Department of Chemistry, Shanghai Stomatological Hospital , Fudan University , Shanghai 200000 , China.
The Second Military Medical University , Changhai Hospital , Shanghai 200433 , China.
Anal Chem. 2018 Mar 20;90(6):3863-3870. doi: 10.1021/acs.analchem.7b04565. Epub 2018 Feb 27.
Antimicrobial resistance (AMR) is one of the most serious problems affecting public health and safety. It is crucial to understand antimicrobial resistance from the molecular level. In this work, TiO-assisted laser desorption/ionization (LDI) mass spectrometry (MS) was used for the fast metabolites analysis from intact bacterial cells to discriminate different strains of bacteria and to detect AMR. With the mass spectra of bacterial metabolites by TiO-LDI MS, multivariable analysis was performed for bacterial identification to determine distinctive metabolites as the potential biomarkers. The most statistically significant metabolites were screened out by the method and further identified using liquid-chromatography (LC) tandem MS (MS/MS). Robustness of our developed methods in bacterial taxonomy was demonstrated by iterative validation using 48 clinical samples. The strategy was further illustrated with three clinical strains of ESBL (extended-spectrum β-lactamase-resistant)-positive Escherichia. coli and four clinical strains of ESBL-negative ones. Eleven key metabolites were identified as potential biomarkers of ESBL-positive E. coli. We also implemented the pathway and network analysis on the key metabolites to prove the feasibility of our method in executing metabolomics analysis. Compared to the most prevalent techniques in a metabolomics study, such as LC-MS, gas chromatography MS, and nuclear magnetic resonance spectroscopy, the current method has advantages in its simple sample preparation and short analysis time, thereby fitting especially into clinical usages and fast analyses.
抗微生物药物耐药性（AMR）是影响公众健康和安全的最严重问题之一。从分子水平理解抗微生物药物耐药性至关重要。在这项工作中，采用 TiO 辅助激光解吸/电离（LDI）质谱（MS）快速分析完整细菌细胞中的代谢物，以区分不同的细菌菌株并检测抗微生物药物耐药性。通过 TiO-LDI MS 的细菌代谢物质谱，进行多变量分析以进行细菌鉴定，以确定有区别的代谢物作为潜在的生物标志物。通过该方法筛选出最具统计学意义的代谢物，并使用液相色谱（LC）串联质谱（MS/MS）进一步鉴定。使用 48 个临床样本进行迭代验证，证明了我们开发的方法在细菌分类学中的稳健性。该策略进一步用三个产超广谱β-内酰胺酶（ESBL）阳性大肠杆菌和四个产 ESBL 阴性大肠杆菌的临床菌株进行了说明。鉴定出 11 个关键代谢物作为 ESBL 阳性大肠杆菌的潜在生物标志物。我们还对关键代谢物进行了途径和网络分析，以证明我们的方法在执行代谢组学分析中的可行性。与代谢组学研究中最常见的技术（如 LC-MS、气相色谱-MS 和核磁共振波谱）相比，当前方法在其简单的样品制备和短的分析时间方面具有优势，因此特别适合临床应用和快速分析。
