Divergent Antibody Subclass and Specificity Profiles but Not Protective HLA-B Alleles Are Associated with Variable Antibody Effector Function among HIV-1 Controllers

Understanding the coordination between humoral and cellular immune responses may be the key to developing protective vaccines, and because genetic studies of long-term HIV-1 nonprogressors have associated specific HLA-B alleles with spontaneous control of viral replication, this subject group presents an opportunity to investigate relationships between arms of the adaptive immune system. Given evidence suggesting that cellular immunity may play a role in viral suppression, we sought to determine whether and how the humoral immune response might vary among controllers. Significantly, Fc-mediated antibody effector functions have likewise been associated with durable viral control. In this study, we compared the effector function and biophysical features of HIV-specific antibodies in a cohort of controllers with and without protective HLA-B alleles in order to investigate whether there was evidence for multiple paths to HIV-1 control, or whether cellular and humoral arms of immunity might exhibit coordinated profiles. However, with the exception of IgG2 antibodies to gp41, HLA status was not associated with divergent humoral responses. This finding did not result from uniform antibody responses across subjects, as controllers could be regrouped according to strong differences in their HIV-specific antibody subclass specificity profiles. These divergent antibody profiles were further associated with significant differences in nonneutralizing antibody effector function, with levels of HIV-specific IgG1 acting as the major distinguishing factor. Thus, while HLA background among controllers was associated with minimal differences in humoral function, antibody subclass and specificity profiles were associated with divergent effector function, suggesting that these features could be used to make functional predictions. Because these nonneutralizing antibody activities have been associated with spontaneous viral control, reduced viral load, and nonprogression in infected subjects and protection in vaccinated subjects, understanding the specific features of IgGs with potentiated effector function may be critical to vaccine and therapeutic antibody development

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.U.S. Military HIV Research ProgramCollaboration for AIDS Vaccine Discover (OPP1032817)National Institutes of Health (U.S.) (3R01AI080289-02S1)National Institutes of Health (U.S.) (5R01AI080289-03)United States. Army Medical Research and Materiel Command (National Institute of Allergy and Infectious Diseases (U.S.) Interagency Agreement Y1-AI-2642-12)Henry M. Jackson Foundation for the Advancement of Military Medicine (U.S.) (United States. Dept. of Defense Cooperative Agreement W81XWH-07-2-0067

Natural Variation in Fc Glycosylation of HIV-Specific Antibodies Impacts Antiviral Activity

While the induction of a neutralizing antibody response against HIV remains a daunting goal, data from both natural infection and vaccine-induced immune responses suggest that it may be possible to induce antibodies with enhanced Fc effector activity and improved antiviral control via vaccination. However, the specific features of naturally induced HIV-specific antibodies that allow for the potent recruitment of antiviral activity and the means by which these functions are regulated are poorly defined. Because antibody effector functions are critically dependent on antibody Fc domain glycosylation, we aimed to define the natural glycoforms associated with robust Fc-mediated antiviral activity. We demonstrate that spontaneous control of HIV and improved antiviral activity are associated with a dramatic shift in the global antibody-glycosylation profile toward agalactosylated glycoforms. HIV-specific antibodies exhibited an even greater frequency of agalactosylated, afucosylated, and asialylated glycans. These glycoforms were associated with enhanced Fc-mediated reduction of viral replication and enhanced Fc receptor binding and were consistent with transcriptional profiling of glycosyltransferases in peripheral B cells. These data suggest that B cell programs tune antibody glycosylation actively in an antigen-specific manner, potentially contributing to antiviral control during HIV infection

Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity

While the induction of a neutralizing antibody response against HIV remains a daunting goal, data from both natural infection and vaccine-induced immune responses suggest that it may be possible to induce antibodies with enhanced Fc effector activity and improved antiviral control via vaccination. However, the specific features of naturally induced HIV-specific antibodies that allow for the potent recruitment of antiviral activity and the means by which these functions are regulated are poorly defined. Because antibody effector functions are critically dependent on antibody Fc domain glycosylation, we aimed to define the natural glycoforms associated with robust Fc-mediated antiviral activity. We demonstrate that spontaneous control of HIV and improved antiviral activity are associated with a dramatic shift in the global antibody-glycosylation profile toward agalac-tosylated glycoforms. HIV-specific antibodies exhibited an even greater frequency of agalactosylated, afucosylated, and asialylated glycans. These glycoforms were associated with enhanced Fc-mediated reduction of viral replication and enhanced Fc receptor binding and were consistent with transcriptional profiling of glycosyl-transferases in peripheral B cells. These data suggest that B cell programs tune antibody glycosylation actively in an antigen-specific manner, potentially contributing to antiviral control during HIV infection.</p

Classification of ADCP from antibody features by penalized logistic regression.

<p>(A-F) Prediction results by 200-replicate five-fold cross-validation, illustrating PLR values (>0.5 predicted high ADCP; <0.5 predicted low) for one replicate (A,C,E) and providing area under the ROC curve (AUC) over all 200 replicates (B,D,F). Box & whisker plots show the median (thick center line), upper and lower quartiles (box), and 1.5 times the interquartile range (whiskers); all points are also plotted in a jittered stripchart. Colors for the classification examples indicate high (red) and low (blue) observed ADCP. (G-I) Coefficients and p-values of the features for a model trained on all subjects. Different input features were used in classification: (A,B,G) the complete set; (C,D,H) the filtered set; (E,F,I) the principal components. Colors for the feature coefficients indicate antibody subclass and antigen-specificity. For convenience, a red line is drawn at p = 0.05.</p

Regression modeling of ADCP from antibody features by Lars.

<p>(A,C,E) Representative regression scatterplot based on leave-one-out cross-validation, (B,D,F) PCCs for 200-replicate five-fold cross-validation. (G-I) Coefficients and p-values of the features for a model trained on all subjects. Different input features were used: (A,B,G) the complete set; (C,D,H) the filtered set; (E,F,I) the principal components. Box & whisker plots show the median (thick center line), upper and lower quartiles (box), and 1.5 times the interquartile range (whiskers); all points are also plotted in a jittered stripchart. Colors for the feature coefficients indicate antibody subclass and antigen-specificity.</p

Input data.

<p>For each of 80 vaccinated subjects (rows), measurements of (A) 20 antibody features (4 IgG subclasses with 5 antigen specificities) and (B) 3 effector functions. The heatmap colors indicate relative values within each column, standardized to a mean of 0, a standard deviation of 1, and truncated at 6σ. Color blocks above the antibody feature columns indicate IgG subclass and antigen specificity.</p

Unsupervised analysis of antibody features and functions.

<p>(A) Antibody feature:function correlations. IgG subclass and antigen specificity are indicated by color blocks. Cell colors indicate Pearson correlation coefficients (PCC), and p-values are represented by asterisks (* < = 0.05; ** < = 0.01; *** < = 0.001). (B) Feature:feature correlations, hierarchically clustered. Antibody feature color blocks, PCCs, and significances are denoted as in (A). Bisecting the dendrogram, as shown by the red line, results in 6 antigen.subclass clusters, each also denoted in the figure by a box. For each function, one feature was selected (starred: blue-ADCP; yellow-ADCC; green-cytokines) from each cluster to yield the filtered feature set. (C) Eigenvectors from principal component analysis. Cell colors indicate feature coefficients in the eigenvectors. Antibody feature color blocks are as in (A).</p