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

A novel biochemistry approach combined with MALDI-TOF MS to discriminate Escherichia coli and Shigella species

Escherichia coli and Shigella spp. are closely related, making it crucial to accurately identify them for disease control and prevention. In this study, we utilized MALDI-TOF MS to identify characteristic peaks of decarboxylation products of lysine and ornithine to distinguish between E. coli and Shigella spp. Our findings indicate that the peak at m/z 103.12 ± 0.1 of the product cadaverine from lysine decarboxylase is unique to E. coli, while all Shigella species lack the m/z 103.12 ± 0.1 peak. However, S. sonnei and S. boydii serotype C13 exhibit a specific peak at m/z 89.10 ± 0.1, which is the product of putrescine from ornithine decarboxylase. We were able to correctly identify 97.06% (132 of 136) of E. coli and Shigella isolates and 100% (8 of 8) of S. sonnei isolates using this biochemical-based MALDI-TOF MS detection system. This technology is advantageous for its high-throughput, high quality, and ease of operation, and is of significant value for the diagnosis of E. coli and Shigella-related diseases.

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

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.

Fractional extraction and structural characterization of glycogen particles from the whole cultivated caterpillar fungus Ophiocordyceps sinensis

Ophiocordyceps sinensis (syn. Cordyceps sinensis) is a valuable medicinal fungus in traditional Chinese medicine, and one or more polysaccharides are the key constituents with important medical effects. Glycogen as a functional polysaccharide is widely identified in eukaryotes including fungi. However, there is no definitive report of glycogen presence in O. sinensis. In this study, we carefully fractionated polysaccharides from cultivated caterpillar fungus O. sinensis, which were then characterized via methods for glycogen analysis. According to the results, 1.03 ± 0.43 % of polysaccharides were quantified via amyloglucosidase digestion in the whole cultivated caterpillar fungus, which had a typical spherical shape under transmission electron microscope with an average peak radius of 37.63 ± 0.57 nm via size exclusion chromatography and an average chain length of 12.47 ± 0.94 degree of polymerization via fluorophore-assisted capillary electrophoresis. Taken together, this study confirmed that the polysaccharides extracted form O. sinensis were mostly glycogen.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles

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.

Ecology, Structure, and Evolution of Shigella Phages

Numerous bacteriophages-viruses of bacteria, also known as phages-have been described for hundreds of bacterial species. The Gram-negative Shigella species are close relatives of Escherichia coli, yet relatively few previously described phages appear to exclusively infect this genus. Recent efforts to isolate Shigella phages have indicated these viruses are surprisingly abundant in the environment and have distinct genomic and structural properties. In addition, at least one model system used for experimental evolution studies has revealed a unique mechanism for developing faster infection cycles. Differences between these bacteriophages and other well-described model systems may mirror differences between their hosts' ecology and defense mechanisms. In this review, we discuss the history of Shigella phages and recent developments in their isolation and characterization and the structural information available for three model systems, Sf6, Sf14, and HRP29; we also provide an overview of potential selective pressures guiding both Shigella phage and host evolution.

Potential regulation of small RNAs on bacterial function activities in pig farm wastewater treatment plants

Small RNAs (sRNAs) are key players in the regulation of bacterial gene expression. However, the distribution and regulatory functions of sRNA in pig farm wastewater treatment plants (WWTPs) remains unknown. In this study, the wastewaters in anoxic and oxic tanks of the WWTPs were collected. The profiles of the community structure, mRNA expression, and sRNA expression of bacteria in pig farm wastewater were investigated using transcriptome sequencing and qPCR. This study demonstrated that there was a higher abundance of sRNA in the pig farm WWTPs and 52 sRNAs were detected. The sRNAs were mainly present in Proteobacteria and Firmicutes, including the potential human pathogenic bacteria (HPB) (Escherichia, Shigella, Bordetella and Morganella), crop pathogen (Pectobacterium) and denitrifying bacteria (Zobellella). And the sRNAs were involved in the bacterial functional activities such as translation, transcription, drug resistance, membrane transport and amino acid metabolism. In addition, most sRNAs had a higher abundance in anoxic tanks which contained a higher abundance of the genes associated with infectious diseases and drug resistance than that in oxic tanks. The results presented here show that in pig farm WWTPs, sRNA played an important role in bacterial function activities, especially the infectious diseases, drug resistance and denitrification, which can provide a new point of penetration for improving the pig farm WWTPs.

Imaging Inflammation and Infection in the Gastrointestinal Tract

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.