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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London, SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London, WC1E 6EA, UK
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge, CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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2
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Federico L, Malone B, Tennøe S, Chaban V, Osen JR, Gainullin M, Smorodina E, Kared H, Akbar R, Greiff V, Stratford R, Clancy T, Munthe LA. Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2024; 15:1377041. [PMID: 38449865 PMCID: PMC10916508 DOI: 10.3389/fimmu.2024.1377041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 03/08/2024] Open
Abstract
[This corrects the article DOI: 10.3389/fimmu.2023.1265044.].
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Affiliation(s)
- Lorenzo Federico
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Viktoriia Chaban
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Julie Røkke Osen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Murat Gainullin
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Hassen Kared
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | | | | | - Ludvig Andre Munthe
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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3
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Iacobescu M, Pop C, Uifălean A, Mogoşan C, Cenariu D, Zdrenghea M, Tănase A, Bergthorsson JT, Greiff V, Cenariu M, Iuga CA, Tomuleasa C, Tătaru D. Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants. Front Immunol 2024; 15:1327035. [PMID: 38433830 PMCID: PMC10904603 DOI: 10.3389/fimmu.2024.1327035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/01/2024] [Indexed: 03/05/2024] Open
Abstract
Despite the numerous advantages of allogeneic hematopoietic stem cell transplants (allo-HSCT), there exists a notable association with risks, particularly during the preconditioning period and predominantly post-intervention, exemplified by the occurrence of graft-versus-host disease (GVHD). Risk stratification prior to symptom manifestation, along with precise diagnosis and prognosis, relies heavily on clinical features. A critical imperative is the development of tools capable of early identification and effective management of patients undergoing allo-HSCT. A promising avenue in this pursuit is the utilization of proteomics-based biomarkers obtained from non-invasive biospecimens. This review comprehensively outlines the application of proteomics and proteomics-based biomarkers in GVHD patients. It delves into both single protein markers and protein panels, offering insights into their relevance in acute and chronic GVHD. Furthermore, the review provides a detailed examination of the site-specific involvement of GVHD. In summary, this article explores the potential of proteomics as a tool for timely and accurate intervention in the context of GVHD following allo-HSCT.
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Affiliation(s)
- Maria Iacobescu
- Department of Proteomics and Metabolomics, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Alina Uifălean
- Department of Pharmaceutical Analysis, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Cristina Mogoşan
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Diana Cenariu
- Department of Translational Medicine, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Mihnea Zdrenghea
- Department of Hematology, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Alina Tănase
- Department of Stem Cell Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Jon Thor Bergthorsson
- Department of Laboratory Hematology, Stem Cell Research Unit, Biomedical Center, School of Health Sciences, University Iceland, Reykjavik, Iceland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Mihai Cenariu
- Department of Animal Reproduction, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
| | - Cristina Adela Iuga
- Department of Proteomics and Metabolomics, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Pharmaceutical Analysis, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ciprian Tomuleasa
- Department of Translational Medicine, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Hematology, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Dan Tătaru
- Department of Internal Medicine, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
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4
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Natali EN, Horst A, Meier P, Greiff V, Nuvolone M, Babrak LM, Fink K, Miho E. Author Correction: The dengue-specific immune response and antibody identification with machine learning. NPJ Vaccines 2024; 9:30. [PMID: 38351085 PMCID: PMC10864368 DOI: 10.1038/s41541-024-00820-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024] Open
Affiliation(s)
- Eriberto Noel Natali
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Alexander Horst
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Patrick Meier
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway
| | - Mario Nuvolone
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Lmar Marie Babrak
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | | | - Enkelejda Miho
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland.
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- aiNET GmbH, Basel, Switzerland.
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5
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Sørensen CV, Hofmann N, Rawat P, Sørensen FV, Ljungars A, Greiff V, Laustsen AH, Jenkins TP. ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns. Bioinform Adv 2024; 4:vbae020. [PMID: 38425781 PMCID: PMC10902677 DOI: 10.1093/bioadv/vbae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/08/2024] [Accepted: 02/08/2024] [Indexed: 03/02/2024]
Abstract
Summary High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner's knowledge. Availability and implementation The pipeline is distributed via GitHub and PyPI, and it can either be installed as a package with pip or the user can choose to clone the repository.
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Affiliation(s)
- Christoffer V Sørensen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Nils Hofmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, NO-0316 Oslo, Norway
| | | | - Anne Ljungars
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, NO-0316 Oslo, Norway
| | - Andreas H Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Timothy P Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
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6
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Balashova D, van Schaik BDC, Stratigopoulou M, Guikema JEJ, Caniels TG, Claireaux M, van Gils MJ, Musters A, Anang DC, de Vries N, Greiff V, van Kampen AHC. Systematic evaluation of B-cell clonal family inference approaches. BMC Immunol 2024; 25:13. [PMID: 38331731 DOI: 10.1186/s12865-024-00600-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024] Open
Abstract
The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.
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Affiliation(s)
- Daria Balashova
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Barbera D C van Schaik
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Maria Stratigopoulou
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
| | - Jeroen E J Guikema
- Cancer Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC location University of Amsterdam, Pathology, Lymphoma and Myeloma Center Amsterdam, Meibergdreef 9, Amsterdam, Netherlands
| | - Tom G Caniels
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Mathieu Claireaux
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Marit J van Gils
- Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Infection and Immunity, Infectious Diseases, Amsterdam, The Netherlands
| | - Anne Musters
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Dornatien C Anang
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Niek de Vries
- Amsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands
- Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Antoine H C van Kampen
- Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands.
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands.
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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Erasmus MF, Ferrara F, D'Angelo S, Spector L, Leal-Lopes C, Teixeira AA, Sørensen J, Nagpal S, Perea-Schmittle K, Choudhary A, Honnen W, Calianese D, Antonio Rodriguez Carnero L, Cocklin S, Greiff V, Pinter A, Bradbury ARM. Author Correction: Insights into next generation sequencing guided antibody selection strategies. Sci Rep 2024; 14:3090. [PMID: 38326401 PMCID: PMC10850126 DOI: 10.1038/s41598-024-53751-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Affiliation(s)
| | | | - Sara D'Angelo
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | - Laura Spector
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | | | | | | | | | - Alok Choudhary
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - William Honnen
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - David Calianese
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | | | - Simon Cocklin
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | - Abraham Pinter
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
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Sharma D, Rawat P, Greiff V, Janakiraman V, Gromiha MM. Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166959. [PMID: 37967796 DOI: 10.1016/j.bbadis.2023.166959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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Affiliation(s)
- Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Puneet Rawat
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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9
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Natali EN, Horst A, Meier P, Greiff V, Nuvolone M, Babrak LM, Fink K, Miho E. The dengue-specific immune response and antibody identification with machine learning. NPJ Vaccines 2024; 9:16. [PMID: 38245547 PMCID: PMC10799860 DOI: 10.1038/s41541-023-00788-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/07/2023] [Indexed: 01/22/2024] Open
Abstract
Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.
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Affiliation(s)
- Eriberto Noel Natali
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Alexander Horst
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Patrick Meier
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway
| | - Mario Nuvolone
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Lmar Marie Babrak
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | | | - Enkelejda Miho
- FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland.
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- aiNET GmbH, Basel, Switzerland.
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10
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Ulvmoen A, Greiff V, Bechensteen AG, Inngjerdingen M. NKG2A discriminates natural killer cells with a suppressed phenotype in pediatric acute leukemia. J Leukoc Biol 2024; 115:334-343. [PMID: 37738462 DOI: 10.1093/jleuko/qiad112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/09/2023] [Accepted: 09/05/2023] [Indexed: 09/24/2023] Open
Abstract
Natural killer (NK) cells are important for early tumor immune surveillance. In patients with hematological cancers, NK cells are generally functional deficient and display dysregulations in their receptor repertoires. Acute leukemia is the most common cancer in children, and we here performed a comparative phenotypic profiling of NK cells from B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients to identify aberrant NK cell phenotypes. NK cell phenotypes, maturation, and function were analyzed in matched bone marrow and blood NK cells from BCP-ALL patients at diagnosis, during treatment, and at end of treatment and compared with age-matched pediatric control subjects. Expression of several markers were skewed in patients, but with large interindividual variations. Undertaking a multiparameter approach, we found that high expression levels of NKG2A was the single predominant marker distinguishing NK cells in BCP-ALL patients compared with healthy control subjects. Moreover, naïve CD57-NKG2A NK cells dominated in BCP-ALL patients at diagnosis. Further, we found dysregulated expression of the activating receptor DNAM-1 in resident bone marrow CXCR6+ NK cells. CXCR6+ NK cells lacking DNAM-1 expressed NKG2A and had a tendency for lower degranulation activity. In conclusion, high expression of NKG2A dominates NK cell phenotypes from pediatric BCP-ALL patients, indicating that NKG2A could be targeted in therapies for this patient group.
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Affiliation(s)
- Aina Ulvmoen
- Department of Pediatrics, Oslo University Hospital, Sognsvannsveien 20, Oslo 0372, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital and University of Oslo, Sognsvannsveien 20, Oslo 0372, Norway
| | - Anne G Bechensteen
- Department of Pediatrics, Oslo University Hospital, Sognsvannsveien 20, Oslo 0372, Norway
| | - Marit Inngjerdingen
- Department of Pharmacology, Oslo University Hospital and University of Oslo, Sognsvannsveien 20, Oslo 0372, Norway
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11
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Erasmus MF, Ferrara F, D'Angelo S, Spector L, Leal-Lopes C, Teixeira AA, Sørensen J, Nagpal S, Perea-Schmittle K, Choudhary A, Honnen W, Calianese D, Antonio Rodriguez Carnero L, Cocklin S, Greiff V, Pinter A, Bradbury ARM. Author Correction: Insights into next generation sequencing guided antibody selection strategies. Sci Rep 2023; 13:22616. [PMID: 38114562 PMCID: PMC10730564 DOI: 10.1038/s41598-023-49214-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023] Open
Affiliation(s)
| | | | - Sara D'Angelo
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | - Laura Spector
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | | | | | | | | | - Alok Choudhary
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - William Honnen
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - David Calianese
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | | | - Simon Cocklin
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | - Abraham Pinter
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
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12
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Federico L, Malone B, Tennøe S, Chaban V, Osen JR, Gainullin M, Smorodina E, Kared H, Akbar R, Greiff V, Stratford R, Clancy T, Munthe LA. Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2023; 14:1265044. [PMID: 38045681 PMCID: PMC10691274 DOI: 10.3389/fimmu.2023.1265044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/01/2023] [Indexed: 12/05/2023] Open
Abstract
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.
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Affiliation(s)
- Lorenzo Federico
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Viktoriia Chaban
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Julie Røkke Osen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Murat Gainullin
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Hassen Kared
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | | | | | - Ludvig Andre Munthe
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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13
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Elster C, Ommer-Bläsius M, Lang A, Vajen T, Pfeiler S, Feige M, Yau Pang T, Böttenberg M, Verheyen S, Lê Quý K, Chernigovskaya M, Kelm M, Winkels H, Schmidt SV, Greiff V, Gerdes N. Application and challenges of TCR and BCR sequencing to investigate T- and B-cell clonality in elastase-induced experimental murine abdominal aortic aneurysm. Front Cardiovasc Med 2023; 10:1221620. [PMID: 38034381 PMCID: PMC10686233 DOI: 10.3389/fcvm.2023.1221620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/16/2023] [Indexed: 12/02/2023] Open
Abstract
Background An abdominal aortic aneurysm (AAA) is a life-threatening cardiovascular disease. Although its pathogenesis is still poorly understood, recent evidence suggests that AAA displays autoimmune disease characteristics. Particularly, T cells responding to AAA-related antigens in the aortic wall may contribute to an initial immune response. Single-cell RNA (scRNA) T cell receptor (TCR) and B cell receptor (BCR) sequencing is a powerful tool for investigating clonality. However, difficulties such as limited numbers of isolated cells must be considered during implementation and data analysis, making biological interpretation challenging. Here, we perform a representative single-cell immune repertoire analysis in experimental murine AAA and show a reliable bioinformatic processing pipeline highlighting opportunities and limitations of this approach. Methods We performed scRNA TCR and BCR sequencing of isolated lymphocytes from the infrarenal aorta of male C57BL/6J mice 3, 7, 14, and 28 days after AAA induction via elastase perfusion of the aorta. Sham-operated mice at days 3 and 28 and non-operated mice served as controls. Results Comparison of complementarity-determining region (CDR3) length distribution of 179 B cells and 796 T cells revealed neither differences between AAA and control nor between the disease stages. We found no clonal expansion of B cells in AAA. For T cells, we identified several clones in 11 of 16 AAA samples and one of eight control samples. Immune receptor repertoire comparison indicated that only a few clones were shared between the individual AAA samples. The most frequently used V-genes in the TCR beta chain in AAA were TRBV3, TRBV19, and the splicing variant TRBV12-2 + TRBV13-2. Conclusion We found no clonal expansion of B cells but evidence for clonal expansion of T cells in elastase-induced AAA in mice. Our findings imply that a more precise characterization of TCR and BCR distribution requires a more extensive number of lymphocytes to prevent undersampling and potentially detect rare clones. Thus, further experiments are necessary to confirm our findings. In summary, this paper examines TCR and BCR sequencing results, identifies limitations and pitfalls, and offers guidance for future studies.
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Affiliation(s)
- Christin Elster
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Miriam Ommer-Bläsius
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Alexander Lang
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Tanja Vajen
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Susanne Pfeiler
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Milena Feige
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Tin Yau Pang
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- Department of Biology, Institute for Computer Science, Heinrich Heine University, Düsseldorf, Germany
| | - Marius Böttenberg
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Verheyen
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Khang Lê Quý
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Malte Kelm
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- Cardiovascular Research Institute Düsseldorf (CARID), Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Holger Winkels
- Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Susanne V. Schmidt
- Institute of Innate Immunity, Medical Faculty and University Hospital, Rheinische Friedrich-Wilhelms-University, Bonn, Germany
- Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, Bonn, Germany
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Norbert Gerdes
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- Cardiovascular Research Institute Düsseldorf (CARID), Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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14
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Erasmus MF, Ferrara F, D'Angelo S, Spector L, Leal-Lopes C, Teixeira AA, Sørensen J, Nagpal S, Perea-Schmittle K, Choudhary A, Honnen W, Calianese D, Antonio Rodriguez Carnero L, Cocklin S, Greiff V, Pinter A, Bradbury ARM. Insights into next generation sequencing guided antibody selection strategies. Sci Rep 2023; 13:18370. [PMID: 37884618 PMCID: PMC10603065 DOI: 10.1038/s41598-023-45538-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Therapeutic antibody discovery often relies on in-vitro display methods to identify lead candidates. Assessing selected output diversity traditionally involves random colony picking and Sanger sequencing, which has limitations. Next-generation sequencing (NGS) offers a cost-effective solution with increased read depth, allowing a comprehensive understanding of diversity. Our study establishes NGS guidelines for antibody drug discovery, demonstrating its advantages in expanding the number of unique HCDR3 clusters, broadening the number of high affinity antibodies, expanding the total number of antibodies recognizing different epitopes, and improving lead prioritization. Surprisingly, our investigation into the correlation between NGS-derived frequencies of CDRs and affinity revealed a lack of association, although this limitation could be moderately mitigated by leveraging NGS clustering, enrichment and/or relative abundance across different regions to enhance lead prioritization. This study highlights NGS benefits, offering insights, recommendations, and the most effective approach to leverage NGS in therapeutic antibody discovery.
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Affiliation(s)
| | | | - Sara D'Angelo
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | - Laura Spector
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | | | | | | | | | - Alok Choudhary
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - William Honnen
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - David Calianese
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | | | - Simon Cocklin
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | - Abraham Pinter
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
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15
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Heimli M, Tennebø Flåm S, Sagsveen Hjorthaug H, Bjørnstad PM, Chernigovskaya M, Le QK, Tekpli X, Greiff V, Lie BA. Human thymic putative CD8αα precursors exhibit a biased TCR repertoire in single cell AIRR-seq. Sci Rep 2023; 13:17714. [PMID: 37853083 PMCID: PMC10584817 DOI: 10.1038/s41598-023-44693-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
Thymic T cell development comprises T cell receptor (TCR) recombination and assessment of TCR avidity towards self-peptide-MHC complexes presented by antigen-presenting cells. Self-reactivity may lead to negative selection, or to agonist selection and differentiation into unconventional lineages such as regulatory T cells and CD8[Formula: see text] T cells. To explore the effect of the adaptive immune receptor repertoire on thymocyte developmental decisions, we performed single cell adaptive immune receptor repertoire sequencing (scAIRR-seq) of thymocytes from human young paediatric thymi and blood. Thymic PDCD1+ cells, a putative CD8[Formula: see text] T cell precursor population, exhibited several TCR features previously associated with thymic and peripheral ZNF683+ CD8[Formula: see text] T cells, including enrichment of large and positively charged complementarity-determining region 3 (CDR3) amino acids. Thus, the TCR repertoire may partially explain the decision between conventional vs. agonist selected thymocyte differentiation, an aspect of importance for the development of therapies for patients with immune-mediated diseases.
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Affiliation(s)
- Marte Heimli
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway
| | - Siri Tennebø Flåm
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway
| | - Hanne Sagsveen Hjorthaug
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway
| | - Pål Marius Bjørnstad
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372, Oslo, Norway
| | - Quy Khang Le
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372, Oslo, Norway
| | - Xavier Tekpli
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372, Oslo, Norway
| | - Benedicte Alexandra Lie
- Department of Medical Genetics, University of Oslo and Oslo University Hospital, 0424, Oslo, Norway.
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16
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Tigu AB, Constantinescu CS, Teodorescu P, Kegyes D, Munteanu R, Feder R, Peters M, Pralea I, Iuga C, Cenariu D, Marcu A, Tanase A, Colita A, Drula R, Bergthorsson JT, Greiff V, Dima D, Selicean C, Rus I, Zdrenghea M, Gulei D, Ghiaur G, Tomuleasa C. Design and preclinical testing of an anti-CD41 CAR T cell for the treatment of acute megakaryoblastic leukaemia. J Cell Mol Med 2023; 27:2864-2875. [PMID: 37667538 PMCID: PMC10538266 DOI: 10.1111/jcmm.17810] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/28/2023] [Accepted: 05/26/2023] [Indexed: 09/06/2023] Open
Abstract
Acute megakaryoblastic leukaemia (AMkL) is a rare subtype of acute myeloid leukaemia (AML) representing 5% of all reported cases, and frequently diagnosed in children with Down syndrome. Patients diagnosed with AMkL have low overall survival and have poor outcome to treatment, thus novel therapies such as CAR T cell therapy could represent an alternative in treating AMkL. We investigated the effect of a new CAR T cell which targets CD41, a specific surface antigen for M7-AMkL, against an in vitro model for AMkL, DAMI Luc2 cell line. The performed flow cytometry evaluation highlighted a percentage of 93.8% CAR T cells eGFP-positive and a limited acute effect on lowering the target cell population. However, the interaction between effector and target (E:T) cells, at a low ratio, lowered the cell membrane integrity, and reduced the M7-AMkL cell population after 24 h of co-culture, while the cytotoxic effect was not significant in groups with higher E:T ratio. Our findings suggest that the anti-CD41 CAR T cells are efficient for a limited time spawn and the cytotoxic effect is visible in all experimental groups with low E:T ratio.
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Affiliation(s)
- Adrian Bogdan Tigu
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Catalin Sorin Constantinescu
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of HematologyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Intensive Care UnitEmergency Clinical HospitalCluj‐NapocaRomania
| | - Patric Teodorescu
- Department of HematologyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of Leukemia, Sidney Kimmel Cancer Center at Johns HopkinsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - David Kegyes
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of HematologyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Raluca Munteanu
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Richard Feder
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Mareike Peters
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of HematologyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Ioana Pralea
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Cristina Iuga
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of Drug AnalysisSchool of PharmacyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Diana Cenariu
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Andra Marcu
- Department of PediatricsCarol Davila University of Medicine and PharmacyBucharestRomania
- Department of Stem Cell TransplantationFundeni Clinical InstituteBucharestRomania
| | - Alina Tanase
- Department of PediatricsCarol Davila University of Medicine and PharmacyBucharestRomania
- Department of Stem Cell TransplantationFundeni Clinical InstituteBucharestRomania
| | - Anca Colita
- Department of PediatricsCarol Davila University of Medicine and PharmacyBucharestRomania
- Department of Stem Cell TransplantationFundeni Clinical InstituteBucharestRomania
| | - Rares Drula
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Jon Thor Bergthorsson
- Stem Cell Research Unit, Biomedical Center, School of Health SciencesUniversity of IcelandReykjavíkIceland
- Department of Laboratory HematologyLandspitali University HospitalReykjavíkIceland
| | - Victor Greiff
- Department of ImmunologyUniversity of Oslo and Oslo University HospitalOsloNorway
| | - Delia Dima
- Department of HematologyIon Chiricuta Clinical Cancer CenterCluj NapocaRomania
| | - Cristina Selicean
- Department of HematologyIon Chiricuta Clinical Cancer CenterCluj NapocaRomania
| | - Ioana Rus
- Department of HematologyIon Chiricuta Clinical Cancer CenterCluj NapocaRomania
| | - Mihnea Zdrenghea
- Department of HematologyIon Chiricuta Clinical Cancer CenterCluj NapocaRomania
| | - Diana Gulei
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
| | - Gabriel Ghiaur
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of Leukemia, Sidney Kimmel Cancer Center at Johns HopkinsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Ciprian Tomuleasa
- Medfuture Research Center for Advanced MedicineIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of HematologyIuliu Hatieganu University of Medicine and PharmacyCluj‐NapocaRomania
- Department of HematologyIon Chiricuta Clinical Cancer CenterCluj NapocaRomania
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17
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Cenariu D, Rus I, Bergthorsson JT, Grewal R, Cenariu M, Greiff V, Tigu AB, Dima D, Selicean C, Petrushev B, Zdrenghea M, Fromm J, Aanei CM, Tomuleasa C. Flow Cytometry of CD5-Positive Hairy Cell Leukemia. Mol Diagn Ther 2023; 27:593-599. [PMID: 37291380 DOI: 10.1007/s40291-023-00658-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Hairy cell leukemia (HCL) is a chronic lymphoproliferative disorder for which diagnosis is typically straightforward, based on bone marrow morphology and flow cytometry (FC) or immunohistochemistry. Nevertheless, variants present atypical expressions of cell surface markers, as is the case of CD5, for which the differential diagnosis can be more difficult. The aim of the current paper was to describe diagnosis of HCL with atypical CD5 expression, with an emphasis on FC. METHODS The detailed diagnostic methodology for HCL with atypical CD5 expression is presented, including differential diagnosis from other lymphoproliferative diseases with similar pathologic features, by FC analysis of the bone marrow aspirate. RESULTS Diagnosis of HCL by means of FC started by gating all events based on side scatter (SSC) versus CD45 and B lymphocytes were selected from the lymphocytes gate as CD45/CD19 positive. The gated cells were positive for CD25, CD11c, CD20, and CD103, while CD10 proved to be dim to negative. Moreover, cells positive for CD3, CD4, and CD8, the three pan-T markers, as well as CD19, showed a bright expression of CD5. The atypical CD5 expression is usually correlated with a negative prognosis and thus chemotherapy with cladribine should be initiated. CONCLUSION HCL is an indolent chronic lymphoproliferative disorder and diagnosis is usually straightforward. However, atypical expression of CD5 renders its differential diagnosis more difficult, but FC is a useful tool that allows an optimal classification of the disease and allows initiation of timely satisfactory therapy.
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Affiliation(s)
- Diana Cenariu
- Medfuture Research Center for Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Louis Pasteur Street, Cluj-Napoca, Romania
| | - Ioana Rus
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
| | - Jon Thor Bergthorsson
- Stem Cell Research Unit, Biomedical Center, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Ravnit Grewal
- Department of Pathology, National Health Laboratory Services, Port Elizabeth, South Africa
| | - Mihai Cenariu
- Department of Clinical Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
| | - Victor Greiff
- Laboratory for Computational and Systems Immunology, University of Oslo, Oslo, Norway
| | - Adrian-Bogdan Tigu
- Medfuture Research Center for Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Louis Pasteur Street, Cluj-Napoca, Romania
| | - Delia Dima
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
| | - Cristina Selicean
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
| | - Bobe Petrushev
- Medfuture Research Center for Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Louis Pasteur Street, Cluj-Napoca, Romania
| | - Mihnea Zdrenghea
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj-Napoca, Romania
| | - Jonathan Fromm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Carmen-Mariana Aanei
- Haematology Laboratory, University Hospital of Saint-Etienne, Saint-Etienne, France
- INSERM U1059-SAINBIOSE, Université de Lyon, Saint-Etienne, France
| | - Ciprian Tomuleasa
- Medfuture Research Center for Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Louis Pasteur Street, Cluj-Napoca, Romania.
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18
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Rappazzo CG, Fernández-Quintero ML, Mayer A, Wu NC, Greiff V, Guthmiller JJ. Defining and Studying B Cell Receptor and TCR Interactions. J Immunol 2023; 211:311-322. [PMID: 37459189 PMCID: PMC10495106 DOI: 10.4049/jimmunol.2300136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/15/2023] [Indexed: 07/20/2023]
Abstract
BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.
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Affiliation(s)
| | | | - Andreas Mayer
- Division of Infection and Immunity, University College London, London WC1E 6BT, UK
| | - Nicholas C. Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Jenna J. Guthmiller
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045
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19
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Timis T, Bergthorsson JT, Greiff V, Cenariu M, Cenariu D. Pathology and Molecular Biology of Melanoma. Curr Issues Mol Biol 2023; 45:5575-5597. [PMID: 37504268 PMCID: PMC10377842 DOI: 10.3390/cimb45070352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023] Open
Abstract
Almost every death in young patients with an advanced skin tumor is caused by melanoma. Today, with the help of modern treatments, these patients survive longer or can even achieve a cure. Advanced stage melanoma is frequently related with poor prognosis and physicians still find this disease difficult to manage due to the absence of a lasting response to initial treatment regimens and the lack of randomized clinical trials in post immunotherapy/targeted molecular therapy settings. New therapeutic targets are emerging from preclinical data on the genetic profile of melanocytes and from the identification of molecular factors involved in the pathogenesis of malignant transformation. In the current paper, we present the diagnostic challenges, molecular biology and genetics of malignant melanoma, as well as the current therapeutic options for patients with this diagnosis.
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Affiliation(s)
- Tanase Timis
- Department of Oncology, Bistrita Emergency Hospital, 420094 Bistrita, Romania;
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Jon Thor Bergthorsson
- Department of Pharmacology and Toxicology, Medical Faculty, University of Iceland, Hofsvallagotu 53, 107 Reykjavík, Iceland;
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo University Hospital, 0372 Oslo, Norway;
| | - Mihai Cenariu
- Department of Animal Reproduction, University of Agricultural Sciences and Veterinary Medicine, 3-5 Calea Manastur Street, 400372 Cluj-Napoca, Romania;
| | - Diana Cenariu
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400337 Cluj-Napoca, Romania
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20
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Tigu AB, Hotea I, Drula R, Zimta AA, Dirzu N, Santa M, Constantinescu C, Dima D, Bergthorsson JT, Greiff V, Gulei D, Coriu D, Serban M, Mahlangu J, Tomuleasa C. RNA sequencing suggests that non-coding RNAs play a role in the development of acquired haemophilia. J Cell Mol Med 2023. [PMID: 37317065 DOI: 10.1111/jcmm.17741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/20/2023] [Accepted: 03/25/2023] [Indexed: 06/16/2023] Open
Abstract
Acquired haemophilia (AH) is a rare disorder characterized by bleeding in patients with no personal or family history of coagulation/clotting-related diseases. This disease occurs when the immune system, by mistake, generates autoantibodies that target FVIII, causing bleeding. Small RNAs from plasma collected from AH patients (n = 2), mild classical haemophilia (n = 3), severe classical haemophilia (n = 3) and healthy donors (n = 2), for sequencing by Illumina, NextSeq500. Based on bioinformatic analysis, AH patients were compared to all experimental groups and a significant number of altered transcripts were identified with one transcript being modified compared to all groups at fold change level. The Venn diagram shows that haemoglobin subunit alpha 1 was highlighted to be the common upregulated transcript in AH compared to classical haemophilia and healthy patients. Non-coding RNAs might play a role in AH pathogenesis; however, due to the rarity of HA, the current study needs to be translated on a larger number of AH samples and classical haemophilia samples to generate more solid data that can confirm our findings.
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Affiliation(s)
- Adrian Bogdan Tigu
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
- Romanian Academy of Scientists, Bucharest, Romania
| | - Ionut Hotea
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Rares Drula
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
- Romanian Academy of Scientists, Bucharest, Romania
| | - Alina-Andreea Zimta
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Noemi Dirzu
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Maria Santa
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Catalin Constantinescu
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Delia Dima
- Department of Hematology, Ion Chiricuta Clinical Cancer Center, Cluj Napoca, Romania
| | - Jon Thor Bergthorsson
- Biomedical Center, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Diana Gulei
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Daniel Coriu
- Department of Hematology, Carol Davila University of Medicine and Pharmacy, Cluj Napoca, Romania
| | - Margit Serban
- Department of Pediatrics, Victor Babes University of Medicine and Pharmacy, Timisoara, Romania
| | - Johnny Mahlangu
- Haemophilia Comprehensive Care Centre, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
| | - Ciprian Tomuleasa
- Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj Napoca, Romania
- Romanian Academy of Scientists, Bucharest, Romania
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21
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Constantinescu C, Constantinescu R, Bergthorsson JT, Greiff V, Tanase A, Colita A, Gulei D, Tomuleasa C. Pitfalls in patenting academic CAR-T cells therapy. Expert Opin Ther Pat 2023. [PMID: 37254751 DOI: 10.1080/13543776.2023.2220883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
INTRODUCTION Emerging immunotherapies are pushing the boundaries of cancer treatment, with chimeric antigen receptor (CAR)-T cell therapy being one of the most advanced. Due to the increasingly crowded CAR-T cell field, patenting and protecting the intellectual property of these CAR-T cells implies a good knowledge of the legal landscape. AREAS COVERED The present manuscript focuses on the challenges regarding the patenting process of CAR-T technology, beginning with a description of the main characteristics of CAR-T cells and their functionalities, continuing with the legal landscape applicable to patenting processes, and concluding by presenting the potential strategies to overcome the impediments that can appear when trying to patent CAR-T cells. It is meant to offer insights for those who are exploring possible patenting options in CAR-T cells territory. PubMed and Patenscope databases were used for patent and literature searching (2013-2023). EXPERT OPINION There is no one-size-fits-all solution in this matter and the medical evolution of this therapy will certainly bring out even more challenges. Comprehensive knowledge of the intellectual property, exposure to potential litigation, growing competition, and the high price of therapy, are strikingly relevant in the broader landscape. Future endeavors would be to take steps towards the harmonization of the CAR-T patenting procedure.
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Affiliation(s)
- Cătălin Constantinescu
- Department of Anesthesia and Intensive Care Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania
- Intensive Care Unit Emergency Hospital Cluj-Napoca Romania
- Medfuture Research Center for Advanced Medicine Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania
| | | | - Jon Thor Bergthorsson
- Stem Cell Research Unit, Department of Laboratory Hematology, Biomedical Center, School of Health Sciences University of Iceland Reykjavík Iceland
| | - Victor Greiff
- Department of Immunology University of Oslo Oslo Norway
| | - Alina Tanase
- Department of Stem Cell Transplantation Fundeni Clinical Institute Bucharest Romania
| | - Anca Colita
- Department of Stem Cell Transplantation Fundeni Clinical Institute Bucharest Romania
| | - Diana Gulei
- Medfuture Research Center for Advanced Medicine Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania
| | - Ciprian Tomuleasa
- Medfuture Research Center for Advanced Medicine Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania
- Department of Hematology Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca Romania
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22
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Rep Methods 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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23
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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24
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Fernández-Quintero ML, Ljungars A, Waibl F, Greiff V, Andersen JT, Gjølberg TT, Jenkins TP, Voldborg BG, Grav LM, Kumar S, Georges G, Kettenberger H, Liedl KR, Tessier PM, McCafferty J, Laustsen AH. Assessing developability early in the discovery process for novel biologics. MAbs 2023; 15:2171248. [PMID: 36823021 PMCID: PMC9980699 DOI: 10.1080/19420862.2023.2171248] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anne Ljungars
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Franz Waibl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Jan Terje Andersen
- Department of Immunology, University of Oslo, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Institute of Clinical Medicine and Department of Pharmacology, University of Oslo, Oslo, Norway
| | | | - Timothy P. Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Bjørn Gunnar Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lise Marie Grav
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R. Liedl
- Center for Molecular Biosciences Innsbruck (CMBI), Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Peter M. Tessier
- Department of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
| | - John McCafferty
- Department of Medicine, Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Maxion Therapeutics, Babraham Research Campus, Cambridge, UK
| | - Andreas H. Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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25
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Kanduri C, Scheffer L, Pavlović M, Rand KD, Chernigovskaya M, Pirvandy O, Yaari G, Greiff V, Sandve GK. simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. Gigascience 2022; 12:giad074. [PMID: 37848619 PMCID: PMC10580376 DOI: 10.1093/gigascience/giad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/20/2023] [Accepted: 08/29/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires. RESULTS We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets. CONCLUSIONS This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.
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Affiliation(s)
- Chakravarthi Kanduri
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Lonneke Scheffer
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Milena Pavlović
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
| | - Knut Dagestad Rand
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Oz Pirvandy
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Gur Yaari
- Faculty of Engineering, Bar-Ilan University, 5290002, Israel
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway
| | - Geir K Sandve
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway
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26
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Høye E, Dagenborg VJ, Torgunrud A, Lund-Andersen C, Fretland ÅA, Lorenz S, Edwin B, Hovig E, Fromm B, Inderberg EM, Greiff V, Ree AH, Flatmark K. T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases. Gigascience 2022; 12:giad032. [PMID: 37161965 PMCID: PMC10170408 DOI: 10.1093/gigascience/giad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/07/2023] [Accepted: 04/25/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Colorectal liver metastasis (CLM) is a leading cause of colorectal cancer mortality, and the response to immune checkpoint inhibition (ICI) in microsatellite-stable CRC has been disappointing. Administration of cytotoxic chemotherapy may cause increased density of tumor-infiltrating T cells, which has been associated with improved response to ICI. This study aimed to quantify and characterize T-cell infiltration in CLM using T-cell receptor (TCR) repertoire sequencing. Eighty-five resected CLMs from patients included in the Oslo CoMet study were subjected to TCR repertoire sequencing. Thirty-five and 15 patients had received neoadjuvant chemotherapy (NACT) within a short or long interval, respectively, prior to resection, while 35 patients had not been exposed to NACT. T-cell fractions were calculated, repertoire clonality was analyzed based on Hill evenness curves, and TCR sequence convergence was assessed using network analysis. RESULTS Increased T-cell fractions (10.6% vs. 6.3%) were detected in CLMs exposed to NACT within a short interval prior to resection, while modestly increased clonality was observed in NACT-exposed tumors independently of the timing of NACT administration and surgery. While private clones made up >90% of detected clones, network connectivity analysis revealed that public clones contributed the majority of TCR sequence convergence. CONCLUSIONS TCR repertoire sequencing can be used to quantify T-cell infiltration and clonality in clinical samples. This study provides evidence to support chemotherapy-driven T-cell clonal expansion in CLM in a clinical context.
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Affiliation(s)
- Eirik Høye
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
- Institute of Clinical Medicine, Medical Faculty, University of Oslo, 0318 Oslo, Norway
| | - Vegar J Dagenborg
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, 0379 Oslo, Norway
| | - Annette Torgunrud
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
| | - Christin Lund-Andersen
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
- Institute of Clinical Medicine, Medical Faculty, University of Oslo, 0318 Oslo, Norway
| | - Åsmund A Fretland
- The Intervention Centre, Rikshospitalet, Oslo University Hospital, 0372 Oslo, Norway
- Department of Hepato-Pancreato-Biliary Surgery, Rikshospitalet, Oslo University Hospital, 0372 Oslo, Norway
| | - Susanne Lorenz
- Department of Core Facilities, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
| | - Bjørn Edwin
- Institute of Clinical Medicine, Medical Faculty, University of Oslo, 0318 Oslo, Norway
- The Intervention Centre, Rikshospitalet, Oslo University Hospital, 0372 Oslo, Norway
- Department of Hepato-Pancreato-Biliary Surgery, Rikshospitalet, Oslo University Hospital, 0372 Oslo, Norway
| | - Eivind Hovig
- Center for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
| | - Bastian Fromm
- The Arctic University Museum of Norway, UiT – The Arctic University of Norway, 9037 Tromsø, Norway
| | - Else M Inderberg
- Translational Research Unit, Department of Cellular Therapy, Oslo University Hospital, 0379 Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Anne H Ree
- Institute of Clinical Medicine, Medical Faculty, University of Oslo, 0318 Oslo, Norway
- Department of Oncology, Akershus University Hospital, 1478 Lørenskog, Norway
| | - Kjersti Flatmark
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway
- Institute of Clinical Medicine, Medical Faculty, University of Oslo, 0318 Oslo, Norway
- Department of Gastroenterological Surgery, The Norwegian Radium Hospital, 0379 Oslo, Norway
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27
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Robert PA, Akbar R, Frank R, Pavlović M, Widrich M, Snapkov I, Slabodkin A, Chernigovskaya M, Scheffer L, Smorodina E, Rawat P, Mehta BB, Vu MH, Mathisen IF, Prósz A, Abram K, Olar A, Miho E, Haug DTT, Lund-Johansen F, Hochreiter S, Haff IH, Klambauer G, Sandve GK, Greiff V. Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. Nat Comput Sci 2022; 2:845-865. [PMID: 38177393 DOI: 10.1038/s43588-022-00372-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/09/2022] [Indexed: 01/06/2024]
Abstract
Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
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Affiliation(s)
- Philippe A Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Michael Widrich
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Igor Snapkov
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrei Slabodkin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway
| | | | - Aurél Prósz
- Danish Cancer Society Research Center, Translational Cancer Genomics, Copenhagen, Denmark
| | - Krzysztof Abram
- The Novo Nordisk Foundation Center for Biosustainability, Autoflow, DTU Biosustain and IT University of Copenhagen, Copenhagen, Denmark
| | - Alex Olar
- Department of Complex Systems in Physics, Eötvös Loránd University, Budapest, Hungary
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | | | - Sepp Hochreiter
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
- Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
| | | | - Günter Klambauer
- ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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28
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Mathew NR, Jayanthan JK, Smirnov IV, Robinson JL, Axelsson H, Nakka SS, Emmanouilidi A, Czarnewski P, Yewdell WT, Schön K, Lebrero-Fernández C, Bernasconi V, Rodin W, Harandi AM, Lycke N, Borcherding N, Yewdell JW, Greiff V, Bemark M, Angeletti D. Single-cell BCR and transcriptome analysis after influenza infection reveals spatiotemporal dynamics of antigen-specific B cells. Cell Rep 2022; 41:111764. [DOI: 10.1016/j.celrep.2022.111764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
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29
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Dorraji E, Borgen E, Segura-Peña D, Rawat P, Smorodina E, Dunn C, Greiff V, Sekulić N, Russnes H, Kyte JA. Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform. Cancers (Basel) 2022; 14:cancers14194859. [PMID: 36230782 PMCID: PMC9563779 DOI: 10.3390/cancers14194859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary In the present study, we addressed the unmet need for a molecular antibody (mAb) with high affinity and specificity against a truncated hyperactive isoform of human epidermal growth factor receptor 2 (HER2), called 611-carboxy terminal fragment (CTF)-p95HER2. Patients with p95HER2+ breast cancer are at risk of developing metastatic breast cancer with a poor prognosis and resistance to therapies targeting full-length HER2. We have generated a mAb named Oslo-2, which react specifically with 611-CTF-p95HER2 and has a high affinity. We also characterized the antigenic determinant (epitope) on the p95HER2 protein and the antigen-binding site (paratope) on the Oslo-2 mAb. The antibody can be used to develop antibody- or cell-based therapies targeting p95HER2, as well as a diagnostic assay to identify p95HER2+ disease. Abstract The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays.
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Affiliation(s)
- Esmaeil Dorraji
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
| | - Elin Borgen
- Department of Pathology, Oslo University Hospital, 0379 Oslo, Norway
| | - Dario Segura-Peña
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Claire Dunn
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0372 Oslo, Norway
| | - Nikolina Sekulić
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
- Department of Chemistry, University of Oslo, 0371 Oslo, Norway
| | - Hege Russnes
- Department of Pathology, Oslo University Hospital, 0379 Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
| | - Jon Amund Kyte
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
- Department of Clinical Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
- Correspondence:
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Greiff V, Luning Prak ET, O'Donnell T, Finotello F, Reddy ST, Walczak A, Mora T. What are the current driving questions in immune repertoire research? Cell Syst 2022; 13:683-686. [PMID: 36137509 DOI: 10.1016/j.cels.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Sandve GK, Greiff V. Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking. Bioinformatics 2022; 38:4994-4996. [PMID: 36073940 PMCID: PMC9620827 DOI: 10.1093/bioinformatics/btac612] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/18/2022] [Accepted: 09/08/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0316 Oslo, Norway
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Han J, Masserey S, Shlesinger D, Kuhn R, Papadopoulou C, Agrafiotis A, Kreiner V, Dizerens R, Hong KL, Weber C, Greiff V, Oxenius A, Reddy ST, Yermanos A. Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes. Bioinform Adv 2022; 2:vbac062. [PMID: 36699357 PMCID: PMC9710610 DOI: 10.1093/bioadv/vbac062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 07/31/2022] [Accepted: 08/26/2022] [Indexed: 02/01/2023]
Abstract
Motivation Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression. Results We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. Availability and implementation The R package and code used in this manuscript can be found at github.com/alexyermanos/echidna and also in the R package Platypus (Yermanos et al., 2021). Installation instructions and the vignette for Echidna is described in the Platypus Computational Ecosystem (https://alexyermanos.github.io/Platypus/index.html). Publicly available data and corresponding sample accession numbers can be found in Supplementary Tables S2 and S3. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Jiami Han
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Solène Masserey
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Danielle Shlesinger
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Raphael Kuhn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Chrysa Papadopoulou
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Andreas Agrafiotis
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Victor Kreiner
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Raphael Dizerens
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Kai-Lin Hong
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Cédric Weber
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo 0450, Norway
| | - Annette Oxenius
- Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
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Weber CR, Rubio T, Wang L, Zhang W, Robert PA, Akbar R, Snapkov I, Wu J, Kuijjer ML, Tarazona S, Conesa A, Sandve GK, Liu X, Reddy ST, Greiff V. Reference-based comparison of adaptive immune receptor repertoires. Cell Rep Methods 2022; 2:100269. [PMID: 36046619 PMCID: PMC9421535 DOI: 10.1016/j.crmeth.2022.100269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/01/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022]
Abstract
B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.
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Affiliation(s)
- Cédric R. Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Teresa Rubio
- Laboratory of Neurobiology, Centro Investigación Príncipe Felipe, Valencia, Spain
| | - Longlong Wang
- BGI-Shenzhen, Shenzhen, China
- BGI-Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Wei Zhang
- BGI-Shenzhen, Shenzhen, China
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Philippe A. Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
| | | | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sonia Tarazona
- Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain
| | - Geir K. Sandve
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Xiao Liu
- BGI-Shenzhen, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Sai T. Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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Perruzza L, Strati F, Raneri M, Li H, Gargari G, Rezzonico-Jost T, Palatella M, Kwee I, Morone D, Seehusen F, Sonego P, Donati C, Franceschi P, Macpherson AJ, Guglielmetti S, Greiff V, Grassi F. Apyrase-mediated amplification of secretory IgA promotes intestinal homeostasis. Cell Rep 2022; 40:111112. [PMID: 35858559 DOI: 10.1016/j.celrep.2022.111112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/15/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022] Open
Abstract
Secretory immunoglobulin A (SIgA) interaction with commensal bacteria conditions microbiota composition and function. However, mechanisms regulating reciprocal control of microbiota and SIgA are not defined. Bacteria-derived adenosine triphosphate (ATP) limits T follicular helper (Tfh) cells in the Peyer's patches (PPs) via P2X7 receptor (P2X7R) and thereby SIgA generation. Here we show that hydrolysis of extracellular ATP (eATP) by apyrase results in amplification of the SIgA repertoire. The enhanced breadth of SIgA in mice colonized with apyrase-releasing Escherichia coli influences topographical distribution of bacteria and expression of genes involved in metabolic versus immune functions in the intestinal epithelium. SIgA-mediated conditioning of bacteria and enterocyte function is reflected by differences in nutrient absorption in mice colonized with apyrase-expressing bacteria. Apyrase-induced SIgA improves intestinal homeostasis and attenuates barrier impairment and susceptibility to infection by enteric pathogens in antibiotic-induced dysbiosis. Therefore, amplification of SIgA by apyrase can be leveraged to restore intestinal fitness in dysbiotic conditions.
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Affiliation(s)
- Lisa Perruzza
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Francesco Strati
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Matteo Raneri
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Hai Li
- Maurice Müller Laboratories, Department of Biomedical Research, Universitätsklinik für Viszerale Chirurgie und Medizin, Inselspital, University of Bern, Bern 3010, Switzerland
| | - Giorgio Gargari
- Division of Food Microbiology and Bioprocesses, Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Milan 20133, Italy
| | - Tanja Rezzonico-Jost
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Martina Palatella
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Ivo Kwee
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Diego Morone
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland
| | - Frauke Seehusen
- Institute of Veterinary Pathology, Vetsuisse Faculty, University of Zurich, Zurich 8057, Switzerland
| | - Paolo Sonego
- Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN) 38098, Italy
| | - Claudio Donati
- Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN) 38098, Italy
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige (TN) 38098, Italy
| | - Andrew J Macpherson
- Maurice Müller Laboratories, Department of Biomedical Research, Universitätsklinik für Viszerale Chirurgie und Medizin, Inselspital, University of Bern, Bern 3010, Switzerland
| | - Simone Guglielmetti
- Division of Food Microbiology and Bioprocesses, Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, Milan 20133, Italy
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo 0372, Norway
| | - Fabio Grassi
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona 6500, Switzerland.
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Rognes T, Scheffer L, Greiff V, Sandve GK. CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. Bioinformatics 2022; 38:4230-4232. [PMID: 35852318 PMCID: PMC9438946 DOI: 10.1093/bioinformatics/btac505] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/20/2022] [Accepted: 07/18/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Adaptive immune receptor (AIR) repertoires (AIRRs) record past immune encounters with exquisite specificity. Therefore, identifying identical or similar AIR sequences across individuals is a key step in AIRR analysis for revealing convergent immune response patterns that may be exploited for diagnostics and therapy. Existing methods for quantifying AIRR overlap scale poorly with increasing dataset numbers and sizes. To address this limitation, we developed CompAIRR, which enables ultra-fast computation of AIRR overlap, based on either exact or approximate sequence matching. RESULTS CompAIRR improves computational speed 1000-fold relative to the state of the art and uses only one-third of the memory: on the same machine, the exact pairwise AIRR overlap of 104 AIRRs with 105 sequences is found in ∼17 min, while the fastest alternative tool requires 10 days. CompAIRR has been integrated with the machine learning ecosystem immuneML to speed up commonly used AIRR-based machine learning applications. AVAILABILITY AND IMPLEMENTATION CompAIRR code and documentation are available at https://github.com/uio-bmi/compairr. Docker images are available at https://hub.docker.com/r/torognes/compairr. The code to replicate the synthetic datasets, scripts for benchmarking and creating figures, and all raw data underlying the figures are available at https://github.com/uio-bmi/compairr-benchmarking. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, 0424 Oslo, Norway
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:6643456. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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Kanduri C, Pavlović M, Scheffer L, Motwani K, Chernigovskaya M, Greiff V, Sandve GK. Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. Gigascience 2022; 11:6593147. [PMID: 35639633 PMCID: PMC9154052 DOI: 10.1093/gigascience/giac046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/23/2021] [Accepted: 04/08/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required. RESULTS To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated sequence patterns (signal) complexity. We trained ≈1,700 ML models with varying assumptions regarding immune signal on ≈1,000 data sets with a total of ≈250,000 AIRRs containing ≈46 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR-ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50,000 AIR sequences. CONCLUSIONS We provide a reference benchmark to guide new AIRR-ML classification methodology by (i) identifying those scenarios characterized by immune signal and data set complexity, where baseline methods already achieve high prediction accuracy, and (ii) facilitating realistic expectations of the performance of AIRR-ML models given training data set properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark data sets for comprehensive benchmarking of AIRR-ML methods.
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Affiliation(s)
- Chakravarthi Kanduri
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Milena Pavlović
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Lonneke Scheffer
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
| | - Keshav Motwani
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, FL 32610, USA
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, 0372, Norway
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, 0372, Norway
| | - Geir K Sandve
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo 0373, Norway
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Dahal-Koirala S, Balaban G, Neumann RS, Scheffer L, Lundin KEA, Greiff V, Sollid LM, Qiao SW, Sandve GK. TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Brief Bioinform 2022; 23:6513728. [PMID: 35062022 PMCID: PMC8921636 DOI: 10.1093/bib/bbab566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 01/19/2023] Open
Abstract
T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.
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Affiliation(s)
- Shiva Dahal-Koirala
- Corresponding authors: Gabriel Balaban, Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway. Tel.: +4767828200; E-mail: . Shiva Dahal-Koirala, Department of Immunology, University of Oslo, Norway. Tel.: +4723072721; E-mail: ; Geir Kjetil Sandve, Department of Informatics, University of Oslo, Norway. Tel.: +4722840861; E-mail: ; Shuo-Wang Qiao, Department of Immunology, University of Oslo, Norway. Tel.: +4722850533; E-mail:
| | - Gabriel Balaban
- Corresponding authors: Gabriel Balaban, Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway. Tel.: +4767828200; E-mail: . Shiva Dahal-Koirala, Department of Immunology, University of Oslo, Norway. Tel.: +4723072721; E-mail: ; Geir Kjetil Sandve, Department of Informatics, University of Oslo, Norway. Tel.: +4722840861; E-mail: ; Shuo-Wang Qiao, Department of Immunology, University of Oslo, Norway. Tel.: +4722850533; E-mail:
| | - Ralf Stefan Neumann
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
| | - Lonneke Scheffer
- Biomedical Informatics, Department of Informatics, University of Oslo, 0373, Oslo, Norway
| | - Knut Erik Aslaksen Lundin
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Gastroenterology, Oslo University Hospital-Rikshospitalet, 0372, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Ludvig Magne Sollid
- K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, 0372, Norway
- Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, 0372, Norway
| | - Shuo-Wang Qiao
- Corresponding authors: Gabriel Balaban, Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway. Tel.: +4767828200; E-mail: . Shiva Dahal-Koirala, Department of Immunology, University of Oslo, Norway. Tel.: +4723072721; E-mail: ; Geir Kjetil Sandve, Department of Informatics, University of Oslo, Norway. Tel.: +4722840861; E-mail: ; Shuo-Wang Qiao, Department of Immunology, University of Oslo, Norway. Tel.: +4722850533; E-mail:
| | - Geir Kjetil Sandve
- Corresponding authors: Gabriel Balaban, Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway. Tel.: +4767828200; E-mail: . Shiva Dahal-Koirala, Department of Immunology, University of Oslo, Norway. Tel.: +4723072721; E-mail: ; Geir Kjetil Sandve, Department of Informatics, University of Oslo, Norway. Tel.: +4722840861; E-mail: ; Shuo-Wang Qiao, Department of Immunology, University of Oslo, Norway. Tel.: +4722850533; E-mail:
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39
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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40
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Marquez S, Babrak L, Greiff V, Hoehn KB, Lees WD, Luning Prak ET, Miho E, Rosenfeld AM, Schramm CA, Stervbo U. Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis. Methods Mol Biol 2022; 2453:297-316. [PMID: 35622333 PMCID: PMC9761518 DOI: 10.1007/978-1-0716-2115-8_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Chaim A Schramm
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Ulrik Stervbo
- Center for Translational Medicine, Immunology, and Transplantation, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany. .,Immundiagnostik, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
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41
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Akbar R, Robert PA, Weber CR, Widrich M, Frank R, Pavlović M, Scheffer L, Chernigovskaya M, Snapkov I, Slabodkin A, Mehta BB, Miho E, Lund-Johansen F, Andersen JT, Hochreiter S, Hobæk Haff I, Klambauer G, Sandve GK, Greiff V. In silico proof of principle of machine learning-based antibody design at unconstrained scale. MAbs 2022; 14:2031482. [PMID: 35377271 PMCID: PMC8986205 DOI: 10.1080/19420862.2022.2031482] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Philippe A Robert
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Cédric R Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michael Widrich
- Ellis Unit Linz and Lit Ai Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Robert Frank
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | | | | | - Maria Chernigovskaya
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Igor Snapkov
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Andrei Slabodkin
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Fridtjof Lund-Johansen
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
| | - Jan Terje Andersen
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway.,Institute of Clinical Medicine, Department of Pharmacology, University of Oslo, Oslo, Norway
| | - Sepp Hochreiter
- Ellis Unit Linz and Lit Ai Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.,Institute of Advanced Research in Artificial Intelligence (IARAI), Austria
| | | | - Günter Klambauer
- Ellis Unit Linz and Lit Ai Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | | | - Victor Greiff
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway
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42
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Grevys A, Frick R, Mester S, Flem-Karlsen K, Nilsen J, Foss S, Sand KMK, Emrich T, Fischer JAA, Greiff V, Sandlie I, Schlothauer T, Andersen JT. Antibody variable sequences have a pronounced effect on cellular transport and plasma half-life. iScience 2022; 25:103746. [PMID: 35118359 PMCID: PMC8800109 DOI: 10.1016/j.isci.2022.103746] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 11/11/2021] [Accepted: 01/05/2022] [Indexed: 11/15/2022] Open
Abstract
Monoclonal IgG antibodies are the fastest growing class of biologics, but large differences exist in their plasma half-life in humans. Thus, to design IgG antibodies with favorable pharmacokinetics, it is crucial to identify the determinants of such differences. Here, we demonstrate that the variable region sequences of IgG antibodies greatly affect cellular uptake and subsequent recycling and rescue from intracellular degradation by endothelial cells. When the variable sequences are masked by the cognate antigen, it influences both their transport behavior and binding to the neonatal Fc receptor (FcRn), a key regulator of IgG plasma half-life. Furthermore, we show how charge patch differences in the variable domains modulate both binding and transport properties and that a short plasma half-life, due to unfavorable charge patches, may partly be overcome by Fc-engineering for improved FcRn binding. IgG variable region sequences greatly affect cellular uptake and recycling Variable region charge patches affect FcRn binding and transport The presence of cognate antigen modulates cellular transport and FcRn binding Fc-engineering for improved FcRn binding can overcome unfavorable charge patches
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Affiliation(s)
- Algirdas Grevys
- Centre for Immune Regulation (CIR) and Department of Biosciences, University of Oslo, 0371 Oslo, Norway
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
- Corresponding author
| | - Rahel Frick
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Simone Mester
- Centre for Immune Regulation (CIR) and Department of Biosciences, University of Oslo, 0371 Oslo, Norway
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Karine Flem-Karlsen
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Jeannette Nilsen
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Stian Foss
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Kine Marita Knudsen Sand
- Centre for Immune Regulation (CIR) and Department of Biosciences, University of Oslo, 0371 Oslo, Norway
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Thomas Emrich
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | | | - Victor Greiff
- Department of Immunology, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Inger Sandlie
- Centre for Immune Regulation (CIR) and Department of Biosciences, University of Oslo, 0371 Oslo, Norway
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
| | - Tilman Schlothauer
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Jan Terje Andersen
- CIR and Department of Immunology, Oslo University Hospital Rikshospitalet, 0372 Oslo, Norway
- Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
- Corresponding author
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43
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Slabodkin A, Chernigovskaya M, Mikocziova I, Akbar R, Scheffer L, Pavlović M, Bashour H, Snapkov I, Mehta BB, Weber CR, Gutierrez-Marcos J, Sollid LM, Haff IH, Sandve GK, Robert PA, Greiff V. Individualized VDJ recombination predisposes the available Ig sequence space. Genome Res 2021; 31:2209-2224. [PMID: 34815307 PMCID: PMC8647828 DOI: 10.1101/gr.275373.121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 10/20/2021] [Indexed: 11/25/2022]
Abstract
The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.
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Affiliation(s)
- Andrei Slabodkin
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Maria Chernigovskaya
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Ivana Mikocziova
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Lonneke Scheffer
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Milena Pavlović
- Department of Informatics, University of Oslo, 0373 Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Igor Snapkov
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Cédric R Weber
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | | | - Ludvig M Sollid
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | | | | | - Philippe A Robert
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, 0372 Oslo, Norway
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44
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Neumeier D, Pedrioli A, Genovese A, Sandu I, Ehling R, Hong KL, Papadopoulou C, Agrafiotis A, Kuhn R, Shlesinger D, Robbiani D, Han J, Hauri L, Csepregi L, Greiff V, Merkler D, Reddy ST, Oxenius A, Yermanos A. Profiling the specificity of clonally expanded plasma cells during chronic viral infection by single-cell analysis. Eur J Immunol 2021; 52:297-311. [PMID: 34727578 PMCID: PMC9299196 DOI: 10.1002/eji.202149331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/02/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022]
Abstract
Plasma cells and their secreted antibodies play a central role in the long-term protection against chronic viral infection. However, due to experimental limitations, a comprehensive description of linked genotypic, phenotypic, and antibody repertoire features of plasma cells (gene expression, clonal frequency, virus specificity, and affinity) has been challenging to obtain. To address this, we performed single-cell transcriptome and antibody repertoire sequencing of the murine BM plasma cell population following chronic lymphocytic choriomeningitis virus infection. Our single-cell sequencing approach recovered full-length and paired heavy- and light-chain sequence information for thousands of plasma cells and enabled us to perform recombinant antibody expression and specificity screening. Antibody repertoire analysis revealed that, relative to protein immunization, chronic infection led to increased levels of clonal expansion, class-switching, and somatic variants. Furthermore, antibodies from the highly expanded and class-switched (IgG) plasma cells were found to be specific for multiple viral antigens and a subset of clones exhibited cross-reactivity to nonviral and autoantigens. Integrating single-cell transcriptome data with antibody specificity suggested that plasma cell transcriptional phenotype was correlated to viral antigen specificity. Our findings demonstrate that chronic viral infection can induce and sustain plasma cell clonal expansion, combined with significant somatic hypermutation, and can generate cross-reactive antibodies.
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Affiliation(s)
- Daniel Neumeier
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | | | | | - Ioana Sandu
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Roy Ehling
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Kai-Lin Hong
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Chrysa Papadopoulou
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Andreas Agrafiotis
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Raphael Kuhn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | | | - Damiano Robbiani
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Jiami Han
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Laura Hauri
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Lucia Csepregi
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Doron Merkler
- Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland.,Division of Clinical Pathology, Geneva University Hospital, Geneva, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | | | - Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Institute of Microbiology, ETH Zurich, Zurich, Switzerland.,Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland
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45
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Mikocziova I, Peres A, Gidoni M, Greiff V, Yaari G, Sollid LM. Germline polymorphisms and alternative splicing of human immunoglobulin light chain genes. iScience 2021; 24:103192. [PMID: 34693229 PMCID: PMC8517844 DOI: 10.1016/j.isci.2021.103192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/17/2021] [Accepted: 09/27/2021] [Indexed: 10/25/2022] Open
Abstract
Inference of germline polymorphisms in immunoglobulin genes from B cell receptor repertoires is complicated by somatic hypermutations, sequencing/PCR errors, and by varying length of reference alleles. The light chain inference is particularly challenging owing to large gene duplications and absence of D genes. We analyzed the light chain cDNA sequences from naïve B cell receptor repertoires from 100 individuals. We optimized light chain allele inference by tweaking parameters of the TIgGER functions, extending the germline reference sequences, and establishing mismatch frequency patterns at polymorphic positions to filter out false-positive candidates. We identified 48 previously unreported variants of light chain variable genes. We selected 14 variants for validation and successfully validated 11 by Sanger sequencing. Clustering of light chain 5'UTR, L-PART1, and L-PART2 revealed partial intron retention in 11 kappa and 9 lambda V alleles. Our results provide insight into germline variation in human light chain immunoglobulin loci.
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Affiliation(s)
- Ivana Mikocziova
- K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
- Department of Immunology, Oslo University Hospital, 0372 Oslo, Norway
| | - Ayelet Peres
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Moriah Gidoni
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, 0372 Oslo, Norway
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Ludvig M. Sollid
- K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, 0372 Oslo, Norway
- Department of Immunology, Oslo University Hospital, 0372 Oslo, Norway
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46
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Öjlert ÅK, Nebdal D, Snapkov I, Olsen V, Kidman J, Greiff V, Chee J, Helland Å. Dynamic changes in the T cell receptor repertoire during treatment with radiotherapy combined with an immune checkpoint inhibitor. Mol Oncol 2021; 15:2958-2968. [PMID: 34402187 PMCID: PMC8564644 DOI: 10.1002/1878-0261.13082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/26/2021] [Accepted: 08/13/2021] [Indexed: 02/01/2023] Open
Abstract
Previous studies have indicated a synergistic effect between radiotherapy and immunotherapy. A better understanding of how this combination affects the immune system can help to clarify its role in the treatment of metastatic cancer. We performed T cell receptor (TCR) sequencing on 46 sequentially collected samples from 15 patients with stage IV non-small cell lung cancer, receiving stereotactic body radiotherapy combined with a programmed cell death ligand-1 (PD-L1) inhibitor. TCR repertoire diversity was assessed using Rényi diversity curves and the Shannon diversity index. TCR clones were tracked over time. We found decreasing or stable diversity in the best responders, and an increase in diversity at progression in patients with an initial response. Expansion of TCR clones was more often seen in responders. Several patients also developed new clones of high abundance. This seemed to be more related to radiotherapy than to immune checkpoint blockade. In summary, we observed similar dynamics in the TCR repertoire as have been described with immunotherapy alone. In addition, the occurrence of new unique clones of high abundance after radiotherapy may indicate that radiotherapy functions as a personalized cancer vaccine.
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Affiliation(s)
- Åsa Kristina Öjlert
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway
| | - Daniel Nebdal
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway
| | - Igor Snapkov
- Department of Immunology, University of Oslo, Norway
| | - Vibeke Olsen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway
| | - Joel Kidman
- National Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.,School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Victor Greiff
- Department of Immunology, University of Oslo, Norway
| | - Jonathan Chee
- National Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.,School of Biomedical Sciences, University of Western Australia, Perth, WA, Australia
| | - Åslaug Helland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway.,Department of Clinical Medicine, University of Oslo, Norway.,Department of Oncology, Oslo University Hospital, Norway
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47
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Mathew NR, Jayanthan JK, Smirnov IV, Robinson JL, Axelsson H, Nakka SS, Emmanouilidi A, Czarnewski P, Yewdell WT, Schön K, Lebrero-Fernández C, Bernasconi V, Rodin W, Harandi AM, Lycke N, Borcherding N, Yewdell JW, Greiff V, Bemark M, Angeletti D. Single-cell BCR and transcriptome analysis after influenza infection reveals spatiotemporal dynamics of antigen-specific B cells. Cell Rep 2021; 35:109286. [PMID: 34161770 PMCID: PMC7612943 DOI: 10.1016/j.celrep.2021.109286] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 05/07/2021] [Accepted: 06/01/2021] [Indexed: 12/15/2022] Open
Abstract
B cell responses are critical for antiviral immunity. However, a comprehensive picture of antigen-specific B cell differentiation, clonal proliferation, and dynamics in different organs after infection is lacking. Here, by combining single-cell RNA and B cell receptor (BCR) sequencing of antigen-specific cells in lymph nodes, spleen, and lungs after influenza infection in mice, we identify several germinal center (GC) B cell subpopulations and organ-specific differences that persist over the course of the response. We discover transcriptional differences between memory cells in lungs and lymphoid organs and organ-restricted clonal expansion. Remarkably, we find significant clonal overlap between GC-derived memory and plasma cells. By combining BCR-mutational analyses with monoclonal antibody (mAb) expression and affinity measurements, we find that memory B cells are highly diverse and can be selected from both low- and high-affinity precursors. By linking antigen recognition with transcriptional programming, clonal proliferation, and differentiation, these finding provide important advances in our understanding of antiviral immunity.
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Affiliation(s)
- Nimitha R Mathew
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Jayalal K Jayanthan
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Ilya V Smirnov
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Jonathan L Robinson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Göteborg, Sweden
| | - Hannes Axelsson
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Sravya S Nakka
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Aikaterini Emmanouilidi
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Paulo Czarnewski
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - William T Yewdell
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Karin Schön
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Cristina Lebrero-Fernández
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Valentina Bernasconi
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - William Rodin
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Ali M Harandi
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden; Vaccine Evaluation Center, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Nils Lycke
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University, St. Louis, MO, USA
| | - Jonathan W Yewdell
- Laboratory of Viral Diseases, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Mats Bemark
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Department of Clinical Immunology and Transfusion Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Davide Angeletti
- Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
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Amoriello R, Chernigovskaya M, Greiff V, Carnasciali A, Massacesi L, Barilaro A, Repice AM, Biagioli T, Aldinucci A, Muraro PA, Laplaud DA, Lossius A, Ballerini C. TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine 2021; 68:103429. [PMID: 34127432 PMCID: PMC8245901 DOI: 10.1016/j.ebiom.2021.103429] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 05/12/2021] [Accepted: 05/19/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND T cells play a key role in the pathogenesis of multiple sclerosis (MS), a chronic, inflammatory, demyelinating disease of the central nervous system (CNS). Although several studies recently investigated the T-cell receptor (TCR) repertoire in cerebrospinal fluid (CSF) of MS patients by high-throughput sequencing (HTS), a deep analysis on repertoire similarities and differences among compartments is still missing. METHODS We performed comprehensive bioinformatics on high-dimensional TCR Vβ sequencing data from published and unpublished MS and healthy donors (HD) studies. We evaluated repertoire polarization, clone distribution, shared CDR3 amino acid sequences (CDR3s-a.a.) across repertoires, clone overlap with public databases, and TCR similarity architecture. FINDINGS CSF repertoires showed a significantly higher public clones percentage and sequence similarity compared to peripheral blood (PB). On the other hand, we failed to reject the null hypothesis that the repertoire polarization is the same between CSF and PB. One Primary-Progressive MS (PPMS) CSF repertoire differed from the others in terms of TCR similarity architecture. Cluster analysis splits MS from HD. INTERPRETATION In MS patients, the presence of a physiological barrier, the blood-brain barrier, does not impact clone prevalence and distribution, but impacts public clones, indicating CSF as a more private site. We reported a high Vβ sequence similarity in the CSF-TCR architecture in one PPMS. If confirmed it may be an interesting insight into MS progressive inflammatory mechanisms. The clustering of MS repertoires from HD suggests that disease shapes the TCR Vβ clonal profile. FUNDING This study was partly financially supported by the Italian Multiple Sclerosis Foundation (FISM), that contributed to Ballerini-DB data collection (grant #2015 R02).
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Affiliation(s)
- Roberta Amoriello
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
| | | | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Alberto Carnasciali
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy
| | - Luca Massacesi
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Alessandro Barilaro
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Anna M Repice
- Dipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Italy
| | - Tiziana Biagioli
- Laboratorio Generale, Careggi University Hospital, Florence, Italy
| | | | - Paolo A Muraro
- Wolfson Neuroscience Laboratory, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - David A Laplaud
- CRTI-Inserm U1064, CIC0004 and Service de Neurologie, CHU de Nantes, Hôpital Nord Laënnec, Nantes, France
| | - Andreas Lossius
- Institute of Clinical Medicine, University of Oslo, Postboks 1105, Blindern 0317 Oslo, Norway.
| | - Clara Ballerini
- Dipartimento di Medicina Sperimentale e Clinica (DMSC), Laboratory of Neuroimmunology, University of Florence, Viale Pieraccini 6, 50139 Florence, Italy.
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49
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Laustsen AH, Greiff V, Karatt-Vellatt A, Muyldermans S, Jenkins TP. Animal Immunization, in Vitro Display Technologies, and Machine Learning for Antibody Discovery. Trends Biotechnol 2021; 39:1263-1273. [PMID: 33775449 DOI: 10.1016/j.tibtech.2021.03.003] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
For years, a discussion has persevered on the benefits and drawbacks of antibody discovery using animal immunization versus in vitro selection from non-animal-derived recombinant repertoires using display technologies. While it has been argued that using recombinant display libraries can reduce animal consumption, we hold that the number of animals used in immunization campaigns is dwarfed by the number sacrificed during preclinical studies. Thus, improving quality control of antibodies before entering in vivo studies will have a larger impact on animal consumption. Both animal immunization and recombinant repertoires present unique advantages for discovering antibodies that are fit for purpose. Furthermore, we anticipate that machine learning will play a significant role within discovery workflows, refining current antibody discovery practices.
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Affiliation(s)
- Andreas H Laustsen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | | | - Serge Muyldermans
- Department of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Timothy P Jenkins
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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50
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Mester S, Evers M, Meyer S, Nilsen J, Greiff V, Sandlie I, Leusen J, Andersen JT. Extended plasma half-life of albumin-binding domain fused human IgA upon pH-dependent albumin engagement of human FcRn in vitro and in vivo. MAbs 2021; 13:1893888. [PMID: 33691596 PMCID: PMC7954421 DOI: 10.1080/19420862.2021.1893888] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Albumin has a serum half-life of 3 weeks in humans. This feature can be used to improve the pharmacokinetics of shorter-lived biologics. For instance, an albumin-binding domain (ABD) can be used to recruit albumin. A prerequisite for such design is that the ABD-albumin interaction does not interfere with pH-dependent binding of albumin to the human neonatal Fc receptor (FcRn), as FcRn acts as the principal regulator of the half-life of albumin. Thus, there is a need to know how ABDs act in the context of fusion partners and human FcRn. Here, we studied the binding and transport properties of human immunoglobulin A1 (IgA1), fused to a Streptococcus protein G-derived engineered ABD, in in vitro and in vivo systems harboring human FcRn. IgA has great potential as a therapeutic protein, but its short half-life is a major drawback. We demonstrate that ABD-fused IgA1 binds human FcRn pH-dependently and is rescued from cellular degradation in a receptor-specific manner in the presence of albumin. This occurs when ABD is fused to either the light or the heavy chain. In human FcRn transgenic mice, IgA1-ABD in complex with human albumin, gave 4-6-fold extended half-life compared to unmodified IgA1, where the light chain fusion showed the longest half-life. When the heavy chain-fused protein was pre-incubated with an engineered human albumin with improved FcRn binding, cellular rescue and half-life was further enhanced. Our study reveals how an ABD, which does not interfere with albumin binding to human FcRn, may be used to extend the half-life of IgA. Abbreviations: ABD - Albumin binding domain, ADA – anti-drug-antibodies, ADCC - Antibody-dependent cellular cytotoxicity, ELISA - Enzyme-linked Immunosorbent assay, FcαRI - Fcα receptor, FcγR - Fcγ receptor, FcRn - The neonatal Fc receptor, GST - Glutathione S-transferase, HC - Heavy chain, HERA - Human endothelial cell-based recycling assay, Her2 - Human epidermal growth factor 2, HMEC - Human microvascular endothelial cells, IgG - Immunoglobulin G, IgA - Immunoglobulin A, LC - Light chain, QMP - E505Q/T527M/K573P, WT - Wild type
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Affiliation(s)
- Simone Mester
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.,Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Mitchell Evers
- Center for Translational Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Saskia Meyer
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Jeannette Nilsen
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.,Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway
| | - Inger Sandlie
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.,Department of Biosciences, University of Oslo, Oslo, Norway
| | - Jeanette Leusen
- Center for Translational Immunology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan Terje Andersen
- Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.,Department of Pharmacology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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