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Dumonteil E, Tu W, Desale H, Goff K, Marx P, Ortega-Lopez J, Herrera C. Immunoglobulin and T cell receptor repertoire changes induced by a prototype vaccine against Chagas disease in naïve rhesus macaques. J Biomed Sci 2024; 31:58. [PMID: 38824576 PMCID: PMC11143712 DOI: 10.1186/s12929-024-01050-5] [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: 12/16/2023] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND A vaccine against Trypanosoma cruzi, the agent of Chagas disease, would be an excellent additional tool for disease control. A recombinant vaccine based on Tc24 and TSA1 parasite antigens was found to be safe and immunogenic in naïve macaques. METHODS We used RNA-sequencing and performed a transcriptomic analysis of PBMC responses to vaccination of naïve macaques after each vaccine dose, to shed light on the immunogenicity of this vaccine and guide the optimization of doses and formulation. We identified differentially expressed genes and pathways and characterized immunoglobulin and T cell receptor repertoires. RESULTS RNA-sequencing analysis indicated a clear transcriptomic response of PBMCs after three vaccine doses, with the up-regulation of several immune cell activation pathways and a broad non-polarized immune profile. Analysis of the IgG repertoire showed that it had a rapid turnover with novel IgGs produced following each vaccine dose, while the TCR repertoire presented several persisting clones that were expanded after each vaccine dose. CONCLUSIONS These data suggest that three vaccine doses may be needed for optimum immunogenicity and support the further evaluation of the protective efficacy of this vaccine.
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Affiliation(s)
- Eric Dumonteil
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA.
| | - Weihong Tu
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
| | - Hans Desale
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
| | - Kelly Goff
- Division of Microbiology, Tulane National Primate Research Center, Tulane University, Covington, LA, USA
| | - Preston Marx
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
- Division of Microbiology, Tulane National Primate Research Center, Tulane University, Covington, LA, USA
| | - Jaime Ortega-Lopez
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de Mexico, México
| | - Claudia Herrera
- Department of Tropical Medicine and Infectious Disease, School of Public Health and Tropical Medicine, and Vector-Borne and Infectious Disease Research Center, Tulane University, 1440 Canal St, New Orleans, Louisiana, 70112, USA
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Chen Y, Ye Z, Zhang Y, Xie W, Chen Q, Lan C, Yang X, Zeng H, Zhu Y, Ma C, Tang H, Wang Q, Guan J, Chen S, Li F, Yang W, Yan H, Yu X, Zhang Z. A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2675-2685. [PMID: 35606050 DOI: 10.4049/jimmunol.2200063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.
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Affiliation(s)
- Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhiming Ye
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yanfang Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenxi Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qingyun Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chunhong Lan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiujia Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huikun Zeng
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Zhu
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Cuiyu Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Junjie Guan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Sen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Fenxiang Li
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Huacheng Yan
- Department of Infectious Disease Control and Prevention, Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhai Zhang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Organ Failure Research, Division of Nephrology, Southern Medical University, Guangzhou, China; and
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China
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