Fecal short chain fatty acids modify therapeutic effects of sleeve gastrectomy.

Aims: We aimed to investigate changes of fecal short chain fatty acids (SCFAs) and their association with metabolic benefits after sleeve gastrectomy (SG). Specifically, whether pre-surgery SCFAs modify surgical therapeutic effects was determined. Methods: 62 participants with measurements of fecal SCFAs and metabolic indices before and 1, 3, 6 months after SG were included. Changes of fecal SCFAs and their association with post-surgery metabolic benefits were calculated. Then, participants were stratified by medians of pre-surgery fecal SCFAs and modification effects of pre-surgery fecal SCFAs on surgical therapeutic effects were investigated, through calculating interaction of group by surgery. Results: Fecal SCFAs were markedly changed by SG. Changes of propionate and acetate were positively correlated with serum triglycerides and total cholesterol, respectively. Notably, high pre-surgery fecal hexanoate group showed a better effect of SG treatment on lowering body weight (P=0.01), BMI (P=0.041) and serum triglycerides (P=0.031), and low pre-surgery fecal butyrate had a better effect of SG on lowering ALT (P=0.003) and AST (P=0.019). Conclusion: Fecal SCFAs were changed and correlated with lipid profiles improvement after SG. Pre-surgery fecal hexanoate and butyrate were potential modifiers impacting metabolic benefits of SG.

Metabolic Reprogramming and Risk Stratification of Hepatocellular Carcinoma Studied by Using Gas Chromatography-Mass Spectrometry-Based Metabolomics.

Hepatocellular carcinoma (HCC) displays a high degree of metabolic and phenotypic heterogeneity and has dismal prognosis in most patients. Here, a gas chromatography-mass spectrometry (GC-MS)-based nontargeted metabolomics method was applied to analyze the metabolic profiling of 130 pairs of hepatocellular tumor tissues and matched adjacent noncancerous tissues from HCC patients. A total of 81 differential metabolites were identified by paired nonparametric test with false discovery rate correction to compare tumor tissues with adjacent noncancerous tissues. Results demonstrated that the metabolic reprogramming of HCC was mainly characterized by highly active glycolysis, enhanced fatty acid metabolism and inhibited tricarboxylic acid cycle, which satisfied the energy and biomass demands for tumor initiation and progression, meanwhile reducing apoptosis by counteracting oxidative stress. Risk stratification was performed based on the differential metabolites between tumor and adjacent noncancerous tissues by using nonnegative matrix factorization clustering. Three metabolic clusters displaying different characteristics were identified, and the cluster with higher levels of free fatty acids (FFAs) in tumors showed a worse prognosis. Finally, a metabolite classifier composed of six FFAs was further verified in a dependent sample set to have potential to define the patients with poor prognosis. Together, our results offered insights into the molecular pathological characteristics of HCC.

Deep Annotation of Hydroxycinnamic Acid Amides in Plants Based on Ultra-High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Its In Silico Database.

Hydroxycinnamic acid amides (HCAAs), diversely distributed secondary metabolites in plants, play essential roles in plant growth and developmental processes. Most current approaches can be used to analyze a few known HCAAs in a given plant. A novel method for comprehensive detection of plant HCAAs is urgently needed. In this study, a deep annotation method of HCAAs was proposed on the basis of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) and its in silico database of HCAAs. To construct an in silico UHPLC-HRMS HCAAs database, a total of 846 HCAAs were generated from the most common phenolic acid and polyamine/aromatic monoamine substrates according to possible biosynthesis reactions, which represent the structures of plant-specialized HCAAs. The characteristic MS/MS fragmentation patterns of HCAAs were extracted from reference mixtures. Four quantitative structure-retention relationship (QSRR) models were developed to predict retention times of mono-trans-HCAAs (aromatic amines conjugates), mono-trans-HCAAs (aliphatic amines conjugates), bis-HCAAs, and tris-HCAAs. The developed method was applied for identifying HCAAs in seeds (maize, wheat, and rice), roots (rice), and leaves (rice and tobacco). A total of 79 HCAAs were detected: 42 of them were identified in these plants for the first time, and 20 of them have never been reported to exist in plants. The results showed that the developed method can be used to identify HCAAs in a plant without prior knowledge of HCAA distributions. To the best of our knowledge, it is the first UHPLC-HRMS database developed for effective deep annotation of HCAAs from nontargeted UHPLC-HRMS data. It is useful for the identification of novel HCAAs in plants.