Understanding the human antibody repertoire

Antibody sequence determinants of viral antigen specificity

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

Discovery of Two Novel Immunoepitopes and Development of a Peptide-based Sarcoidosis Immunoassay

Rationale: Sarcoidosis is a systemic granulomatous disorder associated with hypergammaglobulinemia and the presence of autoantibodies. The specific antigens initiating granulomatous inflammation in sarcoidosis are unknown, and there is no specific test available to diagnose sarcoidosis. To discover novel sarcoidosis antigens, we developed a high-throughput T7 phage display library derived from the sarcoidosis cDNA and identified numerous clones differentiating sarcoidosis from other respiratory diseases. After clone sequencing and a homology search, we identified two epitopes (cofilin μ and chain A) that specifically bind to serum IgGs of patients with sarcoidosis. Objectives: To develop and validate an epitope-specific IgG-based immunoassay specific for sarcoidosis. Methods: We chemically synthesized both immunoepitopes (cofilin μ and chain A) and generated rabbit polyclonal antibodies against both neoantigens. After extensive standardization, we developed a direct peptide ELISA and measured epitope-specific IgG in the sera of 386 subjects, including healthy control subjects (n = 100), three sarcoidosis cohorts (n = 186), pulmonary tuberculosis (n = 70), and lung cancer (n = 30). Measurements and Main Results: To develop a model to classify sarcoidosis distinctly from other groups, data were analyzed using fivefold cross-validation when adjusting for confounders. The cofilin μ IgG model yielded a mean sensitivity, specificity, and positive and negative predictive value of 0.97, 0.9, 0.9, and 0.96, respectively. Those same measures for chain A IgG antibody were 0.9, 0.83, 0.84, and 0.9, respectively. Combining both biomarkers improved the area under the curve, sensitivity, specificity, and positive and negative predictive value. Conclusions: These results provide a novel immunoassay for sarcoidosis. The discovery of two neoantigens facilitates the development of biospecific drug discovery and the sarcoidosis-specific model.

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents

High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development.

Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques

The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.

Antibody design using deep learning: from sequence and structure design to affinity maturation

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

Serum antibody screening using glycan arrays

Humans and other animals produce a diverse collection of antibodies, many of which bind to carbohydrate chains, referred to as glycans. These anti-glycan antibodies are a critical part of our immune systems' defenses. Whether induced by vaccination or natural exposure to a pathogen, anti-glycan antibodies can provide protection against infections and cancers. Alternatively, when an immune response goes awry, antibodies that recognize self-glycans can mediate autoimmune diseases. In any case, serum anti-glycan antibodies provide a rich source of information about a patient's overall health, vaccination history, and disease status. Glycan microarrays provide a high-throughput platform to rapidly interrogate serum anti-glycan antibodies and identify new biomarkers for a variety of conditions. In addition, glycan microarrays enable detailed analysis of the immune system's response to vaccines and other treatments. Herein we review applications of glycan microarray technology for serum anti-glycan antibody profiling.

Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening

Developing therapeutic antibodies is laborious and costly. Here we report a method for antibody discovery that leverages the Illumina HiSeq platform to, within 3 days, screen in the order of 108 antibody-antigen interactions. The method, which we named 'deep screening', involves the clustering and sequencing of antibody libraries, the conversion of the DNA clusters into complementary RNA clusters covalently linked to the instrument's flow-cell surface on the same location, the in situ translation of the clusters into antibodies tethered via ribosome display, and their screening via fluorescently labelled antigens. By using deep screening, we discovered low-nanomolar nanobodies to a model antigen using 4 × 106 unique variants from yeast-display-enriched libraries, and high-picomolar single-chain antibody fragment leads for human interleukin-7 directly from unselected synthetic repertoires. We also leveraged deep screening of a library of 2.4 × 105 sequences of the third complementarity-determining region of the heavy chain of an anti-human epidermal growth factor receptor 2 (HER2) antibody as input for a large language model that generated new single-chain antibody fragment sequences with higher affinity for HER2 than those in the original library.

Building Representation Learning Models for Antibody Comprehension

Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.

Systematic evaluation of B-cell clonal family inference approaches

The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Massive integration of large gene libraries in the chromosome of Escherichia coli

Large gene libraries are frequently created in Escherichia coli plasmids, which can induce cell toxicity and expression instability due to the high gene dosage. To address these limitations, gene libraries can be integrated in a single copy into the bacterial chromosome. Here, we describe an efficient system for the massive integration (MAIN) of large gene libraries in the E. coli chromosome that generates in-frame gene fusions that are expressed stably. MAIN uses a thermosensitive integrative plasmid that is linearized in vivo to promote extensive integration of the gene library via homologous recombination. Positive and negative selections efficiently remove bacteria lacking gene integration in the target site. We tested MAIN with a library of 107 VHH genes that encode nanobodies (Nbs). The integration of VHH genes into a custom target locus of the E. coli chromosome enabled stable expression and surface display of the Nbs. Next-generation DNA sequencing confirmed that MAIN preserved the diversity of the gene library after integration. Finally, we screened the integrated library to select Nbs that bind a specific antigen using magnetic and fluorescence-activated cell sorting. This allowed us to identify Nbs binding the epidermal growth factor receptor that were not previously isolated in a similar screening of a multicopy plasmid library. Our results demonstrate that MAIN enables large gene library integration into the E. coli chromosome, creating stably expressed in-frame fusions for functional screening.

Protective fibroblastic niches in secondary lymphoid organs

Fibroblastic reticular cells (FRCs) are specialized fibroblasts of secondary lymphoid organs that provide the structural foundation of the tissue. Moreover, FRCs guide immune cells to dedicated microenvironmental niches where they provide lymphocytes and myeloid cells with homeostatic growth and differentiation factors. Inflammatory processes, including infection with pathogens, induce rapid morphological and functional adaptations that are critical for the priming and regulation of protective immune responses. However, adverse FRC reprogramming can promote immunopathological tissue damage during infection and autoimmune conditions and subvert antitumor immune responses. Here, we review recent findings on molecular pathways that regulate FRC-immune cell crosstalk in specialized niches during the generation of protective immune responses in the course of pathogen encounters. In addition, we discuss how FRCs integrate immune cell-derived signals to ensure protective immunity during infection and how therapies for inflammatory diseases and cancer can be developed through improved understanding of FRC-immune cell interactions.

Development of Therapeutic Monoclonal Antibodies for Emerging Arbovirus Infections

Antibody-based passive immunotherapy has been used effectively in the treatment and prophylaxis of infectious diseases. Outbreaks of emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to their rapid spread, urging measures and the treatment of infected individuals to combat them. Preparedness in advances in developing antivirals and relevant epidemiological studies protect us from damage and losses. Immunotherapy based on monoclonal antibodies (mAbs) has been shown to be very specific in combating infectious diseases and various other illnesses. Recent advances in mAb discovery techniques have allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and the next-generation strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for dengue, Zika, chikungunya, West Nile and tick-borne encephalitis virus are presented, as well as the protective effect demonstrated in animal model studies.

Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe

The adaptive immune system of jawed vertebrates generates a highly diverse repertoire of antibodies to meet the antigenic challenges of a constantly evolving biological ecosystem. Most of the diversity is generated by two mechanisms: V(D)J gene recombination and somatic hypermutation (SHM). SHM introduces changes in the variable domain of antibodies, mostly in the regions that form the paratope, yielding antibodies with higher antigen binding affinity. However, antigen recognition is only possible if the antibody folds into a stable functional conformation. Therefore, a key force determining the survival of B cell clones undergoing somatic hypermutation is the ability of the mutated heavy and light chains to efficiently fold and assemble into a functional antibody. The antibody is the structural context where the selection of the somatic mutations occurs, and where both the heavy and light chains benefit from protective mechanisms that counteract the potentially deleterious impact of the changes. However, in patients with monoclonal gammopathies, the proliferating plasma cell clone may overproduce the light chain, which is then secreted into the bloodstream. This places the light chain out of the protective context provided by the quaternary structure of the antibody, increasing the risk of misfolding and aggregation due to destabilizing somatic mutations. Light chain-derived (AL) amyloidosis, light chain deposition disease (LCDD), Fanconi syndrome, and myeloma (cast) nephropathy are a diverse group of diseases derived from the pathologic aggregation of light chains, in which somatic mutations are recognized to play a role. In this review, we address the mechanisms by which somatic mutations promote the misfolding and pathological aggregation of the light chains, with an emphasis on AL amyloidosis. We also analyze the contribution of the variable domain (V_L) gene segments and somatic mutations on light chain cytotoxicity, organ tropism, and structure of the AL fibrils. Finally, we analyze the most recent advances in the development of computational algorithms to predict the role of somatic mutations in the cardiotoxicity of amyloidogenic light chains and discuss the challenges and perspectives that this approach faces.

Antigen presentation dynamics shape the antibody response to variants like SARS-CoV-2 Omicron after multiple vaccinations with the original strain

The Omicron variant of SARS-CoV-2 is not effectively neutralized by most antibodies elicited by two doses of mRNA vaccines, but a third dose increases anti-Omicron neutralizing antibodies. We reveal mechanisms underlying this observation by combining computational modeling with data from vaccinated humans. After the first dose, limited antigen availability in germinal centers (GCs) results in a response dominated by B cells that target immunodominant epitopes that are mutated in an Omicron-like variant. After the second dose, these memory cells expand and differentiate into plasma cells that secrete antibodies that are thus ineffective for such variants. However, these pre-existing antigen-specific antibodies transport antigen efficiently to secondary GCs. They also partially mask immunodominant epitopes. Enhanced antigen availability and epitope masking in secondary GCs together result in generation of memory B cells that target subdominant epitopes that are less mutated in Omicron. The third dose expands these cells and boosts anti-variant neutralizing antibodies.

Pathophysiological Effects of Contemporary Lifestyle on Evolutionary-Conserved Survival Mechanisms in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is increasingly being characterized as an evolutionary mismatch disorder that presents with a complex mixture of metabolic and endocrine symptoms. The Evolutionary Model proposes that PCOS arises from a collection of inherited polymorphisms that have been consistently demonstrated in a variety of ethnic groups and races. In utero developmental programming of susceptible genomic variants are thought to predispose the offspring to develop PCOS. Postnatal exposure to lifestyle and environmental risk factors results in epigenetic activation of developmentally programmed genes and disturbance of the hallmarks of health. The resulting pathophysiological changes represent the consequences of poor-quality diet, sedentary behaviour, endocrine disrupting chemicals, stress, circadian disruption, and other lifestyle factors. Emerging evidence suggests that lifestyle-induced gastrointestinal dysbiosis plays a central role in the pathogenesis of PCOS. Lifestyle and environmental exposures initiate changes that result in disturbance of the gastrointestinal microbiome (dysbiosis), immune dysregulation (chronic inflammation), altered metabolism (insulin resistance), endocrine and reproductive imbalance (hyperandrogenism), and central nervous system dysfunction (neuroendocrine and autonomic nervous system). PCOS can be a progressive metabolic condition that leads to obesity, gestational diabetes, type two diabetes, metabolic-associated fatty liver disease, metabolic syndrome, cardiovascular disease, and cancer. This review explores the mechanisms that underpin the evolutionary mismatch between ancient survival pathways and contemporary lifestyle factors involved in the pathogenesis and pathophysiology of PCOS.

Understanding repertoire sequencing data through a multiscale computational model of the germinal center

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

Mitigating Serious Adverse Events in Gene Therapy with AAV Vectors: Vector Dose and Immunosuppression

Gene transfer with high doses of adeno-associated viral (AAV) vectors has resulted in serious adverse events and even death of the recipients. Toxicity could most likely be circumvented by repeated injections of lower and less toxic doses of vectors. This has not been pursued as AAV vectors induce potent neutralizing antibodies, which prevent cell transduction upon reinjection of the same vector. This review discusses different types of immune responses against AAV vectors and how they offer targets for the elimination or inhibition of vector-specific neutralizing antibodies. Such antibodies can be circumvented by using different virus serotypes for sequential injections, they can be removed by plasmapheresis, or they can be destroyed by enzymatic degradation. Antibody producing cells can be eliminated by proteasome inhibitors. Drugs that inhibit T-cell responses, B-cell signaling, or presentation of the vector's antigens to B cells can prevent or reduce induction of AAV-specific antibodies. Combinations of different approaches and drugs are likely needed to suppress or eliminate neutralizing antibodies, which would then allow for repeated dosing. Alternatively, novel AAV vectors with higher transduction efficacy are being developed and may allow for a dose reduction, although it remains unknown if this will completely address the problem of high-dose adverse events.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Toward real-world automated antibody design with combinatorial Bayesian optimization

Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning

Antibodies are a rapidly growing class of therapeutics. Recently, single domain camelid VHH antibodies, and their recognition nanobody domain (Nb) appeared as a cost-effective highly stable alternative to full-length antibodies. There is a growing need for high-throughput epitope mapping based on accurate structural modeling of the variable domains that share a common fold and differ in the Complementarity Determining Regions (CDRs). We develop a deep learning end-to-end model, NanoNet, that given a sequence directly produces the 3D coordinates of the backbone and Cβ atoms of the entire VH domain. For the Nb test set, NanoNet achieves 3.16Å average RMSD for the most variable CDR3 loops and 2.65Å, 1.73Å for the CDR1, CDR2 loops, respectively. The accuracy for antibody VH domains is even higher: 2.38Å RMSD for CDR3 and 0.89Å, 0.96Å for the CDR1, CDR2 loops, respectively. NanoNet run times allow generation of ∼1M nanobody structures in less than 4 hours on a standard CPU computer enabling high-throughput structure modeling. NanoNet is available at GitHub: https://github.com/dina-lab3D/NanoNet.

The Relative Positioning of B and T Cell Epitopes Drives Immunodominance

Humoral immunity is crucial for protection against invading pathogens. Broadly neutralizing antibodies (bnAbs) provide sterilizing immunity by targeting conserved regions of viral variants and represent the goal of most vaccination approaches. While antibodies can be selected to bind virtually any region of a given antigen, the consistent induction of bnAbs in the context of influenza and HIV has represented a major roadblock. Many possible explanations have been considered; however, none of the arguments proposed to date seem to fully recapitulate the observed counter-selection for broadly protective antibodies. Antibodies can influence antigen presentation by enhancing the processing of CD4 epitopes adjacent to the binding region while suppressing the overlapping ones. We analyze the relative positioning of dominant B and T cell epitopes in published antigens that elicit strong and poor humoral responses. In strong immunogenic antigens, regions bound by immunodominant antibodies are frequently adjacent to CD4 epitopes, potentially boosting their presentation. Conversely, poorly immunogenic regions targeted by bnAbs in HIV and influenza overlap with clusters of dominant CD4 epitopes, potentially conferring an intrinsic disadvantage for bnAb-bearing B cells in germinal centers. Here, we propose the theory of immunodominance relativity, according to which the relative positioning of immunodominant B and CD4 epitopes within a given antigen drives immunodominance. Thus, we suggest that the relative positioning of B-T epitopes may be one additional mechanism that cooperates with other previously described processes to influence immunodominance. If demonstrated, this theory can improve the current understanding of immunodominance, provide a novel explanation for HIV and influenza escape from humoral responses, and pave the way for a new rational design of universal vaccines.

Antibody Diversity in Cancer: Translational Implications and Beyond

Patients with cancer tend to develop antibodies to autologous proteins. This phenomenon has been observed across multiple cancer types, including bladder, lung, colon, prostate, and melanoma. These antibodies potentially arise due to induced inflammation or an increase in self-antigens. Studies focusing on antibody diversity are particularly attractive for their diagnostic value considering antibodies are present at an early diseased stage, serum samples are relatively easy to obtain, and the prevalence of antibodies is high even when the target antigen is minimally expressed. Conversely, the surveillance of serum proteins in cancer patients is relatively challenging because they often show variability in expression and are less abundant. Moreover, an antibody’s presence is also useful as it suggests the relative immunogenicity of a given antigen. For these reasons, profiling antibodies’ responses is actively considered to detect the spread of antigens following immunotherapy. The current review focuses on expanding the knowledge of antibodies and their diversity, and the impact of antibody diversity on cancer regression and progression.

Deciphering the language of antibodies using self-supervised learning

An individual’s B cell receptor (BCR) repertoire encodes information about past immune responses and potential for future disease protection. Deciphering the information stored in BCR sequence datasets will transform our understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. A key challenge of BCR sequence analysis is the prediction of BCR properties from their amino acid sequence alone. Here, we present an antibody-specific language model, Antibody-specific Bidirectional Encoder Representation from Transformers (AntiBERTa), which provides a contextualized representation of BCR sequences. Following pre-training, we show that AntiBERTa embeddings capture biologically relevant information, generalizable to a range of applications. As a case study, we fine-tune AntiBERTa to predict paratope positions from an antibody sequence, outperforming public tools across multiple metrics. To our knowledge, AntiBERTa is the deepest protein-family-specific language model, providing a rich representation of BCRs. AntiBERTa embeddings are primed for multiple downstream tasks and can improve our understanding of the language of antibodies.

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AntiBERTa is an antibody-specific transformer model for representation learning

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AntiBERTa embeddings capture aspects of antibody function

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Attention maps of AntiBERTa correspond to structural contacts and binding sites

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AntiBERTa can be fine-tuned for state-of-the-art paratope prediction

Understanding antibody function is critical for deciphering the biology of disease and for the discovery of novel therapeutic antibodies. The challenge is the vast diversity of antibody variants compared with the limited labeled data available. We overcome this challenge by using self-supervised learning to train a large antibody-specific language model, followed by transfer learning, to fine-tune the model for predicting information related to antibody function. We initially demonstrate the success of the model by providing leading results in antibody binding site prediction. The model is amenable to further fine-tuning for diverse applications to improve our understanding of antibody function.

Next-Generation Sequencing-Based Clonality Detection of Immunoglobulin Gene Rearrangements in B-Cell Lymphoma

Immunoglobulin (IG) clonality assessment is a widely used supplementary test for the diagnosis of suspected lymphoid malignancies. The specific rearrangements of the immunoglobulin (IG) heavy and light chain genes act as a unique hallmark of a B-cell lymphoma, a feature that is used in clonality assessment. The widely used BIOMED-2/EuroClonality IG clonality assay, visualized by GeneScanning or heteroduplex analysis, has an unprecedented high detection rate because of the complementarity of this approach. However, the BIOMED-2/EuroClonality clonality assays have been developed for the assessment of specimens with optimal DNA quality. Further improvements for the assessment of samples with suboptimal DNA quality, such as from formalin-fixed paraffin-embedded (FFPE) specimens or specimens with a limited tumor burden, are required. The EuroClonality-NGS Working Group recently developed a next-generation sequencing (NGS)-based clonality assay for the detection of the IG heavy and kappa light chain rearrangements, using the same complementary approach as in the conventional assay. By employing next-generation sequencing, both the sensitivity and specificity of the clonality assay have increased, which not only is very useful for diagnostic clonality testing but also allows robust comparison of clonality patterns in a patient with multiple lymphoma's that have suboptimal DNA quality. Here, we describe the protocols for IG-NGS clonality assessment that are compatible for Ion Torrent and Illumina sequencing platforms including pre-analytical DNA isolation, the analytical phase, and the post-analytical data analysis.

Moving beyond Titers

Vaccination to prevent and even eliminate disease is amongst the greatest achievements of modern medicine. Opportunities remain in vaccine development to improve protection across the whole population. A next step in vaccine development is the detailed molecular characterization of individual humoral immune responses against a pathogen, especially the rapidly evolving pathogens. New technologies such as sequencing the immune repertoire in response to disease, immunogenomics/vaccinomics, particularly the individual HLA variants, and high-throughput epitope characterization offer new insights into disease protection. Here, we highlight the emerging technologies that could be used to identify variation within the human population, facilitate vaccine discovery, improve vaccine safety and efficacy, and identify mechanisms of generating immunological memory. In today’s vaccine-hesitant climate, these techniques used individually or especially together have the potential to improve vaccine effectiveness and safety and thus vaccine uptake rates. We highlight the importance of using these techniques in combination to understand the humoral immune response as a whole after vaccination to move beyond neutralizing titers as the standard for immunogenicity and vaccine efficacy, especially in clinical trials.

Single-Cell Technologies for the Study of Antibody-Secreting Cells

Antibody-secreting cells (ASC), plasmablasts and plasma cells, are terminally differentiated B cells responsible for large-scale production and secretion of antibodies. ASC are derived from activated B cells, which may differentiate extrafollicularly or form germinal center (GC) reactions within secondary lymphoid organs. ASC therefore consist of short-lived, poorly matured plasmablasts that generally secrete lower-affinity antibodies, or long-lived, highly matured plasma cells that generally secrete higher-affinity antibodies. The ASC population is responsible for producing an immediate humoral B cell response, the polyclonal antibody repertoire, as well as in parallel building effective humoral memory and immunity, or potentially driving pathology in the case of autoimmunity. ASC are phenotypically and transcriptionally distinct from other B cells and further distinguishable by morphology, varied lifespans, and anatomical localization. Single cell analyses are required to interrogate the functional and transcriptional diversity of ASC and their secreted antibody repertoire and understand the contribution of individual ASC responses to the polyclonal humoral response. Here we summarize the current and emerging functional and molecular techniques for high-throughput characterization of ASC with single cell resolution, including flow and mass cytometry, spot-based and microfluidic-based assays, focusing on functional approaches of the secreted antibodies: specificity, affinity, and secretion rate.

Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies

Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis.

Antibodies are a key component of the immune system that combat pathogens by binding to a defined region of their molecular surface (known as an ‘epitope’). The ability to map which antibodies target the same epitopes is crucial when designing non-competing antibody therapeutics or predicting the influence of pathogen mutation on population immunity. While one can use laboratory experiments to deduce when pairs of antibodies engage the same epitope, such experiments are very expensive and time consuming if used to compare on the order of thousands of antibodies. In this work, we report a new computational algorithm (SPACE) that clusters antibodies that target the same epitope based on their predicted 3D structure, as binding site structure is a property often conserved between binders complementary to the same epitope. Unlike existing antibody epitope profiling tools which assume two antibodies must share a high sequence identity/similar genetic basis to engage the same region, our orthogonal method can detect broader patterns of convergent evolution across binders to different pathogen strains, and between antibodies with different genetic and even species origins.

Single B cell technologies for monoclonal antibody discovery

Monoclonal antibodies (mAbs) are often selected from antigen-specific single B cells derived from different hosts, which are notably short-lived in ex vivo culture conditions and hence, arduous to interrogate. The development of several new techniques and protocols has facilitated the isolation and retrieval of antibody-coding sequences of antigen-specific B cells by also leveraging miniaturization of reaction volumes. Alternatively, mAbs can be generated independently of antigen-specific B cells, comprising display technologies and, more recently, artificial intelligence-driven algorithms. Consequently, a considerable variety of techniques are used, raising the demand for better consolidation. In this review, we present and discuss the major techniques available to interrogate antigen-specific single B cells to isolate antigen-specific mAbs, including their main advantages and disadvantages.

ssDNA recombineering boosts in vivo evolution of nanobodies displayed on bacterial surfaces

ssDNA recombineering has been exploited to hyperdiversify genomically-encoded nanobodies displayed on the surface of Escherichia coli for originating new binding properties. As a proof-of-principle a nanobody recognizing the antigen TirM from enterohaemorrhagic E. coli (EHEC) was evolved towards the otherwise not recognized TirM antigen from enteropathogenic E. coli (EPEC). To this end, E. coli cells displaying this nanobody fused to the intimin outer membrane-bound domain were subjected to multiple rounds of mutagenic oligonucleotide recombineering targeting the complementarity determining regions (CDRs) of the cognate VHH gene sequence. Binders to the EPEC-TirM were selected upon immunomagnetic capture of bacteria bearing active variants and nanobodies identified with a new ability to strongly bind the new antigen. The results highlight the power of combining evolutionary properties of bacteria in vivo with oligonucleotide synthesis in vitro for the sake of focusing diversification to specific segments of a gene (or protein thereof) of interest.

Could β-Lactam Antibiotics Block Humoral Immunity?

It has been reported that treatment with β-lactam antibiotics induces leukopenia and candidemia, worsens the clinical response to anticancer immunotherapy and decreases immune response to vaccination. β-lactamases can cleave β-lactam antibiotics by blocking their activity. Two distincts superfamilies of β-lactamases are described, the serine β-lactamases and the zinc ion dependent metallo-β-lactamases. In human, 18 metallo-β-lactamases encoding genes (hMBLs) have been identified. While the physiological role of most of them remains unknown, it is well established that the SNM1A, B and C proteins are involved in DNA repair. The SNM1C/Artemis protein is precisely associated in the V(D)J segments rearrangement, that leads to immunoglobulin (Ig) and T-cell receptor variable regions, which have a crucial role in the immune response. Thus in humans, SNM1C/Artemis mutation is associated with severe combined immunodeficiency characterized by hypogammaglobulinemia deficient cellular immunity and opportunistic infections. While catalytic site of hMBLs and especially that of the SNM1 family is highly conserved, in vitro studies showed that some β-lactam antibiotics, and precisely third generation of cephalosporin and ampicillin, inhibit the metallo-β-lactamase proteins SNM1A & B and the SNM1C/Artemis protein complex. By analogy, the question arises as to whether β-lactam antibiotics can block the SNM1C/Artemis protein in humans inducing transient immunodeficiency. We reviewed here the literature data supporting this hypothesis based on in silico, in vitro and in vivo evidences. Understanding the impact of β-lactam antibiotics on the immune cell will offer new therapeutic clues and new clinical approaches in oncology, immunology, and infectious diseases.

New Frontiers for Machine Learning in Protein Science

Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between proteins that exist on a broad spectrum of dynamic conformational states and levels of intrinsic disorder. Additionally, the sizes of the structures formed can range from simple binary complexes to large dynamic biomolecular condensates measuring 100 nm or more. Understanding the parameters that govern such interactions, how they form, how they lead to function and what happens when they take place in unintended manners and lead to disease, represent some of the core questions for molecular biosciences. In light of recent advances made in solving the protein folding problem by machine learning methods, we discuss here the challenges and opportunities brought by these new data-driven approaches for the next frontiers of biomolecular science.

Methods and cell-based strategies to produce antibody libraries: current state

Antibodies are critical components of the adaptive immune system, whose therapeutic applications have been growing exponentially in the last years. Discovery and development of therapeutic antibodies encompasses in vivo immunization, synthetic libraries, and surface display methodologies. To overcome some of their limitations, several platforms in higher eukaryotic cells have been developed. Moreover, these platforms aim to replicate in the bench both primary and secondary antibody diversification mechanisms that occur in vivo. Here, we describe the latest strategies that have been used to mirror both naïve and affinity-maturated antibody repertoire. KEY POINTS: • Therapeutic antibodies are one of the most promising classes of drugs to fight diseases. • Antibodies discovered through hybridoma or display technologies require further engineering. • Innovative antibody discovery platforms in higher eukaryotic cells have been developed.

Progress and challenges in mass spectrometry-based analysis of antibody repertoires

Humoral immunity is divided into the cellular B cell and protein-level antibody responses. High-throughput sequencing has advanced our understanding of both these fundamental aspects of B cell immunology as well as aspects pertaining to vaccine and therapeutics biotechnology. Although the protein-level serum and mucosal antibody repertoire make major contributions to humoral protection, the sequence composition and dynamics of antibody repertoires remain underexplored. This limits insight into important immunological and biotechnological parameters such as the number of antigen-specific antibodies, which are for example, relevant for pathogen neutralization, microbiota regulation, severity of autoimmunity, and therapeutic efficacy. High-resolution mass spectrometry (MS) has allowed initial insights into the antibody repertoire. We outline current challenges in MS-based sequence analysis of antibody repertoires and propose strategies for their resolution.

The RNA regulatory programs that govern lymphocyte development and function

Lymphocytes require of constant and dynamic changes in their transcriptome for timely activation and production of effector molecules to combat external pathogens. Synthesis and translation of messenger (m)RNAs into these effector proteins is controlled both quantitatively and qualitatively by RNA binding proteins (RBPs). RBP-dependent regulation of RNA editing, subcellular location, stability, and translation shapes immune cell development and immunity. Extensive evidences have now been gathered from few model RBPs, HuR, PTBP1, ZFP36, and Roquin. However, recently developed methodologies for global characterization of protein:RNA interactions suggest the existence of complex RNA regulatory networks in which RBPs co-ordinately regulate the fate of sets of RNAs controlling cellular pathways and functions. In turn, RNA can also act as scaffolding of functionally related proteins modulating their activation and function. Here we review current knowledge about how RBP-dependent regulation of RNA shapes our immune system and discuss about the existence of a hidden immune cell epitranscriptome. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 Patients

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8⁺ T cell population. Highly expanded CD8⁺ and CD4⁺ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

EPIphany—A Platform for Analysis and Visualization of Peptide Immunoarray Data

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates. High-resolution analysis of these populations typically requires advanced techniques such as B cell receptor repertoire sequencing, mass spectrometry of isolated immunoglobulins, or phage display libraries that are dependent upon equipment and expertise which are prohibitive for many labs. High-density peptide microarrays representing diverse populations of putative linear epitopes (immunoarrays) are an effective alternative for high-throughput examination of antibody reactivity and diversity. While a promising technology, widespread adoption of immunoarrays has been limited by the need for, and relative absence of, user-friendly tools for consideration and visualization of the emerging data. To address this limitation, we developed EPIphany, a software platform with a simple web-based user interface, aimed at biological users, that provides access to important analysis parameters, data normalization options, and a variety of unique data visualization options. This platform provides researchers the greatest opportunity to extract biologically meaningful information from the immunoarray data, thereby facilitating the discovery and development of novel immuno-therapeutics.

Dichotomous impact of affinity on the function of T cell engaging bispecific antibodies

BACKGROUND

Bispecific T cell engagers represent the majority of bispecific antibodies (BsAbs) entering the clinic to treat metastatic cancer. The ability to apply these agents safely and efficaciously in the clinic, particularly for solid tumors, has been challenging. Many preclinical studies have evaluated parameters related to the activity of T cell engaging BsAbs, but many questions remain.

MAIN BODY

This study investigates the impact of affinity of T cell engaging BsAbs with regards to potency, efficacy, and induction of immunomodulatory receptors/ligands using HER-2/CD3 BsAbs as a model system. We show that an IgG BsAb can be as efficacious as a smaller BsAb format both in vitro and in vivo. We uncover a dichotomous relationship between tumor-associated antigen (TAA) affinity and CD3 affinity requirements for cells that express high versus low levels of TAA. HER-2 affinity directly correlated with the CD3 engager lysis potency of HER-2/CD3 BsAbs when HER-2 receptor numbers are high (~200 K/cell), while the CD3 affinity did not impact potency until its binding affinity was extremely low (<600 nM). When HER-2 receptor numbers were lower (~20 K/cell), both HER-2 and CD3 affinity impacted potency. The high affinity anti-HER-2/low CD3 affinity BsAb also demonstrated lower cytokine induction levels in vivo and a dosing paradigm atypical of extremely high potency T cell engaging BsAbs reaching peak efficacy at doses >3 mg/kg. This data confirms that low CD3 affinity provides an opportunity for improved safety and dosing for T cell engaging BsAbs. T cell redirection also led to upregulation of Programmed cell death 1 (PD-1) and 4-1BB, but not CTLA-4 on T cells, and to Programmed death-ligand 1 (PD-L1) upregulation on HER-2HI SKOV3 tumor cells, but not on HER-2LO OVCAR3 tumor cells. Using this information, we combined anti-PD-1 or anti-4-1BB monoclonal antibodies with the HER-2/CD3 BsAb in vivo and demonstrated significantly increased efficacy against HER-2HI SKOV3 tumors via both combinations.

CONCLUSIONS

Overall, these studies provide an informational dive into the optimization process of CD3 engaging BsAbs for solid tumors indicating that a reduced affinity for CD3 may enable a better therapeutic index with a greater selectivity for the target tumor and a reduced cytokine release syndrome. These studies also provide an additional argument for combining T cell checkpoint inhibition and co-stimulation to achieve optimal efficacy.

BACKGROUND

Animal immunization merges with innovative technologies: A new paradigm shift in antibody discovery

Animal-derived antibody sources, particularly, transgenic mice that are engineered with human immunoglobulin loci, along with advanced antibody generation technology platforms have facilitated the discoveries of human antibody therapeutics. For example, isolation of antigen-specific B cells, microfluidics, and next-generation sequencing have emerged as powerful tools for identifying and developing monoclonal antibodies (mAbs). These technologies enable not only antibody drug discovery but also lead to the understanding of B cell biology, immune mechanisms and immunogenetics of antibodies. In this perspective article, we discuss the scientific merits of animal immunization combined with advanced methods for antibody generation as compared to animal-free alternatives through in-vitro-generated antibody libraries. The knowledge gained from animal-derived antibodies concerning the recombinational diversity, somatic hypermutation patterns, and physiochemical properties is found more valuable and prerequisite for developing in vitro libraries, as well as artificial intelligence/machine learning methods to discover safe and effective mAbs.

A Quest for a Universal Plasma-Derived Antivenom Against All Elapid Neurotoxic Snake Venoms

This review describes the research aimed at the development of universal antivenom against elapid neurotoxic snake venoms. The antivenoms produced in Thailand in the 1980s were of low potency, especially against the elapid venoms. This was thought to be due to the low immunogenicity of the α-neurotoxins, which are the most lethal toxins in these venoms. Comparisons of various α-neurotoxin conjugates and polymers, and also different immunological adjuvants, showed that the adjuvant used is the major determinant in the antibody response in horses. The potent Freund's adjuvant was not used due to its severe local side-effect in horses. Therefore, a novel immunization protocol termed 'low dose, low volume multi-site' was developed for use in horses. This immunization protocol has led to the production of highly potent monospecific antivenoms against several elapid and viperid venoms, and two potent polyspecific antivenoms, one against 4 neurotoxic and another against 3 hematotoxic venoms. The immunization protocol has also led to other improvements in antivenom production including: several fold increases in antiserum potency, a reduction in the time required to reach therapeutically useful antibody titers, a 90% reduction in the amount of venom used, and 100% of the horses responding to the immunization program. This development is partly responsible for significant decrease in the Thailand's annual snakebite death toll from a few dozens to mostly nil in recent years. Finally, a simple and novel immunization strategy, using a 'diverse toxin repertoire' composed of numerous elapid toxin fractions as immunogen, was proposed and tested. This immunization procedure has resulted in the successful production of a widely paraspecific antiserum against at least 36 neurotoxic venoms of 28 species encompassing 10 genera and from 20 countries on four continents, and possibly against all elapid venoms with α-neurotoxins as the lethal toxins. These results indicate that, with optimizations of the composition of the 'diverse toxin repertoire', the immunization scheme and antibody fractionation to increase the antivenom neutralizing potency, an effective universal antivenom against the neurotoxic elapid snakes of the world can be produced.

Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer's disease

The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of individuals exhibiting a pathology and a group of healthy individuals. To achieve this, we focused on developing a predictive tool based on plasma for the amount of brain amyloid-β deposition as observed in PET scans. The accumulation of brain amyloid-β (Aβ) plaques is a key risk factor for the development of Alzheimer’s disease. A contrast was established between cognitively normal individuals above the age of 70 that differed for the amount of brain amyloid-β observed in PET scans (INSIGHT study group). Positive selection was performed against a pool of plasma from individuals with high brain amyloid and negative selection against a pool of plasma from individuals with low brain amyloid This enriched, selected library was then applied to plasma samples from 11 individuals with high levels of brain amyloid and 11 individuals with low levels of brain Aβ accumulation. Each of these individually selected libraries was then characterized by next generation sequencing, and the relative frequency of 10,000 aptamer sequences that were observed in each selection was screened for ability to explain variation in brain amyloid using sparse partial least squares discriminant analysis. From this analysis a subset of 44 aptamers was defined, and the individual aptamers were synthesized. This subset was applied to plasma samples from 70 cognitively normal individuals all above the age of 70 that differed for brain amyloid deposition. 54 individuals were used as a training set, and 15 as a test set. Three of the 15 individuals in the test set were mis-classified resulting in an overall accuracy of 80% with 86% sensitivity and 75% specificity. The aptamers included in the subset serve directly as biomarkers, thus we have named them Aptamarkers. There are two potential applications of these results: extending the predictive capacity of these aptamers across a broader range of individuals, and/or using the individual aptamers to identify targets through covariance analysis and reverse omics approaches. We are currently expanding applications of the Aptamarker platform to other diseases and target matrices.

Current strategies for detecting functional convergence across B-cell receptor repertoires

Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.

B cells were related to HBsAg seroconversion in inactive HBsAg carriers following peginterferon therapy

Our recent study showed high rate of HBsAg seroconversion achieved in inactive HBsAg carriers (IHCs) treated with peginterferon (PEG-IFN). To better understand the immune-mediated component to the HBsAg seroconversion, we investigated the role of B cells in this study. A total of 44 IHCs were given 48 weeks of PEG-IFN. Fifteen cases achieve HBsAg seroconversion (R group), whereas 29 failed (NR group). The proportion of total B cells and plasma B cells were measured before and during treatment. We found that the proportion of total B cells and plasma B cells was no significant between R group and NR group at baseline, but significantly higher in R group than NR group during PEG-IFN treatment, even when the exact age-, sex-, and treatment period-match was made. In conclusion, we demonstrated the increase of total B cell and plasma B cells during PEG-IFN treatment favored HBsAg seroconversion for IHC, and B cells may play a role in HBV seroconversion.

Beyond bulk single-chain sequencing: Getting at the whole receptor

Recent advancements in paired B-cell receptor sequencing technologies have accelerated the development of simpler, high-throughput pipelines for generating native antibody heavy and light chain pairs used to elucidate novel antibodies and provide insights into antibody response against pathogenic targets. These technologies involve single-cell isolation, using either single wells or emulsified droplets to maintain physical separation of individual cells, followed by sequencing. The development of novel single wells and emulsion-based workflows addresses key challenges by improving throughput of single-cell analyses, reducing method complexity, and integrating functional assays into existing workflows. Enabled by paired B-cell receptor sequencing, functional characterization of pathogen-specific antibodies reveals immunological insights beyond bulk sequencing.

Combinatorial mutagenesis with alternative CDR-L1 and -H2 loop lengths contributes to affinity maturation of antibodies

Loop length variation in the complementary determining regions (CDRs) 1 and 2 encoded in germline variable antibody genes provides structural diversity in naïve antibody libraries. In synthetic single framework libraries the parental CDR-1 and CDR-2 length is typically unchanged and alternative lengths are provided only at CDR-3 sites. Based on an analysis of the germline repertoire and structure-solved anti-hapten and anti-peptide antibodies, we introduced combinatorial diversity with alternative loop lengths into the CDR-L1, CDR-L3 and CDR-H2 loops of anti-digoxigenin and anti-microcystin-LR single chain Fv fragments (scFvs) sharing human IGKV3-20/IGHV3-23 frameworks. The libraries were phage display selected for folding and affinity, and analysed by single clone screening and deep sequencing. Among microcystin-LR binders the most frequently encountered alternative loop lengths were one amino acid shorter (6 aa) and four amino acids longer (11 aa) CDR-L1 loops leading up to 17- and 28-fold improved affinity, respectively. Among digoxigenin binders, 2 amino acids longer (10 aa) CDR-H2 loops were strongly enriched, but affinity improved anti-digoxigenin scFvs were also encountered with 7 aa CDR-H2 and 11 aa CDR-L1 loops. Despite the fact that CDR-L3 loop length variants were not specifically enriched in selections, one clone with 22-fold improved digoxigenin binding affinity was identified containing a 2 residues longer (10 aa) CDR-L3 loop. Based on our results the IGKV3-20/IGHV3-23 scaffold tolerates loop length variation, particularly in CDR-L1 and CDR-H2 loops, without compromising antibody stability, laying the foundation for developing novel synthetic antibody libraries with loop length combinations not existing in the natural human Ig gene repertoire.

Immunoglobulins with Non-Canonical Functions in Inflammatory and Autoimmune Disease States

Immunoglobulins are known to combine various effector mechanisms of the adaptive and the innate immune system. Classical immunoglobulin functions are associated with antigen recognition and the initiation of innate immune responses. However, in addition to classical functions, antibodies exhibit a variety of non-canonical functions related to the destruction of various pathogens due to catalytic activity and cofactor effects, the action of antibodies as agonists/antagonists of various receptors, the control of bacterial diversity of the intestine, etc. Canonical and non-canonical functions reflect the extreme human antibody repertoire and the variety of antibody types generated in the organism: antigen-specific, natural, polyreactive, broadly neutralizing, homophilic, bispecific and catalytic. The therapeutic effects of intravenous immunoglobulins (IVIg) are associated with both the canonical and non-canonical functions of antibodies. In this review, catalytic antibodies will be considered in more detail, since their formation is associated with inflammatory and autoimmune diseases. We will systematically summarize the diversity of catalytic antibodies in normal and pathological conditions. Translational perspectives of knowledge about natural antibodies for IVIg therapy will be also discussed.