1
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Abbate MF, Dupic T, Vigne E, Shahsavarian MA, Walczak AM, Mora T. Computational detection of antigen-specific B cell receptors following immunization. Proc Natl Acad Sci U S A 2024; 121:e2401058121. [PMID: 39163333 PMCID: PMC11363332 DOI: 10.1073/pnas.2401058121] [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: 01/16/2024] [Accepted: 07/10/2024] [Indexed: 08/22/2024] Open
Abstract
B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.
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Affiliation(s)
- Maria Francesca Abbate
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
- Large Molecule Research, Sanofi, Vitry-sur-Seine94 400, France
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA02138
| | | | | | - Aleksandra M. Walczak
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
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2
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Lê Quý K, Chernigovskaya M, Stensland M, Singh S, Leem J, Revale S, Yadin DA, Nice FL, Povall C, Minns DH, Galson JD, Nyman TA, Snapkow I, Greiff V. Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling. NPJ Syst Biol Appl 2024; 10:73. [PMID: 38997321 PMCID: PMC11245537 DOI: 10.1038/s41540-024-00402-z] [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: 11/02/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.
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Grants
- The Leona M. and Harry B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO World-Leading Research Community (to VG), UiO: LifeScience Convergence Environment Immunolingo (to VG), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Norwegian Cancer Society Grant (#215817, to VG), Research Council of Norway projects (#300740, (#311341, #331890 to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG). This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 101007799 (Inno4Vac). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA (to VG).
- Mass spectrometry-based proteomic analyses were performed by the Proteomics Core Facility, Department of Immunology, University of Oslo/Oslo University Hospital, which is supported by the Core Facilities program of the South-Eastern Norway Regional Health Authority. This core facility is also a member of the National Network of Advanced Proteomics Infrastructure (NAPI), which is funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910).
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Affiliation(s)
- Khang Lê Quý
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Stensland
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Sachin Singh
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | | | | | | | | | | | | | - Tuula A Nyman
- Proteomics Core Facility, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Igor Snapkow
- Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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3
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Richardson E, Bibi S, McLean F, Schimanski L, Rijal P, Ghraichy M, von Niederhäusern V, Trück J, Clutterbuck EA, O’Connor D, Luhn K, Townsend A, Peters B, Pollard AJ, Deane CM, Kelly DF. Computational mining of B cell receptor repertoires reveals antigen-specific and convergent responses to Ebola vaccination. Front Immunol 2024; 15:1383753. [PMID: 39040106 PMCID: PMC11260629 DOI: 10.3389/fimmu.2024.1383753] [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: 02/08/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Outbreaks of Ebolaviruses, such as Sudanvirus (SUDV) in Uganda in 2022, demonstrate that species other than the Zaire ebolavirus (EBOV), which is currently the sole virus represented in current licensed vaccines, remain a major threat to global health. There is a pressing need to develop effective pan-species vaccines and novel monoclonal antibody-based therapeutics for Ebolavirus disease. In response to recent outbreaks, the two dose, heterologous Ad26.ZEBOV/MVA-BN-Filo vaccine regimen was developed and was tested in a large phase II clinical trial (EBL2001) as part of the EBOVAC2 consortium. Here, we perform bulk sequencing of the variable heavy chain (VH) of B cell receptors (BCR) in forty participants from the EBL2001 trial in order to characterize the BCR repertoire in response to vaccination with Ad26.ZEBOV/MVA-BN-Filo. We develop a comprehensive database, EBOV-AbDab, of publicly available Ebolavirus-specific antibody sequences. We then use our database to predict the antigen-specific component of the vaccinee repertoires. Our results show striking convergence in VH germline gene usage across participants following the MVA-BN-Filo dose, and provide further evidence of the role of IGHV3-15 and IGHV3-13 antibodies in the B cell response to Ebolavirus glycoprotein. Furthermore, we found that previously described Ebola-specific mAb sequences present in EBOV-AbDab were sufficient to describe at least one of the ten most expanded BCR clonotypes in more than two thirds of our cohort of vaccinees following the boost, providing proof of principle for the utility of computational mining of immune repertoires.
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Affiliation(s)
- Eve Richardson
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
- La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Sagida Bibi
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Florence McLean
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Lisa Schimanski
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Pramila Rijal
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Marie Ghraichy
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Valentin von Niederhäusern
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Johannes Trück
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | | | - Daniel O’Connor
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Kerstin Luhn
- Janssen Vaccines and Prevention, Leiden, Netherlands
| | - Alain Townsend
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Andrew J. Pollard
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | | | - Dominic F. Kelly
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
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4
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Liu B, Niu X, Deng Y, Zhang Z, Wang Y, Gao X, Liang H, Li Z, Wang Q, Cheng Y, Chen Q, Huang S, Pan Y, Su M, Lin X, Niu C, Chen Y, Yang W, Zhang Y, Yan Q, He J, Zhao J, Chen L, Xiong X. An unconventional VH1-2 antibody tolerates escape mutations and shows an antigenic hotspot on SARS-CoV-2 spike. Cell Rep 2024; 43:114265. [PMID: 38805396 DOI: 10.1016/j.celrep.2024.114265] [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: 06/22/2023] [Revised: 11/29/2023] [Accepted: 05/07/2024] [Indexed: 05/30/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein continues to evolve antigenically, impacting antibody immunity. D1F6, an affinity-matured non-stereotypic VH1-2 antibody isolated from a patient infected with the SARS-CoV-2 ancestral strain, effectively neutralizes most Omicron variants tested, including XBB.1.5. We identify that D1F6 in the immunoglobulin G (IgG) form is able to overcome the effect of most Omicron mutations through its avidity-enhanced multivalent S-trimer binding. Cryo-electron microscopy (cryo-EM) and biochemical analyses show that three simultaneous epitope mutations are generally needed to substantially disrupt the multivalent S-trimer binding by D1F6 IgG. Antigenic mutations at spike positions 346, 444, and 445, which appeared in the latest variants, have little effect on D1F6 binding individually. However, these mutations are able to act synergistically with earlier Omicron mutations to impair neutralization by affecting the interaction between D1F6 IgG and the S-trimer. These results provide insight into the mechanism by which accumulated antigenic mutations facilitate evasion of affinity-matured antibodies.
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Affiliation(s)
- Banghui Liu
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xuefeng Niu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Yijun Deng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhaoyong Zhang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yanqun Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xijie Gao
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Huan Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zimu Li
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Qian Wang
- Guangzhou Laboratory & Bioland Laboratory, Guangzhou, China
| | - Yuanyi Cheng
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiuluan Chen
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health - Guangdong Laboratory), Guangzhou, China
| | - Shuangshuang Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yingxian Pan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Mengzhen Su
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China; University of Science and Technology of China, Hefei, China
| | - Xiancheng Lin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chuanying Niu
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China; University of Science and Technology of China, Hefei, China
| | - Yinglin Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenyi Yang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yudi Zhang
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China; University of Chinese Academy of Sciences, Beijing, China
| | - Qihong Yan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jun He
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Jincun Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Ling Chen
- Guangzhou Laboratory & Bioland Laboratory, Guangzhou, China.
| | - Xiaoli Xiong
- State Key Laboratory of Respiratory Disease, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
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5
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Wang K, Cai L, Wang H, Shan S, Hu X, Zhang J. Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data. STAR Protoc 2024; 5:102969. [PMID: 38502687 PMCID: PMC10963638 DOI: 10.1016/j.xpro.2024.102969] [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: 11/29/2023] [Revised: 01/26/2024] [Accepted: 03/03/2024] [Indexed: 03/21/2024] Open
Abstract
The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.
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Affiliation(s)
- Kaixuan Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Linru Cai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hao Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Georgia Tech Shenzhen Institute (GTSI), Tianjin University, Shenzhen, Guangdong, China
| | - Shiwen Shan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xihao Hu
- GV20 Therapeutics, Cambridge, MA, USA
| | - Jian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
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6
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Sammut SJ, Galson JD, Minter R, Sun B, Chin SF, De Mattos-Arruda L, Finch DK, Schätzle S, Dias J, Rueda OM, Seoane J, Osbourn J, Caldas C, Bashford-Rogers RJM. Predictability of B cell clonal persistence and immunosurveillance in breast cancer. Nat Immunol 2024; 25:916-924. [PMID: 38698238 PMCID: PMC11065701 DOI: 10.1038/s41590-024-01821-0] [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: 02/20/2024] [Accepted: 03/15/2024] [Indexed: 05/05/2024]
Abstract
B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.
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MESH Headings
- Humans
- Female
- Breast Neoplasms/immunology
- B-Lymphocytes/immunology
- Immunologic Surveillance
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Receptors, Antigen, B-Cell/metabolism
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- T-Lymphocytes/immunology
- Monitoring, Immunologic
- Exome Sequencing
- Antigens, Neoplasm/immunology
- Neoplasm Metastasis
- Clone Cells
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Affiliation(s)
- Stephen-John Sammut
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK.
- The Royal Marsden Hospital NHS Foundation Trust, London, UK.
| | | | | | - Bo Sun
- Wellcome Centre for Human Genetics, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Leticia De Mattos-Arruda
- IrsiCaixa, Germans Trias i Pujol University Hospital, Badalona, Spain
- Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
| | | | | | | | - Oscar M Rueda
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Joan Seoane
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron University Hospital, Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Autònoma de Barcelona (UAB), CIBERONC, Barcelona, Spain
| | | | - Carlos Caldas
- School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| | - Rachael J M Bashford-Rogers
- Wellcome Centre for Human Genetics, Oxford, UK.
- Department of Biochemistry, University of Oxford, Oxford, UK.
- Oxford Cancer Centre, Oxford, UK.
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7
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Townsend DR, Towers DM, Lavinder JJ, Ippolito GC. Innovations and trends in antibody repertoire analysis. Curr Opin Biotechnol 2024; 86:103082. [PMID: 38428225 DOI: 10.1016/j.copbio.2024.103082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/07/2023] [Accepted: 01/28/2024] [Indexed: 03/03/2024]
Abstract
Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.
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Affiliation(s)
- Douglas R Townsend
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Dalton M Towers
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jason J Lavinder
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Gregory C Ippolito
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
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8
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Chomicz D, Kończak J, Wróbel S, Satława T, Dudzic P, Janusz B, Tarkowski M, Deszyński P, Gawłowski T, Kostyn A, Orłowski M, Klaus T, Schulte L, Martin K, Comeau SR, Krawczyk K. Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications. Front Mol Biosci 2024; 11:1352508. [PMID: 38606289 PMCID: PMC11008471 DOI: 10.3389/fmolb.2024.1352508] [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: 12/08/2023] [Accepted: 02/09/2024] [Indexed: 04/13/2024] Open
Abstract
Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.
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Affiliation(s)
| | | | - Sonia Wróbel
- NaturalAntibody, Szczecin, West Pomeranian, Poland
| | | | - Paweł Dudzic
- NaturalAntibody, Szczecin, West Pomeranian, Poland
| | | | | | | | | | | | - Marek Orłowski
- Pure Biologics, Wrocław, Poland
- Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wrocław University of Science and Technology, Wrocław, Poland
| | | | - Lukas Schulte
- Global Computational Biology & Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Kyle Martin
- Biotherapeutics Discovery, Boehringer Ingelheim, Biberach, Germany
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9
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Peres A, Klein V, Frankel B, Lees W, Polak P, Meehan M, Rocha A, Correia Lopes J, Yaari G. Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data. Brief Bioinform 2024; 25:bbae221. [PMID: 38752856 PMCID: PMC11097599 DOI: 10.1093/bib/bbae221] [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: 10/02/2023] [Revised: 03/06/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.
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Affiliation(s)
- Ayelet Peres
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Vered Klein
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Boaz Frankel
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - William Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, United Kingdom
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Pazit Polak
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Mark Meehan
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Artur Rocha
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - João Correia Lopes
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
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10
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Woodruff MC, Faliti CE, Sanz I. Systems biology of B cells in COVID-19. Semin Immunol 2024; 72:101875. [PMID: 38489999 DOI: 10.1016/j.smim.2024.101875] [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: 01/29/2024] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/17/2024]
Abstract
The integration of multi-'omic datasets into complex systems-wide assessments has become a mainstay in immunologic investigation. This focus on high-dimensional data collection and analysis was on full display in the investigation of COVID-19, the respiratory illness resulting from infection by the novel coronavirus SARS-CoV-2. Particularly in the area of B cell biology, tremendous efforts in both cellular and serologic investigation have resulted in an increasingly detailed mapping of the coordinated effector, memory, and antibody secreting cell responses that underpin the development of humoral immunity in response to primary viral infection. Further, the rapid development and deployment of effective vaccines has allowed for the assessment of developing memory responses across a wide variety of immune contexts, including in patients with compromised immune function. The result has been a period of rapid gains in the understanding of B cell biology unrestricted to the study of COVID-19. Here, we outline the systems-level technologies that have been routinely implemented in these investigations throughout the pandemic, and discuss how their use has led to clear and applicable gains in pursuance of the amelioration of human infectious disease and beyond.
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Affiliation(s)
- Matthew C Woodruff
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA.
| | - Caterina E Faliti
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA.
| | - Ignacio Sanz
- Department of Medicine, Division of Rheumatology, Lowance Center for Human Immunology, Emory University, Atlanta, GA, USA; Emory Autoimmunity Center of Excellence, Emory University, Atlanta, GA, USA
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11
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Xiao J, Luo Y, Li Y, Yao X. The characteristics of BCR-CDR3 repertoire in COVID-19 patients and SARS-CoV-2 vaccinated volunteers. J Med Virol 2024; 96:e29488. [PMID: 38415507 DOI: 10.1002/jmv.29488] [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: 11/04/2023] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
The global COVID-19 pandemic has caused more than 1 billion infections, and numerous SARS-CoV-2 vaccines developed rapidly have been administered over 10 billion doses. The world is continuously concerned about the cytokine storms induced by the interaction between SARS-CoV-2 and host, long COVID, breakthrough infections postvaccination, and the impact of SARS-CoV-2 variants. BCR-CDR3 repertoire serves as a molecular target for monitoring the antiviral response "trace" of B cells, evaluating the effects, mechanisms, and memory abilities of individual responses to B cells, and has been successfully applied in analyzing the infection mechanisms, vaccine improvement, and neutralizing antibodies preparation of influenza virus, HIV, MERS, and Ebola virus. Based on research on BCR-CDR3 repertoire of COVID-19 patients and volunteers who received different SARS-CoV-2 vaccines in multiple laboratories worldwide, we focus on analyzing the characteristics and changes of BCR-CDR3 repertoire, such as diversity, clonality, V&J genes usage and pairing, SHM, CSR, shared CDR3 clones, as well as the summary on BCR sequences targeting virus-specific epitopes in the preparation and application research of SARS-CoV-2 potential therapeutic monoclonal antibodies. This review provides comparative data and new research schemes for studying the possible mechanisms of differences in B cell response between SARS-CoV-2 infection or vaccination, and supplies a foundation for improving vaccines after SARS-CoV-2 mutations and potential antibody therapy for infected individuals.
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Affiliation(s)
- Jiaping Xiao
- Department of Immunology, Center of Immunomolecular Engineering, Innovation & Practice Base for Graduate Students Education, Zunyi Medical University, Zunyi, Guizhou, China
- Fushun People's Hospital, Zigong, Sichuan, China
| | - Yan Luo
- Department of Immunology, Center of Immunomolecular Engineering, Innovation & Practice Base for Graduate Students Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Yangyang Li
- Department of Immunology, Center of Immunomolecular Engineering, Innovation & Practice Base for Graduate Students Education, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xinsheng Yao
- Department of Immunology, Center of Immunomolecular Engineering, Innovation & Practice Base for Graduate Students Education, Zunyi Medical University, Zunyi, Guizhou, China
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12
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Tranter E, Frentsch M, Hütter-Krönke ML, Vuong GL, Busch D, Loyal L, Henze L, Rosnev S, Blau IW, Thiel A, Beule D, Bullinger L, Obermayer B, Na IK. Comparable CD8 + T-cell responses to SARS-CoV-2 vaccination in single-cell transcriptomics of recently allogeneic transplanted patients and healthy individuals. J Med Virol 2024; 96:e29539. [PMID: 38516755 DOI: 10.1002/jmv.29539] [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: 11/29/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 03/23/2024]
Abstract
Despite extensive research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination responses in healthy individuals, there is comparatively little known beyond antibody titers and T-cell responses in the vulnerable cohort of patients after allogeneic hematopoietic stem cell transplantation (ASCT). In this study, we assessed the serological response and performed longitudinal multimodal analyses including T-cell functionality and single-cell RNA sequencing combined with T cell receptor (TCR)/B cell receptor (BCR) profiling in the context of BNT162b2 vaccination in ASCT patients. In addition, these data were compared to publicly available data sets of healthy vaccinees. Protective antibody titers were achieved in 40% of patients. We identified a distorted B- and T-cell distribution, a reduced TCR diversity, and increased levels of exhaustion marker expression as possible causes for the poorer vaccine response rates in ASCT patients. Immunoglobulin heavy chain gene rearrangement after vaccination proved to be highly variable in ASCT patients. Changes in TCRα and TCRβ gene rearrangement after vaccination differed from patterns observed in healthy vaccinees. Crucially, ASCT patients elicited comparable proportions of SARS-CoV-2 vaccine-induced (VI) CD8+ T-cells, characterized by a distinct gene expression pattern that is associated with SARS-CoV-2 specificity in healthy individuals. Our study underlines the impaired immune system and thus the lower vaccine response rates in ASCT patients. However, since protective vaccine responses and VI CD8+ T-cells can be induced in part of ASCT patients, our data advocate early posttransplant vaccination due to the high risk of infection in this vulnerable group.
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Affiliation(s)
- Eva Tranter
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marco Frentsch
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Marie Luise Hütter-Krönke
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Giang Lam Vuong
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - David Busch
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lucie Loyal
- Si-M/"Der Simulierte Mensch", A Science Framework of Technische Universität Berlin and Charité-Universitätsmedizin Berlin, Berlin, Germany
- BIH Center of Immunomics-Regenerative Immunology and Aging, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Larissa Henze
- Si-M/"Der Simulierte Mensch", A Science Framework of Technische Universität Berlin and Charité-Universitätsmedizin Berlin, Berlin, Germany
- BIH Center of Immunomics-Regenerative Immunology and Aging, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Stanislav Rosnev
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Igor-Wolfgang Blau
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Thiel
- Si-M/"Der Simulierte Mensch", A Science Framework of Technische Universität Berlin and Charité-Universitätsmedizin Berlin, Berlin, Germany
- BIH Center of Immunomics-Regenerative Immunology and Aging, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lars Bullinger
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Benedikt Obermayer
- Core Unit Bioinformatics, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Il-Kang Na
- Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
- Si-M/"Der Simulierte Mensch", A Science Framework of Technische Universität Berlin and Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
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13
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Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
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Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
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14
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Feng F, Yuen R, Wang Y, Hua A, Kepler TB, Wetzler LM. Characterizing adjuvants' effects at murine immunoglobulin repertoire level. iScience 2024; 27:108749. [PMID: 38269092 PMCID: PMC10805652 DOI: 10.1016/j.isci.2023.108749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 09/29/2023] [Accepted: 12/12/2023] [Indexed: 01/26/2024] Open
Abstract
Generating large-scale, high-fidelity sequencing data is challenging and, furthermore, not much has been done to characterize adjuvants' effects at the repertoire level. Thus, we introduced an IgSeq pipeline that standardized library prep protocols and data analysis functions for accurate repertoire profiling. We then studied systemically effects of CpG and Alum on the Ig heavy chain repertoire using the ovalbumin (OVA) murine model. Ig repertoires of different tissues (spleen and bone marrow) and isotypes (IgG and IgM) were examined and compared in IGHV mutation, gene usage, CDR3 length, clonal diversity, and clonal selection. We found Ig repertoires of different compartments exhibited distinguishable profiles at the non-immunized steady state, and distinctions became more pronounced upon adjuvanted immunizations. Notably, Alum and CpG effects exhibited different tissue- and isotype-preferences. The former led to increased diversity of abundant clones in bone marrow, and the latter promoted the selection of IgG clones in both tissues.
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Affiliation(s)
- Feng Feng
- Department of Microbiology, Boston University, Boston, MA 02118, USA
| | - Rachel Yuen
- Department of Microbiology, Boston University, Boston, MA 02118, USA
| | - Yumei Wang
- Department of Microbiology, Boston University, Boston, MA 02118, USA
| | - Axin Hua
- Department of Microbiology, Boston University, Boston, MA 02118, USA
| | - Thomas B. Kepler
- Department of Microbiology, Boston University, Boston, MA 02118, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02118, USA
| | - Lee M. Wetzler
- Department of Microbiology, Boston University, Boston, MA 02118, USA
- Department of Medicine, Boston University School of Medicine, Boston University, Boston, MA 02118, USA
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15
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Dorey-Robinson D, Maccari G, Hammond JA. IgMAT: immunoglobulin sequence multi-species annotation tool for any species including those with incomplete antibody annotation or unusual characteristics. BMC Bioinformatics 2023; 24:491. [PMID: 38129777 PMCID: PMC10740263 DOI: 10.1186/s12859-023-05624-2] [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: 05/06/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The advent and continual improvement of high-throughput sequencing technologies has made immunoglobulin repertoire sequencing accessible and informative regardless of study species. However, to fully map dynamic changes in polyclonal responses precise framework and complementarity determining region annotation of rearranging genes is pivotal. Most sequence annotation tools are designed primarily for use with human and mouse antibody sequences which use databases with fixed species lists, applying very specific assumptions which select against unique structural characteristics. For this reason, data agnostic tools able to learn from presented data can be very useful with new species or with novel datasets. RESULTS We have developed IgMAT, which utilises a reduced amino acid alphabet, that incorporates multiple HMM alignments into a single consensus to automatically annotate immunoglobulin sequences from most organisms. Additionally, the software allows the incorporation of user defined databases to better represent the species and/or antibody class of interest. To demonstrate the accuracy and utility of IgMAT, we present analysis of sequences extracted from structural data and immunoglobulin sequence datasets from several different species. CONCLUSIONS IgMAT is fully open-sourced and freely available on GitHub ( https://github.com/TPI-Immunogenetics/igmat ) for download under GPLv3 license. It can be used as a CLI application or as a python module to be integrated in custom scripts.
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Affiliation(s)
| | - Giuseppe Maccari
- The Pirbright Institute, Pirbright, UK
- Anthony Nolan Research Institute, London, UK
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16
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Schramm CA, Moon D, Peyton L, Lima NS, Wake C, Boswell KL, Henry AR, Laboune F, Ambrozak D, Darko SW, Teng IT, Foulds KE, Carfi A, Edwards DK, Kwong PD, Koup RA, Seder RA, Douek DC. Interaction dynamics between innate and adaptive immune cells responding to SARS-CoV-2 vaccination in non-human primates. Nat Commun 2023; 14:7961. [PMID: 38042809 PMCID: PMC10693617 DOI: 10.1038/s41467-023-43420-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: 05/31/2023] [Accepted: 11/08/2023] [Indexed: 12/04/2023] Open
Abstract
As SARS-CoV-2 variants continue evolving, testing updated vaccines in non-human primates remains important for guiding human clinical practice. To date, such studies have focused on antibody titers and antigen-specific B and T cell frequencies. Here, we extend our understanding by integrating innate and adaptive immune responses to mRNA-1273 vaccination in rhesus macaques. We sorted innate immune cells from a pre-vaccine time point, as well as innate immune cells and antigen-specific peripheral B and T cells two weeks after each of two vaccine doses and used single-cell sequencing to assess the transcriptomes and adaptive immune receptors of each cell. We show that a subset of S-specific T cells expresses cytokines critical for activating innate responses, with a concomitant increase in CCR5-expressing intermediate monocytes and a shift of natural killer cells to a more cytotoxic phenotype. The second vaccine dose, administered 4 weeks after the first, elicits an increase in circulating germinal center-like B cells 2 weeks later, which are more clonally expanded and enriched for epitopes in the receptor binding domain. Both doses stimulate inflammatory response genes associated with elevated antibody production. Overall, we provide a comprehensive picture of bidirectional signaling between innate and adaptive components of the immune system and suggest potential mechanisms for the enhanced response to secondary exposure.
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Affiliation(s)
- Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Damee Moon
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lowrey Peyton
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Noemia S Lima
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Christian Wake
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kristin L Boswell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Amy R Henry
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Farida Laboune
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David Ambrozak
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Samuel W Darko
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - I-Ting Teng
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kathryn E Foulds
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | | | | | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Richard A Koup
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Robert A Seder
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Daniel C Douek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.
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17
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Wang K, Hu X, Zhang J. Fast clonal family inference from large-scale B cell repertoire sequencing data. CELL REPORTS METHODS 2023; 3:100601. [PMID: 37788671 PMCID: PMC10626204 DOI: 10.1016/j.crmeth.2023.100601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/31/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.
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Affiliation(s)
- Kaixuan Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xihao Hu
- GV20 Therapeutics, Cambridge, MA, USA
| | - Jian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
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18
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Guerra D, Beaumont T, Radić L, Kerster G, van der Straten K, Yuan M, Torres JL, Lee WH, Liu H, Poniman M, Bontjer I, Burger JA, Claireaux M, Caniels TG, Snitselaar JL, Bijl TP, Kruijer S, Ozorowski G, Gideonse D, Sliepen K, Ward AB, Eggink D, de Bree GJ, Wilson IA, Sanders RW, van Gils MJ. Broad SARS-CoV-2 neutralization by monoclonal and bispecific antibodies derived from a Gamma-infected individual. iScience 2023; 26:108009. [PMID: 37841584 PMCID: PMC10570122 DOI: 10.1016/j.isci.2023.108009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 07/10/2023] [Accepted: 09/18/2023] [Indexed: 10/17/2023] Open
Abstract
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has remained a medical threat due to the evolution of multiple variants that acquire resistance to vaccines and prior infection. Therefore, it is imperative to discover monoclonal antibodies (mAbs) that neutralize a broad range of SARS-CoV-2 variants. A stabilized spike glycoprotein was used to enrich antigen-specific B cells from an individual with a primary Gamma variant infection. Five mAbs selected from those B cells showed considerable neutralizing potency against multiple variants, with COVA309-35 being the most potent against the autologous virus, as well as Omicron BA.1 and BA.2, and COVA309-22 having binding and neutralization activity against Omicron BA.4/5, BQ.1.1, and XBB.1. When combining the COVA309 mAbs as cocktails or bispecific antibodies, the breadth and potency were improved. In addition, the mechanism of cross-neutralization of the COVA309 mAbs was elucidated by structural analysis. Altogether these data indicate that a Gamma-infected individual can develop broadly neutralizing antibodies.
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Affiliation(s)
- Denise Guerra
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Tim Beaumont
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Laura Radić
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Gius Kerster
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Karlijn van der Straten
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
- Amsterdam UMC, location University of Amsterdam, Department of Internal Medicine, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
| | - Meng Yuan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Jonathan L. Torres
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Wen-Hsin Lee
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hejun Liu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Meliawati Poniman
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Ilja Bontjer
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Judith A. Burger
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Mathieu Claireaux
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Tom G. Caniels
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Jonne L. Snitselaar
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Tom P.L. Bijl
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Sabine Kruijer
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Gabriel Ozorowski
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David Gideonse
- Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), 3721 MA Bilthoven, the Netherlands
| | - Kwinten Sliepen
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Dirk Eggink
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
- Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), 3721 MA Bilthoven, the Netherlands
| | - Godelieve J. de Bree
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
- Amsterdam UMC, location University of Amsterdam, Department of Internal Medicine, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
| | - Ian A. Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rogier W. Sanders
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
- Department of Microbiology and Immunology, Weill Medical College of Cornell University, New York, NY, USA
| | - Marit J. van Gils
- Amsterdam UMC, location University of Amsterdam, Department of Medical Microbiology and Infection Prevention, Laboratory of Experimental Virology, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands
- Amsterdam Institute for Infection and Immunity, Infectious Diseases, Amsterdam, the Netherlands
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19
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Feng B, Zheng D, Yang L, Su Z, Tang L, Zhu Y, Xu X, Wang Q, Lin Q, Hu J, Lin M, Huang L, Zhou X, Liu H, Li S, Pan W, Shi R, Lu Y, Wu B, Ding B, Wang Z, Guo J, Zhang Z, Zheng G, Liu Y. Post-hospitalization rehabilitation alleviates long-term immune repertoire alteration in COVID-19 convalescent patients. Cell Prolif 2023; 56:e13450. [PMID: 36938980 PMCID: PMC10542649 DOI: 10.1111/cpr.13450] [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: 01/18/2023] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/21/2023] Open
Abstract
The global pandemic of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an once-in-a-lifetime public health crisis. Among hundreds of millions of people who have contracted with or are being infected with COVID-19, the question of whether COVID-19 infection may cause long-term health concern, even being completely recovered from the disease clinically, especially immune system damage, needs to be addressed. Here, we performed seven-chain adaptome immune repertoire analyses on convalescent COVID-19 patients who have been discharged from hospitals for at least 6 months. Surprisingly, we discovered lymphopenia, reduced number of unique CDR3s, and reduced diversity of the TCR/BCR immune repertoire in convalescent COVID-19 patients. In addition, the BCR repertoire appears to be activated, which is consistent with the protective antibody titres, but serological experiments reveal significantly lower IL-4 and IL-7 levels in convalescent patients compared to those in healthy controls. Finally, in comparison with convalescent patients who did not receive post-hospitalization rehabilitation, the convalescent patients who received post-hospitalization rehabilitation had attenuated immune repertoire abnormality, almost back to the level of healthy control, despite no detectable clinic demographic difference. Overall, we report the potential long-term immunological impairment for COVID-19 infection, and correction of this impairment via post-hospitalization rehabilitation may offer a new prospect for COVID-19 recovery strategy.
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20
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Arons E, Henry K, Haas C, Gould M, Tsintolas J, Mauter J, Zhou H, Burbelo PD, Cohen JI, Kreitman RJ. Characterization of B-cell receptor clonality and immunoglobulin gene usage at multiple time points during active SARS-CoV-2 infection. J Med Virol 2023; 95:e29179. [PMID: 37877800 PMCID: PMC11323229 DOI: 10.1002/jmv.29179] [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: 08/08/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
Although monoclonal antibodies to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are known, B-cell receptor repertoire and its change in patients during coronavirus disease-2019 (COVID-19) progression is underreported. We aimed to study this molecularly. We used immunoglobulin heavy chain (IGH) variable region (IGHV) spectratyping and next-generation sequencing of peripheral blood B-cell genomic DNA collected at multiple time points during disease evolution to study B-cell response to SARS-CoV-2 infection in 14 individuals with acute COVID-19. We found a broad distribution of responding B-cell clones. The IGH gene usage was not significantly skewed but frequencies of individual IGH genes changed repeatedly. We found predominant usage of unmutated and low mutation-loaded IGHV rearrangements characterizing naïve and extrafollicular B cells among the majority of expanded peripheral B-cell clonal lineages at most tested time points in most patients. IGH rearrangement usage showed no apparent relation to anti-SARS-CoV-2 antibody titers. Some patients demonstrated mono/oligoclonal populations carrying highly mutated IGHV rearrangements indicating antigen experience at some of the time points tested, including even before anti-SARS-CoV-2 antibodies were detected. We present evidence demonstrating that the B-cell response to SARS-CoV-2 is individual and includes different lineages of B cells at various time points during COVID-19 progression.
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Affiliation(s)
- Evgeny Arons
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
| | - Kiersten Henry
- Medstar Montgomery Medical Center, 18101 Prince Philip Drive, Olney, MD 20832, United States
| | - Christopher Haas
- Medstar Franklin Square Medical Center, 9000 Franklin Square Drive, Baltimore, MD 21237, United States
| | - Mory Gould
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
| | - Jack Tsintolas
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
| | - Jack Mauter
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
| | - Hong Zhou
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
| | - Peter D. Burbelo
- National Institute of Dental and Craniofacial Research, NIH, Bethesda, MD, 20892, United States
| | - Jeffrey I. Cohen
- Laboratory of Infectious Disease, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, United States
| | - Robert J. Kreitman
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, MD, 20892, United States
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21
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Hu Z, Cohen S, Swanson SJ. The immunogenicity of human-origin therapeutic antibodies are associated with V gene usage. Front Immunol 2023; 14:1237754. [PMID: 37720227 PMCID: PMC10502710 DOI: 10.3389/fimmu.2023.1237754] [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: 06/09/2023] [Accepted: 08/18/2023] [Indexed: 09/19/2023] Open
Abstract
Therapeutic antibodies can elicit unwanted immune responses in a subset of patients, which leads to the production of anti-drug antibodies (ADA). Some of these ADAs have been reported to effect the pharmacokinetics, efficacy and/or safety of the therapeutic antibodies. The sequence diversity of antibodies are generated by VDJ recombination and mutagenesis. While the antibody generation process can create a large candidate pool for identifying high-affinity antibodies, it also could produce sequences that are foreign to the human immune system. However, it is not clear how VDJ recombination and mutagenesis impact the clinical ADA rate of therapeutic antibodies. In this study, we identified a positive correlation between the clinical ADA rate and the number of introduced mutations in the antibody sequences. We also found that the use of rare V alleles in human-origin antibody therapeutics is associated with higher risk of immunogenicity. The results suggest that antibody engineering projects should start with frameworks that contain commonly used V alleles and prioritize antibody candidates with low number of mutations to reduce the risk of immunogenicity.
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22
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Andreu-Sánchez S, Bourgonje AR, Vogl T, Kurilshikov A, Leviatan S, Ruiz-Moreno AJ, Hu S, Sinha T, Vich Vila A, Klompus S, Kalka IN, de Leeuw K, Arends S, Jonkers I, Withoff S, Brouwer E, Weinberger A, Wijmenga C, Segal E, Weersma RK, Fu J, Zhernakova A. Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire. Immunity 2023; 56:1376-1392.e8. [PMID: 37164013 DOI: 10.1016/j.immuni.2023.04.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/13/2022] [Accepted: 04/06/2023] [Indexed: 05/12/2023]
Abstract
Phage-displayed immunoprecipitation sequencing (PhIP-seq) has enabled high-throughput profiling of human antibody repertoires. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is lacking. In this study, we investigated the effects of genetic, environmental, and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We detected individual-specificity, temporal consistency, and co-housing similarities in antibody repertoires. Genetic analyses showed the involvement of the HLA, IGHV, and FUT2 gene regions in antibody-bound peptide reactivity. Furthermore, we uncovered associations between phenotypic factors (including age, cell counts, sex, smoking behavior, and allergies, among others) and particular antibody-bound peptides. Our results indicate that human antibody epitope repertoires are shaped by both genetics and environmental exposures and highlight specific signatures of distinct phenotypes and genotypes.
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Affiliation(s)
- Sergio Andreu-Sánchez
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Arno R Bourgonje
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Thomas Vogl
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University Graz, Graz, Austria; Center for Cancer Research, Medical University of Vienna, Wien, Austria.
| | - Alexander Kurilshikov
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sigal Leviatan
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Angel J Ruiz-Moreno
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Shixian Hu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Trishla Sinha
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Arnau Vich Vila
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Shelley Klompus
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Iris N Kalka
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Karina de Leeuw
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Suzanne Arends
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Iris Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Elisabeth Brouwer
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Adina Weinberger
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jingyuan Fu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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23
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Liu KJ, Zelazowska MA, McBride KM. The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq. Viruses 2023; 15:1253. [PMID: 37376553 DOI: 10.3390/v15061253] [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: 04/22/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.
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Affiliation(s)
- Kate J Liu
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Monika A Zelazowska
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kevin M McBride
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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24
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Vakhitova M, Myshkin M, Staroverov D, Shagina I, Izraelson M, Tverdova N, Britanova O, Merzlyak E. A Rapid Method for Detection of Antigen-Specific B Cells. Cells 2023; 12:cells12050774. [PMID: 36899909 PMCID: PMC10000731 DOI: 10.3390/cells12050774] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/17/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023] Open
Abstract
The global SARS-CoV-2 pandemic has united the efforts of many scientists all over the world to develop wet-lab techniques and computational approaches aimed at the identification of antigen-specific T and B cells. The latter provide specific humoral immunity that is essential for the survival of COVID-19 patients, and vaccine development has essentially been based on these cells. Here, we implemented an approach that integrates the sorting of antigen-specific B cells and B-cell receptor mRNA sequencing (BCR-seq), followed by computational analysis. This rapid and cost-efficient method allowed us to identify antigen-specific B cells in the peripheral blood of patients with severe COVID-19 disease. Subsequently, specific BCRs were extracted, cloned, and produced as full antibodies. We confirmed their reactivity toward the spike RBD domain. Such an approach can be effective for the monitoring and identification of B cells participating in an individual immune response.
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Affiliation(s)
- Mariia Vakhitova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Mikhail Myshkin
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Dmitriy Staroverov
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Irina Shagina
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Mark Izraelson
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Nadezhda Tverdova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Olga Britanova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
- Correspondence: (O.B.); (E.M.)
| | - Ekaterina Merzlyak
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
- Correspondence: (O.B.); (E.M.)
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25
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Ruiz Ortega M, Spisak N, Mora T, Walczak AM. Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals. PLoS Genet 2023; 19:e1010652. [PMID: 36827454 PMCID: PMC10075420 DOI: 10.1371/journal.pgen.1010652] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/05/2023] [Accepted: 02/02/2023] [Indexed: 02/26/2023] Open
Abstract
Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnostic tool for predicting SARS-CoV-2 status from repertoire data.
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Affiliation(s)
- María Ruiz Ortega
- Laboratoire de physique de l’École Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Natanael Spisak
- Laboratoire de physique de l’École Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Thierry Mora
- Laboratoire de physique de l’École Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
| | - Aleksandra M. Walczak
- Laboratoire de physique de l’École Normale Supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris, France
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26
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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: 14] [Impact Index Per Article: 14.0] [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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27
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Safra M, Tamari Z, Polak P, Shiber S, Matan M, Karameh H, Helviz Y, Levy-Barda A, Yahalom V, Peretz A, Ben-Chetrit E, Brenner B, Tuller T, Gal-Tanamy M, Yaari G. Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity. Front Immunol 2023; 14:1031914. [PMID: 37153628 PMCID: PMC10154551 DOI: 10.3389/fimmu.2023.1031914] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/22/2023] [Indexed: 05/10/2023] Open
Abstract
Introduction The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. Methods We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls. Results In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. Discussion These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.
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Affiliation(s)
- Modi Safra
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Zvi Tamari
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Pazit Polak
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Shachaf Shiber
- Emergency Department, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Matan
- Clinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel
| | - Hani Karameh
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Yigal Helviz
- Intensive Care Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Adva Levy-Barda
- Biobank, Department of Pathology, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
| | - Vered Yahalom
- Blood Services and Apheresis Institute, Rabin Medical Center, Petah Tikva, Israel
| | - Avi Peretz
- Clinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Eli Ben-Chetrit
- Infectious Diseases Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Baruch Brenner
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Oncology, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering and The Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | - Gur Yaari
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
- *Correspondence: Gur Yaari,
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28
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Katoh H, Komura D, Furuya G, Ishikawa S. Immune repertoire profiling for disease pathobiology. Pathol Int 2023; 73:1-11. [PMID: 36342353 PMCID: PMC10099665 DOI: 10.1111/pin.13284] [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: 05/24/2022] [Revised: 09/20/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022]
Abstract
Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, lifestyle diseases, neurodegenerative diseases, and cancers. Recently, immune repertoire sequencing has attracted much attention because it helps obtain global profiles of the immune receptor sequences of infiltrating T and B cells in specimens. Immune repertoire sequencing not only helps deepen our understanding of the molecular mechanisms of immune-related pathology but also assists in discovering novel therapeutic modalities for diseases, thereby shedding colorful light on otherwise tiny monotonous cells when observed under a microscope. In this review article, we introduce and detail the background and methodology of immune repertoire sequencing and summarize recent scientific achievements in association with human diseases. Future perspectives on this genetic technique in the field of histopathological research will also be discussed.
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Affiliation(s)
- Hiroto Katoh
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Daisuke Komura
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Genta Furuya
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Iwabuchi S, Tsukahara T, Okayama T, Kitabatake M, Motobayashi H, Shichino S, Imafuku T, Yamaji K, Miyamoto K, Tamura S, Ueha S, Ito T, Murata SI, Kondo T, Ikeo K, Suzuki Y, Matsushima K, Kohara M, Torigoe T, Yamaue H, Hashimoto S. B cell receptor repertoire analysis from autopsy samples of COVID-19 patients. Front Immunol 2023; 14:1034978. [PMID: 36911681 PMCID: PMC9996338 DOI: 10.3389/fimmu.2023.1034978] [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: 09/06/2022] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
Neutralizing antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are being developed world over. We investigated the possibility of producing artificial antibodies from the formalin fixation and paraffin-embedding (FFPE) lung lobes of a patient who died by coronavirus disease 2019 (COVID-19). The B-cell receptors repertoire in the lung tissue where SARS-CoV-2 was detected were considered to have highly sensitive virus-neutralizing activity, and artificial antibodies were produced by combining the most frequently detected heavy and light chains. Some neutralizing effects against the SARS-CoV-2 were observed, and mixing two different artificial antibodies had a higher tendency to suppress the virus. The neutralizing effects were similar to the immunoglobulin G obtained from healthy donors who had received a COVID-19 mRNA vaccine. Therefore, the use of FFPE lung tissue, which preserves the condition of direct virus sensitization, to generate artificial antibodies may be useful against future unknown infectious diseases.
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Affiliation(s)
- Sadahiro Iwabuchi
- Department of Molecular Pathophysiology, Institute of Advanced Medicine, Wakayama Medical University, Wakayama, Japan
| | - Tomohide Tsukahara
- Department of Pathology, Sapporo Medical University School of Medicine, Hokkaido, Japan
| | - Toshitugu Okayama
- Laboratory of DNA Data Analysis, National Institute of Genetics, Shizuoka, Japan
| | | | - Hideki Motobayashi
- Second Department of Surgery, Wakayama Medical University, Wakayama, Japan
| | - Shigeyuki Shichino
- Division of Molecular Regulation of Inflammatory and Immune Disease, Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, Japan
| | - Tadashi Imafuku
- Department of Molecular Pathophysiology, Institute of Advanced Medicine, Wakayama Medical University, Wakayama, Japan
| | - Kenzaburo Yamaji
- Department of Diseases and Infection, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kyohei Miyamoto
- Department of Emergency and Critical Care Medicine, Wakayama Medical University, Wakayama, Japan
| | - Shinobu Tamura
- Department of Emergency and Critical Care Medicine, Wakayama Medical University, Wakayama, Japan
| | - Satoshi Ueha
- Division of Molecular Regulation of Inflammatory and Immune Disease, Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, Japan
| | - Toshihiro Ito
- Department of Immunology, Nara Medical University, Nara, Japan
| | - Shin-Ichi Murata
- Departments of Human Pathology, Wakayama Medical University, Wakayama, Japan
| | - Toshikazu Kondo
- Department of Forensic Medicine, Wakayama Medical University, Wakayama, Japan
| | - Kazuho Ikeo
- Laboratory of DNA Data Analysis, National Institute of Genetics, Shizuoka, Japan
| | - Yutaka Suzuki
- Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Kouji Matsushima
- Division of Molecular Regulation of Inflammatory and Immune Disease, Research Institute for Biomedical Sciences, Tokyo University of Science, Chiba, Japan
| | - Michinori Kohara
- Department of Diseases and Infection, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Toshihiko Torigoe
- Department of Pathology, Sapporo Medical University School of Medicine, Hokkaido, Japan
| | - Hiroki Yamaue
- Second Department of Surgery, Wakayama Medical University, Wakayama, Japan.,Departments of Cancer Immunology, Wakayama Medical University, Wakayama, Japan
| | - Shinichi Hashimoto
- Department of Molecular Pathophysiology, Institute of Advanced Medicine, Wakayama Medical University, Wakayama, Japan
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30
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Kanduri C, Scheffer L, Pavlović M, Rand KD, Chernigovskaya M, Pirvandy O, Yaari G, Greiff V, Sandve GK. simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. Gigascience 2022; 12:giad074. [PMID: 37848619 PMCID: PMC10580376 DOI: 10.1093/gigascience/giad074] [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: 02/21/2023] [Revised: 07/20/2023] [Accepted: 08/29/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires. RESULTS We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. CONCLUSIONS This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.
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Affiliation(s)
- Chakravarthi Kanduri
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Lonneke Scheffer
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Milena Pavlović
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Knut Dagestad Rand
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Oz Pirvandy
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Gur Yaari
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Geir K Sandve
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
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31
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He B, Liu S, Xu M, Hu Y, Lv K, Wang Y, Ma Y, Zhai Y, Yue X, Liu L, Lu H, Zhou S, Li P, Mai G, Huang X, Li C, Chen S, Ye S, Zhao P, Yang Y, Li X, Jie Y, Shi M, Yang J, Shu Y, Chen YQ. Comparative global B cell receptor repertoire difference induced by SARS-CoV-2 infection or vaccination via single-cell V(D)J sequencing. Emerg Microbes Infect 2022; 11:2007-2020. [PMID: 35899581 PMCID: PMC9377262 DOI: 10.1080/22221751.2022.2105261] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/30/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023]
Abstract
Dynamic changes of the paired heavy and light chain B cell receptor (BCR) repertoire provide an essential insight into understanding the humoral immune response post-SARS-CoV-2 infection and vaccination. However, differences between the endogenous paired BCR repertoire kinetics in SARS-CoV-2 infection and previously recovered/naïve subjects treated with the inactivated vaccine remain largely unknown. We performed single-cell V(D)J sequencing of B cells from six healthy donors with three shots of inactivated SARS-CoV-2 vaccine (BBIBP-CorV), five people who received the BBIBP-CorV vaccine after having recovered from COVID-19, five unvaccinated COVID-19 recovered patients and then integrated with public data of B cells from four SARS-CoV-2-infected subjects. We discovered that BCR variable (V) genes were more prominently used in the SARS-CoV-2 exposed groups (both in the group with active infection and in the group that had recovered) than in the vaccinated groups. The VH gene that expanded the most after SARS-CoV-2 infection was IGHV3-33, while IGHV3-23 in the vaccinated groups. SARS-CoV-2-infected group enhanced more BCR clonal expansion and somatic hypermutation than the vaccinated healthy group. A small proportion of public clonotypes were shared between the SARS-CoV-2 infected, vaccinated healthy, and recovered groups. Moreover, several public antibodies had been identified against SARS-CoV-2 spike protein. We comprehensively characterize the paired heavy and light chain BCR repertoire from SARS-CoV-2 infection to vaccination, providing further guidance for the development of the next-generation precision vaccine.
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Affiliation(s)
- Bing He
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Shuning Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Mengxin Xu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Yunqi Hu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Kexin Lv
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Yuanyuan Wang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Yong Ma
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Yanmei Zhai
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Xinyu Yue
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Lin Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Hongjie Lu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Siwei Zhou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Pengbin Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Guoqin Mai
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Xiaoping Huang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Chenhang Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Shifeng Chen
- Department of Respiratory and Critical Care Medicine, The 74(th) Group Army Hospital, Guangzhou, People’s Republic of China
| | - Shupei Ye
- SSL Central Hospital of Dongguan City, Dongguan, People’s Republic of China
| | - Pingsen Zhao
- Laboratory for Diagnosis of Clinical Microbiology and Infection, Medical Research Center, Yuebei People’s Hospital, Shantou University Medical College, Shaoguan, People’s Republic of China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xinhua Li
- Department of Infectious Diseases and Key Laboratory of Liver Disease of Guangdong Province, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yusheng Jie
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Mang Shi
- The Centre for Infection and Immunity Studies, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Jingyi Yang
- Vaccine and Immunology Research Center, Translational Medical Research Institute, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People’s Republic of China
| | - Yuelong Shu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
| | - Yao-Qing Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, People’s Republic of China
- b School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- k Ministry of Education, Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Guangzhou, People’s Republic of China
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32
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Ralph DK, Matsen FA. Inference of B cell clonal families using heavy/light chain pairing information. PLoS Comput Biol 2022; 18:e1010723. [PMID: 36441808 PMCID: PMC9731466 DOI: 10.1371/journal.pcbi.1010723] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/08/2022] [Accepted: 11/09/2022] [Indexed: 11/29/2022] Open
Abstract
Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.
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Affiliation(s)
- Duncan K. Ralph
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail:
| | - Frederick A. Matsen
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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33
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Ismanto HS, Xu Z, Saputri DS, Wilamowski J, Li S, Nugraha DK, Horiguchi Y, Okada M, Arase H, Standley DM. Landscape of infection enhancing antibodies in COVID-19 and healthy donors. Comput Struct Biotechnol J 2022; 20:6033-6040. [PMCID: PMC9635252 DOI: 10.1016/j.csbj.2022.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Zichang Xu
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Jan Wilamowski
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Songling Li
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of System Immunology, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan
| | - Dendi K. Nugraha
- Deparment of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Yasuhiko Horiguchi
- Deparment of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan
| | - Masato Okada
- Deparment of Oncogene Research, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Oncogene Research, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan
| | - Hisashi Arase
- Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Immunochemistry, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan
| | - Daron M Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of System Immunology, Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan
- Corresponding author at: Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan.
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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] [Key Words] [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
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35
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Yang X, Chi H, Wu M, Wang Z, Lang Q, Han Q, Wang X, Liu X, Li Y, Wang X, Huang N, Bi J, Liang H, Gao Y, Zhao Y, Feng N, Yang S, Wang T, Xia X, Ge L. Discovery and characterization of SARS-CoV-2 reactive and neutralizing antibodies from humanized CAMouseHG mice through rapid hybridoma screening and high-throughput single-cell V(D)J sequencing. Front Immunol 2022; 13:992787. [PMID: 36211410 PMCID: PMC9545174 DOI: 10.3389/fimmu.2022.992787] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
The coronavirus disease 2019 pandemic has caused more than 532 million infections and 6.3 million deaths to date. The reactive and neutralizing fully human antibodies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are effective detection tools and therapeutic measures. During SARS-CoV-2 infection, a large number of SARS-CoV-2 reactive and neutralizing antibodies will be produced. Most SARS-CoV-2 reactive and neutralizing fully human antibodies are isolated from human and frequently encoded by convergent heavy-chain variable genes. However, SARS-CoV-2 viruses can mutate rapidly during replication and the resistant variants of neutralizing antibodies easily survive and evade the immune response, especially in the face of such focused antibody responses in humans. Therefore, additional tools are needed to develop different kinds of fully human antibodies to compensate for current deficiency. In this study, we utilized antibody humanized CAMouseHG mice to develop a rapid antibody discovery method and examine the antibody repertoire of SARS-CoV-2 RBD-reactive hybridoma cells derived from CAMouseHG mice by using high-throughput single-cell V(D)J sequencing analysis. CAMouseHG mice were immunized by 28-day rapid immunization method. After electrofusion and semi-solid medium screening on day 12 post-electrofusion, 171 hybridoma clones were generated based on the results of SARS-CoV-2 RBD binding activity assay. A rather obvious preferential usage of IGHV6-1 family was found in these hybridoma clones derived from CAMouseHG mice, which was significantly different from the antibodies found in patients with COVID-19. After further virus neutralization screening and antibody competition assays, we generated a noncompeting two-antibody cocktail, which showed a potent prophylactic protective efficacy against SARS-CoV-2 in cynomolgus macaques. These results indicate that humanized CAMouseHG mice not only provide a valuable platform to obtain fully human reactive and neutralizing antibodies but also have a different antibody repertoire from humans. Thus, humanized CAMouseHG mice can be used as a good complementary tool in discovery of fully human therapeutic and diagnostic antibodies.
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Affiliation(s)
- Xi Yang
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Hang Chi
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Meng Wu
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Zhenshan Wang
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- College of Veterinary Medicine, Jilin Agricultural University, Changchun, China
| | - Qiaoli Lang
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Qiuxue Han
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Beijing, China
| | - Xinyue Wang
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China
| | - Xueqin Liu
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Yuanguo Li
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Xiwen Wang
- Food and Drug Inspection Laboratory, Administration for Drug and Instrument Supervision and Inspection, Beijing, China
| | - Nan Huang
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Jinhao Bi
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- College of Veterinary Medicine, Jilin Agricultural University, Changchun, China
| | - Hao Liang
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
| | - Yuwei Gao
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Yongkun Zhao
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Na Feng
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Songtao Yang
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
| | - Tiecheng Wang
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- *Correspondence: Liangpeng Ge, ; Tiecheng Wang, ; Xianzhu Xia,
| | - Xianzhu Xia
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
- *Correspondence: Liangpeng Ge, ; Tiecheng Wang, ; Xianzhu Xia,
| | - Liangpeng Ge
- Institute of Bioengineering, ChongQing Academy of Animal Sciences, Chongqing, China
- *Correspondence: Liangpeng Ge, ; Tiecheng Wang, ; Xianzhu Xia,
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Fraley ER, Khanal S, Pierce SH, LeMaster CA, McLennan R, Pastinen T, Bradley T. Effects of Prior Infection with SARS-CoV-2 on B Cell Receptor Repertoire Response during Vaccination. Vaccines (Basel) 2022; 10:1477. [PMID: 36146555 PMCID: PMC9506540 DOI: 10.3390/vaccines10091477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/24/2022] [Accepted: 08/29/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding the B cell response to SARS-CoV-2 vaccines is a high priority. High-throughput sequencing of the B cell receptor (BCR) repertoire allows for dynamic characterization of B cell response. Here, we sequenced the BCR repertoire of individuals vaccinated by the Pfizer SARS-CoV-2 mRNA vaccine. We compared BCR repertoires of individuals with previous COVID-19 infection (seropositive) to individuals without previous infection (seronegative). We discovered that vaccine-induced expanded IgG clonotypes had shorter heavy-chain complementarity determining region 3 (HCDR3), and for seropositive individuals, these expanded clonotypes had higher somatic hypermutation (SHM) than seronegative individuals. We uncovered shared clonotypes present in multiple individuals, including 28 clonotypes present across all individuals. These 28 shared clonotypes had higher SHM and shorter HCDR3 lengths compared to the rest of the BCR repertoire. Shared clonotypes were present across both serotypes, indicating convergent evolution due to SARS-CoV-2 vaccination independent of prior viral exposure.
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Affiliation(s)
- Elizabeth R. Fraley
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
| | - Santosh Khanal
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
| | - Stephen H. Pierce
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Cas A. LeMaster
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
| | - Rebecca McLennan
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
| | - Tomi Pastinen
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, MO 64108, USA
| | - Todd Bradley
- Genomic Medicine Center, Children’s Mercy Research Institute, Children’s Mercy Kansas City, Kansas City, MO 64108, USA
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, MO 64108, USA
- Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS 66160, USA
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37
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Joseph M, Wu Y, Dannebaum R, Rubelt F, Zlatareva I, Lorenc A, Du ZG, Davies D, Kyle-Cezar F, Das A, Gee S, Seow J, Graham C, Telman D, Bermejo C, Lin H, Asgharian H, Laing AG, del Molino del Barrio I, Monin L, Muñoz-Ruiz M, McKenzie DR, Hayday TS, Francos-Quijorna I, Kamdar S, Davis R, Sofra V, Cano F, Theodoridis E, Martinez L, Merrick B, Bisnauthsing K, Brooks K, Edgeworth J, Cason J, Mant C, Doores KJ, Vantourout P, Luong K, Berka J, Hayday AC. Global patterns of antigen receptor repertoire disruption across adaptive immune compartments in COVID-19. Proc Natl Acad Sci U S A 2022; 119:e2201541119. [PMID: 35943978 PMCID: PMC9407655 DOI: 10.1073/pnas.2201541119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Whereas pathogen-specific T and B cells are a primary focus of interest during infectious disease, we have used COVID-19 to ask whether their emergence comes at a cost of broader B cell and T cell repertoire disruption. We applied a genomic DNA-based approach to concurrently study the immunoglobulin-heavy (IGH) and T cell receptor (TCR) β and δ chain loci of 95 individuals. Our approach detected anticipated repertoire focusing for the IGH repertoire, including expansions of clusters of related sequences temporally aligned with SARS-CoV-2-specific seroconversion, and enrichment of some shared SARS-CoV-2-associated sequences. No significant age-related or disease severity-related deficiencies were noted for the IGH repertoire. By contrast, whereas focusing occurred at the TCRβ and TCRδ loci, including some TCRβ sequence-sharing, disruptive repertoire narrowing was almost entirely limited to many patients aged older than 50 y. By temporarily reducing T cell diversity and by risking expansions of nonbeneficial T cells, these traits may constitute an age-related risk factor for COVID-19, including a vulnerability to new variants for which T cells may provide key protection.
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Affiliation(s)
- Magdalene Joseph
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Yin Wu
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
- cBreast Cancer Now Research Unit, King’s College London, London, SE1 9RT, United Kingdom
- dDepartment of Medical Oncology, Guy’s and St. Thomas’ NHS Foundation Trust, London, SE1 9RT, United Kingdom
- eUCL Cancer Institute, University College London, London, WC1E 6DD, United Kingdom
| | | | | | - Iva Zlatareva
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Anna Lorenc
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | | | - Daniel Davies
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- gDepartment of Plastic and Reconstructive Surgery, Royal Free NHS Foundation Trust, London, NW3 2QG, United Kingdom
| | - Fernanda Kyle-Cezar
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Abhishek Das
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- hLondon School of Hygiene & Tropical Medicine, London, WC1E 7HT, United Kingdom
| | - Sarah Gee
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Jeffrey Seow
- iDepartment of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Carl Graham
- iDepartment of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | | | | | - Hai Lin
- fRoche Diagnostics Solutions, Pleasanton, CA, 94588
| | | | - Adam G. Laing
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Irene del Molino del Barrio
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- eUCL Cancer Institute, University College London, London, WC1E 6DD, United Kingdom
| | - Leticia Monin
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Miguel Muñoz-Ruiz
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Duncan R. McKenzie
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Thomas S. Hayday
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Isaac Francos-Quijorna
- jRegeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AB, United Kingdom
| | - Shraddha Kamdar
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Richard Davis
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Vasiliki Sofra
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Florencia Cano
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
| | - Efstathios Theodoridis
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Lauren Martinez
- kResearch and Development Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, United Kingdom
| | - Blair Merrick
- lCentre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, Guy’s and St Thomas’ NHS Foundation Trust, London, SE1 7EH, United Kingdom
| | - Karen Bisnauthsing
- kResearch and Development Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, United Kingdom
| | - Kate Brooks
- kResearch and Development Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, United Kingdom
| | - Jonathan Edgeworth
- iDepartment of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, United Kingdom
- lCentre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, Guy’s and St Thomas’ NHS Foundation Trust, London, SE1 7EH, United Kingdom
| | - John Cason
- mInfectious Diseases Biobank, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Christine Mant
- mInfectious Diseases Biobank, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Katie J. Doores
- iDepartment of Infectious Diseases, School of Immunology & Microbial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Pierre Vantourout
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
| | - Khai Luong
- fRoche Diagnostics Solutions, Pleasanton, CA, 94588
| | - Jan Berka
- fRoche Diagnostics Solutions, Pleasanton, CA, 94588
| | - Adrian C. Hayday
- aPeter Gorer Department of Immunobiology, School of Immunology and Microbial Sciences, King’s College London, London, SE1 9RT, United Kingdom
- bImmunosurveillance Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom
- 2To whom correspondence may be addressed.
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38
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Välikangas T, Junttila S, Rytkönen KT, Kukkonen-Macchi A, Suomi T, Elo LL. COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts. Front Genet 2022; 13:929887. [PMID: 35991542 PMCID: PMC9388772 DOI: 10.3389/fgene.2022.929887] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/27/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anu Kukkonen-Macchi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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39
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A key F27I substitution within HCDR1 facilitates the rapid maturation of P2C-1F11-like neutralizing antibodies in a SARS-CoV-2-infected donor. Cell Rep 2022; 40:111335. [PMID: 36057256 PMCID: PMC9395280 DOI: 10.1016/j.celrep.2022.111335] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/20/2022] [Accepted: 08/18/2022] [Indexed: 11/23/2022] Open
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40
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Zheng B, Yang Y, Chen L, Wu M, Zhou S. B-Cell Receptor Repertoire Sequencing: Deeper Digging into the Mechanisms and Clinical Aspects of Immune-mediated Diseases. iScience 2022; 25:105002. [PMID: 36157582 PMCID: PMC9494237 DOI: 10.1016/j.isci.2022.105002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
B cells play an essential role in adaptive immunity and are intimately correlated with pleiotropic immune-mediated diseases. Each B cell occupies a unique B cell receptor (BCR), and all BCRs throughout our body form “BCR repertoire.” With the development of sequencing technology and coupled bioinformatics, accumulating evidence indicates that BCR repertoire largely varies under physiological and pathological conditions. Therefore, comprehensive grasp of BCR repertoire will provide new insights into the pathogenesis of immune-mediated diseases and help exploit efficient diagnostic and treatment strategies. In this review, we start with an overview of BCR repertoire and related sequencing technologies and summarize their current applications in immune-mediated diseases. We also underscore the challenges of this emerging field and propose promising future directions in advancing BCR repertoire exploration.
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Affiliation(s)
- Bohao Zheng
- Wuxi School of Medicine, Jiangnan University, Wuxi, P. R. China
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China
| | - Yuqing Yang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China
| | - Lin Chen
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China
| | - Mengrui Wu
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China
- Corresponding author
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41
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Lee H, Yoo DK, Han J, Kim KH, Noh J, Lee Y, Lee E, Kwon S, Chung J. Optimization of peripheral blood volume for in silico reconstitution of the human B cell receptor repertoire. FEBS Open Bio 2022; 12:1634-1643. [PMID: 35866358 PMCID: PMC9433817 DOI: 10.1002/2211-5463.13467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/08/2022] [Accepted: 07/20/2022] [Indexed: 11/30/2022] Open
Abstract
B cells recognize antigens via membrane‐expressed B‐cell receptors (BCR) and antibodies. Similar human BCR sequences are frequently found at a significantly higher frequency than that theoretically calculated. Patients infected with SARS‐CoV2 and HIV or with autoimmune diseases share very similar BCRs. Therefore, in silico reconstitution of BCR repertoires and identification of stereotypical BCR sequences related to human pathology have diagnostic potential. Furthermore, monitoring changes of clinically significant BCR sequences and isotype conversion has prognostic potential. For BCR repertoire analysis, peripheral blood (PB) is the most convenient source. However, the optimal human PB volume for in silico reconstitution of the BCR repertoire has not been studied in detail. Here, we sampled 5, 10, and 20 mL PB from the left arm and 40 mL PB from the right arm of two volunteers, reconstituted in silico PB BCR repertoires, and compared their composition. In both volunteers, PB sampling over 20 mL resulted in slight increases in functional unique sequences (FUSs) or almost no increase in repertoire diversity. All FUSs with a frequency above 0.08% or 0.03% in the 40 mL PB BCR repertoire were detected even in the 5 mL PB BCR repertoire from each volunteer. FUSs with a higher frequency were more likely to be found in BCR repertoires from reduced PB volume, and those coexisting in two repertoires showed a statistically significant correlation in frequency irrespective of sampled anatomical site. The correlation was more significant in higher‐frequency FUSs. These observations support the potential of BCR repertoire analysis for diagnosis.
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Affiliation(s)
- Hyunho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Duck Kyun Yoo
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Jerome Han
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Ki Hyun Kim
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Jinsung Noh
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Yonghee Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea
| | - Eunjae Lee
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.,Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Korea.,BK21+ Creative Research Engineer Development for IT, Seoul National University, Seoul, 08826, Korea.,Bio-MAX Institute, Seoul National University, Seoul, 08826, Korea
| | - Junho Chung
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Korea.,Department of Biomedical Science, Seoul National University College of Medicine, Seoul, 03080, Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Korea
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42
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:bbac267. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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43
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Lee T, Kim Y, Kim HJ, Ha NY, Lee S, Chin B, Cho NH. Acute Surge of Atypical Memory and Plasma B-Cell Subsets Driven by an Extrafollicular Response in Severe COVID-19. Front Cell Infect Microbiol 2022; 12:909218. [PMID: 35899045 PMCID: PMC9309264 DOI: 10.3389/fcimb.2022.909218] [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: 03/31/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022] Open
Abstract
Background Despite the use of vaccines and therapeutics against the coronavirus disease 2019 (COVID-19) pandemic, this severe disease has been a critical burden on public health, whereas the pathogenic mechanism remains elusive. Recently, accumulating evidence underscores the potential role of the aberrant B-cell response and humoral immunity in disease progression, especially in high-risk groups. Methods Using single-cell RNA (scRNA) sequencing analysis, we investigated transcriptional features of B-cell population in peripheral blood from COVID-19 patients and compared them, according to clinical severity and disease course, against a public B-cell dataset. Results We confirmed that acute B cells differentiate into plasma cells, particularly in severe patients, potentially through enhanced extrafollicular (EF) differentiation. In severe groups, the elevated plasma B-cell response displayed increased B-cell receptor (BCR) diversity, as well as higher levels of anti–severe acute respiratory syndrome coronavirus 2 (anti–SARS-CoV-2) spike antibodies in plasma, than those in moderate cases, suggesting more robust and heterogeneous plasma cell response in severe COVID-19 patients. Trajectory analysis identified a differentiation pathway for the EF B-cell response from active naïve to atypical memory B cells (AM2), in addition to the emergence of an aberrant plasma cell subset (PC2), which was associated with COVID-19 progression and severity. The AM2 and PC2 subsets surged in the acute phase of the severe disease and presented multiple inflammatory features, including higher cytokine expression and humoral effector function, respectively. These features differ from other B-cell subsets, suggesting a pathogenic potential for disease progression. Conclusion The acute surge of AM2 and PC2 subsets with lower somatic hypermutation and higher inflammatory features may be driven by the EF B-cell response during the acute phase of severe COVID-19 and may represent one of the critical drivers in disease severity.
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Affiliation(s)
- Taeseob Lee
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea
- Discovery department, Biomarker Laboratory, Geninus Inc., Seoul, South Korea
| | - Yuri Kim
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Hyun Je Kim
- College of Medicine, Genome Medicine Institute, Seoul National University, Seoul, South Korea
| | - Na-Young Ha
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, South Korea
- School of Medicine, Biomedical Research Institute, Chungnam National University, Daejeon, South Korea
| | - Siyoung Lee
- Discovery department, Biomarker Laboratory, Geninus Inc., Seoul, South Korea
| | - BumSik Chin
- Department of Internal Medicine, National Medical Center, Seoul, South Korea
- *Correspondence: Nam-Hyuk Cho, ; BumSik Chin,
| | - Nam-Hyuk Cho
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, South Korea
- Department of Microbiology and Immunology, College of Medicine, Seoul National University, Seoul, South Korea
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, South Korea
- Bundang Hospital, Seoul National University, Seongnam, South Korea
- Wide River Institute of Immunology, Seoul National University, Hongcheon, South Korea
- *Correspondence: Nam-Hyuk Cho, ; BumSik Chin,
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44
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Cullen JN, Martin J, Vilella AJ, Treeful A, Sargan D, Bradley A, Friedenberg SG. Development and application of a next-generation sequencing protocol and bioinformatics pipeline for the comprehensive analysis of the canine immunoglobulin repertoire. PLoS One 2022; 17:e0270710. [PMID: 35802654 PMCID: PMC9269486 DOI: 10.1371/journal.pone.0270710] [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: 12/31/2021] [Accepted: 06/15/2022] [Indexed: 11/18/2022] Open
Abstract
Profiling the adaptive immune repertoire using next generation sequencing (NGS) has become common in human medicine, showing promise in characterizing clonal expansion of B cell clones through analysis of B cell receptors (BCRs) in patients with lymphoid malignancies. In contrast, most work evaluating BCR repertoires in dogs has employed traditional PCR-based approaches analyzing the IGH locus only. The objectives of this study were to: (1) describe a novel NGS protocol to evaluate canine BCRs; (2) develop a bioinformatics pipeline for processing canine BCR sequencing data; and (3) apply these methods to derive insights into BCR repertoires of healthy dogs and dogs undergoing treatment for B-cell lymphoma. RNA from peripheral blood mononuclear cells of healthy dogs (n = 25) and dogs newly diagnosed with intermediate-to-large B-cell lymphoma (n = 18) with intent to pursue chemotherapy was isolated, converted into cDNA and sequenced by NGS. The BCR repertoires were identified and quantified using a novel analysis pipeline. The IGK repertoires of the healthy dogs were far less diverse compared to IGL which, as with IGH, was highly diverse. Strong biases at key positions within the CDR3 sequence were identified within the healthy dog BCR repertoire. For a subset of the dogs with B-cell lymphoma, clonal expansion of specific IGH sequences pre-treatment and reduction post-treatment was observed. The degree of expansion and reduction correlated with the clinical outcome in this subset. Future studies employing these techniques may improve disease monitoring, provide earlier recognition of disease progression, and ultimately lead to more targeted therapeutics.
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Affiliation(s)
- Jonah N. Cullen
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, Minnesota, United States of America
| | - Jolyon Martin
- Wellcome Trust Genome Campus, Hinxton, Saffron Walden, United Kingdom
- PetMedix Ltd, Glenn Berge Building, Babraham Research Campus, Cambridge, United Kingdom
| | - Albert J. Vilella
- PetMedix Ltd, Glenn Berge Building, Babraham Research Campus, Cambridge, United Kingdom
| | - Amy Treeful
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, Minnesota, United States of America
| | - David Sargan
- Department of Veterinary Medicine, Madingley Road, Cambridge, United Kingdom
| | - Allan Bradley
- Wellcome Trust Genome Campus, Hinxton, Saffron Walden, United Kingdom
- PetMedix Ltd, Glenn Berge Building, Babraham Research Campus, Cambridge, United Kingdom
- Department of Medicine, Jeffrey Cheah Biomedical Centre, Cambridge, United Kingdom
| | - Steven G. Friedenberg
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St. Paul, Minnesota, United States of America
- * E-mail:
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Gao H, Yu L, Yan F, Zheng Y, Huang H, Zhuang X, Zeng Y. Landscape of B Cell Receptor Repertoires in COVID-19 Patients Revealed Through CDR3 Sequencing of Immunoglobulin Heavy and Light Chains. Immunol Invest 2022; 51:1994-2008. [PMID: 35797435 DOI: 10.1080/08820139.2022.2092407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The outbreak and persistence of coronavirus disease 2019 (COVID-19) threaten human health. B cells play a vital role in fighting the infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite many studies on the immune responses in COVID-19 patients, it is still unclear how B cell receptor (BCR) constituents, including immunoglobulin heavy (IGHs) and light chains (IGLs), respond to SARS-CoV-2 in patients with varying symptoms. In this study, we conducted complementarity-determining region 3 (CDR3) sequencing of BCR IGHs and IGLs from the peripheral blood of COVID-19 patients and healthy donors. The results showed significantly reduced clonal diversity, more expanded clones, and longer CDR3 lengths of IGH and IGL in COVID-19 patients than those in healthy individuals. The IGLs had a much higher percentage of VJ skew usage (47.83% IGLV and 42.86% IGLJ were significantly regulated) than the IGHs (12.09% IGHV and 0% IGHJ) between the healthy individuals and patients, which indicated the importance of BCR light chains. Furthermore, we found a largely expanded IGLV3-25 gene cluster mostly pairing with IGLJ1 and ILGJ2 in COVID-19 patients and a newly identified upregulated IGLJ1 gene and IGLJ2+IGLV13-21 recombination, both of which are potential sources of SARS-CoV-2-targeting antibodies. Our findings on specific immune B-cell signatures associated with COVID-19 have clinical implications for vaccine and biomarker development for disease diagnosis.
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Affiliation(s)
- Hongzhi Gao
- Central Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.,Department of Respiratory Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Liying Yu
- Central Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Furong Yan
- Central Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Youxian Zheng
- Department of Microbiology, Quanzhou Municipal Center for Disease Control and Prevention, Fujian Province, Quanzhou, China
| | - Hongbo Huang
- Department of Pulmonary and Critical Care Medicine, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xibin Zhuang
- Department of Pulmonary and Critical Care Medicine, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China
| | - Yiming Zeng
- Department of Respiratory Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
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Planchais C, Fernández I, Bruel T, de Melo GD, Prot M, Beretta M, Guardado-Calvo P, Dufloo J, Molinos-Albert LM, Backovic M, Chiaravalli J, Giraud E, Vesin B, Conquet L, Grzelak L, Planas D, Staropoli I, Guivel-Benhassine F, Hieu T, Boullé M, Cervantes-Gonzalez M, Ungeheuer MN, Charneau P, van der Werf S, Agou F, Dimitrov JD, Simon-Lorière E, Bourhy H, Montagutelli X, Rey FA, Schwartz O, Mouquet H. Potent human broadly SARS-CoV-2-neutralizing IgA and IgG antibodies effective against Omicron BA.1 and BA.2. J Exp Med 2022; 219:e20220638. [PMID: 35704748 PMCID: PMC9206116 DOI: 10.1084/jem.20220638] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/11/2022] Open
Abstract
Memory B-cell and antibody responses to the SARS-CoV-2 spike protein contribute to long-term immune protection against severe COVID-19, which can also be prevented by antibody-based interventions. Here, wide SARS-CoV-2 immunoprofiling in Wuhan COVID-19 convalescents combining serological, cellular, and monoclonal antibody explorations revealed humoral immunity coordination. Detailed characterization of a hundred SARS-CoV-2 spike memory B-cell monoclonal antibodies uncovered diversity in their repertoire and antiviral functions. The latter were influenced by the targeted spike region with strong Fc-dependent effectors to the S2 subunit and potent neutralizers to the receptor-binding domain. Amongst those, Cv2.1169 and Cv2.3194 antibodies cross-neutralized SARS-CoV-2 variants of concern, including Omicron BA.1 and BA.2. Cv2.1169, isolated from a mucosa-derived IgA memory B cell demonstrated potency boost as IgA dimers and therapeutic efficacy as IgG antibodies in animal models. Structural data provided mechanistic clues to Cv2.1169 potency and breadth. Thus, potent broadly neutralizing IgA antibodies elicited in mucosal tissues can stem SARS-CoV-2 infection, and Cv2.1169 and Cv2.3194 are prime candidates for COVID-19 prevention and treatment.
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Affiliation(s)
- Cyril Planchais
- Institut Pasteur, Université Paris Cité, Laboratory of Humoral Immunology, Paris, France
- INSERM U1222, Paris, France
| | - Ignacio Fernández
- Institut Pasteur, Université Paris Cité, Structural Virology Unit, Paris, France
- CNRS UMR3569, Paris, France
| | - Timothée Bruel
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Guilherme Dias de Melo
- Institut Pasteur, Université Paris Cité, Lyssavirus Epidemiology and Neuropathology Unit, Paris, France
| | - Matthieu Prot
- Institut Pasteur, Université Paris Cité, G5 Evolutionary Genomics of RNA Viruses, Paris, France
| | - Maxime Beretta
- Institut Pasteur, Université Paris Cité, Laboratory of Humoral Immunology, Paris, France
- INSERM U1222, Paris, France
| | - Pablo Guardado-Calvo
- Institut Pasteur, Université Paris Cité, Structural Virology Unit, Paris, France
- CNRS UMR3569, Paris, France
| | - Jérémy Dufloo
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Luis M. Molinos-Albert
- Institut Pasteur, Université Paris Cité, Laboratory of Humoral Immunology, Paris, France
- INSERM U1222, Paris, France
| | - Marija Backovic
- Institut Pasteur, Université Paris Cité, Structural Virology Unit, Paris, France
- CNRS UMR3569, Paris, France
| | - Jeanne Chiaravalli
- Institut Pasteur, Université Paris Cité, Chemogenomic and Biological Screening Core Facility, C2RT, Paris, France
| | - Emilie Giraud
- Institut Pasteur, Université Paris Cité, Chemogenomic and Biological Screening Core Facility, C2RT, Paris, France
| | - Benjamin Vesin
- Pasteur-TheraVectys, Paris, France
- Institut Pasteur, Université Paris Cité, Molecular Virology & Vaccinology Unit, Paris, France
| | - Laurine Conquet
- Institut Pasteur, Université Paris Cité, Mouse Genetics Laboratory, Paris, France
| | - Ludivine Grzelak
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Delphine Planas
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Isabelle Staropoli
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Florence Guivel-Benhassine
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Thierry Hieu
- Institut Pasteur, Université Paris Cité, Functional Genetics of Infectious Diseases Unit, Paris, France
| | - Mikaël Boullé
- Institut Pasteur, Université Paris Cité, Chemogenomic and Biological Screening Core Facility, C2RT, Paris, France
| | - Minerva Cervantes-Gonzalez
- Department of Epidemiology, Biostatistics and Clinical Research, Assistance Publique-Hôpitaux de Paris, Bichat Claude Bernard University Hospital, INSERM CIC-EC 1425, Paris, France
| | - Marie-Noëlle Ungeheuer
- Institut Pasteur, Université Paris Cité, Investigation Clinique et Accès aux Ressources Biologiques, Center for Translational Research, Paris, France
| | - Pierre Charneau
- Pasteur-TheraVectys, Paris, France
- Institut Pasteur, Université Paris Cité, Molecular Virology & Vaccinology Unit, Paris, France
| | - Sylvie van der Werf
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Molecular Genetics of RNA Viruses, Paris, France
- Université de Paris, Paris, France
| | - Fabrice Agou
- Institut Pasteur, Université Paris Cité, Chemogenomic and Biological Screening Core Facility, C2RT, Paris, France
| | - Jordan D. Dimitrov
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France
| | - Etienne Simon-Lorière
- Institut Pasteur, Université Paris Cité, G5 Evolutionary Genomics of RNA Viruses, Paris, France
| | - Hervé Bourhy
- Institut Pasteur, Université Paris Cité, Lyssavirus Epidemiology and Neuropathology Unit, Paris, France
| | - Xavier Montagutelli
- Institut Pasteur, Université Paris Cité, Mouse Genetics Laboratory, Paris, France
| | - Félix A. Rey
- Institut Pasteur, Université Paris Cité, Structural Virology Unit, Paris, France
- CNRS UMR3569, Paris, France
| | - Olivier Schwartz
- CNRS UMR3569, Paris, France
- Institut Pasteur, Université Paris Cité, Virus & Immunity Unit, Paris, France
| | - Hugo Mouquet
- Institut Pasteur, Université Paris Cité, Laboratory of Humoral Immunology, Paris, France
- INSERM U1222, Paris, France
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Chen Y, Ye Z, Zhang Y, Xie W, Chen Q, Lan C, Yang X, Zeng H, Zhu Y, Ma C, Tang H, Wang Q, Guan J, Chen S, Li F, Yang W, Yan H, Yu X, Zhang Z. A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2675-2685. [PMID: 35606050 DOI: 10.4049/jimmunol.2200063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.
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Affiliation(s)
- Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhiming Ye
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yanfang Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenxi Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qingyun Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chunhong Lan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiujia Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huikun Zeng
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Zhu
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Junjie Guan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Sen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Fenxiang Li
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huacheng Yan
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhai Zhang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Organ Failure Research, Division of Nephrology, Southern Medical University, Guangzhou, China; and
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China
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Abstract
High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.
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49
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Richard D, Phillip S, Hosseinali A, Gracie DZ, Hai L, January W, Holtgrewe M, Charlotte T, Melina M, Xiaomin W, Zehra K, Jacopo S, Jan-Moritz D, Ralf-Harto H, Bernd H, Anja B, Sandra S, Dilduz T, Norbert S, Martin W, Stefan H, Carsten S, Wolfgang P, Leif E S, Dieter B, Florian K, Toumy G, Ulf L, Jan B, Khai L, Rubelt F, Bettina H. Highly multiplexed immune repertoire sequencing links multiple lymphocyte classes with severity of response to COVID-19. EClinicalMedicine 2022; 48:101438. [PMID: 35600330 PMCID: PMC9106482 DOI: 10.1016/j.eclinm.2022.101438] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Disease progression of subjects with coronavirus disease 2019 (COVID-19) varies dramatically. Understanding the various types of immune response to SARS-CoV-2 is critical for better clinical management of coronavirus outbreaks and to potentially improve future therapies. Disease dynamics can be characterized by deciphering the adaptive immune response. METHODS In this cross-sectional study we analyzed 117 peripheral blood immune repertoires from healthy controls and subjects with mild to severe COVID-19 disease to elucidate the interplay between B and T cells. We used an immune repertoire Primer Extension Target Enrichment method (immunoPETE) to sequence simultaneously human leukocyte antigen (HLA) restricted T cell receptor beta chain (TRB) and unrestricted T cell receptor delta chain (TRD) and immunoglobulin heavy chain (IgH) immune receptor repertoires. The distribution was analyzed of TRB, TRD and IgH clones between healthy and COVID-19 infected subjects. Using McFadden's Adjusted R2 variables were examined for a predictive model. The aim of this study is to analyze the influence of the adaptive immune repertoire on the severity of the disease (value on the World Health Organization Clinical Progression Scale) in COVID-19. FINDINGS Combining clinical metadata with clonotypes of three immune receptor heavy chains (TRB, TRD, and IgH), we found significant associations between COVID-19 disease severity groups and immune receptor sequences of B and T cell compartments. Logistic regression showed an increase in shared IgH clonal types and decrease of TRD in subjects with severe COVID-19. The probability of finding shared clones of TRD clonal types was highest in healthy subjects (controls). Some specific TRB clones seems to be present in severe COVID-19 (Figure S7b). The most informative models (McFadden´s Adjusted R2=0.141) linked disease severity with immune repertoire measures across all three cell types, as well as receptor-specific cell counts, highlighting the importance of multiple lymphocyte classes in disease progression. INTERPRETATION Adaptive immune receptor peripheral blood repertoire measures are associated with COVID-19 disease severity. FUNDING The study was funded with grants from the Berlin Institute of Health (BIH).
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Affiliation(s)
| | - Suwalski Phillip
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | | | | | - Lin Hai
- Roche Sequencing Solutions Pleasanton, CA 94588, United States
| | - Weiner January
- Core Unit Bioinformatics Berlin, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, DE 10178, Germany
| | - Manuel Holtgrewe
- Core Unit Bioinformatics Berlin, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, DE 10178, Germany
| | - Thibeault Charlotte
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Müller Melina
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | - Wang Xiaomin
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | - Karadeniz Zehra
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | - Saccomanno Jacopo
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Doehn Jan-Moritz
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Hübner Ralf-Harto
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | | | - Blüher Anja
- Signature Diagnostics GmbH, DE 14473, Germany
| | | | - Telman Dilduz
- Roche Sequencing Solutions Pleasanton, CA 94588, United States
| | - Suttorp Norbert
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Witzenrath Martin
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Hippenstiel Stefan
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Skurk Carsten
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | - Poller Wolfgang
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
| | - Sander Leif E
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | - Beule Dieter
- Core Unit Bioinformatics Berlin, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, DE 10178, Germany
| | - Kurth Florian
- Department of Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, DE 12203, Germany
| | | | - Landmesser Ulf
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Germany
| | - Berka Jan
- Roche Sequencing Solutions Pleasanton, CA 94588, United States
| | - Luong Khai
- Roche Sequencing Solutions Pleasanton, CA 94588, United States
| | | | - Florian Rubelt
- Roche Sequencing Solutions Pleasanton, CA 94588, United States
| | - Heidecker Bettina
- Department of Cardiology, Charité Universitätsmedizin Berlin, Berlin, DE 10117, Germany
- Corresponding authors.
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50
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Paschold L, Klee B, Gottschick C, Willscher E, Diexer S, Schultheiß C, Simnica D, Sedding D, Girndt M, Gekle M, Mikolajczyk R, Binder M. Rapid Hypermutation B Cell Trajectory Recruits Previously Primed B Cells Upon Third SARS-Cov-2 mRNA Vaccination. Front Immunol 2022; 13:876306. [PMID: 35615365 PMCID: PMC9126551 DOI: 10.3389/fimmu.2022.876306] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022] Open
Abstract
The COVID-19 pandemic shows that vaccination strategies building on an ancestral viral strain need to be optimized for the control of potentially emerging viral variants. Therefore, aiming at strong B cell somatic hypermutation to increase antibody affinity to the ancestral strain - not only at high antibody titers - is a priority when utilizing vaccines that are not targeted at individual variants since high affinity may offer some flexibility to compensate for strain-individual mutations. Here, we developed a next-generation sequencing based SARS-CoV-2 B cell tracking protocol to rapidly determine the level of immunoglobulin somatic hypermutation at distinct points during the immunization period. The percentage of somatically hypermutated B cells in the SARS-CoV-2 specific repertoire was low after the primary vaccination series, evolved further over months and increased steeply after boosting. The third vaccination mobilized not only naïve, but also antigen-experienced B cell clones into further rapid somatic hypermutation trajectories indicating increased affinity. Together, the strongly mutated post-booster repertoires and antibodies deriving from this may explain why the third, but not the primary vaccination series, offers some protection against immune-escape variants such as Omicron B.1.1.529.
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Affiliation(s)
- Lisa Paschold
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Bianca Klee
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Cornelia Gottschick
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Edith Willscher
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Sophie Diexer
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Christoph Schultheiß
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Donjete Simnica
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Sedding
- Mid-German Heart Center, Department of Cardiology and Intensive Care Medicine, University Hospital, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Matthias Girndt
- Department of Internal Medicine II, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Michael Gekle
- Julius Bernstein-Institute of Physiology, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Mascha Binder
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
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