A Role for Fc Function in Therapeutic Monoclonal Antibody-Mediated Protection against Ebola Virus

Abstract

The recent Ebola virus (EBOV) epidemic highlighted the need for effective vaccines and therapeutics to limit and prevent outbreaks. Host antibodies against EBOV are critical for controlling disease, and recombinant monoclonal antibodies (mAbs) can protect from infection. However, antibodies mediate an array of antiviral functions including neutralization as well as engagement of Fc-domain receptors on immune cells, resulting in phagocytosis or NK cell-mediated killing of infected cells. Thus, to understand the antibody features mediating EBOV protection, we examined specific Fc features associated with protection using a library of EBOV-specific mAbs. Neutralization was strongly associated with therapeutic protection against EBOV. However, several neutralizing mAbs failed to protect, while several non-neutralizing or weakly neutralizing mAbs could protect. Antibody-mediated effector functions, including phagocytosis and NK cell activation, were associated with protection, particularly for antibodies with moderate neutralizing activity. This framework identifies functional correlates that can inform therapeutic and vaccine design strategies against EBOV and other pathogens.

Keywords: Ebola virus; Fc-receptors; antibodies; effector function; immunotherapeutics; innate immunity; phagocytosis.

Copyright © 2018 Elsevier Inc. All rights reserved.

Figures

Figure 1.. Functionality of Ebola virus GP-specific monoclonal antibodies.

The mAbs within the Zmapp and MB-003 cocktails (A) were evaluated for the induction the following innate immune effector functions: Ab-dependent phagocytosis of GP-coated beads by human monocytes (B); Ab-dependent phagocytosis of GP-coated beads by human neutrophils (C); Ab-dependent activation of NK cells as measured by degranulation (CD107a), and secretion of cytokines (IFNγ) and chemokines (MIP-1β) (C). Red and blue symbols indicate neutralizing and non-neutralizing mAbs, respectively. Each dot represent an independent replicate, the solid line represents the mean response across replicates, and the dashed line indicates the level of the no Ab control. Unsupervised principal component analysis of functional responses separates neutralizing and non-neutralizing mAbs (D), and the scoring plot of component 1 and 2 is shown on the top and the loadings plot is shown on the bottom.

Figure 2.. Functionality of Ebola virus GP-specific mAbs.

The functional profile of each mAb within the panel is graphed by characterizing the magnitude of the functional response for each mAb into high, medium, and low/negligible for each function based on cutoffs defined in Figure S2. Each wedge is color-coded by effector function, with the size of the wedge corresponding to the magnitude of the response in the respective functional assay. The specific VIC Ab number is located at the top of each Ab box, and color-coded according to neutralization group (strongly neutralizing mAbs are indicated in red text, partially neutralizing mAbs in black text, and non-neutralizing mAbs in blue text). The percent protection provided by the indicated Ab is located at the bottom of the Ab box, with protection ranging from 0% (no animals protected) to 100% (all animals protected). The protection data is derived from (Saphire et al., 2018).

Figure 3.. Antibody features that predict protection.

Elastic Net/PSLR were used to identify the minimal mAb features that best predicted level of protection across all mAbs (A-B). Each mAb is color coded by level of protection (blue = 0%; yellow = 100%) in the scoring plot (A), and features are color coded by importance to positively (red) or negatively (blue) predicting protection in the mirrored loadings plot as determined by variable influence on projection (B). All models were tested using two permutation tests (size matched random selection of features and outcome shuffling), and the predictive power of the true model was assessed using 10-fold cross-validation, providing a test for robustness of each true model. The cross validation MSE, cross validation correlation R2 value, and prediction correlation R2 values are shown in Table S5. Correlation network analysis (C) was performed to identify features associated with microneutralization (left) or polyfunctionality (right). Positive correlations between features above a threshold adjusted p-value<0.05 after Benjamini-Hochberg correction for multiple comparisons are indicated by a red connecting line, and negative correlations (adjusted p-value <0.05) are indicated by a blue connecting line. Strength of correlation is indicated by weight of the connecting line, and the background color of the features indicates the category of feature (e.g. neutralization, functional activity, etc.), as indicated in the boxed legend.

Figure 4.. Protective efficacy of partially neutralizing mAbs is associated with induction of effector functions.

The neutralizing activity of the mAbs was evaluated in four neutralization assays, and mAbs were grouped into strong neutralizers (sNeuts; n=44), partial neutralizers (pNeuts; n=33), and non-neutralizers (nNeuts; n=90) by K-means clustering as described in (Saphire et al., 2018). For representation (A), each mAb was categorized into high (neut. activity value = 6-8), medium/low (neut. activity value = 1-5), and no activity (neut. activity value = 0) according to defined cutoff values (Table S3). The neutralizing activity and protection ≥60% (yes/no) of each mAb is plotted, with individual mAbs on the X-axis, and the neutralizing activity and protection on the Y-axis. The colored dots indicate the different neutralization readouts, and white dots indicate protection. The VIC number of the mAbs within each category are listed in Table S4. Correlation analyses was performed between the response in the indicated functional assay and protection (% of mice protected from death) within the sNeut (B; top row), pNeut (B; middle row), or nNeut groups (B; bottom row,). Spearman rho was used to determine significance of association, and an adjusted p<0.05 was considered significant after Benjamini-Hochberg correction for multiple comparisons. Red, blue, and white background of graph indicates a significant positive, negative (none register), or no significant association, respectively.

Figure 5.. Antibody functionality predicts protection mediated by partially and non-neutralizing mAbs.

The minimal features of mAbs within the sNeuts, pNeuts, and nNeuts that predict mAb-mediated protection from lethal EBOV challenge were determined using machine learning analyses. Elastic Net/PLSR analysis was used to identify the minimal mAb features that best predicted level of protection within the sNeuts (A), pNeuts (B), and nNeuts Abs (C). Plots are color coded similar to Figure 3A. Correlation network analyses were performed to identify features associated with Elastic Net-selected features of sNeuts (D; left), pNeuts (D; middle), or nNeuts (D, right). Positive or negative correlations between features above a threshold adjusted p-value<0.05 after Benjamini-Hochberg correction are indicated by a red or blue connecting line, respectively. Strength of correlation and the category of feature are indicated in the boxed legend.

Figure 6.. Non-protective neutralizing mAbs are impaired in recruitment of certain effector functions.

The functional responses (mADCP, mADNP) of protective (≥60% protection; n=35) and non-protective (≤50% protection; n=9) within the sNeut group (A), and the functional responses (mADCP, mADNP, NK cell secretion of IFNγ and MIP1β) of protective (≥60% protection; n=10) and non-protective (≤50% protection; n=23) within pNeut group (B) was compared. Unpaired t-test was used to determine significance between protective and non-protective groups. ****p<0.0005 ***p<0.001 **p<0.005 after correction for multiple comparisons by Benjamini-Hochberg correction. The horizonal line represents the mean functional response within groups, and the error bars represent the standard deviation. The minimal Ab features needed to predict higher NK cell mediated IFNγ secretion (C) or mADCP activity (D) were determined by PLSR analysis using Elastic Net-selected features. All mAbs within the VIC were used in the analysis, and each mAb is color coded in the scoring plot (left graph) by activity response in each assay (blue to yellow = lowest to highest response in assay), and features are color coded by importance to positively (red) or negatively (blue) predicting activity as determined by variable influence on projection in the mirrored loadings plot (right graph). Correlation network analysis was performed to identify features associated with Elastic Net-selected features of NK cell mediated IFNγ secretion (E) or mADCP activity (F). Positive or negative correlations between features above a threshold adjusted p-value<0.05 after Benjamini-Hochberg correction are indicated by a red or blue connecting line, respectively. Strength of correlation and the category of feature are indicated in the boxed legend.

Figure 7.. Exploiting both ends of the antibody to fight Ebola.

A. The overlay of neutralization of live Ebola virus (microneutralization; blue shade), number of functions (polyfunctionality; green shade) and protection of protective Abs is depicted. B. Summary of collaboration between the Fc- and Fab- mediated Ab functions in protective Abs against EBOV.