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Tracking Resilience to Infections by Mapping Disease Space.

Torres BY, Oliveira JH, Thomas Tate A, Rath P, Cumnock K, Schneider DS - PLoS Biol. (2016)

Bottom Line: We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice.We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations.This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

View Article: PubMed Central - PubMed

Affiliation: Program in Immunology, Stanford University, Stanford, California, United States of America.

ABSTRACT
Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through "disease space." We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

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Related in: MedlinePlus

Prediction of mice fated to die based on anemia.(A) Time series data of RBCs for survivors (n = 4, orange) and non-survivors (n = 11, blue). (B–C) Box plots of RBC counts on day 8 (B) and all days (C) of the infection. Significant difference between conditions on day 8 p-value = 0.0015. The p-value when considering all of the time points was p = 0.842. Data provided in S1 Data.
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pbio.1002436.g005: Prediction of mice fated to die based on anemia.(A) Time series data of RBCs for survivors (n = 4, orange) and non-survivors (n = 11, blue). (B–C) Box plots of RBC counts on day 8 (B) and all days (C) of the infection. Significant difference between conditions on day 8 p-value = 0.0015. The p-value when considering all of the time points was p = 0.842. Data provided in S1 Data.

Mentions: It would be useful to measure deviations in the path sick individuals took through disease space using a small number of parameters, like RBC and reticulocyte counts, that could be gathered in a physician’s office rather than a full microarray or flow cytometry analysis of the blood. If we plot our mouse data in a time series (Fig 5A), it is easy to see that the mice that are fated to die become anemic earlier than the resilient mice; thus, a single parameter could be used to predict the fate of these experimental mice (Fig 5B). We can do this in the laboratory because we know when we infected the mice, but we can’t expect a child suffering from malaria to tell us when they were bitten by an infected mosquito; therefore, we can’t depend on a time series for diagnosis in a real medical situation. It is going to be rare that we can ever precisely define time zero for infection in the field. To illustrate this point, we find that if we don’t use time post infection in our analysis of the mouse data and consider all of the data points at once, as we would have to with cross-sectional data, we find no predictive value of RBC levels in our mouse analysis (Fig 5C).


Tracking Resilience to Infections by Mapping Disease Space.

Torres BY, Oliveira JH, Thomas Tate A, Rath P, Cumnock K, Schneider DS - PLoS Biol. (2016)

Prediction of mice fated to die based on anemia.(A) Time series data of RBCs for survivors (n = 4, orange) and non-survivors (n = 11, blue). (B–C) Box plots of RBC counts on day 8 (B) and all days (C) of the infection. Significant difference between conditions on day 8 p-value = 0.0015. The p-value when considering all of the time points was p = 0.842. Data provided in S1 Data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835107&req=5

pbio.1002436.g005: Prediction of mice fated to die based on anemia.(A) Time series data of RBCs for survivors (n = 4, orange) and non-survivors (n = 11, blue). (B–C) Box plots of RBC counts on day 8 (B) and all days (C) of the infection. Significant difference between conditions on day 8 p-value = 0.0015. The p-value when considering all of the time points was p = 0.842. Data provided in S1 Data.
Mentions: It would be useful to measure deviations in the path sick individuals took through disease space using a small number of parameters, like RBC and reticulocyte counts, that could be gathered in a physician’s office rather than a full microarray or flow cytometry analysis of the blood. If we plot our mouse data in a time series (Fig 5A), it is easy to see that the mice that are fated to die become anemic earlier than the resilient mice; thus, a single parameter could be used to predict the fate of these experimental mice (Fig 5B). We can do this in the laboratory because we know when we infected the mice, but we can’t expect a child suffering from malaria to tell us when they were bitten by an infected mosquito; therefore, we can’t depend on a time series for diagnosis in a real medical situation. It is going to be rare that we can ever precisely define time zero for infection in the field. To illustrate this point, we find that if we don’t use time post infection in our analysis of the mouse data and consider all of the data points at once, as we would have to with cross-sectional data, we find no predictive value of RBC levels in our mouse analysis (Fig 5C).

Bottom Line: We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice.We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations.This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

View Article: PubMed Central - PubMed

Affiliation: Program in Immunology, Stanford University, Stanford, California, United States of America.

ABSTRACT
Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through "disease space." We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

Show MeSH
Related in: MedlinePlus