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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

Disease-tolerance curves are sigmoid.(A) Dose-dependent growth of L. monocytogenes during infection. Wild-type flies injected with 10–100,000 L. monocytogenes were homogenized and then plated 2 d post-infection to determine microbe loads. (B) Dose-dependent survival of L. monocytogenes-infected flies. Kaplan-Meier curves were plotted for flies injected with 10–100,000 L. monocytogenes. (C) Disease-tolerance curve. Pairs of microbe load and survival data for 63 microbe load/MTD pairs were plotted. This curve was fit with a four-parameter sigmoid model (r2 > 0.96). (D) A cartoon showing the parameters used to describe a sigmoid disease-tolerance curve including vigor, slope, EC50 (sensitivity), and maximal severity, as well as the measurement of phenotypic range. Data are reported in S2 Data.
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pbio.1002435.g005: Disease-tolerance curves are sigmoid.(A) Dose-dependent growth of L. monocytogenes during infection. Wild-type flies injected with 10–100,000 L. monocytogenes were homogenized and then plated 2 d post-infection to determine microbe loads. (B) Dose-dependent survival of L. monocytogenes-infected flies. Kaplan-Meier curves were plotted for flies injected with 10–100,000 L. monocytogenes. (C) Disease-tolerance curve. Pairs of microbe load and survival data for 63 microbe load/MTD pairs were plotted. This curve was fit with a four-parameter sigmoid model (r2 > 0.96). (D) A cartoon showing the parameters used to describe a sigmoid disease-tolerance curve including vigor, slope, EC50 (sensitivity), and maximal severity, as well as the measurement of phenotypic range. Data are reported in S2 Data.

Mentions: To measure a disease tolerance curve, we recorded the response of the host to a broad range of initial pathogen doses. We did this by injecting L. monocytogenes into flies over a range of ten to 100,000 bacteria and allowing the infected flies to die. We injected L. monocytogenes into the hemocoel of flies and monitored bacterial numbers 2 DPI to measure the ability of the fly to resist microbe growth when challenged with a range of infection intensities (Fig 5). We determined the median time to death (MTD) for each inoculum and used this time as a measurement of health (Fig 5B). Plotting microbe load versus MTD produced a curve that was readily fit by a four-parameter logistic sigmoid model (r2 > 0.96) (Fig 5C).


How Many Parameters Does It Take to Describe Disease Tolerance?

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

Disease-tolerance curves are sigmoid.(A) Dose-dependent growth of L. monocytogenes during infection. Wild-type flies injected with 10–100,000 L. monocytogenes were homogenized and then plated 2 d post-infection to determine microbe loads. (B) Dose-dependent survival of L. monocytogenes-infected flies. Kaplan-Meier curves were plotted for flies injected with 10–100,000 L. monocytogenes. (C) Disease-tolerance curve. Pairs of microbe load and survival data for 63 microbe load/MTD pairs were plotted. This curve was fit with a four-parameter sigmoid model (r2 > 0.96). (D) A cartoon showing the parameters used to describe a sigmoid disease-tolerance curve including vigor, slope, EC50 (sensitivity), and maximal severity, as well as the measurement of phenotypic range. Data are reported in S2 Data.
© Copyright Policy
Related In: Results  -  Collection

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

pbio.1002435.g005: Disease-tolerance curves are sigmoid.(A) Dose-dependent growth of L. monocytogenes during infection. Wild-type flies injected with 10–100,000 L. monocytogenes were homogenized and then plated 2 d post-infection to determine microbe loads. (B) Dose-dependent survival of L. monocytogenes-infected flies. Kaplan-Meier curves were plotted for flies injected with 10–100,000 L. monocytogenes. (C) Disease-tolerance curve. Pairs of microbe load and survival data for 63 microbe load/MTD pairs were plotted. This curve was fit with a four-parameter sigmoid model (r2 > 0.96). (D) A cartoon showing the parameters used to describe a sigmoid disease-tolerance curve including vigor, slope, EC50 (sensitivity), and maximal severity, as well as the measurement of phenotypic range. Data are reported in S2 Data.
Mentions: To measure a disease tolerance curve, we recorded the response of the host to a broad range of initial pathogen doses. We did this by injecting L. monocytogenes into flies over a range of ten to 100,000 bacteria and allowing the infected flies to die. We injected L. monocytogenes into the hemocoel of flies and monitored bacterial numbers 2 DPI to measure the ability of the fly to resist microbe growth when challenged with a range of infection intensities (Fig 5). We determined the median time to death (MTD) for each inoculum and used this time as a measurement of health (Fig 5B). Plotting microbe load versus MTD produced a curve that was readily fit by a four-parameter logistic sigmoid model (r2 > 0.96) (Fig 5C).

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