Deep sequencing and human antibody repertoire analysis

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

SARS-CoV-2 spike-reactive naïve B cells and pre-existing memory B cells contribute to antibody responses in unexposed individuals after vaccination

Introduction

Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has presented considerable public health challenges. Multiple vaccines have been used to induce neutralizing antibodies (nAbs) and memory B-cell responses against the viral spike (S) glycoprotein, and many essential epitopes have been defined. Previous reports have identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-reactive naïve B cells and preexisting memory B cells in unexposed individuals. However, the role of these spike-reactive B cells in vaccine-induced immunity remains unknown.

Methods

To elucidate the characteristics of preexisting SARS-CoV-2 S-reactive B cells as well as their maturation after antigen encounter, we assessed the relationship of spike-reactive B cells before and after vaccination in unexposed human individuals. We further characterized the sequence identity, targeting domain, broad-spectrum binding activity and neutralizing activity of these SARS-CoV-2 S-reactive B cells by isolating monoclonal antibodies (mAbs) from these B cells.

Results

The frequencies of both spike-reactive naïve B cells and preexisting memory B cells before vaccination correlated with the frequencies of spike-reactive memory B cells after vaccination. Isolated mAbs from spike-reactive naïve B cells before vaccination had fewer somatic hypermutations (SHMs) than mAbs isolated from spike-reactive memory B cells before and after vaccination, but bound SARS-CoV-2 spike in vitro. Intriguingly, these germline-like mAbs possessed broad binding profiles for SARS-CoV-2 and its variants, although with low or no neutralizing capacity. According to tracking of the evolution of IGHV4-4/IGKV3-20 lineage antibodies from a single donor, the lineage underwent SHMs and developed increased binding activity after vaccination.

Discussion

Our findings suggest that spike-reactive naïve B cells can be expanded and matured by vaccination and cocontribute to vaccine-elicited antibody responses with preexisting memory B cells. Selectively and precisely targeting spike-reactive B cells by rational antigen design may provide a novel strategy for next-generation SARS-CoV-2 vaccine development.

Characterizing adjuvants' effects at murine immunoglobulin repertoire level

Generating large-scale, high-fidelity sequencing data is challenging and, furthermore, not much has been done to characterize adjuvants' effects at the repertoire level. Thus, we introduced an IgSeq pipeline that standardized library prep protocols and data analysis functions for accurate repertoire profiling. We then studied systemically effects of CpG and Alum on the Ig heavy chain repertoire using the ovalbumin (OVA) murine model. Ig repertoires of different tissues (spleen and bone marrow) and isotypes (IgG and IgM) were examined and compared in IGHV mutation, gene usage, CDR3 length, clonal diversity, and clonal selection. We found Ig repertoires of different compartments exhibited distinguishable profiles at the non-immunized steady state, and distinctions became more pronounced upon adjuvanted immunizations. Notably, Alum and CpG effects exhibited different tissue- and isotype-preferences. The former led to increased diversity of abundant clones in bone marrow, and the latter promoted the selection of IgG clones in both tissues.

Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods

BACKGROUND

The mammalian immune system is able to generate antibodies against a huge variety of antigens, including bacteria, viruses, and toxins. The ultradeep DNA sequencing of rearranged immunoglobulin genes has considerable potential in furthering our understanding of the immune response, but it is limited by the lack of a high-throughput, sequence-based method for predicting the antigen(s) that a given immunoglobulin recognizes.

OBJECTIVE

As a step toward the prediction of antibody-antigen binding from sequence data alone, we aimed to compare a range of machine learning approaches that were applied to a collated data set of antibody-antigen pairs in order to predict antibody-antigen binding from sequence data.

METHODS

Data for training and testing were extracted from the Protein Data Bank and the Coronavirus Antibody Database, and additional antibody-antigen pair data were generated by using a molecular docking protocol. Several machine learning methods, including the weighted nearest neighbor method, the nearest neighbor method with the BLOSUM62 matrix, and the random forest method, were applied to the problem.

RESULTS

The final data set contained 1157 antibodies and 57 antigens that were combined in 5041 antibody-antigen pairs. The best performance for the prediction of interactions was obtained by using the nearest neighbor method with the BLOSUM62 matrix, which resulted in around 82% accuracy on the full data set. These results provide a useful frame of reference, as well as protocols and considerations, for machine learning and data set creation in the prediction of antibody-antigen binding.

CONCLUSIONS

Several machine learning approaches were compared to predict antibody-antigen interaction from protein sequences. Both the data set (in CSV format) and the machine learning program (coded in Python) are freely available for download on GitHub.

Evaluation and comparison of adaptive immunity through analyzing the diversities and clonalities of T-cell receptor repertoires in the peripheral blood

The adaptive immune system plays an important role in defending against different kinds of diseases, including infection and cancer. There has been a longtime need for a simple method to quantitatively evaluate the potency of adaptive immunity in our bodies. The tremendously diversified T-cell receptor (TCR) repertoires are the foundation of the adaptive immune system. In this study, we analyzed the expressed TCRβ repertoires in the peripheral blood of 582 healthy donors and 60 cancer patients. The TCR repertoire in each individual is different, with different usages of TCR Vβ and Jβ genes. Importantly, the TCR diversity and clonality change along with age and disease situation. Most elder individuals and cancer patients have elevated numbers of large TCRβ clones and reduced numbers of shared common clones, and thus, they have very low TCR diversity index (D₅₀) values. These results reveal the alteration of the expressed TCRβ repertoire with aging and oncogenesis, and thus, we hypothesize that the TCR diversity and clonality in the peripheral blood might be used to evaluate and compare the adaptive immunities among different individuals in clinical practice.

Advances in Electrochemical and Acoustic Aptamer-Based Biosensors and Immunosensors in Diagnostics of Leukemia

Early diagnostics of leukemia is crucial for successful therapy of this disease. Therefore, development of rapid, sensitive, and easy-to-use methods for detection of this disease is of increased interest. Biosensor technology is challenged for this purpose. This review includes a brief description of the methods used in current clinical diagnostics of leukemia and provides recent achievements in sensor technology based on immuno- and DNA aptamer-based electrochemical and acoustic biosensors. The comparative analysis of immuno- and aptamer-based sensors shows a significant advantage of DNA aptasensors over immunosensors in the detection of cancer cells. The acoustic technique is of comparable sensitivity with those based on electrochemical methods; moreover, it is label-free and provides straightforward evaluation of the signal. Several examples of sensor development are provided and discussed.

Shared B cell memory to coronaviruses and other pathogens varies in human age groups and tissues

It remains unclear whether B cell repertoires against coronaviruses and other pathogens differ between adults and children and how important these distinctions are. Yang et al. analyzed blood samples from young children and adults, as well as tissues from deceased organ donors, characterizing the B cell receptor (BCR) repertoires specific to six common pathogens and two viruses that they had not seen before: Ebola virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Children had higher frequencies of B cells with convergent BCR heavy chains against previously encountered pathogens and higher frequencies of class-switched convergent B cell clones against SARS-CoV-2 and related coronaviruses. These findings suggest that encounters with coronaviruses in early life may produce cross-reactive memory B cell populations that contribute to divergent COVID-19 susceptibilities.

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Vaccination and infection promote the formation, tissue distribution, and clonal evolution of B cells, which encode humoral immune memory. We evaluated pediatric and adult blood and deceased adult organ donor tissues to determine convergent antigen-specific antibody genes of similar sequences shared between individuals. B cell memory varied for different pathogens. Polysaccharide antigenspecific clones were not exclusive to the spleen. Adults had higher clone frequencies and greater class switching in lymphoid tissues than blood, while pediatric blood had abundant class-switched convergent clones. Consistent with reported serology, prepandemic children had class-switched convergent clones to severe acute respiratory syndrome coronavirus 2 with weak cross-reactivity to other coronaviruses, while adult blood or tissues showed few such clones. These results highlight the prominence of early childhood B cell clonal expansions and cross-reactivity for future responses to novel pathogens.

An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity

B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB⁺CD27⁻ early memory population, a class-switched CD39⁺ tonsil-resident population, and a CD19^hiCD11c⁺ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

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A mass cytometry screen reveals 98 surface molecules expressed by human B cells

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High-dimensional analysis identifies twelve B cell subsets across four tissues

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CD45RB, CD11c, CD39, CD73, and CD95 define subsets of antigen-experienced B cells

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Isotype usage, signaling, and metabolism vary in accordance with cell surface phenotype

Sequencing B cell receptors from ferrets (Mustela putorius furo)

The domestic ferret (Mustela putorius furo) provides a critical animal model to study human respiratory diseases. However immunological insights are restricted due to a lack of ferret-specific reagents and limited genetic information about ferret B and T cell receptors. Here, variable, diversity and joining genes within the ferret kappa, lambda and heavy chain immunoglobulin loci were annotated using available genomic information. A multiplex PCR approach was derived that facilitated the recovery of paired heavy and light chain immunoglobulin sequences from single sorted ferret B cells, allowing validation of predicted germline gene sequences and the identification of putative novel germlines. Eukaryotic expression vectors were developed that enabled the generation of recombinant ferret monoclonal antibodies. This work advances the ferret as an informative immunological model for viral diseases by allowing the in-depth interrogation of antibody-based immunity.

The Extended Impact of Human Immunodeficiency Virus/AIDS Research

Human immunodeficiency virus (HIV) is one of the most extensively studied viruses in history, and numerous extraordinary scientific advances, including an in-depth understanding of viral biology, pathogenesis, and life-saving antiretroviral therapies, have resulted from investments in HIV/AIDS research. While the substantial investments in HIV/AIDS research are validated solely on these advances, the collateral broader scientific progress resulting from the support of HIV/AIDS research over the past 30 years is extraordinary as well. The positive impact has ranged from innovations in basic immunology and structural biology to treatments for immune-mediated diseases and cancer and has had an enormous effect on the research and public and global health communities well beyond the field of HIV/AIDS. This article highlights a few select examples of the unanticipated and substantial positive spin-offs of HIV/AIDS research on other scientific areas.

Application of cell-free DNA sequencing in characterization of bloodborne microbes and the study of microbe-disease interactions

It is an important issue whether microorganisms can live harmoniously with normal cells in the cardiovascular system. The answer to the question will have enormous impact on medical microbiology. To address the issue, it is essential to identify and characterize the bloodborne microbes in an efficient and comprehensive manner. Due to microbial sequence complexity and the composition of significant number of unknown microbial species in the circulatory system, traditional approaches using cell culture, PCR, or microarray are not suitable for the purpose. Recent reports indicate that cell-free DNA (cfDNA) sequencing using next-generation sequencing (NGS) or single-molecule sequencing (SMS), together with bioinformatics approaches, possesses a strong potential enabling us to distinguish microbial species at the nucleotide level. Multiple studies using microbial cfDNA sequencing to identify microbes for septic patients have shown strong agreement with cell culture. Similar approaches have also been applied to reveal previously unidentified microorganisms or to demonstrate the feasibility of comprehensive assessment of bloodborne microorganisms for healthy and/or diseased individuals. SMS using either SMRT (single-molecule real-time) sequencing or Nanopore sequencing are providing new momentum to reinforce this line of investigation. Taken together, microbial cfDNA sequencing provides a novel opportunity allowing us to further understand the involvement of bloodborne microbes in development of diseases. Similar approaches should also be applicable to the study of metagenomics for sufficient and comprehensive analysis of microbial species living in various environments. This article reviews this line of research and discuss the methodological approaches that have been developed, or are likely to be developed in the future, which may have strong potential to facilitate cfDNA- and cfRNA-based studies of cancer and acute/chronic diseases, in the hope that a better understanding of the hidden microbes in the circulatory system will improve diagnosis, prevention and treatment of problematic diseases.

Signatures of B-cell receptor diversity in B lymphocytes following Epstein-Barr virus transformation

Epstein-Barr virus (EBV) is a widespread human virus that establishes latent infection, potentially leading to tumors, hematological disorders, and other severe diseases. EBV infections are associated with diverse symptoms and affect various organs; therefore, early diagnosis and treatment are crucial. B cell receptor (BCR) repertoires of B cell surface immunoglobulins have been widely studied for their association with various infectious diseases. However, the specific genetic changes that modulate the BCR repertoires after an EBV infection are still poorly understood. In this study, we employed high-throughput sequencing (HTS) to investigate the diversity of BCR repertoires in an EBV-transformed lymphoblastic cell line (LCL). Compared with the noninfected control B cell line, the LCL exhibited a decrease in overall BCR diversity but displayed an increase in the expansion of some dominant rearrangements such as IGHV4-31/IGHJ4, IGHV4-59/IGHJ4, IGHV5-51/IGHJ3, and IGHV3-74/IGHJ3. A higher frequency of occurrence of these rearrangement types was confirmed in patients with EBV infection. Interestingly, the IGHV3-74 rearrangement was only detected in EBV-infected children, suggesting that our experimental observations were not coincidental. In addition, we identified a highly dominant consensus motif, CAR(xRx)YGSG(xYx)FD, in complementarity-determining region 3 (CDR3) sequences of the heavy chain in the LCL. Our findings demonstrated the utility of HTS technology for studying the variations in signature motifs of the BCR repertoires after EBV infection. We propose that the analysis of BCR repertoire sequences represents a promising method for diagnosing early EBV infections and developing novel antibody- and vaccine-based therapies against such infections.

High-Quality Library Preparation for NGS-Based Immunoglobulin Germline Gene Inference and Repertoire Expression Analysis

Next generation sequencing (NGS) of immunoglobulin (Ig) repertoires (Rep-seq) enables examination of the adaptive immune system at an unprecedented level. Applications include studies of expressed repertoires, gene usage, somatic hypermutation levels, Ig lineage tracing and identification of genetic variation within the Ig loci through inference methods. All these applications require starting libraries that allow the generation of sequence data with low error rate and optimal representation of the expressed repertoire. Here, we provide detailed protocols for the production of libraries suitable for human Ig germline gene inference and Ig repertoire studies. Various parameters used in the process were tested in order to demonstrate factors that are critical to obtain high quality libraries. We demonstrate an improved 5'RACE technique that reduces the length constraints of Illumina MiSeq based Rep-seq analysis but allows for the acquisition of sequences upstream of Ig V genes, useful for primer design. We then describe a 5' multiplex method for library preparation, which yields full length V(D)J sequences suitable for genotype identification and novel gene inference. We provide comprehensive sets of primers targeting IGHV, IGKV, and IGLV genes. Using the optimized protocol, we produced IgM, IgG, IgK, and IgL libraries and analyzed them using the germline inference tool IgDiscover to identify expressed germline V alleles. This process additionally uncovered three IGHV, one IGKV, and six IGLV novel alleles in a single individual, which are absent from the IMGT reference database, highlighting the need for further study of Ig genetic variation. The library generation protocols presented here enable a robust means of analyzing expressed Ig repertoires, identifying novel alleles and producing individualized germline gene databases from humans.

Top-down Mass Spectrometry Analysis of Human Serum Autoantibody Antigen-Binding Fragments

Detecting autoimmune diseases at an early stage is crucial for effective treatment and disease management to slow disease progression and prevent irreversible organ damage. In many autoimmune diseases, disease-specific autoantibodies are produced by B cells in response to soluble autoantigens due to defects in B cell tolerance mechanisms. Autoantibodies accrue early in disease development, and several are so disease-specific they serve as classification criteria. In this study, we established a high-throughput, sensitive, intact serum autoantibody analysis platform based on the optimization of a one dimensional ultra-high-pressure liquid chromatography top-down mass spectrometry platform (1D UPLC-TDMS). This approach has been successfully applied to a 12 standard monoclonal antibody antigen-binding fragment (Fab) mixture, demonstrating the feasibility to separate and sequence intact antibodies with high sequence coverage and high sensitivity. We then applied the optimized platform to characterize total serum antibody Fabs in a systemic lupus erythematosus (SLE) patient sample and compared it to healthy control samples. From this analysis, we show that the SLE sample has many dominant antibody Fab-related mass features unlike the healthy controls. To our knowledge, this is the first top-down demonstration of serum autoantibody pool analysis. Our proposed approach holds great promise for discovering novel serum autoantibody biomarkers that are of interest for diagnosis, prognosis, and tolerance induction, as well as improving our understanding of pathogenic autoimmune processes.

Non-H3 CDR template selection in antibody modeling through machine learning

Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.

Guinea pig immunoglobulin VH and VL naïve repertoire analysis

The guinea pig has been used as a model to study various human infectious diseases because of its similarity to humans regarding symptoms and immune response, but little is known about the humoral immune response. To better understand the mechanism underlying the generation of the antibody repertoire in guinea pigs, we performed deep sequencing of full-length immunoglobulin variable chains from naïve B and plasma cells. We gathered and analyzed nearly 16,000 full-length V_H, V_κ and V_λ genes and analyzed V and J gene segment usage profiles and mutation statuses by annotating recently reported genome data of guinea pig immunoglobulin genes. We found that approximately 70% of heavy, 73% of kappa and 81% of lambda functional germline V gene segments are integrated into the actual V(D)J recombination events. We also found preferential use of a particular V gene segment and accumulated mutation in CDRs 1 and 2 in antigen-specific plasma cells. Our study represents the first attempt to characterize sequence diversity in the expressed guinea pig antibody repertoire and provides significant insight into antibody repertoire generation and Ig-based immunity of guinea pigs.

Applications of Nanomaterials for Immunosensing

In biomedical science among several other growing fields, the detection of specific biological agents or biomolecular markers, from biological samples is crucial for early diagnosis and decision-making in terms of appropriate treatment, influencing survival rates. In this regard, immunosensors are based on specific antibody-antigen interactions, forming a stable immune complex. The antigen-specific detection antibodies (i.e., biomolecular recognition element) are generally immobilized on the nanomaterial surfaces and their interaction with the biomolecular markers or antigens produces a physico-chemical response that modulates the signal readout. Lowering the detection limits for particular biomolecules is one of the key parameters when designing immunosensors. Thus, their design by combining the specificity and versatility of antibodies with the intrinsic properties of nanomaterials offers a plethora of opportunities for clinical diagnosis. In this review, we show a comprehensive set of recent developments in the field of nanoimmunosensors and how they are progressing the detection and validation for a wide range of different biomarkers in multiple diseases and what are some drawbacks and considerations of the uses of such devices and their expansion.

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.

Immunoglobulin gene analysis as a tool for investigating human immune responses

The human immunoglobulin repertoire is a hugely diverse set of sequences that are formed by processes of gene rearrangement, heavy and light chain gene assortment, class switching and somatic hypermutation. Early B cell development produces diverse IgM and IgD B cell receptors on the B cell surface, resulting in a repertoire that can bind many foreign antigens but which has had self-reactive B cells removed. Later antigen-dependent development processes adjust the antigen affinity of the receptor by somatic hypermutation. The effector mechanism of the antibody is also adjusted, by switching the class of the antibody from IgM to one of seven other classes depending on the required function. There are many instances in human biology where positive and negative selection forces can act to shape the immunoglobulin repertoire and therefore repertoire analysis can provide useful information on infection control, vaccination efficacy, autoimmune diseases, and cancer. It can also be used to identify antigen-specific sequences that may be of use in therapeutics. The juxtaposition of lymphocyte development and numerical evaluation of immune repertoires has resulted in the growth of a new sub-speciality in immunology where immunologists and computer scientists/physicists collaborate to assess immune repertoires and develop models of immune action.

Computational Modeling of Hepatitis C Virus Envelope Glycoprotein Structure and Recognition

Hepatitis C virus (HCV) is a major global health concern, and though therapeutic options have improved, no vaccine is available despite decades of research. As HCV can rapidly mutate to evade the immune response, an effective HCV vaccine must rely on identification and characterization of sites critical for broad immune protection and viral neutralization. This knowledge depends on structural and mechanistic insights of the E1 and E2 envelope glycoproteins, which assemble as a heterodimer on the surface of the virion, engage coreceptors during host cell entry, and are the primary targets of antibodies. Due to the challenges in determining experimental structures, structural information on E1 and E2 and their interaction is relatively limited, providing opportunities to model the structures, interactions, and dynamics of these proteins. This review highlights efforts to model the E2 glycoprotein structure, the assembly of the functional E1E2 heterodimer, the structure and binding of human coreceptors, and recognition by key neutralizing antibodies. We also discuss a comparison of recently described models of full E1E2 heterodimer structures, a simulation of the dynamics of key epitope sites, and modeling glycosylation. These modeling efforts provide useful mechanistic hypotheses for further experimental studies of HCV envelope assembly, recognition, and viral fitness, and underscore the benefit of combining experimental and computational modeling approaches to reveal new insights. Additionally, computational design approaches have produced promising candidates for epitope-based vaccine immunogens that specifically target key epitopes, providing a possible avenue to optimize HCV vaccines versus using native glycoproteins. Advancing knowledge of HCV envelope structure and immune recognition is highly applicable toward the development of an effective vaccine for HCV and can provide lessons and insights relevant to modeling and characterizing other viruses.

Shaping a universally broad antibody response to influenza amidst a variable immunoglobulin landscape

One hundred years ago, the 1918 H1N1 Pandemic killed 20 million people worldwide. Despite the introduction of a worldwide surveillance system, large-scale production of influenza vaccines coupled with annual vaccination schemes, influenza remains a major public health concern. Prevention of influenza on a population basis requires intimate knowledge of the interplay between the virus' ability to escape the immune response and persistent recall and regeneration of the antibody response. Here we will briefly outline the nature of the antibody response, focusing on the response to intransigent regions of the hemagglutinin (HA) and speculate on the how this data may be used to inform and ultimately develop a universal influenza vaccine.

Next-Generation Immune Repertoire Sequencing as a Clue to Elucidate the Landscape of Immune Modulation by Host-Gut Microbiome Interactions

The human immune system is a fine network consisted of the innumerable numbers of functional cells that balance the immunity and tolerance against various endogenous and environmental challenges. Although advances in modern immunology have revealed a role of many unique immune cell subsets, technologies that enable us to capture the whole landscape of immune responses against specific antigens have been not available to date. Acquired immunity against various microorganisms including host microbiome is principally founded on T cell and B cell populations, each of which expresses antigen-specific receptors that define a unique clonotype. Over the past several years, high-throughput next-generation sequencing has been developed as a powerful tool to profile T- and B-cell receptor repertoires in a given individual at the single-cell level. Sophisticated immuno-bioinformatic analyses by use of this innovative methodology have been already implemented in clinical development of antibody engineering, vaccine design, and cellular immunotherapy. In this article, we aim to discuss the possible application of high-throughput immune receptor sequencing in the field of nutritional and intestinal immunology. Although there are still unsolved caveats, this emerging technology combined with single-cell transcriptomics/proteomics provides a critical tool to unveil the previously unrecognized principle of host–microbiome immune homeostasis. Accumulation of such knowledge will lead to the development of effective ways for personalized immune modulation through deeper understanding of the mechanisms by which the intestinal environment affects our immune ecosystem.

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.

Antibody Heavy Chain Variable Domains of Different Germline Gene Origins Diversify through Different Paths

B cells produce antibodies, key effector molecules in health and disease. They mature their properties, including their affinity for antigen, through hypermutation events; processes that involve, e.g., base substitution, codon insertion and deletion, often in association with an isotype switch. Investigations of antibody evolution define modes whereby particular antibody responses are able to form, and such studies provide insight important for instance for development of efficient vaccines. Antibody evolution is also used in vitro for the design of antibodies with improved properties. To better understand the basic concepts of antibody evolution, we analyzed the mutational paths, both in terms of amino acid substitution and insertions and deletions, taken by antibodies of the IgG isotype. The analysis focused on the evolution of the heavy chain variable domain of sets of antibodies, each with an origin in 1 of 11 different germline genes representing six human heavy chain germline gene subgroups. Investigated genes were isolated from cells of human bone marrow, a major site of antibody production, and characterized by next-generation sequencing and an in-house bioinformatics pipeline. Apart from substitutions within the complementarity determining regions, multiple framework residues including those in protein cores were targets of extensive diversification. Diversity, both in terms of substitutions, and insertions and deletions, in antibodies is focused to different positions in the sequence in a germline gene-unique manner. Altogether, our findings create a framework for understanding patterns of evolution of antibodies from defined germline genes.

In Silico Prediction Analysis of Idiotope-Driven T-B Cell Collaboration in Multiple Sclerosis

Memory B cells acting as antigen-presenting cells are believed to be important in multiple sclerosis (MS), but the antigen they present remains unknown. We hypothesized that B cells may activate CD4⁺ T cells in the central nervous system of MS patients by presenting idiotopes from their own immunoglobulin variable regions on human leukocyte antigen (HLA) class II molecules. Here, we use bioinformatics prediction analysis of B cell immunoglobulin variable regions from 11 MS patients and 6 controls with other inflammatory neurological disorders (OINDs), to assess whether the prerequisites for such idiotope-driven T–B cell collaboration are present. Our findings indicate that idiotopes from the complementarity determining region (CDR) 3 of MS patients on average have high predicted affinities for disease associated HLA-DRB1*15:01 molecules and are predicted to be endosomally processed by cathepsin S and L in positions that allows such HLA binding to occur. Additionally, complementarity determining region 3 sequences from cerebrospinal fluid (CSF) B cells from MS patients contain on average more rare T cell-exposed motifs that could potentially escape tolerance and stimulate CD4⁺ T cells than CSF B cells from OIND patients. Many of these features were associated with preferential use of the IGHV4 gene family by CSF B cells from MS patients. This is the first study to combine high-throughput sequencing of patient immune repertoires with large-scale prediction analysis and provides key indicators for future in vitro and in vivo analyses.

Rare, high-affinity anti-pathogen antibodies from human repertoires, discovered using microfluidics and molecular genomics

Affinity-matured, functional anti-pathogen antibodies are present at low frequencies in natural human repertoires. These antibodies are often excellent candidates for therapeutic monoclonal antibodies. However, mining natural human antibody repertoires is a challenge. In this study, we demonstrate a new method that uses microfluidics, yeast display, and deep sequencing to identify 247 natively paired anti-pathogen single-chain variable fragments (scFvs), which were initially as rare as 1 in 100,000 in the human repertoires. Influenza A vaccination increased the frequency of influenza A antigen-binding scFv within the peripheral B cell repertoire from <0.1% in non-vaccinated donors to 0.3-0.4% in vaccinated donors, whereas pneumococcus vaccination did not increase the frequency of antigen-binding scFv. However, the pneumococcus scFv binders from the vaccinated library had higher heavy and light chain Replacement/Silent mutation (R/S) ratios, a measure of affinity maturation, than the pneumococcus binders from the corresponding non-vaccinated library. Thus, pneumococcus vaccination may increase the frequency of affinity-matured antibodies in human repertoires. We synthesized 10 anti-influenza A and nine anti-pneumococcus full-length antibodies that were highly abundant among antigen-binding scFv. All 10 anti-influenza A antibodies bound the appropriate antigen at K_D<10 nM and neutralized virus in cellular assays. All nine anti-pneumococcus full-length antibodies bound at least one polysaccharide serotype, and 71% of the anti-pneumococcus antibodies that we tested were functional in cell killing assays. Our approach has future application in a variety of fields, including the development of therapeutic antibodies for emerging viral diseases, autoimmune disorders, and cancer.

Promoting immunology: The future is here

Science Immunology will provide a broad platform for the most exciting findings in this growing field.