Explore the vast range of titles available.

Rotavirus (RV) is a common double-stranded RNA virus that causes diarrheal disease in young children. The prevalent species, Rotavirus A (RVA), is responsible for over 90% of human RV infections. With significant morbidity and mortality, this pathogen poses a serious global health challenge, particularly in underdeveloped countries. This study presents an immunoinformatics approach for designing an mRNA vaccine based on a multi-peptide construct to elicit robust immune responses against RVA. The VP4 was analyzed from 40 sequences using phylogenetic analysis. Prediction of cytotoxic (CTL) and helper T cell (HTL) epitopes was performed and validated. The 17 high-conservancy CTL/HTL epitopes were selected for vaccine construction. The mRNA vaccine based on multi-peptide was engineered with human beta-defensin 3 (hBD3) adjuvant and linkers to enhance immunogenicity. The designed mRNA vaccine product exhibited favorable physicochemical properties and was predicted to be a probable antigen, non-allergenic, and non-toxic. 2D and 3D structure validation demonstrated the quality of the model. Molecular docking with Toll-like receptor 2/3 (TLR2/3) indicated favorable interaction, and peptide docking with MHC-I/II alleles showed strong binding affinities and have significant Residue-Residue interactions. Simulation of immune responses revealed potent B-cell and T-cell activities, macrophage responses, and significant cytokine synthesis. Molecular dynamics simulation (MDS) confirmed the structural stability of the TLR3-vaccine complex, and MHC-peptide in 200ns and STQFTDFVSLNSLRF peptide have shown good interaction with MHC molecule. In addition, the MM/GBSA analysis yielded a binding free energy of − 89.77 kcal/mol, indicating a strong and stable interaction between the vaccine construct and the target receptor. Codon optimization and mRNA secondary structure prediction were carried out for efficient translation. Additionally, population coverage analysis indicated the vaccine’s effectiveness worldwide with 100% value. Overall, this study showcases a promising immunoinformatics approach for designing an mRNA vaccine based on a multi-peptide construct targeting RVA. The findings support the potential of this vaccine design to elicit robust and widespread immune responses against RVA infection, paving the way for future vaccine development strategies and this study needs experimental validation.

Natural infections expose the immune system to escalating antigen and inflammation over days to weeks, whereas nonlive vaccines are single bolus events. We explored whether the immune system responds optimally to antigen kinetics most similar to replicating infections, rather than a bolus dose. Using HIV antigens, we found that administering a given total dose of antigen and adjuvant over 1–2 wk through repeated injections or osmotic pumps enhanced humoral responses, with exponentially increasing (exp-inc) dosing profiles eliciting >10-fold increases in antibody production relative to bolus vaccination post prime. Computational modeling of the germinal center response suggested that antigen availability as higher-affinity antibodies evolve enhances antigen capture in lymph nodes. Consistent with these predictions, we found that exp-inc dosing led to prolonged antigen retention in lymph nodes and increased Tfh cell and germinal center B-cell numbers. Thus, regulating the antigen and adjuvant kinetics may enable increased vaccine potency.

The immune system is a distributed decentralized system that functions without any centralized control. The immune system has millions of cells that function somewhat independently and can detect and respond to pathogens with considerable speed and efficiency. Lymph nodes are physical anatomical structures that allow the immune system to rapidly detect pathogens and mobilize cells to respond to it. Lymph nodes function as: 1) information processing centres, and 2) a distributed detection and response network. We introduce biologically inspired computing that uses lymph nodes as inspiration. We outline applications to diverse domains like mobile robots, distributed computing clusters, peer-to-peer networks and online social networks. We argue that lymph node inspired computing systems provide powerful metaphors for distributed computing and complement existing artificial immune systems. We view our work as a first step towards holistic simulations of the immune system that would capture all the complexities and the power of a complex adaptive system like the immune system. Ultimately this would lead to immune system inspired computing that captures all the complexities and power of the immune system in human-engineered complex systems.

The immune system is a network of specialized cell types and tissues that communicates via cytokines and direct contact, to orchestrate specific types of defensive responses. Until recently, we could only study immune responses in a piecemeal, highly focused fashion, on major components like antibodies to the pathogen. But recent advances in technology and in our understanding of the many components of the system, innate and adaptive, have made possible a broader approach, where both the multiple responding cells and cytokines in the blood are measured. This systems immunology approach to a vaccine response or an infection gives us a more holistic picture of the different parts of the immune system that are mobilized and should allow us a much better understanding of the pathways and mechanisms of such responses, as well as to predict vaccine efficacy in different populations well in advance of efficacy studies. Here we summarize the different technologies and methods and discuss how they can inform us about the differences between diseases and vaccines, and how they can greatly accelerate vaccine development.

HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides, and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential noncanonical binding modes, we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01, and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8⁺ T cells of a 10-mer epitope from cytomegalovirus predicted to follow the C-terminal extension binding mode.

Background Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. Results We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. Conclusions CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.

Antigen presentation lies at the heart of immune recognition of infected or malignant cells. For this reason, important efforts have been made to predict which peptides are more likely to bind and be presented by the human leukocyte antigen (HLA) complex at the surface of cells. These predictions have become even more important with the advent of next-generation sequencing technologies that enable researchers and clinicians to rapidly determine the sequences of pathogens (and their multiple variants) or identify non-synonymous genetic alterations in cancer cells. Here, we review recent advances in predicting HLA binding and antigen presentation in human cells. We argue that the very large amount of high-quality mass spectrometry data of eluted (mainly self) HLA ligands generated in the last few years provides unprecedented opportunities to improve our ability to predict antigen presentation and learn new properties of HLA molecules, as demonstrated in many recent studies of naturally presented HLA-I ligands. Although major challenges still lie on the road toward the ultimate goal of predicting immunogenicity, these experimental and computational developments will facilitate screening of putative epitopes, which may eventually help decipher the rules governing T cell recognition.

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.

Glioblastoma (GBM) is an aggressive brain tumour with limited responsiveness to current immunotherapeutic approaches, partly due to its low mutational burden and intra-tumour heterogeneity. A systematic understanding of the tumour antigen landscape is therefore essential for advancing tumour immunology and supporting rational development of immunotherapeutic strategies. In this study, we performed whole-transcriptome sequencing of RNA extracted from 79 formalin-fixed paraffin-embedded (FFPE) IDH-wildtype GBM samples to systematically identify and prioritise candidate tumour antigens derived from three sources: single-nucleotide variants (SNVs), overexpressed tumour-associated antigens (TAAs), and gene fusion events. Candidate peptides were evaluated using integrated computational criteria, including transcript expression, predicted antigen processing features, peptide–HLA binding affinity and stability. Across the cohort, mutation-derived tumor-specific antigens (TSAs) were largely private to individual samples, whereas TAAs constituted a larger and more recurrent candidate pool. Despite comparable predicted binding characteristics across antigen classes, recurrence patterns differed substantially, reflecting their distinct biological origins. Fusion-derived candidates were rare and sample-specific. Predicted peptide presentation was disproportionately associated with a limited subset of HLA class I alleles. Collectively, this study provides a systematically prioritized catalogue of transcriptionally expressed GBM antigen candidates and offers a comparative evaluation of mutation-, expression-, and fusion-derived antigen sources within a unified transcriptome-based framework.

Adaptive immune activation in lymph nodes requires rare antigen-specific naïve T cells to locate antigen-bearing dendritic cells within a spatially structured stromal network. Reduced priming efficiency is typically attributed to weak T cell receptor signaling, yet it remains unclear whether failure arises from impaired signaling or from limited access to antigen-bearing dendritic cells during early scanning. We developed COORDINATE, a spatially explicit agent-based model of lymph node microanatomy that integrates fibroblastic reticular cell topology, chemokine-guided migration, and competition for dendritic cell access. We show that stromal architecture and trafficking biases strongly influence which T cells encounter antigen, while competition for limited dendritic cell access can exclude a substantial fraction of cells from forming any productive contact. Consequently, reduced activation can arise from failed clonal recruitment rather than diminished signaling following contact. These results support a view of early T cell priming as an access-limited process and indicate that commonly used endpoint measurements can conflate failure to access antigen with failure to activate. Together, our findings suggest that improving early antigen access, rather than strengthening signaling alone, may represent an alternative strategy to enhance adaptive immune responses.