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

Autoři: Jin Ling aff001;  Hong Wang aff001;  Gaomin Li aff001;  Zhen Feng aff004;  Yufei Song aff005;  Peng Wang aff006;  Hong Shao aff001;  Hu Zhou aff007;  Gang Chen aff001
Působiště autorů: Department of Biochemical Drugs and Biological Products, Shanghai Institute for Food and Drug Control, Shanghai, China aff001;  NMPA Key Laboratory for Quality Control of Therapeutic Monoclonal Antibodies, Shanghai Institute for Food and Drug Control, Shanghai, China aff002;  Department of Pharmacy, Zhejiang Jinhua Guangfu Hospital, Jinhua, China aff003;  Department of Antibiotics and Microbiology, Shanghai Institute for Food and Drug Control, Shanghai, China aff004;  Department of Gastroenterology, Lihuili Hospital of Ningbo Medical Center, Ningbo, China aff005;  Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China aff006;  Department of Analytical Chemistry, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China aff007
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222636



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.


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.


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.

Klíčová slova:

Artificial neural networks – Biomarkers – Escherichia – Escherichia coli – Matrix-assisted laser desorption ionization time-of-flight mass spectrometry – Shigella – Matrix-assisted laser desorption ionization mass spectrometry – Lactose


1. Buchanan R, Ball D, Dolphin H, Dave J. Matrix-assisted laser desorption-ionization time-of-flight mass spectrometry for the identification of Neisseria gonorrhoeae. Clin Microbiol Infect.2016; 22: 815.e815–815.e817.

2. Mari-Almirall M, Cosgaya C, Higgins PG, Van Assche A, Telli M, Huys G, et al. MALDI-TOF/MS identification of species from the Acinetobacter baumannii (Ab) group revisited: inclusion of the novel A. seifertii and A. dijkshoorniae species. Clin Microbiol Infect.2017; 23: 210.e211–210.e219.

3. Yu J, Liu J, Li Y, Yu J, Zhu W, Liu Y, et al. Rapid detection of carbapenemase activity of Enterobacteriaceae isolated from positive blood cultures by MALDI-TOF MS. Ann Clin Microbiol Antimicrob.2018; 17: 22. doi: 10.1186/s12941-018-0274-9 29776363

4. Dallagassa CB, Huergo LF, Stets MI, Pedrosa FO, Souza EM, Cruz LM, et al. Matrix-assisted laser desorption ionization-time of flight mass spectrometry analysis of Escherichia coli categories. Genet Mol Res.2014; 13: 716–722. doi: 10.4238/2014.January.29.2 24615036

5. Devanga Ragupathi NK, Muthuirulandi Sethuvel DP, Inbanathan FY, Veeraraghavan B. Accurate differentiation of Escherichia coli and Shigella serogroups: challenges and strategies. New Microbes New Infect.2018; 21: 58–62. doi: 10.1016/j.nmni.2017.09.003 29204286

6. Chattaway MA, Schaefer U, Tewolde R, Dallman TJ, Jenkins C. Identification of Escherichia coli and Shigella Species from Whole-Genome Sequences. J Clin Microbiol.2017; 55: 616–623. doi: 10.1128/JCM.01790-16 27974538

7. Campilongo R, Di Martino ML, Marcocci L, Pietrangeli P, Leuzzi A, Grossi M, et al. Molecular and functional profiling of the polyamine content in enteroinvasive E. coli: looking into the gap between commensal E. coli and harmful Shigella. PLoS One.2014; 9: e106589. doi: 10.1371/journal.pone.0106589 25192335

8. Ud-Din A, Wahid S. Relationship among Shigella spp. and enteroinvasive Escherichia coli (EIEC) and their differentiation. Braz J Microbiol.2014; 45: 1131–1138. doi: 10.1590/s1517-83822014000400002 25763015

9. Schulthess B, Brodner K, Bloemberg GV, Zbinden R, Bottger EC, Hombach M. Identification of Gram-positive cocci by use of matrix-assisted laser desorption ionization-time of flight mass spectrometry: comparison of different preparation methods and implementation of a practical algorithm for routine diagnostics. J Clin Microbiol.2013; 51: 1834–1840. doi: 10.1128/JCM.02654-12 23554198

10. Harju I, Lange C, Kostrzewa M, Maier T, Rantakokko-Jalava K, Haanperä M. Improved Differentiation of Streptococcus pneumoniae and Other S. mitis Group Streptococci by MALDI Biotyper Using an Improved MALDI Biotyper Database Content and a Novel Result Interpretation Algorithm. J Clin Microbiol.2017; 55: 914–922. doi: 10.1128/JCM.01990-16 28053215

11. Schulthess B, Bloemberg GV, Zbinden R, Bottger EC, Hombach M. Evaluation of the Bruker MALDI Biotyper for identification of Gram-positive rods: development of a diagnostic algorithm for the clinical laboratory. J Clin Microbiol.2014; 52: 1089–1097. doi: 10.1128/JCM.02399-13 24452159

12. Khot PD, Fisher MA. Novel approach for differentiating Shigella species and Escherichia coli by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol.2013; 51: 3711–3716. doi: 10.1128/JCM.01526-13 23985919

13. Paauw A, Jonker D, Roeselers G, Heng JM, Mars-Groenendijk RH, Trip H, et al. Rapid and reliable discrimination between Shigella species and Escherichia coli using MALDI-TOF mass spectrometry. Int J Med Microbiol.2015; 305: 446–452. doi: 10.1016/j.ijmm.2015.04.001 25912807

14. Veloo AC, de Vries ED, Jean-Pierre H, Justesen US, Morris T, Urban E, et al. The optimization and validation of the Biotyper MALDI-TOF MS database for the identification of Gram-positive anaerobic cocci. Clin Microbiol Infect.2016; 22: 793–798. doi: 10.1016/j.cmi.2016.06.016 27404365

15. Seputiene V, Motiejunas D, Suziedelis K, Tomenius H, Normark S, Melefors O, et al. Molecular characterization of the acid-inducible asr gene of Escherichia coli and its role in acid stress response. J Bacteriol.2003; 185: 2475–2484. doi: 10.1128/JB.185.8.2475-2484.2003 12670971

16. Bergey DH. Bergey’s manual of systematic bacteriology 2nd Edition. Springer New York Dordrecht Heidelberg London; 2010.

17. Chen YD, Zheng S, Yu JK, Hu X. Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res.2004; 10: 8380–8385. doi: 10.1158/1078-0432.CCR-1162-03 15623616

18. Fangous MS, Mougari F, Gouriou S, Calvez E, Raskine L, Cambau E, et al. Classification algorithm for subspecies identification within the Mycobacterium abscessus species, based on matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol.2014; 52: 3362–3369. doi: 10.1128/JCM.00788-14 25009048

19. Huang B, Zhang L, Zhang W, Liao K, Zhang S, Zhang Z, et al. Direct Detection and Identification of Bacterial Pathogens from Urine with Optimized Specimen Processing and Enhanced Testing Algorithm.2017; 55: 1488–1495. doi: 10.1128/JCM.02549-16 28249997

20. Lasch P, Beyer W, Nattermann H, Stammler M, Siegbrecht E, Grunow R, et al. Identification of Bacillus anthracis by using matrix-assisted laser desorption ionization-time of flight mass spectrometry and artificial neural networks. Appl Environ Microbiol.2009; 75: 7229–7242. doi: 10.1128/AEM.00857-09 19767470

21. Feng L, Zhu S, Lin F, Su Z, Yuan K, Zhao Y, et al. Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks. Sensors (Basel).2018; 18: E1944

Článek vyšel v časopise


2019 Číslo 10
Nejčtenější tento týden