Robert Koch-Institute, Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS6), 13353 Berlin, Germany.
Nosocomial Pathogens and Antibiotic Resistance (FG13), Robert Koch-Institute, 38855 Wernigerode, Germany.
Anal Chem. 2021 Nov 9;93(44):14599-14608. doi: 10.1021/acs.analchem.1c00594. Epub 2021 Oct 26.
Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently from species identification. Sequencing-based approaches are possible alternatives for AMR detection, although the analysis of proteins should be superior to gene or transcript sequencing for phenotype prediction as the actual resistance to antibiotics is almost exclusively mediated by proteins. In this proof-of-concept study, we present an unbiased proteomics workflow for detecting both bacterial species and AMR-related proteins in the absence of secondary antibiotic cultivation within <4 h from a primary culture. The workflow was designed to meet the needs in clinical microbiology. It introduces a new data analysis concept for bacterial proteomics, and a software (rawDIAtect) for the prediction and reporting of AMR from peptide identifications. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms, which resulted in a sensitivity of 98% and a specificity of 100%.
抗菌药物耐药性 (AMR) 对细菌感染的治疗和临床管理构成了日益严峻的挑战。目前，抗菌药物耐药性检测依赖于表型检测，该检测与物种鉴定是相互独立进行的。基于测序的方法是抗菌药物耐药性检测的可行替代方法，尽管与基因或转录本测序相比，分析蛋白质对于表型预测更具优势，因为抗生素的实际耐药性几乎完全由蛋白质介导。在本概念验证研究中，我们提出了一种无偏蛋白质组学工作流程，用于在没有二级抗生素培养的情况下，从初次培养物中在 <4 小时内检测细菌物种和与抗菌药物耐药性相关的蛋白质。该工作流程旨在满足临床微生物学的需求。它引入了一种新的细菌蛋白质组学数据分析概念，以及一种软件（rawDIAtect），用于从肽鉴定中预测和报告抗菌药物耐药性。该方法使用由 13 种蛋白质同工型代表的 7 种细菌物种和 11 种抗菌药物耐药性决定因素的样本队列进行了验证，其灵敏度为 98%，特异性为 100%。
