UC Santa Barbara

The serum antibody repertoire is a unique repository of information regarding past immune encounters. Antibodies are produced in response to exposures and bind specifically to their target antigens. Antibodies associated with infection and disease can serve as diagnostic biomarkers upon detection. However, many disease-specific antibodies and their targets remain undiscovered. Therefore, to discover antibody biomarkers, reagents must be developed to specifically bind the disease-specific antibodies. Peptides are suitable reagents as they can often mimic native antibody epitopes with high affinity and specificity. Moreover, the sequences of antibody-binding peptides can be determined and used to identify the original antibody targets, revealing previously unknown antigens that contribute to disease progression and creating new opportunities for therapeutic development. Here, we developed and applied high-throughput methods to discover and characterize peptide epitopes from immune-related diseases.

A large bacterial display peptide library composed of randomized peptides was constructed to screen against human serum specimens and discover peptides that bind antibodies. For each specimen, millions of antibody-binding peptide sequences were determined using next-generation sequencing and analyzed computationally to reveal disease-specific binding motifs. This screening methodology was first employed to discover and characterize epitopes from two highly similar viruses, herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2). Following antibody repertoire analysis, we discovered HSV type-specific motifs that could be used as diagnostic classifiers to achieve 100% diagnostic accuracy when distinguishing HSV-1 from HSV-2 and vice versa. Furthermore, numerous type-specific motifs were mapped to HSV antigens using protein sequence database alignments, including known antigens such as glycoproteins G and D as well as previously unreported antigens. We then applied this screening methodology to age-related macular degeneration (AMD). We identified a large panel of antibody-binding motifs associated with the onset of advanced AMD and developed a classifier with 84% accuracy when distinguishing specimens at high risk of advanced AMD from those at low risk. However, as the risk of developing advanced AMD increased, the classifier performance worsened suggesting a unique antibody signature associated with AMD progression.

Additional methods were developed to improve the discovery and characterization of epitopes from the antibody repertoire. We developed a method to selectively deplete highly abundant antibodies from a specimen to reduce repertoire complexity, improve the limits of detection, and enable the discovery of rare antibodies and their targets. Additionally, we developed a novel computational algorithm to rapidly determine binding motifs for epitopes by utilizing the full extent of next-generation sequencing datasets. Ultimately, these tools for analyzing the antibody repertoire at great depth can be applied to a broad range of infectious, autoimmune, and allergic diseases. The discovery of disease-associated epitopes will create new opportunities for the development of diagnostics, vaccines, and therapeutics.