RESUMO
We used HIV-1C sequences to predict (in silico) resistance to 33 known broadly neutralizing antibodies (bnAbs) and evaluate the different HIV-1 Env characteristics that may affect virus neutralization. We analyzed proviral sequences from adults with documented HIV-1 seroconversion (N = 140) in Botswana (2013-2018). HIV-1 env sequences were used to predict bnAb resistance using bNAb-ReP, to determine the number of potential N-linked glycosylation sites (PNGS) and evaluate Env variable region characteristics (VC). We also assessed the presence of signature mutations that may affect bnAb sensitivity in vitro. We observe varied results for predicted bnAb resistance among our cohort. 3BNC117 showed high predicted resistance (72%) compared to intermediate levels of resistance to VRC01 (57%). We predict low resistance to PGDM100 and 10-1074 and no resistance to 4E10. No difference was observed in the frequency of PNGS by bNAb susceptibility patterns except for higher number of PNGs in V3 bnAb resistant strains. Associations of VC were observed for V1, V4 and V5 loop length and net charge. We also observed few mutations that have been reported to confer bnAb resistance in vitro. Our results support use of sequence data and machine learning tools to predict the best bnAbs to use within populations.
Assuntos
Infecções por HIV , Soropositividade para HIV , HIV-1 , Humanos , Adulto , Anticorpos Amplamente Neutralizantes , Anticorpos Neutralizantes , Anticorpos Anti-HIV , HIV-1/genética , Botsuana , Produtos do Gene env do Vírus da Imunodeficiência Humana , EpitoposRESUMO
We used HIV-1C sequences to predict (in silico) resistance to 33 known broadly neutralizing antibodies (bNAbs) and evaluate the different HIV-1 env characteristics that may affect virus neutralization. We analyzed proviral sequences from adults with documented HIV-1 seroconversion (N=140) in Botswana (2013-2018). HIV-1 env sequences were used to predict bnAb resistance using bNAb-ReP, to determine the number of potential N-linked glycosylation sites (PNGS) and evaluate env variable region characteristics (VC). We also assessed the presence of signature mutations that may affect bnAb sensitivity in vitro. We observe varied results for predicted bnAb resistance among our cohort. 3BNC117 showed high predicted resistance (72%) compared to intermediate levels of resistance to VRC01 (57%). We predict low resistance to PGDM100 and 10-1074 and no resistance to 4E10. No difference was observed in the frequency of PNGS by bNAb susceptibility patterns except for higher number of PNGs in V3 bnAb resistant strains. Associations of VC were observed for V1, V4 and V5 loop length and net charge. We also observed few mutations that have been reported to confer bnAb resistance in vitro. Our results support use of sequence data and machine learning tools to predict the best bnAbs to use within populations.
