Divergent antibody subclass and specificity profiles but not protective HLA-B alleles are associated with variable antibody effector function among HIV-1 controllers

Abstract

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.

Importance: In this study, we investigated whether the humoral and cellular arms of adaptive immunity exhibit coordinated or compensatory activity by studying the antibody response among HIV-1 controllers with different genetic backgrounds.

FIG 1

IgG2 responses to gp41. A comparison of antibody properties between elite controllers with (P) and without (NP) protective HLA-B alleles revealed only a significant difference in IgG2 antibodies against gp41 (P = 0.03, uncorrected; significance threshold of 0.0018 following Bonferroni correction is unmet). All other antibody properties tested (n = 27) showed no significant differences between these HLA-defined groups.

FIG 2

Antibody subclass specificity correlation matrix defines new subject clusters. Correlations between subjects were calculated based on the 16 antibody subclass specificity features measured on the customized microsphere array. Clustering patients according to their Pearson correlation coefficients, performed by Ward linkage, revealed two well-differentiated groups of subjects marked by the two branches of the dendrogram (labeled H and L). Subjects with (p) or without (np) protective alleles were evenly distributed between branches.

FIG 3

Array-defined subject groups distinguish antibody function. Comparison of functional measurements showed that the array-defined subject groups (blue, designated H and L) distinguish patients by phagocytic activity (A), NK cell-mediated cytotoxicity (B), and MBL (C) and FcγR2a (D) binding. (E to H) No differences in these functions are observed comparing patients based on HLA status (protective [P] and nonprotective [NP]; in green). The Bonferroni-corrected significance threshold given multiple comparisons is 0.0125.

FIG 4

Array-defined groups are distinguished by HIV-specific IgG1 levels. (A) A clustering analysis of patients and features based on scaled and centered antibody subclass specificity measurements is represented by a heat map. Notably, all antigen-specific IgG1 features clustered together, with patients grouped consistently with differences in the levels of HIV-specific IgG1 antibodies. (B to E) Closer inspection of the array-defined groups showed that groups were differentiated by high (H) and low (L) IgG1 levels of antibodies to gp120 (B), gp140 (C), gp41 (D), and p24 (E). No difference was observed between HLA-defined groups of ECs with (P) or without (NP) protective alleles (F to I) or among other antibody subclasses (***, P < 0.0001). The Bonferroni-corrected significance threshold given multiple comparisons is 0.0125.

FIG 5

HIV-specific IgG1 levels correlate with enhanced effector function and elevation of total plasma IgG1. (A) Correlation coefficient plot of HIV-specific IgG1 versus phagocytosis (ADCP), cytotoxic activity (RFADCC), and the percentage of bulk plasma IgG of the IgG1 subclass (*, P < 0.05; **, P < 0.01; ***, P < 0.001). (B to D) Scatterplots of gp120-specific IgG1 with ADCP (B), RFADCC (C), and plasma IgG1 levels as a percentage of total plasma IgG (D).

FIG 6

Correlations between bulk IgG subclass measurements and functional measurements. Shown are orrelation coefficients and scatterplots of bulk plasma IgG composition, bulk plasma IgG FcγR binding, IgG fucosylation (AAL binding), IgG agalactosylation (MBL binding), ADCP and RFADCC activity, and subject CD4 count. Strength of correlation is represented with color, with orange representing strong negative correlations and blue representing strong positive correlations. Relationships between bulk plasma IgG subclasses are boxed in green; %IgG1 and %IgG3 showed strong positive correlation, while %IgG1 and %IgG2 showed strong negative correlation. Relationships between FcγR binding profiles are boxed in red; binding to FcγR2A, FcγR2B, and FcγR3A showed strong positive correlation and no relationship with the high-affinity FcγR1 receptor. Relationships between bulk plasma IgG subclasses and FcγR binding are boxed in blue; %IgG3 showed strong positive correlation with binding to all FcγRs despite its low serum abundance. %IgG1 showed strong correlation with binding to FcγR1, while %IgG2 showed strong negative correlation to FcγR1 binding. Phagocytic activity (ADCP) was positively associated with IgG1 and IgG3 levels and binding to FcγR1 and FcγR2B. Cytolytic activity (RFADCC) was positively associated most strongly with binding to FcγR3A and inversely with antibody fucosylation as measured by binding to the fucose-sensitive lectin AAL (*, P < 0.05; **, P < 0.01; ***, P < 0.001).