Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia.

BACKGROUND: Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia. METHODS: Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia. RESULTS: 16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia. CONCLUSIONS: Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

A comprehensive metabolomics analysis of volatile and non-volatile compounds in matcha processed from different tea varieties.

Tea varieties play a crucial role on the quality formation of matcha. This research aimed to examine the impact of four specific tea plant varieties (Okumidori, Longjing 43, Zhongcha108, and E'Cha 1) on various aspects of matcha, including sensory evaluation, major components, color quality, volatile and non-volatile metabolomic profiles. The findings revealed that the levels of tea polyphenols, ester catechins, nonester catechins, and amino acids varied among these four varieties. Notably, 177 significant different metabolites, such as phenolic acids, flavonoids, tannins, alkaloids were identified among 1383 non-volatile compounds. In addition, 97 key aroma-active compounds were identified based on their odor activity value exceeding 1. Aldehydes, heterocyclic compounds, and ketones were closely associated with the formation of volatile metabolites. Overall, this study enhances our understanding of how different tea plant varieties impact the quality of matcha, and can provide valuable guidance for improving matcha varieties in a favorable direction.

GC-MS and LC-MS/MS metabolomics revealed dynamic changes of volatile and non-volatile compounds during withering process of black tea.

High-performance liquid chromatography (HPLC), headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and ultra-high performance liquid chromatography-Q-Exactive HF-X mass spectrometer (UHPLC-Q-Exactive HF/MS) were carried out to reveal dynamic changes of volatile and non-volatile compounds during the withering process of black tea. A total of 118 volatile organic compounds (VOCs) and 648 metabolites were identified in fresh and withered tea-leaves, respectively. Among them, 47 VOCs (OAV > 1.0) for the aroma formation, and 46 characteristic metabolites (VIP > 1.50, p < 0.01) selected through orthonormal partial least squares-discriminant analysis, indicated the withering contribution during black tea processing. Overall, the withering promoted alcohols, aldehydes, phenols, heterocyclic oxygen, hydrocarbons and halogenated hydrocarbons through relevant hydrolyzation, decomposition, terpene synthesis, and O-methylation. The hydrolyzation, O-methylation, condensation and N-acylation of kaempferol glycosides, quercetin glycosides, ester catechins, and gallic acid generated the accumulation of methoxyl flavonoids and flavonoid glucosides, dihydrokaempferol, syringic acid, theaflavins, and N-acylated amino acids, respectively.

New insights into the mechanisms of high-fat diet mediated gut microbiota in chronic diseases.

High-fat diet (HFD) has been recognized as a primary factor in the risk of chronic disease. Obesity, diabetes, gastrointestinal diseases, neurodegenerative diseases, and cardiovascular diseases have long been known as chronic diseases with high worldwide incidence. In this review, the influences of gut microbiota and their corresponding bacterial metabolites on the mechanisms of HFD-induced chronic diseases are systematically summarized. Gut microbiota imbalance is also known to increase susceptibility to diseases. Several studies have proven that HFD has a negative impact on gut microbiota, also exacerbating the course of many chronic diseases through increased populations of Erysipelotrichaceae, facultative anaerobic bacteria, and opportunistic pathogens. Since bile acids, lipopolysaccharide, short-chain fatty acids, and trimethylamine N-oxide have long been known as common features of bacterial metabolites, we will explore the possibility of synergistic mechanisms among those metabolites and gut microbiota in the context of HFD-induced chronic diseases. Recent literature concerning the mechanistic actions of HFD-mediated gut microbiota have been collected from PubMed, Google Scholar, and Scopus. The aim of this review is to provide new insights into those mechanisms and to point out the potential biomarkers of HFD-mediated gut microbiota.

The investigation on the characteristic metabolites of Lactobacillus plantarum RLL68 during fermentation of beverage from by-products of black tea manufacture.

At present, lactic acid bacteria (LAB) fermentation is commonly considered as an effective strategy to remarkably drive the improvement of flavor and nutritional value, and extend shelf-life of fermented foods. In this study, the by-product of tea manufacture, including broken tea segments and tea stalk, was used to produce fermented tea beverages. In addition, the residual components of matrices and bacterial metabolites were measured, as well as the sensory quality of the beverage was evaluated. Subsequently, the determination of monosaccharides, volatile aroma profile, free amino acids, biogenic amines and organic acids, and several functional substances involving γ-aminobutyric acid (GABA), polyphenols, caffeine and L-theanine were carried out. The results revealed that glucose, fructose, mannose and xylose are principal carbon source of Lactobacillus plantarum RLL68 during the fermentation; moreover, the abundance of aromatic substances is varied dramatically and the characteristic flavors of the beverages, particularly fermentation for 48 h and 72 h, are imparted with sweet and fruity odor on the basis of initial nutty and floral odor; Meanwhile, the organoleptic qualities of fermented beverages is also enhanced. Furthermore, the levels of organic acids and GABA are elevated, while the bitter amino acids, as well as some bioactive substances including tea polyphenols and L-theanine are declined; Besides, the caffeine level almost remains constant, and quite low levels of various biogenic amines are also observed. The results of this study will provide the theoretical basis to steer and control the flavor and quality of the fermented tea beverages in the future.

Characterization of a lactic acid bacteria using branched-chain amino acid transaminase and protease from Jinhua Ham and application in myofibrillar protein model.

This study was to screen a strain with both branched-chain amino acid transaminase (BCAT) and protease activities for producing methyl-branched flavor compounds in a myofibrillar protein extract model. Lactic acid bacteria (LAB) was isolated from Jinhua ham and screened with BCAT activity by an electrochemical sensor and protease activity by the agar plate method. In the culture medium, strain L6 was selected with high utilization rate and characteristic metabolites content of branched-chain amino acids (BCAAs), and identified as Lactobacillus fermentum YZU-06 (L. fermentum). Compared with the previously reported L. plantarum, L. fermentum exhibited an excellent capacity of hydrolyzing myofibrillar protein with the higher contents of free amino acids, peptides and small molecular weight proteins. Moreover, L. fermentum group presented more BCAAs metabolites of 2-methylbutanal and 3-methylbutanal than that of L. plantarum group. In conclusion, L. fermentum YZU-06 is a promising starter culture to improve the flavor of fermented meat products.

Comparative Analysis of Fruit Metabolome Using Widely Targeted Metabolomics Reveals Nutritional Characteristics of Different Rosa roxburghii Genotypes.

The fruits of Rosa roxburghii (R. roxburghii) Tratt., which are rich in bioactive compounds, provide numerous health benefits, yet the overall metabolism of R. roxburghii fruits and the metabolic profiles among different genotypes of R. roxburghii fruits are not fully understood. In the research, we used ultra-performance liquid chromatography/tandem mass spectrometry analysis to identify and quantify metabolites including phenolic acids, amino acids, and organic acids in six R. roxburghii genotypes; a total of 723 metabolites were identified. Comparative analysis showed some different characteristic metabolites in each genotype. Moreover, flavonoids, triterpenoids, and phenolic acids were significantly correlated with the antioxidant capacity of the fruit extract. Our results suggest that R. roxburghii fruits have rich bioactive metabolites beneficial to human health and that Rr-7 and Rr-f have more potential to be used as medicinal material or functional food than other genotypes. This research provides helpful information for developing new functional foods of R. roxburghii genotypes.

The application of metabolomics analysis in the research of gestational diabetes mellitus and preeclampsia.

AIM: The aim of the study was to investigate the difference of the serum metabolic profile between gestational diabetes mellitus (GDM) patients and preeclampsia (PE) patients, to establish the disease differentiation model and to find characteristic metabolites, in order to provide a new idea for the occurrence, development and treatment of the disease. METHODS: Twenty-nine patients with GDM group and 29 PE group who were examined in Tianjin No. 3 Central Hospital from March 2018 to August 2018 were enrolled as case group, and 29 normal pregnant women were selected as control group. All the serum samples were analyzed by using the ultra-performance liquid chromatography and mass spectrometry. Based on the multivariate statistical analysis method of pattern recognition, we screened out and identified the differential characteristic metabolites. RESULTS: The serum metabolic profile model of GDM group and PE group was successfully constructed. A total of nine characteristic metabolites were screened and identified in this study, including LPC 18:0, LPC 22:6, LPC 16:0, (S)-14-methylhexadecanoic acid, behenic acid, palmitic acid, sphingosine, phytosphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone. Among them, six characteristic metabolites which were LPC 18:0, LPC 22:6, behenic acid, palmitic acid, sphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone all had a significant statistical difference among GDM, PE and normal pregnancy groups (P < 0.05). CONCLUSION: The construction of metabolic profile discriminant model has a strong ability to differentiate GDM patients from PE pregnant women. The screened characteristic metabolites can early reflect the disorder of lipid, calcium and phosphorus metabolism of patients, and provide reference and help for the discussion of the occurrence, development and treatment of diseases.

Gene co-expression network analysis reveals coordinated regulation of three characteristic secondary biosynthetic pathways in tea plant (Camellia sinensis).

BACKGROUND: The leaves of tea plants (Camellia sinensis) are used to produce tea, which is one of the most popular beverages consumed worldwide. The nutritional value and health benefits of tea are mainly related to three abundant characteristic metabolites; catechins, theanine and caffeine. Weighted gene co-expression network analysis (WGCNA) is a powerful system for investigating correlations between genes, identifying modules among highly correlated genes, and relating modules to phenotypic traits based on gene expression profiling. Currently, relatively little is known about the regulatory mechanisms and correlations between these three secondary metabolic pathways at the omics level in tea. RESULTS: In this study, levels of the three secondary metabolites in ten different tissues of tea plants were determined, 87,319 high-quality unigenes were assembled, and 55,607 differentially expressed genes (DEGs) were identified by pairwise comparison. The resultant co-expression network included 35 co-expression modules, of which 20 modules were significantly associated with the biosynthesis of catechins, theanine and caffeine. Furthermore, we identified several hub genes related to these three metabolic pathways, and analysed their regulatory relationships using RNA-Seq data. The results showed that these hub genes are regulated by genes involved in all three metabolic pathways, and they regulate the biosynthesis of all three metabolites. It is notable that light was identified as an important regulator for the biosynthesis of catechins. CONCLUSION: Our integrated omics-level WGCNA analysis provides novel insights into the potential regulatory mechanisms of catechins, theanine and caffeine metabolism, and the identified hub genes provide an important reference for further research on the molecular biology of tea plants.

Metabolic profiling of hepatitis B virus-related hepatocellular carcinoma with diverse differentiation grades.

The most effective diagnostic tool for the majority of hepatocellular carcinoma (HCC) patients is determining the differentiation grade of their tumors. However liver biopsies, which are currently the most effective way of determining tumor differentiation grade, have several limitations. The present study was designed to select serum characteristic metabolites that correlate with the differentiation grades of hepatitis B virus (HBV)-related HCC, and so could be used in the clinic as a non-invasive method of differentiating patients with different grades of HCC. A total of 58 patients with HBV-related HCC were included in the present study, and divided into three groups according to their tumor differentiation grade. A further 20 patients with HBV-related liver cirrhosis and 19 healthy volunteers were enrolled. Ultra-performance liquid chromatography-mass spectrometry was used to analyze endogenous metabolites. Multivariate statistical analysis was used to examine the data using MZmine 2.0 software. The 14 metabolites that were highly correlated with specific differentiation grades of HCC were then selected for additional study. Receiver operator characteristic curve analysis was used to evaluate their clinical value. In total, 5 metabolites were finally identified, including lysophosphatidylcholine (16:0), oleamide, monoglyceride (0:0/15:0/0:0), lysophosphatidylcholine (18:0) and lysophosphatidylcholine [22:5(7Z,10Z,13Z,16Z,19Z)]. All these metabolites exhibited an excellent ability to distinguish different types of HCC with various differentiation grades and the area under the curve of these metabolites was up to 0.942, showing promising clinical value.

Metabolic analysis of immune suppression mice based on NMR / 中国药理学通报

Aim Toanalyzethecharacteristicsofser-um metabolites in immune suppression mice by using 1HNMR-basedmetabonomics.Methods Twentymale Kunming(KM)mice were randomly divided into two groups:normal group and cyclophosphamide (Cy ) model group.Cy model group was given Cy to induce immune suppression.NMR technology was used to find out the variability of serum metabolites by the method ofOPLS-DA.Results Comparedwithnormalgroup, Cy model group showed a significant decrease in the serum levels of glutamine,glycolprotein,unsaturated lipid,VLDL,LDL,acetone and showed a significant increase in the serum levels of leucine,alanine,tyro-sine, histidine, lactate, glycine, creatine, me-theionine,valine,β-hydrocxy butyrate and carnitine. Conclusions Immunesuppressionmiceareimbal-anced in metabolic network.These findings indicate that lipid metabolism-related metabolites are de-creased,however,β-oxidation of fatty acid,proteoly-sis and glycolysis level are increased.

Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma.

AIM: To select characteristic endogenous metabolites in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients and to identify their molecular mechanism and potential clinical value. METHODS: An ultra performance liquid chromatography and linear trap quadrupole-Orbitrap XL-mass spectrometry platform was used to analyze endogenous metabolites in the homogenate of central tumor tissue, adjacent tissue and distant tissue obtained from 10 HBV-related HCC patients. After pretreatment with Mzmine software, including peak detection, alignment and normalization, the acquired data were treated with Simca-P+software to establish multivariate statistical analysis based on a pattern recognition technique and characteristic metabolites highly correlated with changing trends in metabolic profiling were selected and further identified. RESULTS: Based on data acquired using Mzmine software, a principal component analysis model (R2X = 66.9%, Q2 = 21.7%) with 6 principal components and an orthogonal partial least squares discriminant analysis model (R2X = 76.5%, R2Y = 93.7%, Q2 = 68.7%) with 2 predicted principal components and 5 orthogonal principal components were established in the three tissue groups. Forty-nine ions were selected, 33 ions passed the 2 related samples nonparametric test (P < 0.05) and 14 of these were further identified as characteristic metabolites that showed significant differences in levels between the central tumor tissue group and distant tumor tissue group, including 9 metabolites (L-phenylalanine, glycerophosphocholine, lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate) which had been reported as serum metabolite biomarkers for HCC diagnosis in previous research, and 5 metabolites (beta-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide) which had not been reported before. CONCLUSION: Characteristic metabolites and metabolic pathways highly related to HCC pathogenesis and progression are identified through metabolic profiling analysis of HCC tissue homogenates.