1
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Sakakibara S, Liu YC, Ishikawa M, Edahiro R, Shirai Y, Haruna S, El Hussien MA, Xu Z, Li S, Yamaguchi Y, Murakami T, Morita T, Kato Y, Hirata H, Takeda Y, Sugihara F, Naito Y, Motooka D, Tsai CY, Ono C, Matsuura Y, Wing JB, Matsumoto H, Ogura H, Okada M, Kumanogoh A, Okada Y, Standley DM, Kikutani H, Okuzaki D. Clonal landscape of autoantibody-secreting plasmablasts in COVID-19 patients. Life Sci Alliance 2024; 7:e202402774. [PMID: 39288992 PMCID: PMC11408605 DOI: 10.26508/lsa.202402774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/19/2024] Open
Abstract
Whereas severe COVID-19 is often associated with elevated autoantibody titers, the underlying mechanism behind their generation has remained unclear. Here we report clonal composition and diversity of autoantibodies in humoral response to SARS-CoV-2. Immunoglobulin repertoire analysis and characterization of plasmablast-derived monoclonal antibodies uncovered clonal expansion of plasmablasts producing cardiolipin (CL)-reactive autoantibodies. Half of the expanded CL-reactive clones exhibited strong binding to SARS-CoV-2 antigens. One such clone, CoV1804, was reactive to both CL and viral nucleocapsid (N), and further showed anti-nucleolar activity in human cells. Notably, antibodies sharing genetic features with CoV1804 were identified in COVID-19 patient-derived immunoglobulins, thereby constituting a novel public antibody. These public autoantibodies had numerous mutations that unambiguously enhanced anti-N reactivity, when causing fluctuations in anti-CL reactivity along with the acquisition of additional self-reactivities, such as anti-nucleolar activity, in the progeny. Thus, potentially CL-reactive precursors may have developed multiple self-reactivities through clonal selection, expansion, and somatic hypermutation driven by viral antigens. Our results revealed the nature of autoantibody production during COVID-19 and provided novel insights into the origin of virus-induced autoantibodies.
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Affiliation(s)
- Shuhei Sakakibara
- https://ror.org/035t8zc32 Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yu-Chen Liu
- https://ror.org/035t8zc32 Laboratory of Human Immunology (Single Cell Genomics), Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Masakazu Ishikawa
- https://ror.org/035t8zc32 Laboratory of Human Immunology (Single Cell Genomics), Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Ryuya Edahiro
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Statistical Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yuya Shirai
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Statistical Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Soichiro Haruna
- https://ror.org/035t8zc32 Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Marwa Ali El Hussien
- https://ror.org/035t8zc32 Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Zichang Xu
- https://ror.org/035t8zc32 Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Songling Li
- https://ror.org/035t8zc32 Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yuta Yamaguchi
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Teruaki Murakami
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Takayoshi Morita
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Yasuhiro Kato
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Haruhiko Hirata
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yoshito Takeda
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Fuminori Sugihara
- https://ror.org/035t8zc32 Core Instrumentation Facility, Immunology Frontier Research Center and Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Yoko Naito
- https://ror.org/035t8zc32 Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Daisuke Motooka
- https://ror.org/035t8zc32 Laboratory of Human Immunology (Single Cell Genomics), Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
| | - Chao-Yuan Tsai
- https://ror.org/035t8zc32 Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Chikako Ono
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
| | - Yoshiharu Matsuura
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
| | - James B Wing
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Human Single Cell Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
| | - Hisatake Matsumoto
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hiroshi Ogura
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masato Okada
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
| | - Atsushi Kumanogoh
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan
| | - Yukinari Okada
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Statistical Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Wakō, japan
| | - Daron M Standley
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Laboratory of Systems Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Advanced Modalities and DDS, Osaka University, Osaka, Japan
| | - Hitoshi Kikutani
- https://ror.org/035t8zc32 Laboratory of Immune Regulation, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Daisuke Okuzaki
- https://ror.org/035t8zc32 Laboratory of Human Immunology (Single Cell Genomics), Immunology Frontier Research Center, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
- https://ror.org/035t8zc32 Japan Agency for Medical Research and Development - Core Research for Evolutional Science and Technology (AMED-CREST), Osaka University, Osaka, Japan
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2
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Yamamoto Y, Shirai Y, Edahiro R, Kumanogoh A, Okada Y. Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases. Immunol Med 2024:1-10. [PMID: 39171621 DOI: 10.1080/25785826.2024.2394258] [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: 06/25/2024] [Accepted: 08/15/2024] [Indexed: 08/23/2024] Open
Abstract
Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.
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Affiliation(s)
- Yuji Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Japan Agency for Medical Research and Development, Core Research for Evolutional Science and Technology (AMED-CREST), Tokyo, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- RIKEN Center for Integrative Medical Sciences, Laboratory for Systems Genetics, Yokohama, Japan
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Japan
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
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3
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Tomofuji Y, Edahiro R, Sonehara K, Shirai Y, Kock KH, Wang QS, Namba S, Moody J, Ando Y, Suzuki A, Yata T, Ogawa K, Naito T, Namkoong H, Xuan Lin QX, Buyamin EV, Tan LM, Sonthalia R, Han KY, Tanaka H, Lee H, Okuno T, Liu B, Matsuda K, Fukunaga K, Mochizuki H, Park WY, Yamamoto K, Hon CC, Shin JW, Prabhakar S, Kumanogoh A, Okada Y. Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues. CELL GENOMICS 2024; 4:100625. [PMID: 39084228 PMCID: PMC11406184 DOI: 10.1016/j.xgen.2024.100625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/09/2024] [Accepted: 07/05/2024] [Indexed: 08/02/2024]
Abstract
Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.
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Affiliation(s)
- Yoshihiko Tomofuji
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8654, Japan.
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8654, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kian Hong Kock
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore
| | - Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8654, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8654, Japan
| | - Jonathan Moody
- Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Yoshinari Ando
- RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Tomohiro Yata
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Neurology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Kotaro Ogawa
- Department of Neurology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Shinanomachi 160-8582, Japan
| | - Quy Xiao Xuan Lin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore
| | - Eliora Violain Buyamin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore
| | - Le Min Tan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore
| | - Radhika Sonthalia
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore
| | - Kyung Yeon Han
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Shinanomachi 160-8582, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Shinanomachi 160-8582, Japan
| | - Tatsusada Okuno
- Department of Neurology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Boxiang Liu
- Department of Pharmacy, National University of Singapore, Singapore 117549, Republic of Singapore
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Shirokanedai 108-8639, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Shinanomachi 160-8582, Japan
| | - Hideki Mochizuki
- Department of Neurology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Jay W Shin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore; Laboratory for Advanced Genomics Circuit, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Shyam Prabhakar
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A∗STAR), Singapore 138672, Republic of Singapore; Lee Kong Chian School of Medicine, Singapore 308232, Republic of Singapore; Cancer Science Institute of Singapore, Singapore 117599, Republic of Singapore
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Immunopathology, Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8654, Japan; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita 565-0871, Japan.
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4
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Xu Z, Ismanto HS, Saputri DS, Haruna S, Sun G, Wilamowski J, Teraguchi S, Sengupta A, Li S, Standley DM. Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors. Brief Bioinform 2024; 25:bbae431. [PMID: 39226888 PMCID: PMC11370640 DOI: 10.1093/bib/bbae431] [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: 05/22/2024] [Revised: 07/19/2024] [Accepted: 08/21/2024] [Indexed: 09/05/2024] Open
Abstract
Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.
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Affiliation(s)
- Zichang Xu
- Department of Systems Immunology, Immunology Frontier Research Institute (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Hendra S Ismanto
- Department of Systems Immunology, Immunology Frontier Research Institute (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Dianita S Saputri
- Department of Systems Immunology, Immunology Frontier Research Institute (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Soichiro Haruna
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Guanqun Sun
- School of information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
| | - Jan Wilamowski
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Shunsuke Teraguchi
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Faculty of Data Science, Shiga University 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
| | - Ayan Sengupta
- Cogent Labs, 3-2-1 Roppongi, Minato-ku, Tokyo 106-6122, Japan
| | - Songling Li
- Department of Systems Immunology, Immunology Frontier Research Institute (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
| | - Daron M Standley
- Department of Systems Immunology, Immunology Frontier Research Institute (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases (RIMD), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Japan
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5
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Pacheco-García U, Varela-López E, Serafín-López J. Immune Stimulation with Imiquimod to Best Face SARS-CoV-2 Infection and Prevent Long COVID. Int J Mol Sci 2024; 25:7661. [PMID: 39062904 PMCID: PMC11277483 DOI: 10.3390/ijms25147661] [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: 05/24/2024] [Revised: 07/04/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Through widespread immunization against SARS-CoV-2 prior to or post-infection, a substantial segment of the global population has acquired both humoral and cellular immunity, and there has been a notable reduction in the incidence of severe and fatal cases linked to this virus and accelerated recovery times for those infected. Nonetheless, a significant demographic, comprising around 20% to 30% of the adult population, remains unimmunized due to diverse factors. Furthermore, alongside those recovered from the infection, there is a subset of the population experiencing persistent symptoms referred to as Long COVID. This condition is more prevalent among individuals with underlying health conditions and immune system impairments. Some Long COVID pathologies stem from direct damage inflicted by the viral infection, whereas others arise from inadequate immune system control over the infection or suboptimal immunoregulation. There are differences in the serum cytokines and miRNA profiles between infected individuals who develop severe COVID-19 or Long COVID and those who control adequately the infection. This review delves into the advantages and constraints associated with employing imiquimod in human subjects to enhance the immune response during SARS-CoV-2 immunization. Restoration of the immune system can modify it towards a profile of non-susceptibility to SARS-CoV-2. An adequate immune system has the potential to curb viral propagation, mitigate symptoms, and ameliorate the severe consequences of the infection.
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Affiliation(s)
- Ursino Pacheco-García
- Department of Cardio-Renal Pathophysiology, Instituto Nacional de Cardiología “Ignacio Chávez”, Mexico City 14080, Mexico
| | - Elvira Varela-López
- Laboratory of Translational Medicine, Instituto Nacional de Cardiología “Ignacio Chávez”, Mexico City 14080, Mexico;
| | - Jeanet Serafín-López
- Department of Immunology, Escuela Nacional de Ciencias Biológicas (ENCB), Instituto Politécnico Nacional (IPN), Mexico City 11340, Mexico;
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6
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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7
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Svensson Akusjärvi S, Zanoni I. Yin and yang of interferons: lessons from the coronavirus disease 2019 (COVID-19) pandemic. Curr Opin Immunol 2024; 87:102423. [PMID: 38776716 PMCID: PMC11162909 DOI: 10.1016/j.coi.2024.102423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 03/05/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024]
Abstract
The host immune response against severe acute respiratory syndrome coronavirus 2 includes the induction of a group of natural antiviral cytokines called interferons (IFNs). Although originally recognized for their ability to potently counteract infections, the mechanistic functions of IFNs in patients with varying severities of coronavirus disease 2019 (COVID-19) have highlighted a more complex scenario. Cellular and molecular analyses have revealed that timing, location, and subtypes of IFNs produced during severe acute respiratory syndrome coronavirus 2 infection play a major role in determining disease progression and severity. In this review, we summarize what the COVID-19 pandemic has taught us about the protective and detrimental roles of IFNs during the inflammatory response elicited against a new respiratory virus across different ages and its longitudinal consequences in driving the development of long COVID-19.
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Affiliation(s)
- Sara Svensson Akusjärvi
- Harvard Medical School, Division of Immunology, Boston Children's Hospital, Boston, MA, USA; Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Ivan Zanoni
- Harvard Medical School, Division of Immunology, Boston Children's Hospital, Boston, MA, USA; Division of Gastroenterology, Boston Children's Hospital, Boston, MA, USA.
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8
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Singh MS, Pyati A, Rubi RD, Subramanian R, Muley VY, Ansari MA, Yellaboina S. Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis. iScience 2024; 27:109087. [PMID: 38384846 PMCID: PMC10879696 DOI: 10.1016/j.isci.2024.109087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/07/2023] [Accepted: 01/29/2024] [Indexed: 02/23/2024] Open
Abstract
The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.
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Affiliation(s)
- Mairembam Stelin Singh
- Department of Biochemistry, SCLS, Jamia Hamdard, New Delhi, India
- Department of Zoology, Rajiv Gandhi University, Itanagar, Arunachal Pradesh, India
| | - Anand Pyati
- All India Institute of Medical Sciences, Bibinagar, Hyderabad, Telangana 508126, India
| | - R. Devika Rubi
- Department of Computer Science and Engineering, Keshav Memorial Institute of Technology, Hyderabad, Telangana State, India
| | - Rajasekaran Subramanian
- Department of Computer Science and Engineering, Keshav Memorial Institute of Technology, Hyderabad, Telangana State, India
| | | | - Mairaj Ahmed Ansari
- Department of Biotechnology, SCLS, Jamia Hamdard, New Delhi, India
- Centre for Virology, SIST, Jamia Hamdard, New Delhi, India
| | - Sailu Yellaboina
- All India Institute of Medical Sciences, Bibinagar, Hyderabad, Telangana 508126, India
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9
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Kenney D, O’Connell AK, Tseng AE, Turcinovic J, Sheehan ML, Nitido AD, Montanaro P, Gertje HP, Ericsson M, Connor JH, Vrbanac V, Crossland NA, Harly C, Balazs AB, Douam F. Resolution of SARS-CoV-2 infection in human lung tissues is driven by extravascular CD163+ monocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.583965. [PMID: 38496468 PMCID: PMC10942442 DOI: 10.1101/2024.03.08.583965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The lung-resident immune mechanisms driving resolution of SARS-CoV-2 infection in humans remain elusive. Using mice co-engrafted with a genetically matched human immune system and fetal lung xenograft (fLX), we mapped the immunological events defining resolution of SARS-CoV-2 infection in human lung tissues. Viral infection is rapidly cleared from fLX following a peak of viral replication. Acute replication results in the emergence of cell subsets enriched in viral RNA, including extravascular inflammatory monocytes (iMO) and macrophage-like T-cells, which dissipate upon infection resolution. iMO display robust antiviral responses, are transcriptomically unique among myeloid lineages, and their emergence associates with the recruitment of circulating CD4+ monocytes. Consistently, mice depleted for human CD4+ cells but not CD3+ T-cells failed to robustly clear infectious viruses and displayed signatures of chronic infection. Our findings uncover the transient differentiation of extravascular iMO from CD4+ monocytes as a major hallmark of SARS-CoV-2 infection resolution and open avenues for unravelling viral and host adaptations defining persistently active SARS-CoV-2 infection.
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Affiliation(s)
- Devin Kenney
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Aoife K. O’Connell
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Anna E. Tseng
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Jacquelyn Turcinovic
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Meagan L. Sheehan
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally to the work
| | - Adam D. Nitido
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally to the work
| | - Paige Montanaro
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Hans P. Gertje
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Maria Ericsson
- Electron Microscopy Core Facility, Harvard Medical School, Boston, MA, USA
| | - John H. Connor
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | | | - Nicholas A. Crossland
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Christelle Harly
- Université de Nantes, INSERM, CNRS, CRCINA, Nantes, France
- LabEx IGO ‘Immunotherapy, Graft, Oncology’, Nantes, France
- These authors contributed equally to the work
| | - Alejandro B. Balazs
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally to the work
| | - Florian Douam
- Department of Virology, Immunology, and Microbiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
- These authors contributed equally to the work
- Lead contact
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10
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Matsubara Y, Kiyohara H, Mikami Y, Nanki K, Namkoong H, Chubachi S, Tanaka H, Azekawa S, Sugimoto S, Yoshimatsu Y, Sujino T, Takabayashi K, Hosoe N, Sato T, Ishii M, Hasegawa N, Okada Y, Koike R, Kitagawa Y, Kimura A, Imoto S, Miyano S, Ogawa S, Fukunaga K, Kanai T. Gastrointestinal symptoms in COVID-19 and disease severity: a Japanese registry-based retrospective cohort study. J Gastroenterol 2024; 59:195-208. [PMID: 38270615 DOI: 10.1007/s00535-023-02071-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 12/24/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Research on whether gastrointestinal symptoms correlate with the severity of Coronavirus Disease 2019 (COVID-19) has been inconclusive. This study aimed to clarify any associations between gastrointestinal symptoms and the prognosis of COVID-19. METHODS We collected data from the Japanese nationwide registry for COVID-19 to conduct a retrospective cohort study. Data from 3498 Japanese COVID-19 patients, diagnosed at 74 facilities between February 2020 and August 2022, were analyzed in this study. Hospitalized patients were followed up until discharge or transfer to another hospital. Outpatients were observed until the end of treatment. Associations between gastrointestinal symptoms and clinical outcomes were investigated using multivariable-adjusted logistic regression models. RESULTS The prevalence of diarrhea, nausea/vomiting, abdominal pain, and melena were 16.6% (581/3498), 8.9% (311/3498), 3.5% (121/3498), and 0.7% (23/3498), respectively. In the univariable analysis, admission to intensive care unit (ICU) and requirement for mechanical ventilation were less common in patients with diarrhea than those without (ICU, 15.7% vs. 20.6% (p = 0.006); mechanical ventilation, 7.9% vs. 11.4% (p = 0.013)). In the multivariable-adjusted analysis, diarrhea was associated with lower likelihood of ICU admission (adjusted odds ratio (aOR), 0.70; 95% confidence interval (CI), 0.53-0.92) and mechanical ventilation (aOR, 0.61; 95% CI, 0.42-0.89). Similar results were obtained in a sensitivity analysis with another logistic regression model that adjusted for 14 possible covariates with diarrhea (ICU; aOR, 0.70; 95% CI, 0.53-0.93; mechanical ventilation; aOR 0.62; 95% CI, 0.42-0.92). CONCLUSIONS Diarrhea was associated with better clinical outcomes in COVID-19 patients.
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Affiliation(s)
- Yuta Matsubara
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Hiroki Kiyohara
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan.
| | - Yohei Mikami
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Kosaku Nanki
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shinya Sugimoto
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Yusuke Yoshimatsu
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Tomohisa Sujino
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Naoki Hosoe
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Toshiro Sato
- Department of Integrative Medicine and Biochemistry, Keio University School of Medicine, Tokyo, Japan
| | - Makoto Ishii
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
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11
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Uslu K, Ozcelik F, Zararsiz G, Eldem V, Cephe A, Sahin IO, Yuksel RC, Sipahioglu H, Ozer Simsek Z, Baspinar O, Akalin H, Simsek Y, Gundogan K, Tutar N, Karayol Akin A, Ozkul Y, Yildiz O, Dundar M. Deciphering the host genetic factors conferring susceptibility to severe COVID-19 using exome sequencing. Genes Immun 2024; 25:14-42. [PMID: 38123822 DOI: 10.1038/s41435-023-00232-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/23/2023]
Abstract
The COVID-19 pandemic remains a significant public health concern despite the new vaccines and therapeutics. The clinical course of acute SARS-CoV-2 infection is highly variable and influenced by several factors related to the virus and the host. Numerous genetic studies, including candidate gene, exome, and genome sequencing studies, genome-wide association studies, and other omics efforts, have proposed various Mendelian and non-Mendelian associations with COVID-19 course. In this study, we conducted whole-exome sequencing on 90 unvaccinated patients from Turkey with no known comorbidities associated with severe COVID-19. Of these patients, 30 had severe, 30 had moderate, and 30 had mild/asymptomatic disease. We identified rare variants in genes associated with SARS-CoV-2 susceptibility and pathogenesis, with an emphasis on genes related to the regulation of inflammation, and discussed these in the context of the clinical course of the patients. In addition, we compared the frequencies of common variants between each group. Even though no variant remained statistically significant after correction for multiple testing, we observed that certain previously associated genes and variants showed significant associations before correction. Our study contributes to the existing literature regarding the genetic susceptibility to SARS-CoV-2. Future studies would be beneficial characterizing the host genetic properties in different populations.
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Affiliation(s)
- Kubra Uslu
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Firat Ozcelik
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Gokmen Zararsiz
- Department of Biostatistics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
- Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Vahap Eldem
- Department of Biology, Faculty of Science, Istanbul University, Istanbul, Turkey
| | - Ahu Cephe
- Institutional Data Management and Analytics Units, Erciyes University Rectorate, Kayseri, Turkey
| | - Izem Olcay Sahin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Recep Civan Yuksel
- Division of Intensive Care Medicine, Department of Internal Medicine, Kayseri City Education and Research Hospital, Kayseri, Turkey
| | - Hilal Sipahioglu
- Division of Intensive Care Medicine, Department of Internal Medicine, Kayseri City Education and Research Hospital, Kayseri, Turkey
| | - Zuhal Ozer Simsek
- Division of Intensive Care Medicine, Department of Internal Medicine, Kayseri City Education and Research Hospital, Kayseri, Turkey
| | - Osman Baspinar
- Department of Internal Medicine, Kayseri City Education and Research Hospital, Kayseri, Turkey
| | - Hilal Akalin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Yasin Simsek
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kayseri City Education and Research Hospital, Kayseri, Turkey
| | - Kursat Gundogan
- Division of Intensive Care Medicine, Department of Internal Medicine, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Nuri Tutar
- Department of Chest Diseases, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Aynur Karayol Akin
- Department of Anesthesiology and Reanimation, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Yusuf Ozkul
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Orhan Yildiz
- Department of Infectious Diseases, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Munis Dundar
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
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12
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Tanaka H, Okada Y, Nakayamada S, Miyazaki Y, Sonehara K, Namba S, Honda S, Shirai Y, Yamamoto K, Kubo S, Ikari K, Harigai M, Sonomoto K, Tanaka Y. Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases by cohort-wide immunophenotyping. Ann Rheum Dis 2024; 83:242-252. [PMID: 37903543 PMCID: PMC10850648 DOI: 10.1136/ard-2023-224537] [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: 06/09/2023] [Accepted: 09/13/2023] [Indexed: 11/01/2023]
Abstract
OBJECTIVE Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases (AIRDs) is essential towards personalised medicine. METHODS We conducted large-scale and cohort-wide immunophenotyping of 46 peripheral immune cells using Human Immunology Protocol of comprehensive 8-colour flow cytometric analysis. Dataset consisted of >1000 Japanese patients of 11 AIRDs with deep clinical information registered at the FLOW study, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). In-depth clinical and immunological characterisation was conducted for the identified RA patient clusters, including associations of inborn human genetics represented by Polygenic Risk Score (PRS). RESULTS Multimodal clustering of immunophenotypes deciphered underlying disease-cell type network in immune cell, disease and patient cluster resolutions. This provided immune cell type specificity shared or distinct across AIRDs, such as close immunological network between mixed connective tissue disease and SLE. Individual patient-level clustering dissected patients with AIRD into several clusters with different immunological features. Of these, RA-like or SLE-like clusters were exclusively dominant, showing immunological differentiation between RA and SLE across AIRDs. In-depth clinical analysis of RA revealed that such patient clusters differentially defined clinical heterogeneity in disease activity and treatment responses, such as treatment resistance in patients with RA with SLE-like immunophenotypes. PRS based on RA case-control and within-case stratified genome-wide association studies were associated with clinical and immunological characteristics. This pointed immune cell type implicated in disease biology such as dendritic cells for RA-interstitial lung disease. CONCLUSION Cohort-wide and cross-disease immunophenotyping elucidate clinically heterogeneous patient subtypes existing within single disease in immune cell type-specific manner.
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Affiliation(s)
- Hiroaki Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
| | - Shingo Nakayamada
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yusuke Miyazaki
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
| | - Suguru Honda
- Department of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University, Shinjuku-ku, Tokyo, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University School of Medicine Graduate School of Medicine, Suita, Osaka, Japan
- Laboratory of Children's health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Satoshi Kubo
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Katsunori Ikari
- Department of Orthopedics, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayoshi Harigai
- Department of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University, Shinjuku-ku, Tokyo, Japan
| | - Koshiro Sonomoto
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
- Department of Clinical Nursing, School of Health Sciences, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Yoshiya Tanaka
- First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
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Saputri DS, Ismanto HS, Nugraha DK, Xu Z, Horiguchi Y, Sakakibara S, Standley DM. Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences. mSystems 2023; 8:e0072223. [PMID: 37975681 PMCID: PMC10734444 DOI: 10.1128/msystems.00722-23] [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: 08/07/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.
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Affiliation(s)
- Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dendi K. Nugraha
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Zichang Xu
- Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Yasuhiko Horiguchi
- Graduate School of Medicine, Osaka University, Suita, Japan
- Department of Molecular Bacteriology, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
| | - Shuhei Sakakibara
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Graduate School of Medical Safety Management, Jikei University of Health Care Sciences, Osaka, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Graduate School of Medicine, Osaka University, Suita, Japan
- Immunology Frontier Research Center, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Japan
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14
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López-Bielma MF, Falfán-Valencia R, Abarca-Rojano E, Pérez-Rubio G. Participation of Single-Nucleotide Variants in IFNAR1 and IFNAR2 in the Immune Response against SARS-CoV-2 Infection: A Systematic Review. Pathogens 2023; 12:1320. [PMID: 38003785 PMCID: PMC10675296 DOI: 10.3390/pathogens12111320] [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: 09/21/2023] [Revised: 10/22/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Host genetic factors significantly influence susceptibility to SARS-CoV-2 infection and COVID-19 severity. Among these genetic factors are single-nucleotide variants (SNVs). IFNAR2 and IFNAR1 genes have been associated with severe COVID-19 in populations from the United Kingdom, Africa, and Latin America. IFNAR1 and IFNAR2 are subunits forming the type I interferon receptor (IFNAR). SNVs in the IFNAR genes impact protein function, affecting antiviral response and disease phenotypes. This systematic review aimed to describe IFNAR1 and IFNAR2 variants associated with COVID-19 susceptibility and severity. Accordingly, the current review focused on IFNAR1 and IFNAR2 studies published between January 2021 and February 2023, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. The electronic search was conducted in PubMed databases using Boolean operators and inclusion and exclusion criteria. Of the 170 literature pieces, 11 studies were included. We include case reports of rare SNVs, defined by minor allele frequency (MAF) < 1%, and genome-wide associated studies (GWAS). Variants in IFNAR1 and IFNAR2 could potentially be new targets for therapies that limit the infection and the resulting inflammation by SARS-CoV-2 infection.
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Affiliation(s)
- María Fernanda López-Bielma
- HLA Laboratory, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City 14080, Mexico (R.F.-V.)
- Sección de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City 11340, Mexico
| | - Ramcés Falfán-Valencia
- HLA Laboratory, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City 14080, Mexico (R.F.-V.)
| | - Edgar Abarca-Rojano
- Sección de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City 11340, Mexico
| | - Gloria Pérez-Rubio
- HLA Laboratory, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City 14080, Mexico (R.F.-V.)
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15
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Sun Q, Rowland B, Wang W, Miller-Fleming TW, Cox N, Graff M, Faucon A, Shuey MM, Blue EE, Auer P, Li Y, Sankaran VG, Reiner AP, Raffield LM. Genetic examination of hematological parameters in SARS-CoV-2 infection and COVID-19. Blood Cells Mol Dis 2023; 103:102782. [PMID: 37558590 PMCID: PMC10507673 DOI: 10.1016/j.bcmd.2023.102782] [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: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023]
Abstract
People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 related outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Wanjiang Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Nancy Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Misa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Annika Faucon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Megan M Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Elizabeth E Blue
- Department of Medicine, Division of Medical Genetics, University of Washington, Brotman Baty Institute for Precision Medicine, Seattle, WA, United States
| | - Paul Auer
- Division of Biostatistics, Institute for Health and Equity, Cancer Center, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
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16
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Kong W, Zhu J, Bi S, Huang L, Wu P, Zhu S. Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients. IMETA 2023; 2:e126. [PMID: 38867930 PMCID: PMC10989835 DOI: 10.1002/imt2.126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2024]
Abstract
We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts.
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Affiliation(s)
- Weikaixin Kong
- Institute for Molecular Medicine Finland (FIMM), HiLIFEUniversity of HelsinkiHelsinkiFinland
| | - Jie Zhu
- Institute for Molecular Medicine Finland (FIMM), HiLIFEUniversity of HelsinkiHelsinkiFinland
| | - Suzhen Bi
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of MedicineQingdao UniversityQingdaoChina
| | - Liting Huang
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of MedicineQingdao UniversityQingdaoChina
| | - Peng Wu
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Medical College, Tongji HospitalHuazhong University of Science and TechnologyWuhanChina
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Su‐Jie Zhu
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of MedicineQingdao UniversityQingdaoChina
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