Uri Laserson

Autoantigen discovery with a synthetic human peptidome

Immune responses targeting self-proteins (autoantigens) can lead to a variety of autoimmune diseases. Identification of these antigens is important for both diagnostic and therapeutic reasons. However, current approaches to characterize autoantigens have, in most cases, met only with limited success. Here we present a synthetic representation of the complete human proteome, the T7 peptidome phage display library (T7-Pep), and demonstrate its application to autoantigen discovery. T7-Pep is composed of >413,000 36-residue, overlapping peptides that cover all open reading frames in the human genome, and can be analyzed using high-throughput DNA sequencing. We developed a phage immunoprecipitation sequencing (PhIP-Seq) methodology to identify known and previously unreported autoantibodies contained in the spinal fluid of three individuals with paraneoplastic neurological syndromes. We also show how T7-Pep can be used more generally to identify peptide-protein interactions, suggesting the broader utility of our approach for proteomic research.Immune responses targeting self-proteins (autoantigens) can lead to a variety of autoimmune diseases. Identification of these antigens is important for both diagnostic and therapeutic reasons. However, current approaches to characterize autoantigens have, in most cases, met only with limited success. Here we present a synthetic representation of the complete human proteome, the T7 peptidome phage display library (T7-Pep), and demonstrate its application to autoantigen discovery. T7-Pep is composed of >413,000 36-residue, overlapping peptides that cover all open reading frames in the human genome, and can be analyzed using high-throughput DNA sequencing. We developed a phage immunoprecipitation sequencing (PhIP-Seq) methodology to identify known and previously unreported autoantibodies contained in the spinal fluid of three individuals with paraneoplastic neurological syndromes. We also show how T7-Pep can be used more generally to identify peptide-protein interactions, suggesting the broader utility of our approach for proteomic research.

High-resolution antibody dynamics of vaccine-induced immune responses

Significance The immune system must constantly adapt to combat infections and other challenges. This is accomplished by continuously evolving the antibody repertoire, and by maintaining memory of prior challenges. By using next-generation DNA sequencing technology, we have examined the shear amount of antibody made by individuals during a flu vaccination trial. We demonstrate one of the first characterizations of the fast antibody dynamics through time in multiple individuals responding to an immune challenge. The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

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/SHM

Two Classes of Broadly Neutralizing Antibodies within a Single Lineage Directed to the High-Mannose Patch of HIV Envelope

ABSTRACT The high-mannose patch of human immunodeficiency virus (HIV) envelope (Env) elicits broadly neutralizing antibodies (bnAbs) during natural infection relatively frequently, and consequently, this region has become a major target of vaccine design. However, it has also become clear that antibody recognition of the region is complex due, at least in part, to variability in neighboring loops and glycans critical to the epitopes. bnAbs against this region have some shared features and some distinguishing features that are crucial to understand in order to design optimal immunogens that can induce different classes of bnAbs against this region. Here, we compare two branches of a single antibody lineage, in which all members recognize the high-mannose patch. One branch (prototype bnAb PGT128) has a 6-amino-acid insertion in CDRH2 that is crucial for broad neutralization. Antibodies in this branch appear to favor a glycan site at N332 on gp120, and somatic hypermutation is required to accommodate the neighboring V1 loop glycans and glycan heterogeneity. The other branch (prototype bnAb PGT130) lacks the CDRH2 insertion. Antibodies in this branch are noticeably effective at neutralizing viruses with an alternate N334 glycan site but are less able to accommodate glycan heterogeneity. We identify a new somatic variant within this branch that is predominantly dependent on N334. The crystal structure of PGT130 offers insight into differences from PGT128. We conclude that different immunogens may be required to elicit bnAbs that have the optimal characteristics of the two branches of the lineage described. IMPORTANCE Development of an HIV vaccine is of vital importance for prevention of new infections, and it is thought that elicitation of HIV bnAbs will be an important component of an effective vaccine. Increasingly, bnAbs that bind to the cluster of high-mannose glycans on the HIV envelope glycoprotein, gp120, are being highlighted as important templates for vaccine design. In particular, bnAbs from IAVI donor 36 (PGT125 to PGT131) have been shown to be extremely broad and potent. Combination of these bnAbs enhanced neutralization breadth considerably, suggesting that an optimal immunogen should elicit several antibodies from this family. Here we study the evolution of this antibody family to inform immunogen design. We identify two classes of bnAbs that differ in their recognition of the high-mannose patch and show that different immunogens may be required to elicit these different classes.

Protein interaction discovery using parallel analysis of translated ORFs (PLATO)

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 for human proteins by identifying known and previously unidentified interacting partners of LYN kinase, patient autoantibodies, and the small-molecules gefitinib and dasatinib.

PhIP-Seq characterization of autoantibodies from patients with multiple sclerosis, type 1 diabetes and rheumatoid arthritis

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 individuals 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.

Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data

High-throughput sequencing of B and T cell receptors is routinely being applied in studies of adaptive immunity. The Adaptive Immune Receptor Repertoire (AIRR) Community was formed in 2015 to address issues in AIRR sequencing studies, including the development of reporting standards for the sharing of data sets.

Reproducibility and reuse of adaptive immune receptor repertoire data

High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1–3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community’s founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).

MHCflurry: open-source class I MHC binding affinity prediction

Predicting the binding affinity of major histocompatibility complex I (MHC I) proteins and their peptide ligands is important for vaccine design. We introduce an open-source package for MHC I binding prediction, MHCflurry. The software implements allele-specific neural networks that use a novel architecture and peptide encoding scheme. When trained on affinity measurements, MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 overall and particularly on non-9-mer peptides in a benchmark of ligands identified by mass spectrometry. The released predictor, MHCflurry 1.2.0, uses mass spectrometry datasets for model selection and showed competitive accuracy with standard tools, including the recently released NetMHCpan 4.0, on a small benchmark of affinity measurements. MHCflurrys prediction speed exceeded 7,000 predictions per second, 396 times faster than NetMHCpan 4.0. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, may be installed using package managers, and applies software development best practices.

Improved Analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) Data Using a Z-score Algorithm

Phage ImmunoPrecipitation Sequencing (PhIP-Seq) is a massively multiplexed, phage-display based methodology for analyzing antibody binding specificities, with several advantages over existing techniques, including the uniformity and completeness of proteomic libraries, as well as high sample throughput and low cost. Data generated by the PhIP-Seq assay are unique in many ways. The only published analytical approach for these data suffers from important limitations. Here, we propose a new statistical framework with several improvements. Using a set of replicate mock immunoprecipitations (negative controls lacking antibody input) to generate background binding distributions, we establish a statistical model to quantify antibody-dependent changes in phage clone abundance. Our approach incorporates robust regression of experimental samples against the mock IPs as a means to calculate the expected phage clone abundance, and provides a generalized model for calculating each clone’s expected abundance-associated standard deviation. In terms of bias removal and detection sensitivity, we demonstrate that this z-score algorithm outperforms the previous approach. Further, in a large cohort of autoantibody-defined Sjögren’s Syndrome (SS) patient sera, PhIP-Seq robustly identified Ro52, Ro60, and SSB/La as known autoantigens associated with SS. In an effort to identify novel SS-specific binding specificities, SS z-scores were compared with z-scores obtained by screening Ropositive sera from patients with systemic lupus erythematosus (SLE). This analysis did not yield any commonly targeted SS-specific autoantigens, suggesting that if they exist at all, their epitopes are likely to be discontinuous or post-translationally modified. In summary, we have developed an improved algorithm for PhIP-Seq data analysis, which was validated using a large set of sera with clinically characterized autoantibodies. This z-score approach will substantially improve the ability of PhIP-Seq to detect and interpret antibody binding specificities. The associated Python code is freely available for download here: https://github.com/LarmanLab/PhIP-Seq-Analyzer.