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AIDS Res Hum Retroviruses ; 14(2): 139-49, 1998 Jan 20.
Article in English | MEDLINE | ID: mdl-9462924

ABSTRACT

HIV-1 V3 serotyping is used to classify immunodeficiency viruses on the basis of antibody binding to V3 peptides derived from env genetic subtypes. Although it shows a reasonable overlap, it has been reported to be distinct from viral genetic subtypes. The aim of this study is to determine the feasibility of HIV-1 serotyping to predict genetic subtypes in an East African setting, where multiple HIV-1 subtypes have coexisted for many years. HIV-1 genetic subtypes of 86 AIDS patients in Mbeya Town, southwest Tanzania, were determined, using env nucleic acid sequencing as the basis for comparison. Those data were compared with V3 serotyping results obtained by four different methodologies. Four HIV-1 genetic subtypes were identified, including A (25, 29%), C (47, 55%), D (13, 15%), and G (1, 1%). The sensitivity and specificity of those serotyping assays varied considerably: sensitivity for genetic subtype A (40-48%), C (52-96%), and D (9-31%); and specificity for genetic subtype A (77-95%), C (46-63%), and D (97-100%). We further tried to identify reasons for the discrepancies between serotyping results and genetic subtypes. By means of logistic regression analysis three amino acid residues within the V3 loop (positions 12, 13, and 19; V, H, and A for serotype A, I, R, and T for serotype C) were found to be most important for antibody binding; a deviation from the subtype-specific amino acids was highly related to mismatched results. In addition, we have shown that phenetic analysis of V3 amino acid sequence data could be used to predict the majority of V3 serotypes (93-94%). Our data demonstrated that for the majority of specimens HIV-1 V3 serotyping results closely match the subtype of the analyzed sample as revealed by the V3 loop amino acid sequence. However, our data demonstrate that HIV-1 serotyping is not sufficiently accurate to predict genetic subtypes in Tanzania, where subtypes A, C, D, and G are circulating. This was due to highly similar amino acid sequences throughout the prevalent genetic subtypes, which caused the inability of HIV-1 V3 serotyping to differentiate subtype A from C as well as D from C. Instead, the serotyping results reflect the frequency distribution of V3 serotypes. To investigate HIV-1 genetic subtypes in population-based studies in this African setting additional or modified algorithms are needed.


PIP: HIV-1 V3 serotyping is used to classify immunodeficiency viruses on the basis of antibody binding to V3 peptides derived from env genetic subtypes. Findings are reported from a study conducted to determine whether HIV-1 serotyping could be effectively used to predict genetic subtypes in an East African setting, where multiple HIV-1 subtypes have coexisted for many years. The HIV-1 genetic subtypes of 86 people with AIDS in Mbeya Town, southwest Tanzania, were determined, using env nucleic acid sequencing as the basis for comparison. Those data were then compared with V3 serotyping results obtained by analysis with tests manufactured by Behring and the Pettenkofer Institute, tests conducted by St. Mary's Hospital Medical School, tests conducted by Georg-Speyer-Haus, and tests conducted by Universite Francois Rabelais. The following HIV-1 genetic subtypes were identified: 25 cases of A (29%), 47 of C (55%), 13 of D (15%), and 1 of G (1%). The sensitivity and specificity of the serotyping assays varied considerably. These data indicate that HIV-1 serotyping is not accurate enough to predict genetic subtypes in Tanzania. This conclusion was reached based upon the highly similar amino acid sequences throughout the prevalent genetic subtypes, which caused the inability of HIV-1 V3 serotyping to differentiate subtype A from C as well as D from C. The serotyping results instead reflect the frequency distribution of V3 serotypes.


Subject(s)
Acquired Immunodeficiency Syndrome/virology , HIV Envelope Protein gp120/genetics , HIV Envelope Protein gp120/immunology , HIV-1/genetics , Peptide Fragments/genetics , Peptide Fragments/immunology , Acquired Immunodeficiency Syndrome/blood , Acquired Immunodeficiency Syndrome/immunology , Amino Acid Sequence , Data Interpretation, Statistical , Enzyme-Linked Immunosorbent Assay , Genotype , HIV Envelope Protein gp120/chemistry , HIV Envelope Protein gp120/classification , HIV-1/immunology , Humans , Molecular Sequence Data , Neural Networks, Computer , Peptide Fragments/chemistry , Peptide Fragments/classification , Protein Structure, Secondary , Serotyping , Tanzania , World Health Organization
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