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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 space analysis of infected and recovering flies.(A) Infection map colored by treatment groups. The network was built using Ayasdi Core (data from S1 Table). Samples with similar expression patterns are binned together. Nodes are bins of individual samples. Bins containing the same sample are connected by edges. Cyan is the overlap of uninfected and no treatment groups, and orange is the overlap of ampicillin treatment and no treatment groups. (B) Infection map colored by CFU. The green arrow marks disease progression. Pathways of upregulated genes are numbered. Phases of infection are indicated by Roman numerals. (C) Infection map colored by heat shock protein 26. (D) Infection map colored by Attacin A. (E) Infection map colored by CG3117 (peptidase and death gene). (F) Infection map colored by CG32444 (aldose-1 epimerase and recovery gene).
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pbio.1002435.g004: Disease space analysis of infected and recovering flies.(A) Infection map colored by treatment groups. The network was built using Ayasdi Core (data from S1 Table). Samples with similar expression patterns are binned together. Nodes are bins of individual samples. Bins containing the same sample are connected by edges. Cyan is the overlap of uninfected and no treatment groups, and orange is the overlap of ampicillin treatment and no treatment groups. (B) Infection map colored by CFU. The green arrow marks disease progression. Pathways of upregulated genes are numbered. Phases of infection are indicated by Roman numerals. (C) Infection map colored by heat shock protein 26. (D) Infection map colored by Attacin A. (E) Infection map colored by CG3117 (peptidase and death gene). (F) Infection map colored by CG32444 (aldose-1 epimerase and recovery gene).

Mentions: Fig 4 shows the TDA graph we built to describe our data, and 4A describes the three basic treatment groups in the dataset (uninfected, infected, and antibiotic-treated). In Fig 4B, the phase curve is colored by CFU (blue indicates low and red indicate high microbe loads). The green arrow indicates progression from health to infection and back, moving clockwise around the figure. Uninfected flies are at the top of the figure, and the figure progresses through acutely wounded flies, sick flies, and on to recovering flies, which link back to the original uninfected flies. Infection was initiated by the injection of 100 CFU, and L. monocytogenes levels increase following this. The path followed by moribund flies deviates from recovering flies to form a spur on the loop (marked as section iv). Flies that received ampicillin decrease in CFUs, and flies that have low CFUs loop back to overlap with uninfected controls.


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 space analysis of infected and recovering flies.(A) Infection map colored by treatment groups. The network was built using Ayasdi Core (data from S1 Table). Samples with similar expression patterns are binned together. Nodes are bins of individual samples. Bins containing the same sample are connected by edges. Cyan is the overlap of uninfected and no treatment groups, and orange is the overlap of ampicillin treatment and no treatment groups. (B) Infection map colored by CFU. The green arrow marks disease progression. Pathways of upregulated genes are numbered. Phases of infection are indicated by Roman numerals. (C) Infection map colored by heat shock protein 26. (D) Infection map colored by Attacin A. (E) Infection map colored by CG3117 (peptidase and death gene). (F) Infection map colored by CG32444 (aldose-1 epimerase and recovery gene).
© Copyright Policy
Related In: Results  -  Collection

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

pbio.1002435.g004: Disease space analysis of infected and recovering flies.(A) Infection map colored by treatment groups. The network was built using Ayasdi Core (data from S1 Table). Samples with similar expression patterns are binned together. Nodes are bins of individual samples. Bins containing the same sample are connected by edges. Cyan is the overlap of uninfected and no treatment groups, and orange is the overlap of ampicillin treatment and no treatment groups. (B) Infection map colored by CFU. The green arrow marks disease progression. Pathways of upregulated genes are numbered. Phases of infection are indicated by Roman numerals. (C) Infection map colored by heat shock protein 26. (D) Infection map colored by Attacin A. (E) Infection map colored by CG3117 (peptidase and death gene). (F) Infection map colored by CG32444 (aldose-1 epimerase and recovery gene).
Mentions: Fig 4 shows the TDA graph we built to describe our data, and 4A describes the three basic treatment groups in the dataset (uninfected, infected, and antibiotic-treated). In Fig 4B, the phase curve is colored by CFU (blue indicates low and red indicate high microbe loads). The green arrow indicates progression from health to infection and back, moving clockwise around the figure. Uninfected flies are at the top of the figure, and the figure progresses through acutely wounded flies, sick flies, and on to recovering flies, which link back to the original uninfected flies. Infection was initiated by the injection of 100 CFU, and L. monocytogenes levels increase following this. The path followed by moribund flies deviates from recovering flies to form a spur on the loop (marked as section iv). Flies that received ampicillin decrease in CFUs, and flies that have low CFUs loop back to overlap with uninfected controls.

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