Human cytomegalovirus (CMV) is a globally widespread pathogen associated with significant morbidity in immunocompromised individuals. Despite its clinical importance, no licensed vaccine is currently available. This study aimed to design a rational multi-epitope vaccine candidate targeting CMV using an integrative approach combining immunoinformatics and structural biology. Viral proteins were screened to identify epitopes with high affinity for B cells, cytotoxic T cells (CTLs), and helper T cells (HTLs) using the Immune Epitope Database (IEDB). Selected epitopes were filtered according to their antigenicity and toxicity and then assembled into a chimeric construct incorporating an immunostimulatory adjuvant. The designed vaccine was evaluated for its physicochemical properties, validated by Ramchandran and ERRAT analyses. Molecular modeling demonstrated strong and stable interactions with key innate immunity receptors, including TLR7 and TLR9, interactions confirmed by molecular dynamics simulations. In silico immune simulation predicted a robust and durable immune response, characterized by high levels of IgM and IgG, as well as significant activation of CD4 + and CD8 + lymphocytes and innate immunity components. These results highlight the potential of the proposed multi-epitope construct as a promising vaccine candidate against HCMV. However, experimental validation is essential to confirm its immunogenicity, safety, and translational applicability.
BACKGROUND: Therapeutic heterogeneity limits the efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). Shuyu Wan (SYW), a classic TCM formula, has shown potential in modulating gut microbiota (GM) and enhancing immunotherapy, yet its synergistic mechanism with PD-1 inhibitors remains unclear. MATERIALS AND METHODS: SYW components were identified by UPLC-MS. NSCLC-related targets were integrated with SYW targets for pathway enrichment, and molecular docking validated component-target binding. NSCLC syngeneic mice were treated with SYW and/or PD-1 inhibitor (RMP1-14). Tumor growth, histopathology, serum cytokines, tumor-infiltrating CD8+T cell subsets (flow cytometry), PD-1/PD-L1 expression and co-localization (immunofluorescence), GM composition (16S rRNA), and metabolomics were assessed. FMT verified the role of GM-TME crosstalk. RESULTS: SYW monotherapy showed no significant tumor inhibition, whereas SYW combined with PD-1 inhibitor dose-dependently suppressed NSCLC growth. The combination reduced PD-1 expression and PD-1/PD-L1 co-localization, elevated serum IL-12, IFN-γ, and TNF-α, increased total tumor-infiltrating CD8+ T cells, decreased PD-1+ and TIM-3+ exhausted subsets, and expanded IFN-γ+ and Granzyme B+ effector subsets. Concurrently, it reshaped GM (increased Bacillota, decreased Patescibacteria) and altered metabolites (L-glycine, L-proline). These effects were abolished in antibiotic treated mice and restored by FMT, suggesting GM-TME crosstalk as essential. CONCLUSION: SYW acts as a microbiota-dependent immune sensitizer that potentiates PD-1 inhibitor efficacy in NSCLC by remodeling GM and enhancing effector CD8+ T cell infiltration while reducing exhaustion. GM-TME crosstalk is the potential mechanism, supporting SYW as an adjunct to PD-1 blockade in NSCLC therapy.
BACKGROUND: Neoantigens-tumor-specific peptides generated by somatic mutations-are central targets of effective anticancer T cell immunity and underpin the clinical success of immune checkpoint blockade and personalized cancer vaccines. Advances in high-throughput sequencing, immunopeptidomics, and artificial intelligence (AI) have transformed neoantigen discovery from tailored experimental workflows into scalable, computational pipelines. However, accurately identifying the small subset of tumor mutations that yield processed, presented, and immunogenic epitopes remains a major bottleneck. METHODS: This review summarizes how AI is reshaping neoantigen discovery, from somatic variant calling, HLA typing, and peptide processing to peptide-MHC binding, presentation, and T cell recognition. We first outline the immunobiological foundations of antigen presentation, emphasizing class I and II peptide-binding grooves and their allele-specific motifs, then describe AI workflows that integrate somatic mutation calling, HLA typing, transcriptomics, and immunopeptidomics to nominate candidate neoepitopes. We highlight recent AI-driven tools for presentation and immunogenicity prediction, integrative pipelines that support personal and shared neoantigen targeting, and early clinical applications in vaccination and T cell therapies. RESULTS: AI-driven models trained on eluted ligand datasets substantially outperform affinity-only predictors for peptide presentation across diverse HLA alleles and populations. Consortium-scale benchmarking demonstrates that integrating features of antigen processing, presentation, and TCR recognition can eliminate the majority of non-immunogenic candidates while retaining clinically relevant neoepitopes. Immunopeptidomics provides essential ground truth, revealing that only a small fraction of genomically predicted candidates are naturally presented and uncovering noncanonical antigen sources, including splice variants, post-translational modifications, and noncoding regions. Integrative pipelines now support both personal (private) and shared (public) neoantigen prioritization, enabling translational applications such as personalized vaccines and TCR-based therapies. CONCLUSIONS: AI-guided neoantigen discovery is now clinically actionable, enabled by immunopeptidomics and deep learning models. Despite significant progress, key challenges remain, including limited class II prediction accuracy, incomplete coverage of rare HLA alleles, tumor heterogeneity, and the need for standardized benchmarking and validation. Anchoring computational predictions to mass spectrometry-derived ligands and incorporating tumor evolution and immune escape mechanisms will be critical for improving target selection. Continued integration of AI, proteogenomics, and clinical data is poised to accelerate the development of effective, precision neoantigen-based cancer immunotherapies.
Human vaccines & immunotherapeuticsTina Zarrinpanah, Maryam Mashhadi Abolghasem Shirazi, Seyed Mohammad Hasan Modarressi, Setareh Haghighat
Vaccines based on purified antigens, recombinant proteins, and nucleic acid platforms increasingly depend on adjuvants to induce robust, durable, and appropriately polarized immune responses in humans. While classical adjuvants such as aluminum salts and oil-in-water emulsions have enabled the success of many licensed vaccines, their largely empirical design limits adaptability to emerging pathogens and population-specific needs. This review presents a translational framework for next-generation vaccine adjuvant development by integrating nanotechnology-based delivery systems, innate immune signaling mechanisms, and systems-level computational strategies relevant to human vaccination. We summarize the mechanisms and clinical relevance of licensed and advanced adjuvants, including alum, MF59, AS01/AS04, saponins, toll-like receptor agonists, and lipid nanoparticles, with emphasis on influenza, HPV, herpes zoster, and COVID-19 vaccines. By linking immunological mechanisms with delivery engineering and predictive modeling, this review highlights rational strategies to support safer and more effective human vaccines.
Reliable structural prediction of T-cell receptors (TCRs) is essential for dissecting antigen recognition and accelerating TCR-based therapeutic development, yet the performance of emerging computational structure prediction models for this task requires further systematic evaluation under practical usage conditions. Here, we assembled a manually curated dataset of TCR crystal structures and compared five state-of-the-art predictors-AlphaFold2 (v2.3.1), TCRmodel2, AlphaFold3, ESMFold, and tFold-TCR-in both single-chain and paired-chain modes. Using pLDDT and model confidence scores (pTM/ipTM), we defined optimized quality thresholds for assessing model reliability. Our analyses revealed a pronounced context dependence in model performance: AlphaFold3 achieved the highest accuracy for paired-chain TCR predictions (excluding the α-FRs domain), while tFold-TCR excelled in single-chain modeling (excluding Vα, CDR1α, CDR1β, and CDR2β domains), indicating that their performance varies significantly depending on the prediction mode and specific structural domains. We further showed that, under our evaluation conditions, structure-guided clustering of predicted CDR3β loops showed improved sensitivity and achieved higher conformational consistency for certain antigen-specific TCR groups compared with conventional sequence-based methods. Applying this framework, we successfully identified two functional SARS-CoV-2-specific TCRs using a TPS (TPSGTWLTY)-reactive TCR template. Our study establishes a practical comparative framework and highlights the translational potential of structure-guided computational workflows for antigen-specific TCR discovery.
Briefings in bioinformaticsYi Zhong, Volker M Lauschke, Yi Wang, Yitian Zhou
Human leukocyte antigen (HLA)-B*57:01 associated with abacavir-induced hypersensitivity syndrome (ABC-HSS) is one of the most extensively studied immune-mediated drug hypersensitivity reactions (DHRs). The high odds ratio and strong predictive values of HLA-B*57:01 for ABC-HSS have prompted the Food and Drug Administration and European Medicines Agency to require genetic testing before abacavir treatment. Abacavir binds to HLA-B*57:01 and alters the repertoire of presented peptides, resulting in the activation of autoimmunity. Previous studies employing computational approaches to investigate such DHRs have relied solely on a few crystallized tripartite structures, thus overlooking the full presented peptidome, leading to unsatisfactory predictive results. Here, we employed a state-of-the-art modeling approach to generate HLA structures complexed with over 13 000 presented peptides. We then established a novel computational modeling pipeline to simulate the binding of abacavir to these HLA-peptide complexes. Benchmarking against experimentally determined structures showed that this approach successfully recapitulated the crystalized tripartite structures with high accuracy (RMSD<2.2 Å). We then profiled alterations of the peptide repertoire at key positions in the presence of abacavir and proposed a method that accurately predicts compounds known to trigger T-cell activation. Overall, these results show that comprehensive modeling of the HLA-bound peptidome using advanced structural approaches can enhance the prediction and mechanistic understanding of immune-mediated DHRs.
Briefings in bioinformaticsFrank Qingyun Wang, Caicai Zhang, Xiao Dang, Huidong Su, Yao Lei, Youming Guo, Xinxin Chen, Wanling Yang
Traditional, knowledge-driven pathway annotations and bulk transcriptomic analyses often fail to capture the cellular specificity and mechanistic heterogeneity of immune responses. We present scImmuneCo, a comprehensive resource of immune cell-specific co-expression modules derived from single-cell RNA sequencing across 17 immunological conditions and 1.78 million cells. Using a modified graph-based framework, we constructed 873 robust modules spanning 7 major immune cell types, providing stable, cell-type-specific interaction networks for functional inference. scImmuneCo resolves complex biology at cellular resolution. We identify 20 interferon-related modules that reveal both conserved and cell-type-specific regulatory programs, clarifying disease-dependent differences that are invisible to pathway tools treating interferon signaling as a unitary process. We also uncover age-associated CD8+ T cell programs, capturing state transitions from naive to effector/memory cells and exposing a progressive imbalance in translation and cytotoxicity with age. Together, these results demonstrate the power of high-resolution, data-driven functional inference to link gene groups to biological roles and disease processes. To support broad application, we provide an R package (https://github.com/FrankQYW/scImmuneCo_R) for module-based analysis of both single-cell and bulk transcriptomic data, along with an interactive web portal (http://www.scimmuneco.site/) for visualization and gene-module exploration. scImmuneCo offers a scalable and interpretable framework for dissecting immune mechanisms and identifying disease-relevant transcriptional programs with cellular resolution.
Briefings in bioinformaticsDevora Siminovsky, Yoram Louzoun
Binding of T-cell receptors (TCRs) and their cognate peptide-major histocompatibility complex (pMHC) target is determined by both TCR$\alpha $ and TCR$\beta $ chains. However, not all TCR$\alpha $ and TCR$\beta $ can bind to each other. Predicting their pairing is crucial for understanding the TCR-pMHC interaction and developing effective de novo TCRs. Here, we show that in the general TCR repertoire, TCR$\alpha $ and TCR$\beta $ chain compositions are independent. However, in pMHC-binding TCRs, clear associations between TCR$\alpha $ and TCR$\beta $ chains are found, also for TCRs binding to the same pMHC. The association between the CDR3 amino acid composition and $V$, $J$ usage of TCR$\alpha $ and TCR$\beta $ reveals distinct binding patterns between specific $V$ and $J$ genes, as well as negative correlations between the charge and polarity of the TCR$\alpha $ and TCR$\beta $ chains, but positive associations between their molecular weights. These associations are used for the development of a prediction model for TCR$\alpha $ and TCR$\beta $ pairing. We present here TCR-BARN (TCR Beta-Alpha chains paiRing using Nlp) that employs an initial embedding for each amino acid in the TCR alpha and beta CDR3 sequences, followed by long short-term memory (LSTM) networks to capture sequence dependencies. The $V$ and $J$ genes are represented using one-hot encoding. LSTM outputs are concatenated and passed through a fully connected feedforward layer for binding prediction. TCR-BARN reaches an area under the curve $>0.65\pm 0.007$ for epitope-bound TCRs. TCR-BARN can be used for generating cognate TCRs resembling natural TCRs and evaluating the generated TCR quality.
Briefings in bioinformaticsNomathamsanqa Tholo, Gavin Markey, Ruairidh Harrigan, Preeti Pandey, Bodhayan Prasad, Ram Shankar Barai, David Samuel Gibson, Priyank Shukla
Peptide-based vaccines, enabled by bioinformatics and machine learning (ML), have emerged as one of the most promising approaches for rapid, safe, and cost-effective vaccine design against infectious diseases. Unlike conventional approaches that depend heavily on whole-pathogen cultures or recombinant protein expression, peptide vaccines can be designed in silico and synthesized quickly. Rational and targeted in silico approaches for the discovery of peptide-based vaccine candidates include B-cell and T-cell epitope prediction, immunogenicity, antigenicity, allergenicity, autoimmunity, population coverage, sequence conservation, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation analyses. The combination of these comprehensive computational methods can effectively generate high-quality vaccine candidates for subsequent validation via in vitro and in vivo experiments. This review contextualizes the historical trajectory of peptide-based vaccinology, from early linear epitope discoveries in the 1960s to multi-epitope constructs and clinically tested candidates such as UB-612 and PepGNP-Covid19. It examines critical challenges in immunoinformatics, including performance gaps in epitope prediction tools, complexities in human leucocyte antigen (HLA) mapping, and the need for extensive manual intervention in pipelines. Artificial intelligence-driven approaches, spanning deep learning, and interpretable ML, are positioned to transform epitope prediction, reduce human error, and standardize reproducibility. These advances have the potential to support global outbreak response targets such as the Coalition for Epidemic Preparedness Innovations (CEPI) 100 Days Mission and the World Health Organization (WHO) Research and Development (R&D) Blueprint. However, their performance remains constrained by data quality, dataset imbalance, limited benchmark standardization, and persistent underrepresentation of many HLA alleles and population groups. Key Points Peptide-based vaccines, accelerated by bioinformatics and machine learning, offer a potentially rapid, relatively safe, and cost-effective alternative to traditional vaccine design, enabling in silico development and swift synthetic manufacturing. Computational methods such as B-cell and T-cell epitope prediction, immunogenicity analysis, and molecular simulations allow for rational and targeted vaccine candidate discovery, enhancing quality and efficiency. The field has evolved from early linear epitope discoveries in the 1960s to sophisticated multi-epitope constructs and clinically tested candidates like UB-612 and PepGNP-Covid19. Major challenges in immunoinformatics include performance limitations in epitope prediction tools, complexities in HLA mapping, and the necessity for manual intervention in data pipelines. Artificial intelligence-driven models, including deep learning and interpretable machine learning, promise to overcome these challenges by improving prediction accuracy, reducing errors, and supporting global epidemic response efforts such as CEPI's 100 Days Mission and the WHO R&D Blueprint.
Veterinary medicine and scienceYasaman Dini, Tohid Piri-Gharaghie, Elahe Hamdi, Pegah Goodarzi, Ronak Ahmadi
BACKGROUND: Bovine leukaemia virus (BLV) is the causative agent of enzootic bovine leukosis, a chronic infectious disease that causes significant economic losses in the dairy and beef industries worldwide. Despite extensive research, there is no licensed vaccine available for effective prevention and control of BLV infection. OBJECTIVES: This study aimed to design and evaluate a novel multi-epitope vaccine candidate against BLV using an integrated computational and experimental approach to enhance immunogenicity, expression efficiency, and molecular stability. METHODS: Major BLV structural proteins (gp51, gp30, and p24) were analyzed for B- and T-cell epitope prediction using immunoinformatics tools. Selected epitopes were assembled into a single chimeric construct with appropriate linkers and an N-terminal β-defensin adjuvant. The vaccine was evaluated for antigenicity, allergenicity, and physicochemical properties. Structural modelling, molecular docking with Toll-like receptors (TLRs), and RNA stability analyses were performed to assess receptor binding affinity and translational efficiency. Codon optimization for Lactococcus lactis expression was conducted using the JCAT server, and in silico cloning was verified in the NICE pNZ8148 vector. RESULTS: The designed vaccine showed high antigenicity (VaxiJen score: 0.7261), non-allergenicity, and stability, with an optimal codon adaptation index (0.94) and GC content (48.55%). Molecular docking revealed strong interactions with TLR9 (z-score = -2.5; van der Waals energy = -56.5 ± 3.8 kcal/mol), suggesting effective immune receptor engagement. RNAfold analysis indicated a stable mRNA structure (MFE = -155.20 kcal/mol), supporting efficient expression. CONCLUSIONS: The multi-epitope vaccine candidate demonstrated favourable immunological, structural, and translational properties, indicating its strong potential as a next-generation recombinant vaccine against BLV. Further in vitro and in vivo validation is warranted to confirm its immunogenicity and protective efficacy in cattle.
HLAAntonio Milano, Roberto Crocchiolo, Noemi Anzaldi, Andrea Calligaro, Lorenzo Gangi, Mariarosa Riva, Chiara Mariadele Scollo
Celiac disease (CD) is classically associated with the HLA-DQ2.5 and HLA-DQ8 heterodimers; however, a clinically relevant subset of patients fulfilling diagnostic criteria lacks these canonical risk HLA-DQ molecules, indicating the presence of alternative antigen-presentation pathways. Among potential contributors, HLA-DQ5 and HLA-DQ9 remain poorly characterised at the immunopeptidomic level. In this study, we applied an integrated in silico framework to systematically investigate the gliadin-derived peptide repertoire presented by canonical and non-canonical HLA-DQ molecules. Peptides derived from α-, γ- and ω-gliadin families were generated following simulated tissue transglutaminase-mediated deamidation and multi-enzyme gastrointestinal digestion. Binding affinities to HLA-DQ2.5, DQ8, DQ5 and DQ9 were predicted using NetMHCIIpan 4.3 and integrated with peptide stability and sequence abundance into a composite Biological Plausibility score. The analysis identified γ-gliadin-derived peptides as the dominant immunogenic drivers across all investigated HLA-DQ molecules, owing to their enhanced digestive stability and high allelic promiscuity. Hierarchical clustering of binding profiles revealed a repertoire-dependent functional overlap between HLA-DQ9 and the canonical DQ8 molecule, particularly for γ-gliadin motifs. In contrast, HLA-DQ5 displayed functional complementarity, selectively presenting a distinct subset of gliadin peptides that were poorly recognised by classical risk HLA-DQ molecules. Collectively, these findings provide a mechanistic framework for CD pathogenesis in individuals lacking HLA-DQ2.5 and HLA-DQ8, demonstrating that non-canonical HLA-DQ molecules can sustain pathogenic CD4+ T-cell responses through convergent and complementary antigen-presentation pathways. As a predictive in silico framework, this work refines the genetic paradigm of CD by emphasising functional organisation of the gluten immunopeptidome over simple allele presence and provides a mechanistic rationale for future experimental validation of non-canonical HLA-DQ-restricted gliadin presentation.
Immunological reviewsRomi Vandoren, Vincent Van Deuren, Fabio Affaticati, Sofie Gielis, Kris Laukens, Pieter Meysman
T cells are central to adaptive immunity, recognizing antigenic peptides, called epitopes, via the T cell receptor (TCR). The immense diversity and cross-reactivity of the TCR repertoire makes direct interpretation of antigen specificity from repertoire sequencing challenging. High-throughput sequencing enables large-scale profiling of TCRs but does not directly reveal their target epitopes, requiring computational approaches to bridge this gap. This review outlines two complementary strategies, bottom-up and top-down approaches, to annotate TCR specificity. Bottom-up methods predict TCR-epitope specificity from curated TCR-epitope databases, identifying recurring patterns through distance-based, feature-based, or deep learning models. While effective for well-characterized epitopes, they are limited by biased training data, absence of negative data, and weak generalization to unseen epitopes. Top-down approaches instead infer antigen-driven responses from repertoire-level signals such as sequence similarity, enrichment, and TCR convergence. These methods enable discovery of disease- or exposure-associated TCR signatures without prior epitope knowledge but are sensitive to technical noise and biological confounding. Both approaches are complementary as bottom-up provides mechanistic specificity, while top-down enables discovery in complex datasets. Their integration, alongside multimodal modeling and improved benchmarking, is key to advancing TCR-epitope annotation and understanding adaptive immune responses.
Immunological reviewsDavid Gfeller, Julien Racle, Rita Ann Roessner
T-cell recognition of infected or malignant cells is central to both spontaneous and therapy-induced cellular immune responses against pathogens and cancer. This recognition is elicited by the interaction between T-Cell Receptors (TCRs) and epitopes, which consist of antigenic peptides displayed on major histocompatibility complex molecules. TCR-epitope interactions are characterized by high diversity in TCR and epitope sequences and high structural flexibility in TCR loops. As a result, deciphering the rules of TCR-epitope recognition specificity and accurately predicting these interactions remains challenging. Here, we review the different strategies developed to predict TCR-epitope recognition, classify the principal computational frameworks, examine the data modalities on which they depend and discuss their current limitations. We then synthesize key conceptual insights that have emerged from recent research and outline how these lessons should inform the design of future experiments and next-generation computational tools.
Journal of the National Comprehensive Cancer Network : JNCCNMallik Greene, Brad Stieber, A Burak Ozbay, Joseph Anderson, Joseph LeMaster, Michael Dore, Igor Stukalin, Jeffrey Arroyo, Derek W Ebner, A Mark Fendrick, Jord…
BACKGROUND: Fecal immunochemical tests (FITs), fecal occult blood tests (FOBTs), and multitarget stool DNA (mt-sDNA) tests are convenient, noninvasive colorectal cancer (CRC) screening strategies. However, delays in or failure to complete follow-up colonoscopy (FU-CY) after a positive noninvasive test are associated with higher CRC incidence, complications, and mortality. We sought to evaluate FU-CY rates, time to colonoscopy, and compliance predictors. PATIENTS AND METHODS: This retrospective cohort study linked 2 data sources: Komodo Health Data with Exact Sciences Laboratories. Data were collected from January 2016 to June 2023 and analyzed in 2024. Study participants (ages 45-75 years) were individuals with positive stool-based CRC screening tests (FIT, guaiac-based FOBT, or mt-sDNA tests). FU-CY compliance was assessed at 30-day intervals up to 365 days, and logistic regression was used to identify predictors of FU-CY. RESULTS: Among 362,646 participants, colonoscopy compliance was 77.1% for mt-sDNA and 45.1% for FIT/FOBT (P<.001). Overall FU-CY compliance was lowest among Asian and Black populations, but was significantly higher in these subgroups for mt-sDNA versus FIT (Asian: 74.2% vs 41.1%; Black: 71.3% vs 44.6%). The strongest predictor of compliance was use of mt-sDNA (odds ratio, 3.98; P<.001). More than half of participants across all race groups who completed FU-CY after mt-sDNA did so within the first 120 days, whereas none of the FIT/FOBT racial subgroups reached 50% completion within 365 days. CONCLUSIONS: Timely completion of colonoscopy after a noninvasive test is critical, as failure to undergo colonoscopy or delays in completion are associated with increased CRC incidence and more advanced-stage disease. These findings demonstrate that mt-sDNA use may help close CRC screening disparity gaps compared with FIT testing. FU-CY adherence is higher, and time to colonoscopy is shorter following a positive mt-sDNA test versus FIT across all racial and ethnic subgroups.
Background: Allergy/immunology (A/I) fellowship recruitment occurs within a competitive match environment, with most interviews conducted virtually. Program web sites play a critical role in informing applicants and shaping program perception. Despite established literature that demonstrates the importance of program web sites across specialties, the comprehensiveness of A/I fellowship web sites has not been systematically evaluated. Objective: The purpose of this study was to evaluate existing A/I fellowship program web sites with the intent to identify opportunities for improvement. Methods: A list of 91 A/I fellowship programs was obtained from the American Academy of Allergy, Asthma & Immunology web site. Eighty-nine programs with accessible web sites were included. Each web site was evaluated for the presence of 24 predefined metrics derived from previous residency and fellowship web site studies. Sixteen programs affiliated with World Allergy Organization (WAO) Centers of Excellence in the United States were analyzed as a subgroup. Mean metric counts were compared by using a two-sample t-test. Results: Web sites contained a mean ± standard deviation (SD) of 13.7 ± 4.9 of 24 metrics. WAO-affiliated programs contained a mean ± SD of 13.2 ± 4.6 metrics, with no statistically significant difference when compared with all the programs (p = 0.70). Although most web sites listed program leadership, curriculum, and training sites, fewer included alumni information (40%), job placement (38%), or wellness initiatives (18%). Conclusion: A/I fellowship program web sites vary widely in content and may lack information, particularly with regard to wellness, alumni outcomes, and trainee demographics. Enhancing web site comprehensiveness represents an opportunity to improve recruitment, branding, and applicant engagement in a competitive application environment.
Bioinformatics (Oxford, England)Sakina Amin, Lauren Overend, Felicia Tucci, Bo Sun, Justin Whalley, Michael L Dustin, Julian C Knight, Rachael Bashford-Rogers
MOTIVATION: High-throughput sequencing of B and T cell repertoires provides unprecedented insights into adaptive immunity but generates high-dimensional feature sets that are challenging to interpret. Standard dimensionality reduction techniques are often suboptimal for adaptive immune receptor repertoire (AIRR) data, which exhibits multi-collinearity, heterogeneous data types, and missingness. RESULTS: Here, we present VDJ-REMIX, an R package implementing a robust, network-based framework to deconstruct complex repertoire feature matrices into biologically interpretable modules. By refactoring weighted correlation network analysis (WGCNA), VDJ-REMIX provides a tailored workflow for preprocessing, imputation, and modularization of immune repertoire data. We demonstrate its utility across diverse contexts, including autoimmunity, inflammation, and acute infection. In autoimmune patients, VDJ-REMIX identified distinct B cell signatures that stratified diseases and revealed opposing dynamic responses to B cell-depleting versus anti-proliferative therapies. In COVID-19 and non-COVID-19 sepsis patients, it distinguished disease-specific signatures from shared severe infection responses and identified modules correlated with severity. Analysis of flow-sorted B cell populations stratified by FCGR2B genotype recapitulated known tolerance defects and uncovered population-specific repertoire signatures linked to inhibitory receptor dysfunction, providing orthogonal validation of module biological coherence. Finally, applied to a single-cell multi-omics dataset of immune cells in pancreatic ductal adenocarcinoma (PDAC) combining gene expression with AIRR-seq, VDJ-REMIX recovered modules linking BCR isotype usage and clonality to cytotoxic, interferon-responsive, and regulatory immune programmes. VDJ-REMIX is a versatile tool enabling systematic exploration of immunological variation and biomarker discovery from complex immune repertoire data. AVAILABILITY AND IMPLEMENTATION: VDJ-REMIX is freely available at https://github.com/Bashford-Rogers-lab/vdjremix.
PloS oneLucas Dos Reis de Souza, Monique Ferreira Silva Souza, Pâmela Aparecida Lima, Tatyane Martins Cirilo, Letícia Neves Ribeiro, Samuel Alexandre Pimenta Carvalho,…
Serological methods are valuable diagnostic tools for bovine brucellosis, a zoonotic infectious disease with a worldwide distribution. Currently employed diagnostic methods utilize crude bacterial extract or regions of LPS, a molecule that can exhibit cross-reactions with other infectious agents. The goal of this study was to develop an indirect enzyme-linked immunosorbent assay (ELISAi) using synthetic peptides or a multi-epitope protein based on in silico-predicted B cell epitopes. Peptides were synthesized on a cellulose membrane using spot synthesis, and immunoblots were performed to evaluate reactivity by densitometry against bovine sera of interest. Peptides reacting with serum from positive cattle and non-reactive to the serum from negative controls were selected, synthesized in a soluble form, and used as antigens for the development of the ELISAi. Two peptides (P1 and P2) were selected and, after standardization of the ELISAi, positive (25) and negative (175) samples were tested, resulting in a sensitivity of 84% (21/25) and specificity of 83.43% (146/175). The sequence of the two peptides in replicates with spacers were inserted into a multi-epitope protein, which was also used as antigen in an ELISAi resulting in a sensitivity of 72% (18/25) and a specificity of 61.71% (108/175). This study provided a preliminary analytical assessment of in silico-predicted epitopes for developing novel serologic diagnostic tests.
INTRODUCTION: Clostridium perfringens is the primary causative agent of enterotoxemia in yaks, resulting in substantial economic losses on the Qinghai-Tibet Plateau. Conventional vaccines exhibit limited protective breadth and suboptimal efficacy, highlighting the need for innovative strategies. Here, we aimed to construct a novel vaccine candidate incorporating epitopes from multiple prevalent toxinotypes (A, C, E) of C. perfringens affecting yaks, using immunoinformatics approach. METHODS: A hierarchical immunoinformatics pipeline was implemented, encompassing subtractive genomics to identify core virulence factors, prediction and filtering of immunogenic T-cell and B-cell epitopes, rational multi-epitope vaccine design incorporating adjuvant and linkers, three-dimensional structure modeling and validation, molecular docking to evaluate interactions with TLR4, molecular dynamics simulations to confirm complex stability, and codon optimization to facilitate heterologous expression. RESULTS AND DISCUSSION: Five core virulence proteins (Iap, CpsE, NanH, Plc, Pfo) were identified from genomic data, leading to the prediction and selection of ten cytotoxic T lymphocyte (CTL) epitopes, five helper T lymphocyte (HTL) epitopes, and five B-cell epitopes. The final 352-amino-acid multi-epitope vaccine (MEV) construct was assembled using the adjuvant human β-defensin-3 and specific linkers (AAY, GPGPG, KK). Computational evaluations confirmed the vaccine's high antigenicity (VaxiJen score: 0.9092), non-allergenic nature, and structural stability. Molecular docking revealed strong binding affinity with TLR2 (-1024.6 kcal/mol) and TLR4 (-1104.4 kcal/mol). Molecular dynamics simulations over 100 ns confirmed stable TLR4 complex with an average RMSD of 0.1971 ± 0.0377 nm, while the TLR2 complex showed an average RMSD of 0.2692 ± 0.0420 nm. Immune simulation profiles predicted the induction of robust humoral and cellular immune responses, including elevated antibody titers, T-cell activation, and cytokine production. In silico cloning verified the potential for efficient expression in E. coli. CONCLUSION: This study designed a novel multi-epitope vaccine against C. perfringens in yaks using an immunoinformatics approach. The vaccine showed high antigenicity, stability, and broad allelic coverage in silico, providing a promising candidate that requires rigorous in vitro and in vivo experimental validation to confirm these computational predictions. This work offers a foundation for the development of effective vaccines against yak C. perfringens infections on the Qinghai-Tibet Plateau.
Advances in physiology educationVisula Abeysuriya, Sachintha N Alwis, Vasitha Abeysuriya
Problem-based learning (PBL) is an effective strategy for developing higher-order cognitive skills in biomedical education; however, structured exposure is often introduced late in postgraduate immunology curricula, leaving students insufficiently prepared for complex, independent learning tasks. Teaching immunity to COVID-19 presents additional challenges due to the need to integrate innate, adaptive, and cytokine-mediated immune pathways. The objective of this study was to quantitatively evaluate the effect of a pyramidal PBL intervention using structured mind mapping on academic performance, conceptual organization, and learner engagement among postgraduate immunology students. The intervention group (n = 18) replaced a scheduled didactic lecture with an intergroup pyramidal PBL activity involving domain-specific mind mapping, iterative peer and facilitator feedback, and whole class synthesis, while a comparable control cohort (n = 20) received conventional lecture-based instruction. Knowledge retention was assessed using a delayed summative examination administered 2 wk postintervention, and student perceptions were measured using a structured Likert-scale questionnaire. The intervention group achieved significantly higher examination scores than controls (means ± SD: 73.5 ± 4.4 vs. 66.2 ± 6.5), with a mean difference of 7.3 percentage points [95% confidence interval (CI): 3.8-10.7; P < 0.001] and a large effect size (Cohen's d = 1.30). Questionnaire findings demonstrated high acceptability and perceived effectiveness, with most participants reporting improved conceptual understanding, communication skills, and engagement with complex immunological content. These findings indicate that pyramidal mind-mapping-based PBL produces educationally meaningful improvements in learning outcomes and supports earlier scaffolding of problem-based learning within postgraduate immunology curricula.NEW & NOTEWORTHY This study introduces a pyramidal problem-based learning (PBL) model combined with structured mind mapping for postgraduate immunology, applied for the first time to teaching complex COVID-19 immunity. Early scaffolding of PBL significantly enhanced students' conceptual organization, engagement, and examination performance, with a large measurable effect. These findings provide the first evidence that this interactive, structured approach can accelerate mastery of challenging biomedical content in postgraduate curricula.