Literature DB >> 32634369

A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse.

Myrto Vlazaki1, Omar Rossi2, David J Price3,4, Callum McLean1, Andrew J Grant1, Pietro Mastroeni1, Olivier Restif1.   

Abstract

Antibiotic therapy has drastically reduced the mortality and sequelae of bacterial infections. From naturally occurring to chemically synthesized, different classes of antibiotics have been successfully used without detailed knowledge of how they affect bacterial dynamics in vivo. However, a proportion of patients receiving antimicrobial therapy develop recrudescent infections post-treatment. Relapsing infections are attributable to incomplete clearance of bacterial populations following antibiotic administration; the metabolic profile of this antibiotic-recalcitrant bacterial subpopulation, the spatio-temporal context of its emergence and the variance of antibiotic-bacterial interactions in vivo remain unclear. Here, we develop and apply a mechanistic mathematical model to data from a study comparing the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica serovar Typhimurium in murine infections. Using the inferential capacity of our model, we show that the antibiotic-recalcitrant bacteria following ampicillin, but not ciprofloxacin, treatment belong to a non-replicating phenotype. Aligning with previous studies, we independently estimate that the lymphoid tissues and spleen are important reservoirs of non-replicating bacteria. Finally, we predict that post-treatment, the progenitors of the non-growing and growing bacterial populations replicate and die at different rates. Ultimately, the liver, spleen and mesenteric lymph nodes are all repopulated by progenitors of the previously non-growing phenotype in ampicillin-treated mice.

Entities:  

Keywords:  bacterial persistence; data-based model; heterogeneity; isogenic tagged strains; mechanistic model; model selection

Mesh:

Substances:

Year:  2020        PMID: 32634369      PMCID: PMC7423434          DOI: 10.1098/rsif.2020.0299

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  43 in total

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Authors:  Karl Drlica; Muhammad Malik; Robert J Kerns; Xilin Zhao
Journal:  Antimicrob Agents Chemother       Date:  2007-08-27       Impact factor: 5.191

2.  Phenotypic variation of Salmonella in host tissues delays eradication by antimicrobial chemotherapy.

Authors:  Beatrice Claudi; Petra Spröte; Anna Chirkova; Nicolas Personnic; Janine Zankl; Nura Schürmann; Alexander Schmidt; Dirk Bumann
Journal:  Cell       Date:  2014-08-14       Impact factor: 41.582

3.  Antagonism between bacteriostatic and bactericidal antibiotics is prevalent.

Authors:  Paolo S Ocampo; Viktória Lázár; Balázs Papp; Markus Arnoldini; Pia Abel zur Wiesch; Róbert Busa-Fekete; Gergely Fekete; Csaba Pál; Martin Ackermann; Sebastian Bonhoeffer
Journal:  Antimicrob Agents Chemother       Date:  2014-05-27       Impact factor: 5.191

4.  Time-kill kinetics of antibiotics active against rapidly growing mycobacteria.

Authors:  Beatriz E Ferro; Jakko van Ingen; Melanie Wattenberg; Dick van Soolingen; Johan W Mouton
Journal:  J Antimicrob Chemother       Date:  2014-10-25       Impact factor: 5.790

5.  Antibiotic treatment selects for cooperative virulence of Salmonella typhimurium.

Authors:  Médéric Diard; Mikael E Sellin; Tamas Dolowschiak; Markus Arnoldini; Martin Ackermann; Wolf-Dietrich Hardt
Journal:  Curr Biol       Date:  2014-08-14       Impact factor: 10.834

6.  Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut.

Authors:  Médéric Diard; Wolf-Dietrich Hardt; Erik Bakkeren; Jana S Huisman; Stefan A Fattinger; Annika Hausmann; Markus Furter; Adrian Egli; Emma Slack; Mikael E Sellin; Sebastian Bonhoeffer; Roland R Regoes
Journal:  Nature       Date:  2019-09-04       Impact factor: 49.962

7.  Antibiotic Killing of Diversely Generated Populations of Nonreplicating Bacteria.

Authors:  Ingrid C McCall; Nilang Shah; Adithi Govindan; Fernando Baquero; Bruce R Levin
Journal:  Antimicrob Agents Chemother       Date:  2019-06-24       Impact factor: 5.191

8.  Internalization of Salmonella by macrophages induces formation of nonreplicating persisters.

Authors:  Sophie Helaine; Angela M Cheverton; Kathryn G Watson; Laura M Faure; Sophie A Matthews; David W Holden
Journal:  Science       Date:  2014-01-10       Impact factor: 47.728

9.  Modelling within-host spatiotemporal dynamics of invasive bacterial disease.

Authors:  Andrew J Grant; Olivier Restif; Trevelyan J McKinley; Mark Sheppard; Duncan J Maskell; Pietro Mastroeni
Journal:  PLoS Biol       Date:  2008-04-08       Impact factor: 8.029

Review 10.  Persisters-as elusive as ever.

Authors:  Niilo Kaldalu; Vasili Hauryliuk; Tanel Tenson
Journal:  Appl Microbiol Biotechnol       Date:  2016-06-04       Impact factor: 4.813

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  2 in total

1.  An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo.

Authors:  Myrto Vlazaki; David J Price; Olivier Restif
Journal:  J R Soc Interface       Date:  2020-12-16       Impact factor: 4.118

2.  Tissue compartmentalization enables Salmonella persistence during chemotherapy.

Authors:  Jiagui Li; Beatrice Claudi; Joseph Fanous; Natalia Chicherova; Francesca Romana Cianfanelli; Robert A A Campbell; Dirk Bumann
Journal:  Proc Natl Acad Sci U S A       Date:  2021-12-21       Impact factor: 11.205

  2 in total

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