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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 ofLeptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the sameLeptospiraserovar, 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 titers (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. 

Abstract Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regardingLeptospiracirculation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessedLeptospiraprevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%).Leptospiraphylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions ofLeptospirainto local marine and island ecosystems. Overall, we found evidence of widespreadLeptospiraexposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas. 

Abstract Studies of infectious disease ecology would benefit greatly from 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.We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected withLeptospira interrogans.Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times.The method substantially simplifies the challenge of modelling 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. 

Diseases of tropical reef organisms is an intensive area of study, but despite significant advances in methodology and the global knowledge base, identifying the proximate causes of disease outbreaks remains difficult. The dynamics of infectious wildlife diseases are known to be influenced by shifting interactions among the host, pathogen, and other members of the microbiome, and a collective body of work clearly demonstrates that this is also the case for the main foundation species on reefs, corals. Yet, among wildlife, outbreaks of coral diseases stand out as being driven largely by a changing environment. These outbreaks contributed not only to significant losses of coral species but also to whole ecosystem regime shifts. Here we suggest that to better decipher the disease dynamics of corals, we must integrate more holistic and modern paradigms that consider multiple and variable interactions among the three major players in epizootics: the host, its associated microbiome, and the environment. In this perspective, we discuss how expanding the pathogen component of the classic host-pathogen-environment disease triad to incorporate shifts in the microbiome leading to dysbiosis provides a better model for understanding coral disease dynamics. We outline and discuss issues arising when evaluating each component of this trio and make suggestions for bridging gaps between them. We further suggest that to best tackle these challenges, researchers must adjust standard paradigms, like the classic one pathogen-one disease model, that, to date, have been ineffectual at uncovering many of the emergent properties of coral reef disease dynamics. Lastly, we make recommendations for ways forward in the fields of marine disease ecology and the future of coral reef conservation and restoration given these observations. 

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of ‘permeability’ from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.