Tracking Resilience to Infections by Mapping Disease Space

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...

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Published inPLoS biology Vol. 14; no. 4; p. e1002436
Main Authors Torres, Brenda Y, Oliveira, Jose Henrique M, Thomas Tate, Ann, Rath, Poonam, Cumnock, Katherine, Schneider, David S
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 18.04.2016
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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.
AbstractList   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.
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.
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. This study shows that infections cause sick hosts to loop through disease space on their return back to health; resilient individuals take tiny loops through this space, minimizing the impact of the infection. When we get sick, we long for recovery; thus, a major goal of medicine is to promote resilience—the ability of a host to return to its original health following an infection. While in the laboratory we can study the response to infection with precise knowledge of inoculation time and dose, sick patients in the clinic do not have this information. This creates a problem because we can’t easily differentiate between patients who are early in the stages of infection that will develop severe disease from more disease-tolerant patients who present later in the infection. The distinction between these two types of patients is important, as the less disease-tolerant patient would require a more aggressive treatment regime. To determine where patients lie along the infection timeline, we charted “disease maps” that trace a patient’s route through “disease space.” We select symptoms that produce looping graphs as patients grow sick and recover. Using a mouse–malaria model, we demonstrate that less resilient individuals take wider loops through this space, representing a longer infection time with more severe symptoms. We find this looping behavior also applies to humans and suggest that people carrying the sickle cell trait are more resilient to malaria infections.
Audience Academic
Author Torres, Brenda Y
Oliveira, Jose Henrique M
Rath, Poonam
Schneider, David S
Cumnock, Katherine
Thomas Tate, Ann
AuthorAffiliation 3 Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
Princeton University, UNITED STATES
1 Program in Immunology, Stanford University, Stanford, California, United States of America
2 Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America
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– name: 3 Department of Biology and Biochemistry, University of Houston, Houston, Texas, United States of America
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  organization: Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America
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  organization: Department of Microbiology and Immunology, Stanford University, Stanford, California, United States of America
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27088359$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2016 Public Library of Science
2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Torres BY, Oliveira JHM, Thomas Tate A, Rath P, Cumnock K, Schneider DS (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14(4): e1002436. doi:10.1371/journal.pbio.1002436
2016 Torres et al 2016 Torres et al
2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Torres BY, Oliveira JHM, Thomas Tate A, Rath P, Cumnock K, Schneider DS (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14(4): e1002436. doi:10.1371/journal.pbio.1002436
Copyright_xml – notice: COPYRIGHT 2016 Public Library of Science
– notice: 2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Torres BY, Oliveira JHM, Thomas Tate A, Rath P, Cumnock K, Schneider DS (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14(4): e1002436. doi:10.1371/journal.pbio.1002436
– notice: 2016 Torres et al 2016 Torres et al
– notice: 2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Torres BY, Oliveira JHM, Thomas Tate A, Rath P, Cumnock K, Schneider DS (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14(4): e1002436. doi:10.1371/journal.pbio.1002436
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The authors have declared that no competing interests exist.
Conceived and designed the experiments: DSS BYT JHMO. Performed the experiments: BYT JHMO KC. Analyzed the data: BYT JHMO ATT DSS PR. Wrote the paper: BYT JHMO ATT PR KC DSS.
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  year: 2016
  text: 2016-04-18
  day: 18
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PublicationTitle PLoS biology
PublicationTitleAlternate PLoS Biol
PublicationYear 2016
Publisher Public Library of Science
Public Library of Science (PLoS)
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SSID ssj0022928
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Snippet Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and...
  Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and...
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SubjectTerms Animals
Biology and Life Sciences
Blood
Computer and Information Sciences
Confidence intervals
Erythrocytes
Funding
Genetic engineering
Host-parasite relationships
Humans
Infections
Infections - physiopathology
Infectious diseases
Malaria
Malaria - blood
Malaria - physiopathology
Medicine and Health Sciences
Methods
Mice
Observations
Pathogenesis
Pathogens
Patients
Plasmodium chabaudi
Plasmodium chabaudi - pathogenicity
Research and Analysis Methods
Statistical analysis
Studies
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Title Tracking Resilience to Infections by Mapping Disease Space
URI https://www.ncbi.nlm.nih.gov/pubmed/27088359
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http://dx.doi.org/10.1371/journal.pbio.1002436
Volume 14
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