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How Many Parameters Does It Take to Describe Disease Tolerance?

Louie A, Song KH, Hotson A, Thomas Tate A, Schneider DS - PLoS Biol. (2016)

Bottom Line: Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models.Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model.With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America.

ABSTRACT
The study of infectious disease has been aided by model organisms, which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however, the lack of a conceptual framework for unifying findings across models, combined with host variability, has impeded progress and translation. Here, we fill this gap with a simple graphical and mathematical framework to study disease tolerance, the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked. Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models. As we altered the system by varying host or pathogen genetics, disease tolerance varied, as we would expect if it was indeed governed by parameters controlling the sensitivity of the system (the number of bacteria required to trigger a response) and maximal effect size according to a logistic equation. Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model. With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

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Related in: MedlinePlus

Listeria monocytogenes growth characteristics in the fly.(A) L. monocytogenes growth dynamics during infection. w1118 flies were injected with 1,000 CFUs. Individual flies were homogenized and plated, and bacterial colonies were counted at each time point. The data are fit with a logistic curve with a maximal growth ceiling of approximately 17,000 bacteria and a growth rate of 0.1659/h. The dotted lines indicate the 95% confidence interval. (B) L. monocytogenes dynamics during antibiotic treatment. Flies were injected with 100 CFUs and treated with ampicillin. The data are fit with a plateau followed by a one-phase decay with a half-life of 2.4 d. The dotted lines indicate the 95% confidence interval. (C) Survival curves for treated and untreated flies demonstrate that antibiotic treatment led to recovery of the flies, which lived as long as wounding control flies. Data are reported in S1 Data.
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pbio.1002435.g002: Listeria monocytogenes growth characteristics in the fly.(A) L. monocytogenes growth dynamics during infection. w1118 flies were injected with 1,000 CFUs. Individual flies were homogenized and plated, and bacterial colonies were counted at each time point. The data are fit with a logistic curve with a maximal growth ceiling of approximately 17,000 bacteria and a growth rate of 0.1659/h. The dotted lines indicate the 95% confidence interval. (B) L. monocytogenes dynamics during antibiotic treatment. Flies were injected with 100 CFUs and treated with ampicillin. The data are fit with a plateau followed by a one-phase decay with a half-life of 2.4 d. The dotted lines indicate the 95% confidence interval. (C) Survival curves for treated and untreated flies demonstrate that antibiotic treatment led to recovery of the flies, which lived as long as wounding control flies. Data are reported in S1 Data.

Mentions: When L. monocytogenes is injected into flies that are incubated at 29°C, the resulting bacterial growth is best described as logistic (Fig 2A, logistic adjusted r2 = 0.594, adjusted linear and adjusted exponential r2 < 0.1) with a maximum growth rate of 0.1656 log10 (CFU)/h. L. monocytogenes loads reach a plateau at 24 h post infection.


How Many Parameters Does It Take to Describe Disease Tolerance?

Louie A, Song KH, Hotson A, Thomas Tate A, Schneider DS - PLoS Biol. (2016)

Listeria monocytogenes growth characteristics in the fly.(A) L. monocytogenes growth dynamics during infection. w1118 flies were injected with 1,000 CFUs. Individual flies were homogenized and plated, and bacterial colonies were counted at each time point. The data are fit with a logistic curve with a maximal growth ceiling of approximately 17,000 bacteria and a growth rate of 0.1659/h. The dotted lines indicate the 95% confidence interval. (B) L. monocytogenes dynamics during antibiotic treatment. Flies were injected with 100 CFUs and treated with ampicillin. The data are fit with a plateau followed by a one-phase decay with a half-life of 2.4 d. The dotted lines indicate the 95% confidence interval. (C) Survival curves for treated and untreated flies demonstrate that antibiotic treatment led to recovery of the flies, which lived as long as wounding control flies. Data are reported in S1 Data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835111&req=5

pbio.1002435.g002: Listeria monocytogenes growth characteristics in the fly.(A) L. monocytogenes growth dynamics during infection. w1118 flies were injected with 1,000 CFUs. Individual flies were homogenized and plated, and bacterial colonies were counted at each time point. The data are fit with a logistic curve with a maximal growth ceiling of approximately 17,000 bacteria and a growth rate of 0.1659/h. The dotted lines indicate the 95% confidence interval. (B) L. monocytogenes dynamics during antibiotic treatment. Flies were injected with 100 CFUs and treated with ampicillin. The data are fit with a plateau followed by a one-phase decay with a half-life of 2.4 d. The dotted lines indicate the 95% confidence interval. (C) Survival curves for treated and untreated flies demonstrate that antibiotic treatment led to recovery of the flies, which lived as long as wounding control flies. Data are reported in S1 Data.
Mentions: When L. monocytogenes is injected into flies that are incubated at 29°C, the resulting bacterial growth is best described as logistic (Fig 2A, logistic adjusted r2 = 0.594, adjusted linear and adjusted exponential r2 < 0.1) with a maximum growth rate of 0.1656 log10 (CFU)/h. L. monocytogenes loads reach a plateau at 24 h post infection.

Bottom Line: Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models.Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model.With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America.

ABSTRACT
The study of infectious disease has been aided by model organisms, which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however, the lack of a conceptual framework for unifying findings across models, combined with host variability, has impeded progress and translation. Here, we fill this gap with a simple graphical and mathematical framework to study disease tolerance, the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked. Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models. As we altered the system by varying host or pathogen genetics, disease tolerance varied, as we would expect if it was indeed governed by parameters controlling the sensitivity of the system (the number of bacteria required to trigger a response) and maximal effect size according to a logistic equation. Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model. With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

Show MeSH
Related in: MedlinePlus