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Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study.
PLoS Negl Trop Dis. 2020 06; 14(6):e0008407.PN

Abstract

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Authors+Show Affiliations

Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America.Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America. Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, Illinois, United States of America.The Marine Mammal Center, Sausalito, California, United States of America. California Academy of Sciences, San Francisco, California, United States of America.Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America.U.S. Navy Marine Mammal Program, Naval Information Warfare Center Pacific, San Diego, California, United States of America.National Marine Mammal Foundation, San Diego, California, United States of America.Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.Hollings Marine Laboratory, National Ocean Service, Charleston, South Carolina, United States of America.The Marine Mammal Center, Sausalito, California, United States of America. Karen Dryer Wildlife Health Center, University of California Davis, California, United States of America.Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

32598393

Citation

Prager, K C., et al. "Linking Longitudinal and Cross-sectional Biomarker Data to Understand Host-pathogen Dynamics: Leptospira in California Sea Lions (Zalophus Californianus) as a Case Study." PLoS Neglected Tropical Diseases, vol. 14, no. 6, 2020, pp. e0008407.
Prager KC, Buhnerkempe MG, Greig DJ, et al. Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study. PLoS Negl Trop Dis. 2020;14(6):e0008407.
Prager, K. C., Buhnerkempe, M. G., Greig, D. J., Orr, A. J., Jensen, E. D., Gomez, F., Galloway, R. L., Wu, Q., Gulland, F. M. D., & Lloyd-Smith, J. O. (2020). Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study. PLoS Neglected Tropical Diseases, 14(6), e0008407. https://doi.org/10.1371/journal.pntd.0008407
Prager KC, et al. Linking Longitudinal and Cross-sectional Biomarker Data to Understand Host-pathogen Dynamics: Leptospira in California Sea Lions (Zalophus Californianus) as a Case Study. PLoS Negl Trop Dis. 2020;14(6):e0008407. PubMed PMID: 32598393.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study. AU - Prager,K C, AU - Buhnerkempe,Michael G, AU - Greig,Denise J, AU - Orr,Anthony J, AU - Jensen,Eric D, AU - Gomez,Forrest, AU - Galloway,Renee L, AU - Wu,Qingzhong, AU - Gulland,Frances M D, AU - Lloyd-Smith,James O, Y1 - 2020/06/29/ PY - 2019/12/19/received PY - 2020/05/21/accepted PY - 2020/07/10/revised PY - 2020/7/1/pubmed PY - 2020/7/1/medline PY - 2020/6/30/entrez SP - e0008407 EP - e0008407 JF - PLoS neglected tropical diseases JO - PLoS Negl Trop Dis VL - 14 IS - 6 N2 - Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale. SN - 1935-2735 UR - https://www.unboundmedicine.com/medline/citation/32598393/Linking_longitudinal_and_cross-sectional_biomarker_data_to_understand_host-pathogen_dynamics:_Leptospira in_California_sea_lions_(Zalophus_californianus)_as_a_case_study L2 - https://dx.plos.org/10.1371/journal.pntd.0008407 DB - PRIME DP - Unbound Medicine ER -
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