1
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Liu L, Xu B, Chen L, Liu J, Liu W, Xue F, Feng S, Jiang E, Han M, Shao W, Zhang L, Pei X. An investigation of the immune epitopes of adeno-associated virus capsid-derived peptides among hemophilia patients. Mol Ther Methods Clin Dev 2024; 32:101245. [PMID: 38660620 PMCID: PMC11039395 DOI: 10.1016/j.omtm.2024.101245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Adeno-associated virus (AAV) is an optimal gene vector for monogenic disorders. However, neutralizing antibodies (Nabs) against AAV hinder its widespread application in gene therapy. In this study, we biosynthesized peptides recognized by the binding antibodies (Babs) from the sera containing high Nab titers against AAV2. We established four immunological methods to detect immune epitopes of the AAV2-derived peptides, including a Bab assay, Nab assay, B cell receptor (BCR) detecting assay, and immunoglobin-producing B cell enzyme-linked immunosorbent spot (B cell ELISpot) assay. Correlations among the epitopes determined by these four methods were analyzed using the serum samples and peripheral blood mononuclear cells (PBMC) from 89 patients with hemophilia A/B. As decoys, the peptides' ability to block the Nab of AAV2 particles was assessed using AAV transduction models both in vitro and in vivo. Overall, we provide insights into AAV2-capsid-derived peptide immune epitopes, involving the Nab, Bab, BCR, and B cell ELISpot assays, offering alternative immunological evaluation approaches and strategies to overcome Nab barriers in AAV-mediated gene therapy.
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Affiliation(s)
- Li Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Bingqi Xu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Lingling Chen
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Jia Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Wei Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Feng Xue
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Sizhou Feng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Erlie Jiang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Mingzhe Han
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Wenwei Shao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R. China
| | - Lei Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
| | - Xiaolei Pei
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, P.R. China
- Tianjin Institutes of Health Science, Tianjin 300020, P.R. China
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2
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Lin N, Miyamoto K, Ogawara T, Sakurai S, Kizaka-Kondoh S, Kadonosono T. Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing. Commun Biol 2024; 7:652. [PMID: 38806676 PMCID: PMC11133372 DOI: 10.1038/s42003-024-06363-7] [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: 01/08/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024] Open
Abstract
Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.
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Affiliation(s)
- Ning Lin
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
| | - Kotaro Miyamoto
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
| | - Takumi Ogawara
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
| | - Saki Sakurai
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
| | - Shinae Kizaka-Kondoh
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
| | - Tetsuya Kadonosono
- School of Life Science and Technology, Tokyo Institute of Technology, Yokohama, 226-8501, Japan.
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3
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Rong Y, Chen IL, Larrabee L, Sawant MS, Fuh G, Koenig P. An Engineered Mouse Model That Generates a Diverse Repertoire of Endogenous, High-Affinity Common Light Chain Antibodies. Antibodies (Basel) 2024; 13:14. [PMID: 38390875 PMCID: PMC10885109 DOI: 10.3390/antib13010014] [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: 10/24/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
Bispecific antibodies have gained increasing popularity as therapeutics as they enable novel activities that cannot be achieved with monospecific antibodies. Some of the most popular bispecific formats are molecules in which two Fab arms with different antigen specificities are combined into one IgG-like molecule. One way to produce these bispecific molecules requires the discovery of antibodies against the two antigens of interest that share a common light chain. Here, we present the generation and characterization of a common light chain mouse model, in which the endogenous IGKJ cluster is replaced with a prearranged, modified murine IGKV10-96/IGKJ1 segment. We demonstrate that genetic modification does not impact B-cell development. Upon immunization with ovalbumin, the animals generate an antibody repertoire with VH gene segment usage of a similar diversity to wildtype mice, while the light chain diversity is restricted to antibodies derived from the prearranged IGKV10-96/IGKJ1 germline. We further show that the clonotype diversity of the common light chain immune repertoire matches the diversity of immune repertoire isolated from wildtype mice. Finally, the common light chain anti-ovalbumin antibodies have only slightly lower affinities than antibodies isolated from wildtype mice, demonstrating the suitability of these animals for antibody discovery for bispecific antibody generation.
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Affiliation(s)
- Yinghui Rong
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - I-Ling Chen
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - Lance Larrabee
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - Manali S Sawant
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - Germaine Fuh
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - Patrick Koenig
- 23andMe, Inc. Therapeutics, 349 Oyster Point Boulevard, South San Francisco, CA 94080, USA
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4
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Spoendlin FC, Abanades B, Raybould MIJ, Wong WK, Georges G, Deane CM. Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope. Front Mol Biosci 2023; 10:1237621. [PMID: 37790877 PMCID: PMC10544996 DOI: 10.3389/fmolb.2023.1237621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).
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Affiliation(s)
- Fabian C. Spoendlin
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Matthew I. J. Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Wing Ki Wong
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Guy Georges
- Large Molecule Research, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Penzberg, Germany
| | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom
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5
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Ghanbarpour A, Jiang M, Foster D, Chai Q. Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models. iScience 2023; 26:106036. [PMID: 36824280 PMCID: PMC9941125 DOI: 10.1016/j.isci.2023.106036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.
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Affiliation(s)
- Ahmadreza Ghanbarpour
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA
| | - Min Jiang
- Advanced Analytics and Data Sciences, Lilly Corporate Center, Indianapolis, IN 46225, USA
| | - Denisa Foster
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA
| | - Qing Chai
- Biotechnology Discovery Research, Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA 92121, USA,Corresponding author
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6
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [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: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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7
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Cunha DM, Hernández-Pérez S, Mattila PK. Isolation of the B Cell Immune Synapse for Proteomic Analysis. Methods Mol Biol 2023; 2654:393-408. [PMID: 37106196 DOI: 10.1007/978-1-0716-3135-5_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Recent technical developments have fueled increasing utilization of proteomics to gain new insights into various aspects of cellular behavior. In this chapter, we describe a method to specifically isolate immune synapses from mouse primary B cells. The method utilizes antibody-coated magnetic beads to induce the formation of the immune synapses and describes a protocol for the extraction of the cell-bead adhesions for mass spectrometry analysis. Finally, this method enables unveiling the large-scale protein content of the immune synapse.
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Affiliation(s)
- Diogo M Cunha
- Institute of Biomedicine, MediCity Research Laboratories, and InFLAMES Research Flagship, University of Turku, Turku, Finland
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sara Hernández-Pérez
- Institute of Biomedicine, MediCity Research Laboratories, and InFLAMES Research Flagship, University of Turku, Turku, Finland
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland
| | - Pieta K Mattila
- Institute of Biomedicine, MediCity Research Laboratories, and InFLAMES Research Flagship, University of Turku, Turku, Finland.
- Turku Bioscience, University of Turku and Åbo Akademi University, Turku, Finland.
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8
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Jaffe DB, Shahi P, Adams BA, Chrisman AM, Finnegan PM, Raman N, Royall AE, Tsai F, Vollbrecht T, Reyes DS, Hepler NL, McDonnell WJ. Functional antibodies exhibit light chain coherence. Nature 2022; 611:352-357. [PMID: 36289331 PMCID: PMC9607724 DOI: 10.1038/s41586-022-05371-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 09/21/2022] [Indexed: 11/08/2022]
Abstract
The vertebrate adaptive immune system modifies the genome of individual B cells to encode antibodies that bind particular antigens1. In most mammals, antibodies are composed of heavy and light chains that are generated sequentially by recombination of V, D (for heavy chains), J and C gene segments. Each chain contains three complementarity-determining regions (CDR1-CDR3), which contribute to antigen specificity. Certain heavy and light chains are preferred for particular antigens2-22. Here we consider pairs of B cells that share the same heavy chain V gene and CDRH3 amino acid sequence and were isolated from different donors, also known as public clonotypes23,24. We show that for naive antibodies (those not yet adapted to antigens), the probability that they use the same light chain V gene is around 10%, whereas for memory (functional) antibodies, it is around 80%, even if only one cell per clonotype is used. This property of functional antibodies is a phenomenon that we call light chain coherence. We also observe this phenomenon when similar heavy chains recur within a donor. Thus, although naive antibodies seem to recur by chance, the recurrence of functional antibodies reveals surprising constraint and determinism in the processes of V(D)J recombination and immune selection. For most functional antibodies, the heavy chain determines the light chain.
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9
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Xu Z, Ismanto HS, Zhou H, Saputri DS, Sugihara F, Standley DM. Advances in antibody discovery from human BCR repertoires. FRONTIERS IN BIOINFORMATICS 2022; 2:1044975. [PMID: 36338807 PMCID: PMC9631452 DOI: 10.3389/fbinf.2022.1044975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.
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Affiliation(s)
- Zichang Xu
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hao Zhou
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Department Systems Immunology, Immunology Frontier Research Center, Osaka University, Suita, Japan
- *Correspondence: Daron M. Standley,
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10
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Waltari E, Nafees S, McCutcheon KM, Wong J, Pak JE. AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses. PLoS Comput Biol 2022; 18:e1010052. [PMID: 36126074 PMCID: PMC9524643 DOI: 10.1371/journal.pcbi.1010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/30/2022] [Accepted: 09/04/2022] [Indexed: 11/18/2022] Open
Abstract
The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.
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Affiliation(s)
- Eric Waltari
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (EW); (JEP)
| | - Saba Nafees
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
| | | | - Joan Wong
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
| | - John E. Pak
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
- * E-mail: (EW); (JEP)
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11
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Weber CR, Rubio T, Wang L, Zhang W, Robert PA, Akbar R, Snapkov I, Wu J, Kuijjer ML, Tarazona S, Conesa A, Sandve GK, Liu X, Reddy ST, Greiff V. Reference-based comparison of adaptive immune receptor repertoires. CELL REPORTS METHODS 2022; 2:100269. [PMID: 36046619 PMCID: PMC9421535 DOI: 10.1016/j.crmeth.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.
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Affiliation(s)
- Cédric R. Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Teresa Rubio
- Laboratory of Neurobiology, Centro Investigación Príncipe Felipe, Valencia, Spain
| | - Longlong Wang
- BGI-Shenzhen, Shenzhen, China
- BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Wei Zhang
- BGI-Shenzhen, Shenzhen, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Philippe A. Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | | | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sonia Tarazona
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Xiao Liu
- BGI-Shenzhen, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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12
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Bourgonje AR, Vogl T, Segal E, Weersma RK. Antibody signatures in inflammatory bowel disease: current developments and future applications. Trends Mol Med 2022; 28:693-705. [DOI: 10.1016/j.molmed.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/01/2022] [Accepted: 05/03/2022] [Indexed: 11/25/2022]
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13
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Hummer AM, Abanades B, Deane CM. Advances in computational structure-based antibody design. Curr Opin Struct Biol 2022; 74:102379. [PMID: 35490649 DOI: 10.1016/j.sbi.2022.102379] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/28/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022]
Abstract
Antibodies are currently the most important class of biotherapeutics and are used to treat numerous diseases. Recent advances in computational methods are ushering in a new era of antibody design, driven in part by accurate structure prediction. Previously, structure-based antibody design has been limited to a relatively small number of cases where accurate structures or models of both the target antigen and antibody were available. As we move towards a time where it is possible to accurately model most antibodies and antigens, and to reliably predict their binding site, there is vast potential for true computational antibody design. In this review, we describe the latest methods that promise to launch a paradigm shift towards entirely in silico structure-based antibody design.
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Affiliation(s)
- Alissa M Hummer
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@AlissaHummer
| | - Brennan Abanades
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK. https://twitter.com/@brennanaba
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
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14
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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