This study aimed to elucidate the potential regulatory mechanism of mung bean peptides (MBPs) on glucolipid metabolism in insulin-resistant mice induced by high-fat diet (HFD) using untargeted serum metabolomics, enzyme linked immunosorbent assay (ELISA), intraperitoneal injection glucose tolerance test (IPGTT), insulin tolerance test (IPITT), and hematoxylin-eosin staining (H&E). The regulatory effect of MBPs for alleviating insulin resistance was studied by measuring body weight, fasting blood glucose (FBG) and serum insulin levels, C-Peptide levels, inflammatory and antioxidant factors, and histopathological observation of C57BL/6 mice. The experimental results showed that dietary intervention with MBPs (245 mg/kg/d) for 5 weeks significantly relieved insulin resistance in HFD mice. The body weight, insulin resistance index, and the levels of FBG, C-Peptide, IL-6, TNF-α, and MDA in the serum of HFD mice significantly decreased (P < 0.05). Conversely, SOD content and pancreatic β cell function index significantly increased (P < 0.05), and the damaged pancreatic tissue was repaired. One biomarker associated with insulin resistance was glycine. In addition, there were four important differential metabolites: pyroglutamate, D-glutamine, aminoadipic acid, and nicotinamide, involved in 12 metabolic pathway changes. It was found that MBPs may regulate amino acid, glycerol phospholipid, fatty acid, alkaloid, and nicotinamide metabolism to regulate the metabolic profile of HFD mice in a beneficial direction.
BackgroundStorage is an essential part of brown rice circulation. During the storage process, the metabolic activity of brown rice is still ongoing, and long‐term storage leads to the deterioration of brown rice. Metabolomics analysis was performed using gas chromatography–mass spectrometry to investigate the changes in metabolites of brown rice after storage at 18 °C for 12 months.ResultsIn terms of quantity, sugar, fatty acids, and other metabolites in brown rice decreased after storage, and alcohols, aldehydes, phenols, and amines increased. A total of 34 differential metabolites were screened. In terms of contents, carbohydrates, amino acids, and fatty acids of brown rice decreased after storage, while those of sugar alcohol, amines, and aldehydes increased after storage. Cluster analysis of the samples at zero storage time revealed that the metabolites expressed least became highly expressed after storage and those expressed highly became low after storage. Metabolic pathway analysis showed that storage significantly influenced the lipid metabolism in brown rice. Palmitoleic acid, cholesterol, linoleic acid, and lauric acid are four key metabolites in lipid metabolism during storage of brown rice.ConclusionSignificant changes occurred in quantity and type of brown rice metabolites after storage. Storage has the greatest effect on lipids. Storage caused a ‘reverse change’ in the metabolites content of brown rice. The results obtained may help in understanding the changes in metabolites profile and delaying of the quality deterioration of brown rice during storage.
Metabolomic studies were carried out using gas chromatography and mass spectrometry (GC‐MS) on Daohuaxiang variety rice (Oryza sativa L.) from the Wuchang Geographical Indication Rice Protection Area in Heilongjiang Province, to investigate the effects of storage on brown rice metabolism. The data were subjected to principal component analysis (PCA), orthogonal partial least squares‐discriminant analysis (OPLS‐DA), and cluster analysis using software such as SIMCA. Analysis of the samples led to the identification of a total of 160 metabolites. No significant differences were found in the amount of metabolites before and after storage. A total of 31 differential metabolites were screened, and the changes in metabolite content showed a “reverse change” overall. Storage significantly changed the content of various metabolites in rice, with fatty acids impacted most significantly. Metabolic pathway analysis revealed that fatty acid biosynthesis is a key metabolic pathway in rice storage. The degradation of brown rice quality caused by storage is closely related to the composition and content of its metabolites, and that change in lipid content significantly affects brown rice quality during storage.
This study aimed to develop a nomogram for predicting the progression-free survival (PFS) of testicular germ cell tumors (TGCT) patients based on DNA methylation signature and clinicopathological characteristics. The DNA methylation profiles, transcriptome data, and clinical information of TGCT patients were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a prognostic CpG sites-derived risk signature. Differential expression analysis, functional enrichment analysis, immunoinfiltration analysis, chemotherapy sensitivity analysis, and clinical feature correlation analysis were performed to elucidate the differences among risk groups. A prognostic nomogram integrating CpG sites-derived risk signature and clinicopathological features was further established and evaluated likewise. A risk score model based on 7 CpG sites was developed and found to exhibit significant differences among different survival, staging, radiotherapy, and chemotherapy subgroups. There were 1452 differentially expressed genes between the high- and low-risk groups, with 666 being higher expressed and 786 being lower expressed. Genes highly expressed were significantly enriched in immune-related biological processes and related to T-cell differentiation pathways; meanwhile, down-regulated genes were significantly enriched in extracellular matrix tissue organization-related biological processes and involved in multiple signaling pathways such as PI3K-AKT. As compared with the low-risk group, patients in the high-risk group had decreased lymphocyte infiltration (including T-cell and B-cell) and increased macrophage infiltration (M2 macrophages). They also showed decreased sensitivity to etoposide and bleomycin chemotherapy. Three clusters were obtained by consensus clustering analysis based on the 7 CpG sites and showed distinct prognostic features, and the risk scores in each cluster were significantly different. Multivariate Cox regression analysis found that the risk scores, age, chemotherapy, and staging were independent prognostic factors of PFS of TGCT, and the results were used to formulate a nomogram model that was validated to have a C-index of 0.812. Decision curve analysis showed that the nomogram model was superior to other strategies in the prediction of PFS of TGCT. In this study, we successfully established CpG sites-derived risk signature, which might serve as a useful tool in the prediction of PFS, immunoinfiltration, and chemotherapy sensitivity for TGCT patients.
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