Diversity in the CDR3 region of V(H) is sufficient for most antibody specificities

Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma

PURPOSE

Colorectal medullary carcinoma (MeC) is extensive lymphocyte infiltration and is associated with an active immune response. However, studies to comprehensively explore the immune landscape and efficacy of immune checkpoint blockade (ICB) therapy in MeC are limited.

EXPERIMENTAL DESIGN

We screened 47 cases of MeC from the Harbin Medical University Cancer Hospital cohort. The immunologic characteristics of MeC were analyzed by targeted exon sequencing, NanoString nCounter gene expression sequencing, IHC, multiplexed immunofluorescence, and T-cell antigen receptor sequencing. An additional 47 patients with MeC who received ICB therapy were included in the retrospective analysis to verify the efficacy of immunotherapy.

RESULTS

Genomically, MeC tends to have a higher proportion of mismatch repair protein deficiency/microsatellite instability (MSI), ARID1A mutation, and ASCL2 amplification. Gene expression shows enriched immune response-related pathways while downregulating oncogenic pathways, such as glycolysis, epithelial-mesenchymal transition, and Wnt/β-catenin signaling. Further immune characterization showed that MeC showed advantages in antigen presentation, co-stimulatory molecules, effector molecules, immune checkpoints, and immune cell abundance. More importantly, both MSI and microsatellite-stable type MeC showed a similar state of high infiltration of immune cells, even better than MSI non-MeC. MeC infiltrated massive highly clonal immune cells, especially intraepithelial CD8+ T cells. In the retrospective cohort, there were 30 patients with MeC who received ICB therapy and achieved complete or partial response with an objective response rate of 63.8%, especially including 16 patients with microsatellite-stable colorectal cancer.

CONCLUSIONS

MeC is a pathologic subtype with an active immune response and is a promising group for ICB therapy. This heightened immune response was not limited to the patients' microsatellite status.

Screening of deoxyribonuclease I inhibitors from autodisplayed Fv-antibody library

Deoxyribonuclease (DNase) I inhibitors have been developed based on proteins, nucleotides and synthetic compounds. In this work, amino acid sequences with the activity of DNase I inhibitor were screened from an Fv-antibody library expressed on the outer membrane of Escherichia coli. The Fv-antibody indicated the heavy chain variable region (VH) of immunoglobulin G (IgG) and the Fv-antibody library was generated with a randomized complementarity-determining region 3 (CDR3). From the Fv-antibody library, two clones were screened for their binding affinity to DNase I and expressed as soluble recombinant proteins as well as peptides. The binding affinity (KD) to DNase I was estimated for the expressed Fv-antibodies (73.4 nM for Fv-1 and 89.0 nM for Fv-19) and synthesized peptides (279.2 nM for Peptide-1 and 243.2 nM for Peptide-19) using SPR biosensor. The inhibitory activity (IC50) of the expressed Fv-antibodies (550.0 nM for Fv-1 and 660.2 nM for Fv-19) and synthetic peptides (864.5 nM for Peptide-1 and 974.6 nM for Peptide-19) was measured using agarose-gel assay and TaqMan-like fluorescence assay. These IC50 values indicated that both expressed Fv-antibodies and synthesized peptides exerted an effective inhibitory activity against DNase I. The interaction between the screened inhibitors and DNase I was analyzed by docking simulation.

Persistent splenic-derived IgMs preferentially recognize factor VIII A2 and C2 domain epitopes but do not alter antibody production

BACKGROUND

The most significant treatment complication for patients with hemophilia A is the development of neutralizing immunoglobins (Igs) G, termed inhibitors, against factor VIII (FVIII), which prevent FVIII replacement therapy. Low titers of FVIII-specific IgMs have been identified in hemophilia A patients with and without inhibitors, as well as in healthy individuals. However, the duration and influence of IgMs on the immune response to FVIII remains unclear.

OBJECTIVES

To characterize the binding interactions of persistently secreted FVIII-specific IgMs in hemophilia A mice and assess their effect on IgG antibody development.

METHODS

Splenic-derived monoclonal antibodies (mAbs) from immunized FVIII knockout mice were isolated and purified using hybridoma technology. Binding interactions were assessed utilizing a novel fluid-phase enzyme-linked immunosorbent assay and computational modeling with High Ambiguity-Driven protein-protein DOCKing to account for weak IgM binding.

RESULTS

Sixteen porcine cross-reactive and noninhibitory FVIII-specific IgM mAbs were identified. RNA sequencing of FVIII-specific IgMs revealed 13 unique variable, diversity, and joining (VDJ)/variable and joining (VJ) sequences indicating derivation from 13 unique B cell clones. The IgMs demonstrated polyclonal and polyreactive binding to FVIII in vitro and in silico. Molecular docking studies with reconstructed IgM variable, diversity, and joining/variable and joining regions identified frequent IgM interactions with amino acid residues K376, T381, K437, R2215, or K2249 within the FVIII A2 and C2 domains. Injections of individual IgMs prior to FVIII exposure and co-injection of FVIII/IgM immune complexes did not affect de novo FVIII antibody production.

CONCLUSION

Persistent FVIII-specific IgMs are polyclonal but preferentially bind the A2 and C2 domains. FVIII/IgM immune complex formation does not significantly alter inhibitor development.

A computational approach to matching multiple sclerosis-related, IGH CDR3s with a MBP epitope

In multiple sclerosis (MS), T-cell receptors (TCRs) and antibodies specifically target the main structural proteins of myelin, including myelin basic protein (MBP), especially a specific, canonical, immunoglobulin (IG)-targeted MBP epitope. Efficient computational analyses to diagnose or monitor autoimmune conditions, which could have broad applicability in clinical trials or in diagnoses, remains a challenge. As such, we considered the possibility that focusing on the immunoglobin heavy chain (IGH) complementarity determining region-3 (CDR3) amino acid sequences could support the development of an efficient, convenient, and user-friendly approach to detecting or assessing IGH targets in MS. Thus, we applied a chemical complementarity scoring algorithm, extensively benchmarked in many cancer settings, to assess the combined electrostatic and hydrophobic attractiveness of large numbers of (individual patient) IGH CDR3s and the canonical IG MBP epitope. Samples and controls were filtered to only include CDR3s above a baseline chemical complementarity score. Then, the frequency of each unique IGH CDR3 (with the minimum MBP epitope complementarity) in the MS samples was compared to the same parameter for the control sample. Specifically, a greater number of high frequency IGH CDR3s, with chemically complementary to the canonical MBP epitope, was detected in 47 out of 48 MS-control comparisons, in most cases representing a p < 0.0001. With continued development, this approach has the potential to lead to a user-friendly computational screening tool for patients at risk for developing MS. Additional results indicate that the methodology could also be applied to antigen epitope discovery.

Unveiling inverted D genes and D-D fusions in human antibody repertoires unlocks novel antibody diversity

Antibodies, essential components of adaptive immunity, derive their remarkable diversity primarily from V(D)J gene rearrangements, particularly within the heavy chain complementarity-determining region 3 (CDR-H3) where D genes play a major role. Traditionally, D genes were thought to recombine only in the forward direction, despite having identical recombination signal sequences (12 base pair spacers) at both ends. This observation led us to question whether these symmetrical sequences might enable bidirectional recombination. We identified 25 unique inverted D genes (InvDs) in both naive and memory B cells from antibody repertoires of 13 healthy donors. These InvDs utilize all three reading frames during translation, producing distinct amino acid profiles enriched in histidine, proline, and lysine in CDR-H3s of antibodies with potential functional diversity. Notably, our analysis revealed a broader range of D-D fusions, including D-D, D-InvD, InvD-D, and InvD-InvD configurations, opening new perspectives for antibody engineering and therapeutic development.

Amino acids characterization based on frequency and interaction analysis in human antigen-antibody complexes from Thera-SAbDab

BACKGROUND

Antibodies are composed of light and heavy chains, both of which have constant and variable regions. The diversity, specific binding ability and therapeutic potential of antibodies are determined by hypervariable loops called complementarity-determining regions (CDRs), with the other regions being the framework regions.

OBJECTIVE

To investigate the key amino acid patterns in various antibody regions in the human therapeutic antigen-antibody (Ag-Ab) complexes collected from the Thera-SAbDab database.

METHOD

The study focuses on identifying the amino acid frequency, diversity index in CDRs, paratope-epitope amino acid interactions, amino acid bond formation frequency, and bond types among selected therapeutic Ag-Ab complexes.

RESULTS

The results revealed that Ser is highly distributed in the overall light chain CDRs while Gly is highly distributed in the heavy chain CDRs. CDR profiling analysis indicated that the average amino acid diversity in heavy chain CDRs is 60% to 70%, while in the light chain, it is 50% to 60%. Aromatic residues such as Tyr, Trp and Phe are the top contributors to these paratope-epitope interactions in the light and heavy chains. Moreover, we examined the frequency of amino acids in light and heavy chains of Ag-Ab complexes. Importantly, the outcome of this study leverages the in depth analysis on single residues, dipeptides, and tripeptides for the therapeutic Ag-Ab complexes.

CONCLUSION

We conclude that the amino acid frequency and interaction analysis centered on therapeutic Ag-Ab complexes will benefit antibody engineering parameters such as antibody design, optimization, affinity maturation, and overall antibody development.

SARS-CoV-2 vaccine based on ferritin complexes with screened immunogenic sequences from the Fv-antibody library

In this study, the vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was developed using ferritin complexes with the immunogenic sequences screened against the SARS-CoV-2 spike protein (SP) from the Fv-antibody library. The Fv-antibody library was prepared on the outer membrane of E. coli by the expression of the VH region of immunoglobulin G (IgG) with a randomized complementarity-determining region 3 (CDR3). Four Fv-antibodies to the receptor-binding domain (RBD) were screened from the Fv-antibody library, which had a comparable binding constant (KD) between SARS-CoV-2 SP and the angiotensin-converting enzyme 2 (ACE2) receptor. The binding sites of screened Fv-antibodies on the RBD were analyzed using a docking analysis, and these binding sites were used as immunogenic sequences for the vaccine. The four immunogenic sequences were modified and co-expressed as a part of ferritin which was assembled into a ferritin complex. After the vaccination of ferritin complexes to mice, the anti-sera were analyzed to have a high enough titer. Additionally, the immune responses were found to be activated by vaccination, such as the expression of IgG subclasses and the increased level of cytokines. The neutralizing activity of the anti-sera was estimated using a cell-based infection assay based on pseudo-virus expressing the SP of SARS-CoV-2 variants.

An allelic atlas of immunoglobulin heavy chain variable regions reveals antibody binding epitope preference resilient to SARS-CoV-2 mutation escape

Background

Although immunoglobulin (Ig) alleles play a pivotal role in the antibody response to pathogens, research to understand their role in the humoral immune response is still limited.

Methods

We retrieved the germline sequences for the IGHV from the IMGT database to illustrate the amino acid polymorphism present within germline sequences of IGHV genes. We aassembled the sequences of IgM and IgD repertoire from 130 people to investigate the genetic variations in the population. A dataset comprising 10,643 SARS-CoV-2 spike-specific antibodies, obtained from COV-AbDab, was compiled to assess the impact of SARS-CoV-2 infection on allelic gene utilization. Binding affinity and neutralizing activity were determined using bio-layer interferometry and pseudovirus neutralization assays. Primary docking was performed using ZDOCK (3.0.2) to generate the initial conformation of the antigen-antibody complex, followed by simulations of the complete conformations using Rosetta SnugDock software. The original and simulated structural conformations were visualized and presented using ChimeraX (v1.5).

Results

We present an allelic atlas of immunoglobulin heavy chain (IgH) variable regions, illustrating the diversity of allelic variants across 33 IGHV family germline sequences by sequencing the IgH repertoire of in the population. Our comprehensive analysis of SARS-CoV-2 spike-specific antibodies revealed the preferential use of specific Ig alleles among these antibodies. We observed an association between Ig alleles and antibody binding epitopes. Different allelic genotypes binding to the same RBD epitope on the spike show different neutralizing potency and breadth. We found that antibodies carrying the IGHV1-69*02 allele tended to bind to the RBD E2.2 epitope. The antibodies carrying G50 and L55 amino acid residues exhibit potential enhancements in binding affinity and neutralizing potency to SARS-CoV-2 variants containing the L452R mutation on RBD, whereas R50 and F55 amino acid residues tend to have reduced binding affinity and neutralizing potency. IGHV2-5*02 antibodies using the D56 allele bind to the RBD D2 epitope with greater binding and neutralizing potency due to the interaction between D56 on HCDR2 and K444 on RBD of most Omicron subvariants. In contrast, IGHV2-5*01 antibodies using the N56 allele show increased binding resistance to the K444T mutation on RBD.

Discussion

This study provides valuable insights into humoral immune responses from the perspective of Ig alleles and population genetics. These findings underscore the importance of Ig alleles in vaccine design and therapeutic antibody development.

Stereotyped B-Cell Receptor Immunoglobulins in B-Cell Lymphomas

Thorough examination of clonotypic B-cell receptor immunoglobulin (BcR IG) gene rearrangement sequences in patients with mature B-cell malignancies has revealed significant repertoire restrictions, leading to the identification of subsets of patients expressing highly similar, stereotyped BcR IG. This discovery strongly suggests selection by common epitopes or classes of structurally similar epitopes in the development of these tumors. Initially observed in chronic lymphocytic leukemia (CLL), where the stereotyped fraction accounts for a substantial fraction of patients, stereotyped BcR IGs have also been identified in other mature B-cell malignancies, including mantle cell lymphoma (MCL) and splenic marginal zone lymphoma (SMZL).Further comparisons across different entities have indicated that stereotyped IGs are predominantly "disease-biased," indicating distinct immune pathogenetic trajectories. Notably, accumulating evidence suggests that molecular subclassification of mature B-cell malignancies based on BcR IG stereotypy holds biological and clinical relevance. Particularly in CLL, patients belonging to the same subset due to the expression of a specific stereotyped BcR IG exhibit consistent biological backgrounds and clinical courses, especially for major and extensively studied subsets. Therefore, robust assignment to stereotyped subsets may aid in uncovering mechanisms underlying disease initiation and progression, as well as refining patient risk stratification. In this chapter, we offer an overview of recent studies on BcR IG stereotypy in mature B-cell malignancies and delineate past and present methodological approaches utilized for the identification of stereotyped BcR IG.

Lowering the affinity of single-chain monovalent BBB shuttle scFc-scFv8D3 prolongs its half-life and increases brain concentration

Monoclonal antibody therapeutics is a massively growing field. Progress in providing monoclonal antibody therapeutics to treat brain disorders is complicated, due to the impermeability of the blood-brain barrier (BBB) to large macromolecular structures. To date, the most successful approach for delivering antibody therapeutics to the brain is by targeting the transferrin receptor (TfR) using anti-TfR BBB shuttles, with the 8D3 antibody being one of the most extensively studied in the field. The strategy of fine-tuning TfR binding affinity has shown promise, with previous results showing an improved brain delivery of bivalent 8D3-BBB constructs. In the current study, a fine-tuning TfR affinity strategy has been employed to improve single-chain variable fragment (scFv) 8D3 (scFv8D3) affinity mutants. Initially, in silico protein-protein docking analysis was performed to identify amino acids (AAs) likely to contribute to 8D3s TfR binding affinity. Mutating the identified AAs resulted in decreased TfR binding affinity, increased blood half-life and increased brain concentration. As monovalent BBB shuttles are seemingly superior for delivering antibodies at therapeutically relevant doses, our findings and approach may be relevant for optimizing brain delivery.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Screened Fv-Antibodies against the Angiotensin-Converting Enzyme 2 (ACE2) Receptor Neutralizing the Infection of SARS-CoV-2

For the prevention of SARS-CoV-2 infection, four Fv-antibodies with binding affinity for the ACE2 receptor were screened from an Fv-antibody library. The screened Fv-antibodies were expressed as soluble proteins and estimated to have a high binding affinity, comparable to that between SARS-CoV-2 and the ACE2 receptor. The interaction between the Fv-antibodies and the ACE2 receptor was analyzed using docking simulation, and the significant binding affinity of the screened Fv-antibodies was attributed to the homology in amino acid sequence with the ACE2 receptor. The neutralizing activities of the Fv-antibodies were demonstrated using a cell-based infection assay based on four pseudo-virus types with SARS-CoV-2 variant spike proteins (Wild-type D614, Delta B.1.617.2, and Omicron BA.2, and Omicron BA.4/5).

Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection

Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.

Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and machine learning

Given the crucial role of specific matrix metalloproteinases (MMPs) in the extracellular matrix, an imbalance in the regulation of activation of matrix metalloproteinase-9 (MMP-9) zymogen and inhibition of the enzyme can result in various diseases, such as cancer, neurodegenerative, and gynecological diseases. Thus, developing novel therapeutics that target MMP-9 with single-chain antibody fragments (scFvs) is a promising approach. We used fluorescent-activated cell sorting (FACS) to screen a synthetic scFv antibody library displayed on yeast for enhanced binding to MMP-9. The screened scFv mutants demonstrated improved binding to MMP-9 compared to the natural inhibitor of MMPs, tissue inhibitor of metalloproteinases (TIMPs). To identify the molecular determinants of these engineered scFv variants that affect binding to MMP-9, we used next-generation DNA sequencing and computational protein structure analysis. Additionally, a deep-learning language model was trained on the screened scFv library of variants to predict the binding affinities of scFv variants based on their CDR-H3 sequences.

A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Development of a new genotype-phenotype linked antibody screening system

Antibodies are powerful tools for the therapy and diagnosis of various diseases. In addition to conventional hybridoma-based screening, recombinant antibody-based screening has become a common choice; however, its application is hampered by two factors: (1) screening starts after Ig gene cloning and recombinant antibody production only, and (2) the antibody is composed of paired chains, heavy and light, commonly expressed by two independent expression vectors. Here, we introduce a method for the rapid screening of recombinant monoclonal antibodies by establishing a Golden Gate-based dual-expression vector and in-vivo expression of membrane-bound antibodies. Using this system, we demonstrate the rapid isolation of influenza cross-reactive antibodies with high affinity from immunized mice within 7 days. This system is particularly useful for isolating therapeutic or diagnostic antibodies, for example during foreseen pandemics.

Immunisation of chickens with inactivated and/or infectious H9N2 avian influenza virus leads to differential immune B-cell repertoire development

Avian influenza viruses (AIVs) are a major economic burden to the poultry industry and pose serious zoonotic risks, with human infections being reported every year. To date, the vaccination of birds remains the most important method for the prevention and control of AIV outbreaks. Most national vaccination strategies against AIV infection use whole virus-inactivated vaccines, which predominantly trigger a systemic antibody-mediated immune response. There are currently no studies that have examined the antibody repertoire of birds that were infected with and/or vaccinated against AIV. To this end, we evaluate the changes in the H9N2-specific IgM and IgY repertoires in chickens subjected to vaccination(s) and/or infectious challenge. We show that a large proportion of the IgM and IgY clones were shared across multiple individuals, and these public clonal responses are dependent on both the immunisation status of the birds and the specific tissue that was examined. Furthermore, the analysis revealed specific clonal expansions that are restricted to particular H9N2 immunisation regimes. These results indicate that both the nature and number of immunisations are important drivers of the antibody responses and repertoire profiles in chickens following H9N2 antigenic stimulation. We discuss how the repertoire biology of avian B-cell responses may affect the success of AIV vaccination in chickens, in particular the implications of public versus private clonal selection.

Rapid affinity optimization of an anti-TREM2 clinical lead antibody by cross-lineage immune repertoire mining

We describe a process for rapid antibody affinity optimization by repertoire mining to identify clones across B cell clonal lineages based on convergent immune responses where antigen-specific clones with the same heavy (VH) and light chain germline segment pairs, or parallel lineages, bind a single epitope on the antigen. We use this convergence framework to mine unique and distinct VH lineages from rat anti-triggering receptor on myeloid cells 2 (TREM2) antibody repertoire datasets with high diversity in the third complementarity-determining loop region (CDR H3) to further affinity-optimize a high-affinity agonistic anti-TREM2 antibody while retaining critical functional properties. Structural analyses confirm a nearly identical binding mode of anti-TREM2 variants with subtle but significant structural differences in the binding interface. Parallel lineage repertoire mining is uniquely tailored to rationally explore the large CDR H3 sequence space in antibody repertoires and can be easily and generally applied to antibodies discovered in vivo.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

Epidermal Growth Factor Receptor (EGFR) Inhibitors Screened from Autodisplayed Fv-Antibody Library

Inhibitors of the epithermal growth factor receptor (EGFR) were screened from an autodisplayed Fv-antibody library using an anti-EGF antibody. The Fv-antibody library was expressed on the outer membrane of Escherichia coli, which corresponds to the heavy chain VH region of immunoglobulin G. The library was constructed by randomizing the CDR3 region of expressed VH regions (11 amino acid residues) by site-directed mutagenesis. Using an anti-EGF antibody as a screening probe, amino acid sequences (CDR3 region) with antibody binding affinity were screened from the Fv-antibody library. These amino acid sequences were considered to have similar chemical properties to EGF, which can bind to EGFR. Two autodisplayed clones with Fv-antibodies against EGFR were screened from the Fv-antibody library, and the screened Fv-antibodies were expressed as soluble proteins. The binding affinity (KD) was estimated using an SPR biosensor, and the inhibitory activity of expressed Fv-antibodies was observed for PANC-1 pancreatic tumor cells and T98G glioblastoma cells using Western blot analysis of proteins in the EGFR-mediated signaling pathway. The viability of PANC-1 and T98G cells was observed to decrease via the inhibitory activity of expressed Fv-antibodies. Finally, interactions between Fv-antibodies and EGFR were analyzed by using molecular docking simulations.

Bacterial cell surface characterization by phage display coupled to high-throughput sequencing

The remarkable capacity of bacteria to adapt in response to selective pressures drives antimicrobial resistance. Pseudomonas aeruginosa illustrates this point, establishing chronic infections during which it evolves to survive antimicrobials and evade host defenses. Many adaptive changes occur on the P. aeruginosa cell surface but methods to identify these are limited. Here we combine phage display with high-throughput DNA sequencing to create a high throughput, multiplexed technology for surveying bacterial cell surfaces, Phage-seq. By applying phage display panning to hundreds of bacterial genotypes and analyzing the dynamics of the phage display selection process, we capture important biological information about cell surfaces. This approach also yields camelid single-domain antibodies that recognize key P. aeruginosa virulence factors on live cells. These antibodies have numerous potential applications in diagnostics and therapeutics. We propose that Phage-seq establishes a powerful paradigm for studying the bacterial cell surface by identifying and profiling many surface features in parallel.

De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model

Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.

Bat humoral immunity and its role in viral pathogenesis, transmission, and zoonosis

Bats harbor viruses that can cause severe disease and death in humans including filoviruses (e.g., Ebola virus), henipaviruses (e.g., Hendra virus), and coronaviruses (e.g., SARS-CoV). Bats often tolerate these viruses without noticeable adverse immunological effects or succumbing to disease. Previous studies have largely focused on the role of the bat's innate immune response to control viral pathogenesis, but little is known about bat adaptive immunity. A key component of adaptive immunity is the humoral response, comprised of antibodies that can specifically recognize viral antigens with high affinity. The antibody genes within the 1,400 known bat species are highly diverse, and these genetic differences help shape fundamental aspects of the antibody repertoire, including starting diversity and viral antigen recognition. Whether antibodies in bats protect, mediate viral clearance, and prevent transmission within bat populations is poorly defined. Furthermore, it is unclear how neutralizing activity and Fc-mediated effector functions contribute to bat immunity. Although bats have canonical Fc genes (e.g., mu, gamma, alpha, and epsilon), the copy number and sequences of their Fc genes differ from those of humans and mice. The function of bat antibodies targeting viral antigens has been speculated based on sequencing data and polyclonal sera, but functional and biochemical data of monoclonal antibodies are lacking. In this review, we summarize current knowledge of bat humoral immunity, including variation between species, their potential protective role(s) against viral transmission and replication, and address how these antibodies may contribute to population dynamics within bats communities. A deeper understanding of bat adaptive immunity will provide insight into immune control of transmission and replication for emerging viruses with the potential for zoonotic spillover.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.

Identification of heparin-binding amino acid residues in antibody HS4C3 with the potential to design antibodies against heparan sulfate domains

Heparan sulfate (HS) is a linear polysaccharide with high structural and functional diversity. Detection and localization of HS in tissues can be performed using single chain variable fragment (scFv) antibodies. Although several anti-HS antibodies recognizing different sulfation motifs have been identified, little is known about their interaction with HS. In this study the interaction between the scFv antibody HS4C3 and heparin was investigated. Heparin-binding lysine and arginine residues were identified using a protect and label methodology. Site-directed mutagenesis was applied to further identify critical heparin-binding lysine/arginine residues using immunohistochemical and biochemical assays. In addition, computational docking of a heparin tetrasaccharide towards a 3-D homology model of HS4C3 was applied to identify potential heparin-binding sites. Of the 12 lysine and 15 arginine residues within the HS4C3 antibody, 6 and 9, respectively, were identified as heparin-binding. Most of these residues are located within one of the complementarity determining regions (CDR) or in their proximity. All basic amino acid residues in the CDR3 region of the heavy chain were involved in binding. Computational docking showed a heparin tetrasaccharide close to these regions. Mutagenesis of heparin-binding residues reduced or altered reactivity towards HS and heparin. Identification of heparin-binding arginine and lysine residues in HS4C3 allows for better understanding of the interaction with HS and creates a framework to rationally design antibodies targeting specific HS motifs.

Structural, Biophysical, and Computational Studies of a Murine Light Chain Dimer

Antibodies are widely used in medicinal and scientific research due to their ability to bind to a specific antigen. Most often, antibodies are composed of heavy and light chain domains. Under physiological conditions, light chains are produced in excess, as compared to the heavy chain. It is now known that light chains are not silent partners of the heavy chain and can modulate the immune response independently. In this work, the first crystal structure of a light chain dimer originating from mice is described. It represents the light chain dimer of 6A8, a monoclonal antibody specific to the allergen Der f 1. Building on the unexpected occurrence of this kind of dimer, we have demonstrated that this light chain is stable in solution alone. Moreover, enzyme-linked immunosorbent assays (ELISA) have revealed that, when the light chain is not partnered to its corresponding heavy chain, it interacts non-specifically with a wide range of proteins. Computational studies were used to provide insight on the role of the 6A8 heavy chain domain in the specific binding to Der f 1. Overall, this work demonstrates and supports the ongoing notion that light chains can function by themselves and are not silent partners of heavy chains.

Elucidating key determinants of engineered scFv antibody in MMP-9 binding using high throughput screening and machine learning

An imbalance in matrix metalloproteinase-9 (MMP-9) regulation can lead to numerous diseases, including neurological disorders, cancer, and pre-term labor. Engineering single-chain antibody fragments (scFvs) Targeting MMP-9 to develop novel therapeutics for such diseases is desirable. We screened a synthetic scFv antibody library displayed on the yeast surface for binding improvement to MMP-9 using FACS (fluorescent-activated cell sorting). The scFv antibody clones isolated after FACS showed improvement in binding to MMP-9 compared to the endogenous inhibitor. To understand molecular determinants of binding between engineered scFv antibody variants and MMP-9, next-generation DNA sequencing, and computational protein structure analysis were used. Additionally, a deep-learning language model was trained on the synthetic library to predict the binding of scFv variants using their CDR-H3 sequences.

Immunoglobulin and T cell receptor repertoire changes induced by a prototype vaccine against Chagas disease in naïve rhesus macaques

BACKGROUND

A vaccine against Trypanosoma cruzi, the agent of Chagas disease, would be an excellent additional tool for disease control. A recombinant vaccine based on Tc24 and TSA1 parasite antigens was found to be safe and immunogenic in naïve macaques.

METHODS

We used RNA-sequencing and performed a transcriptomic analysis of PBMC responses to vaccination of naïve macaques after each vaccine dose, to shed light on the immunogenicity of this vaccine and guide the optimization of doses and formulation. We identified differentially expressed genes and pathways and characterized immunoglobulin and T cell receptor repertoires.

RESULTS

RNA-sequencing analysis indicated a clear transcriptomic response of PBMCs after three vaccine doses, with the up-regulation of several immune cell activation pathways and a broad non-polarized immune profile. Analysis of the IgG repertoire showed that it had a rapid turnover with novel IgGs produced following each vaccine dose, while the TCR repertoire presented several persisting clones that were expanded after each vaccine dose.

CONCLUSIONS

These data suggest that three vaccine doses may be needed for optimum immunogenicity and support the further evaluation of the protective efficacy of this vaccine.

Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser

Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

Development of a new affinity maturation protocol for the construction of an internalizing anti-nucleolin antibody library

Over the last decades, monoclonal antibodies have substantially improved the treatment of several conditions. The continuous search for novel therapeutic targets and improvements in antibody's structure, demands for a constant optimization of their development. In this regard, modulation of an antibody's affinity to its target has been largely explored and culminated in the discovery and optimization of a variety of molecules. It involves the creation of antibody libraries and selection against the target of interest. In this work, we aimed at developing a novel protocol to be used for the affinity maturation of an antibody previously developed by our group. An antibody library was constructed using an in vivo random mutagenesis approach that, to our knowledge, has not been used before for antibody development. Then, a cell-based phage display selection protocol was designed to allow the fast and simple screening of antibody clones capable of being internalized by target cells. Next generation sequencing coupled with computer analysis provided an extensive characterization of the created library and post-selection pool, that can be used as a guide for future antibody development. With a single selection step, an enrichment in the mutated antibody library, given by a decrease in almost 50% in sequence diversity, was achieved, and structural information useful in the study of the antibody-target interaction in the future was obtained.

Proteomics Platform Reveals Broad-Spectrum Nanobodies for SARS-CoV-2 Variant Neutralization

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of different variants of concerns with immune evasion that have been prevalent over the past three years. Nanobodies, the functional variable regions of camelid heavy-chain-only antibodies, have garnered interest in developing neutralizing antibodies due to their smaller size, structural stability, ease of production, high affinity, and low immunogenicity, among other characteristics. In this work, we describe an integrated proteomics platform for the high-throughput screening of nanobodies against different SARS-CoV-2 spike variants. To demonstrate this platform, we immunized a camel with subunit 1 (S1) of the wild-type spike protein and constructed a nanobody phage library. The binding and neutralizing activities of the nanobodies against 72 spike variants were then measured, resulting in the identification of two nanobodies (C-282 and C-39) with broad neutralizing activity against six non-Omicron variants (D614G, Alpha, Beta, Gamma, Delta, Kappa) and five Omicron variants (BA.1-5). Their neutralizing capability was validated using in vitro pseudovirus-based neutralization assays. All these results demonstrate the utility of our proteomics platform to identify new nanobodies with broad neutralizing capability and to develop a treatment for patients with SARS-CoV-2 variant infection in the future.

Computational Modeling and Characterization of Peptides Derived from Nanobody Complementary-Determining Region 2 (CDR2) Targeting Active-State Conformation of the β2-Adrenergic Receptor (β2AR)

This study assessed the suitability of the complementarity-determining region 2 (CDR2) of the nanobody (Nb) as a template for the derivation of nanobody-derived peptides (NDPs) targeting active-state β2-adrenergic receptor (β2AR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied β2AR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with β2AR were subjected to protein-peptide docking, molecular dynamics (MD) simulations, and metadynamics-based free-energy binding calculations. Computational analyses identified a 25-amino-acid-long CDR2-NDP of Nb71, designated P4, which exhibited the following binding free-energy for the formation of the β2AR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 μM at 310 K) and mapped the β2AR:P4 amino acid interaction network. In vitro characterization showed that P4 (i) can cross the plasma membrane, (ii) reduces the maximum isoproterenol-induced cAMP level by approximately 40% and the isoproterenol potency by up to 20-fold at micromolar concentration, (iii) has a very low affinity to interact with unstimulated β2AR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated β2AR/β-arrestin-2 interaction in the BRET2-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated β2AR and disrupts Gαs-mediated signaling.

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity

Recent studies have demonstrated a role for Ten-Eleven Translocation-2 (TET2), an epigenetic modulator, in regulating germinal center formation and plasma cell differentiation in B-2 cells, yet the role of TET2 in regulating B-1 cells is largely unknown. Here, B-1 cell subset numbers, IgM production, and gene expression were analyzed in mice with global knockout of TET2 compared to wildtype (WT) controls. Results revealed that TET2-KO mice had elevated numbers of B-1a and B-1b cells in their primary niche, the peritoneal cavity, as well as in the bone marrow (B-1a) and spleen (B-1b). Consistent with this finding, circulating IgM, but not IgG, was elevated in TET2-KO mice compared to WT. Analysis of bulk RNASeq of sort purified peritoneal B-1a and B-1b cells revealed reduced expression of heavy and light chain immunoglobulin genes, predominantly in B-1a cells from TET2-KO mice compared to WT controls. As expected, the expression of IgM transcripts was the most abundant isotype in B-1 cells. Yet, only in B-1a cells there was a significant increase in the proportion of IgM transcripts in TET2-KO mice compared to WT. Analysis of the CDR3 of the BCR revealed an increased abundance of replicated CDR3 sequences in B-1 cells from TET2-KO mice, which was more clearly pronounced in B-1a compared to B-1b cells. V-D-J usage and circos plot analysis of V-J combinations showed enhanced usage of VH11 and VH12 pairings. Taken together, our study is the first to demonstrate that global loss of TET2 increases B-1 cell number and IgM production and reduces CDR3 diversity, which could impact many biological processes and disease states that are regulated by IgM.

Extraction of the CDRH3 sequence of the mouse antibody repertoire selected upon influenza virus infection by subtraction of the background antibody repertoire

Historically, antibody reactivity to pathogens and vaccine antigens has been evaluated using serological measurements of antigen-specific antibodies. However, it is difficult to evaluate all antibodies that contribute to various functions in a single assay, such as the measurement of the neutralizing antibody titer. Bulk antibody repertoire analysis using next-generation sequencing is a comprehensive method for analyzing the overall antibody response; however, it is unreliable for estimating antigen-specific antibodies due to individual variation. To address this issue, we propose a method to subtract the background signal from the repertoire of data of interest. In this study, we analyzed changes in antibody diversity and inferred the heavy-chain complementarity-determining region 3 (CDRH3) sequences of antibody clones that were selected upon influenza virus infection in a mouse model using bulk repertoire analysis. A decrease in the diversity of the antibody repertoire was observed upon viral infection, along with an increase in neutralizing antibody titers. Using kernel density estimation of sequences in a high-dimensional sequence space with background signal subtraction, we identified several clusters of CDRH3 sequences induced upon influenza virus infection. Most of these repertoires were detected more frequently in infected mice than in uninfected control mice, suggesting that infection-specific antibody sequences can be extracted using this method. Such an accurate extraction of antigen- or infection-specific repertoire information will be a useful tool for vaccine evaluation in the future.

IMPORTANCE

As specific interactions between antigens and cell-surface antibodies trigger the proliferation of B-cell clones, the frequency of each antibody sequence in the samples reflects the size of each clonal population. Nevertheless, it is extremely difficult to extract antigen-specific antibody sequences from the comprehensive bulk antibody sequences obtained from blood samples due to repertoire bias influenced by exposure to dietary antigens and other infectious agents. This issue can be addressed by subtracting the background noise from the post-immunization or post-infection repertoire data. In the present study, we propose a method to quantify repertoire data from comprehensive repertoire data. This method allowed subtraction of the background repertoire, resulting in more accurate extraction of expanded antibody repertoires upon influenza virus infection. This accurate extraction of antigen- or infection-specific repertoire information is a useful tool for vaccine evaluation.

AbFlex: designing antibody complementarity determining regions with flexible CDR definition

MOTIVATION

Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

RESULTS

We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

AVAILABILITY AND IMPLEMENTATION

The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

Precision engineering for localization, validation, and modification of allergenic epitopes

The allergen-IgE interaction is essential for the genesis of allergic responses, yet investigation of the molecular basis of these interactions is in its infancy. Precision engineering has unveiled the molecular features of allergen-antibody interactions at the atomic level. High-resolution technologies, including x-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, determine allergen-antibody structures. X-ray crystallography of an allergen-antibody complex localizes in detail amino acid residues and interactions that define the epitope-paratope interface. Multiple structures involving murine IgG mAbs have recently been resolved. The number of amino acids forming the epitope broadly correlates with the epitope area. The production of human IgE mAbs from B cells of allergic subjects is an exciting recent development that has for the first time enabled an actual IgE epitope to be defined. The biologic activity of defined IgE epitopes can be validated in vivo in animal models or by measuring mediator release from engineered basophilic cell lines. Finally, gene-editing approaches using the Clustered Regularly Interspaced Short Palindromic Repeats technology to either remove allergen genes or make targeted epitope engineering at the source are on the horizon. This review presents an overview of the identification and validation of allergenic epitopes by precision engineering.

The characteristics of BCR-CDR3 repertoire in COVID-19 patients and SARS-CoV-2 vaccinated volunteers

The global COVID-19 pandemic has caused more than 1 billion infections, and numerous SARS-CoV-2 vaccines developed rapidly have been administered over 10 billion doses. The world is continuously concerned about the cytokine storms induced by the interaction between SARS-CoV-2 and host, long COVID, breakthrough infections postvaccination, and the impact of SARS-CoV-2 variants. BCR-CDR3 repertoire serves as a molecular target for monitoring the antiviral response "trace" of B cells, evaluating the effects, mechanisms, and memory abilities of individual responses to B cells, and has been successfully applied in analyzing the infection mechanisms, vaccine improvement, and neutralizing antibodies preparation of influenza virus, HIV, MERS, and Ebola virus. Based on research on BCR-CDR3 repertoire of COVID-19 patients and volunteers who received different SARS-CoV-2 vaccines in multiple laboratories worldwide, we focus on analyzing the characteristics and changes of BCR-CDR3 repertoire, such as diversity, clonality, V&J genes usage and pairing, SHM, CSR, shared CDR3 clones, as well as the summary on BCR sequences targeting virus-specific epitopes in the preparation and application research of SARS-CoV-2 potential therapeutic monoclonal antibodies. This review provides comparative data and new research schemes for studying the possible mechanisms of differences in B cell response between SARS-CoV-2 infection or vaccination, and supplies a foundation for improving vaccines after SARS-CoV-2 mutations and potential antibody therapy for infected individuals.

Characterization of chicken-derived antibody against Alpha-Enolase of Streptococcus pneumoniae

Streptococcus pneumoniae is a clinically relevant pathogen notorious for causing pneumonia, meningitis, and otitis media in immunocompromised patients. Currently, antibiotic therapy is the most efficient treatment for fighting pneumococcal infections. However, an arise in antimicrobial resistance in S. pneumoniae has become a serious health issue globally. To resolve the problem, alternative and cost-effective strategies, such as monoclonal antibody-based targeted therapy, are needed for combating bacterial infection. S. pneumoniae alpha-enolase (spEno1), which is thought to be a great target, is a surface protein that binds and converts human plasminogen to plasmin, leading to accelerated bacterial infections. We first purified recombinant spEno1 protein for chicken immunization to generate specific IgY antibodies. We next constructed two single-chain variable fragments (scFv) antibody libraries by phage display technology, containing 7.2 × 107 and 4.8 × 107 transformants. After bio-panning, ten scFv antibodies were obtained, and their binding activities to spEno1 were evaluated on ELISA, Western blot and IFA. The epitopes of spEno1 were identified by these scFv antibodies, which binding affinities were determined by competitive ELISA. Moreover, inhibition assay displayed that the scFv antibodies effectively inhibit the binding between spEno1 and human plasminogen. Overall, the results suggested that these scFv antibodies have the potential to serve as an immunotherapeutic drug against S. pneumoniae infections.

Microfluidic-assisted single-cell RNA sequencing facilitates the development of neutralizing monoclonal antibodies against SARS-CoV-2

As a class of antibodies that specifically bind to a virus and block its entry, neutralizing monoclonal antibodies (neutralizing mAbs) have been recognized as a top choice for combating COVID-19 due to their high specificity and efficacy in treating serious infections. Although conventional approaches for neutralizing mAb development have been optimized for decades, there is an urgent need for workflows with higher efficiency due to time-sensitive concerns, including the high mutation rate of SARS-CoV-2. One promising approach is the identification of neutralizing mAb candidates via single-cell RNA sequencing (RNA-seq), as each B cell has a unique transcript sequence corresponding to its secreted antibody. The state-of-the-art high-throughput single-cell sequencing technologies, which have been greatly facilitated by advances in microfluidics, have greatly accelerated the process of neutralizing mAb development. Here, we provide an overview of the general procedures for high-throughput single-cell RNA-seq enabled by breakthroughs in droplet microfluidics, introduce revolutionary approaches that combine single-cell RNA-seq to facilitate the development of neutralizing mAbs against SARS-CoV-2, and outline future steps that need to be taken to further improve development strategies for effective treatments against infectious diseases.

One-step immunoassay of SARS-CoV-2 using screened Fv-antibodies and switching peptides

The Fv-antibodies were correponded to VH region of immunoglobulin G, which were composed of three complementarity determining regions (CDRs) for the specific binding of antigens. In this work, the Fv-antibodies against SARS-CoV-2 spike protein (SP) were screened from an autodisplayed Fv-antibody library which was expressed on E. coli outer membrane, and the receptor binding domain (RBD) of SP was used as a screening probe. The screened target clones were analyzed to have quantitative binding properties to the RBD, and the Fv-antibodies from the screened target clones were expressed as soluble proteins. The binding affinity (KD) of expressed Fv-antibodies to the RBD was estimated to be 70-85 nM using SPR biosensor. The specific binding properties of Fv-antibodies were analyzed for pseudo-virus particles with SARS-CoV-2 SP on the Lenti-virus envelope, such as wild type (Wuhan-1) and variants (Delta, Omicron BA.2, Omicron BA.4/5) using a SPR biosensor. The detection of real SARS-CoV-2 (Wild type, Wuhan-1) based on a SPR biosensor was also presented using the Fv-antibodies with the binding constant (KD) of cycle threshold value (Ct) = 33.8-32.9 (2.19-4.08 copies/μL) and LOD of 0.67-0.83 copies/μL (Ct = 35.5-35.2). Finally, one-step immunoassay based on switching peptide was demonstrated for the detection of the real SARS-CoV-2 (Wuhan-1) without any washing step. The binding constant (KD) was estimated to be Ct = 35.2-33.9 (0.83-2.04 copies/μL), and LOD was estimated to be 0.14-0.47 copies/μL (Ct = 37.8-36.0). Considering the LOD of the conventional RT-PCR (Ct = 35), the LOD of the one-step immunoassay based on the switching peptide was determined to be feasible for the medical diagnosis of COVID-19.

A bispecific antibody NXT007 exerts a hemostatic activity in hemophilia A monkeys enough to keep a nonhemophilic state

BACKGROUND

Emicizumab, a factor (F) VIIIa-function mimetic bispecific antibody (BsAb) to FIXa and FX, has become an indispensable treatment option for people with hemophilia A (PwHA). However, a small proportion of PwHA still experience bleeds even under emicizumab prophylaxis, as observed in the long-term outcomes of clinical studies. A more potent BsAb may be desirable for such patients.

OBJECTIVES

To identify a potent BsAb to FIXa and FX, NXT007, surpassing emicizumab by in vitro and in vivo evaluation.

METHODS

New pairs of light chains for emicizumab's heavy chains were screened from phage libraries, and subsequent antibody optimization was performed. For in vitro evaluation, thrombin generation assays were performed with hemophilia A plasma. In vivo hemostatic activity was evaluated in a nonhuman primate model of acquired hemophilia A.

RESULTS

NXT007 exhibited an in vitro thrombin generation activity comparable to the international standard activity of FVIII (100 IU/dL), much higher than emicizumab, when triggered by tissue factor. NXT007 also demonstrated a potent in vivo hemostatic activity at approximately 30-fold lower plasma concentrations than emicizumab's historical data. In terms of dose shift between NXT007 and emicizumab, the in vitro and in vivo results were concordant. Regarding pharmacokinetics, NXT007 showed lower in vivo clearance than those shown by typical monoclonal antibodies, suggesting that the Fc engineering to enhance FcRn binding worked well.

CONCLUSION

NXT007, a potent BsAb, was successfully created. Nonclinical results suggest that NXT007 would have a potential to keep a nonhemophilic range of coagulation potential in PwHA or to realize more convenient dosing regimens than emicizumab.

IgM marks persistent IgG anti-human leukocyte antigen antibodies in highly sensitized heart transplant patients

BACKGROUND

Sensitization to human leukocyte antigens (HLA) is a persistent problem in heart transplant (HT) candidates. We sought to characterize the anti-HLA antibody and circulating B cell repertoire in a cohort of highly sensitized HT candidates.

METHODS

We assessed immunoglobulin G (IgG) and immunoglobulin M (IgM) anti-HLA antibodies using Luminex single antigen bead assays in a cohort of 11 highly sensitized (HS; calculated panel reactive antibody ≥ 90%) and 3 mildly sensitized (MS) candidates. We also performed B cell receptor repertoire sequencing (BCRseq) in HS candidates and 33 non-candidate controls. HLA antibody strength was measured by mean fluorescence intensity (MFI).

RESULTS

We found that IgM anti-HLA antibodies were present in all HS candidates, but with a lower breadth and strength as compared to IgG. When anti-HLA IgG specificities intersected with IgM, binding strength was higher. In contrast, there were IgM but no intersecting IgG specificities for the MS group. In four candidates in the HS group, IgG anti-HLA antibodies decreased in both breadth and strength after HT, but the decrease in strength was smaller if the IgG possessed a specificity that intersected with pre-transplant IgM. BCRseq revealed larger B cell clonotypes in HS candidates but similar diversity as compared to controls.

CONCLUSIONS

IgM marks IgG anti-HLA antibodies with higher strength before HT and persistence after HT. The presence of IgM intersecting IgG for an anti-HLA specificity may be a useful approach to determine which donor HLA should be avoided for a sensitized candidate.

The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing

The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.

Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity

High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In addition, incorporating full-length heavy chains and paired light chain sequences improves the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.

The dengue-specific immune response and antibody identification with machine learning

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.

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.

Monoamine Oxidase-A (MAO-A) Inhibitors Screened from the Autodisplayed Fv-Antibody Library

Serotonin-like mimotopes were screened from the Fv-antibody library to be used as inhibitors against monoamine oxidase A (MAO-A). The Fv-antibody [corresponding to the VH region of immunoglobulin G (IgG)] consists of three complementarity-determining regions and four frame regions. The Fv-antibody library was prepared by site-directed mutagenesis of CDR3, which consists of 11 amino acid residues. Three target clones were screened from the Fv-antibody library, and the binding affinity of the screened clones to the monoclonal anti-serotonin antibody was analyzed using fluorescence-activated cell sorting. The screened Fv-antibodies were expressed as soluble proteins fused with green fluorescence protein. Additionally, the screened CDR3 regions (11 residues) of the selected Fv-antibodies were synthesized as peptides with linking amino acid residues. The binding constants (KD) of the three serotonin-like mimotopes (Fv-antibodies and peptides) were estimated using a surface plasmon resonance biosensor. The inhibitory activity (IC50) of the serotonin-like mimotopes (Fv-antibodies and peptides) was estimated separately for MAO-A and MAO-B enzymes and compared with that of conventional inhibitors. Finally, the screened serotonin-like mimotopes were used to treat a cell line (SH-SY5Y, ATCC code: CRL-2266) expressing serotonin receptors. This was done to confirm the following two aspects: (1) the binding of mimotopes to the serotonin receptors on the cell surface and (2) the inhibitory activity of mimotopes against MAO-A enzymes in the cell lysates.