Background: Gut microbiota are known to be closely related to irritable bowel syndrome (IBS). However, not much is known about characteristic fecal metabolic profiles of IBS. We aimed to characterize fecal metabolites in patients with IBS with predominant diarrhea (IBS-D) using 1 H-nuclear magnetic resonance ( 1 H-NMR) spectroscopy. Methods:In this study, we enrolled 29 patients diagnosed with IBS-D according to the Rome IV criteria, 22 healthy controls (HC) and 11 HC administered laxatives (HC-L) in the age group of 20-69 year. The usual diet of the patients and HC was maintained, their fecal samples were collected and investigated by NMR-based global metabolic profiling coupled with multivariate statistical analysis. Results:We detected 55 metabolites in 1 H-NMR spectra of fecal samples: four amines, 16 amino acids, six fatty acids, eight organic acids, three sugars, and 18 other compounds. Orthogonal partial least square-discriminant analysis derived score plots showed clear separation between the IBS-D group and the HC and HC-L groups.Among the 55 metabolites identified, we found five disease-relevant potential biomarkers distinguishing the IBS-D from the HC, namely, cadaverine, putrescine, threonine, tryptophan, and phenylalanine. Conclusions:The patients with IBS-D were clearly differentiated from the HC and HC-L by fecal metabolite analysis using 1 H-NMR spectroscopy, and five fecal metabolites characteristic of IBS-D were found. The findings of this study could be used to develop alternative and complementary diagnostic methods and as a source of fundamental information for developing novel therapies for IBS-D.
The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm–1 region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans.
In this study, the effects of coronatine treatment on the growth, comprehensive metabolic profiles, and productivity of bioactive compounds, including phenolics and phytosterols, in whole plant cultures of Lemna paucicostata were investigated using gas chromatography-mass spectrometry (GC-MS) coupled with multivariate statistical analysis. To determine the optimal timing of coronatine elicitation, coronatine was added on days 0, 23, and 28 after inoculation. The total growth of L. paucicostata was not significantly different between the coronatine treated groups and the control. The coronatine treatment in L. paucicostata induced increases in the content of hydroxycinnamic acids, such as caffeic acid, isoferulic acid, ρ-coumaric acid, sinapic acid, and phytosterols, such as campesterol and β-sitosterol. The productivity of these useful metabolites was highest when coronatine was added on day 0 and harvested on day 32. These results suggest that coronatine treatment on day 0 activates the phenolic and phytosterol biosynthetic pathways in L. paucicostata to a greater extent than in the control. To the best of our knowledge, this is the first report to investigate the effects of coronatine on the alteration of metabolism in L. paucicostata based on GC-MS profiling. The results of this research provide a foundation for designing strategies for enhanced production of useful metabolites for pharmaceutical and nutraceutical industries by cultivation of L. paucicostata.
With the increase in soybean trade between countries, the intentional mislabeling of the origin of soybeans has become a serious problem worldwide. In this study, metabolic profiling of soybeans from the Republic of Korea and China was performed by nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to predict the geographical origin of soybeans. The optimal orthogonal partial least squares-discriminant analysis (OPLS-DA) model was obtained using total area normalization and unit variance (UV) scaling, without applying the variable influences on projection (VIP) cut-off value, resulting in 96.9% sensitivity, 94.4% specificity, and 95.6% accuracy in the leave-one-out cross validation (LOO-CV) test for discriminating between Korean and Chinese soybeans. Soybeans from the northeastern, middle, and southern regions of China were successfully differentiated by standardized area normalization and UV scaling with a VIP cut-off value of 1.0, resulting in 100% sensitivity, 91.7%–100% specificity, and 94.4%–100% accuracy in a LOO-CV test. The methods employed in this study can be used to obtain essential information for the authentication of soybean samples from diverse geographical locations in future studies.
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