Proteomics platform, CHU de Québec - Université Laval Research Center, Québec City, Québec, Canada.
Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, Québec, Canada.
Mol Cell Proteomics. 2019 Dec;18(12):2492-2505. doi: 10.1074/mcp.TIR119.001559. Epub 2019 Oct 4.
Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.
快速鉴定临床样本中的微生物物种对于为患者提供适当的抗生素治疗以及减少广谱抗生素的处方以防止抗生素耐药至关重要。MALDI-TOF-MS 技术已成为微生物鉴定的首选工具，但存在几个缺点：它在分析之前需要很长的细菌培养步骤（≥24 小时），特异性低，并且不是定量的。我们开发了一种使用特定 LC-MS/MS 肽特征来鉴定尿液中细菌物种的新策略。在第一步的训练中，在 DDA 模式下获得纯细菌菌落的肽文库，然后在 DIA 模式下验证其在尿液中的检测，随后使用机器学习分类器（朴素贝叶斯、贝叶斯网络和赫夫丁树）来定义一个肽特征，以将每种细菌与其他细菌区分开来。然后，在第二步中，使用靶向蛋白质组学监测未知尿液样本中的这种特征。该方法可在不到 4 小时内完成细菌鉴定，已应用于代表所有尿路感染 84%的十五种细菌。190 个样本中的 31,000 多个肽通过 DIA 进行定量，并通过机器学习进行分类，以确定 82 个肽特征并构建预测模型。该特征用于在两种不同仪器上使用平行反应监测进行常规验证。该方法的线性度和重现性以及对供体标本的准确性得到了证明。在 4 小时内且无需进行细菌培养，我们的方法能够以 97%的病例和 100%以上的标准阈值预测感染样本的主要细菌。这项工作证明了我们的方法在快速和特异性鉴定引起尿路感染的细菌物种方面的效率，并且将来可以扩展到其他生物样本和具有特定毒力或耐药因子的细菌。
