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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

Reconstructed mouse and human disease space maps from longitudinal and cross-sectional data.(A–C) show nearest neighbor networks for mouse and human data plotted in physical spaces. Arrows indicate the direction of movement through phase space. (A–B) show longitudinal data. (C) shows cross-sectional data. (A) Mouse red blood cell (RBC) by parasite density plotted by connecting five nearest neighbors. (B) Mouse RBC by C1qB by reticulocyte (Trim10) connecting five nearest neighbors. (C) Human RBC by C1qb by Trim10 map connecting five nearest neighbors. Children reported as “uninfected” are marked in red, while those infected are marked in blue. Topological network maps of mice (D) and humans (E) suffering from malaria. The known timeline in the mice runs clockwise as marked by the white arrow. The inferred human timeline is marked similarly. The color scheme in (D) and (E) marks parasite density, where blue represents low values while red represents high values. Segments of the map are marked in grey to show transcript or cell counts reporting the relative abundance of marked cell types. Red blood cells were counted directly using flow cytometry. The markers for B cells, granulocytes, NK cells, and reticulocytes are: Faim3, Lcn2, Nkg7, Trim10 for both mice and humans. (F,G) show mouse and human malaria maps reporting different parameters. Colors mark the progression of time or the relative abundance of marked parameters. Ranges for (D–G) and parameters for deriving the graphs are listed in S6 Table.
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pbio.1002436.g003: Reconstructed mouse and human disease space maps from longitudinal and cross-sectional data.(A–C) show nearest neighbor networks for mouse and human data plotted in physical spaces. Arrows indicate the direction of movement through phase space. (A–B) show longitudinal data. (C) shows cross-sectional data. (A) Mouse red blood cell (RBC) by parasite density plotted by connecting five nearest neighbors. (B) Mouse RBC by C1qB by reticulocyte (Trim10) connecting five nearest neighbors. (C) Human RBC by C1qb by Trim10 map connecting five nearest neighbors. Children reported as “uninfected” are marked in red, while those infected are marked in blue. Topological network maps of mice (D) and humans (E) suffering from malaria. The known timeline in the mice runs clockwise as marked by the white arrow. The inferred human timeline is marked similarly. The color scheme in (D) and (E) marks parasite density, where blue represents low values while red represents high values. Segments of the map are marked in grey to show transcript or cell counts reporting the relative abundance of marked cell types. Red blood cells were counted directly using flow cytometry. The markers for B cells, granulocytes, NK cells, and reticulocytes are: Faim3, Lcn2, Nkg7, Trim10 for both mice and humans. (F,G) show mouse and human malaria maps reporting different parameters. Colors mark the progression of time or the relative abundance of marked parameters. Ranges for (D–G) and parameters for deriving the graphs are listed in S6 Table.

Mentions: To examine the data using nearest neighbor analysis, we took the 78 time points from the study in which we followed three mice longitudinally and stripped these data of time information. We then plotted the remaining data in two- and three-dimensional spaces that we had identified as producing looping hysteretic curves. We then connected individual data points to their five nearest neighbors using a subset of the transcriptome data that focused on identifiable cell types (S5 Table). We chose five nearest neighbors as, with this data set, this number produced a graph that revealed the shape of the disease path without being overly dense. This generates a network, and the shape of that network overlapped with the actual paths the mice took through disease space (Fig 3A and 3B). We extended this approach to published data for humans, analyzing cross-sectional transcriptome results from the blood of malaria-infected and uninfected children (Fig 3C) [12]. Comparison with the mouse disease map suggests that the human infection also follows a loop, though the loop has an obvious low-density gap corresponding to the recovery stages of the disease.


Tracking Resilience to Infections by Mapping Disease Space.

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

Reconstructed mouse and human disease space maps from longitudinal and cross-sectional data.(A–C) show nearest neighbor networks for mouse and human data plotted in physical spaces. Arrows indicate the direction of movement through phase space. (A–B) show longitudinal data. (C) shows cross-sectional data. (A) Mouse red blood cell (RBC) by parasite density plotted by connecting five nearest neighbors. (B) Mouse RBC by C1qB by reticulocyte (Trim10) connecting five nearest neighbors. (C) Human RBC by C1qb by Trim10 map connecting five nearest neighbors. Children reported as “uninfected” are marked in red, while those infected are marked in blue. Topological network maps of mice (D) and humans (E) suffering from malaria. The known timeline in the mice runs clockwise as marked by the white arrow. The inferred human timeline is marked similarly. The color scheme in (D) and (E) marks parasite density, where blue represents low values while red represents high values. Segments of the map are marked in grey to show transcript or cell counts reporting the relative abundance of marked cell types. Red blood cells were counted directly using flow cytometry. The markers for B cells, granulocytes, NK cells, and reticulocytes are: Faim3, Lcn2, Nkg7, Trim10 for both mice and humans. (F,G) show mouse and human malaria maps reporting different parameters. Colors mark the progression of time or the relative abundance of marked parameters. Ranges for (D–G) and parameters for deriving the graphs are listed in S6 Table.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4835107&req=5

pbio.1002436.g003: Reconstructed mouse and human disease space maps from longitudinal and cross-sectional data.(A–C) show nearest neighbor networks for mouse and human data plotted in physical spaces. Arrows indicate the direction of movement through phase space. (A–B) show longitudinal data. (C) shows cross-sectional data. (A) Mouse red blood cell (RBC) by parasite density plotted by connecting five nearest neighbors. (B) Mouse RBC by C1qB by reticulocyte (Trim10) connecting five nearest neighbors. (C) Human RBC by C1qb by Trim10 map connecting five nearest neighbors. Children reported as “uninfected” are marked in red, while those infected are marked in blue. Topological network maps of mice (D) and humans (E) suffering from malaria. The known timeline in the mice runs clockwise as marked by the white arrow. The inferred human timeline is marked similarly. The color scheme in (D) and (E) marks parasite density, where blue represents low values while red represents high values. Segments of the map are marked in grey to show transcript or cell counts reporting the relative abundance of marked cell types. Red blood cells were counted directly using flow cytometry. The markers for B cells, granulocytes, NK cells, and reticulocytes are: Faim3, Lcn2, Nkg7, Trim10 for both mice and humans. (F,G) show mouse and human malaria maps reporting different parameters. Colors mark the progression of time or the relative abundance of marked parameters. Ranges for (D–G) and parameters for deriving the graphs are listed in S6 Table.
Mentions: To examine the data using nearest neighbor analysis, we took the 78 time points from the study in which we followed three mice longitudinally and stripped these data of time information. We then plotted the remaining data in two- and three-dimensional spaces that we had identified as producing looping hysteretic curves. We then connected individual data points to their five nearest neighbors using a subset of the transcriptome data that focused on identifiable cell types (S5 Table). We chose five nearest neighbors as, with this data set, this number produced a graph that revealed the shape of the disease path without being overly dense. This generates a network, and the shape of that network overlapped with the actual paths the mice took through disease space (Fig 3A and 3B). We extended this approach to published data for humans, analyzing cross-sectional transcriptome results from the blood of malaria-infected and uninfected children (Fig 3C) [12]. Comparison with the mouse disease map suggests that the human infection also follows a loop, though the loop has an obvious low-density gap corresponding to the recovery stages of the disease.

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