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Yi L, Guo X, Liu Y, Jirimutu, Wang Z. Single-cell 5' RNA sequencing of camelid peripheral B cells provides insights into cellular basis of heavy-chain antibody production. Comput Struct Biotechnol J 2024; 23:1705-1714. [PMID: 38689719 PMCID: PMC11059136 DOI: 10.1016/j.csbj.2024.04.041] [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: 01/19/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024] Open
Abstract
Camelids produce both conventional tetrameric antibodies (Abs) and dimeric heavy-chain antibodies (HCAbs). Although B cells that generate these two types of Abs exhibit distinct B cell receptors (BCRs), whether these two B cell populations differ in their phenotypes and developmental processes remains unclear. Here, we performed single-cell 5' RNA profiling of peripheral blood mononuclear cell samples from Bactrian camels before and after immunization. We characterized the functional subtypes and differentiation trajectories of circulating B cells in camels, and reconstructed single-cell BCR sequences. We found that in contrast to humans, the proportion of T-bet+ B cells was high among camelid peripheral B cells. Several marker genes of human B cell subtypes, including CD27 and IGHD, were expressed at low levels in the corresponding camel B cell subtypes. Camelid B cells expressing variable genes of HACbs (VHH) were widely present in various functional subtypes and showed highly overlapping differentiation trajectories with B cells expressing variable genes of conventional Abs (VH). After immunization, the transcriptional changes in VHH+ and VH+ B cells were largely consistent. Through structure modeling, we identified a variety of scaffold types among the reconstructed VHH sequences. Our study provides insights into the cellular context of HCAb production in camels and lays the foundation for developing single-B cell-based camelid single-domain Ab screening.
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Affiliation(s)
- Li Yi
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, College of Food Science and Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China
| | - Xin Guo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuexing Liu
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Jirimutu
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, College of Food Science and Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China
- Inner Mongolia China-Kazakhstan Camel Research Institute, Alxa 750306, China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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2
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Yi X, Qiu Y, Wang S, Sun X. Analysis of immunoglobulin organization and complexity in mink (Neovison vison). DEVELOPMENTAL AND COMPARATIVE IMMUNOLOGY 2024; 160:105234. [PMID: 39069110 DOI: 10.1016/j.dci.2024.105234] [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: 06/04/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 07/30/2024]
Abstract
Mink are susceptible to viruses such as SARS-CoV-2, H1N1 and H9N2, so they are considered a potential animal model for studying human viral infections. Therefore, it is important to study the immune system of mink. Immunoglobulin (Ig) is an important component of humoral immunity and plays an important role in the body's immune defense. In this study, we described the gene loci structure of mink Ig germline by genome comparison, and analysed the mechanism of expression diversity of mink antibody library by 5'RACE and next-generation sequencing (NGS). The results were as follows: the IgH, Igκ and Igλ loci of mink were located on chromosome 13, chromosome 8 and chromosome 3, respectively, and they had 25, 36 and 7 V genes, 3, 5 and 7 J genes and 10 DH genes, respectively. Mink Ig heavy chain preferred the IGHV1, IGHD2 and IGHJ4 subgroups, κ chain mainly use the IGKV1, IGKJ1 and IGHL4 subgroups, and λ chain mainly use the IGLV3 and IGLJ3 subgroups. Linkage diversity analysis revealed that N nucleotide insertion was the main factor affecting the linkage diversity of mink Igs. On the mutation types of mink Ig Somatic Hypermutation (SHM), the high mutation types of heavy chain were mainly G > A, C > T, T > C, A > G, C > A, G > T, A > C, and T > G; the high mutation types of κ chain were G > A and T > C; and the high mutation types of λ chain were G > A and A > G. The objective of this study was to analyse the loci structure and expression diversity of Ig in mink. The results contribute to our comprehension of Ig expression patterns in mink and were valuable for advancing knowledge in mink immunogenetics, exploring the evolution of adaptive immune systems across different species, and conducting comparative genomics research.
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Affiliation(s)
- Xiaohua Yi
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Yanbo Qiu
- College of Grassland Agriculture, Northwest A&F University, Yangling, 712100, China
| | - Shuhui Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Xiuzhu Sun
- College of Grassland Agriculture, Northwest A&F University, Yangling, 712100, China.
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3
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Sherpa P, Chong KT, Tayara H. FvFold: A model to predict antibody Fv structure using protein language model with residual network and Rosetta minimization. Comput Biol Med 2024; 182:109128. [PMID: 39270460 DOI: 10.1016/j.compbiomed.2024.109128] [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: 05/24/2024] [Revised: 08/22/2024] [Accepted: 09/05/2024] [Indexed: 09/15/2024]
Abstract
The immune system depends on antibodies (Abs) to recognize and attach to a wide range of antigens, playing a pivotal role in immunity. The precise prediction of the variable fragment (Fv) region of antibodies is vital for the progress of therapeutic and commercial applications, particularly in the treatment of diseases such as cancer. Although deep learning models exist for accurate antibody structure prediction, challenges persist, particularly in modeling complementarity-determining regions (CDRs) and the overall antibody Fv structures. Introducing the FvFold model, a deep learning approach harnessing the capabilities of the ProtT5-XL-UniRef50 protein language model which is capable of predicting accurate antibody Fv structure. Through evaluations on various benchmarks, our model outperforms existing models, demonstrating superior accuracy by achieving lower Root Mean Square Deviation (RMSD) in almost all loops and Orientational Coordinate Distance (OCD) values in the RosettaAntibody benchmark, Therapeutic benchmark and IgFold benchmark compared to the previous top-performing model.
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Affiliation(s)
- Pasang Sherpa
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
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4
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Underwood AP, Gupta M, Wu BR, Eltahla AA, Boo I, Wang JJ, Agapiou D, Abayasingam A, Reynaldi A, Keoshkerian E, Zhao Y, Brasher N, Walker MR, Bukh J, Maher L, Gordon T, Davenport MP, Luciani F, Drummer HE, Lloyd AR, Bull RA. B-cell characteristics define HCV reinfection outcome. J Hepatol 2024; 81:415-428. [PMID: 38604387 DOI: 10.1016/j.jhep.2024.04.004] [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: 08/22/2023] [Revised: 03/20/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND & AIMS In individuals highly exposed to HCV, reinfection is common, suggesting that natural development of sterilising immunity is difficult. In those that are reinfected, some will develop a persistent infection, while a small proportion repeatedly clear the virus, suggesting natural protection is possible. The aim of this study was to characterise immune responses associated with rapid natural clearance of HCV reinfection. METHODS Broad neutralising antibodies (nAbs) and Envelope 2 (E2)-specific memory B cell (MBC) responses were examined longitudinally in 15 individuals with varied reinfection outcomes. RESULTS Broad nAb responses were associated with MBC recall, but not with clearance of reinfection. Strong evidence of antigen imprinting was found, and the B-cell receptor repertoire showed a high level of clonality with ongoing somatic hypermutation of many clones over subsequent reinfection events. Single-cell transcriptomic analyses showed that cleared reinfections featured an activated transcriptomic profile in HCV-specific B cells that rapidly expanded upon reinfection. CONCLUSIONS MBC quality, but not necessarily breadth of nAb responses, is important for protection against antigenically diverse variants, which is encouraging for HCV vaccine development. IMPACT AND IMPLICATIONS HCV continues to have a major health burden globally. Limitations in the health infrastructure for diagnosis and treatment, as well as high rates of reinfection, indicate that a vaccine that can protect against chronic HCV infection will greatly complement current efforts to eliminate HCV-related disease. With alternative approaches to testing vaccines, such as controlled human inoculation trials under consideration, we desperately need to identify the correlates of immune protection. In this study, in a small but rare cohort of high-risk injecting drug users who were reinfected multiple times, breadth of neutralisation was not associated with ultimate clearance of the reinfection event. Alternatively, characteristics of the HCV-specific B-cell response associated with B-cell proliferation were. This study indicates that humoral responses are important for protection and suggests that for genetically very diverse viruses, such as HCV, it may be beneficial to look beyond just antibodies as correlates of protection.
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Affiliation(s)
- Alexander P Underwood
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; Copenhagen Hepatitis C Program (CO-HEP), Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre and Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Money Gupta
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Bing-Ru Wu
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Auda A Eltahla
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Irene Boo
- Burnet Institute, Melbourne, VIC, Australia
| | - Jing Jing Wang
- Department of Immunology Flinders Medical Centre and Flinders University, SA Pathology Bedford Park, SA, Australia
| | - David Agapiou
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Arunasingam Abayasingam
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Arnold Reynaldi
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | | | - Yanran Zhao
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Nicholas Brasher
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Melanie R Walker
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Jens Bukh
- Copenhagen Hepatitis C Program (CO-HEP), Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre and Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lisa Maher
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Tom Gordon
- Department of Immunology Flinders Medical Centre and Flinders University, SA Pathology Bedford Park, SA, Australia
| | - Miles P Davenport
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Fabio Luciani
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Heidi E Drummer
- Burnet Institute, Melbourne, VIC, Australia; Department of Microbiology, Monash University, Clayton, VIC, Australia; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Andrew R Lloyd
- The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia
| | - Rowena A Bull
- School of Biomedical Science, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia; The Kirby Institute, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia.
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5
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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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6
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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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7
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Gabernet G, Marquez S, Bjornson R, Peltzer A, Meng H, Aron E, Lee NY, Jensen CG, Ladd D, Polster M, Hanssen F, Heumos S, Yaari G, Kowarik MC, Nahnsen S, Kleinstein SH. nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework. PLoS Comput Biol 2024; 20:e1012265. [PMID: 39058741 PMCID: PMC11305553 DOI: 10.1371/journal.pcbi.1012265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 08/07/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.
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Affiliation(s)
- Gisela Gabernet
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
| | - Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Robert Bjornson
- Yale Center for Research Computing, New Haven, Connecticut, United States of America
| | | | - Hailong Meng
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Edel Aron
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Noah Y. Lee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Cole G. Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - David Ladd
- oNKo-Innate Pty Ltd, Melbourne, Victoria, Australia
| | - Mark Polster
- Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital, Tübingen, Germany
| | - Friederike Hanssen
- Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital, Tübingen, Germany
| | - Simon Heumos
- Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital, Tübingen, Germany
| | | | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | - Markus C. Kowarik
- Department of Neurology and Stroke, Center for Neurology, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, Eberhard-Karls University of Tübingen, Tübingen, Germany
| | - Sven Nahnsen
- Quantitative Biology Center, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard-Karls University of Tübingen, Tübingen, Germany
- M3 Research Center, University Hospital, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard-Karls University of Tübingen, Tübingen, Germany
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Immunobiology, Yale School of Medicine, New Haven, Connecticut, United States of America
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8
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Stingl C, VanDuijn MM, Dejoie T, Sillevis Smitt PAE, Luider TM. Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques. CELL REPORTS METHODS 2024; 4:100795. [PMID: 38861989 PMCID: PMC11228375 DOI: 10.1016/j.crmeth.2024.100795] [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: 11/03/2023] [Revised: 03/30/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.
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Affiliation(s)
- Christoph Stingl
- Clinical and Cancer Proteomics, Department of Neurology, Erasmus MC, 3000 CA Rotterdam, the Netherlands.
| | - Martijn M VanDuijn
- Clinical and Cancer Proteomics, Department of Neurology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Thomas Dejoie
- Laboratoire de Biochimie, Centre Hospitalier Universitaire (CHU), 44000 Nantes, France
| | - Peter A E Sillevis Smitt
- Clinical and Cancer Proteomics, Department of Neurology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Theo M Luider
- Clinical and Cancer Proteomics, Department of Neurology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
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9
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Dumonteil E, Tu W, Desale H, Goff K, Marx P, Ortega-Lopez J, Herrera C. Immunoglobulin and T cell receptor repertoire changes induced by a prototype vaccine against Chagas disease in naïve rhesus macaques. J Biomed Sci 2024; 31:58. [PMID: 38824576 PMCID: PMC11143712 DOI: 10.1186/s12929-024-01050-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND A vaccine against Trypanosoma cruzi, the agent of Chagas disease, would be an excellent additional tool for disease control. A recombinant vaccine based on Tc24 and TSA1 parasite antigens was found to be safe and immunogenic in naïve macaques. METHODS We used RNA-sequencing and performed a transcriptomic analysis of PBMC responses to vaccination of naïve macaques after each vaccine dose, to shed light on the immunogenicity of this vaccine and guide the optimization of doses and formulation. We identified differentially expressed genes and pathways and characterized immunoglobulin and T cell receptor repertoires. RESULTS RNA-sequencing analysis indicated a clear transcriptomic response of PBMCs after three vaccine doses, with the up-regulation of several immune cell activation pathways and a broad non-polarized immune profile. Analysis of the IgG repertoire showed that it had a rapid turnover with novel IgGs produced following each vaccine dose, while the TCR repertoire presented several persisting clones that were expanded after each vaccine dose. CONCLUSIONS These data suggest that three vaccine doses may be needed for optimum immunogenicity and support the further evaluation of the protective efficacy of this vaccine.
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Affiliation(s)
- Eric Dumonteil
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA.
| | - Weihong Tu
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
| | - Hans Desale
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
| | - Kelly Goff
- Division of Microbiology, Tulane National Primate Research Center, Tulane University, Covington, LA, USA
| | - Preston Marx
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
- Division of Microbiology, Tulane National Primate Research Center, Tulane University, Covington, LA, USA
| | - Jaime Ortega-Lopez
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de Mexico, México
| | - Claudia Herrera
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
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10
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Chen X, Mohapatra A, Nguyen HTV, Schimanski L, Kit Tan T, Rijal P, Chen CP, Cheng SH, Lee WH, Chou YC, Townsend AR, Ma C, Huang KYA. The presence of broadly neutralizing anti-SARS-CoV-2 RBD antibodies elicited by primary series and booster dose of COVID-19 vaccine. PLoS Pathog 2024; 20:e1012246. [PMID: 38857264 PMCID: PMC11192315 DOI: 10.1371/journal.ppat.1012246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 06/21/2024] [Accepted: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
Antibody-mediated immunity plays a key role in protection against SARS-CoV-2. We characterized B-cell-derived anti-SARS-CoV-2 RBD antibody repertoires from vaccinated and infected individuals and elucidate the mechanism of action of broadly neutralizing antibodies and dissect antibodies at the epitope level. The breadth and clonality of anti-RBD B cell response varies among individuals. The majority of neutralizing antibody clones lose or exhibit reduced activities against Beta, Delta, and Omicron variants. Nevertheless, a portion of anti-RBD antibody clones that develops after a primary series or booster dose of COVID-19 vaccination exhibit broad neutralization against emerging Omicron BA.2, BA.4, BA.5, BQ.1.1, XBB.1.5 and XBB.1.16 variants. These broadly neutralizing antibodies share genetic features including a conserved usage of the IGHV3-53 and 3-9 genes and recognize three clustered epitopes of the RBD, including epitopes that partially overlap the classically defined set identified early in the pandemic. The Fab-RBD crystal and Fab-Spike complex structures corroborate the epitope grouping of antibodies and reveal the detailed binding mode of broadly neutralizing antibodies. Structure-guided mutagenesis improves binding and neutralization potency of antibody with Omicron variants via a single amino-substitution. Together, these results provide an immunological basis for partial protection against severe COVID-19 by the ancestral strain-based vaccine and indicate guidance for next generation monoclonal antibody development and vaccine design.
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Affiliation(s)
- Xiaorui Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | | | - Hong Thuy Vy Nguyen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Chemical Biology and Molecular Biophysics program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan
| | - Lisa Schimanski
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Tiong Kit Tan
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Pramila Rijal
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Cheng-Pin Chen
- Department of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, and Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shu-Hsing Cheng
- Department of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, and School of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hsin Lee
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Yu-Chi Chou
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
| | - Alain R. Townsend
- MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Che Ma
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Kuan-Ying A. Huang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Immunology and Department of Pediatrics, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
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11
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Jing H, Gao Z, Xu S, Shen T, Peng Z, He S, You T, Ye S, Lin W, Sun S. Accurate prediction of antibody function and structure using bio-inspired antibody language model. Brief Bioinform 2024; 25:bbae245. [PMID: 38797969 PMCID: PMC11128484 DOI: 10.1093/bib/bbae245] [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: 10/12/2023] [Revised: 04/08/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.
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Affiliation(s)
- Hongtai Jing
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
| | - Zhengtao Gao
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Sheng Xu
- Shanghai AI Laboratory, Shanghai 200232, China
| | - Tao Shen
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Zelixir Biotech, Shanghai 201206, China
| | - Zhangzhi Peng
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Shwai He
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Tao You
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Shuang Ye
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200032, China
- Shanghai AI Laboratory, Shanghai 200232, China
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Siqi Sun
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- Shanghai AI Laboratory, Shanghai 200232, China
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12
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Strazza V, Rossi M, Avati A, Tiseo G, Falcone M, Cusi MG, Menichetti F, Ricciardi-Castagnoli P, Tinti C, Pileri P. Rapid generation of human recombinant monoclonal antibodies from antibody-secreting cells using ferrofluid-based technology. Front Immunol 2024; 15:1341389. [PMID: 38698845 PMCID: PMC11064063 DOI: 10.3389/fimmu.2024.1341389] [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: 11/20/2023] [Accepted: 03/06/2024] [Indexed: 05/05/2024] Open
Abstract
Monoclonal antibodies (mAbs) are one of the most important classes of biologics with high therapeutic and diagnostic value, but traditional methods for mAbs generation, such as hybridoma screening and phage display, have limitations, including low efficiency and loss of natural chain pairing. To overcome these challenges, novel single B cell antibody technologies have emerged, but they also have limitations such as in vitro differentiation of memory B cells and expensive cell sorters. In this study, we present a rapid and efficient workflow for obtaining human recombinant monoclonal antibodies directly from single antigen-specific antibody secreting cells (ASCs) in the peripheral blood of convalescent COVID-19 patients using ferrofluid technology. This process allows the identification and expression of recombinant antigen-specific mAbs in less than 10 days, using RT-PCR to generate linear Ig heavy and light chain gene expression cassettes, called "minigenes", for rapid expression of recombinant antibodies without cloning procedures. This approach has several advantages. First, it saves time and resources by eliminating the need for in vitro differentiation. It also allows individual antigen-specific ASCs to be screened for effector function prior to recombinant antibody cloning, enabling the selection of mAbs with desired characteristics and functional activity. In addition, the method allows comprehensive analysis of variable region repertoires in combination with functional assays to evaluate the specificity and function of the generated antigen-specific antibodies. Our approach, which rapidly generates recombinant monoclonal antibodies from single antigen-specific ASCs, could help to identify functional antibodies and deepen our understanding of antibody dynamics in the immune response through combined antibody repertoire sequence analysis and functional reactivity testing.
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Affiliation(s)
- Veronica Strazza
- Hyper Antibody Research & Development (HARD) -Lab, Toscana Life Sciences Foundation, Siena, Italy
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
| | - Marco Rossi
- Hyper Antibody Research & Development (HARD) -Lab, Toscana Life Sciences Foundation, Siena, Italy
| | - Andrea Avati
- Hyper Antibody Research & Development (HARD) -Lab, Toscana Life Sciences Foundation, Siena, Italy
| | - Giusy Tiseo
- Infectious Diseases Unit, Department of Clinical and Experimental Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, Pisa, Italy
| | - Marco Falcone
- Infectious Diseases Unit, Department of Clinical and Experimental Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, Pisa, Italy
| | - Maria Grazia Cusi
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Francesco Menichetti
- Infectious Diseases Unit, Department of Clinical and Experimental Medicine, Azienda Ospedaliero Universitaria Pisana, University of Pisa, Pisa, Italy
| | | | - Cristina Tinti
- Hyper Antibody Research & Development (HARD) -Lab, Toscana Life Sciences Foundation, Siena, Italy
| | - Piero Pileri
- Hyper Antibody Research & Development (HARD) -Lab, Toscana Life Sciences Foundation, Siena, Italy
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13
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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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14
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Beaulaurier J, Ly L, Duty JA, Tyer C, Stevens C, Hung CT, Sookdeo A, Drong AW, Kowdle S, Turner DJ, Juul S, Hickey S, Lee B. De novo antibody discovery in human blood from full-length single B cell transcriptomics and matching haplotyped-resolved germline assemblies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586834. [PMID: 38585716 PMCID: PMC10996687 DOI: 10.1101/2024.03.26.586834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully-phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells resulted in whole transcriptome characterization of each cell, as well as highly-accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrated binding to measles antigens and neutralization of measles live virus.
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15
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Gallo E. The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design. J Biomed Sci 2024; 31:29. [PMID: 38491519 PMCID: PMC10943851 DOI: 10.1186/s12929-024-01018-5] [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: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.
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Affiliation(s)
- Eugenio Gallo
- Department of Medicinal Chemistry, Avance Biologicals, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- Department of Protein Engineering, RevivAb, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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16
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Aterido A, López-Lasanta M, Blanco F, Juan-Mas A, García-Vivar ML, Erra A, Pérez-García C, Sánchez-Fernández SÁ, Sanmartí R, Fernández-Nebro A, Alperi-López M, Tornero J, Ortiz AM, Fernández-Cid CM, Palau N, Pan W, Byrne-Steele M, Starenki D, Weber D, Rodriguez-Nunez I, Han J, Myers RM, Marsal S, Julià A. Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes. Genome Biol 2024; 25:68. [PMID: 38468286 PMCID: PMC10926600 DOI: 10.1186/s13059-024-03210-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA. RESULTS In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool. CONCLUSIONS Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.
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Affiliation(s)
- Adrià Aterido
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - María López-Lasanta
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - Francisco Blanco
- Rheumatology Department, Hospital Juan Canalejo, A Coruña, Spain
| | | | | | - Alba Erra
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
- Rheumatology Department, Hospital Sant Rafael, Barcelona, Spain
| | | | | | - Raimon Sanmartí
- Rheumatology Department, Hospital Clínic de Barcelona and IDIBAPS, Barcelona, Spain
| | | | | | - Jesús Tornero
- Rheumatology Department, Hospital Universitario Guadalajara, Guadalajara, Spain
| | - Ana María Ortiz
- Rheumatology Department, Hospital Universitario La Princesa, IIS La Princesa, Madrid, Spain
| | | | - Núria Palau
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | | | | | | | | | | | - Jian Han
- iRepertoire Inc, Huntsville, AL, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Sara Marsal
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall Hebron Research Institute, 08035, Barcelona, Spain.
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17
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Grauslund LR, Ständer S, Veggi D, Andreano E, Rand KD, Norais N. Epitope Mapping of Human Polyclonal Antibodies to the fHbp Antigen of a Neisseria Meningitidis Vaccine by Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS). Mol Cell Proteomics 2024; 23:100734. [PMID: 38342408 PMCID: PMC10959699 DOI: 10.1016/j.mcpro.2024.100734] [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: 11/02/2022] [Revised: 12/22/2023] [Accepted: 02/08/2024] [Indexed: 02/13/2024] Open
Abstract
Antigen-antibody interactions play a key role in the immune response post vaccination and the mechanism of action of antibody-based biopharmaceuticals. 4CMenB is a multicomponent vaccine against Neisseria meningitidis serogroup B in which factor H binding protein (fHbp) is one of the key antigens. In this study, we use hydrogen/deuterium exchange mass spectrometry (HDX-MS) to identify epitopes in fHbp recognized by polyclonal antibodies (pAb) from two human donors (HDs) vaccinated with 4CMenB. Our HDX-MS data reveal several epitopes recognized by the complex mixture of human pAb. Furthermore, we show that the pAb from the two HDs recognize the same epitope regions. Epitope mapping of total pAb and purified fHbp-specific pAb from the same HD reveals that the two antibody samples recognize the same main epitopes, showing that HDX-MS based epitope mapping can, in this case at least, be performed directly using total IgG pAb samples that have not undergone Ab-selective purification. Two monoclonal antibodies (mAb) were previously produced from B-cell repertoire sequences from one of the HDs and used for epitope mapping of fHbp with HDX-MS. The epitopes identified for the pAb from the same HD in this study, overlap with the epitopes recognized by the two individual mAbs. Overall, HDX-MS epitope mapping appears highly suitable for simultaneous identification of epitopes recognized by pAb from human donors and to thus both guide vaccine development and study basic human immunity to pathogens, including viruses.
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Affiliation(s)
- Laura R Grauslund
- Protein Analysis Group, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark; GSK Vaccines, GSK, Siena, Italy
| | - Susanne Ständer
- Protein Analysis Group, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark; GSK Vaccines, GSK, Siena, Italy
| | | | - Emanuele Andreano
- Monoclonal Antibody Discovery (MAD) Lab, Fondazione Toscana Life Sciences, Siena, Italy
| | - Kasper D Rand
- Protein Analysis Group, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark.
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18
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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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19
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Schlotheuber LJ, Lüchtefeld I, Eyer K. Antibodies, repertoires and microdevices in antibody discovery and characterization. LAB ON A CHIP 2024; 24:1207-1225. [PMID: 38165819 PMCID: PMC10898418 DOI: 10.1039/d3lc00887h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/01/2023] [Indexed: 01/04/2024]
Abstract
Therapeutic antibodies are paramount in treating a wide range of diseases, particularly in auto-immunity, inflammation and cancer, and novel antibody candidates recognizing a vast array of novel antigens are needed to expand the usefulness and applications of these powerful molecules. Microdevices play an essential role in this challenging endeavor at various stages since many general requirements of the overall process overlap nicely with the general advantages of microfluidics. Therefore, microfluidic devices are rapidly taking over various steps in the process of new candidate isolation, such as antibody characterization and discovery workflows. Such technologies can allow for vast improvements in time-lines and incorporate conservative antibody stability and characterization assays, but most prominently screenings and functional characterization within integrated workflows due to high throughput and standardized workflows. First, we aim to provide an overview of the challenges of developing new therapeutic candidates, their repertoires and requirements. Afterward, this review focuses on the discovery of antibodies using microfluidic systems, technological aspects of micro devices and small-scale antibody protein characterization and selection, as well as their integration and implementation into antibody discovery workflows. We close with future developments in microfluidic detection and antibody isolation principles and the field in general.
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Affiliation(s)
- Luca Johannes Schlotheuber
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
| | - Ines Lüchtefeld
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
- ETH Laboratory for Tumor and Stem Cell Dynamics, Institute of Molecular Health Sciences, D-BIOL, ETH Zürich, 8093 Zürich, Switzerland
| | - Klaus Eyer
- ETH Laboratory for Functional Immune Repertoire Analysis, Institute of Pharmaceutical Sciences, D-CHAB, ETH Zürich, 8093 Zürich, Switzerland.
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20
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Shukla ND, Schroers-Martin JG, Sworder BJ, Kathuria KR, Alig SK, Frank MJ, Miklos DB, Coutre S, Diehn M, Khodadoust MS, Roschewski M, Kurtz DM, Alizadeh AA. Specificity of immunoglobulin high-throughput sequencing minimal residual disease monitoring in non-Hodgkin lymphomas. Blood Adv 2024; 8:780-784. [PMID: 38147627 PMCID: PMC10847740 DOI: 10.1182/bloodadvances.2023011997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023] Open
Affiliation(s)
- Navika D. Shukla
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
| | | | - Brian J. Sworder
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Karan Raj Kathuria
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Stefan K. Alig
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Matthew J. Frank
- Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford University, Stanford, CA
| | - David B. Miklos
- Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford University, Stanford, CA
| | - Steven Coutre
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University, Stanford, CA
| | | | - Mark Roschewski
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - David M. Kurtz
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Ash A. Alizadeh
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA
- Division of Hematology, Department of Medicine, Stanford University, Stanford, CA
- Stanford Cancer Institute, Stanford University, Stanford, CA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA
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21
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Kim D, Song J, Mancuso N, Mangul S, Jung J, Jang W. Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis. Arthritis Res Ther 2024; 26:47. [PMID: 38336809 PMCID: PMC10858498 DOI: 10.1186/s13075-024-03280-2] [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: 09/29/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. METHODS To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. RESULTS We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks. CONCLUSION Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.
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Affiliation(s)
- Daeun Kim
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Serghei Mangul
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
| | - Junghyun Jung
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA.
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
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22
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Gallo E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol 2024:10.1007/s12033-024-01064-2. [PMID: 38308755 DOI: 10.1007/s12033-024-01064-2] [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: 11/03/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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23
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Islam Z, Vaikath NN, Hmila I, El‐Agnaf OMA, Kolatkar PR. Structural insights into the unique recognition module between α-synuclein peptide and nanobody. Protein Sci 2024; 33:e4875. [PMID: 38105512 PMCID: PMC10807187 DOI: 10.1002/pro.4875] [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: 08/16/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Nanobodies are single-domain fragments of antibodies with comparable specificity and affinity to antibodies. They are emerging as versatile tools in biology due to their relatively small size. Here, we report the crystal structure of a specific nanobody Nbα-syn01, bound to a 14 amino acid long peptide of α-synuclein (αSyn), a 140-residue protein whose aggregation is associated with Parkinson's disease. The complex structure exhibits a unique binding pattern where the αSyn peptide replaces the N-terminal region of nanobody. Recognition is mediated principally by extended main chain interaction of the αSyn peptide and specificity of the interaction lies in the central 48-52 region of αSyn peptide. Structure-guided truncation of Nbα-syn01 shows tighter binding to αSyn peptide and improved inhibition of α-synuclein aggregation. The structure of the truncated complex was subsequently determined and was indistinguishable to full length complex as the full-length form had no visible electron density for the N-terminal end. These findings reveal the molecular basis for a previously unobserved binding mode for nanobody recognition of α-synuclein, providing an explanation for the enhanced binding, and potential for an alternate framework for structure-based protein engineering of nanobodies to develop better diagnostic and therapeutic tools.
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Affiliation(s)
- Zeyaul Islam
- Diabetes CenterQatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar FoundationDohaQatar
| | - Nishant N. Vaikath
- Neurological Disorder Research CenterQatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar FoundationDohaQatar
| | - Issam Hmila
- Neurological Disorder Research CenterQatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar FoundationDohaQatar
| | - Omar M. A. El‐Agnaf
- Neurological Disorder Research CenterQatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar FoundationDohaQatar
| | - Prasanna R. Kolatkar
- Diabetes CenterQatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar FoundationDohaQatar
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24
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Wang Q, Feng D, Jia S, Lu Q, Zhao M. B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases. Clin Rev Allergy Immunol 2024; 66:76-98. [PMID: 38459209 DOI: 10.1007/s12016-024-08984-6] [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] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
In the field of contemporary medicine, autoimmune diseases (AIDs) are a prevalent and debilitating group of illnesses. However, they present extensive and profound challenges in terms of etiology, pathogenesis, and treatment. A major reason for this is the elusive pathophysiological mechanisms driving disease onset. Increasing evidence suggests the indispensable role of B cells in the pathogenesis of autoimmune diseases. Interestingly, B-cell receptor (BCR) repertoires in autoimmune diseases display a distinct skewing that can provide insights into disease pathogenesis. Over the past few years, advances in high-throughput sequencing have provided powerful tools for analyzing B-cell repertoire to understand the mechanisms during the period of B-cell immune response. In this paper, we have provided an overview of the mechanisms and analytical methods for generating BCR repertoire diversity and summarize the latest research progress on BCR repertoire in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), multiple sclerosis (MS), and type 1 diabetes (T1D). Overall, B-cell repertoire analysis is a potent tool to understand the involvement of B cells in autoimmune diseases, facilitating the creation of innovative therapeutic strategies targeting specific B-cell clones or subsets.
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Affiliation(s)
- Qian Wang
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Delong Feng
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Sujie Jia
- Department of Pharmacy, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
| | - Ming Zhao
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China.
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
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25
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Gabernet G, Marquez S, Bjornson R, Peltzer A, Meng H, Aron E, Lee NY, Jensen C, Ladd D, Hanssen F, Heumos S, Yaari G, Kowarik MC, Nahnsen S, Kleinstein SH. nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576147. [PMID: 38293151 PMCID: PMC10827190 DOI: 10.1101/2024.01.18.576147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow).
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26
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Zhu Y, Tang H, Xie W, Chen S, Zeng H, Lan C, Guan J, Ma C, Yang X, Wang Q, Wei L, Zhang Z, Yu X. The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing. SCIENCE ADVANCES 2024; 10:eadj5640. [PMID: 38266093 PMCID: PMC10807814 DOI: 10.1126/sciadv.adj5640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.
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Affiliation(s)
- Yan Zhu
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Haipei Tang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Wenxi Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sen Chen
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Huikun Zeng
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Division of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Chunhong Lan
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Junjie Guan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiujia Yang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Qilong Wang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhenhai Zhang
- Center for Precision Medicine, Medical Research Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- 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 510515, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Division of Nephrology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
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27
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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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28
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Wang L, Xu Z, Zhang W, Li L, Liu X, Zhang J. Comprehensive characterization and database construction of immune repertoire in the largest Chinese glioma cohort. iScience 2024; 27:108661. [PMID: 38205245 PMCID: PMC10777385 DOI: 10.1016/j.isci.2023.108661] [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: 08/14/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Immune receptor repertoire is valuable for developing immunotherapeutic interventions, but remains poorly understood across glioma subtypes including IDH wild type, IDH mutation without 1p/19q codeletion (IDHmut-noncodel) and IDH mutation with 1p/19q codeletion (IDHmut-codel). We assembled over 320,000 TCR/BCR clonotypes from the largest glioma cohort of 913 RNA sequencing samples in the Chinese population, finding that immune repertoire diversity was more prominent in the IDH wild type (the most aggressive glioma). Fewer clonotypes were shared within each glioma subtype, indicating high heterogeneity of the immune repertoire. The TRA-CDR3 was longer in private than in public clonotypes in IDH wild type. CDR3 variable motifs had higher proportions of hydrophobic residues in private than in public clonotypes, suggesting private CDR3 sequences have greater potential for tumor antigen recognition. Finally, we developed GTABdb, a web-based database designed for hosting, exploring, visualizing, and analyzing glioma immune repertoire. Our study will facilitate developing glioma immunotherapy.
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Affiliation(s)
- Lu Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Zhiyuan Xu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Wei Zhang
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, People’s Republic of China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, Beijing 100070, People’s Republic of China
| | - Lin Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiao Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Jing Zhang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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29
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Gallo E. Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies. Mol Biol Rep 2024; 51:134. [PMID: 38236361 DOI: 10.1007/s11033-023-08941-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/13/2023] [Indexed: 01/19/2024]
Abstract
Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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30
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Matsuda T, Akazawa-Ogawa Y, Komaba LK, Kiyose N, Miyazaki N, Mizuguchi Y, Fukuta T, Ito Y, Hagihara Y. Prediction of antigen-responding VHH antibodies by tracking the evolution of antibody along the time course of immunization. Front Immunol 2024; 14:1335462. [PMID: 38292485 PMCID: PMC10825579 DOI: 10.3389/fimmu.2023.1335462] [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: 11/09/2023] [Accepted: 12/22/2023] [Indexed: 02/01/2024] Open
Abstract
Antibody maturation is the central function of the adaptive immune response. This process is driven by the repetitive selection of mutations that increase the affinity toward antigens. We hypothesized that a precise observation of this process by high-throughput sequencing along the time course of immunization will enable us to predict the antibodies reacting to the immunized antigen without any additional in vitro screening. An alpaca was immunized with IgG fragments using multiple antigen injections, and the antibody repertoire development was traced via high-throughput sequencing periodically for months. The sequences were processed into clusters, and the antibodies in the 16 most abundant clusters were generated to determine whether the clusters included antigen-binding antibodies. The sequences of most antigen-responsive clusters resembled those of germline cells in the early stages. These sequences were observed to accumulate significant mutations and also showed a continuous sequence turnover throughout the experimental period. The foregoing characteristics gave us >80% successful prediction of clusters composed of antigen-responding VHHs against IgG fragment. Furthermore, when the prediction method was applied to the data from other alpaca immunized with epidermal growth factor receptor, the success rate exceeded 80% as well, confirming the general applicability of the prediction method. Superior to previous studies, we identified the immune-responsive but very rare clusters or sequences from the immunized alpaca without any empirical screening data.
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Affiliation(s)
- Tomonari Matsuda
- Research Center for Environmental Quality Management, Kyoto University, Otsu, Japan
| | - Yoko Akazawa-Ogawa
- Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ikeda, Japan
| | - Lilian-Kaede Komaba
- Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ikeda, Japan
| | - Norihiko Kiyose
- Division of Antibody Operations, ARK Resource. Co., Ltd., Kumamoto, Japan
| | - Nobuo Miyazaki
- Division of Antibody Operations, ARK Resource. Co., Ltd., Kumamoto, Japan
| | | | | | - Yuji Ito
- Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan
| | - Yoshihisa Hagihara
- Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
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van Rijswijck DMH, Bondt A, de Kat N, Lood R, Heck AJR. Direct Comparison of the Hinge-Cleaving Proteases IgdE and BdpK for LC-MS-Based IgG1 Clonal Profiling. Anal Chem 2024; 96:23-27. [PMID: 38105593 PMCID: PMC10782413 DOI: 10.1021/acs.analchem.3c03712] [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: 08/18/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/19/2023]
Abstract
Human antibodies are heterogeneous molecules primarily due to clonal sequence variations. Analytical techniques to assess antibody levels quantitatively, such as ELISA, lack the power to resolve abundances at the clonal level. Recently, we introduced an LC-MS-based approach that can distinguish and quantify antibody clones using the mass and retention time of their corresponding Fab-fragments. We used specific hinge-cleaving protease IgdE (FabALACTICA) to release the Fab-fragments from the constant Fc region of the antibody. Here, we explore an alternative IgG1 hinge-cleaving protease, BdpK (FabDELLO), and compare it directly to IgdE for use in IgG1 repertoire profiling. We used IgdE and BdpK in parallel to digest all IgG1s from the same set of plasma samples. Both proteases cleave IgG1 specifically in the hinge, albeit via different mechanisms and at two distinct cleavage sites. Notwithstanding these differences, the Fab fragments generated by IgdE or BdpK produced highly similar clonal repertoires. However, IgdE required ∼16 h of incubation to digest plasma IgG1s, while BdpK required ∼2 h. We authenticated the similarity of the clones by top-down proteomics using electron transfer dissociation. We conclude that BdpK performs very well in digesting polyclonal plasma IgG1s and that neither BdpK nor IgdE displays detectable biases in cleaving IgG1s. We anticipate that BdpK may emerge as the preferred protease for IgG1 hinge-digestion because it offers a shorter digestion time compared to IgdE, an equally specific digestion site, and no bias against any IgG1 present in plasma.
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Affiliation(s)
- Danique M H van Rijswijck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Albert Bondt
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Naomi de Kat
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Rolf Lood
- Genovis AB, Scheelevägen 2, 223 63 Lund, Sweden
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
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Yin R, Pierce BG. Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy. Protein Sci 2024; 33:e4865. [PMID: 38073135 PMCID: PMC10751731 DOI: 10.1002/pro.4865] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 12/26/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
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Mai G, Zhang C, Lan C, Zhang J, Wang Y, Tang K, Tang J, Zeng J, Chen Y, Cheng P, Liu S, Long H, Wen Q, Li A, Liu X, Zhang R, Xu S, Liu L, Niu Y, Yang L, Wang Y, Yin D, Sun C, Chen YQ, Shen W, Zhang Z, Du X. Characterizing the dynamics of BCR repertoire from repeated influenza vaccination. Emerg Microbes Infect 2023; 12:2245931. [PMID: 37542407 PMCID: PMC10438862 DOI: 10.1080/22221751.2023.2245931] [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: 05/21/2023] [Revised: 07/12/2023] [Accepted: 08/03/2023] [Indexed: 08/06/2023]
Abstract
Yearly epidemics of seasonal influenza cause an enormous disease burden around the globe. An understanding of the rules behind the immune response with repeated vaccination still presents a significant challenge, which would be helpful for optimizing the vaccination strategy. In this study, 34 healthy volunteers with 16 vaccinated were recruited, and the dynamics of the BCR repertoire for consecutive vaccinations in two seasons were tracked. In terms of diversity, length, network, V and J gene segments usage, somatic hypermutation (SHM) rate and isotype, it was found that the overall changes were stronger in the acute phase of the first vaccination than the second vaccination. However, the V gene segments of IGHV4-39, IGHV3-9, IGHV3-7 and IGHV1-69 were amplified in the acute phase of the first vaccination, with IGHV3-7 dominant. On the other hand, for the second vaccination, the changes were dominated by IGHV1-69, with potential for coding broad neutralizing antibody. Additional analysis indicates that the application of V gene segment for IGHV3-7 in the acute phase of the first vaccination was due to the elevated usage of isotypes IgM and IgG3. While for IGHV1-69 in the second vaccination, it was contributed by isotypes IgG1 and IgG2. Finally, 41 public BCR clusters were identified in the vaccine group, with both IGHV3-7 and IGHV1-69 were involved and representative complementarity determining region 3 (CDR3) motifs were characterized. This study provides insights into the immune response dynamics following repeated influenza vaccination in humans and can inform universal vaccine design and vaccine strategies in the future.
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Affiliation(s)
- Guoqin Mai
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Chunhong Lan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- Center for Precision Medicine, Guangdong Academy of Medical Sciences, Medical Research Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jie Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yuanyuan Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Kang Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Jing Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yilin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shuning Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Haoyu Long
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qilan Wen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Aqin Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xuan Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Ruitong Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shuyang Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lin Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yanlan Niu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lan Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yihan Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Di Yin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Caijun Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yao-Qing Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wei Shen
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China
| | - Zhenhai Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- Center for Precision Medicine, Guangdong Academy of Medical Sciences, Medical Research Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- 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, People’s Republic of China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, People’s Republic of China
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Dumonteil E, Tu W, Desale H, Goff K, Marx P, Ortega-Lopez J, Herrera C. Immunoglobin and T cell receptor repertoire changes induced by a prototype vaccine against Chagas disease in naïve rhesus macaques. RESEARCH SQUARE 2023:rs.3.rs-3453582. [PMID: 37961272 PMCID: PMC10635379 DOI: 10.21203/rs.3.rs-3453582/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A vaccine against Trypanosoma cruzi, the agent of Chagas disease, would be an excellent additional tool for disease control. A recombinant vaccine based on Tc24 and TSA1 parasite antigens was found to be safe and immunogenic in naïve macaques. Here we performed a transcriptomic analysis of PBMC responses to vaccination, to shed light on the immunogenicity of this vaccine and guide the optimization of doses and formulation. RNA-sequencing analysis indicated a clear transcriptomic response of PBMCs from macaques after three vaccine doses, with the up-regulation of several immune cell activation pathways and a broad non-polarized immune profile. Analysis of the IgG repertoire showed that it had a rapid turnover with novel IgGs produced following each vaccine dose, while the TCR repertoire presented several persisting clones that were expanded after each vaccine dose. These data suggest that three vaccine doses may be needed for optimum immunogenecity and support the further evaluation of the protective efficacy of this vaccine.
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Yin R, Pierce BG. Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547832. [PMID: 37461571 PMCID: PMC10349958 DOI: 10.1101/2023.07.05.547832] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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Troelnikov A, Armour B, Putty T, Aggarwal A, Akerman A, Milogiannakis V, Chataway T, King J, Turville SG, Gordon TP, Wang JJ. Immunoglobulin repertoire restriction characterizes the serological responses of patients with predominantly antibody deficiency. J Allergy Clin Immunol 2023; 152:290-301.e7. [PMID: 36965845 DOI: 10.1016/j.jaci.2023.02.033] [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: 08/22/2022] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 03/27/2023]
Abstract
BACKGROUND Predominantly antibody deficiency (PAD) is the most common category of inborn errors of immunity and is underpinned by impaired generation of appropriate antibody diversity and quantity. In the clinic, responses are interrogated by assessment of vaccination responses, which is central to many PAD diagnoses. However, the composition of the generated antibody repertoire is concealed from traditional quantitative measures of serological responses. Leveraging modern mass spectrometry-based proteomics (MS-proteomics), it is possible to elaborate the molecular features of specific antibody repertoires, which may address current limitations of diagnostic vaccinology. OBJECTIVES We sought to evaluate serum antibody responses in patients with PAD following vaccination with a neo-antigen (severe acute respiratory syndrome coronavirus-2 vaccination) using MS-proteomics. METHODS Following severe acute respiratory syndrome coronavirus-2 vaccination, serological responses in individuals with PAD and healthy controls (HCs) were assessed by anti-S1 subunit ELISA and neutralization assays. Purified anti-S1 subunit IgG and IgM was profiled by MS-proteomics for IGHV subfamily usage and somatic hypermutation analysis. RESULTS Twelve patients with PAD who were vaccine-responsive were recruited with 11 matched vaccinated HCs. Neutralization and end point anti-S1 titers were lower in PAD. All subjects with PAD demonstrated restricted anti-S1 IgG antibody repertoires, with usage of <5 IGHV subfamilies (median: 3; range 2-4), compared to ≥5 for the 11 HC subjects (P < .001). IGHV3-7 utilization was far less common in patients with PAD than in HCs (2 of 12 vs 10 of 11; P = .001). Amino acid substitutions due to somatic hypermutation per subfamily did not differ between groups. Anti-S1 IgM was present in 64% and 50% of HC and PAD cohorts, respectively, and did not differ significantly between HCs and patients with PAD. CONCLUSIONS This study demonstrates the breadth of anti-S1 antibodies elicited by vaccination at the proteome level and identifies stereotypical restriction of IGHV utilization in the IgG repertoire in patients with PAD compared with HC subjects. Despite uniformly pauci-clonal antibody repertoires some patients with PAD generated potent serological responses, highlighting a possible limitation of traditional serological techniques. These findings suggest that IgG repertoire restriction is a key feature of antibody repertoires in PAD.
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Affiliation(s)
- Alexander Troelnikov
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia; SA Pathology, Adelaide, Australia.
| | - Bridie Armour
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia; SA Pathology, Adelaide, Australia
| | - Trishni Putty
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia; SA Pathology, Adelaide, Australia
| | | | | | | | - Tim Chataway
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia
| | - Jovanka King
- SA Pathology, Adelaide, Australia; Women's and Children's Hospital Network, Adelaide, Australia; Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | | | - Tom P Gordon
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia; SA Pathology, Adelaide, Australia; Flinders Medical Centre, Bedford Park, Australia
| | - Jing Jing Wang
- College of Medicine and Public Health, Flinders University, Bedford Park, Australia; SA Pathology, Adelaide, Australia
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Ruffolo JA, Chu LS, Mahajan SP, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun 2023; 14:2389. [PMID: 37185622 PMCID: PMC10129313 DOI: 10.1038/s41467-023-38063-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.
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Affiliation(s)
- Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.
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Nau A, Shen Y, Sanchorawala V, Prokaeva T, Morgan GJ. Complete variable domain sequences of monoclonal antibody light chains identified from untargeted RNA sequencing data. Front Immunol 2023; 14:1167235. [PMID: 37143670 PMCID: PMC10151772 DOI: 10.3389/fimmu.2023.1167235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/31/2023] [Indexed: 05/06/2023] Open
Abstract
Introduction Monoclonal antibody light chain proteins secreted by clonal plasma cells cause tissue damage due to amyloid deposition and other mechanisms. The unique protein sequence associated with each case contributes to the diversity of clinical features observed in patients. Extensive work has characterized many light chains associated with multiple myeloma, light chain amyloidosis and other disorders, which we have collected in the publicly accessible database, AL-Base. However, light chain sequence diversity makes it difficult to determine the contribution of specific amino acid changes to pathology. Sequences of light chains associated with multiple myeloma provide a useful comparison to study mechanisms of light chain aggregation, but relatively few monoclonal sequences have been determined. Therefore, we sought to identify complete light chain sequences from existing high throughput sequencing data. Methods We developed a computational approach using the MiXCR suite of tools to extract complete rearranged IGVL-IGJL sequences from untargeted RNA sequencing data. This method was applied to whole-transcriptome RNA sequencing data from 766 newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass study. Results Monoclonal IGVL-IGJL sequences were defined as those where >50% of assigned IGK or IGL reads from each sample mapped to a unique sequence. Clonal light chain sequences were identified in 705/766 samples from the CoMMpass study. Of these, 685 sequences covered the complete IGVL-IGJL region. The identity of the assigned sequences is consistent with their associated clinical data and with partial sequences previously determined from the same cohort of samples. Sequences have been deposited in AL-Base. Discussion Our method allows routine identification of clonal antibody sequences from RNA sequencing data collected for gene expression studies. The sequences identified represent, to our knowledge, the largest collection of multiple myeloma-associated light chains reported to date. This work substantially increases the number of monoclonal light chains known to be associated with non-amyloid plasma cell disorders and will facilitate studies of light chain pathology.
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Affiliation(s)
- Allison Nau
- Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Yun Shen
- Research Computing Services, Boston University, Boston, MA, United States
| | - Vaishali Sanchorawala
- Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Section of Hematology and Medical Oncology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Tatiana Prokaeva
- Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Gareth J. Morgan
- Amyloidosis Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Section of Hematology and Medical Oncology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
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Zhang Y, Li Q, Luo L, Duan C, Shen J, Wang Z. Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis. Biotechnol Adv 2023; 65:108143. [PMID: 37023966 DOI: 10.1016/j.biotechadv.2023.108143] [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: 01/13/2023] [Revised: 03/26/2023] [Accepted: 03/29/2023] [Indexed: 04/08/2023]
Abstract
Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.
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Affiliation(s)
- Yingjie Zhang
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Qing Li
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Liang Luo
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Changfei Duan
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Jianzhong Shen
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China
| | - Zhanhui Wang
- National Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, 100193 Beijing, People's Republic of China.
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41
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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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42
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Park M, de Villavicencio Diaz TN, Lange V, Wu L, Le Bihan T, Ma B. Exploring the sheep (Ovis aries) immunoglobulin repertoire by next generation sequencing. Mol Immunol 2023; 156:20-30. [PMID: 36867981 DOI: 10.1016/j.molimm.2023.02.008] [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: 12/15/2022] [Revised: 02/10/2023] [Accepted: 02/23/2023] [Indexed: 03/05/2023]
Abstract
Next-generation sequencing (NGS) has revolutionized the way we determine the antibody repertoires encoded by B cells in the blood or lymphoid organs and transformed our understanding of adaptive immune responses in many species. Sheep (Ovis aries) have been widely used as a host for therapeutic antibody production since the early 1980s, however, little is known about their immune repertoires or immunological processes affecting the antibody generation. The objective of this study was to employ NGS for a comprehensive analysis of immunoglobulin heavy and light chain repertoires in four healthy sheep. We obtained > 90 % complete antibody sequences and nearly 130,000, 48,000 and 218,000 unique CDR3 reads for the heavy chain (IGH), kappa chain (IGK), and lambda chain (IGL) loci, respectively. Consistent with other species, we observed biased usage of germline variable (V), diversity (D) and joining (J) genes in the heavy and kappa loci, but not in the lambda loci. Moreover, the enormous diversity of CDR3 sequences was observed through sequence clustering and convergent recombination. These data will build a foundation for future studies investigating immune repertoires in health and disease as well as contribute to further refinement of ovine-derived therapeutic antibody drugs.
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Affiliation(s)
| | | | | | - Lin Wu
- Rapid Novor Inc., Kitchener, Ontario, Canada
| | | | - Bin Ma
- Rapid Novor Inc., Kitchener, Ontario, Canada
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43
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Snir T, Philip H, Gordin M, Zilberberg A, Efroni S. The temporal behavior of the murine T cell receptor repertoire following Immunotherapy. Sci Data 2023; 10:108. [PMID: 36823176 PMCID: PMC9950060 DOI: 10.1038/s41597-023-01982-x] [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: 08/08/2022] [Accepted: 01/24/2023] [Indexed: 02/25/2023] Open
Abstract
Immunotherapy is now an essential tool for cancer treatment, and the unique features of an individual's T cell receptor repertoire are known to play a key role in its effectiveness. The repertoire, famously vast due to a cascade of cellular mechanisms, can be quantified using repertoire sequencing. In this study, we sampled the repertoire over several time points following treatment with anti-CTLA-4, in a syngeniec mouse model for colorectal cancer, generating a longitudinal dataset of T cell clones and their abundance. The dynamics of the repertoire can be observed in response to treatment over a period of four weeks, as clonal expansion of specific clones ascends and descends. The data made available here can be used to determine treatment and predict its effect, while also providing a unique look at the behavior of the immune system over time.
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Affiliation(s)
- Tom Snir
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Hagit Philip
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Miri Gordin
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Alona Zilberberg
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Sol Efroni
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
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44
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Akerman O, Isakov H, Levi R, Psevkin V, Louzoun Y. Counting is almost all you need. Front Immunol 2023; 13:1031011. [PMID: 36741395 PMCID: PMC9896581 DOI: 10.3389/fimmu.2022.1031011] [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: 08/29/2022] [Accepted: 12/27/2022] [Indexed: 01/21/2023] Open
Abstract
The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.
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Affiliation(s)
- Ofek Akerman
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
- Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel
| | - Haim Isakov
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Reut Levi
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Vladimir Psevkin
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
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45
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Fahad AS, Madan B, DeKosky BJ. Bioinformatic Analysis of Natively Paired VH:VL Antibody Repertoires for Antibody Discovery. Methods Mol Biol 2023; 2552:447-463. [PMID: 36346608 DOI: 10.1007/978-1-0716-2609-2_25] [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] [Indexed: 06/16/2023]
Abstract
Next-generation DNA sequencing (NGS) of human antibody repertoires has been extensively implemented to discover novel antibody drugs, to analyze B-cell developmental features, and to investigate antibody responses to infectious diseases and vaccination. Because the antibody repertoire encoded by human B cells is highly diverse, NGS analyses of antibody genes have provided a new window into understanding antibody responses for basic immunology, biopharmaceutical drug discovery, and immunotherapy. However, many antibody discovery protocols analyze the heavy and light chains separately due to the short-read nature of most NGS technologies, whereas paired heavy and light chain data are required for complete antibody characterization. Here, we describe a computational workflow to process millions of paired antibody heavy and light chain DNA sequence reads using the Illumina MiSeq 2x300 NGS platform. In this workflow, we describe raw NGS read processing and initial quality filtering, the annotation and assembly of antibody clonotypes relating to paired heavy and light chain antibody lineages, and the generation of complete heavy+light consensus sequences for the downstream cloning and expression of human antibody proteins.
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Affiliation(s)
- Ahmed S Fahad
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA
| | - Bharat Madan
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA
| | - Brandon J DeKosky
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA.
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Chemical Engineering, The University of Kansas, Lawrence, KS, USA.
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46
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Hayashi S, Ishikawa S. Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning. Methods Mol Biol 2023; 2552:465-473. [PMID: 36346609 DOI: 10.1007/978-1-0716-2609-2_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Advances in high-throughput sequencing technologies have enabled comprehensive sequencing of the immune repertoire. Since repertoire analysis can help to explain the relationship between the immune system and diseases, several methods have been developed for repertoire analysis. Here, using simulated and real-world datasets, we describe how to use DeepRC, a method that applies cutting-edge machine learning techniques.
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Affiliation(s)
- Shuto Hayashi
- 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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47
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Benítez R, Yu K, Sirota M, Malats N, Pineda S. Characterization of the tumor-infiltrating immune repertoire in muscle invasive bladder cancer. Front Immunol 2023; 14:986598. [PMID: 36817478 PMCID: PMC9936234 DOI: 10.3389/fimmu.2023.986598] [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: 07/05/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease with several taxonomic molecular subtypes showing different genetic, clinical, and epidemiological profiles. It has been suggested that MIBC-subtypes follow different tumorigenesis pathways playing decisive roles at different stages of tumor development, resulting in distinct tumor microenvironment containing both innate and adaptive immune cells (T and B lymphocytes). We aim to characterize the MIBC tumor microenvironment by analyzing the tumor-infiltrating B and T cell repertoire according to the taxonomic molecular subtypes. Methods RNAseq data from 396 MIBC samples included in TCGA were considered. The subtype information was collected from the international consensus taxonomic classification describing six subtypes: Basal/Squamous-like (Ba/Sq), Luminal papillary (LumP), Luminal non-Specify (LumNS), Luminal unstable (LumU), Stroma-rich, and Neuroendocrine-like (NE-like). Using MiXCR, we mapped the RNA read sequences to their respective B-cell receptor (BCR) and T-cell receptor (TCR) clonotypes. To evaluate the BCR and TCR differences among subtypes, we compared diversity measures (richness and diversity) using a Wilcoxon test and we performed a network analysis to characterize the clonal expansion. For the survival analysis stratified by subtypes, Cox regression models adjusted for age, region, and pathological stage were performed. Results Overall, we found different patterns of tumor-infiltrating immune repertoire among the different MIBC subtypes. Stroma-rich and Ba/Sq tumors showed the highest BCR and TCR infiltration while LumP showed the lowest. In addition, we observed that the Ba/Sq and Stroma-rich tumors were more clonally expanded than the Luminal subtypes. Moreover, higher TCR richness and diversity were significantly associated with better survival in the Stroma-rich and Ba/Sq subtypes. Discussion This study provides evidence that MIBC subtypes present differences in the tumor microenvironment, in particular, the Ba/Sq and the Stroma-rich are related with a higher tumoral-infiltrating immune repertoire, which seems to be translated into better survival. Determining the causes of the different tumoral-infiltrating immune repertoire according to the MIBC molecular subtypes will help to improve our understanding of the disease and the distinct responses to immunotherapy of MIBC.
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Affiliation(s)
- Raquel Benítez
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO) and CIBERONC, Madrid, Spain
| | - Katherine Yu
- Bakar Computational Health Sciences Institute, University of California, San Francisco (UCSF), San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco (UCSF), San Francisco, CA, United States
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO) and CIBERONC, Madrid, Spain
| | - Silvia Pineda
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO) and CIBERONC, Madrid, Spain.,Bakar Computational Health Sciences Institute, University of California, San Francisco (UCSF), San Francisco, CA, United States.,Department of Statistics and Data Science, Complutense University of Madrid (UCM), Madrid, Spain
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48
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Lowden MJ, Lei EK, Hussack G, Henry KA. Applications of High-Throughput DNA Sequencing to Single-Domain Antibody Discovery and Engineering. Methods Mol Biol 2023; 2702:489-540. [PMID: 37679637 DOI: 10.1007/978-1-0716-3381-6_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Next-generation DNA sequencing (NGS) technologies have made it possible to interrogate antibody repertoires to unprecedented depths, typically via sequencing of cDNAs encoding immunoglobulin variable domains. In the absence of heavy-light chain pairing, the variable domains of heavy chain-only antibodies (HCAbs), referred to as single-domain antibodies (sdAbs), are uniquely amenable to NGS analyses. In this chapter, we provide simple and rapid protocols for producing and sequencing multiplexed immunoglobulin variable domain (VHH, VH, or VL) amplicons derived from a variety of sources using the Illumina MiSeq platform. Generation of such amplicon libraries is relatively inexpensive, requiring no specialized equipment and only a limited set of PCR primers. We also present several applications of NGS to sdAb discovery and engineering, including: (1) evaluation of phage-displayed sdAb library sequence diversity and monitoring of panning experiments; (2) identification of sdAbs of predetermined epitope specificity following competitive elution of phage-displayed sdAb libraries; (3) direct selection of B cells expressing antigen-specific, membrane-bound HCAb using antigen-coupled magnetic beads and identification of antigen-specific sdAbs, and (4) affinity maturation of lead sdAbs using tandem phage display selection and NGS. These methods can easily be adapted to other types of proteins and libraries and expand the utility of in vitro display technology.
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Affiliation(s)
- Michael J Lowden
- Human Health Therapeutics Research Centre, National Research Council Canada, Ottawa, ON, Canada
| | - Eric K Lei
- Human Health Therapeutics Research Centre, National Research Council Canada, Ottawa, ON, Canada
| | - Greg Hussack
- Human Health Therapeutics Research Centre, National Research Council Canada, Ottawa, ON, Canada
| | - Kevin A Henry
- Human Health Therapeutics Research Centre, National Research Council Canada, Ottawa, ON, Canada.
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada.
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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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50
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Liu Y, Yi L, Li Y, Wang Z, Jirimutu. Characterization of heavy-chain antibody gene repertoires in Bactrian camels. J Genet Genomics 2023; 50:38-45. [PMID: 35500746 DOI: 10.1016/j.jgg.2022.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023]
Abstract
Camelids are the only mammals that can produce functional heavy-chain antibodies (HCAbs). Although HCAbs were discovered over 30 years ago, the antibody gene repertoire of Bactrian camels remains largely underexplored. To characterize the diversity of variable genes of HCAbs (VHHs), germline and rearranged VHH repertoires are constructed. Phylogenetics analysis shows that all camelid VHH genes are derived from a common ancestor and the nucleotide diversity of VHHs is similar across all camelid species. While species-specific hallmark sites are identified, the non-canonical cysteines specific to VHHs are distinct in Bactrian camels and dromedaries compared with alpacas. Though low divergence at the germline repertoire between wild and domestic Bactrian camels, higher expression of VHHs is observed in some wild Bactrian camels than that of domestic ones. This study not only adds our understanding of VHH repertoire diversity across camelids, but also provides useful resources for HCAb engineering.
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Affiliation(s)
- Yuexing Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Yi
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, College of Food Science and Engineering, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia 010018, China
| | - Yixue Li
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang 310024, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Guangzhou Laboratory, Guangzhou, Guangdong 510005, China; Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China.
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Jirimutu
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, College of Food Science and Engineering, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia 010018, China; Inner Mongolia Institute of Camel Research, West Alax, Inner Mongolia 737399, China.
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