1
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Lee HE, Cho AH, Hwang JH, Kim JW, Yang HR, Ryu T, Jung Y, Lee S. Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library. Int J Mol Sci 2024; 25:4791. [PMID: 38732011 PMCID: PMC11083953 DOI: 10.3390/ijms25094791] [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: 03/20/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.
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Affiliation(s)
- Hee Eon Lee
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
| | - Ah Hyun Cho
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
| | - Jae Hyeon Hwang
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
| | - Ji Woong Kim
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
| | - Ha Rim Yang
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul 08507, Republic of Korea; (T.R.); (Y.J.)
| | - Yushin Jung
- ATG Lifetech Inc., Seoul 08507, Republic of Korea; (T.R.); (Y.J.)
| | - Sukmook Lee
- Department of Biopharmaceutical Chemistry, Kookmin University, Seoul 02707, Republic of Korea; (H.E.L.); (A.H.C.); (J.H.H.); (J.W.K.); (H.R.Y.)
- Department of Applied Chemistry, Kookmin University, Seoul 02707, Republic of Korea
- Antibody Research Institute, Kookmin University, Seoul 02707, Republic of Korea
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2
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Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
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Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
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3
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Hoffmann M, Willruth LL, Dietrich A, Lee HK, Knabl L, Trummer N, Baumbach J, Furth PA, Hennighausen L, List M. Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2. Sci Rep 2024; 14:2808. [PMID: 38307916 PMCID: PMC10837437 DOI: 10.1038/s41598-024-53117-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/28/2024] [Indexed: 02/04/2024] Open
Abstract
Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.
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Affiliation(s)
- Markus Hoffmann
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany.
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA.
| | - Lina-Liv Willruth
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Alexander Dietrich
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
| | | | - Nico Trummer
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Priscilla A Furth
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
| | - Markus List
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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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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Yuuki H, Itamiya T, Nagafuchi Y, Ota M, Fujio K. B cell receptor repertoire abnormalities in autoimmune disease. Front Immunol 2024; 15:1326823. [PMID: 38361948 PMCID: PMC10867955 DOI: 10.3389/fimmu.2024.1326823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
B cells play a crucial role in the immune response and contribute to various autoimmune diseases. Recent studies have revealed abnormalities in the B cell receptor (BCR) repertoire of patients with autoimmune diseases, with distinct features observed among different diseases and B cell subsets. Classically, BCR repertoire was used as an identifier of distinct antigen-specific clonotypes, but the recent advancement of analyzing large-scale repertoire has enabled us to use it as a tool for characterizing cellular biology. In this review, we provide an overview of the BCR repertoire in autoimmune diseases incorporating insights from our latest research findings. In systemic lupus erythematosus (SLE), we observed a significant skew in the usage of VDJ genes, particularly in CD27+IgD+ unswitched memory B cells and plasmablasts. Notably, autoreactive clones within unswitched memory B cells were found to be increased and strongly associated with disease activity, underscoring the clinical significance of this subset. Similarly, various abnormalities in the BCR repertoire have been reported in other autoimmune diseases such as rheumatoid arthritis. Thus, BCR repertoire analysis holds potential for enhancing our understanding of the underlying mechanisms involved in autoimmune diseases. Moreover, it has the potential to predict treatment effects and identify therapeutic targets in autoimmune diseases.
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Affiliation(s)
- Hayato Yuuki
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takahiro Itamiya
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yasuo Nagafuchi
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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6
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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7
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Hoffmann M, Willruth LL, Dietrich A, Lee HK, Knabl L, Trummer N, Baumbach J, Furth PA, Hennighausen L, List M. Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.564190. [PMID: 38076885 PMCID: PMC10705570 DOI: 10.1101/2023.11.03.564190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.
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Affiliation(s)
- Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Lina-Liv Willruth
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Alexander Dietrich
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | | | - Nico Trummer
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Priscilla A. Furth
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, United States of America
| | - Lothar Hennighausen
- Institute for Advanced Study (Lichtenbergstrasse 2 a, D-85748 Garching, Germany), Technical University of Munich, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD 20892, United States of America
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
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8
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Chowdhury R, Taguchi AT, Kelbauskas L, Stafford P, Diehnelt C, Zhao ZG, Williamson PC, Green V, Woodbury NW. Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease. PLoS Comput Biol 2023; 19:e1010773. [PMID: 37339137 DOI: 10.1371/journal.pcbi.1010773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/15/2023] [Indexed: 06/22/2023] Open
Abstract
Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.
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Affiliation(s)
- Robayet Chowdhury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Laimonas Kelbauskas
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Phillip Stafford
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Chris Diehnelt
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
| | | | - Valerie Green
- Creative Testing Solutions, Tempe, Arizona, United States of America
| | - Neal W Woodbury
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, fsupArizona, United States of America
- School of Molecular Sciences, Arizona State University, Tempe, Arizona, United States of America
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9
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Sutherland C, Cowan GJM. AIRRSHIP: simulating human B cell receptor repertoire sequences. Bioinformatics 2023; 39:btad365. [PMID: 37279738 PMCID: PMC10272706 DOI: 10.1093/bioinformatics/btad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/05/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023] Open
Abstract
SUMMARY Adaptive Immune Receptor Repertoire Sequencing is a rapidly developing field that has advanced understanding of the role of the adaptive immune system in health and disease. Numerous tools have been developed to analyse the complex data produced by this technique but work to compare their accuracy and reliability has been limited. Thorough, systematic assessment of their performance is dependent on the ability to produce high quality simulated datasets with known ground truth. We have developed AIRRSHIP, a flexible and fast Python package that produces synthetic human B cell receptor sequences. AIRRSHIP uses a comprehensive set of reference data to replicate key mechanisms in the immunoglobulin recombination process, with a particular focus on junctional complexity. Repertoires generated by AIRRSHIP are highly similar to published data and all steps in the sequence generation process are recorded. These data can be used to not only determine the accuracy of repertoire analysis tools but can also, by tuning of the large number of user-controllable parameters, give insight into factors that contribute to inaccuracies in results. AVAILABILITY AND IMPLEMENTATION AIRRSHIP is implemented in Python. It is available via https://github.com/Cowanlab/airrship and on PyPI at https://pypi.org/project/airrship/. Documentation can be found at https://airrship.readthedocs.io/.
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Affiliation(s)
- Catherine Sutherland
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FL, United Kingdom
| | - Graeme J M Cowan
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FL, United Kingdom
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10
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Vakhitova M, Myshkin M, Staroverov D, Shagina I, Izraelson M, Tverdova N, Britanova O, Merzlyak E. A Rapid Method for Detection of Antigen-Specific B Cells. Cells 2023; 12:cells12050774. [PMID: 36899909 PMCID: PMC10000731 DOI: 10.3390/cells12050774] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/17/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023] Open
Abstract
The global SARS-CoV-2 pandemic has united the efforts of many scientists all over the world to develop wet-lab techniques and computational approaches aimed at the identification of antigen-specific T and B cells. The latter provide specific humoral immunity that is essential for the survival of COVID-19 patients, and vaccine development has essentially been based on these cells. Here, we implemented an approach that integrates the sorting of antigen-specific B cells and B-cell receptor mRNA sequencing (BCR-seq), followed by computational analysis. This rapid and cost-efficient method allowed us to identify antigen-specific B cells in the peripheral blood of patients with severe COVID-19 disease. Subsequently, specific BCRs were extracted, cloned, and produced as full antibodies. We confirmed their reactivity toward the spike RBD domain. Such an approach can be effective for the monitoring and identification of B cells participating in an individual immune response.
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Affiliation(s)
- Mariia Vakhitova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Mikhail Myshkin
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Dmitriy Staroverov
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Irina Shagina
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Mark Izraelson
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Nadezhda Tverdova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Olga Britanova
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
- Correspondence: (O.B.); (E.M.)
| | - Ekaterina Merzlyak
- Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Institute of Translation Medicine, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
- Correspondence: (O.B.); (E.M.)
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