High-throughput methods for characterizing the immune repertoire

Thesis (Ph. D. in Biomedical Engineering and Computational Biology)--Harvard-MIT Program in Health Sciences and Technology, February 2013."September 2012." Cataloged from PDF version of thesis.Includes bibliographical references (p. 147-160).The adaptive immune system is one of the primary mediators in almost every major human disease, including infections, cancer, autoimmunity, and inflammation-based disorders. It fundamentally functions as a molecular classifier, and stores a memory of its previous exposures. However, until recently, methods to unlock this information or to exploit its power in the form of new therapeutic antibodies or affinity reagents have been limited by the use of traditional, low-throughput technologies. In this thesis, we leverage recent advances in high-throughput DNA sequencing technology to develop new methods to characterize and probe the immune repertoire in unprecedented detail. We use this technology to 1) characterize the rapid dynamics of the immune repertoire in response to influenza vaccination, 2) characterize elite neutralizing antibodies to HIV, to better understand the constraints for designing an HIV vaccine, and 3) develop new methodologies for discovering auto-antigens, and assaying large libraries of protein antigens in general. We hope that these projects will serve as stepping-stones towards filling the gap left by low-throughput methods in the development of antibody technologies.by Uri Laserson.Ph.D.in Biomedical Engineering and Computational Biolog

Predicting candidate genomic sequences that correspond to synthetic functional RNA motifs

Riboswitches and RNA interference are important emerging mechanisms found in many organisms to control gene expression. To enhance our understanding of such RNA roles, finding small regulatory motifs in genomes presents a challenge on a wide scale. Many simple functional RNA motifs have been found by in vitro selection experiments, which produce synthetic target-binding aptamers as well as catalytic RNAs, including the hammerhead ribozyme. Motivated by the prediction of Piganeau and Schroeder [(2003) Chem. Biol., 10, 103–104] that synthetic RNAs may have natural counterparts, we develop and apply an efficient computational protocol for identifying aptamer-like motifs in genomes. We define motifs from the sequence and structural information of synthetic aptamers, search for sequences in genomes that will produce motif matches, and then evaluate the structural stability and statistical significance of the potential hits. Our application to aptamers for streptomycin, chloramphenicol, neomycin B and ATP identifies 37 candidate sequences (in coding and non-coding regions) that fold to the target aptamer structures in bacterial and archaeal genomes. Further energetic screening reveals that several candidates exhibit energetic properties and sequence conservation patterns that are characteristic of functional motifs. Besides providing candidates for experimental testing, our computational protocol offers an avenue for expanding natural RNA's functional repertoire

RAG: RNA-As-Graphs web resource

BACKGROUND: The proliferation of structural and functional studies of RNA has revealed an increasing range of RNA's structural repertoire. Toward the objective of systematic cataloguing of RNA's structural repertoire, we have recently described the basis of a graphical approach for organizing RNA secondary structures, including existing and hypothetical motifs. DESCRIPTION: We now present an RNA motif database based on graph theory, termed RAG for RNA-As-Graphs, to catalogue and rank all theoretically possible, including existing, candidate and hypothetical, RNA secondary motifs. The candidate motifs are predicted using a clustering algorithm that classifies RNA graphs into RNA-like and non-RNA groups. All RNA motifs are filed according to their graph vertex number (RNA length) and ranked by topological complexity. CONCLUSIONS: RAG's quantitative cataloguing allows facile retrieval of all classes of RNA secondary motifs, assists identification of structural and functional properties of user-supplied RNA sequences, and helps stimulate the search for novel RNAs based on predicted candidate motifs

Protein interaction mapping with ribosome-displayed using PLATO ORF libraries

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

Application of a synthetic human proteome to autoantigen discovery through PhIP-Seq

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

Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting “S5F” models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at http://clip.med.yale.edu/SH

AIRR Community Standardized Representations for Annotated Immune Repertoires

Increased interest in the immune system's involvement in pathophysiological phenomena coupled with decreased DNA sequencing costs have led to an explosion of antibody and T cell receptor sequencing data collectively termed “adaptive immune receptor repertoire sequencing” (AIRR-seq or Rep-Seq). The AIRR Community has been actively working to standardize protocols, metadata, formats, APIs, and other guidelines to promote open and reproducible studies of the immune repertoire. In this paper, we describe the work of the AIRR Community's Data Representation Working Group to develop standardized data representations for storing and sharing annotated antibody and T cell receptor data. Our file format emphasizes ease-of-use, accessibility, scalability to large data sets, and a commitment to open and transparent science. It is composed of a tab-delimited format with a specific schema. Several popular repertoire analysis tools and data repositories already utilize this AIRR-seq data format. We hope that others will follow suit in the interest of promoting interoperable standards

A Comprehensive Phenotypic and Functional Immune Analysis Unravels Circulating Anti-Phospholipase A2 Receptor Antibody Secreting Cells in Membranous Nephropathy Patients

Introduction: Primary membranous nephropathy (MN) is characterized by the presence of antipodocyte antibodies, but studies describing phenotypic and functional abnormalities in circulating lymphocytes are limited. Methods: We analyzed 68 different B- and T-cell subsets using flow cytometry in 30 MN patients (before initiating immunosuppression) compared with 31 patients with non-immune-mediated chronic kidney disease (CKD) and 12 healthy individuals. We also measured 19 serum cytokines in MN patients and in healthy controls. Lastly, we quantified the ex vivo production of phospholipase A2 receptor (PLA2R)-specific IgG by plasmablasts (measuring antibodies in culture supernatants and by the newly developed FluoroSpot assay [AutoImmun Diagnostika, Strasberg, Germany]) and assessed the circulating antibody repertoire by phage immunoprecipitation sequencing (PhIP-Seq). Results: After adjusting for multiple testing, plasma cells and regulatory B cells (BREG) were significantly higher (P < 0.05) in MN patients compared with both control groups. The percentages of circulating plasma cells correlated with serum anti-PLA2R antibody levels (P = 0.042) and were associated with disease activity. Ex vivo-expanded PLA2R-specific IgG-producing plasmablasts generated from circulating PLA2R-specific memory B cells (mBCs) correlated with serum anti-PLA2R IgG antibodies (P < 0.001) in MN patients. Tumor necrosis factor-alpha (TNF-alpha) was the only significantly increased cytokine in MN patients (P < 0.05), whereas there was no significant difference across study groups in the autoantibody and antiviral antibody repertoire. Conclusion: This extensive phenotypic and functional immune characterization shows that autoreactive plasma cells are present in the circulation of MN patients, providing a new therapeutic target and a candidate biomarker of disease activity