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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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2
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Schaffer AM, Fiala GJ, Hils M, Natali E, Babrak L, Herr LA, Romero-Mulero MC, Cabezas-Wallscheid N, Rizzi M, Miho E, Schamel WWA, Minguet S. Kidins220 regulates the development of B cells bearing the λ light chain. eLife 2024; 13:e83943. [PMID: 38271217 PMCID: PMC10810608 DOI: 10.7554/elife.83943] [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: 10/12/2022] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
The ratio between κ and λ light chain (LC)-expressing B cells varies considerably between species. We recently identified Kinase D-interacting substrate of 220 kDa (Kidins220) as an interaction partner of the BCR. In vivo ablation of Kidins220 in B cells resulted in a marked reduction of λLC-expressing B cells. Kidins220 knockout B cells fail to open and recombine the genes of the Igl locus, even in genetic scenarios where the Igk genes cannot be rearranged or where the κLC confers autoreactivity. Igk gene recombination and expression in Kidins220-deficient B cells is normal. Kidins220 regulates the development of λLC B cells by enhancing the survival of developing B cells and thereby extending the time-window in which the Igl locus opens and the genes are rearranged and transcribed. Further, our data suggest that Kidins220 guarantees optimal pre-BCR and BCR signaling to induce Igl locus opening and gene recombination during B cell development and receptor editing.
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Affiliation(s)
- Anna-Maria Schaffer
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Signalling Research Centers BIOSS and CIBSS, University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
| | - Gina Jasmin Fiala
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Signalling Research Centers BIOSS and CIBSS, University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
| | - Miriam Hils
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
- Department of Dermatology and Allergy Biederstein, School of Medicine, Technical University of MunichMunichGermany
| | - Eriberto Natali
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW 15 University of Applied Sciences and Arts Northwestern SwitzerlandMuttenzSwitzerland
| | - Lmar Babrak
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW 15 University of Applied Sciences and Arts Northwestern SwitzerlandMuttenzSwitzerland
| | - Laurenz Alexander Herr
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Signalling Research Centers BIOSS and CIBSS, University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
| | - Mari Carmen Romero-Mulero
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Max Planck Institute of Immunobiology and EpigeneticsFreiburgGermany
| | - Nina Cabezas-Wallscheid
- Max Planck Institute of Immunobiology and EpigeneticsFreiburgGermany
- CIBSS – Centre for Integrative Biological Signalling Studies, University of FreiburgFreiburgGermany
| | - Marta Rizzi
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
- CIBSS – Centre for Integrative Biological Signalling Studies, University of FreiburgFreiburgGermany
- Division of Clinical and Experimental Immunology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of ViennaViennaAustria
- Department of Rheumatology and Clinical Immunology, University Medical Center Freiburg, Faculty of Medicine, University of FreiburgFreiburgGermany
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW 15 University of Applied Sciences and Arts Northwestern SwitzerlandMuttenzSwitzerland
- aiNET GmbHBaselSwitzerland
- SIB Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Wolfgang WA Schamel
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Signalling Research Centers BIOSS and CIBSS, University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
| | - Susana Minguet
- Faculty of Biology, Albert-Ludwigs-University of FreiburgFreiburgGermany
- Signalling Research Centers BIOSS and CIBSS, University of FreiburgFreiburgGermany
- Center of Chronic Immunodeficiency CCI, University Clinics and Medical FacultyFreiburgGermany
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3
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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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4
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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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5
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Cascino P, Nevone A, Piscitelli M, Scopelliti C, Girelli M, Mazzini G, Caminito S, Russo G, Milani P, Basset M, Foli A, Fazio F, Casarini S, Massa M, Bozzola M, Ripepi J, Sesta MA, Acquafredda G, De Cicco M, Moretta A, Rognoni P, Milan E, Ricagno S, Lavatelli F, Petrucci MT, Miho E, Klersy C, Merlini G, Palladini G, Nuvolone M. Single-molecule real-time sequencing of the M protein: Toward personalized medicine in monoclonal gammopathies. Am J Hematol 2022; 97:E389-E392. [PMID: 35997169 DOI: 10.1002/ajh.26684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 01/28/2023]
Affiliation(s)
- Pasquale Cascino
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Alice Nevone
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Maggie Piscitelli
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Claudia Scopelliti
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Maria Girelli
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giulia Mazzini
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Serena Caminito
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giancarlo Russo
- EMBL partner institute for genome editing, Life Science Center, Vilnius University, Vilnius, Lithuania
| | - Paolo Milani
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marco Basset
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Andrea Foli
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Francesca Fazio
- Hematology, Department of Translational and Precision Medicine, Azienda Ospedaliera Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Simona Casarini
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Margherita Massa
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Margherita Bozzola
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Jessica Ripepi
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Melania Antonietta Sesta
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Gloria Acquafredda
- Pediatric Hematology Oncology Unit, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Cell Factory and Center for Advanced Cellular Therapies, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marica De Cicco
- Pediatric Hematology Oncology Unit, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Cell Factory and Center for Advanced Cellular Therapies, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Antonia Moretta
- Pediatric Hematology Oncology Unit, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Cell Factory and Center for Advanced Cellular Therapies, Department of Maternal and Children's Health, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Paola Rognoni
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Enrico Milan
- Age related Diseases Unit, Division of Genetics and Cell Biology, San Raffaele Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy
| | - Stefano Ricagno
- Department of Biosciences, Università degli Studi di Milano, Milan, Italy.,Institute of Molecular and Translational Cardiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Francesca Lavatelli
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Maria Teresa Petrucci
- Hematology, Department of Translational and Precision Medicine, Azienda Ospedaliera Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,aiNET GmbH, Basel, Switzerland
| | - Catherine Klersy
- Clinical Epidemiology and Biometry Service, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giampaolo Merlini
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Giovanni Palladini
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Mario Nuvolone
- Department of Molecular Medicine, University of Pavia, Pavia, Italy.,Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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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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7
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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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8
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Horst A, Smakaj E, Natali EN, Tosoni D, Babrak LM, Meier P, Miho E. Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences. Front Artif Intell 2021; 4:715462. [PMID: 34708197 PMCID: PMC8542978 DOI: 10.3389/frai.2021.715462] [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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.
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Affiliation(s)
- Alexander Horst
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Erand Smakaj
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Eriberto Noel Natali
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Deniz Tosoni
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Lmar Marie Babrak
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Patrick Meier
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Enkelejda Miho
- School of Life Sciences, Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,aiNET GmbH, Basel, Switzerland
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9
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Grimberg F, Asprion PM, Schneider B, Miho E, Babrak L, Habbabeh A. The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data. Digit Biomark 2021; 5:148-157. [PMID: 34414352 DOI: 10.1159/000516178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 11/29/2020] [Accepted: 03/28/2021] [Indexed: 12/19/2022] Open
Abstract
Background The life science industry has a strong interest in real-world data (RWD), a term that is currently being used in many ways and with varying definitions depending on the source. In this review article, we provide a summary overview of the challenges and risks regarding the use of RWD and its translation into real-world evidence and provide a classification and visualization of RWD challenges by means of the RWD Challenges Radar. Summary Based on a systematic literature search, we identified 3 types of challenges - organizational, technological, and people-based - that must be addressed when deriving evidence from RWD to be used in drug approval and other applications. It further demonstrates that numerous different aspects, for example, related to the application field and the associated industry, must be considered. A key finding in our review is that the regulatory landscape must be carefully assessed before utilizing RWD. Key Messages Establishing awareness and insight into the challenges and risks regarding the use of RWD will be key to taking full advantage of the RWD potential. As a result of this review, an "RWD Challenges Radar" will support the establishment of awareness by providing a comprehensive overview of the relevant aspects to be considered when employing RWD.
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Affiliation(s)
- Frank Grimberg
- Institute of Information Systems, Competence Center Cyber Security and Resilience, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
| | - Petra Maria Asprion
- Institute of Information Systems, Competence Center Cyber Security and Resilience, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
| | - Bettina Schneider
- Institute of Information Systems, Competence Center Cyber Security and Resilience, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
| | - Enkelejda Miho
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Lmar Babrak
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Ali Habbabeh
- Institute of Information Systems, Competence Center Cyber Security and Resilience, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
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10
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Natali EN, Babrak LM, Miho E. Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics. Front Immunol 2021; 12:574411. [PMID: 34211454 PMCID: PMC8239437 DOI: 10.3389/fimmu.2021.574411] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 05/17/2021] [Indexed: 01/02/2023] Open
Abstract
Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.
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Affiliation(s)
- Eriberto N. Natali
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Lmar M. Babrak
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- aiNET GmbH, Basel, Switzerland
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11
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Babrak LM, Smakaj E, Agac T, Asprion PM, Grimberg F, der Werf DV, van Ginkel EW, Tosoni DD, Clay I, Degen M, Brodbeck D, Natali EN, Schkommodau E, Miho E. RWD-Cockpit: Application for Quality Assessment of Real-World Data (Preprint). JMIR Form Res 2021; 6:e29920. [PMID: 35266872 PMCID: PMC9627468 DOI: 10.2196/29920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 04/25/2021] [Revised: 10/31/2021] [Accepted: 02/19/2022] [Indexed: 11/22/2022] Open
Abstract
Background Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. Objective To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets—molecular, phenotypical, and social—and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies—de novo–generated sleep data and publicly available data sets—the RWD-Cockpit could identify and provide researchers with variables that might increase quality. Conclusions The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores—quality identifiers—provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.
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Affiliation(s)
- Lmar Marie Babrak
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Erand Smakaj
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Teyfik Agac
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Petra Maria Asprion
- Fachhochschule Nordwestschweiz University of Applied Sciences and Arts Northwestern Switzerland, School of Business, Olten, Switzerland
| | - Frank Grimberg
- Fachhochschule Nordwestschweiz University of Applied Sciences and Arts Northwestern Switzerland, School of Business, Olten, Switzerland
| | | | | | - Deniz David Tosoni
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Ieuan Clay
- Evidation Health Inc, San Mateo, CA, United States
| | - Markus Degen
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Dominique Brodbeck
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Eriberto Noel Natali
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Erik Schkommodau
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Enkelejda Miho
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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12
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Akbar R, Robert PA, Pavlović M, Jeliazkov JR, Snapkov I, Slabodkin A, Weber CR, Scheffer L, Miho E, Haff IH, Haug DTT, Lund-Johansen F, Safonova Y, Sandve GK, Greiff V. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell Rep 2021; 34:108856. [PMID: 33730590 DOI: 10.1016/j.celrep.2021.108856] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [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: 03/12/2020] [Revised: 11/29/2020] [Accepted: 02/22/2021] [Indexed: 12/16/2022] Open
Abstract
Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo, Oslo, Norway.
| | | | - Milena Pavlović
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, University of Oslo, Norway; K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Igor Snapkov
- Department of Immunology, University of Oslo, Oslo, Norway
| | | | - Cédric R Weber
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Lonneke Scheffer
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, 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
| | | | | | | | - Yana Safonova
- Computer Science and Engineering Department, University of California, San Diego, La Jolla, CA, USA
| | - Geir K Sandve
- Department of Informatics, University of Oslo, Oslo, Norway; Centre for Bioinformatics, University of Oslo, Norway; K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway.
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13
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Smakaj E, Babrak L, Ohlin M, Shugay M, Briney B, Tosoni D, Galli C, Grobelsek V, D'Angelo I, Olson B, Reddy S, Greiff V, Trück J, Marquez S, Lees W, Miho E. Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences. Bioinformatics 2020; 36:1731-1739. [PMID: 31873728 PMCID: PMC7075533 DOI: 10.1093/bioinformatics/btz845] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [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: 06/21/2019] [Revised: 10/21/2019] [Accepted: 12/19/2019] [Indexed: 01/01/2023] Open
Abstract
Summary Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics. There are currently multiple tools available for the annotation of antibody sequences. All downstream analyses such as choosing lead drug candidates depend on the correct annotation of these sequences; however, a thorough comparison of the performance of these tools has not been investigated. Here, we benchmark the performance of commonly used immunoinformatic tools, i.e. IMGT/HighV-QUEST, IgBLAST and MiXCR, in terms of reproducibility of annotation output, accuracy and speed using simulated and experimental high-throughput sequencing datasets. We analyzed changes in IMGT reference germline database in the last 10 years in order to assess the reproducibility of the annotation output. We found that only 73/183 (40%) V, D and J human genes were shared between the reference germline sets used by the tools. We found that the annotation results differed between tools. In terms of alignment accuracy, MiXCR had the highest average frequency of gene mishits, 0.02 mishit frequency and IgBLAST the lowest, 0.004 mishit frequency. Reproducibility in the output of complementarity determining three regions (CDR3 amino acids) ranged from 4.3% to 77.6% with preprocessed data. In addition, run time of the tools was assessed: MiXCR was the fastest tool for number of sequences processed per unit of time. These results indicate that immunoinformatic analyses greatly depend on the choice of bioinformatics tool. Our results support informed decision-making to immunoinformaticians based on repertoire composition and sequencing platforms. Availability and implementation All tools utilized in the paper are free for academic use. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erand Smakaj
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz 4132, Switzerland
| | - Lmar Babrak
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz 4132, Switzerland
| | - Mats Ohlin
- Department of Immunotechnology, Lund University, Lund 223, Sweden
| | - Mikhail Shugay
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Bryan Briney
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Deniz Tosoni
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz 4132, Switzerland
| | - Christopher Galli
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz 4132, Switzerland
| | - Vendi Grobelsek
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Igor D'Angelo
- One Amgen Center Drive, Amgen, Inc., Therapeutic Discovery/Molecular Engineering, Thousand Oaks, CA 91320, USA
| | - Branden Olson
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.,Department of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Sai Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo 0372, Norway
| | - Johannes Trück
- Paediatric Immunology, Children's Research Center, University Children's Hospital, University of Zurich, Zurich 8032, Switzerland
| | - Susanna Marquez
- Department of Pathology, Yale School of Medicine, New Haven, CT 06511, USA
| | - William Lees
- Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, UK
| | - Enkelejda Miho
- Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz 4132, Switzerland.,aiNET GmbH, Switzerland Innovation Park Basel Area AG, Basel 4057, Switzerland
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14
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Ghraichy M, Galson JD, Kovaltsuk A, von Niederhäusern V, Pachlopnik Schmid J, Recher M, Jauch AJ, Miho E, Kelly DF, Deane CM, Trück J. Maturation of the Human Immunoglobulin Heavy Chain Repertoire With Age. Front Immunol 2020; 11:1734. [PMID: 32849618 PMCID: PMC7424015 DOI: 10.3389/fimmu.2020.01734] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [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: 04/06/2020] [Accepted: 06/29/2020] [Indexed: 01/01/2023] Open
Abstract
B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.
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Affiliation(s)
- Marie Ghraichy
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Jacob D Galson
- Children's Research Center, University of Zurich, Zurich, Switzerland.,Alchemab Therapeutics Ltd, London, United Kingdom
| | | | - Valentin von Niederhäusern
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Jana Pachlopnik Schmid
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
| | - Mike Recher
- Immunodeficiency Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland
| | - Annaïse J Jauch
- Immunodeficiency Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland
| | - Enkelejda Miho
- Institute of Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Muttenz, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,aiNET GmbH, Basel, Switzerland
| | - Dominic F Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Johannes Trück
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland.,Children's Research Center, University of Zurich, Zurich, Switzerland
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15
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Babrak LM, Menetski J, Rebhan M, Nisato G, Zinggeler M, Brasier N, Baerenfaller K, Brenzikofer T, Baltzer L, Vogler C, Gschwind L, Schneider C, Streiff F, Groenen PM, Miho E. Traditional and Digital Biomarkers: Two Worlds Apart? Digit Biomark 2019; 3:92-102. [PMID: 32095769 PMCID: PMC7015353 DOI: 10.1159/000502000] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [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: 05/02/2019] [Accepted: 07/08/2019] [Indexed: 11/19/2022] Open
Abstract
The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society. The increase of digital devices and the rise in popularity of health-related mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their intersection with traditional biomarkers hinders their discovery and validation. We have identified current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.
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Affiliation(s)
- Lmar M. Babrak
- FHNW University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland
| | - Joseph Menetski
- Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
| | - Michael Rebhan
- Novartis Institutes for Biomedical Research, Basel, Switzerland
- DayOne, BaselArea. Swiss, Basel, Switzerland
| | | | | | - Noé Brasier
- CMIO Research Group, University Hospital Basel, Basel, Switzerland
| | - Katja Baerenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, and Swiss Institute of Bioinformatics (SIB), Davos, Switzerland
| | | | | | | | | | - Cornelia Schneider
- Clinical Pharmacy and Epidemiology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
- Hospital Pharmacy, University Hospital Basel, Basel, Switzerland
| | | | - Peter M.A. Groenen
- DayOne, BaselArea. Swiss, Basel, Switzerland
- Idorsia Pharmaceuticals Ltd., Translational Science, Allschwil, Switzerland
| | - Enkelejda Miho
- FHNW University of Applied Sciences Northwestern Switzerland, Muttenz, Switzerland
- DayOne, BaselArea. Swiss, Basel, Switzerland
- aiNET GmbH, Basel, Switzerland
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16
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Miho E, Roškar R, Greiff V, Reddy ST. Large-scale network analysis reveals the sequence space architecture of antibody repertoires. Nat Commun 2019; 10:1321. [PMID: 30899025 PMCID: PMC6428871 DOI: 10.1038/s41467-019-09278-8] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [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: 04/06/2017] [Accepted: 03/01/2019] [Indexed: 12/23/2022] Open
Abstract
The architecture of mouse and human antibody repertoires is defined by the sequence similarity networks of the clones that compose them. The major principles that define the architecture of antibody repertoires have remained largely unknown. Here, we establish a high-performance computing platform to construct large-scale networks from comprehensive human and murine antibody repertoire sequencing datasets (>100,000 unique sequences). Leveraging a network-based statistical framework, we identify three fundamental principles of antibody repertoire architecture: reproducibility, robustness and redundancy. Antibody repertoire networks are highly reproducible across individuals despite high antibody sequence dissimilarity. The architecture of antibody repertoires is robust to the removal of up to 50-90% of randomly selected clones, but fragile to the removal of public clones shared among individuals. Finally, repertoire architecture is intrinsically redundant. Our analysis provides guidelines for the large-scale network analysis of immune repertoires and may be used in the future to define disease-associated and synthetic repertoires.
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Affiliation(s)
- Enkelejda Miho
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.,Institute of Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, 4132, Muttenz, Switzerland.,aiNET GmbH, c/o Switzerland Innovation Park Basel Area AG, Hochbergstrasse 60C, 4057, Basel, Switzerland
| | - Rok Roškar
- Research Informatics, Scientific IT Services, ETH Zürich, 8001, Zürich, Switzerland
| | - Victor Greiff
- Department of Immunology, University of Oslo, 0372, Oslo, Norway.
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
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17
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Yermanos A, Greiff V, Krautler NJ, Menzel U, Dounas A, Miho E, Oxenius A, Stadler T, Reddy ST. Comparison of methods for phylogenetic B-cell lineage inference using time-resolved antibody repertoire simulations (AbSim). Bioinformatics 2018; 33:3938-3946. [PMID: 28968873 DOI: 10.1093/bioinformatics/btx533] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 08/30/2017] [Indexed: 01/13/2023] Open
Abstract
Motivation The evolution of antibody repertoires represents a hallmark feature of adaptive B-cell immunity. Recent advancements in high-throughput sequencing have dramatically increased the resolution to which we can measure the molecular diversity of antibody repertoires, thereby offering for the first time the possibility to capture the antigen-driven evolution of B cells. However, there does not exist a repertoire simulation framework yet that enables the comparison of commonly utilized phylogenetic methods with regard to their accuracy in inferring antibody evolution. Results Here, we developed AbSim, a time-resolved antibody repertoire simulation framework, which we exploited for testing the accuracy of methods for the phylogenetic reconstruction of B-cell lineages and antibody molecular evolution. AbSim enables the (i) simulation of intermediate stages of antibody sequence evolution and (ii) the modeling of immunologically relevant parameters such as duration of repertoire evolution, and the method and frequency of mutations. First, we validated that our repertoire simulation framework recreates replicates topological similarities observed in experimental sequencing data. Second, we leveraged Absim to show that current methods fail to a certain extent to predict the true phylogenetic tree correctly. Finally, we formulated simulation-validated guidelines for antibody evolution, which in the future will enable the development of accurate phylogenetic methods. Availability and implementation https://cran.r-project.org/web/packages/AbSim/index.html. Contact sai.reddy@ethz.ch. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander Yermanos
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | - Victor Greiff
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | | | - Ulrike Menzel
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | - Andreas Dounas
- Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Enkelejda Miho
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | | | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
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18
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Friedensohn S, Lindner JM, Cornacchione V, Iazeolla M, Miho E, Zingg A, Meng S, Traggiai E, Reddy ST. Synthetic Standards Combined With Error and Bias Correction Improve the Accuracy and Quantitative Resolution of Antibody Repertoire Sequencing in Human Naïve and Memory B Cells. Front Immunol 2018; 9:1401. [PMID: 29973938 PMCID: PMC6019461 DOI: 10.3389/fimmu.2018.01401] [Citation(s) in RCA: 26] [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] [Received: 03/19/2018] [Accepted: 06/06/2018] [Indexed: 11/13/2022] Open
Abstract
High-throughput sequencing of immunoglobulin (Ig) repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy-chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naïve and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment, and isotype subclass usage, and somatic hypermutation. We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling.
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Affiliation(s)
- Simon Friedensohn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - John M Lindner
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Enkelejda Miho
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Andreas Zingg
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Simon Meng
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | | | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
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Miho E, Yermanos A, Weber CR, Berger CT, Reddy ST, Greiff V. Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires. Front Immunol 2018; 9:224. [PMID: 29515569 PMCID: PMC5826328 DOI: 10.3389/fimmu.2018.00224] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [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: 11/22/2017] [Accepted: 01/26/2018] [Indexed: 12/21/2022] Open
Abstract
The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
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Affiliation(s)
- Enkelejda Miho
- Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- aiNET GmbH, ETH Zürich, Basel, Switzerland
| | - Alexander Yermanos
- Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Cédric R. Weber
- Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Christoph T. Berger
- Department of Biomedicine, University Hospital Basel, Basel, Switzerland
- Department of Internal Medicine, Clinical Immunology, University Hospital Basel, Basel, Switzerland
| | - Sai T. Reddy
- Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Victor Greiff
- Department for Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Department of Immunology, University of Oslo, Oslo, Norway
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Greiff V, Weber CR, Palme J, Bodenhofer U, Miho E, Menzel U, Reddy ST. Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires. J I 2017; 199:2985-2997. [DOI: 10.4049/jimmunol.1700594] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 08/16/2017] [Indexed: 11/19/2022]
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