Dextran Sulfate Sodium Inhibits Alanine Synthesis in Caco-2 Cells

To understand and characterize the pathogenic mechanisms of inflammatory bowel disease, dextran sulfate sodium (DSS) has been used to induce acute and chronic colitis in animal models by causing intestinal epithelium damage. The mechanism of action of DSS in producing this outcome is not well understood. In an effort to understand how DSS might impact epithelial cell metabolism, we studied the intestinal epithelial cell line Caco-2 incubated with 1% DSS over 56 hours using 1H NMR spectroscopy. We observed no difference in cell viability as compared to control cultures, and an approximately 1.5-fold increase in IL-6 production upon incubation with 1% DSS. The effect on Caco-2 cell metabolism as measured through changes in the concentration of metabolites in the cell supernatant included a three-fold decrease in the concentration of alanine. Given that the concentrations of other amino acids in the cell culture supernatant were not different between treated and control cultures over 56 hours suggest that DSS inhibits alanine synthesis, specifically alanine aminotransferase, without affecting other key metabolic pathways. The importance of alanine aminotransferase in inflammatory bowel disease is discussed

NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system

BACKGROUND: Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample. METHODOLOGY/PRINCIPAL FINDINGS: 1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity. CONCLUSIONS/SIGNIFICANCE: These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes

Metabolite signature of Candidatus Liberibacter asiaticus infection in two citrus varieties.

Huanglongbing (HLB), also known as Citrus Greening Disease, is caused by the bacterium 'Candidatus Liberibacter asiaticus' (CLas) and is a serious threat to the citrus industry. To understand the effect of CLas infection on the citrus metabolome, juice from healthy (n = 18), HLB-asymptomatic (n = 18), and HLB-symptomatic Hamlin (n = 18), as well as from healthy (n = 18) and HLB-symptomatic (n = 18) Valencia sweet oranges (from southern and eastern Florida) were evaluated using (1)H NMR-based metabolomics. Differences in the concentration of several metabolites including phenylalanine, histidine, limonin, and synephrine between control or asymptomatic fruit and symptomatic fruit were observed regardless of the citrus variety or location. There were no clear differences between the metabolite profiles of Hamlin fruits classified by PCR as asymptomatic and control, suggesting that some of the control fruit may have been infected. Taken together, these data indicate that infection due to CLas presents a strong metabolic response that is observed across different cultivars and regions, suggesting the potential for generation of metabolite-based biomarkers of CLas infection

Cross-talk between E. coli strains and a human colorectal adenocarcinoma-derived cell line.

Although there is great interest in the specific mechanisms of how gut microbiota modulate the biological processes of the human host, the extent of host-microbe interactions and the bacteria-specific metabolic activities for survival in the co-evolved gastrointestinal environment remain unclear. Here, we demonstrate a comprehensive comparison of the host epithelial response induced by either a pathogenic or commensal strain of Escherichia coli using a multi-omics approach. We show that Caco-2 cells incubated with E. coli display an activation of defense response genes associated with oxidative stress. Indeed, in the bacteria co-culture system, the host cells experience an altered environment compared with the germ-free system that includes reduced pH, depletion of major energy substrates, and accumulation of fermentation by-products. Measurement of intracellular Caco-2 cell metabolites revealed a significantly increased lactate concentration, as well as changes in TCA cycle intermediates. Our results will lead to a deeper understanding of acute microbial-host interactions

Cross-talk between E. coli strains and a human colorectal adenocarcinoma-derived cell line.

Although there is great interest in the specific mechanisms of how gut microbiota modulate the biological processes of the human host, the extent of host-microbe interactions and the bacteria-specific metabolic activities for survival in the co-evolved gastrointestinal environment remain unclear. Here, we demonstrate a comprehensive comparison of the host epithelial response induced by either a pathogenic or commensal strain of Escherichia coli using a multi-omics approach. We show that Caco-2 cells incubated with E. coli display an activation of defense response genes associated with oxidative stress. Indeed, in the bacteria co-culture system, the host cells experience an altered environment compared with the germ-free system that includes reduced pH, depletion of major energy substrates, and accumulation of fermentation by-products. Measurement of intracellular Caco-2 cell metabolites revealed a significantly increased lactate concentration, as well as changes in TCA cycle intermediates. Our results will lead to a deeper understanding of acute microbial-host interactions

Quantification of Water-Soluble Metabolites in Medicinal Mushrooms Using Proton NMR Spectroscopy.

The water-soluble metabolites in 5 mushrooms were identified and quantified using proton nuclear magnetic resonance (NMR) spectroscopy and software for targeted metabolite detection and quantification. In total, 35 compounds were found in Agaricus brasiliensis, 25 in Taiwanofungus camphoratus, 23 in Ganoderma lucidum (Taiwan) and Lentinus edodes, and 16 in G. lucidum (China). Total amounts of all identified metabolites in A. brasiliensis, T. camphoratus, G. lucidum, G. lucidum (China), and L. edodes were 149,950.51, 12,834.18, 9,549.09, 2,788.41, and 111,726.51 mg/kg dry weight, respectively. These metabolites were categorized into 4 groups: free amino acids and derivatives, carbohydrates, carboxylic acids, and nucleosides. Carbohydrates were the most abundant metabolites among all 4 groups, with mannitol having the highest concentration among all analyzed metabolites (848-94,104 mg/kg dry weight). Principal components analysis (PCA) showed obvious distinction among the metabolites of the 5 different kinds of mushrooms analyzed in this study. Thus PCA could provide an optional analytical way of identifying and recognizing the compositions of flavor products. Furthermore, the results of this study demonstrate that NMRbased metabolomics is a powerful tool for differentiating between various medicinal mushrooms

Quantification of Water-Soluble Metabolites in Medicinal Mushrooms Using Proton NMR Spectroscopy.

The water-soluble metabolites in 5 mushrooms were identified and quantified using proton nuclear magnetic resonance (NMR) spectroscopy and software for targeted metabolite detection and quantification. In total, 35 compounds were found in Agaricus brasiliensis, 25 in Taiwanofungus camphoratus, 23 in Ganoderma lucidum (Taiwan) and Lentinus edodes, and 16 in G. lucidum (China). Total amounts of all identified metabolites in A. brasiliensis, T. camphoratus, G. lucidum, G. lucidum (China), and L. edodes were 149,950.51, 12,834.18, 9,549.09, 2,788.41, and 111,726.51 mg/kg dry weight, respectively. These metabolites were categorized into 4 groups: free amino acids and derivatives, carbohydrates, carboxylic acids, and nucleosides. Carbohydrates were the most abundant metabolites among all 4 groups, with mannitol having the highest concentration among all analyzed metabolites (848-94,104 mg/kg dry weight). Principal components analysis (PCA) showed obvious distinction among the metabolites of the 5 different kinds of mushrooms analyzed in this study. Thus PCA could provide an optional analytical way of identifying and recognizing the compositions of flavor products. Furthermore, the results of this study demonstrate that NMRbased metabolomics is a powerful tool for differentiating between various medicinal mushrooms

Quantification of Water-Soluble Metabolites in Medicinal Mushrooms Using Proton NMR Spectroscopy

The water-soluble metabolites in 5 mushrooms were identified and quantified using proton nuclear magnetic resonance (NMR) spectroscopy and software for targeted metabolite detection and quantification. In total, 35 compounds were found in Agaricus brasiliensis, 25 in Taiwanofungus camphoratus, 23 in Ganoderma lucidum (Taiwan) and Lentinus edodes, and 16 in G. lucidum (China). Total amounts of all identified metabolites in A. brasiliensis, T. camphoratus, G. lucidum, G. lucidum (China), and L. edodes were 149,950.51, 12,834.18, 9,549.09, 2,788.41, and 111,726.51 mg/kg dry weight, respectively. These metabolites were categorized into 4 groups: free amino acids and derivatives, carbohydrates, carboxylic acids, and nucleosides. Carbohydrates were the most abundant metabolites among all 4 groups, with mannitol having the highest concentration among all analyzed metabolites (848-94,104 mg/kg dry weight). Principal components analysis (PCA) showed obvious distinction among the metabolites of the 5 different kinds of mushrooms analyzed in this study. Thus PCA could provide an optional analytical way of identifying and recognizing the compositions of flavor products. Furthermore, the results of this study demonstrate that NMRbased metabolomics is a powerful tool for differentiating between various medicinal mushrooms