Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota.

Emerging evidence suggests that modulation of gut microbiota by dietary fibre may offer solutions for metabolic disorders. In a randomized placebo-controlled crossover design trial (ChiCTR-TTRCC-13003333) in 37 participants with overweight or obesity, we test whether resistant starch (RS) as a dietary supplement influences obesity-related outcomes. Here, we show that RS supplementation for 8 weeks can help to achieve weight loss (mean -2.8 kg) and improve insulin resistance in individuals with excess body weight. The benefits of RS are associated with changes in gut microbiota composition. Supplementation with Bifidobacterium adolescentis, a species that is markedly associated with the alleviation of obesity in the study participants, protects male mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota alter the bile acid profile, reduce inflammation by restoring the intestinal barrier and inhibit lipid absorption. We demonstrate that RS can facilitate weight loss at least partially through B. adolescentis and that the gut microbiota is essential for the action of RS.

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.

Maturation of the gut metabolome during the first year of life in humans.

The gut microbiota is involved in the production of numerous metabolites that maintain host wellbeing. The assembly of the gut microbiome is highly dynamic, and influenced by many postnatal factors, moreover, little is known about the development of the gut metabolome. We showed that geography has an important influence on the microbiome dynamics in the first year of life based on two independent cohorts from China and Sweden. Major compositional differences since birth were the high relative abundance of Bacteroides in the Swedish cohort and Streptococcus in the Chinese cohort. We analyzed the development of the fecal metabolome in the first year of life in the Chinese cohort. Lipid metabolism, especially acylcarnitines and bile acids, was the most abundant metabolic pathway in the newborn gut. Delivery mode and feeding induced particular differences in the gut metabolome since birth. In contrast to C-section newborns, medium- and long-chain acylcarnitines were abundant at newborn age only in vaginally delivered infants, associated by the presence of bacteria such as Bacteroides vulgatus and Parabacteroides merdae. Our data provide a basis for understanding the maturation of the fecal metabolome and the metabolic role of gut microbiota in infancy.

PFO5DoDA disrupts hepatic homeostasis primarily through glucocorticoid signaling inhibition.

Legacy per- and polyfluoroalkyl substances (PFASs) are a worldwide health concern due to their potential bioaccumulation and toxicity in humans. A variety of perfluoroether carboxylic acids (PFECAs) have been developed as next-generation replacements of legacy PFASs. However, information regarding their possible environmental and human health risks is limited. In the present study, we explored the effects of PFECAs on mice based on long-term exposure to environmentally relevant doses of perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoDA). Results showed that PFECAs exposure suppressed many cellular stress signals and resulted in hepatomegaly. PFO5DoDA acted as an agonist of the peroxisome proliferator-activated receptor (PPAR) in vitro and modulated PPAR-dependent gene expression in the liver. Importantly, PFECAs had an inhibitory effect on the glucocorticoid receptor (GR), which may contribute to the extensive suppression of stress signals. Of note, the GR suppression induced by PFECAs was not reported by legacy perfluorooctanoic acid (PFOA). PFO5DoDA-induced changes in both GR and PPAR signals remodeled hepatic metabolic profiles, including decreased fatty acids and amino acids and increased ß-oxidation. Mechanistically, PFO5DoDA inhibited GR transactivation by degradation of GR proteins. Our results emphasize the potential risk of PFECAs to human health, which were introduced to ease concerns regarding legacy PFASs.

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.

Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites.

Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MS n-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS2 data, including tR, MS1, and MS2 information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS n-based nontargeted metabolomics study.