A competitive hemagglutination inhibition assay for dissecting functional antibody activity to influenza virus

Influenza remains one of the most contagious infectious diseases. Approximately, 25-50 million people suffer from influenza-like illness in the United States annually, leading to almost 1 million hospitalizations. Globally, the World Health Organization (WHO) estimates 250,000-500,000 mortalities associated with secondary respiratory complications due to influenza virus infection every year. Currently, seasonal vaccination represents the best countermeasure to prevent influenza virus spread and transmission in the general population. However, presently licensed influenza vaccines are about 60% effective on average, and their effectiveness varies from season to season and among age groups, as well as between different influenza subtypes within a single season. The hemagglutination inhibition (HAI) assay represents the gold standard method for measuring the functional antibody response elicited following standard-of-care vaccination, along with evaluating the efficacy of under-development influenza vaccines in both animal models and clinical trial settings. However, using the classical HAI approach it is not possible to dissect the complexities of variable epitope recognition within a polyclonal antibody response. In this communication, we describe a straightforward competitive HAI-based method using a combination of influenza virus and recombinant hemagglutinin (HA) proteins to dissect the HAI functional activity of HA-specific antibody populations in a single assay format. Importance: The hemagglutination inhibition (HAI) assay is a well-established and reproducible method that quantifies functional antibody activity against influenza viruses and in particular the capability of an antibody formulation to inhibit the binding of HA to sialic acid. However, the HAI assay does not provide with full insights on the breadth and epitope recognition of the antibody formulation, especially in the context of polyclonal sera where multiple antibody specificities contribute to the overall observed functional activity. In this report we introduce the use of Y98F point-mutated recombinant HA (HAΔSA) proteins, which lack sialic acid binding activity, in the context of the HAI assay as a mean to absorb out certain HA-directed (i.e., strain-specific or cross-reactive) antibody populations. This modification to the classical HAI assay, referred to as the competitive HAI assay, represents a new tool to dissect the magnitude and breadth of polyclonal antibodies elicited through vaccination or natural infection.

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

CD8+ T cell immune monitoring aims at measuring the size and functions of antigen-specific CD8+ T cell populations, thereby providing insights into cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical because within a complex antigen exists a multitude of potential epitopes that can be presented by HLA class I molecules. Further complicating this task, there is HLA class I polygenism and polymorphism which predisposes CD8+ T cell responses towards individualized epitope recognition profiles. In this study, we compare the actual CD8+ T cell recognition of a well-characterized model antigen, human cytomegalovirus (HCMV) pp65 protein, with its anticipated epitope coverage. Due to the abundance of experimentally defined HLA-A*02:01-restricted pp65 epitopes, and because in silico epitope predictions are most advanced for HLA-A*02:01, we elected to focus on subjects expressing this allele. In each test subject, every possible CD8+ T cell epitope was systematically covered testing 553 individual peptides that walk the sequence of pp65 in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. No correlation was found between epitopes’ ranking on the prediction scale and their actual immune dominance. Collectively, these data suggest that accurate CD8+ T cell immune monitoring may necessitate reliance on agnostic mega peptide pools, or brute force mapping, rather than electing individual peptides as representative epitopes for tetramer and other multimer labeling of surface antigen receptors.

Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

Cytomegalovirus-Specific T Cell Epitope Recognition in Congenital Cytomegalovirus Mother-Infant Pairs

Background: Congenital cytomegalovirus (cCMV) infection is the most common infection acquired before birth and from which about 20% of infants develop permanent neurodevelopmental effects regardless of presence or absence of symptoms at birth. Viral escape from host immune control may be a mechanism of CMV transmission and infant disease severity. We sought to identify and compare CMV epitopes recognized by mother-infant pairs. We also hypothesized that if immune escape were occurring, then one pattern of longitudinal CD8 T cell responses restricted by shared HLA alleles would be maternal loss (by viral escape) and infant gain (by viral reversion to wildtype) of CMV epitope recognition.Methods: The study population consisted of 6 women with primary CMV infection during pregnancy and their infants with cCMV infection. CMV UL83 and UL123 peptides with known or predicted restriction by maternal MHC class I alleles were identified, and a subset was selected for testing based on several criteria. Maternal or infant cells were stimulated with CMV peptides in the IFN-γ ELISpot assay.Results: Overall, 14 of 25 (56%; 8 UL83 and 6 UL123) peptides recognized by mother-infant pairs were not previously reported as CD8 T cell epitopes. Of three pairs with longitudinal samples, one showed maternal loss and infant gain of responses to a CMV epitope restricted by a shared HLA allele.Conclusions: CD8 T cell responses to multiple novel CMV epitopes were identified, particularly in infants. Moreover, the hypothesized pattern of CMV immune escape was observed in one mother-infant pair. These findings emphasize that knowledge of paired CMV epitope recognition allows exploration of viral immune escape that may operate within the maternal-fetal system. Our work provides rationale for future studies of this potential mechanism of CMV transmission during pregnancy or clinical outcomes of infants with cCMV infection.

Discordance between the predicted vs. the actually recognized CD8+ T cell epitopes of HCMV pp65 antigen and aleatory epitope dominance

CD8+ T cell immune monitoring aims at measuring the numbers and functions of antigen-specific CD8+ T cell populations engaged during immune responses, providing insights into the magnitude and quality of cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical, however, because for each restricting HLA class I molecule present in a human individual there is a multitude of potential epitopes within complex antigens, and HLA diversity between the test subjects predisposes CD8+ T cell responses to individualized epitope recognition profiles. We report here on a brute force CD8+ T cell epitope mapping approach for the human cytomegalovirus (HCMV) pp65 antigen on ten HLA-A*02:01-matched HCMV infected human subjects. In this approach, in each test subject, every possible CD8+ T cell epitope was systematically tested; that is 553 individual peptides that walk the sequence of the HCMV pp65 protein in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. We compared the actually detected epitope utilization in each individual with epitope prediction ranking for the shared HLA-A*02:01 allele, and for additional HLA class I alleles expressed by each individual. No correlation was found between epitopes’ ranking on the prediction scale and their actual immune dominance. The data suggest that accurate CD8+ T cell immune monitoring might depend on the agnostic reliance on mega peptide pools, or brute force mapping, rather than individualized epitope predictions.