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      Mathematical Model of Plasmid-Mediated Resistance to Ceftiofur in Commensal Enteric Escherichia coli of Cattle

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          Abstract

          Antimicrobial use in food animals may contribute to antimicrobial resistance in bacteria of animals and humans. Commensal bacteria of animal intestine may serve as a reservoir of resistance-genes. To understand the dynamics of plasmid-mediated resistance to cephalosporin ceftiofur in enteric commensals of cattle, we developed a deterministic mathematical model of the dynamics of ceftiofur-sensitive and resistant commensal enteric Escherichia coli ( E. coli) in the absence of and during parenteral therapy with ceftiofur. The most common treatment scenarios including those using a sustained-release drug formulation were simulated; the model outputs were in agreement with the available experimental data. The model indicated that a low but stable fraction of resistant enteric E. coli could persist in the absence of immediate ceftiofur pressure, being sustained by horizontal and vertical transfers of plasmids carrying resistance-genes, and ingestion of resistant E. coli. During parenteral therapy with ceftiofur, resistant enteric E. coli expanded in absolute number and relative frequency. This expansion was most influenced by parameters of antimicrobial action of ceftiofur against E. coli. After treatment (>5 weeks from start of therapy) the fraction of ceftiofur-resistant cells among enteric E. coli, similar to that in the absence of treatment, was most influenced by the parameters of ecology of enteric E. coli, such as the frequency of transfer of plasmids carrying resistance-genes, the rate of replacement of enteric E. coli by ingested E. coli, and the frequency of ceftiofur resistance in the latter.

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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            Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae.

            The mammalian gut harbors a dense microbial community interacting in multiple ways, including horizontal gene transfer (HGT). Pangenome analyses established particularly high levels of genetic flux between Gram-negative Enterobacteriaceae. However, the mechanisms fostering intraenterobacterial HGT are incompletely understood. Using a mouse colitis model, we found that Salmonella-inflicted enteropathy elicits parallel blooms of the pathogen and of resident commensal Escherichia coli. These blooms boosted conjugative HGT of the colicin-plasmid p2 from Salmonella enterica serovar Typhimurium to E. coli. Transconjugation efficiencies of ~100% in vivo were attributable to high intrinsic p2-transfer rates. Plasmid-encoded fitness benefits contributed little. Under normal conditions, HGT was blocked by the commensal microbiota inhibiting contact-dependent conjugation between Enterobacteriaceae. Our data show that pathogen-driven inflammatory responses in the gut can generate transient enterobacterial blooms in which conjugative transfer occurs at unprecedented rates. These blooms may favor reassortment of plasmid-encoded genes between pathogens and commensals fostering the spread of fitness-, virulence-, and antibiotic-resistance determinants.
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              Human intestinal bacteria as reservoirs for antibiotic resistance genes.

              Human intestinal bacteria have many roles in human health, most of which are beneficial or neutral for the host. In this review, we explore a more sinister side of intestinal bacteria; their role as traffickers in antibiotic resistance genes. Evidence is accumulating to support the hypothesis that intestinal bacteria not only exchange resistance genes among themselves but might also interact with bacteria that are passing through the colon, causing these bacteria to acquire and transmit antibiotic resistance genes.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                16 May 2012
                : 7
                : 5
                : e36738
                Affiliations
                [1 ]Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America
                [2 ]Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, Tennessee, United States of America
                Massey University, New Zealand
                Author notes

                Conceived and designed the experiments: CL YTG. Wrote the paper: VVV. Partook in model development: VVV CL ZL YTG. Supported VVV in model implementation: ZL. Worked on model parameterization, collated the treatment scenarios, and solved the models: VVV. Helped edit the manuscript: CL ZL YTG. Read and approved the final version of the manuscript: VVV CL ZL YTG.

                Article
                PONE-D-12-03143
                10.1371/journal.pone.0036738
                3353932
                22615803
                eb9a8d23-aac0-4cba-9548-428cebb682f2
                Volkova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 31 January 2012
                : 12 April 2012
                Page count
                Pages: 15
                Categories
                Research Article
                Agriculture
                Animal Management
                Biology
                Computational Biology
                Microbiology
                Bacterial Pathogens
                Escherichia Coli
                Virology
                Mechanisms of Resistance and Susceptibility
                Mathematics
                Applied Mathematics
                Medicine
                Epidemiology
                Infectious Diseases
                Infectious Disease Modeling
                Veterinary Science
                Animal Types
                Large Animals
                Veterinary Diseases
                Veterinary Bacteriology
                Veterinary Epidemiology

                Uncategorized
                Uncategorized

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