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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

Predictions of disease space map behavior.(A) This figure imagines a map tracing the path that a resilient infected individual might travel through disease space. The host starts at a position of comfort, but as pathogen load increases and health decreases, it enters a state of sickness. Once pathogen load decreases, the host starts to actively repair its health, resulting in an open loop in which every position along the path is unique. (B) For a system in which two parameters oscillate through a single cycle, they will trace three basic shapes through phase space. If the parameters do not overlap with each other in time, they will trace an L-shaped curve (i, iv). Complete overlap produces a line (ii, v), and partial overlap produces a loop (iii, vi). The color gradients show increasing concentrations of each parameter.
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pbio.1002436.g001: Predictions of disease space map behavior.(A) This figure imagines a map tracing the path that a resilient infected individual might travel through disease space. The host starts at a position of comfort, but as pathogen load increases and health decreases, it enters a state of sickness. Once pathogen load decreases, the host starts to actively repair its health, resulting in an open loop in which every position along the path is unique. (B) For a system in which two parameters oscillate through a single cycle, they will trace three basic shapes through phase space. If the parameters do not overlap with each other in time, they will trace an L-shaped curve (i, iv). Complete overlap produces a line (ii, v), and partial overlap produces a loop (iii, vi). The color gradients show increasing concentrations of each parameter.

Mentions: We started with the proposition that infected patients will trace a path over a multidimensional manifold in disease space; resilient patients will travel predictable paths as they sicken and recover, and patients who do poorly will also take predictable paths when they sicken and die. We can examine a patient’s progress along this disease path by observing two-dimensional projections of this space using readily measured symptoms. We call these two-dimensional shadows of the multidimensional manifold “disease maps.” These maps trace the route a patient follows as they travel from a region of comfort through sickness and back along a path to recovery (Fig 1A). Disease maps have the potential to be useful because they define a patient’s position in disease space, and, with experience, we could predict whether this position places the patient on a well-travelled route back to health or whether they are headed into a dangerous neighborhood where they risk permanent harm or death. We hypothesize that these dangerous neighborhoods lie on the outside of the paths taken by resilient individuals; extending this idea, we expect that resilient individuals will take tight loops through disease space while those at risk of severe pathology or death will take wide loops.


Tracking Resilience to Infections by Mapping Disease Space.

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

Predictions of disease space map behavior.(A) This figure imagines a map tracing the path that a resilient infected individual might travel through disease space. The host starts at a position of comfort, but as pathogen load increases and health decreases, it enters a state of sickness. Once pathogen load decreases, the host starts to actively repair its health, resulting in an open loop in which every position along the path is unique. (B) For a system in which two parameters oscillate through a single cycle, they will trace three basic shapes through phase space. If the parameters do not overlap with each other in time, they will trace an L-shaped curve (i, iv). Complete overlap produces a line (ii, v), and partial overlap produces a loop (iii, vi). The color gradients show increasing concentrations of each parameter.
© Copyright Policy
Related In: Results  -  Collection

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

pbio.1002436.g001: Predictions of disease space map behavior.(A) This figure imagines a map tracing the path that a resilient infected individual might travel through disease space. The host starts at a position of comfort, but as pathogen load increases and health decreases, it enters a state of sickness. Once pathogen load decreases, the host starts to actively repair its health, resulting in an open loop in which every position along the path is unique. (B) For a system in which two parameters oscillate through a single cycle, they will trace three basic shapes through phase space. If the parameters do not overlap with each other in time, they will trace an L-shaped curve (i, iv). Complete overlap produces a line (ii, v), and partial overlap produces a loop (iii, vi). The color gradients show increasing concentrations of each parameter.
Mentions: We started with the proposition that infected patients will trace a path over a multidimensional manifold in disease space; resilient patients will travel predictable paths as they sicken and recover, and patients who do poorly will also take predictable paths when they sicken and die. We can examine a patient’s progress along this disease path by observing two-dimensional projections of this space using readily measured symptoms. We call these two-dimensional shadows of the multidimensional manifold “disease maps.” These maps trace the route a patient follows as they travel from a region of comfort through sickness and back along a path to recovery (Fig 1A). Disease maps have the potential to be useful because they define a patient’s position in disease space, and, with experience, we could predict whether this position places the patient on a well-travelled route back to health or whether they are headed into a dangerous neighborhood where they risk permanent harm or death. We hypothesize that these dangerous neighborhoods lie on the outside of the paths taken by resilient individuals; extending this idea, we expect that resilient individuals will take tight loops through disease space while those at risk of severe pathology or death will take wide loops.

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