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Celis-Giraldo C, Ordoñez D, Díaz-Arévalo D, Bohórquez MD, Ibarrola N, Suárez CF, Rodríguez K, Yepes Y, Rodríguez A, Avendaño C, López-Abán J, Manzano-Román R, Patarroyo MA. Identifying major histocompatibility complex class II-DR molecules in bovine and swine peripheral blood monocyte-derived macrophages using mAb-L243. Vaccine 2024; 42:3445-3454. [PMID: 38631956 DOI: 10.1016/j.vaccine.2024.04.042] [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: 12/22/2023] [Revised: 04/04/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
Major histocompatibility complex class II (MHC-II) molecules are involved in immune responses against pathogens and vaccine candidates' immunogenicity. Immunopeptidomics for identifying cancer and infection-related antigens and epitopes have benefited from advances in immunopurification methods and mass spectrometry analysis. The mouse anti-MHC-II-DR monoclonal antibody L243 (mAb-L243) has been effective in recognising MHC-II-DR in both human and non-human primates. It has also been shown to cross-react with other animal species, although it has not been tested in livestock. This study used mAb-L243 to identify Staphylococcus aureus and Salmonella enterica serovar Typhimurium peptides binding to cattle and swine macrophage MHC-II-DR molecules using flow cytometry, mass spectrometry and two immunopurification techniques. Antibody cross-reactivity led to identifying expressed MHC-II-DR molecules, together with 10 Staphylococcus aureus peptides in cattle and 13 S. enterica serovar Typhimurium peptides in swine. Such data demonstrates that MHC-II-DR expression and immunocapture approaches using L243 mAb represents a viable strategy for flow cytometry and immunopeptidomics analysis of bovine and swine antigen-presenting cells.
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Affiliation(s)
- Carmen Celis-Giraldo
- Animal Science Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Bogotá, Colombia; PhD Programme in Tropical Health and Development, Doctoral School "Studii Salamantini", Universidad de Salamanca, Salamanca, Spain
| | - Diego Ordoñez
- Animal Science Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Bogotá, Colombia; PhD Programme in Tropical Health and Development, Doctoral School "Studii Salamantini", Universidad de Salamanca, Salamanca, Spain
| | - Diana Díaz-Arévalo
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Michel D Bohórquez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia; MSc Programme in Microbiology, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Nieves Ibarrola
- Centro de Investigación del Cáncer and Instituto de Biología Molecular y Celular del Cáncer (IBMCC), CSIC-University of Salamanca, Salamanca, Spain
| | - Carlos F Suárez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Kewin Rodríguez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Yoelis Yepes
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Alexander Rodríguez
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia
| | - Catalina Avendaño
- Department of Immunology and Theranostics, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute of City of Hope, National Medical Center, Duarte, CA, United States
| | - Julio López-Abán
- Infectious and Tropical Diseases Group (e-INTRO), IBSAL-CIETUS (Instituto de Investigación Biomédica de Salamanca - Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca), Pharmacy Faculty, Universidad de Salamanca, C/ L. Méndez Nieto s/n, 37007 Salamanca, Spain
| | - Raúl Manzano-Román
- Infectious and Tropical Diseases Group (e-INTRO), IBSAL-CIETUS (Instituto de Investigación Biomédica de Salamanca - Centro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca), Pharmacy Faculty, Universidad de Salamanca, C/ L. Méndez Nieto s/n, 37007 Salamanca, Spain
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia.
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Adams C, Gabriel W, Laukens K, Picciani M, Wilhelm M, Bittremieux W, Boonen K. Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nat Commun 2024; 15:3956. [PMID: 38730277 PMCID: PMC11087512 DOI: 10.1038/s41467-024-48322-0] [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: 07/17/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.
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Affiliation(s)
- Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Kris Laukens
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Mario Picciani
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, 85354, Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, Antwerp, Belgium.
| | - Kurt Boonen
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Sustainable Health Department, Flemish Institute for Technological Research (VITO), Antwerp, Belgium.
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Adams C, Laukens K, Bittremieux W, Boonen K. Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome. Proteomics 2024; 24:e2300336. [PMID: 38009585 DOI: 10.1002/pmic.202300336] [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: 09/06/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
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Affiliation(s)
- Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- ImmuneSpec BV, Niel, Belgium
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Chavda VP, Redwan EM. SARS-CoV-2: Immunopeptidomics and Other Immunological Studies. Vaccines (Basel) 2022; 10:vaccines10111975. [PMID: 36423070 PMCID: PMC9694091 DOI: 10.3390/vaccines10111975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/09/2022] [Accepted: 11/15/2022] [Indexed: 11/23/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced a significant continuing epidemic worldwide [...]
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Affiliation(s)
- Vivek P. Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L M College of Pharmacy, Ahmedabad 380008, India
- Correspondence: (V.P.C.); (E.M.R.); Tel.: +91-7030-919-407 (V.P.C.)
| | - Elrashdy M. Redwan
- Biological Science Department, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
- Protein Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA City), Alexandria 21934, Egypt
- Correspondence: (V.P.C.); (E.M.R.); Tel.: +91-7030-919-407 (V.P.C.)
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Acharjee A, Stephen Kingsly J, Kamat M, Kurlawala V, Chakraborty A, Vyas P, Vaishnav R, Srivastava S. Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet? Expert Rev Proteomics 2022; 19:197-212. [PMID: 35655386 DOI: 10.1080/14789450.2022.2085564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
INTRODUCTION The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role. AREAS COVERED In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms. EXPERT OPINION The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.
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Affiliation(s)
- Arup Acharjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | | | - Madhura Kamat
- Department of Biological Sciences, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be University), Mumbai, India
| | - Vishakha Kurlawala
- Department of Biological Sciences, Sunandan Divatia School of Science, SVKM's NMIMS (Deemed-to-be University), Mumbai, India
| | | | - Priyanka Vyas
- Department of Biotechnology and Botany, Mahila PG Mahavidyalaya, J. N. V University, Jodhpur, India
| | - Radhika Vaishnav
- Department of Life Sciences, Ivy Tech Community College, Indianapolis, Indiana, USA
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
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