Suppr超能文献

用于 SARS-CoV-2 研究和检测的蛋白质组学的数据、试剂、检测方法和优点。

Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing.

机构信息

Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany.

Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich, Freising, Germany.

出版信息

Mol Cell Proteomics. 2020 Sep;19(9):1503-1522. doi: 10.1074/mcp.RA120.002164. Epub 2020 Jun 26.

Abstract

As the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC-MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing.

摘要

随着 COVID-19 大流行的持续蔓延,全球数千名科学家改变了研究方向,以更好地了解病毒的工作原理,并找出应对病毒的方法。预印本服务器上的手稿数量正在飙升,使用基于 MS 的蛋白质组学的同行评审出版物也开始出现。为了促进 SARS-CoV-2(导致 COVID-19 的病毒)的蛋白质组学研究,本报告介绍了常见细胞系模型(特别是 Vero E6、Calu-3、Caco-2 和 ACE2-A549)的深度蛋白质组(10000 种蛋白质;>130000 种肽),这些模型的蛋白质表达谱特征包括 ACE2 或 TMPRSS2 等病毒进入因子。我们以 9 kDa 蛋白 SRP9 和乳腺癌致癌基因 BRCA1 为例,展示了如何使用蛋白质组表达数据来完善非洲绿猴和 Vero 细胞系基因组中蛋白质编码区的注释。监测病毒感染时蛋白质组的变化揭示了广泛的表达变化,包括转录调节剂、蛋白酶抑制剂和参与固有免疫的蛋白质。基于代表 11 种 SARS-CoV-2 蛋白的 98 种稳定同位素标记合成肽的文库,我们开发了用于纳流和微流 LC-MS/MS 的 PRM(平行反应监测)测定法。我们使用感染病毒的 Vero E6 细胞的上清液评估了这些 PRM 测定法的优点,并通过分析两个包含 24(+30)例 SARS-CoV-2 阳性和 28(+9)例阴性病例的诊断队列来检验这些测定法。鉴于获得的结果,并包括预印本服务器上的最新出版物或手稿,我们批判性地讨论了基于 MS 的蛋白质组学在 SARS-CoV-2 研究和检测中的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e14/8143634/38ed9327fe73/gr6.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验