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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description | 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. |
doi_str_mv | 10.1371/journal.pbio.1002436 |
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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.</description><identifier>ISSN: 1545-7885</identifier><identifier>ISSN: 1544-9173</identifier><identifier>EISSN: 1545-7885</identifier><identifier>DOI: 10.1371/journal.pbio.1002436</identifier><identifier>PMID: 27088359</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PLoS biology, 2016-04, Vol.14 (4), p.e1002436-e1002436</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>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</rights><rights>2016 Torres et al 2016 Torres et al</rights><rights>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</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c794t-f4c288a97a28832344ae331b540d02232e4499402607c4e1232e57203e5c20ca3</citedby><cites>FETCH-LOGICAL-c794t-f4c288a97a28832344ae331b540d02232e4499402607c4e1232e57203e5c20ca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835107/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835107/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27088359$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Torres, Brenda Y</creatorcontrib><creatorcontrib>Oliveira, Jose Henrique M</creatorcontrib><creatorcontrib>Thomas Tate, Ann</creatorcontrib><creatorcontrib>Rath, Poonam</creatorcontrib><creatorcontrib>Cumnock, Katherine</creatorcontrib><creatorcontrib>Schneider, David S</creatorcontrib><title>Tracking Resilience to Infections by Mapping Disease Space</title><title>PLoS biology</title><addtitle>PLoS Biol</addtitle><description>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.</description><subject>Animals</subject><subject>Biology and Life Sciences</subject><subject>Blood</subject><subject>Computer and Information Sciences</subject><subject>Confidence intervals</subject><subject>Erythrocytes</subject><subject>Funding</subject><subject>Genetic engineering</subject><subject>Host-parasite relationships</subject><subject>Humans</subject><subject>Infections</subject><subject>Infections - physiopathology</subject><subject>Infectious diseases</subject><subject>Malaria</subject><subject>Malaria - blood</subject><subject>Malaria - physiopathology</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Mice</subject><subject>Observations</subject><subject>Pathogenesis</subject><subject>Pathogens</subject><subject>Patients</subject><subject>Plasmodium chabaudi</subject><subject>Plasmodium chabaudi - 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27088359</pmid><doi>10.1371/journal.pbio.1002436</doi><oa>free_for_read</oa></addata></record> |
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subjects | 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 |
title | Tracking Resilience to Infections by Mapping Disease Space |
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