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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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Disease space maps of malaria-infected mice.(A) Average values for eight parameters for three mice measured (and averaged) daily for 20 d are plotted in a timeline marked in blue. The paths for the three mice plotted individually are shown in S2 Fig. The transcript markers used to define B cells, NK cells, granulocytes, and reticulocytes are, respectively, Cd79b, Nkg7, Camp, and Trim 10, which are reported as log2 values. Time is indicated by the increasing thickness of the curve. (B,C) Phase plots for representative looping pairs of parameters. Note that the axes have been flipped so that all graphs start at the top, and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue) and “recovering” (days 11–15, yellow) regions. See also S1 Fig.
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pbio.1002436.g002: Disease space maps of malaria-infected mice.(A) Average values for eight parameters for three mice measured (and averaged) daily for 20 d are plotted in a timeline marked in blue. The paths for the three mice plotted individually are shown in S2 Fig. The transcript markers used to define B cells, NK cells, granulocytes, and reticulocytes are, respectively, Cd79b, Nkg7, Camp, and Trim 10, which are reported as log2 values. Time is indicated by the increasing thickness of the curve. (B,C) Phase plots for representative looping pairs of parameters. Note that the axes have been flipped so that all graphs start at the top, and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue) and “recovering” (days 11–15, yellow) regions. See also S1 Fig.

Mentions: To establish which parameters should be plotted to build informative disease maps, we gathered a multi-dimensional dataset over the course of infections of mice challenged with the malaria parasite Plasmodium chabaudi. We chose this parasite because it is readily measurable in the blood of the host and causes pathology that can also be measured in the blood; another useful feature is that the parasite causes a self-resolving infection. Together, these properties let us easily follow the progression of an infection in a resilient system. We mapped the behavior of circulating blood cells and parasites by following infected mice over the 26 d infection course and performing microarray analysis on daily blood samples. We grouped the transcriptome data using a k-means analysis, and these groups were characterized as reporting circulating cells based on the composition of their members (S1 Fig, S1–S3 Tables) [10,11]. We plotted pairs of these k-means averages against each other to observe the phase curves and found hysteretic relationships that produced open loops that could be used as maps (Fig 2A). In Fig 2B and 2C, we show how some of these loops can be considered with respect to the cartoon model of disease space we introduced in Fig 1A.


Tracking Resilience to Infections by Mapping Disease Space.

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

Disease space maps of malaria-infected mice.(A) Average values for eight parameters for three mice measured (and averaged) daily for 20 d are plotted in a timeline marked in blue. The paths for the three mice plotted individually are shown in S2 Fig. The transcript markers used to define B cells, NK cells, granulocytes, and reticulocytes are, respectively, Cd79b, Nkg7, Camp, and Trim 10, which are reported as log2 values. Time is indicated by the increasing thickness of the curve. (B,C) Phase plots for representative looping pairs of parameters. Note that the axes have been flipped so that all graphs start at the top, and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue) and “recovering” (days 11–15, yellow) regions. See also S1 Fig.
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pbio.1002436.g002: Disease space maps of malaria-infected mice.(A) Average values for eight parameters for three mice measured (and averaged) daily for 20 d are plotted in a timeline marked in blue. The paths for the three mice plotted individually are shown in S2 Fig. The transcript markers used to define B cells, NK cells, granulocytes, and reticulocytes are, respectively, Cd79b, Nkg7, Camp, and Trim 10, which are reported as log2 values. Time is indicated by the increasing thickness of the curve. (B,C) Phase plots for representative looping pairs of parameters. Note that the axes have been flipped so that all graphs start at the top, and the sick mice follow a clockwise path through phase space. The graph shows “comfortable” (days 0–6, green), “sick” (days 7–10, blue) and “recovering” (days 11–15, yellow) regions. See also S1 Fig.
Mentions: To establish which parameters should be plotted to build informative disease maps, we gathered a multi-dimensional dataset over the course of infections of mice challenged with the malaria parasite Plasmodium chabaudi. We chose this parasite because it is readily measurable in the blood of the host and causes pathology that can also be measured in the blood; another useful feature is that the parasite causes a self-resolving infection. Together, these properties let us easily follow the progression of an infection in a resilient system. We mapped the behavior of circulating blood cells and parasites by following infected mice over the 26 d infection course and performing microarray analysis on daily blood samples. We grouped the transcriptome data using a k-means analysis, and these groups were characterized as reporting circulating cells based on the composition of their members (S1 Fig, S1–S3 Tables) [10,11]. We plotted pairs of these k-means averages against each other to observe the phase curves and found hysteretic relationships that produced open loops that could be used as maps (Fig 2A). In Fig 2B and 2C, we show how some of these loops can be considered with respect to the cartoon model of disease space we introduced in Fig 1A.

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