NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

Design and evaluation of a multi-epitope DNA vaccine against HPV16

Cervical cancer, among the deadliest cancers affecting women globally, primarily arises from persistent infection with high-risk human papillomavirus (HPV). To effectively combat persistent infection and prevent the progression of precancerous lesions into malignancy, a therapeutic HPV vaccine is under development. This study utilized an immunoinformatics approach to predict epitopes of cytotoxic T lymphocytes (CTLs) and helper T lymphocytes (HTLs) using the E6 and E7 oncoproteins of the HPV16 strain as target antigens. Subsequently, through meticulous selection of T-cell epitopes and other necessary elements, a multi-epitope vaccine was constructed, exhibiting good immunogenic, physicochemical, and structural characteristics. Furthermore, in silico simulations showed that the vaccine not only interacted well with toll-like receptors (TLR2/TLR3/TLR4), but also induced a strong innate and adaptive immune response characterized by elevated Th1-type cytokines, such as interferon-gamma (IFN-γ) and interleukin-2 (IL2). Additionally, our study investigated the effects of different immunization intervals on immune responses, aiming to optimize a time-efficient immunization program. In animal model experiments, the vaccine exhibited robust immunogenic, therapeutic, and prophylactic effects. Administered thrice, it consistently induced the expansion of specific CD4 and CD8 T cells, resulting in substantial cytokines release and increased proliferation of memory T cell subsets in splenic cells. Overall, our findings support the potential of this multi-epitope vaccine in combating HPV16 infection and signify its candidacy for future HPV vaccine development.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Development of multi-epitope mRNA vaccine against Clostridioides difficile using reverse vaccinology and immunoinformatics approaches

Clostridioides difficile (C. difficile), as the major pathogen of diarrhea in healthcare settings, has become increasingly prevalent within community populations, resulting in significant morbidity and mortality. However, the therapeutic options for Clostridioides difficile infection (CDI) remain limited, and as of now, no authorized vaccine is available to combat this disease. Therefore, the development of a novel vaccine against C. difficile is of paramount importance. In our study, the complete proteome sequences of 118 strains of C. difficile were downloaded and analyzed. We found four antigenic proteins that were highly conserved and can be used for epitope identification. We designed two vaccines, WLcd1 and WLcd2, that contain the ideal T-cell and B-cell epitopes, adjuvants, and the pan HLA DR-binding epitope (PADRE) sequences. The biophysical and chemical assessments of these vaccine candidates indicated that they were suitable for immunogenic applications. Molecular docking analyses revealed that WLcd1 bonded with higher affinity to Toll-like receptors (TLRs) than WLcd2. Furthermore, molecular dynamics (MD) simulations, performed using Gmx_MMPBSA v1.56, confirmed the binding stability of WLcd1 with TLR2 and TLR4. The preliminary findings suggested that this multi-epitope vaccine could be a promising candidate for protection against CDI; however, experimental studies are necessary to confirm these predictions.

Tumour mutational burden: clinical utility, challenges and emerging improvements

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Mosaic neuraminidase-based vaccine induces antigen-specific T cell responses against homologous and heterologous influenza viruses

Seasonal influenza is an annually severe crisis for global public health, and an ideal influenza vaccine is expected to provide broad protection against constantly drifted strains. Compared to highly flexible hemagglutinin (HA), increasing data have demonstrated that neuraminidase (NA) might be a potential target against influenza variants. In the present study, a series of genetic algorithm-based mosaic NA were designed, and then cloned into recombinant DNA and replication-defective Vesicular Stomatitis Virus (VSV) vector as a novel influenza vaccine candidate. Our Results showed that DNA prime/VSV boost strategy elicited a robust NA-specific Th1-dominated immune response, but the traditional inactivated influenza vaccine elicited a Th2-dominated immune response. More importantly, the superior NA-specific immunity induced by our strategy could confer both a full protection against lethal homologous influenza challenge and a partial protection against heterologous influenza infection. These findings will provide insights on designing NA-based universal vaccine strategy against influenza variants.

In silico design of Mycobacterium tuberculosis multi-epitope adhesin protein vaccines

Mycobacterium tuberculosis (Mtb) adhesin proteins are promising candidates for subunit vaccine design. Multi-epitope Mtb vaccine and diagnostic candidates were designed using immunoinformatic tools. The antigenic potential of 26 adhesin proteins were determined using VaxiJen 2.0. The truncated heat shock protein 70 (tnHSP70), 19 kDa antigen lipoprotein (lpqH), Mtb curli pili (MTP), and Phosphate transport protein S1 (PstS1) were selected based on the number of known epitopes on the Immune Epitope Database (IEDB). B- and T-cell epitopes were identified using BepiPred2.0, ABCpred, SVMTriP, and IEDB, respectively. Population coverage was analysed using prominent South African specific alleles on the IEDB. The allergenicity, physicochemical characteristics and tertiary structure of the tri-fusion proteins were determined. The in silico immune simulation was performed using C-ImmSim. Three truncated sequences, with predicted B and T cell epitopes, and without allergenicity or signal peptides were linked by three glycine-serine residues, resulting in the stable, hydrophilic molecules, tnlpqH-tnPstS1-tnHSP70 (64,86 kDa) and tnMTP-tnPstS1-tnHSP70 (63,96 kDa). Restriction endonuclease recognition sequences incorporated at the N- and C-terminal ends of each construct, facilitated virtual cloning using Snapgene, into pGEX6P-1, resulting in novel, highly immunogenic vaccine candidates (0,912-0,985). Future studies will involve the cloning, recombinant protein expression and purification of these constructs for downstream applications.

Peptide vaccine design against glioblastoma by applying immunoinformatics approach

Brain tumors are considered to be one of the most fatal forms of cancer owing to their highly aggressive attributes, diverse characteristics, and notably low rate of survival. Among these tumors, glioblastoma stands out as the prevalent and perilous variant Despite the present advancements in surgical procedures, pharmacological treatment, and radiation therapy, the overall prognosis remains notably unfavorable, as merely 4.3 % of individuals manage to attain a five-year survival rate; For this reason, it has emerged as a challenge for cancer researchers. Therefore, among several immunotherapy methods, using peptide-based vaccines for cancer treatment is considered promising due to their ability to generate a focused immune response with minimal damage. This study endeavors to devise a multi-epitope vaccine utilizing an immunoinformatics methodology to address the challenge posed by glioblastoma disease. Through this approach, it is anticipated that the duration and expenses associated with vaccine manufacturing can be diminished, while simultaneously enhancing the characteristics of the vaccine. The target gene in this research is ITGA5, which was achieved through TCGA analysis by targeting the PI3K-Akt pathway as a significant association with patient survival. Subsequently, the suitable epitopes of T and B cells were selected through various immunoinformatics tools by analyzing their sequence. Then, nine epitopes were merged with GM-CSF as an adjuvant to enhance immunogenicity. The outcomes encompass molecular docking, molecular dynamics (MD) simulation, simulation of the immune response, prognosis and confirmation of the secondary and tertiary structure, Chemical and physical characteristics, toxicity, as well as antigenicity and allergenicity of the potential vaccine candidate against glioblastoma.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization

BACKGROUND

Antigen presentation is a central step in initiating and shaping the adaptive immune response. To activate CD8+ T cells, pathogen-derived peptides are presented on the cell surface of antigen-presenting cells bound to major histocompatibility complex (MHC) class I molecules. CD8+ T cells that recognize these complexes with their T cell receptor are activated and ideally eliminate infected cells. Prediction of putative peptides binding to MHC class I (MHC-I) is crucial for understanding pathogen recognition in specific immune responses and for supporting drug and vaccine design. There are reliable databases for epitope prediction algorithms available however they primarily focus on the prediction of epitopes in single immunogenic proteins.

RESULTS

We have developed the tool DiscovEpi to establish an interface between whole proteomes and epitope prediction. The tool allows the automated identification of all potential MHC-I-binding peptides within a proteome and calculates the epitope density and average binding score for every protein, a protein-centric approach. DiscovEpi provides a convenient interface between automated multiple sequence extraction by organism and cell compartment from the database UniProt for subsequent epitope prediction via NetMHCpan. Furthermore, it allows ranking of proteins by their predicted immunogenicity on the one hand and comparison of different proteomes on the other. By applying the tool, we predict a higher immunogenic potential of membrane-associated proteins of SARS-CoV-2 compared to those of influenza A based on the presented metrics epitope density and binding score. This could be confirmed visually by comparing the epitope maps of the influenza A strain and SARS-CoV-2.

CONCLUSION

Automated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.

TCR3d 2.0: expanding the T cell receptor structure database with new structures, tools and interactions

Recognition of antigens by T cell receptors (TCRs) is a key component of adaptive immunity. Understanding the structures of these TCR interactions provides major insights into immune protection and diseases, and enables design of therapeutics, vaccines and predictive modeling algorithms. Previously, we released TCR3d, a database and resource for structures of TCRs and their recognition. Due to the growth of available structures and categories of complexes, the content of TCR3d has expanded substantially in the past 5 years. This expansion includes new tables dedicated to TCR mimic antibody complex structures, TCR-CD3 complexes and annotated Class I and II peptide-MHC complexes. Additionally, tools are available for users to calculate docking geometries for input TCR and TCR mimic complex structures. The core tables of TCR-peptide-MHC complexes have grown by 50%, and include binding affinity data for experimentally determined structures. These major content and feature updates enhance TCR3d as a resource for immunology, therapeutics and structural biology research, and enable advanced approaches for predictive TCR modeling and design. TCR3d is available at: https://tcr3d.ibbr.umd.edu.

Advancing one health vaccination: In silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus-swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.

Overcoming immune evasion from post-translational modification of a mutant KRAS epitope to achieve TCR-engineered T cell-mediated antitumor activity

T cell receptor (TCR)-T cell immunotherapy, in which T cells are engineered to express a TCR specific for a tumor epitope, is a form of adoptive cell therapy (ACT) that has demonstrated promise against various tumor types. Mutants of oncoprotein KRAS, particularly at glycine-12 (G12), are frequent drivers of tumorigenicity, but also attractive targets for TCR-T cell therapy. However, MHC class I-restricted TCRs specifically targeting G12-mutant KRAS epitopes in the context of tumors expressing HLA-A2, the most common human HLA-A allele, have remained elusive despite evidence that an epitope encompassing the mutation can bind HLA-A2 and induce T cell responses. We report that post-translational modifications of the protein on this epitope may allow tumor cells to evade immunologic pressure from TCR-T cells. A lysine side chain-methylated KRAS G12V peptide, rather than unmodified epitope, may be presented in HLA-A2 by tumor cells and impact recognition by TCRs. Using a novel computationally guided approach to design TCRs, we developed by mutagenesis TCRs that recognize this methylated peptide, enhancing tumor recognition and destruction. Additionally, we identified TCRs with similar functional activity in normal repertoires from rare primary T cells by stimulation with modified peptide, clonal expansion, and selection. Mechanistically, a gene knockout screen to identify mechanism(s) by which tumor cells methylate or demethylate this epitope unveiled SPT6 as a demethylating protein that could be targeted to improve effectiveness of these TCRs. These findings highlight the role of post-translational modifications in immune evasion and suggest that identifying and targeting such modifications should facilitate development of more effective TCR-T cell therapies.

Identification of immunogenic HLA class I and II neoantigens using surrogate immunopeptidomes

Neoantigens arising from somatic mutations are tumor specific and induce antitumor host T cell responses. However, their sequences are individual specific and need to be identified for each patient for therapeutic applications. Here, we present a proteogenomic approach for neoantigen identification, named Neoantigen Selection using a Surrogate Immunopeptidome (NESSIE). This approach uses an autologous wild-type immunopeptidome as a surrogate for the tumor immunopeptidome and allows human leukocyte antigen (HLA)-agnostic identification of both HLA class I (HLA-I) and HLA class II (HLA-II) neoantigens. We demonstrate the direct identification of highly immunogenic HLA-I and HLA-II neoantigens using NESSIE in patients with colorectal cancer and endometrial cancer. Fresh or frozen tumor samples are not required for analysis, making it applicable to many patients in clinical settings. We also demonstrate tumor prevention by vaccination with selected neoantigens in a preclinical mouse model. This approach may benefit personalized T cell-mediated immunotherapies.

Designing of a chimeric multiepitope vaccine against bancroftian lymphatic filariasis through immunoinformatics approaches

The filarial worms of Wuchereria bancrofti are the primary cause of lymphatic filariasis (LF), a mosquito-borne disease among the neglected tropical parasitic diseases. Considering the global endemic consequences of the disease, there is a need to develop a successful vaccine candidate against LF. Using advanced immunoinformatics approaches, we designed two multiepitope vaccines targeting W. bancrofti's glutathione S-transferase and thioredoxin. Therefore, we predicted several MHC-1, MHC-2, and B-cell epitopes from these proteins and mapped two vaccine candidates (V1 and V2). The vaccines were subsequently employed for physicochemical analysis, structural prediction and validation, docking and normal mode analysis, codon optimization, and immune simulation. The selected MHC-1, MHC-2, and B-cell epitopes were antigenic without allergenicity or toxicity. The designed vaccines were expected to be soluble, stable proteins under physiological conditions. Compared to V2, V1's secondary and tertiary structures were simultaneously favorable, with Ramachandran plot analysis revealing 95.6% residues in favored areas. Subsequently, the molecular docking analysis indicated that the V1 had a high binding affinity for the TLR-2, TLR-4 and TLR-5, as suggested by the docking scores of -1248.7, -1038.5 and -1562.8, respectively. The NMA of these complexes further indicated their structural flexibility. Molecular dynamics simulations of V1-TLR complexes revealed V1-TLR-4 as the most stable, with the lowest free energy and minimal fluctuations, indicating the strongest binding affinity. The results of the codon optimization showed high levels of expression, with a favorable CAI score (<1.0). A three-dose vaccination analysis showed significant and persistent immunological responses, including adaptive and innate immune responses. The findings emphasize the potential of the V1 against W. bancrofti, but further validation is required through in vitro, in vivo, and clinical trials.

HLA-C Peptide Repertoires as Predictors of Clinical Response during Early SARS-CoV-2 Infection

The human leukocyte antigen (HLA) system plays a pivotal role in the immune response to viral infections, mediating the presentation of viral peptides to T cells and influencing both the strength and specificity of the host immune response. Variations in HLA genotypes across individuals lead to differences in susceptibility to viral infection and severity of illness. This study uses observations from the early phase of the COVID-19 pandemic to explore how specific HLA class I molecules affect clinical responses to SARS-CoV-2 infection. By analyzing paired high-resolution HLA types and viral genomic sequences from 60 patients, we assess the relationship between predicted HLA class I peptide binding repertoires and infection severity as measured by the sequential organ failure assessment score. This approach leverages functional convergence across HLA-C alleles to identify relationships that may otherwise be inaccessible due to allelic diversity and limitations in sample size. Surprisingly, our findings show that severely symptomatic infection in this cohort is associated with disproportionately abundant binding of SARS-CoV-2 structural and non-structural protein epitopes by patient HLA-C molecules. In addition, the extent of overlap between a given patient's predicted HLA-C and HLA-A peptide binding repertoires correlates with worse prognoses in this cohort. The findings highlight immunologic mechanisms linking HLA-C molecules with the human response to viral pathogens that warrant further investigation.

Strategies to Overcome Hurdles in Cancer Immunotherapy

Despite marked advancements in cancer immunotherapy over the past few decades, there remains an urgent need to develop more effective treatments in humans. This review explores strategies to overcome hurdles in cancer immunotherapy, leveraging innovative technologies including multi-specific antibodies, chimeric antigen receptor (CAR) T cells, myeloid cells, cancer-associated fibroblasts, artificial intelligence (AI)-predicted neoantigens, autologous vaccines, and mRNA vaccines. These approaches aim to address the diverse facets and interactions of tumors' immune evasion mechanisms. Specifically, multi-specific antibodies and CAR T cells enhance interactions with tumor cells, bolstering immune responses to facilitate tumor infiltration and destruction. Modulation of myeloid cells and cancer-associated fibroblasts targets the tumor's immunosuppressive microenvironment, enhancing immunotherapy efficacy. AI-predicted neoantigens swiftly and accurately identify antigen targets, which can facilitate the development of personalized anticancer vaccines. Additionally, autologous and mRNA vaccines activate individuals' immune systems, fostering sustained immune responses against cancer neoantigens as therapeutic vaccines. Collectively, these strategies are expected to enhance efficacy of cancer immunotherapy, opening new horizons in anticancer treatment.

Mesothelin- and nucleolin-specific T cells from combined short peptides effectively kill triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer (TNBC), known for its aggressiveness and limited treatment options, presents a significant challenge. Adoptive cell transfer, involving the ex vivo generation of antigen-specific T cells from peripheral blood mononuclear cells (PBMCs), emerges as a promising approach. The overexpression of mesothelin (MSLN) and nucleolin (NCL) in TNBC samples underscores their potential as targets for T cell therapy. This study explored the efficacy of multi-peptide pulsing of PBMCs to generate MSLN/NCL-specific T cells targeting MSLN+/NCL+ TNBC cells.

METHODS

TNBC patient samples were confirmed for both MSLN and NCL expression via immunohistochemistry. Synthesized MSLN and NCL peptides were combined and administered to activate PBMCs from healthy donors. The cancer-killing ability of the resultant T cells was assessed using crystal violet staining, and their subtypes and cytotoxic cytokines were characterized through flow cytometry and cytokine bead array.

RESULTS

Findings showed that 85.3% (127/149) of TNBC cases were positive for either MSLN or NCL, or both; with single positivity rates for MSLN and NCL of 14.1% and 28.9%, respectively. MSLN and NCL peptides, with high binding affinity for HLA-A*02, were combined and introduced to activated PBMCs from healthy donors. The co-pulsed PBMCs significantly induced TEM and TEMRA CD3+/CD8+ T cells and IFN-γ production, compared to single-peptide pulsed or unpulsed conditions. Notably, MSLN/NCL-specific T cells successfully induced cell death in MSLN+/NCL+ MDA-MB-231 cells, releasing key cytotoxic factors such as perforin, granzymes A and B, Fas ligand, IFN-γ, and granulysin.

CONCLUSIONS

These findings serve as a proof-of-concept for using multiple immunogenic peptides as a novel therapeutic approach in TNBC patients.

Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease

The gut microbiome is crucial for nutrient metabolism, immune regulation, and intestinal homeostasis with changes in its composition linked to complex diseases like inflammatory bowel disease (IBD). Although the precise host-microbial mechanisms in disease pathogenesis remain unclear, high-throughput sequencing have opened new ways to unravel the role of interspecies interactions in IBD. Systems biology-a holistic computational framework for modeling complex biological systems-is critical for leveraging multi-omics datasets to identify disease mechanisms. This review highlights the significance of multi-omics data in IBD research and provides an overview of state-of-the-art systems biology resources and computational tools for data integration. We explore gaps, challenges, and future directions in the research field aiming to uncover novel biomarkers and therapeutic targets, ultimately advancing personalized treatment strategies. While focusing on IBD, the proposed approaches are applicable for other complex diseases, like cancer, and neurodegenerative diseases, where the microbiome has also been implicated.

VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

An immunoinformatics approach for a potential NY-ESO-1 and WT1 based multi-epitope vaccine designing against triple-negative breast cancer

Breast cancer emerges as one of the most prevalent malignancies in women, its incidence showing a concerning upward trend. Among the diverse array of breast cancer subtypes, triple-negative breast cancer (TNBC) assumes notable significance, due to lack of estrogen, progesterone, and HER-2 receptors. More focus has to be placed on creating effective therapy due to the high prevalence and rising incidence of TNBC. Currently, conventional passive treatments have several drawbacks that have not yet been resolved. On the other hand, as innovative immunotherapy approaches, cancer vaccines have offered promising prospects in combatting advanced stages of TNBC. Therefore, the main objective of this study was to utilize WT1 and NY-ESO-1 antigenic proteins in designing a multiepitope vaccine against TNBC. Initially, to generate robust immune responses, we identified antigenic epitopes of both proteins and assessed their immunogenicity. In order to reduce junctional immunogenicity, promiscuous epitopes were joined using the suitable adjuvant (50S ribosomal L7/L12 protein) and incorporated appropriate linkers (GPGPG, AAY, and EAAAK). The best predicted 3D model was refined and validated to achieve an excellent 3D model. Molecular docking analysis and dynamic simulation were conducted to demonstrate the structural stability and integrity of the vaccine/TLR-4 complex. Finally, the vaccine was cloned into the vector pET28 (+). Thus, analysis of the constructed vaccine through immunoinformatics indicates its capability to elicit robust humoral and cellular immune responses in the targeted organism. As such, it holds promise as a therapeutic weapon against TNBC and may open doors for further research in the field.

An isoform-resolution transcriptomic atlas of colorectal cancer from long-read single-cell sequencing

Colorectal cancer (CRC) ranks as the second leading cause of cancer deaths globally. In recent years, short-read single-cell RNA sequencing (scRNA-seq) has been instrumental in deciphering tumor heterogeneities. However, these studies only enable gene-level quantification but neglect alterations in transcript structures arising from alternative end processing or splicing. In this study, we integrated short- and long-read scRNA-seq of CRC samples to build an isoform-resolution CRC transcriptomic atlas. We identified 394 dysregulated transcript structures in tumor epithelial cells, including 299 resulting from various combinations of splicing events. Second, we characterized genes and isoforms associated with epithelial lineages and subpopulations exhibiting distinct prognoses. Among 31,935 isoforms with novel junctions, 330 were supported by The Cancer Genome Atlas RNA-seq and mass spectrometry data. Finally, we built an algorithm that integrated novel peptides derived from open reading frames of recurrent tumor-specific transcripts with mass spectrometry data and identified recurring neoepitopes that may aid the development of cancer vaccines.

PathMHC: a workflow to selectively target pathogen-derived MHC peptides in discovery immunopeptidomics experiments for vaccine target identification

Vaccine-elicited T cell responses can contribute to immune protection against emerging infectious disease risks such as antimicrobials-resistant (AMR) microbial pathogens and viruses with pandemic potential, but rapidly identifying appropriate targets for T cell priming vaccines remains challenging. Mass spectrometry (MS) analysis of peptides presented on major histocompatibility complexes (MHCs) can identify potential targets for protective T cell responses in a proteome-wide manner. However, pathogen-derived peptides are outnumbered by self peptides in the MHC repertoire and may be missed in untargeted MS analyses. Here we present a novel approach, termed PathMHC, that uses computational analysis of untargeted MS data followed by targeted MS to discover novel pathogen-derived MHC peptides more efficiently than untargeted methods alone. We applied this workflow to identify MHC peptides derived from multiple microbes, including potential vaccine targets presented on MHC-I by human dendritic cells infected with Mycobacterium tuberculosis . PathMHC will facilitate antigen discovery campaigns for vaccine development.

Predicting T cell receptor functionality against mutant epitopes

Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Immunogenetic profiles of 9 human herpes virus envelope glycoproteins

Human herpes viruses (HHV) are ubiquitous and have been implicated in numerous long-term health conditions. Since the association between viral exposure and long-term health impacts is partially influenced by variation in human leukocyte antigen (HLA) genes, we evaluated in silico the binding affinities of 9 HHV envelope glycoproteins with 127 common HLA Class I and Class II molecules. The findings show substantial variability in HHV binding affinity across viruses, HLA Class, HLA genes, and HLA alleles. Specific findings were as follows: (1) the predicted binding affinities of HHVs were characterized by four distinct groupings-[HHV1, HHV2], [HHV3, HHV4, HHV5], [HHV6A], [HHV6B, HHV7, HHV8]-with relatively lower binding affinities for HHV1, HHV2, and HHV6a compared to other HHVs; (2) significantly higher binding affinity was found for HLA Class I relative to Class II; (3) analyses within each class demonstrated that alleles of the C gene (for Class I) and DRB1 gene (for Class II) had the highest binding affinities; and (4) for each virus, predicted binding affinity to specific alleles varied, with HHV6a having the lowest affinity for HHV-HLA complexes, and HHV3, HHV4, and HHV5 having the highest. Since HLA-antigen binding is the first step in initiating an immune response to foreign antigens, these relative differences in HHV binding affinities are likely to influence long-term health impacts such that the cells infected with viruses associated with higher binding affinities across common HLA alleles may be more reduced in numbers, thereby lowering the potential for long-term sequelae of their infections.

Deciphering the HLA-E immunopeptidome with mass spectrometry: an opportunity for universal mRNA vaccines and T-cell-directed immunotherapies

Advances in immunotherapy rely on targeting novel cell surface antigens, including therapeutically relevant peptide fragments presented by HLA molecules, collectively known as the actionable immunopeptidome. Although the immunopeptidome of classical HLA molecules is extensively studied, exploration of the peptide repertoire presented by non-classical HLA-E remains limited. Growing evidence suggests that HLA-E molecules present pathogen-derived and tumor-associated peptides to CD8+ T cells, positioning them as promising targets for universal immunotherapies due to their minimal polymorphism. This mini-review highlights recent developments in mass spectrometry (MS) technologies for profiling the HLA-E immunopeptidome in various diseases. We discuss the unique features of HLA-E, its expression patterns, stability, and the potential for identifying new therapeutic targets. Understanding the broad repertoire of actionable peptides presented by HLA-E can lead to innovative treatments for viral and pathogen infections and cancer, leveraging its monomorphic nature for broad therapeutic efficacy.

ARID1A-Deficient Tumors Acquire Immunogenic Neoantigens during the Development of Resistance to Targeted Therapy

Neoantigen-based immunotherapy is an attractive potential treatment for previously intractable tumors. To effectively broaden the application of this approach, stringent biomarkers are crucial to identify responsive patients. ARID1A, a frequently mutated subunit of SWI/SNF chromatin remodeling complex, has been reported to determine tumor immunogenicity in some cohorts; however, mutations and deletions of ARID1A are not always linked to clinical responses to immunotherapy. In this study, we investigated immunotherapeutic responses based on ARID1A status in targeted therapy-resistant cancers. Mouse and human BRAFV600E melanomas with or without ARID1A expression were transformed into resistant to vemurafenib, an FDA-approved specific BRAFV600E inhibitor. Anti-PD-1 antibody treatment enhanced antitumor immune responses in vemurafenib-resistant ARID1A-deficient tumors but not in ARID1A-intact tumors or vemurafenib-sensitive ARID1A-deficient tumors. Neoantigens derived from accumulated somatic mutations during vemurafenib resistance were highly expressed in ARID1A-deficient tumors and promoted tumor immunogenicity. Furthermore, the newly generated neoantigens could be utilized as immunotherapeutic targets by vaccines. Finally, targeted therapy resistance-specific neoantigen in experimental human melanoma cells lacking ARID1A were validated to elicit T-cell receptor responses. Collectively, the classification of ARID1A-mutated tumors based on vemurafenib resistance as an additional indicator of immunotherapy response will enable a more accurate prediction to guide cancer treatment. Furthermore, the neoantigens that emerge with therapy resistance can be promising therapeutic targets for refractory tumors. Significance: Chemotherapy resistance promotes the acquisition of immunogenic neoantigens in ARID1A-deficient tumors that confer sensitivity to immune checkpoint blockade and can be utilized for developing antitumor vaccines, providing strategies to improve immunotherapy efficacy.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

Nivolumab Reaches Brain Lesions in Patients with Recurrent Glioblastoma and Induces T-cell Activity and Upregulation of Checkpoint Pathways

Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. Although immunotherapy is being explored as a potential treatment option for patients with GBM, it is unclear whether systemic immunotherapy can reach and modify the tumor microenvironment in the brain. We evaluated immune characteristics in patients receiving the anti-PD-1 immune checkpoint inhibitor nivolumab 1 week prior to surgery, compared with control patients receiving salvage resection without prior nivolumab treatment. We observed saturating levels of nivolumab bound to intratumorally and tissue-resident T cells in the brain, implicating saturating levels of nivolumab reaching brain tumors. Following nivolumab treatment, significant changes in T-cell activation and proliferation were observed in the tumor-resident T-cell population, and peripheral T cells upregulated chemokine receptors related to brain homing. A strong nivolumab-driven upregulation in compensatory checkpoint inhibition molecules, i.e., TIGIT, LAG-3, TIM-3, and CTLA-4, was observed, potentially counteracting the treatment effect. Finally, tumor-reactive tumor-infiltrating lymphocytes (TIL) were found in a subset of nivolumab-treated patients with prolonged survival, and neoantigen-reactive T cells were identified in both TILs and blood. This indicates a systemic response toward GBM in a subset of patients, which was further boosted by nivolumab, with T-cell responses toward tumor-derived neoantigens. Our study demonstrates that nivolumab does reach the GBM tumor lesion and enhances antitumor T-cell responses both intratumorally and systemically. However, various anti-inflammatory mechanisms mitigate the clinical efficacy of the anti-PD-1 treatment.

Targeting Yezo Virus Structural Proteins for Multi-Epitope Vaccine Design Using Immunoinformatics Approach

A novel tick-borne orthonairovirus called the Yezo virus (YEZV), primarily transmitted by the Ixodes persulcatus tick, has been recently discovered and poses significant threats to human health. The YEZV is considered endemic in Japan and China. Clinical symptoms associated with this virus include thrombocytopenia, fatigue, headache, leukopenia, fever, depression, and neurological complications ranging from mild febrile illness to severe outcomes like meningitis and encephalitis. At present, there is no treatment or vaccine readily accessible for this pathogenic virus. Therefore, this research employed an immunoinformatics approach to pinpoint potential vaccine targets within the YEZV through an extensive examination of its structural proteins. Three structural proteins were chosen using specific criteria to pinpoint T-cell and B-cell epitopes, which were subsequently validated through interferon-gamma induction. Six overlapping epitopes for cytotoxic T-lymphocytes (CTL), helper T-lymphocytes (HTL), and linear B-lymphocytes (LBL) were selected to construct a multi-epitope vaccine, achieving a 92.29% coverage of the global population. These epitopes were then fused with the 50S ribosomal protein L7/L12 adjuvant to improve protection against international strains. The three-dimensional structure of the designed vaccine construct underwent an extensive evaluation through structural analysis. Following molecular docking studies, the YEZV vaccine construct emerged as a candidate for further investigation, showing the lowest binding energy (-78.7 kcal/mol) along with favorable physiochemical and immunological properties. Immune simulation and molecular dynamics studies demonstrated its stability and potential to induce a strong immune response within the host cells. This comprehensive analysis indicates that the designed vaccine construct could offer protection against the YEZV. It is crucial to conduct additional in vitro and in vivo experiments to verify its safety and effectiveness.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes

MOTIVATION

Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy development. However, the accuracy of current bioinformatic methods remains unsatisfactory. Surface and structural features of peptide-HLA class I (pHLA-I) complexes offer valuable insight into the immunogenicity of neoantigens.

RESULTS

We present NeoaPred, a deep-learning framework for neoantigen prediction. NeoaPred accurately constructs pHLA-I complex structures, with 82.37% of the predicted structures showing an RMSD of < 1 Å. Using these structures, NeoaPred integrates differences in surface, structural, and atom group features between the mutant peptide and its wild-type counterpart to predict a foreignness score. This foreignness score is an effective factor for neoantigen prediction, achieving an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.81 and an AUPRC (Area Under the Precision-Recall Curve) of 0.54 in the test set, outperforming existing methods.

AVAILABILITY AND IMPLEMENTATION

The source code is released under an Apache v2.0 license and is available at the GitHub repository (https://github.com/Dulab2020/NeoaPred).

Distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia

The evolution of T cell molecular signatures in the distal lung of patients with severe pneumonia is understudied. Here, we analyzed T cell subsets in longitudinal bronchoalveolar lavage fluid samples from 273 patients with severe pneumonia, including unvaccinated patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or with respiratory failure not linked to pneumonia. In patients with SARS-CoV-2 pneumonia, activation of interferon signaling pathways, low activation of the NF-κB pathway and preferential targeting of spike and nucleocapsid proteins early after intubation were associated with favorable outcomes, whereas loss of interferon signaling, activation of NF-κB-driven programs and specificity for the ORF1ab complex late in disease were associated with mortality. These results suggest that in patients with severe SARS-CoV-2 pneumonia, alveolar T cell interferon responses targeting structural SARS-CoV-2 proteins characterize individuals who recover, whereas responses against nonstructural proteins and activation of NF-κB are associated with poor outcomes.

In silico designing of multi-epitope vaccine against canine parvovirus using reverse vaccinology

Canine parvovirus (CPV-2) is a highly contagious virus affecting dogs worldwide, posing a significant threat. The VP2 protein stands out as the predominant and highly immunogenic structural component of CPV-2. Soon after its emergence, CPV-2 was replaced by variants known as CPV-2a, 2b and 2c, marked by changes in amino acid residue 426 of VP2. Additional amino acid alterations have been identified within VP2, with certain modifications serving as signatures of emerging variants. In Brazil, CPV-2 outbreaks persist with diverse VP2 profiles. Vaccination is the main preventive measure against the virus. However, the emergence of substitutions presents challenges to conventional vaccine methods. Commercial vaccines are formulated with strains that usually do not match those currently circulating in the field. To address this, the study aimed to investigate CPV-2 variants in Brazil, predict epitopes, and design an in silico vaccine tailored to local variants employing reverse vaccinology. The methodology involved data collection, genetic sequence analysis, and amino acid comparison between field strains and vaccines, followed by the prediction of B and T cell epitope regions. The predicted epitopes were evaluated for antigenicity, allergenicity and toxicity. The final vaccine construct consisted of selected epitopes linked to an adjuvant and optimized for expression in Escherichia coli. Structural predictions confirmed the stability and antigenicity of the vaccine, while molecular docking demonstrated interaction with the canine toll-like receptor 4. Molecular dynamics simulations indicated a stable complex formation. In silico immune simulations demonstrated a progressive immune response post-vaccination, including increased antibody production and T-helper cell activity. The multi-epitope vaccine design targeted prevalent CPV-2 variants in Brazil and potentially other regions globally. However, experimental validation is essential to confirm our in silico findings.

A review of CD4+ T cell differentiation and diversity in dogs

CD4+ T cells are an integral component of the adaptive immune response, carrying out many functions to combat a diverse range of pathogenic challenges. These cells exhibit remarkable plasticity, differentiating into specialized subsets such as T helper type 1 (TH1), TH2, TH9, TH17, TH22, regulatory T cells (Tregs), and follicular T helper (TFH) cells. Each subset is capable of addressing a distinct immunological need ranging from pathogen eradication to regulation of immune homeostasis. As the immune response subsides, CD4+ T cells rest down into long-lived memory phenotypes-including central memory (TCM), effector memory (TEM), resident memory (TRM), and terminally differentiated effector memory cells (TEMRA) that are localized to facilitate a swift and potent response upon antigen re-encounter. This capacity for long-term immunological memory and rapid reactivation upon secondary exposure highlights the role CD4+ T cells play in sustaining both adaptive defense mechanisms and maintenance. Decades of mouse, human, and to a lesser extent, pig T cell research has provided the framework for understanding the role of CD4+ T cells in immune responses, but these model systems do not always mimic each other. Although our understanding of pig immunology is not as extensive as mouse or human research, we have gained valuable insight by studying this model. More akin to pigs, our understanding of CD4+ T cells in dogs is much less complete. This disparity exists in part because canine immunologists depend on paradigms from mouse and human studies to characterize CD4+ T cells in dogs, with a fraction of available lineage-defining antibody markers. Despite this, every major CD4+ T cell subset has been described to some extent in dogs. These subsets have been studied in various contexts, including in vitro stimulation, homeostatic conditions, and across a range of disease states. Canine CD4+ T cells have been categorized according to lineage-defining characteristics, trafficking patterns, and what cytokines they produce upon stimulation. This review addresses our current understanding of canine CD4+ T cells from a comparative perspective by highlighting both the similarities and differences from mouse, human, and pig CD4+ T cell biology. We also discuss knowledge gaps in our current understanding of CD4+ T cells in dogs that could provide direction for future studies in the field.

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Functional metagenomic analysis reveals potential inflammatory triggers associated with genetic risk for autoimmune disease

To assess functional differences between the microbiomes of individuals with autoimmune risk-associated human leukocyte antigen (HLA) genetics and autoimmune protection-associated HLA, we performed a metagenomic analysis of stool samples from 72 infants in the All Babies in Southeast Sweden general-population cohort and assessed haplotype-peptide binding affinities. Infants with risk-associated HLA DR3-DQ2.5 and DR4-DQ8 had a higher abundance of known pathogen-associated molecular patterns and virulence related genes than infants with protection-associated HLA DR15-DQ6.2. However, there was limited overlap in the type of inflammatory trigger between risk groups. Supported by a high Firmicutes/Bacteroides ratio and differentially abundant flagellated species, genes related to the synthesis of flagella were prominent in those with HLA DR3-DQ2.5. However, this haplotype had a significantly lower likelihood of binding affinity to flagellin peptides. O-antigen biosynthesis genes were significantly correlated with the risk genotypes and absent from protective genotype association, supported by the differential abundance of gram-negative bacteria seen in the risk-associated groups. Genes related to vitamin B biosynthesis stood out in higher abundance in infants with HLA DR3-DQ2.5/DR4-DQ8 heterozygosity compared to those with autoimmune-protective genetics. Prevotella species and genus were significantly abundant in all infant groups with high risk for autoimmune disease. The potential inflammatory triggers associated with genetic risk for autoimmunity have significant implications. These results suggest that certain HLA haplotypes may be creating the opportunity for dysbiosis and subsequent inflammation early in life by clearing beneficial microbes or not clearing proinflammatory microbes. This HLA gatekeeping may prevent genetically at-risk individuals from benefiting from probiotic therapies by restricting the colonization of those beneficial bacteria.

Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity

T and B cell activation are equally important in triggering and orchestrating adaptive host responses to design multi-epitope African swine fever virus (ASFV) vaccines. However, few design methods have considered the trade-off between T and B cell immunogenicity when identifying promising ASFV epitopes. This work proposed a novel Pareto front-based ASFV screening method PFAS to identify promising epitopes for designing multi-epitope vaccines utilizing five ASFV Georgia 2007/1 sequences. To accurately predict T cell immunogenicity, four scoring methods were used to estimate the T cell activation in the four stages, including proteasomal cleavage probability, transporter associated with antigen processing transport efficiency, class I binding affinity of the major histocompatibility complex, and CD8 + cytotoxic T cell immunogenicity. PFAS ranked promising epitopes using a Pareto front method considering T and B cell immunogenicity. The coefficient of determination between the Pareto ranks of multi-epitope vaccines and survival days of swine vaccinations was R2 = 0.95. Consequently, PFAS scored complete epitope profiles and identified 72 promising top-ranked epitopes, including 46 CD2v epitopes, two p30 epitopes, 10 p72 epitopes, and 14 pp220 epitopes. PFAS is the first method of using the Pareto front approach to identify promising epitopes that considers the objectives of maximizing both T and B cell immunogenicity. The top-ranked promising epitopes can be cost-effectively validated in vitro. The Pareto front approach can be adaptively applied to various epitope predictors for bacterial, viral and cancer vaccine developments. The MATLAB code of the Pareto front method was available at https://github.com/NYCU-ICLAB/PFAS .

Molecular and Evolutionary Characteristics of Chicken Parvovirus (ChPV) Genomes Detected in Chickens with Runting-Stunting Syndrome

Chicken Parvovirus (ChPV) belongs to the genus Aveparvovirus and is implicated in enteric diseases like runting-stunting syndrome (RSS) in poultry. In RSS, chicken health is affected by diarrhea, depression, and increased mortality, causing significant economic losses in the poultry industry. This study aimed to characterize the ChPV genomes detected in chickens with RSS through a metagenomic approach and compare the molecular and evolutionary characteristics within the Aveparvovirus galliform1 species. The intestinal content of broiler flocks affected with RSS was submitted to viral metagenomics. The assembled prevalent genomes were identified as ChPV after sequence and phylogenetic analysis, which consistently clustered separately from Turkey Parvovirus (TuPV). The strain USP-574-A presented signs of genomic recombination. The selective pressure analysis indicated that most of the coding genes in A. galliform1 are evolving under diversifying (negative) selection. Protein modeling of ChPV and TuPV viral capsids identified high conservancy over the VP2 region. The prediction of epitopes identified several co-localized antigenic peptides from ChPV and TuPV, especially for T-cell epitopes, highlighting the immunological significance of these sites. However, most of these peptides presented host-specific variability, obeying an adaptive scenario. The results of this study show the evolutionary path of ChPV and TuPV, which are influenced by diversifying events such as genomic recombination and selective pressure, as well as by adaptation processes, and their subsequent immunological impact.

Nine Human Leukocyte Antigen (HLA) Class I Alleles are Omnipotent Against 11 Antigens Expressed in Melanoma Tumors

Objective

Host immunogenetics (Human Leukocyte Antigen, HLA) play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes hinge on the successful binding of epitopes of melanoma antigens to HLA Class I molecules for an effective engagement of cytotoxic CD8+ lymphocytes and subsequent elimination of the cancerous cell. This study evaluated the binding affinity and immunogenicity of HLA Class I to melanoma tumor antigens to identify alleles best suited to facilitate elimination of melanoma antigens.

Methods

In this study, we used freely available software tools to determine in silico the binding affinity and immunogenicity of 2462 reported HLA Class I alleles to all linear nonamer epitopes of 11 known antigens expressed in melanoma tumors (TRP2, S100, Tyrosinase, TRP1, PMEL(17), MAGE1, MAGE4, CTA, BAGE, GAGE/SSX2, Melan).

Results

We identified the following 9 HLA Class I alleles with very high immunogenicity and binding affinity against all 11 melanoma antigens: A*02:14, B*07:10, B*35:10, B*40:10, B*40:12, B*44:10, C*07:11, and C*07:13, and C*07:14.

Conclusion

These 9 HLA alleles possess the potential to aid in the elimination of melanoma both by themselves and by enhancing the beneficial effect of immune checkpoint inhibitors.

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

Developing Vaccines in Pancreatic Adenocarcinoma: Trials and Tribulations

Pancreatic adenocarcinoma represents one of the most challenging malignancies to treat, with dismal survival rates despite advances in therapeutic modalities. Immunotherapy, particularly vaccines, has emerged as a promising strategy to harness the body's immune system in combating this aggressive cancer. This abstract reviews the trials and tribulations encountered in the development of vaccines targeting pancreatic adenocarcinoma. Key challenges include the immunosuppressive tumor microenvironment, the heterogeneity of tumor antigens, and a limited understanding of immune evasion mechanisms employed by pancreatic cancer cells. Various vaccine platforms, including peptide-based, dendritic cell-based, and viral vector-based vaccines, have been explored in preclinical and clinical settings. However, translating promising results from preclinical models to clinical efficacy has proven elusive. In recent years, mRNA vaccines have emerged as a promising immunotherapeutic strategy in the fight against various cancers, including pancreatic adenocarcinoma. We will discuss the potential applications, opportunities, and challenges associated with mRNA vaccines in pancreatic cancer treatment.

Altered epitopes enhance macrophage-mediated anti-tumour immunity to low-immunogenic tumour mutations

Personalized neoantigen therapy has shown long-term and stable efficacy in specific patient populations. However, not all patients have sufficient levels of neoantigens for treatment. Although somatic mutations are commonly found in tumours, a significant portion of these mutations do not trigger an immune response. Patients with low mutation burdens continue to exhibit unresponsiveness to this treatment. We propose a design paradigm for neoantigen vaccines by utilizing the highly immunogenic unnatural amino acid p-nitrophenylalanine (pNO2Phe) for sequence alteration of somatic mutations that failed to generate neoepitopes. This enhances the immunogenicity of the mutations and transforms it into a suitable candidate for immunotherapy. The nitrated altered epitope vaccines designed according to this paradigm is capable of activating circulating CD8+ T cells and inducing immune cross-reactivity against autologous mutated epitopes in different MHC backgrounds (H-2Kb, H-2Kd, and human HLA-A02:01), leading to the elimination of tumour cells carrying the mutation. After immunization with the altered epitopes, tumour growth was significantly inhibited. It is noteworthy that nitrated epitopes induce tumour-infiltrating macrophages to differentiate into the M1 phenotype, surprisingly enhancing the MHC II molecule presenting pathway of macrophages. Nitrated epitope-treated macrophages have the potential to cross-activate CD4+ and CD8+ T cells, which may explain why pNO2Phe can enhance the immunogenicity of epitopes. Meanwhile, the immunosuppressive microenvironment of the tumour is altered due to the activation of macrophages. The nitrated neoantigen vaccine strategy enables the design of vaccines targeting non-immunogenic tumour mutations, expanding the pool of potential peptides for personalized and shared novel antigen therapy. This approach provides treatment opportunities for patients previously ineligible for new antigen vaccine therapy.

Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Porcine epidemic diarrhea virus (PEDV) is a serious disease that poses a significant threat to the pig industry. This study focused on analyzing the Spike protein of PEDV, which harbors crucial antigenic determinants, in identifying dominant epitopes. Immunoinformatics tools were used to screen for B-cell, CD4+ and CD8+ predominance epitopes. These epitopes were then connected to the N-terminal of ferritin to form a self-assembled nanoparticle vaccine. Various physical and chemical properties of the candidate vaccine were analyzed, including secondary structure prediction, tertiary structure modeling, molecular docking, immune response simulation and computer cloning. The results demonstrated that the candidate vaccine was antigenic, soluble, stable, non-allergic, and formed a stable complex with the target receptor TLR-3. Immune simulation analysis showed that the candidate vaccine effectively stimulated both cellular and humoral reactions, leading to increased related cytokines production. Furthermore, efficient and stable expression of the candidate vaccine was achieved through reverse translation in the Escherichia coli K12 expression system following codon optimization and in silico cloning. The developed nanoparticle candidate vaccine in this study holds promise as an effective PEDV vaccine candidate, offering a new approach for the research, development and improvement of vaccines targeting porcine enteric diarrhea coronavirus.

Emerging biochemical, microbial and immunological evidence in the search for why HLA-B∗27 confers risk for spondyloarthritis

The strong association of the human leukocyte antigen B∗27 alleles (HLA-B∗27) with spondyloarthritis and related rheumatic conditions has long fascinated researchers, yet the precise mechanisms underlying its pathogenicity remain elusive. Here, we review how interplay between the microbiome, the immune system, and the enigmatic HLA-B∗27 could trigger spondyloarthritis, with a focus on whether HLA-B∗27 presents an arthritogenic peptide. We propose mechanisms by which the unique biochemical characteristics of the HLA-B∗27 protein structure, particularly its peptide binding groove, could dictate its propensity to induce pathological T cell responses. We further provide new insights into how TRBV9+ CD8+ T cells are implicated in the disease process, as well as how the immunometabolism of T cells modulates tissue-specific inflammatory responses in spondyloarthritis. Finally, we present testable models and suggest approaches to this problem in future studies given recent advances in computational biology, chemical biology, structural biology, and small-molecule therapeutics.

Identification of tumor rejection antigens and the immunologic landscape of medulloblastoma

BACKGROUND

The current standard of care treatments for medulloblastoma are insufficient as these do not take tumor heterogeneity into account. Newer, safer, patient-specific treatment approaches are required to treat high-risk medulloblastoma patients who are not cured by the standard therapies. Immunotherapy is a promising treatment modality that could be key to improving survival and avoiding morbidity. For an effective immune response, appropriate tumor antigens must be targeted. While medulloblastoma patients with subgroup-specific genetic substitutions have been previously reported, the immunogenicity of these genetic alterations remains unknown. The aim of this study is to identify potential tumor rejection antigens for the development of antigen-directed cellular therapies for medulloblastoma.

METHODS

We developed a cancer immunogenomics pipeline and performed a comprehensive analysis of medulloblastoma subgroup-specific transcription profiles (n = 170, 18 WNT, 46 SHH, 41 Group 3, and 65 Group 4 patient tumors) available through International Cancer Genome Consortium (ICGC) and European Genome-Phenome Archive (EGA). We performed in silico antigen prediction across a broad array of antigen classes including neoantigens, tumor-associated antigens (TAAs), and fusion proteins. Furthermore, we evaluated the antigen processing and presentation pathway in tumor cells and the immune infiltrating cell landscape using the latest computational deconvolution methods.

RESULTS

Medulloblastoma patients were found to express multiple private and shared immunogenic antigens. The proportion of predicted TAAs was higher than neoantigens and gene fusions for all molecular subgroups, except for sonic hedgehog (SHH), which had a higher neoantigen burden. Importantly, cancer-testis antigens, as well as previously unappreciated neurodevelopmental antigens, were found to be expressed by most patients across all medulloblastoma subgroups. Despite being immunologically cold, medulloblastoma subgroups were found to have distinct immune cell gene signatures.

CONCLUSIONS

Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with important implications for the development of immunotherapy for medulloblastoma.

Immunopeptidomics Mapping of Listeria monocytogenes T Cell Epitopes in Mice

Listeria monocytogenes is a foodborne intracellular bacterial model pathogen. Protective immunity against Listeria depends on an effective CD8+ T cell response, but very few T cell epitopes are known in mice as a common animal infection model for listeriosis. To identify epitopes, we screened for Listeria immunopeptides presented in the spleen of infected mice by mass spectrometry-based immunopeptidomics. We mapped more than 6000 mouse self-peptides presented on MHC class I molecules, including 12 high confident Listeria peptides from 12 different bacterial proteins. Bacterial immunopeptides with confirmed fragmentation spectra were further tested for their potential to activate CD8+ T cells, revealing VTYNYINI from the putative cell wall surface anchor family protein LMON_0576 as a novel bona fide peptide epitope. The epitope showed high biological potency in a prime boost model and can be used as a research tool to probe CD8+ T cell responses in the mouse models of Listeria infection. Together, our results demonstrate the power of immunopeptidomics for bacterial antigen identification.

A novel multi-epitope peptide vaccine targeting immunogenic antigens of Ebola and monkeypox viruses with potential of immune responses provocation in silico

The emergence or reemergence of monkeypox (Mpox) and Ebola virus (EBOV) agents causing zoonotic diseases remains a huge threat to human health. Our study aimed at designing a multi-epitope vaccine (MEV) candidate to target both the Mpox and EBOV agents using immunoinformatics tools. Viral protein sequences were retrieved, and potential nonallergenic, nontoxic, and antigenic epitopes were obtained. Next, cytotoxic and helper T-cell (CTL and HTL, respectively) and B-cell (BCL) epitopes were predicted, and those potential epitopes were fused utilizing proper linkers. The in silico cloning and expression processes were implemented using Escherichia coli K12. The immune responses were prognosticated using the C-ImmSim server. The MEV construct (29.53 kDa) included four BCL, two CTL, and four HTL epitopes and adjuvant. The MEV traits were pertinent in terms of antigenicity, non-allergenicity, nontoxicity, physicochemical characters, and stability. The MEV candidate was also highly expressed in E. coli K12. The strong affinity of MEV-TLR3 was confirmed using molecular docking and molecular dynamics simulation analyses. Immune simulation analyses unraveled durable activation and responses of cellular and humoral arms alongside innate immune responses. The designed MEV candidate demonstrated appropriate traits and was promising in the prediction of immune responses against both Mpox and EBOV agents. Further experimental assessments of the MEV are required to verify its efficacy.

Profiling IgG and IgA antibody responses during vaccination and infection in a high-risk gonorrhoea population

Development of a vaccine against gonorrhoea is a global priority, driven by the rise in antibiotic resistance. Although Neisseria gonorrhoeae (Ng) infection does not induce substantial protective immunity, highly exposed individuals may develop immunity against re-infection with the same strain. Retrospective epidemiological studies have shown that vaccines containing Neisseria meningitidis (Nm) outer membrane vesicles (OMVs) provide a degree of cross-protection against Ng infection. We conducted a clinical trial (NCT04297436) of 4CMenB (Bexsero, GSK), a licensed Nm vaccine containing OMVs and recombinant antigens, comprising a single arm, open label study of two doses with 50 adults in coastal Kenya who have high exposure to Ng. Data from a Ng antigen microarray established that serum IgG and IgA reactivities against the gonococcal homologs of the recombinant antigens in the vaccine peaked at 10 but had declined by 24 weeks. For most reactive OMV-derived antigens, the reverse was the case. A cohort of similar individuals with laboratory-confirmed gonococcal infection were compared before, during, and after infection: their reactivities were weaker and differed from the vaccinated cohort. We conclude that the cross-protection of the 4CMenB vaccine against gonorrhoea could be explained by cross-reaction against a diverse selection of antigens derived from the OMV component.