1
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Schlegel B, Morikone M, Mu F, Tang WY, Kohanbash G, Rajasundaram D. bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data. NAR Genom Bioinform 2024; 6:lqae137. [PMID: 39411512 PMCID: PMC11474772 DOI: 10.1093/nargab/lqae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 08/13/2024] [Accepted: 09/23/2024] [Indexed: 10/19/2024] Open
Abstract
B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.
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Affiliation(s)
- Brent T Schlegel
- Department of Pediatrics, Division of Health Informatics, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, John G. Rangos Sr. Research Center, 4401 Penn Avenue, Pittsburgh, PA 15224, USA
| | - Michael Morikone
- Department of Pediatrics, Division of Health Informatics, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, John G. Rangos Sr. Research Center, 4401 Penn Avenue, Pittsburgh, PA 15224, USA
| | - Fangping Mu
- Center for Research Computing, University of Pittsburgh, 312 Schenley Place, 4420 Bayard Street, Pittsburgh, PA 15260, USA
| | - Wan-Yee Tang
- Department of Environmental and Occupational Health, University of Pittsburgh, School of Public Health, 130 DeSoto Street, Pittsburgh, PA 15261, USA
| | - Gary Kohanbash
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, John G. Rangos Sr. Research Center, 4401 Penn Avenue, Pittsburgh, PA 15224, USA
| | - Dhivyaa Rajasundaram
- Department of Pediatrics, Division of Health Informatics, University of Pittsburgh School of Medicine, UPMC Children's Hospital of Pittsburgh, John G. Rangos Sr. Research Center, 4401 Penn Avenue, Pittsburgh, PA 15224, USA
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2
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Mallaby J, Mwangi W, Ng J, Stewart A, Dorey-Robinson D, Kipling D, Hershberg U, Fraternali F, Nair V, Dunn-Walters D. Diversification of immunoglobulin genes by gene conversion in the domestic chicken ( Gallus gallus domesticus). DISCOVERY IMMUNOLOGY 2023; 2:kyad002. [PMID: 38567069 PMCID: PMC10917233 DOI: 10.1093/discim/kyad002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 12/29/2022] [Accepted: 01/18/2023] [Indexed: 04/04/2024]
Abstract
Sustainable modern poultry production depends on effective protection against infectious diseases and a diverse range of antibodies is key for an effective immune response. In the domestic chicken, somatic gene conversion is the dominant process in which the antibody immunoglobulin genes are diversified. Affinity maturation by somatic hypermutation (SHM) also occurs, but the relative contribution of gene conversion versus somatic hypermutation to immunoglobulin (Ig) gene diversity is poorly understood. In this study, we use high throughput long-read sequencing to study immunoglobulin diversity in multiple immune-associated tissues in Rhode Island Red chickens. To better understand the impact of genetic diversification in the chicken, a novel gene conversion identification software was developed (BrepConvert). In this study, BrepConvert enabled the identification of over 1 million gene conversion events. Mapping the occurrence of putative somatic gene conversion (SGC) events throughout the variable gene region revealed repetitive and highly restricted patterns of genetic insertions in both the antibody heavy and light chains. These patterns coincided with the locations of genetic variability in available pseudogenes and align with antigen binding sites, predominately the complementary determining regions (CDRs). We found biased usage of pseudogenes during gene conversion, as well as immunoglobulin heavy chain diversity gene (IGHD) preferences during V(D)J gene rearrangement, suggesting that antibody diversification in chickens is more focused than the genetic potential for diversity would suggest.
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Affiliation(s)
- Jessica Mallaby
- Department of Bioscience and Medicine, University of Surrey, Guildford, UK
- Pirbright Institute, Woking, UK
| | | | - Joseph Ng
- Randall Centre for Cell and Molecular Biophysics, King’s College London, London, UK
| | - Alexander Stewart
- Department of Bioscience and Medicine, University of Surrey, Guildford, UK
| | | | - David Kipling
- Department of Bioscience and Medicine, University of Surrey, Guildford, UK
| | - Uri Hershberg
- Department of Human Biology, University of Haifa, Haifa, Israel
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King’s College London, London, UK
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3
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Pennell M, Rodriguez OL, Watson CT, Greiff V. The evolutionary and functional significance of germline immunoglobulin gene variation. Trends Immunol 2023; 44:7-21. [PMID: 36470826 DOI: 10.1016/j.it.2022.11.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/07/2022] [Indexed: 12/04/2022]
Abstract
The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.
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Affiliation(s)
- Matt Pennell
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Oscar L Rodriguez
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Corey T Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
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4
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Mallaby J, Ng J, Stewart A, Sinclair E, Dunn-Walters D, Hershberg U. Chickens, more than humans, focus the diversity of their immunoglobulin genes on the complementarity-determining region but utilise amino acids, indicative of a more cross-reactive antibody repertoire. Front Immunol 2022; 13:837246. [PMID: 36569888 PMCID: PMC9772431 DOI: 10.3389/fimmu.2022.837246] [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: 12/16/2021] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
The mechanisms of B-cell diversification differ greatly between aves and mammals, but both produce B cells and antibodies capable of supporting an effective immune response. To see how differences in the generation of diversity might affect overall repertoire diversity, we have compared the diversity characteristics of immunoglobulin genes from domestic chickens to those from humans. Both use V(D)J gene rearrangement and somatic hypermutation, but only chickens use somatic gene conversion. A range of diversity analysis tools were used to investigate multiple aspects of amino acid diversity at both the germline and repertoire levels. The effect of differing amino acid usages on antibody characteristics was assessed. At both the germline and repertoire levels, chickens exhibited lower amino acid diversity in comparison to the human immunoglobulin genes, especially outside of the complementarity-determining region (CDR). Chickens were also found to possess much larger and more hydrophilic CDR3s with a higher predicted protein binding potential, suggesting that the antigen-binding site in chicken antibodies is more flexible and more polyreactive than that seen in human antibodies.
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Affiliation(s)
- Jessica Mallaby
- Department of Bioscience and Medicine, University of Surrey, Guildford, United Kingdom
| | - Joseph Ng
- Randall Centre for Cell and Molecular Biophysics, King’s College London, London, United Kingdom
| | - Alex Stewart
- Department of Bioscience and Medicine, University of Surrey, Guildford, United Kingdom
| | - Emma Sinclair
- Department of Bioscience and Medicine, University of Surrey, Guildford, United Kingdom
| | - Deborah Dunn-Walters
- Department of Bioscience and Medicine, University of Surrey, Guildford, United Kingdom
| | - Uri Hershberg
- Department of Human Biology, University of Haifa, Haifa, Israel
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5
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Inter- and intraspecies comparison of phylogenetic fingerprints and sequence diversity of immunoglobulin variable genes. Immunogenetics 2020; 72:279-294. [PMID: 32367185 DOI: 10.1007/s00251-020-01164-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 04/13/2020] [Indexed: 10/24/2022]
Abstract
Protection and neutralization of a vast array of pathogens is accomplished by the tremendous diversity of the B cell receptor (BCR) repertoire. For jawed vertebrates, this diversity is initiated via the somatic recombination of immunoglobulin (Ig) germline elements. While it is clear that the number of these germline segments differs from species to species, the extent of cross-species sequence diversity remains largely uncharacterized. Here we use extensive computational and statistical methods to investigate the sequence diversity and evolutionary relationship between Ig variable (V), diversity (D), and joining (J) germline segments across nine commonly studied species ranging from zebrafish to human. Metrics such as guanine-cytosine (GC) content showed low redundancy across Ig germline genes within a given species. Other comparisons, including amino acid motifs, evolutionary selection, and sequence diversity, revealed species-specific properties. Additionally, we showed that the germline-encoded diversity differs across antibody (recombined V-D-J) repertoires of various B cell subsets. To facilitate future comparative immunogenomics analysis, we created VDJgermlines, an R package that contains the germline sequences from multiple species. Our study informs strategies for the humanization and engineering of therapeutic antibodies.
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6
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Schwartz GW, Zhou Y, Petrovic J, Fasolino M, Xu L, Shaffer SM, Pear WS, Vahedi G, Faryabi RB. TooManyCells identifies and visualizes relationships of single-cell clades. Nat Methods 2020; 17:405-413. [PMID: 32123397 PMCID: PMC7439807 DOI: 10.1038/s41592-020-0748-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 01/15/2020] [Indexed: 01/24/2023]
Abstract
Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.
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Affiliation(s)
- Gregory W Schwartz
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yeqiao Zhou
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jelena Petrovic
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Maria Fasolino
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Lanwei Xu
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney M Shaffer
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Warren S Pear
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert B Faryabi
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Abramson Family Cancer Research Institute Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Schwartz GW, Shauli T, Linial M, Hershberg U. Serine substitutions are linked to codon usage and differ for variable and conserved protein regions. Sci Rep 2019; 9:17238. [PMID: 31754132 PMCID: PMC6872785 DOI: 10.1038/s41598-019-53452-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 11/01/2019] [Indexed: 11/11/2022] Open
Abstract
Serine is the only amino acid that is encoded by two disjoint codon sets (TCN & AGY) so that a tandem substitution of two nucleotides is required to switch between the two sets. We show that these codon sets underlie distinct substitution patterns at positions subject to purifying and diversifying selections. We found that in humans, positions that are conserved among ~100 vertebrates, and thus subjected to purifying selection, are enriched for substitutions involving serine (TCN, denoted S'), proline, and alanine, (S'PA). In contrast, the less conserved positions are enriched for serine encoded with AGY codons (denoted S″), glycine and asparagine, (GS″N). We tested this phenomenon in the HIV envelope glycoprotein (gp120), and the V-gene that encodes B-cell receptors/antibodies. These fast evolving proteins both have hypervariable positions, which are under diversifying selection, closely adjacent to highly conserved structural regions. In both instances, we identified an opposite abundance of two groups of serine substitutions, with enrichment of S'PA in the conserved positions, and GS″N in the hypervariable regions. Finally, we analyzed the substitutions across 60,000 individual human exomes to show that, when serine has a specific functional constraint of phosphorylation capability, S' codons are 32-folds less prone than S″ to substitutions to Threonine or Tyrosine that could potentially retain the phosphorylation site capacity. Combined, our results, that cover evolutionary signals at different temporal scales, demonstrate that through its encoding by two codon sets, serine allows for the existence of alternating substitution patterns within positions of functional maintenance versus sites of rapid diversification.
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Affiliation(s)
- Gregory W Schwartz
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Tair Shauli
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Uri Hershberg
- Drexel School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USA.
- Department of Microbiology and Immunology, Drexel College of Medicine, Drexel University, Philadelphia, USA.
- Department of Human Biology, Faculty of Science, University of Haifa, Haifa, Israel.
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8
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Collins AM, Watson CT. Immunoglobulin Light Chain Gene Rearrangements, Receptor Editing and the Development of a Self-Tolerant Antibody Repertoire. Front Immunol 2018; 9:2249. [PMID: 30349529 PMCID: PMC6186787 DOI: 10.3389/fimmu.2018.02249] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/10/2018] [Indexed: 11/13/2022] Open
Abstract
Discussion of the antibody repertoire usually emphasizes diversity, but a conspicuous feature of the light chain repertoire is its lack of diversity. The diversity of reported allelic variants of germline light chain genes is also limited, even in well-studied species. In this review, the implications of this lack of diversity are considered. We explore germline and rearranged light chain genes in a variety of species, with a particular focus on human and mouse genes. The importance of the number, organization and orientation of the genes for the control of repertoire development is discussed, and we consider how primary rearrangements and receptor editing together shape the expressed light chain repertoire. The resulting repertoire is dominated by just a handful of IGKV and IGLV genes. It has been hypothesized that an important function of the light chain is to guard against self-reactivity, and the role of secondary rearrangements in this process could explain the genomic organization of the light chain genes. It could also explain why the light chain repertoire is so limited. Heavy and light chain genes may have co-evolved to ensure that suitable light chain partners are usually available for each heavy chain that forms early in B cell development. We suggest that the co-evolved loci of the house mouse often became separated during the inbreeding of laboratory mice, resulting in new pairings of loci that are derived from different sub-species of the house mouse. A resulting vulnerability to self-reactivity could explain at least some mouse models of autoimmune disease.
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Affiliation(s)
- Andrew M. Collins
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Corey T. Watson
- Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States
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9
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Rosenfeld AM, Meng W, Luning Prak ET, Hershberg U. ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data. Front Immunol 2018; 9:2107. [PMID: 30298069 PMCID: PMC6161679 DOI: 10.3389/fimmu.2018.02107] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 08/28/2018] [Indexed: 11/16/2022] Open
Abstract
ImmuneDB is a system for storing and analyzing high-throughput immune receptor sequencing data. Unlike most existing tools, which utilize flat-files, ImmuneDB stores data in a well-structured MySQL database, enabling efficient data queries. It can take raw sequencing data as input and annotate receptor gene usage, infer clonotypes, aggregate results, and run common downstream analyses such as calculating selection pressure and constructing clonal lineages. Alternatively, pre-annotated data can be imported and analyzed data can be exported in a variety of common Adaptive Immune Receptor Repertoire (AIRR) file formats. To validate ImmuneDB, we compare its results to those of another pipeline, MiXCR. We show that the biological conclusions drawn would be similar with either tool, while ImmuneDB provides the additional benefits of integrating other common tools and storing data in a database. ImmuneDB is freely available on GitHub at https://github.com/arosenfeld/immunedb, on PyPi at https://pypi.org/project/ImmuneDB, and a Docker container is provided at https://hub.docker.com/r/arosenfeld/immunedb. Full documentation is available at http://immunedb.com.
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Affiliation(s)
- Aaron M Rosenfeld
- School of Biomedical Engineering Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Uri Hershberg
- School of Biomedical Engineering Science and Health Systems, Drexel University, Philadelphia, PA, United States.,Department of Microbiology and Immunology, College of Medicine, Drexel University, Philadelphia, PA, United States.,Department of Human Biology, Faculty of Sciences, University of Haifa, Haifa, Israel
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10
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Miron M, Kumar BV, Meng W, Granot T, Carpenter DJ, Senda T, Chen D, Rosenfeld AM, Zhang B, Lerner H, Friedman AL, Hershberg U, Shen Y, Rahman A, Luning Prak ET, Farber DL. Human Lymph Nodes Maintain TCF-1 hi Memory T Cells with High Functional Potential and Clonal Diversity throughout Life. THE JOURNAL OF IMMUNOLOGY 2018; 201:2132-2140. [PMID: 30111633 DOI: 10.4049/jimmunol.1800716] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/23/2018] [Indexed: 11/19/2022]
Abstract
Translating studies on T cell function and modulation from mouse models to humans requires extrapolating in vivo results on mouse T cell responses in lymphoid organs (spleen and lymph nodes [LN]) to human peripheral blood T cells. However, our understanding of T cell responses in human lymphoid sites and their relation to peripheral blood remains sparse. In this study, we used a unique human tissue resource to study human T cells in different anatomical compartments within individual donors and identify a subset of memory CD8+ T cells in LN, which maintain a distinct differentiation and functional profile compared with memory CD8+ T cells in blood, spleen, bone marrow, and lungs. Whole-transcriptome and high-dimensional cytometry by time-of-flight profiling reveals that LN memory CD8+ T cells express signatures of quiescence and self-renewal compared with corresponding populations in blood, spleen, bone marrow, and lung. LN memory T cells exhibit a distinct transcriptional signature, including expression of stem cell-associated transcription factors TCF-1 and LEF-1, T follicular helper cell markers CXCR5 and CXCR4, and reduced expression of effector molecules. LN memory T cells display high homology to a subset of mouse CD8+ T cells identified in chronic infection models that respond to checkpoint blockade immunotherapy. Functionally, human LN memory T cells exhibit increased proliferation to TCR-mediated stimulation and maintain higher TCR clonal diversity compared with memory T cells from blood and other sites. These findings establish human LN as reservoirs for memory T cells with high capacities for expansion and diverse recognition and important targets for immunotherapies.
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Affiliation(s)
- Michelle Miron
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032.,Department of Microbiology and Immunology, Columbia University, New York, NY 10032
| | - Brahma V Kumar
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19102
| | - Tomer Granot
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032
| | - Dustin J Carpenter
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032
| | - Takashi Senda
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032
| | - Dora Chen
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19102
| | - Aaron M Rosenfeld
- School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104
| | - Bochao Zhang
- School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104
| | | | | | - Uri Hershberg
- School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104
| | - Yufeng Shen
- Department of Systems Biology, Columbia University, New York, NY 10032.,Department of Biomedical Informatics, Columbia University, New York, NY 10032
| | - Adeeb Rahman
- Icahn School of Medicine at Mount Sinai, New York, NY 10029; and
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19102
| | - Donna L Farber
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY 10032; .,Department of Microbiology and Immunology, Columbia University, New York, NY 10032.,Department of Surgery, Columbia University, New York, NY 10032
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11
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Rosenfeld AM, Meng W, Chen DY, Zhang B, Granot T, Farber DL, Hershberg U, Luning Prak ET. Computational Evaluation of B-Cell Clone Sizes in Bulk Populations. Front Immunol 2018; 9:1472. [PMID: 30008715 PMCID: PMC6034424 DOI: 10.3389/fimmu.2018.01472] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 06/13/2018] [Indexed: 12/27/2022] Open
Abstract
B cell clones expand and contract during adaptive immune responses and can persist or grow uncontrollably in lymphoproliferative disorders. One way to monitor and track B cell clones is to perform large-scale sampling of bulk cell populations, amplifying, and sequencing antibody gene rearrangements by next-generation sequencing (NGS). Here, we describe a series of computational approaches for estimating B cell clone size in NGS immune repertoire profiling data of antibody heavy chain gene rearrangements. We define three different measures of B cell clone size-copy numbers, instances, and unique sequences-and show how these measures can be used to rank clones, analyze their diversity, and study their distribution within and between individuals. We provide a detailed, step-by-step procedure for performing these analyses using two different data sets of spleen samples from human organ donors. In the first data set, 19 independently generated biological replicates from a single individual are analyzed for B cell clone size, diversity and sampling sufficiency for clonal overlap analysis. In the second data set, B cell clones are compared in eight different organ donors. We comment upon frequently encountered pitfalls and offer practical advice with alternative approaches. Overall, we provide a series of pragmatic analytical approaches and show how different clone size measures can be used to study the clonal landscape in bulk B cell immune repertoire profiling data.
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Affiliation(s)
- Aaron M. Rosenfeld
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Dora Y. Chen
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Bochao Zhang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | - Tomer Granot
- Columbia Center for Translational Immunology, Columbia University, New York, NY, United States
| | - Donna L. Farber
- Columbia Center for Translational Immunology, Columbia University, New York, NY, United States
| | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Department of Microbiology and Immunology, Drexel College of Medicine, Drexel University, Philadelphia, PA, United States
| | - Eline T. Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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12
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Meng W, Zhang B, Schwartz GW, Rosenfeld AM, Ren D, Thome JJ, Carpenter DJ, Matsuoka N, Lerner H, Friedman AL, Granot T, Farber DL, Shlomchik MJ, Hershberg U, Luning Prak ET. An atlas of B-cell clonal distribution in the human body. Nat Biotechnol 2017; 35:879-884. [PMID: 28829438 PMCID: PMC5679700 DOI: 10.1038/nbt.3942] [Citation(s) in RCA: 119] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 07/13/2017] [Indexed: 12/12/2022]
Abstract
B-cell responses result in clonal expansion, and can occur in a variety of tissues. To define how B-cell clones are distributed in the body, we sequenced 933,427 B-cell clonal lineages and mapped them to eight different anatomic compartments in six human organ donors. We show that large B-cell clones partition into two broad networks-one spans the blood, bone marrow, spleen and lung, while the other is restricted to tissues within the gastrointestinal (GI) tract (jejunum, ileum and colon). Notably, GI tract clones display extensive sharing of sequence variants among different portions of the tract and have higher frequencies of somatic hypermutation, suggesting extensive and serial rounds of clonal expansion and selection. Our findings provide an anatomic atlas of B-cell clonal lineages, their properties and tissue connections. This resource serves as a foundation for studies of tissue-based immunity, including vaccine responses, infections, autoimmunity and cancer.
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Affiliation(s)
- Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Bochao Zhang
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Gregory W. Schwartz
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Aaron M. Rosenfeld
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
| | - Daqiu Ren
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Joseph J.C. Thome
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY
| | - Dustin J. Carpenter
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY
| | - Nobuhide Matsuoka
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY
| | | | | | - Tomer Granot
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY
| | - Donna L. Farber
- Columbia Center for Translational Immunology, Columbia University Medical Center, New York, NY
- Department of Surgery and Department of Microbiology and Immunology, Columbia University School of Medicine, New York, NY
| | | | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA
- Department of Microbiology and Immunology, Drexel College of Medicine, Drexel University, Philadelphia, PA
| | - Eline T. Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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13
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Antell GC, Dampier W, Aiamkitsumrit B, Nonnemacher MR, Pirrone V, Zhong W, Kercher K, Passic S, Williams J, Liu Y, James T, Jacobson JM, Szep Z, Wigdahl B, Krebs FC. Evidence of Divergent Amino Acid Usage in Comparative Analyses of R5- and X4-Associated HIV-1 Vpr Sequences. Int J Genomics 2017; 2017:4081585. [PMID: 28620613 PMCID: PMC5460428 DOI: 10.1155/2017/4081585] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 03/20/2017] [Indexed: 11/25/2022] Open
Abstract
Vpr is an HIV-1 accessory protein that plays numerous roles during viral replication, and some of which are cell type dependent. To test the hypothesis that HIV-1 tropism extends beyond the envelope into the vpr gene, studies were performed to identify the associations between coreceptor usage and Vpr variation in HIV-1-infected patients. Colinear HIV-1 Env-V3 and Vpr amino acid sequences were obtained from the LANL HIV-1 sequence database and from well-suppressed patients in the Drexel/Temple Medicine CNS AIDS Research and Eradication Study (CARES) Cohort. Genotypic classification of Env-V3 sequences as X4 (CXCR4-utilizing) or R5 (CCR5-utilizing) was used to group colinear Vpr sequences. To reveal the sequences associated with a specific coreceptor usage genotype, Vpr amino acid sequences were assessed for amino acid diversity and Jensen-Shannon divergence between the two groups. Five amino acid alphabets were used to comprehensively examine the impact of amino acid substitutions involving side chains with similar physiochemical properties. Positions 36, 37, 41, 89, and 96 of Vpr were characterized by statistically significant divergence across multiple alphabets when X4 and R5 sequence groups were compared. In addition, consensus amino acid switches were found at positions 37 and 41 in comparisons of the R5 and X4 sequence populations. These results suggest an evolutionary link between Vpr and gp120 in HIV-1-infected patients.
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Affiliation(s)
- Gregory C. Antell
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Will Dampier
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Benjamas Aiamkitsumrit
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Michael R. Nonnemacher
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Vanessa Pirrone
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Clinical and Translational Medicine, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Wen Zhong
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Katherine Kercher
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Shendra Passic
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jean Williams
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Yucheng Liu
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Tony James
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jeffrey M. Jacobson
- Center for Clinical and Translational Medicine, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- Division of Infectious Diseases and HIV Medicine, Department of Medicine, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Zsofia Szep
- Center for Clinical and Translational Medicine, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- Division of Infectious Diseases and HIV Medicine, Department of Medicine, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Brian Wigdahl
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Clinical and Translational Medicine, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Fred C. Krebs
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA
- Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
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14
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Antell GC, Dampier W, Aiamkitsumrit B, Nonnemacher MR, Jacobson JM, Pirrone V, Zhong W, Kercher K, Passic S, Williams JW, Schwartz G, Hershberg U, Krebs FC, Wigdahl B. Utilization of HIV-1 envelope V3 to identify X4- and R5-specific Tat and LTR sequence signatures. Retrovirology 2016; 13:32. [PMID: 27143130 PMCID: PMC4855882 DOI: 10.1186/s12977-016-0266-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/20/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND HIV-1 entry is a receptor-mediated process directed by the interaction of the viral envelope with the host cell CD4 molecule and one of two co-receptors, CCR5 or CXCR4. The amino acid sequence of the third variable (V3) loop of the HIV-1 envelope is highly predictive of co-receptor utilization preference during entry, and machine learning predictive algorithms have been developed to characterize sequences as CCR5-utilizing (R5) or CXCR4-utilizing (X4). It was hypothesized that while the V3 loop is predominantly responsible for determining co-receptor binding, additional components of the HIV-1 genome may contribute to overall viral tropism and display sequence signatures associated with co-receptor utilization. RESULTS The accessory protein Tat and the HlV-1 long terminal repeat (LTR) were analyzed with respect to genetic diversity and compared by Jensen-Shannon divergence which resulted in a correlation with both mean genetic diversity as well as the absolute difference in genetic diversity between R5- and X4-genome specific trends. As expected, the V3 domain of the gp120 protein was enriched with statistically divergent positions. Statistically divergent positions were also identified in Tat amino acid sequences within the transactivation and TAR-binding domains, and in nucleotide positions throughout the LTR. We further analyzed LTR sequences for putative transcription factor binding sites using the JASPAR transcription factor binding profile database and found several putative differences in transcription factor binding sites between R5 and X4 HIV-1 genomes, specifically identifying the C/EBP sites I and II, and Sp site III to differ with respect to sequence configuration for R5 and X4 LTRs. CONCLUSION These observations support the hypothesis that co-receptor utilization coincides with specific genetic signatures in HIV-1 Tat and the LTR, likely due to differing transcriptional regulatory mechanisms and selective pressures applied within specific cellular targets during the course of productive HIV-1 infection.
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Affiliation(s)
- Gregory C Antell
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA.,School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Will Dampier
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA.,School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Benjamas Aiamkitsumrit
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Michael R Nonnemacher
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jeffrey M Jacobson
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Division of Infectious Diseases and HIV Medicine, Department of Medicine, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Clinical and Translational Medicine, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Vanessa Pirrone
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Wen Zhong
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Katherine Kercher
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Shendra Passic
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jean W Williams
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Gregory Schwartz
- School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Uri Hershberg
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Fred C Krebs
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Brian Wigdahl
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA. .,Center for Molecular Virology and Translational Neuroscience, Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, PA, USA. .,Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA.
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15
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HIV-1 Promoter Single Nucleotide Polymorphisms Are Associated with Clinical Disease Severity. PLoS One 2016; 11:e0150835. [PMID: 27100290 PMCID: PMC4839606 DOI: 10.1371/journal.pone.0150835] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 02/20/2016] [Indexed: 12/13/2022] Open
Abstract
The large majority of human immunodeficiency virus type 1 (HIV-1) markers of disease progression/severity previously identified have been associated with alterations in host genetic and immune responses, with few studies focused on viral genetic markers correlate with changes in disease severity. This study presents a cross-sectional/longitudinal study of HIV-1 single nucleotide polymorphisms (SNPs) contained within the viral promoter or long terminal repeat (LTR) in patients within the Drexel Medicine CNS AIDS Research and Eradication Study (CARES) Cohort. HIV-1 LTR SNPs were found to associate with the classical clinical disease parameters CD4+ T-cell count and log viral load. They were found in both defined and undefined transcription factor binding sites of the LTR. A novel SNP identified at position 108 in a known COUP (chicken ovalbumin upstream promoter)/AP1 transcription factor binding site was significantly correlated with binding phenotypes that are potentially the underlying cause of the associated clinical outcome (increase in viral load and decrease in CD4+ T-cell count).
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16
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Schwartz GW, Shokoufandeh A, Ontañón S, Hershberg U. Using a novel clumpiness measure to unite data with metadata: Finding common sequence patterns in immune receptor germline V genes. Pattern Recognit Lett 2016. [DOI: 10.1016/j.patrec.2016.01.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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17
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Zhang B, Meng W, Prak ETL, Hershberg U. Discrimination of germline V genes at different sequencing lengths and mutational burdens: A new tool for identifying and evaluating the reliability of V gene assignment. J Immunol Methods 2015; 427:105-16. [PMID: 26529062 DOI: 10.1016/j.jim.2015.10.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Revised: 10/28/2015] [Accepted: 10/30/2015] [Indexed: 12/25/2022]
Abstract
Immune repertoires are collections of lymphocytes that express diverse antigen receptor gene rearrangements consisting of Variable (V), (Diversity (D) in the case of heavy chains) and Joining (J) gene segments. Clonally related cells typically share the same germline gene segments and have highly similar junctional sequences within their third complementarity determining regions. Identifying clonal relatedness of sequences is a key step in the analysis of immune repertoires. The V gene is the most important for clone identification because it has the longest sequence and the greatest number of sequence variants. However, accurate identification of a clone's germline V gene source is challenging because there is a high degree of similarity between different germline V genes. This difficulty is compounded in antibodies, which can undergo somatic hypermutation. Furthermore, high-throughput sequencing experiments often generate partial sequences and have significant error rates. To address these issues, we describe a novel method to estimate which germline V genes (or alleles) cannot be discriminated under different conditions (read lengths, sequencing errors or somatic hypermutation frequencies). Starting with any set of germline V genes, this method measures their similarity using different sequencing lengths and calculates their likelihood of unambiguous assignment under different levels of mutation. Hence, one can identify, under different experimental and biological conditions, the germline V genes (or alleles) that cannot be uniquely identified and bundle them together into groups of specific V genes with highly similar sequences.
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Affiliation(s)
- Bochao Zhang
- School of Biomedical Engineering, Science and Health Systems, 711 Bossone Building, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA
| | - Wenzhao Meng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 405B Stellar Chance Labs, 422 Curie Boulevard, Philadelphia, PA 19104, USA
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 405B Stellar Chance Labs, 422 Curie Boulevard, Philadelphia, PA 19104, USA
| | - Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, 711 Bossone Building, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA; Department of Microbiology and Immunology, College of Medicine, 2900 Queen Lane, Philadelphia, PA 19129, USA.
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18
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Schwartz GW, Hershberg U. Germline Amino Acid Diversity in B Cell Receptors is a Good Predictor of Somatic Selection Pressures. Front Immunol 2013; 4:357. [PMID: 24265630 PMCID: PMC3820969 DOI: 10.3389/fimmu.2013.00357] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2013] [Accepted: 10/21/2013] [Indexed: 11/13/2022] Open
Abstract
The diversity of the immune repertoire is important for the adaptive immune system’s ability to detect pathogens. Much of this diversity is generated in two steps, first through the recombination of germline gene segments and second through hypermutation during an immune response. While both steps are to some extent based on the germline level repertoire of genes, the final structure and selection of specific receptors is at the somatic level. How germline diversity and selection relate to somatic diversity and selection has not been clear. To investigate how germline diversity relates to somatic diversity and selection, we considered the published repertoire of Ig heavy chain V genes taken from the blood of 12 individuals, post-vaccination against influenza, sequenced by 454 high-throughput sequencing. We here show that when we consider individual amino acid positions in the heavy chain V gene sequence, there exists a strong correlation between the diversity of the germline repertoire at a position and the number of B cell clones that change amino acids at that position. At the same time, we find that the diversity of amino acids used in the mutated positions is greater than in the germline, albeit still correlated to germline diversity. From these findings, we propose that while germline diversity and germline amino acid usage at a given position do not fully specify the amino acid mutant needed to promote survival of specific clones, germline diversity at a given position is a good indicator for the potential to survive after somatic mutation at that position. We would therefore suggest that germline diversity at each specific position is the better a priori model for the effects of somatic mutation and selection, than simply the division into complementarity determining and framework regions.
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Affiliation(s)
- Gregory W Schwartz
- Systems Immunology Laboratory, School of Biomedical Engineering, Science, and Health Systems, Drexel University , Philadelphia, PA , USA
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Nemenman I, Gnanakaran S, Munsky B, Wall ME, Jiang Y, Hlavacek WS, Faeder JR. Special section dedicated to The Sixth q-bio Conference: meeting report and preface. Phys Biol 2013; 10:030301. [DOI: 10.1088/1478-3975/10/3/030301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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