Insights into the B-cell response in a natural human infection: high-throughput mapping of epitopes using next-generation peptide chips

CARMONA SJ; SCHÄFER-NIELSEN, C; JUAN MUCCI,; ALTCHEH J; TEKIEL V; CAMPETELLA O; A BUSCAGLIA, CARLOS; NIELSEN M; AGÜERO F

Rosario

Congreso; 4to Congreso Argentino de Bioinformática y Biología Computacional; 2013

Asociacion Argentina de Bioinformática y Biología Computacional

Background. The full set of specificities in a human antibody response to a natural infection remains largely unexplored. Here, we used next-generation high-density peptide microarrays to demonstrate for the first time that it is feasible to identify and map hundreds of B-cell epitopes from a complex natural human infection. In this work, we have analyzed the B-cell immune response in humans with Chagas Disease, caused by a protozoan parasite. Description. The chip consists on a tiling array of ~200K 15-mer peptides synthesized using a maskless photolithographic technique, which in concert cover >500 individual proteins. This represents a coverage of ~5% of the proteome, including known antigens, previously uncharacterized proteins selected using a recently published bioinformatic method (Carmona SJ, et al 2012,), randomly selected proteins, and random sequences following parasite's proteome di-peptide composition. Antibody pools from healthy individuals and infected patients were assayed in a single chip, and data processed to obtain disease-specific signal for each peptide. These were used to reconstruct full-length protein antigenicity profiles (Figure 1, left panel). A smoothing procedure showed significant improvement on signal to noise ratio. A testing set of previously known Chagas antigens with fine mapped epitopes was used to assess our performance on linear B-cell epitope identification. Performance on this task was excellent, with an area under the ROC curve of 0.92 (Fig1). Discrimination of antigens from non- antigens is a more challenging task, however. Using a threshold of 20u (1u = background interquartile range) and a setup with an antigen-non-antigen ratio of 1%, we were able to detect 20 out of the set of 45 known antigens (44.4%) with a Positive Predictive Value (PPV) of 91% corresponding to 2 false positive predictions. Applying this threshold to the complete set of proteins analyzed on the chip, we detected 78 novel potential antigens, with an average of 1.5 epitopes per protein. Conclusions. In this work we show that high-density peptide chips allow rapid, high-throughput identification of B-cell epitopes from a natural infection, caused by a complex pathogen. These findings open the door to complete B-cell response maps of complex human infections.