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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.

plos one

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