1
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Abbate MF, Dupic T, Vigne E, Shahsavarian MA, Walczak AM, Mora T. Computational detection of antigen-specific B cell receptors following immunization. Proc Natl Acad Sci U S A 2024; 121:e2401058121. [PMID: 39163333 PMCID: PMC11363332 DOI: 10.1073/pnas.2401058121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/10/2024] [Indexed: 08/22/2024] Open
Abstract
B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.
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Affiliation(s)
- Maria Francesca Abbate
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
- Large Molecule Research, Sanofi, Vitry-sur-Seine94 400, France
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA02138
| | | | | | - Aleksandra M. Walczak
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de physique de l’École normale supérieure, CNRS, Paris Sciences et Lettres University, Sorbonne Université, and Université Paris-Cité, Paris75005, France
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2
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Spisak N, Athènes G, Dupic T, Mora T, Walczak AM. Combining mutation and recombination statistics to infer clonal families in antibody repertoires. eLife 2024; 13:e86181. [PMID: 39120133 DOI: 10.7554/elife.86181] [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: 01/14/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B-cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution, and dynamics. We present HILARy (high-precision inference of lineages in antibody repertoires), an efficient, fast, and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.
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Affiliation(s)
- Natanael Spisak
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, Paris, France
| | - Gabriel Athènes
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, Paris, France
- Saber Bio SAS, Institut du Cerveau, iPEPS The Healthtech Hub, Paris, France
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Thierry Mora
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, Paris, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL University, Sorbonne Université and Université de Paris, Paris, France
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3
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Lanahan SM, Yang L, Jones KM, Qi Z, Cabrera EC, Cominsky LY, Ramaswamy A, Barmada A, Gabernet G, Uthaya Kumar DB, Xu L, Shan P, Wymann MP, Kleinstein SH, Rao VK, Mustillo P, Romberg N, Abraham RS, Lucas CL. PI3Kγ in B cells promotes antibody responses and generation of antibody-secreting cells. Nat Immunol 2024; 25:1422-1431. [PMID: 38961274 DOI: 10.1038/s41590-024-01890-1] [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: 07/06/2023] [Accepted: 06/07/2024] [Indexed: 07/05/2024]
Abstract
The differentiation of naive and memory B cells into antibody-secreting cells (ASCs) is a key feature of adaptive immunity. The requirement for phosphoinositide 3-kinase-delta (PI3Kδ) to support B cell biology has been investigated intensively; however, specific functions of the related phosphoinositide 3-kinase-gamma (PI3Kγ) complex in B lineage cells have not. In the present study, we report that PI3Kγ promotes robust antibody responses induced by T cell-dependent antigens. The inborn error of immunity caused by human deficiency in PI3Kγ results in broad humoral defects, prompting our investigation of roles for this kinase in antibody responses. Using mouse immunization models, we found that PI3Kγ functions cell intrinsically within activated B cells in a kinase activity-dependent manner to transduce signals required for the transcriptional program supporting differentiation of ASCs. Furthermore, ASC fate choice coincides with upregulation of PIK3CG expression and is impaired in the context of PI3Kγ disruption in naive B cells on in vitro CD40-/cytokine-driven activation, in memory B cells on toll-like receptor activation, or in human tonsillar organoids. Taken together, our study uncovers a fundamental role for PI3Kγ in supporting humoral immunity by integrating signals instructing commitment to the ASC fate.
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Affiliation(s)
- Stephen M Lanahan
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Lucas Yang
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Kate M Jones
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Zhihong Qi
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Emylette Cruz Cabrera
- Division of Immunology and Allergy, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lauren Y Cominsky
- Immunology Graduate Group, Perelman School of Medicine, Philadelphia, PA, USA
| | - Anjali Ramaswamy
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Anis Barmada
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Gisela Gabernet
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Lan Xu
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Peiying Shan
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Steven H Kleinstein
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - V Koneti Rao
- Primary Immunodeficiency Clinic, Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Peter Mustillo
- Division of Allergy and Immunology, Department of Pediatrics, Nationwide Children's Hospital, Columbus, OH, USA
| | - Neil Romberg
- Division of Immunology and Allergy, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, Philadelphia, PA, USA
| | - Roshini S Abraham
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Carrie L Lucas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
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4
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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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5
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Foglierini M, Nortier P, Schelling R, Winiger RR, Jacquet P, O'Dell S, Demurtas D, Mpina M, Lweno O, Muller YD, Petrovas C, Daubenberger C, Perreau M, Doria-Rose NA, Gottardo R, Perez L. RAIN: machine learning-based identification for HIV-1 bNAbs. Nat Commun 2024; 15:5339. [PMID: 38914562 PMCID: PMC11196741 DOI: 10.1038/s41467-024-49676-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.
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Affiliation(s)
- Mathilde Foglierini
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
- Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pauline Nortier
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Rachel Schelling
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Rahel R Winiger
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Philippe Jacquet
- Scientific Computing and Research Support Unit, University of Lausanne, Lausanne, Switzerland
| | - Sijy O'Dell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Davide Demurtas
- Interdisciplinary center of electron microscopy, CIME, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Omar Lweno
- Ifakara Health Institute, Bagamoyo, United Republic of Tanzania
| | - Yannick D Muller
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Centre for Human Immunology, Lausanne, Switzerland
| | - Constantinos Petrovas
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Claudia Daubenberger
- Department of Medical Parasitology and Infection Biology, Clinical Immunology Unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Matthieu Perreau
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nicole A Doria-Rose
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Raphael Gottardo
- Biomedical Data Science Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Laurent Perez
- Department of Medicine, Service of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Centre for Human Immunology, Lausanne, Switzerland.
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6
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Wang K, Cai L, Wang H, Shan S, Hu X, Zhang J. Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data. STAR Protoc 2024; 5:102969. [PMID: 38502687 PMCID: PMC10963638 DOI: 10.1016/j.xpro.2024.102969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/26/2024] [Accepted: 03/03/2024] [Indexed: 03/21/2024] Open
Abstract
The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.
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Affiliation(s)
- Kaixuan Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Linru Cai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hao Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Georgia Tech Shenzhen Institute (GTSI), Tianjin University, Shenzhen, Guangdong, China
| | - Shiwen Shan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xihao Hu
- GV20 Therapeutics, Cambridge, MA, USA
| | - Jian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
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7
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Voss K, Kaur KM, Banerjee R, Breden F, Pennell M. Evaluating methods for B-cell clonal family assignment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596491. [PMID: 38853833 PMCID: PMC11160721 DOI: 10.1101/2024.05.29.596491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.
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Affiliation(s)
- Katalin Voss
- Department of Quantitative and Computational Biology, University of Southern California, USA
| | - Katrina M. Kaur
- Department of Zoology, University of British Columbia, Canada
| | - Rituparna Banerjee
- Bioinformatics Graduate Program, Faculty of Science, University of British Columbia, Canada
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Canada
| | - Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, USA
- Department of Biological Sciences, University of Southern California, USA
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8
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Jensen CG, Sumner JA, Kleinstein SH, Hoehn KB. Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1579-1588. [PMID: 38557795 PMCID: PMC11073909 DOI: 10.4049/jimmunol.2300851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/07/2024] [Indexed: 04/04/2024]
Abstract
Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.
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Affiliation(s)
- Cole G. Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Jacob A. Sumner
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, Connecticut, 06520, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
- Current address: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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9
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [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: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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10
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Peres A, Klein V, Frankel B, Lees W, Polak P, Meehan M, Rocha A, Correia Lopes J, Yaari G. Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data. Brief Bioinform 2024; 25:bbae221. [PMID: 38752856 PMCID: PMC11097599 DOI: 10.1093/bib/bbae221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 03/06/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.
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Affiliation(s)
- Ayelet Peres
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Vered Klein
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Boaz Frankel
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - William Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, United Kingdom
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Pazit Polak
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
| | - Mark Meehan
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Artur Rocha
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - João Correia Lopes
- INESC TEC – Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, 5290002 Ramat Gan, Israel
- Bar Ilan institute of nanotechnology and advanced materials, Bar Ilan university, 5290002 Ramat Gan, Israel
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11
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Perez L, Foglierini M. RAIN: a Machine Learning-based identification for HIV-1 bNAbs. RESEARCH SQUARE 2024:rs.3.rs-4023897. [PMID: 38903123 PMCID: PMC11188109 DOI: 10.21203/rs.3.rs-4023897/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infection. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoire is still lacking. Here, we developed a straightforward computational method for Rapid Automatic Identification of bNAbs (RAIN) based on Machine Learning methods. In contrast to other approaches using one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of novel HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.
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Affiliation(s)
- Laurent Perez
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mathilde Foglierini
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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12
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Balashova D, van Schaik BDC, Stratigopoulou M, Guikema JEJ, Caniels TG, Claireaux M, van Gils MJ, Musters A, Anang DC, de Vries N, Greiff V, van Kampen AHC. Systematic evaluation of B-cell clonal family inference approaches. BMC Immunol 2024; 25:13. [PMID: 38331731 PMCID: PMC11370117 DOI: 10.1186/s12865-024-00600-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.
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Affiliation(s)
- Daria Balashova
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Barbera D C van Schaik
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Maria Stratigopoulou
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
| | - Jeroen E J Guikema
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Pathology, Lymphoma and Myeloma Center Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
| | - Tom G Caniels
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Mathieu Claireaux
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Marit J van Gils
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Anne Musters
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Dornatien C Anang
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Niek de Vries
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Antoine H C van Kampen
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands.
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands.
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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13
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Ota M, Hoehn KB, Fernandes-Braga W, Ota T, Aranda CJ, Friedman S, Miranda-Waldetario MG, Redes J, Suprun M, Grishina G, Sampson HA, Malbari A, Kleinstein SH, Sicherer SH, de Lafaille MAC. CD23 +IgG1 + memory B cells are poised to switch to pathogenic IgE production in food allergy. Sci Transl Med 2024; 16:eadi0673. [PMID: 38324641 PMCID: PMC11008013 DOI: 10.1126/scitranslmed.adi0673] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/15/2023] [Indexed: 02/09/2024]
Abstract
Food allergy is caused by allergen-specific immunoglobulin E (IgE) antibodies, but little is known about the B cell memory of persistent IgE responses. Here, we describe, in human pediatric peanut allergy, a population of CD23+IgG1+ memory B cells arising in type 2 immune responses that contain high-affinity peanut-specific clones and generate IgE-producing cells upon activation. The frequency of CD23+IgG1+ memory B cells correlated with circulating concentrations of IgE in children with peanut allergy. A corresponding population of "type 2-marked" IgG1+ memory B cells was identified in single-cell RNA sequencing experiments. These cells differentially expressed interleukin-4 (IL-4)- and IL-13-regulated genes, such as FCER2/CD23+, IL4R, and germline IGHE, and carried highly mutated B cell receptors (BCRs). In children with high concentrations of serum peanut-specific IgE, high-affinity B cells that bind the main peanut allergen Ara h 2 mapped to the population of "type 2-marked" IgG1+ memory B cells and included clones with convergent BCRs across different individuals. Our findings indicate that CD23+IgG1+ memory B cells transcribing germline IGHE are a unique memory population containing precursors of high-affinity pathogenic IgE-producing cells that are likely to be involved in the long-term persistence of peanut allergy.
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Affiliation(s)
- Miyo Ota
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
| | - Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine; New Haven, CT 06520, USA
| | - Weslley Fernandes-Braga
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
| | - Takayuki Ota
- Department of Dermatology, Janssen Research & Development LLC; San Diego, CA 92121, USA
| | - Carlos J. Aranda
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
| | - Sara Friedman
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
| | - Mariana G.C. Miranda-Waldetario
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
| | - Jamie Redes
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
- Graduate School of Biomedical Sciences, ISMMS; New York, NY 10029, USA
| | - Maria Suprun
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
| | - Galina Grishina
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
| | - Hugh A. Sampson
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
| | - Alefiyah Malbari
- Kravis Children’s Hospital, Department of Pediatrics, ISMMS; New York, NY 10029, USA
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine; New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine; New Haven, CT 06520, USA
- Program in Computational Biology & Bioinformatics, Yale University; New Haven, CT 06511, USA
| | - Scott H. Sicherer
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
| | - Maria A. Curotto de Lafaille
- Jaffe Food Allergy Institute, Division of Allergy and Immunology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai (ISMMS); New York, NY 10029, USA
- Precision Immunology Institute (PrIISM), and Department of Immunology and Immunotherapy, ISMMS; New York, NY. 10029, USA
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14
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Li F, Gragert L, Giovanni Biagini D, Patel JK, Kobashigawa JA, Trück J, Rodriguez O, Watson CT, Gibb DR, Zhang X, Kransdorf EP. IgM marks persistent IgG anti-human leukocyte antigen antibodies in highly sensitized heart transplant patients. J Heart Lung Transplant 2024; 43:314-323. [PMID: 37793509 DOI: 10.1016/j.healun.2023.09.022] [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: 02/05/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Sensitization to human leukocyte antigens (HLA) is a persistent problem in heart transplant (HT) candidates. We sought to characterize the anti-HLA antibody and circulating B cell repertoire in a cohort of highly sensitized HT candidates. METHODS We assessed immunoglobulin G (IgG) and immunoglobulin M (IgM) anti-HLA antibodies using Luminex single antigen bead assays in a cohort of 11 highly sensitized (HS; calculated panel reactive antibody ≥ 90%) and 3 mildly sensitized (MS) candidates. We also performed B cell receptor repertoire sequencing (BCRseq) in HS candidates and 33 non-candidate controls. HLA antibody strength was measured by mean fluorescence intensity (MFI). RESULTS We found that IgM anti-HLA antibodies were present in all HS candidates, but with a lower breadth and strength as compared to IgG. When anti-HLA IgG specificities intersected with IgM, binding strength was higher. In contrast, there were IgM but no intersecting IgG specificities for the MS group. In four candidates in the HS group, IgG anti-HLA antibodies decreased in both breadth and strength after HT, but the decrease in strength was smaller if the IgG possessed a specificity that intersected with pre-transplant IgM. BCRseq revealed larger B cell clonotypes in HS candidates but similar diversity as compared to controls. CONCLUSIONS IgM marks IgG anti-HLA antibodies with higher strength before HT and persistence after HT. The presence of IgM intersecting IgG for an anti-HLA specificity may be a useful approach to determine which donor HLA should be avoided for a sensitized candidate.
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Affiliation(s)
- Fang Li
- Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, California
| | - Loren Gragert
- Department of Pathology, Tulane University School of Medicine, New Orleans, Louisiana
| | - D Giovanni Biagini
- Department of Pathology, Tulane University School of Medicine, New Orleans, Louisiana
| | - Jignesh K Patel
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jon A Kobashigawa
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Johannes Trück
- Division of Immunology, University Children's Hospital and Children's Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Oscar Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, Kentucky
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, Kentucky
| | - David R Gibb
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Xiaohai Zhang
- Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, California
| | - Evan P Kransdorf
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
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15
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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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16
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Chang H, Ashlock DA, Graether SP, Keller SM. Anchor Clustering for million-scale immune repertoire sequencing data. BMC Bioinformatics 2024; 25:42. [PMID: 38273275 PMCID: PMC10809746 DOI: 10.1186/s12859-024-05659-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The clustering of immune repertoire data is challenging due to the computational cost associated with a very large number of pairwise sequence comparisons. To overcome this limitation, we developed Anchor Clustering, an unsupervised clustering method designed to identify similar sequences from millions of antigen receptor gene sequences. First, a Point Packing algorithm is used to identify a set of maximally spaced anchor sequences. Then, the genetic distance of the remaining sequences to all anchor sequences is calculated and transformed into distance vectors. Finally, distance vectors are clustered using unsupervised clustering. This process is repeated iteratively until the resulting clusters are small enough so that pairwise distance comparisons can be performed. RESULTS Our results demonstrate that Anchor Clustering is faster than existing pairwise comparison clustering methods while providing similar clustering quality. With its flexible, memory-saving strategy, Anchor Clustering is capable of clustering millions of antigen receptor gene sequences in just a few minutes. CONCLUSIONS This method enables the meta-analysis of immune-repertoire data from different studies and could contribute to a more comprehensive understanding of the immune repertoire data space.
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Affiliation(s)
- Haiyang Chang
- Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Daniel A Ashlock
- Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Steffen P Graether
- Department of Molecular and Cellular Biology, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Stefan M Keller
- Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA.
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17
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Hoehn KB, Kleinstein SH. B cell phylogenetics in the single cell era. Trends Immunol 2024; 45:62-74. [PMID: 38151443 PMCID: PMC10872299 DOI: 10.1016/j.it.2023.11.004] [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: 10/26/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.
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Affiliation(s)
- Kenneth B Hoehn
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
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18
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Dudzic P, Chomicz D, Kończak J, Satława T, Janusz B, Wrobel S, Gawłowski T, Jaszczyszyn I, Bielska W, Demharter S, Spreafico R, Schulte L, Martin K, Comeau SR, Krawczyk K. Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery. MAbs 2024; 16:2361928. [PMID: 38844871 PMCID: PMC11164219 DOI: 10.1080/19420862.2024.2361928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Lukas Schulte
- Global Computational Biology & Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Kyle Martin
- Biotherapeutics Discovery, Boehringer Ingelheim, Ridgefield, CT, USA
| | - Stephen R. Comeau
- Biotherapeutics Discovery, Boehringer Ingelheim, Ridgefield, CT, USA
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19
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Gervásio J, Ferreira A, Felicori LF. Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data. J Immunol Methods 2023; 523:113576. [PMID: 37966818 DOI: 10.1016/j.jim.2023.113576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023]
Abstract
MOTIVATION The next-generation sequencing technologies have transformed our understanding of immunoglobulin (Ig) profiles in various immune states. Clonotyping, which groups Ig sequences into B cell clones, is crucial in investigating the diversity of repertoires and changes in antigen exposure. Despite its importance, there is no widely accepted method for clonotyping, and existing methods are computationally intensive for large sequencing datasets. RESULTS To address this challenge, we introduce YClon, a fast and efficient approach for clonotyping Ig repertoire data. YClon uses a hierarchical clustering approach, similar to other methods, to group Ig sequences into B cell clones in a highly sensitive and specific manner. Notably, our approach outperforms other methods by being more than 30 to 5000 times faster in processing the repertoires analyzed. Astonishingly, YClon can effortlessly handle up to 2 million Ig sequences on a standard laptop computer. This enables in-depth analysis of large and numerous antibody repertoires. AVAILABILITY AND IMPLEMENTATION YClon was implemented in Python3 and is freely available on GitHub.
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Affiliation(s)
- João Gervásio
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Alice Ferreira
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Liza F Felicori
- Laboratory of Synthetic Biology and Biomimetics, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil.
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20
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Wang K, Hu X, Zhang J. Fast clonal family inference from large-scale B cell repertoire sequencing data. CELL REPORTS METHODS 2023; 3:100601. [PMID: 37788671 PMCID: PMC10626204 DOI: 10.1016/j.crmeth.2023.100601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/31/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.
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Affiliation(s)
- Kaixuan Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xihao Hu
- GV20 Therapeutics, Cambridge, MA, USA
| | - Jian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
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21
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Jensen CG, Sumner JA, Kleinstein SH, Hoehn KB. Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.29.560187. [PMID: 37873135 PMCID: PMC10592837 DOI: 10.1101/2023.09.29.560187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated heavy and light chains, but advancements in single cell sequencing now pair heavy and light chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired heavy and light chain sequences to build phylogenetic trees. We found incorporating light chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree building methods and persisted even when mixing bulk and single cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some light chains were missing, such as when mixing single cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for heavy and light chain gene partitions. Thus, we recommend using maximum likelihood methods with separate heavy and light chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.
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Affiliation(s)
- Cole G. Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Jacob A. Sumner
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, Connecticut, 06520, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
- Current address: Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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22
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Kastenschmidt JM, Sureshchandra S, Jain A, Hernandez-Davies JE, de Assis R, Wagoner ZW, Sorn AM, Mitul MT, Benchorin AI, Levendosky E, Ahuja G, Zhong Q, Trask D, Boeckmann J, Nakajima R, Jasinskas A, Saligrama N, Davies DH, Wagar LE. Influenza vaccine format mediates distinct cellular and antibody responses in human immune organoids. Immunity 2023; 56:1910-1926.e7. [PMID: 37478854 PMCID: PMC10433940 DOI: 10.1016/j.immuni.2023.06.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/11/2023] [Accepted: 06/20/2023] [Indexed: 07/23/2023]
Abstract
Highly effective vaccines elicit specific, robust, and durable adaptive immune responses. To advance informed vaccine design, it is critical that we understand the cellular dynamics underlying responses to different antigen formats. Here, we sought to understand how antigen-specific B and T cells were activated and participated in adaptive immune responses within the mucosal site. Using a human tonsil organoid model, we tracked the differentiation and kinetics of the adaptive immune response to influenza vaccine and virus modalities. Each antigen format elicited distinct B and T cell responses, including differences in their magnitude, diversity, phenotype, function, and breadth. These differences culminated in substantial changes in the corresponding antibody response. A major source of antigen format-related variability was the ability to recruit naive vs. memory B and T cells to the response. These findings have important implications for vaccine design and the generation of protective immune responses in the upper respiratory tract.
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Affiliation(s)
- Jenna M Kastenschmidt
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Suhas Sureshchandra
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Aarti Jain
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Jenny E Hernandez-Davies
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Rafael de Assis
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Zachary W Wagoner
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Andrew M Sorn
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Mahina Tabassum Mitul
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Aviv I Benchorin
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Elizabeth Levendosky
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63112, USA
| | - Gurpreet Ahuja
- Department of Pediatric Otolaryngology, Children's Hospital of Orange County, Orange, CA 92868, USA; Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, CA 92868, USA
| | - Qiu Zhong
- Department of Pediatric Otolaryngology, Children's Hospital of Orange County, Orange, CA 92868, USA; Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, CA 92868, USA
| | - Douglas Trask
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, CA 92868, USA
| | - Jacob Boeckmann
- Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, Orange, CA 92868, USA
| | - Rie Nakajima
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Algimantas Jasinskas
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Naresha Saligrama
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63112, USA; Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, MO 63112, USA; Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine in St. Louis, St. Louis, MO 63112, USA
| | - D Huw Davies
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA
| | - Lisa E Wagar
- Department of Physiology & Biophysics, University of California Irvine, Irvine, CA 92617, USA; Institute for Immunology, University of California Irvine, Irvine, CA 92617, USA; Center for Virus Research, University of California Irvine, Irvine, CA 92617, USA; Vaccine R&D Center, University of California Irvine, Irvine, CA 92617, USA.
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23
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Lopes de Assis F, Hoehn KB, Zhang X, Kardava L, Smith CD, El Merhebi O, Buckner CM, Trihemasava K, Wang W, Seamon CA, Chen V, Schaughency P, Cheung F, Martins AJ, Chiang CI, Li Y, Tsang JS, Chun TW, Kleinstein SH, Moir S. Tracking B cell responses to the SARS-CoV-2 mRNA-1273 vaccine. Cell Rep 2023; 42:112780. [PMID: 37440409 PMCID: PMC10529190 DOI: 10.1016/j.celrep.2023.112780] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/15/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Protective immunity following vaccination is sustained by long-lived antibody-secreting cells and resting memory B cells (MBCs). Responses to two-dose SARS-CoV-2 mRNA-1273 vaccination are evaluated longitudinally by multimodal single-cell analysis in three infection-naïve individuals. Integrated surface protein, transcriptomics, and B cell receptor (BCR) repertoire analysis of sorted plasmablasts and spike+ (S-2P+) and S-2P- B cells reveal clonal expansion and accumulating mutations among S-2P+ cells. These cells are enriched in a cluster of immunoglobulin G-expressing MBCs and evolve along a bifurcated trajectory rooted in CXCR3+ MBCs. One branch leads to CD11c+ atypical MBCs while the other develops from CD71+ activated precursors to resting MBCs, the dominant population at month 6. Among 12 evolving S-2P+ clones, several are populated with plasmablasts at early timepoints as well as CD71+ activated and resting MBCs at later timepoints, and display intra- and/or inter-cohort BCR convergence. These relationships suggest a coordinated and predictable evolution of SARS-CoV-2 vaccine-generated MBCs.
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Affiliation(s)
- Felipe Lopes de Assis
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Xiaozhen Zhang
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lela Kardava
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Connor D Smith
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Omar El Merhebi
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Clarisa M Buckner
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Krittin Trihemasava
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Wei Wang
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Catherine A Seamon
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Vicky Chen
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Paul Schaughency
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Foo Cheung
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD 20892, USA
| | - Andrew J Martins
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD 20892, USA
| | - Chi-I Chiang
- Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Yuxing Li
- Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Microbiology and Immunology and Center for Biomolecular Therapeutics, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - John S Tsang
- NIH Center for Human Immunology, NIAID, NIH, Bethesda, MD 20892, USA; Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD 20892, USA
| | - Tae-Wook Chun
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA; Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Susan Moir
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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24
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Sagan SA, Moinfar Z, Moseley CE, Dandekar R, Spencer CM, Verkman AS, Ottersen OP, Sobel RA, Sidney J, Sette A, Anderson MS, Steinman L, Wilson MR, Sabatino JJ, Zamvil SS. T cell deletional tolerance restricts AQP4 but not MOG CNS autoimmunity. Proc Natl Acad Sci U S A 2023; 120:e2306572120. [PMID: 37463205 PMCID: PMC10372680 DOI: 10.1073/pnas.2306572120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/08/2023] [Indexed: 07/20/2023] Open
Abstract
Aquaporin-4 (AQP4)-specific Th17 cells are thought to have a central role in neuromyelitis optica (NMO) pathogenesis. When modeling NMO, only AQP4-reactive Th17 cells from AQP4-deficient (AQP4-/-), but not wild-type (WT) mice, caused CNS autoimmunity in recipient WT mice, indicating that a tightly regulated mechanism normally ensures tolerance to AQP4. Here, we found that pathogenic AQP4 T cell epitopes bind MHC II with exceptionally high affinity. Examination of T cell receptor (TCR) α/β usage revealed that AQP4-specific T cells from AQP4-/- mice employed a distinct TCR repertoire and exhibited clonal expansion. Selective thymic AQP4 deficiency did not fully restore AQP4-reactive T cells, demonstrating that thymic negative selection alone did not account for AQP4-specific tolerance in WT mice. Indeed, AQP4-specific Th17 cells caused paralysis in recipient WT or B cell-deficient mice, which was followed by complete recovery that was associated with apoptosis of donor T cells. However, donor AQP4-reactive T cells survived and caused persistent paralysis in recipient mice deficient in both T and B cells or mice lacking T cells only. Thus, AQP4 CNS autoimmunity was limited by T cell-dependent deletion of AQP4-reactive T cells. In contrast, myelin oligodendrocyte glycoprotein (MOG)-specific T cells survived and caused sustained disease in WT mice. These findings underscore the importance of peripheral T cell deletional tolerance to AQP4, which may be relevant to understanding the balance of AQP4-reactive T cells in health and in NMO. T cell tolerance to AQP4, expressed in multiple tissues, is distinct from tolerance to MOG, an autoantigen restricted in its expression.
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Affiliation(s)
- Sharon A Sagan
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Program in Immunology, University of California, San Francisco, CA 94143
| | - Zahra Moinfar
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Program in Immunology, University of California, San Francisco, CA 94143
| | - Carson E Moseley
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Program in Immunology, University of California, San Francisco, CA 94143
| | - Ravi Dandekar
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Collin M Spencer
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Program in Immunology, University of California, San Francisco, CA 94143
| | - Alan S Verkman
- Department of Medicine, University of California, San Francisco, CA 94143
- Department of Physiology, University of California, San Francisco, CA 94143
| | - Ole Petter Ottersen
- Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo NO-0316, Norway
| | - Raymond A Sobel
- Department of Pathology, Stanford University School of Medicine, Palo Alto VA Health Care System, Palo Alto, CA 94305
| | - John Sidney
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA 92093
| | - Mark S Anderson
- Program in Immunology, University of California, San Francisco, CA 94143
- Diabetes Center, University of California, San Francisco, CA 94143
| | - Lawrence Steinman
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305
| | - Michael R Wilson
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Joseph J Sabatino
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
| | - Scott S Zamvil
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94143
- Program in Immunology, University of California, San Francisco, CA 94143
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25
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Ford EE, Tieri D, Rodriguez OL, Francoeur NJ, Soto J, Kos JT, Peres A, Gibson WS, Silver CA, Deikus G, Hudson E, Woolley CR, Beckmann N, Charney A, Mitchell TC, Yaari G, Sebra RP, Watson CT, Smith ML. FLAIRR-Seq: A Method for Single-Molecule Resolution of Near Full-Length Antibody H Chain Repertoires. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:1607-1619. [PMID: 37027017 PMCID: PMC10152037 DOI: 10.4049/jimmunol.2200825] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/14/2023] [Indexed: 04/08/2023]
Abstract
Current Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using short-read sequencing strategies resolve expressed Ab transcripts with limited resolution of the C region. In this article, we present the near-full-length AIRR-seq (FLAIRR-seq) method that uses targeted amplification by 5' RACE, combined with single-molecule, real-time sequencing to generate highly accurate (99.99%) human Ab H chain transcripts. FLAIRR-seq was benchmarked by comparing H chain V (IGHV), D (IGHD), and J (IGHJ) gene usage, complementarity-determining region 3 length, and somatic hypermutation to matched datasets generated with standard 5' RACE AIRR-seq using short-read sequencing and full-length isoform sequencing. Together, these data demonstrate robust FLAIRR-seq performance using RNA samples derived from PBMCs, purified B cells, and whole blood, which recapitulated results generated by commonly used methods, while additionally resolving H chain gene features not documented in IMGT at the time of submission. FLAIRR-seq data provide, for the first time, to our knowledge, simultaneous single-molecule characterization of IGHV, IGHD, IGHJ, and IGHC region genes and alleles, allele-resolved subisotype definition, and high-resolution identification of class switch recombination within a clonal lineage. In conjunction with genomic sequencing and genotyping of IGHC genes, FLAIRR-seq of the IgM and IgG repertoires from 10 individuals resulted in the identification of 32 unique IGHC alleles, 28 (87%) of which were previously uncharacterized. Together, these data demonstrate the capabilities of FLAIRR-seq to characterize IGHV, IGHD, IGHJ, and IGHC gene diversity for the most comprehensive view of bulk-expressed Ab repertoires to date.
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Affiliation(s)
- Easton E. Ford
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Oscar L. Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Nancy J. Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Juan Soto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Ayelet Peres
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - William S. Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Catherine A. Silver
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Elizabeth Hudson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Cassandra R. Woolley
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Noam Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Thomas C. Mitchell
- Department of Microbiology and Immunology, University of Louisville School of Medicine, Louisville, KY
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Robert P. Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
| | - Melissa L. Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY
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26
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Nickerson KM, Smita S, Hoehn KB, Marinov AD, Thomas KB, Kos JT, Yang Y, Bastacky SI, Watson CT, Kleinstein SH, Shlomchik MJ. Age-associated B cells are heterogeneous and dynamic drivers of autoimmunity in mice. J Exp Med 2023; 220:e20221346. [PMID: 36828389 PMCID: PMC9997508 DOI: 10.1084/jem.20221346] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/23/2022] [Accepted: 02/09/2023] [Indexed: 02/26/2023] Open
Abstract
Age-associated B cells (ABCs) are formed under inflammatory conditions and are considered a type of memory B cell (MBC) expressing the transcription factor T-bet. In SLE, ABC frequency is correlated with disease, and they are thought to be the source of autoantibody-secreting cells. However, in inflammatory conditions, whether autoreactive B cells can become resting MBCs is uncertain. Further, the phenotypic identity of ABCs and their relationship to other B cell subsets, such as plasmablasts, is unclear. Whether ABCs directly promote disease is untested. Here we report, in the MRL/lpr SLE model, unexpected heterogeneity among ABC-like cells for expression of the integrins CD11b and CD11c, T-bet, and memory or plasmablast markers. Transfer and labeling studies demonstrated that ABCs are dynamic, rapidly turning over. scRNA-seq identified B cell clones present in multiple subsets, revealing that ABCs can be plasmablast precursors or undergo cycles of reactivation. Deletion of CD11c-expressing B cells revealed a direct role for ABC-like B cells in lupus pathogenesis.
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Affiliation(s)
- Kevin M. Nickerson
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shuchi Smita
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Anthony D. Marinov
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kayla B. Thomas
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Justin T. Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Yi Yang
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sheldon I. Bastacky
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Mark J. Shlomchik
- Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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27
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Scheid JF, Eraslan B, Hudak A, Brown EM, Sergio D, Delorey TM, Phillips D, Lefkovith A, Jess AT, Duck LW, Elson CO, Vlamakis H, Plichta DR, Deguine J, Ananthakrishnan AN, Graham DB, Regev A, Xavier RJ. Remodeling of colon plasma cell repertoire within ulcerative colitis patients. J Exp Med 2023; 220:e20220538. [PMID: 36752797 PMCID: PMC9949229 DOI: 10.1084/jem.20220538] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 10/03/2022] [Accepted: 01/11/2023] [Indexed: 02/09/2023] Open
Abstract
Plasma cells (PCs) constitute a significant fraction of colonic mucosal cells and contribute to inflammatory infiltrates in ulcerative colitis (UC). While gut PCs secrete bacteria-targeting IgA antibodies, their role in UC pathogenesis is unknown. We performed single-cell V(D)J- and RNA-seq on sorted B cells from the colon of healthy individuals and patients with UC. A large fraction of B cell clones is shared between different colon regions, but inflammation in UC broadly disrupts this landscape, causing transcriptomic changes characterized by an increase in the unfolded protein response (UPR) and antigen presentation genes, clonal expansion, and isotype skewing from IgA1 and IgA2 to IgG1. We also directly expressed and assessed the specificity of 152 mAbs from expanded PC clones. These mAbs show low polyreactivity and autoreactivity and instead target both shared bacterial antigens and specific bacterial strains. Altogether, our results characterize the microbiome-specific colon PC response and how its disruption might contribute to inflammation in UC.
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Affiliation(s)
- Johannes F. Scheid
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Hudak
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric M. Brown
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Dallis Sergio
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Toni M. Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Alison T. Jess
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lennard W. Duck
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Charles O. Elson
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Ashwin N. Ananthakrishnan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel B. Graham
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ramnik J. Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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28
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Ota M, Hoehn KB, Ota T, Aranda CJ, Friedman S, Braga WF, Malbari A, Kleinstein SH, Sicherer SH, Curotto de Lafaille MA. The memory of pathogenic IgE is contained within CD23 + IgG1 + memory B cells poised to switch to IgE in food allergy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525506. [PMID: 36747707 PMCID: PMC9900782 DOI: 10.1101/2023.01.25.525506] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Food allergy is caused by allergen-specific IgE antibodies but little is known about the B cell memory of persistent IgE responses. Here we describe in human pediatric peanut allergy CD23 + IgG1 + memory B cells arising in type 2 responses that contain peanut specific clones and generate IgE cells on activation. These 'type2-marked' IgG1 + memory B cells differentially express IL-4/IL-13 regulated genes FCER2 / CD23, IL4R , and germline IGHE and carry highly mutated B cell receptors (BCRs). Further, high affinity memory B cells specific for the main peanut allergen Ara h 2 mapped to the population of 'type2-marked' IgG1 + memory B cells and included convergent BCRs across different individuals. Our findings indicate that CD23 + IgG1 + memory B cells transcribing germline IGHE are a unique memory population containing precursors of pathogenic IgE. One-Sentence Summary We describe a unique population of IgG + memory B cells poised to switch to IgE that contains high affinity allergen-specific clones in peanut allergy.
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29
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Mlynarczyk C, Teater M, Pae J, Chin CR, Wang L, Arulraj T, Barisic D, Papin A, Hoehn KB, Kots E, Ersching J, Bandyopadhyay A, Barin E, Poh HX, Evans CM, Chadburn A, Chen Z, Shen H, Isles HM, Pelzer B, Tsialta I, Doane AS, Geng H, Rehman MH, Melnick J, Morgan W, Nguyen DTT, Elemento O, Kharas MG, Jaffrey SR, Scott DW, Khelashvili G, Meyer-Hermann M, Victora GD, Melnick A. BTG1 mutation yields supercompetitive B cells primed for malignant transformation. Science 2023; 379:eabj7412. [PMID: 36656933 PMCID: PMC10515739 DOI: 10.1126/science.abj7412] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 12/12/2022] [Indexed: 01/21/2023]
Abstract
Multicellular life requires altruistic cooperation between cells. The adaptive immune system is a notable exception, wherein germinal center B cells compete vigorously for limiting positive selection signals. Studying primary human lymphomas and developing new mouse models, we found that mutations affecting BTG1 disrupt a critical immune gatekeeper mechanism that strictly limits B cell fitness during antibody affinity maturation. This mechanism converted germinal center B cells into supercompetitors that rapidly outstrip their normal counterparts. This effect was conferred by a small shift in MYC protein induction kinetics but resulted in aggressive invasive lymphomas, which in humans are linked to dire clinical outcomes. Our findings reveal a delicate evolutionary trade-off between natural selection of B cells to provide immunity and potentially dangerous features that recall the more competitive nature of unicellular organisms.
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Affiliation(s)
- Coraline Mlynarczyk
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Matt Teater
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Juhee Pae
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - Christopher R. Chin
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional PhD Program in Computational Biomedicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Ling Wang
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Theinmozhi Arulraj
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Darko Barisic
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Antonin Papin
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kenneth B. Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Ekaterina Kots
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jonatan Ersching
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - Arnab Bandyopadhyay
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Ersilia Barin
- Department of Pharmacology and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Hui Xian Poh
- Department of Pharmacology and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Chiara M. Evans
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, and Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Chadburn
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Zhengming Chen
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Hao Shen
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Hannah M. Isles
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Benedikt Pelzer
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Ioanna Tsialta
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Ashley S. Doane
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Huimin Geng
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Muhammad Hassan Rehman
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Weill Cornell Medicine–Qatar, Doha, Qatar
| | - Jonah Melnick
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Wyatt Morgan
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Diu T. T. Nguyen
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, and Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Olivier Elemento
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine and Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Michael G. Kharas
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, and Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samie R. Jaffrey
- Department of Pharmacology and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - David W. Scott
- Centre for Lymphoid Cancer, BC Cancer, Vancouver, BC, Canada
| | - George Khelashvili
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany
- Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Gabriel D. Victora
- Laboratory of Lymphocyte Dynamics, The Rockefeller University, New York, NY, USA
| | - Ari Melnick
- Division of Hematology and Oncology, Department of Medicine and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
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30
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Xu Q, Milanez-Almeida P, Martins AJ, Radtke AJ, Hoehn KB, Oguz C, Chen J, Liu C, Tang J, Grubbs G, Stein S, Ramelli S, Kabat J, Behzadpour H, Karkanitsa M, Spathies J, Kalish H, Kardava L, Kirby M, Cheung F, Preite S, Duncker PC, Kitakule MM, Romero N, Preciado D, Gitman L, Koroleva G, Smith G, Shaffer A, McBain IT, McGuire PJ, Pittaluga S, Germain RN, Apps R, Schwartz DM, Sadtler K, Moir S, Chertow DS, Kleinstein SH, Khurana S, Tsang JS, Mudd P, Schwartzberg PL, Manthiram K. Adaptive immune responses to SARS-CoV-2 persist in the pharyngeal lymphoid tissue of children. Nat Immunol 2023; 24:186-199. [PMID: 36536106 PMCID: PMC10777159 DOI: 10.1038/s41590-022-01367-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Most studies of adaptive immunity to SARS-CoV-2 infection focus on peripheral blood, which may not fully reflect immune responses at the site of infection. Using samples from 110 children undergoing tonsillectomy and adenoidectomy during the COVID-19 pandemic, we identified 24 samples with evidence of previous SARS-CoV-2 infection, including neutralizing antibodies in serum and SARS-CoV-2-specific germinal center and memory B cells in the tonsils and adenoids. Single-cell B cell receptor (BCR) sequencing indicated virus-specific BCRs were class-switched and somatically hypermutated, with overlapping clones in the two tissues. Expanded T cell clonotypes were found in tonsils, adenoids and blood post-COVID-19, some with CDR3 sequences identical to previously reported SARS-CoV-2-reactive T cell receptors (TCRs). Pharyngeal tissues from COVID-19-convalescent children showed persistent expansion of germinal center and antiviral lymphocyte populations associated with interferon (IFN)-γ-type responses, particularly in the adenoids, and viral RNA in both tissues. Our results provide evidence for persistent tissue-specific immunity to SARS-CoV-2 in the upper respiratory tract of children after infection.
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Affiliation(s)
- Qin Xu
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | | | - Andrew J Martins
- Multiscale Systems Biology Section, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Andrea J Radtke
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Cihan Oguz
- NIAID Collaborative Bioinformatics Resource (NCBR), NIAID, NIH, Bethesda, MD, USA
- Axle Informatics, Bethesda, MD, USA
| | - Jinguo Chen
- Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Can Liu
- Multiscale Systems Biology Section, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Juanjie Tang
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD, USA
| | - Gabrielle Grubbs
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD, USA
| | - Sydney Stein
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD, USA
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Sabrina Ramelli
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD, USA
| | - Juraj Kabat
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Hengameh Behzadpour
- Division of Pediatric Otolaryngology, Children's National Hospital, Washington, DC, USA
| | - Maria Karkanitsa
- Laboratory of Immuno-Engineering, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD, USA
| | - Jacquelyn Spathies
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, NIBIB, NIH, Bethesda, MD, USA
| | - Heather Kalish
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, NIBIB, NIH, Bethesda, MD, USA
| | - Lela Kardava
- B-cell Immunology Section, Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Martha Kirby
- National Human Genome Research Institute (NHGRI), NIH, Bethesda, MD, USA
| | - Foo Cheung
- Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | - Silvia Preite
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | | | | | - Nahir Romero
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Diego Preciado
- Division of Pediatric Otolaryngology, Children's National Hospital, Washington, DC, USA
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Lyuba Gitman
- Division of Pediatric Otolaryngology, Children's National Hospital, Washington, DC, USA
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | | | - Grace Smith
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute (NCI), NIH, Bethesda, MD, USA
| | - Arthur Shaffer
- Lymphoid Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD, USA
| | - Ian T McBain
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Peter J McGuire
- National Human Genome Research Institute (NHGRI), NIH, Bethesda, MD, USA
| | - Stefania Pittaluga
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute (NCI), NIH, Bethesda, MD, USA
| | - Ronald N Germain
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH, Bethesda, MD, USA
- Lymphocyte Biology Section, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Richard Apps
- Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
| | | | - Kaitlyn Sadtler
- Laboratory of Immuno-Engineering, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD, USA
| | - Susan Moir
- B-cell Immunology Section, Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Daniel S Chertow
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD, USA
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Surender Khurana
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD, USA
| | - John S Tsang
- Center for Human Immunology, NIAID, NIH, Bethesda, MD, USA
- Multiscale Systems Biology Section, LISB, NIAID, NIH, Bethesda, MD, USA
| | - Pamela Mudd
- Division of Pediatric Otolaryngology, Children's National Hospital, Washington, DC, USA
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pamela L Schwartzberg
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.
- National Human Genome Research Institute (NHGRI), NIH, Bethesda, MD, USA.
| | - Kalpana Manthiram
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, USA.
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31
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Ralph DK, Matsen FA. Inference of B cell clonal families using heavy/light chain pairing information. PLoS Comput Biol 2022; 18:e1010723. [PMID: 36441808 PMCID: PMC9731466 DOI: 10.1371/journal.pcbi.1010723] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 12/08/2022] [Accepted: 11/09/2022] [Indexed: 11/29/2022] Open
Abstract
Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.
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Affiliation(s)
- Duncan K. Ralph
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail:
| | - Frederick A. Matsen
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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32
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Abdollahi N, Jeusset L, De Septenville AL, Ripoche H, Davi F, Bernardes JS. A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data. PLoS Comput Biol 2022; 18:e1010411. [PMID: 36037250 PMCID: PMC9462827 DOI: 10.1371/journal.pcbi.1010411] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 09/09/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022] Open
Abstract
The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies. High-throughput sequencing can produce a large set of sequences and has profoundly changed our ability to study immune repertoires, particularly B cell receptor sequences. An important application is the analysis of the clonal lineage composition of B cell populations; it is the starting point of many immune repertoire studies, for instance, to differentiate between healthy individuals and those with lymphoid malignancies or other diseases. Several computational methods have been developed to identify clonal lineages from a set of B cell receptor sequences. Most of them apply clustering algorithms and optimize a single objective function that typically minimizes intra-clonal distances. However, optimizing several objective functions in parallel can benefit and increase the clustering performance and efficiency. We propose MobiLLe, the first multi-objective clonal lineage grouping method, which simultaneously optimizes two objective functions for minimizing intra-clonal diversity and maximizing inter-clonal differences. Our approach greatly improved clonal grouping on simulated benchmarks and performed comparably to the most powerful and recent methods on experimental samples. MobiLLe is computationally more efficient than existing tools and does not require any training process or hyper-parameter optimization. It can easily manage large-scale experimental repertoires, providing useful plots to help researchers detect clonally-related sequences in high-throughput B cell repertoire data.
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Affiliation(s)
- Nika Abdollahi
- Sorbonne Université, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Lucile Jeusset
- Sorbonne Université, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
- Sorbonne Université, AP-HP, Hôpital Pitié-Salpêtrière, UMR_S 1138 Department of Hematology, Paris, France
| | | | - Hugues Ripoche
- Sorbonne Université, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Frédéric Davi
- Sorbonne Université, AP-HP, Hôpital Pitié-Salpêtrière, UMR_S 1138 Department of Hematology, Paris, France
| | - Juliana Silva Bernardes
- Sorbonne Université, CNRS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
- * E-mail:
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33
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Shiohara M, Suzuki S, Shichinohe S, Ishigaki H, Nakayama M, Nomura N, Shingai M, Sekiya T, Ohno M, Iida S, Kawai N, Kawahara M, Yamagishi J, Ito K, Mitsumata R, Ikeda T, Motokawa K, Sobue T, Kida H, Ogasawara K, Itoh Y. Inactivated whole influenza virus particle vaccines induce neutralizing antibodies with an increase in immunoglobulin gene subclones of B-lymphocytes in cynomolgus macaques. Vaccine 2022; 40:4026-4037. [PMID: 35641357 DOI: 10.1016/j.vaccine.2022.05.045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/03/2022] [Accepted: 05/17/2022] [Indexed: 10/18/2022]
Abstract
The All-Japan Influenza Vaccine Study Group has been developing a more effective vaccine than the current split vaccines for seasonal influenza virus infection. In the present study, the efficacy of formalin- and/or β-propiolactone-inactivated whole virus particle vaccines for seasonal influenza was compared to that of the current ether-treated split vaccines in a nonhuman primate model. The monovalent whole virus particle vaccines or split vaccines of influenza A virus (H1N1) and influenza B virus (Victoria lineage) were injected subcutaneously into naïve cynomolgus macaques twice. The whole virus particle vaccines induced higher titers of neutralizing antibodies against H1N1 influenza A virus and influenza B virus in the plasma of macaques than did the split vaccines. At challenge with H1N1 influenza A virus or influenza B virus, the virus titers in nasal swabs and the increases in body temperatures were lower in the macaques immunized with the whole virus particle vaccine than in those immunized with the split vaccine. Repertoire analyses of immunoglobulin heavy chain genes demonstrated that the number of B-lymphocyte subclones was increased in macaques after the 1st vaccination with the whole virus particle vaccine, but not with the split vaccine, indicating that the whole virus particle vaccine induced the activation of vaccine antigen-specific B-lymphocytes more vigorously than did the split vaccine at priming. Thus, the present findings suggest that the superior antibody induction ability of the whole virus particle vaccine as compared to the split vaccine is attributable to its stimulatory properties on the subclonal differentiation of antigen-specific B-lymphocytes.
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Affiliation(s)
- Masanori Shiohara
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan
| | - Saori Suzuki
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan
| | - Shintaro Shichinohe
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan
| | - Hirohito Ishigaki
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan
| | - Misako Nakayama
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan
| | - Naoki Nomura
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Masashi Shingai
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Toshiki Sekiya
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Global Station for Zoonosis Control, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, Japan
| | - Marumi Ohno
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Sayaka Iida
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Naoko Kawai
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Mamiko Kawahara
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Junya Yamagishi
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Kimihito Ito
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | | | | | - Kenji Motokawa
- Business Planning & Management Department, Daiichi Sankyo Biotech Co. Ltd., Saitama, Japan
| | - Tomoyoshi Sobue
- Group III, Modality Research Laboratories, Daiichi Sankyo Co. Ltd., Tokyo, Japan
| | - Hiroshi Kida
- International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan; Global Station for Zoonosis Control, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, Japan; Collaborating Research Center for the Control of Infectious Diseases, Nagasaki University, Nagasaki, Japan
| | - Kazumasa Ogasawara
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan; International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Yasushi Itoh
- Division of Pathogenesis and Disease Regulation, Department of Pathology, Shiga University of Medical Science, Otsu, Japan.
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Gilboa A, Hope R, Ben Simon S, Polak P, Koren O, Yaari G. Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2713-2725. [PMID: 35623663 DOI: 10.4049/jimmunol.2100955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/30/2022] [Indexed: 06/15/2023]
Abstract
The immune system matures throughout childhood to achieve full functionality in protecting our bodies against threats. The immune system has a strong reciprocal symbiosis with the host bacterial population and the two systems co-develop, shaping each other. Despite their fundamental role in health physiology, the ontogeny of these systems is poorly characterized. In this study, we investigated the development of the BCR repertoire by analyzing high-throughput sequencing of their receptors in several time points of young C57BL/6J mice. In parallel, we explored the development of the gut microbiome. We discovered that the gut IgA repertoires change from birth to adolescence, including an increase in CDR3 lengths and somatic hypermutation levels. This contrasts with the spleen IgM repertoires that remain stable and distinct from the IgA repertoires in the gut. We also discovered that large clones that germinate in the gut are initially confined to a specific gut compartment, then expand to nearby compartments and later on expand also to the spleen and remain there. Finally, we explored the associations between diversity indices of the B cell repertoires and the microbiome, as well as associations between bacterial and BCR clusters. Our results shed light on the ontogeny of the adaptive immune system and the microbiome, providing a baseline for future research.
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Affiliation(s)
- Amit Gilboa
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
| | - Ronen Hope
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | - Shira Ben Simon
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Pazit Polak
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Gur Yaari
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel;
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
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35
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Akama-Garren EH, Carroll MC. Lupus Susceptibility Loci Predispose Mice to Clonal Lymphocytic Responses and Myeloid Expansion. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2403-2424. [PMID: 35477687 PMCID: PMC9254690 DOI: 10.4049/jimmunol.2200098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/14/2022] [Indexed: 05/17/2023]
Abstract
Lupus susceptibility results from the combined effects of numerous genetic loci, but the contribution of these loci to disease pathogenesis has been difficult to study due to the large cellular heterogeneity of the autoimmune immune response. We performed single-cell RNA, BCR, and TCR sequencing of splenocytes from mice with multiple polymorphic lupus susceptibility loci. We not only observed lymphocyte and myeloid expansion, but we also characterized changes in subset frequencies and gene expression, such as decreased CD8 and marginal zone B cells and increased Fcrl5- and Cd5l-expressing macrophages. Clonotypic analyses revealed expansion of B and CD4 clones, and TCR repertoires from lupus-prone mice were distinguishable by algorithmic specificity prediction and unsupervised machine learning classification. Myeloid differential gene expression, metabolism, and altered ligand-receptor interaction were associated with decreased Ag presentation. This dataset provides novel mechanistic insight into the pathophysiology of a spontaneous model of lupus, highlighting potential therapeutic targets for autoantibody-mediated disease.
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Affiliation(s)
- Elliot H Akama-Garren
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA; and
- Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA
| | - Michael C Carroll
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA; and
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36
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Xu Q, Milanez-Almeida P, Martins AJ, Radtke AJ, Hoehn KB, Chen J, Liu C, Tang J, Grubbs G, Stein S, Ramelli S, Kabat J, Behzadpour H, Karkanitsa M, Spathies J, Kalish H, Kardava L, Kirby M, Cheung F, Preite S, Duncker PC, Romero N, Preciado D, Gitman L, Koroleva G, Smith G, Shaffer A, McBain IT, Pittaluga S, Germain RN, Apps R, Sadtler K, Moir S, Chertow DS, Kleinstein SH, Khurana S, Tsang JS, Mudd P, Schwartzberg PL, Manthiram K. Robust, persistent adaptive immune responses to SARS-CoV-2 in the oropharyngeal lymphoid tissue of children. RESEARCH SQUARE 2022:rs.3.rs-1276578. [PMID: 35350206 PMCID: PMC8963700 DOI: 10.21203/rs.3.rs-1276578/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
SARS-CoV-2 infection triggers adaptive immune responses from both T and B cells. However, most studies focus on peripheral blood, which may not fully reflect immune responses in lymphoid tissues at the site of infection. To evaluate both local and systemic adaptive immune responses to SARS-CoV-2, we collected peripheral blood, tonsils, and adenoids from 110 children undergoing tonsillectomy/adenoidectomy during the COVID-19 pandemic and found 24 with evidence of prior SARS-CoV-2 infection, including detectable neutralizing antibodies against multiple viral variants. We identified SARS-CoV-2-specific germinal center (GC) and memory B cells; single cell BCR sequencing showed that these virus-specific B cells were class-switched and somatically hypermutated, with overlapping clones in the adenoids and tonsils. Oropharyngeal tissues from COVID-19-convalescent children showed persistent expansion of GC and anti-viral lymphocyte populations associated with an IFN-γ-type response, with particularly prominent changes in the adenoids, as well as evidence of persistent viral RNA in both tonsil and adenoid tissues of many participants. Our results show robust, tissue-specific adaptive immune responses to SARS-CoV-2 in the upper respiratory tract of children weeks to months after acute infection, providing evidence of persistent localized immunity to this respiratory virus.
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Affiliation(s)
- Qin Xu
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD
| | | | | | - Andrea J. Radtke
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH Bethesda, MD
| | | | - Jinguo Chen
- Center for Human Immunology, NIAID, NIH, Bethesda, MD
| | - Can Liu
- Multiscale Systems Biology Section, LISB, NIAID, NIH, Bethesda, MD
| | - Juanjie Tang
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD
| | - Gabrielle Grubbs
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD
| | - Sydney Stein
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD
| | - Sabrina Ramelli
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD
| | - Juraj Kabat
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH Bethesda, MD
| | - Hengameh Behzadpour
- Division of Pediatric Otolaryngology, Children’s National Hospital, Washington, DC
| | - Maria Karkanitsa
- Laboratory of Immuno-Engineering, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD
| | - Jacquelyn Spathies
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, NIBIB, NIH, Bethesda, MD
| | - Heather Kalish
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, NIBIB, NIH, Bethesda, MD
| | - Lela Kardava
- B-cell Immunology Section, Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD
| | - Martha Kirby
- National Human Genome Research Institute (NHGRI), NIH, Bethesda, MD
| | - Foo Cheung
- Center for Human Immunology, NIAID, NIH, Bethesda, MD
| | - Silvia Preite
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD
| | | | - Nahir Romero
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Diego Preciado
- Division of Pediatric Otolaryngology, Children’s National Hospital, Washington, DC
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Lyuba Gitman
- Division of Pediatric Otolaryngology, Children’s National Hospital, Washington, DC
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | | | - Grace Smith
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute (NCI), NIH, Bethesda, MD
| | - Arthur Shaffer
- Lymphoid Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, MD
| | - Ian T. McBain
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD
| | - Stefania Pittaluga
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute (NCI), NIH, Bethesda, MD
| | - Ronald N. Germain
- Center for Advanced Tissue Imaging, LISB, NIAID, NIH Bethesda, MD
- Lymphocyte Biology Section, LISB, NIAID, NIH, Bethesda, MD
| | - Richard Apps
- Center for Human Immunology, NIAID, NIH, Bethesda, MD
| | - Kaitlyn Sadtler
- Laboratory of Immuno-Engineering, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD
| | - Susan Moir
- B-cell Immunology Section, Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD
| | - Daniel S. Chertow
- Emerging Pathogens Section, Critical Care Medicine Department, Clinical Center (CC), NIH, Bethesda, MD
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT
- Department of Immunobiology, Yale School of Medicine, New Haven, CT
| | - Surender Khurana
- Division of Viral Products, Center for Biologics Evaluation and Research (CBER), Food and Drug Administration (FDA), Silver Spring, MD
| | - John S. Tsang
- Center for Human Immunology, NIAID, NIH, Bethesda, MD
- Multiscale Systems Biology Section, LISB, NIAID, NIH, Bethesda, MD
| | - Pamela Mudd
- Division of Pediatric Otolaryngology, Children’s National Hospital, Washington, DC
- Division of Otolaryngology, Department of Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC
| | - Pamela L. Schwartzberg
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD
- National Human Genome Research Institute (NHGRI), NIH, Bethesda, MD
| | - Kalpana Manthiram
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology (LISB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD
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37
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Marquez S, Babrak L, Greiff V, Hoehn KB, Lees WD, Luning Prak ET, Miho E, Rosenfeld AM, Schramm CA, Stervbo U. Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis. Methods Mol Biol 2022; 2453:297-316. [PMID: 35622333 PMCID: PMC9761518 DOI: 10.1007/978-1-0716-2115-8_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.
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Affiliation(s)
- Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Lmar Babrak
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo University Hospital, Oslo, Norway
| | - Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - William D Lees
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, UK
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Enkelejda Miho
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- aiNET GmbH, Basel, Switzerland
| | - Aaron M Rosenfeld
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
- Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
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38
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Fiorentino G, Visintainer R, Domenici E, Lauria M, Marchetti L. MOUSSE: Multi-Omics Using Subject-Specific SignaturEs. Cancers (Basel) 2021; 13:cancers13143423. [PMID: 34298641 PMCID: PMC8304726 DOI: 10.3390/cancers13143423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature. Abstract High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.
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Affiliation(s)
- Giuseppe Fiorentino
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Roberto Visintainer
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
| | - Enrico Domenici
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Cellular, Computational, and Integrative Biology (CiBio), University of Trento, 38123 Povo, Italy
| | - Mario Lauria
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Department of Mathematics, University of Trento, 38123 Povo, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research, University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy; (G.F.); (R.V.); (E.D.); (M.L.)
- Correspondence:
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39
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Jiang R, Meng H, Raddassi K, Fleming I, Hoehn KB, Dardick KR, Belperron AA, Montgomery RR, Shalek AK, Hafler DA, Kleinstein SH, Bockenstedt LK. Single-cell immunophenotyping of the skin lesion erythema migrans identifies IgM memory B cells. JCI Insight 2021; 6:148035. [PMID: 34061047 PMCID: PMC8262471 DOI: 10.1172/jci.insight.148035] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/19/2021] [Indexed: 11/17/2022] Open
Abstract
The skin lesion erythema migrans (EM) is an initial sign of the Ixodes tick-transmitted Borreliella spirochetal infection known as Lyme disease. T cells and innate immune cells have previously been shown to predominate the EM lesion and promote the reaction. Despite the established importance of B cells and antibodies in preventing infection, the role of B cells in the skin immune response to Borreliella is unknown. Here, we used single-cell RNA-Seq in conjunction with B cell receptor (BCR) sequencing to immunophenotype EM lesions and their associated B cells and BCR repertoires. We found that B cells were more abundant in EM in comparison with autologous uninvolved skin; many were clonally expanded and had circulating relatives. EM-associated B cells upregulated the expression of MHC class II genes and exhibited preferential IgM isotype usage. A subset also exhibited low levels of somatic hypermutation despite a gene expression profile consistent with memory B cells. Our study demonstrates that single-cell gene expression with paired BCR sequencing can be used to interrogate the sparse B cell populations in human skin and reveals that B cells in the skin infection site in early Lyme disease expressed a phenotype consistent with local antigen presentation and antibody production.
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Affiliation(s)
| | | | - Khadir Raddassi
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ira Fleming
- Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA
| | | | | | - Alexia A. Belperron
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ruth R. Montgomery
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alex K. Shalek
- Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA
- The Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
- Institute for Medical Engineering & Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA
| | - David A. Hafler
- Department of Immunobiology
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA
| | - Steven H. Kleinstein
- Department of Immunobiology
- Department of Pathology, and
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
| | - Linda K. Bockenstedt
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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40
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Scheid JF, Barnes CO, Eraslan B, Hudak A, Keeffe JR, Cosimi LA, Brown EM, Muecksch F, Weisblum Y, Zhang S, Delorey T, Woolley AE, Ghantous F, Park SM, Phillips D, Tusi B, Huey-Tubman KE, Cohen AA, Gnanapragasam PNP, Rzasa K, Hatziioanno T, Durney MA, Gu X, Tada T, Landau NR, West AP, Rozenblatt-Rosen O, Seaman MS, Baden LR, Graham DB, Deguine J, Bieniasz PD, Regev A, Hung D, Bjorkman PJ, Xavier RJ. B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV. Cell 2021; 184:3205-3221.e24. [PMID: 34015271 PMCID: PMC8064835 DOI: 10.1016/j.cell.2021.04.032] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/26/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
Monoclonal antibodies (mAbs) are a focus in vaccine and therapeutic design to counteract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Here, we combined B cell sorting with single-cell VDJ and RNA sequencing (RNA-seq) and mAb structures to characterize B cell responses against SARS-CoV-2. We show that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells. Cryo-electron microscopy structures of selected nAbs from these two clusters complexed with SARS-CoV-2 spike trimers show recognition of various receptor-binding domain (RBD) epitopes. One of these mAbs, BG10-19, locks the spike trimer in a closed conformation to potently neutralize SARS-CoV-2, the recently arising mutants B.1.1.7 and B.1.351, and SARS-CoV and cross-reacts with heterologous RBDs. Together, our results characterize transcriptional differences among SARS-CoV-2-specific B cells and uncover cross-neutralizing Ab targets that will inform immunogen and therapeutic design against coronaviruses.
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MESH Headings
- Antibodies, Monoclonal/chemistry
- Antibodies, Monoclonal/immunology
- Antibodies, Neutralizing/blood
- Antibodies, Neutralizing/chemistry
- Antibodies, Neutralizing/immunology
- Antibodies, Viral/blood
- Antibodies, Viral/chemistry
- Antibodies, Viral/immunology
- Antigen-Antibody Complex/chemistry
- Antigen-Antibody Complex/metabolism
- Antigen-Antibody Reactions
- B-Lymphocytes/cytology
- B-Lymphocytes/metabolism
- B-Lymphocytes/virology
- COVID-19/pathology
- COVID-19/virology
- Cryoelectron Microscopy
- Crystallography, X-Ray
- Gene Expression Profiling
- Humans
- Immunoglobulin A/immunology
- Immunoglobulin Variable Region/chemistry
- Immunoglobulin Variable Region/genetics
- Protein Domains/immunology
- Protein Multimerization
- Protein Structure, Quaternary
- SARS-CoV-2/immunology
- SARS-CoV-2/isolation & purification
- SARS-CoV-2/metabolism
- Sequence Analysis, RNA
- Spike Glycoprotein, Coronavirus/chemistry
- Spike Glycoprotein, Coronavirus/genetics
- Spike Glycoprotein, Coronavirus/immunology
- Spike Glycoprotein, Coronavirus/metabolism
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Affiliation(s)
- Johannes F Scheid
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Christopher O Barnes
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Basak Eraslan
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Andrew Hudak
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Jennifer R Keeffe
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Lisa A Cosimi
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Eric M Brown
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Frauke Muecksch
- Laboratory of Molecular Virology, The Rockefeller University, New York, NY 10065, USA
| | - Yiska Weisblum
- Laboratory of Molecular Virology, The Rockefeller University, New York, NY 10065, USA
| | - Shuting Zhang
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Toni Delorey
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ann E Woolley
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Fadi Ghantous
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Sung-Moo Park
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Devan Phillips
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Betsabeh Tusi
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Kathryn E Huey-Tubman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Alexander A Cohen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | | | - Kara Rzasa
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Theodora Hatziioanno
- Laboratory of Molecular Virology, The Rockefeller University, New York, NY 10065, USA
| | - Michael A Durney
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Xiebin Gu
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Takuya Tada
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Nathaniel R Landau
- Department of Microbiology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Anthony P West
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Orit Rozenblatt-Rosen
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michael S Seaman
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Lindsey R Baden
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel B Graham
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Jacques Deguine
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Paul D Bieniasz
- Laboratory of Molecular Virology, The Rockefeller University, New York, NY 10065, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Deborah Hung
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Pamela J Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Ramnik J Xavier
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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41
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Jin X, Zhou W, Luo M, Wang P, Xu Z, Ma K, Cao H, Xu C, Huang Y, Cheng R, Xiao L, Lin X, Pang F, Li Y, Nie H, Jiang Q. Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing. Brief Bioinform 2021; 22:6278607. [PMID: 34015809 PMCID: PMC8194558 DOI: 10.1093/bib/bbab192] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 04/14/2021] [Accepted: 04/26/2021] [Indexed: 12/18/2022] Open
Abstract
The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.
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Affiliation(s)
- Xiyun Jin
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Wenyang Zhou
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Meng Luo
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Pingping Wang
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Zhaochun Xu
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Kexin Ma
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Huimin Cao
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Chang Xu
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Yan Huang
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Rui Cheng
- Harbin Institute of Technology, China
| | - Lixing Xiao
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | | | | | - Yiqun Li
- Harbin Institute of Technology, China
| | - Huan Nie
- School of Life Science and Technology at the Harbin Institute of Technology, China
| | - Qinghua Jiang
- School of Life Science and Technology at the Harbin Institute of Technology, China
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42
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Lindenbaum O, Nouri N, Kluger Y, Kleinstein SH. Alignment free identification of clones in B cell receptor repertoires. Nucleic Acids Res 2021; 49:e21. [PMID: 33330933 PMCID: PMC7913774 DOI: 10.1093/nar/gkaa1160] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 11/22/2022] Open
Abstract
Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method.
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Affiliation(s)
- Ofir Lindenbaum
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | - Nima Nouri
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.,Center for Medical Informatics, Yale University, New Haven, CT 06511, USA
| | - Yuval Kluger
- Program in Applied Mathematics, Yale University, New Haven, CT, USA.,Department of Pathology, Yale School of Medicine, New Haven, CT, USA.,Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.,Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.,Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
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43
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de Oliveira TC, Corder RM, Early A, Rodrigues PT, Ladeia-Andrade S, Alves JMP, Neafsey DE, Ferreira MU. Population genomics reveals the expansion of highly inbred Plasmodium vivax lineages in the main malaria hotspot of Brazil. PLoS Negl Trop Dis 2020; 14:e0008808. [PMID: 33112884 PMCID: PMC7592762 DOI: 10.1371/journal.pntd.0008808] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/21/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Plasmodium vivax is a neglected human malaria parasite that causes significant morbidity in the Americas, the Middle East, Asia, and the Western Pacific. Population genomic approaches remain little explored to map local and regional transmission pathways of P. vivax across the main endemic sites in the Americas, where great progress has been made towards malaria elimination over the past decades. METHODOLOGY/PRINCIPAL FINDINGS We analyze 38 patient-derived P. vivax genome sequences from Mâncio Lima (ML)-the Amazonian malaria hotspot next to the Brazil-Peru border-and 24 sequences from two other sites in Acre State, Brazil, a country that contributes 23% of malaria cases in the Americas. We show that the P. vivax population of ML is genetically diverse (π = 4.7 × 10-4), with a high polymorphism particularly in genes encoding proteins putatively involved in red blood cell invasion. Paradoxically, however, parasites display strong genome-wide linkage disequilibrium, being fragmented into discrete lineages that are remarkably stable across time and space, with only occasional recombination between them. Using identity-by-descent approaches, we identified a large cluster of closely related sequences that comprises 16 of 38 genomes sampled in ML over 26 months. Importantly, we found significant ancestry sharing between parasites at a large geographic distance, consistent with substantial gene flow between regional P. vivax populations. CONCLUSIONS/SIGNIFICANCE We have characterized the sustained expansion of highly inbred P. vivax lineages in a malaria hotspot that can seed regional transmission. Potential source populations in hotspots represent a priority target for malaria elimination in the Amazon.
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Affiliation(s)
- Thaís Crippa de Oliveira
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Rodrigo M. Corder
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Angela Early
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Departament of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Priscila T. Rodrigues
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Simone Ladeia-Andrade
- Laboratory of Parasitic Diseases, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil
| | - João Marcelo P. Alves
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Daniel E. Neafsey
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Departament of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Marcelo U. Ferreira
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
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44
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Jiang R, Fichtner ML, Hoehn KB, Pham MC, Stathopoulos P, Nowak RJ, Kleinstein SH, O'Connor KC. Single-cell repertoire tracing identifies rituximab-resistant B cells during myasthenia gravis relapses. JCI Insight 2020; 5:136471. [PMID: 32573488 DOI: 10.1172/jci.insight.136471] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 06/11/2020] [Indexed: 12/19/2022] Open
Abstract
Rituximab, a B cell-depleting therapy, is indicated for treating a growing number of autoantibody-mediated autoimmune disorders. However, relapses can occur after treatment, and autoantibody-producing B cell subsets may be found during relapses. It is not understood whether these autoantibody-producing B cell subsets emerge from the failed depletion of preexisting B cells or are generated de novo. To further define the mechanisms that cause postrituximab relapse, we studied patients with autoantibody-mediated muscle-specific kinase (MuSK) myasthenia gravis (MG) who relapsed after treatment. We carried out single-cell transcriptional and B cell receptor profiling on longitudinal B cell samples. We identified clones present before therapy that persisted during relapse. Persistent B cell clones included both antibody-secreting cells and memory B cells characterized by gene expression signatures associated with B cell survival. A subset of persistent antibody-secreting cells and memory B cells were specific for the MuSK autoantigen. These results demonstrate that rituximab is not fully effective at eliminating autoantibody-producing B cells and provide a mechanistic understanding of postrituximab relapse in MuSK MG.
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Affiliation(s)
| | - Miriam L Fichtner
- Department of Immunobiology and.,Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Panos Stathopoulos
- Department of Immunobiology and.,Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Richard J Nowak
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Steven H Kleinstein
- Department of Immunobiology and.,Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, USA.,Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kevin C O'Connor
- Department of Immunobiology and.,Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
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45
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Nouri N, Kleinstein SH. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data. PLoS Comput Biol 2020; 16:e1007977. [PMID: 32574157 PMCID: PMC7347241 DOI: 10.1371/journal.pcbi.1007977] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 07/09/2020] [Accepted: 05/21/2020] [Indexed: 01/11/2023] Open
Abstract
Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license.
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Affiliation(s)
- Nima Nouri
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Center for Medical Informatics, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Steven H. Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, United States of America
- Center for Medical Informatics, Yale School of Medicine, New Haven, Connecticut, United States of America
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
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46
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Omer A, Shemesh O, Peres A, Polak P, Shepherd AJ, Watson C, Boyd SD, Collins AM, Lees W, Yaari G. VDJbase: an adaptive immune receptor genotype and haplotype database. Nucleic Acids Res 2020; 48:D1051-D1056. [PMID: 31602484 PMCID: PMC6943044 DOI: 10.1093/nar/gkz872] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/19/2019] [Accepted: 10/01/2019] [Indexed: 12/14/2022] Open
Abstract
VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.
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Affiliation(s)
- Aviv Omer
- Bioengineering, Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Or Shemesh
- Bioengineering, Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Ayelet Peres
- Bioengineering, Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Pazit Polak
- Bioengineering, Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - Adrian J Shepherd
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | - Corey T Watson
- University of Louisville School of Medicine, Biochemistry and Molecular Genetics, Louisville, KY 40292, USA
| | - Scott D Boyd
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Andrew M Collins
- School of Biotechnology and Biomolecular Sciences, University of NSW, Kensington, Sydney, NSW 2052, Australia
| | - William Lees
- Institute of Structural and Molecular Biology, Birkbeck, University of London, London, UK
| | - Gur Yaari
- Bioengineering, Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
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47
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Govek KW, Yamajala VS, Camara PG. Clustering-independent analysis of genomic data using spectral simplicial theory. PLoS Comput Biol 2019; 15:e1007509. [PMID: 31756191 PMCID: PMC6897424 DOI: 10.1371/journal.pcbi.1007509] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 12/06/2019] [Accepted: 10/25/2019] [Indexed: 12/20/2022] Open
Abstract
The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them (e.g. differentially expressed genes). However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score when restricted to graphs. We demonstrate the utility of this framework with several applications to the analysis of gene expression and multi-modal genomic data. Specifically, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate differentially expressed genes according to the topology of the expression space (e.g. alternative paths of differentiation). We also apply this formalism to identify genes with spatial patterns of expression using fluorescence in-situ hybridization data and to establish associations between genetic alterations and global expression patterns in large cross-sectional studies. Our results provide a unifying perspective on topological data analysis and manifold learning approaches to the analysis of large-scale biological datasets. Manifold learning methods have emerged as a way of analyzing the large high-dimensional data sets that are currently generated in many areas of science. They assume the data has been sampled from an unknown manifold which is approximated with a graph and utilize spectral graph techniques to perform unsupervised feature selection and dimensionality reduction. However, graphs provide only partial approximations to manifolds, precluding the application to features with a complex combinatorial structure. Relatedly, these methods cannot take into account the topology of the manifold. In this work, we extend spectral methods for feature selection to topological spaces built from data and present a general framework for feature selection. We present specific applications of this framework to clustering-independent analysis of gene expression and multi-modal genomic data. In particular, using these methods, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate the results according to topological features of the expression space. In addition, we identify genes with spatial patterns of expression using spatially-resolved transcriptomic data and establish associations between genetic alterations and global expression patterns in large cross-sectional cancer studies.
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Affiliation(s)
- Kiya W. Govek
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Venkata S. Yamajala
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Pablo G. Camara
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- * E-mail:
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48
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Zhou JQ, Kleinstein SH. Cutting Edge: Ig H Chains Are Sufficient to Determine Most B Cell Clonal Relationships. THE JOURNAL OF IMMUNOLOGY 2019; 203:1687-1692. [PMID: 31484734 DOI: 10.4049/jimmunol.1900666] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/02/2019] [Indexed: 01/10/2023]
Abstract
B cell clonal expansion is vital for adaptive immunity. High-throughput BCR sequencing enables investigating this process but requires computational inference to identify clonal relationships. This inference usually relies on only the BCR H chain, as most current protocols do not preserve H:L chain pairing. The extent to which paired L chains aids inference is unknown. Using human single-cell paired BCR datasets, we assessed the ability of H chain-based clonal clustering to identify clones. Of the expanded clones identified, <20% grouped cells expressing inconsistent L chains. H chains from these misclustered clones contained more distant junction sequences and shared fewer V segment mutations than the accurate clones. This suggests that additional H chain information could be leveraged to refine clonal relationships. Conversely, L chains were insufficient to refine H chain-based clonal clusters. Overall, the BCR H chain alone is sufficient to identify clonal relationships with confidence.
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Affiliation(s)
- Julian Q Zhou
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511
| | - Steven H Kleinstein
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511; .,Department of Pathology, Yale School of Medicine, New Haven, CT 06520; and.,Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520
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