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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

Predicted and observed variation of tolerance curves.(A–D) Predicted changes in infection tolerance curves as the rate of immune effector production (τ) and the inflection points for microbe-induced immunity or damage are altered in the immunological damage model (α only) and the bacterial damage model (ϕ only). Each line is the sigmoid fit of values computed by the model. The colors represent the value of the altered parameter, moving from violet to blue to green to red as the value increases. (E–H) Tolerance curve of a resistance-deficient fly strain (CG2247) infected with wild-type L. monocytogenes. (I–L) Tolerance curve of a natural variant D. melanogaster strain infected with wild-type L. monocytogenes. (M–P) Tolerance curve of L. monocytogenes ΔactA mutants injected into w1118 control flies, (M–P). Microbe loads are recorded in (E), (I), and (M). Survival curves are recorded in (F), (J), and (N). Tolerance curves for the condition being tested are reported in (G), (K), and (O), with the corresponding data points in blue. The tolerance curve for w1118 is shown in black without data points. Panels (H), (L), and (P) show illustrations of the changes in parameters seen in the tolerance curves. Data are reported in S2 Data.
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pbio.1002435.g006: Predicted and observed variation of tolerance curves.(A–D) Predicted changes in infection tolerance curves as the rate of immune effector production (τ) and the inflection points for microbe-induced immunity or damage are altered in the immunological damage model (α only) and the bacterial damage model (ϕ only). Each line is the sigmoid fit of values computed by the model. The colors represent the value of the altered parameter, moving from violet to blue to green to red as the value increases. (E–H) Tolerance curve of a resistance-deficient fly strain (CG2247) infected with wild-type L. monocytogenes. (I–L) Tolerance curve of a natural variant D. melanogaster strain infected with wild-type L. monocytogenes. (M–P) Tolerance curve of L. monocytogenes ΔactA mutants injected into w1118 control flies, (M–P). Microbe loads are recorded in (E), (I), and (M). Survival curves are recorded in (F), (J), and (N). Tolerance curves for the condition being tested are reported in (G), (K), and (O), with the corresponding data points in blue. The tolerance curve for w1118 is shown in black without data points. Panels (H), (L), and (P) show illustrations of the changes in parameters seen in the tolerance curves. Data are reported in S2 Data.

Mentions: In the model shown in Fig 1A, depletion of health could be induced either by bacterial damage effectors or indirectly by self-harm caused by the resistance response. We modeled the two possibilities by observing how the shape of the curve changed as we altered the model such that either the resistance response or bacteria were the sole cause of damage. We concentrated on changes in the rate that the immune response was turned on (τ), and the inflection points for the relationships between microbe density and the rates of immune induction and microbe-induced damage induction (σI and σM) (Fig 6A–6D, S4 Table). In the case in which resistance mechanisms drove damage (Fig 6A and 6C) and bacterial damage was set to zero, changes in τ or the inflection point for microbe-induced immunity caused shifts in both the EC50 and microbe loads. The effect is more extreme when the inoculum is far below the microbial carrying capacity, as this gives the immune response an opportunity to control microbe loads. This results in a reciprocal mechanistic link between resistance and tolerance in which one is always high when the other is low. In the model in which bacteria drive damage, a loss of resistance results in high microbe loads and health pegged at maximum severity (Fig 6B). Shifts in the EC50 of the bacterial damage-driven system, in which resistance-induced damage is set to zero, are caused by changes in the inflection point for bacterial-induced damage, as might be expected for differentially virulent strains of microbes or hosts that are better at neutralizing bacterial toxins (Fig 6D). In this second case, there is no mechanistic reciprocal link between resistance and tolerance, and the two vary independently of each other.


How Many Parameters Does It Take to Describe Disease Tolerance?

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

Predicted and observed variation of tolerance curves.(A–D) Predicted changes in infection tolerance curves as the rate of immune effector production (τ) and the inflection points for microbe-induced immunity or damage are altered in the immunological damage model (α only) and the bacterial damage model (ϕ only). Each line is the sigmoid fit of values computed by the model. The colors represent the value of the altered parameter, moving from violet to blue to green to red as the value increases. (E–H) Tolerance curve of a resistance-deficient fly strain (CG2247) infected with wild-type L. monocytogenes. (I–L) Tolerance curve of a natural variant D. melanogaster strain infected with wild-type L. monocytogenes. (M–P) Tolerance curve of L. monocytogenes ΔactA mutants injected into w1118 control flies, (M–P). Microbe loads are recorded in (E), (I), and (M). Survival curves are recorded in (F), (J), and (N). Tolerance curves for the condition being tested are reported in (G), (K), and (O), with the corresponding data points in blue. The tolerance curve for w1118 is shown in black without data points. Panels (H), (L), and (P) show illustrations of the changes in parameters seen in the tolerance curves. Data are reported in S2 Data.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4835111&req=5

pbio.1002435.g006: Predicted and observed variation of tolerance curves.(A–D) Predicted changes in infection tolerance curves as the rate of immune effector production (τ) and the inflection points for microbe-induced immunity or damage are altered in the immunological damage model (α only) and the bacterial damage model (ϕ only). Each line is the sigmoid fit of values computed by the model. The colors represent the value of the altered parameter, moving from violet to blue to green to red as the value increases. (E–H) Tolerance curve of a resistance-deficient fly strain (CG2247) infected with wild-type L. monocytogenes. (I–L) Tolerance curve of a natural variant D. melanogaster strain infected with wild-type L. monocytogenes. (M–P) Tolerance curve of L. monocytogenes ΔactA mutants injected into w1118 control flies, (M–P). Microbe loads are recorded in (E), (I), and (M). Survival curves are recorded in (F), (J), and (N). Tolerance curves for the condition being tested are reported in (G), (K), and (O), with the corresponding data points in blue. The tolerance curve for w1118 is shown in black without data points. Panels (H), (L), and (P) show illustrations of the changes in parameters seen in the tolerance curves. Data are reported in S2 Data.
Mentions: In the model shown in Fig 1A, depletion of health could be induced either by bacterial damage effectors or indirectly by self-harm caused by the resistance response. We modeled the two possibilities by observing how the shape of the curve changed as we altered the model such that either the resistance response or bacteria were the sole cause of damage. We concentrated on changes in the rate that the immune response was turned on (τ), and the inflection points for the relationships between microbe density and the rates of immune induction and microbe-induced damage induction (σI and σM) (Fig 6A–6D, S4 Table). In the case in which resistance mechanisms drove damage (Fig 6A and 6C) and bacterial damage was set to zero, changes in τ or the inflection point for microbe-induced immunity caused shifts in both the EC50 and microbe loads. The effect is more extreme when the inoculum is far below the microbial carrying capacity, as this gives the immune response an opportunity to control microbe loads. This results in a reciprocal mechanistic link between resistance and tolerance in which one is always high when the other is low. In the model in which bacteria drive damage, a loss of resistance results in high microbe loads and health pegged at maximum severity (Fig 6B). Shifts in the EC50 of the bacterial damage-driven system, in which resistance-induced damage is set to zero, are caused by changes in the inflection point for bacterial-induced damage, as might be expected for differentially virulent strains of microbes or hosts that are better at neutralizing bacterial toxins (Fig 6D). In this second case, there is no mechanistic reciprocal link between resistance and tolerance, and the two vary independently of each other.

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