To investigate the modulatory effect of oolong tea polyphenols (OTP) on intestinal microbiota, OTP was prepared by column chromatography and its influence on the gut flora structure was analyzed by high-throughput sequencing with a human flora-associated high fat diet (HFD) induced obesity mouse model. We observed a robust increase in bacterial biodiversity and the abundance of genera known to be butyrate- and acetate-producing bacteria. A large increase in Bacteroidetes with a decrease in Firmicutes was observed after the administration of OTP for 4 weeks, and the corresponding decrease in the Firmicutes/Bacteroidetes ratio reflected the positive modulatory effect of OTP on the intestinal microbiota. In addition, KEGG pathways for the biosynthesis of amino acids, carbon metabolism, and the ribosome were among the most differentially expressed genes after OTP intervention. The current study revealed that OTP rich in tea catechins, especially O-methylated derivatives, may have prebiotic-like activity and can be used as a functional food component with potential therapeutic utility to prevent obesity-related metabolic disorders by manipulating the intestinal microbiota.
In the present study, polyphenols from green tea (GTP), oolong tea (OTP) and black tea (BTP) were prepared by extraction with hot water and polyamide column chromatography. In antioxidant assay in vitro, each tea polyphenols exhibited potential activity; the intestinal absorption of GTP, OTP and BTP was investigated individually by Caco-2 transwell system, and each sample was poorly transported, illustrating a low transport rate for tea polyphenols through cell monolayers. The effects of GTP, OTP and BTP on human intestinal microbiota were also evaluated, and each sample induced the proliferation of certain beneficial bacteria and inhibited - and . Moreover, the short-chain fatty acids (SCFA) produced in cultures with tea polyphenols were relatively higher. Together, these results suggested GTP, OTP and BTP may modulate the intestinal flora and generate SCFA, and contribute to the improvements of human health.
Diabetic nephropathy (DN) is a major cause of morbidity and mortality in diabetic patients. To prevent the development of this disease and to improve advanced kidney injury, effective therapies directed toward the key molecular target are required. Grape seed proanthocyanidin extracts (GSPE) have been reported to be effective in treating DN, while little is known about the functional protein changes. In this study, we used streptozotocin (STZ) to induce diabetic rats. GSPE (250 mg/kg body weight/day) were administrated to diabetic rats for 24 weeks. Serum glucose, glycated hemoglobin, and advanced glycation end products were determined. Consequently, 2-D difference gel electrophoresis and mass spectrometry were used to investigate kidney protein profiles among the control, untreated and GSPE treated diabetic rats. Twenty-five proteins were found either up-regulated or down-regulated in the kidneys of untreated diabetic rats. Only nine proteins in the kidneys of diabetic rats were found to be back-regulated to normal levels after GSPE therapy. These back-regulated proteins are involved in oxidative stress, glycosylation damage, and amino acids metabolism. Our findings might help to better understanding of the mechanism of DN, and provide novel targets for estimating the effects of GSPE therapy.
Metabolite identification remains a bottleneck in mass spectrometry (MS)-based metabolomics. Currently, this process relies heavily on tandem mass spectrometry (MS/MS) spectra generated separately for peaks of interest identified from previous MS runs. Such a delayed and labor-intensive procedure creates a barrier to automation. Further, information embedded in MS data has not been used to its full extent for metabolite identification. Multimers, adducts, multiply charged ions, and fragments of given metabolites occupy a substantial proportion (40-80%) of the peaks of a quantitation result. However, extensive information on these derivatives, especially fragments, may facilitate metabolite identification. We propose a procedure with automation capability to group and annotate peaks associated with the same metabolite in the quantitation results of opposite modes and to integrate this information for metabolite identification. In addition to the conventional mass and isotope ratio matches, we would match annotated fragments with low-energy MS/MS spectra in public databases. For identification of metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. The accuracy and effectiveness of the procedure were evaluated using one public and two in-house liquid chromatography-mass spectrometry (LC-MS) data sets. The procedure accurately identified 89% of 28 standard metabolites with derivative ions in the data sets. With respect to effectiveness, the procedure confidently identified the correct chemical formula of at least 42% of metabolites with derivative ions via MS/MS spectrum, characteristic fragment, and common substructure matches. The confidence level was determined according to the fulfilled identification criteria of various matches and relative retention time.
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