In silico prediction and ex vivo evaluation of potential T-cell epitopes in glycoproteins 4 and 5 and nucleocapsid protein of genotype-I (European) of porcine reproductive and respiratory syndrome virus
Introduction
Porcine reproductive and respiratory syndrome virus (PRRSV) was firstly isolated in 1991 by Dutch researchers [1] and thereafter classified as a member of the genus Arteriviridae[2]. At present, two genotypes of PRRSV are known: genotype-I (European) and genotype-II (American). Antigenic and genetic diversity appears to be high within each genotype, particularly within European strains [3], [4], [5], [6], [7], [8].
PRRSV genome is composed of nine open reading frames (ORFs). ORFs 1a and 1b account for approximately 75% of the viral genome and encode the non-structural proteins (nsp). ORFs 2a, 2b and 3–7 encode the PRRSV structural proteins of which resulting proteins 2a, 3, 4 and 5 are glycosylated (namely GP2a, GP3, GP4 and GP5, respectively). ORF6 encodes the viral matrix protein (M) and ORF7 encodes the nucleocapsid (N) [9], [10], [11], [12]. Neutralizing antibodies are thought to be mainly induced by GP5 and GP4 [13]; in contrast, a very limited knowledge is available regarding T-cell epitopes of PRRSV. Recently, two T-cell epitopes have been identified in GP5 of American-type strains based on their ability to induce IFN-γ responses in cultures of peripheral blood mononuclear cells (PBMC) obtained from PRRSV-immunized and later challenged pigs [14].
Determination of T-cell epitopes is a difficult task. The systematic approach based on the synthesis and testing of large sets of overlapping peptides is indicated when location of T-epitopes can, somehow, be estimated; however, when this knowledge lacks, that is a very expensive and cumbersome way. In contrast, bioinformatic prediction is extremely cheap and may help to restrict the number of peptides to be screened. Combination of T-cell epitope prediction and classical immunization experiments can be a useful strategy that permits to speed research in this area [15]. In the present study, bioinformatics were used to predict potential T-cell epitopes in GP4, GP5 and N proteins of PRRSV and were later tested on immunized animals.
Section snippets
Peptide design and synthesis
Putative MHC-I epitopes were predicted after Lelystad virus (LV) sequences combining two different types of prediction algorithms: binding matrices and artificial neural networks. Thus, in first step, peptides were predicted using MAPP with the SYFPEITHY matrix (available at http://www.mpiib-berlin.mpg.de/MAPPP/binding.html) considering all nonamers with potential binding for any of the human or cattle alleles available, since swine genes software and haplotypes are not yet available. For each
Screening of peptides using IFN-γ-SC by ELISPOT
In the first study, 14 peptides were tested (nos. 2–15); 6 of them were predicted to bind MHC-I and 8 were predicted to bind MHC-II. The initial screening of those peptides by ELISPOT showed that 8 of them (8/14; 57.1%) – peptides 2, 3, 5, 6, 7, 8, 9, 15 – were recognized by at least 1/5 immunized pigs at both 42 and 63 days post-vaccination with OverELISPOT values ranging from 3% to 18%. That group of peptides included five potential MHC-I epitopes (5/6; 83.3%) and three (3/8; 37.5%) potential
Discussion
Control of PRRSV has proven to be difficult. Several reasons can explain this difficulty but one of the most important factors is that available vaccines provide a limited protection against the infection, particularly taking into account the high genetic diversity of PRRSV. Development of new and more efficacious vaccines against this virus faces the fact that our understanding of the host's protective immune responses against the virus or about what parts of the virus induce protective
Acknowledgements
We would like to thank Núria Navarro and Esmeralda Cano for their excellent technical assistance. Our grateful thanks to Joan Tarradas and Cristina Lorca for their help in the construction of DNA vaccines. Thanks to Dr. Javier Dominguez and Dr. Belén Álvarez for help and advice. Dr. Ivan Díaz has been financially supported by project “Porcivir” CDS2006-00007 of the Spanish Ministry of Science and Innovation. This study has been funded by the Spanish Ministry of Science and Innovation project
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