Biology subjects

Prager, K. C.

Publications and source records attributed to Prager, K. C..

5 recordsLinked to original sources

bioRxiv2026

Leptospirosis in Southern Sea Otters from Central California: Detection of Fatal Infections and Pathologic Findings

Leptospira interrogans serovar Pomona infections cause periodic outbreaks among California sea lions (CSL; Zalophus californianus) and sporadic deaths in phocids; however, frequency of infection and associated health impacts are uncharacterized among sympatric threatened southern sea otters (SSO; Enhydra lutris nereis), which are important sentinels of coastal health. Given the broad impacts of L. interrogans on other marine mammals, our objective was to screen selected SSO for infection, determine whether leptospirosis contributes to SSO mortality, and describe leptospiral-associated lesions. A retrospective review (2005-2025) yielded 19 candidates that underwent case review, including Leptospira immunohistochemistry (IHC), serology, and polymerase chain reaction (PCR), with a special focus on renal and hepatic lesions. Kidney was PCR-positive for 74% (14/19) of suspected cases and for eight of these, DNA sequence-based serogroup typing detected L. interrogans serogroup Pomona. Seven of the 14 PCR-positive leptospirosis cases were classed as fatal based on positive renal IHC and moderate to severe tubulointerstitial nephritis. All fatal cases had anti-L. interrogans serovar Pomona antibody titers [≥]1:25,600. All remaining (n=7) PCR-positive cases were considered nonfatal leptospirosis cases due to minimal and/or unrelated renal lesions, and negative IHC. Nonfatal Leptospira-infected cases ranged from seronegative to low positive (1:400) for serovar Pomona. Antibody titers for Leptospira PCR-negative cases were negative. For fatal cases, gross renal changes were often inapparent or characterized by miliary white cortical foci. Renal histologic lesions included tubulointerstitial nephritis, acute tubular necrosis and suppurative tubulitis with intratubular bacteria in fatal cases, and positive IHC staining for leptospiral antigen in lesions. Gross hepatic changes were also inapparent in fatal cases and histologic lesions were rare, characterized in one animal by hepatocellular disassociation and in two sea otters by limited leptospiral antigen detection by IHC. Most Leptospira-infected sea otters (71%, 10/14) stranded during higher rainfall months in California, suggesting possible land-to-sea transmission from terrestrial hosts. Given these findings, and because L. interrogans serovar Pomona infections have been confirmed in sympatric CSLs and terrestrial mammals from adjacent watersheds, focused investigation of potential marine and terrestrial disease transmission dynamics could provide new information to reduce mortalities of SSO.

pathology↗

bioRxiv2024

Navigating cross-reactivity and host species effects in a serological assay: A case study of the microscopic agglutination test for Leptospira serology

BackgroundSerology (the detection of antibodies formed by the host against an infecting pathogen) is frequently used to assess current infections and past exposure to specific pathogens. However, the presence of cross-reactivity among host antibodies in serological data makes it challenging to interpret the patterns and draw reliable conclusions about the infecting pathogen or strain. Methodology/Principal FindingsIn our study, we use microscopic agglutination test (MAT) serological data from three host species [California sea lion (Zalophus californianus), island fox (Urocyon littoralis), and island spotted skunk (Spilogale gracilis)] with confirmed infections to assess differences in cross-reactivity by host species and diagnostic laboratory. All host species are known to be infected with the same serovar of Leptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the same Leptospira serovar, three host species exhibit different cross-reactivity profiles to a 5-serovar diagnostic panel. We also observe that the cross-reactive antibody titer against a non-infecting serovar can remain detectable after the antibody titer against the infecting serovar declines below detectable levels. Conclusions/SignificanceCross-reactivity in serological data makes interpretation difficult and can lead to common pitfalls. Our results show that the highest antibody titer is not a reliable indicator of infecting serovar and highlight an intriguing role of host species in shaping reactivity patterns. On the other side, seronegativity against a given serovar does not rule out that serovar as the cause of infection. We show that titer magnitudes can be influenced by both host species and diagnostic laboratory, indicating that efforts to interpret absolute titer levels (e.g., as indicators of recent infection) must be calibrated to the system under study. Thus, we implore scientists and health officials using serological data for surveillance to interpret the data with caution. AUTHOR SUMMARYSerology is frequently used for disease surveillance, especially in systems that are resource constrained or logistically challenging. Serological testing involves analyzing blood serum samples to detect antibodies with reactivity toward specific pathogens (or more generally, molecular antigens), with the goal of characterizing past exposure to those pathogens. However, these antibodies can be non-specific and may react against other related pathogens or strains - a phenomenon known as cross-reactivity. Interpretation of serological data exhibiting cross-reactivity is difficult and simplifying assumptions are often made (e.g., to interpret the strain that elicits the highest antibody titer level as the infecting pathogen strain). Our work shows that interpreting antibody data requires more nuance and more caution. Both absolute titer levels and relative reactivity against different strains can vary across host species and diagnostic laboratories, so it is essential to interpret these data in the appropriate context. These host species differences in antibody reactivity and cross-reactivity patterns make direct comparisons across species inadvisable.

ecology↗

bioRxiv2022

Inferring time of infection from field data using dynamic models of antibody decay

Studies of infectious disease ecology often rely heavily on knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody level data being one of the most promising sources of information. The use of antibody levels to back-calculate infection time requires the development of a host-pathogen system-specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. Here, we present a way to do this in a Bayesian framework that facilitates the incorporation of all available information about potential infection times. We apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans, leading to reductions of 51-92% in the window of possible infection times. Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements of infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. The method substantially simplifies the challenge of modeling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology, and can even be applied to cross-sectional data once the model is trained.

ecology↗

bioRxiv2019

Mapping the Host-Pathogen Space to Link Longitudinal and Cross-sectional Biomarker Data: Leptospira Infection in California Sea Lions (Zalophus californianus) as a Case Study

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 bacterial infection (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve both our individual-level and population-level understanding of this host-pathogen 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 visualize and characterize changes in these values through time. We 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. This mapping framework enables us to determine an individuals location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure, including severe acute disease, chronic subclinical infection, and recovery. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and in certain cases we can impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space and using quantitative biomarker data provides more nuanced approaches for understanding and modeling disease dynamics in a system, yielding benefits for the clinician who needs to triage patients and prevent transmission, and for the disease ecologist or epidemiologist wishing to develop appropriate risk management strategies and assess health impacts on a population scale.\n\nAuthor SummaryA pathogen can cause a range of disease severity across different host individuals, and these presentations change over the time-course from infection to recovery. These facts complicate the work of epidemiologists and disease ecologists seeking to understand the factors governing disease spread, often working with cross-sectional data. Recognizing these facts also highlights the shortcomings of 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.

ecology↗

bioRxiv2018

Proteomic Analysis of Urine from California Sea Lions (Zalophus californianus): a Resource for Urinary Biomarker Discovery

Urinary markers for the assessment of kidney diseases in wild animals are limited, in part, due to the lack of urinary proteome data, especially for marine mammals. One of the most prevalent kidney diseases in marine mammals is caused by Leptospira interrogans, which is the second most common etiology linked to stranding of California sea lions (Zalophus californianus). Urine proteins from eleven sea lions with leptospirosis kidney disease and eight sea lions without leptospirosis or kidney disease were analyzed using shotgun proteomics. In total, 2694 protein groups were identified and 316 were differentially abundant between groups. Major urine proteins in sea lions were similar to major urine proteins in dogs and humans except for the preponderance of resistin, lysozyme C, and PDZ domain containing 1, which appear to be over-represented. Previously reported urine protein markers of kidney injury in humans and animals were also identified. Notably, neutrophil gelatinase-associated lipocalin, osteopontin, and epidermal fatty acid binding protein were elevated over 20-fold in the leptospirosis-infected sea lions. Consistent with leptospirosis infection in rodents, urinary proteins associated with the renin-angiotensin system were depressed, including neprilysin. This study represents a foundation from which to explore the clinical use of urinary protein markers in California sea lions.\n\nAbbreviations

biochemistry↗