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Introduction: The SARS-CoV-2 pandemic has had a widespread and severe impact on society, yet there have also been instances of remarkable recovery, even in critically ill patients., Materials and Methods: In this study, we used single-cell RNA sequencing to analyze the immune responses in recovered and deceased COVID-19 patients during moderate and critical stages., Results: Expanded T cell receptor (TCR) clones were predominantly SARS-CoV-2-specific, but represented only a small fraction of the total repertoire in all patients. In contrast, while deceased patients exhibited monoclonal B cell receptor (BCR) expansions without COVID-19 specificity, survivors demonstrated diverse and specific BCR clones. These findings suggest that neither TCR diversity nor BCR monoclonal expansions are sufficient for viral clearance and subsequent recovery. Differential gene expression analysis revealed that protein biosynthetic processes were enriched in survivors, but that potentially damaging mitochondrial ATP metabolism was activated in the deceased., Conclusion: This study underscores that BCR repertoire diversity, but not TCR diversity, correlates with favorable outcomes in COVID-19., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Paran, Oyama, Khasawneh, Ai, Ismanto, Sherif, Saputri, Ono, Saita, Takei, Horiuchi, Yagi, Matsuura, Okazaki, Takahashi, Standley, Tabe and Naito.)

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs., (© The Author(s) 2024. Published by Oxford University Press.)

Importance: Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities., Competing Interests: We wish to disclose that we are in the process of applying for a patent describing the antibody clustering method.

The entry of SARS-CoV-2 into host cells is mediated by the interaction between the spike receptor-binding domain (RBD) and host angiotensin-converting enzyme 2 (ACE2). Certain human antibodies, which target the spike N-terminal domain (NTD) at a distant epitope from the host cell binding surface, have been found to augment ACE2 binding and enhance SARS-CoV-2 infection. Notably, these antibodies exert their effect independently of the antibody fragment crystallizable (Fc) region, distinguishing their mode of action from previously described antibody-dependent infection-enhancing (ADE) mechanisms. Building upon previous hypotheses and experimental evidence, we propose that these NTD-targeting infection-enhancing antibodies (NIEAs) achieve their effect through the crosslinking of neighboring spike proteins. In this study, we present refined structural models of NIEA fragment antigen-binding region (Fab)-NTD complexes, supported by molecular dynamics simulations and hydrogen-deuterium exchange mass spectrometry (HDX-MS). Furthermore, we provide direct evidence confirming the crosslinking of spike NTDs by NIEAs. Collectively, our findings advance our understanding of the molecular mechanisms underlying NIEAs and their impact on SARS-CoV-2 infection.

Antibody and TCR modeling are becoming important as more and more sequence data becomes available to the public. One of the pressing questions now is how to use such data to understand adaptive immune responses to disease. Infectious disease is of particular interest because the antigens driving such responses are often known to some extent. Here, we describe tips for gathering data and cleaning it for use in downstream analysis. We present a method for high-throughput structural modeling of antibodies or TCRs using Repertoire Builder and its extensions. AbAdapt is an extension of Repertoire Builder for antibody-antigen docking from antibody and antigen sequences. ImmuneScape is a corresponding extension for TCR-pMHC 3D modeling. Together, these pipelines can help researchers to understand immune responses to infection from a structural point of view., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Ismanto, Zhou, Saputri, Sugihara and Standley.)

The covalent fusion of immunostimulatory adjuvants to immunogenic antigens is a promising strategy for the development of effective synthetic vaccines for infectious diseases. Herein, we describe the conjugation of a mycobacterial peptide antigen from the 6 kDa early secretory antigenic target (ESAT6) to a suitably functionalised trehalose dibehenate (TDB), a potent glycolipid adjuvant targeting macrophage inducible C-type lectin (Mincle).

To assess the frequency of SARS-CoV-2 infection in the general population, we searched over 64 million heavy chain antibody sequences from healthy unvaccinated, healthy BNT162b2 vaccinated and COVID-19 patient repertoires for sequences similar to 11 previously reported enhancing antibodies. Although the distribution of sequence identities was similar in all three groups of repertoires, the COVID-19 and healthy vaccinated hits were significantly more clonally expanded than healthy unvaccinated hits. Furthermore, among the tested hits, 17 out of 94 from COVID-19 and 9 out of 59 from healthy vaccinated, compared with only 2 out of 96 from healthy unvaccinated, bound to the enhancing epitope. A total of 9 of the 28 epitope-binding antibodies enhanced ACE2 receptor binding to the spike protein. Together, this study revealed that infection enhancing-like antibodies are far more frequent in COVID-19 patients or healthy vaccinated donors than in healthy unvaccinated donors, but a reservoir of potential enhancing antibodies exists in healthy donors that could potentially mature to actual enhancing antibodies upon infection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

The SARS-CoV-2 S protein is a major point of interaction between the virus and the human immune system. As a consequence, the S protein is not a static target but undergoes rapid molecular evolution. In order to more fully understand the selection pressure during evolution, we examined residue positions in the S protein that vary greatly across closely related viruses but are conserved in the subset of viruses that infect humans. These "evolutionarily important" residues were not distributed evenly across the S protein but were concentrated in two domains: the N-terminal domain and the receptor-binding domain, both of which play a role in host cell binding in a number of related viruses. In addition to being localized in these two domains, evolutionary importance correlated with structural flexibility and inversely correlated with distance from known or predicted host receptor-binding residues. Finally, we observed a bias in the composition of the amino acids that make up such residues toward more human-like, rather than virus-like, sequence motifs., (Copyright © 2020 Saputri, Li, van Eerden, Rozewicki, Xu, Ismanto, Davila, Teraguchi, Katoh and Standley.)

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context., (© 2020 The Author(s).)

Background: Langerin, a C-type lectin receptor (CLR) expressed in a subset of dendritic cells (DCs), binds to glycan ligands for pathogen capture and clearance. Previous studies revealed that langerin has an unusual binding affinity toward 6-sulfated galactose (Gal), a structure primarily found in keratan sulfate (KS). However, details and biological outcomes of this interaction have not been characterized. Based on a recent discovery that the disaccharide L4, a KS component that contains 6-sulfo-Gal, exhibits anti-inflammatory activity in mouse lung, we hypothesized that L4-related compounds are useful tools for characterizing the langerin-ligand interactions and their therapeutic application., Methods: We performed binding analysis between purified long and short forms of langerin and a series of KS disaccharide components. We also chemically synthesized oligomeric derivatives of L4 to develop a new high-affinity ligand of langerin., Results: We show that the binding critically requires the 6-sulfation of Gal and that the long form of langerin displays higher affinity than the short form. The synthesized trimeric (also designated as triangle or Tri) and polymeric (pendant) L4 derivatives displayed over 1000-fold higher affinity toward langerin than monomeric L4. The pendant L4, but not the L4 monomer, was found to effectively transduce langerin signaling in a model cell system., Conclusions: L4 is a specific ligand for langerin. Oligomerization of L4 unit increased the affinity toward langerin., General Significance: These results suggest that oligomeric L4 derivatives will be useful for clarifying the langerin functions and for the development of new glycan-based anti-inflammatory drugs., (Copyright © 2018 Elsevier B.V. All rights reserved.)