Predicting personal immune scenarios

Antibodies neutralize pathogens and are important therapeutics and diagnostics.

The antibody repertoire is the collection of distinct B-cell receptors and secreted antibodies, each represented by a sequence of 20 unique amino acids (a.a.). The theoretical diversity of antibody repertoire counts 10140 possible sequences. B-cell kinetics results in an ever-changing personalized antibody repertoire and a dynamic immune status. The record of sequence diversity in antibody repertoires has been recently made available from the advancement of high-throughput sequencing technologies. Antibody repertoire networks, where antibodies are sequence-nodes connected by similarity-edges, have been shown to have reproducible (exponential), robust and redundant structure. Thus, network analysis can capture sequence relations in this complex system, track the potential proliferation and predict the disappearance of certain sequence features. We currently use high-throughput sequencing data combined with network analysis to measure, track and predict the change in sequence space of the antibody repertoire. We investigate how this network model can serve as the base to track entire personalized antibody repertoires in the theoretical antibody sequence space, thus predicting immune status scenarios.