Serum and Cervicovaginal Fluid Antibody Profiling in Herpes Simplex Virus-Seronegative Recipients of the HSV529 Vaccine.

Previous herpes simplex virus type 2 (HSV-2) vaccines have not prevented genital herpes. Concerns have been raised about the choice of antigen, the type of antibody induced by the vaccine, and whether antibody is present in the genital tract where infection occurs. We reported results of a trial of an HSV-2 replication-defective vaccine, HSV529, that induced serum neutralizing antibody responses in 78% of HSV-1-/HSV-2- vaccine recipients. Here we show that HSV-1-/HSV-2- vaccine recipients developed antibodies to epitopes of several viral proteins; however, fewer antibody epitopes were detected in vaccine recipients compared with naturally infected persons. HSV529 induced antibodies that mediated HSV-2-specific natural killer (NK) cell activation. Depletion of glycoprotein D (gD)-binding antibody from sera reduced neutralizing titers by 62% and NK cell activation by 81%. HSV-2 gD antibody was detected in cervicovaginal fluid at about one-third the level of that in serum. A vaccine that induces potent serum antibodies transported to the genital tract might reduce HSV genital infection.

Serum Epitope Repertoire Analysis Enables Early Detection of Lyme Disease with Improved Sensitivity in an Expandable Multiplex Format.

Widely employed diagnostic antibody serology for Lyme disease, known as standard two-tier testing (STTT), exhibits insufficient sensitivity in early Lyme disease, yielding many thousands of false-negative test results each year. Given this problem, we applied serum antibody repertoire analysis (SERA), or next-generation sequencing (NGS)-based serology, to discover IgG and IgM antibody epitope motifs capable of detecting Lyme disease-specific antibodies with high sensitivity and specificity. Iterative motif discovery and bioinformatic analysis of epitope repertoires from subjects with Lyme disease (n = 264) and controls (n = 391) yielded a set of 28 epitope motifs representing 20 distinct IgG antibody epitopes and a set of 38 epitope motifs representing 21 distinct IgM epitopes, which performed equivalently in a large validation cohort of STTT-positive samples. In a second validation set from subjects with clinically defined early Lyme disease (n = 119) and controls (n = 257), the SERA Lyme IgG and IgM assay exhibited significantly improved sensitivity relative to STTT (77% versus 62%; Z-test; P = 0.013) and improved specificity (99% versus 97%). Early Lyme disease subjects exhibited significantly fewer reactive epitopes (Mann-Whitney U test; P < 0.0001) relative to subjects with Lyme arthritis. Thus, SERA Lyme IgG and M panels provided increased accuracy in early Lyme disease in a readily expandable multiplex assay format.

High-affinity, neutralizing antibodies to SARS-CoV-2 can be made without T follicular helper cells.

T follicular helper (TFH) cells are the conventional drivers of protective, germinal center (GC)­based antiviral antibody responses. However, loss of TFH cells and GCs has been observed in patients with severe COVID-19. As T cell­B cell interactions and immunoglobulin class switching still occur in these patients, noncanonical pathways of antibody production may be operative during SARS-CoV-2 infection. We found that both TFH-dependent and -independent antibodies were induced against SARS-CoV-2 infection, SARS-CoV-2 vaccination, and influenza A virus infection. Although TFH-independent antibodies to SARS-CoV-2 had evidence of reduced somatic hypermutation, they were still high affinity, durable, and reactive against diverse spike-derived epitopes and were capable of neutralizing both homologous SARS-CoV-2 and the B.1.351 (beta) variant of concern. We found by epitope mapping and B cell receptor sequencing that TFH cells focused the B cell response, and therefore, in the absence of TFH cells, a more diverse clonal repertoire was maintained. These data support an alternative pathway for the induction of B cell responses during viral infection that enables effective, neutralizing antibody production to complement traditional GC-derived antibodies that might compensate for GCs damaged by viral inflammation.

Mapping antibody binding using multiplexed epitope substitution analysis.

A more complete understanding of antibody epitopes would aid the development of diagnostics, therapeutic antibodies, and vaccines. However, current methods for mapping antibody binding to epitopes require a targeted experimental approach, which limits throughput. To address these limitations, we developed Multiplexed Epitope Substitution Analysis (MESA) which can rapidly characterize various distinct epitopes using millions of antibody-binding peptides. We screened peptides from a random 12-mer library that bound to human serum antibody repertoires and determined their sequences using next-generation sequencing (NGS). Computationally, we divided target epitope sequences into overlapping k-mer subsequences and substituted the positions in each k-mer with all 20 amino acids, mimicking a saturation mutagenesis. We then determined enrichments of the substituted k-mers in the screened peptide dataset and used these enrichments to identify substitutions favored for binding at each position in the target epitope, ultimately revealing the precise binding motif. To validate MESA, we determined binding motifs for monoclonal antibodies spiked into serum, recovering the expected binding positions and amino acid preferences. To characterize epitopes bound by a population, we analyzed 50 serum specimens to determine the binding motifs within various target epitopes from common pathogens. Additionally, by analyzing various HSV-1 glycoprotein epitopes, MESA revealed unique binding signatures for HSV-1 seropositive specimens and demonstrated the variability of binding signatures within a population. These results demonstrate that MESA can rapidly identify and characterize binding motifs for an unlimited number of epitopes from a single experiment, accelerating discoveries and enhancing our understanding of antibody-epitope interactions.

High-resolution epitope mapping and characterization of SARS-CoV-2 antibodies in large cohorts of subjects with COVID-19.

As Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to spread, characterization of its antibody epitopes, emerging strains, related coronaviruses, and even the human proteome in naturally infected patients can guide the development of effective vaccines and therapies. Since traditional epitope identification tools are dependent upon pre-defined peptide sequences, they are not readily adaptable to diverse viral proteomes. The Serum Epitope Repertoire Analysis (SERA) platform leverages a high diversity random bacterial display library to identify proteome-independent epitope binding specificities which are then analyzed in the context of organisms of interest. When evaluating immune response in the context of SARS-CoV-2, we identify dominant epitope regions and motifs which demonstrate potential to classify mild from severe disease and relate to neutralization activity. We highlight SARS-CoV-2 epitopes that are cross-reactive with other coronaviruses and demonstrate decreased epitope signal for mutant SARS-CoV-2 strains. Collectively, the evolution of SARS-CoV-2 mutants towards reduced antibody response highlight the importance of data-driven development of the vaccines and therapies to treat COVID-19.

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes.

Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.

High-titer antibody depletion enhances discovery of diverse serum antibody specificities.

The human antibody repertoire is a unique repository of information regarding infection, inflammation, and autoimmunity of the past, present, and future. However, antibodies can span vast ranges of concentrations with varying affinities and the repertoire is often heavily polarized by a few species. These complexities lead to difficulties detecting and characterizing low abundance antibody species that may be relevant to disease. We therefore developed a method to selectively remove antibodies from a sample in proportion to the titer of the species prior to analysis, referred to as high-titer depletion (HTD). Peptides from a large random peptide display library were enriched towards binding high-titer antibody species and utilized as binding reagents to deplete the corresponding species from the specimen. HTD enabled the discovery of antibody binding specificities using random peptide library screening with reduced cross-reactivity and background signal and improved coverage of low abundance species. With HTD, three monoclonal antibody species were detected at concentrations at least an order of magnitude lower than without HTD. Additionally, 92 serum antibody specificities were readily discovered from an individual specimen using HTD compared to only 25 specificities without HTD. Parameters affecting the extent of depletion such as the concentration of depleted serum were also adjusted to reproducibly improve the coverage of antibody specificities. These results demonstrate that HTD could be employed for the discovery of rare specificities related to disease and enable extensive characterization of the antibody repertoire. Moreover, the strategy of depletion in proportion to titer could be extended to other applications with complex biological samples to improve discovery.

Identification of disease-specific motifs in the antibody specificity repertoire via next-generation sequencing.

Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.