FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses

A benchmark dataset of protein antigens for antigenicity measurement

Antigenicity measurement plays a fundamental role in vaccine design, which requires antigen selection from a large number of mutants. To augment traditional cross-reactivity experiments, computational approaches for predicting the antigenic distance between multiple protein antigens are highly valuable. The performance of in silico models relies heavily on large-scale benchmark datasets, which are scattered among public databases and published articles or reports. Here, we present the first benchmark dataset of protein antigens with experimental evidence to guide in silico antigenicity calculations. This dataset includes (1) standard haemagglutination-inhibition (HI) tests for 3,867 influenza A/H3N2 strain pairs, (2) standard HI tests for 559 influenza virus B strain pairs, and (3) neutralization titres derived from 1,073 Dengue virus strain pairs. All of these datasets were collated and annotated with experimentally validated antigenicity relationships as well as sequence information for the corresponding protein antigens. We anticipate that this work will provide a benchmark dataset for in silico antigenicity prediction that could be further used to assist in epidemic surveillance and therapeutic vaccine design for viruses with variable antigenicity.

Virological Surveillance of Influenza in the eight epidemic seasons after the 2009 pandemic in Emilia-Romagna (Northern Italy)

BACKGROUND AND AIM OF THE WORK

Influenza virological surveillance is essential for monitoring the evolution of influenza viruses (IVs) as well as for annual updating of the vaccine composition. The aim of this study is to analyse IVs circulation in Emilia-Romagna during the eight epidemic seasons after the 2009 pandemic and to evaluate their match with seasonal vaccine strains.

METHODS

A total of 7882 respiratory specimens from patients with influenza-like illness (ILI), were collected by regional sentinel practitioners and hospital physicians. Viral investigations were conducted by rRT-PCR assay. Genetic characterization was performed for a spatial-temporal representative number of influenza laboratory-confirmed specimens.

RESULTS

Influenza-positive samples per season ranged between 28.9% (2013-2014) and 66.8% (2012-2013). Co-circulation of IVs type A and type B was observed in all seasons, although with a different intensity. In all seasons, the highest number of positive samples was recorded in younger patients aged 5-14 years with relative frequencies ranging from 40% in the 2013-2014 season and 78% in the 2012-2013 season. Since the 2009 pandemic, A/H1N1pdm09 IVs circulating were closely related to the vaccine strain A/California/7/2009. Antigenic mismatch between vaccine strain and A/H3N2 IVs was observed in the 2011-2012 and 2014-2015 seasons. During 2015-2016, 2016-2017 and 2017-2018 seasons a complete or nearly complete mismatch between the predominant influenza B lineage of IVs type B circulating and vaccine B lineage occurred.

CONCLUSIONS

This analysis confirms the importance of the virological surveillance and highlights the need of a continuous monitoring of IVs circulation, to improve the most appropriate vaccination strategies. (www.actabiomedica.it).

Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2

The rapid and consistent mutation of influenza requires frequent evaluation of antigenicity variation among newly emerged strains, during which several in-silico methods have been reported to facilitate the assays. In this paper, we designed a structure-based antigenicity scoring model instead of those sequence-based previously published. Protein structural context was adopted to derive the antigenicity-dominant positions, as well as the physic-chemical change of local micro-environment in correlation with antigenicity change. Then a position specific scoring matrix (PSSM) profile and local environmental change over above positions were integrated to predict the antigenicity variance. Independent testing showed a high accuracy of 0.875, and sensitivity of 0.986, with a significant ability to discover antigenic-escaping strains. When applying this model to the historical data, global and regional antigenic drift events can be successfully detected. Furthermore, two well-known vaccine failure events were clearly suggested. Therefore, this structure-context model may be particularly useful to identify those to-be-failed vaccine strains, in addition to suggest potential new vaccine strains.

A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding

Background

Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells often target diverse regions in highly variable viral pathogens and this diversity may need to be addressed through redefinition of suitable peptide targets.

Methods

We have developed a method for antigen assessment and target selection for polyvalent vaccines, with which we identified immune epitopes from variable regions, where all variants bind HLA. These regions, although variable, can thus be considered stable in terms of HLA binding and represent valuable vaccine targets.

Results

We applied this method to predict CD8⁺ T-cell targets in influenza A H7N9 hemagglutinin and significantly increased the number of potential vaccine targets compared to the number of targets discovered using the traditional approach where low-frequency peptides are excluded.

Conclusions

We developed a webserver with an intuitive visualization scheme for summarizing the T cell-based antigenic potential of any given protein or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/.