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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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Antimicrobial peptide expression dynamics.(A) AMP dose response curve. Expression of AMPs was determined by microarray. Fold change is plotted relative to log microbe load (grey: uninfected control, black: infected, no treatment, red: infected, ampicillin treatment). The data are fit with a four-parameter sigmoid model. Adjusted r2: Drosomycin = 0.9521, Attacin A = 0.8509, Cecropin A = 0.8933. The dotted lines indicate the 95% confidence interval. (B) EC50 of antimicrobial peptides. Error bars mark the 95% confidence interval. (C) Maximum expression level of antimicrobial peptides. Error bars mark the 95% confidence interval. (D) Relationship between Hill slope and EC50. The data are fit with an exponential growth function with an adjusted r2 = 0.5126. Data are reported in S1 Data.
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pbio.1002435.g003: Antimicrobial peptide expression dynamics.(A) AMP dose response curve. Expression of AMPs was determined by microarray. Fold change is plotted relative to log microbe load (grey: uninfected control, black: infected, no treatment, red: infected, ampicillin treatment). The data are fit with a four-parameter sigmoid model. Adjusted r2: Drosomycin = 0.9521, Attacin A = 0.8509, Cecropin A = 0.8933. The dotted lines indicate the 95% confidence interval. (B) EC50 of antimicrobial peptides. Error bars mark the 95% confidence interval. (C) Maximum expression level of antimicrobial peptides. Error bars mark the 95% confidence interval. (D) Relationship between Hill slope and EC50. The data are fit with an exponential growth function with an adjusted r2 = 0.5126. Data are reported in S1 Data.

Mentions: We analyzed the expression of the known and presumed AMPs with respect to microbe loads and found that AMPs are expressed according to a sigmoid relationship with respect to microbe load. We plotted the expression of AMPs to CFU levels in the fly and found the relationship between AMP transcription and microbe load is best fit by a four-parameter sigmoid (Fig 3A). Accordingly, each AMP transcript has a baseline (bottom plateau), maximal concentration (top plateau), effective bacterial concentration producing a half maximal effective concentration (EC50), and slope. We observe that the AMPs expressed during infection differ in their EC50 (Fig 3B) over a range of about 100-fold (20–2,000 CFUs). AMP expression does not continue to increase as microbe number increases past a certain point, and each AMP transcript reaches a characteristic plateau. Each AMP transcript can thus be characterized by its maximum expression in relation to its baseline, and this relationship varies over an 8-fold range between AMPs. We observed no significant relationship between EC50 and maximum expression, suggesting that EC50 and maximum expression can vary independently of each other. We found an exponential relationship between Hill slope and EC50 (Fig 3D), suggesting that AMPs requiring large numbers of bacteria for induction turn on in a switch-like manner, while those turned on at low microbe loads are turned on gradually. This gives us insight into the tuning of the fly’s immune response; overall, AMPs will be turned on gradually at microbe loads below 100 bacteria per fly but will rapidly increase as loads pass 1,000 bacteria per fly.


How Many Parameters Does It Take to Describe Disease Tolerance?

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

Antimicrobial peptide expression dynamics.(A) AMP dose response curve. Expression of AMPs was determined by microarray. Fold change is plotted relative to log microbe load (grey: uninfected control, black: infected, no treatment, red: infected, ampicillin treatment). The data are fit with a four-parameter sigmoid model. Adjusted r2: Drosomycin = 0.9521, Attacin A = 0.8509, Cecropin A = 0.8933. The dotted lines indicate the 95% confidence interval. (B) EC50 of antimicrobial peptides. Error bars mark the 95% confidence interval. (C) Maximum expression level of antimicrobial peptides. Error bars mark the 95% confidence interval. (D) Relationship between Hill slope and EC50. The data are fit with an exponential growth function with an adjusted r2 = 0.5126. Data are reported in S1 Data.
© Copyright Policy
Related In: Results  -  Collection

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

pbio.1002435.g003: Antimicrobial peptide expression dynamics.(A) AMP dose response curve. Expression of AMPs was determined by microarray. Fold change is plotted relative to log microbe load (grey: uninfected control, black: infected, no treatment, red: infected, ampicillin treatment). The data are fit with a four-parameter sigmoid model. Adjusted r2: Drosomycin = 0.9521, Attacin A = 0.8509, Cecropin A = 0.8933. The dotted lines indicate the 95% confidence interval. (B) EC50 of antimicrobial peptides. Error bars mark the 95% confidence interval. (C) Maximum expression level of antimicrobial peptides. Error bars mark the 95% confidence interval. (D) Relationship between Hill slope and EC50. The data are fit with an exponential growth function with an adjusted r2 = 0.5126. Data are reported in S1 Data.
Mentions: We analyzed the expression of the known and presumed AMPs with respect to microbe loads and found that AMPs are expressed according to a sigmoid relationship with respect to microbe load. We plotted the expression of AMPs to CFU levels in the fly and found the relationship between AMP transcription and microbe load is best fit by a four-parameter sigmoid (Fig 3A). Accordingly, each AMP transcript has a baseline (bottom plateau), maximal concentration (top plateau), effective bacterial concentration producing a half maximal effective concentration (EC50), and slope. We observe that the AMPs expressed during infection differ in their EC50 (Fig 3B) over a range of about 100-fold (20–2,000 CFUs). AMP expression does not continue to increase as microbe number increases past a certain point, and each AMP transcript reaches a characteristic plateau. Each AMP transcript can thus be characterized by its maximum expression in relation to its baseline, and this relationship varies over an 8-fold range between AMPs. We observed no significant relationship between EC50 and maximum expression, suggesting that EC50 and maximum expression can vary independently of each other. We found an exponential relationship between Hill slope and EC50 (Fig 3D), suggesting that AMPs requiring large numbers of bacteria for induction turn on in a switch-like manner, while those turned on at low microbe loads are turned on gradually. This gives us insight into the tuning of the fly’s immune response; overall, AMPs will be turned on gradually at microbe loads below 100 bacteria per fly but will rapidly increase as loads pass 1,000 bacteria per fly.

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