Immune Repertoire after Immunization As Seen by Next-Generation Sequencing and Proteomics

Evaluation of the Immune Response of Patulin by Proteomics

Patulin, an emerging mycotoxin with high toxicity, poses great risks to public health. Considering the poor antibody production in patulin immunization, this study focuses on the four-dimensional data-independent acquisition (4D-DIA) quantitative proteomics to reveal the immune response of patulin in rabbits. The rabbit immunization was performed with the complete developed antigens of patulin, followed by the identification of the immune serum. A total of 554 differential proteins, including 292 up-regulated proteins and 262 down-regulated proteins, were screened; the differential proteins were annotated; and functional enrichment analysis was performed. The differential proteins were associated with the pathways of metabolism, gene information processing, environmental information processing, cellular processes, and organismal systems. The functional enrichment analysis indicated that the immunization procedures mostly resulted in the regulation of biochemical metabolic and signal transduction pathways, including the biosynthesis of amino acid (glycine, serine, and threonine), ascorbate, and aldarate metabolism; fatty acid degradation; and antigen processing and presentation. The 14 key proteins with high connectivity included G1U9T1, B6V9S9, G1SCN8, G1TMS5, G1U9U0, A0A0G2JH20, G1SR03, A0A5F9DAT4, G1SSA2, G1SZ14, G1T670, P30947, P29694, and A0A5F9C804, which were obtained by the analysis of protein-protein interaction networks. This study could provide potential directions for protein interaction and antibody production for food hazards in animal immunization.

Exploring the sheep (Ovis aries) immunoglobulin repertoire by next generation sequencing

Next-generation sequencing (NGS) has revolutionized the way we determine the antibody repertoires encoded by B cells in the blood or lymphoid organs and transformed our understanding of adaptive immune responses in many species. Sheep (Ovis aries) have been widely used as a host for therapeutic antibody production since the early 1980s, however, little is known about their immune repertoires or immunological processes affecting the antibody generation. The objective of this study was to employ NGS for a comprehensive analysis of immunoglobulin heavy and light chain repertoires in four healthy sheep. We obtained > 90 % complete antibody sequences and nearly 130,000, 48,000 and 218,000 unique CDR3 reads for the heavy chain (IGH), kappa chain (IGK), and lambda chain (IGL) loci, respectively. Consistent with other species, we observed biased usage of germline variable (V), diversity (D) and joining (J) genes in the heavy and kappa loci, but not in the lambda loci. Moreover, the enormous diversity of CDR3 sequences was observed through sequence clustering and convergent recombination. These data will build a foundation for future studies investigating immune repertoires in health and disease as well as contribute to further refinement of ovine-derived therapeutic antibody drugs.

An Immunoproteomic Survey of the Antibody Landscape: Insights and Opportunities Revealed by Serological Repertoire Profiling

Immunoproteomics has emerged as a versatile tool for analyzing the antibody repertoire in various disease contexts. Until recently, characterization of antibody molecules in biological fluids was limited to bulk serology, which identifies clinically relevant features of polyclonal antibody responses. The past decade, however, has seen the rise of mass-spectrometry-enabled proteomics methods that have allowed profiling of the antibody response at the molecular level, with the disease-specific serological repertoire elucidated in unprecedented detail. In this review, we present an up-to-date survey of insights into the disease-specific immunological repertoire by examining how quantitative proteomics-based approaches have shed light on the humoral immune response to infection and vaccination in pathogenic illnesses, the molecular basis of autoimmune disease, and the tumor-specific repertoire in cancer. We address limitations of this technology with a focus on emerging potential solutions and discuss the promise of high-resolution immunoproteomics in therapeutic discovery and novel vaccine design.

Maturation trajectories and transcriptional landscape of plasmablasts and autoreactive B cells in COVID-19

In parasite and viral infections, aberrant B cell responses can suppress germinal center reactions thereby blunting long-lived memory and may provoke immunopathology including autoimmunity. Using COVID-19 as model, we set out to identify serological, cellular, and transcriptomic imprints of pathological responses linked to autoreactive B cells at single-cell resolution. We show that excessive plasmablast expansions are prognostically adverse and correlate with autoantibody production but do not hinder the formation of neutralizing antibodies. Although plasmablasts followed interleukin-4 (IL-4) and BAFF-driven developmental trajectories, were polyclonal, and not enriched in autoreactive B cells, we identified two memory populations (CD80⁺/ISG15⁺ and CD11c⁺/SOX5⁺/T-bet^+/−) with immunogenetic and transcriptional signs of autoreactivity that may be the cellular source of autoantibodies in COVID-19 and that may persist beyond recovery. Immunomodulatory interventions discouraging such adverse responses may be useful in selected patients to shift the balance from autoreactivity toward long-term memory.

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Plasmablast expansions correlate with disease severity and autoantibodies in COVID-19

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Patients with high plasmablast levels exhibit IGHV4-34 skewing

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Autoreactive BCRs are enriched in atypical memory, not plasmablast populations

SARS-CoV-2-specific antibody rearrangements in prepandemic immune repertoires of risk cohorts and patients with COVID-19

A considerable fraction of B cells recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with germline-encoded elements of their B cell receptor, resulting in the production of neutralizing and nonneutralizing antibodies. We found that antibody sequences from different discovery cohorts shared biochemical properties and could be retrieved across validation cohorts, confirming the stereotyped character of this naive response in coronavirus disease 2019 (COVID-19). While neutralizing antibody sequences were found independently of disease severity, in line with serological data, individual nonneutralizing antibody sequences were associated with fatal clinical courses, suggesting detrimental effects of these antibodies. We mined 200 immune repertoires from healthy individuals and 500 repertoires from patients with blood or solid cancers - all acquired prior to the pandemic - for SARS-CoV-2 antibody sequences. While the largely unmutated B cell rearrangements occurred in a substantial fraction of immune repertoires from young and healthy individuals, these sequences were less likely to be found in individuals over 60 years of age and in those with cancer. This reflects B cell repertoire restriction in aging and cancer, and may to a certain extent explain the different clinical courses of COVID-19 observed in these risk groups. Future studies will have to address if this stereotyped B cell response to SARS-CoV-2 emerging from unmutated antibody rearrangements will create long-lived memory.

Next-Generation Sequencing of T and B Cell Receptor Repertoires from COVID-19 Patients Showed Signatures Associated with Severity of Disease

We profiled adaptive immunity in COVID-19 patients with active infection or after recovery and created a repository of currently >14 million B and T cell receptor (BCR and TCR) sequences from the blood of these patients. The B cell response showed converging IGHV3-driven BCR clusters closely associated with SARS-CoV-2 antibodies. Clonality and skewing of TCR repertoires were associated with interferon type I and III responses, early CD4+ and CD8+ T cell activation, and counterregulation by the co-receptors BTLA, Tim-3, PD-1, TIGIT, and CD73. Tfh, Th17-like, and nonconventional (but not classical antiviral) Th1 cell polarizations were induced. SARS-CoV-2-specific T cell responses were driven by TCR clusters shared between patients with a characteristic trajectory of clonotypes and traceability over the disease course. Our data provide fundamental insight into adaptive immunity to SARS-CoV-2 with the actively updated repository providing a resource for the scientific community urgently needed to inform therapeutic concepts and vaccine development.

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species

Immunoglobulin genes are formed through V(D)J recombination, which joins the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics focuses on finding alleles of germline genes across various patients. Although reconstruction of V and J genes is a well-studied problem, the more challenging task of reconstructing D genes remained open until the IgScout algorithm was developed in 2019. In this work, we address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction, apply it to hundreds of immunosequencing datasets from multiple species, and validate the newly inferred D genes by analyzing diverse whole genome sequencing datasets and haplotyping heterozygous V genes.

Antibodies provide specific binding to an enormous range of antigens and represent a key component of the adaptive immune system. Immunosequencing has emerged as a method of choice for generating millions of reads that sample antibody repertoires and provides insights into monitoring immune response to disease and vaccination. Most of the previous immunogenomics studies rely on the reference germline genes in the immunoglobulin locus rather than the germline genes in a specific patient. This approach is deficient since the set of known germline genes is incomplete (particularly for non-European humans and non-human species) and contains alleles that resulted from sequencing and annotation errors. The problem of de novo inference of diversity (D) genes from immunosequencing data remained open until the IgScout algorithm was developed in 2019. We address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction and infer multiple D genes across multiple species that are not present in standard databases.

Quantitative Proteomics and Metabolomics Reveal Biomarkers of Disease as Potential Immunotherapy Targets and Indicators of Therapeutic Efficacy

Quantitative mass spectrometry (MS) continues to deepen our understanding of the immune system, quickly becoming the gold standard for obtaining high-throughput, quantitative data on biomolecules. The development of targeted and multiplexed assays for biomarker quantification makes MS an attractive tool both for diagnosing diseases and for quantifying the effects of immunotherapeutics. Because of its accuracy, the use of MS for identifying biomarkers of disease reduces the potential for misdiagnosis and overtreatment. Advances in workflows for sample processing have drastically reduced processing time and complexities due to sample preparation, making MS a more accessible technology. In this review, we present how recent developments in proteomics and metabolomics make MS an essential component of enhancing and monitoring the efficacy of immunotherapeutic treatments.

Role of the Microbiota in the Modulation of Vaccine Immune Responses

The human immune system and the microbiota co-evolve, and their balanced relationship is based on crosstalk between the two systems through the course of life. This tight association and the overall composition and richness of the microbiota play an important role in the modulation of host immunity and may impact the immune response to vaccination. The availability of innovative technologies, such as next-generation sequencing (NGS) and correlated bioinformatics tools, allows a deeper investigation of the crosstalk between the microbiota and human immune responses. This review discusses the current knowledge on the influence of the microbiota on the immune response to vaccination and novel tools to deeply analyze the impact of the microbiome on vaccine responses.

Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires

Abs are immune system proteins that recognize noxious molecules for elimination. Their sequence diversity and binding versatility have made Abs the primary class of biopharmaceuticals. Recently, it has become possible to query their immense natural diversity using next-generation sequencing of Ig gene repertoires (Ig-seq). However, Ig-seq outputs are currently fragmented across repositories and tend to be presented as raw nucleotide reads, which means nontrivial effort is required to reuse the data for analysis. To address this issue, we have collected Ig-seq outputs from 55 studies, covering more than half a billion Ab sequences across diverse immune states, organisms (primarily human and mouse), and individuals. We have sorted, cleaned, annotated, translated, and numbered these sequences and make the data available via our Observed Antibody Space (OAS) resource at http://antibodymap.org The data within OAS will be regularly updated with newly released Ig-seq datasets. We believe OAS will facilitate data mining of immune repertoires for improved understanding of the immune system and development of better biotherapeutics.

ASAP - A Webserver for Immunoglobulin-Sequencing Analysis Pipeline

Reproducible and robust data on antibody repertoires are invaluable for basic and applied immunology. Next-generation sequencing (NGS) of antibody variable regions has emerged as a powerful tool in systems immunology, providing quantitative molecular information on antibody polyclonal composition. However, major computational challenges exist when analyzing antibody sequences, from error handling to hypermutation profiles and clonal expansion analyses. In this work, we developed the ASAP (A webserver for Immunoglobulin-Seq Analysis Pipeline) webserver (https://asap.tau.ac.il). The input to ASAP is a paired-end sequence dataset from one or more replicates, with or without unique molecular identifiers. These datasets can be derived from NGS of human or murine antibody variable regions. ASAP first filters and annotates the sequence reads using public or user-provided germline sequence information. The ASAP webserver next performs various calculations, including somatic hypermutation level, CDR3 lengths, V(D)J family assignments, and V(D)J combination distribution. These analyses are repeated for each replicate. ASAP provides additional information by analyzing the commonalities and differences between the repeats (“joint” analysis). For example, ASAP examines the shared variable regions and their frequency in each replicate to determine which sequences are less likely to be a result of a sample preparation derived and/or sequencing errors. Moreover, ASAP clusters the data to clones and reports the identity and prevalence of top ranking clones (clonal expansion analysis). ASAP further provides the distribution of synonymous and non-synonymous mutations within the V genes somatic hypermutations. Finally, ASAP provides means to process the data for proteomic analysis of serum/secreted antibodies by generating a variable region database for liquid chromatography high resolution tandem mass spectrometry (LC-MS/MS) interpretation. ASAP is user-friendly, free, and open to all users, with no login requirement. ASAP is applicable for researchers interested in basic questions related to B cell development and differentiation, as well as applied researchers who are interested in vaccine development and monoclonal antibody engineering. By virtue of its user-friendliness, ASAP opens the antibody analysis field to non-expert users who seek to boost their research with immune repertoire analysis.

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.