Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics

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（Antimicrobial resistance, AMR）正日益成为细菌感染治疗与临床管理领域的严峻挑战。当前，抗菌药物耐药性检测依赖于与菌种鉴定独立开展的表型检测法。基于测序的检测方法可作为抗菌药物耐药性检测的备选方案，但由于抗生素耐药性几乎完全由蛋白质介导，在表型预测任务中，蛋白质层面的分析应优于基因或转录组测序。在本概念验证研究中，我们报道了一种无偏倚蛋白质组学流程，可在从初代培养开始的4小时内、无需次级抗生素培养的前提下，同时完成细菌菌种与抗菌药物耐药相关蛋白的检测。该流程贴合临床微生物学的实际需求，提出了全新的细菌蛋白质组学数据分析理念，并开发了一款用于基于肽段鉴定预测并报告抗菌药物耐药性的软件（rawDIAtect）。本研究使用包含7种细菌菌种、13种蛋白质同工型所代表的11种抗菌药物耐药决定因子的样本队列对该方法进行验证，最终获得了98%的灵敏度与100%的特异度。