Avidity of the immunoglobulin G response to a Neisseria meningitidis group C polysaccharide conjugate vaccine as measured by inhibition and chaotropic enzyme-linked immunosorbent assays

Abstract

Antibody avidity, the strength of the multivalent interaction between antibodies and their antigens, is an important characteristic of protective immune responses. We have developed an inhibition enzyme-linked immunosorbent assay (ELISA) to measure antibody avidity for the capsular polysaccharide (PS) of Neisseria meningitidis group C (MnC) and determined the avidity constants (K(D)s) for 100 sera from children immunized with an MnC PS conjugate vaccine. The avidity constants were compared to the avidity indices (AI) obtained for the same sera using a chaotropic ELISA protocol. After the primary immunization series, the geometric mean (GM) K(D) was 674 nM and did not change in the months following immunization. However, the GM avidity did increase after the booster dose (GM K(D), 414 nM 1 month after booster immunization). In contrast, the GM AI increased from an initial value of 118 after the primary immunization series to 147 6 months after the completion of the primary immunization series and then further increased to 178 after booster immunization. At the individual subject level, the avidity constant and AI correlated after the primary immunization series and after booster immunization but not prior to boosting. This work suggests that the AI, as measured by the chaotropic ELISA, in contrast to the K(D), reflects changes that render antibody populations less susceptible to disruption by chaotropic agents without directly affecting the strength of the binding interactions.

FIG. 1.

Isotherm and nonlinear least-squares fit of data from isothermal titration microcalorimetry experiment with acetylated MnC PS and MAb MNC46-1, specific for deacetylated MnC PS. The average stoichiometry of binding (N), from three experiments with MAb 46-1 and two experiments with MAb MNC207-1, specific for acetylated MnC PS (data not shown), is 10.7 ± 1.5. K, thermodynamic binding constant; ΔH, enthalpy of binding; ΔS, entropy of binding.

FIG. 2.

Linearity of inhibition ELISA. After normalizing for antibody concentration, serum from adult 1, which had a high K_D, was mixed with serum from adult 3, which had a low K_D, to produce a mixed serum with a linear gradient of K_Ds. The obtained K_Ds are plotted against the percentage of serum from adult 1 for each mixed serum. The results of two independent experiments are shown. Experiment 1, black diamonds and solid line (R² = 0.83). Experiment 2, open circles and dashed line (R² = 0.95).

FIG. 3.

GM AI and GM K_D for sera from children immunized with an MnC conjugate vaccine. Bars represent GM AI determined via chaotropic ELISA. Filled diamonds represent GM K_D determined via inhibition ELISA for the same subjects. Error bars represent 95% confidence intervals for the geometric means. GM AI shows a significant increase at prebooster and postbooster time points, whereas GM K_D shows a significant decrease (i.e., increased avidity) at the postbooster point only.

FIG. 4.

Correlation between K_D and AI after primary-series immunization. At the post-primary-series time point, K_D is 929.31 − 1.79 × AI (model F test: P = 0.0054 from the ANOVA table, with an R² of 0.08). Data points are shown as squares and the regression function as a solid line. The dashed lines around the regression line represent the 95% confidence curves for the mean of K_D.

FIG. 5.

Correlation between K_D and AI before booster immunization. At the prebooster time point there is no linear relationship between K_D and AI.

FIG. 6.

(a) Correlation between K_D and AI post-booster immunization. At the postbooster time point, the regression model between K_D and AI is a curve (model F test: P < 0.001 from the ANOVA table, with an R² of 0.296). Data points are shown as squares and the regression function as a solid line. (b) Correlation between K_D and AI after booster immunization. At the postbooster time point, K_{D_X} is 167.53 − 56.59*log₁₀(AI), where K_{D_X} = (K_D)^0.6 and log₁₀(AI) equals the log-transformed values of AI (model F test: P < 0.001 from the ANOVA table, with an R² of 0.296). Data points are shown as squares and the regression function as a solid line. The dashed lines around the regression line represent the 95% confidence curves for the mean of K_D.