Systematic analysis of human antibody response to ebolavirus glycoprotein reveals high prevalence of neutralizing public clonotypes

Understanding the human antibody response to emerging viral pathogens is key to epidemic preparedness. As the size of the B cell response to a pathogenic virus protective antigen is undefined, we performed deep paired heavy and light chain sequencing in EBOV-GP specific memory B cells, allowing analysis of the ebolavirus-specific antibody repertoire both genetically and functionally. This approach facilitated investigation of the molecular and genetic basis for evolution of cross-reactive antibodies by elucidating germline-encoded properties of antibodies to EBOV and identification of the overlap between antibodies in the memory B-cell and serum repertoire. We identified 73 public clonotypes to EBOV, 20% of which encoded antibodies with neutralization activity and capacity to protect in vivo. This comprehensive analysis of the public and private antibody repertoire provides insight into the molecular basis of the humoral immune response to EBOV-GP, which informs vaccine design of new vaccines and improved therapeutics.

CellCelector™ as a platform in isolating primary B cells for antibody discovery

Background The most robust strategy in antibody discovery is the use of immunized animals and the ability to isolate and immortalize immune B-cells to hybridoma for further interrogation. However, capturing the full repertoire of an immunized animal is labor intensive, time consuming, and limited in throughput. Therefore, techniques to directly mine the antibody repertoire of primary B-cells are of great importance in antibody discovery. Methods In the current study, we present a method to isolate individual antigen specific primary B-cells using the CellCellector™ single-cell isolation platform from XenoMouse® (XM) immunized with a recombinant therapeutic protein, EGFR. We screened a subset of CD138+ B-cells and identified 238 potential EGFR specific B-cells from 1,189 antibody secreting cells (ASCs) and isolated 94 by CellCellector. Results We identified a diverse set of heavy chain CDR sequences and cloned and expressed 20 into a standard human IgG1 antibody format. We further characterized and identified 13 recombinant antibodies that engage soluble and native forms of EGFR. By extrapolating the method to all 400,000 CD138+ B-cells extracted from one EGFR immunized XM, a potential 1,196 unique EGFR-specific antibodies could be discovered. Conclusions CellCelector allows for interrogating the B-cell pool directly and isolating B-cells specific to the therapeutic target of interest. Furthermore, antibody sequences recovered from isolated B-cells engage the native and recombinant target, demonstrating the CellCellector can serve as a platform in antibody discovery.

Dual UMIs and Dual Barcodes With Minimal PCR Amplification Removes Artifacts and Acquires Accurate Antibody Repertoire

Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.

Public Immunity: Evolutionary Spandrels for Pathway-Amplifying Protective Antibodies

Humoral immunity is seeded by affinity between the B cell receptor (BCR) and cognate antigen. While the BCR is a chimeric display of diverse antigen engagement solutions, we discuss its functional activity as an ‘innate-like’ immune receptor, wherein genetically hardwired antigen complementarity can serve as reproducible templates for pathway-amplifying otherwise immunologically recessive antibody responses. We propose that the capacity for germline reactivity to new antigen emerged as a set of evolutionary spandrels or coupled traits, which can now be exploited by rational vaccine design to focus humoral immunity upon conventionally immune-subdominant antibody targets. Accordingly, we suggest that evolutionary spandrels account for the necessary but unanticipated antigen reactivity of the germline antibody repertoire.

Novel Allele Detection Tool Benchmark and Application With Antibody Repertoire Sequencing Dataset

Detailed knowledge of the diverse immunoglobulin germline genes is critical for the study of humoral immunity. Hundreds of alleles have been discovered by analyzing antibody repertoire sequencing (Rep-seq or Ig-seq) data via multiple novel allele detection tools (NADTs). However, the performance of these NADTs through antibody sequences with intrinsic somatic hypermutations (SHMs) is unclear. Here, we developed a tool to simulate repertoires by integrating the full spectrum features of an antibody repertoire such as germline gene usage, junctional modification, position-specific SHM and clonal expansion based on 2152 high-quality datasets. We then systematically evaluated these NADTs using both simulated and genuine Ig-seq datasets. Finally, we applied these NADTs to 687 Ig-seq datasets and identified 43 novel allele candidates (NACs) using defined criteria. Twenty-five alleles were validated through findings of other sources. In addition to the NACs detected, our simulation tool, the results of our comparison, and the streamline of this process may benefit further humoral immunity studies via Ig-seq.

B Cell Mobilization, Dissemination, Fine Tuning of Local Antigen Specificity and Isotype Selection in Asthma

In order to better understand how the immune system interacts with environmental triggers to produce organ-specific disease, we here address the hypothesis that B and plasma cells are free to migrate through the mucosal surfaces of the upper and lower respiratory tracts, and that their total antibody repertoire is modified in a common respiratory tract disease, in this case atopic asthma. Using Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) we have catalogued the antibody repertoires of B cell clones retrieved near contemporaneously from multiple sites in the upper and lower respiratory tract mucosa of adult volunteers with atopic asthma and non-atopic controls and traced their migration. We show that the lower and upper respiratory tracts are immunologically connected, with trafficking of B cells directionally biased from the upper to the lower respiratory tract and points of selection when migrating from the nasal mucosa and into the bronchial mucosa. The repertoires are characterized by both IgD-only B cells and others undergoing class switch recombination, with restriction of the antibody repertoire distinct in asthmatics compared with controls. We conclude that B cells and plasma cells migrate freely throughout the respiratory tract and exhibit distinct antibody repertoires in health and disease.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.