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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 using polar transformed data.(A) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. Data collected through qRT-PCR. The grey area shows the range of angles analyzed just before the time of death. (B) Radius and angle for live (circle, orange) and dead mice (x, blue). (C) Box plot measuring the radius of live and dead mice. (D) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line). Observed values are plotted as histograms. (E) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. The grey area shows the range of angles analyzed around time zero. (F) Radius and angle for live (circle, orange) and dead (x, blue) mice at early time points. (G) Box plot measuring the radius of live and dead mice at early time points; p = 0.0477. (H) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line) for early time points. Data provided in S3 Data.
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pbio.1002436.g007: Prediction of mice fated to die using polar transformed data.(A) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. Data collected through qRT-PCR. The grey area shows the range of angles analyzed just before the time of death. (B) Radius and angle for live (circle, orange) and dead mice (x, blue). (C) Box plot measuring the radius of live and dead mice. (D) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line). Observed values are plotted as histograms. (E) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. The grey area shows the range of angles analyzed around time zero. (F) Radius and angle for live (circle, orange) and dead (x, blue) mice at early time points. (G) Box plot measuring the radius of live and dead mice at early time points; p = 0.0477. (H) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line) for early time points. Data provided in S3 Data.

Mentions: Once cross-sectional data has been ordered using a polar transformation, we can easily analyze deviations from the resilient path at the point of separation. If we plot RBC numbers by reticulocyte numbers predicted from the microarray in Cartesian space, we find that the resilient mice loop and those fated to die explore areas outside of this loop (Fig 7A). This danger zone is difficult to define in a Cartesian plot because we have to follow variation in two dimensions using a small number of samples. To explore this relationship further, we collected a larger dataset from mice that lived or died during the infection (four surviving and 11 dying mice) and tracked RBCs by flow cytometry and reticulocyte counts by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (Fig 7, S10 Table). When we plot radius versus angle (polar transformed), data that formed a circle in Cartesian space are plotted as a line, and data that deviates from the circle rises above or below the line (Fig 7B). We can analyze these data by performing an ANOVA over interesting ranges of the angle. We transformed these RBC by reticulocyte data to polar coordinates and compared the animals over the angles corresponding to the period where the dying mice diverged. We found that the dying mice differed significantly in terms of radius with respect to the surviving mice (Fig 7C and 7D). Further examination of the mice in polar space showed that we could find a significant difference in radius at the start of the infection, suggesting that there were pre-existing conditions in these mice that made them susceptible to death upon infection with P. chabaudi (Fig 7E–7H).


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 using polar transformed data.(A) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. Data collected through qRT-PCR. The grey area shows the range of angles analyzed just before the time of death. (B) Radius and angle for live (circle, orange) and dead mice (x, blue). (C) Box plot measuring the radius of live and dead mice. (D) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line). Observed values are plotted as histograms. (E) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. The grey area shows the range of angles analyzed around time zero. (F) Radius and angle for live (circle, orange) and dead (x, blue) mice at early time points. (G) Box plot measuring the radius of live and dead mice at early time points; p = 0.0477. (H) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line) for early time points. Data provided in S3 Data.
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pbio.1002436.g007: Prediction of mice fated to die using polar transformed data.(A) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. Data collected through qRT-PCR. The grey area shows the range of angles analyzed just before the time of death. (B) Radius and angle for live (circle, orange) and dead mice (x, blue). (C) Box plot measuring the radius of live and dead mice. (D) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line). Observed values are plotted as histograms. (E) Disease map of live (n = 4) and dead (n = 11) mice in Fech by RBC space. The grey area shows the range of angles analyzed around time zero. (F) Radius and angle for live (circle, orange) and dead (x, blue) mice at early time points. (G) Box plot measuring the radius of live and dead mice at early time points; p = 0.0477. (H) Binomial generalized linear model (glm) showing the probability of survival decrease as the length of the radius increases (red line) for early time points. Data provided in S3 Data.
Mentions: Once cross-sectional data has been ordered using a polar transformation, we can easily analyze deviations from the resilient path at the point of separation. If we plot RBC numbers by reticulocyte numbers predicted from the microarray in Cartesian space, we find that the resilient mice loop and those fated to die explore areas outside of this loop (Fig 7A). This danger zone is difficult to define in a Cartesian plot because we have to follow variation in two dimensions using a small number of samples. To explore this relationship further, we collected a larger dataset from mice that lived or died during the infection (four surviving and 11 dying mice) and tracked RBCs by flow cytometry and reticulocyte counts by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (Fig 7, S10 Table). When we plot radius versus angle (polar transformed), data that formed a circle in Cartesian space are plotted as a line, and data that deviates from the circle rises above or below the line (Fig 7B). We can analyze these data by performing an ANOVA over interesting ranges of the angle. We transformed these RBC by reticulocyte data to polar coordinates and compared the animals over the angles corresponding to the period where the dying mice diverged. We found that the dying mice differed significantly in terms of radius with respect to the surviving mice (Fig 7C and 7D). Further examination of the mice in polar space showed that we could find a significant difference in radius at the start of the infection, suggesting that there were pre-existing conditions in these mice that made them susceptible to death upon infection with P. chabaudi (Fig 7E–7H).

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