1
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Pahkuri S, Katayama S, Valta M, Nygård L, Knip M, Kere J, Ilonen J, Lempainen J. The effect of type 1 diabetes protection and susceptibility associated HLA class II genotypes on DNA methylation in immune cells. HLA 2024; 103:e15548. [PMID: 38887913 DOI: 10.1111/tan.15548] [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: 01/02/2024] [Revised: 04/24/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024]
Abstract
The HLA region, especially HLA class I and II genes, which encode molecules for antigen presentation to T cells, plays a major role in the predisposition to autoimmune disorders. To clarify the mechanisms behind this association, we examined genome-wide DNA methylation by microarrays to cover over 850,000 CpG sites in the CD4+ T cells and CD19+ B cells of healthy subjects homozygous either for DRB1*15-DQA1*01-DQB1*06:02 (DR2-DQ6, n = 14), associated with a strongly decreased T1D risk, DRB1*03-DQA1*05-DQB1*02 (DR3-DQ2, n = 19), or DRB1*04:01-DQA1*03-DQB1*03:02 (DR4-DQ8, n = 17), associated with a moderately increased T1D risk. In total, we discovered 14 differentially methylated CpG probes, of which 10 were located in the HLA region and six in the HLA-DRB1 locus. The main differences were between the protective genotype DR2-DQ6 and the risk genotypes DR3-DQ2 and DR4-DQ8, where the DR2-DQ6 group was hypomethylated compared to the other groups in all but four of the differentially methylated probes. The differences between the risk genotypes DR3-DQ2 and DR4-DQ8 were small. Our results indicate that HLA variants have few systemic effects on methylation and that their effect on autoimmunity is conveyed directly by HLA molecules, possibly by differences in expression levels or function.
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Affiliation(s)
- Sirpa Pahkuri
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Shintaro Katayama
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Milla Valta
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Lucas Nygård
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Mikael Knip
- Faculty of Medicine, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Juha Kere
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Johanna Lempainen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- Clinical Microbiology, Turku University Hospital, Turku, Finland
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2
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Ungar RA, Goddard PC, Jensen TD, Degalez F, Smith KS, Jin CA, Bonner DE, Bernstein JA, Wheeler MT, Montgomery SB. Impact of genome build on RNA-seq interpretation and diagnostics. Am J Hum Genet 2024:S0002-9297(24)00168-X. [PMID: 38834072 DOI: 10.1016/j.ajhg.2024.05.005] [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: 01/04/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.
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Affiliation(s)
- Rachel A Ungar
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Pagé C Goddard
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Tanner D Jensen
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Kevin S Smith
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Christopher A Jin
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Devon E Bonner
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA; Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Jonathan A Bernstein
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Matthew T Wheeler
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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3
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Yu D, Ayyala R, Sadek SH, Chittampalli L, Farooq H, Jung J, Nahid AA, Boldirev G, Jung M, Park S, Nguyen A, Zelikovsky A, Mancuso N, Joo JWJ, Thompson RF, Alachkar H, Mangul S. A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.22.541750. [PMID: 38293199 PMCID: PMC10827116 DOI: 10.1101/2023.05.22.541750] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.
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Affiliation(s)
- Dottie Yu
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA
| | - Ram Ayyala
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA
| | - Sarah Hany Sadek
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
- Department of Biology, and Department of Computer Science, California State University, Fullerton, Fullerton, CA 92831
| | - Likhitha Chittampalli
- Department of Computer Science, Viterbi School of Engineering University of Southern California, Los Angeles, CA, USA
| | - Hafsa Farooq
- Department of Computer Science, Georgia State University Atlanta, GA 30303 USA
| | - Junghyun Jung
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Abdullah Al Nahid
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Grigore Boldirev
- Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA, 30303, USA
| | - Mina Jung
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, US
| | - Sungmin Park
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Austin Nguyen
- Computational Biologist, Immune Monitoring & Cancer Omics Oregon Health & Science University, Biomedical Engineering, 3181 S.W. Sam Jackson Park Road Portland, OR 97239-3098
| | - Alex Zelikovsky
- Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA, 30303, USA
| | - Nicholas Mancuso
- Assistant Professor of Population and Public Health Sciences, Keck School of Medicina, University of Southern California, 1845 N. Soto Street, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea
- Division of AI Software Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Reid F Thompson
- Assistant Professor of Radiation Medicine, School of Medicine, OHSU, Portland, OR 97239
- Assistant Professor of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97239
- Staff Physician, VA Portland Healthcare System, Portland OR 97239
| | - Houda Alachkar
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, CA, USA
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, 1540 Alcazar Street, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles
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4
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Almanaa TN. Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach. Front Immunol 2023; 14:1332378. [PMID: 38143752 PMCID: PMC10739491 DOI: 10.3389/fimmu.2023.1332378] [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: 11/02/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.
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Affiliation(s)
- Taghreed N. Almanaa
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
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5
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Aguiar VRC, Castelli EC, Single RM, Bashirova A, Ramsuran V, Kulkarni S, Augusto DG, Martin MP, Gutierrez-Arcelus M, Carrington M, Meyer D. Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression. Immunogenetics 2023; 75:249-262. [PMID: 36707444 PMCID: PMC9883133 DOI: 10.1007/s00251-023-01296-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023]
Abstract
Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques.
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Affiliation(s)
- Vitor R. C. Aguiar
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP Brazil ,Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA ,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Erick C. Castelli
- Molecular Genetics and Bioinformatics Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University, Botucatu, SP Brazil
| | - Richard M. Single
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT USA
| | - Arman Bashirova
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA
| | - Veron Ramsuran
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa ,School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Smita Kulkarni
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Host-Pathogen Interactions Program, Texas Biomedical Research Institute, San Antonio, TX USA
| | - Danillo G. Augusto
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Department of Biological Sciences, The University of North Carolina at Charlotte, Charlotte, NC USA ,Programa de Pós-Graduação em Genética, Universidade Federal do Paraná, Curitiba, PR Brazil
| | - Maureen P. Martin
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA ,Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Mary Carrington
- Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD USA ,Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD USA ,Ragon Institute of MGH, MIT and Harvard, Cambridge, MA USA
| | - Diogo Meyer
- Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP Brazil
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6
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Li T, Du D, Zhang D, Lin Y, Ma J, Zhou M, Meng W, Jin Z, Chen Z, Yuan H, Wang J, Dong S, Sun S, Ye W, Li B, Liu H, Zhang Z, Jiao Y, Xie Z, Qiu W, Liu Y. CRISPR-based targeted haplotype-resolved assembly of a megabase region. Nat Commun 2023; 14:22. [PMID: 36596772 PMCID: PMC9810730 DOI: 10.1038/s41467-022-35389-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 11/29/2022] [Indexed: 01/04/2023] Open
Abstract
Constructing high-quality haplotype-resolved genome assemblies has substantially improved the ability to detect and characterize genetic variants. A targeted approach providing readily access to the rich information from haplotype-resolved genome assemblies will be appealing to groups of basic researchers and medical scientists focused on specific genomic regions. Here, using the 4.5 megabase, notoriously difficult-to-assemble major histocompatibility complex (MHC) region as an example, we demonstrated an approach to construct haplotype-resolved assembly of the targeted genomic region with the CRISPR-based enrichment. Compared to the results from haplotype-resolved genome assembly, our targeted approach achieved comparable completeness and accuracy with reduced computing complexity, sequencing cost, as well as the amount of starting materials. Moreover, using the targeted assembled personal MHC haplotypes as the reference both improves the quantification accuracy for sequencing data and enables allele-specific functional genomics analyses of the MHC region. Given its highly efficient use of resources, our approach can greatly facilitate population genetic studies of targeted regions, and may pave a new way to elucidate the molecular mechanisms in disease etiology.
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Affiliation(s)
- Taotao Li
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Duo Du
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Dandan Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Yicheng Lin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Jiakang Ma
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Mengyu Zhou
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Weida Meng
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Zelin Jin
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Ziqiang Chen
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Haozhe Yuan
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Jue Wang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Shulong Dong
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China
| | - Shaoyang Sun
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Wenjing Ye
- Division of Rheumatology and Immunology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bosen Li
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Houbao Liu
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhao Zhang
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Yuchen Jiao
- State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Wenqing Qiu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China. .,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China. .,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, China.
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7
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Sapart E, Faria M, Dierckx S, Durez P, Fonseca JE. Editorial: Risk factors for Rheumatoid Arthritis and pre-Rheumatoid Arthritis. Front Med (Lausanne) 2022; 9:1052618. [PMID: 36590974 PMCID: PMC9795167 DOI: 10.3389/fmed.2022.1052618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 09/26/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Emillie Sapart
- Department of Rheumatology, Cliniques Universitaires Saint-Luc, Université catholique de Louvain (UCLouvain), Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium
| | - Margarida Faria
- Serviço de Reumatologia e Doenças Ósseas Metabólicas, Centro Hospitalar Lisboa Norte, Centro Académico de Medicina de Lisboa, Lisbon, Portugal
| | - Stephanie Dierckx
- Department of Rheumatology, Center Hospitalier Universitaire (CHU) Mont-Godinne, Mont-Godinne, Belgium
| | - Patrick Durez
- Department of Rheumatology, Cliniques Universitaires Saint-Luc, Université catholique de Louvain (UCLouvain), Institut de Recherche Expérimentale et Clinique (IREC), Brussels, Belgium
| | - João Eurico Fonseca
- Serviço de Reumatologia e Doenças Ósseas Metabólicas, Centro Hospitalar Lisboa Norte, Centro Académico de Medicina de Lisboa, Lisbon, Portugal,Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon, Portugal,*Correspondence: João Eurico Fonseca
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8
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Houtman M, Dzebisashvili A, Dubnovitsky A, Kozhukh G, Rönnblom L, Klareskog L, Malmström V, Padyukov L. Five commercially-available antibodies react differentially with allelic forms of human HLA-DR beta chain. Mol Immunol 2022; 152:106-110. [DOI: 10.1016/j.molimm.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/05/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
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9
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Johansson T, Partanen J, Saavalainen P. HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation. Front Immunol 2022; 13:1007425. [PMID: 36248878 PMCID: PMC9554311 DOI: 10.3389/fimmu.2022.1007425] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn’s disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.
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Affiliation(s)
- Tiira Johansson
- Translational Immunology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
- *Correspondence: Tiira Johansson,
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Päivi Saavalainen
- Translational Immunology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Genetics Research Program, Folkhälsan Research Center, Helsinki, Finland
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10
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Panhuber A, Lamorte G, Bruno V, Cetin H, Bauer W, Höftberger R, Erber AC, Frommlet F, Koneczny I. A systematic review and meta-analysis of HLA class II associations in patients with IgG4 autoimmunity. Sci Rep 2022; 12:9229. [PMID: 35654912 PMCID: PMC9163138 DOI: 10.1038/s41598-022-13042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 05/13/2022] [Indexed: 11/22/2022] Open
Abstract
Autoimmune diseases caused by pathogenic IgG4 subclass autoantibodies (IgG4-AID) include diseases like MuSK myasthenia gravis, pemphigus vulgaris or thrombotic thrombocytopenic purpura. Their etiology is still unknown. Polymorphisms in the human leukocyte antigen (HLA) gene locus, particularly in HLA-DRB1, are known genetic susceptibility factors for autoimmune diseases. We hypothesized a similar role for HLA polymorphisms in IgG4-AID and conducted a systematic review and meta-analysis with case-control studies on IgG4-AID based on MOOSE/ HuGENet guidelines. Genotype (G) and allele (A) frequencies of HLA-DQB1*05 (G: OR 3.8; 95% CI 2.44-5.9; p < 0.00001; A: OR 2.54; 95% CI 1.82-3.55; p < 0.00001) and HLA-DRB1*14 (G: OR 4.31; 95% CI 2.82-6.59; p < 0.00001; A: OR 4.78; 95% CI 3.52-6.49; p < 0.00001) and the HLA-DRB1*14-DQB1*05 haplotype (OR 6.3; 95% CI 3.28-12.09; p < 0.00001/OR 4.98; 95% CI 3.8-6.53; p < 0.00001) were increased while HLA-DRB1*13 (G: OR 0.48; 95% CI 0.34-0.68; p < 0.0001; A: OR 0.46; 95% CI 0.34-0.62; p < 0.00001) was decreased in IgG4-AID patients. In conclusion, the HLA-DQB1*05, HLA-DRB1*14 alleles and the HLA-DQB1*05-DRB1*14 haplotype could be genetic risk factors that predispose for the production of pathogenic IgG4 autoantibodies and the HLA-DRB1*13 allele may protect from IgG4 autoimmunity.
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Affiliation(s)
- Anja Panhuber
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Giovanni Lamorte
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Veronica Bruno
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Hakan Cetin
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Bauer
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Astrid C Erber
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Florian Frommlet
- Center for Medical Statistics Informatics and Intelligent Systems, Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Inga Koneczny
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria.
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11
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Hu H, Tang N, Zhang F, Li L, Li L. Bioinformatics and System Biology Approach to Identify the Influences of COVID-19 on Rheumatoid Arthritis. Front Immunol 2022; 13:860676. [PMID: 35464423 PMCID: PMC9021444 DOI: 10.3389/fimmu.2022.860676] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 03/16/2022] [Indexed: 02/05/2023] Open
Abstract
Background Severe coronavirus disease 2019 (COVID -19) has led to a rapid increase in mortality worldwide. Rheumatoid arthritis (RA) was a high-risk factor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, whereas the molecular mechanisms underlying RA and CVOID-19 are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19 and RA using bioinformatics and a systems biology approach. Methods Two Differentially expressed genes (DEGs) sets extracted from GSE171110 and GSE1775544 datasets were intersected to generate common DEGs, which were used for functional enrichment, pathway analysis, and candidate drugs analysis. Results A total of 103 common DEGs were identified in the two datasets between RA and COVID-19. A protein-protein interaction (PPI) was constructed using various combinatorial statistical methods and bioinformatics tools. Subsequently, hub genes and essential modules were identified from the PPI network. In addition, we performed functional analysis and pathway analysis under ontological conditions and found that there was common association between RA and progression of COVID-19 infection. Finally, transcription factor-gene interactions, protein-drug interactions, and DEGs-miRNAs coregulatory networks with common DEGs were also identified in the datasets. Conclusion We successfully identified the top 10 hub genes that could serve as novel targeted therapy for COVID-19 and screened out some potential drugs useful for COVID-19 patients with RA.
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Affiliation(s)
- Huan Hu
- Department of Rheumatology and Immunology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.,Clinical Medical College, Guizhou Medical University, Guiyang, China
| | - Nana Tang
- Medical Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Facai Zhang
- Department of Urology/Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Li Li
- Medical Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Long Li
- Department of Rheumatology and Immunology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
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