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1.
Cotyledons of tomato seedlings that germinated in a 20 µM AlK(SO4)2 solution remained chlorotic while those germinated in an aluminum free medium were normal (green) in color. Previously, we have reported the effect of aluminum toxicity on root proteome in tomato seedlings (Zhou et al.1). Two dimensional DIGE protein analysis demonstrated that Al stress affected three major processes in the chlorotic cotyledons: antioxidant and detoxification metabolism (induced), glyoxylate and glycolytic processes (enhanced), and the photosynthetic and carbon fixation machinery (suppressed).Key words: aluminum, cotyledons, proteome, tomatoDifferent biochemical processes occur depending on the developmental stages of cotyledons. During early seed germination, before the greening of the cotyledons, glyoxysomes enzymes are very active. Fatty acids are converted to glucose via the gluconeogenesis pathway.2,3 In greening cotyledons, chloroplast proteins for photosynthesis and leaf peroxisomal enzymes in the glycolate pathway for photorespiration are metabolized.24 Enzymes involved in regulatory mechanisms such as protein kinases, protein phosphatases, and mitochondrial enzymes are highly expressed.3,5,6The chlorotic cotyledons are similar to other chlorotic counterparts in that both contains lower levels of chlorophyll, thus the photosynthetic activities are not as active. In order to understand the impact of Al on tomato cotyledon development, a comparative proteome analysis was performed using 2D-DIGE following the as previously described procedure.1 Some proteins accumulated differentially in Al-treated (chlorotic) and untreated cotyledons (Fig. 1). Mass spectrometry of tryptic digestion fragments of the proteins followed by database search has identified some of the differentially expressed proteins (Open in a separate windowFigure 1Image of protein spots generated by Samspot analysis of Al treated and untreated tomato cotyledons proteomes separated on 2D-DIGE.

Table 1

Proteins identified from tomato cotyledons of seeds germinating in Al-solution
Spot No.Fold (treated/ctr)ANOVA (p value)AnnotationSGN accession
12.340.00137412S seed storages protein (CRA1)SGN-U314355
22.130.003651unidentified
32.00.006353lipase class 3 familySGN-U312972
41.960.002351large subunit of RUBISCOSGN-U346314
51.952.66E-05arginine-tRNA ligaseSGN-U316216
61.950.003343unidentified
71.780.009219Monodehydroascorbate reductase (NADH)SGN-U315877
81.780.000343unidentified
91.754.67E-05unidentified
121.700.002093unidentified
131.680.004522unidentified
151.660.019437Glutamate dehydrogenase 1SGN-U312368
161.660.027183unidentified
171.622.01E-08Major latex protein-related, pathogenesis-relatedSGN-U312368
18−1.610.009019RUBisCo activaseSGN-U312543
191.610.003876Cupin family proteinSGN-U312537
201.600.000376unidentified
221.590.037216unidentified
0.003147unidentified
29−1.560.001267RUBisCo activaseSGN-U312543
351.520.001955unidentified
401.470.007025unidentified
411.470.009446unidentified
451.450.001134unidentified
59−1.405.91E-0512 S seed storage proteinSGN-U314355
611.391.96E-05MD-2-related lipid recognition domain containing proteinSGN-U312452
651.370.000608triosephosphate isomerase, cytosolicSGN-U312988
681.360.004225unidentified
811.320.001128unidentified
82−1.310.00140833 kDa precursor protein of oxygen-evolving complexSGN-U312530
871.300.002306unidentified
89−1.30.000765unidentified
921.290.000125superoxide dismutaseSGN-U314405
981.280.000246triosephosphate isomerase, cytosolicSGN-U312988
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2.
Up to 60 different proteins are recruited to the site of clathrin-mediated endocytosis in an ordered sequence. These accessory proteins have roles during all the different stages of clathrin-mediated endocytosis. First, they participate in the initiation of the endocytic event, thereby determining when and where endocytic vesicles are made; later they are involved in the maturation of the clathrin coat, recruitment of specific cargo molecules, bending of the membrane, and finally in scission and uncoating of the nascent vesicle. In addition, many of the accessory components are involved in regulating and coupling the actin cytoskeleton to the endocytic membrane. We will discuss the different accessory components and their various roles. Most of the data comes from studies performed with cultured mammalian cells or yeast cells. The process of endocytosis is well conserved between these different organisms, but there are also many interesting differences that may shed light on the mechanistic principles of endocytosis.Receptor-mediated endocytosis is the process by which eukaryotic cells concentrate and internalize cell surface receptors from the plasma membrane into small (∼50 nm– ∼100 nm diameter) membrane vesicles (Chen et al. 2011; McMahon and Boucrot 2011; Weinberg and Drubin 2012). This mechanism has been studied extensively in mammalian tissue culture cells and in yeast, and despite the evolutionary distance between yeast and mammalian cells the mechanism of receptor-mediated endocytosis in the respective cell types show remarkable similarities. Indeed many of the ∼60 endocytic accessory proteins (EAPs) found in yeast have homologs in mammalian cells, although both cell types also have unique EAPs (McMahon and Boucrot 2011; Weinberg and Drubin 2012).In the following, we briefly describe known yeast and mammalian EAPs (Sigismund et al. 2012; see also Bökel and Brand 2013; Cosker and Segal 2014; Di Fiore and von Zastrow 2014).

Table 1.

Key endocytic proteins in mammals and in yeast
MammalsYeastFunction
Coat proteinsClathrinChc1, Clc1Coat protein
AP-2 (4 subunits)AP-2 (4 subunits)Adaptor protein
EpsinEnt1/2Adaptor protein
AP180Yap1801/2Adaptor protein
CALMAdaptor protein
NECAPAdaptor protein
FCHo1/2Syp1Adaptor protein
Eps15Ede1Scaffold protein
IntersectinPan1Scaffold protein
Sla1Scaffold protein
End3Scaffold protein
N-BAR proteinsAmphiphysinRvs161/167Membrane curvature sensor/generator
EndophilinMembrane curvature sensor/generator
BIN1Membrane curvature sensor/generator
DynaminDynamin1/2Vps1Mechanoenzyme, GTPase
Actin cytoskeletonActinAct1Actin monomer
Arp2/3 complexArp2/3 complexActin filament nucleator
ABP1Abp1Actin-binding protein
CortactinActin-binding protein
CoroninCrn1Actin-binding protein
CofilinCof1Actin depolymerizing protein
Actin regulatorsMyosin 1EMyo3/5Actin motor
Myosin 6Actin motor
Hip1R, Hip1Sla2Actin-membrane coupler
SyndapinBzz1BAR domain protein
N-WASPLas17Regulator of actin nucleation
WIP/WIREVrp1Regulator of actin nucleation
SNX9Regulator of actin nucleation
Bbc1Regulator of actin nucleation
Other regulatorsAAK1Ark1/Prk1Protein kinase
Auxilin, GAKUncoating factor
SynaptojaninSjl2Lipid phosphatase
OCRL1Lipid phosphatase
Open in a separate windowThe proteins are grouped into functional categories and the homologous proteins are listed on the same line.  相似文献   

3.
4.
5.
All published records for the 49 species of moth flies known from North Africa are reviewed and discussed: Morocco (27 species), Algeria (33 species), Tunisia (18 species) and Egypt (five species). In addition, records of seven species of Psychodinae new to the fauna of Morocco are added, of which three are new mentions for North Africa (Table (Table1)1) and one is a new record for Egypt. Telmatoscopus squamifer Tonnoir, 1922 is transferred to the genus Iranotelmatoscopus Ježek, 1987, comb. n. Satchelliella reghayana Boumezzough & Vaillant, 1987 is transferred to the genus Pneumia Enderlein, 1935, comb. n. Pneumia aberrans Tonnoir, 1922 is transferred to the subgenus Logima.

Table 1.

Species (in alphabetical order) of Psychodinae known from the North African countries. Libya has been omitted because no information exists in the literature from Libya.
MoroccoAlgeriaTunisiaEgypt
Bazarella atra (Vaillant, 1955)X*X
Berdeniella lucasii (Satchell, 1955)X
Clogmia albipunctata (Williston, 1893)X**XX
Clytocerus kabylicus Wagner, 1987X
Iranotelmatoscopus numidicus (Satchell, 1955)X
Iranotelmatoscopus squamifer (Tonnoir, 1922)X
Lepiseodina tristis (Meigen, 1830)X
Mormia tenebricosa (Vaillant, 1954)X*XX
Mormia riparia (Satchell, 1955)X
Mormia similis Wagner, 1987X
Panimerus goetghebueri (Tonnoir, 1919)XX
Panimerus thienemanni (Vaillant, 1954)XXX
Paramormia ustulata (Walker, 1856)X*XX
Pericoma barbarica Vaillant, 1955X*XX
Pericoma blandula Eaton, 1893XXX
Pericoma diversa Tonnoir, 1920X*
Pericoma exquisita Eaton, 1893XXX
Pericoma granadica Vaillant, 1978X*
Pericoma latina Sarà, 1954X*X
Pericoma maroccana Vaillant, 1955X*
Pericoma modesta Tonnoir, 1922XX
Pericoma pseudexquisita Tonnoir, 1940X***
Philosepedon beaucournui Vaillant, 1974XX
Philosepedon humerale (Meigen, 1818)X**X
Pneumia nubila (Meigen, 1818)X***
Pneumia pilularia (Tonnoir, 1940)XX
Pneumia propinqua (Satchell, 1955)X**X
Pneumia reghayana (Boumezzough & Vaillant, 1986)X
Pneumia toubkalensis (Omelková & Ježek 2012)X*
Psychoda aberrans Tonnoir, 1922X
Psychoda (Falsologima) savaiiensis Edwards, 1928X
Psychoda (Logima) albipennis Zetterstedt, 1850XX
Psychoda (Logima) erminea Eaton, 1893X
Psychoda (Psycha) grisescens Tonnoir, 1922XXX
Psychoda (Psychoda) phalaenoides (Linnaeus, 1758)X
Psychoda (Psychoda) uniformata Haseman, 1907X
Psychoda (Psychodocha) cinerea Banks, 1894X**XX
Psychoda (Psychodocha) gemina (Eaton, 1904)X***
Psychoda (Psychomora) trinodulosa Tonnoir, 1922X
Psychoda (Tinearia) alternata Say, 1824X*XXX**
Psychoda (Tinearia) efflatouni Tonnoir, 1922X
Psychoda (Tinearia) lativentris Berden, 1952X
Telmatoscopus advena (Eaton, 1893)X
Thornburghiella quezeli (Vaillant, 1955)XX
Tonnoiriella atlantica (Satchell, 1953)XX
Tonnoiriella paveli Ježek, 1999X
Tonnoiriella pulchra (Eaton, 1893)XX
Vaillantodes fraudulentus (Eaton, 1896)XX
Vaillantodes malickyi (Wagner, 1987)X
Open in a separate windowX***: new species for North Africa; X**: new species for Morocco or Egypt; X*: new species for the Rif Mountains.  相似文献   

6.
The Invasion of Ukraine prompts us to support our Ukranian colleagues but also to keep open communication with the Russian scientists who oppose the war.

In the eyes of the civilized world, Russia has already lost the war: politically, it is becoming ever more isolated; economically as the sanctions take an enormous toll; militarily as the losses of the Russian army mount. In contrast, the courage of Ukrainian people fighting for their independence has united the Western world that is providing enormous support for those Ukrainians who fight the Russian invasion and those who have fled their war‐torn country. Once this war is over, Ukraine will have to heal the wounds of war, reunite families, restore its economy, reestablish infrastructure, and rebuild science and education. Russia will have to restore its dignity and overcome its self‐inflicted isolation.Europe’s unity in condemning Russia’s war of aggression and showing its solidarity with Ukraine has been impressive. This includes not the least welcoming and accommodating millions of refugees. We, the scientific community in Europe, have a moral obligation to help Ukrainian students and colleagues by providing safe space to study and to continue their research. First, European research organizations and funding agencies should develop strategies to support them in the years to come. Second, efforts by EMBO, research funders, universities, and research institutions to support Ukrainian students and scientists are necessary. As a first priority, dedicated and unbureaucratic short‐term scholarship and grant programs are required to accommodate Ukrainian scientists; such programs have been already initiated by many organizations, for example, by EMBO, Volkswagen Stiftung, Max Planck Society, and the ERC among others. These help Ukrainian scientists to stay connected to research and become integrated into the European research landscape. In the long‐term and after the war, this aid should be complemented by funding for research centers of excellence in Ukraine, to which scientists could then return.Even though the priority must be to help Ukrainians, we must also think of students and colleagues in Russia who oppose the war and are affected by the sanctions. As the Iron Curtain closes again, we have to think differently about our ongoing and future collaborations. Although freezing most, if not all, research collaborations with official Russian organizations is justified, it would be a mistake to extend these sanctions to all scientists and students. There is already an exodus of Russian and Belarusian scholars, which will only accelerate in the next months and years, and accepting scientists who ask for political asylum will be beneficial for Europe.The fraction of Russian society in open opposition to the war is, unfortunately, smaller than that officially in support of it. At the beginning of the war, a number of Russian scientists published an open letter on the internet, in which they condemn this war (https://t‐invariant.org/2022/02/we‐are‐against‐war/). They clearly state that "The responsibility for unleashing a new war in Europe lies entirely with Russia. There is no rational justification for this war”, and “demand an immediate halt to all military operations directed against Ukraine". At the same time, other prominent Russian science and education officials signed the “Statement of the Russian Union of University Rectors (Provosts)”, which expressed unwavering support for Russia, its president and its Army and their goal to “to achieve demilitarization and denazification of Ukraine and thus to defend ourselves from the ever‐growing military threat” (https://www.rsr‐online.ru/news/2022‐god/obrashchenie‐rossiyskogo‐soyuza‐rektorov1/).Inevitably, Russian scientists must decide themselves how to live and continue their scientific work under the increasingly tight surveillance of the Kremlin regime. History is repeating itself. Not long ago, during the Cold War, Soviet scientists were largely isolated from the international research community and worked in government‐controlled research. In some fields, no one knew what they were working on or where. However, even in those dark times, courageous individuals such as Andrei Sakharov spoke out against the regime and tried to educate the next generation about the importance of free will. Many Soviet geneticists had been arrested under Stalin’s regime of terror and as a result of Lysenkoism and were executed or sent to the Gulag or had to emigrate, such as Nikolaj Timofeev‐Resovskij, one of the great geneticists of his time and an opponent of communism. As a result of sending dissident scientists to Siberia, great educational institutions were created in the region, which trained many famous scientists. History tells us that it is impossible to kill free will and the search for truth.The Russian invasion of Ukraine is a major humanitarian tragedy and a tragedy for science at many levels. Our hope is that the European science community, policymakers, and funders will be prepared to continue and expand support for our colleagues from Ukraine and eventually help to rebuild the bridges with Russian science that have been torn down.This commentary has been endorsed and signed by the EMBO Young Investigators and former Young Investigators listed below.

All signatories are current and former EMBO Young Investigators and endorse the statements in this article.
Igor AdameykoKarolinska Institut, Stockholm, Sweden
Bungo AkiyoshiUniversity of Oxford, United Kingdom
Leila AkkariNetherlands Cancer Institute, Amsterdam, Netherlands
Panagiotis AlexiouMasaryk University, Brno, Czech Republic
Hilary AsheFaculty of Life Sciences, University of Manchester, United Kingdom
Michalis AverofInstitut de Génomique Fonctionnelle de Lyon (IGFL), France
Katarzyna BandyraUniversity of Warsaw, Poland
Cyril BarinkaInstitute of Biotechnology AS CR, Prague, Czech Republic
Frédéric BergerGregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna, Austria
Vitezslav BryjaInstitute of Experimental Biology, Masaryk University, Brno, Czech Republic
Janusz BujnickiInternational Institute of Molecular and Cell Biology, Warsaw, Poland
Björn BurmannUniversity Gothenburg, Sweden
Andrew CarterMRC Laboratory of Molecular Biology, Cambridge, United Kingdom
Pedro CarvalhoSir William Dunn School of Pathology University of Oxford, United Kingdom
Ayse Koca CaydasiKoç University, Istanbul, Turkey
Hsu‐Wen ChaoMedical University, Taipei, Taiwan
Jeffrey ChaoFriedrich Miescher Institute, Basel, Switzerland
Alan CheungUniversity of Bristol, United Kingdom
Tim ClausenResearch Institute for Molecular Pathology (IMP), Vienna, Austria
Maria Luisa CochellaThe Johns Hopkins University School of Medicine, USA
Francisco CubillosSantiago de Chile, University, Chile
Uri Ben‐DavidTel Aviv University, Tel Aviv, Israel
Sebastian DeindlUppsala University, Sweden
Pierre‐Marc DelauxLaboratoire de Recherche en Sciences Végétales, Castanet‐Tolosan, France
Christophe DessimozUniversity, Lausanne, Switzerland
Maria DominguezInstitute of Neuroscience, CSIC ‐ University Miguel Hernandez, Alicante, Spain
Anne DonaldsonInstitute of Medical Sciences, University of Aberdeen, United Kingdom
Peter DraberBIOCEV, First Faculty of Medicine, Charles University, Vestec, Czech Republic
Xiaoqi FengJohn Innes Centre, Norwich, United Kingdom
Luisa FigueiredoInstitute of Molecular Medicine, Lisbon, Portugal
Reto GassmannInstitute for Molecular and Cell Biology, Porto, Portugal
Kinga Kamieniarz‐GdulaAdam Mickiewicz University in Poznań, Poland
Roger GeigerInstitute for Research in Biomedicine, Bellinzona, Switzerland
Niko GeldnerUniversity of Lausanne, Switzerland
Holger GerhardtMax Delbrück Center for Molecular Medicine, Berlin, Germany
Daniel Wolfram GerlichInstitute of Molecular Biotechnology (IMBA), Vienna, Austria
Jesus GilMRC Clinical Sciences Centre, Imperial College London, United Kingdom
Sebastian GlattMalopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
Edgar GomesInstitute of Molecular Medicine, Lisbon, Portugal
Pierre GönczySwiss Institute for Experimental Cancer Research (ISREC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Maria GornaUniversity of Warsaw, Poland
Mina GoutiMax‐Delbrück‐Centrum, Berlin, Germany
Jerome GrosInstitut Pasteur, Paris, France
Anja GrothBiotech Research and Innovation Centre (BRIC), University of Copenhagen, Denmark
Annika GuseCentre for Organismal Studies, Heidelberg, Germany
Ricardo HenriquesInstituto Gulbenkian de Ciência, Oeiras, Portugal
Eva HoffmannCenter for Chromosome Stability, University of Copenhagen, Denmark
Thorsten HoppeCECAD at the Institute for Genetics, University of Cologne, Germany
Yen‐Ping HsuehAcademia Sinica, Taipei, Taiwan
Pablo HuertasAndalusian Molecular Biology and Regenerative Medicine Centre (CABIMER), Seville, Spain
Matteo IannaconeIRCCS San Raffaele Scientific Institute, Milan, Italy
Alvaro Rada‐IglesiasInstitue of Biomedicine and Biotechnology of Cantabria (IBBTEC)
University of Cantabria, Santander, Spain
Axel InnisInstitut Européen de Chimie et Biologie (IECB), Pessac, France
Nicola IovinoMPI für Immunbiologie und Epigenetik, Freiburg, Germany
Carsten JankeInstitut Curie, France
Ralf JansenInterfaculty Institute for Biochemistry, Eberhard‐Karls‐University Tübingen, Germany
Sebastian JessbergerHiFo / Brain Research Institute, University of Zurich, Switzerland
Martin JinekUniversity of Zurich, Switzerland
Simon Bekker‐JensenUniversity, Copenhagen, Denmark
Nicole JollerUniversity of Zurich, Switzerland
Luca JovineDepartment of Biosciences and Nutrition & Center for
Biosciences, Karolinska Institutet, Stockholm, Sweden
Jan Philipp JunkerMax‐Delbrück‐Centrum, Berlin, Germany
Anna KarnkowskaUniversity, Warsaw, Poland
Zuzana KeckesovaInstitute of Organic Chemistry and Biochemistry AS CR, Prague, Czech Republic
René KettingInstitute of Molecular Biology (IMB), Mainz, Germany
Bruno KlaholzInstitute of Genetics and Molecular and Cellular Biology (IGBMC), University of Strasbourg, Illkirch, France
Jürgen KnoblichInstitute of Molecular Biotechnology (IMBA), Vienna, Austria
Taco KooijCentre for Molecular Life Sciences, Nijmegen, Netherlands
Romain KoszulInstitut Pasteur, Paris, France
Claudine KraftInstitute for Biochemistry and Molecular Biology, Universität Freiburg, Germany
Alena KrejciFaculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic
Lumir KrejciNational Centre for Biomolecular Research (NCBR), Masaryk University, Brno, Czech Republic
Arnold KristjuhanInstitute of Molecular and Cell Biology, University of Tartu, Estonia
Yogesh KulathuMRC Protein Phosphorylation & Ubiquitylation Unit, University of Dundee, United Kingdom
Edmund KunjiMRC Mitochondrial Biology Unit, Cambridge, United Kingdom
Karim LabibMRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, United Kingdom
Thomas LecuitDevelopmental Biology Institute of Marseilles ‐ Luminy (IBDML), France
Gaëlle LegubeCenter for Integrative Biology in Toulouse, Paul Sabatier University, France
Suewei LinAcademia Sinica, Taipei, Taiwan
Ming‐Jung LiuAcademia Sinica, Taipei, Taiwan
Malcolm LoganRandall Division of Cell and Molecular Biophysics, King’s College London, United Kingdom
Massimo LopesUniversity of Zurich, Switzerland
Jan LöweStructural Studies Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
Martijn LuijsterburgUniversity Medical Centre, Leiden, Netherlands
Taija MakinenUppsala University, Sweden
Sandrine Etienne‐MannevilleInstitut Pasteur, Paris, France
Miguel ManzanaresSpanish National Center for Cardiovascular Research (CNIC), Madrid, Spain
Jean‐Christophe MarineCenter for Biology of Disease, Laboratory for Molecular Cancer Biology, VIB & KU Leuven, Belgium
Sascha MartensMax F. Perutz Laboratories, University of Vienna, Austria
Elvira MassUniversität Bonn, Germany
Olivier MathieuClermont Université, Aubière, France
Ivan MaticMax Planck Institute for Biology of Ageing, Cologne, Germany
Joao MatosMax Perutz Laboratories, Vienna, Austria
Nicholas McGranahanUniversity College London, United Kingdom
Hind MedyoufGeorg‐Speyer‐Haus, Frankfurt, Germany
Patrick MeraldiUniversity of Geneva, Switzerland
Marco MilánICREA & Institute for Research in Biomedicine (IRB), Barcelona, Spain
Eric MiskaWellcome Trust/Cancer Research UK Gurdon Institute,
University of Cambridge, United Kingdom
Nuria MontserratInstitut de Bioenginyeria de Catalunya (IBEC), Barcelona, Spain
Nuno Barbosa‐MoraisInstitute of Molecular Medicine, Lisbon, Portugal
Antonin MorillonInstitut Curie, Paris, France
Rafal MostowyJagiellonian University, Krakow, Poland
Patrick MüllerUniversity of Konstanz, Konstanz, Germany
Miratul MuqitUniversity of Dundee, United Kigdom
Poul NissenCentre for Structural Biology, Aarhus University, Denmark
Ellen NollenEuropean Research Institute for the Biology of Ageing, University of Groningen, Netherlands
Marcin NowotnyInternational Institute of Molecular and Cell Biology, Warsaw, Poland
John O''NeillMRC Laboratory of Molecular Biology, Cambridge, United Kigdom
Tamer ÖnderKoc University School of Medicine, Istanbul, Turkey
Elin OrgUniversity of Tartu, Estonia
Nurhan ÖzlüKoç University, Istanbul, Turkey
Bjørn Panyella PedersenAarhus University, Denmark
Vladimir PenaLondon, The Institute of Cancer Research, United Kingdom
Camilo PerezBiozentrum, University of Basel, Switzerland
Antoine PetersFriedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland
Clemens PlaschkaIMP, Vienna, Austria
Pavel PlevkaCEITEC, Masaryk University, Brno, Czech Republic
Hendrik PoeckTechnische Universität, München, , Germany
Sophie PoloUniversité Diderot (Paris 7), Paris, France
Simona PoloIFOM ‐ The FIRC Institute of Molecular Oncology, Milan, Italy
Magdalini PolymenidouUniversity of Zurich, Switzerland
Freddy RadtkeSwiss Institute for Experimental Cancer Research (ISREC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Markus RalserInstitute of Biochemistry Charité, Berlin, Germany & MRC National Institute for Medical Research, London, United Kingdom
Jan RehwinkelJohn Radcliffe Hospital, Oxford, United Kingdom
Maria RescignoEuropean Institute of Oncology (IEO), Milan, Italy
Katerina RohlenovaPrague, Institute of Biotechnology, Czech Republic
Guadalupe SabioCentro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
Ana Jesus Garcia SaezUniversity of Cologne, CECAD Research Center, Germany
Iris SaleckerInstitut de Biologie de l''Ecole Normale Supérieure (IBENS), Paris, France
Peter SarkiesUniversity of Oxford, United Kingdom
Frédéric SaudouGrenoble Institute of Neuroscience, France
Timothy SaundersCentre for Mechanochemical Cell Biology, Interdisciplinary Biomedical Research Building, Warwick Medical School, Coventry, United Kingdom
Orlando D. SchärerIBS Center for Genomic Integrity, Ulsan, South Korea
Arp SchnittgerBiozentrum Klein Flottbek, University of Hamburg, Germnay
Frank SchnorrerAix Marseille University, CNRS, IBDM, Turing Centre for Living Systems, Marseille, France
Maya SchuldinerDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
Schraga SchwartzWeizmann Institute of Science, Rehovot, Israel
Martin SchwarzerInstitute of Microbiology, Academy of Sciences of the Czech Republic
Claus MariaInstituto de Medicina Molecular Faculdade de Medicina da Universidade de Lisboa, Portugal
Hayley SharpeThe Babraham Institute, United Kingdom
Halyna ShcherbataInstitute of Cell Biochemistry, Hannover Medical School, Hannover, Germany
Eric SoDepartment of Haematological Medicine, King''s College London, United Kingdom
Victor SourjikMax Planck Institute for Terrestrial Microbiology, Marburg, Germany
Anne SpangBiozentrum, University of Basel, Switzerland
Irina StanchevaInstitute of Cell Biology, University of Edinburgh, United Kingdom
Bas van SteenselDepartment of Gene Regulation, The Netherlands Cancer Institute, Amsterdam, Netherlands
Richard SteflCEITEC, Masaryk University, Brno, Czech Republic
Yonatan StelzerWeizmann Institute of Science, Rehovot, Israel
Julian StingeleLudwig‐Maximilians‐Universität, München, Germany
Katja SträßerInstitute for Biochemistry, University of Giessen, Germany
Kvido StrisovskyInstitute of Organic Chemistry and Biochemistry ASCR, Prague, Czech Republic
Joanna SulkowskaUniversity, Warsaw, Poland
Grzegorz SumaraNencki Institute of Experimental Biology, Warsaw, Poland
Karolina SzczepanowskaInternational Institute Molecular Mechanisms & Machines PAS, Warsaw, Poland
Luca TamagnoneInstitute for Cancer Research and Treatment, University of Torino Medical School, Italy
Meng How TanSingapore, Nanyang Technological University, Singapore
Nicolas TaponCancer Research UK London Research Institute, United Kingdom
Nicholas M. I. TaylorUniversity, Copenhagen, Denmark
Sven Van TeeffelenUniversité de Montréal, Canada
Maria Teresa TeixeiraLaboratory of Molecular and Cellular Biology of Eukaryotes, IBPC, Paris, France
Aurelio TelemanGerman Cancer Research Center (DKFZ), Heidelberg, Germany
Pascal TherondInstitute Valrose Biology, University of Nice‐Sophia Antipolis, France
Pavel TolarUniversity College London, United Kingdom
Isheng Jason TsaiAcademia Sinica, Taipei, Taiwan
Helle UlrichInstitute of Molecular Biology (IMB), Mainz, Germany
Stepanka VanacovaCentral European Institute of Technology, Masaryk University, Brno, Czech Republic
Henrique Veiga‐FernandesChampalimaud Center for the Unknown, Lisboa, Portugal
Marc VeldhoenInstituto de Medicina Molecular, Lisbon, Portugal
Louis VermeulenAcademic Medical Centre, Amsterdam, Netherlands
Uwe VinkemeierUniversity of Nottingham Medical School, United Kingdom
Helen WaldenMRC Protein Phosphorylation & Ubiquitylation Unit, University of Dundee, United Kingdom
Michal WandelInstitute of Biochemistry and Biophysics, PAS, Warsaw, Poland
Julie WelburnWellcome Trust Centre, Edinburgh, United Kingdom
Ervin WelkerInstitute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Szeged, Hungary
Gerhard WingenderIzmir Biomedicine and Genome Center, Dokuz Eylul University, Izmir, Turkey
Thomas WollertInstitute Pasteur, Membrane Biochemistry and Transport, Centre François Jacob, Paris, France
Hyun YoukUniversity of Massachusetts Medical School, USA
Christoph ZechnerMPI für molekulare Zellbiologie und Genetik, Dresden, Germany
Philip ZegermanWellcome Trust / Cancer Research UK Gurdon Institute, University of Cambridge, United Kingdom
Alena ZikováInstitute of Parasitology, Biology Centre AS CR, Ceske Budejovice, Czech Republic
Piotr ZiolkowskiAdam Mickiewicz University, Poznan, Poland
David ZwickerMPI für Dynamik und Selbstorganisation, Göttingen, Germany
Open in a separate window  相似文献   

7.
Genital coevolution between the sexes is expected to be common because of the direct interaction between male and female genitalia during copulation. Here we review the diverse mechanisms of genital coevolution that include natural selection, female mate choice, male–male competition, and how their interactions generate sexual conflict that can lead to sexually antagonistic coevolution. Natural selection on genital morphology will result in size coevolution to allow for copulation to be mechanically possible, even as other features of genitalia may reflect the action of other mechanisms of selection. Genital coevolution is explicitly predicted by at least three mechanisms of genital evolution: lock and key to prevent hybridization, female choice, and sexual conflict. Although some good examples exist in support of each of these mechanisms, more data on quantitative female genital variation and studies of functional morphology during copulation are needed to understand more general patterns. A combination of different approaches is required to continue to advance our understanding of genital coevolution. Knowledge of the ecology and behavior of the studied species combined with functional morphology, quantitative morphological tools, experimental manipulation, and experimental evolution have been provided in the best-studied species, all of which are invertebrates. Therefore, attention to vertebrates in any of these areas is badly needed.Of all the evolutionary interactions between the sexes, the mechanical interaction of genitalia during copulation in species with internal fertilization is perhaps the most direct. For this reason alone, coevolution between genital morphologies of males and females is expected. Morphological and genetic components of male and female genitalia have been shown to covary in many taxa (Sota and Kubota 1998; Ilango and Lane 2000; Arnqvist and Rowe 2002; Brennan et al. 2007; Rönn et al. 2007; Kuntner et al. 2009; Tatarnic and Cassis 2010; Cayetano et al. 2011; Evans et al. 2011, 2013; Simmons and García-González 2011; Yassin and Orgogozo 2013; and see examples in
TaxaMale structuresFemale structuresEvidenceLikely mechanismReferences
Mollusks
 Land snails (Xerocrassa)Spermatophore-producing organsSpermatophore-receiving organsComparative among speciesSAC or female choiceSauder and Hausdorf 2009
 SatsumaPenis lengthVagina lengthCharacter displacementLock and keyKameda et al. 2009
Arthropods
 Arachnids (Nephilid spiders)MultipleMultipleComparative among speciesSACKuntner et al. 2009
 Pholcidae spidersCheliceral apophysisEpigynal pocketsComparative (no phylogenetic analysis)Female choiceHuber 1999
 Harvestmen (Opiliones)Hardened penes and loss of nuptial giftsSclerotized pregenital barriersComparative among speciesSACBurns et al. 2013
Millipedes
Parafontaria tonomineaGonopod sizeGenital segment sizeComparative in species complexMechanical incompatibility resulting from Intersexual selectionSota and Tanabe 2010
Antichiropus variabilisGonopod shape and sizeAccesory lobe of the vulva and distal projectionFunctional copulatory morphologyLock and keyWojcieszek and Simmons 2012
Crustacean
 Fiddler crabs, UcaGonopodeVulva, vagina, and spermathecaTwo-species comparison, shape correspondenceNatural selection against fluid loss, lock and key, and sexual selectionLautenschlager et al. 2010
Hexapodes
 OdonatesClasping appendagesAbdominal shape and sensory hairsFunctional morphology, comparative among speciesLock and key via female sensory systemRobertson and Paterson 1982; McPeek et al. 2009
Insects
 Coleoptera: seed beetlesSpiny aedagusThickened walls of copulatory ductComparative among speciesSACRönn et al. 2007
 Callosobruchus: Callosobruchus maculatusDamage inflictedSusceptibility to damageFull sib/half sib mating experimentsSACGay et al. 2011
Reduced spinesNo correlated responseExperimental evolutionSACCayetano et al. 2011
 Carabid beetles (Ohomopterus)Apophysis of the endophallusVaginal appendix (pocket attached to the vaginal apophysis)Cross-species matingsLock and keySota and Kubota 1998; Sasabi et al. 2010
 Dung beetle: Onthophagus taurusShape of the parameres in the aedagusSize and location of genital pitsExperimental evolutionFemale choiceSimmons and García-González 2011
 Diptera: Drosophila santomea and D. yakubaSclerotized spikes on the aedagusCavities with sclerotized plateletsCross-species matingsSACKamimura 2012
Drosophila melanogaster species complexEpandrial posterior lobes
Oviscapt pouchesComparative among speciesSAC or female choiceYassin and Orgogozo 2013
Phallic spikesOviscapt furrows
Cercal teeth, phallic hook, and spinesUterine, vulval, and vaginal shields
D. mauritiana and D. secheliaPosterior lobe of the genital archWounding of the female abdomenMating with introgressed linesSACMasly and Kamimura 2014
 Stalk-eyed flies (Diopsidae)Genital processCommon spermathecal ductComparative among species and morphologicalFemale choiceKotrba et al. 2014
 Tse-tse flies: Glossina pallidipesCercal teethFemale-sensing structuresExperimental copulatory functionFemale choiceBriceño and Eberhard 2009a,b
 Phelebotomine: sand fliesAedagal filaments, aedagal sheathsSpermathecal ducts length, base of the ductComparative among speciesNone specifiedIlango and Lane 2000
 Heteroptera: Bed bugs (Cimiciidae)Piercing genitaliaSpermalege (thickened exosqueleton)Comparative among speciesSACCarayon 1966; Morrow and Arnqvist 2003
 Plant bugs (Coridromius)Changes in male genital shapeExternal female paragenitaliaComparative among speciesSACTatarnic and Cassis 2010
 Waterstriders (Gerris sp.)Grasping appendagesAntigrasping appendagesComparative among speciesSACArnqvist and Rowe 2002
Gerris incognitusGrasping appendagesAntigrasping appendagesComparative among populationsSACPerry and Rowe 2012
 Bee assassins (Apiomerus)AedagusBursa copulatrixComparative among speciesNoneForero et al. 2013
 Cave insects (Psocodea), NeotroglaMale genital chamberPenis-like gynosomeComparative among speciesFemale competition (role reversal), coevolution SACYoshizawa et al. 2014
 Butterflies (Heliconiinae)Thickness of spermatophore wallSigna: Sclerotized structure to break spermatophoresComparative among speciesSACSánchez and Cordero 2014
Fish
 Basking shark: Cetorhinus maximusClasper clawThick vaginal padsMorphological observationNoneMatthews 1950
GambusiaGonopodial tipsGenital papillae within openingsComparative among speciesStrong character displacementLangerhans 2011
Poecilia reticulataGonopodium tip shapeFemale gonopore shapeComparative among populationsSACEvans et al. 2011
Reptiles
 AnolesHemipene shapeVagina shapeShape correspondence, two speciesSexual selectionKöhler et al. 2012
 Several speciesHemipene shapeVagina shapeShape correspondenceLock and key, female choice, and SACPope 1941; Böhme and Ziegler 2009; King et al. 2009
 Asiatic pit vipersSpininess in hemipenesThickness of vagina wallTwo-species comparisonNonePope 1941
 Garter snake: Thamnophis sirtalisBasal hempene spineVaginal muscular controlExperimental manipulationSACFriesen et al. 2014
Birds
 WaterfowlPenis lengthVaginal elaborationComparative among speciesSACBrennan et al. 2007
 TinamousPenis length/presenceVaginal elaborationComparative among speciesFemale choice/natural selectionPLR Brennan, K Zyscowski, and RO Prum, unpubl.
Mammals
 MarsupialsBifid penisTwo lateral vaginaeShape correspondenceNoneRenfree 1987
 EquidnaBifid penis with four rosettesSingle vagina splits into two uteriShape correspondenceNoneAugee et al. 2006; Johnston et al. 2007
 Insectivores: Short-tailed shrew: Blarina brevicaudaS-shaped curve of the erect penisCoincident curve in the vaginaShape correspondenceNoneBedford et al. 2004
 Common tenrec: Tenrec caudatusFiliform penis (up to 70% of the male’s body length)Internal circular folds in the vaginaLength correspondenceNoneBedford et al. 2004
 Rodents: Cape dune mole: Bathyergus suillusPenis and baculum lengthVaginal lengthAllometric relationships within speciesNoneKinahan et al. 2007
 Australian hopping mice (Notomys)Spiny penisDerived distal region in the vaginaMorphological observation and two-species comparisonCopulatory lockBreed et al. 2013
 Pig: Sus domesticusFiliform penis endCervical ridgesArtificial inseminationFemale choiceBonet et al. 2013
 Primates: Macaca arctoidesLong and filamentous glansVestibular colliculus (fleshy fold) that partially obstructs the entrance to the vaginaShape correspondence and comparison with close relativesNoneFooden 1967
Open in a separate windowThe likely mechanism is that suggested by the authors, and it includes sexually antagonistic coevolution (SAC), natural selection, sexual selection, female choice, or none specified. The evidence provided by the studies can be comparative among species or among populations, experimental evolution, cross-species matings, full-sibling (sib)/half-sib matings, shape, and length correspondence. Shape correspondence is often taken as evidence of coevolution, although it is not as conclusive as other approaches.Male genitalia are among the most variable structures in nature (Eberhard 1985). In contrast, female genitalia have typically been found not to be as interspecifically variable as male genitalia in several studies that specifically examined and described them (Eberhard 1985, 2010a,b). Female genitalia are not studied as often as male genitalia, perhaps because of a male-biased view of evolutionary processes by researchers (Ah-King et al. 2014). However, studying female genitalia is undeniably challenging. Male genitalia are generally kept inside of the body cavity, but are everted before, or during copulation, so their functional morphology can be more easily studied than the internal genitalia of females. Female genitalia also tend to be softer than male genitalia and thus their morphology may be more difficult to describe, and can more easily be distorted on dissection and preservation. Female adaptations to sense or oppose features of male genitalia can be subtle, requiring careful study. Female genital tracts are under multiple sources of selection: not just mating, but also storing sperm, egg laying, birthing, and often interfacing with the terminal portion of the digestive tract. Therefore, selection balancing multiple functions may further constrain morphological evolution in female genitalia. However, even small morphological changes in female genitalia, for example, increases in vaginal muscle, may change a female’s ability to choose or reject a male during mating, or to manage the costs of mating. Thus, the functional consequences to male and female genital morphology are hard to predict unless one knows how genitalia function during intromission. Despite these challenges, recent studies have examined variation of female genitalia and evidence is accumulating that features of female genitalia are variable enough to support coevolutionary processes (Polihronakis 2006; Puniamoorthy et al. 2010; Siegel et al. 2011; Showalter et al. 2013; and see additional references in Ah-King et al. 2014).In this article, we will discuss different hypotheses of genital evolution that predict coevolution; however, this is not a review of that entire subject (but see Eberhard et al. 2010b; Simmons 2013). Rather, we discuss the various mechanisms of genital coevolution differentiating the potentially independent or overlapping roles of natural selection, female choice, and male–male competition (Fig. 1). This classification allows us to distinguish specifically those mechanisms of genital coevolution that involve sexual conflict (i.e., when the evolutionary interests of individuals of different sexes, particularly over mating, are different). We then highlight examples in different taxa organisms with particular emphasis on those that provide evidence of sexual conflict.Open in a separate windowFigure 1.Graphical classification of mechanisms of genital evolution and coevolution. Three circles depict the independent and co-occurring actions of natural selection, female choice, and male–male competition. Different specific versions of genital coevolution can occur depending on which of the three broader evolutionary mechanisms are occurring. Sexual conflict (hatched lines) occurs through the simultaneous action of male–male competition and female choice, or male–male competition and natural selection. SAC, sexually antagonistic coevolution. See text for explanation.  相似文献   

8.
Endoplasmic Reticulum Targeting and Insertion of Tail-Anchored Membrane Proteins by the GET Pathway     
Vladimir Denic  Volker D?tsch  Irmgard Sinning 《Cold Spring Harbor perspectives in biology》2013,5(8)
Hundreds of eukaryotic membrane proteins are anchored to membranes by a single transmembrane domain at their carboxyl terminus. Many of these tail-anchored (TA) proteins are posttranslationally targeted to the endoplasmic reticulum (ER) membrane for insertion by the guided-entry of TA protein insertion (GET) pathway. In recent years, most of the components of this conserved pathway have been biochemically and structurally characterized. Get3 is the pathway-targeting factor that uses nucleotide-linked conformational changes to mediate the delivery of TA proteins between the GET pretargeting machinery in the cytosol and the transmembrane pathway components in the ER. Here we focus on the mechanism of the yeast GET pathway and make a speculative analogy between its membrane insertion step and the ATPase-driven cycle of ABC transporters.The mechanism of membrane protein insertion into the endoplasmic reticulum (ER) has been extensively studied for many years (Shao and Hegde 2011). From this work, the signal recognition particle (SRP)/Sec61 pathway has emerged as a textbook example of a cotranslational membrane insertion mechanism (Grudnik et al. 2009). The SRP binds a hydrophobic segment (either a cleavable amino-terminal signal sequence or a transmembrane domain) immediately after it emerges from the ribosomal exit tunnel. This results in a translational pause that persists until SRP engages its receptor in the ER and delivers the ribosome-nascent chain complex to the Sec61 channel. Last, the Sec61 channel enables protein translocation into the ER lumen along with partitioning of hydrophobic transmembrane domains into the lipid bilayer through the Sec61 lateral gate (Rapoport 2007).Approximately 5% of all eukaryotic membrane proteins have an ER targeting signal in a single carboxy-terminal transmembrane domain that emerges from the ribosome exit tunnel following completion of protein synthesis and is not recognized by the SRP (Stefanovic and Hegde 2007). Nonetheless, because hydrophobic peptides in the cytoplasm are prone to aggregation and subject to degradation by quality control systems (Hessa et al. 2011), these tail-anchored (TA) proteins still have to be specifically recognized, shielded from the aqueous environment, and guided to the ER membrane for insertion. In the past five years, the guided-entry of TA proteins (GET) pathway has come to prominence as the major machinery for performing these tasks and the enabler of many key cellular processes mediated by TA proteins including vesicle fusion, membrane protein insertion, and apoptosis. This research has rapidly yielded biochemical and structural insights (and2)2) into many of the GET pathway components (Hegde and Keenan 2011; Chartron et al. 2012a; Denic 2012). In particular, Get3 is an ATPase that uses metabolic energy to bridge recognition of TA proteins by upstream pathway components with TA protein recruitment to the ER for membrane insertion. However, the precise mechanisms of nucleotide-dependent TA protein binding to Get3 and how the GET pathway inserts tail anchors into the membrane are still poorly understood. Here, we provide an overview of the budding yeast GET pathway with emphasis on mechanistic insights that have come from structural studies of its membrane-associated steps and make a speculative juxtaposition with the ABC transporter mechanism.

Table 1.

A catalog of GET pathway component structures
ComponentRole in the pathwayPDB ID
Sgt2Component of the pretargeting complex that delivers TA proteins to Get3; dimer interacts with Get4/Get5, contains TPR repeats that interact with Hsps3SZ7
Get5Component of the pretargeting complex that delivers TA proteins to Get3; dimer interacts with Get4 via amino-terminal domain and with Sgt2 via its ubiquitin-like domain2LNZ
3VEJ
2LO0
Get4Component of the pretargeting complex that delivers TA proteins to Get3; interacts with Get3 via amino-terminal domain and with Get4 via carboxy-terminal domain3LPZ
3LKU
3WPV
Get3ATPase that binds the TA protein; dimer interacts with the pretargeting complex in the cytosol, and with Get1/2 at the ER membraneTable 2
Get1ER receptor for Get3; integral ER membrane
protein, three TMDs; forms a complex with Get2
3SJA, 3SJB
3SJC, 3ZS8
3VLC, 3B2E
Get2ER receptor for Get3; integral ER membrane
protein, three TMDs; forms a complex with Get1
3SJD
3ZS9
Open in a separate windowTA, tail anchored; TPR, tetratricopeptide repeat; TMDs, transmembrane domains.

Table 2.

An itemized list of published Get3 structures with associated nucleotides and conformation nomenclature
OrganismNucleotideConformationPDB IDReferences
Get3
Schizosaccharomyces pombeNoneOpen2WOOMateja et al. 2009
Saccharomyces cerevisiaeNoneOpen3H84Hu et al. 2009
3A36Yamagata et al. 2010
Aspergillus fumigatusADPOpen3IBGSuloway et al. 2009
S. cerevisiaeADPOpen3A37Yamagata et al. 2010
Debaryomyces hanseniiADPClosed3IO3Hu et al. 2009
Chaetomium thermophilumAMPPNP-Mg2+Closed3IQWBozkurt et al. 2009
C. thermophilumADP-Mg2+Closed3IQXBozkurt et al. 2009
S. cerevisiaeADP•AlF4-Mg2+Fully closed2WOJMateja et al. 2009
Methanothermobacter thermautotrophicusADP•AlF4-Mg2+Fully closed3ZQ6Sherill et al. 2011
Methanococcus jannaschiiADP•AlF4-Mg2+Tetrameric3UG6Suloway et al. 2012
3UG7
Get3/Get2cyto
S. cerevisiaeADP-Mg2+Closed3SJDStefer et al. 2011
S. cerevisiaeADP•AlF4-Mg2+Closed3ZS9Mariappan et al. 2011
Get3/Get1cyto
S. cerevisiaeNoneSemiopen3SJCStefer et al. 2011
S. cerevisiaeADPSemiopen3VLCKubota et al. 2012
S. cerevisiaeNoneOpen3SJAStefer et al. 2011
3SJBStefer et al. 2011
3ZS8Mariappan et al. 2011
ADPOpen3B2EKubota et al. 2012
Open in a separate windowADP, adenosine diphosphate.  相似文献   

9.
Diminished Susceptibility to Cefoperazone/Sulbactam and Piperacillin/Tazobactam in Enterobacteriaceae Due to Narrow-Spectrum β-Lactamases as Well as Omp Mutation     
Fengzhen Yang  Qi Zhao  Lipeng Wang  Jinying Wu  Lihua Jiang  Li Sheng  Leyan Zhang  Zhaoping Xue  Maoli Yi 《Polish journal of microbiology》2022,71(2):251
Cefoperazone/sulbactam (CSL) and piperacillin/tazobactam (TZP) are commonly used in clinical practice in China because of their excellent antimicrobial activity. CSL and TZP-nonsusceptible Enterobacteriaceae are typically resistant to extended-spectrum cephalosporins such as ceftriaxone (CRO). However, 11 nonrepetitive Enterobacteriaceae strains, which were resistant to CSL and TZP yet susceptible to CRO, were collected from January to December 2020. Antibiotic susceptibility tests and whole-genome sequencing were conducted to elucidate the mechanism for this rare phenotype. Antibiotic susceptibility tests showed that all isolates were amoxicillin/clavulanic-acid resistant and sensitive to ceftazidime, cefepime, cefepime/tazobactam, cefepime/zidebactam, ceftazidime/avibactam, and ceftolozane/tazobactam. Whole-genome sequencing revealed three of seven Klebsiella pneumoniae strains harbored blaSHV-1 only, and four harbored blaSHV-1 and blaTEM-1B. Two Escherichia coli strains carried blaTEM-1B only, while two Klebsiella oxytoca isolates harbored blaOXY-1-3 and blaOXY-1-1, respectively. No mutation in the β-lactamase gene and promoter sequence was found. Outer membrane protein (Omp) gene detection revealed that numerous missense mutations of OmpK36 and OmpK37 were found in all strains of K. pneumoniae. Numerous missense mutations of OmpK36 and OmpK35 and OmpK37 deficiency were found in one K. oxytoca strain, and no OmpK gene was found in the other. No Omp mutations were found in E. coli isolates. These results indicated that narrow spectrum β-lactamases, TEM-1, SHV-1, and OXY-1, alone or in combination with Omp mutation, contributed to the resistance to CSL and TZP in CRO-susceptible Enterobacteriaceae.Antibiotic susceptibility tests
AntibioticsBreakpoint, (μg/ml)Klebsiella pneumoniae
Escherichia cou
Klebriehd axyoca
E1E3E4E7E9E10E11E6E8E2E5
CRO≤1≥4≤0.5≤0.5≤0.5≤0.5 1≤0.51≤0.5≤0.511
CAZ4 ≥161214444241 1
FEP≤2 216 110.2512220.521 1
AMC≤8 ≥32≥128≥128≥128≥128≥128≥128≥128≥128≥128≥128≥128
CSL≤16 ≥6464646464≥128128≥12864128128≥128
TZP≤16 ≥128≥256≥256≥256≥25622562256≥256≥256≥256≥256≥256
FPT≤2 ≥1610.50.060.1252120.2510.1250.25
FPZ≤2 2160.250.250.060.1250.250.25 10.1250.250.1250.125
CZA≤8 216 10.50.250.2510.2510.50.50.50.25
CZT≤2 28210.5 1222 1122
Open in a separate windowCROceftriaxone, CAZceftazidime, FEPcefepime, AMC:amoxicillin clavulanic-acid, CSLcefoperazone/sulbactam, TZP:piperadllin/tazobactam, FPT:cefepime tazobactam, FPZ:cefepime/zidebactam, CZA:ceftazidime/avibactam, CZTceftolozane/tazobactam Gene sequencing results
NumberStrainSTp-Lactamase genePromoter sequence mutationOmp mutation
ElKpn45blaSHV-1, blaTEM-lBnoneOmpK36, OmpK3 7
E3Kpn45blaSHV-1, blaTEM-lBnoneOmpK36. OmpK3 7
E4Kpn2854blaSHV-1noneOmpK36, OmpK3 7
E7Kpn2358blaSHV-1 - blaTEM-lBnoneOmpK36, OmpK3 7
E9Kpn2358blaSHV-1. blaTEM-lBnoneOmpK36. OmpK3 7
E10Kpn 189blaSHV-1noneOmpK36. OmpK3 7
EllKpn45blaSHV-1noneOmpK36, OmpK3 7
E6Eco88blaTEM-lBnonenone
ESEco409blaTEM-1Bnonenone
E2Kox194blaOXY-1-3noneOmpK36 mutations. OmpK35 and OmpK 37 deficiency
E5Kox 11blaOXY-1-1noneno OmpK (OmpK3 5, OmpK36 and OmpK37) gene found
Open in a separate window  相似文献   

10.
Measuring disease activity in adults with systemic lupus erythematosus: the challenges of administrative burden and responsiveness to patient concerns in clinical research     
Jamal Mikdashi  Ola Nived 《Arthritis research & therapy》2015,17(1)
  相似文献   

11.
BANK1 and BLK Act through Phospholipase C Gamma 2 in B-Cell Signaling     
Manuel Bernal-Quirós  Ying-Yu Wu  Marta E. Alarcón-Riquelme  Casimiro Castillejo-López 《PloS one》2013,8(3)
  相似文献   

12.
Use of methotrexate therapy is not associated with decreased prevalence of metabolic syndrome     
Hennie G Raterman  Alexandre E Voskuyl  Ben AC Dijkmans  Michael T Nurmohamed 《Arthritis research & therapy》2009,11(5):413-2
With great interest, we read the article by Toms and colleagues [1] in the previous issue of Arthritis Research & Therapy, in which they assessed prevalences of metabolic syndrome (MetS) in rheumatoid arthritis (RA) patients. Moreover, they identified demographic and clinical factors that may be associated with MetS. Toms and colleagues found prevalences of up to 45% of MetS and demonstrated older age and health status (health assessment questionnaire) to be associated with MetS irrespectively of the definition used. Of most interest, an association between methotrexate (MTX) use and decreased presence of MetS was observed in patients more than 60 years of age. The investigators hypothesized that this may be attributed to a drug-specific effect (and not to an anti-inflammatory effect) either by changing levels of adenosine, which is known to interact with glucose and lipid metabolism, or by an indirect effect mediated through concomitant folic acid administration, thereby decreasing homocysteine levels.Recently, we also examined the prevalence of MetS in (a subgroup of) RA patients in the CARRÉ investigation, a prospective cohort study on prevalent and incident cardiovascular disease and its underlying cardiovascular risk factors [2]. The findings of Toms and colleagues stimulated us to perform additional analyses in our total study population (n = 353).The prevalences of MetS were 35% and 25% (Table (Table1)1) according to criteria of National Cholesterol Education Program (NCEP) 2004 and NCEP 2001, respectively. In multivariate backward regression analyses, we found significant associations between body mass index, pulse rate, creatinine levels, hypothyroidism and diabetes mellitus and the presence of MetS independently of the criteria used (Table (Table2).2). However, an independent association between single use of MTX or use of MTX in combination with other disease-modifying antirheumatic drugs, on the one hand, and a decreased prevalence of MetS, on the other hand, could not be demonstrated (even in the subgroup of patients over the age of 60).

Table 1

Characteristics of the study population
MetS presentaMetS absentaMetS presentbMetS absentb
n = 84n = 265n = 121n = 228P valueaP valueb
Demographics
 Age, years63.8 (± 8)63.1 (± 7)64.3 (± 8)62.7 (± 7)0.460.045
 Female, percentage766374620.0220.028
RA-related characteristics
 DAS284.2 (± 1.3)3.9 (± 1.4)4.1 (± 1.3)3.8 (± 1.4)0.210.062
 ESR, mm/hour22 (10-35)16 (9-30)20 (10-34)17 (9-31)0.0590.33
 CRP, mg/L11 (4-21)6 (3-16)8 (3-18)6 (3-19)0.0210.46
 RA duration, years7 (4-10)7 (4-10)7 (4-10)7 (5-10)0.830.19
 Erosion, percentage778379830.200.36
 Number of DMARDs1 (1-2)1 (1-1)1 (1-2)1 (1-1)0.260.43
 MTX current, percentage626063590.710.46
 MTX only, percentage393941380.950.67
 SSZ only, percentage8139140.230.22
 HCQ only, percentage14340.310.55
 Combination of DMARDs, percentage312529250.240.38
 TNF-blocking agent, percentage1191190.730.65
 Prednisolone only, percentage12311.000.42
Cardiovascular risk factors
 Current smoker, percentage263125320.420.15
 Pack-years, years17 (0-34)19 (2-38)19 (0-35)18 (2-38)0.230.75
 BMI, kg/m230 (± 4)26 (± 5)29 (± 4)25 (± 5)< 0.001< 0.001
 Creatinine, μmol/L89 (± 21)89 (± 16)91 (± 22)87 (± 14)0.990.070
 Renal clearance, mL/minute81 (± 24)72 (± 19)77 (± 23)73 (± 19)0.0030.062
 Pulse, beats per minute76 (± 11)73 (± 9)75 (± 11)73 (± 9)0.0050.015
 Diabetes mellitus, percentage143123< 0.0010.001
 Hypothyroidism, percentage122920.0010.003
Open in a separate windowaMetabolic syndrome (MetS) according to National Cholesterol Education Program (NCEP) 2001; bMetS according to NCEP 2004. Continuous variables are presented as means (± standard deviations) in cases of normal distribution or as medians (interquartile ranges) in cases of non-normal distribution. BMI, body mass index; CRP, C-reactive protein; DAS28, disease activity score using 28 joint counts; DMARD, disease-modifying antirheumatic drug; ESR, erythrocyte sedimentation rate; HCQ, hydroxychloroquine; MTX, methotrexate; RA, rheumatoid arthritis; SSZ, sulfasalazine; TNF, tumour necrosis factor.

Table 2

Variables associated with metabolic syndrome
UnivariateMultivariatea


OR95% CIP valueOR95% CIP value
Body mass index1.21.1-1.3< 0.0011.21.1-1.3< 0.001
Pulse1.031.01-1.060.0111.031.00-1.060.020
Creatinine1.011.00-1.020.0801.021.00-1.030.017
Hypothyroidism4.51.5-13.20.0074.71.5-15.00.009
Diabetes mellitus4.81.8-12.90.0024.51.4-15.20.014
Open in a separate windowaIn multivariate analyses, the following variables were used: gender, age, prednisolone only, methotrexate only, sulfasalazine only, hydroxychloroquine only, tumour necrosis factor-blocking agents, combination of disease-modifying antirheumatic drugs, pack-years, smoking, erosions, DAS28 (disease activity score using 28 joint counts), body mass index, pulse rate, creatinine levels, renal clearance, hypothyroidism and diabetes mellitus. CI, confidence interval; OR, odds ratio.Therefore, to get more support for a drug-specific effect, it is of interest to know whether or not in the study of Toms and colleagues the MTX effect was present only in the group of RA patients with single use of MTX or in the group of MTX-treated patients with other antirheumatic drugs. As patients with MetS were significantly older, it would give further information whether age was an independent risk factor for MetS in regression analyses. Moreover, as readers, we are not informed about comorbidities like diabetes and clinical hypothyroidism, which are notorious cardiometabolic risk factors. On the whole, we could not confirm a plausible protective role for the use of MTX and presence of MetS, and hence further investigation is required to explain the discrepancy between our findings and those of Toms and colleagues.  相似文献   

13.
Receptor Tyrosine Kinases in the Nucleus     
Graham Carpenter  Hong-Jun Liao 《Cold Spring Harbor perspectives in biology》2013,5(10)
  相似文献   

14.
Metrics for antibody therapeutics development     
Janice M Reichert 《MABS-AUSTIN》2010,2(6):695-700
  相似文献   

15.
Rab Proteins and the Compartmentalization of the Endosomal System     
Angela Wandinger-Ness  Marino Zerial 《Cold Spring Harbor perspectives in biology》2014,6(11)
  相似文献   

16.
Allelic frequency and genotypes of prion protein at codon 136 and 171 in Iranian Ghezel sheep breeds     
Siamak Salami  Reza Ashrafi Zadeh  Mir Davood Omrani  Fatemeh Ramezani  Amir Amniattalab 《朊病毒》2011,5(3):228-231
PrP genotypes at codons 136 and 171 in 120 Iranian Ghezel sheep breeds were studied using allele-specific PCR amplification and compared with the well-known sheep breeds in North America, the United States and Europe. The frequency of V allele and VV genotype at codon 136 of Ghezel sheep breed was significantly lower than AA and AV. At codon 171, the frequency of allele H was significantly lower than Q and R. Despite the similarities of PrP genotypes at codons 136 and 171 between Iranian Ghezel sheep breeds and some of the studied breeds, significant differences were found with others. Planning of effective breeding control and successful eradication of susceptible genotypes in Iranian Ghezel sheep breeds will not be possible unless the susceptibility of various genotypes in Ghezel sheep breeds to natural or experimental scrapie has been elucidated.Key words: scrapie, Ghezel sheep breed, PrP genotyping, allele specific amplification, codon 136, codon 171Scrapie was first described in England in 1732,1 and it is an infectious neurodegenerative fatal disease of sheep and goats belonging to the group of transmissible subacute spongiform encephalopathies (TSEs), along with bovine spongiform encephalopathy (BSE), chronic wasting disease and Creutzfeldt-Jakob disease.2,3 The term prion, proteinaceous infectious particles, coined by Stanley B. Prusiner, was introduced, and he presents the idea that the causal agent is a protein.4 Prion proteins are discovered in two forms, the wild-type form (PrPc) and the mutant form (PrPSc).5 Although scrapie is an infectious disease, the susceptibility of sheep is influenced by genotypes of the prion protein (PrP) gene.2,6 Researchers have found that the PrP allelic variant alanine/arginine/arginine (ARR) at codons 136, 154 and 171 is associated with resistance to scrapie in several breeds.714 Most of the sheep populations in the Near East and North African Region (84% of the total population of 255 million) are raised in Iran, Turkey, Pakistan, Sudan, Algeria, Morocco, Afghanistan, Syria and Somalia.15 In 2003, the Iranian sheep population was estimated at 54,000,000 head. The Ghezel sheep breed, which also is known as Kizil-Karaman, Mor-Karaman, Dugli, Erzurum, Chacra, Chagra, Chakra, Gesel, Gezel, Kazil, Khezel, Khizel, Kizil, Qezel, Qizil and Turkish Brown, originated in northwestern Iran and northeastern Turkey. By considering sheep breeds as one of the main sources of meat, dairy products and related products, a global screening attempt is started in different areas. In compliance with European Union Decision 2003/100/EC, each member state has introduced a breeding program to select for resistance to TSEs in sheep populations to increase the frequency of the ARR allele. A similar breeding program is established in United States and Canada. The Near East and North African Region still needs additional programs to help the global plan of eradication of scrapie-susceptible genotypes. The current study was the first to assess the geographical and molecular variation of codons 136 and 171 polymorphism between Iranian Ghezel sheep breed and well-known sheep breeds.Polymorphism at codon 136 is associated with susceptibility to scrapie in both experimental and natural models.10,11,13,16 17 and Austrian Carynthian sheep.18 Swiss White Alpine showed higher frequency of allele V at position 136 than Swiss Oxford Down, Swiss Black-Brown Mountain and Valais Blacknose.19 Comparison of polymorphism at codon 136 in the current study with some of other breeds (20 some flock of Hampshire sheep21 with current study, but the frequency of it is higher than that of some other breeds.

Table 1

Comparison of PrP allelic and genotype frequencies at codon 136 in different breeds
BreedA (%)V (%)AA (%)AV (%)VV (%)Reference
Iranian Ghezel breeds (n = 120)77.5022.565.0025.0010.00Current study
Oklahoma sheep (n = 334)De Silva, et al.27
Suffolk99.240.7698.481.520.00
Hampshire1000.001000.000.00
Dorset92.67.9487.309.523.17
Montadale77.6622.3459.5736.174.26
Hampshire (n = 48)93.756.2588.0012.000.00Youngs, et al.21
German Sheep Breeds (n = 660)92.897.1187.8010.471.73Kutzer, et al.28
Bleu du Maine83.4716.5369.5627.832.61
Friesian Milk S.1000.001000.000.00
Nolana90.139.8785.908.465.64
Suffolk1000.001000.000.00
Texel90.879.1382.1617.410.43
Swiss Sheep (n = 200)92.57.5Gmur, et al.19
Swiss Oxford Down93.007.00---
Swiss Black-Brown M.99.001.00---
Valais Blacknose1000.00---
Swiss White Alpine88.0022.00---
Austrian Sheep (n = 112)98.951.0598.950.001.05Sipos, et al.18
Tyrolean mountain sheep1000.001000.000.00
Forest sheep1000.001000.000.00
Tyrolean stone sheep1000.001000.000.00
Carynthian sheep95.804.2095.800.004.20
Open in a separate windowIt has been found that a polymorphism at codon 171 also is associated with susceptibility to experimental scrapie in Cheviot sheep16 and natural scrapie in Suffolk sheep.22 As shown in 23 They also found that different breeds show different predominant genotypes in ewes and rams.23 Different PrP genotypes were found at codon 171 in Austrian sheep breeds, but QQ has higher frequency than others.18 In some kinds of Swiss breeds, allelic frequencies of allele Q was higher than R.19 Distribution of prion protein codon 171 genotypes in Hampshire sheep revealed that different flocks shows different patterns.21 The frequency of PrP genotypes at codon 171 in Iranian Ghezel breeds was similar to some sheep breeds, like the Suffolk breed of Oklahoma sheep, but it was completely different from others (PrP genotypes at codon 172BreedAllelic frequencyGenotypesReferenceQRHRRQRQQQHRHHHIranian Iranian Ghezel breeds (n = 120)55.0043.331.6723.3336.6736.670.003.330.00Current studyOklahoma sheep (n = 334)De Silva, et al.20Suffolk40.9559.050.0037.0743.9718.970.000.000.00Hampshire51.8948.110.0021.7052.8325.470.000.000.00Dorset67.7531.250.007.9546.5945.450.000.000.00Montadale62.9637.040.0014.8144.4440.740.000.000.00Hampshire (n = 201)72.1426.601.265.0042.0050.002.001.000.00Youngs, et al.21German Sheep Breeds (n = 660)Kutzer, et al.28Bleu du Maine37.862.20.0046.9630.4422.60.000.000.00Friesian Milk S.90.458.90.651.2715.382.80.000.000.64Nolana42.357.80.0036.6242.2621.130.000.000.00Suffolk68.427.64.016.121.8455.174.61.151.15Texel55.3529.714.912.5626.8336.3611.257.365.63Swiss Sheep (n = 200)Gmur, et al.19Swiss Oxford Down32.0068.00-------Swiss Black-Brown M.70.0030.00-------Valais Blacknose85.0015.00-------Swiss White Alpine27.0073.00-------Austrian Sheep (n = 112)Sipos, et al.18Tyrolean mountain sheep74.3025.800.002.9045.7051.400.000.000.00Forest sheep77.0019.203.8011.5015.4069.200.000.003.80Tyrolean stone sheep81.5014.803.700.0029.6062.907.400.000.00Carynthian sheep72.8023.004.204.2041.7013.008.400.000.00Open in a separate windowThe association between scrapie susceptibility and polymorphism at codon154 is unclear, and fewer evidences were found that support it.24,25 So the frequency of different genotypes at codon 154 in Iranian Sheep breeds has not been included in the current study.In addition to difference in number of included animals and methodology of genotyping, the apparent discrepancies among reported allelic frequency might be caused by the difference in geographical dissemination of sheep breeds and related purity.26 The deviations from Hardy-Weinberg equilibrium, which were assumed in the current study, were checked using Pearson''s chi-squared test or Fisher''s exact test. Although the number of animals in this study is acceptable, a population study is still suggested. In conclusion, fairly different patterns of PrP genotypes in this common Near eastern sheep breed are an evidence for geographical variation of molecular susceptibility to scrapie. Because other report from Turkey also has shown a prevalence of genotypes, which is different from western countries,26 and no reports have been published yet to show which of the genotypes in that breed are actually resistant or susceptible to natural or experimental scrapie, our results is an authentic platform to motivate further studies. Actually, extrapolation of the existing general pattern of susceptibility or resistance for all breeds and current plan of elimination would not be successful unless the susceptible genotypes in the Near East with numerous breeds will be identified. Hence, the current study could be used as an important pilot study for further investigation.Genomic DNA was isolated from fresh EDTA-treated blood of 120 healthy, randomly chosen sheep of Iranian Ghezel sheep breeds using a mammalian blood DNA isolation kit (Bioflux, Japan). The allelic frequencies of prion protein codons 171 and 136 were determined by allele-specific PCR amplifications using scrapie susceptibility test kit (Elchrom Scientific AG). Primer sets were designed by manufacturer to amplify specific gene targets according to possible genotypes of positions 136 and 171.The amplification reactions were performed using iCycler™ (BioRad Inc.,), and PCR products (PositionGenotypeFragment size136A133136V139171H170171Q247171R155Open in a separate window  相似文献   

17.
Does an Eye-Hand Coordination Test Have Added Value as Part of Talent Identification in Table Tennis? A Validity and Reproducibility Study     
Irene R. Faber  Frits G. J. Oosterveld  Maria W. G. Nijhuis-Van der Sanden 《PloS one》2014,9(1)
This study investigated the added value, i.e. discriminative and concurrent validity and reproducibility, of an eye-hand coordination test relevant to table tennis as part of talent identification. Forty-three table tennis players (7–12 years) from national (n = 13), regional (n = 11) and local training centres (n = 19) participated. During the eye-hand coordination test, children needed to throw a ball against a vertical positioned table tennis table with one hand and to catch the ball correctly with the other hand as frequently as possible in 30 seconds. Four different test versions were assessed varying the distance to the TotalNationalRegionalLocalTotal43131119Boys268810Girls17539Age (years)10.4±1.410.9±1.510.4±1.510.1±1.47 year olds1--18 year olds51139 year olds3-3-10 year olds1232711 year olds1151512 year olds11443Length (cm)149±11150±12150±12148±10Weight (kg )38±837±737±738±9Right-handed359917Left-handed8422Training (hours*week-1)6 (0–20)11 (7–20)7 (4–11)2 (0–3)Competition (points)173 (−52–430)297 (144–430)188 (72–317)36 (−52–130)Open in a separate windowData are frequencies, except for age, length and weight (years±SD), and training and competition (mean (range)).  相似文献   

18.
Over-represented promoter motifs in abiotic stress-induced DREB genes of rice and sorghum and their probable role in regulation of gene expression     
Amrita Srivastav  Sameet Mehta  Angelica Lindlof  Sujata Bhargava 《Plant signaling & behavior》2010,5(7):775-784
  相似文献   

19.
Demonstrated and inferred metabolism associated with cytosolic lipid droplets     
Joel M. Goodman 《Journal of lipid research》2009,50(11):2148-2156
Cytosolic lipid droplets were considered until recently to be rather inert particles of stored neutral lipid. Largely through proteomics is it now known that droplets are dynamic organelles and that they participate in several important metabolic reactions as well as trafficking and interorganellar communication. In this review, the role of droplets in metabolism in the yeast Saccharomyces cerevisiae, the fly Drosophila melanogaster, and several mammalian sources are discussed, particularly focusing on those reactions shared by these organisms. From proteomics and older work, it is clear that droplets are important for fatty acid and sterol biosynthesis, fatty acid activation, and lipolysis. However, many droplet-associated enzymes are predicted to span a membrane two or more times, which suggests either that droplet structure is more complex than the current model posits, or that there are tightly bound membranes, particularly derived from the endoplasmic reticulum, which account for the association of several of these proteins.Cytosolic lipid droplets, originally thought to be simply coalesced neutral lipids waiting for lipolysis at metabolic demand, are now known to be considerably more complicated both structurally and functionally. There is general agreement that droplets are comprised of a core of neutral lipids, principally triglycerides and steryl esters, surrounded by a leaflet of phospholipids into which are embedded a specific subset of cellular proteins, the most abundant of which are members of the PAT family (see below) in animal cells (1). However, this model is probably too simple; there is evidence from physical probes of droplets isolated from yeast mutants unable to synthesize triglycerides or steryl esters that these two molecular families are partially segregated within the core, with thin shells of steryl esters forming concentric hollow spheres around an inner core composed principally of triglycerides (2).The next layer of complexity is the functional inhomogeneity of droplets. Subsets of droplets within the same cells exist with different populations of PAT proteins, differentiating among different sizes, ages, and levels of metabolic activity (3, 4). Perhaps most surprisingly, droplets may be comprised, at least in some cases, not of the layered core-phospholipid shell architecture at all but a knot of tightly woven endoplasmic reticulum (ER) surrounded by secreted neutral lipid, itself encased with a single leaflet. Such a model is based on electron microscopic thin sections (5), freeze fracture-immunogold evidence (6), immunohistochemical studies of ER luminal proteins within the droplet (7), and the identification of these proteins, notably ER chaperones, in several proteomic studies. Although certainly, such a complex structure must obey physical laws governing aqueous interactions with hydrophobic lipids and artifacts in processing for electron microscopy do occur, it may be best at present to keep an open mind and consider that droplets may not have the same structure among tissues and that they may take multiple physical forms in rapid order as they dynamically perform their functions.What are these functions? The most obvious one is lipid metabolism, namely the biogenesis and breakdown of the neutral lipids contained within the droplet. Although this conclusion predates proteomic studies (8), these recent studies have revealed the breadth and conservation of metabolic reactions that occur at or near the droplet surface, the subject of this review. Moreover, proteomics has demonstrated the surprising fact that droplets are likely to be very active in organellar communication because they are replete in rab proteins and other trafficking molecules. Our knowledge from proteomic studies of droplet trafficking and communication is discussed separately in this thematic review series.A major caveat must be kept in mind when evaluating droplet proteomics data: besides droplet trafficking through transient interactions with vesicles or target organelles such as early endosomes (9), droplets make extensive, tight, and long-lasting synapses with the endoplasmic reticulum, mitochondria, and peroxisomes (10, 11). The fact that ER, mitochondrial, peroxisomal, and a few plasma membrane proteins are found with such high frequency in the droplet proteome probably reflects these tight interorganellar interactions, perhaps similar to the mitochondrially associated membranes (MAMs) that link mitochondria with ER (12). The molecular basis for droplet-mediated synapses are not yet known. Besides the frequent occurrence of specific nondroplet organelle proteins in the droplet proteome, adventitious contamination of droplets is unlikely in view of the unique density of droplets that allow their flotation to the top of aqueous buffers and density gradients after centrifugation while all other cell components sink (which also permits several washes with high recovery), and the nonrandom coisolation of subsets of proteins from other organelles, such as the β-oxidation peroxisomal enzymes (10), which suggests specialized regions for metabolically-productive droplet interactions at the synapses.Droplet-ER interactions are a special case; it is the rule rather than the exception that enzymes of lipid metabolism that are found in the droplet proteome are also found to varying extents in the ER. This has been well documented in yeast through genome-wide green fluorescent protein (GFP)-tagging (13, 14). Erg6p, an enzyme in the latter part of the ergosterol biosynthetic pathway, is the only droplet protein in the pathway with a near-exclusive droplet localization in yeast; Erg1p, Erg7p, and Erg 27p are dually localized, and the pattern changes depending on metabolic state. Whether this general rule is specific for yeast, in which droplets remain on the ER surface (15), is not yet clear. However, several examples already exist in mammalian cells: cytochrome b5 reductase (DT diaphorase) and various sterol dehydrogenases (see 12).

TABLE 1.

Metabolic functions of droplets as revealed by proteomics
ProteinReference(s)Comments
Fatty Acid Synthesis
ATP citrate lyase(e)Generates acetyl-CoA
Acetyl-CoA carboxylase/ACC1(i) (j) (n) (o)(e)Generates malonyl CoA
3-Oxoacyl(ACP) synthase(e)Drosophila; early step in FA synthesis
Fatty acid synthase(e)Drosophila
Diaphorase 1/Cytochrome b5 reductase(g)(h)(j) (l) (n) (o)Redox carrier in FA elongation and many others
Fatty acid desaturase 2(e) (m)Many hydrophobic spans likely
Fatty Acid Activation
Acyl-CoA synthetase/ACSL1(g) (n)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL3(g)(h)(i) (j) (l) (n) (o)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL4(g)(h) (j) (l) (n)Fatty acid-CoA ligase
Acyl-CoA synthetase/ACSL5(m)LACS2
Acyl-CoA synthetases/FAA1, FAA4, FAT1(a) (d)Yeast enzymes; FAT1 is a FA transporter; may have synthetase activity
Steroid Synthesis
Squalene epoxidase/ERG1(a) (i) (j) (o)(d)
Lanosterol synthase/ERG7(a)(g) (h) (i) (j) (m) (o)(d)
NAD(P) steroid dehydrogenase like (NSDHL)/ERG26(g)(h) (i)(m) (o)Sterol synthesis
3-keto reductase 17 βHSD7/ERG27(b)*(c)*(g) (j)(n) (o)(d)Sterol synthesis
C24-methyltransferase/ERG6(a) (c)* (d)Specific to ergosterol synthesis in fungi
17 β-HSD11 (retinal short chain dehydrogenase)(h) (i) (j) (l) (m) (n) (o) (e)Testosterone biosynthesis; steroid metabolism
17 β-HSD4(l)Bile salt snthesis
17 β-HSD13(m)A short-chain dehydrogenase
17 β-HSD3(m)Steroid metabolism
Triglyceride Synthesis
AcylDHAP reductase/AYR1(d)Determined early biochemically (68)
LysoPA acyltransferase/SLC1(d)Determined earlier biochemically (69)
DAG acyltransferase/DGA1Determined biochemically in yeast (70)
Lipolysis
Hormone-sensitive lipase(f)(g)Diglyceride lipase [first characterized in (71)]
Fat-specific gene 27(g)Lipase activity
ATGL(n) (o)Triglyceride lipase
Monoglyceride lipase(m)
Tgl3, Tgl4, Tgl5(a)Yeast triglyceride lipases [for Tgl4 and 5 see (60)]
Tgl1p, Yeh1p(a)Yeast steryl ester lipases; Yeh1 localized in (62)
PLC α(n)
Phospholipase A1(n)
Lipase Modulators
Perilipin(g)PAT family
ADRP(g)(h) (i) (k) (l) (m) (n) (o)PAT family
TIP47(g)(h) (l) (m) (o)PAT family
S3-12(g)PAT family
LSD2(e)(f)PAT family (Drosophila)
CGI-58(g) (i) (n) (o) (f)Regulator of ATGL; has endogenous acyltransferase activity (72)
Caveolin 1(g) (m) (n)May bridge perilipin with PKA to stimulate lipolysis
Other Redox Enzymes
Cytochrome p450(e)Mostly in ER
Cytochrome b5(e)Mostly in ER
Alcohol dehydrogenase 4(j) (m)(n) (e)Most in cytoplasm. Broad specificity, including retinols, aliphatic alcohols, and steroids
Aldehyde dehydrogenase /ALDH3B1(g)Can oxidize medium and long chain aldehydes
Glyceraldehyde phosphate dehydrogenase(a)(h) (l) (m) (n) (o) (e)Cytosolic glycolytic enzyme, but often found with droplets
Xanthine oxidoreductase(k)Identified in mammary tissue only
Gulonolactone oxidase(m)Drosophila; missing in humans. Role in ascorbic acid synthesis
Short-chain dehydrogenase/reductase member 1(g) (j) (n)(e)Unknown substrate
Other Enzymes
Acyl-CoA:ethanol o-acyltransferase /EHT1(a)(d)Generation of medium-chain ethyl esters
SCCPDH (CGI49)(h)(n) (o)Degradation of lysine
PI4 phosphatase/SAC1(n)
Serine palmitoyltransferase subunit 1 isoform a(n)Sphingolipid synthesis
SAM-dependent methyltransferase(j)Biosynthesis of phosphatidylcholine
Possible Contamination
Sterol carrier protein 2-related form(l) (e)May have thiolase activity. Peroxisomal contamination?
Palmitoyl-protein thioesterase(j) (n)Lysosomal contamination?
ER carboxyesterase(k)Mammary; used to make triglyc for lipooproteins
ATPsynthase2(g)Mitochondrial contamination
Carbamoyl P Synthetase 1(m)Mitochondrial contamination
Pyruvate carboxylase(g)(k)(e)Mitochondrial contamination?
Fatty acid translocase/CD36(g)Plasma membrane contamination?
Lipoprotein lipase (LPL)(g)Plasma membrane contamination
Open in a separate window*Non proteomics screens.(a) (29).*(b) (GFP screen) (13).*(c) (GFP screen) (14).(d) (10).(e) (73).(f) (74).(g) (23).(h) (75).(i) (76).(j) (24).(k) (77).(l) (78).(m) (79).(n) (40).(o) (5).The metabolic functions of droplets, as revealed or confirmed by proteomic studies, can be grouped into fatty acid synthesis and activation, sterol biosynthesis, triglyceride biosynthesis, and fatty acid mobilization from sterol esters and triglycerides. 相似文献   

20.
Comparative Pathobiology of Kaposi Sarcoma-associated Herpesvirus and Related Primate Rhadinoviruses     
Susan V Westmoreland  Keith G Mansfield 《Comparative medicine》2008,58(1):31-42
With the emergence of the AIDS epidemic over the last 2 decades and the more recent identification of Kaposi sarcoma-associated herpesvirus (KSHV, Human herpesvirus 8), the genera of rhadinoviruses have gained importance as a family of viruses with oncogenic potential. First recognized in New World primates more than 30 y ago, the rhadinoviruses Saimiriine herpesvirus 2 and Ateline herpesvirus 2 have well-described transforming capabilities. Recently several new species-specific rhadinoviruses of Old World primates have been described, including retroperitoneal fibromatosis herpesvirus and rhesus rhadinovirus (Cercopithecine herpesvirus 17). Molecular analysis of these viruses has elucidated several functionally conserved genes and properties shared with KSHV involved in cellular proliferation, transformation, and immune evasion that facilitate the oncogenic potential of these viruses. This review examines the comparative pathobiology of KSHV, discusses the role of macaque rhadinoviruses as models of human disease, and outlines the derivation of specific pathogen-free animals.Abbreviations: CCL, cellular chemokine ligand; IRF, interferon regulatory factors; KSHV, Kaposi sarcoma-associated herpesvirus; LANA, latent nuclear antigen; MCD, multicentric Castleman disease; MCP1, monocyte chemotactic protein 1; miRNA, microRNA; ORF, open reading frame; PEL, primary effusion lymphoma; RFHV, retroperitoneal fibromatosis herpesvirus; RVV, rhesus rhadinovirus; SaHV2, Saimiriine herpesvirus 2; SPF, specific pathogen-free; SRV2, simian retrovirus type 2; THBS1, thrombospondinMembers of the herpesviridae are enveloped DNA viral agents that can infect a variety of host species, resulting in lifelong infection. The family is divided into Alphaherpesvirinae, Betaherpesvirinae, and Gammaherpesvirinae, according to biologic behavior and phylogenetic relationship. As a group, synthesis of viral DNA occurs in the nucleus, and production of infectious virions is associated with destruction of the cell. Herpesviruses have large complex genomes and often have acquired host genes that allow these viruses to modulate and persist in the face of host immune responses.25,71 This condition (termed ‘latency’) is characteristic of all herpesviral infections of the natural host. Although most members of the herpesviridae are of relatively low virulence in their respective hosts, some lack strict host specificity, and cross-species transmission to an inadvertent host can be associated with severe and fatal disease.The gammaherpesvirinae subfamily is characterized by in vitro and in vivo infection of lymphoblastoid cells and is further divided into the lymphocryptovirus (γ1 herpesviruses) and rhadinovirus (γ2 herpesviruses) genera. Rhadinoviruses have taken on increased importance with the identification of the novel Kaposi sarcoma-associated herpesvirus (KSHV, Human herpesvirus 8) in association with Kaposi sarcoma, an inflammatory and neoplastic condition seen in many HIV-infected patients with AIDS.20,22 Until the recognition of KSHV more than a decade ago, rhadinovirus infection of primates was thought to be restricted to the New World primate lineages, but subsequent investigation revealed a number of novel species-specific viruses in a variety of Old World primates (28 As discussed later, based largely on phylogenetic analysis, it is now believed that the rhadinoviruses are subdivided into 2 distinct groupings (rhadinovirus [RV] 1 and 2).77 This review will examine 2 recently recognized rhadinoviruses of macaques (retroperitoneal fibromatosis virus [RFHV] and rhesus rhadinovirus [RRV, Cercopethecine herpesvirus 17]), focusing on their comparative pathobiology with KSHV, their impact on naturally occurring disease entities, and their roles as animal models of human disease.

Table 1.

Nomenclature of primate rhadinoviruses (RV)
GroupAbbreviationOfficial designationaAlternative designationHostVirus isolatedGenomic sequence available
RV1
HHV8Human herpesvirus 8KSHVHomo sapiensyesyes
RV1mmunot availableRFHVmmuMacaca mulattanono
RV1mnenot availableRFHVmneMacaca nemestrinanono
RV1pannot availablePtRV1a andPan troglodytesnono
PtRV1b
RV1gornot availableGorRV1Gorilla gorillanono
RV1agmnot availableChRV1Chlorocebus aethiopsnono
RV2
HVSSaimirine herpesvirus 2 (SaHV2)noneS. sciureusyesyes
HVAAteline herpesvirus 2 (AtHV2)noneAteles geoffroyiyesyes
RV2mmuCercopethecine herpesvirus 17 (CeHV17)RRVMacaca mulattayesyes
RV2mnenot availablePRVMacaca nemestrinayesno
RV2pannot availablePtRV2Pan troglodytesnono
RV2agmnot availableChRV2Chlorocebus aethiopsnono
RV2pannot availablePapRV2Pan anubisnono
Open in a separate windowaFrom the International Committee on Taxonomy of Viruses.  相似文献   

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