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Zhao K, Zheng X, Liu X, Liu T, Ke Z, Zhu F, Wen Q, Xin B, Li Q, Zhang L. Tissue-Matched IgH Gene Rearrangement of Circulating Tumor DNA Shows Significant Value in Predicting the Progression of Diffuse Large B Cell Lymphoma. Oncologist 2024; 29:e672-e680. [PMID: 38297976 PMCID: PMC11067791 DOI: 10.1093/oncolo/oyae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Evidence has demonstrated that monitoring of the variable, diversity, and joining gene segments (VDJ) rearrangement of the immunoglobulin (Ig) genes in the circulating tumor DNA (ctDNA) is of value in predicting the outcomes of diffuse large B cell lymphoma (DLBCL). In this study, we investigated the role of VDJ rearrangement proportion in ctDNA for predicting DLBCL progression. METHODS Patients diagnosed with newly diagnosed DLBCL were included in this study. The VDJ sequences of IgH were detected using next-generation sequencing (NGS) in formalin-fixed paraffin-embedded tissue and/or peripheral blood. The clonotype of the highest proportion in the peripheral blood was defined as the "dominant circulating clonotype," whilst the clonotype of the highest proportion in matched tissue that is detected in peripheral blood was defined as the "dominant tissue-matched clonotype." The decision tree, a machine learning-based methodology, was used to establish a progression-predicting model through a combination of "dominant tissue-matched clonotype" proportion or "dominant circulating clonotype" proportion, and the clinicopathological information, including age, sex, cell of origin, stage, international prognostic index, lactate dehydrogenase, number of extranodal involvements and β2-microglobulin. RESULTS A total of 55 patients with eligible sequencing data were used for prognosis analysis, among which 36 patients had matched tissue samples. The concordance rate of "dominant circulating clonotype" and "dominant tissue-matched clonotype" was 19.44% (7/36). The decision tree model showed that the combination of extranodal involvement event and "dominant circulating clonotype" proportion (≥37%) had a clinical value in predicting the prognosis of DLBCL following combined chemotherapy (sensitivity, 0.63; specificity, 0.81; positive prediction value (PPV), 0.59; negative prediction value, 0.83; kappa value, 0.42). Noticeably, the combination of the "dominant tissue-matched clonotype" and extranodal involvement event showed a higher value in predicting the progression (sensitivity, 0.85; specificity, 0.78; PPV, 0.69; kappa value, 0.64). CONCLUSION IgH proportion detected in the ctDNA samples traced from tissue samples has a high clinical value in predicting the progression of DLBCL.
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Affiliation(s)
- Kewei Zhao
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Xin Zheng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Xinxiu Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Tao Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Zhonghe Ke
- Department of Research and Development, Shanghai Rightongene Biotechnology, Shanghai, People’s Republic of China
| | - Fang Zhu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Qiuyue Wen
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Beibei Xin
- Department of Medicine, Shanghai Rightongene Biotechnology, Shanghai, People’s Republic of China
| | - Qiuhui Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Liling Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
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Shingai M, Iida S, Kawai N, Kawahara M, Sekiya T, Ohno M, Nomura N, Handabile C, Kawakita T, Omori R, Yamagishi J, Sano K, Ainai A, Suzuki T, Ohnishi K, Ito K, Kida H. Extraction of the CDRH3 sequence of the mouse antibody repertoire selected upon influenza virus infection by subtraction of the background antibody repertoire. J Virol 2024; 98:e0199523. [PMID: 38323813 PMCID: PMC10949447 DOI: 10.1128/jvi.01995-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 02/08/2024] Open
Abstract
Historically, antibody reactivity to pathogens and vaccine antigens has been evaluated using serological measurements of antigen-specific antibodies. However, it is difficult to evaluate all antibodies that contribute to various functions in a single assay, such as the measurement of the neutralizing antibody titer. Bulk antibody repertoire analysis using next-generation sequencing is a comprehensive method for analyzing the overall antibody response; however, it is unreliable for estimating antigen-specific antibodies due to individual variation. To address this issue, we propose a method to subtract the background signal from the repertoire of data of interest. In this study, we analyzed changes in antibody diversity and inferred the heavy-chain complementarity-determining region 3 (CDRH3) sequences of antibody clones that were selected upon influenza virus infection in a mouse model using bulk repertoire analysis. A decrease in the diversity of the antibody repertoire was observed upon viral infection, along with an increase in neutralizing antibody titers. Using kernel density estimation of sequences in a high-dimensional sequence space with background signal subtraction, we identified several clusters of CDRH3 sequences induced upon influenza virus infection. Most of these repertoires were detected more frequently in infected mice than in uninfected control mice, suggesting that infection-specific antibody sequences can be extracted using this method. Such an accurate extraction of antigen- or infection-specific repertoire information will be a useful tool for vaccine evaluation in the future. IMPORTANCE As specific interactions between antigens and cell-surface antibodies trigger the proliferation of B-cell clones, the frequency of each antibody sequence in the samples reflects the size of each clonal population. Nevertheless, it is extremely difficult to extract antigen-specific antibody sequences from the comprehensive bulk antibody sequences obtained from blood samples due to repertoire bias influenced by exposure to dietary antigens and other infectious agents. This issue can be addressed by subtracting the background noise from the post-immunization or post-infection repertoire data. In the present study, we propose a method to quantify repertoire data from comprehensive repertoire data. This method allowed subtraction of the background repertoire, resulting in more accurate extraction of expanded antibody repertoires upon influenza virus infection. This accurate extraction of antigen- or infection-specific repertoire information is a useful tool for vaccine evaluation.
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Affiliation(s)
- Masashi Shingai
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
- Division of Vaccine Immunology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Sayaka Iida
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Naoko Kawai
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Mamiko Kawahara
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Toshiki Sekiya
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Marumi Ohno
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
- One Health Research Center, Hokkaido University, Sapporo, Japan
| | - Naoki Nomura
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
| | - Chimuka Handabile
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
| | - Tomomi Kawakita
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
- Division of Vaccine Immunology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Ryosuke Omori
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Junya Yamagishi
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Kaori Sano
- Department of Pathology, National Institute of Infectious Diseases, Tokyo, Japan
| | - Akira Ainai
- Department of Pathology, National Institute of Infectious Diseases, Tokyo, Japan
| | - Tadaki Suzuki
- Department of Pathology, National Institute of Infectious Diseases, Tokyo, Japan
| | - Kazuo Ohnishi
- Department of Immunology, National Institute of Infectious Diseases, Tokyo, Japan
| | - Kimihito Ito
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Hiroshi Kida
- Division of Biologics Development, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
- Institute for Vaccine Research and Development (HU-IVReD), Hokkaido University, Sapporo, Japan
- International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
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Qiao Y, Huang X, Moos PJ, Ahmann JM, Pomicter AD, Deininger MW, Byrd JC, Woyach JA, Stephens DM, Marth GT. A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data. Genome Res 2024; 34:94-105. [PMID: 38195207 PMCID: PMC10903947 DOI: 10.1101/gr.278234.123] [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: 06/29/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024]
Abstract
Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.
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Affiliation(s)
- Yi Qiao
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Xiaomeng Huang
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Philip J Moos
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 84112, USA
| | - Jonathan M Ahmann
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Anthony D Pomicter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Michael W Deininger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
- Division of Hematology and Hematologic Malignancies, University of Utah, Salt Lake City, Utah 84112, USA
| | - John C Byrd
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Jennifer A Woyach
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Deborah M Stephens
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Gabor T Marth
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA;
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4
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Wang Q, Feng D, Jia S, Lu Q, Zhao M. B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases. Clin Rev Allergy Immunol 2024; 66:76-98. [PMID: 38459209 DOI: 10.1007/s12016-024-08984-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
In the field of contemporary medicine, autoimmune diseases (AIDs) are a prevalent and debilitating group of illnesses. However, they present extensive and profound challenges in terms of etiology, pathogenesis, and treatment. A major reason for this is the elusive pathophysiological mechanisms driving disease onset. Increasing evidence suggests the indispensable role of B cells in the pathogenesis of autoimmune diseases. Interestingly, B-cell receptor (BCR) repertoires in autoimmune diseases display a distinct skewing that can provide insights into disease pathogenesis. Over the past few years, advances in high-throughput sequencing have provided powerful tools for analyzing B-cell repertoire to understand the mechanisms during the period of B-cell immune response. In this paper, we have provided an overview of the mechanisms and analytical methods for generating BCR repertoire diversity and summarize the latest research progress on BCR repertoire in autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), multiple sclerosis (MS), and type 1 diabetes (T1D). Overall, B-cell repertoire analysis is a potent tool to understand the involvement of B cells in autoimmune diseases, facilitating the creation of innovative therapeutic strategies targeting specific B-cell clones or subsets.
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Affiliation(s)
- Qian Wang
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Delong Feng
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China
| | - Sujie Jia
- Department of Pharmacy, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
| | - Ming Zhao
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
- Clinical Medical Research Center of Major Skin Diseases and Skin Health of Hunan Province, Changsha, China.
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China.
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China.
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5
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Mai G, Zhang C, Lan C, Zhang J, Wang Y, Tang K, Tang J, Zeng J, Chen Y, Cheng P, Liu S, Long H, Wen Q, Li A, Liu X, Zhang R, Xu S, Liu L, Niu Y, Yang L, Wang Y, Yin D, Sun C, Chen YQ, Shen W, Zhang Z, Du X. Characterizing the dynamics of BCR repertoire from repeated influenza vaccination. Emerg Microbes Infect 2023; 12:2245931. [PMID: 37542407 PMCID: PMC10438862 DOI: 10.1080/22221751.2023.2245931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/12/2023] [Accepted: 08/03/2023] [Indexed: 08/06/2023]
Abstract
Yearly epidemics of seasonal influenza cause an enormous disease burden around the globe. An understanding of the rules behind the immune response with repeated vaccination still presents a significant challenge, which would be helpful for optimizing the vaccination strategy. In this study, 34 healthy volunteers with 16 vaccinated were recruited, and the dynamics of the BCR repertoire for consecutive vaccinations in two seasons were tracked. In terms of diversity, length, network, V and J gene segments usage, somatic hypermutation (SHM) rate and isotype, it was found that the overall changes were stronger in the acute phase of the first vaccination than the second vaccination. However, the V gene segments of IGHV4-39, IGHV3-9, IGHV3-7 and IGHV1-69 were amplified in the acute phase of the first vaccination, with IGHV3-7 dominant. On the other hand, for the second vaccination, the changes were dominated by IGHV1-69, with potential for coding broad neutralizing antibody. Additional analysis indicates that the application of V gene segment for IGHV3-7 in the acute phase of the first vaccination was due to the elevated usage of isotypes IgM and IgG3. While for IGHV1-69 in the second vaccination, it was contributed by isotypes IgG1 and IgG2. Finally, 41 public BCR clusters were identified in the vaccine group, with both IGHV3-7 and IGHV1-69 were involved and representative complementarity determining region 3 (CDR3) motifs were characterized. This study provides insights into the immune response dynamics following repeated influenza vaccination in humans and can inform universal vaccine design and vaccine strategies in the future.
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Affiliation(s)
- Guoqin Mai
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Chunhong Lan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- Center for Precision Medicine, Guangdong Academy of Medical Sciences, Medical Research Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jie Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yuanyuan Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Kang Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Jing Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yilin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shuning Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Haoyu Long
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qilan Wen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Aqin Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xuan Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Ruitong Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shuyang Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lin Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yanlan Niu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lan Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yihan Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Di Yin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Caijun Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yao-Qing Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wei Shen
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People’s Republic of China
| | - Zhenhai Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- Center for Precision Medicine, Guangdong Academy of Medical Sciences, Medical Research Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, People’s Republic of China
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, People’s Republic of China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, People’s Republic of China
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6
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Zehnder JL, Bussel JB, Cooper N. The role of genetics in refractory immune thrombocytopenia. Br J Haematol 2023; 203:62-64. [PMID: 37735556 DOI: 10.1111/bjh.19110] [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: 06/15/2023] [Accepted: 07/31/2023] [Indexed: 09/23/2023]
Abstract
Patients with refractory immune thrombocytopenia (rITP) have increased morbidity and mortality. Currently, there is limited understanding of the cause of refractoriness and no markers to help direct novel treatment options. Understanding the reason(s) for refractoriness is crucial to determining novel treatment options. The pathogenesis underlying rITP has generally been thought to be an underlying genetic predisposition with an environmental trigger. Familial ITP remains rare, and there are few twin studies, suggesting that a simple genetic cause is unlikely. However, genetic mutations provide the background for several autoimmune diseases. In this review, we explore the evidence of either an inherited genetic cause of rITP or an acquired mutation, in particular one resulting in clonal expansion of cytotoxic T cells.
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Affiliation(s)
- James L Zehnder
- Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - James B Bussel
- Division of Hematology/Oncology, Department of Pediatrics, Weill Cornell Medicine, New York, New York, USA
| | - Nichola Cooper
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK
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7
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Kaiser FMP, Janowska I, Menafra R, de Gier M, Korzhenevich J, Pico-Knijnenburg I, Khatri I, Schulz A, Kuijpers TW, Lankester AC, Konstantinidis L, Erlacher M, Kloet S, van Schouwenburg PA, Rizzi M, van der Burg M. IL-7 receptor signaling drives human B-cell progenitor differentiation and expansion. Blood 2023; 142:1113-1130. [PMID: 37369082 PMCID: PMC10644098 DOI: 10.1182/blood.2023019721] [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: 01/26/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Although absence of interleukin-7 (IL-7) signaling completely abrogates T and B lymphopoiesis in mice, patients with severe combined immunodeficiency caused by mutations in the IL-7 receptor α chain (IL-7Rα) still generate peripheral blood B cells. Consequently, human B lymphopoiesis has been thought to be independent of IL-7 signaling. Using flow cytometric analysis and single-cell RNA sequencing of bone marrow samples from healthy controls and patients who are IL-7Rα deficient, in combination with in vitro modeling of human B-cell differentiation, we demonstrate that IL-7R signaling plays a crucial role in human B lymphopoiesis. IL-7 drives proliferation and expansion of early B-cell progenitors but not of pre-BII large cells and has a limited role in the prevention of cell death. Furthermore, IL-7 guides cell fate decisions by enhancing the expression of BACH2, EBF1, and PAX5, which jointly orchestrate the specification and commitment of early B-cell progenitors. In line with this observation, early B-cell progenitors of patients with IL-7Rα deficiency still expressed myeloid-specific genes. Collectively, our results unveil a previously unknown role for IL-7 signaling in promoting the B-lymphoid fate and expanding early human B-cell progenitors while defining important differences between mice and humans. Our results have implications for hematopoietic stem cell transplantation strategies in patients with T- B+ severe combined immunodeficiency and provide insights into the role of IL-7R signaling in leukemogenesis.
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Affiliation(s)
- Fabian M. P. Kaiser
- Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Iga Janowska
- Department of Rheumatology and Clinical Immunology, Freiburg University Medical Center, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Melanie de Gier
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children’s Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Jakov Korzhenevich
- Division of Clinical and Experimental Immunology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Ingrid Pico-Knijnenburg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children’s Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Indu Khatri
- Department of Immunology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ansgar Schulz
- Department of Pediatrics and Adolescent Medicine, University Medical Center, University Ulm, Ulm, Germany
| | - Taco W. Kuijpers
- Department of Pediatrics, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Arjan C. Lankester
- Department of Pediatrics, Hematology and Stem Cell Transplantation, Willem-Alexander Children’s Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Lukas Konstantinidis
- Department of Orthopedics and Trauma Surgery, Freiburg University Medical Center, University of Freiburg, Freiburg, Germany
| | - Miriam Erlacher
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine, Freiburg University Medical Center, University of Freiburg, Freiburg, Germany
| | - Susan Kloet
- Leiden Genome Technology Center, Leiden, The Netherlands
| | - Pauline A. van Schouwenburg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children’s Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Marta Rizzi
- Department of Rheumatology and Clinical Immunology, Freiburg University Medical Center, University of Freiburg, Freiburg, Germany
- Center for Chronic Immunodeficiency, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Division of Clinical and Experimental Immunology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
- Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Mirjam van der Burg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children’s Hospital, Leiden University Medical Center, Leiden, The Netherlands
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8
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Gazzola A, Navari M, Mannu C, Donelli R, Etebari M, Piccaluga PP. Single-Step IGHV Next-Generation Sequencing Detects Clonality and Somatic Hypermutation in Lymphoid Malignancies: A Phase III Diagnostic Accuracy Study. Cancers (Basel) 2023; 15:4624. [PMID: 37760593 PMCID: PMC10526376 DOI: 10.3390/cancers15184624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Multiplex PCR based on consensus primers followed by capillary electrophoresis and Sanger sequencing are considered as the gold standard method for the evaluation of clonality and somatic hypermutation in lymphoid malignancies. As an alternative, the next-generation sequencing (NGS) of immune receptor genes has recently been proposed as a solution, due to being highly effective and sensitive. Here, we designed a phase III diagnostic accuracy study intended to compare the current gold standard methods versus the first commercially available NGS approaches for testing immunoglobulin heavy chain gene rearrangements. METHODS We assessed IGH rearrangements in 68 samples by means of both the NGS approach (LymphoTrack® IGH assay, and LymphoTrack® IGH somatic hypermutation assay, run on Illumina MiSeq) and capillary electrophoresis/Sanger sequencing to assess clonality and somatic hypermutations (SHM). RESULTS In comparison to the routine capillary-based analysis, the NGS clonality assay had an overall diagnostic accuracy of 96% (63/66 cases). Other studied criteria included sensitivity (95%), specificity (100%), positive predictive value (100%) and negative predictive value (75%). In discrepant cases, the NGS results were confirmed by a different set of primers that provided coverage of the IGH leader sequence. Furthermore, there was excellent agreement of the SHM determination with both the LymphoTrack® FR1 and leader assays when compared to the Sanger sequencing analysis (84%), with NGS able to assess the SHM rate even in cases where the conventional approach failed. CONCLUSION Overall, conventional Sanger sequencing and next-generation-sequencing-based clonality and somatic hypermutation analyses gave comparable results. For future use in a routine diagnostic workflow, NGS-based approaches should be evaluated prospectively and an analysis of cost-effectiveness should be performed.
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Affiliation(s)
- Anna Gazzola
- Hematopathology Unit, IRCCS Azienda Opedaliera-Universitaria di Bologna S. Orsola-Malpighi, 40138 Bologna, Italy; (A.G.); (C.M.)
| | - Mohsen Navari
- Department of Medical Biotechnology, School of Paramedical Sciences, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh 95196-33787, Iran;
- Research Center of Advanced Technologies in Medicine, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh 95196-33787, Iran
- Bioinformatics Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran
| | - Claudia Mannu
- Hematopathology Unit, IRCCS Azienda Opedaliera-Universitaria di Bologna S. Orsola-Malpighi, 40138 Bologna, Italy; (A.G.); (C.M.)
| | - Riccardo Donelli
- Biobank of Research, IRCCS Azienda Opedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
- Department of Medical and Surgical Sciences, Institute of Hematology and Medical Oncology “L&A Seràgnoli”, Bologna University School of Medicine, 40126 Bologna, Italy
| | - Maryam Etebari
- Health Sciences Research Center, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh 33787-95196, Iran;
| | - Pier Paolo Piccaluga
- Biobank of Research, IRCCS Azienda Opedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
- Department of Medical and Surgical Sciences, Institute of Hematology and Medical Oncology “L&A Seràgnoli”, Bologna University School of Medicine, 40126 Bologna, Italy
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9
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Ji J, Tang Y, Ke Z, Xin B, Wu Y. NGS-based IgH gene rearrangement monitoring predicts relapse and guides maintenance therapy in DLBCL: A case report from indolent lymphoma to aggressive lymphoma. Pathol Res Pract 2023; 248:154644. [PMID: 37441867 DOI: 10.1016/j.prp.2023.154644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 05/29/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023]
Abstract
This report describes a case of extranodal marginal zone B-cell lymphoma (ENMZL) of the mucosa-associated lymphoid tissue (MALT) lymphoma that transformed to diffuse large B cell lymphoma (DLBCL) in a 39-year-old female patient with Hashimoto's thyroiditis (HT). The patient presented with MALT lymphoma in the thyroid tissue and DLBCL in the multiple site invasions, including the ovary, breast, and lymph nodes. We assessed the Ig gene rearrangement and mutation profile in lymphoma involved tissues and the collected stem cells. V(D)J sequence of the tumor clonotype detected in thyroid, ovary, and breast was identical, revealing a shared origin of the malignant lymphoma. Noticeably, a small percentage of tumor clonotype (the highest-ranking clonotype in tumor tissues) was detected in the stem cell sample, suggesting the malignant cells was residual in the stem cells, likely conferred disease relapse following ASCT. This patient recieved BTK inhibitor combined with radiotherapy to eradicate the residual tumor cells based on the V(D)J sequence monitoring after ASCT. Now the patient remains in complete remission following 12 months of ASCT.
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MESH Headings
- Female
- Humans
- Adult
- Lymphoma, B-Cell, Marginal Zone/genetics
- Lymphoma, B-Cell, Marginal Zone/therapy
- Lymphoma, B-Cell, Marginal Zone/pathology
- Lymphoma, Large B-Cell, Diffuse/genetics
- Lymphoma, Large B-Cell, Diffuse/pathology
- Hashimoto Disease
- Gene Rearrangement
- Recurrence
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Affiliation(s)
- Jie Ji
- Department of Hematology and Hematology Research Institute, West China Hospital, Sichuan University, Chengdu 610044, China; Stem Cell Transplantation & Cellular Therapy division, Clinic Trial Center, West China Hospital, Sichuan University, Chengdu 610044, China
| | - Yuan Tang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu 610044, China
| | - Zhonghe Ke
- Department of Research and Development, Shanghai Rightongene Biotechnology Co., Ltd., Shanghai 201403, China
| | - Beibei Xin
- Department of Medicine, Shanghai Rightongene Biotechnology Co., Ltd., Shanghai 201403, China
| | - Yu Wu
- Department of Hematology and Hematology Research Institute, West China Hospital, Sichuan University, Chengdu 610044, China.
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10
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Antigen Receptors Gene Analysis for Minimal Residual Disease Detection in Acute Lymphoblastic Leukemia: The Role of High Throughput Sequencing. HEMATO 2023. [DOI: 10.3390/hemato4010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The prognosis of adult acute lymphoblastic leukemia (ALL) is variable but more often dismal. Indeed, its clinical management is challenging, current therapies inducing complete remission in 65–90% of cases, but only 30–40% of patients being cured. The major determinant of treatment failure is relapse; consequently, measurement of residual leukemic blast (minimal residual disease, MRD) has become a powerful independent prognostic indicator in adults. Numerous evidences have also supported the clinical relevance of MRD assessment for risk class assignment and treatment selection. MRD can be virtually evaluated in all ALL patients using different technologies, such as polymerase chain reaction amplification of fusion transcripts and clonal rearrangements of antigen receptor genes, flow cytometric study of leukemic immunophenotypes and, the most recent, high throughput sequencing (HTS). In this review, the authors focused on the latest developments on MRD monitoring with emphasis on the use of HTS, as well as on the clinical impact of MRD monitoring.
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11
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Duncavage EJ, Coleman JF, de Baca ME, Kadri S, Leon A, Routbort M, Roy S, Suarez CJ, Vanderbilt C, Zook JM. Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation: A Joint Report of the Association for Molecular Pathology, Association for Pathology Informatics, and College of American Pathologists. J Mol Diagn 2023; 25:3-16. [PMID: 36244574 DOI: 10.1016/j.jmoldx.2022.09.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented.
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Affiliation(s)
- Eric J Duncavage
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Joshua F Coleman
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Monica E de Baca
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Pacific Pathology Partners, Seattle, Washington
| | - Sabah Kadri
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Anne and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Annette Leon
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Color Health, Burlingame, California
| | - Mark Routbort
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas
| | - Somak Roy
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio
| | - Carlos J Suarez
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Palo Alto, California
| | - Chad Vanderbilt
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Justin M Zook
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Biomarker and Genomic Sciences Group, National Institute of Standards and Technology, Gaithersburg, Maryland
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12
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Koraichi MB, Touzel MP, Mazzolini A, Mora T, Walczak AM. NoisET: Noise Learning and Expansion Detection of T-Cell Receptors. J Phys Chem A 2022; 126:7407-7414. [PMID: 36178325 DOI: 10.1021/acs.jpca.2c05002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals. However, quantitative comparison between repertoires is confounded by variability in the read count of each receptor clonotype due to sampling, library preparation, and expression noise. We review methods for accounting for both biological and experimental noise and present an easy-to-use python package NoisET that implements and generalizes a previously developed Bayesian method. It can be used to learn experimental noise models for repertoire sequencing from replicates, and to detect responding clones following a stimulus. We test the package on different repertoire sequencing technologies and data sets. We review how such approaches have been used to identify responding clonotypes in vaccination and disease data. Availability: NoisET is freely available to use with source code at github.com/statbiophys/NoisET.
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Affiliation(s)
- Meriem Bensouda Koraichi
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | | | - Andrea Mazzolini
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Thierry Mora
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
| | - Aleksandra M Walczak
- Laboratoire de physique de l' École normale supérieure, CNRS, PSL University, Sorbonne Université, and Université de Paris, Paris75005, France
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13
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van Schouwenburg PA, van der Burg M, IJspeert H. NGS-Based B-Cell Receptor Repertoire AnalysisRepertoire analyses in the Context of Inborn Errors of Immunity. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2453:169-190. [PMID: 35622327 DOI: 10.1007/978-1-0716-2115-8_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Inborn errors of immunity (IEI) are genetic defects that can affect both the innate and the adaptive immune system. Patients with IEI usually present with recurrent infections, but many also suffer from immune dysregulation, autoimmunity, and malignancies.Inborn errors of the immune system can cause defects in the development and selection of the B-cell receptor (BCR ) repertoire. Patients with IEI can have a defect in one of the key processes of immune repertoire formation like V(D)J recombination, somatic hypermutation (SHM), class switch recombination (CSR), or (pre-)BCR signalling and proliferation. However, also other genetic defects can lead to quantitative and qualitative differences in the immune repertoire.In this chapter, we will give an overview of protocols that can be used to study the immune repertoire in patients with IEI, provide considerations to take into account before setting up experiments, and discuss analysis of the immune repertoire data using Antigen Receptor Galaxy (ARGalaxy).
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Affiliation(s)
- Pauline A van Schouwenburg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children's Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Mirjam van der Burg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children's Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Hanna IJspeert
- Department of Immunology, Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
- Academic Center for Rare Immunological Diseases (RIDC), Erasmus University Medical Center, Rotterdam, The Netherlands.
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14
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Gydush G, Nguyen E, Bae JH, Blewett T, Rhoades J, Reed SC, Shea D, Xiong K, Liu R, Yu F, Leong KW, Choudhury AD, Stover DG, Tolaney SM, Krop IE, Christopher Love J, Parsons HA, Mike Makrigiorgos G, Golub TR, Adalsteinsson VA. Massively parallel enrichment of low-frequency alleles enables duplex sequencing at low depth. Nat Biomed Eng 2022; 6:257-266. [PMID: 35301450 PMCID: PMC9089460 DOI: 10.1038/s41551-022-00855-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 01/28/2022] [Indexed: 02/07/2023]
Abstract
Assaying for large numbers of low-frequency mutations requires sequencing at extremely high depth and accuracy. Increasing sequencing depth aids the detection of low-frequency mutations yet limits the number of loci that can be simultaneously probed. Here we report a method for the accurate tracking of thousands of distinct mutations that requires substantially fewer reads per locus than conventional hybrid-capture duplex sequencing. The method, which we named MAESTRO (for minor-allele-enriched sequencing through recognition oligonucleotides), combines massively parallel mutation enrichment with duplex sequencing to track up to 10,000 low-frequency mutations, with up to 100-fold fewer reads per locus. We show that MAESTRO can be used to test for chimaerism by tracking donor-exclusive single-nucleotide polymorphisms in sheared genomic DNA from human cell lines, to validate whole-exome sequencing and whole-genome sequencing for the detection of mutations in breast-tumour samples from 16 patients, and to monitor the patients for minimal residual disease via the analysis of cell-free DNA from liquid biopsies. MAESTRO improves the breadth, depth, accuracy and efficiency of mutation testing by sequencing.
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Affiliation(s)
| | - Erica Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jin H Bae
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Douglas Shea
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kan Xiong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ruolin Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fangyan Yu
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA
| | - Ka Wai Leong
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Atish D Choudhury
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel G Stover
- Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Sara M Tolaney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ian E Krop
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - J Christopher Love
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Heather A Parsons
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - G Mike Makrigiorgos
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA.
| | - Todd R Golub
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Viktor A Adalsteinsson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA.
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15
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Luo L, Luo Y, Xu J, Zhu R, Wu J, Liu X, Li W, Yao X. Heterogeneous origin of IgE in atopic dermatitis and psoriasis revealed by B cell receptor repertoire analysis. Allergy 2022; 77:559-568. [PMID: 34738638 DOI: 10.1111/all.15173] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/03/2021] [Accepted: 10/18/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Epicutaneous sensitization is an important route for the production of IgE, and skin inflammation-induced IgE has recently been reported having features of natural antibody. Atopic dermatitis (AD) and psoriasis have differentially increased level of serum IgE; however, the production mechanism of IgE in these inflammatory skin diseases remains unknown. OBJECTIVE To explore the origin of IgE in AD and psoriasis by analyzing the B cell receptor repertoire. METHODS mRNA was prepared from peripheral blood mononuclear cells of AD and psoriasis patients that had elevated serum levels of IgE, and immunoglobulin heavy chain (IGH) repertoires were sequenced after reverse transcription. Clonal lineages of B cells containing members expressing IgE were identified, and somatic hypermutations in IGH inherited from common ancestors within the clonal lineage were used to infer the relationships between B cells. RESULTS The proportions of IGHE from AD and psoriasis were higher than that of normal control, which were positively correlated with the levels of serum total IgE. The somatic hypermutation value of IGHE variable region was lower than that of IGHG and IGHA, but higher than IGHM and IGHD, indicating a mixed natural and adaptive origins of IgE; and psoriasis demonstrated lower level of hypermutation than AD. The Shannon indexes of CDR3 in IGHE of AD and psoriasis were higher than that of normal control, also supporting the natural origin. The VH usage of IgE was weakly biased in AD and psoriasis patients with high level of house dust mite-specific IgE. Comparison of the number of shared mutations in multi-isotype lineages containing IgE showed that isotype-switching from IgG-expressing B cells might be the major source of IgE in AD and psoriasis. CONCLUSION IgE has heterogeneous origin in AD and psoriasis, and skin inflammation may contribute to the increased production of natural IgE.
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Affiliation(s)
- Lihua Luo
- Department of Dermatology Huashan Hospital Fudan University Shanghai China
- University of Chinese Academy of Sciences Beijing China
| | - Yang Luo
- Department of Allergy and Rheumatology Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs Hospital for Skin Diseases Institute of Dermatology Chinese Academy of Medical Sciences and Peking Union Medical College Nanjing China
| | - Jing Xu
- Department of Dermatology Huashan Hospital Fudan University Shanghai China
| | - Ronghui Zhu
- Department of Dermatology Huashan Hospital Fudan University Shanghai China
| | - Jinghua Wu
- University of Chinese Academy of Sciences Beijing China
| | - Xiao Liu
- Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China
| | - Wei Li
- Department of Dermatology Huashan Hospital Fudan University Shanghai China
- Department of Allergy Huashan Hospital Fudan University Shanghai China
| | - Xu Yao
- Department of Allergy and Rheumatology Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs Hospital for Skin Diseases Institute of Dermatology Chinese Academy of Medical Sciences and Peking Union Medical College Nanjing China
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16
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Framme JL, Lundqvist C, Lundell AC, van Schouwenburg PA, Lemarquis AL, Thörn K, Lindgren S, Gudmundsdottir J, Lundberg V, Degerman S, Zetterström RH, Borte S, Hammarström L, Telemo E, Hultdin M, van der Burg M, Fasth A, Oskarsdóttir S, Ekwall O. Long-Term Follow-Up of Newborns with 22q11 Deletion Syndrome and Low TRECs. J Clin Immunol 2022; 42:618-633. [PMID: 35080750 PMCID: PMC9016018 DOI: 10.1007/s10875-021-01201-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/12/2021] [Indexed: 01/03/2023]
Abstract
Background Population-based neonatal screening using T-cell receptor excision circles (TRECs) identifies infants with profound T lymphopenia, as seen in cases of severe combined immunodeficiency, and in a subgroup of infants with 22q11 deletion syndrome (22q11DS). Purpose To investigate the long-term prognostic value of low levels of TRECs in newborns with 22q11DS. Methods Subjects with 22q11DS and low TRECs at birth (22q11Low, N=10), matched subjects with 22q11DS and normal TRECs (22q11Normal, N=10), and matched healthy controls (HC, N=10) were identified. At follow-up (median age 16 years), clinical and immunological characterizations, covering lymphocyte subsets, immunoglobulins, TRECs, T-cell receptor repertoires, and relative telomere length (RTL) measurements were performed. Results At follow-up, the 22q11Low group had lower numbers of naïve T-helper cells, naïve T-regulatory cells, naïve cytotoxic T cells, and persistently lower TRECs compared to healthy controls. Receptor repertoires showed skewed V-gene usage for naïve T-helper cells, whereas for naïve cytotoxic T cells, shorter RTL and a trend towards higher clonality were found. Multivariate discriminant analysis revealed a clear distinction between the three groups and a skewing towards Th17 differentiation of T-helper cells, particularly in the 22q11Low individuals. Perturbations of B-cell subsets were found in both the 22q11Low and 22q11Normal group compared to the HC group, with larger proportions of naïve B cells and lower levels of memory B cells, including switched memory B cells. Conclusions This long-term follow-up study shows that 22q11Low individuals have persistent immunologic aberrations and increased risk for immune dysregulation, indicating the necessity of lifelong monitoring. Clinical Implications This study elucidates the natural history of childhood immune function in newborns with 22q11DS and low TRECs, which may facilitate the development of programs for long-term monitoring and therapeutic choices. Supplementary Information The online version contains supplementary material available at 10.1007/s10875-021-01201-5.
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Affiliation(s)
- Jenny Lingman Framme
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
- Department of Pediatrics, Halland Hospital Halmstad, Halmstad, Region Halland, Sweden.
| | - Christina Lundqvist
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Anna-Carin Lundell
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Pauline A van Schouwenburg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children's Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Andri L Lemarquis
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Karolina Thörn
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Susanne Lindgren
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Judith Gudmundsdottir
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Children's Medical Center, National University Hospital of Iceland, Reykjavík, Iceland
| | - Vanja Lundberg
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Sofie Degerman
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
| | - Rolf H Zetterström
- Centre for Inherited Metabolic Diseases, Karolinska University Hospital Solna, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - Stephan Borte
- ImmunoDeficiencyCenter Leipzig (IDCL), Municipal Hospital St. Georg Leipzig, Leipzig, Germany
| | - Lennart Hammarström
- Department of Biosciences and Nutrition, Neo, Karolinska Institute, Stockholm, Sweden
| | - Esbjörn Telemo
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Magnus Hultdin
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Mirjam van der Burg
- Department of Pediatrics, Laboratory for Pediatric Immunology, Willem-Alexander Children's Hospital, Leiden University Medical Center, Leiden, The Netherlands
| | - Anders Fasth
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Sólveig Oskarsdóttir
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Olov Ekwall
- Department of Pediatrics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
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17
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Vetter J, Schaller S, Heinzel A, Aschauer C, Reindl-Schwaighofer R, Jelencsics K, Hu K, Oberbauer R, Winkler SM. ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data. BMC Bioinformatics 2022; 23:21. [PMID: 34991455 PMCID: PMC8734366 DOI: 10.1186/s12859-021-04535-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 12/14/2021] [Indexed: 11/14/2022] Open
Abstract
Background Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. Results ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. Conclusions IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.
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Affiliation(s)
- Julia Vetter
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 13, 4232, Hagenberg im Muehlkreis, Austria. .,Department of Biosciences, University of Salzburg, Hellbrunnerstrasse 34, 5020, Salzburg, Austria.
| | - Susanne Schaller
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 13, 4232, Hagenberg im Muehlkreis, Austria
| | - Andreas Heinzel
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Constantin Aschauer
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Roman Reindl-Schwaighofer
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Kira Jelencsics
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Karin Hu
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Rainer Oberbauer
- Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Stephan M Winkler
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 13, 4232, Hagenberg im Muehlkreis, Austria
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18
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Zhu Y, Yang X, Ma C, Tang H, Wang Q, Guan J, Xie W, Chen S, Chen Y, Wang M, Lan C, Sun D, Wei L, Sun C, Yu X, Zhang Z. Antibody upstream sequence diversity and its biological implications revealed by repertoire sequencing. J Genet Genomics 2021; 48:936-945. [PMID: 34420911 DOI: 10.1016/j.jgg.2021.06.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/10/2021] [Accepted: 06/16/2021] [Indexed: 12/26/2022]
Abstract
The sequence upstream of the antibody variable region (antibody upstream sequence [AUS]) consists of a 5' untranslated region (5' UTR) and a preceding leader region. The sequence variations in AUS affect antibody engineering and PCR based antibody quantification and may also be implicated in mRNA transcription and translation. However, the diversity of AUSs remains elusive. Using 5' rapid amplification of cDNA ends and high-throughput antibody repertoire sequencing technique, we acquired full-length AUSs for human, rhesus macaque, cynomolgus macaque, mouse, and rat. We designed a bioinformatics pipeline and identified 3307 unique AUSs, corresponding to 3026 and 1457 unique sequences for 5' UTR and leader region, respectively. Comparative analysis indicated that 928 (63.69%) leader sequences are novel relative to those recorded in the international ImMunoGeneTics information system. Evolutionarily, leader sequences are more conserved than 5' UTR and seem to coevolve with their downstream V genes. Besides, single-nucleotide polymorphisms are position dependent for leader regions and may contribute to the functional reversal of the downstream V genes. Finally, the AUGs in AUSs were found to have little impact on gene expression. Taken together, our findings can facilitate primer design for capturing antibodies efficiently and provide a valuable resource for antibody engineering and molecule-level antibody studies.
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Affiliation(s)
- Yan Zhu
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
| | - Xiujia Yang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
| | - Cuiyu Ma
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Junjie Guan
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenxi Xie
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sen Chen
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Minhui Wang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Nephrology, Hainan Affiliated Hospital of Hainan Medical College, Haikou 570311, China; Department of Nephrology, Hainan General Hospital, Haikou 570311, China
| | - Chunhong Lan
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Deqiang Sun
- Department of Center Laboratory, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510700, China
| | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Caijun Sun
- School of Public Health, Sun Yat-sen University, Shenzhen 510006, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
| | - Zhenhai Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China.
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19
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Ding H, Xu J, Lin Z, Huang J, Wang F, Yang Y, Cui Y, Luo H, Gao Y, Zhai X, Pang W, Zhang L, Zheng Y. Minimal residual disease in multiple myeloma: current status. Biomark Res 2021; 9:75. [PMID: 34649622 PMCID: PMC8515655 DOI: 10.1186/s40364-021-00328-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 09/17/2021] [Indexed: 02/08/2023] Open
Abstract
Multiple myeloma (MM) is a treatable plasma cell cancer with no cure. Clinical evidence shows that the status of minimal residual disease (MRD) after treatment is an independent prognostic factor of MM. MRD indicates the depth of post-therapeutic remission. In this review article, we outlined the major clinical trials that have determined the prognostic value of MRD in MM. We also reviewed different methods that were used for MM MRD assessment. Most important, we reviewed our current understanding of MM MRD biology. MRD studies strongly indicate that MRD is not a uniform declination of whole MM tumor population. Rather, MM MRD exhibits unique signatures of cytogenetic aberration and gene expression profiles, unlike those of MM cells before therapy. Diagnostic high-risk MM and low-risk MM exhibited a diversity of MRD features. Clonal evaluation may occur at the MRD stage in MM. The dynamics from the diagnostic MM to MRD correlate with the disease prognosis. Lastly, on the aspect of omics, we performed data-based analysis to address the biological features underlying the course of diagnostic-to-MRD MM. To summarize, the MRD stage of disease represents a critical step in MM pathogenesis and progression. Demonstration of MM MRD biology should help us to deal with the curative difficulties.
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Affiliation(s)
- Hong Ding
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Juan Xu
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Zhimei Lin
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China.,Department of Hematology, The Affiliated Hospital of Chengdu University, Chengdu, China
| | - Jingcao Huang
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Fangfang Wang
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Yan Yang
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Yushan Cui
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Hongmei Luo
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Yuhan Gao
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Xinyu Zhai
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Weicui Pang
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China
| | - Li Zhang
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China.
| | - Yuhuan Zheng
- Department of Hematology, West China Hospital, and State Key Laboratory of Biotherapy and Cancer Center, Sichuan University, #37 GuoXue Xiang Street, Chengdu, China.
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20
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Kim M, Jeon K, Hutt K, Zlotnicki AM, Kim HJ, Lee J, Kim HS, Kang HJ, Lee YK. Immunoglobulin gene rearrangement in Koreans with multiple myeloma: Clonality assessment and repertoire analysis using next-generation sequencing. PLoS One 2021; 16:e0253541. [PMID: 34166440 PMCID: PMC8224885 DOI: 10.1371/journal.pone.0253541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/07/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction We assessed the applicability of next-generation sequencing (NGS)-based IGH/IGK clonality testing and analyzed the repertoire of immunoglobulin heavy chain (IGH) or immunoglobulin kappa light chain (IGK) gene usage in Korean patients with multiple myeloma (MM) for the first time. Methods Fifty-nine bone marrow samples from 57 Korean patients with MM were analyzed, and NGS-based clonality testing that targeted the IGH and IGK genes was performed using IGH FR1 and IGK primer sets. Results Clonal IGH and IGK rearrangements were observed in 74.2% and 67.7% of samples from Korean patients with kappa-restricted MM, respectively (90.3% had one or both), and in 60.7% and 95.5% of samples from those with lambda-restricted MM, respectively (85.7% had one or both). In total, 88.1% of samples from Koreans with MM had clonal IGH and/or IGK rearrangement. Clonal rearrangement was not significantly associated with the bone marrow plasma cells as a proportion of all BM lymphoid cells. IGHV3-9 (11.63%) and IGHV4-31 (9.30%) were the most frequently reported IGHV genes and were more common in Koreans with MM than in Western counterparts. IGHD3-10 and IGHD3-3 (13.95% each) were the most frequent IGHD genes; IGHD3-3 was more common in Koreans with MM. No IGK rearrangement was particularly prevalent, but single IGKV-J rearrangements were less common in Koreans with kappa-restricted MM than in Western counterparts. IGKV4-1 was less frequent in Koreans regardless of light chain type. Otherwise, the usages of the IGH V, D, and J genes and of the IGK gene were like those observed in previous Western studies. Conclusion NGS-based IGH/IGK clonality testing ought to be applicable to most Koreans with MM. The overrepresentation of IGHV3-9, IGHV4-31, and IGHD3-3 along with the underrepresentation of IGKV4-1 and the differences in IGK gene rearrangement types suggest the existence of ethnicity-specific variations in this disease.
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Affiliation(s)
- Miyoung Kim
- Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kibum Jeon
- Department of Laboratory Medicine, Hangang Sacred Heart Hospital, Seoul, South Korea
| | - Kasey Hutt
- Invivoscribe, Inc., San Diego, California, United States of America
| | | | - Hyo Jung Kim
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea
| | - Jiwon Lee
- Department of Laboratory Medicine, Green Cross Laboratories, Yongin, South Korea
| | - Han-Sung Kim
- Department of Laboratory Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea
| | - Hee Jung Kang
- Department of Laboratory Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea
| | - Young Kyung Lee
- Department of Laboratory Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea
- * E-mail:
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21
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Frank MJ, Hossain NM, Bukhari A, Dean E, Spiegel JY, Claire GK, Kirsch I, Jacob AP, Mullins CD, Lee LW, Kong KA, Craig J, Mackall CL, Rapoport AP, Jain MD, Dahiya S, Locke FL, Miklos DB. Monitoring of Circulating Tumor DNA Improves Early Relapse Detection After Axicabtagene Ciloleucel Infusion in Large B-Cell Lymphoma: Results of a Prospective Multi-Institutional Trial. J Clin Oncol 2021; 39:3034-3043. [PMID: 34133196 PMCID: PMC10166351 DOI: 10.1200/jco.21.00377] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Although the majority of patients with relapsed or refractory large B-cell lymphoma respond to axicabtagene ciloleucel (axi-cel), only a minority of patients have durable remissions. This prospective multicenter study explored the prognostic value of circulating tumor DNA (ctDNA) before and after standard-of-care axi-cel for predicting patient outcomes. METHODS Lymphoma-specific variable, diversity, and joining gene segments (VDJ) clonotype ctDNA sequences were frequently monitored via next-generation sequencing from the time of starting lymphodepleting chemotherapy until progression or 1 year after axi-cel infusion. We assessed the prognostic value of ctDNA to predict outcomes and axi-cel-related toxicity. RESULTS A tumor clonotype was successfully detected in 69 of 72 (96%) enrolled patients. Higher pretreatment ctDNA concentrations were associated with progression after axi-cel infusion and developing cytokine release syndrome and/or immune effector cell-associated neurotoxicity syndrome. Twenty-three of 33 (70%) durably responding patients versus 4 of 31 (13%) progressing patients demonstrated nondetectable ctDNA 1 week after axi-cel infusion (P < .0001). At day 28, patients with detectable ctDNA compared with those with undetectable ctDNA had a median progression-free survival and OS of 3 months versus not reached (P < .0001) and 19 months versus not reached (P = .0080), respectively. In patients with a radiographic partial response or stable disease on day 28, 1 of 10 patients with concurrently undetectable ctDNA relapsed; by contrast, 15 of 17 patients with concurrently detectable ctDNA relapsed (P = .0001). ctDNA was detected at or before radiographic relapse in 29 of 30 (94%) patients. All durably responding patients had undetectable ctDNA at or before 3 months after axi-cel infusion. CONCLUSION Noninvasive ctDNA assessments can risk stratify and predict outcomes of patients undergoing axi-cel for the treatment of large B-cell lymphoma. These results provide a rationale for designing ctDNA-based risk-adaptive chimeric antigen receptor T-cell clinical trials.
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Affiliation(s)
- Matthew J Frank
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
| | | | - Ali Bukhari
- University of Maryland School of Medicine, Greenebaum Comprehensive Cancer Center, Baltimore, MD
| | | | - Jay Y Spiegel
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
| | - Gursharan K Claire
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
| | | | | | | | | | - Katherine A Kong
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
| | - Juliana Craig
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
| | - Crystal L Mackall
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA.,Division of Pediatric Hematology/Oncology/Stem Cell Transplantation, Department of Pediatrics, Stanford University, Stanford, CA
| | - Aaron P Rapoport
- University of Maryland School of Medicine, Greenebaum Comprehensive Cancer Center, Baltimore, MD
| | | | - Saurabh Dahiya
- University of Maryland School of Medicine, Greenebaum Comprehensive Cancer Center, Baltimore, MD
| | | | - David B Miklos
- Division of Blood and Stem Cell Transplantation, Department of Medicine, Stanford University, Stanford, CA.,Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford, CA
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22
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Ashkenazy H, Avram O, Ryvkin A, Roitburd-Berman A, Weiss-Ottolenghi Y, Hada-Neeman S, Gershoni JM, Pupko T. Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning. J Mol Biol 2021; 433:167071. [PMID: 34052285 DOI: 10.1016/j.jmb.2021.167071] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/26/2021] [Accepted: 05/22/2021] [Indexed: 11/26/2022]
Abstract
Antibodies provide a comprehensive record of the encounters with threats and insults to the immune system. The ability to examine the repertoire of antibodies in serum and discover those that best represent "discriminating features" characteristic of various clinical situations, is potentially very useful. Recently, phage display technologies combined with Next-Generation Sequencing (NGS) produced a powerful experimental methodology, coined "Deep-Panning", in which the spectrum of serum antibodies is probed. In order to extract meaningful biological insights from the tens of millions of affinity-selected peptides generated by Deep-Panning, advanced bioinformatics algorithms are a must. In this study, we describe Motifier, a computational pipeline comprised of a set of algorithms that systematically generates discriminatory peptide motifs based on the affinity-selected peptides identified by Deep-Panning. These motifs are shown to effectively characterize antibody binding activities and through the implementation of machine-learning protocols are shown to accurately classify complex antibody mixtures representing various biological conditions.
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Affiliation(s)
- Haim Ashkenazy
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Oren Avram
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Arie Ryvkin
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Anna Roitburd-Berman
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yael Weiss-Ottolenghi
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Smadar Hada-Neeman
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jonathan M Gershoni
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
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23
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Montague Z, Lv H, Otwinowski J, DeWitt WS, Isacchini G, Yip GK, Ng WW, Tsang OTY, Yuan M, Liu H, Wilson IA, Peiris JSM, Wu NC, Nourmohammad A, Mok CKP. Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19. Cell Rep 2021; 35:109173. [PMID: 33991510 PMCID: PMC8106887 DOI: 10.1016/j.celrep.2021.109173] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/05/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023] Open
Abstract
Individuals with the 2019 coronavirus disease (COVID-19) show varying severity of the disease, ranging from asymptomatic to requiring intensive care. Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, we still lack an understanding of the overall landscape of B cell receptor (BCR) repertoires in individuals with COVID-19. We use high-throughput sequencing of bulk and plasma B cells collected at multiple time points during infection to characterize signatures of the B cell response to SARS-CoV-2 in 19 individuals. Using principled statistical approaches, we associate differential features of BCRs with different disease severity. We identify 38 significantly expanded clonal lineages shared among individuals as candidates for responses specific to SARS-CoV-2. Using single-cell sequencing, we verify the reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identify the natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in some individuals. Our results provide insights important for development of rational therapies and vaccines against COVID-19.
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Affiliation(s)
- Zachary Montague
- Department of Physics, University of Washington, 3910 15th Ave. Northeast, Seattle, WA 98195, USA
| | - Huibin Lv
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jakub Otwinowski
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - William S DeWitt
- Department of Genome Sciences, University of Washington, 3720 15th Ave. NE, Seattle, WA 98195, USA; Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, WA 98109, USA
| | - Giulio Isacchini
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany; Laboratoire de physique de l'ecole normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Garrick K Yip
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wilson W Ng
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Owen Tak-Yin Tsang
- Infectious Diseases Centre, Princess Margaret Hospital, Hospital Authority of Hong Kong, Hong Kong SAR, China
| | - Meng Yuan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hejun Liu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - J S Malik Peiris
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, 3910 15th Ave. Northeast, Seattle, WA 98195, USA; Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany; Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, WA 98109, USA.
| | - Chris Ka Pun Mok
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
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24
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Large-scale analysis of 2,152 Ig-seq datasets reveals key features of B cell biology and the antibody repertoire. Cell Rep 2021; 35:109110. [PMID: 33979623 DOI: 10.1016/j.celrep.2021.109110] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/09/2021] [Accepted: 04/20/2021] [Indexed: 12/20/2022] Open
Abstract
Antibody repertoire sequencing enables researchers to acquire millions of B cell receptors and investigate these molecules at the single-nucleotide level. This power and resolution in studying humoral responses have led to its wide applications. However, most of these studies were conducted with a limited number of samples. Given the extraordinary diversity, assessment of these key features with a large sample set is demanded. Thus, we collect and systematically analyze 2,152 high-quality heavy-chain antibody repertoires. Our study reveals that 52 core variable genes universally contribute to more than 99% of each individual's repertoire; a distal interspersed preferences characterize V gene recombination; the number of public clones between two repertoires follows a linear model, and the positive selection dominates at RGYW motif in somatic hypermutations. Thus, this population-level analysis resolves some critical features of the antibody repertoire and may have significant value to the large cadre of scientists.
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25
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Soto C, Bombardi RG, Kozhevnikov M, Sinkovits RS, Chen EC, Branchizio A, Kose N, Day SB, Pilkinton M, Gujral M, Mallal S, Crowe JE. High Frequency of Shared Clonotypes in Human T Cell Receptor Repertoires. Cell Rep 2021; 32:107882. [PMID: 32668251 PMCID: PMC7433715 DOI: 10.1016/j.celrep.2020.107882] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 04/18/2020] [Accepted: 06/16/2020] [Indexed: 01/30/2023] Open
Abstract
The collection of T cell receptors (TCRs) generated by somatic recombination is large but unknown. We generate large TCR repertoire datasets as a resource to facilitate detailed studies of the role of TCR clonotypes and repertoires in health and disease. We estimate the size of individual human recombined and expressed TCRs by sequence analysis and determine the extent of sharing between individual repertoires. Our experiments reveal that each blood sample contains between 5 million and 21 million TCR clonotypes. Three individuals share 8% of TCRβ- or 11% of TCRα-chain clonotypes. Sorting by T cell phenotypes in four individuals shows that 5% of naive CD4+ and 3.5% of naive CD8+ subsets share their TCRβ clonotypes, whereas memory CD4+ and CD8+ subsets share 2.3% and 0.4% of their clonotypes, respectively. We identify the sequences of these shared TCR clonotypes that are of interest for studies of human T cell biology. Soto et al. examine the extent to which five healthy adults share their T cell receptor (TCR) repertoire. Using sequencing and bioinformatics, they show a high prevalence of shared clonotypes even considering different T cell phenotypes. Possible functions for some clonotypes are inferred based on homology with TCRs in GenBank.
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Affiliation(s)
- Cinque Soto
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Robin G Bombardi
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Morgan Kozhevnikov
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Robert S Sinkovits
- San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, USA
| | - Elaine C Chen
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Andre Branchizio
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Nurgun Kose
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Samuel B Day
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mark Pilkinton
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Madhusudan Gujral
- San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, USA
| | - Simon Mallal
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - James E Crowe
- The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA.
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26
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Montague Z, Lv H, Otwinowski J, DeWitt WS, Isacchini G, Yip GK, Ng WW, Tsang OTY, Yuan M, Liu H, Wilson IA, Peiris JSM, Wu NC, Nourmohammad A, Mok CKP. Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2020.07.13.20153114. [PMID: 32699862 PMCID: PMC7373151 DOI: 10.1101/2020.07.13.20153114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.
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Affiliation(s)
- Zachary Montague
- Department of Physics, University of Washington, 3910 15th Ave Northeast, Seattle, WA 98195, USA
| | - Huibin Lv
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jakub Otwinowski
- Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - William S. DeWitt
- Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - Giulio Isacchini
- Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, 37077 Göttingen, Germany
- Laboratoire de physique de l’ecole normale supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Garrick K. Yip
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wilson W. Ng
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Owen Tak-Yin Tsang
- Infectious Diseases Centre, Princess Margaret Hospital, Hospital Authority of Hong Kong
| | - Meng Yuan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hejun Liu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ian A. Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
- The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - J. S. Malik Peiris
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Armita Nourmohammad
- Department of Physics, University of Washington, 3910 15th Ave Northeast, Seattle, WA 98195, USA
- Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, 37077 Göttingen, Germany
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - Chris Ka Pun Mok
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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27
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Wolf KJ, Adeyemo AA, Williamson KC. Plasma cell immunoglobulin heavy chain repertoire dynamics before and after tetanus booster vaccination. Immunogenetics 2021; 73:321-332. [PMID: 33768273 DOI: 10.1007/s00251-021-01215-8] [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: 01/18/2021] [Accepted: 03/09/2021] [Indexed: 11/25/2022]
Abstract
Antibody sequence repertoire analysis of plasma cells (PC) isolated before and 1 week after a vaccine provides time-specific snapshots of the antibody response. Comparison of the immunoglobulin (Ig) sequences pre- and post-vaccination allows analysis of maturation over time and identification of antigen specific Ig. Here we compare the Ig heavy chain (Ig-H) repertoire of circulating PCs isolated from 109 peripheral blood mononuclear cells (PBMC) collected by apheresis 1 week after a tetanus toxoid vaccine booster with the Ig-H repertoire of PCs collected 2 and 11 weeks prior to the booster. A total of 21,060 unique Ig nucleotide sequences encoding 14,307 unique heavy chain complementarity determining region 3 (CDR-H3) amino acid sequences, also called clonotypes, were identified. Only 466 clonotypes (3.3%) were present at all 3 time points. In contrast, 90% of the 30 highest frequency CDR-H3 regions at +1w were also identified at another time point and 50% were present at all time points, suggesting the rapid expansion of a memory B cell population. The tetanus toxoid specificity of the CDR-H3 region with the 7th highest frequency at +1w was confirmed using immunoprecipitation and mass spectroscopy, and two public tetanus toxoid-specific CDR-H3 regions were also overrepresented at +1w. In summary, we have used the tetanus vaccine model system to demonstrate that bulk PC Ig repertoire analysis can identify PC populations that expand and mature following antigen exposure. The application of this approach before and after clinical infections should advance our understanding of clinical protection and facilitate vaccine design.
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Affiliation(s)
- Kyle J Wolf
- Department of Molecular Microbiology and Immunology, Saint Louis University, Saint Louis, MO, 63104, USA
- ArcherDX, Inc., Boulder, CO, 80301, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20814, USA
| | - Kim C Williamson
- Department of Microbiology and Immunology, Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
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28
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Hong B, Wang L, Huang C, Hong X, Liu A, Li Q, Liu Q, Su L, Wang L, Wang C, Ying T. Decrease of Clone Diversity in IgM Repertoires of HBV Chronically Infected Individuals With High Level of Viral Replication. Front Microbiol 2021; 11:615669. [PMID: 33519772 PMCID: PMC7843509 DOI: 10.3389/fmicb.2020.615669] [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: 10/09/2020] [Accepted: 12/22/2020] [Indexed: 01/05/2023] Open
Abstract
High-throughput antibody sequencing allows in-depth insights into human antibody repertoires. To investigate the characteristics of antibody repertoires in patients with chronic HBV infection, we performed Illumina sequencing and IMGT/HighV-QUEST analysis of B lymphocytes from healthy adults and the HBV carriers with high or low level of viral replication. The comparative study revealed high levels of similarity between the IgM and IgG repertoires of the HBV carriers and the healthy adults, including the somatic mutations in V regions, the average CDR3 length, and the occurrence of junctional modifications. Nevertheless, the diversity of the unique clones decreased and some clusters of unique clones expanded in the IgM repertoire of chronic HBV carriers (CHB) compared with healthy adults (HH) and inactive HBV carriers (IHB). Such difference in clone diversity and expansion was not observed in the IgG repertoires of the three populations. More shared antibody clones were found between the IgM repertoires of IHB and HH than that found between CHB and HH (7079 clones vs. 2304 clones). Besides, the biased used IGHD genes were IGHD2-2 and IGHD3-3 in CHB library but were IGHD3-10 and IGHD3-22 in IHB and HH library. In contrast, for IgG repertories, the preferred used VDJ genes were similar in all the three populations. These results indicated that low level of serum HBV might not induce significant changes in BCR repertoires, and high level of HBV replication could have more impacts on IgM repertories than IgG repertoires. Taken together, our findings provide a better understanding of the antibody repertoires of HBV chronically infected individuals.
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Affiliation(s)
- Binbin Hong
- Central Laboratory, Second Affiliated Hospital, Fujian Medical University, Quanzhou, China
| | - Lizhi Wang
- Traditional Chinese Medicine Department, Rehabilitation Hospital, Quanzhou, China
| | - Chunlan Huang
- Traditional Chinese Medicine Department, Rehabilitation Hospital, Quanzhou, China
| | - Xiaoju Hong
- Traditional Chinese Medicine Department, Rehabilitation Hospital, Quanzhou, China
| | - Alan Liu
- Traditional Chinese Medicine Department, Rehabilitation Hospital, Quanzhou, China
| | - Qiulan Li
- Central Laboratory, Second Affiliated Hospital, Fujian Medical University, Quanzhou, China
| | - Qiaoling Liu
- Central Laboratory, Second Affiliated Hospital, Fujian Medical University, Quanzhou, China
| | - Lili Su
- Central Laboratory, Second Affiliated Hospital, Fujian Medical University, Quanzhou, China
| | - Lixing Wang
- Central Laboratory, Second Affiliated Hospital, Fujian Medical University, Quanzhou, China
| | - Chunyu Wang
- Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Tianlei Ying
- Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, China
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29
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Richardson E, Galson JD, Kellam P, Kelly DF, Smith SE, Palser A, Watson S, Deane CM. A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies. MAbs 2021; 13:1869406. [PMID: 33427589 PMCID: PMC7808390 DOI: 10.1080/19420862.2020.1869406] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis. Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid
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Affiliation(s)
- Eve Richardson
- Department of Statistics, University of Oxford , Oxford, UK
| | - Jacob D Galson
- Alchemab Therapeutics Ltd , London, UK.,Division of Immunology, University Children's Hospital, University of Zurich, Zurich , Switzerland
| | - Paul Kellam
- Kymab Ltd , Cambridge, UK.,Department of Infectious Diseases, Faculty of Medicine, Imperial College London , London, UK
| | - Dominic F Kelly
- Department of Paediatrics, University of Oxford , Oxford, UK.,Oxford University Hospitals NHS Foundation Trust , Oxford, UK
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30
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Hoh RA, Joshi SA, Lee JY, Martin BA, Varma S, Kwok S, Nielsen SCA, Nejad P, Haraguchi E, Dixit PS, Shutthanandan SV, Roskin KM, Zhang W, Tupa D, Bunning BJ, Manohar M, Tibshirani R, Fernandez-Becker NQ, Kambham N, West RB, Hamilton RG, Tsai M, Galli SJ, Chinthrajah RS, Nadeau KC, Boyd SD. Origins and clonal convergence of gastrointestinal IgE + B cells in human peanut allergy. Sci Immunol 2020; 5:5/45/eaay4209. [PMID: 32139586 DOI: 10.1126/sciimmunol.aay4209] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 02/07/2020] [Indexed: 12/18/2022]
Abstract
B cells in human food allergy have been studied predominantly in the blood. Little is known about IgE+ B cells or plasma cells in tissues exposed to dietary antigens. We characterized IgE+ clones in blood, stomach, duodenum, and esophagus of 19 peanut-allergic patients, using high-throughput DNA sequencing. IgE+ cells in allergic patients are enriched in stomach and duodenum, and have a plasma cell phenotype. Clonally related IgE+ and non-IgE-expressing cell frequencies in tissues suggest local isotype switching, including transitions between IgA and IgE isotypes. Highly similar antibody sequences specific for peanut allergen Ara h 2 are shared between patients, indicating that common immunoglobulin genetic rearrangements may contribute to pathogenesis. These data define the gastrointestinal tract as a reservoir of IgE+ B lineage cells in food allergy.
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Affiliation(s)
- Ramona A Hoh
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shilpa A Joshi
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ji-Yeun Lee
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Brock A Martin
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sushama Varma
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Kwok
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sandra C A Nielsen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Parastu Nejad
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Emily Haraguchi
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Priya S Dixit
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Swetha V Shutthanandan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Krishna M Roskin
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH 45267, USA.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Wenming Zhang
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dana Tupa
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bryan J Bunning
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Monali Manohar
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Robert Tibshirani
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA.,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Nielsen Q Fernandez-Becker
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Neeraja Kambham
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Robert B West
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Robert G Hamilton
- Division of Allergy and Clinical Immunology, Department of Medicine, and Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Mindy Tsai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.,Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Stephen J Galli
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.,Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Rebecca S Chinthrajah
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA.,Division of Pulmonary, Allergy and Critical Care Medicine and Division of Allergy, Immunology and Rheumatology, Stanford University, Stanford, CA 94305, USA
| | - Kari C Nadeau
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA.,Division of Pulmonary, Allergy and Critical Care Medicine and Division of Allergy, Immunology and Rheumatology, Stanford University, Stanford, CA 94305, USA
| | - Scott D Boyd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. .,Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA 94305, USA
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31
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Spisak N, Walczak AM, Mora T. Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data. Nucleic Acids Res 2020; 48:10702-10712. [PMID: 33035336 PMCID: PMC7641750 DOI: 10.1093/nar/gkaa825] [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: 07/21/2020] [Revised: 09/07/2020] [Accepted: 09/18/2020] [Indexed: 01/23/2023] Open
Abstract
Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors’ ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation.
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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, 24 rue Lhomond, 75005 Paris, France
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32
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Zhang W, Wang L, Liu K, Wei X, Yang K, Du W, Wang S, Guo N, Ma C, Luo L, Wu J, Lin L, Yang F, Gao F, Wang X, Li T, Zhang R, Saksena NK, Yang H, Wang J, Fang L, Hou Y, Xu X, Liu X. PIRD: Pan Immune Repertoire Database. Bioinformatics 2020; 36:897-903. [PMID: 31373607 DOI: 10.1093/bioinformatics/btz614] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/17/2019] [Accepted: 08/01/2019] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. RESULTS We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. AVAILABILITY AND IMPLEMENTATION PIRD can be freely accessed at https://db.cngb.org/pird.
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Affiliation(s)
- Wei Zhang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Longlong Wang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China.,BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Ke Liu
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xiaofeng Wei
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Kai Yang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Wensi Du
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Shiyu Wang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China.,BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Nannan Guo
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Chuanchuan Ma
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Lihua Luo
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China.,BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Jinghua Wu
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China.,BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Liya Lin
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Fan Yang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Fei Gao
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xie Wang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Tao Li
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Ruifang Zhang
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Nitin K Saksena
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518083, China.,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Lin Fang
- BGI-Shenzhen, Shenzhen 518083, China.,Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yong Hou
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xiao Liu
- BGI-Shenzhen, Shenzhen 518083, China.,China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
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33
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Ultra-efficient sequencing of T Cell receptor repertoires reveals shared responses in muscle from patients with Myositis. EBioMedicine 2020; 59:102972. [PMID: 32891935 PMCID: PMC7484536 DOI: 10.1016/j.ebiom.2020.102972] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/10/2020] [Accepted: 08/10/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Myositis, or idiopathic inflammatory myopathy (IIM), is a group disorders of unknown etiology characterized by the inflammation of skeletal muscle. The role of T cells and their antigenic targets in IIM initiation and progression is poorly understood. T cell receptor (TCR) repertoire sequencing is a powerful approach for characterizing complex T cell responses. However, current TCR sequencing methodologies are complex, expensive, or both, greatly limiting the scale of feasible studies. METHODS Here we present Framework Region 3 AmplifiKation sequencing ("FR3AK-seq"), a simplified multiplex PCR-based approach for the ultra-efficient and quantitative analysis of TCR complementarity determining region 3 (CDR3) repertoires. By using minimal primer sets targeting a conserved region immediately upstream of CDR3, undistorted amplicons are analyzed via short read, single-end sequencing. We also introduce the novel algorithm Inferring Sequences via Efficiency Projection and Primer Incorporation ("ISEPPI") for linking CDR3s to their associated variable genes. FINDINGS We find that FR3AK-seq is sensitive and quantitative, performing comparably to two different industry standards. FR3AK-seq and ISEPPI were used to efficiently and inexpensively characterize the T cell infiltrates of surgical muscle biopsies obtained from 145 patients with IIM and controls. A cluster of closely related TCRs was identified in samples from patients with sporadic inclusion body myositis (IBM). INTERPRETATION The ease and minimal cost of FR3AK-seq removes critical barriers to routine, large-scale TCR CDR3 repertoire analyses, thereby democratizing the quantitative assessment of human TCR repertoires in disease-relevant target tissues. Importantly, discovery of closely related TCRs in muscle from patients with IBM provides evidence for a shared antigen-driven T cell response in this disease of unknown pathogenesis. FUNDING This work was supported by NIH grant U24AI118633 and a Prostate Cancer Foundation Young Investigator Award.
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34
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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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35
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Flach J, Shumilov E, Joncourt R, Porret N, Novak U, Pabst T, Bacher U. Current concepts and future directions for hemato-oncologic diagnostics. Crit Rev Oncol Hematol 2020; 151:102977. [DOI: 10.1016/j.critrevonc.2020.102977] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 03/22/2020] [Accepted: 04/28/2020] [Indexed: 02/07/2023] Open
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36
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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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37
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Nielsen SCA, Yang F, Hoh RA, Jackson KJL, Roeltgen K, Lee JY, Rustagi A, Rogers AJ, Powell AE, Kim PS, Wang TT, Pinsky B, Blish CA, Boyd SD. B cell clonal expansion and convergent antibody responses to SARS-CoV-2. RESEARCH SQUARE 2020. [PMID: 32702737 PMCID: PMC7336706 DOI: 10.21203/rs.3.rs-27220/v1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
During virus infection B cells are critical for the production of antibodies and protective immunity. Establishment of a diverse antibody repertoire occurs by rearrangement of germline DNA at the immunoglobulin heavy and light chain loci to encode the membrane-bound form of antibodies, the B cell antigen receptor. Little is known about the B cells and antigen receptors stimulated by the novel human coronavirus SARS-CoV-2. Here we show that the human B cell compartment in patients with diagnostically confirmed SARS-CoV-2 and clinical COVID-19 is rapidly altered with the early recruitment of B cells expressing a limited subset of V genes, and extensive activation of IgG and IgA subclasses without significant somatic mutation. We detect expansion of B cell clones as well as convergent antibodies with highly similar sequences across SARS-CoV-2 patients, highlighting stereotyped naïve responses to this virus. A shared convergent B cell clonotype in SARS-CoV-2 infected patients was previously seen in patients with SARS. These findings offer molecular insights into shared features of human B cell responses to SARS-CoV-2 and other zoonotic spillover coronaviruses.
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Affiliation(s)
| | - Fan Yang
- Department of Pathology, Stanford University
| | | | | | | | - Ji-Yeun Lee
- Department of Pathology, Stanford University
| | - Arjun Rustagi
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University
| | - Angela J Rogers
- Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, Stanford University
| | - Abigail E Powell
- Stanford ChEM-H and Department of Biochemistry, Stanford University
| | - Peter S Kim
- Stanford ChEM-H and Department of Biochemistry, Stanford University
| | - Taia T Wang
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University
| | | | - Catherine A Blish
- Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University
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38
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Aversa I, Malanga D, Fiume G, Palmieri C. Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy. Int J Mol Sci 2020; 21:ijms21072378. [PMID: 32235561 PMCID: PMC7177412 DOI: 10.3390/ijms21072378] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/22/2020] [Accepted: 03/28/2020] [Indexed: 02/08/2023] Open
Abstract
The T cells are key players of the response to checkpoint blockade immunotherapy (CBI) and monitoring the strength and specificity of antitumor T-cell reactivity remains a crucial but elusive component of precision immunotherapy. The entire assembly of T-cell receptor (TCR) sequences accounts for antigen specificity and strength of the T-cell immune response. The TCR repertoire hence represents a “footprint” of the conditions faced by T cells that dynamically evolves according to the challenges that arise for the immune system, such as tumor neo-antigenic load. Hence, TCR repertoire analysis is becoming increasingly important to comprehensively understand the nature of a successful antitumor T-cell response, and to improve the success and safety of current CBI.
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Affiliation(s)
- Ilenia Aversa
- Research Center of Biochemistry and Advanced Molecular Biology, Department of Experimental and Clinical Medicine, University “Magna Græcia” of Catanzaro, 88100 Catanzaro, Italy;
| | - Donatella Malanga
- Interdepartmental Center of Services (CIS), Department of Experimental and Clinical Medicine, University “Magna Græcia” of Catanzaro, 88100 Catanzaro, Italy;
| | - Giuseppe Fiume
- Department of Experimental and Clinical Medicine, University “Magna Græcia” of Catanzaro, 88100 Catanzaro, Italy;
| | - Camillo Palmieri
- Department of Experimental and Clinical Medicine, University “Magna Græcia” of Catanzaro, 88100 Catanzaro, Italy;
- Correspondence:
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39
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Malekzadeh P, Yossef R, Cafri G, Paria BC, Lowery FJ, Jafferji M, Good ML, Sachs A, Copeland AR, Kim SP, Kivitz S, Parkhurst MR, Robbins PF, Ray S, Xi L, Raffeld M, Yu Z, Restifo NP, Somerville RPT, Rosenberg SA, Deniger DC. Antigen Experienced T Cells from Peripheral Blood Recognize p53 Neoantigens. Clin Cancer Res 2020; 26:1267-1276. [PMID: 31996390 DOI: 10.1158/1078-0432.ccr-19-1874] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/21/2023]
Abstract
PURPOSE The purpose of this study was to evaluate antigen experienced T cells in peripheral blood lymphocytes (PBL) for responses to p53 neoantigens. EXPERIMENTAL DESIGN PBLs from patients with a mutated TP53 tumor were sorted for antigen-experienced T cells and in vitro stimulation (IVS) was performed with p53 neoantigens. The IVS cultures were stimulated with antigen-presenting cells expressing p53 neoantigens, enriched for 41BB/OX40 and grown with rapid expansion protocol. RESULTS T-cell responses were not observed in the PBLs of 4 patients who did not have tumor-infiltrating lymphocyte (TIL) responses to mutated TP53. In contrast, 5 patients with TIL responses to mutated TP53 also had similar T-cell responses in their PBLs, indicating that the PBLs and TILs were congruent in p53 neoantigen reactivity. CD4+ and CD8+ T cells were specific for p53R175H, p53Y220C, or p53R248W neoantigens, including a 78% reactive T-cell culture against p53R175H and HLA-A*02:01. Tracking TCRB clonotypes (clonality, top ranked, and TP53 mutation-specific) supported the enrichment of p53 neoantigen-reactive T cells from PBLs. The same T-cell receptor (TCR) from the TIL was found in the IVS cultures in three cases and multiple unique TCRs were found in another patient. TP53 mutation-specific T cells also recognized tumor cell lines bearing the appropriate human leukocyte antigen restriction element and TP53 mutation, indicating these T cells could recognize processed and presented p53 neoantigens. CONCLUSIONS PBL was a noninvasive source of T cells targeting TP53 mutations for cell therapy and can provide a window into intratumoral p53 neoantigen immune responses.See related commentary by Olivera et al., p. 1203.
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Affiliation(s)
| | - Rami Yossef
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Gal Cafri
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Biman C Paria
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Frank J Lowery
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | | | - Meghan L Good
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Abraham Sachs
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Amy R Copeland
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Sanghyun P Kim
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Scott Kivitz
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | | | - Paul F Robbins
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Satyajit Ray
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | - Liqiang Xi
- Hematopathology Section, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Mark Raffeld
- Hematopathology Section, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Zhiya Yu
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
| | | | | | | | - Drew C Deniger
- Surgery Branch, National Cancer Institute, Bethesda, Maryland
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40
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Kreer C, Gruell H, Mora T, Walczak AM, Klein F. Exploiting B Cell Receptor Analyses to Inform on HIV-1 Vaccination Strategies. Vaccines (Basel) 2020; 8:vaccines8010013. [PMID: 31906351 PMCID: PMC7157687 DOI: 10.3390/vaccines8010013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 12/22/2022] Open
Abstract
The human antibody repertoire is generated by the recombination of different gene segments as well as by processes of somatic mutation. Together these mechanisms result in a tremendous diversity of antibodies that are able to combat various pathogens including viruses and bacteria, or malignant cells. In this review, we summarize the opportunities and challenges that are associated with the analyses of the B cell receptor repertoire and the antigen-specific B cell response. We will discuss how recent advances have increased our understanding of the antibody response and how repertoire analyses can be exploited to inform on vaccine strategies, particularly against HIV-1.
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Affiliation(s)
- Christoph Kreer
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; (C.K.); (H.G.)
| | - Henning Gruell
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; (C.K.); (H.G.)
- German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
| | - Thierry Mora
- Laboratoire de Physique de l’École Normale Supérieure (PSL University), CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France; (T.M.); (A.M.W.)
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’École Normale Supérieure (PSL University), CNRS, Sorbonne Université, Université de Paris, 75005 Paris, France; (T.M.); (A.M.W.)
| | - Florian Klein
- Laboratory of Experimental Immunology, Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; (C.K.); (H.G.)
- German Center for Infection Research, Partner Site Bonn-Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
- Correspondence:
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41
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Sevy AM, Soto C, Bombardi RG, Meiler J, Crowe JE. Immune repertoire fingerprinting by principal component analysis reveals shared features in subject groups with common exposures. BMC Bioinformatics 2019; 20:629. [PMID: 31801472 PMCID: PMC6894320 DOI: 10.1186/s12859-019-3281-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 11/18/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. RESULTS We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as "repertoire fingerprinting." We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. CONCLUSIONS Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.
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Affiliation(s)
- Alexander M Sevy
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA.,Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Cinque Soto
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Robin G Bombardi
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Jens Meiler
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA.,Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA.,Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
| | - James E Crowe
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA. .,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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42
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Hoehn KB, Vander Heiden JA, Zhou JQ, Lunter G, Pybus OG, Kleinstein SH. Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination. Proc Natl Acad Sci U S A 2019; 116:22664-22672. [PMID: 31636219 PMCID: PMC6842591 DOI: 10.1073/pnas.1906020116] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
In order to produce effective antibodies, B cells undergo rapid somatic hypermutation (SHM) and selection for binding affinity to antigen via a process called affinity maturation. The similarities between this process and evolution by natural selection have led many groups to use phylogenetic methods to characterize the development of immunological memory, vaccination, and other processes that depend on affinity maturation. However, these applications are limited by the fact that most phylogenetic models are designed to be applied to individual lineages comprising genetically diverse sequences, while B cell repertoires often consist of hundreds to thousands of separate low-diversity lineages. Further, several features of affinity maturation violate important assumptions in standard phylogenetic models. Here, we introduce a hierarchical phylogenetic framework that integrates information from all lineages in a repertoire to more precisely estimate model parameters while simultaneously incorporating the unique features of SHM. We demonstrate the power of this repertoire-wide approach by characterizing previously undescribed phenomena in affinity maturation. First, we find evidence consistent with age-related changes in SHM hot-spot targeting. Second, we identify a consistent relationship between increased tree length and signs of increased negative selection, apparent in the repertoires of recently vaccinated subjects and those without any known recent infections or vaccinations. This suggests that B cell lineages shift toward negative selection over time as a general feature of affinity maturation. Our study provides a framework for undertaking repertoire-wide phylogenetic testing of SHM hypotheses and provides a means of characterizing dynamics of mutation and selection during affinity maturation.
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Affiliation(s)
- Kenneth B Hoehn
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520
| | - Jason A Vander Heiden
- Department of Bioinformatics & Computational Biology, Genentech, South San Francisco, CA 94080
| | - Julian Q Zhou
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511
| | - Gerton Lunter
- Wellcome Centre for Human Genetics, Oxford OX3 7BN, United Kingdom
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520;
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511
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43
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Crowe JE. Influenza Virus-Specific Human Antibody Repertoire Studies. THE JOURNAL OF IMMUNOLOGY 2019; 202:368-373. [PMID: 30617118 DOI: 10.4049/jimmunol.1801459] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2018] [Indexed: 12/19/2022]
Abstract
The diversity of Ag-specific adaptive receptors on the surface of B cells and in the population of secreted Abs is enormous, but increasingly, we are acquiring the technical capability to interrogate Ab repertoires in great detail. These Ab technologies have been especially pointed at understanding the complex issues of immunity to infection and disease caused by influenza virus, one of the most common and vexing medical problems in man. Influenza immunity is particularly interesting as a model system because the antigenic diversity of influenza strains and proteins is high and constantly evolving. Discovery of canonical features in the subset of the influenza repertoire response that is broadly reactive for diverse influenza strains has spurred the recent optimism for creating universal influenza vaccines. Using new technologies for sequencing Ab repertoires at great depth is helping us to understand the central features of influenza immunity.
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Affiliation(s)
- James E Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232; .,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232; and .,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232
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44
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Nouri N, Kleinstein SH. A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data. Bioinformatics 2019; 34:i341-i349. [PMID: 29949968 PMCID: PMC6022594 DOI: 10.1093/bioinformatics/bty235] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Motivation B cells derive their antigen-specificity through the expression of Immunoglobulin (Ig) receptors on their surface. These receptors are initially generated stochastically by somatic re-arrangement of the DNA and further diversified following antigen-activation by a process of somatic hypermutation, which introduces mainly point substitutions into the receptor DNA at a high rate. Recent advances in next-generation sequencing have enabled large-scale profiling of the B cell Ig repertoire from blood and tissue samples. A key computational challenge in the analysis of these data is partitioning the sequences to identify descendants of a common B cell (i.e. a clone). Current methods group sequences using a fixed distance threshold, or a likelihood calculation that is computationally-intensive. Here, we propose a new method based on spectral clustering with an adaptive threshold to determine the local sequence neighborhood. Validation using simulated and experimental datasets demonstrates that this method has high sensitivity and specificity compared to a fixed threshold that is optimized for these measures. In addition, this method works on datasets where choosing an optimal fixed threshold is difficult and is more computationally efficient in all cases. The ability to quickly and accurately identify members of a clone from repertoire sequencing data will greatly improve downstream analyses. Clonally-related sequences cannot be treated independently in statistical models, and clonal partitions are used as the basis for the calculation of diversity metrics, lineage reconstruction and selection analysis. Thus, the spectral clustering-based method here represents an important contribution to repertoire analysis. Availability and implementation Source code for this method is freely available in the SCOPe (Spectral Clustering for clOne Partitioning) R package in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nima Nouri
- Department of Pathology, Yale School of Medicine, 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
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45
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Davis MM, Boyd SD. Recent progress in the analysis of αβT cell and B cell receptor repertoires. Curr Opin Immunol 2019; 59:109-114. [PMID: 31326777 PMCID: PMC7075470 DOI: 10.1016/j.coi.2019.05.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 05/28/2019] [Indexed: 01/10/2023]
Abstract
T cell receptors (TCRs) and B cell receptors (BCRs) are vertebrate evolution's best answer to the threat of microbial pathogens that can evolve much faster than ourselves. These antigen receptors are generated during T cell or B cell development by combinatorial rearrangement of germline genome V, D and J gene segments, and with junctional residues capable of enormous diversity. For decades the complexity of these receptor repertoires has limited their analysis, but advances in DNA sequencing technology and an array of complementary tools have now made their study much more tractable, filling a major gap in our ability to understand immunology as a system. Here, we summarize the recent approaches and discoveries that are enabling these advances, with some suggestions as to what may lie ahead.
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Affiliation(s)
- Mark M Davis
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; The Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| | - Scott D Boyd
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA; The Sean N. Parker Center for Allergy and Asthma Research at Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
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46
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RNase H-dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis. Nat Protoc 2019; 14:2571-2594. [PMID: 31341290 DOI: 10.1038/s41596-019-0195-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 05/07/2019] [Indexed: 11/08/2022]
Abstract
RNase H-dependent PCR-enabled T-cell receptor sequencing (rhTCRseq) can be used to determine paired alpha/beta T-cell receptor (TCR) clonotypes in single cells or perform alpha and beta TCR repertoire analysis in bulk RNA samples. With the enhanced specificity of RNase H-dependent PCR (rhPCR), it achieves TCR-specific amplification and addition of dual-index barcodes in a single PCR step. For single cells, the protocol includes sorting of single cells into plates, generation of cDNA libraries, a TCR-specific amplification step, a second PCR on pooled sample to generate a sequencing library, and sequencing. In the bulk method, sorting and cDNA library steps are replaced with a reverse-transcriptase (RT) reaction that adds a unique molecular identifier (UMI) to each cDNA molecule to improve the accuracy of repertoire-frequency measurements. Compared to other methods for TCR sequencing, rhTCRseq has a streamlined workflow and the ability to analyze single cells in 384-well plates. Compared to TCR reconstruction from single-cell transcriptome sequencing data, it improves the success rate for obtaining paired alpha/beta information and ensures recovery of complete complementarity-determining region 3 (CDR3) sequences, a prerequisite for cloning/expression of discovered TCRs. Although it has lower throughput than droplet-based methods, rhTCRseq is well-suited to analysis of small sorted populations, especially when analysis of 96 or 384 single cells is sufficient to identify predominant T-cell clones. For single cells, sorting typically requires 2-4 h and can be performed days, or even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol takes ~3 d.
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47
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Vlachonikola E, Vardi A, Stamatopoulos K, Hadzidimitriou A. High-Throughput Sequencing of the T-Cell Receptor Beta Chain Gene Repertoire in Chronic Lymphocytic Leukemia. Methods Mol Biol 2019; 1881:355-363. [PMID: 30350216 DOI: 10.1007/978-1-4939-8876-1_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
High-throughput, next-generation sequencing (NGS) offers a unique opportunity for in-depth characterization of adaptive immune receptor repertoires. Nevertheless, limitations and pitfalls exist in every step of both the experimental and the analytical procedure, leading to discrepancies in the literature and incomprehensive and/or altogether misleading results. Thus, standardization of protocols in NGS immunogenetics is urgently needed.Here, we describe the experimental protocol that we developed for T-cell receptor beta chain (TRB) gene repertoire analysis in chronic lymphocytic leukemia, aiming to provide a reproducible and biologically meaningful output. Although optimized for TRBV-TRBD-TRBJ gene rearrangements, this protocol may be customized for other adaptive immune receptor sequences, as well.
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Affiliation(s)
- E Vlachonikola
- Institute of Applied Biosciences (INAB), Center for Research and Technology (CERTH), Thessaloniki, Greece
- Department of Genetics, Development and Molecular Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - A Vardi
- Institute of Applied Biosciences (INAB), Center for Research and Technology (CERTH), Thessaloniki, Greece
- HCT Unit, Hematology Department, G. Papanikolaou Hospital, Thessaloniki, Greece
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - K Stamatopoulos
- Institute of Applied Biosciences (INAB), Center for Research and Technology (CERTH), Thessaloniki, Greece
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - A Hadzidimitriou
- Institute of Applied Biosciences (INAB), Center for Research and Technology (CERTH), Thessaloniki, Greece.
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48
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Zhuang Y, Zhang C, Wu Q, Zhang J, Ye Z, Qian Q. Application of immune repertoire sequencing in cancer immunotherapy. Int Immunopharmacol 2019; 74:105688. [PMID: 31276974 DOI: 10.1016/j.intimp.2019.105688] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 05/05/2019] [Accepted: 06/05/2019] [Indexed: 12/21/2022]
Abstract
With the prominent breakthrough in the field of tumor immunology, diverse cancer immunotherapies have attracted great attention in the last decade. The immune checkpoint inhibitors, adoptive cell therapies, and therapeutic cancer vaccines have already achieved impressive clinical success. However, the fact that only a small subset of patients with specific tumor types can benefit from these treatments limits the application of cancer immunotherapy. To seek out the molecular mechanisms behind this challenge and to select cancer precision medicine for different individuals, researchers apply the immune repertoire sequencing (IRS) to evaluate genetic responses of each patient to current immunotherapies. This review summarizes the technical advances and recent applications of IRS in cancer immunotherapy, indicates the limitations of this technique, and predicts future perspectives both in basic studies and clinical trials.
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Affiliation(s)
- Yuan Zhuang
- Shanghai Baize Medical Laboratory, Shanghai, China
| | - Changzheng Zhang
- Shanghai Baize Medical Laboratory, Shanghai, China; Shanghai Engineering Research Center for Cell Therapy, Shanghai, China
| | - Qiong Wu
- Shanghai Baize Medical Laboratory, Shanghai, China
| | - Jing Zhang
- Shanghai Baize Medical Laboratory, Shanghai, China
| | - Zhenlong Ye
- Shanghai Baize Medical Laboratory, Shanghai, China; Shanghai Cell Therapy Research Institute, Shanghai, China; Shanghai Engineering Research Center for Cell Therapy, Shanghai, China.
| | - Qijun Qian
- Shanghai Baize Medical Laboratory, Shanghai, China; Shanghai Cell Therapy Research Institute, Shanghai, China; Shanghai Engineering Research Center for Cell Therapy, Shanghai, China.
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49
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Standardized next-generation sequencing of immunoglobulin and T-cell receptor gene recombinations for MRD marker identification in acute lymphoblastic leukaemia; a EuroClonality-NGS validation study. Leukemia 2019; 33:2241-2253. [PMID: 31243313 PMCID: PMC6756028 DOI: 10.1038/s41375-019-0496-7] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 02/20/2019] [Indexed: 01/09/2023]
Abstract
Amplicon-based next-generation sequencing (NGS) of immunoglobulin (IG) and T-cell receptor (TR) gene rearrangements for clonality assessment, marker identification and quantification of minimal residual disease (MRD) in lymphoid neoplasms has been the focus of intense research, development and application. However, standardization and validation in a scientifically controlled multicentre setting is still lacking. Therefore, IG/TR assay development and design, including bioinformatics, was performed within the EuroClonality-NGS working group and validated for MRD marker identification in acute lymphoblastic leukaemia (ALL). Five EuroMRD ALL reference laboratories performed IG/TR NGS in 50 diagnostic ALL samples, and compared results with those generated through routine IG/TR Sanger sequencing. A central polytarget quality control (cPT-QC) was used to monitor primer performance, and a central in-tube quality control (cIT-QC) was spiked into each sample as a library-specific quality control and calibrator. NGS identified 259 (average 5.2/sample, range 0–14) clonal sequences vs. Sanger-sequencing 248 (average 5.0/sample, range 0–14). NGS primers covered possible IG/TR rearrangement types more completely compared with local multiplex PCR sets and enabled sequencing of bi-allelic rearrangements and weak PCR products. The cPT-QC showed high reproducibility across all laboratories. These validated and reproducible quality-controlled EuroClonality-NGS assays can be used for standardized NGS-based identification of IG/TR markers in lymphoid malignancies.
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50
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Scheijen B, Meijers RWJ, Rijntjes J, van der Klift MY, Möbs M, Steinhilber J, Reigl T, van den Brand M, Kotrová M, Ritter JM, Catherwood MA, Stamatopoulos K, Brüggemann M, Davi F, Darzentas N, Pott C, Fend F, Hummel M, Langerak AW, Groenen PJTA. Next-generation sequencing of immunoglobulin gene rearrangements for clonality assessment: a technical feasibility study by EuroClonality-NGS. Leukemia 2019; 33:2227-2240. [PMID: 31197258 PMCID: PMC6756030 DOI: 10.1038/s41375-019-0508-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/25/2019] [Accepted: 04/26/2019] [Indexed: 11/09/2022]
Abstract
One of the hallmarks of B lymphoid malignancies is a B cell clone characterized by a unique footprint of clonal immunoglobulin (IG) gene rearrangements that serves as a diagnostic marker for clonality assessment. The EuroClonality/BIOMED-2 assay is currently the gold standard for analyzing IG heavy chain (IGH) and κ light chain (IGK) gene rearrangements of suspected B cell lymphomas. Here, the EuroClonality-NGS Working Group presents a multicentre technical feasibility study of a novel approach involving next-generation sequencing (NGS) of IGH and IGK loci rearrangements that is highly suitable for detecting IG gene rearrangements in frozen and formalin-fixed paraffin-embedded tissue specimens. By employing gene-specific primers for IGH and IGK amplifying smaller amplicon sizes in combination with deep sequencing technology, this NGS-based IG clonality analysis showed robust performance, even in DNA samples of suboptimal DNA integrity, and a high clinical sensitivity for the detection of clonal rearrangements. Bioinformatics analyses of the high-throughput sequencing data with ARResT/Interrogate, a platform developed within the EuroClonality-NGS Working Group, allowed accurate identification of clonotypes in both polyclonal cell populations and monoclonal lymphoproliferative disorders. This multicentre feasibility study is an important step towards implementation of NGS-based clonality assessment in clinical practice, which will eventually improve lymphoma diagnostics.
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Affiliation(s)
- Blanca Scheijen
- Department of Pathology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Ruud W J Meijers
- Department of Immunology, Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, 3015 CN, Rotterdam, The Netherlands
| | - Jos Rijntjes
- Department of Pathology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Michèle Y van der Klift
- Department of Immunology, Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, 3015 CN, Rotterdam, The Netherlands
| | - Markus Möbs
- Charité-Universitätsmedizin Berlin, Institute of Pathology, D-10117, Berlin, Germany
| | - Julia Steinhilber
- Institute of Pathology and Neuropathology, Comprehensive Cancer Center, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Tomas Reigl
- Molecular Medicine Program, Central European Institute of Technology, Masaryk University, 62500, Brno, Czech Republic
| | - Michiel van den Brand
- Department of Pathology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Michaela Kotrová
- Department of Hematology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Julia-Marie Ritter
- Charité-Universitätsmedizin Berlin, Institute of Pathology, D-10117, Berlin, Germany
| | - Mark A Catherwood
- Department of Haematology, Belfast City Hospital, Belfast BT9 7AB, UK
| | | | - Monika Brüggemann
- Department of Hematology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Frédéric Davi
- Hematology Department, Hospital Pitié-Salpêtrière and Sorbonne University, 75013, Paris, France
| | - Nikos Darzentas
- Molecular Medicine Program, Central European Institute of Technology, Masaryk University, 62500, Brno, Czech Republic.,Department of Hematology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Christiane Pott
- Department of Hematology, University Hospital Schleswig-Holstein, 24105, Kiel, Germany
| | - Falko Fend
- Institute of Pathology and Neuropathology, Comprehensive Cancer Center, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Michael Hummel
- Charité-Universitätsmedizin Berlin, Institute of Pathology, D-10117, Berlin, Germany
| | - Anton W Langerak
- Department of Immunology, Laboratory Medical Immunology, Erasmus MC, University Medical Center Rotterdam, 3015 CN, Rotterdam, The Netherlands
| | - Patricia J T A Groenen
- Department of Pathology, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands.
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