Limits...
Cecum lymph node dendritic cells harbor slow-growing bacteria phenotypically tolerant to antibiotic treatment.

Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD - PLoS Biol. (2014)

Bottom Line: High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs.The slow growth was sufficient to explain recalcitrance to antibiotics treatment.Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microbiology, Eidgenössische Technische Hochschule ETH, Zurich, Switzerland.

ABSTRACT
In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103⁺CX₃CR1⁻CD11c⁺ dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

Show MeSH

Related in: MedlinePlus

S. TmWITS infection and a modeling reveal slow growth of tolerant bacteria.(A) Schematic representation of the stochastic birth-death process modified by immigration and its parameterization (for details, see Materials and Methods). During the first 24 h p.i., we analyzed cLN colonization dynamics in absence of ciprofloxacin. Ciprofloxacin treatment was started by 24 h p.i. and continued until day 3, 5, or 10, as indicated. The model for analyzing the latter data is displayed on the right side. (B) Mice were infected with S. TmWITS and treated with ciprofloxacin (2×62 mg/kg/d by gavage) from day 1 p.i. until the indicated end of the experiment (day 1, n = 49; day 3, n = 28; day 5, n = 28; day 10, n = 28 data points). CFU determination and analysis of tag abundance using rtqPCR (experimental data, Table S2) was used to fit the mathematical model. Graphic display of experimental data (x) and 100 simulations (grey lines). (C) Parameters (with confidence intervals) derived from fitting the mathematical model to our experimental data. r, replication rate; doubling time, ln2/r×h; c, clearance rate; half-life, ln0.5/c×h.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3928039&req=5

pbio-1001793-g004: S. TmWITS infection and a modeling reveal slow growth of tolerant bacteria.(A) Schematic representation of the stochastic birth-death process modified by immigration and its parameterization (for details, see Materials and Methods). During the first 24 h p.i., we analyzed cLN colonization dynamics in absence of ciprofloxacin. Ciprofloxacin treatment was started by 24 h p.i. and continued until day 3, 5, or 10, as indicated. The model for analyzing the latter data is displayed on the right side. (B) Mice were infected with S. TmWITS and treated with ciprofloxacin (2×62 mg/kg/d by gavage) from day 1 p.i. until the indicated end of the experiment (day 1, n = 49; day 3, n = 28; day 5, n = 28; day 10, n = 28 data points). CFU determination and analysis of tag abundance using rtqPCR (experimental data, Table S2) was used to fit the mathematical model. Graphic display of experimental data (x) and 100 simulations (grey lines). (C) Parameters (with confidence intervals) derived from fitting the mathematical model to our experimental data. r, replication rate; doubling time, ln2/r×h; c, clearance rate; half-life, ln0.5/c×h.

Mentions: To that end, we devised a novel approach extending our recent work on the population dynamics of cLN colonization by S. Tm in the mouse model for complicated Salmonellosis [40]. The original method employed a defined mixture of differentially tagged, isogenic S. Tm strains—that is, wild-type S. Tm (SL1344 S. Tm) spiked with limiting amounts of phenotypically identical strains (S. TmWITS). These strains are isogenic to wild-type S. Tm, except for a 40-nucleotide sequence, which serves as an identifiable neutral marker that can be quantified by real-time qPCR [40],[41]. The inoculum contained a total of seven different S. TmWITS, each carrying a unique 40 nucleotide sequence (mixed in a 1∶1∶1∶1∶1∶1∶1 ratio) and a 20-fold excess of untagged wild-type S. Tm ([40], this work; Figure S12). In our initial work, this approach was used to estimate the net bacterial replication rate in the cLN by analyzing the S. TmWITS infection data with a stochastic birth-death model extended by immigration [40]. The model predicted the fraction of strains that successfully migrated to the cLN and their population size as a function of the rate of immigration, μ, replication, r, and clearance, c (Figure 4A). Fitting this model to our experimentally determined number of each tagged strain in the cLN, we could infer rates at which bacteria immigrate into the cLN (298 bacteria during the first 24 h p.i.) and replicate therein. The validity and robustness of this approach was verified analyzing S. TmWITS infections in wild-type and knockout mice [40]. In our present analysis of the tolerant S. Tm cells in the cLN, we used these parameters to estimate the composition of the S. Tm population at the beginning of the ciprofloxacin treatment (Figure 4A,B), and extended this approach to quantify the growth rate of the S. Tm cells surviving the ciprofloxacin treatment in the cLN (Figure 4A, Figure S12, Materials and Methods, Text S1).


Cecum lymph node dendritic cells harbor slow-growing bacteria phenotypically tolerant to antibiotic treatment.

Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD - PLoS Biol. (2014)

S. TmWITS infection and a modeling reveal slow growth of tolerant bacteria.(A) Schematic representation of the stochastic birth-death process modified by immigration and its parameterization (for details, see Materials and Methods). During the first 24 h p.i., we analyzed cLN colonization dynamics in absence of ciprofloxacin. Ciprofloxacin treatment was started by 24 h p.i. and continued until day 3, 5, or 10, as indicated. The model for analyzing the latter data is displayed on the right side. (B) Mice were infected with S. TmWITS and treated with ciprofloxacin (2×62 mg/kg/d by gavage) from day 1 p.i. until the indicated end of the experiment (day 1, n = 49; day 3, n = 28; day 5, n = 28; day 10, n = 28 data points). CFU determination and analysis of tag abundance using rtqPCR (experimental data, Table S2) was used to fit the mathematical model. Graphic display of experimental data (x) and 100 simulations (grey lines). (C) Parameters (with confidence intervals) derived from fitting the mathematical model to our experimental data. r, replication rate; doubling time, ln2/r×h; c, clearance rate; half-life, ln0.5/c×h.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928039&req=5

pbio-1001793-g004: S. TmWITS infection and a modeling reveal slow growth of tolerant bacteria.(A) Schematic representation of the stochastic birth-death process modified by immigration and its parameterization (for details, see Materials and Methods). During the first 24 h p.i., we analyzed cLN colonization dynamics in absence of ciprofloxacin. Ciprofloxacin treatment was started by 24 h p.i. and continued until day 3, 5, or 10, as indicated. The model for analyzing the latter data is displayed on the right side. (B) Mice were infected with S. TmWITS and treated with ciprofloxacin (2×62 mg/kg/d by gavage) from day 1 p.i. until the indicated end of the experiment (day 1, n = 49; day 3, n = 28; day 5, n = 28; day 10, n = 28 data points). CFU determination and analysis of tag abundance using rtqPCR (experimental data, Table S2) was used to fit the mathematical model. Graphic display of experimental data (x) and 100 simulations (grey lines). (C) Parameters (with confidence intervals) derived from fitting the mathematical model to our experimental data. r, replication rate; doubling time, ln2/r×h; c, clearance rate; half-life, ln0.5/c×h.
Mentions: To that end, we devised a novel approach extending our recent work on the population dynamics of cLN colonization by S. Tm in the mouse model for complicated Salmonellosis [40]. The original method employed a defined mixture of differentially tagged, isogenic S. Tm strains—that is, wild-type S. Tm (SL1344 S. Tm) spiked with limiting amounts of phenotypically identical strains (S. TmWITS). These strains are isogenic to wild-type S. Tm, except for a 40-nucleotide sequence, which serves as an identifiable neutral marker that can be quantified by real-time qPCR [40],[41]. The inoculum contained a total of seven different S. TmWITS, each carrying a unique 40 nucleotide sequence (mixed in a 1∶1∶1∶1∶1∶1∶1 ratio) and a 20-fold excess of untagged wild-type S. Tm ([40], this work; Figure S12). In our initial work, this approach was used to estimate the net bacterial replication rate in the cLN by analyzing the S. TmWITS infection data with a stochastic birth-death model extended by immigration [40]. The model predicted the fraction of strains that successfully migrated to the cLN and their population size as a function of the rate of immigration, μ, replication, r, and clearance, c (Figure 4A). Fitting this model to our experimentally determined number of each tagged strain in the cLN, we could infer rates at which bacteria immigrate into the cLN (298 bacteria during the first 24 h p.i.) and replicate therein. The validity and robustness of this approach was verified analyzing S. TmWITS infections in wild-type and knockout mice [40]. In our present analysis of the tolerant S. Tm cells in the cLN, we used these parameters to estimate the composition of the S. Tm population at the beginning of the ciprofloxacin treatment (Figure 4A,B), and extended this approach to quantify the growth rate of the S. Tm cells surviving the ciprofloxacin treatment in the cLN (Figure 4A, Figure S12, Materials and Methods, Text S1).

Bottom Line: High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs.The slow growth was sufficient to explain recalcitrance to antibiotics treatment.Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microbiology, Eidgenössische Technische Hochschule ETH, Zurich, Switzerland.

ABSTRACT
In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103⁺CX₃CR1⁻CD11c⁺ dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

Show MeSH
Related in: MedlinePlus