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Dysentery is a major health threat that dramatically impacts childhood morbidity and mortality in developing countries. Various pathogenic agents cause dysentery, such as Shigella spp. and Escherichia coli , which are very closely related if not identical species. Sensitive and precise detection and identification of the infectious agent is important to target the best therapeutic strategy, but the differential diagnosis of these two groups remains a challenge using conventional methods. Here, we present a nuclear magnetic resonance (NMR) based multivariate classification model employing bacterial metabolic footprints in postculture growth media with remarkable segregation capability, including the discrimination of lactose negative E. coli and Shigella spp. Our results confirm the potential of metabolomic markers in the field of bacterial identification for the distinction of even very closely related species.

Discrimination of Escherichia coli and Shigella spp. by Nuclear Magnetic Resonance Based Metabolomic Characterization of Culture Media