Factors influencing estimates of HIV-1 infection timing using BEAST

Using viral sequence diversity to estimate time of HIV infection in infants

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

Optimal sequence-based design for multi-antigen HIV-1 vaccines using minimally distant antigens

The immense global diversity of HIV-1 is a significant obstacle to developing a safe and effective vaccine. We recently showed that infections established with multiple founder variants are associated with the development of neutralization breadth years later. We propose a novel vaccine design strategy that integrates the variability observed in acute HIV-1 infections with multiple founder variants. We developed a probabilistic model to simulate this variability, yielding a set of sequences that present the minimal diversity seen in an infection with multiple founders. We applied this model to a subtype C consensus sequence for the Envelope (Env) (used as input) and showed that the simulated Env sequences mimic the mutational landscape of an infection with multiple founder variants, including diversity at antibody epitopes. The derived set of multi-founder-variant-like, minimally distant antigens is designed to be used as a vaccine cocktail specific to a HIV-1 subtype or circulating recombinant form and is expected to promote the development of broadly neutralizing antibodies.

Diverse HIV-1 populations are generally thought to promote neutralizing responses. Current leading HIV-1 vaccine design strategies maximize the distance between antigens to attempt to cover global HIV-1 diversity or serialize immunizations to recapitulate the temporal evolution of HIV-1 during infection. To date, no vaccine has elicited broadly neutralizing antibodies. As we recently demonstrated that infection with multiple HIV-1 founder variants is predictive of neutralization breadth, we propose a novel strategy that endeavors to promote the development of broadly neutralizing antibodies by replicating the diversity of multi-founder variant acute infections. By training an HIV-1 Env consensus sequence on the diversity from acute infections with multiple founders, we derived in silico a set of minimally distant antigens that is representative of the diversity seen in a multi-founder acute infection. As the model is particular to the input sequence, it can produce antigens specific to any HIV-1 subtype or circulating recombinant form (CRF). We applied this to HIV-1 subtype C and obtained a set of minimally distant antigens that can be used as a vaccine cocktail.

HIV-1 infections with multiple founders associate with the development of neutralization breadth

Eliciting broadly neutralizing antibodies (bnAbs) is a cornerstone of HIV-1 vaccine strategies. Comparing HIV-1 envelope (env) sequences from the first weeks of infection to the breadth of antibody responses observed several years after infection can help define viral features critical to vaccine design. We investigated the relationship between HIV-1 env genetics and the development of neutralization breadth in 70 individuals enrolled in a prospective acute HIV-1 cohort. Half of the individuals who developed bnAbs were infected with multiple HIV-1 founder variants, whereas all individuals with limited neutralization breadth had been infected with single HIV-1 founders. Accordingly, at HIV-1 diagnosis, env diversity was significantly higher in participants who later developed bnAbs compared to those with limited breadth (p = 0.012). This association between founder multiplicity and the subsequent development of neutralization breadth was also observed in 56 placebo recipients in the RV144 vaccine efficacy trial. In addition, we found no evidence that neutralization breath was heritable when analyzing env sequences from the 126 participants. These results demonstrate that the presence of slightly different HIV-1 variants in acute infection could promote the induction of bnAbs, suggesting a novel vaccine strategy, whereby an initial immunization with a cocktail of minimally distant antigens would be able to initiate bnAb development towards breadth.

Vaccines against viral pathogens protect through the induction of broadly neutralizing antibodies (bnAbs). No HIV-1 vaccine has successfully elicited bnAbs, and a successful HIV-1 vaccine will need to accelerate the process of development of a broadly neutralizing response that typically takes a couple of years to develop in natural infection. We studied diversity in the HIV-1 envelope gene from initial infection to several years out in 126 individuals from two cohorts. We showed that the development of bnAbs at 2–3 years was not due to transmissible viral genetics, but rather associated with diversity during the first month of infection. We propose that designing a vaccine that mimics an infection with multiple, minimally distant founder variants may successfully elicit the development of bnAbs and provide effective prophylaxis against HIV-1.

Timing HIV infection with a simple and accurate population viral dynamics model

Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials.