Development and evaluation of a diagnostic cytokine-release assay for Mycobacterium suricattae infection in meerkats (Suricata suricatta)

CITATION: Clarke, C., et al. 2017. Development and evaluation of a diagnostic cytokine-release assay for mycobacterium suricattae infection in meerkats (Suricata suricatta). BMC Veterinary Research, 13:2, doi:10.1186/s12917-016-0927-x.The original publication is available at http://bmcvetres.biomedcentral.comBackground: Sensitive diagnostic tools are necessary for the detection of Mycobacterium suricattae infection in meerkats (Suricata suricatta) in order to more clearly understand the epidemiology of tuberculosis and the ecological consequences of the disease in this species. We therefore aimed to develop a cytokine release assay to measure antigen-specific cell-mediated immune responses of meerkats. Results: Enzyme-linked immunosorbent assays (ELISAs) were evaluated for the detection of interferon-gamma (IFN-γ) and IFN-γ inducible protein 10 (IP-10) in meerkat plasma. An IP-10 ELISA was selected to measure the release of this cytokine in whole blood in response to Bovigam® PC-HP Stimulating Antigen, a commercial peptide pool of M. bovis antigens. Using this protocol, captive meerkats with no known M. suricattae exposure (n = 10) were tested and results were used to define a diagnostic cut off value (mean plus 2 standard deviations). This IP-10 release assay (IPRA) was then evaluated in free-living meerkats with known M. suricattae exposure, categorized as having either a low, moderate or high risk of infection with this pathogen. In each category, respectively, 24.7%, 27.3% and 82.4% of animals tested IPRA-positive. The odds of an animal testing positive was 14.0 times greater for animals with a high risk of M. suricattae infection compared to animals with a low risk. Conclusion: These results support the use of this assay as a measure of M. suricattae exposure in meerkat populations. Ongoing longitudinal studies aim to evaluate the value of the IPRA as a diagnostic test of M. suricattae infection in individual animals.http://bmcvetres.biomedcentral.com/articles/10.1186/s12917-016-0927-xPublisher's versio

Inference of the infection status of individuals using longitudinal testing data from cryptic populations: Towards a probabilistic approach to diagnosis

Effective control of many diseases requires the accurate detection of infected individuals. Confidently ascertaining whether an individual is infected can be challenging when diagnostic tests are imperfect and when some individuals go for long periods of time without being observed or sampled. Here, we use a multi-event capture-recapture approach to model imperfect observations of true epidemiological states. We describe a method for interpreting potentially disparate results from individuals sampled multiple times over an extended period, using empirical data from a wild badger population naturally infected with Mycobacterium bovis as an example. We examine the effect of sex, capture history and current and historical diagnostic test results on the probability of being truly infected, given any combination of diagnostic test results. In doing so, we move diagnosis away from the traditional binary classification of apparently infected versus uninfected to a probability-based interpretation which is updated each time an individual is re-sampled. Our findings identified temporal variation in infection status and suggest that capture probability is influenced by year, season and infection status. This novel approach to combining ecological and epidemiological data may aid disease management decision-making by providing a framework for the integration of multiple diagnostic test data with other information

Network position: A key component in the characterization of social personality types

In recent years, animal social interactions have received much attention in terms of personality research (e. g. aggressive or cooperative interactions). However, other components of social behaviour such as those describing the intensity, frequency, directedness and individual repeatability of interactions in animal groups have largely been neglected. Network analysis offers a valuable opportunity to characterize individual consistency of traits in labile social groups and therein provide novel insights to personality research in ways previously not possible using traditional techniques. Should individual network positions be consistently different between individuals under changing conditions, they might reflect expressions of an individual's personality. Here, we discuss a conceptual framework for using network analyses to infer the presence of individual differences and present a statistical test based on randomization techniques for testing the consistency of network positions in individuals. The statistical tools presented are useful because if particular individuals consistently occupy key positions in social networks, then this is also likely to have consequences for their fitness as well as for that of others in the population. These consequences may be particularly significant since individual network position has been shown to be important for the transmission of diseases, socially learnt information and genetic material between individuals and populations. © 2012 Springer-Verlag Berlin Heidelberg

Recent advances in the analysis of behavioural organization and interpretation as indicators of animal welfare

While the incorporation of mathematical and engineering methods has greatly advanced in other areas of the life sciences, they have been under-utilized in the field of animal welfare. Exceptions are beginning to emerge and share a common motivation to quantify ‘hidden’ aspects in the structure of the behaviour of an individual, or group of animals. Such analyses have the potential to quantify behavioural markers of pain and stress and quantify abnormal behaviour objectively. This review seeks to explore the scope of such analytical methods as behavioural indicators of welfare. We outline four classes of analyses that can be used to quantify aspects of behavioural organization. The underlying principles, possible applications and limitations are described for: fractal analysis, temporal methods, social network analysis, and agent-based modelling and simulation. We hope to encourage further application of analyses of behavioural organization by highlighting potential applications in the assessment of animal welfare, and increasing awareness of the scope for the development of new mathematical methods in this area