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Sharma L, Rahman F, Sharma RA. The emerging role of biotechnological advances and artificial intelligence in tackling gluten sensitivity. Crit Rev Food Sci Nutr 2024:1-17. [PMID: 39145745 DOI: 10.1080/10408398.2024.2392158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Gluten comprises an intricate network of hundreds of related but distinct proteins, mainly "gliadins" and "glutenins," which play a vital role in determining the rheological properties of wheat dough. However, ingesting gluten can trigger severe conditions in susceptible individuals, including celiac disease, wheat allergy, or non-celiac gluten sensitivity, collectively known as gluten-related disorders. This review provides a panoramic view, delving into the various aspects of gluten-triggered disorders, including symptoms, diagnosis, mechanism, and management. Though a gluten-free diet remains the primary option to manage gluten-related disorders, the emerging microbial and plant biotechnology tools are playing a transformative role in reducing the immunotoxicity of gluten. The enzymatic hydrolysis of gluten and the development of gluten-reduced/free wheat lines using RNAi and CRISPR/Cas technology are laying the foundation for creating safer wheat products. In addition to biotechnological interventions, the emerging artificial intelligence technologies are also bringing about a paradigm shift in the diagnosis and management of gluten-related disorders. Here, we provide a comprehensive overview of the latest developments and the potential these technologies hold for tackling gluten sensitivity.
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Affiliation(s)
- Lakshay Sharma
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
| | - Farhanur Rahman
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
| | - Rita A Sharma
- Department of Biological Sciences, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani, India
- National Agri-Food Biotechnology Institute (NABI), Mohali, India
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2
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Zaslavsky ME, Craig E, Michuda JK, Sehgal N, Ram-Mohan N, Lee JY, Nguyen KD, Hoh RA, Pham TD, Röltgen K, Lam B, Parsons ES, Macwana SR, DeJager W, Drapeau EM, Roskin KM, Cunningham-Rundles C, Moody MA, Haynes BF, Goldman JD, Heath JR, Nadeau KC, Pinsky BA, Blish CA, Hensley SE, Jensen K, Meyer E, Balboni I, Utz PJ, Merrill JT, Guthridge JM, James JA, Yang S, Tibshirani R, Kundaje A, Boyd SD. Disease diagnostics using machine learning of immune receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2022.04.26.489314. [PMID: 35547855 PMCID: PMC9094102 DOI: 10.1101/2022.04.26.489314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.
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3
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Yuuki H, Itamiya T, Nagafuchi Y, Ota M, Fujio K. B cell receptor repertoire abnormalities in autoimmune disease. Front Immunol 2024; 15:1326823. [PMID: 38361948 PMCID: PMC10867955 DOI: 10.3389/fimmu.2024.1326823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
B cells play a crucial role in the immune response and contribute to various autoimmune diseases. Recent studies have revealed abnormalities in the B cell receptor (BCR) repertoire of patients with autoimmune diseases, with distinct features observed among different diseases and B cell subsets. Classically, BCR repertoire was used as an identifier of distinct antigen-specific clonotypes, but the recent advancement of analyzing large-scale repertoire has enabled us to use it as a tool for characterizing cellular biology. In this review, we provide an overview of the BCR repertoire in autoimmune diseases incorporating insights from our latest research findings. In systemic lupus erythematosus (SLE), we observed a significant skew in the usage of VDJ genes, particularly in CD27+IgD+ unswitched memory B cells and plasmablasts. Notably, autoreactive clones within unswitched memory B cells were found to be increased and strongly associated with disease activity, underscoring the clinical significance of this subset. Similarly, various abnormalities in the BCR repertoire have been reported in other autoimmune diseases such as rheumatoid arthritis. Thus, BCR repertoire analysis holds potential for enhancing our understanding of the underlying mechanisms involved in autoimmune diseases. Moreover, it has the potential to predict treatment effects and identify therapeutic targets in autoimmune diseases.
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Affiliation(s)
- Hayato Yuuki
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takahiro Itamiya
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yasuo Nagafuchi
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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4
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Saputri DS, Ismanto HS, Nugraha DK, Xu Z, Horiguchi Y, Sakakibara S, Standley DM. Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences. mSystems 2023; 8:e0072223. [PMID: 37975681 PMCID: PMC10734444 DOI: 10.1128/msystems.00722-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.
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Affiliation(s)
- Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dendi K. Nugraha
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Zichang Xu
- Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Yasuhiko Horiguchi
- Graduate School of Medicine, Osaka University, Suita, Japan
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
| | - Shuhei Sakakibara
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Graduate School of Medical Safety Management, Jikei University of Health Care Sciences, Osaka, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
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5
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Tapkire MD, Arun V. Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review. JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023; 60:2350-2357. [PMID: 37424577 PMCID: PMC10326233 DOI: 10.1007/s13197-022-05550-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 07/11/2023]
Abstract
Clinicians and administrators are applying Artificial Intelligence (AI) Techniques widely as the promising results of their applications in the healthcare have been established. The meaningful impact of the AI applications will be limited unless it is coherently applied with human diagnosis and inputs from specialist clinician. This will help to address limitations and take advantage of the promises of the AI techniques. Machine Learning is one of the AI technique that finds high relevance in the medicine and health care. This review provides an overall glimpse of current practices and research outcomes of the application of the AI techniques in the healthcare and medical practices. It further describes Machine Learning Techniques in disease prediction and scope for food formulations for combatting disease.
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Affiliation(s)
- Mayura D. Tapkire
- Department of Information Science and Engineering, National Institute of Engineering, Mysuru, India
| | - Vanishri Arun
- Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Constituent College of JSS Science and Technology University, Mysuru, India
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Rodriguez OL, Safonova Y, Silver CA, Shields K, Gibson WS, Kos JT, Tieri D, Ke H, Jackson KJL, Boyd SD, Smith ML, Marasco WA, Watson CT. Genetic variation in the immunoglobulin heavy chain locus shapes the human antibody repertoire. Nat Commun 2023; 14:4419. [PMID: 37479682 PMCID: PMC10362067 DOI: 10.1038/s41467-023-40070-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
Variation in the antibody response has been linked to differential outcomes in disease, and suboptimal vaccine and therapeutic responsiveness, the determinants of which have not been fully elucidated. Countering models that presume antibodies are generated largely by stochastic processes, we demonstrate that polymorphisms within the immunoglobulin heavy chain locus (IGH) impact the naive and antigen-experienced antibody repertoire, indicating that genetics predisposes individuals to mount qualitatively and quantitatively different antibody responses. We pair recently developed long-read genomic sequencing methods with antibody repertoire profiling to comprehensively resolve IGH genetic variation, including novel structural variants, single nucleotide variants, and genes and alleles. We show that IGH germline variants determine the presence and frequency of antibody genes in the expressed repertoire, including those enriched in functional elements linked to V(D)J recombination, and overlapping disease-associated variants. These results illuminate the power of leveraging IGH genetics to better understand the regulation, function, and dynamics of the antibody response in disease.
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Affiliation(s)
- Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Yana Safonova
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Catherine A Silver
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Kaitlyn Shields
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - William S Gibson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Justin T Kos
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Hanzhong Ke
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Scott D Boyd
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Melissa L Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
| | - Wayne A Marasco
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
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7
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Konstantinovsky T, Yaari G. A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression. Bioinformatics 2023; 39:btad426. [PMID: 37417959 PMCID: PMC10348835 DOI: 10.1093/bioinformatics/btad426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023] Open
Abstract
MOTIVATION T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses. However, their high diversity and complexity present significant challenges in representation and analysis. The main motivation of this study is to develop a unified and compact representation of a TCRB repertoire that can efficiently capture its inherent complexity and diversity and allow for direct inference. RESULTS We introduce a novel approach to TCRB repertoire encoding and analysis, leveraging the Lempel-Ziv 76 algorithm. This approach allows us to create a graph-like model, identify-specific sequence features, and produce a new encoding approach for an individual's repertoire. The proposed representation enables various applications, including generation probability inference, informative feature vector derivation, sequence generation, a new measure for diversity estimation, and a new sequence centrality measure. The approach was applied to four large-scale public TCRB sequencing datasets, demonstrating its potential for a wide range of applications in big biological sequencing data. AVAILABILITY AND IMPLEMENTATION Python package for implementation is available https://github.com/MuteJester/LZGraphs.
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Affiliation(s)
- Thomas Konstantinovsky
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
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8
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Neuman H, Arrouasse J, Benjamini O, Mehr R, Kedmi M. B cell M-CLL clones retain selection against replacement mutations in their immunoglobulin gene framework regions. Front Oncol 2023; 13:1115361. [PMID: 37007112 PMCID: PMC10060519 DOI: 10.3389/fonc.2023.1115361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/03/2023] [Indexed: 03/18/2023] Open
Abstract
IntroductionChronic lymphocytic leukemia (CLL) is the most common adult leukemia, accounting for 30–40% of all adult leukemias. The dynamics of B-lymphocyte CLL clones with mutated immunoglobulin heavy chain variable region (IgHV) genes in their tumor (M-CLL) can be studied using mutational lineage trees.MethodsHere, we used lineage tree-based analyses of somatic hypermutation (SHM) and selection in M-CLL clones, comparing the dominant (presumably malignant) clones of 15 CLL patients to their non-dominant (presumably normal) B cell clones, and to those of healthy control repertoires. This type of analysis, which was never previously published in CLL, yielded the following novel insights. ResultsCLL dominant clones undergo – or retain – more replacement mutations that alter amino acid properties such as charge or hydropathy. Although, as expected, CLL dominant clones undergo weaker selection for replacement mutations in the complementarity determining regions (CDRs) and against replacement mutations in the framework regions (FWRs) than non-dominant clones in the same patients or normal B cell clones in healthy controls, they surprisingly retain some of the latter selection in their FWRs. Finally, using machine learning, we show that even the non-dominant clones in CLL patients differ from healthy control clones in various features, most notably their expression of higher fractions of transition mutations. DiscussionOverall, CLL seems to be characterized by significant loosening – but not a complete loss – of the selection forces operating on B cell clones, and possibly also by changes in SHM mechanisms.
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Affiliation(s)
- Hadas Neuman
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
| | - Jessica Arrouasse
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
| | - Ohad Benjamini
- Division of Hematology and Bone Marrow Transplantation, Chaim Sheba Medical Center, Ramat-Gan, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Ramit Mehr
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
- *Correspondence: Ramit Mehr,
| | - Meirav Kedmi
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
- Division of Hematology and Bone Marrow Transplantation, Chaim Sheba Medical Center, Ramat-Gan, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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9
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Safra M, Tamari Z, Polak P, Shiber S, Matan M, Karameh H, Helviz Y, Levy-Barda A, Yahalom V, Peretz A, Ben-Chetrit E, Brenner B, Tuller T, Gal-Tanamy M, Yaari G. Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity. Front Immunol 2023; 14:1031914. [PMID: 37153628 PMCID: PMC10154551 DOI: 10.3389/fimmu.2023.1031914] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/22/2023] [Indexed: 05/10/2023] Open
Abstract
Introduction The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. Methods We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls. Results In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. Discussion These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.
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Affiliation(s)
- Modi Safra
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Zvi Tamari
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Pazit Polak
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Shachaf Shiber
- Emergency Department, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Matan
- Clinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel
| | - Hani Karameh
- Jesselson Integrated Heart Center, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Yigal Helviz
- Intensive Care Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Adva Levy-Barda
- Biobank, Department of Pathology, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
| | - Vered Yahalom
- Blood Services and Apheresis Institute, Rabin Medical Center, Petah Tikva, Israel
| | - Avi Peretz
- Clinical Microbiology Laboratory, Baruch Padeh Medical Center, Poriya, Israel
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Eli Ben-Chetrit
- Infectious Diseases Unit, Shaare Zedek Medical Center, Hebrew University School of Medicine, Jerusalem, Israel
| | - Baruch Brenner
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Oncology, Rabin Medical Center-Belinson Campus, Petah Tikva, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering and The Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | - Gur Yaari
- Bio-engineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
- *Correspondence: Gur Yaari,
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10
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Safra M, Werner L, Peres A, Polak P, Salamon N, Schvimer M, Weiss B, Barshack I, Shouval DS, Yaari G. A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls. Genome Res 2023; 33:71-79. [PMID: 36526432 PMCID: PMC9977146 DOI: 10.1101/gr.276683.122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
Crohn's disease (CD) is a chronic relapsing-remitting inflammatory disorder of the gastrointestinal tract that is characterized by altered innate and adaptive immune function. Although massively parallel sequencing studies of the T cell receptor repertoire identified oligoclonal expansion of unique clones, much less is known about the B cell receptor (BCR) repertoire in CD. Here, we present a novel BCR repertoire sequencing data set from ileal biopsies from pediatric patients with CD and controls, and identify CD-specific somatic hypermutation (SHM) patterns, revealed by a machine learning (ML) algorithm trained on BCR repertoire sequences. Moreover, ML classification of a different data set from blood samples of adults with CD versus controls identified that V gene usage, clusters, or mutation frequencies yielded excellent results in classifying the disease (F1 > 90%). In summary, we show that an ML algorithm enables the classification of CD based on unique BCR repertoire features with high accuracy.
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Affiliation(s)
- Modi Safra
- The Alexander Kofkin Faculty of Engineering, Bar Ilan University, 5290002, Ramat Gan, Israel;,Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, 5290002, Ramat Gan, Israel
| | - Lael Werner
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children's Medical Center of Israel, Petah Tikva 4920235, Israel;,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Ayelet Peres
- The Alexander Kofkin Faculty of Engineering, Bar Ilan University, 5290002, Ramat Gan, Israel;,Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, 5290002, Ramat Gan, Israel
| | - Pazit Polak
- The Alexander Kofkin Faculty of Engineering, Bar Ilan University, 5290002, Ramat Gan, Israel;,Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, 5290002, Ramat Gan, Israel
| | - Naomi Salamon
- Pediatric Gastroenterology Unit, Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan 5262100, Israel
| | - Michael Schvimer
- Institute of Pathology, Sheba Medical Center, Ramat Gan 5262100, Israel
| | - Batia Weiss
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel;,Pediatric Gastroenterology Unit, Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan 5262100, Israel
| | - Iris Barshack
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel;,Institute of Pathology, Sheba Medical Center, Ramat Gan 5262100, Israel
| | - Dror S. Shouval
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children's Medical Center of Israel, Petah Tikva 4920235, Israel;,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Gur Yaari
- The Alexander Kofkin Faculty of Engineering, Bar Ilan University, 5290002, Ramat Gan, Israel;,Bar Ilan Institute of Nanotechnology and Advanced Materials, Bar Ilan University, 5290002, Ramat Gan, Israel
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11
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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] [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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12
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Neuman H, Arrouasse J, Kedmi M, Cerutti A, Magri G, Mehr R. IgTreeZ, A Toolkit for Immunoglobulin Gene Lineage Tree-Based Analysis, Reveals CDR3s Are Crucial for Selection Analysis. Front Immunol 2022; 13:822834. [PMID: 36389731 PMCID: PMC9643157 DOI: 10.3389/fimmu.2022.822834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 02/08/2022] [Indexed: 01/23/2024] Open
Abstract
Somatic hypermutation (SHM) is an important diversification mechanism that plays a part in the creation of immune memory. Immunoglobulin (Ig) variable region gene lineage trees were used over the last four decades to model SHM and the selection mechanisms operating on B cell clones. We hereby present IgTreeZ (Immunoglobulin Tree analyZer), a python-based tool that analyses many aspects of Ig gene lineage trees and their repertoires. Using simulations, we show that IgTreeZ can be reliably used for mutation and selection analyses. We used IgTreeZ on empirical data, found evidence for different mutation patterns in different B cell subpopulations, and gained insights into antigen-driven selection in corona virus disease 19 (COVID-19) patients. Most importantly, we show that including the CDR3 regions in selection analyses - which is only possible if these analyses are lineage tree-based - is crucial for obtaining correct results. Overall, we present a comprehensive lineage tree analysis tool that can reveal new biological insights into B cell repertoire dynamics.
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Affiliation(s)
- Hadas Neuman
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
| | - Jessica Arrouasse
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
| | - Meirav Kedmi
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
- Division of Hematology and Bone Marrow Transplantation, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Andrea Cerutti
- Translational Clinical Research Program, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Giuliana Magri
- Translational Clinical Research Program, Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - Ramit Mehr
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel
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13
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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 REPORTS 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] [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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14
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Chen Y, Ye Z, Zhang Y, Xie W, Chen Q, Lan C, Yang X, Zeng H, Zhu Y, Ma C, Tang H, Wang Q, Guan J, Chen S, Li F, Yang W, Yan H, Yu X, Zhang Z. A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2675-2685. [PMID: 35606050 DOI: 10.4049/jimmunol.2200063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.
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Affiliation(s)
- Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhiming Ye
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yanfang Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenxi Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qingyun Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chunhong Lan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiujia Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huikun Zeng
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Zhu
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Junjie Guan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Sen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Fenxiang Li
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huacheng Yan
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhai Zhang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Organ Failure Research, Division of Nephrology, Southern Medical University, Guangzhou, China; and
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China
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15
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Gilboa A, Hope R, Ben Simon S, Polak P, Koren O, Yaari G. Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2713-2725. [PMID: 35623663 DOI: 10.4049/jimmunol.2100955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/30/2022] [Indexed: 06/15/2023]
Abstract
The immune system matures throughout childhood to achieve full functionality in protecting our bodies against threats. The immune system has a strong reciprocal symbiosis with the host bacterial population and the two systems co-develop, shaping each other. Despite their fundamental role in health physiology, the ontogeny of these systems is poorly characterized. In this study, we investigated the development of the BCR repertoire by analyzing high-throughput sequencing of their receptors in several time points of young C57BL/6J mice. In parallel, we explored the development of the gut microbiome. We discovered that the gut IgA repertoires change from birth to adolescence, including an increase in CDR3 lengths and somatic hypermutation levels. This contrasts with the spleen IgM repertoires that remain stable and distinct from the IgA repertoires in the gut. We also discovered that large clones that germinate in the gut are initially confined to a specific gut compartment, then expand to nearby compartments and later on expand also to the spleen and remain there. Finally, we explored the associations between diversity indices of the B cell repertoires and the microbiome, as well as associations between bacterial and BCR clusters. Our results shed light on the ontogeny of the adaptive immune system and the microbiome, providing a baseline for future research.
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Affiliation(s)
- Amit Gilboa
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
| | - Ronen Hope
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | - Shira Ben Simon
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Pazit Polak
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Gur Yaari
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel;
- Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel; and
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16
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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: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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17
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Gnodi E, Meneveri R, Barisani D. Celiac disease: From genetics to epigenetics. World J Gastroenterol 2022; 28:449-463. [PMID: 35125829 PMCID: PMC8790554 DOI: 10.3748/wjg.v28.i4.449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/16/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.
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Affiliation(s)
- Elisa Gnodi
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Raffaella Meneveri
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
| | - Donatella Barisani
- School of Medicine and Surgery, University of Milano-Bicocca, Monza 20900, Italy
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18
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Arunkumar M, Zielinski CE. T-Cell Receptor Repertoire Analysis with Computational Tools-An Immunologist's Perspective. Cells 2021; 10:cells10123582. [PMID: 34944090 PMCID: PMC8700004 DOI: 10.3390/cells10123582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 12/25/2022] Open
Abstract
Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth a revolution in the characterization of immune repertoires. T-cell receptor (TCR) repertoires carry comprehensive information on the history of an individual’s antigen exposure. They serve as correlates of host protection and tolerance, as well as biomarkers of immunological perturbation by natural infections, vaccines or immunotherapies. Their interrogation yields large amounts of data. This requires a suite of highly sophisticated bioinformatics tools to leverage the meaning and complexity of the large datasets. Many different tools and methods, specifically designed for various aspects of immunological research, have recently emerged. Thus, researchers are now confronted with the issue of having to choose the right kind of approach to analyze, visualize and ultimately solve their task at hand. In order to help immunologists to choose from the vastness of available tools for their data analysis, this review addresses and compares commonly used bioinformatics tools for TCR repertoire analysis and illustrates the advantages and limitations of these tools from an immunologist’s perspective.
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Affiliation(s)
- Mahima Arunkumar
- Department of Infection Immunology, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, 07745 Jena, Germany;
- Department of Biological Sciences, Friedrich Schiller University, 07743 Jena, Germany
- Bioinformatics, Ludwig Maximilians University Munich, 80539 Munich, Germany
| | - Christina E. Zielinski
- Department of Infection Immunology, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, 07745 Jena, Germany;
- Department of Biological Sciences, Friedrich Schiller University, 07743 Jena, Germany
- Correspondence:
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19
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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: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [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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20
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The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00413-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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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] [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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22
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Ghraichy M, von Niederhäusern V, Kovaltsuk A, Galson JD, Deane CM, Trück J. Different B cell subpopulations show distinct patterns in their IgH repertoire metrics. eLife 2021; 10:73111. [PMID: 34661527 PMCID: PMC8560093 DOI: 10.7554/elife.73111] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/17/2021] [Indexed: 12/11/2022] Open
Abstract
Several human B cell subpopulations are recognised in the peripheral blood, which play distinct roles in the humoral immune response. These cells undergo developmental and maturational changes involving VDJ recombination, somatic hypermutation and class switch recombination, altogether shaping their immunoglobulin heavy chain (IgH) repertoire. Here, we sequenced the IgH repertoire of naïve, marginal zone, switched and plasma cells from 10 healthy adults along with matched unsorted and in silico separated CD19+ bulk B cells. Using advanced bioinformatic analysis and machine learning, we show that sorted B cell subpopulations are characterised by distinct repertoire characteristics on both the individual sequence and the repertoire level. Sorted subpopulations shared similar repertoire characteristics with their corresponding in silico separated subsets. Furthermore, certain IgH repertoire characteristics correlated with the position of the constant region on the IgH locus. Overall, this study provides unprecedented insight over mechanisms of B cell repertoire control in peripherally circulating B cell subpopulations.
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Affiliation(s)
- Marie Ghraichy
- Division of Immunology, University Children's Hospital and Children's Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Valentin von Niederhäusern
- Division of Immunology, University Children's Hospital and Children's Research Center, University of Zurich (UZH), Zurich, Switzerland
| | | | - Jacob D Galson
- Division of Immunology, University Children's Hospital and Children's Research Center, University of Zurich (UZH), Zurich, Switzerland.,Alchemab Therapeutics Ltd, London, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Johannes Trück
- Division of Immunology, University Children's Hospital and Children's Research Center, University of Zurich (UZH), Zurich, Switzerland
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Ostrovsky-Berman M, Frankel B, Polak P, Yaari G. Immune2vec: Embedding B/T Cell Receptor Sequences in ℝ N Using Natural Language Processing. Front Immunol 2021; 12:680687. [PMID: 34367141 PMCID: PMC8340020 DOI: 10.3389/fimmu.2021.680687] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
The adaptive branch of the immune system learns pathogenic patterns and remembers them for future encounters. It does so through dynamic and diverse repertoires of T- and B- cell receptors (TCR and BCRs, respectively). These huge immune repertoires in each individual present investigators with the challenge of extracting meaningful biological information from multi-dimensional data. The ability to embed these DNA and amino acid textual sequences in a vector-space is an important step towards developing effective analysis methods. Here we present Immune2vec, an adaptation of a natural language processing (NLP)-based embedding technique for BCR repertoire sequencing data. We validate Immune2vec on amino acid 3-gram sequences, continuing to longer BCR sequences, and finally to entire repertoires. Our work demonstrates Immune2vec to be a reliable low-dimensional representation that preserves relevant information of immune sequencing data, such as n-gram properties and IGHV gene family classification. Applying Immune2vec along with machine learning approaches to patient data exemplifies how distinct clinical conditions can be effectively stratified, indicating that the embedding space can be used for feature extraction and exploratory data analysis.
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Affiliation(s)
- Miri Ostrovsky-Berman
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Boaz Frankel
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Pazit Polak
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - Gur Yaari
- Bioengineering, Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
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