Cowpea viruses: Quantitative and qualitative effects of single and mixed viral infections

Multiple viral infections have been reported on cultivated commercial cowpeas (Vigna unguiculata) in Nigeria. In this study, the effect of inoculating two commercial cultivars (cvs) (“Oloyin” and “Olo II’)and two lines from IITA (Ife Brown and TVu-76) with buffer, Cowpea aphid-borne mosaic (CABMV), Cowpea mottle (CMeV) and Southern bean mosaic (SBMV) viruses individually as well as in mixtures(CABMV+ CMeV, CABMV+ SBMV, CMeV+SBMV, and CABMV+ CMeV+SBMV) at 10 and 28 days after planting (DAP) on the growth, yield and nutritive content of seeds from infected plants were evaluated.The age of the plants at time of infection and the different viral treatments significantly affected the different parameters assessed. The average height of plants inoculated 10 DAP were significantlyshorter than those of plants inoculated 28 DAP. Inoculating with single, double and triple viruses (10 DAP) resulted in 19-34%, 31-46% and 42-53% reductions in plant height, respectively. Viral infectionsalso resulted in significant reductions in the number of pods and seeds produced. Plants inoculated with the three viruses 10 DAP produced the least number of pods and seed. Viral treatments resulted inthe production of seeds with a lower protein content of 24.8-28.9% compared with the 28.5-30.4% protein in seeds from the control plants. Plants inoculated 10 DAP with the triple viruses produced theseeds with the least protein content (24.8-27.1%). The carbohydrate, fat and moisture contents of seeds from virus infected plants were however slightly higher than those of the control plants while the ashcontents were lower. Generally, the commercial cowpea cvs were more severely affected by the viral treatments. These results indicate that infection at an early age and by multiple viruses can havedevastating effects on the growth, yield and the nutritional quality of cowpea

Immunological and molecular diagnostic methods for detection of viruses infecting cowpea (Vigna unquiculata)

Cowpea viruses are difficult to identify using morphological criteria which can be time consuming, challenging, and require extensive knowledge in taxonomy. In order to improve the quality and quantityof the germplasms and to significantly reduce the infection and transmission of virus to different cultivars of cowpea, proper diagnosis and control is essential. The immuno-diagnostic and  moleculardiagnostic methods have shown great potential as far as specificity and sensitivity are concerned and can generate accurate results rapidly. The aim of this overview is to discuss the various immunodiagnostic and molecular diagnostic methods such as enzymes linked immunosorbent assay (ELISA), immunosorbent electron microscopy (ISEM), polymerase chain reaction (PCR), nucleic acidhybridization, dot immunoblotting assay (DTBIA) found suitable for diagnosis of Cowpea aphid-borne mosaic virus (CABMV), Cucumber mosaic virus (CMV) and Cowpea mottle virus (CMeV) infectingcowpea. These techniques do not only provide information for epidemiological purposes, but also help to develop disease free stock of cowpeas. Therefore, these various techniques with symptoms andhistory are of immense value to diagnose cowpea viruses and are the cornerstone of the management of cowpea cultivars

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

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

Microbial and physico-chemical quality of powdered soymilk samples in Akwa Ibom, South Southern Nigeria

Branded and unbranded powdered soymilks were evaluated for microbial quality, physicochemical parameters and aflatoxin level. The total bacterial count ranged from 4~104 to 1.1~105 cfu/g and 2.0 ~104 to 7.2 ~ 104 cfu/g for branded and unbranded powdered soymilk samples, respectively. Coliform were not detected in the branded and unbranded powdered soymilk samples. The fungal count rangedfrom 2.1~104 to 4.9 ~104cfu/g and 1.5 ~104 to 2.6 ~104 cfu/g for branded and unbranded soymilk, respectively. The isolated microorganisms were Micrococcus sp., Staphylococcus aureus, Bacillussubtilis, Pseudomonas sp., Streptococcus sp., Aspergillus flavus, Candida pseudotropicalis, Saccharomyces cerevisae, Penicillium citrium and Cladosporium sp. in branded samples while Pseudomonas sp., B. subtilis, Yersinia enterocolitica, Streptococcus sp., A. flavus, Aspergillus niger, C. pseudotropicalis, Saccharomyces sp. were isolated from unbranded samples. The pH ranged from 7.9 .8.0 and 7.8 . 8.0 while the lactic acid ranged from 90.08 . 144.13 mg and 81.07 . 162.144 mg for branded and unbranded samples in that order. The moisture content ranged from 12.76 . 13.05%. The proximate analysis revealed that the crude protein ranged from 16.68 . 17.74% branded and unbranded powdered soymilk samples. Aflatoxin B1 were detected in the samples and it ranged from 7.87 . 19.76 ƒÊ/kg and 4.58 . 16.74 ƒÊ/kg for branded and unbranded samples respectively while Aflatoxin B2 ranged from 3.43 . 11.74 ƒÊ/kg and 2.57 . 9.79 ƒÊ/kg. The microbial population in terms of numbers and types reflected poor standard of production constituting a serious health hazard among populace. Thus, this calls for proper monitoring and quality assurance