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Stimulating broadly neutralizing antibodies (bnAbs) directly from germline remains a barrier for HIV vaccines. HIV superinfection elicits bnAbs more frequently than single infection, providing clues of how to elicit such responses. We used longitudinal antibody sequencing and structural studies to characterize bnAb development from a superinfection case. BnAb QA013.2 bound initial and superinfecting viral Env, despite its probable naive progenitor only recognizing the superinfecting strain, suggesting both viruses influenced this lineage. A 4.15 Å cryo-EM structure of QA013.2 bound to native-like trimer showed recognition of V3 signatures (N301/N332 and GDIR). QA013.2 relies less on CDRH3 and more on framework and CDRH1 for affinity and breadth compared to other V3/glycan-specific bnAbs. Antigenic profiling revealed that viral escape was achieved by changes in the structurally-defined epitope and by mutations in V1. These results highlight shared and novel properties of QA013.2 relative to other V3/glycan-specific bnAbs in the setting of sequential, diverse antigens.

A prerequisite for the design of an HIV vaccine that elicits protective antibodies is understanding the developmental pathways that result in desirable antibody features. The development of antibodies that mediate antibody-dependent cellular cytotoxicity (ADCC) is particularly relevant because such antibodies have been associated with HIV protection in humans. We reconstructed the developmental pathways of six human HIV-specific ADCC antibodies using longitudinal antibody sequencing data. Most of the inferred naive antibodies did not mediate detectable ADCC. Gain of antigen binding and ADCC function typically required mutations in complementarity determining regions of one or both chains. Enhancement of ADCC potency often required additional mutations in framework regions. Antigen binding affinity and ADCC activity were correlated, but affinity alone was not sufficient to predict ADCC potency. Thus, elicitation of broadly active ADCC antibodies may require mutations that enable high-affinity antigen recognition along with mutations that optimize factors contributing to functional ADCC activity.

A reservoir of HIV-infected cells that persists despite suppressive antiretroviral therapy (ART) is the source of viral rebound upon ART cessation and the major barrier to a cure. Understanding reservoir seeding dynamics will help identify the best timing for HIV cure strategies. Here we characterize reservoir seeding using longitudinal samples from before and after ART initiation in individuals who sequentially became infected with genetically distinct HIV variants (superinfected). We previously identified cases of superinfection in a cohort of Kenyan women, and the dates of both initial infection and superinfection were determined. Six women, superinfected 0.2-5.2 years after initial infection, were subsequently treated with ART 5.4-18.0 years after initial infection. We performed next-generation sequencing of HIV gag and env RNA from plasma collected during acute infection as well as every ~2 years thereafter until ART initiation, and of HIV DNA from PBMCs collected 0.9-4.8 years after viral suppression on ART. We assessed phylogenetic relationships between HIV DNA reservoir sequences and longitudinal plasma RNA sequences prior to ART, to determine proportions of initial and superinfecting variants in the reservoir. The proportions of initial and superinfection lineage variants present in the HIV DNA reservoir were most similar to the proportions present in HIV RNA immediately prior to ART initiation. Phylogenetic analysis confirmed that the majority of HIV DNA reservoir sequences had the smallest pairwise distance to RNA sequences from timepoints closest to ART initiation. Our data suggest that while reservoir cells are created throughout pre-ART infection, the majority of HIV-infected cells that persist during ART entered the reservoir near the time of ART initiation. We estimate the half-life of pre-ART DNA reservoir sequences to be ~25 months, which is shorter than estimated reservoir decay rates during suppressive ART, implying continual decay and reseeding of the reservoir up to the point of ART initiation.

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.