مقالهها، منابع و پژوهشهای تازه حوزه ایمنیشناسی
This article uses a hypothetical quality improvement project to discuss the process of the Model for Improvement and ways to incorporate this into allergy practice.
In early 2026, the world lost Barry Bloom, a great advocate for public health, an extraordinarily accomplished immunologist, and a science advisor who helped refocus policy on controlling infectious diseases, including neglected diseases such as leprosy. Barry's career took him from a life of laboratory discovery where he was enormously influential in catalyzing the late 20th century shift from studying the immune response of simple molecules to pathogenic microbes. As Dean of Harvard T.H. Chan School of Public Health he had a profound influence on the growth of the institution and the careers of many who have continued his work.
Immunometabolism has rapidly evolved from an emerging area within immunology into a topic that not only impregnates most aspects of immune research but also reveals itself as a defining feature of the immune response. At the European Immunometabolism Conference celebrated in June, we asked some of the speakers to share their personal journey as researchers in this field and what questions they are currently working on.
I was pleased to receive an invitation to write a historical perspective on my career. As I understand it, these perspectives aim to illustrate the often-twisted paths to discoveries, the fortuitous events that often enable them, and the pitfalls along the way. At the same time, they can be expected to illustrate the influence of the particular "tastes" and interests of a scientist in the choices that are made along the way and that there are many ways to position oneself in a field of study. This article describes my initial interest in molecular biology, the early events that led me to undertake graduate studies in cellular immunology with a focus on MHC restriction of T cell specificity and T cell activation, and the serendipitous events that led to my eventual transition to a focus on natural killer cells and cancer immunotherapy. I hope that this piece provides some of the illustrations mentioned and will therefore be of interest to a range of readers with an interest in immunology research.
Genetically driven immune diseases are immune-mediated disorders involving germline and/or somatic host genetic changes that make a major contribution to disease pathogenesis. These conditions, which include the inborn errors of immunity, may predispose patients to the development of immunodeficiency, immune dysregulation, atopy, autoinflammation or hyperinflammation, and malignancy. Since the development of massively parallel sequencing, also known as next-generation sequencing, and its introduction into clinical use, clinical genetic testing has expanded exponentially across medical specialties, including clinical immunology, with approximately 28% of patients with suspected genetically driven immune diseases undergoing genetic testing in the United States. Despite rapid uptake, knowledge gaps persist regarding when to order genetic testing, how to choose the appropriate assay, and how to interpret the results in clinical context. In this review, we provide a practical framework for genetic testing strategy and interpretation and discuss current limitations, as well as emerging advances that may help further enhance diagnostic yield and clinical impact.
The integration of research discoveries into routine clinical care is often slow and resource-intensive, particularly in allergy and immunology, where advances in biologics, immunotherapy, precision diagnostics, and guideline-driven management continue to evolve rapidly. Implementation science provides a structured approach to understand and address the gaps between research discovery and real-world patient care. In asthma, food allergy, atopic dermatitis, drug allergy, and primary immune disorders, variation in clinician knowledge, health system infrastructure, reimbursement models, patient demographics, and staffing patterns contribute to inconsistent uptake of evidence-based interventions. Implementation science offers tools to systematically evaluate these contextual factors and develop strategies that improve adoption, fidelity, sustainability, and equity in allergic and immunologic care. By offering theory-informed frameworks to design, adapt, and evaluate interventions across diverse clinical settings, implementation science advances the study of evidence-to-practice care gaps in allergy and immunology. When applied thoughtfully, these methods enhance implementation of specialty guidelines, practice parameters, and emerging therapies, while addressing multilevel determinants of care delivery. With the rapidly growing application of precision medicine and augmented intelligence to allergy and immunology practice, the study of how to best apply technological and research discovery to clinical practice will be needed to promote equitable uptake of evidence-based care.
Megalocytivirus pagrus 1 infection is a World Organisation for Animal Health-listed aquatic animal disease caused by a virus species comprising the RSIV, ISKNV, and TRBIV genogroups. Here, we integrated comparative genomics and immunoinformatics to prioritize a multi-epitope protein construct, pMEV, and to design a DNA vaccine candidate encoding it, with emphasis on RSIV-type infection relevant to rock bream aquaculture. Analysis of 61 complete genomes identified 28 core gene clusters, from which myristoylated membrane protein (MMP) and major capsid protein (MCP) were prioritized as source antigens for epitope screening. Four cytotoxic T-cell, five helper T-cell, and five linear B-cell epitope candidates were selected based on sequence-based screening and exploratory peptide-MHC docking. The selected epitopes were assembled with rock bream beta-defensin-3, PADRE, and peptide linkers to generate the 283-aa pMEV construct. Sequence-based physicochemical analyses indicated properties relevant to subsequent structural and expression-based evaluation, while computationally refined structural modeling identified nine putative conformational B-cell epitope regions. TLR3 docking, normal mode analysis, and a 200-ns molecular dynamics simulation characterized the structural behavior of the selected computational complex without inferring receptor activation. C-ImmSim further generated model-dependent generic humoral and helper T-cell-associated response patterns within a mammalian-based simulation framework. Finally, the pMEV coding sequence was codon-optimized and incorporated into an in silico pcDNA3.1(+)-based DNA vaccine design. Collectively, this study provides a comparative genomics-guided framework for prioritizing an experimentally testable multi-epitope DNA vaccine candidate against M. pagrus 1, while construct expression, immunogenicity, and protective efficacy remain to be evaluated experimentally.
The immune system leverages B and T cells to recognize specific molecular patterns, known as epitopes, on pathogens and cancer cells to effectively combat infections and diseases. The critical success of immunotherapies in cancer treatment and COVID-19 vaccine development has established precise epitope identification as a central and rapidly growing priority in therapeutic design. Because traditional wet-lab based identification of B- and T-cell epitopes is expensive and timeconsuming, our systematic literature search (2015-2026) identified 148 Artificial Intelligence (AI) based linear B-cell, conformational B-cell, and T-cell epitope prediction models within the stated search scope. However, the true potential of these models remains unclear due to fragmented evaluation practices, with models rarely tested across a comprehensive range of datasets, insufficient comparison with existing predictors, systematic under-utilization of available public databases, and other methodological inconsistencies across five different stages of the predictive pipeline. Moreover, the 7 existing review papers fail to adequately highlight these research gaps or to support the development of robust AI models. This review paper consolidates the computational landscape of B-cell and T-cell epitope recognition and introduces a unified taxonomy that organises the field into twelve prediction tasks: linear and conformational B-cell prediction together with ten T-cell subtasks (T1-T10) that span the antigen-recognition cascade from human leukocyte antigen (HLA) typing to vaccine design. It analyses these twelve tasks across five major stages of a shared predictive pipeline. Within this taxonomy, the recognition sub-tasks (T1-T7) apply to the epitopes of any antigen, whereas neoantigen identification (T8) and tumor T-cell antigen (TTCA) classification (T9) are oncology-specific translational applications and multi-epitope vaccine design (T10) spans infectious-disease and cancer targets. It systematically examines 155 studies published from 2015 to 2026 to perform comprehensive categorization of 43 dedicated epitope/immunology databases, 144 benchmark datasets, 272 representation learning approaches, 148 classifiers, 54 evaluation and optimization approaches, and 148 predictive models accessibility status across all twelve prediction tasks. Additionally, it highlights persistent challenges across each stage of the predictive pipeline and offers key directions for improvement. This comprehensive analysis provides actionable recommendations for developing more robust, generalizable epitope predictors, and it ultimately accelerates the translation of computational predictions into effective immunotherapies against diverse diseases.
Repeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms, particularly the disruption of maternal-fetal tolerance, remain incompletely understood. Although immune tolerance is critical for pregnancy success, the specific immune dysregulations contributing to RPL, particularly in euploid pregnancies, have been difficult to characterize. To address this, we performed single-cell RNA sequencing of decidual tissues from RPL patients and first-trimester controls. Our analysis initially revealed elevated expression of a transcriptional module of immune activation genes in RPL decidual tissues. To dissect the cellular drivers of this complex landscape, we employed genotype-based origin analysis coupled with a supervised machine learning model and a transformer-based foundation model (scGPT). This hierarchical approach prioritized maternal T cells over other immune subsets as the population carrying the most distinct and generalizable RPL-associated signatures. Through the convergence of computational drug repurposing, network centrality analysis, and a rigorous origin-controlled expression filtering strategy, we identified CXCR4 and JUN as druggable molecular candidates strongly associated with this T-cell dysregulation. Collectively, our machine learning-driven approach characterizes the maternal immune landscape of euploid RPL in the context of immune tolerance breakdown, and nominates candidate targets for future functional investigation.
Human metapneumovirus (HMPV) is a primary cause of global respiratory infections yet no approved vaccine currently exists. This study computationally predicts a multi-epitope vaccine candidate using a diverse dataset of 65 HMPV sequences spanning five continents. Following the screening of lead proteins for antigenicity and virulence, fifteen highly conserved MHC-I, MHC-II and B-cell epitopes were prioritized. These were integrated with a putative L7/L12 adjuvant using optimized AAY, GPGPG, and KK linkers to design three constructs (HMPV_V1-V3). Structural validation identified HMPV-V2 as the lead candidate that exhibits a Z-score of-5.24 and 87.7% of residues in favored Ramachandran regions indicating excellent stereochemical quality and structural stability. In silico docking indicated a strong predicted binding affinity between HMPV-V2 and the TLR4 receptor (energy: -969.2). Immune simulations predicted a robust adaptive response characterized by high IgG1 titers, memory B-cell maturation, and a Th1-dominant cytokine profile. Furthermore, molecular dynamics simulations suggested exceptional structural integrity for HMPV-V2, maintaining a low RMSD of 8.213 and RMSF of 0.737 throughout the simulation. Optimized in silico cloning into the pET28a (+) vector indicated a high potential for protein expression in E. coli systems. While these findings provide a theoretically grounded blueprint for vaccine development, this study is entirely computational and lacks experimental validation. Further in vitro and in vivo testing is required to confirm the actual safety and immunogenicity of the proposed candidate.
BACKGROUND: Ocular infectious diseases remain an important cause of preventable visual impairment worldwide, yet vaccine development for eye-specific pathogens has lagged behind systemic infections. This gap reflects the biology of the eye, including immune privilege, mucosal immunity, pathogen diversity, and limited understanding of ocular correlates of protection. Advances in data science, artificial intelligence, genomics, and systems biology are reshaping vaccine research by enabling antigen discovery, immune modeling, and surveillance. However, these approaches have not been integrated into a coherent framework for ocular vaccinology. PURPOSE OF THE REVIEW: This review examines how data science and computational methods may help accelerate ocular vaccine development by addressing the biological, immunological, and translational constraints that have limited progress in the field. Rather than providing a descriptive overview of ocular pathogens and vaccine candidates, it proposes a challenge-solution framework linking ocular immunobiology, computational vaccinology, and translational implementation. MAIN THEMES COVERED: The review is organized around three main themes. First, it outlines the biological and immunological barriers that complicate vaccine development for ocular infections, including immune privilege, mucosal immune constraints, antigenic diversity, and poorly defined correlates of protection. Second, it evaluates how computational approaches, including reverse vaccinology, machine learning-based epitope prediction, structural vaccinology, systems immunology, genomic epidemiology, and real-world data analytics, may support antigen discovery, immune modeling, and population-targeted vaccination strategies. Third, it examines the translational barriers that continue to limit clinical implementation, including inadequate ocular disease models, fragmented ophthalmic datasets, challenges in data standardization and interoperability, regulatory uncertainty, and financial constraints. KEY CONCLUSIONS: Progress in ocular vaccinology is likely to depend on an integrated, systems-level strategy that combines immunology, ophthalmology, and data science. Computational methods may reduce the cost, time, and uncertainty associated with antigen discovery and candidate prioritization, whereas epidemiological and real-world data may strengthen vaccine targeting, surveillance, and post-implementation evaluation. However, meaningful translation will require ocular-specific immune models, standardized multimodal ophthalmic datasets, improved experimental systems, and iterative validation pipelines that connect in silico prediction with biological and clinical evidence. Collectively, these advances could help shift ocular vaccinology from a largely reactive field toward a more predictive and evidence-based approach to preventing vision-threatening infections.
INTRODUCTION: Peptide therapeutics are increasingly explored to treat challenging diseases, but immunogenicity risks limit their clinical success. In silico tools enable immunogenicity screening through prediction of peptide-MHCII binding, yet current methods do not account for chemical properties of non-natural amino acids routinely incorporated to improve peptide properties. METHODS: Here, we present a machine learning approach combining chemical fingerprints with sequence information to predict MHC class II binding for peptides including both natural (NAA) and non-natural (NNAA) amino acids. We evaluate peptide-level fingerprints against residue-level fingerprints (direct-encoding and similarity-based fingerprints) that preserve positional information while encoding chemical diversity, for a total of 31 different representations. RESULTS: Peptide-level fingerprints showed poor performance due to loss of positional information, while residue-level fingerprints matched the performance of sequence-based encodings (BLOSUM62 and one-hot) for NAA-based peptides while accurately identifying binding cores and motifs. Considering citrullinated peptides as a case study, we observed similar linear correlation performance across encoding strategies, with residue-level fingerprints showing marginal improvements in quantitative prediction accuracy. Citrulline was rarely found at canonical anchor positions, suggesting that the advantage of explicit chemical encoding may be more pronounced for modifications occurring at positions critical for binding. DISCUSSION: The proposed framework allows for inclusion of diverse modifications, including synthetic amino acids, and is compatible with existing pan-allele architectures. While the full advantage of explicit chemical encoding remains to be demonstrated, this framework is designed to capture these effects as experimental data becomes available, supporting immunogenicity risk assessment for emerging peptide therapeutics.
BACKGROUND: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the COVID-19 pandemic, which resulted in millions of deaths globally and had profound social, economic, and political consequences. Although effective vaccines and antiviral therapies have substantially reduced the global burden of COVID-19, the continued emergence of viral variants highlights the need for next-generation effective vaccine strategies capable of providing broader and more durable immune response. METHODS: In this work, we provide an immunoinformatic approach for multi-epitope vaccine (MEV) design and prediction. Based on the spike (S) and nucleocapsid (N) proteins of SARS-CoV-2, immunoinformatic methods were used to identify the epitopes for B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL). The B cell, CTL, and HTL epitopes were conjugated with flexible linkers GSG, GSGG, and a Gb-1 peptide conjugated to the C-terminal of the MEV ccandidate. RESULTS: The final MEV candidate exhibited favorable predicted characteristics, with a molecular weight of approximately 55.47 kDa and a length of 498 amino acid residues. Computational analyses indicated that the designed construct was antigenic, non-toxic, non-allergenic, and possessed suitable physicochemical properties and predicted solubility, supporting its potential as a vaccine candidate for further investigation. Molecular docking analysis demonstrated favorable interactions between the MEV construct and selected Toll-like receptors (TLRs), while molecular dynamics (MD) simulations suggested the stability of the vaccine-receptor complexes throughout the simulation period. Furthermore, C-ImmSim-based immune simulation predicted the induction of both humoral and cellular immune responses following the proposed immunization schedule. Collectively, these findings highlight the potential of the designed MEV construct as a computationally optimized vaccine candidate and provide a framework for future experimental evaluation. CONCLUSION: This study presents a computationally designed MEV candidate against SARS-CoV-2 by integrating immunoinformatics approaches, structural modeling, molecular docking, molecular dynamics simulations, and immune response prediction. The findings suggest that the proposed MEV construct may possess favorable immunogenic and structural properties; however, experimental validation through in vitro and in vivo studies remains essential to confirm its safety, immunogenicity, and protective efficacy. The proposed approach provides a valuable strategy for accelerating rational vaccine design and may serve as a foundation for future development of experimentally validated vaccine candidates.
Legionella pneumophila is a Gram-negative opportunistic pathogen responsible for Legionnaires' disease, a severe form of atypical pneumonia associated with high morbidity and mortality, particularly in immunocompromised individuals. Despite its growing global burden and the persistence of environmental reservoirs, no licensed vaccine is currently available. In this study, an integrative immunoinformatics and reverse vaccinology approach was employed to design a multi-epitope vaccine candidate targeting L. pneumophila. The complete proteome was systematically analyzed to identify essential, virulent, and surface-accessible proteins. Subsequent antigenicity assessment and non-homology screening against the human proteome led to the selection of key immunogenic targets, including KDO transferase and TolR. B-cell and T-cell epitopes were predicted and rigorously filtered based on antigenicity, non-allergenicity, and non-toxicity. Selected epitopes were assembled into a multi-epitope vaccine construct using appropriate linkers and adjuvants to enhance immunogenicity and structural stability. The designed construct exhibited favorable physicochemical properties, high antigenicity, and good solubility. Structural modeling and refinement confirmed the reliability of the predicted three-dimensional structure. Molecular docking analysis demonstrated strong binding interactions with immune receptors TLR-2 and TLR-9, which were further validated by molecular dynamics simulations indicating stable complex formation under physiological conditions. In addition, immune simulation predicted robust humoral and cellular immune responses, including memory cell generation. Overall, this study presents a promising computationally designed multi-epitope vaccine candidate against L. pneumophila. However, further in vitro and in vivo studies are required to validate its immunogenicity, safety, and protective efficacy.
In this review, linear epitope-based subunit vaccines are systematically profiled to outline their current developmental status, integrating recent advances in computational immunogenic epitope screening, site-specific chemical modification for stability enhancement, next-generation delivery platform matching, and ongoing clinical translation efforts. Deep learning-based MHC-binding prediction tools including NetMHCpan, MHCflurry 2.0, and MARIA, supported by robust empirical evidence, are rigorously evaluated, with notable improvements observed in the efficiency of identifying B/T cell linear epitopes with high immunogenicity, while it is also noted that the field still faces core bottlenecks such as weak in vivo immunogenicity, rapid enzymatic degradation, and insufficient cross-protection against viral variants. According to this integrative evidence-based assessment, feasible optimization pathways are further outlined, including the synergy between AI-driven epitope structural design and virus-like particle-based delivery systems, to provide practical references for the rational development of next-generation broad-spectrum linear epitope-based vaccines.
BACKGROUND: Trypanosoma brucei causes Human African Trypanosomiasis (HAT), which has a devastating impact on an individual's health. Currently, there is no FDA-approved vaccine for HAT prevention. Therefore, reverse vaccinology approaches were utilized to design an mRNA vaccine candidate. METHODS: Variant surface glycoprotein, heat shock protein 70, and vacuolar transporter chaperone complex of T. brucei were targeted to predict immunogenic, non-allergenic, and non-toxic peptides. The vaccine candidate was evaluated for population coverage, biophysical attributes, structural stability, and refinement. Molecular docking, MD simulation, and MM-GBSA analyses evaluate receptor binding and complex stability. Codon optimization and in-silico cloning were conducted in Escherichia coli (strain K12) using pET-28a( +). Immune simulations predicted humoral and cellular responses, while mRNA integrity was evaluated through MFE analysis. RESULTS: The vaccine candidate achieved 100% global population coverage. Biophysical attributes indicated aliphaticity 71.23, and GRAVY score -0.719. Predicted tertiary structure (TM-score 0.65 ± 0.13, and C-score -0.50) was refined with stable validation metrics (Ramachandran score 86.8%, and Z-score -5.26). Docking predicted significant binding with TLR-2 and TLR-4 (energy scores -1013.5 and -1002.8 kJ/mol), validated through MD simulation, PCA, DCCM, MM-GBSA analyses. Codon optimization (CAI 0.9688; GC 44.70%) indicated high expression potential, and immune simulation exhibits robust antibody and cell-mediated responses, including elevated B lymphocyte, T lymphocyte levels, and IgM, IgG titers. Finally, the structural integrity of mRNA was predicted by MFE values. CONCLUSION: This in-silico designed vaccine demonstrated strong structural stability, receptor interactions, and immunogenic potential against T. brucei. Experimental validation and in-vivo studies are required to verify safety and efficacy of vaccine candidate.
BACKGROUND: Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC) is distinct molecular subtype of gastric cancer for which effective preventive and targeted therapeutic strategies remain limited. This study aimed to design and evaluate multiepitope vaccine candidates targeting four EBV proteins critically involved in this disease, namely BZLF1, EBNA1, LMP1, and LMP2. METHODS: An integrated immunoinformatics and reverse vaccinology approach was employed to predict and screen MHC-I (CTL), MHC-II (HTL), and B-cell epitopes. Selected epitopes were assembled into multiepitope vaccine constructs, which were then evaluated for physicochemical properties, potential interactions with immune receptors, and post-injection immune responses using computational simulations. RESULTS: A total of 20 immunodominant epitopes in each CTL, HTL and LBL were identified and selected based on their predicted antigenicity, immunogenicity, non-allergenicity, and non-toxicity, while the selected MHC-II epitopes were additionally predicted to induce IFN-γ, IL-2, IL-4, and IL-10 responses. Global population coverage analysis estimated worldwide coverage of 98.74%. The final vaccine construct comprised 1,091 amino acids and exhibited favorable physicochemical properties with acceptable structural quality, as indicated by a ProSA Z-score of - 2.28 and 79.1% of residues located in the most favored regions of the Ramachandran plot. Disulfide engineering identified six residue pairs with the potential to enhance structural stability. Molecular docking demonstrated favorable binding of the vaccine construct to TLR9, with a HADDOCK score of - 165.4 ± 0.7 kcal/mol. Molecular dynamics simulations further supported the structural feasibility of the vaccine-TLR9 complex, yielding an average RMSD of 1.253 nm; 963.3 hydrogen bonds, and SASA of 911.3 nm². Meanwhile, normal mode analysis corroborated the dynamic stability of the complex. Codon optimization in the pET-28a(+) vector resulted an optimal codon adaptation index (CAI = 1.0) and GC content of 57.86%, suggesting high potential for recombinant expression in Escherichia coli. Immune simulations further predicted coordinated humoral and cellular immune responses, accompanied by immunological memory formation following simulated vaccination regimen. CONCLUSION: The study proposed multiepitope vaccine candidate targeting crucial antigen of EBVaGC based immunoinformatics-based approach. The construct predicted to have favorable in silico immunological and structural properties. However, further experimental validation is required to confirm its safety and immunogenic potential.
Immune-mediated diseases display substantial variability in clinical expression, treatment durability, relapse timing, and long-term trajectories that are not always fully explained by genetic predisposition, molecular pathways, or pharmacological exposure alone. Increasing observations from systems immunology suggest that immune behavior emerges through interactions across molecular, environmental, and temporal dimensions. This article introduces Adaptive Bandwidth of Immunity (ABI) as a conceptual systems-level framework describing the functional range within which immune regulatory networks preserve adaptive responsiveness, proportionality, reversibility, and coordinated recovery across biological perturbations. ABI is not proposed as a discrete biological pathway or a directly measurable variable. Instead, it is introduced as an emergent property inferred through longitudinal clinical trajectories, temporal response dynamics, and integrated biological observations. The framework draws upon concepts from immune homeostasis, trained immunity, immune tolerance, systems immunology, and environmental modulation while emphasizing preservation of adaptive flexibility across time. Within this interpretation, immune-mediated disease may be viewed not only through dysregulated activation but also through progressive restriction of adaptive regulatory capacity. The framework generates empirically approachable predictions and outlines potential directions for future operationalization through longitudinal cohorts, trajectory-based analyses, and integration of multi-omics with temporal clinical data. ABI is presented as a hypothesis-generating framework intended to support future investigation into adaptive regulation and dynamic immune behavior.
BACKGROUND: Chronic cough is a common reason for pediatric consultation and may lead to repeated medical visits, caregiver anxiety, inappropriate use of diagnostic tests, and unnecessary treatments. In children and adolescents, chronic cough should be regarded primarily as a symptom of an underlying condition rather than as a standalone diagnosis, requiring age-specific diagnostic pathways. In 2020, the Italian Society of Pediatric Allergy and Immunology (SIAIP) published a document on this issue. OBJECTIVE: To develop a pragmatic, algorithm-based approach for the evaluation and management of chronic cough in children and adolescents, reflecting contemporary evidence and expert consensus; the SIAIP promoted an updated position paper. METHODS: A multidisciplinary panel of experts reviewed the previous document, taking into account international guidelines and key publications on pediatric chronic cough, and integrated the available evidence with clinical expertise. Consensus discussions focused on red flags and specific cough pointers, the classification of cough as wet or dry, treatable traits, and the role of time-limited therapeutic trials with predefined reassessment. RESULTS: The proposed algorithm prioritizes the early identification of red flags that indicate the need for targeted investigations and specialist referral. In children without warning features, cough quality guides subsequent management. Wet cough is addressed primarily through the recognition and appropriate treatment of infective causes, such as protracted bacterial bronchitis. Dry cough is evaluated sequentially for cough-predominant asthma, upper airway cough syndrome, gastroesophageal reflux-related mechanisms, and functional cough. At each step, empirical therapies are limited and require scheduled reassessment, with discontinuation if ineffective. The approach emphasizes the avoidance of prolonged empirical therapy, the rational use of antibiotics, inhaled corticosteroids, alginates, and proton pump inhibitors, and the early reconsideration of the diagnosis when the response is lacking. CONCLUSIONS: This pragmatic algorithm provides a structured, clinically oriented approach to standardize the evaluation and management of chronic cough in children and adolescents. It is intended to support clinical judgment, reduce unwarranted variation in practice, minimize unnecessary investigations and treatments, and promote the timely identification of conditions requiring specialist care. Prospective validation in real-world settings is warranted to assess its impact on outcomes and healthcare utilization.
Background: Effective communication between physicians and the public has become increasingly important in the modern media environment. Pediatricians and allergist/immunologists play a critical role in shaping public understanding of conditions such as food allergy, asthma, and vaccine safety. Objective: The objective was to provide a practical framework for clinicians to engage effectively with media, improve public health messaging, and enhance patient outcomes through clear, accurate, and impactful communication. Methods: This was a narrative review of literature that integrates health communication, media engagement, pediatric advocacy, allergy and immunology, public health messaging, vaccine communication, and asthma and food allergy education. Results: Successful media engagement requires preparation, audience awareness, message clarity, and strategic delivery. Core principles include simplifying complex medical information; maintaining credibility; and focusing on key public health messages, such as early food introduction, asthma control, and vaccine safety. The RATIO framework (Research, Audience, Targeted Topic, Interview Redirecting, Optimism) provides a structured approach to media advocacy. Conclusion: Physician engagement with media is an essential extension of clinical care. Effective communication can improve public understanding, counter misinformation, promote evidence-based health behaviors, and substantially affect adherence and preventive behaviors, particularly in vaccination and chronic disease management, particularly in pediatric populations.
IMGT®, the international information system® (IMGT), was created in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS) in Montpellier, France, to deal with and to manage the huge diversity of immunoglobulins (IG) or antibodies and T-cell receptors (TR), which are the antigen receptors (AR) of the adaptive immune response (AIR) of jawed vertebrates. The founding of IMGT® marked the advent of immunoinformatics, a new science which emerged at the interface between immunogenetics and bioinformatics. The biocuration of the IMGT data (IG and TR sequences, genes and structures) and the implementation of the IMGT system (7 databases, 17 tools, 25,000 Web resources pages) are based on the IMGT Scientific chart rules (keywords, labels, nomenclature, numbering…) generated from the IMGT-ONTOLOGY axioms and concepts. The IMGT nomenclature (IMGT-NC) and the IMGT unique numbering, the two pillars of immunoinformatics, have been used to define 335 engineered variants for effector properties and formats of therapeutic antibodies (including 12 chimerisotypes) and TR, fusion proteins for immune applications (FPIA) and composite proteins for clinical applications (CPCA). IMGT-NC engineered variant names from the World Health Organization (WHO) International Nonproprietary Name (INN) programme descriptions contribute to the common language for immunoinformatics and artificial intelligence (AI).
Cross-immunity, defined as the ability of T-cells to recognize multiple antigen peptide-major histocompatibility complexes, is a fundamental feature of adaptive immunity. However, the prediction of different peptide epitopes that can be recognized by the same T-cell receptor remains challenging. Currently, artificial intelligent (AI)-based machine learning (ML) methods can be successfully used for pattern recognition in epitope molecular space by detecting the functional similarity between peptide sequences. In this study, using literature-based experimental data, we examined ML-based binary classification models trained on small datasets to predict the activity of nine-amino-acid-long peptides. Our results suggest that the consensus function of well-established similarity matrix-based representations and structural-based descriptors of epitopes yields better performance because representation-specific noises are reduced and individual model weaknesses are partially compensated. We also sought to determine the extent to which the predictive power of the applied AIs procedure depended on the physicochemical content of the descriptor set during the training process. In addition, challenging the models, we applied them to an independent experimental dataset to examine the effects of diverse laboratory conditions on a regulated biological measurement. In summary, applying a consensus function can capture the biological complexity of cross-reactivity at the binary classification level, even when applied to relatively small datasets.
Autoimmune diseases arise from the breakdown of immune tolerance through complex interactions between genetic predisposition, environmental exposures, and adaptive immune responses. High-throughput T-cell receptor (TCR) repertoire sequencing has transformed our ability to characterize these responses, providing unprecedented insights into clonal dynamics, antigen-driven selection, and immune history. In this review, we summarize the major alterations of TCR repertoires reported across autoimmune diseases, including changes in diversity, clonal expansion, repertoire architecture, and tissue distribution. We discuss the principal biological and technical challenges that currently limit repertoire interpretation, with particular emphasis on the concept that bulk repertoires represent composite mixtures of biologically distinct T-cell populations. Finally, we highlight emerging approaches integrating single-cell profiling, computational modeling, and antigen-specificity inference that are reshaping the field. We propose that TCR repertoires should be viewed as dynamic molecular footprints of autoimmune disease, providing a systems-level framework to improve mechanistic understanding, biomarker discovery, and the development of precision immunotherapies.
BACKGROUND: Toxoplasma gondii is a protozoan parasite of medical and veterinary importance causing abortion. While current therapies are limited to address the chronic phase of infection, effective prophylactic vaccines are warranted. OBJECTIVES: In this study, we applied a rational vaccine design approach centred around immunodominant dense granule antigens (GRAs). METHODS: In silico epitope mapping was performed on six GRAs (GRA15, GRA60, GRA76, GRA83, GRA-α and GRA-β). Using web servers, B-cell, cytotoxic T-lymphocyte and helper T-lymphocyte epitopes were predicted and then filtered for antigenicity, solubility and allergenicity. We designed four vaccine constructs by fusing predicted epitopes with specific linkers and adjuvants (RS-09, RpfE/50S ribosomal protein of Mycobacterium tuberculosis, and human interferon gamma [IFN-γ]). Constructs were subjected to rigorous evaluation, which included physicochemical evaluation, 3D structural prediction and refinement, toll-like receptor-4 (TLR-4) docking, immune simulation and codon adaptation. RESULTS: All candidates exhibited high antigenicity (VaxiJen scores > 0.9) and solubility (> 0.6). Rpf-Toxo emerged optimal, with non-allergenic, antigenic (0.9148), soluble (0.658), stable (instability index: 35.41) and hydrophilic (grand average of hydropathicity: -0.641) properties. Docking revealed strong TLR-4 binding activity (affinity: -20.6 kcal/mol; Kd: 7.3e-16 M) and immune simulation predicted robust responses, including antibody titers > 170,000, Th1-skewed IFN-γ (380,000 ng/mL) and memory cell activation; however, these in silico predictions need experimental validation. Codon optimization enhanced expression (CAI: 1.00; GC: 65.63%), and in silico cloning indicated compatibility with pET28a(+). CONCLUSION: These computational predictions require future experimental validation through in vitro and in vivo studies to confirm safety and protective efficacy.
The development of highly accurate deep learning models for protein structure prediction has transformed the landscape of T-cell receptor (TCR) structure data, which can now be accessed at repertoire scale. We provide a perspective on the growing field of structural TCR immunoinformatics, summarizing core principles of TCR structural biology and highlighting existing resources and tools. We outline computational methods for TCR structure prediction, and discuss outstanding challenges faced by current tools, as well as potential avenues to address these. We expand on the research enabled by the availability of predicted TCR structures, exploring the utility of TCR structure predictions for computationally inferring TCR specificity, as well as summarizing opportunities emerging from the adjacent field of antibody research. Finally, we provide a forward-looking perspective on the advances in deep learning research which have recently enabled computational design of TCRs and TCR-like binders.
Porcine contagious pleuropneumonia (PCP) is caused by Actinobacillus pleuropneumoniae (APP) and inflicts heavy economic losses on the swine industry. However, existing inactivated vaccines provide limited cross-protection, highlighting the need for improved vaccine strategies. In this study, we combined pangenome analysis with subtractive proteomics to screen the APP core genome and identified 11 potential antigens. Seven of them showed immunoreactivity by ELISA and Western blotting. These antigens, together with the ApxI-III toxins, were used for T and B cell epitope prediction. On this basis, a multi-epitope fusion protein MVAPP was constructed. In silico molecular docking with swine immune receptors and immune simulations suggested that MVAPP has the potential to induce immune responses. In the mouse model, that MVAPP elicited specific antibody responses, shifted the splenic T-cell subset distribution toward CD4+ T cells, and provided partial protection against challenge with strains from two serovars. In conclusion, MVAPP represents a potential multi-epitope vaccine candidate for further development against APP.
ABSTRACT: BACKGROUND: Large language models (LLMs) are rapidly transforming medical education, yet their performance in Allergy/Immunology remains insufficiently characterized. Furthermore, concerns regarding accuracy, consistency, and sensitivity to input format persist. ABSTRACT: OBJECTIVES: This study aimed to evaluate and compare the accuracy and response consistency of three leading LLMs-ChatGPT-5, Gemini 2.5, and Grok 4-on Allergy/Immunology United States Medical Licensing Examination (USMLE) Step 1-style questions under different prompt conditions. ABSTRACT: METHODS: Thirty-five USMLE Step 1-style questions were selected. Questions were presented to each model in two formats: single-question prompts and a combined prompt containing all questions. Fifteen trials were conducted for each format per model. Performance was assessed using mean accuracy, and variability was measured using Shannon entropy. Mixed-effects models tested the effects of model, prompt condition, and question difficulty. ABSTRACT: RESULTS: Overall accuracy differed significantly (p < 0.001), with Gemini (80.7%) and Grok (80.5%) achieving higher mean scores than ChatGPT (74.3%). Single-item prompts yielded superior performance, with Grok (93.1%) and Gemini (90.9%) demonstrating the highest accuracy. Transitioning to a combined prompt significantly reduced accuracy for all models. Accuracy also decreased with increasing question difficulty for all models. Grok demonstrated superior reliability, maintaining the lowest overall response entropy, whereas ChatGPT exhibited the highest variability. ABSTRACT: CONCLUSION: On Allergy/Immunology Step 1-style questions, Gemini and Grok demonstrated higher accuracy than ChatGPT, although their overall accuracies remained approximately 81%. Grok offered the most consistent performance. All models demonstrated substantial sensitivity to prompt complexity and inherent performance limitations. These findings underscore the importance of prompt optimization and support the supplementary role of these models in medical education.
Identification of the origin of pathogenic immune cells is crucial for therapeutic interventions and diagnosis but pseudotime methods struggle to trace immune cells accurately. Current trajectory inference methods for B cell development and response in health and disease either ignore or underutilize antigen receptor sequence information, limiting their ability to resolve developmental pathways, particularly for pathogenic populations. Widely used methods such as Monocle 3 reconstruct developmental paths from transcriptomic similarity alone, discarding the features from immune receptors. Dandelion has combined the immune receptor features with transcriptomics but it struggles to simulate the trajectory path of B cells. Here we present ClonoTrace, a computational framework that integrates BCR sequence features with transcriptomic trajectory inference through gated fusion of multimodal embeddings. In fetal B cell development and germinal centre development, ClonoTrace demonstrates closer concordance with the canonical reference ordering than Monocle 3 and Dandelion. Applied to systemic lupus erythematosus, ClonoTrace indicates a memory B cell extrafollicular maturation route alongside the naïve B cell route, accompanied by induction of ZEB2 with a concomitant decline of BACH2 along the trajectory, as a candidate alternative route to pathogenic double negative 2 B cells (DN2) in systemic lupus erythematosus (SLE) patients. In healthy ageing, ClonoTrace resolved three candidate age-related B cell maturation routes, from naïve, IgM+ memory and switched-memory B cells, each passing through a DN2-associated transcriptional state that is ordered before age-associated B cells along the inferred trajectory. ClonoTrace's fate probability algorithm indicated that IgM+ memory B cell to ABC transition as the leading candidate age-associated transition, which may be distinct from SLE DN2 maturation. ClonoTrace provides a generalizable framework for receptor-informed trajectory inference, describing candidate developmental routes of pathogenic B cell populations in autoimmunity and ageing.
BACKGROUND: Human papillomavirus (HPV) drives both malignant and benign tumours. Current prophylactic vaccines are type-restricted, not optimised for T-cell induction, and lack therapeutic efficacy. Although T-cells are critical for both preventing and clearing HPV infection, experimentally validated HPV T-cell epitopes remain fragmented across the literature, limiting systematic evaluation of cellular immune targets. METHODS: We curated experimentally validated HPV T-cell epitopes from the Immune Epitope Database (IEDB). Epitopes were mapped across HPV proteins and genotypes, and analysed for response rate, sequence conservation across 454 representative HPV genomes, and HLA restriction patterns. RESULTS: 485 unique experimentally validated HPV epitopes have been described (133 studies; 1,494 functional assays). Consistent with research focus and viral biology, E6 and E7 proteins account for >60% of known HPV epitopes despite accounting for ~10% of the viral proteome. High-risk HPV types, especially HPV16 and HPV18, were the most studied (p <.001) and were enriched for CD8+ epitopes (p <.001). We identified major knowledge gaps, including: underrepresentation of structural proteins such as L2; limited epitope coverage for low-prevalence HPV genotypes; a bias towards common HLA alleles. In silico analysis indicated greater conservation of epitopes in L1/L2 and across high-risk HPV types. Conserved, commonly detected, and HLA-promiscuous epitopes were highlighted and we provide panels of candidate epitopes for consideration in immune monitoring, broad-spectrum prophylactic vaccines, and high-risk targeted therapeutic vaccines. CONCLUSION: This study provides the first comprehensive atlas of experimentally validated HPV T-cell epitopes and ranked epitope candidates for translational application. We demonstrate that our understanding of HPV T-cell immunity is constrained by biases in antigen, genotype and HLA focus and by incomplete epitope mapping. Addressing these gaps will be essential for a comprehensive assessment of cellular immunity and for utilising T-cells in next-generation vaccines.
Varicellovirus bovinealpha1 (BoAHV-1) and 5 (BoAHV-5) are important pathogens associated with respiratory, reproductive, and neurological disorders in cattle, remaining globally relevant since their first reports in the 1950s and 1960s. Envelope glycoproteins B, C, and D play critical roles in viral attachment and fusion, making them key targets for immune responses and promising candidates for vaccine development. This study aimed to design a multi-epitope protein using immunoinformatic approaches, incorporating highly conserved, antigenic B- and T- cell epitopes with strong predicted MHC-binding affinity from glycoproteins B, C, and D of BoAHV-1/5, followed by in silico characterization and heterologous expression in Escherichia coli. The construct was designed using bioinformatics tools, cloned into the pET-24a vector, and expressed in a prokaryotic system. The resulting protein consists of 321 amino acids, with a predicted molecular weight of 33.24 kDa, high antigenicity (1.1543) and non-allergenicity. Molecular docking analyses indicated strong interactions with bovine Toll-like receptors TLR2/6 and TLR4. The protein was successfully expressed in E. coli and detected by anti-His6x antibody in Western blot, showing the expected molecular weight (~33 kDa). It was also recognized by bovine serum containing neutralizing antibodies against BoAHV-1/5. Overall, these findings support the feasibility of the multi-epitope construct and provide a foundation for future investigations of its immunogenicity and protective efficacy studies in animal models.
Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a critical public health concern due to the limited efficacy of the Bacillus Calmette-Guerin (BCG) vaccine, the only WHO-approved vaccine so far. Based on the reverse vaccinology approach, this study involves in silico prediction of a fusion constructed from four highly immunogenic antigens from the latent, early, and active stages of the disease. The fusion named TetraFuVac11 consists of the complete sequences of the antigens CFP-7 and EspC and the truncated sequences of the antigens HspX and Hrp1. Major Histocompatibility Complex II (MHC II) binding Th-cell-specific epitopes were predicted through tools provided in the Immune Epitope Database (IEDB). The designed fusion molecule was found to be antigenic, non-allergenic and non-toxic. The instability index II and the GRAND Average of Hydropathy values were predicted to be 29.51 and -0.182, respectively. The refinement of the predicted 3D structure resulted in an improved stereochemical profile. The Z-score was predicted to be -6.8, and the ERRAT score was improved from 94.928 for the unrefined model to 97.1591 for the refined model. The data obtained from molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) of the docked complex between the refined fusion protein and Toll-like Receptor 4 (TLR4) demonstrated a s interaction. The fusion construct was successfully predicted to be cloned into the pET-28a(+) vector to make a recombinant plasmid. The predicted solubility of the fusion protein exceeded the threshold values, indicating soluble expression in Escherichia coli. Finally, based on the encouraging in silico data, the proposed construct could serve as a potential vaccine candidate for detailed experimental validation towards developing an Mtb-specific vaccine.
Vaccination is one of the greatest triumphs in human history. Traditionally, vaccines were designed to stimulate antibody responses that block infection, but this overlooks the immune system's complex and multifaceted defence mechanisms. Here we review the current state of the field of systems vaccinology, which has transformed vaccine research by using vaccines as controlled probes of the human immune system and by applying multi-omics and computational approaches to reveal the nature of human immunity. These approaches have identified molecular signatures that predict the magnitude and durability of immune responses and revealed new human biology, including how host genetics, metabolism and the microbiome shape immunity, and demonstrated that host defence emerges from coordinated immune programs spanning baseline immune state, early response dynamics and tissue-level interactions. Rapid advances in artificial intelligence are beginning to accelerate the distillation of knowledge and understanding from vast multi-omics datasets. These developments position systems vaccinology as a powerful framework for rational vaccine design. However, despite its considerable impact on discovery and human immunology, considerable challenges remain in translating these insights into clinical and regulatory practice. Addressing this translational gap will be essential for realizing the full potential of systems vaccinology to deliver safer, more effective vaccines against existing and emerging infectious threats.
INTRODUCTION: The Leishmania parasite is phagocytized by macrophages and stored in the parasitophorous vacuole; therefore, the activation of Th1-CD4+ lymphocytes is essential to control the infection. Consequently, antigen presentation is critical for such activation, occurring through the binding of short parasite sequences to the MHC class II pocket. OBJECTIVE: To identify linear sequences from immunogenic proteins of Leishmania (Viannia) spp. through bioinformatic analysis of human MHC class II molecules. MATERIALS AND METHODS: Linear sequences of GP63, HSP70, and PEPCK were obtained from Leishmania panamensis, and the binding of 15-mer peptides to various HLA-DRβ*04 alleles was predicted. Highly conserved sequences among parasite species with null or low analogy to human proteins were selected. Finally, molecular docking was performed against the HLA-DRβ*04:01 pocket. RESULTS: Six sequences were initially selected as potential natural T-cell epitopes. Among these, GP63267-282, GP63282-297, and PEPCK535-550 demonstrated multiple interactions with the HLA-DR: 04:01 pocket and a consistent N-terminus to C-terminus orientation. CONCLUSION: Three sequences were identified as potential Leishmania Th1 epitopes; however, in vitro validation is necessary to confirm these findings.
Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self-peptides during their "training" in the thymus, and how tolerance is generalized to the remaining "test" self-peptides across peripheral tissues in the body remains an open question. We show that this can be achieved because the immune system satisfies two conditions necessary for generalization in machine learning settings. Consequently, sparse, random sampling of only 10% of self-peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self. We support this result and validate predictions from our model with diverse experimental data. Overall, we provide a plausible answer to a long-standing question underlying adaptive immunity, and we highlight how generalization, a fundamental challenge faced by nearly every learning algorithm, is tackled by the immune system.
BACKGROUND: Established screening programs for colorectal cancer (CRC) based on fecal immunochemical tests (FITs) employ fixed screening intervals of either one year or two years for all participants. We aimed to assess the potential design of risk-adapted FIT screening intervals based on quantitative values of the fecal hemoglobin concentration among FIT negative participants. METHODS: Using COSIMO, a previously validated simulation tool, we compared CRC risks in subgroups with initially negative FIT. Subgroups were stratified by stool hemoglobin levels at screening (< 8 (low), 8-<10 (medium), 10-<17 µg (high) Hb/g stool), a routinely available measure in FIT-based screening programs. Colorectal neoplasia prevalences at model start were informed by 6,661 screening colonoscopy participants in Germany at average CRC risk with negative FIT and available colonoscopy results (BLITZ study). Simulations were run for hypothetical cohorts of 100,000 individuals. RESULTS: Of 6,661 FIT-negative participants, 6,016 (90%), 273 (4%), and 372 (6%) were in the low, medium, and high-negative subgroups, respectively. In those low-negative, expected cancer detection rates were 0.17%, 0.30%, 0.42%, 0.53%, and 0.62%, after screening intervals of 1, 2, 3, 4, and 5 years, respectively. Cancer detection rates among the small groups of medium- and high-negative patients were up to six times higher and exceeded the expected 2-year detection rates of the low-negative group already after 1 year of follow-up. CONCLUSIONS: Fecal hemoglobin concentrations among FIT negative participants may be a readily available, highly informative tool for defining risk-adapted annual or biennial FIT screening intervals.
INTRODUCTION: CD40L (CD154) is a homotrimeric member of the TNF superfamily protein expressed on activated CD4+ T cells whose interaction with CD40 is required for class-switch recombination, somatic hypermutation, and B cell terminal differentiation. Loss-of-function mutations in gene CD40LG cause X-linked hyper-IgM syndrome type 1 (HIGM1), characterized by recurrent infections, lack of IgG, IgA and IgE, and near-normal or elevated IgM. METHODS: We implemented computational strategies to describe the structure and dynamics of the CD40L-CD40 complex for the native protein and three missense variants not previously characterized at the molecular level: A123E, S222F and G257R. Two independent 300 ns simulations were run per system in explicit solvent at 300 K and 0.15 M NaCl. In addition, we evaluated the energetics and electrostatics of the complex using continuum solvent models, such as molecular mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) and Adaptive Poisson-Boltzmann Solver (APBS). RESULTS: We found that A123E caused a moderate, energetically coherent reduction in binding affinity consistent with partial loss of function. S222F caused the largest reduction in binding free energy (weakest binder by MM-PBSA) along with an increase of the CD40L-CD40 contact area, consistent with a geometrically expanded but energetically disrupted interface where the aromatic side chain offsets paradoxically favorable electrostatic reorganization. G257R maintained near-native binding free energies but reduced the trimeric contact area by 20-22%, and increased the radius of gyration of CD40L, consistent with structural loosening of the homotrimer that we predict could uncouple binding competence from signaling competence, with potential effects on CD40 receptor mobility and mechanotransduction at the immunological synapse that remain to be tested experimentally. DISCUSSION: These findings may offer a structural basis for the clinical heterogeneity of HIGM1 and illustrate what computational approaches can contribute to the interpretation of CD40LG variants, including novel variants identified in Latin American patients where experimental validation is rarely available.
INTRODUCTION: Toxoplasma gondii is a significant zoonotic pathogen responsible for severe disease in immunocompromised individuals, adverse pregnancy outcomes, and substantial economic losses in the livestock industry. Given the limitations of current therapeutic strategies and vaccines, this study utilized immunoinformatics and reverse vaccinology approaches to design a multi-epitope candidate vaccine. METHODS: A total of 49 T. gondii proteins from the SAG, GRA, MIC, and ROP families were screened. Epitopes were selected based on antigenicity, immunogenicity, cytokine-inducing potential, toxicity, and allergenicity. The selected epitopes were assembled using AAY, GPGPG, and KK linkers, with the 50S ribosomal protein L7/L12 as an adjuvant. Physicochemical properties, molecular docking, molecular dynamics simulations, immune simulations, and codon optimization were subsequently evaluated. RESULTS: We identified 9 cytotoxic T lymphocyte (CTL), 6 helper T lymphocyte (HTL), and 11 B-cell epitopes. The finalized 556-amino-acid construct (61.13 kDa) demonstrated favorable antigenicity (VaxiJen score: 0.6843), stability (instability index: 39.05), solubility (0.663), and hydrophilicity (GRAVY: -0.502), while maintaining safety profiles. Molecular docking and dynamics simulations confirmed robust and stable binding to TLR2 (-32.8 kcal/mol) and TLR4 (-72.37 kcal/mol). Furthermore, immune simulations predicted a strong, memory-forming humoral and cellular immune response. Codon optimization (CAI: 0.93, GC content: 56.43%) suggested high expression efficiency in Escherichia coli. DISCUSSION: The rationally designed multi-epitope vaccine demonstrates robust theoretical potential to elicit comprehensive, long-lasting immunity in humans, although its safety and effectiveness require additional experimental validation.
The Middle East is a significant hotspot for human hydatidosis caused by Echinococcus granulosus, a complex multi-stage pathogen exhibiting antigenic variation with diverse epitopes. Multi-epitope vaccines, designed using immunoinformatics, represent a promising approach to effectively control this challenging parasite. A total of 1150 secreted, non-toxic, antigenic proteins were obtained from database and analyzed for their immunogenic potential, leading to the identification of 1024 CTL, 80 HTL, and 172 LBL prioritized epitopes, which were then ranked and clustered to prefer the most immunogenicity epitopes to include in novel vaccine construct. The chosen Construct (Con2) was modified for dual-plasmid insertion, facilitating the expression of three different vaccine constructs which were predicted by C-ImmSim to stimulate robust IFN-γ/IL-2 production and enhance IgM/IgG antibody secretion. Utilizing in silico, next-generation vaccine design, we propose several vaccine candidates that warrant further validation through in vitro laboratory testing and in vivo studies to assess their immunological efficacy.
Hantaviruses, the etiological agents of hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS), represent a high-risk zoonotic threat with substantial global health impact. Currently, there is no FDA-approved vaccine. The viral surface glycoprotein (GP) is crucial for host cell entry and is regarded as a key target for vaccine development. However, its variability among hantavirus species limits the effectiveness of conventional vaccine strategies. Epitope-based vaccines offer a promising alternative by enabling the design of broadly protective constructs. In this study, we applied immunoinformatics approaches to design a universal multi-epitope vaccine candidate targeting both HFRS- and HPS-associated hantaviruses through a multi-layered workflow integrating B-cell and T-cell epitope prediction, antigenicity scoring, IFN-γ induction potential, conservation analysis, and population coverage assessment. Viral GPs from SEOV, PUUV, SNV, and ANDV were analyzed using algorithms for B-cell and T-cell epitope prediction. Predicted epitopes were assessed for allergenicity, toxicity, conservation, and population coverage. Two vaccine constructs incorporating β-defensin or 50S ribosomal protein L7/L12 as adjuvants were assessed for physicochemical properties, structural stability and immunogenic potential. Molecular docking analyses provided exploratory ectodomain-compatibility screening, suggesting potential interactions with TLR4 that require future experimental confirmation. The in silico immune simulations suggested potential robust and long-lasting responses with memory cell persistence exceeding one year. Simulations also indicated balanced humoral and cellular responses, robust antibody production, and long-term memory formation suggestive of durable protective immunity. These findings support the rational design of broad-spectrum multi-epitope vaccines against genetically diverse hantaviruses, offering a rational framework for preclinical development of next-generation universal vaccines against hantavirus-associated diseases.
Human antibody diversification, achieved through gene selection and somatic hypermutation (SHM), is critical for protecting against diverse pathogens. This study investigates whether specific immune responses possess distinct receptor sequence patterns that differentiate them from the general immune repertoire. Utilizing data from an anti-SARS-CoV-2 vaccination study, we analyzed two properties of SARS-CoV-2 specific memory B-cells and compared them to the background immune repertoire. Driven by somatic hypermutation (SHM), B cells exhibit a highly dynamic nature. Consequently, groups sharing a direct lineage from a common progenitor are defined as B-cell clones. First, we studied substitution survival - the number of clones to survive amino acid substitutions across the variable region of the B cell receptor (BCR). Second, we analyzed clonal amino acid trimer usage patterns across the BCR gene to gain insight into prevalent genomic motifs found in different immune sub-repertoires. We demonstrated that these two metrics can effectively cluster and distinguish SARS-CoV-2 specific B cell responses. Furthermore, we observed that SARS-CoV-2-specific B-cells show an increased tendency to utilize and conserve a specific CDR2 motif derived from the VH3-30 gene and its alleles. Beyond identifying a specific germline motif related to SARS-CoV-2-specific B-cells response, our findings demonstrate that our novel analysis pipeline can successfully identify signatures of specific immune responses. We therefore suggest that using the methods described here could be key for the study of the substrate of B-cell selection and protective immunity in other vaccine and pathogen responses.