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Sobczyńska-Konefał A, Jasek M, Karabon L, Jaskuła E. Insights into genetic aberrations and signalling pathway interactions in chronic lymphocytic leukemia: from pathogenesis to treatment strategies. Biomark Res 2024; 12:162. [PMID: 39732734 DOI: 10.1186/s40364-024-00710-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 12/17/2024] [Indexed: 12/30/2024] Open
Abstract
Chronic lymphocytic leukemia (CLL) is prevalent in adults and is characterized by the accumulation of mature B cells in the blood, bone marrow, lymph nodes, and spleens. Recent progress in therapy and the introduction of targeted treatments [inhibitors of Bruton's tyrosine kinase (BTKi) or inhibitor of anti-apoptotic B-cell lymphoma-2 (Bcl-2i) protein (venetoclax)] in place of chemoimmunotherapy have significantly improved the outcomes of patients with CLL. These advancements have shifted the importance of traditional predictive markers, leading to a greater focus on resistance genes and reducing the significance of mutations, such as TP53 and del(17p). Despite the significant progress in CLL treatment, some patients still experience disease relapse. This is due to the substantial heterogeneity of CLL as well as the interconnected genetic resistance mechanisms and pathway adaptive resistance mechanisms to targeted therapies in CLL. Although the knowledge of the pathomechanism of CLL has expanded significantly in recent years, the precise origins of CLL and the interplay between various genetic factors remain incompletely understood, necessitating further research. This review enhances the molecular understanding of CLL by describing how BCR signalling, NF-κB PI3K/AKT, and ROR1 pathways sustain CLL cell survival, proliferation, and resistance to apoptosis. It also presents genetic and pathway-adaptive resistance mechanisms in CLL. Identifying B-cell receptor (BCR) signalling as a pivotal driver of CLL progression, the findings advocate personalized treatment strategies based on molecular profiling, emphasizing the need for further research to unravel the complex interplay between BCR signalling and its associated pathways to improve patient outcomes.
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Affiliation(s)
- Anna Sobczyńska-Konefał
- L. Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Rudolf Weigl 12, 53-114, Wroclaw, Poland
- Lower Silesian Oncology Hematology and Pulmonology Center, Ludwik Hirszfeld square 12, 53-413, Wroclaw, Poland
| | - Monika Jasek
- L. Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Rudolf Weigl 12, 53-114, Wroclaw, Poland
| | - Lidia Karabon
- L. Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Rudolf Weigl 12, 53-114, Wroclaw, Poland
| | - Emilia Jaskuła
- L. Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Rudolf Weigl 12, 53-114, Wroclaw, Poland.
- Lower Silesian Oncology Hematology and Pulmonology Center, Ludwik Hirszfeld square 12, 53-413, Wroclaw, Poland.
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2
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Mikelov A, Nefediev G, Tashkeev A, Rodriguez OL, Aguilar Ortmans D, Skatova V, Izraelson M, Davydov AN, Poslavsky S, Rahmouni S, Watson CT, Chudakov D, Boyd SD, Bolotin D. Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR. Genome Res 2024; 34:2293-2303. [PMID: 39433438 DOI: 10.1101/gr.278775.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 10/03/2024] [Indexed: 10/23/2024]
Abstract
Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), is of critical importance for immune responses to pathogens and vaccines. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci. Here, we present a novel algorithm for extrasensitive and specific variable (V) and joining (J) gene allele inference, allowing the reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput novel allele discovery from a wide variety of existing data sets. The developed algorithm is a part of the MiXCR software. We demonstrate the accuracy of this approach using AIRR-seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) AIRR-seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA and TRB) AIRR-seq data set, representing 134 individuals. This allowed us to assess the genetic diversity within the IGH, TRA, and TRB loci in different populations and to establish a database of alleles of V and J genes inferred from AIRR-seq data and their population frequencies with free public access through VDJ.online database.
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Affiliation(s)
- Artem Mikelov
- Department of Pathology, Stanford University, Stanford, California 94305, USA;
| | - George Nefediev
- MiLaboratories Incorporated, San Francisco, California 94114, USA
| | - Alexander Tashkeev
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège (B34), 4000 Liège, Belgium
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, Kentucky 40202, USA
| | - Diego Aguilar Ortmans
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège (B34), 4000 Liège, Belgium
| | - Valeriia Skatova
- MiLaboratories Incorporated, San Francisco, California 94114, USA
| | - Mark Izraelson
- MiLaboratories Incorporated, San Francisco, California 94114, USA
| | - Alexey N Davydov
- MiLaboratories Incorporated, San Francisco, California 94114, USA
- Central European Institute of Technology, Masaryk University, 601 77 Brno, Czech Republic
| | | | - Souad Rahmouni
- Unit of Animal Genomics, WELBIO, GIGA-R and Faculty of Veterinary Medicine, University of Liège (B34), 4000 Liège, Belgium
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, Kentucky 40202, USA
| | - Dmitriy Chudakov
- MiLaboratories Incorporated, San Francisco, California 94114, USA
- Central European Institute of Technology, Masaryk University, 601 77 Brno, Czech Republic
| | - Scott D Boyd
- Department of Pathology, Stanford University, Stanford, California 94305, USA
| | - Dmitry Bolotin
- MiLaboratories Incorporated, San Francisco, California 94114, USA;
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3
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Moya ND, Yan SM, McCoy RC, Andersen EC. The long and short of hyperdivergent regions. Trends Genet 2024:S0168-9525(24)00269-5. [PMID: 39706705 DOI: 10.1016/j.tig.2024.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 11/11/2024] [Accepted: 11/14/2024] [Indexed: 12/23/2024]
Abstract
The increasing prevalence of genome sequencing and assembly has uncovered evidence of hyperdivergent genomic regions - loci with excess genetic diversity - in species across the tree of life. Hyperdivergent regions are often enriched for genes that mediate environmental responses, such as immunity, parasitism, and sensory perception. Especially in self-fertilizing species where the majority of the genome is homozygous, the existence of hyperdivergent regions might imply the historical action of evolutionary forces such as introgression and/or balancing selection. We anticipate that the application of new sequencing technologies, broader taxonomic sampling, and evolutionary modeling of hyperdivergent regions will provide insights into the mechanisms that generate and maintain genetic diversity within and between species.
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Affiliation(s)
- Nicolas D Moya
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie M Yan
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA.
| | - Erik C Andersen
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA.
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4
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O'Donnell TJ, Kanduri C, Isacchini G, Limenitakis JP, Brachman RA, Alvarez RA, Haff IH, Sandve GK, Greiff V. Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning. Cell Syst 2024; 15:1168-1189. [PMID: 39701034 DOI: 10.1016/j.cels.2024.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 08/16/2024] [Accepted: 11/14/2024] [Indexed: 12/21/2024]
Abstract
The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.
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Affiliation(s)
| | - Chakravarthi Kanduri
- Department of Informatics, University of Oslo, Oslo, Norway; UiO:RealArt Convergence Environment, University of Oslo, Oslo, Norway
| | | | | | - Rebecca A Brachman
- Imprint Labs, LLC, New York, NY, USA; Cornell Tech, Cornell University, New York, NY, USA
| | | | - Ingrid H Haff
- Department of Mathematics, University of Oslo, 0371 Oslo, Norway
| | - Geir K Sandve
- Department of Informatics, University of Oslo, Oslo, Norway; UiO:RealArt Convergence Environment, University of Oslo, Oslo, Norway
| | - Victor Greiff
- Imprint Labs, LLC, New York, NY, USA; Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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5
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Marsden AA, Corcoran M, Hedestam GK, Garrett N, Karim SSA, Moore PL, Kitchin D, Morris L, Scheepers C. Novel polymorphic and copy number diversity in the antibody IGH locus of South African individuals. Immunogenetics 2024; 77:6. [PMID: 39627383 PMCID: PMC11615098 DOI: 10.1007/s00251-024-01363-7] [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: 05/31/2024] [Accepted: 11/19/2024] [Indexed: 12/06/2024]
Abstract
The heavy chain of an antibody is crucial for mediating antigen binding. IGHV genes, which partially encode the heavy chain of antibodies, exhibit vast genetic diversity largely through polymorphism and copy number variation (CNV). These genetic variations impact population-level expression levels. In this study, we analyzed expressed antibody transcriptomes and matched germline IGHV genes from donors from KwaZulu-Natal, South Africa. Amplicon NGS targeting germline IGHV sequences was performed on genomic DNA from 70 participants, eight of whom had matched datasets of expressed antibody transcriptomes. Germline IGHV sequencing identified 161 unique IGHV alleles, of which 32 were novel. A further 21 novel IGHV alleles were detected in the expressed transcriptomes of these donors. We also examined the datasets for CNV, uncovering gene duplications of 10 IGHV genes from germline sequencing and 33 genes in the expressed transcriptomes. Many of the IGHV gene duplications have not been described in other populations. This study expands our understanding of genetic differences in distinct populations and suggests the potential impact of genetic diversity on immune responses.
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Affiliation(s)
- Alaine A Marsden
- SA MRC Antibody Immunity Research Unit (AIRU), University of the Witwatersrand, Johannesburg, South Africa
- Centre for HIV and STIs, HIV Virology Section, National Institute for Communicable Diseases (NICD), a Division of the National Health Laboratory Service (NHLS), Johannesburg, South Africa
| | - Martin Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Nigel Garrett
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Salim S Abdool Karim
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
- Department of Epidemiology, Mailman School of Public Health, Columbia University, Columbia, NY, USA
| | - Penny L Moore
- SA MRC Antibody Immunity Research Unit (AIRU), University of the Witwatersrand, Johannesburg, South Africa
- Centre for HIV and STIs, HIV Virology Section, National Institute for Communicable Diseases (NICD), a Division of the National Health Laboratory Service (NHLS), Johannesburg, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
| | - Dale Kitchin
- SA MRC Antibody Immunity Research Unit (AIRU), University of the Witwatersrand, Johannesburg, South Africa
- Centre for HIV and STIs, HIV Virology Section, National Institute for Communicable Diseases (NICD), a Division of the National Health Laboratory Service (NHLS), Johannesburg, South Africa
| | - Lynn Morris
- SA MRC Antibody Immunity Research Unit (AIRU), University of the Witwatersrand, Johannesburg, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
| | - Cathrine Scheepers
- SA MRC Antibody Immunity Research Unit (AIRU), University of the Witwatersrand, Johannesburg, South Africa.
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6
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Voss K, Kaur KM, Banerjee R, Breden F, Pennell M. Applying phylogenetic methods for species delimitation to distinguish B-cell clonal families. Front Immunol 2024; 15:1505032. [PMID: 39687606 PMCID: PMC11646844 DOI: 10.3389/fimmu.2024.1505032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Accepted: 11/07/2024] [Indexed: 12/18/2024] Open
Abstract
The adaptive immune system generates a diverse array of B-cell receptors through the processes of V(D)J recombination and somatic hypermutation. B-cell receptors that bind to an antigen will undergo clonal expansion, creating a Darwinian evolutionary dynamic within individuals. A key step in studying these dynamics is to identify sequences derived from the same ancestral V(D)J recombination event (i.e. a clonal family). There are a number of widely used methods for accomplishing this task but a major limitation of all of them is that they rely, at least in part, on the ability to map sequences to a germline reference set. This requirement is particularly problematic in non-model systems where we often know little about the germline allelic diversity in the study population. Recognizing that delimiting B-cell clonal families is analogous to delimiting species from single locus data, we propose a novel strategy of reconstructing the phylogenetic tree of all B-cell sequences in a sample and using a popular species delimitation method, multi-rate Poisson Tree Processes (mPTP), to delimit clonal families. Using extensive simulations, we show that not only does this phylogenetically explicit approach perform well for the purpose of delimiting clonal families when no reference allele set is available, it performs similarly to state-of-the-art techniques developed specifically for B-cell data even when we have a complete reference allele set. Additionally, our analysis of an empirical dataset shows that mPTP performs similarly to leading methods in the field. These findings demonstrate the utility of using off-the-shelf phylogenetic techniques for analyzing B-cell clonal dynamics in non-model systems, and suggests that phylogenetic inference techniques may be potentially combined with mapping based approaches for even more robust inferences, even in model systems.
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Affiliation(s)
- Katalin Voss
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States
| | - Katrina M. Kaur
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
| | - Rituparna Banerjee
- Bioinformatics Graduate Program, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, United States
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7
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Sung K, Johnson MM, Dumm W, Simon N, Haddox H, Fukuyama J, Matsen FA. Thrifty wide-context models of B cell receptor somatic hypermutation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.26.625407. [PMID: 39651125 PMCID: PMC11623647 DOI: 10.1101/2024.11.26.625407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, for understanding the selective forces guiding affinity maturation, and for understanding the underlying biochemical process. High throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this paper we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM, however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop "thrifty" models of SHM that have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model - on out-of-frame sequence data and on synonymous mutations - produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.
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8
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Madden PJ, Marina-Zárate E, Rodrigues KA, Steichen JM, Shil M, Ni K, Michaels KK, Maiorino L, Upadhyay AA, Saha S, Pradhan A, Kalyuzhiny O, Liguori A, Lopez PG, Phung I, Phelps N, Georgeson E, Alavi N, Kubitz M, Lu D, Eskandarzadeh S, Metz A, Rodriguez OL, Shields K, Schultze S, Smith ML, Healy BS, Lim D, Lewis VR, Ben-Akiva E, Pinney W, Gregory J, Xiao S, Carnathan DG, Kasturi SP, Watson CT, Bosinger SE, Silvestri G, Schief WR, Irvine DJ, Crotty S. Diverse priming outcomes under conditions of very rare precursor B cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.21.624746. [PMID: 39651117 PMCID: PMC11623517 DOI: 10.1101/2024.11.21.624746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Rare B cells can have special pathogen-recognition features giving them the potential to make outsized contributions to protective immunity. However, rare naive B cells infrequently participate in immune responses. We investigated how germline-targeting vaccine antigen delivery and adjuvant selection affect priming of exceptionally rare BG18-like HIV broadly neutralizing antibody-precursor B cells (~1 in 50 million) in non-human primates. Only escalating dose (ED) priming immunization using the saponin adjuvant SMNP elicited detectable BG18-like cells in germinal centers (GCs). All groups had strong GC responses, but only ED+SMNP and bolus+SMNP induced BG18-like memory B cells in >50% of animals. One group had vaccine-specific GC responses equivalent to ED+SMNP, but BG18-like memory B cells were rarely detected. Following homologous boosting, BG18-like memory B cells were more frequent in a bolus priming group, but had lower somatic hypermutation and affinities. This outcome was inversely associated with post-prime antibody titers, suggesting antibody feedback can significantly influence rare precursor B cell responses.
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9
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Versoza CJ, Jensen JD, Pfeifer SP. The landscape of structural variation in aye-ayes ( Daubentonia madagascariensis). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.08.622672. [PMID: 39605644 PMCID: PMC11601217 DOI: 10.1101/2024.11.08.622672] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Aye-ayes (Daubentonia madagascariensis) are one of the 25 most critically endangered primate species in the world. Endemic to Madagascar, their small and highly fragmented populations make them particularly vulnerable to both genetic disease and anthropogenic environmental changes. Over the past decade, conservation genomic efforts have largely focused on inferring and monitoring population structure based on single nucleotide variants to identify and protect critical areas of genetic diversity. However, the recent release of a highly contiguous genome assembly allows, for the first time, for the study of structural genomic variation (deletions, duplications, insertions, and inversions) which are likely to impact a substantial proportion of the species' genome. Based on whole-genome, short-read sequencing data from 14 individuals, >1,000 high-confidence autosomal structural variants were detected, affecting ~240 kb of the aye-aye genome. The majority of these variants (>85%) were deletions shorter than 200 bp, consistent with the notion that longer structural mutations are often associated with strongly deleterious fitness effects. For example, two deletions longer than 850 bp located within disease-linked genes were predicted to impose substantial fitness deficits owing to a resulting frameshift and gene fusion, respectively; whereas several other major effect variants outside of coding regions are likely to impact gene regulatory landscapes. Taken together, this first glimpse into the landscape of structural variation in aye-ayes will enable future opportunities to advance our understanding of the traits impacting the fitness of this endangered species, as well as allow for enhanced evolutionary comparisons across the full primate clade.
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Affiliation(s)
- Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jeffrey D. Jensen
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Susanne P. Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA
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10
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Shi Y, Zhu Z, Li Q, Chen Q, Jiang W, Chen C, Chen X. Molecular characterization of the IgH locus and V(D)J recombination in large yellow croaker (Larimichthys crocea). FISH & SHELLFISH IMMUNOLOGY 2024; 154:109909. [PMID: 39284538 DOI: 10.1016/j.fsi.2024.109909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Revised: 09/13/2024] [Accepted: 09/13/2024] [Indexed: 09/20/2024]
Abstract
V(D)J recombination is crucial for generating a diverse repertoire of immunoglobulins. Although the V(D)J recombination process has been well characterized in mammals, this process remains largely unexplored in teleosts. In this study, we comprehensively analyzed the IgH locus of a marine fish species large yellow croaker (Larimichthys crocea), and identified 28 V, 19 D, and 8 J gene segments, following a pattern of V-Dζ-Jζ-Cζ-Dμ-Jμ-Cμ1-Cμ2. The V, D, and J gene segments are flanked by consensus recombination signal sequences, with spacer lengths similar to those observed in mammals. The V gene segments are categorized into three distinct families, and exhibited a higher sequence identity compared to those in mammals. Additionally, we designed a set of primers for the examination of the V(D)J recombination in large yellow croaker. RNA-seq analysis showed increased expression of genes related to immunoglobulin production and lymphocyte chemotaxis in IgM + B cells upon Pseudomonas plecoglossicida infection, accompanied by altered expression of V gene segments, suggesting their involvement in the response to P. plecoglossicida infection. Taken together, we identified the IgH locus and V(D)J recombination process of large yellow croaker, which contribute to the understanding of immunoglobulin production and B cell immunity in teleosts, and may provide insights into vaccine development in large yellow croaker.
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Affiliation(s)
- Yuan Shi
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhuo Zhu
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Qiuhua Li
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Qiuxuan Chen
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Wenwu Jiang
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Chenyi Chen
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Xinhua Chen
- State Key Laboratory of Mariculture Breeding, Key Laboratory of Marine Biotechnology of Fujian Province, College of Marine Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China; Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, 266237, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China.
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11
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Yoo D, Rhie A, Hebbar P, Antonacci F, Logsdon GA, Solar SJ, Antipov D, Pickett BD, Safonova Y, Montinaro F, Luo Y, Malukiewicz J, Storer JM, Lin J, Sequeira AN, Mangan RJ, Hickey G, Anez GM, Balachandran P, Bankevich A, Beck CR, Biddanda A, Borchers M, Bouffard GG, Brannan E, Brooks SY, Carbone L, Carrel L, Chan AP, Crawford J, Diekhans M, Engelbrecht E, Feschotte C, Formenti G, Garcia GH, de Gennaro L, Gilbert D, Green RE, Guarracino A, Gupta I, Haddad D, Han J, Harris RS, Hartley GA, Harvey WT, Hiller M, Hoekzema K, Houck ML, Jeong H, Kamali K, Kellis M, Kille B, Lee C, Lee Y, Lees W, Lewis AP, Li Q, Loftus M, Loh YHE, Loucks H, Ma J, Mao Y, Martinez JFI, Masterson P, McCoy RC, McGrath B, McKinney S, Meyer BS, Miga KH, Mohanty SK, Munson KM, Pal K, Pennell M, Pevzner PA, Porubsky D, Potapova T, Ringeling FR, Roha JL, Ryder OA, Sacco S, Saha S, Sasaki T, Schatz MC, Schork NJ, Shanks C, Smeds L, Son DR, Steiner C, Sweeten AP, Tassia MG, Thibaud-Nissen F, Torres-González E, Trivedi M, Wei W, Wertz J, Yang M, Zhang P, Zhang S, Zhang Y, Zhang Z, Zhao SA, Zhu Y, Jarvis ED, Gerton JL, Rivas-González I, Paten B, Szpiech ZA, Huber CD, Lenz TL, Konkel MK, Yi SV, Canzar S, Watson CT, Sudmant PH, Molloy E, Garrison E, Lowe CB, Ventura M, O’Neill RJ, Koren S, Makova KD, Phillippy AM, Eichler EE. Complete sequencing of ape genomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.605654. [PMID: 39131277 PMCID: PMC11312596 DOI: 10.1101/2024.07.31.605654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives.
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Affiliation(s)
- DongAhn Yoo
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arang Rhie
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Prajna Hebbar
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Francesca Antonacci
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - Glennis A. Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Steven J. Solar
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Dmitry Antipov
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Brandon D. Pickett
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yana Safonova
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Francesco Montinaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yanting Luo
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Joanna Malukiewicz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Jessica M. Storer
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - Jiadong Lin
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Abigail N. Sequeira
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Riley J. Mangan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Genetics Training Program, Harvard Medical School, Boston, MA 02115, USA
| | - Glenn Hickey
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | | | | | - Anton Bankevich
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Christine R. Beck
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Arjun Biddanda
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Matthew Borchers
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Gerard G. Bouffard
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Emry Brannan
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA
| | - Shelise Y. Brooks
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lucia Carbone
- Department of Medicine, KCVI, Oregon Health Sciences University, Portland, OR, USA
- Division of Genetics, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Laura Carrel
- PSU Medical School, Penn State University School of Medicine, Hershey, PA, USA
| | - Agnes P. Chan
- The Translational Genomics Research Institute, a part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Juyun Crawford
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Cedric Feschotte
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY 10021, USA
| | - Gage H. Garcia
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Luciana de Gennaro
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - David Gilbert
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | | | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Ishaan Gupta
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - Diana Haddad
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Junmin Han
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Robert S. Harris
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Gabrielle A. Hartley
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
| | - William T. Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Michael Hiller
- LOEWE Centre for Translational Biodiversity Genomics, Senckenberg Research Institute, Goethe University, Frankfurt, Germany
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Marlys L. Houck
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Hyeonsoo Jeong
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Kaivan Kamali
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bryce Kille
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Chul Lee
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Youngho Lee
- Laboratory of bioinformatics and population genetics, Interdisciplinary program in bioinformatics, Seoul National University, Republic of Korea
| | - William Lees
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Alexandra P. Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mark Loftus
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Yong Hwee Eddie Loh
- Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Hailey Loucks
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Yafei Mao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- Center for Genomic Research, International Institutes of Medicine, Fourth Affiliated Hospital, Zhejiang University, Yiwu, Zhejiang, China
- Shanghai Jiao Tong University Chongqing Research Institute, Chongqing, China
| | - Juan F. I. Martinez
- Computer Science and Engineering Department, Huck Institutes of Life Sciences, Pennsylvania State University, State College, PA 16801, USA
| | - Patrick Masterson
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Rajiv C. McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Barbara McGrath
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Sean McKinney
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Britta S. Meyer
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Karen H. Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Saswat K. Mohanty
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Katherine M. Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Karol Pal
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Pavel A. Pevzner
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tamara Potapova
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Francisca R. Ringeling
- Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany
| | - Joana L. Roha
- Department of Integrative Biology, University of California, Berkeley, Berkeley, USA
| | - Oliver A. Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Samuel Sacco
- University of California Santa Cruz, Santa Cruz, CA, USA
| | - Swati Saha
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Takayo Sasaki
- San Diego Biomedical Research Institute, San Diego, CA, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Nicholas J. Schork
- The Translational Genomics Research Institute, a part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Cole Shanks
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Linnéa Smeds
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Dongmin R. Son
- Department of Ecology, Evolution and Marine Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Cynthia Steiner
- San Diego Zoo Wildlife Alliance, Escondido, CA, 92027-7000, USA
| | - Alexander P. Sweeten
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael G. Tassia
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Mihir Trivedi
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Wenjie Wei
- School of Life Sciences, Westlake University, Hangzhou 310024, China
- National Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070, Wuhan, China
| | - Julie Wertz
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Panpan Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Shilong Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, PA, USA
| | - Zhenmiao Zhang
- Department of Computer Science and Engineering, University of California San Diego, CA, USA
| | - Sarah A. Zhao
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yixin Zhu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Erich D. Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - Iker Rivas-González
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Zachary A. Szpiech
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Christian D. Huber
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Tobias L. Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Miriam K. Konkel
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Soojin V. Yi
- Department of Ecology, Evolution and Marine Biology, Department of Molecular, Cellular and Developmental Biology, Neuroscience Research Institute, University of California, Santa Barbara, CA, USA
| | - Stefan Canzar
- Faculty of Informatics and Data Science, University of Regensburg, 93053 Regensburg, Germany
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Peter H. Sudmant
- Department of Integrative Biology, University of California, Berkeley, Berkeley, USA
- Center for Computational Biology, University of California, Berkeley, Berkeley, USA
| | - Erin Molloy
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Craig B. Lowe
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Mario Ventura
- Department of Biosciences, Biotechnology and Environment, University of Bari, Bari, 70124, Italy
| | - Rachel J. O’Neill
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- Departments of Molecular and Cell Biology, UConn Storrs, CT, USA
| | - Sergey Koren
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kateryna D. Makova
- Department of Biology, Penn State University, University Park, PA 16802, USA
| | - Adam M. Phillippy
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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12
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De-Paula RB, Bacolla A, Syed A, Tainer JA. Enriched G4 forming repeats in the human genome are associated with robust well-coordinated transcription and reduced cancer transcriptome variation. J Biol Chem 2024; 300:107822. [PMID: 39341500 PMCID: PMC11532954 DOI: 10.1016/j.jbc.2024.107822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 09/01/2024] [Accepted: 09/21/2024] [Indexed: 10/01/2024] Open
Abstract
Non-B DNA G-quadruplex (G4) structures with guanine (G) runs of 2 to 4 repeats can trigger opposing experimental transcriptional impacts. Here, we used bioinformatic algorithms to comprehensively assess correlations of steady-state RNA transcript levels with all putative G4 sequence (pG4) locations genome-wide in three mammalian genomes and in normal and tumor human tissues. The human pG4-containing gene set displays higher expression levels than the set without pG4, supporting and extending some prior observations. pG4 enrichment at transcription start sites (TSSs) in human, but not chimpanzee and mouse genomes, suggests possible positive selection pressure for pG4 at human TSS, potentially driving genome rewiring and gene expression divergence between human and chimpanzee. Comprehensive bioinformatic analyses revealed lower pG4-containing gene set variability in humans and among different pG4 genes in tumors. As G4 stabilizers are under therapeutic consideration for cancer and pathogens, such distinctions between human normal and tumor G4s along with other species merit attention. Furthermore, in germline and cancer sequences, the most mutagenic pG4 mapped to regions promoting alternative DNA structures. Overall findings establish high pG4 at TSS as a human genome attribute statistically associated with robust well-coordinated transcription and reduced cancer transcriptome variation with implications for biology, model organisms, and medicine.
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Affiliation(s)
- Ruth B De-Paula
- Graduate School of Biomedical Sciences, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Albino Bacolla
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Aleem Syed
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - John A Tainer
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
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13
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Konstantinovsky T, Peres A, Polak P, Yaari G. An unbiased comparison of immunoglobulin sequence aligners. Brief Bioinform 2024; 25:bbae556. [PMID: 39489605 PMCID: PMC11531861 DOI: 10.1093/bib/bbae556] [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: 06/14/2024] [Revised: 09/11/2024] [Accepted: 10/19/2024] [Indexed: 11/05/2024] Open
Abstract
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our understanding of the adaptive immune system's dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate rearranged immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, a modular simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.
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Affiliation(s)
- Thomas Konstantinovsky
- 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
| | - Ayelet Peres
- 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
- 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
| | - Gur Yaari
- 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
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
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14
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Trendowski MR, Watza D, Lusk CM, Lonardo F, Ratliff V, Wenzlaff AS, Mamdani H, Neslund-Dudas C, Boerner JL, Schwartz AG, Gibson HM. Evaluation of the Immune Response within the Tumor Microenvironment in African American and Non-Hispanic White Patients with Non-Small Cell Lung Cancer. Cancer Epidemiol Biomarkers Prev 2024; 33:1220-1228. [PMID: 38953893 PMCID: PMC11371519 DOI: 10.1158/1055-9965.epi-24-0333] [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: 03/01/2024] [Revised: 05/20/2024] [Accepted: 06/28/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND African Americans have higher incidence and mortality from lung cancer than non-Hispanic Whites, but investigations into differences in immune response have been minimal. Therefore, we compared components of the tumor microenvironment among African Americans and non-Hispanic Whites diagnosed with non-small cell lung cancer based on PDL1 or tertiary lymphoid structure (TLS) status to identify differences of translational relevance. METHODS Using a cohort of 280 patients with non-small cell lung cancer from the Inflammation, Health, Ancestry, and Lung Epidemiology study (non-Hispanic White: n = 155; African American: n = 125), we evaluated PDL1 tumor proportion score (<1% vs. ≥1%) and TLS status (presence/absence), comparing differences within the tumor microenvironment based on immune cell distribution and differential expression of genes. RESULTS Tumors from African Americans had a higher proportion of plasma cell signatures within the tumor microenvironment than non-Hispanic Whites. In addition, gene expression patterns in African American PDL1-positive samples suggest that these tumors contained greater numbers of γδ T cells and resting dendritic cells, along with fewer CD8+ T cells after adjusting for age, sex, pack-years, stage, and histology. Investigation of differential expression of B cell/plasma cell-related genes between the two patient populations revealed that two immunoglobulin genes (IGKV2-29 and IGLL5) were associated with decreased mortality risk in African Americans. CONCLUSIONS In the first known race-stratified analysis of tumor microenvironment components in lung cancer based on PDL1 expression or TLS status, differences within the immune cell composition and transcriptomic signature were identified that may have therapeutic implications. IMPACT Future investigation of racial variation within the tumor microenvironment may help direct the use of immunotherapy.
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Affiliation(s)
- Matthew R Trendowski
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Donovan Watza
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Christine M Lusk
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Fulvio Lonardo
- Department of Pathology, Wayne State University School of Medicine, Detroit, Michigan
| | - Valerie Ratliff
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Angela S Wenzlaff
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Hirva Mamdani
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | | | - Julie L Boerner
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Ann G Schwartz
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Heather M Gibson
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
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15
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Deguine J, Xavier RJ. B cell tolerance and autoimmunity: Lessons from repertoires. J Exp Med 2024; 221:e20231314. [PMID: 39093312 PMCID: PMC11296956 DOI: 10.1084/jem.20231314] [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: 04/25/2024] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Adaptive immune cell function is regulated by a highly diverse receptor recombined from variable germline-encoded segments that can recognize an almost unlimited array of epitopes. While this diversity enables the recognition of any pathogen, it also poses a risk of self-recognition, leading to autoimmunity. Many layers of regulation are present during both the generation and activation of B cells to prevent this phenomenon, although they are evidently imperfect. In recent years, our ability to analyze immune repertoires at scale has drastically increased, both through advances in sequencing and single-cell analyses. Here, we review the current knowledge on B cell repertoire analyses, focusing on their implication for autoimmunity. These studies demonstrate that a failure of tolerance occurs at multiple independent checkpoints in different autoimmune contexts, particularly during B cell maturation, plasmablast differentiation, and within germinal centers. These failures are marked by distinct repertoire features that may be used to identify disease- or patient-specific therapeutic approaches.
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Affiliation(s)
- Jacques Deguine
- Immunology Program, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, MA, USA
| | - Ramnik J Xavier
- Immunology Program, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School , Boston, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
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16
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Engelbrecht E, Rodriguez OL, Watson CT. Addressing Technical Pitfalls in Pursuit of Molecular Factors That Mediate Immunoglobulin Gene Regulation. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 213:651-662. [PMID: 39007649 PMCID: PMC11333172 DOI: 10.4049/jimmunol.2400131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 06/13/2024] [Indexed: 07/16/2024]
Abstract
The expressed Ab repertoire is a critical determinant of immune-related phenotypes. Ab-encoding transcripts are distinct from other expressed genes because they are transcribed from somatically rearranged gene segments. Human Abs are composed of two identical H and L chain polypeptides derived from genes in IGH locus and one of two L chain loci. The combinatorial diversity that results from Ab gene rearrangement and the pairing of different H and L chains contributes to the immense diversity of the baseline Ab repertoire. During rearrangement, Ab gene selection is mediated by factors that influence chromatin architecture, promoter/enhancer activity, and V(D)J recombination. Interindividual variation in the composition of the Ab repertoire associates with germline variation in IGH, implicating polymorphism in Ab gene regulation. Determining how IGH variants directly mediate gene regulation will require integration of these variants with other functional genomic datasets. In this study, we argue that standard approaches using short reads have limited utility for characterizing regulatory regions in IGH at haplotype resolution. Using simulated and chromatin immunoprecipitation sequencing reads, we define features of IGH that limit use of short reads and a single reference genome, namely 1) the highly duplicated nature of the DNA sequence in IGH and 2) structural polymorphisms that are frequent in the population. We demonstrate that personalized diploid references enhance performance of short-read data for characterizing mappable portions of the locus, while also showing that long-read profiling tools will ultimately be needed to fully resolve functional impacts of IGH germline variation on expressed Ab repertoires.
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Affiliation(s)
- Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY
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17
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Jackson KJ. RAGging on recombination signal sequence strength for diffusion-mediated recombination. Immunol Cell Biol 2024; 102:648-650. [PMID: 38973226 DOI: 10.1111/imcb.12803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
In this article, we discuss new insights into the distinct mechanisms for V(D)J recombination for different immunoglobulin loci. This follows the recent revelation that recombination signal sequences (RSS) within the IGKV locus have evolved to be more efficient mediators of recombination activating gene (RAG) recombination compared to the same elements in the IGH locus. This difference in RSS strength is proposed to be driven by different molecular mechanisms for RAG-mediated recombination between the two loci.
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Affiliation(s)
- Katherine Jl Jackson
- Immunology Program, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
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18
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Zhu Y, Watson C, Safonova Y, Pennell M, Bankevich A. Assessing Assembly Errors in Immunoglobulin Loci: A Comprehensive Evaluation of Long-read Genome Assemblies Across Vertebrates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604360. [PMID: 39091785 PMCID: PMC11291089 DOI: 10.1101/2024.07.19.604360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Long-read sequencing technologies have revolutionized genome assembly producing near-complete chromosome assemblies for numerous organisms, which are invaluable to research in many fields. However, regions with complex repetitive structure continue to represent a challenge for genome assembly algorithms, particularly in areas with high heterozygosity. Robust and comprehensive solutions for the assessment of assembly accuracy and completeness in these regions do not exist. In this study we focus on the assembly of biomedically important antibody-encoding immunoglobulin (IG) loci, which are characterized by complex duplications and repeat structures. High-quality full-length assemblies for these loci are critical for resolving haplotype-level annotations of IG genes, without which, functional and evolutionary studies of antibody immunity across vertebrates are not tractable. To address these challenges, we developed a pipeline, "CloseRead", that generates multiple assembly verification metrics for analysis and visualization. These metrics expand upon those of existing quality assessment tools and specifically target complex and highly heterozygous regions. Using CloseRead, we systematically assessed the accuracy and completeness of IG loci in publicly available assemblies of 74 vertebrate species, identifying problematic regions. We also demonstrated that inspecting assembly graphs for problematic regions can both identify the root cause of assembly errors and illuminate solutions for improving erroneous assemblies. For a subset of species, we were able to correct assembly errors through targeted reassembly. Together, our analysis demonstrated the utility of assembly assessment in improving the completeness and accuracy of IG loci across species.
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Affiliation(s)
- Yixin Zhu
- Department of Quantitative and Computational Biology and Biological Sciences, University of Southern California, Los Angeles, CA, United States
| | - Corey Watson
- Department of Biochemistry and Molecular Biology, University of Louisville School of Medicine, Louisville, KY, United States
| | - Yana Safonova
- Department of Computer Science and Engineering, Pennsylvania State University, PA, United States
| | - Matt Pennell
- Department of Quantitative and Computational Biology and Biological Sciences, University of Southern California, Los Angeles, CA, United States
| | - Anton Bankevich
- Department of Computer Science and Engineering, Pennsylvania State University, PA, United States
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19
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Engelbrecht E, Rodriguez OL, Shields K, Schultze S, Tieri D, Jana U, Yaari G, Lees WD, Smith ML, Watson CT. Resolving haplotype variation and complex genetic architecture in the human immunoglobulin kappa chain locus in individuals of diverse ancestry. Genes Immun 2024; 25:297-306. [PMID: 38844673 PMCID: PMC11327106 DOI: 10.1038/s41435-024-00279-2] [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: 10/27/2023] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 08/17/2024]
Abstract
Immunoglobulins (IGs), critical components of the human immune system, are composed of heavy and light protein chains encoded at three genomic loci. The IG Kappa (IGK) chain locus consists of two large, inverted segmental duplications. The complexity of the IG loci has hindered use of standard high-throughput methods for characterizing genetic variation within these regions. To overcome these limitations, we use long-read sequencing to create haplotype-resolved IGK assemblies in an ancestrally diverse cohort (n = 36), representing the first comprehensive description of IGK haplotype variation. We identify extensive locus polymorphism, including novel single nucleotide variants (SNVs) and novel structural variants harboring functional IGKV genes. Among 47 functional IGKV genes, we identify 145 alleles, 67 of which were not previously curated. We report inter-population differences in allele frequencies for 10 IGKV genes, including alleles unique to specific populations within this dataset. We identify haplotypes carrying signatures of gene conversion that associate with SNV enrichment in the IGK distal region, and a haplotype with an inversion spanning the proximal and distal regions. These data provide a critical resource of curated genomic reference information from diverse ancestries, laying a foundation for advancing our understanding of population-level genetic variation in the IGK locus.
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Affiliation(s)
- Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Kaitlyn Shields
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Steven Schultze
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - David Tieri
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Uddalok Jana
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
- Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan, Israel
| | - William D Lees
- Faculty of Engineering, Bar Ilan University, Ramat Gan, Israel
| | - Melissa L Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA.
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY, USA.
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20
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Richardson E, Bibi S, McLean F, Schimanski L, Rijal P, Ghraichy M, von Niederhäusern V, Trück J, Clutterbuck EA, O’Connor D, Luhn K, Townsend A, Peters B, Pollard AJ, Deane CM, Kelly DF. Computational mining of B cell receptor repertoires reveals antigen-specific and convergent responses to Ebola vaccination. Front Immunol 2024; 15:1383753. [PMID: 39040106 PMCID: PMC11260629 DOI: 10.3389/fimmu.2024.1383753] [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: 02/08/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Outbreaks of Ebolaviruses, such as Sudanvirus (SUDV) in Uganda in 2022, demonstrate that species other than the Zaire ebolavirus (EBOV), which is currently the sole virus represented in current licensed vaccines, remain a major threat to global health. There is a pressing need to develop effective pan-species vaccines and novel monoclonal antibody-based therapeutics for Ebolavirus disease. In response to recent outbreaks, the two dose, heterologous Ad26.ZEBOV/MVA-BN-Filo vaccine regimen was developed and was tested in a large phase II clinical trial (EBL2001) as part of the EBOVAC2 consortium. Here, we perform bulk sequencing of the variable heavy chain (VH) of B cell receptors (BCR) in forty participants from the EBL2001 trial in order to characterize the BCR repertoire in response to vaccination with Ad26.ZEBOV/MVA-BN-Filo. We develop a comprehensive database, EBOV-AbDab, of publicly available Ebolavirus-specific antibody sequences. We then use our database to predict the antigen-specific component of the vaccinee repertoires. Our results show striking convergence in VH germline gene usage across participants following the MVA-BN-Filo dose, and provide further evidence of the role of IGHV3-15 and IGHV3-13 antibodies in the B cell response to Ebolavirus glycoprotein. Furthermore, we found that previously described Ebola-specific mAb sequences present in EBOV-AbDab were sufficient to describe at least one of the ten most expanded BCR clonotypes in more than two thirds of our cohort of vaccinees following the boost, providing proof of principle for the utility of computational mining of immune repertoires.
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Affiliation(s)
- Eve Richardson
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
- La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Sagida Bibi
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Florence McLean
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Lisa Schimanski
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Pramila Rijal
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Marie Ghraichy
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Valentin von Niederhäusern
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | - Johannes Trück
- Divisions of Allergy and Immunology, University Children’s Hospital and Children’s Research Center, University of Zurich (UZH), Zurich, Switzerland
| | | | - Daniel O’Connor
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | - Kerstin Luhn
- Janssen Vaccines and Prevention, Leiden, Netherlands
| | - Alain Townsend
- Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Andrew J. Pollard
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
| | | | - Dominic F. Kelly
- Oxford Vaccine Group, Department of Pediatrics, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
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21
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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22
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du Pre MF, Iversen R, Sollid LM. Coeliac disease: the paradox of diagnosing a food hypersensitivity disorder with autoantibodies. Gut 2024; 73:844-853. [PMID: 38378252 DOI: 10.1136/gutjnl-2023-331595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024]
Abstract
Serum antibodies to the autoantigen transglutaminase 2 (TG2) are increasingly harnessed to diagnose coeliac disease. Diagnostic guidelines for children give recommendation for a no-biopsy-based diagnosis through detection of high amounts of IgA anti-TG2 antibodies in serum with confirmation of positivity in a separate blood sample by characteristic autoantibody-staining of tissue. While measurement of IgA anti-TG2 also is important in the diagnostic workup of adults, the adult guidelines still mandate examination of gut biopsies. This requirement might well change in the future, as might the necessity for confirming autoantibody positivity by tissue staining. The key role of autoantibody serology for diagnosis of coeliac disease is paradoxical. Coeliac disease was considered, and still can be considered, a food intolerance disorder where autoantibodies at face value are out of place. The immunological mechanisms underlying the formation of autoantibodies in response to gluten exposure have been dissected. This review presents the current insights demonstrating that the autoantibodies in coeliac disease are intimately integrated in the maladapted immune response to gluten.
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Affiliation(s)
- M Fleur du Pre
- Norwegian Coeliac Disease Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Immunology, Oslo University Hosptial - Rikshospitalet, Oslo, Norway
| | - Rasmus Iversen
- Norwegian Coeliac Disease Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Immunology, Oslo University Hosptial - Rikshospitalet, Oslo, Norway
| | - Ludvig M Sollid
- Norwegian Coeliac Disease Research Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Immunology, Oslo University Hosptial - Rikshospitalet, Oslo, Norway
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23
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Corcoran MM, Karlsson Hedestam GB. Adaptive immune receptor germline gene variation. Curr Opin Immunol 2024; 87:102429. [PMID: 38805851 DOI: 10.1016/j.coi.2024.102429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 05/30/2024]
Abstract
Recognition of antigens by T cell receptors (TCRs) and B cell receptors (BCRs) is a key step in lymphocyte activation. T and B cells mediate adaptive immune responses, which protect us against infections and provide immunological memory, and also, in some instances, drive pathogenic responses in autoimmune diseases. TCRs and BCRs are encoded within loci that are known to be genetically diverse. However, the extent and functional impact of this variation, both in humans and model animals used in immunological research, remain largely unknown. Experimental and genetic evidence has demonstrated that the complementarity determining regions 1 and 2 (HCDR1 and HCDR2), encoded by the variable (V) region of TCRs and BCRs, also often make critical contacts with the targeted antigen. Thus, knowledge about allelic variation in the genes encoding TCRs and BCRs is critically important for understanding adaptive immune responses in outbred populations and to define responder and non-responder phenotypes.
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Affiliation(s)
- Martin M Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17177 Stockholm, Sweden
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24
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Townsend DR, Towers DM, Lavinder JJ, Ippolito GC. Innovations and trends in antibody repertoire analysis. Curr Opin Biotechnol 2024; 86:103082. [PMID: 38428225 DOI: 10.1016/j.copbio.2024.103082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/07/2023] [Accepted: 01/28/2024] [Indexed: 03/03/2024]
Abstract
Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.
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Affiliation(s)
- Douglas R Townsend
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Dalton M Towers
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jason J Lavinder
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Gregory C Ippolito
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
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25
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Beaulaurier J, Ly L, Duty JA, Tyer C, Stevens C, Hung CT, Sookdeo A, Drong AW, Kowdle S, Turner DJ, Juul S, Hickey S, Lee B. De novo antibody discovery in human blood from full-length single B cell transcriptomics and matching haplotyped-resolved germline assemblies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.26.586834. [PMID: 38585716 PMCID: PMC10996687 DOI: 10.1101/2024.03.26.586834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully-phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells resulted in whole transcriptome characterization of each cell, as well as highly-accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrated binding to measles antigens and neutralization of measles live virus.
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26
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Collins AM, Ohlin M, Corcoran M, Heather JM, Ralph D, Law M, Martínez-Barnetche J, Ye J, Richardson E, Gibson WS, Rodriguez OL, Peres A, Yaari G, Watson CT, Lees WD. AIRR-C IG Reference Sets: curated sets of immunoglobulin heavy and light chain germline genes. Front Immunol 2024; 14:1330153. [PMID: 38406579 PMCID: PMC10884231 DOI: 10.3389/fimmu.2023.1330153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/27/2023] [Indexed: 02/27/2024] Open
Abstract
Introduction Analysis of an individual's immunoglobulin (IG) gene repertoire requires the use of high-quality germline gene reference sets. When sets only contain alleles supported by strong evidence, AIRR sequencing (AIRR-seq) data analysis is more accurate and studies of the evolution of IG genes, their allelic variants and the expressed immune repertoire is therefore facilitated. Methods The Adaptive Immune Receptor Repertoire Community (AIRR-C) IG Reference Sets have been developed by including only human IG heavy and light chain alleles that have been confirmed by evidence from multiple high-quality sources. To further improve AIRR-seq analysis, some alleles have been extended to deal with short 3' or 5' truncations that can lead them to be overlooked by alignment utilities. To avoid other challenges for analysis programs, exact paralogs (e.g. IGHV1-69*01 and IGHV1-69D*01) are only represented once in each set, though alternative sequence names are noted in accompanying metadata. Results and discussion The Reference Sets include less than half the previously recognised IG alleles (e.g. just 198 IGHV sequences), and also include a number of novel alleles: 8 IGHV alleles, 2 IGKV alleles and 5 IGLV alleles. Despite their smaller sizes, erroneous calls were eliminated, and excellent coverage was achieved when a set of repertoires comprising over 4 million V(D)J rearrangements from 99 individuals were analyzed using the Sets. The version-tracked AIRR-C IG Reference Sets are freely available at the OGRDB website (https://ogrdb.airr-community.org/germline_sets/Human) and will be regularly updated to include newly observed and previously reported sequences that can be confirmed by new high-quality data.
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Affiliation(s)
- Andrew M. Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mats Ohlin
- Department of Immunotechnology, and SciLifeLab, Lund University, Lund, Sweden
| | - Martin Corcoran
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - James M. Heather
- Mass General Cancer Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Duncan Ralph
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Mansun Law
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Jesus Martínez-Barnetche
- Centro de Investigación Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico
| | - Jian Ye
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Eve Richardson
- La Jolla Institute for Immunology, San Diego, CA, United States
| | - William S. Gibson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Oscar L. Rodriguez
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, United States
| | - Ayelet Peres
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Gur Yaari
- Bioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, United States
| | - William D. Lees
- Institute of Structural and Molecular Biology, Birkbeck College, London, United Kingdom
- Human-Centered Computing and Information Science, Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
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27
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Watson CT, Rodriguez OL, Engelbrecht E, Safonova Y, Marasco WA, Smith ML. Looking to the future of antibody genetics: resolving the roles of immunoglobulin diversity in gene regulation, function, and immunity. Genes Immun 2024; 25:92-94. [PMID: 38097744 DOI: 10.1038/s41435-023-00238-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 02/18/2024]
Affiliation(s)
- Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Eric Engelbrecht
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Yana Safonova
- Computer Science and Engineering Department, Huck Institutes of the Life Sciences, Penn State University, State College, PA, 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
| | - Melissa L Smith
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
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28
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Mikelov A, Nefediev G, Tashkeev A, Rodriguez OL, Ortmans DA, Skatova V, Izraelson M, Davydov A, Poslavsky S, Rahmouni S, Watson CT, Chudakov D, Boyd SD, Bolotin D. Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561703. [PMID: 38014266 PMCID: PMC10680553 DOI: 10.1101/2023.10.10.561703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), has been shown to be of critical importance for immune responses to pathogens and vaccines. In recent years, B cell and T cell receptor repertoire sequencing (Rep-Seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci in different populations. Here we present a novel algorithm for extra-sensitive and specific variable (V) and joining (J) gene allele inference and genotyping allowing reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput genotyping and novel allele discovery from a wide variety of existing datasets. The developed algorithm is a part of the MiXCR software ( https://mixcr.com ) and can be incorporated into any pipeline utilizing upstream processing with MiXCR. We demonstrate the accuracy of this approach using Rep-Seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) Rep-Seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA; TRB) Rep-Seq dataset, representing 134 individuals. This allowed us to assess the genetic diversity of genes within the IGH, TRA and TRB loci in different populations and demonstrate the connection between antibody repertoire gene usage and the number of allelic variants present in the population. Finally we established a database of allelic variants of V and J genes inferred from Rep-Seq data and their population frequencies with free public access at https://vdj.online .
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29
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Yuan M, Feng Z, Lv H, So N, Shen IR, Tan TJC, Teo QW, Ouyang WO, Talmage L, Wilson IA, Wu NC. Widespread impact of immunoglobulin V-gene allelic polymorphisms on antibody reactivity. Cell Rep 2023; 42:113194. [PMID: 37777966 PMCID: PMC10636607 DOI: 10.1016/j.celrep.2023.113194] [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: 06/07/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 10/03/2023] Open
Abstract
The ability of the human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V-gene allelic polymorphisms. However, previous studies have provided only limited examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many V-gene allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiments further demonstrate that paratope allelic polymorphisms on both heavy and light chains often abolish antibody binding. We also illustrate the importance of minor V-gene allelic polymorphisms with low frequency in several broadly neutralizing antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus. Overall, this study not only highlights the pervasive impact of V-gene allelic polymorphisms on antibody binding but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery.
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Affiliation(s)
- Meng Yuan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ziqi Feng
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Huibin Lv
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Natalie So
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Ivana R Shen
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Timothy J C Tan
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Qi Wen Teo
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Wenhao O Ouyang
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Logan Talmage
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nicholas C Wu
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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30
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Richardson E, Binter Š, Kosmac M, Ghraichy M, von Niederhäusern V, Kovaltsuk A, Galson JD, Trück J, Kelly DF, Deane CM, Kellam P, Watson SJ. Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing. eLife 2023; 12:e81629. [PMID: 36971345 PMCID: PMC10115447 DOI: 10.7554/elife.81629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/26/2023] [Indexed: 03/29/2023] Open
Abstract
Immunoglobulin loci-transgenic animals are widely used in antibody discovery and increasingly in vaccine response modelling. In this study, we phenotypically characterised B-cell populations from the Intelliselect Transgenic mouse (Kymouse) demonstrating full B-cell development competence. Comparison of the naïve B-cell receptor (BCR) repertoires of Kymice BCRs, naïve human, and murine BCR repertoires revealed key differences in germline gene usage and junctional diversification. These differences result in Kymice having CDRH3 length and diversity intermediate between mice and humans. To compare the structural space explored by CDRH3s in each species' repertoire, we used computational structure prediction to show that Kymouse naïve BCR repertoires are more human-like than mouse-like in their predicted distribution of CDRH3 shape. Our combined sequence and structural analysis indicates that the naïve Kymouse BCR repertoire is diverse with key similarities to human repertoires, while immunophenotyping confirms that selected naïve B cells are able to go through complete development.
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Affiliation(s)
- Eve Richardson
- Kymab, a Sanofi Company, Babraham Research CampusCambridgeUnited Kingdom
- Department of Statistics, University of OxfordOxfordUnited Kingdom
| | - Špela Binter
- Kymab, a Sanofi Company, Babraham Research CampusCambridgeUnited Kingdom
| | - Miha Kosmac
- Kymab, a Sanofi Company, Babraham Research CampusCambridgeUnited Kingdom
| | - Marie Ghraichy
- Division of Immunology, University Children's Hospital, University of ZurichZurichSwitzerland
- Children's Research Center, University of ZurichZurichSwitzerland
| | - Valentin von Niederhäusern
- Division of Immunology, University Children's Hospital, University of ZurichZurichSwitzerland
- Children's Research Center, University of ZurichZurichSwitzerland
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, Kings CrossLondonUnited Kingdom
| | - Johannes Trück
- Division of Immunology, University Children's Hospital, University of ZurichZurichSwitzerland
- Children's Research Center, University of ZurichZurichSwitzerland
| | - Dominic F Kelly
- Department of Paediatrics, University of OxfordOxfordUnited Kingdom
| | | | - Paul Kellam
- Kymab, a Sanofi Company, Babraham Research CampusCambridgeUnited Kingdom
- Department of Infectious Disease, Faculty of Medicine, Imperial College LondonLondonUnited Kingdom
| | - Simon J Watson
- Kymab, a Sanofi Company, Babraham Research CampusCambridgeUnited Kingdom
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