Karimzadeh, H. | bioRxiv Science

bioRxiv2021

A high-throughput neutralization assay for yellow fever serodiagnostics

Quick and accurate detection of neutralizing antibodies (nAbs) against yellow fever is essential in serodiagnosis during outbreaks, for surveillance and to evaluate vaccine efficacy in population-wide studies. All this requires serological assays that can process a large number of samples in a highly standardized format. Albeit being laborious, time-consuming and limited in throughput, classical plaque reduction neutralization test (PRNT) is still considered gold standard for the detection and quantification of nAbs due to its sensitivity and specificity. Here we report the development of an alternative fluorescence-based serological assay (SNTFLUO) with an equally high sensitivity and specificity that is fit for high-throughput testing with the potential for automation. Finally, our novel SNTFLUO was cross-validated in several reference laboratories and against international WHO standards showing its potential to be implemented in clinical use. SNTFLUO assays with similar performance are available for the Japanese encephalitis, Zika and dengue viruses amenable for differential diagnostics. IMPORTANCEFast and accurate detection of neutralizing antibodies (nAbs) against yellow fever virus (YFV) is key in yellow fever serodiagnosis, outbreak surveillance and monitoring of vaccine efficacy. Although classical PRNT still remains gold standard for measuring YFV nAbs, this methodology suffers from inherent limitations such as a low throughput and an overall high labor intensity. We present a novel fluorescence-based serum neutralization test (SNTFLUO) with equally high sensitivity and specificity that is fit for processing large number of samples in a highly standardized manner and has the potential to be implemented in clinical use. In addition, we present SNTFLUO assays with similar performance for Japanese encephalitis, Zika and dengue viruses opening new avenues for differential diagnostics.

microbiology↗

bioRxiv2021

HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations

MotivationA key process in anti-viral adaptive immunity is that the Human Leukocyte Antigen system (HLA) presents epitopes as Major Histocompatibility Complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-Lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system, e.g. by point mutations that decrease binding of viral peptides to MHC I. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the host HLA alleles and its MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature HAMs are amenable to detection by statistical methods in paired sequence / HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. ResultsWe present a new regression model for the detection of HAMs. As we choose a Bayesian approach we can include the novel sparsity-inducing priors, and we obtain easily interpretable quantitative information on HAM candidates. The basic model can be extended to include prior information relevant to HAM detection, which we demonstrate by integrating predictions of epitope affinities to MHC I, predictions of epitope peptide processing, and computation of phylogenetic background. This integrative method improves performance in HAM detection considerably over state-of-the-art methods. AvailabilityThe source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license. Contactdaniel.habermann@uni-due.de, daniel.hoffmann@uni-due.de

bioinformatics↗