氨基酸分辨率下无症状组粒ba .2感染者抗体反应的蛋白质组分析

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Hongye Wang, Huixia Gao, Mansheng Li, Linlin Cheng, Xin Zhang, Xiaomei Zhang, Haoting Zhan, Yongmei Liu, Yuling Wang, Jing Ren, Di Hu, Fuchu He*, Erhei Dai*, Yongzhe Li* and Xiaobo Yu*, 
{"title":"氨基酸分辨率下无症状组粒ba .2感染者抗体反应的蛋白质组分析","authors":"Hongye Wang,&nbsp;Huixia Gao,&nbsp;Mansheng Li,&nbsp;Linlin Cheng,&nbsp;Xin Zhang,&nbsp;Xiaomei Zhang,&nbsp;Haoting Zhan,&nbsp;Yongmei Liu,&nbsp;Yuling Wang,&nbsp;Jing Ren,&nbsp;Di Hu,&nbsp;Fuchu He*,&nbsp;Erhei Dai*,&nbsp;Yongzhe Li* and Xiaobo Yu*,&nbsp;","doi":"10.1021/acs.jproteome.4c0054610.1021/acs.jproteome.4c00546","DOIUrl":null,"url":null,"abstract":"<p >Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":"24 1","pages":"189–201 189–201"},"PeriodicalIF":3.6000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Proteome-Wide Analysis of Antibody Responses in Asymptomatic Omicron BA.2-Infected Individuals at the Amino Acid Resolution\",\"authors\":\"Hongye Wang,&nbsp;Huixia Gao,&nbsp;Mansheng Li,&nbsp;Linlin Cheng,&nbsp;Xin Zhang,&nbsp;Xiaomei Zhang,&nbsp;Haoting Zhan,&nbsp;Yongmei Liu,&nbsp;Yuling Wang,&nbsp;Jing Ren,&nbsp;Di Hu,&nbsp;Fuchu He*,&nbsp;Erhei Dai*,&nbsp;Yongzhe Li* and Xiaobo Yu*,&nbsp;\",\"doi\":\"10.1021/acs.jproteome.4c0054610.1021/acs.jproteome.4c00546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.</p>\",\"PeriodicalId\":48,\"journal\":{\"name\":\"Journal of Proteome Research\",\"volume\":\"24 1\",\"pages\":\"189–201 189–201\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Proteome Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00546\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Proteome Research","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00546","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

在病毒入侵期间,体液免疫在清除SARS-CoV-2中起着关键作用。然而,在无症状和有症状的Omicron变异感染个体中,抗体反应的蛋白质组特征仍然知之甚少。我们使用SARS-CoV-2蛋白质组芯片在氨基酸分辨率上分析了108人的血清抗体,包括健康对照和感染了Omicron BA.2的人。我们在有症状和无症状个体中构建了横跨SARS-CoV-2蛋白质组的b细胞表位图谱。免疫优势表位主要来源于S、N、Nsp3、M和ORF3a蛋白,部分表位与t细胞表位重叠。使用机器学习,我们确定了一个蛋白质组学特征,能够在训练和验证队列中区分无症状个体和健康对照,auc分别为0.988和0.857。这些发现为欧米克隆的BA.2感染提供了重要的免疫学见解,并对未来的COVID-19诊断和治疗具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Proteome-Wide Analysis of Antibody Responses in Asymptomatic Omicron BA.2-Infected Individuals at the Amino Acid Resolution

Proteome-Wide Analysis of Antibody Responses in Asymptomatic Omicron BA.2-Infected Individuals at the Amino Acid Resolution

Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信