Use of cross-reactive serological assays for detecting novel pathogens in wildlife: assessing an appropriate cutoff for henipavirus assays in African bats

Abstract

Reservoir hosts of novel pathogens are often identified or suspected as such on the basis of serological assay results, prior to the isolation of the pathogen itself. Serological assays might therefore be used outside of their original, validated scope in order to infer seroprevalences in reservoir host populations, until such time that specific diagnostic assays can be developed. This is particularly the case in wildlife disease research. The absence of positive and negative control samples and gold standard diagnostic assays presents challenges in determining an appropriate threshold, or 'cutoff', for the assay that enables differentiation between seronegative and seropositive individuals. Here, multiple methods were explored to determine an appropriate cutoff for a multiplexed microsphere assay that is used to detect henipavirus antibody binding in fruit bat plasma. These methods included calculating multiples of 'negative' control assay values, receiver operating characteristic curve analyses, and Bayesian mixture models to assess the distribution of assay outputs for classifying seropositive and seronegative individuals within different age classes. As for any diagnostic assay, the most appropriate cutoff determination method and value selected must be made according to the aims of the study. This study is presented as an example for others where reference samples, and assays that have been characterised previously, are absent.

Keywords: Eidolon helvum; Emerging diseases; HeV; Hendra virus; MCMC; MFI; Markov chain Monte Carlo; Microsphere binding assay; Multiplex; NiV; Nipah virus; ROC; Serology; median fluorescence intensity; receiver operating characteristic.

Figure 1:

Proportion of 402 E. helvum samples capable of henipavirus neutralisation at dilutions of ≥1:10, by a) NiV sG binding assay MFI result b) NiV sG binding assay ln(MFI) result.

Figure 2:

ROC curve analysis of sensitivity and specificity afforded by NiV sG binding assay MFIs for predicting HeV or NiV neutralisation. Two values are highlighted on the curve, with specificity and sensitivity in brackets. To the left is the ‘best’ value for optimal sensitivity and specificity, and to the right, the point that gives maximum specificity while maintaining 100% sensitivity.

Figure 3:

Frequency distribution histograms of NiV sG binding assay ln(MFI) values for each age group (Neonate: n = 42, Juvenile (J): n = 142, Sexually Immature (SI): n = 328, Adult (A): n = 986). The red lines correspond to the predictive posterior means generated from the fitted mixture model, fitted to all age groups simultaneously.

Figure 4:

A plot of the posterior mean (and 95% credible intervals) for the relative probability of a given NiV sG binding assay ln(MFI) value belonging to the seronegative, rather than seropositive groups (generated from the fitted mixture model, fitted to all age groups simultaneously). The coloured lines represents the posterior mean probability of being seronegative at each of the cutoffs identified by the various methods.