Data_Sheet_1.PDF

<p>Antibody subclasses exhibit extensive polymorphisms (allotypes) that could potentially impact the quality of HIV-vaccine induced B cell responses. Allotypes of immunoglobulin (Ig) G1, the most abundant serum antibody, have been shown to display altered functional properties in regard to serum half-life, Fc-receptor binding and FcRn-mediated mucosal transcytosis. To investigate the potential link between allotypic IgG1-variants and vaccine-generated humoral responses in a cohort of 14 HIV vaccine recipients, we developed a novel protocol for rapid IgG1-allotyping. We combined PCR and ELISA assays in a dual approach to determine the IgG1 allotype identity (G1m3 and/or G1m1) of trial participants, using human plasma and RNA isolated from PBMC. The IgG1-allotype distribution of our participants mirrored previously reported results for caucasoid populations. We observed elevated levels of HIV gp140-specific IgG1 and decreased IgG2 levels associated with the G1m1-allele, in contrast to G1m3 carriers. These data suggest that vaccinees homozygous for G1m1 are predisposed to develop elevated Ag-specific IgG1:IgG2 ratios compared to G1m3-carriers. This elevated IgG1:IgG2 ratio was further associated with higher FcγR-dimer engagement, a surrogate for potential antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) function. Although preliminary, these results suggest that IgG1 allotype may have a significant impact on IgG subclass distribution in response to vaccination and associated Fc-mediated effector functions. These results have important implications for ongoing HIV vaccine efficacy studies predicated on engagement of FcγR-mediated cellular functions including ADCC and ADCP, and warrant further investigation. Our novel allotyping protocol provides new tools to determine the potential impact of IgG1 allotypes on vaccine efficacy.</p

IgG isolated from individuals that develop bNAbs shows increased gp120-specific binding to Fc receptors and complement proteins.

<p>(<b>A</b>) Binding gp120 ConC-specific IgG isolated from bNAb (red) and no-bNAb (blue) individuals to Fc receptors and C1q measured by an antigen-specific Fc receptor multiplex array. Significant differences (calculated by Mann-Whitney U test) in binding are shown as *p<0.05; **p<0.001; ***p<0.0001. Data are representative of 2 independent experiments. (<b>B</b>) The ratio of activating FcγRIIa (either H131 or R131) to inhibitory FcγRIIb receptor binding at 6 months post infection for bNAb and no-bNAb individuals. Medians are shown and significance was calculated by the Mann-Whitney U test. (<b>C</b>) Correlations between ADCT or ADCD and binding to Fc receptors and C1q shown as MFI. Significant Spearman´s correlation coefficients are indicated. Lines indicate the trend of the correlations.</p

Multivariate classifications reveal that individuals who develop bNAbs can be reliably identified by their Fc features at 6 months of infection.

<p>(<b>A</b>) Principal components analysis of 13 bNAb (red) and 10 no-bNAb (blue) using 17 variables. Individual CAPRISA identifiers are shown, with component 1 and 2 explaining 52.3% of the variance in the data set. (<b>B</b>) Confusion matrix showing the classification of bNAb and no-bNAb individuals achieved by random forest classification. Shown are the numbers of individuals for each predicted or observed group with correct classifications indicated in color and misclassifications indicated in white. The 2 bNAb (CAP257 and CAP292) and 2 no-bNAb (CAP88 and CAP228) individuals that were incorrectly classified can be seen in 5A. (<b>C</b>) Importance of the features employed in the random forest classification is indicated by the mean decrease in Gini importance weighting. (<b>D</b>) The model was verified by permutation testing following random shuffling of the classification data 100,000 times. The dashed line indicates the accuracy of the proposed model (82.6%), with shuffles resulting in accuracy greater than this shown as a proportion of the total shuffles (0.38%).</p

bNAb individuals have higher HIV-specific IgG subclass diversity.

<p>(<b>A</b>) A multiplex assay was used to measure levels of HIV-specific IgG subclasses present in 6 month samples from bNAb and no-bNAb individuals to 12 different HIV antigens. Median abundance of antigen-specific IgG2, IgG3 and IgG4 (orange, yellow and purple respectively) are represented as a ratio to IgG1 calculated using median fluorescence intensities. Data are representative of 2 independent experiments. Spearman´s correlations between subclass diversity score and (<b>B</b>) neutralization breadth and (<b>C</b>) Fc polyfunctionality are shown. The score was calculated as the ratio of gp120 ConC IgG2 and IgG4 relative to IgG1 levels. bNAb individuals are shown in red and no-bNAb in blue with dotted trend lines.</p

Fc effector function early in HIV infection is higher in individuals that develop bNAbs.

<p>(<b>A</b>) Purified IgG from 13 bNAb, 10 no-bNAb and 5 HIV-negative individuals (in red, blue and grey respectively) at 6 months post-infection was tested for antibody dependent cellular phagocytosis (ADCP), complement deposition (ADCD), cellular trogocytosis (ADCT) and cellular cytoxicity (ADCC) using three HIV-specific antigens gp120 ConC, gp140 C.ZA.1197MB and gp120 CAP45.G3. Significant differences between groups determined by the Mann-Whitney U test are indicated by *p<0.05; **p<0.001. (<b>B</b>) Medians and IQR of different Fc effector functions for bNAb and no-bNAb individuals against all tested antigens over 36 months of infection are indicated as cumulative Fc effector function. Data are representative of 3 independent experiments. (<b>C</b>) Each Fc function was standardized by calculating a Z-score and polyfunctionality determined by addition of the Z-scores for all functions for each individual. Bars above the x-axis indicate Fc polyfunctional individuals, while those below indicate poor Fc polyfunctionality. bNAb and no-bNAb individuals are indicated in red and blue respectively. (<b>D</b>) Spearman´s correlation coefficient for the relationship between the Fc polyfunctionality Z-score and % neutralization breadth calculated by a 44 multi-clade virus panel is shown. The dashed diagonal line indicates the trend of the relationship.</p

Geographic location affects bulk IgG glycosylation.

<p>Bulk IgG glycosylation was assessed in subjects from three regions: Unite States (blue, n = 43), Kenya and Rwanda (maroon, n = 69), and South Africa (yellow, n = 47). (A) Bulk antibody Fc glycosylation in vaccine recipients from each of the three regions was measured via capillary electrophoresis, and the mean proportion of total galactosylated, sialylated, fucosylated, and bisected structures was compared using Kruskal-Wallis one-way ANOVA (*<i>p</i><0.05, **<i>p</i><0.01, ***<i>p</i><0.001, ****<i>p</i><0.0001). (B) Multivariate comparison of antibody Fc glycosylation among the three geographic sites was performed using PCA. The score plot (left panel) depicts the principal component analysis of samples collected in the three regions (each dot represents a vaccinee, and colors are as described above), and the loadings plot of the PCA (right panel) shows the contribution of particular glycan structures to driving the observed separation, where longer arrows signify a greater contribution to separating glycan profiles. This PCA describes 55% of the total variance among these samples.</p

Vaccine-elicited antibody glycosylation profiles are distinct from bulk antibody glycosylation.

<p>Viral vector–induced gp120-specific and influenza specific antibodies were isolated from vaccinees, and the attached glycans were analyzed by capillary electrophoresis. (A) Multivariate PCA was used to compare bulk antibody glycoprofiles (blue, n = 32) and vaccine-elicited antigen-specific antibody glycoprofiles (maroon, n = 20), and both the scores plot (left) and loadings plot (right) are shown. This analysis describes 69% of the variation. (B) The mean proportions of bulk and vaccine-elicited antibody glycan were compared using students two-tailed paired t tests (n = 13 for bulk, n = 20 for gp120 (*<i>p</i><0.05, **<i>p</i><0.01, ***<i>p</i><0.001, ****<i>p</i><0.0001) (C) The mean proportions of vaccine-elicited antibody glycan structures were compared across vaccine groups using Kruskal-Wallis ANOVA (n = 9 for United States, n = 6 for Kenya/Rwanda, n = 4 for South Africa). No statistically significant differences were found. (D) The mean proportions of influenza-specific antibody glycans at baseline (magenta), post-first (green), and post-boost (purple) vaccine timepoints were compared using non-parametric two-way ANOVA (n = 18 for United States, n = 11 for Kenya/Rwanda, n = 5 for South Africa). No significant differences were found between the three timepoints for either antigen or glycan type.</p

Different vaccines induce distinct vaccine-elicited antibody glycosylation profiles.

<p>(A) The antigen-specific IgG glycans from the B003/IPCAVD-004/HVTN 091 (blue) and VAX003 (maroon) studies were compared using PCA. This analysis described 56% of the variation. (B) The mean gp120-specific glycan profiles induced by the B003/IPCAVD-004/HVTN 091 (n = 19) and VAX003 (n = 17) trials were compared using the Mann-Whitney <i>U</i> test (*<i>p</i><0.05, **<i>p</i><0.01, ***<i>p</i><0.001, ****<i>p</i><0.0001).</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