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How affinity influences tolerance in an idiotypic network.

Hart, Emma; Bersini, Hugues; Santos, Francisco

Authors

Hugues Bersini

Francisco Santos



Abstract

The mutability of bacteriophages offers a particular advantage in the treatment of bacterial infections not afforded by other antimicrobial therapies. When phage-resistant bacteria emerge, mutation may generate phage capable of exploiting and thus limiting population expansion among these emergent types. However, while mutation potentially generates beneficial variants, it also contributes to a genetic load of deleterious mutations. Here, we model the influence of varying phage mutation rate on the efficacy of phage therapy. All else being equal, phage types with historical mutation rates of approximately 0.1 deleterious mutations per genome per generation offer a reasonable balance between beneficial mutational diversity and deleterious mutational load. We determine that increasing phage inoculum density can undesirably increase the peak density of a mutant bacterial class by limiting the in situ production of mutant phage variants. For phage populations with minimal genetic load, engineering mutation rate increases beyond the mutation–selection balance optimum may provide even greater protection against emergent bacterial types, but only with very weak selective coefficients for de novo deleterious mutations (below 0.01). Increases to the mutation rate beyond the optimal value at mutation–selection balance may therefore prove generally undesirable.

Citation

Hart, E., Bersini, H., & Santos, F. (2007). How affinity influences tolerance in an idiotypic network. Journal of Theoretical Biology, 249, 422-436. https://doi.org/10.1016/j.jtbi.2007.07.019

Journal Article Type Article
Publication Date Jul 1, 2007
Deposit Date May 26, 2008
Print ISSN 0022-5193
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 249
Pages 422-436
DOI https://doi.org/10.1016/j.jtbi.2007.07.019
Keywords Phage therapy; Mutation rate; Mutational load; Bacteriophage; Host range; Computer modelling;
Public URL http://researchrepository.napier.ac.uk/id/eprint/1825
Publisher URL http://dx.doi.org/10.1016/j.jtbi.2007.07.019