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Long F, Xiao C, Cui H, Wang W, Jiang Z, Tang M, Zhang W, Liu Y, Xiang R, Zhang L, Zhao X, Yang C, Yan P, Wu X, Wang Y, Zhou Y, Lu R, Chen Y, Li J, Jiang X, Fan C, Zhang B. The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study. Front Immunol 2023; 14:1217444. [PMID: 37662938 PMCID: PMC10472139 DOI: 10.3389/fimmu.2023.1217444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial. Methods We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran's Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results. Results We found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy. Conclusions Our study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.
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Affiliation(s)
- Feiwu Long
- Department of Gastrointestinal, Bariatric and Metabolic Surgery, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wei Wang
- Department of Gastrointestinal, Bariatric and Metabolic Surgery, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zongze Jiang
- Department of Gastrointestinal, Bariatric and Metabolic Surgery, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Rong Xiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xunying Zhao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Zhou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ran Lu
- Department of Urology and Pelvic Surgery, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health, and West China Fourth Hospital, Sichuan University, Chengdu, China
- Laboratory of Stem Cell Biology, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
- Department of Environment and Occupational Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yulin Chen
- Laboratory of Stem Cell Biology, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Nutrition and Food Hygiene, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chuanwen Fan
- Department of Gastrointestinal, Bariatric and Metabolic Surgery, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Laboratory of Stem Cell Biology, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China
- Department of Oncology, Linköping University, Linköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Environment and Occupational Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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Rudman N, Kaur S, Simunović V, Kifer D, Šoić D, Keser T, Štambuk T, Klarić L, Pociot F, Morahan G, Gornik O. Integrated glycomics and genetics analyses reveal a potential role for N-glycosylation of plasma proteins and IgGs, as well as the complement system, in the development of type 1 diabetes. Diabetologia 2023; 66:1071-1083. [PMID: 36907892 PMCID: PMC10163086 DOI: 10.1007/s00125-023-05881-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 12/21/2022] [Indexed: 03/14/2023]
Abstract
AIMS/HYPOTHESIS We previously demonstrated that N-glycosylation of plasma proteins and IgGs is different in children with recent-onset type 1 diabetes compared with their healthy siblings. To search for genetic variants contributing to these changes, we undertook a genetic association study of the plasma protein and IgG N-glycome in type 1 diabetes. METHODS A total of 1105 recent-onset type 1 diabetes patients from the Danish Registry of Childhood and Adolescent Diabetes were genotyped at 183,546 genetic markers, testing these for genetic association with variable levels of 24 IgG and 39 plasma protein N-glycan traits. In the follow-up study, significant associations were validated in 455 samples. RESULTS This study confirmed previously known plasma protein and/or IgG N-glycosylation loci (candidate genes MGAT3, MGAT5 and ST6GAL1, encoding beta-1,4-mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase, alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase and ST6 beta-galactoside alpha-2,6-sialyltransferase 1 gene, respectively) and identified novel associations that were not previously reported for the general European population. First, novel genetic associations of IgG-bound glycans were found with SNPs on chromosome 22 residing in two genomic intervals close to candidate gene MGAT3; these include core fucosylated digalactosylated disialylated IgG N-glycan with bisecting N-acetylglucosamine (GlcNAc) (pdiscovery=7.65 × 10-12, preplication=8.33 × 10-6 for the top associated SNP rs5757680) and core fucosylated digalactosylated glycan with bisecting GlcNAc (pdiscovery=2.88 × 10-10, preplication=3.03 × 10-3 for the top associated SNP rs137702). The most significant genetic associations of IgG-bound glycans were those with MGAT3. Second, two SNPs in high linkage disequilibrium (missense rs1047286 and synonymous rs2230203) located on chromosome 19 within the protein coding region of the complement C3 gene (C3) showed association with the oligomannose plasma protein N-glycan (pdiscovery=2.43 × 10-11, preplication=8.66 × 10-4 for the top associated SNP rs1047286). CONCLUSIONS/INTERPRETATION This study identified novel genetic associations driving the distinct N-glycosylation of plasma proteins and IgGs identified previously at type 1 diabetes onset. Our results highlight the importance of further exploring the potential role of N-glycosylation and its influence on complement activation and type 1 diabetes susceptibility.
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Affiliation(s)
- Najda Rudman
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | | | - Vesna Simunović
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Domagoj Kifer
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Dinko Šoić
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Toma Keser
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Tamara Štambuk
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Lucija Klarić
- Institute of Genetics and Cancer, MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
| | - Flemming Pociot
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Grant Morahan
- Centre for Diabetes Research, The Harry Perkins Institute for Medical Research, University of Western Australia, Perth, WA, Australia.
- Australian Centre for Accelerating Diabetes Innovations, University of Melbourne, Melbourne, VIC, Australia.
| | - Olga Gornik
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia.
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Meng X, Wang F, Gao X, Wang B, Xu X, Wang Y, Wang W, Zeng Q. Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint. EPMA J 2023; 14:1-20. [PMID: 36866157 PMCID: PMC9971369 DOI: 10.1007/s13167-022-00311-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 12/25/2022]
Abstract
Objectives Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM. Methods Case-control and nested case-control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument. Results After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case-control setting, baseline SHS, and baseline optimal health participants from the nested case-control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case-control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case-control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone. Conclusions This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM. Supplementary information The online version contains supplementary material available at 10.1007/s13167-022-00311-3.
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Affiliation(s)
- Xiaoni Meng
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Beijing, 100069 China
| | - Fei Wang
- Health Management Institute, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853 China
| | - Xiangyang Gao
- Health Management Institute, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853 China
| | - Biyan Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Beijing, 100069 China
| | - Xizhu Xu
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 China
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Beijing, 100069 China
| | - Wei Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen, Fengtai District, Beijing, 100069 China
- School of Public Health, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 China
- Centre for Precision Health, Edith Cowan University, 270 Joondalup Drive, Joondalup, Perth, WA 6027 Australia
| | - Qiang Zeng
- Health Management Institute, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People’s Liberation Army General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853 China
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Zhang Z, Wu J, Liu P, Kang L, Xu X. Diagnostic Potential of Plasma IgG N-glycans in Discriminating Thyroid Cancer from Benign Thyroid Nodules and Healthy Controls. Front Oncol 2021; 11:658223. [PMID: 34476207 PMCID: PMC8406750 DOI: 10.3389/fonc.2021.658223] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 07/27/2021] [Indexed: 12/16/2022] Open
Abstract
Background Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer. Methods Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits. Results Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer. Conclusions IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.
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Affiliation(s)
- Zejian Zhang
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianqiang Wu
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Liu
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Kang
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiequn Xu
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Mahan AE, Tedesco J, Dionne K, Baruah K, Cheng HD, De Jager PL, Barouch DH, Suscovich T, Ackerman M, Crispin M, Alter G. A method for high-throughput, sensitive analysis of IgG Fc and Fab glycosylation by capillary electrophoresis. J Immunol Methods 2014; 417:34-44. [PMID: 25523925 DOI: 10.1016/j.jim.2014.12.004] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Revised: 12/08/2014] [Accepted: 12/09/2014] [Indexed: 02/02/2023]
Abstract
The N-glycan of the IgG constant region (Fc) plays a central role in tuning and directing multiple antibody functions in vivo, including antibody-dependent cellular cytotoxicity, complement deposition, and the regulation of inflammation, among others. However, traditional methods of N-glycan analysis, including HPLC and mass spectrometry, are technically challenging and ill suited to handle the large numbers of low concentration samples analyzed in clinical or animal studies of the N-glycosylation of polyclonal IgG. Here we describe a capillary electrophoresis-based technique to analyze plasma-derived polyclonal IgG-glycosylation quickly and accurately in a cost-effective, sensitive manner that is well suited for high-throughput analyses. Additionally, because a significant fraction of polyclonal IgG is glycosylated on both Fc and Fab domains, we developed an approach to separate and analyze domain-specific glycosylation in polyclonal human, rhesus and mouse IgGs. Overall, this protocol allows for the rapid, accurate, and sensitive analysis of Fc-specific IgG glycosylation, which is critical for population-level studies of how antibody glycosylation may vary in response to vaccination or infection, and across disease states ranging from autoimmunity to cancer in both clinical and animal studies.
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Affiliation(s)
- Alison E Mahan
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States
| | | | - Kendall Dionne
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States
| | - Kavitha Baruah
- Department of Biochemistry, Oxford University, Oxford, United Kingdom
| | - Hao D Cheng
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Philip L De Jager
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States
| | - Dan H Barouch
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Todd Suscovich
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States
| | - Margaret Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Max Crispin
- Department of Biochemistry, Oxford University, Oxford, United Kingdom
| | - Galit Alter
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States.
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