Neutralizing Antibodies Targeting BK Polyomavirus: Clinical Importance and Therapeutic Potential for Kidney Transplant Recipients.

Most of the world's adult population is latently infected by the BK polyomavirus. It causes asymptomatic infection in healthy individuals but emerged as a threat to kidney transplant recipients because of virus-associated nephropathy caused by immunosuppressive therapy. In these conditions, when a functional cellular response is impaired by immunosuppression, neutralizing antibodies may play a major role because they can directly prevent infection of target cells, independently of cell-mediated immunity, by binding to the viral particles. Studying the contribution of anti-BK virus neutralizing antibodies in viral control has long been hampered by the lack of convenient in vitro models, but major progress has been made in the past decade. The four BK virus genotypes have been demonstrated to behave as distinct serotypes. A low recipient neutralizing antibody titer against the donor's serotype before kidney transplant has been significantly associated with BK virus replication after transplant. Different mechanisms exploited by the BK virus to evade neutralizing antibodies have been described. Recent studies also support the potential benefit of administering intravenous Igs or monoclonal neutralizing antibodies as a therapeutic strategy, and more interestingly, this could also be used as preventive or preemptive therapy before advanced kidney damage has occurred. Besides, neutralizing antibodies could be induced by vaccination. In this review, we summarize accumulated knowledge on anti-BK virus neutralizing antibodies as well as their clinical importance and therapeutic potential for kidney transplant recipients.

Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2 nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Broadly potent spike-specific human monoclonal antibodies inhibit SARS-CoV-2 Omicron sub-lineages.

The continuous emergence of SARS-CoV-2 variants of concern has rendered many therapeutic monoclonal antibodies (mAbs) ineffective. To date, there are no clinically authorized therapeutic antibodies effective against the recently circulating Omicron sub-lineages BA.2.86 and JN.1. Here, we report the isolation of broad and potent neutralizing human mAbs (HuMabs) from a healthcare worker infected with SARS-CoV-2 early in the pandemic. These include a genetically unique HuMab, named K501SP6, which can neutralize different Omicron sub-lineages, including BQ.1, XBB.1, BA.2.86 and JN.1, by targeting a highly conserved epitope on the N terminal domain, as well as an RBD-specific HuMab (K501SP3) with high potency towards earlier circulating variants that was escaped by the more recent Omicron sub-lineages through spike F486 and E484 substitutions. Characterizing SARS-CoV-2 spike-specific HuMabs, including broadly reactive non-RBD-specific HuMabs, can give insight into the immune mechanisms involved in neutralization and immune evasion, which can be a valuable addition to already existing SARS-CoV-2 therapies.

Impaired mucosal IgA response in patients with severe COVID-19.

Several studies have investigated the antibody response to SARS-CoV-2, focusing particularly on the systemic humoral immune response and the production of immunoglobulin G (IgG) antibodies. IgA antibodies play a crucial role in protecting against respiratory viral infections but have also been associated with the pathophysiology of COVID-19. We performed a prospective study of 169 COVID-19 patients - 50 with critical/severe (ICU), 47 with moderate (Non-ICU), and 72 with asymptomatic COVID-19 - to explore the humoral immune response to SARS-CoV-2 infection. We found that the early systemic IgA response strongly induced in patients with severe disease did not block IgG neutralization functions and activated FcRs more effectively than IgG. However, even if SIgA levels were high, mucosal IgA antibodies could not control the infection effectively in patients with severe disease. Our findings highlight the complexity of the immune response to SARS-CoV-2 exhibiting high systemic levels of IgA with strong neutralizing capacity in severe cases, together with higher levels of IgA-FcR activation than in asymptomatic patients. They also suggest the need for further research to fully understand the role of IgA and its structural alterations in mucosal tissues in cases of severe disease and the impact of these antibodies on disease progression.

Robust and persistent B-cell responses following SARS-CoV-2 vaccine determine protection from SARS-CoV-2 infection.

Introduction: A clear immune correlate of protection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been defined. We explored antibody, B-cell, and T-cell responses to the third-dose vaccine and relationship to incident SARS-CoV-2 infection. Methods: Adults in a prospective cohort provided blood samples at day 0, day 14, and 10 months after the third-dose SARS-CoV-2 vaccine. Participants self-reported incident SARS-CoV-2 infection. Plasma anti-SARS-CoV-2 receptor-binding domain (RBD) and spike-subunit-1 and spike-subunit-2 antibodies were measured. A sub-study assessed SARS-CoV-2-specific plasma and memory B-cell and memory T-cell responses in peripheral blood mononuclear cells by enzyme-linked immunospot. Comparative analysis between participants who developed incident infection and uninfected participants utilised non-parametric t-tests, Kaplan-Meier survival analysis, and Cox proportional hazard ratios. Results: Of the 132 participants, 47 (36%) reported incident SARS-CoV-2 infection at a median 16.5 (16.25-21) weeks after the third-dose vaccination. RBD titres and B-cell responses, but not T-cell responses, increased after the third-dose vaccine. Whereas no significant difference in day 14 antibody titres or T-cell responses was observed between participants with and without incident SARS-CoV-2 infection, RBD memory B-cell frequencies were significantly higher in those who did not develop infection [10.0% (4.5%-16.0%) versus 4.9% (1.6%-9.3%), p = 0.01]. RBD titres and memory B-cell frequencies remained significantly higher at 10 months than day 0 levels (p < 0.01). Discussion: Robust antibody and B-cell responses persisted at 10 months following the third-dose vaccination. Higher memory B-cell frequencies, rather than antibody titres or T-cell responses, predicted protection from subsequent infection, identifying memory B cells as a correlate of protection.

Tonsil explants as a human in vitro model to study vaccine responses.

Introduction: Vaccination is one of the most effective infection prevention strategies. Viruses with high mutation rates -such as influenza- escape vaccine-induced immunity and represent significant challenges to vaccine design. Influenza vaccine strain selection is based on circulating strains and immunogenicity testing in animal models with limited predictive outcomes for vaccine effectiveness in humans. Methods: We developed a human in vitro vaccination model using human tonsil tissue explants cultured in 3D perfusion bioreactors to be utilized as a platform to test and improve vaccines. Results: Tonsils cultured in bioreactors showed higher viability, metabolic activity, and more robust immune responses than those in static cultures. The in vitro vaccination system responded to various premanufactured vaccines, protein antigens, and antigen combinations. In particular, a multivalent in vitro immunization with three phylogenetically distant H3N2 influenza strains showed evidence for broader B cell activation and induced higher antibody cross-reactivity than combinations with more related strains. Moreover, we demonstrate the capacity of our in vitro model to generate de novo humoral immune responses to a model antigen. Discussion: Perfusion-cultured tonsil tissue may be a valuable human in vitro model for immunology research with potential application in vaccine candidate selection.

Improved deep learning prediction of antigen-antibody interactions.

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

[The Molecular and Biological Patterns Underlying Sustained SARS-CoV-2 Circulation in the Human Population].

INTRODUCTION: For four years, SARS-CoV-2, the etiological agent of COVID-19, has been circulating among humans. By the end of the second year, an absence of immunologically naive individuals was observed, attributable to extensive immunization efforts and natural viral exposure. This study focuses on delineating the molecular and biological patterns that facilitate the persistence of SARS-CoV-2, thereby informing predictions on the epidemiological trajectory of COVID-19 toward refining pandemic countermeasures. The aim of this study was to describe the molecular biological patterns identified that contribute to the persistence of the virus in the human population. MATERIALS AND METHODS: For over three years since the beginning of the COVID-19 pandemic, molecular genetic monitoring of SARS-CoV-2 has been conducted, which included the collection of nasopharyngeal swabs from infected individuals, assessment of viral load, and subsequent whole-genome sequencing. RESULTS: We discerned dominant genetic lineages correlated with rising disease incidence. We scrutinized amino acid substitutions across SARS-CoV-2 proteins and quantified viral loads in swab samples from patients with emerging COVID-19 variants. Our findings suggest a model of viral persistence characterized by 1) periodic serotype shifts causing substantial diminutions in serum virus-neutralizing activity (> 10-fold), 2) serotype-specific accrual of point mutations in the receptor-binding domain (RBD) to modestly circumvent neutralizing antibodies and enhance receptor affinity, and 3) a gradually increasing amount of virus being shed in mucosal surfaces within a single serotype. CONCLUSION: This model aptly accounts for the dynamics of COVID-19 incidence in Moscow. For a comprehensive understanding of these dynamics, acquiring population-level data on immune tension and antibody neutralization relative to genetic lineage compositions is essential.

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health. MATERIALS AND METHODS: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting. The calculations used the AAindex matrices in parallel to the Hamming distance. The calculations were carried out with different types and values of antigenic escape thresholds, on four data sets. The results were compared using common binary classification metrics. RESULTS: Significant differentiation is shown depending on the data sets used. The best results were demonstrated by all three models for the forecast autumn season of 2022, which were preliminary trained on the February season of the same year (Auroc 0.934; 0.958; 0.956, respectively). The lowest results were obtained for the entire forecast year 2023, they were set up on data from two seasons of 2022 (Aucroc 0.614; 0.658; 0.775). The dependence of the results on the types of thresholds used and their values turned out to be insignificant. The additional use of AAindex matrices did not significantly improve the results of the models without introducing significant deterioration. CONCLUSION: More complex models show better results. When developing cross-immunity models, testing on a variety of data sets is important to make strong claims about their prognostic robustness.

Development of novel canine phage display-derived neutralizing monoclonal antibody fragments against rabies virus from immunized dogs.

Animal rabies is a potentially fatal infectious disease in mammals, especially dogs. Currently, the number of rabies cases in pet dogs is increasing in several regions of Thailand. However, no passive postexposure prophylaxis (PEP) has been developed to combat rabies infection in animals. As monoclonal antibodies (MAbs) are promising biological therapies for postinfection, we developed a canine-neutralizing MAb against rabies virus (RABV) via the single-chain variable fragment (scFv) platform. Immunized phage-displaying scFv libraries were constructed from PBMCs via the pComb3XSS system. Diverse canine VHVLκ and VHVLλ libraries containing 2.4 × 108 and 1.3 × 106 clones, respectively, were constructed. Five unique clones that show binding affinity with the RABV glycoprotein were then selected, of which K9RABVscFv1 and K9RABVscFv16 showed rapid fluorescent foci inhibition test (RFFIT) neutralizing titers above the human protective level of 0.5 IU/ml. Finally, in silico docking predictions revealed that the residues on the CDRs of these neutralizing clones interact mainly with similar antigenic sites II and III on the RABV glycoprotein. These candidates may be used to develop complete anti-RABV MAbs as a novel PEP protocol in pet dogs and other animals.