Novel skewed usage of B-cell receptors in COVID-19 patients with various clinical presentations

AntiFormer: graph enhanced large language model for binding affinity prediction

Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.

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.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

AT2 cell-derived IgA trapped by the extracellular matrix in silica-induced pulmonary fibrosis

Pulmonary fibrosis is an interstitial lung disease caused by various factors such as exposure to workplace environmental contaminants, drugs, or X-rays. Epithelial cells are among the driving factors of pulmonary fibrosis. Immunoglobulin A (IgA), traditionally thought to be secreted by B cells, is an important immune factor involved in respiratory mucosal immunity. In the current study, we found that lung epithelial cells are involved in IgA secretion, which, in turn, promotes pulmonary fibrosis. Spatial transcriptomics and single-cell sequencing suggest that Igha transcripts were highly expressed in the fibrotic lesion areas of lungs from silica-treated mice. Reconstruction of B-cell receptor (BCR) sequences revealed a new cluster of AT2-like epithelial cells with a shared BCR and high expression of genes related to IgA production. Furthermore, the secretion of IgA by AT2-like cells was trapped by the extracellular matrix and aggravated pulmonary fibrosis by activating fibroblasts. Targeted blockade of IgA secretion by pulmonary epithelial cells may be a potential strategy for treating pulmonary fibrosis.

Proof-of-Concept Analysis of B Cell Receptor Repertoire in COVID-19 Patients Undergoing ECMO by Single-Cell V(D)J and Gene Expression Sequencing

SARS-CoV-2, which causes COVID-19, has altered human activities all over the world and has become a global hazard to public health. Despite considerable advancements in pandemic containment techniques, in which vaccination played a key role, COVID-19 remains a global threat, particularly for frail patients and unvaccinated individuals, who may be more susceptible to developing ARDS. Several studies reported that patients with COVID-19-related ARDS who were treated with ECMO had a similar survival rate to those with COVID-19-unrelated ARDS. In order to shed light on the potential mechanisms underlying the COVID-19 infection, we conducted this proof-of-concept study using single-cell V(D)J and gene expression sequencing of B cells to examine the dynamic changes in the transcriptomic BCR repertoire present in patients with COVID-19 at various stages. We compared a recovered and a deceased COVID-19 patient supported by ECMO with one COVID-19-recovered patient who did not receive ECMO treatment and one healthy subject who had never been infected previously. Our analysis revealed a downregulation of FXYD, HLA-DRB1, and RPS20 in memory B cells; MTATP8 and HLA-DQA1 in naïve cells; RPS4Y1 in activated B cells; and IGHV3-73 in plasma cells in COVID-19 patients. We further described an increased ratio of IgA + IgG to IgD + IgM, suggestive of an intensive memory antibody response, in the COVID ECMO D patient. Finally, we assessed a V(D)J rearrangement of heavy chain IgHV3, IGHJ4, and IGHD3/IGHD2 families in COVID-19 patients regardless of the severity of the disease.