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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
- Source :
- PLoS Neglected Tropical Diseases, PLoS Neglected Tropical Diseases, Vol 14, Iss 6, p e0008407 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
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.<br />Author summary A pathogen can cause variable disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. In addition, different pathogens may induce similar clinical presentations. These facts complicate efforts to identify infections caused by rare or neglected pathogens and to understand factors governing disease spread in humans and animals, particularly when data are limited. These biological complexities are omitted from classical approaches to modeling infectious disease, which typically rely on discrete and well-defined disease states. Here we show that by analyzing multiple biomarkers of health and infection simultaneously, treating these values as quantitative rather than binary indicators, and including a modest amount of longitudinal sampling of hosts, we can create a map of the host-pathogen interaction that shows the full spectrum of disease presentations and opens doors for new insights and predictions. By accounting for individual variation and capturing changes through time since infection, this mapping framework enables more robust interpretation of cross-sectional data; e.g., to detect predictive relationships between biomarkers and key outcomes such as survival, or to assess whether observed disease is associated with the pathogen of interest. This approach can help epidemiologists, ecologists and clinicians to better study and manage the many infectious diseases that exhibit complex relationships with their hosts.
- Subjects :
- 0301 basic medicine
Bacterial Diseases
Zalophus californianus
Physiology
RC955-962
Marine and Aquatic Sciences
Disease
Pathogenesis
Molting
Pathology and Laboratory Medicine
Biochemistry
California
Animal Diseases
0302 clinical medicine
Arctic medicine. Tropical medicine
Immune Physiology
Zoonoses
Medicine and Health Sciences
Mammals
Leptospira
Bacterial Shedding
education.field_of_study
Immune System Proteins
Transmission (medicine)
Eukaryota
Antibodies, Bacterial
Sea Lions
Bacterial Pathogens
Survival Rate
Infectious Diseases
Medical Microbiology
Vertebrates
Host-Pathogen Interactions
Biomarker (medicine)
Public aspects of medicine
RA1-1270
Pathogens
Leptospira interrogans
Research Article
Neglected Tropical Diseases
030231 tropical medicine
Population
Immunology
Marine Biology
Biology
Microbiology
Antibodies
03 medical and health sciences
Environmental health
medicine
Animals
Leptospirosis
education
Marine Mammals
Microbial Pathogens
Bacteria
Public Health, Environmental and Occupational Health
Organisms
Immunity
Tropical disease
Biology and Life Sciences
Proteins
Missing data
biology.organism_classification
medicine.disease
Tropical Diseases
Kinetics
030104 developmental biology
Cross-Sectional Studies
Amniotes
Earth Sciences
Physiological Processes
Biomarkers
Subjects
Details
- Language :
- English
- ISSN :
- 19352735 and 19352727
- Volume :
- 14
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS Neglected Tropical Diseases
- Accession number :
- edsair.doi.dedup.....c1bcc8e1957ef439625056d1a2714946