Interpreting ELISA analyses from wild animal samples: Some recurrent issues and solutions

Abstract

The NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Accepted ArticleThis article is protected by copyright. All rights reserved. 2. Here, we outline two solutions to deal with these issues. First, we show that implementing two assays with differing detection methods can help validate the use of reagents, such as antibodies, in species different from their intended target. We illustrate this by comparing the quantification of specific vaccinal antibodies against Newcastle Disease Virus (NDV) using two ELISA approaches in four seabird species (Cory's shearwater, European shag, European storm petrel, and Southern rockhopper penguin). Accepted ArticleThis article is protected by copyright. All rights reserved.3. Second, we provide a simple way to determine from the distribution of ELISA values whether the assayed samples are likely to be made of a single group of individuals (likely negative) or of two groups of individuals (negative and positive). We illustrate the use of this approach with two independent datasets: NDV antibody levels following vaccination and anti-Borrelia antibody levels following natural exposure. 4.The practical implementation of these methodological approaches could provide a way to efficiently apply ELISAs and other immune-based assays to address questions in the growing fields of ecological immunology and disease ecology.

Novel insights into serodiagnosis and epidemiology of Erysipelothrix rhusiopathiae, a newly recognized pathogen in muskoxen (Ovibos moschatus)

“…For both serum and FP samples, the estimated distribution of PP values for positive samples was much wider (larger standard deviation) compared to negative samples (Fig 2). This large standard deviation in the distribution of positive samples was previously described by Garnier et al [39] in a Table 1 for references).…”

“…To estimate the optimal cut-off value of PP to discriminate between negative and positive samples, we used a mixture-distribution modelling (MDM) approach. This method is commonly used to estimate cut-offs for diagnostic tests both in animals [39][40][41] and in humans [42,43]. Briefly, we assumed that the test results from our sampled population could be represented as a mixture of two underlying subpopulations, corresponding to negative and positive samples, with Gaussian distributions and distinct parameters (mean and standard deviation).…”

“…experimental trials) [40][41][42]48]. However, to effectively use a MDM approach, the investigated dataset must contain both negative and positive samples in sufficient quantities to allow the estimation of the two subpopulations [39]. We assumed that there were seropositive muskoxen in our sample set as samples originated from the same populations where the bacterium E. rhusiopathiae had recently been detected [1,27].…”

“…We assumed that there were seropositive muskoxen in our sample set as samples originated from the same populations where the bacterium E. rhusiopathiae had recently been detected [1,27]. Further, the distribution of the PP values was skewed to the right with a long tail, suggesting negative and positive subpopulations [39].…”

Exposure of breeding albatrosses to the agent of avian cholera: dynamics of antibody levels and ecological implications

“…Results are expressed as optical densities (ODs). The use of anti-chicken IgY to detect yellow-nosed albatross IgY has previously been validated using an experimental design and the positivity threshold was adapted using a method relying on fitting a mixture of normal distributions to the values of ODs (see Garnier et al 2017). The two assays, thus, only partly overlap in their targeted immunoglobulins: they detect different isotypes, directed against different epitopes, which may or may not be agglutinating.…”