(559e) Identifying Motifs Associated with Autoimmune Diseases Using Next Generation Sequencing Experiments

Autoimmune diseases are typically chronic, have major quality-of-life effects and afflict a large minority of the population. A hallmark of autoimmune diseases is the presence of antibodies that bind to “self” targets. Unfortunately, it has proven challenging to identify these autoantibodies and develop accurate diagnostic tests for their presence in many diseases.

We developed a method to Identify Motifs Using Next generation sequencing Experiments (the IMUNE method) capable of discovering disease-associated motifs. First, a large, bacterial-display peptide library is screened using magnetic and/or fluorescence-activated cell sorting to recover peptides bound by an individual’s antibodies. Next generation sequencing technologies (e.g. Illumina NextSeq) are utilized to identify these millions of peptides. Because of the large diversities in the peptide library and in patients’ antibodies, it is unlikely that this experimental approach alone will identify peptides bound by many disease patients but not control patients. Therefore, computational analysis is employed to identify patterns of amino acids that are statistically enriched in a cohort of patients compared to control patients, where a pattern is a series of specific amino acids possibly interspersed with undefined residues. Finally, these enriched patterns are clustered together to create the disease-associated motifs. We will present an overview of the experimental and computational methods, and show examples from Celiac disease and multiple sclerosis to highlight the method’s ability to discover known and novel disease-associated motifs.