We report, for the first time, prediction of resistance to 33 different bnAbs using HIV-1 proviral sequences from adults with HIV-1 seroconversion in Botswana, using a readily available in silico approach (bNab-ReP)[15]. There are limited data on the diversity of HIV-1 env and how it relates to probability of the successful use of bnAbs as immunotherapies in sub-Saharan African countries. We here report on the variability of the HIV env characteristics that may affect susceptibility and potential use of these bnAbs in Botswana, including signature mutations that may facilitate bnAb escape in the setting of HIV-1 subtype C.
Generally, predicted bnAb resistance varied among different bnAbs even if they were of the same binding type class, indicating variability in the potential neutralization capabilities of different bnAbs in the HIV-1C setting. For instance, we observe high rates of predicted resistance to 2F5 and no resistance to 4E10 among the two evaluated MPER binding site bnAbs. Similarly, the prediction of resistance also varied among other classes, including V2 apex and V3 binding bnAbs. Consistent with other studies that have analyzed phenotypic neutralization of HIV-1C envelopes[9, 25], we observed diversity in the predicted response to bnAbs that target V2. Recently, Mandzivo et al. 2022 demonstrated differences in neutralization breath of different bnAbs; 2G12 (23%) compared to 10-1074 (80%) among V3 binding bnAbs, as well as 3BNC117 (59%) compared to VRC07-LS (100%) among CD4 binding bnAbs in single env genomes from nine South African individuals followed longitudinally post-acute HIV-1 infection [26]. The authors also describe that transmitted founder viruses were observed to be more resistant to VRC01 and sensitive to PGDM1400, but a reverse observation is seen for more chronic infections, and we observed similar results for adults with documented seroconversion. In Botswana, several studies have evaluated VRC01 and 10-1074 bnAbs in both adult and infant populations showing moderate to good neutralization breath, especially for 10-1074 [27, 28]; we demonstrate that the prediction model of choice was able to predict similar results in the setting of adults with HIV-1 seroconversion, further highlighting the potential use of machine learning and artificial intelligence to determine bnAbs to use in different clinical settings.
Assessing N-linked glycans found in PNG sites in HIV-1 is important as they take up an average 50% of total mass of gp120 and are used by the virus to escape bnAb neutralization and increase viral pathogenesis [29]. Our data reveal high conservation of signature PNG sites across HIV-1C sequences, and higher levels of PNG sites were mostly associated with V3 binding bnAbs. We observed highly conserved PNG sites: N88, N156, N160, N197, N241, N262, N276, N289, N301, N386, N448. These data agree with results from Sutar et al. 2021, who evaluated the geospatial differences in HIV-1C genetic signatures and what attributes differentiate region-specific HIV-1C with virus neutralization to key bnAbs using full-length sequences across 37 different countries (including Botswana) retrieved form the Los Alamos National Laboratory HIV database. They reported that N88, N156, N160, N197, N276, N301 and N386 were observed to be highly conserved in all countries with over 70% abundance, where N301 was the most conserved (89–100%). Similarly, in our cohort, N301 conservation was 95% among our sequences, with N262 as the most conserved (100%) PNG site which has been previously predicted to interact with PGT151[30]. Furthermore, Sutar et al. 2021 also observed significant differences in PNG sites from India and South Africa, where N130, N295, N392 and N448 which represent C1,C2,V4 and C4, these sites are responsible for the integrity of the mannose patch as well as associations with bnAbs such as 2G12, VRC-PG05, PGT135 and PGT135 [29]. We observe low conservation at PNG site N295, which is one of the sites where glycans that interact with V3 bnAbs are often positive for neutralization; this supports our finding of significant differences between resistant and sensitive strains within the V3 binding bnAbs. However, the extreme variability of V3 positions 336–442 may lead to inconclusive observations in terms of bnAb sensitivity[22].
Traditionally, variable region characteristics of HIV env have been used to inform HIV vaccine designs, where the higher variation requires a robust and potent intervention. Our results report minor differences in variable characteristics by predicted bnAb resistance, although these differences which were most likely to affect bnAb sensitivity were observed in V1, V4 and V5. The variations observed in these sites have been reported to have impact on neutralization breath of majority of different bnAbs, this has been the main driver for development of bnAb cocktails targeting different regions of the env gene for treatment and prevention of HIV-1[31–33].
The presence of mutations in majority of sequences with high levels of predicted bnAb resistance has been a general observation for several other studies[5, 22, 25]. We observe similar results to what has been observed in different studies done both in vitro and in silico[34], where introduction of several mutations in the env backbone of most HIV-1 pseudoviruses has led to reduced potency and neutralization breath of several bnAbs. We also reported the T234N which has been a glycosylation site associated with resistance to several CD4 binding bnAbs[22, 35, 36], including strong associations with HIV-1 vertical transmission[37].
Our study had several limitations. We used the bNab-ReP tool to predict HIV-1 resistance/sensitivity to 33 different known bnAbs. Although the tool has been shown to have a great correlation between in silico predictions and in vitro data through the CATNAP database (96% prediction accuracy)[15], several newer algorithms have been deposited in Github which may yield more accurate results by updating the tools with other predictors selected apriori through time[13]. Although this is a limitation, it also highlights the need to develop within-country, regional and subtype-specific algorithms for such predictions with the hope of getting predictions that are able to be used across all subtypes and easier to perform by end-users in resource-limited countries. We only evaluated a subset of mutations for a subset of bnAbs of interest, and future studies will include HIV-1C-specific mutations that correlate with phenotypic data demonstrating actual resistance caused by these mutations. Another limitation is the lack of phenotype data from Botswana or from our current cohort of interest, as we are unable to correlate genotypic prediction data with in vitro neutralization which may allow us to draw more firm conclusions. The strengths of our analyses include the ability to use a readily available prediction tool using sequences from recent seroconversions in Botswana. The predictions of resistance and sensitivity/susceptibility shown in our results are in line with what has already been published for teams that used phenotypic assays within the region to detect resistance to several bnAbs.