161 research outputs found

    Interactions between KSHV ORF57 and the novel human TREX proteins, CHTOP and CIP29

    Get PDF
    The coupling of mRNA processing steps is essential for precise and efficient gene expression. The human transcription/export (hTREX) complex is a highly conserved multi-protein complex responsible for eukaryotic mRNA stability and nuclear export. We have previously shown that the Kaposi's sarcoma-associated ORF57 protein orchestrates the recruitment of the hTREX complex onto viral intronless mRNA forming a stable and export competent viral ribonucleoprotein particle (vRNP). Recently, additional cellular proteins, namely CHTOP, CIP29 and POLDIP3 have been proposed as novel hTREX components. Herein we extend our previous research and provide evidence that ORF57 interacts with CHTOP and CIP29, in contrast to POLDIP3. Moreover, depletion studies show both CHTOP and CIP29 effect ORF57-mediated viral mRNA processing. As such, these results suggest both CHTOP and CIP29 are hTREX components and are recruited to an ORF57-mediated vRNP

    Utilising proteomic approaches to understand oncogenic human herpesviruses

    Get PDF
    The γ‑herpesviruses Epstein-Barr virus and Kaposi's sarcoma‑associated herpesvirus are successful pathogens, each infecting a large proportion of the human population. These viruses persist for the life of the host and may each contribute to a number of malignancies, for which there are currently no cures. Large‑scale proteomic-based approaches provide an excellent means of increasing the collective understanding of the proteomes of these complex viruses and elucidating their numerous interactions within the infected host cell. These large‑scale studies are important for the identification of the intricacies of viral infection and the development of novel therapeutics against these two important pathogens

    A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES)

    Get PDF
    In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysisThis work has also been supported by grants BFU2012-39816-C02-01 (co-financed by FEDER funds and the Ministry of Economy and Competitiveness, Spain) to AL and Prometeo/2009/092 (Ministry of Education, Government of Valencia, Spain) and Explora Ciencia y Explora Tecnologia/SAF2013-49788-EXP (Spanish Ministry of Economy and Competitiveness) to AM. IRF is recipient of a "Sara Borrell" postdoctoral fellowship (Ref. CD12/00492) from the Ministry of Economy and Competitiveness (Spain). We are also grateful to the Spanish Network for the Study of Plasmids and Extrachromosomal Elements (REDEEX) for encouraging and funding cooperation among Spanish microbiologists working on the biology of mobile genetic elements (Spanish Ministry of Science and Innovation, reference number BFU2011-14145-E).Campos Frances, M.; Llorens, C.; Sempere Luna, JM.; Futami, R.; Rodríguez, I.; Carrasco, P.; Capilla, R.... (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct. 10(41):1-13. https://doi.org/10.1186/s13062-015-0070-9S1131041Baquero F, Coque TM, Canton R. Counteracting antibiotic resistance: breaking barriers among antibacterial strategies. Expert Opin Ther Targets. 2014;18:851–61.Baquero F, Lanza VF, Canton R, Coque TM. Public health evolutionary biology of antimicrobial resistance: priorities for intervention. Evol Appl. 2014;8:223–39.Baquero F, Coque TM, de la Cruz F. Ecology and evolution as targets: the need for novel eco-evo drugs and strategies to fight antibiotic resistance. Antimicrob Agents Chemother. 2011;55:3649–60.Carlet J, Jarlier V, Harbarth S, Voss A, Goossens H, Pittet D, et al. Ready for a world without antibiotics? The pensieres antibiotic resistance call to action. Antimicrob Resist Infect Control. 2012;1:11.Laxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, et al. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13:1057–98.G8-Science-Ministers-Statement. 2013. https://www.gov.uk/government/news/g8-science-ministers-statement .Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nat Med. 2004;10:S122–9.Wellington EM, Boxall AB, Cross P, Feil EJ, Gaze WH, Hawkey PM, et al. The role of the natural environment in the emergence of antibiotic resistance in gram-negative bacteria. Lancet Infect Dis. 2013;13:155–65.Marshall BM, Levy SB. Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev. 2011;24:718–33.Marshall BM, Ochieng DJ, Levy SB. Commensals: underappreciated reservoir of antibiotic resistance. Microbe. 2009;4:231–8.Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science. 2012;337:1107–11.Heuer H, Schmitt H, Smalla K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr Opin Microbiol. 2011;14:236–43.Teillant A, Laxminarayan R. Economics of Antibiotic Use in U.S. Swine and Poultry Production. Choices. 2015;30:1. 1st Quarter 2015.ANTIBIOTIC RESISTANCE THREATS in the United States. http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf .Gillings MR. Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome. Front Microbiol. 2013;4:4.Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74:417–33.Palmer AC, Kishony R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat Rev Genet. 2013;14:243–8.Baquero F, Tedim AP, Coque TM. Antibiotic resistance shaping multi-level population biology of bacteria. Front Microbiol. 2013;4:15.Partridge SR. Analysis of antibiotic resistance regions in Gram-negative bacteria. FEMS Microbiol Rev. 2011;35:820–55.Baquero F, Coque TM. Multilevel population genetics in antibiotic resistance. FEMS Microbiol Rev. 2011;35:705–6.Martinez JL, Baquero F, Andersson DI. Predicting antibiotic resistance. Nat Rev Microbiol. 2007;5:958–65.Martinez JL, Baquero F. Emergence and spread of antibiotic resistance: setting a parameter space. Upsala Journal of Medical Sciences. Upsala J Med Sci. 2014, Early Online: 1–10, doi: 10.3109/03009734.2014.901444 ).Baquero F, Nombela C. The microbiome as a human organ. Clin Microbiol Infect. 2012;18 Suppl 4:2–4.Kumsa B, Socolovschi C, Parola P, Rolain JM, Raoult D. Molecular detection of Acinetobacter species in lice and keds of domestic animals in Oromia Regional State. Ethiopia PLoS One. 2012;7:e52377.Ahmad A, Ghosh A, Schal C, Zurek L. Insects in confined swine operations carry a large antibiotic resistant and potentially virulent enterococcal community. BMC Microbiol. 2011;11:23.Graczyk TK, Knight R, Gilman RH, Cranfield MR. The role of non-biting flies in the epidemiology of human infectious diseases. Microbes Infect. 2001;3:231–5.Limoee M, Enayati AA, Khassi K, Salimi M, Ladonni H. Insecticide resistance and synergism of three field-collected strains of the German cockroach Blattella germanica (L.) (Dictyoptera: Blattellidae) from hospitals in Kermanshah, Iran. Trop Biomed. 2011;28:111–8.Salehzadeha A, Tavacolb P, Mahjubc H. Bacterial, fungal and parasitic contamination of cockroaches in public hospitals of Hamadan, Iran. J Vect Borne Dis. 2007;44:105–10.Akinjogunla OJ, Odeyemi AT, Udoinyang EP. Cockroaches (periplaneta americana and blattella germanica): reservoirs of multi drug resistant (MDR) bacteria in Uyo, Akwa Ibom State. Scientific J Biol Sci. 2012;1:19–30.Mideo N, Alizon S, Day T. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends Ecol Evol. 2008;23:511–7.Gillings MR, Stokes HW. Are humans increasing bacterial evolvability? Trends EcolEvol. 2012;27:346–52.Baquero F. Environmental stress and evolvability in microbial systems. Clin Microbiol Infect. 2009;15 Suppl 1:5–10.Paun G, Rozemberg G, Salomaa A. The Oxford Handbook of Membrane Computing. Oxford, London. Oxford University Press. 2010.Paun G. Membrane Computing. An Introduction. Berlin, Heidelberg. Springer-Verlag GmbH. 2002.Paun G. Computing with membranes. J Comput Syst Sci. 2000;61:108–43.Fontana F, Biancom L, Manca V. P systems and the modeling of biochemical oscillations. Lect Notes Comput Sci. 2006;3850:199–208.Cheruku S, Paun A, Romero-Campero FJ, Perez-Jimenez MJ, Ibarra OH. Simulating FAS-induced apoptosis by using P systems. Prog Nat Sci. 2007;4:424–31.Perez-Jimenez MJ, Romero-Campero FJ. P systems, a new computational modelling tool for systems biology. Transactions on computational systems. Lect N Bioinformat. 2006;Biology VI:176–97.Romero-Campero FJ, Perez-Jimenez MJ. Modelling gene expression control using P systems: The Lac Operon, a case study. Biosystems. 2008;91:438–57.Romero-Campero FJ, Perez-Jimenez MJ. A model of the quorum sensing system in Vibrio fischeri using P systems. Artif Life. 2008;14:95–109.Besozzi D, Cazzaniga P, Pescini D, Mauri G. Modelling metapopulations with stochastic membrane systems. Biosystems. 2008;91:499–514.Cardona M, Colomer MA, Perez-Jimenez MJ, Sanuy D, Margalida A. Modelling ecosystems using P Systems: The Bearded Vulture, a case of study. Lect Notes Comput Sci. 2009;5391:137–56.Cardona M, Colomer MA, Margalida A, Perez-Hurtado I, Perez-Jimenez MJ, Sanuy D. A P system based model of an ecosystem of some scavenger birds. Lect Notes Comput Sci. 2010;5957:182–95.Frisco P, Gheorghe M, Perez-Jimenez M. Applications of Membrane Computing in Systems and Synthetic biology. Cham. Springer International Publishing. 2014.Membrane Computing Community. http://ppage.psystems.eu .P-Lingua. http://www.p-lingua.org/wiki/index.php/Main_Page .Llorens C, Futami R, Covelli L, Dominguez-Escriba L, Viu JM, Tamarit D, et al. The Gypsy Database (GyDB) of mobile genetic elements: release 2.0. Nucleic Acids Res. 2011;39:D70–4.Baquero F. From pieces to patterns: evolutionary engineering in bacterial pathogens. Nat Rev Microbiol. 2004;2:510–8.Java. http://www.java.com .Garcia-Quismondo M, Gutierrez-Escudero R, Martinez-del-Amor MA, Orejuela-Pinedo E, Pérez-Hurtado I. P-Lingua 2.0: a software framework for cell-like P systems. Int J Comput Commun. 2009;IV:234.R programming language. http://www.r-project.org .Maciel A, Sankaranarayanan G, Halic T, Arikatla VS, Lu Z, De S. Surgical model-view-controller simulation software framework for local and collaborative applications. Int J Comput Assist Radiol Surg. 2011;6:457–71.Dethlefsen L, McFall-Ngai M, Relman DA. An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature. 2007;449:811–8.Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol. 2008;6:776–88.Pallen MJ, Wren BW. Bacterial pathogenomics. Nature. 2007;449:835–42.Carrasco P, Perez-Cobas AE, Van de Pol C, Baixeras J, Moya A, Latorre A. Succession of the gut microbiota in the cockroach Blattella germanica. Int Microbiol. 2014;17:99–109

    Physics with antihydrogen

    Get PDF
    Performing measurements of the properties of antihydrogen, the bound state of an antiproton and a positron, and comparing the results with those for ordinary hydrogen, has long been seen as a route to test some of the fundamental principles of physics. There has been much experimental progress in this direction in recent years, and antihydrogen is now routinely created and trapped and a range of exciting measurements probing the foundations of modern physics are planned or underway. In this contribution we review the techniques developed to facilitate the capture and manipulation of positrons and antiprotons, along with procedures to bring them together to create antihydrogen. Once formed, the antihydrogen has been detected by its destruction via annihilation or field ionization, and aspects of the methodologies involved are summarized. Magnetic minimum neutral atom traps have been employed to allow some of the antihydrogen created to be held for considerable periods. We describe such devices, and their implementation, along with the cusp magnetic trap used to produce the first evidence for a low-energy beam of antihydrogen. The experiments performed to date on antihydrogen are discussed, including the first observation of a resonant quantum transition and the analyses that have yielded a limit on the electrical neutrality of the anti-atom and placed crude bounds on its gravitational behaviour. Our review concludes with an outlook, including the new ELENA extension to the antiproton decelerator facility at CERN, together with summaries of how we envisage the major threads of antihydrogen physics will progress in the coming years

    Mammalian innate resistance to highly pathogenic avian influenza H5N1 virus infection is mediated through reduced proinflammation and infectious virus release

    Get PDF
    Respiratory epithelial cells and macrophages are the key innate immune cells that play an important role in the pathogenesis of influenza A virus infection. We found that these two cell types from both human and pig showed comparable susceptibilities to initial infection with a highly pathogenic avian influenza (HPAI) H5N1 virus (A/turkey/Turkey/1/05) and a moderately pathogenic human influenza H1N1 virus (A/USSR/77), but there were contrasting differences in host innate immune responses. Human cells mounted vigorous cytokine (tumor necrosis factor alpha [TNF-α] and interleukin-6 [IL-6]) and chemokine (CXCL9, CXCL10, and CXCL11) responses to H5N1 virus infection. However, pig epithelial cells and macrophages showed weak or no TNF-α and chemokine induction with the same infections. The apparent lack of a strong proinflammatory response, corroborated by the absence of TNF-α induction in H5N1 virus-challenged pigs, coincided with greater cell death and the reduced release of infectious virus from infected pig epithelial cells. Suppressor of cytokine signaling 3 (SOCS3), a protein suppressor of the JAK-STAT pathway, was constitutively highly expressed and transcriptionally upregulated in H5N1 virus-infected pig epithelial cells and macrophages, in contrast to the corresponding human cells. The overexpression of SOCS3 in infected human macrophages dampened TNF-α induction. In summary, we found that the reported low susceptibility of pigs to contemporary Eurasian HPAI H5N1 virus infections coincides at the level of innate immunity of respiratory epithelial cells and macrophages with a reduced output of viable virus and an attenuated proinflammatory response, possibly mediated in part by SOCS3, which could serve as a target in the treatment or prevention of virus-induced hypercytokinemia, as observed for humans

    Performance and Operation of the CMS Electromagnetic Calorimeter

    Get PDF
    The operation and general performance of the CMS electromagnetic calorimeter using cosmic-ray muons are described. These muons were recorded after the closure of the CMS detector in late 2008. The calorimeter is made of lead tungstate crystals and the overall status of the 75848 channels corresponding to the barrel and endcap detectors is reported. The stability of crucial operational parameters, such as high voltage, temperature and electronic noise, is summarised and the performance of the light monitoring system is presented

    Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

    Get PDF
    BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

    Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks : The GR@ACE project

    Get PDF
    Introduction: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. Methods: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. Results: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. Discussion: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series

    Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

    Get PDF
    Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.
    corecore