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一种新型的短期高乳糖培养方法结合基质辅助激光解吸电离飞行时间质谱法,利用人工神经网络区分大肠杆菌和志贺氏菌属。

A novel short-term high-lactose culture approach combined with a matrix-assisted laser desorption ionization-time of flight mass spectrometry assay for differentiating Escherichia coli and Shigella species using artificial neural networks.

机构信息

Department of Biochemical Drugs and Biological Products, Shanghai Institute for Food and Drug Control, Shanghai, China.

NMPA Key Laboratory for Quality Control of Therapeutic Monoclonal Antibodies, Shanghai Institute for Food and Drug Control, Shanghai, China.

出版信息

PLoS One. 2019 Oct 8;14(10):e0222636. doi: 10.1371/journal.pone.0222636. eCollection 2019.

Abstract

BACKGROUND

Escherichia coli is currently unable to be reliably differentiated from Shigella species by routine matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis. In the present study, a reliable and rapid identification method was established for Escherichia coli and Shigella species based on a short-term high-lactose culture using MALDI-TOF MS and artificial neural networks (ANN).

MATERIALS AND METHODS

The Escherichia coli and Shigella species colonies, treated with (Condition 1)/without (Condition 2) a short-term culture with an in-house developed high-lactose fluid medium, were prepared for MALDI-TOF MS assays. The MS spectra were acquired in linear positive mode, with a mass range from 2000 to 12000 Da and were then compared to discover new biomarkers for identification. Finally, MS spectra data sets 1 and 2, extracted from the two conditions, were used for ANN training to investigate the benefit on bacterial classification produced by the new biomarkers.

RESULTS

Twenty-seven characteristic MS peaks from the Escherichia coli and Shigella species were summarized. Seven unreported MS peaks, with m/z 2330.745, m/z 2341.299, m/z 2371.581, m/z 2401.038, m/z 3794.851, m/z 3824.839 and m/z 3852.548, were discovered in only the spectra from the E. coli strains after a short-term high-lactose culture and were identified as belonging to acid shock protein. The prediction accuracies of the ANN models, based on data set 1 and 2, were 97.71±0.16% and 74.39±0.34% (n = 5), with an extremely remarkable difference (p < 0.001), and the areas under the curve of the receiver operating characteristic curve were 0.72 and 0.99, respectively.

CONCLUSIONS

In summary, adding a short-term high-lactose culture approach before the analysis enabled a reliable and easy differentiation of Escherichia coli from the Shigella species using MALDI-TOF MS and ANN.

摘要

背景

目前,常规基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)分析无法可靠地区分大肠杆菌和志贺菌属。本研究基于 MALDI-TOF MS 和人工神经网络(ANN)建立了一种快速可靠的大肠杆菌和志贺菌属鉴定方法。

材料和方法

用自制高乳糖液体培养基进行短期高乳糖培养(条件 1)/不培养(条件 2)处理大肠杆菌和志贺菌属的菌落,进行 MALDI-TOF MS 检测。采用线性正模式采集 MS 谱,质量范围为 2000-12000 Da,然后进行比较以发现新的鉴定生物标志物。最后,分别从两种条件下提取 MS 谱数据集 1 和 2 进行 ANN 训练,以研究新生物标志物对细菌分类的益处。

结果

总结了大肠杆菌和志贺菌属的 27 个特征 MS 峰。在仅经过短期高乳糖培养的大肠杆菌菌株的光谱中发现了 7 个未报道的 MS 峰,其质荷比(m/z)分别为 2330.745、2341.299、2371.581、2401.038、3794.851、3824.839 和 3852.548,被鉴定为酸休克蛋白。基于数据集 1 和 2 的 ANN 模型的预测准确率分别为 97.71±0.16%和 74.39±0.34%(n=5),差异极其显著(p<0.001),受试者工作特征曲线下面积分别为 0.72 和 0.99。

结论

总之,在分析前增加短期高乳糖培养方法,可以使用 MALDI-TOF MS 和 ANN 可靠且轻松地区分大肠杆菌和志贺菌属。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffb1/6782097/26acb02fa7d1/pone.0222636.g001.jpg

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