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

doi:10.1371/journal.pone.0222636

	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
	Ling, Jin 2,3,4; Wang, Hong 2,3; Li, Gaomin 2,3; Feng, Zhen 5; Song, Yufei 7; Wang, Peng 6; Shao, Hong 2,3; Zhou, Hu 1; Chen, Gang 2,3
刊名	PLOS ONE
	2019-10-08
卷号	14 期号:10 页码:10
ISSN号	1932-6203
DOI	10.1371/journal.pone.0222636
通讯作者	Chen, Gang(chengang@sifdc.org.cn)
英文摘要	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.
资助项目	Shanghai Municipal Food and Drug Administration Research Projects[KT-201708210037] ; Chinese Pharmacopoeia Commission Research Projects[2019S05]
WOS关键词	MALDI BIOTYPER ; IDENTIFICATION ; ALGORITHM ; DATABASE
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000532431700002
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/280185]
专题	中国科学院上海药物研究所
通讯作者	Chen, Gang
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, Dept Analyt Chem, Shanghai, Peoples R China 2.Shanghai Inst Food & Drug Control, Dept Biochem Drugs & Biol Prod, Shanghai, Peoples R China 3.Shanghai Inst Food & Drug Control, NMPA Key Lab Qual Control Therapeut Monoclonal An, Shanghai, Peoples R China 4.Zhejiang Jinhua Guangfu Hosp, Dept Pharm, Jinhua, Zhejiang, Peoples R China 5.Shanghai Inst Food & Drug Control, Dept Antibiot & Microbiol, Shanghai, Peoples R China 6.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China 7.Ningbo Med Ctr, Lihuili Hosp, Dept Gastroenterol, Ningbo, Peoples R China
推荐引用方式 GB/T 7714	Ling, Jin,Wang, Hong,Li, Gaomin,et al. 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[J]. PLOS ONE,2019,14(10):10.
APA	Ling, Jin.,Wang, Hong.,Li, Gaomin.,Feng, Zhen.,Song, Yufei.,...&Chen, Gang.(2019).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.PLOS ONE,14(10),10.
MLA	Ling, Jin,et al."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".PLOS ONE 14.10(2019):10.