title: Modeling the infection dynamics of bacteriophages in enteric Escherichia coli: estimating the contribution of transduction to antimicrobial gene spread.
abstract: Animal-associated bacterial communities are infected by bacteriophages, although the dynamics of these infections are poorly understood. Transduction by bacteriophages may contribute to transfer of antimicrobial resistance genes, but the relative importance of transduction among other gene transfer mechanisms is unknown. We therefore developed a candidate deterministic mathematical model of the infection dynamics of enteric coliphages in commensal Escherichia coli in the large intestine of cattle. We assumed the phages were associated with the intestine and were predominantly temperate. Model simulations demonstrated how, given the bacterial ecology and infection dynamics, most (>90%) commensal enteric E. coli bacteria may become lysogens of enteric coliphages during intestinal transit. Using the model and the most liberal assumptions about transduction efficiency and resistance gene frequency, we approximated the upper numerical limits ("worst-case scenario") of gene transfer through specialized and generalized transduction in E. coli by enteric coliphages when the transduced genetic segment is picked at random. The estimates were consistent with a relatively small contribution of transduction to lateral gene spread; for example, generalized transduction delivered the chromosomal resistance gene to up to 8 E. coli bacteria/hour within the population of 1.47 × 10(8) E. coli bacteria/liter luminal contents. In comparison, the plasmidic blaCMY-2 gene carried by ~2% of enteric E. coli was transferred by conjugation at a rate at least 1.4 × 10(3) times greater than our generalized transduction estimate. The estimated numbers of transductants varied nonlinearly depending on the ecology of bacteria available for phages to infect, that is, on the assumed rates of turnover and replication of enteric E. coli.
authors.affiliation: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA vv87@cornell.edu.
authors.forename: Victoriya V
authors.initials: VV
authors.lastname: Volkova
authors.affiliation: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Besser
authors.affiliation: Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.
authors.forename: Yrjö T
authors.initials: YT
authors.lastname: Gröhn
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D002417:Cattle; D003090:Coliphages / Q000254:growth & development / Q000302:isolation & purification*; D004269:DNA, Bacterial / Q000235:genetics; D024881:Drug Resistance, Bacterial / Q000235:genetics*; D004926:Escherichia coli / Q000187:drug effects / Q000235:genetics* / Q000821:virology; D005787:Gene Frequency; D005798:Genes, Bacterial; D007422:Intestines / Q000382:microbiology / Q000821:virology; D008954:Models, Biological; D010957:Plasmids / Q000235:genetics; D012984:Software; D014161:Transduction, Genetic
chemical_list: D000900:Anti-Bacterial Agents; D004269:DNA, Bacterial