Maternal Humoral Immune Correlates of Peripartum Transmission of Clade C HIV-1 in the Setting of Peripartum Antiretrovirals

ABSTRACT Despite the widespread use of antiretrovirals (ARV), more than 150,000 pediatric HIV-1 infections continue to occur annually. Supplemental strategies are necessary to eliminate pediatric HIV infections. We previously reported that maternal HIV envelope-specific anti-V3 IgG and CD4 binding site-directed antibodies, as well as tier 1 virus neutralization, predicted a reduced risk of mother-to-child transmission (MTCT) of HIV-1 in the pre-ARV era U.S.-based Women and Infants Transmission Study (WITS) cohort. As the majority of ongoing pediatric HIV infections occur in sub-Saharan Africa, we sought to determine if the same maternal humoral immune correlates predicted MTCT in a subset of the Malawian Breastfeeding, Antiretrovirals, and Nutrition (BAN) cohort of HIV-infected mothers ( n = 88, with 45 transmitting and 43 nontransmitting). Women and infants received ARV at delivery; thus, the majority of MTCT was in utero (91%). In a multivariable logistic regression model, neither maternal anti-V3 IgG nor clade C tier 1 virus neutralization was associated with MTCT. Unexpectedly, maternal CD4 binding-site antibodies and anti-variable loop 1 and 2 (V1V2) IgG were associated with increased MTCT, independent of maternal viral load. Neither infant envelope (Env)-specific IgG levels nor maternal IgG transplacental transfer efficiency was associated with transmission. Distinct humoral immune correlates of MTCT in the BAN and WITS cohorts could be due to differences between transmission modes, virus clades, or maternal antiretroviral use. The association between specific maternal antibody responses and in utero transmission, which is distinct from potentially protective maternal antibodies in the WITS cohort, underlines the importance of investigating additional cohorts with well-defined transmission modes to understand the role of antibodies during HIV-1 MTCT

Multiple HIV-1-specific IgG3 responses decline during acute HIV-1: implications for detection of incident HIV infection

Different HIV-1 antigen specificities appear in sequence after HIV-1 transmission and the immunoglobulin G (IgG) subclass responses to HIV antigens are distinct from each other. The initial predominant IgG subclass response to HIV-1 infection consists of IgG1 and IgG3 antibodies with a noted decline in some IgG3 antibodies during acute HIV-1 infection. Thus, we postulate that multiple antigen-specific IgG3 responses may serve as surrogates for the relative time since HIV-1 acquisition

Initial antibodies binding to HIV-1 gp41 in acutely infected subjects are polyreactive and highly mutated

Many HIV-1 envelope-reactive antibodies shortly after HIV-1 transmission may arise from crow-reactive memory B cells previously stimulated by non-HIV-1 host or microbial antigen

Polyclonal B Cell Differentiation and Loss of Gastrointestinal Tract Germinal Centers in the Earliest Stages of HIV-1 Infection

The antibody response to HIV-1 does not appear in the plasma until approximately 2–5 weeks after transmission, and neutralizing antibodies to autologous HIV-1 generally do not become detectable until 12 weeks or more after transmission. Moreover, levels of HIV-1–specific antibodies decline on antiretroviral treatment. The mechanisms of this delay in the appearance of anti-HIV-1 antibodies and of their subsequent rapid decline are not known. While the effect of HIV-1 on depletion of gut CD4+ T cells in acute HIV-1 infection is well described, we studied blood and tissue B cells soon after infection to determine the effect of early HIV-1 on these cells

Computational analysis of antibody dynamics identifies recent HIV-1 infection.

CAPRISA, 2017.Abstract available in pdf

Pentavalent HIV-1 vaccine protects against simian-human immunodeficiency virus challenge

The RV144 Thai trial HIV-1 vaccine of recombinant poxvirus (ALVAC) and recombinant HIV-1 gp120 subtype B/subtype E (B/E) proteins demonstrated 31% vaccine efficacy. Here we design an ALVAC/Pentavalent B/E/E/E/E vaccine to increase the diversity of gp120 motifs in the immunogen to elicit a broader antibody response and enhance protection. We find that immunization of rhesus macaques with the pentavalent vaccine results in protection of 55% of pentavalent-vaccine-immunized macaques from simian–human immunodeficiency virus (SHIV) challenge. Systems serology of the antibody responses identifies plasma antibody binding to HIV-infected cells, peak ADCC antibody titres, NK cell-mediated ADCC and antibody-mediated activation of MIP-1β in NK cells as the four immunological parameters that best predict decreased infection risk that are improved by the pentavalent vaccine. Thus inclusion of additional gp120 immunogens to a pox-prime/protein boost regimen can augment antibody responses and enhance protection from a SHIV challenge in rhesus macaques

Strain-Specific V3 and CD4 Binding Site Autologous HIV-1 Neutralizing Antibodies Select Neutralization-Resistant Viruses.

The third variable (V3) loop and the CD4 binding site (CD4bs) of the HIV-1 envelope are frequently targeted by neutralizing antibodies (nAbs) in infected individuals. In chronic infection, HIV-1 escape mutants repopulate the plasma, and V3 and CD4bs nAbs emerge that can neutralize heterologous tier 1 easy-to-neutralize but not tier 2 difficult-to-neutralize HIV-1 isolates. However, neutralization sensitivity of autologous plasma viruses to this type of nAb response has not been studied. We describe the development and evolution in vivo of antibodies distinguished by their target specificity for V3 and CD4bs epitopes on autologous tier 2 viruses but not on heterologous tier 2 viruses. A surprisingly high fraction of autologous circulating viruses was sensitive to these antibodies. These findings demonstrate a role for V3 and CD4bs antibodies in constraining the native envelope trimer in vivo to a neutralization-resistant phenotype, explaining why HIV-1 transmission generally occurs by tier 2 neutralization-resistant viruses

Strain-Specific V3 and CD4 Binding Site Autologous HIV-1 Neutralizing Antibodies Select Neutralization-Resistant Viruses

The third variable (V3) loop and the CD4 binding site (CD4bs) of the HIV-1 envelope are frequently targeted by neutralizing antibodies (nAbs) in infected individuals. In chronic infection, HIV-1 escape mutants repopulate the plasma, and V3 and CD4bs nAbs emerge that can neutralize heterologous tier 1 easy-to-neutralize, but not tier 2 difficult-to-neutralize HIV-1 isolates. However, neutralization sensitivity of autologous plasma viruses to this type of nAb response has not been studied. We describe the development and evolution in vivo of antibodies distinguished by their target specificity for V3and CD4bs epitopes on autologous tier 2 viruses but not on heterologous tier 2 viruses. A surprisingly high fraction of autologous circulating viruses was sensitive to these antibodies. These findings demonstrate a role for V3 and CD4bs antibodies in constraining the native envelope trimer in vivo to a neutralization-resistant phenotype, explaining why HIV-1 transmission generally occurs by tier 2 neutralization-resistant viruses

Marginal modeling in community randomized trials with rare events: Utilization of the negative binomial regression model

This work is motivated by the HEALing Communities Study, which is a post-test only cluster randomized trial in which communities are randomized to two different trial arms. The primary interest is in reducing opioid overdose fatalities, which will be collected as a count outcome at the community level. Communities range in size from thousands to over one million residents, and fatalities are expected to be rare. Traditional marginal modeling approaches in the cluster randomized trial literature include the use of generalized estimating equations with an exchangeable correlation structure when utilizing subject-level data, or analogously quasi-likelihood based on an over-dispersed binomial variance when utilizing community-level data. These approaches account for and estimate the intra-cluster correlation coefficient, which should be provided in the results from a cluster randomized trial. Alternatively, the coefficient of variation or R coefficient could be reported. In this article, we show that negative binomial regression can also be utilized when communities are large and events are rare. The objectives of this article are (1) to show that the negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model and to explain why the estimates may differ; (2) to derive formulas relating the negative binomial overdispersion parameter k with the intra-cluster correlation coefficient, coefficient of variation, and R coefficient; and (3) analyze pre-intervention data from the HEALing Communities Study to demonstrate and contrast models and to show how to report the intra-cluster correlation coefficient, coefficient of variation, and R coefficient when utilizing negative binomial regression. Negative binomial and over-dispersed binomial regression modeling are contrasted in terms of model setup, regression parameter estimation, and formulation of the overdispersion parameter. Three specific models are used to illustrate concepts and address the third objective. The negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model, although estimates may differ. Practical differences arise in regard to how overdispersion, and hence the intra-cluster correlation coefficient is modeled. The negative binomial overdispersion parameter is approximately equal to the ratio of the intra-cluster correlation coefficient and marginal probability, the square of the coefficient of variation, and the R coefficient minus 1. As a result, estimates corresponding to all four of these different types of overdispersion parameterizations can be reported when utilizing negative binomial regression. Negative binomial regression provides a valid, practical, alternative approach to the analysis of count data, and corresponding reporting of overdispersion parameters, from community randomized trials in which communities are large and events are rare