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Nanobody-Based Blocking of Binding ELISA for the Detection of Anti-NS1 Zika-Virus-Specific Antibodies in Convalescent Patients.
Delfin-Riela, Triana; Rossotti, Martín A; Mattiuzzo, Giada; Echaides, César; González-Sapienza, Gualberto.
Afiliação
  • Delfin-Riela T; Cátedra de Inmunología, DEPBIO, Facultad de Química, Instituto de Higiene, UDELAR, Montevideo 11600, Uruguay.
  • Rossotti MA; Cátedra de Inmunología, DEPBIO, Facultad de Química, Instituto de Higiene, UDELAR, Montevideo 11600, Uruguay.
  • Mattiuzzo G; Division of Virology, National Institute for Biological Standards and Control (NIBSC)-MHRA, Hertfordshire EN6 3QG, UK.
  • Echaides C; Parque Lecoq, IMM, Montevideo 11300, Uruguay.
  • González-Sapienza G; Cátedra de Inmunología, DEPBIO, Facultad de Química, Instituto de Higiene, UDELAR, Montevideo 11600, Uruguay.
Trop Med Infect Dis ; 8(1)2023 Jan 10.
Article em En | MEDLINE | ID: mdl-36668962
ABSTRACT
Zika virus has spread around the world with rapid pace in the last five years. Although symptoms are typically mild and unspecific, Zika's major impact occurs during pregnancy, generating a congenital syndrome. Serology plays a key role in its diagnosis. However, its use is limited due to the uncertainty caused by the cross-reaction of antibodies elicited in response to other flavivirus infections when tested in direct immunoassays. Using a panel of previously generated anti-Zika non-structural protein 1 (NS1) nanobodies, a set was selected that only recognizes epitopes present in Zika and is immunogenic to humans. A proper arrangement of these nanobodies was made and conditions were optimized in order to develop a novel serology assay. This new ELISA relies on the inhibition of the binding of a set of selected nanobodies to Zika-immobilized NS1 when previously incubated with Zika convalescent sera. Using the developed blocking of binding assay, it was possible to discriminate between Zika-specific and cross-reactive antibodies in serum samples from infections with Zika and other flaviviruses.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article