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Röltgen K, Boyd SD. Antibody and B Cell Responses to SARS-CoV-2 Infection and Vaccination: The End of the Beginning. Annu Rev Pathol 2024; 19:69-97. [PMID: 37738512 DOI: 10.1146/annurev-pathmechdis-031521-042754] [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] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
As the COVID-19 pandemic has evolved during the past years, interactions between human immune systems, rapidly mutating and selected SARS-CoV-2 viral variants, and effective vaccines have complicated the landscape of individual immunological histories. Here, we review some key findings for antibody and B cell-mediated immunity, including responses to the highly mutated omicron variants; immunological imprinting and other impacts of successive viral antigenic variant exposures on antibody and B cell memory; responses in secondary lymphoid and mucosal tissues and non-neutralizing antibody-mediated immunity; responses in populations vulnerable to severe disease such as those with cancer, immunodeficiencies, and other comorbidities, as well as populations showing apparent resistance to severe disease such as many African populations; and evidence of antibody involvement in postacute sequelae of infection or long COVID. Despite the initial phase of the pandemic ending, human populations will continue to face challenges presented by this unpredictable virus.
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Affiliation(s)
- Katharina Röltgen
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Scott D Boyd
- Department of Pathology, Stanford University School of Medicine, Stanford, California, USA;
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, California, USA
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Chang YS, Hsu MH, Tu SJ, Yen JC, Lee YT, Fang HY, Chang JG. Metatranscriptomic Analysis of Human Lung Metagenomes from Patients with Lung Cancer. Genes (Basel) 2021; 12:1458. [PMID: 34573440 DOI: 10.3390/genes12091458] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 02/07/2023] Open
Abstract
This study was designed to characterize the microbiomes of the lung tissues of lung cancer patients. RNA-sequencing was performed on lung tumor samples from 49 patients with lung cancer. Metatranscriptomics data were analyzed using SAMSA2 and Kraken2 software. 16S rRNA sequencing was also performed. The heterogeneous cellular landscape and immune repertoires of the lung samples were examined using xCell and TRUST4, respectively. We found that nine bacteria were significantly enriched in the lung tissues of cancer patients, and associated with reduced overall survival (OS). We also found that subjects with mutations in the epidermal growth factor receptor gene were less likely to experience the presence of Pseudomonas. aeruginosa. We found that the presence of CD8+ T-cells, CD4+ naive T-cells, dendritic cells, and CD4+ central memory T cells were associated with a good prognosis, while the presence of pro B-cells was associated with a poor prognosis. Furthermore, high clone numbers were associated with a high ImmuneScore for all immune receptor repertoires. Clone numbers and diversity were significantly higher in unpresented subjects compared to presented subjects. Our results provide insight into the microbiota of human lung cancer, and how its composition is linked to the tumor immune microenvironment, immune receptor repertoires, and OS.
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Zhang J, Hu X, Wang J, Sahu AD, Cohen D, Song L, Ouyang Z, Fan J, Wang B, Fu J, Gu S, Sade-Feldman M, Hacohen N, Li W, Ying X, Li B, Liu XS. Immune receptor repertoires in pediatric and adult acute myeloid leukemia. Genome Med 2019; 11:73. [PMID: 31771646 PMCID: PMC6880565 DOI: 10.1186/s13073-019-0681-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [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: 01/28/2019] [Accepted: 10/31/2019] [Indexed: 02/08/2023] Open
Abstract
Background Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized. Methods In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples. Results We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, β, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival. Conclusions Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.
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Affiliation(s)
- Jian Zhang
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China.,Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xihao Hu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jin Wang
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Avinash Das Sahu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - David Cohen
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Li Song
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zhangyi Ouyang
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jingyu Fan
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Binbin Wang
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Jingxin Fu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shengqing Gu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Moshe Sade-Feldman
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA.,Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA.,Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Nir Hacohen
- Massachusetts General Hospital Cancer Center, Harvard Medical School (HMS), Boston, MA, USA.,Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA.,Department of Medicine, Massachusetts General Hospital, HMS, Boston, MA, USA
| | - Wuju Li
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Xiaomin Ying
- Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China.
| | - Bo Li
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
| | - X Shirley Liu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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