Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/4920
Título: Molecular data mining to improve antibody-based detection of grapevine leafroll-associated virus 1 (GLRaV-1).
Autor: Esteves, Filipa
Teixeira Santos, Margarida
Eiras-Dias, José Eduardo
Fonseca, Filomena
Palavras-chave: Antibodies
Tissue print immunoblotting
In situ immunoassay
Capsid protein
Intra-isolate genetic structure
Molecular variability
Data: 2013
Editora: Elsevier
Resumo: Testing for Grapevine leafroll-associated virus 1 (GLRaV-1) is mandatory in certification schemes of propagation material within the EU. Accurate and reliable diagnostic assays are necessary for implementation of this measure. During routine detection of GLRaV-1, using double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) and reverse transcription (RT) followed by polymerase chain reaction (PCR), evidence was obtained that positive samples could be overlooked by either or both detection methods. With the aim of improving serological detection tools for GLRaV-1, a total of 20 isolates were analyzed and 83 new complete capsid protein (CP) gene sequences were obtained. This set, together with the CP sequences available at GenBank was used for a comprehensive in silico analysis. To obtain a specific antibody able to recognize all known CP variants, conserved regions with suitable antigenicity profile were identified along the deduced CP AA sequences and a 14 AA sequence was chosen for commercial peptide synthesis and immunization. Initially polyclonal antibodies were produced and tested, followed by purification of the respective monospecific antibody and conjugation with alkaline phosphatase or fluorescein isothiocyanate (FITC). These serological tools were tested successfully on all the available positive samples and proved adequate for in situ immunoassay (ISIA). Further testing showed that the monospecific antibody could also be used in tissue print immunoblotting (TPIB), a technique that allows rapid processing of large sample sets, which is highly suitable to implement protocols ensuring that, for each vine analyzed, enough random samples are taken and processed, before certification.
Peer review: yes
URI: http://hdl.handle.net/10400.1/4920
DOI: http://dx.doi.org/10.1016/j.jviromet.2013.09.004
ISSN: 0166-0934
Versão do Editor: http://www.sciencedirect.com/science/article/pii/S0166093413003911#
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