, from 6 at least 72 different sources (e.g., strains of bacteria) (ETA, 2004). In this Technical Report, information is 7 provided about animal, microbial, and plant-derived enzymes generally, and more detailed information 8 is presented for at least one model enzyme in each group. 9 10 Enzymes Derived from Animal Sources: 11 Commonly used animal-derived enzymes include animal lipase, bovine liver catalase, egg white 12 lysozyme, pancreatin, pepsin, rennet, and trypsin. The model enzyme is rennet. Additional details are 13 also provided for egg white lysozyme. 14 15 Chemical Name: Trade Name: 16 Rennet (animal-derived) Rennet 17 18 Other Names: CAS Number: 19 Bovine rennet 9001-98-3 20 Rennin 25 21 Chymosin 26 Other Codes: 22 Prorennin 27 Enzyme Commission number: 3.4.23.4 23 Rennase 28 24 29 30 31 Chemical Name: CAS Number: 32 Peptidoglycan N-acetylmuramoylhydrolase 9001-63-2 33 34 Other Name: Other Codes: 35 Muramidase Enzyme Commission number: 3.2.1.17 36 37 Trade Name: 38 Egg white lysozyme 39 40 Enzymes Derived from Plant Sources: 41 Commonly used plant-derived enzymes include bromelain, papain, chinitase, plant-derived phytases, and 42 ficin. The model enzyme is bromelain. -
Molybdoproteomes and Evolution of Molybdenum Utilization
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Vadim Gladyshev Publications Biochemistry, Department of April 2008 Molybdoproteomes and evolution of molybdenum utilization Yan Zhang University of Nebraska-Lincoln, [email protected] Vadim N. Gladyshev University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/biochemgladyshev Part of the Biochemistry, Biophysics, and Structural Biology Commons Zhang, Yan and Gladyshev, Vadim N., "Molybdoproteomes and evolution of molybdenum utilization" (2008). Vadim Gladyshev Publications. 78. https://digitalcommons.unl.edu/biochemgladyshev/78 This Article is brought to you for free and open access by the Biochemistry, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Vadim Gladyshev Publications by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Published in Journal of Molecular Biology (2008); doi: 10.1016/j.jmb.2008.03.051 Copyright © 2008 Elsevier. Used by permission. http://www.sciencedirect.com/science/journal/00222836 Submitted November 26, 2007; revised March 15, 2008; accepted March 25, 2008; published online as “Accepted Manuscript” April 1, 2008. Molybdoproteomes and evolution of molybdenum utilization Yan Zhang and Vadim N. Gladyshev* Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, NE 685880664 *Corresponding author—tel 402 472-4948, fax 402 472-7842, email [email protected] Abstract The trace element molybdenum (Mo) is utilized in many life forms, where it is a key component of several enzymes involved in nitrogen, sulfur, and carbon metabolism. With the exception of nitrogenase, Mo is bound in proteins to a pterin, thus forming the molybdenum cofactor (Moco) at the catalytic sites of molybdoenzymes. -
Sulfite Dehydrogenases in Organotrophic Bacteria : Enzymes
Sulfite dehydrogenases in organotrophic bacteria: enzymes, genes and regulation. Dissertation zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften (Dr. rer. nat.) an der Universität Konstanz Fachbereich Biologie vorgelegt von Sabine Lehmann Tag der mündlichen Prüfung: 10. April 2013 1. Referent: Prof. Dr. Bernhard Schink 2. Referent: Prof. Dr. Andrew W. B. Johnston So eine Arbeit wird eigentlich nie fertig, man muss sie für fertig erklären, wenn man nach Zeit und Umständen das möglichste getan hat. (Johann Wolfgang von Goethe, Italienische Reise, 1787) DANKSAGUNG An dieser Stelle möchte ich mich herzlich bei folgenden Personen bedanken: . Prof. Dr. Alasdair M. Cook (Universität Konstanz, Deutschland), der mir dieses Thema und seine Laboratorien zur Verfügung stellte, . Prof. Dr. Bernhard Schink (Universität Konstanz, Deutschland), für seine spontane und engagierte Übernahme der Betreuung, . Prof. Dr. Andrew W. B. Johnston (University of East Anglia, UK), für seine herzliche und bereitwillige Aufnahme in seiner Arbeitsgruppe, seiner engagierten Unter- stützung, sowie für die Übernahme des Koreferates, . Prof. Dr. Frithjof C. Küpper (University of Aberdeen, UK), für seine große Hilfsbereitschaft bei der vorliegenden Arbeit und geplanter Manuskripte, als auch für die mentale Unterstützung während der letzten Jahre! Desweiteren möchte ich herzlichst Dr. David Schleheck für die Übernahme des Koreferates der mündlichen Prüfung sowie Prof. Dr. Alexander Bürkle, für die Übernahme des Prüfungsvorsitzes sowie für seine vielen hilfreichen Ratschläge danken! Ein herzliches Dankeschön geht an alle beteiligten Arbeitsgruppen der Universität Konstanz, der UEA und des SAMS, ganz besonders möchte ich dabei folgenden Personen danken: . Dr. David Schleheck und Karin Denger, für die kritische Durchsicht dieser Arbeit, der durch und durch sehr engagierten Hilfsbereitschaft bei Problemen, den zahlreichen wissenschaftlichen Diskussionen und für die aufbauenden Worte, . -
Genome 559 Intro to Statistical and Computational Genomics
Genome 559! Intro to Statistical and Computational Genomics! Lecture 16a: Computational Gene Prediction Larry Ruzzo Today: Finding protein-coding genes coding sequence statistics prokaryotes mammals More on classes More practice Codons & The Genetic Code Ala : Alanine Second Base Arg : Arginine U C A G Asn : Asparagine Phe Ser Tyr Cys U Asp : Aspartic acid Phe Ser Tyr Cys C Cys : Cysteine U Leu Ser Stop Stop A Gln : Glutamine Leu Ser Stop Trp G Glu : Glutamic acid Leu Pro His Arg U Gly : Glycine Leu Pro His Arg C His : Histidine C Leu Pro Gln Arg A Ile : Isoleucine Leu Pro Gln Arg G Leu : Leucine Ile Thr Asn Ser U Lys : Lysine Ile Thr Asn Ser C Met : Methionine First Base A Third Base Ile Thr Lys Arg A Phe : Phenylalanine Met/Start Thr Lys Arg G Pro : Proline Val Ala Asp Gly U Ser : Serine Val Ala Asp Gly C Thr : Threonine G Val Ala Glu Gly A Trp : Tryptophane Val Ala Glu Gly G Tyr : Tyrosine Val : Valine Idea #1: Find Long ORF’s Reading frame: which of the 3 possible sequences of triples does the ribosome read? Open Reading Frame: No stop codons In random DNA average ORF = 64/3 = 21 triplets 300bp ORF once per 36kbp per strand But average protein ~ 1000bp So, coding DNA is not random–stops are rare Scanning for ORFs 1 2 3 U U A A U G U G U C A U U G A U U A A G! A A U U A C A C A G U A A C U A A U A C! 4 5 6 Idea #2: Codon Frequency,… Even between stops, coding DNA is not random In random DNA, Leu : Ala : Tryp = 6 : 4 : 1 But in real protein, ratios ~ 6.9 : 6.5 : 1 Even more: synonym usage is biased (in a species dependant way) Examples known with 90% AT 3rd base Why? E.g. -
Enzyme Characterisation and Kinetic Modelling of the Pentose Phosphate
1 Enzyme characterisation and kinetic modelling of the pentose 2 phosphate pathway in yeast 1;2 3 Hanan L. Messiha Edward Kent 1;3;4 Naglis Malys 1;2;6 Kathleen M. Carroll 1;5 Neil Swainston 1;4 s 1;4;7;∗ t Pedro Mendes 1;4 n Kieran Smallbone i r P 1Manchester Centre for Integrative Systems Biology e r 2Faculty of Life Sciences 3 P Doctoral Training Centre in Integrative Systems Biology 4School of Computer Science 5School of Chemistry University of Manchester, M13 9PL, UK. 6School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry, UK. 7Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06030, USA. 4 Abstract 5 We present the quantification and kinetic characterisation of the enzymes of the pentose 6 phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical 7 model that describes the dynamics of this system and allows us to predict changes in metabo- 8 lite concentrations and fluxes in response to perturbations. We use the model to study the 9 response of yeast to a glucose pulse. We then combine the model with an existing glycolysis 10 model to study the effect of oxidative stress on carbohydrate metabolism. The combina- 11 tion of these two models was made possible by the standardised enzyme kinetic experiments 12 carried out in both studies. This work demonstrates the feasibility of constructing larger 13 network-scale models by merging smaller pathway-scale models. ∗To whom correspondence should be addressed at [email protected] PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.146v4 | CC-BY 3.0 Open Access | received: 10 Apr 2014, published: 10 Apr 2014 1 14 Introduction 15 The pentose phosphate pathway (PPP) is a central and widely conserved metabolic pathway of car- 16 bohydrate metabolism which, in eukaryotic cells, is located in the cytoplasm (see Figure 1). -
Adaptive Laboratory Evolution Enhances Methanol Tolerance and Conversion in Engineered Corynebacterium Glutamicum
ARTICLE https://doi.org/10.1038/s42003-020-0954-9 OPEN Adaptive laboratory evolution enhances methanol tolerance and conversion in engineered Corynebacterium glutamicum Yu Wang 1, Liwen Fan1,2, Philibert Tuyishime1, Jiao Liu1, Kun Zhang1,3, Ning Gao1,3, Zhihui Zhang1,3, ✉ ✉ 1234567890():,; Xiaomeng Ni1, Jinhui Feng1, Qianqian Yuan1, Hongwu Ma1, Ping Zheng1,2,3 , Jibin Sun1,3 & Yanhe Ma1 Synthetic methylotrophy has recently been intensively studied to achieve methanol-based biomanufacturing of fuels and chemicals. However, attempts to engineer platform micro- organisms to utilize methanol mainly focus on enzyme and pathway engineering. Herein, we enhanced methanol bioconversion of synthetic methylotrophs by improving cellular tolerance to methanol. A previously engineered methanol-dependent Corynebacterium glutamicum is subjected to adaptive laboratory evolution with elevated methanol content. Unexpectedly, the evolved strain not only tolerates higher concentrations of methanol but also shows improved growth and methanol utilization. Transcriptome analysis suggests increased methanol con- centrations rebalance methylotrophic metabolism by down-regulating glycolysis and up- regulating amino acid biosynthesis, oxidative phosphorylation, ribosome biosynthesis, and parts of TCA cycle. Mutations in the O-acetyl-L-homoserine sulfhydrylase Cgl0653 catalyzing formation of L-methionine analog from methanol and methanol-induced membrane-bound transporter Cgl0833 are proven crucial for methanol tolerance. This study demonstrates the importance of -
Contig Protein Description Symbol Anterior Posterior Ratio
Table S2. List of proteins detected in anterior and posterior intestine pooled samples. Data on protein expression are mean ± SEM of 4 pools fed the experimental diets. The number of the contig in the Sea Bream Database (http://nutrigroup-iats.org/seabreamdb) is indicated. Contig Protein Description Symbol Anterior Posterior Ratio Ant/Pos C2_6629 1,4-alpha-glucan-branching enzyme GBE1 0.88±0.1 0.91±0.03 0.98 C2_4764 116 kDa U5 small nuclear ribonucleoprotein component EFTUD2 0.74±0.09 0.71±0.05 1.03 C2_299 14-3-3 protein beta/alpha-1 YWHAB 1.45±0.23 2.18±0.09 0.67 C2_268 14-3-3 protein epsilon YWHAE 1.28±0.2 2.01±0.13 0.63 C2_2474 14-3-3 protein gamma-1 YWHAG 1.8±0.41 2.72±0.09 0.66 C2_1017 14-3-3 protein zeta YWHAZ 1.33±0.14 4.41±0.38 0.30 C2_34474 14-3-3-like protein 2 YWHAQ 1.3±0.11 1.85±0.13 0.70 C2_4902 17-beta-hydroxysteroid dehydrogenase 14 HSD17B14 0.93±0.05 2.33±0.09 0.40 C2_3100 1-acylglycerol-3-phosphate O-acyltransferase ABHD5 ABHD5 0.85±0.07 0.78±0.13 1.10 C2_15440 1-phosphatidylinositol phosphodiesterase PLCD1 0.65±0.12 0.4±0.06 1.65 C2_12986 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase delta-1 PLCD1 0.76±0.08 1.15±0.16 0.66 C2_4412 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase gamma-2 PLCG2 1.13±0.08 2.08±0.27 0.54 C2_3170 2,4-dienoyl-CoA reductase, mitochondrial DECR1 1.16±0.1 0.83±0.03 1.39 C2_1520 26S protease regulatory subunit 10B PSMC6 1.37±0.21 1.43±0.04 0.96 C2_4264 26S protease regulatory subunit 4 PSMC1 1.2±0.2 1.78±0.08 0.68 C2_1666 26S protease regulatory subunit 6A PSMC3 1.44±0.24 1.61±0.08 -
Lecture 7 - the Calvin Cycle and the Pentose Phosphate Pathway
Lecture 7 - The Calvin Cycle and the Pentose Phosphate Pathway Chem 454: Regulatory Mechanisms in Biochemistry University of Wisconsin-Eau Claire 1 Introduction The Calvin cycle Text The dark reactions of photosynthesis in green plants Reduces carbon from CO2 to hexose (C6H12O6) Requires ATP for free energy and NADPH as a reducing agent. 2 2 Introduction NADH versus Text NADPH 3 3 Introduction The Pentose Phosphate Pathway Used in all organisms Glucose is oxidized and decarboxylated to produce reduced NADPH Used for the synthesis and degradation of pentoses Shares reactions with the Calvin cycle 4 4 1. The Calvin Cycle Source of carbon is CO2 Text Takes place in the stroma of the chloroplasts Comprises three stages Fixation of CO2 by ribulose 1,5-bisphosphate to form two 3-phosphoglycerate molecules Reduction of 3-phosphoglycerate to produce hexose sugars Regeneration of ribulose 1,5-bisphosphate 5 5 1. Calvin Cycle Three stages 6 6 1.1 Stage I: Fixation Incorporation of CO2 into 3-phosphoglycerate 7 7 1.1 Stage I: Fixation Rubisco: Ribulose 1,5- bisphosphate carboxylase/ oxygenase 8 8 1.1 Stage I: Fixation Active site contains a divalent metal ion 9 9 1.2 Rubisco Oxygenase Activity Rubisco also catalyzes a wasteful oxygenase reaction: 10 10 1.3 State II: Formation of Hexoses Reactions similar to those of gluconeogenesis But they take place in the chloroplasts And use NADPH instead of NADH 11 11 1.3 State III: Regeneration of Ribulose 1,5-Bisphosphosphate Involves a sequence of transketolase and aldolase reactions. 12 12 1.3 State III: -
Ijphs Jan Feb 07.Pmd
www.ijpsonline.com Review Article Pharmacogenomics and Computational Genomics: An Appraisal P. K. TRIPATHI*, SHALINI TRIPATHI AND N. K. JAIN1 Rajarshi Rananjay Sinh College of Pharmacy, Amethi-227 405, 1Department of Pharmaceutical Sciences, Dr. H. S. Gour Vishwavidyalaya, Sagar-470 003, India. Pharmacogenomics deals with the interactions of individual genetic constitution with drug therapy. It is very likely that pharmacogenetic tests will make up a significant proportion of total molecular biology testing in future. Therefore, this article emphasizes the applications of pharmacogenomics, and computational genome analysis in drug therapy. Patients sometimes experience adverse drug reactions for both efficacy and safety of a given drug regimen and (ADR) leading to deterioration of their underlying this is the central topic of pharmacogenomics. Drug condition in clinical practice. Physiology of individual development and pretreatment genetic analysis of patients patient can vary, so the response of an individual to drug will be two major practical aspects in pharmacogenomics. therapy may also be highly variable. This is a major Highly specialized drug research laboratories will achieve clinical problem, since this inter-individual variability is the first goal, while the second goal has to be achieved until now only partly predictable. Besides these medical by clinical .com).laboratories 2,3. problems, cost-benefit calculations for a given pharmacological therapy will be affected significantly. Drug development: This is exemplified by a recent US study, which estimates There are two pharmacogenomic approaches. First, for that over 100,000 patients die every year from ADRs1. most therapies a correct diagnosis is mandatory for a satisfactory therapeutic result. This is more relevant There are many factors such as age, sex, nutritional.medknow because many diseases may be caused by different status, kidney and liver function, concomitant diseases and genetic defects or be significantly affected by the genetic medications, and the disease that affects drug responses.