1
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Hamza H, Ghosh M, Löffler MW, Rammensee HG, Planz O. Identification and relative abundance of naturally presented and cross-reactive influenza A virus MHC class I-restricted T cell epitopes. Emerg Microbes Infect 2024; 13:2306959. [PMID: 38240239 PMCID: PMC10854457 DOI: 10.1080/22221751.2024.2306959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 01/14/2024] [Indexed: 02/10/2024]
Abstract
Cytotoxic T lymphocytes are key for controlling viral infection. Unravelling CD8+ T cell-mediated immunity to distinct influenza virus strains and subtypes across prominent HLA types is relevant for combating seasonal infections and emerging new variants. Using an immunopeptidomics approach, naturally presented influenza A virus-derived ligands restricted to HLA-A*24:02, HLA-A*68:01, HLA-B*07:02, and HLA-B*51:01 molecules were identified. Functional characterization revealed multifunctional memory CD8+ T cell responses for nine out of sixteen peptides. Peptide presentation kinetics was optimal around 12 h post infection and presentation of immunodominant epitopes shortly after infection was not always persistent. Assessment of immunogenic epitopes revealed that they are highly conserved across the major zoonotic reservoirs and may contain a single substitution in the vicinity of the anchor residues. These findings demonstrate how the identified epitopes promote T cell pools, possibly cross-protective in individuals and can be potential targets for vaccination.
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Affiliation(s)
- Hazem Hamza
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Virology Laboratory, Environmental Research Division, National Research Centre, Giza, Egypt
| | - Michael Ghosh
- Institute for Immunology, University of Tübingen, Tübingen, Germany
| | - Markus W Löffler
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Institute for Clinical and Experimental Transfusion Medicine, Medical Faculty of Tübingen, Tübingen, Germany
- Centre for Clinical Transfusion Medicine, University Hospital Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
| | - Hans-Georg Rammensee
- Institute for Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence CMFI (EXC2124) "Controlling Microbes to Fight Infections", University of Tübingen, Tübingen, Germany
| | - Oliver Planz
- Institute for Immunology, University of Tübingen, Tübingen, Germany
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2
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Su Z, Wu Y, Cao K, Du J, Cao L, Wu Z, Wu X, Wang X, Song Y, Wang X, Duan H. APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules. Methods 2024; 228:38-47. [PMID: 38772499 DOI: 10.1016/j.ymeth.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/28/2024] [Accepted: 05/18/2024] [Indexed: 05/23/2024] Open
Abstract
Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.
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Affiliation(s)
- Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Yejian Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Kaiqiang Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Jie Du
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Zhipeng Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Xinqiao Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Xudong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
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3
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Pan X, Guo X, Shi J. Design of a novel multiepitope vaccine with CTLA-4 extracellular domain against Mycoplasma pneumoniae: A vaccine-immunoinformatics approach. Vaccine 2024; 42:3883-3898. [PMID: 38777697 DOI: 10.1016/j.vaccine.2024.04.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 04/16/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Community-acquired pneumonia often stems from the macrolide-resistant strain of Mycoplasma pneumoniae, yet no effective vaccine exists against it. METHODS This study proposes a vaccine-immunoinformatics strategy for Mycoplasma pneumoniae and other pathogenic microbes. Specifically, dominant B and T cell epitopes of the Mycoplasma pneumoniae P30 adhesion protein were identified through immunoinformatics method. The vaccine sequence was then constructed by coupling with CTLA-4 extracellular region, a novel molecular adjuvant for antigen-presenting cells. Subsequently, the vaccine's physicochemical properties, antigenicity, and allergenicity were verified. Molecular dynamics modeling was employed to confirm interaction with TLR-2, TLR-4, B7-1, and B7-2. Finally, the vaccine underwent in silico cloning for expression. RESULTS The vaccine exhibited both antigenicity and non-allergenicity. Molecular dynamics simulation, post-docking with TLR-2, TLR-4, B7-1, and B7-2, demonstrated stable interaction between the vaccine and these molecules. In silico cloning confirmed effective expression of the vaccine gene in insect baculovirus vectors. CONCLUSION This vaccine-immunoinformatics approach holds promise for the development of vaccines against Mycoplasma pneumoniae and other pathogenic non-viral and non-bacterial microbes.
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Affiliation(s)
- Xiaohong Pan
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China
| | - Xiaomei Guo
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China; Kunming Medical University, Kunming, Yunnan, China
| | - Jiandong Shi
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China; National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan China.
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4
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Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification. Mol Cell Proteomics 2024:100798. [PMID: 38871251 DOI: 10.1016/j.mcpro.2024.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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5
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Yao P, Gao M, Hu W, Wang J, Wang Y, Wang Q, Ji J. Proteogenomic analysis identifies neoantigens and bacterial peptides as immunotherapy targets in colorectal cancer. Pharmacol Res 2024; 204:107209. [PMID: 38740147 DOI: 10.1016/j.phrs.2024.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.
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Affiliation(s)
- Pengju Yao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Mingjie Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Weiyi Hu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Jiahao Wang
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Yuhao Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Qingsong Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Jianguo Ji
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China.
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6
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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7
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Elrashedy A, Nayel M, Salama A, Salama MM, Hasan ME. Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31. Sci Rep 2024; 14:11951. [PMID: 38789443 PMCID: PMC11126717 DOI: 10.1038/s41598-024-61991-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.
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Affiliation(s)
- Alyaa Elrashedy
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt.
| | - Mohamed Nayel
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Akram Salama
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Mohammed M Salama
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
| | - Mohamed E Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt
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8
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Feng M, Chan KC, Zhong Q, Zhou R. In silico design of high-affinity antigenic peptides for HLA-B44. Int J Biol Macromol 2024; 267:131356. [PMID: 38574928 DOI: 10.1016/j.ijbiomac.2024.131356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024]
Abstract
Cancer cell-killing by CD8+ T cells demands effective tumor antigen presentation by human leukocyte antigen class I (HLA-I) molecules. Screening and designing highly immunogenic neoantigens require quantitative computations to reliably predict HLA-peptide binding affinities. Here, with all-atom molecular dynamics (MD) simulations and free energy perturbation (FEP) methods, we design a collection of antigenic peptide candidates through in silico mutagenesis studies on immunogenic neoantigens, yielding enhanced binding affinities to HLA-B*44:02. In-depth structural dissection shows that introducing positively charged residues such as arginine to position 6 or lysine to position 7 of the candidates triggers conformational shifts in both peptides and the antigen-binding groove of the HLA, following the "induced-fit" mechanism. Enhancement in binding affinities compared to the wild-type was found in three out of five mutated candidates. The HLA pocket, capable of accommodating positively charged residues in positions from 5 to 7, is designated as the "dynamic pocket". Taken together, we showcase an effective structure-based binding affinity optimization framework for antigenic peptides of HLA-B*44:02 and underscore the importance of dynamic nature of the antigen-binding groove in concert with the anchoring motifs. This work provides structural insights for rational design of favorable HLA-peptide bindings and future developments in neoantigen-based therapeutics.
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Affiliation(s)
- Mei Feng
- Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China; Institute of Quantitative Biology, and College of Life Sciences, Zhejiang University, 310027 Hangzhou, China; Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China
| | - Kevin C Chan
- Institute of Quantitative Biology, and College of Life Sciences, Zhejiang University, 310027 Hangzhou, China; Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China
| | - Qinglu Zhong
- Institute of Quantitative Biology, and College of Life Sciences, Zhejiang University, 310027 Hangzhou, China; Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China
| | - Ruhong Zhou
- Institute of Quantitative Biology, and College of Life Sciences, Zhejiang University, 310027 Hangzhou, China; Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China; The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China; Department of Chemistry, Columbia University, New York, NY 10027, USA.
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9
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Wilson E, Cava JK, Chowell D, Raja R, Mangalaparthi KK, Pandey A, Curtis M, Anderson KS, Singharoy A. The electrostatic landscape of MHC-peptide binding revealed using inception networks. Cell Syst 2024; 15:362-373.e7. [PMID: 38554709 DOI: 10.1016/j.cels.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/24/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Wilson
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Kevin Cava
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85207, USA
| | - Diego Chowell
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Remya Raja
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Marion Curtis
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA; College of Medicine and Science, Mayo Clinic, Scottsdale, AZ 85259, USA; Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Karen S Anderson
- School of Life Sciences, Arizona State University, Tempe, AZ 85207, USA.
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA.
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10
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Yasmin S, Ansari MY, Pandey K, Dikhit MR. Identification of potential vaccine targets for elicitation of host immune cells against SARS-CoV-2 by reverse vaccinology approach. Int J Biol Macromol 2024; 265:130754. [PMID: 38508555 DOI: 10.1016/j.ijbiomac.2024.130754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024]
Abstract
The COVID-19 pandemic has emerged as a critical global health crisis, demanding urgent and effective strategies for containment. While some knowledge exists about epitope sequences recognized by human immune cells and their activation of CD8+ T cells within the HLA context, comprehensive information remains limited. This study employs reverse vaccinology to explore antigenic HLA-restricted T-cell epitopes capable of eliciting durable immunity. Screening reveals 187 consensus epitopes, with 23 offering broad population coverage worldwide, spanning over 5000 HLA alleles. Sequence alignment analysis highlights the genetic distinctiveness of these peptides from Homo sapiens and their intermediate to high TAP binding efficiency. Notably, these epitopes share 100 % sequence identity across strains from nine countries, indicating potential for a uniform protective immune response among diverse ethnic populations. Docking simulations further confirm their binding capacity with the HLA allele, validating them as promising targets for SARS-CoV-2 immune recognition. The anticipated epitopes are connected with suitable linkers and adjuvant, and then assessed for its translational efficacy within a bacterial expression vector through computational cloning. Through docking, it is observed that the chimeric vaccine construct forms lasting hydrogen bonds with Toll-like receptor (TLR4), while immune simulation illustrates an increased cytotoxic response aimed at CD8+ T cells. This comprehensive computational analysis suggests the chimeric vaccine construct's potential to provoke a robust immune response against SARS-CoV-2. By delineating these antigenic fragments, our study offers valuable insights into effective vaccine and immunotherapy development against COVID-19, contributing significantly to global efforts in combating this infectious threat.
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Affiliation(s)
- Sabina Yasmin
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University (KKU), Abha 62529, Saudi Arabia
| | - Mohammad Yousuf Ansari
- Department of Pharmaceutical Chemistry, M.M. College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 133207, India.
| | - Krishna Pandey
- Department of Clinical Medicine, ICMR-Rajendra Memorial Research Institute of Medical Sciences, Agamkuan, Patna 800007, India
| | - Manas Ranjan Dikhit
- Department of Bioinformatics, ICMR-Rajendra Memorial Research Institute of Medical Sciences, Agamkuan, Patna 800007, India.
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11
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Lin Y, Ma J, Yuan H, Chen Z, Xu X, Jiang M, Zhu J, Meng W, Qiu W, Liu Y. Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design. Brief Bioinform 2024; 25:bbae247. [PMID: 38770719 PMCID: PMC11107383 DOI: 10.1093/bib/bbae247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/22/2024] Open
Abstract
Recent advances in cancer immunotherapy have highlighted the potential of neoantigen-based vaccines. However, the design of such vaccines is hindered by the possibility of weak binding affinity between the peptides and the patient's specific human leukocyte antigen (HLA) alleles, which may not elicit a robust adaptive immune response. Triggering cross-immunity by utilizing peptide mutations that have enhanced binding affinity to target HLA molecules, while preserving their homology with the original one, can be a promising avenue for neoantigen vaccine design. In this study, we introduced UltraMutate, a novel algorithm that combines Reinforcement Learning and Monte Carlo Tree Search, which identifies peptide mutations that not only exhibit enhanced binding affinities to target HLA molecules but also retains a high degree of homology with the original neoantigen. UltraMutate outperformed existing state-of-the-art methods in identifying affinity-enhancing mutations in an independent test set consisting of 3660 peptide-HLA pairs. UltraMutate further showed its applicability in the design of peptide vaccines for Human Papillomavirus and Human Cytomegalovirus, demonstrating its potential as a promising tool in the advancement of personalized immunotherapy.
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Affiliation(s)
- Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Jiakang Ma
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Haozhe Yuan
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Xingyu Xu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Mengping Jiang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Jialiang Zhu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
| | - Wenqing Qiu
- Shanghai Xuhui Central Hospital, 366 North Longchuan Road, Shanghai, 200231, China
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, 131 DongAn Road, Shanghai, 200032, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, 131 DongAn Road, Shanghai, 200032, China
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12
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Wang M, Lei C, Wang J, Li Y, Li M. TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. Brief Bioinform 2024; 25:bbae154. [PMID: 38600667 PMCID: PMC11006794 DOI: 10.1093/bib/bbae154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/16/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.
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Affiliation(s)
- Meng Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Chuqi Lei
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- School of Computer Science and engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Min Li
- School of Computer Science and engineering, Central South University, Changsha 410083, China
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13
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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14
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Borole P, Rajan A. Building trust in deep learning-based immune response predictors with interpretable explanations. Commun Biol 2024; 7:279. [PMID: 38448546 PMCID: PMC10917751 DOI: 10.1038/s42003-024-05968-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/23/2024] [Indexed: 03/08/2024] Open
Abstract
The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations.
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Affiliation(s)
- Piyush Borole
- School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Newington, Edinburgh, EH8 9AB, Scotland, UK.
| | - Ajitha Rajan
- School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Newington, Edinburgh, EH8 9AB, Scotland, UK.
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15
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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16
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Kono M, Wakisaka R, Komatsuda H, Hayashi R, Kumai T, Yamaki H, Sato R, Nagato T, Ohkuri T, Kosaka A, Ohara K, Kishibe K, Kobayashi H, Hayashi T, Takahara M. Immunotherapy targeting tumor-associated antigen in a mouse model of head and neck cancer. Head Neck 2024. [PMID: 38390628 DOI: 10.1002/hed.27703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND The identification of epitope peptides from tumor-associated antigens (TAAs) is informative for developing tumor-specific immunotherapy. However, only a few epitopes have been detected in mouse TAAs of head and neck cancer (HNSCC). METHODS Novel mouse c-Met-derived T-cell epitopes were predicted by computer-based algorithms. Mouse HNSCC cell line-bearing mice were treated with a c-Met peptide vaccine. The effects of CD8 and/or CD4 T-cell depletion, and vaccine combination with immune checkpoint inhibitors (ICIs) were evaluated. Tumor re-inoculation was performed to assess T-cell memory. RESULTS We identified c-Met-derived short and long epitopes that elicited c-Met-reactive antitumor CD8 and/or CD4 T-cell responses. Vaccination using these peptides showed remarkable antitumor responses via T cells in which ICIs were not required. The c-Met peptide-vaccinated mice rejected the re-inoculated tumors. CONCLUSIONS We demonstrated that novel c-Met peptide vaccines can induce antitumor T-cell response, and could be a potent immunotherapy in a syngeneic mouse HNSCC model.
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Affiliation(s)
- Michihisa Kono
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Risa Wakisaka
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Hiroki Komatsuda
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Ryusuke Hayashi
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Takumi Kumai
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Innovative Head & Neck Cancer Research and Treatment, Asahikawa Medical University, Asahikawa, Japan
| | - Hidekiyo Yamaki
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Ryosuke Sato
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Toshihiro Nagato
- Department of Pathology, Asahikawa Medical University, Asahikawa, Japan
| | - Takayuki Ohkuri
- Department of Pathology, Asahikawa Medical University, Asahikawa, Japan
| | - Akemi Kosaka
- Department of Pathology, Asahikawa Medical University, Asahikawa, Japan
| | - Kenzo Ohara
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Kan Kishibe
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Hiroya Kobayashi
- Department of Pathology, Asahikawa Medical University, Asahikawa, Japan
| | - Tatsuya Hayashi
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
| | - Miki Takahara
- Department of Otolaryngology-Head & Neck Surgery, Asahikawa Medical University, Asahikawa, Japan
- Department of Innovative Head & Neck Cancer Research and Treatment, Asahikawa Medical University, Asahikawa, Japan
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17
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Zhou M, Zhao F, Yu L, Liu J, Wang J, Zhang JZH. An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1. Molecules 2024; 29:881. [PMID: 38398632 PMCID: PMC10892774 DOI: 10.3390/molecules29040881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.
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Affiliation(s)
- Mengchen Zhou
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
| | - Fanyu Zhao
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
| | - Lan Yu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jinfeng Liu
- Department of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Jian Wang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China;
- NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China;
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
- Department of Chemistry, New York University, New York, NY 10003, USA
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China
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18
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Yu Y, Zu L, Jiang J, Wu Y, Wang Y, Xu M, Liu Q. Structure-aware deep model for MHC-II peptide binding affinity prediction. BMC Genomics 2024; 25:127. [PMID: 38291350 PMCID: PMC10826266 DOI: 10.1186/s12864-023-09900-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/12/2023] [Indexed: 02/01/2024] Open
Abstract
The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future.
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Affiliation(s)
- Ying Yu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Lipeng Zu
- Department of Computer Science, Florida State University, Tallahassee, 32306, USA
| | - Jiaye Jiang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yafang Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yinglin Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Midie Xu
- Department of Pathology, Fudan University, Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Institute of Pathology, Fudan University, Shanghai, 200032, China.
| | - Qing Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
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19
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Wang X, Wu T, Jiang Y, Chen T, Pan D, Jin Z, Xie J, Quan L, Lyu Q. RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding. Bioinformatics 2024; 40:btad785. [PMID: 38175759 PMCID: PMC10796178 DOI: 10.1093/bioinformatics/btad785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.
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Affiliation(s)
- Xuejiao Wang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Yelu Jiang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Taoning Chen
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Deng Pan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhi Jin
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Jingxin Xie
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
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20
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Barra C, Nilsson JB, Saksager A, Carri I, Deleuran S, Garcia Alvarez HM, Høie MH, Li Y, Clifford JN, Wan YTR, Moreta LS, Nielsen M. In Silico Tools for Predicting Novel Epitopes. Methods Mol Biol 2024; 2813:245-280. [PMID: 38888783 DOI: 10.1007/978-1-0716-3890-3_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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Affiliation(s)
- Carolina Barra
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
| | | | - Astrid Saksager
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Sebastian Deleuran
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Magnus Haraldson Høie
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Yuchen Li
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | | | - Yat-Tsai Richie Wan
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Lys Sanz Moreta
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
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21
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Huang D, Zhu X, Ye S, Zhang J, Liao J, Zhang N, Zeng X, Wang J, Yang B, Zhang Y, Lao L, Chen J, Xin M, Nie Y, Saw PE, Su S, Song E. Tumour circular RNAs elicit anti-tumour immunity by encoding cryptic peptides. Nature 2024; 625:593-602. [PMID: 38093017 DOI: 10.1038/s41586-023-06834-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/03/2023] [Indexed: 12/23/2023]
Abstract
Emerging data have shown that previously defined noncoding genomes might encode peptides that bind human leukocyte antigen (HLA) as cryptic antigens to stimulate adaptive immunity1,2. However, the significance and mechanisms of action of cryptic antigens in anti-tumour immunity remain unclear. Here mass spectrometry of the HLA class I (HLA-I) peptidome coupled with ribosome sequencing of human breast cancer samples identified HLA-I-binding cryptic antigenic peptides that were noncanonically translated by a tumour-specific circular RNA (circRNA): circFAM53B. The cryptic peptides efficiently primed naive CD4+ and CD8+ T cells in an antigen-specific manner and induced anti-tumour immunity. Clinically, the expression of circFAM53B and its encoded peptides was associated with substantial infiltration of antigen-specific CD8+ T cells and better survival in patients with breast cancer and patients with melanoma. Mechanistically, circFAM53B-encoded peptides had strong binding affinity to both HLA-I and HLA-II molecules. In vivo, administration of vaccines consisting of tumour-specific circRNA or its encoded peptides in mice bearing breast cancer tumours or melanoma induced enhanced infiltration of tumour-antigen-specific cytotoxic T cells, which led to effective tumour control. Overall, our findings reveal that noncanonical translation of circRNAs can drive efficient anti-tumour immunity, which suggests that vaccination exploiting tumour-specific circRNAs may serve as an immunotherapeutic strategy against malignant tumours.
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Affiliation(s)
- Di Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaofeng Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Shuying Ye
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jiahui Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jianyou Liao
- Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ning Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xin Zeng
- Program of Molecular Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Jiawen Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bing Yang
- Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yin Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Liyan Lao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jianing Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Min Xin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Nie
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Phei Er Saw
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Shicheng Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China.
- Department of Infectious Diseases, the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
- Biotherapy Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
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22
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Storz U. The rebirth of epitope-based patent claims. Hum Antibodies 2024; 32:35-49. [PMID: 38640147 DOI: 10.3233/hab-240006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
BACKGROUND Patent protection of therapeutic antibodies and T cell receptors is an important tool to enable the path to the market. In view of the substantial spendings for R&D and regulatory approval, sponsors expect exclusivity for their drug for a given period of time. Different categories exist to protect therapeutic antibodies and T cell receptors. One of these categories are epitope-based patent claims, with regard to which in the different jurisdictions, different patentability standards exist, which, furthermore, are constantly changed by courts and lawmakers. OBJECTIVE This article tries to explain the patentability issues related to epitope-based patent claims. METHODS For this purpose, an overview is given on the respective legal provisions and court decisions. RESULTS The study reveals that the respective patentability standards are constantly changed by courts and lawmakers. CONCLUSIONS Companies developing therapeutic antibodies or T cell receptors need to consider these developments in their strategic planning.
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23
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Pande S, Guo HC. Structure-guided discovery of aminopeptidase ERAP1 variants capable of processing antigens with novel PC anchor specificities. Immunology 2024; 171:131-145. [PMID: 37858978 PMCID: PMC10841542 DOI: 10.1111/imm.13709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
Endoplasmic reticulum aminopeptidase 1 (ERAP1) belongs to the oxytocinase subfamily of M1 aminopeptidases (M1APs), which are a diverse family of metalloenzymes involved in a wide range of functions and have been implicated in various chronic and infectious diseases of humans. ERAP1 trims antigenic precursors into correct sizes (8-10 residues long) for Major Histocompatibility Complex (MHC) presentation, by a unique molecular ruler mechanism in which it makes concurrent bindings to substrate N- and C-termini. We have previously determined four crystal structures of ERAP1 C-terminal regulatory domain (termed ERAP1_C domain) in complex with peptide carboxyl (PC)-ends that carry various anchor residues, and identified a specificity subsite for recognizing the PC anchor side chain, denoted as the SC subsite to follow the conventional notations: S1 site for P1, S2 site for P2, and so forth. In this study, we report studies on structure-guided mutational and hydrolysis kinetics, and peptide trimming assays to further examine the functional roles of this SC subsite. Most strikingly, a point mutation V737R results in a change of substrate preference from a hydrophobic to a negatively charged PC anchor residue; the latter is presumed to be a poor substrate for WT ERAP1. These studies validate the crystallographic observations that this SC subsite is directly involved in binding and recognition of the substrate PC anchor and presents a potential target to modulate MHC-restricted immunopeptidomes.
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Affiliation(s)
- Suchita Pande
- Department of Biological Sciences, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, USA
- Present Address: Molecular Cardiology Research Institute, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA
| | - Hwai-Chen Guo
- Department of Biological Sciences, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854, USA
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24
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Kumari S, Kessel A, Singhal D, Kaur G, Bern D, Lemay-St-Denis C, Singh J, Jain S. Computational identification of a multi-peptide vaccine candidate in E2 glycoprotein against diverse Hepatitis C virus genotypes. J Biomol Struct Dyn 2023; 41:11044-11061. [PMID: 37194293 DOI: 10.1080/07391102.2023.2212777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 12/11/2022] [Indexed: 05/18/2023]
Abstract
Hepatitis C Virus (HCV) is estimated to affect nearly 180 million people worldwide, culminating in ∼0.7 million yearly casualties. However, a safe vaccine against HCV is not yet available. This study endeavored to identify a multi-genotypic, multi-epitopic, safe, and globally competent HCV vaccine candidate. We employed a consensus epitope prediction strategy to identify multi-epitopic peptides in all known envelope glycoprotein (E2) sequences, belonging to diverse HCV genotypes. The obtained peptides were screened for toxicity, allergenicity, autoimmunity and antigenicity, resulting in two favorable peptides viz., P2 (VYCFTPSPVVVG) and P3 (YRLWHYPCTV). Evolutionary conservation analysis indicated that P2 and P3 are highly conserved, supporting their use as part of a designed multi-genotypic vaccine. Population coverage analysis revealed that P2 and P3 are likely to be presented by >89% Human Leukocyte Antigen (HLA) molecules from six geographical regions. Indeed, molecular docking predicted the physical binding of P2 and P3 to various representative HLAs. We designed a vaccine construct using these peptides and assessed its binding to toll-like receptor 4 (TLR-4) by molecular docking and simulation. Subsequent analysis by energy-based and machine learning tools predicted high binding affinity and pinpointed the key binding residues (i.e. hotspots) in P2 and P3. Also, a favorable immunogenic profile of the construct was predicted by immune simulations. We encourage the scientific community to validate our vaccine construct in vitro and in vivo.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shweta Kumari
- University Institute of Biotechnology, Chandigarh University, Mohali, Punjab, India
| | - Amit Kessel
- Department of Biochemistry and Molecular Biology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Divya Singhal
- University Institute of Biotechnology, Chandigarh University, Mohali, Punjab, India
| | - Gurpreet Kaur
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - David Bern
- Department of Biochemistry and Molecular Biology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Claudèle Lemay-St-Denis
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, Canada
- PROTEO, The Québec Network for Research on Protein, Function, Engineering and Applications, Québec, QC, Canada
- CGCC, Center in Green Chemistry and Catalysis, Montréal, QC, Canada
| | - Jasdeep Singh
- University Institute of Biotechnology, Chandigarh University, Mohali, Punjab, India
| | - Sahil Jain
- University Institute of Biotechnology, Chandigarh University, Mohali, Punjab, India
- Department of Biochemistry and Molecular Biology, Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
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25
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Hashemzadeh P, Nezhad SA, Khoshkhabar H. Immunoinformatics analysis of Brucella melitensis to approach a suitable vaccine against brucellosis. J Genet Eng Biotechnol 2023; 21:152. [PMID: 38019359 PMCID: PMC10686926 DOI: 10.1186/s43141-023-00614-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
BACKGROUND Brucellosis caused by B. melitensis is one of the most important common diseases between humans and livestock. Currently, live attenuated vaccines are used for this disease, which causes many problems, and unfortunately, there is no effective vaccine for human brucellosis. The aim of our research was to design a recombinant vaccine containing potential immunogenic epitopes against B. melitensis. METHODS In this study, using immunoinformatics approaches, 3 antigens Omp31, Omp25, and Omp28 were identified and the amino acid sequence of the selected antigens was determined in NCBI. Signal peptides were predicted by SignaIP-5.0 server. To predict B-cell epitopes from ABCpred and Bcepred servers, to predict MHC-I epitopes from RANKPEP and SYFPEITHI servers, to predict MHC-II epitopes from RANKPEP and MHCPred servers, and to predict CTL epitopes were used from the CTLPred server. Potentially immunogenic final epitopes were joined by flexible linkers. Finally, allergenicity (AllerTOP 2.0 server), antigenicity (Vaxijen server), physicochemical properties (ProtParam server), solubility (Protein-sol server), secondary (PSIPRED and GRO4 servers) and tertiary structure (I-TASSER server), refinement (GalaxyWEB server), validation (ProSA-web, Molprobity, and ERRAT servers), and optimization of the codon sequence (JCat server) of the structure of the multi-epitope vaccine were analyzed. RESULTS The analysis of immunoinformatics tools showed that the designed vaccine has high quality, acceptable physicochemical properties, and can induce humoral and cellular immune responses against B. melitensis bacteria. In addition, the high expression level of recombinant antigens in the E. coli host was observed through in silico simulation. CONCLUSION According to the results in silico, the designed vaccine can be a suitable candidate to fight brucellosis and in vitro and in vivo studies are needed to evaluate the research of this study.
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Affiliation(s)
- Pejman Hashemzadeh
- Department of Medical Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Lorestan, Iran.
| | - Saba Asgari Nezhad
- Department of Immunology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Lorestan, Iran
| | - Hossein Khoshkhabar
- Department of Medical Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Lorestan, Iran
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26
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Nelde A, Schuster H, Heitmann JS, Bauer J, Maringer Y, Zwick M, Volkmer JP, Chen JY, Stanger AMP, Lehmann A, Appiah B, Märklin M, Rücker-Braun E, Salih HR, Roerden M, Schroeder SM, Häring MF, Schlosser A, Schetelig J, Schmitz M, Boerries M, Köhler N, Lengerke C, Majeti R, Weissman IL, Rammensee HG, Walz JS. Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells. Blood Cancer Discov 2023; 4:468-489. [PMID: 37847741 PMCID: PMC10618727 DOI: 10.1158/2643-3230.bcd-23-0020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/13/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML. SIGNIFICANCE The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.
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Affiliation(s)
- Annika Nelde
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Heiko Schuster
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
| | - Jonas S. Heitmann
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Jens Bauer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Yacine Maringer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Melissa Zwick
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jens-Peter Volkmer
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - James Y. Chen
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Anna M. Paczulla Stanger
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Ariane Lehmann
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Bismark Appiah
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Melanie Märklin
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Elke Rücker-Braun
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Helmut R. Salih
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Malte Roerden
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Sarah M. Schroeder
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Max-Felix Häring
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | | | - Johannes Schetelig
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- German Bone Marrow Donor Center (DKMS), Clinical Trials Unit, Dresden, Germany
| | - Marc Schmitz
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), partner site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Boerries
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
- Comprehensive Cancer Center Freiburg (CCCF), Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site, Freiburg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Natalie Köhler
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Claudia Lengerke
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
- Clinic for Hematology, University of Basel and University Hospital Basel, Basel, Switzerland
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Ravindra Majeti
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
- Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Irving L. Weissman
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Juliane S. Walz
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
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27
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Zhang J, Sun B, Shen W, Wang Z, Liu Y, Sun Y, Zhang J, Liu R, Wang Y, Bai T, Ma Z, Luo C, Qiao X, Zhang X, Yang S, Sun Y, Jiang D, Yang K. In Silico Analyses, Experimental Verification and Application in DNA Vaccines of Ebolavirus GP-Derived pan-MHC-II-Restricted Epitopes. Vaccines (Basel) 2023; 11:1620. [PMID: 37897022 PMCID: PMC10610722 DOI: 10.3390/vaccines11101620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background and Purpose: Ebola virus (EBOV) is the causative agent of Ebola virus disease (EVD), which causes extremely high mortality and widespread epidemics. The only glycoprotein (GP) on the surface of EBOV particles is the key to mediating viral invasion into host cells. DNA vaccines for EBOV are in development, but their effectiveness is unclear. The lack of immune characteristics resides in antigenic MHC class II reactivity. (2) Methods: We selected MHC-II molecules from four human leukocyte antigen II (HLA-II) superfamilies with 98% population coverage and eight mouse H2-I alleles. IEDB, NetMHCIIpan, SYFPEITHI, and Rankpep were used to screen MHC-II-restricted epitopes with high affinity for EBOV GP. Further immunogenicity and conservation analyses were performed using VaxiJen and BLASTp, respectively. EpiDock was used to simulate molecular docking. Cluster analysis and binding affinity analysis of EBOV GP epitopes and selected MHC-II molecules were performed using data from NetMHCIIpan. The selective GP epitopes were verified by the enzyme-linked immunospot (ELISpot) assay using splenocytes of BALB/c (H2d), C3H, and C57 mice after DNA vaccine pVAX-GPEBO immunization. Subsequently, BALB/c mice were immunized with Protein-GPEBO, plasmid pVAX-GPEBO, and pVAX-LAMP/GPEBO, which encoded EBOV GP. The dominant epitopes of BALB/c (H-2-I-AdEd genotype) mice were verified by the enzyme-linked immunospot (ELISpot) assay. It is also used to evaluate and explore the advantages of pVAX-LAMP/GPEBO and the reasons behind them. (3) Results: Thirty-one HLA-II-restricted and 68 H2-I-restricted selective epitopes were confirmed to have high affinity, immunogenicity, and conservation. Nineteen selective epitopes have cross-species reactivity with good performance in MHC-II molecular docking. The ELISpot results showed that pVAX-GPEBO could induce a cellular immune response to the synthesized selective peptides. The better immunoprotection of the DNA vaccines pVAX-LAMP/GPEBO coincides with the enhancement of the MHC class II response. (4) Conclusions: Promising MHC-II-restricted candidate epitopes of EBOV GP were identified in humans and mice, which is of great significance for the development and evaluation of Ebola vaccines.
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Affiliation(s)
- Junqi Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Baozeng Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
- Yingtan Detachment, Jiangxi Corps, Chinese People’s Armed Police Force, Yingtan 335000, China
| | - Wenyang Shen
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Zhenjie Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Yang Liu
- Institute of AIDS Prevention and Control, Shaanxi Provincial Center for Disease Control and Prevention, Xi’an 710054, China;
| | - Yubo Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Jiaxing Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Ruibo Liu
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Yongkai Wang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Tianyuan Bai
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Zilu Ma
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Cheng Luo
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Xupeng Qiao
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Xiyang Zhang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Shuya Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Yuanjie Sun
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
| | - Dongbo Jiang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
- Institute of AIDS Prevention and Control, Shaanxi Provincial Center for Disease Control and Prevention, Xi’an 710054, China;
| | - Kun Yang
- Department of Immunology, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China; (J.Z.); (B.S.); (W.S.); (Z.W.); (Y.S.); (J.Z.); (R.L.); (Y.W.); (T.B.); (Z.M.); (C.L.); (X.Q.); (X.Z.); (S.Y.); (Y.S.)
- The Key Laboratory of Bio-Hazard Damage and Prevention Medicine, Basic Medicine School, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710032, China
- Department of Rheumatology, Tangdu Hospital, Air-Force Medical University (The Fourth Military Medical University), Xi’an 710038, China
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28
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Massa C, Seliger B. Combination of multiple omics techniques for a personalized therapy or treatment selection. Front Immunol 2023; 14:1258013. [PMID: 37828984 PMCID: PMC10565668 DOI: 10.3389/fimmu.2023.1258013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/05/2023] [Indexed: 10/14/2023] Open
Abstract
Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.
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Affiliation(s)
- Chiara Massa
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Barbara Seliger
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
- Institute of Medical Immunology, Martin Luther University Halle-Wittenberg, Halle, Germany
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
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29
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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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30
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Guo X, Pan X, Sun Q, Hu Y, Shi J. Design of a novel multiepitope vaccine against Chlamydia pneumoniae using the extracellular protein as a target. Sci Rep 2023; 13:15070. [PMID: 37700027 PMCID: PMC10497608 DOI: 10.1038/s41598-023-42222-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/06/2023] [Indexed: 09/14/2023] Open
Abstract
Chlamydia pneumoniae (C. pneumoniae) infection in humans is universal and causes various respiratory infectious diseases, making a safe and effective preventive vaccine essential. In this study, a multi-epitope vaccine with CTLA-4 extracellular structure was constructed by an immunoinformatics approach. Since MOMP protein is the major extracellular protein in C. pneumoniae and has good immunogenicity and high conservation, we selected the MOMP protein of C. pneumoniae as the antigen target, predicted the T and B cell epitopes of the MOMP protein and then connected the CTLA-4 extracellular structure with the predicted dominant epitopes by various linkers to construct a multi-epitope vaccine. The biochemical characterization of the multi-epitope vaccine showed its immunogenicity and anti-allergic properties. The tertiary structure of this vaccine, along with molecular docking, molecular dynamics simulation, and principal component analysis, showed that the multi-epitope vaccine structure interacted with B7 (B7-1, B7-2) and toll-like receptors (TLR-2, TLR-4). Ultimately, the vaccine was cloned and effectively expressed in silico on an insect baculovirus expression vector (pFastBac1). These analyses showed that the designed vaccine could potentially target antigen-presenting cells and was immune to C. pneumoniae, which provided novel strategies for developing the vaccine.
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Affiliation(s)
- Xiaomei Guo
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China
- Kunming Medical University, Kunming, Yunnan, China
| | - Xiaohong Pan
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China
| | - Qiangming Sun
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- National Kunming High-Level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China.
| | - Yunzhang Hu
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- Kunming Medical University, Kunming, Yunnan, China.
| | - Jiandong Shi
- Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 935 Jiaoling Road, Kunming, 650118, Yunnan, China.
- National Kunming High-Level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China.
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31
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Landry SJ, Mettu RR, Kolls JK, Aberle JH, Norton E, Zwezdaryk K, Robinson J. Structural Framework for Analysis of CD4+ T-Cell Epitope Dominance in Viral Fusion Proteins. Biochemistry 2023; 62:2517-2529. [PMID: 37554055 PMCID: PMC10483696 DOI: 10.1021/acs.biochem.3c00335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/31/2023] [Indexed: 08/10/2023]
Abstract
Antigen conformation shapes CD4+ T-cell specificity through mechanisms of antigen processing, and the consequences for immunity may rival those from conformational effects on antibody specificity. CD4+ T cells initiate and control immunity to pathogens and cancer and are at least partly responsible for immunopathology associated with infection, autoimmunity, and allergy. The primary trigger for CD4+ T-cell maturation is the presentation of an epitope peptide in the MHC class II antigen-presenting protein (MHCII), most commonly on an activated dendritic cell, and then the T-cell responses are recalled by subsequent presentations of the epitope peptide by the same or other antigen-presenting cells. Peptide presentation depends on the proteolytic fragmentation of the antigen in an endosomal/lysosomal compartment and concomitant loading of the fragments into the MHCII, a multistep mechanism called antigen processing and presentation. Although the role of peptide affinity for MHCII has been well studied, the role of proteolytic fragmentation has received less attention. In this Perspective, we will briefly summarize evidence that antigen resistance to unfolding and proteolytic fragmentation shapes the specificity of the CD4+ T-cell response to selected viral envelope proteins, identify several remarkable examples in which the immunodominant CD4+ epitopes most likely depend on the interaction of processing machinery with antigen conformation, and outline how knowledge of antigen conformation can inform future efforts to design vaccines.
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Affiliation(s)
- Samuel J. Landry
- Department
of Biochemistry and Molecular Biology, Tulane
University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Ramgopal R. Mettu
- Department
of Computer Science, Tulane University, New Orleans, Louisiana 70118, United States
| | - Jay K. Kolls
- John
W. Deming Department of Internal Medicine, Center for Translational
Research in Infection and Inflammation, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Judith H. Aberle
- Center
for Virology, Medical University of Vienna, 1090 Vienna, Austria
| | - Elizabeth Norton
- Department
of Microbiology & Immunology, Tulane
University School of Medicine, New Orleans, Louisiana 70112, United States
| | - Kevin Zwezdaryk
- Department
of Microbiology & Immunology, Tulane
University School of Medicine, New Orleans, Louisiana 70112, United States
| | - James Robinson
- Department
of Pediatrics, Tulane University School
of Medicine, New Orleans, Louisiana 70112, United States
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32
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Schmalen A, Kammerl IE, Meiners S, Noessner E, Deeg CA, Hauck SM. A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability. Biomolecules 2023; 13:1300. [PMID: 37759700 PMCID: PMC10527444 DOI: 10.3390/biom13091300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.
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Affiliation(s)
- Adrian Schmalen
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
| | - Ilona E. Kammerl
- Comprehensive Pneumology Center (CPC), University Hospital, Ludwig-Maximilians-University, Helmholtz Center Munich, Member of the German Center for Lung Research (DZL), 81377 Munich, Germany
| | - Silke Meiners
- Research Center Borstel, Leibniz Lung Center, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), 23845 Borstel, Germany
- Institute of Experimental Medicine, Christian-Albrechts University Kiel, 24118 Kiel, Germany
| | - Elfriede Noessner
- Immunoanalytics Research Group—Tissue Control of Immunocytes, Helmholtz Center Munich, 81377 Munich, Germany
| | - Cornelia A. Deeg
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
| | - Stefanie M. Hauck
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
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33
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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34
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Medici G, Freudenmann LK, Velz J, Wang SSY, Kapolou K, Paramasivam N, Mühlenbruch L, Kowalewski DJ, Vasella F, Bilich T, Frey BM, Dubbelaar ML, Patterson AB, Zeitlberger AM, Silginer M, Roth P, Weiss T, Wirsching HG, Krayenbühl N, Bozinov O, Regli L, Rammensee HG, Rushing EJ, Sahm F, Walz JS, Weller M, Neidert MC. A T-cell antigen atlas for meningioma: novel options for immunotherapy. Acta Neuropathol 2023; 146:173-190. [PMID: 37368072 PMCID: PMC10329067 DOI: 10.1007/s00401-023-02605-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 06/28/2023]
Abstract
Meningiomas are the most common primary intracranial tumors. Although most symptomatic cases can be managed by surgery and/or radiotherapy, a relevant number of patients experience an unfavorable clinical course and additional treatment options are needed. As meningiomas are often perfused by dural branches of the external carotid artery, which is located outside the blood-brain barrier, they might be an accessible target for immunotherapy. However, the landscape of naturally presented tumor antigens in meningioma is unknown. We here provide a T-cell antigen atlas for meningioma by in-depth profiling of the naturally presented immunopeptidome using LC-MS/MS. Candidate target antigens were selected based on a comparative approach using an extensive immunopeptidome data set of normal tissues. Meningioma-exclusive antigens for HLA class I and II are described here for the first time. Top-ranking targets were further functionally characterized by showing their immunogenicity through in vitro T-cell priming assays. Thus, we provide an atlas of meningioma T-cell antigens which will be publicly available for further research. In addition, we have identified novel actionable targets that warrant further investigation as an immunotherapy option for meningioma.
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Affiliation(s)
- Gioele Medici
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland.
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
| | - Lena K Freudenmann
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Julia Velz
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Sophie Shih-Yüng Wang
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery and Neurotechnology, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Konstantina Kapolou
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Roche Diagnostics International Ltd, Rotkreuz, Switzerland
| | - Nagarajan Paramasivam
- Computational Oncology Group, Molecular Precision Oncology Program, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Lena Mühlenbruch
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
| | - Daniel J Kowalewski
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Flavio Vasella
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Tatjana Bilich
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Beat M Frey
- Blood Transfusion Service, Swiss Red Cross, Schlieren, Switzerland
| | - Marissa L Dubbelaar
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karls University Tübingen, 72076, Tübingen, Baden-Württemberg, Germany
| | | | - Anna Maria Zeitlberger
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
| | - Manuela Silginer
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Patrick Roth
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Tobias Weiss
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Hans-Georg Wirsching
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Oliver Bozinov
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Elisabeth Jane Rushing
- Department of Neuropathology, University Hospital and University of Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Juliane S Walz
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Michael Weller
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Marian C Neidert
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
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35
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Ramachandran S, Grozdanov V, Leins B, Kandler K, Witzel S, Mulaw M, Ludolph AC, Weishaupt JH, Danzer KM. Low T-cell reactivity to TDP-43 peptides in ALS. Front Immunol 2023; 14:1193507. [PMID: 37545536 PMCID: PMC10401033 DOI: 10.3389/fimmu.2023.1193507] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Background Dysregulation of the immune system in amyotrophic lateral sclerosis (ALS) includes changes in T-cells composition and infiltration of T cells in the brain and spinal cord. Recent studies have shown that cytotoxic T cells can directly induce motor neuron death in a mouse model of ALS and that T cells from ALS patients are cytotoxic to iPSC-derived motor neurons from ALS patients. Furthermore, a clonal expansion to unknown epitope(s) was recently found in familial ALS and increased peripheral and intrathecal activation of cytotoxic CD8+ T cells in sporadic ALS. Results Here, we show an increased activation of peripheral T cells from patients with sporadic ALS by IL-2 treatment, suggesting an increase of antigen-experienced T cells in ALS blood. However, a putative antigen for T-cell activation in ALS has not yet been identified. Therefore, we investigated if peptides derived from TDP-43, a key protein in ALS pathogenesis, can act as epitopes for antigen-mediated activation of human T cells by ELISPOT and flow cytometry. We found that TDP-43 peptides induced only a weak MHCI or MHCII-restricted activation of both naïve and antigen-experienced T cells from healthy controls and ALS patients. Interestingly, we found less activation in T cells from ALS patients to TDP-43 and control stimuli. Furthermore, we found no change in the levels of naturally occurring auto-antibodies against full-length TDP-43 in ALS. Conclusion Our data suggests a general increase in antigen-experienced T cells in ALS blood, measured by in-vitro culture with IL-2 for 14 days. Furthermore, it suggests that TDP-43 is a weak autoantigen.
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Affiliation(s)
| | | | - Bianca Leins
- Neurology, University Clinic, University of Ulm, Ulm, Germany
| | | | - Simon Witzel
- Neurology, University Clinic, University of Ulm, Ulm, Germany
| | - Medhanie Mulaw
- Institute of Experimental Cancer Research, Medical Faculty, University of Ulm, Ulm, Germany
| | - Albert C. Ludolph
- Neurology, University Clinic, University of Ulm, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Jochen H. Weishaupt
- Neurology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Karin M. Danzer
- Neurology, University Clinic, University of Ulm, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
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36
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Gouttefangeas C, Klein R, Maia A. The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer. Front Immunol 2023; 14:1212546. [PMID: 37409132 PMCID: PMC10319254 DOI: 10.3389/fimmu.2023.1212546] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/24/2023] [Indexed: 07/07/2023] Open
Abstract
T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.
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Affiliation(s)
- Cécile Gouttefangeas
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) partner site Tübingen, Tübingen, Germany
| | - Reinhild Klein
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Ana Maia
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
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37
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Fonseca AF, Antunes DA. CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases. Front Immunol 2023; 14:1142573. [PMID: 37377956 PMCID: PMC10291144 DOI: 10.3389/fimmu.2023.1142573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.
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38
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Castro JT, Brito R, Hojo-Souza NS, Azevedo B, Salazar N, Ferreira CP, Junqueira C, Fernandes AP, Vasconcellos R, Cardoso JM, Aguiar-Soares RDO, Vieira PMA, Carneiro CM, Valiate B, Toledo C, Salazar AM, Caballero O, Lannes-Vieira J, Teixeira SR, Reis AB, Gazzinelli RT. ASP-2/Trans-sialidase chimeric protein induces robust protective immunity in experimental models of Chagas' disease. NPJ Vaccines 2023; 8:81. [PMID: 37258518 DOI: 10.1038/s41541-023-00676-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 05/16/2023] [Indexed: 06/02/2023] Open
Abstract
Immunization with the Amastigote Surface Protein-2 (ASP-2) and Trans-sialidase (TS) antigens either in the form of recombinant protein, encoded in plasmids or human adenovirus 5 (hAd5) confers robust protection against various lineages of Trypanosoma cruzi. Herein we generated a chimeric protein containing the most immunogenic regions for T and B cells from TS and ASP-2 (TRASP) and evaluated its immunogenicity in comparison with our standard protocol of heterologous prime-boost using plasmids and hAd5. Mice immunized with TRASP protein associated to Poly-ICLC (Hiltonol) were highly resistant to challenge with T. cruzi, showing a large decrease in tissue parasitism, parasitemia and no lethality. This protection lasted for at least 3 months after the last boost of immunization, being equivalent to the protection induced by DNA/hAd5 protocol. TRASP induced high levels of T. cruzi-specific antibodies and IFNγ-producing T cells and protection was primarily mediated by CD8+ T cells and IFN-γ. We also evaluated the toxicity, immunogenicity, and efficacy of TRASP and DNA/hAd5 formulations in dogs. Mild collateral effects were detected at the site of vaccine inoculation. While the chimeric protein associated with Poly-ICLC induced high levels of antibodies and CD4+ T cell responses, the DNA/hAd5 induced no antibodies, but a strong CD8+ T cell response. Immunization with either vaccine protected dogs against challenge with T. cruzi. Despite the similar efficacy, we conclude that moving ahead with TRASP together with Hiltonol is advantageous over the DNA/hAd5 vaccine due to pre-existing immunity to the adenovirus vector, as well as the cost-benefit for development and large-scale production.
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Affiliation(s)
- Julia T Castro
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
- Plataforma de Medicina Translacional, Fundação Oswaldo Cruz-Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
| | - Rory Brito
- Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Natalia S Hojo-Souza
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
| | - Bárbara Azevedo
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
| | - Natalia Salazar
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
| | | | - Caroline Junqueira
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
| | - Ana Paula Fernandes
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
| | | | | | | | | | | | - Bruno Valiate
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
| | - Cristiane Toledo
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil
| | | | | | | | - Santuza R Teixeira
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil
| | | | - Ricardo T Gazzinelli
- Centro de Tecnologia em Vacinas, Universidade Federal de Minas Gerais, Parque Tecnológico de Belo Horizonte, Belo Horizonte, Brazil.
- Centro de Pesquisas Rene Rachou, Fundação Osvaldo Cruz, Rio de Janeiro, Brazil.
- Plataforma de Medicina Translacional, Fundação Oswaldo Cruz-Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil.
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39
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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40
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Naghavian R, Faigle W, Oldrati P, Wang J, Toussaint NC, Qiu Y, Medici G, Wacker M, Freudenmann LK, Bonté PE, Weller M, Regli L, Amigorena S, Rammensee HG, Walz JS, Brugger SD, Mohme M, Zhao Y, Sospedra M, Neidert MC, Martin R. Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma. Nature 2023; 617:807-817. [PMID: 37198490 PMCID: PMC10208956 DOI: 10.1038/s41586-023-06081-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/13/2023] [Indexed: 05/19/2023]
Abstract
Microbial organisms have key roles in numerous physiological processes in the human body and have recently been shown to modify the response to immune checkpoint inhibitors1,2. Here we aim to address the role of microbial organisms and their potential role in immune reactivity against glioblastoma. We demonstrate that HLA molecules of both glioblastoma tissues and tumour cell lines present bacteria-specific peptides. This finding prompted us to examine whether tumour-infiltrating lymphocytes (TILs) recognize tumour-derived bacterial peptides. Bacterial peptides eluted from HLA class II molecules are recognized by TILs, albeit very weakly. Using an unbiased antigen discovery approach to probe the specificity of a TIL CD4+ T cell clone, we show that it recognizes a broad spectrum of peptides from pathogenic bacteria, commensal gut microbiota and also glioblastoma-related tumour antigens. These peptides were also strongly stimulatory for bulk TILs and peripheral blood memory cells, which then respond to tumour-derived target peptides. Our data hint at how bacterial pathogens and bacterial gut microbiota can be involved in specific immune recognition of tumour antigens. The unbiased identification of microbial target antigens for TILs holds promise for future personalized tumour vaccination approaches.
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Affiliation(s)
- Reza Naghavian
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
| | - Wolfgang Faigle
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
- Immunity and Cancer, Institut Curie, PSL University, INSERM U932, Paris, France
| | - Pietro Oldrati
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Jian Wang
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Nora C Toussaint
- NEXUS Personalized Health Technologies, ETH Zurich, Schlieren, Switzerland
- Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Yuhan Qiu
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Gioele Medici
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marcel Wacker
- Department of Peptide-based Immunotherapy, University of Tübingen, University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | - Lena K Freudenmann
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | | | - Michael Weller
- Laboratory of Molecular Neuro-Oncology, Department of Neurology and Clinical Neuroscience, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian Amigorena
- Immunity and Cancer, Institut Curie, PSL University, INSERM U932, Paris, France
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | - Juliane S Walz
- Department of Peptide-based Immunotherapy, University of Tübingen, University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Silvio D Brugger
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Malte Mohme
- Department of Neurosurgery, University Hospital Hamburg Eppendorf, University of Hamburg, Hamburg, Germany
| | - Yingdong Zhao
- Computational and Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, NCI, NIH, Rockville, MD, USA
| | - Mireia Sospedra
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
| | - Marian C Neidert
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
- Cellerys AG, Schlieren, Switzerland.
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland.
- Therapeutic Immune Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden.
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41
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Tagliamonte M, Cavalluzzo B, Mauriello A, Ragone C, Buonaguro FM, Tornesello ML, Buonaguro L. Molecular mimicry and cancer vaccine development. Mol Cancer 2023; 22:75. [PMID: 37101139 PMCID: PMC10131527 DOI: 10.1186/s12943-023-01776-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/14/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND The development of cancer immunotherapeutic strategies relies on the identification and validation of optimal target tumor antigens, which should be tumor-specific as well as able to elicit a swift and potent anti-tumor immune response. The vast majority of such strategies are based on tumor associated antigens (TAAs) which are shared wild type cellular self-epitopes highly expressed on tumor cells. Indeed, TAAs can be used to develop off-the-shelf cancer vaccines appropriate to all patients affected by the same malignancy. However, given that they may be also presented by HLAs on the surface of non-malignant cells, they may be possibly affected by immunological tolerance or elicit autoimmune responses. MAIN BODY In order to overcome such limitations, analogue peptides with improved antigenicity and immunogenicity able to elicit a cross-reactive T cell response are needed. To this aim, non-self-antigens derived from microorganisms (MoAs) may be of great benefit.
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Affiliation(s)
- Maria Tagliamonte
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori, IRCCS - "Fond. G. Pascale", Naples, Italy
| | - Beatrice Cavalluzzo
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori, IRCCS - "Fond. G. Pascale", Naples, Italy
| | - Angela Mauriello
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori, IRCCS - "Fond. G. Pascale", Naples, Italy
| | - Concetta Ragone
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori, IRCCS - "Fond. G. Pascale", Naples, Italy
| | - Franco M Buonaguro
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale Tumori, IRCCS - "Fond G. Pascale", Naples, Italy
| | - Maria Lina Tornesello
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale Tumori, IRCCS - "Fond G. Pascale", Naples, Italy
| | - Luigi Buonaguro
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori, IRCCS - "Fond. G. Pascale", Naples, Italy.
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42
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Gao F, Mallajosyula V, Arunachalam PS, van der Ploeg K, Manohar M, Röltgen K, Yang F, Wirz O, Hoh R, Haraguchi E, Lee JY, Willis R, Ramachandiran V, Li J, Kathuria KR, Li C, Lee AS, Shah MM, Sindher SB, Gonzalez J, Altman JD, Wang TT, Boyd SD, Pulendran B, Jagannathan P, Nadeau KC, Davis MM. Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8 + T cell responses post SARS-CoV-2 infection. Immunity 2023; 56:864-878.e4. [PMID: 36996809 PMCID: PMC10017386 DOI: 10.1016/j.immuni.2023.03.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 01/05/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023]
Abstract
T cells are a critical component of the response to SARS-CoV-2, but their kinetics after infection and vaccination are insufficiently understood. Using "spheromer" peptide-MHC multimer reagents, we analyzed healthy subjects receiving two doses of the Pfizer/BioNTech BNT162b2 vaccine. Vaccination resulted in robust spike-specific T cell responses for the dominant CD4+ (HLA-DRB1∗15:01/S191) and CD8+ (HLA-A∗02/S691) T cell epitopes. Antigen-specific CD4+ and CD8+ T cell responses were asynchronous, with the peak CD4+ T cell responses occurring 1 week post the second vaccination (boost), whereas CD8+ T cells peaked 2 weeks later. These peripheral T cell responses were elevated compared with COVID-19 patients. We also found that previous SARS-CoV-2 infection resulted in decreased CD8+ T cell activation and expansion, suggesting that previous infection can influence the T cell response to vaccination.
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Affiliation(s)
- Fei Gao
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Vamsee Mallajosyula
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Prabhu S Arunachalam
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Kattria van der Ploeg
- Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, USA
| | - Monali Manohar
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Katharina Röltgen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Fan Yang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Oliver Wirz
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramona Hoh
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Emily Haraguchi
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ji-Yeun Lee
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard Willis
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Jiefu Li
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Karan Raj Kathuria
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Chunfeng Li
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra S Lee
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Mihir M Shah
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sayantani B Sindher
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph Gonzalez
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - John D Altman
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Taia T Wang
- Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Scott D Boyd
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bali Pulendran
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Prasanna Jagannathan
- Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Kari C Nadeau
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; Sean N. Parker Center for Allergy and Asthma Research, Stanford University and Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard, MA, USA
| | - Mark M Davis
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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Pyke RM, Mellacheruvu D, Dea S, Abbott C, Zhang SV, Phillips NA, Harris J, Bartha G, Desai S, McClory R, West J, Snyder MP, Chen R, Boyle SM. Precision Neoantigen Discovery Using Large-Scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation. Mol Cell Proteomics 2023; 22:100506. [PMID: 36796642 PMCID: PMC10114598 DOI: 10.1016/j.mcpro.2023.100506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past 2 decades. However, improvement in the accuracy of prediction algorithms is needed for clinical applications like the development of personalized cancer vaccines, the discovery of biomarkers for response to immunotherapies, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic Human Leukocyte Antigen (HLA) Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA allele to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.17-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.
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Affiliation(s)
| | | | - Steven Dea
- Personalis, Inc, Menlo Park, California, USA
| | | | | | | | | | | | - Sejal Desai
- Personalis, Inc, Menlo Park, California, USA
| | | | - John West
- Personalis, Inc, Menlo Park, California, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University, Palo Alto, California, USA
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Zhai Y, Chen L, Zhao Q, Zheng ZH, Chen ZN, Bian H, Yang X, Lu HY, Lin P, Chen X, Chen R, Sun HY, Fan LN, Zhang K, Wang B, Sun XX, Feng Z, Zhu YM, Zhou JS, Chen SR, Zhang T, Chen SY, Chen JJ, Zhang K, Wang Y, Chang Y, Zhang R, Zhang B, Wang LJ, Li XM, He Q, Yang XM, Nan G, Xie RH, Yang L, Yang JH, Zhu P. Cysteine carboxyethylation generates neoantigens to induce HLA-restricted autoimmunity. Science 2023; 379:eabg2482. [PMID: 36927018 DOI: 10.1126/science.abg2482] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Autoimmune diseases such as ankylosing spondylitis (AS) can be driven by emerging neoantigens that disrupt immune tolerance. Here, we developed a workflow to profile posttranslational modifications involved in neoantigen formation. Using mass spectrometry, we identified a panel of cysteine residues differentially modified by carboxyethylation that required 3-hydroxypropionic acid to generate neoantigens in patients with AS. The lysosomal degradation of integrin αIIb [ITGA2B (CD41)] carboxyethylated at Cys96 (ITGA2B-ceC96) generated carboxyethylated peptides that were presented by HLA-DRB1*04 to stimulate CD4+ T cell responses and induce autoantibody production. Immunization of HLA-DR4 transgenic mice with the ITGA2B-ceC96 peptide promoted colitis and vertebral bone erosion. Thus, metabolite-induced cysteine carboxyethylation can give rise to pathogenic neoantigens that lead to autoreactive CD4+ T cell responses and autoantibody production in autoimmune diseases.
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Affiliation(s)
- Yue Zhai
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Liang Chen
- School of Medicine, Shanghai University, Shanghai 200444, China
| | - Qian Zhao
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Zhao-Hui Zheng
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Zhi-Nan Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Huijie Bian
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Xu Yang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Huan-Yu Lu
- Department of Occupational and Environmental Health and the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Fourth Military Medical University, Xi'an 710032, China
| | - Peng Lin
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Xi Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Ruo Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Hao-Yang Sun
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Lin-Ni Fan
- State Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Kun Zhang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Bin Wang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Xiu-Xuan Sun
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Zhuan Feng
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Yu-Meng Zhu
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Jian-Sheng Zhou
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Shi-Rui Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Tao Zhang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Si-Yu Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Jun-Jie Chen
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Kui Zhang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Yan Wang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Yang Chang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Rui Zhang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Bei Zhang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Li-Juan Wang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Xiao-Min Li
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Qian He
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Xiang-Min Yang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Gang Nan
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Rong-Hua Xie
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Liu Yang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
| | - Jing-Hua Yang
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
- Clinical Systems Biology Laboratories, Translational Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Ping Zhu
- Department of Clinical Immunology, Xijing Hospital, and Department of Cell Biology of National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an 710032, China
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Bhattacharjee M, Banerjee M, Mukherjee A. In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross-immunity against human visceral and cutaneous leishmaniasis: an immunoinformatics-based approach. J Mol Model 2023; 29:99. [PMID: 36928431 PMCID: PMC10018593 DOI: 10.1007/s00894-023-05503-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 03/08/2023] [Indexed: 03/18/2023]
Abstract
CONTEXT Leishmaniasis is a group of vector-borne infectious diseases caused by over 20 pathogenic Leishmania species that are endemic in many tropical and subtropical countries. The emergence of drug-resistant strains, the adverse side effects of anti-Leishmania drugs, and the absence of a preventative vaccination strategy threaten the sensitive population. Recently, many groups of researchers have exploited the field of reverse vaccinology to develop vaccines, focusing chiefly on inducing immunity against either visceral or cutaneous leishmaniasis. METHODS This present work involves retrieving twelve experimentally validated leishmanial antigenic protein sequences from the UniProt database, followed by their antigenicity profiling employing ANTIGENpro and Vaxijen 2.0 servers. MHC-binding epitopes for the same were predicted using both NetCTL 1.2 and SYFPEITHI servers, while epitopes for B cell were computed using ABCpred and BepiPred 2.0 servers. The screened epitopes with significantly higher scores were utilized for designing the vaccine construct with appropriate linkers and natural adjuvant. The secondary and tertiary structures of the synthetic peptide were determined by conditional random fields, shallow neural networks, and profile-profile threading alignment with iterative structure assembly simulations, respectively. The 3-D vaccine model was validated through CASP10-tested refinement and the MolProbity web server. Molecular docking and multi-scale normal mode analysis simulation were performed to analyze the best vaccine-TLR complex. Finally, computational immune simulation findings revealed promising cellular and humoral immune responses, suggesting that the engineered chimeric peptide is a potential broad-spectrum vaccine against visceral and cutaneous leishmaniasis.
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Affiliation(s)
- Mainak Bhattacharjee
- Department of Biotechnology, Heritage Institute of Technology, 994, Madurdaha, Kolkata, 700107, India
| | - Monojit Banerjee
- Department of Zoology, Triveni Devi Bhalotia College, Raniganj, 713347, India
| | - Arun Mukherjee
- Department of Zoology, Triveni Devi Bhalotia College, Raniganj, 713347, India.
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Song Y, Hu H, Xiao K, Huang X, Guo H, Shi Y, Zhao J, Zhu S, Ji T, Xia B, Jiang J, Cao L, Zhang Y, Zhang Y, Xu W. A Synthetic SARS-CoV-2-Derived T-Cell and B-Cell Peptide Cocktail Elicits Full Protection against Lethal Omicron BA.1 Infection in H11-K18-hACE2 Mice. Microbiol Spectr 2023; 11:e0419422. [PMID: 36912685 PMCID: PMC10100915 DOI: 10.1128/spectrum.04194-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/19/2023] [Indexed: 03/14/2023] Open
Abstract
Emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developing the capacity for immune evasion and resistance to existing vaccines and drugs. To address this, development of vaccines against coronavirus disease 2019 (COVID-19) has focused on universality, strong T cell immunity, and rapid production. Synthetic peptide vaccines, which are inexpensive and quick to produce, show low toxicity, and can be selected from the conserved SARS-CoV-2 proteome, are promising candidates. In this study, we evaluated the effectiveness of a synthetic peptide cocktail containing three murine CD4+ T-cell epitopes from the SARS-CoV-2 nonspike proteome and one B-cell epitope from the Omicron BA.1 receptor-binding domain (RBD), along with aluminum phosphate (Al) adjuvant and 5' cytosine-phosphate-guanine 3' oligodeoxynucleotide (CpG-ODN) adjuvant in mice. The peptide cocktail induced good Th1-biased T-cell responses and effective neutralizing-antibody titers against the Omicron BA.1 variant. Additionally, H11-K18-hACE2 transgenic mice were fully protected against lethal challenge with the BA.1 strain, with a 100% survival rate and reduced pulmonary viral load and pathological lesions. Subcutaneous administration was found to be the superior route for synthetic peptide vaccine delivery. Our findings demonstrate the effectiveness of the peptide cocktail in mice, suggesting the feasibility of synthetic peptide vaccines for humans. IMPORTANCE Current vaccines based on production of neutralizing antibodies fail to prevent the infection and transmission of SARS-CoV-2 Omicron and its subvariants. Understanding the critical factors and avoiding the disadvantages of vaccine strategies are essential for developing a safe and effective COVID-19 vaccine, which would include a more effective and durable cellular response, minimal effects of viral mutations, rapid production against emerging variants, and good safety. Peptide-based vaccines are an excellent alternative because they are inexpensive, quick to produce, and very safe. In addition, human leukocyte antigen T-cell epitopes could be targeted at robust T-cell immunity and selected in the conserved region of the SARS-CoV-2 variants. Our study showed that a synthetic SARS-CoV-2-derived peptide cocktail induced full protection against lethal infection with Omicron BA.1 in H11-K18-hACE2 mice for the first time. This could have implications for the development of effective COVID-19 peptide vaccines for humans.
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Affiliation(s)
- Yang Song
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hongqiao Hu
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kang Xiao
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xinghu Huang
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hong Guo
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuqing Shi
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiannan Zhao
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shuangli Zhu
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tianjiao Ji
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Baicheng Xia
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jie Jiang
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lei Cao
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yong Zhang
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yan Zhang
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wenbo Xu
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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47
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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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Lim CP, Kok BH, Lim HT, Chuah C, Abdul Rahman B, Abdul Majeed AB, Wykes M, Leow CH, Leow CY. Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design. Pathog Glob Health 2023; 117:134-151. [PMID: 35550001 PMCID: PMC9970233 DOI: 10.1080/20477724.2022.2072456] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.
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Affiliation(s)
- Chin Peng Lim
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia.,Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Boon Hui Kok
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Hui Ting Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Candy Chuah
- Faculty of Health Sciences, Universiti Teknologi MARA, Penang, Malaysia
| | | | | | - Michelle Wykes
- Molecular Immunology Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Chiuan Herng Leow
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor, Malaysia
| | - Chiuan Yee Leow
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
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49
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Schroeder SM, Nelde A, Walz JS. Viral T-cell epitopes - Identification, characterization and clinical application. Semin Immunol 2023; 66:101725. [PMID: 36706520 DOI: 10.1016/j.smim.2023.101725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/26/2023]
Abstract
T-cell immunity, mediated by CD4+ and CD8+ T cells, represents a cornerstone in the control of viral infections. Virus-derived T-cell epitopes are represented by human leukocyte antigen (HLA)-presented viral peptides on the surface of virus-infected cells. They are the prerequisite for the recognition of infected cells by T cells. Knowledge of viral T-cell epitopes provides on the one hand a diagnostic tool to decipher protective T-cell immune responses in the human population and on the other hand various prophylactic and therapeutic options including vaccination approaches and the transfer of virus-specific T cells. Such approaches have already been proven to be effective against various viral infections, particularly in immunocompromised patients lacking sufficient humoral, antibody-based immune response. This review provides an overview on the state of the art as well as current studies regarding the identification and characterization of viral T-cell epitopes and approaches of clinical application. In the first chapter in silico prediction tools and direct, mass spectrometry-based identification of viral T-cell epitopes is compared. The second chapter provides an overview of commonly used assays for further characterization of T-cell responses and phenotypes. The final chapter presents an overview of clinical application of viral T-cell epitopes with a focus on human immunodeficiency virus (HIV), human cytomegalovirus (HCMV) and severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), being representatives of relevant viruses.
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Affiliation(s)
- Sarah M Schroeder
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany; Department for Otorhinolaryngology, Head, and Neck Surgery, University Hospital Tübingen, Tübingen, Germany; Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
| | - Annika Nelde
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany; Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | - Juliane S Walz
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany; Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany; Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany.
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Mühlenbruch L, Abou-Kors T, Dubbelaar ML, Bichmann L, Kohlbacher O, Bens M, Thomas J, Ezić J, Kraus JM, Kestler HA, von Witzleben A, Mytilineos J, Fürst D, Engelhardt D, Doescher J, Greve J, Schuler PJ, Theodoraki MN, Brunner C, Hoffmann TK, Rammensee HG, Walz JS, Laban S. The HLA ligandome of oropharyngeal squamous cell carcinomas reveals shared tumour-exclusive peptides for semi-personalised vaccination. Br J Cancer 2023; 128:1777-1787. [PMID: 36823366 PMCID: PMC9949688 DOI: 10.1038/s41416-023-02197-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/24/2023] [Accepted: 02/01/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND The immune peptidome of OPSCC has not previously been studied. Cancer-antigen specific vaccination may improve clinical outcome and efficacy of immune checkpoint inhibitors such as PD1/PD-L1 antibodies. METHODS Mapping of the OPSCC HLA ligandome was performed by mass spectrometry (MS) based analysis of naturally presented HLA ligands isolated from tumour tissue samples (n = 40) using immunoaffinity purification. The cohort included 22 HPV-positive (primarily HPV-16) and 18 HPV-negative samples. A benign reference dataset comprised of the HLA ligandomes of benign haematological and tissue datasets was used to identify tumour-associated antigens. RESULTS MS analysis led to the identification of naturally HLA-presented peptides in OPSCC tumour tissue. In total, 22,769 peptides from 9485 source proteins were detected on HLA class I. For HLA class II, 15,203 peptides from 4634 source proteins were discovered. By comparative profiling against the benign HLA ligandomic datasets, 29 OPSCC-associated HLA class I ligands covering 11 different HLA allotypes and nine HLA class II ligands were selected to create a peptide warehouse. CONCLUSION Tumour-associated peptides are HLA-presented on the cell surfaces of OPSCCs. The established warehouse of OPSCC-associated peptides can be used for downstream immunogenicity testing and peptide-based immunotherapy in (semi)personalised strategies.
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Affiliation(s)
- Lena Mühlenbruch
- grid.10392.390000 0001 2190 1447Institute for Cell Biology, Department of Immunology, Eberhard Karls University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Department of Peptide-based Immunotherapy, Eberhard Karls University and University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,German Cancer Consortium (DKTK), Partner Site Tübingen, 72076 Tübingen, Baden-Württemberg Germany
| | - Tsima Abou-Kors
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Marissa L. Dubbelaar
- grid.10392.390000 0001 2190 1447Institute for Cell Biology, Department of Immunology, Eberhard Karls University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Department of Peptide-based Immunotherapy, Eberhard Karls University and University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Quantitative Biology Center (QBiC), Eberhard Karls University Tübingen, 72076 Tübingen, Baden-Württemberg Germany
| | - Leon Bichmann
- grid.10392.390000 0001 2190 1447Institute for Cell Biology, Department of Immunology, Eberhard Karls University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, 72076 Tübingen, Baden-Württemberg Germany
| | - Oliver Kohlbacher
- grid.10392.390000 0001 2190 1447Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Cluster of Excellence Machine Learning in the Sciences (EXC2064), Eberhard Karls University Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.411544.10000 0001 0196 8249Institute for Translational Bioinformatics, University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Institute for Bioinformatics and Medical Informatics, Eberhard Karls University Tübingen, 72076 Tübingen, Baden-Württemberg Germany
| | - Martin Bens
- grid.418245.e0000 0000 9999 5706Leibniz-Institute on Aging, Fritz-Lipmann-Institute, 07745 Jena, Thüringen Germany
| | - Jaya Thomas
- grid.5491.90000 0004 1936 9297CRUK and NIHR Experimental Cancer Medicine Center & School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ UK
| | - Jasmin Ezić
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Johann M. Kraus
- grid.6582.90000 0004 1936 9748Ulm University, Institute of Medical Systems Biology, Ulm, Germany
| | - Hans A. Kestler
- grid.6582.90000 0004 1936 9748Ulm University, Institute of Medical Systems Biology, Ulm, Germany
| | - Adrian von Witzleben
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Joannis Mytilineos
- grid.410712.10000 0004 0473 882XInstitute of Clinical Transfusion Medicine and Immunogenetics Ulm, German Red Cross Blood Transfusion Service, Baden–Württemberg–Hessen, and University Hospital Ulm, Ulm, Germany ,grid.6582.90000 0004 1936 9748Institute of Transfusion Medicine, Ulm University, Ulm, Germany ,German Stem Cell Donor Registry, German Red Cross Blood Transfusion Service, Ulm, Germany
| | - Daniel Fürst
- grid.410712.10000 0004 0473 882XInstitute of Clinical Transfusion Medicine and Immunogenetics Ulm, German Red Cross Blood Transfusion Service, Baden–Württemberg–Hessen, and University Hospital Ulm, Ulm, Germany ,grid.6582.90000 0004 1936 9748Institute of Transfusion Medicine, Ulm University, Ulm, Germany
| | - Daphne Engelhardt
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Johannes Doescher
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Jens Greve
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Patrick J. Schuler
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Marie-Nicole Theodoraki
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Cornelia Brunner
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Thomas K. Hoffmann
- grid.410712.10000 0004 0473 882XDepartment of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany
| | - Hans-Georg Rammensee
- grid.10392.390000 0001 2190 1447Institute for Cell Biology, Department of Immunology, Eberhard Karls University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Department of Peptide-based Immunotherapy, Eberhard Karls University and University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,German Cancer Consortium (DKTK), Partner Site Tübingen, 72076 Tübingen, Baden-Württemberg Germany
| | - Juliane S. Walz
- grid.10392.390000 0001 2190 1447Institute for Cell Biology, Department of Immunology, Eberhard Karls University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Department of Peptide-based Immunotherapy, Eberhard Karls University and University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.10392.390000 0001 2190 1447Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg Germany ,grid.411544.10000 0001 0196 8249Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Baden-Württemberg 72076 Germany
| | - Simon Laban
- Department of Otorhinolaryngology and Head & Neck Surgery, Ulm University Medical Center, Head and Neck Cancer Center of the Comprehensive Cancer Center Ulm, Ulm, Germany.
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