- -

Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Campos Frances, Marcelino es_ES
dc.contributor.author San Millan, Alvaro es_ES
dc.contributor.author Sempere Luna, José María es_ES
dc.contributor.author Lanza, Val F. es_ES
dc.contributor.author Coque, Teresa M. es_ES
dc.contributor.author Llorens, Carlos es_ES
dc.contributor.author Baquero, Fernando es_ES
dc.date.accessioned 2020-12-18T04:31:09Z
dc.date.available 2020-12-18T04:31:09Z
dc.date.issued 2020-08 es_ES
dc.identifier.issn 0066-4804 es_ES
dc.identifier.uri http://hdl.handle.net/10251/157354
dc.description.abstract [EN] Bacterial plasmids harboring antibiotic resistance genes are critical in the spread of antibiotic resistance. It is known that plasmids differ in their kinetic values, i.e., conjugation rate, segregation rate by copy number incompatibility with related plasmids, and rate of stochastic loss during replication. They also differ in cost to the cell in terms of reducing fitness and in the frequency of compensatory mutations compensating plasmid cost. However, we do not know how variation in these values influences the success of a plasmid and its resistance genes in complex ecosystems, such as the microbiota. Genes are in plasmids, plasmids are in cells, and cells are in bacterial populations and microbiotas, which are inside hosts, and hosts are in human communities at the hospital or the community under various levels of cross-colonization and antibiotic exposure. Differences in plasmid kinetics might have consequences on the global spread of antibiotic resistance. New membrane computing methods help to predict these consequences. In our simulation, conjugation frequency of at least 10(-3) influences the dominance of a strain with a resistance plasmid. Coexistence of different antibiotic resistances occurs if host strains can maintain two copies of similar plasmids. Plasmid loss rates of 10(-4) or 10(-5) or plasmid fitness costs of >= 0.06 favor plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost at high mutation frequencies (10(-3) to 10(-5)). The results of this computational model clearly show how changes in plasmid kinetics can modify the entire population ecology of antibiotic resistance in the hospital setting. es_ES
dc.description.sponsorship F. Baquero, M. Campos, and T. M. Coque were supported by EU Joint Programming Initiative JPIAMR2016-AC16/00043 (JPIonAMR-Third call on Transmission, ST131TS project), the Health Institute Carlos III of Spain (grants PI15-00818 and PI18-01942 and CIBER [CIBER in Epidemiology and Public Health, CIBERESP; CB06/02/0053]), and the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691), all of them cofinanced by the European Development Regional Fund (ERDF) "A Way to Achieve Europe." A. San Millan was supported by the European Research Council under the European Union's Horizon 2020 Research and Innovation Program (ERC grant agreement number 757440-PLASREVOLUTION) es_ES
dc.language Inglés es_ES
dc.publisher American Society for Microbiology es_ES
dc.relation.ispartof Antimicrobial Agents and Chemotherapy es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Antibiotic resistance es_ES
dc.subject Complex systems es_ES
dc.subject Computational biology es_ES
dc.subject Computer modeling es_ES
dc.subject Conjugation es_ES
dc.subject Ecosystems es_ES
dc.subject Membrane computing es_ES
dc.subject Plasmids es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1128/AAC.00593-20 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//AC16%2F00043/ES/Escherichia coli ST131: a model for high-risk transmission dynamics of antimicrobial resistance/
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/757440/EU/Understanding the evolution of plasmid-mediated antibiotic resistance in real life scenarios/
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//PI15%2F00818/ES/Desarrollo de un simulador computacional de membranas para el estudio de la dinámica trans-jerárquica en la evolución de resistencia bacteriana a los antibióticos./
dc.relation.projectID info:eu-repo/grantAgreement/ISCIII//PI18%2F01942/ES/Evolución de Resistomas y Resistotipos en la UVI: hacia un Análisis Particularizado de Riesgo de Emergencia e Infección por Bacterias Resistentes a Antibióticos/
dc.relation.projectID info:eu-repo/grantAgreement/CIBER-BBN//CB06%2F02%2F0053/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Campos Frances, M.; San Millan, A.; Sempere Luna, JM.; Lanza, VF.; Coque, TM.; Llorens, C.; Baquero, F. (2020). Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. Antimicrobial Agents and Chemotherapy. 64(8):1-19. https://doi.org/10.1128/AAC.00593-20 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1128/AAC.00593-20 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 64 es_ES
dc.description.issue 8 es_ES
dc.identifier.pmid 32457104 es_ES
dc.identifier.pmcid PMC7526830 es_ES
dc.relation.pasarela S\419527 es_ES
dc.contributor.funder European Commission
dc.contributor.funder Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references De Gelder, L., Ponciano, J. M., Joyce, P., & Top, E. M. (2007). Stability of a promiscuous plasmid in different hosts: no guarantee for a long-term relationship. Microbiology, 153(2), 452-463. doi:10.1099/mic.0.2006/001784-0 es_ES
dc.description.references Norman, A., Hansen, L. H., & Sørensen, S. J. (2009). Conjugative plasmids: vessels of the communal gene pool. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1527), 2275-2289. doi:10.1098/rstb.2009.0037 es_ES
dc.description.references Andam, C. P., Fournier, G. P., & Gogarten, J. P. (2011). Multilevel populations and the evolution of antibiotic resistance through horizontal gene transfer. FEMS Microbiology Reviews, 35(5), 756-767. doi:10.1111/j.1574-6976.2011.00274.x es_ES
dc.description.references Baquero, F., Tedim, A. P., & Coque, T. M. (2013). Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4. doi:10.3389/fmicb.2013.00015 es_ES
dc.description.references Wein, T., Hülter, N. F., Mizrahi, I., & Dagan, T. (2019). Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance. Nature Communications, 10(1). doi:10.1038/s41467-019-10600-7 es_ES
dc.description.references Yano, H., Shintani, M., Tomita, M., Suzuki, H., & Oshima, T. (2019). Reconsidering plasmid maintenance factors for computational plasmid design. Computational and Structural Biotechnology Journal, 17, 70-81. doi:10.1016/j.csbj.2018.12.001 es_ES
dc.description.references Gumpert, H., Kubicek-Sutherland, J. Z., Porse, A., Karami, N., Munck, C., Linkevicius, M., … Sommer, M. O. A. (2017). Transfer and Persistence of a Multi-Drug Resistance Plasmid in situ of the Infant Gut Microbiota in the Absence of Antibiotic Treatment. Frontiers in Microbiology, 8. doi:10.3389/fmicb.2017.01852 es_ES
dc.description.references Durão, P., Balbontín, R., & Gordo, I. (2018). Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance. Trends in Microbiology, 26(8), 677-691. doi:10.1016/j.tim.2018.01.005 es_ES
dc.description.references Campos, M., Llorens, C., Sempere, J. M., Futami, R., Rodriguez, I., Carrasco, P., … Baquero, F. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct, 10(1). doi:10.1186/s13062-015-0070-9 es_ES
dc.description.references Campos, M., Capilla, R., Naya, F., Futami, R., Coque, T., Moya, A., … Baquero, F. (2019). Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. mBio, 10(1), e02460-18. doi:10.1128/mbio.02460-18 es_ES
dc.description.references 13. Baquero F, Campos M, Llorens C, Sempere JM. 2018. A model of antibiotic resistance evolution dynamics through P systems with active membranes and communication rules, p 33–44. In Graciani C, Agustín Riscos-Núñez A, Păun Gh, Rozenberg G, Salomaa A (ed), Enjoying natural computing. Springer, Cham, Switzerland. es_ES
dc.description.references Leclerc, Q. J., Lindsay, J. A., & Knight, G. M. (2019). Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations. Journal of The Royal Society Interface, 16(157), 20190260. doi:10.1098/rsif.2019.0260 es_ES
dc.description.references Blanquart, F. (2019). Evolutionary epidemiology models to predict the dynamics of antibiotic resistance. Evolutionary Applications, 12(3), 365-383. doi:10.1111/eva.12753 es_ES
dc.description.references 16. Rozenberg G, Salomaa A, Păun G (ed). 2010. The Oxford handbook of membrane computing. Oxford University Press, Oxford, England. es_ES
dc.description.references 17. Păun G. 2002. Membrane computing. An introduction. Springer-Verlag, Heidelberg, Germany. es_ES
dc.description.references Novais, A., Cantón, R., Moreira, R., Peixe, L., Baquero, F., & Coque, T. M. (2006). Emergence and Dissemination of Enterobacteriaceae Isolates Producing CTX-M-1-Like Enzymes in Spain Are Associated with IncFII (CTX-M-15) and Broad-Host-Range (CTX-M-1, -3, and -32) Plasmids. Antimicrobial Agents and Chemotherapy, 51(2), 796-799. doi:10.1128/aac.01070-06 es_ES
dc.description.references Mathers, A. J., Peirano, G., & Pitout, J. D. D. (2015). The Role of Epidemic Resistance Plasmids and International High-Risk Clones in the Spread of Multidrug-Resistant Enterobacteriaceae. Clinical Microbiology Reviews, 28(3), 565-591. doi:10.1128/cmr.00116-14 es_ES
dc.description.references 20. Poirel L, Madec JY, Lupo A, Schink AK, Kieffer N, Nordmann P, Schwarz S. 2018. Antimicrobial resistance in Escherichia coli, p 289–316. In Schwarz S, Cavaco LM, Shen J (ed), Antimicrobial resistance in bacteria from livestock and companion animals. ASM Press, Washington, DC. es_ES
dc.description.references Livermore, D. M., & Hawkey, P. M. (2005). CTX-M: changing the face of ESBLs in the UK. Journal of Antimicrobial Chemotherapy, 56(3), 451-454. doi:10.1093/jac/dki239 es_ES
dc.description.references 23. European Centre for Disease Prevention and Control. 2015. Antimicrobial resistance surveillance in Europe 2015. Annual report of the European Antimicrobial Resistance Surveillance Network (EARS-Net). European Centre for Disease Prevention and Control, Stockholm, Sweden. es_ES
dc.description.references Bush, K., & Fisher, J. F. (2011). Epidemiological Expansion, Structural Studies, and Clinical Challenges of New β-Lactamases from Gram-Negative Bacteria. Annual Review of Microbiology, 65(1), 455-478. doi:10.1146/annurev-micro-090110-102911 es_ES
dc.description.references Bush, K. (2018). Past and Present Perspectives on β-Lactamases. Antimicrobial Agents and Chemotherapy, 62(10). doi:10.1128/aac.01076-18 es_ES
dc.description.references Hawser, S. P., Bouchillon, S. K., Hoban, D. J., Badal, R. E., Cantón, R., & Baquero, F. (2010). Incidence and Antimicrobial Susceptibility of Escherichia coli and Klebsiella pneumoniae with Extended-Spectrum β-Lactamases in Community- and Hospital-Associated Intra-Abdominal Infections in Europe: Results of the 2008 Study for Monitoring Antimicrobial Resistance Trends (SMART). Antimicrobial Agents and Chemotherapy, 54(7), 3043-3046. doi:10.1128/aac.00265-10 es_ES
dc.description.references Simonsen, L., Gordon, D. M., Stewart, F. M., & Levin, B. R. (1990). Estimating the rate of plasmid transfer: an end-point method. Journal of General Microbiology, 136(11), 2319-2325. doi:10.1099/00221287-136-11-2319 es_ES
dc.description.references Levin, B. R., Stewart, F. M., & Rice, V. A. (1979). The kinetics of conjugative plasmid transmission: Fit of a simple mass action model. Plasmid, 2(2), 247-260. doi:10.1016/0147-619x(79)90043-x es_ES
dc.description.references Turner, P. E., Williams, E. S. C. P., Okeke, C., Cooper, V. S., Duffy, S., & Wertz, J. E. (2014). Antibiotic resistance correlates with transmission in plasmid evolution. Evolution, 68(12), 3368-3380. doi:10.1111/evo.12537 es_ES
dc.description.references Porse, A., Schønning, K., Munck, C., & Sommer, M. O. A. (2016). Survival and Evolution of a Large Multidrug Resistance Plasmid in New Clinical Bacterial Hosts. Molecular Biology and Evolution, 33(11), 2860-2873. doi:10.1093/molbev/msw163 es_ES
dc.description.references Smillie, C., Garcillán-Barcia, M. P., Francia, M. V., Rocha, E. P. C., & de la Cruz, F. (2010). Mobility of Plasmids. Microbiology and Molecular Biology Reviews, 74(3), 434-452. doi:10.1128/mmbr.00020-10 es_ES
dc.description.references 38. Taylor DE, Gibreel A, Tracz DM, Lawley TD. 2004. Antibiotic resistance plasmids, p 473–492. In Funnell BE, Phillips GJ (ed), Plasmid biology. American Society of Microbiology, Washington, DC. es_ES
dc.description.references Million-Weaver, S., & Camps, M. (2014). Mechanisms of plasmid segregation: Have multicopy plasmids been overlooked? Plasmid, 75, 27-36. doi:10.1016/j.plasmid.2014.07.002 es_ES
dc.description.references Lau, B. T. C., Malkus, P., & Paulsson, J. (2013). New quantitative methods for measuring plasmid loss rates reveal unexpected stability. Plasmid, 70(3), 353-361. doi:10.1016/j.plasmid.2013.07.007 es_ES
dc.description.references Vogwill, T., & MacLean, R. C. (2014). The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evolutionary Applications, 8(3), 284-295. doi:10.1111/eva.12202 es_ES
dc.description.references Andersson, D. I., & Levin, B. R. (1999). The biological cost of antibiotic resistance. Current Opinion in Microbiology, 2(5), 489-493. doi:10.1016/s1369-5274(99)00005-3 es_ES
dc.description.references Andersson, D. I., & Hughes, D. (2010). Antibiotic resistance and its cost: is it possible to reverse resistance? Nature Reviews Microbiology, 8(4), 260-271. doi:10.1038/nrmicro2319 es_ES
dc.description.references Loftie-Eaton, W., Bashford, K., Quinn, H., Dong, K., Millstein, J., Hunter, S., … Top, E. M. (2017). Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nature Ecology & Evolution, 1(9), 1354-1363. doi:10.1038/s41559-017-0243-2 es_ES
dc.description.references Zwanzig, M., Harrison, E., Brockhurst, M. A., Hall, J. P. J., Berendonk, T. U., & Berger, U. (2019). Mobile Compensatory Mutations Promote Plasmid Survival. mSystems, 4(1). doi:10.1128/msystems.00186-18 es_ES
dc.description.references Yang, Q. E., MacLean, C., Papkou, A., Pritchard, M., Powell, L., Thomas, D., … Walsh, T. R. (2020). Compensatory mutations modulate the competitiveness and dynamics of plasmid-mediated colistin resistance in Escherichia coli clones. The ISME Journal, 14(3), 861-865. doi:10.1038/s41396-019-0578-6 es_ES
dc.description.references Gama, J. A., Zilhão, R., & Dionisio, F. (2018). Impact of plasmid interactions with the chromosome and other plasmids on the spread of antibiotic resistance. Plasmid, 99, 82-88. doi:10.1016/j.plasmid.2018.09.009 es_ES
dc.description.references Harrison, E., Dytham, C., Hall, J. P. J., Guymer, D., Spiers, A. J., Paterson, S., & Brockhurst, M. A. (2016). Rapid compensatory evolution promotes the survival of conjugative plasmids. Mobile Genetic Elements, 6(3), e1179074. doi:10.1080/2159256x.2016.1179074 es_ES
dc.description.references Hall, J. P. J., Brockhurst, M. A., Dytham, C., & Harrison, E. (2017). The evolution of plasmid stability: Are infectious transmission and compensatory evolution competing evolutionary trajectories? Plasmid, 91, 90-95. doi:10.1016/j.plasmid.2017.04.003 es_ES
dc.description.references 54. Shintani M, Suzuki H. 2019. Plasmids and their hosts, p 109–133. In Nishida H, Oshima T (ed), DNA traffic in the environment. Springer, Singapore. es_ES
dc.description.references Komp Lindgren, P., Karlsson, A., & Hughes, D. (2003). Mutation Rate and Evolution of Fluoroquinolone Resistance in Escherichia coli Isolates from Patients with Urinary Tract Infections. Antimicrobial Agents and Chemotherapy, 47(10), 3222-3232. doi:10.1128/aac.47.10.3222-3232.2003 es_ES
dc.description.references Krone, S. M., Lu, R., Fox, R., Suzuki, H., & Top, E. M. (2007). Modelling the spatial dynamics of plasmid transfer and persistence. Microbiology, 153(8), 2803-2816. doi:10.1099/mic.0.2006/004531-0 es_ES
dc.description.references Baquero, F., Coque, T. M., & de la Cruz, F. (2011). Ecology and Evolution as Targets: the Need for Novel Eco-Evo Drugs and Strategies To Fight Antibiotic Resistance. Antimicrobial Agents and Chemotherapy, 55(8), 3649-3660. doi:10.1128/aac.00013-11 es_ES
dc.description.references Buckner, M. M. C., Ciusa, M. L., & Piddock, L. J. V. (2018). Strategies to combat antimicrobial resistance: anti-plasmid and plasmid curing. FEMS Microbiology Reviews, 42(6), 781-804. doi:10.1093/femsre/fuy031 es_ES
dc.description.references Bush, K. (2008). Extended-spectrum β-lactamases in North America, 1987–2006. Clinical Microbiology and Infection, 14, 134-143. doi:10.1111/j.1469-0691.2007.01848.x es_ES
dc.description.references Jacoby, G. A., & Han, P. (1996). Detection of extended-spectrum beta-lactamases in clinical isolates of Klebsiella pneumoniae and Escherichia coli. Journal of clinical microbiology, 34(4), 908-911. doi:10.1128/jcm.34.4.908-911.1996 es_ES
dc.description.references Valverde, A., Coque, T. M., Sanchez-Moreno, M. P., Rollan, A., Baquero, F., & Canton, R. (2004). Dramatic Increase in Prevalence of Fecal Carriage of Extended-Spectrum  -Lactamase-Producing Enterobacteriaceae during Nonoutbreak Situations in Spain. Journal of Clinical Microbiology, 42(10), 4769-4775. doi:10.1128/jcm.42.10.4769-4775.2004 es_ES
dc.description.references Hernández, J. R., Martínez-Martínez, L., Cantón, R., Coque, T. M., & Pascual, A. (2005). Nationwide Study of Escherichia coli and Klebsiella pneumoniae Producing Extended-Spectrum β-Lactamases in Spain. Antimicrobial Agents and Chemotherapy, 49(5), 2122-2125. doi:10.1128/aac.49.5.2122-2125.2005 es_ES
dc.description.references PEREZ, F., ENDIMIANI, A., HUJER, K., & BONOMO, R. (2007). The continuing challenge of ESBLs. Current Opinion in Pharmacology, 7(5), 459-469. doi:10.1016/j.coph.2007.08.003 es_ES
dc.description.references Hernández-García, M., Pérez-Viso, B., Navarro-San Francisco, C., Baquero, F., Morosini, M. I., Ruiz-Garbajosa, P., & Cantón, R. (2019). Intestinal co-colonization with different carbapenemase-producing Enterobacterales isolates is not a rare event in an OXA-48 endemic area. EClinicalMedicine, 15, 72-79. doi:10.1016/j.eclinm.2019.09.005 es_ES
dc.description.references Jensen, R. B., & Gerdes, K. (1995). Programmed cell death in bacteria: proteic plasmid stabilization systems. Molecular Microbiology, 17(2), 205-210. doi:10.1111/j.1365-2958.1995.mmi_17020205.x es_ES
dc.description.references Stalder, T., Cornwell, B., Lacroix, J., Kohler, B., Dixon, S., Yano, H., … Top, E. M. (2020). Evolving Populations in Biofilms Contain More Persistent Plasmids. Molecular Biology and Evolution, 37(6), 1563-1576. doi:10.1093/molbev/msaa024 es_ES
dc.description.references McNally, A., Oren, Y., Kelly, D., Pascoe, B., Dunn, S., Sreecharan, T., … Corander, J. (2016). Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations. PLOS Genetics, 12(9), e1006280. doi:10.1371/journal.pgen.1006280 es_ES
dc.description.references Baquero, M.-R., Galán, J. C., del Carmen Turrientes, M., Cantón, R., Coque, T. M., Martínez, J. L., & Baquero, F. (2005). Increased Mutation Frequencies in Escherichia coli Isolates Harboring Extended-Spectrum β-Lactamases. Antimicrobial Agents and Chemotherapy, 49(11), 4754-4756. doi:10.1128/aac.49.11.4754-4756.2005 es_ES
dc.description.references Baquero, F. (2004). From pieces to patterns: evolutionary engineering in bacterial pathogens. Nature Reviews Microbiology, 2(6), 510-518. doi:10.1038/nrmicro909 es_ES
dc.description.references Andersson, D. I., Balaban, N. Q., Baquero, F., Courvalin, P., Glaser, P., Gophna, U., … Tønjum, T. (2020). Antibiotic resistance: turning evolutionary principles into clinical reality. FEMS Microbiology Reviews, 44(2), 171-188. doi:10.1093/femsre/fuaa001 es_ES
dc.description.references Jernberg, C., Löfmark, S., Edlund, C., & Jansson, J. K. (2010). Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology, 156(11), 3216-3223. doi:10.1099/mic.0.040618-0 es_ES
dc.description.references Sommer, F., Anderson, J. M., Bharti, R., Raes, J., & Rosenstiel, P. (2017). The resilience of the intestinal microbiota influences health and disease. Nature Reviews Microbiology, 15(10), 630-638. doi:10.1038/nrmicro.2017.58 es_ES
dc.description.references Novais, C., Tedim, A. P., Lanza, V. F., Freitas, A. R., Silveira, E., Escada, R., … Coque, T. M. (2016). Co-diversification of Enterococcus faecium Core Genomes and PBP5: Evidences of pbp5 Horizontal Transfer. Frontiers in Microbiology, 7. doi:10.3389/fmicb.2016.01581 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem