Person:

Elledge, Stephen

Identifying physical interactions between proteins and other molecules is a critical aspect of biological analysis. Here we describe PLATO, an in vitro method for mapping such interactions by affinity enrichment of a library of full-length open reading frames displayed on ribosomes, followed by massively parallel analysis using DNA sequencing. We demonstrate the broad utility of the method by identifying known and new interacting partners of LYN kinase, patient autoantibodies and the small molecules gefitinib and dasatinib.

In this study, we improve on current autoantigen discovery approaches by creating a synthetic representation of the complete human proteome, the T7 “peptidome” phage display library (T7-Pep), and use it to profile the autoantibody repertoires of individual patients. We provide methods for 1) designing and cloning large libraries of DNA microarray-derived oligonucleotides encoding peptides for display on bacteriophage, and 2) analysis of the peptide libraries using high throughput DNA sequencing. We applied phage immunoprecipitation sequencing (PhIP-Seq) to identify both known and novel autoantibodies contained in the spinal fluid of three patients with paraneoplastic neurological syndromes. We also show how our approach can be used more generally to identify peptide-protein interactions and point toward ways in which this technology will be further developed in the future. We envision that PhIP-Seq can become an important new tool in autoantibody analysis, as well as proteomic research in general.

Autoimmune disease results from a loss of tolerance to self-antigens in genetically susceptible individuals. Completely understanding this process requires that targeted antigens be identified, and so a number of techniques have been developed to determine immune receptor specificities. We previously reported the construction of a phage-displayed synthetic human peptidome and a proof-of-principle analysis of antibodies from three patients with neurological autoimmunity. Here we present data from a large-scale screen of 298 independent antibody repertoires, including those from 73 healthy sera, using phage immunoprecipitation sequencing. The resulting database of peptide-antibody interactions characterizes each individual’s unique autoantibody fingerprint, and includes specificities found to occur frequently in the general population as well as those associated with disease. Screening type 1 diabetes (T1D) patients revealed a prematurely polyautoreactive phenotype compared with their matched controls. A collection of cerebrospinal fluids and sera from 63 multiple sclerosis patients uncovered novel, as well as previously reported antibody-peptide interactions. Finally, a screen of synovial fluids and sera from 64 rheumatoid arthritis patients revealed novel disease-associated antibody specificities that were independent of seropositivity status. This work demonstrates the utility of performing PhIP-Seq screens on large numbers of individuals and is another step toward defining the full complement of autoimmunoreactivities in health and disease.