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

Rautureau, Gilles; Palama, Tony; Canard, Isabelle; Mirande, Caroline; Chatellier, Sonia; Belkum, Alex van; Elena-Herrmann, Bénédicte

doi:10.1021/acsinfecdis.9b00199

Cited by 11 publications

(11 citation statements)

References 43 publications

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“…Globally, NTS is estimated to result in 90 million illnesses and 155,000 deaths per year [64]. In the United states, 40,000 infections and 450 deaths are reported annually as a result of NTS infection [45,64]. Children and immunocompromised youths have a higher risk of infection, with the latter being more susceptible for more severe disease manifestations or death [64].…”

Section: Salmonellamentioning

confidence: 99%

“…Treatment of Salmonella infection often includes lessening of symptoms and administration of antibiotics [65]. To provide accurate diagnosis of salmonellosis, the current practice is to collect stool samples from patients and culture them in Salmonella selective media [45,64,65]. In this method, enrichment broths suited for enhanced isolation of Salmonella may be used to increase diagnostic accuracy [65,66].…”

Section: Salmonellamentioning

confidence: 99%

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Imaging Inflammation and Infection in the Gastrointestinal Tract

Frickenstein

Jones

Behkam

et al. 2019

IJMS

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A variety of seemingly non-specific symptoms manifest within the gastrointestinal (GI) tract, particularly in the colon, in response to inflammation, infection, or a combination thereof. Differentiation between symptom sources can often be achieved using various radiologic studies. Although it is not possible to provide a comprehensive survey of imaging gastrointestinal GI tract infections in a single article, the purpose of this review is to survey several topics on imaging of GI tract inflammation and infections. The review discusses such modalities as computed tomography, positron emission tomography, ultrasound, endoscopy, and magnetic resonance imaging while looking at up-an-coming technologies that could improve diagnoses and patient comfort. The discussion is accomplished through examining a combination of organ-based and organism-based approaches, with accompanying selected case examples. Specific focus is placed on the bacterial infections caused by Shigella spp., Escherichia coli, Clostridium difficile, Salmonella, and inflammatory conditions of diverticulitis and irritable bowel disease. These infectious and inflammatory diseases and their detection via molecular imaging will be compared including the appropriate differential diagnostic considerations.

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Section: Salmonellamentioning

confidence: 99%

Section: Salmonellamentioning

confidence: 99%

Imaging Inflammation and Infection in the Gastrointestinal Tract

Frickenstein

Jones

Behkam

et al. 2019

IJMS

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“…coli ( Pupo et al, 2000 ), while pH-indicator-based triple sugar ion (TSI) test showed poor sensitivity and specificity in terms of discriminating E . coli and Shigella spp., from other bacterial species ( Rautureau et al, 2019 ). Other methods commonly used in clinical laboratory such as 16S rRNA gene sequencing and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) cannot differentiate Shigella spp., from E .…”

Section: Introductionmentioning

confidence: 99%

“…From clinical perspective, Shigella spp., are responsible for life-threatening dysentery (also known as shigellosis) and the infection leads to high childhood morbidity and mortality globally ( Wang et al, 2019 ; Zhu et al, 2020 ), while E . coli are normally commensals found in human gut microbiota ( Rautureau et al, 2019 ). However, among the many E .…”

Section: Introductionmentioning

confidence: 99%

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Liu

Tang²,

Mou³

et al. 2023

Front. Microbiol.

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Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

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“…Metabolomics, the comprehensive measurement of metabolites in a system, is a promising technique in this case because of its speed in delivering results [3]. Nuclear magnetic resonance (NMR) metabolomics has been proposed as a method to distinguish between E. coli and Shigella strains [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles

2021

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Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D 1H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

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

Cited by 11 publications

References 43 publications

Imaging Inflammation and Infection in the Gastrointestinal Tract

Imaging Inflammation and Infection in the Gastrointestinal Tract

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles

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