MetaboLights

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BQC2pos.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/blk2pos.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/QC03neg.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/CK-5neg.wiffftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/CK-1neg.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/BQC2neg.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/CK-3pos.wiffftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/OB-7pos.wiff.scanftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668/FILES/RAW_FILES/CK-1neg.wiffprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12668To evaluate the rationality of candidate metabolites and to more comprehensively and intuitively display the relationships between samples as well as the differences in metabolite expression patterns across different samples, we performed hierarchical clustering on the expression levels of qualitatively significant differential metabolites for each group of samples. This approach assists us in accurately screening marker metabolites and investigating alterations in related metabolic processes.Generally speaking, when the screened candidate metabolites are reasonable and accurate, samples from the same group can appear in the same cluster through clustering(Cluster). At the same time, metabolites clustered within the same group exhibit similar expression patterns and may be involved in relatively close reaction steps in the metabolic process.Correlation analysis can help measure the degree of correlation between significantly different metabolites, further elucidating the interrelationships among metabolites during biological state changes.KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp/) [5] is one of the commonly used databases for pathway studies. It employs specific graphical language to describe numerous metabolic pathways and their interrelationships based on researchers' extensive literature review. The KEGG pathway database encompasses information on metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, human diseases, and drug development. For details on the KEGG database and related website resources, please refer to: http://www.genome.jp/kegg/pathway.html. KEGG pathway enrichment analysis takes KEGG pathways as units and uses the metabolic pathways involved by the species or closely related species as background. Through Fisher's exact test, it calculates the significance level of metabolite enrichment in each pathway to identify significantly affected metabolic and signal transduction pathways. Generally, the smaller the P-value (P<<0.05) in KEGG pathway enrichment results, the more statistically significant the enrichment. The number of differentially expressed metabolites within a KEGG pathway reflects, to some extent, the degree of biological treatment impact on each pathway in the experimental design. Therefore, by considering both factors, researchers can select metabolic or signal transduction pathways of interest along with the differentially expressed metabolites that significantly affect these pathways for subsequent biological experimental validation or mechanistic studies.MetaboLightsPublicLiquid Chromatography MS - positive - HILICLiquid Chromatography MS - negative - HILICThe samples were separated using an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC) with a HILIC column; the column temperature was maintained at 25°C; the flow rate was 0.3 mL/min; the mobile phase consisted of A: water + 25 mM ammonium acetate + 25 mM ammonia, and B: acetonitrile; the gradient elution program was as follows: 0 --- 0.5 min, 95% B; 0.5 --- 7 min, B linearly changed from 95% to 65%; 7 --- 8 min, B linearly changed from 65% to 40%; 8 --- 9 min, B was maintained at 40%; 9 --- 9.1 min, B linearly changed from 40% to 95%; 9.1 --- 12 min, B was maintained at 95%; throughout the analysis, the samples were placed in the autosampler at 4°C. To minimize the impact of instrument signal fluctuations, the samples were analyzed in a randomized order. Quality control (QC) samples were inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data.Gut Microbiota Regulates Serum Metabolites in Mice with Nonalcoholic Fatty Liver Disease via Gut Metabolites: Mechanisms Involving Branched-Chain Amino Acids and Unsaturated Fatty Acids.Peking UniversityDuo Keaiserummass spectrometryThe samples were taken out at -80°C, slowly thawed at 4°C, and then 100 μl of each group sample was taken. 400 μl of pre-cooled methanol-acetonitrile solution (1:1, v/v) was added, vortexed for 60 s, and placed at -20°C for 1 h to precipitate proteins. The samples were centrifuged at 14,000 rcf at 4°C for 20 min, the supernatant was collected and freeze-dried, and the samples were stored at -80°C.Rattus norvegicushttps://www.ebi.ac.uk/metabolights/MTBLS12668Congwei Gu. Southwest Medical University. gcw081543@swmu.edu.cn.Xiaoxia Zou. Southwest Medical University. zouxiaoxia0625@163.com.Mingde Zhao. Southwest Medical University. 786497714@qq.com.Hao Qiu. Southwest Medical University. 2078907209@qq.com.The raw data were converted into .mzXML format using ProteoWizard, followed by peak alignment, retention time correction, and peak area extraction using the XCMS program. Metabolite structural identification was performed by matching exact mass (<25 ppm) and secondary spectra against a laboratory-built database. After Pareto-scaling preprocessing, the data underwent multivariate statistical analysis, including unsupervised principal component analysis (PCA), supervised partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Univariate statistical analyses included Student's t-test and fold-change analysis, with volcano plots generated using R software.Groupkeaiduoduo998@126.comSix-week-old male C57BL/6 mice (weighing 17-19g) were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., LTD and raised in a special pathogen-free (SPF) facility in the Experimental Animal Center of Southwest Medical University at an ambient temperature of 22±2, a relative humidity of 50%-60% and a light/dark cycle of 12 hours. After a week's adaptation period, 20 mice were randomly divided into two groups, fed for 12 weeks: CK group (n=10, Standard chow diet) and NAFLD group (n=10, High fat diet, HFD). Standard chow diet and HFD were purchased from Beijing Keao Xieli Co., LTD. See (Supplementary Table S2, S3) for energy supply and high-fat feed formula for mice. All animals were free to drink water and eat freely. Observed the physical activity, food and water consumption and defecation of experimental mice every day. At the end of the specified feeding period, all mice were fasted overnight and anesthetized by intraperitoneal injection of 1% pentobarbital sodium (50 mg/kg body weight). Serum was collected for non-targeted metabolome detection. The data of serum metabolites are related to obesity index (Body weight), liver function index (ALT, AST), serum biochemical index (TG, TC, HDL and LDL), gut microbiome and gut metabolites, which have been presented in previous studies(Gu et al., 2022).Metabolomicsserum metabolitesmetabonomicsMetOriginNonalcoholic fatty liver diseasegut microbiotagut metabolitesserum metabolitesmetabonomicsMetOriginNonalcoholic fatty liver diseasegut microbiotagut metabolitesDetection was performed using electrospray ionization (ESI) in both positive and negative ion modes. After separation by UHPLC, the samples were analyzed using an Agilent 6550 mass spectrometer. The ESI source conditions were as follows: Gas Temperature: 250°C, Drying Gas: 16 L/min, Nebulizer: 20 psig, Sheath Gas Temperature: 400°C, Sheath Gas Flow: 12 L/min, Vcap: 3000 V, Nozzle Voltage: 0 V. Fragment: 175 V, Mass Range: 50-1200, Acquisition Rate: 4 Hz, Cycle Time: 250 ms.After sample testing was completed, an AB Triple TOF 6600 mass spectrometer was used to identify metabolites, collecting both primary and secondary spectra from QC samples. The ESI source conditions were as follows: Ion Source Gas1 (Gas1): 40, Ion Source Gas2 (Gas2): 80, Curtain gas (CUR): 30, source temperature: 650°C, IonSpray Voltage Floating (ISVF) ±5000 V (both positive and negative modes). Secondary mass spectra were acquired using information-dependent acquisition (IDA) in high-sensitivity mode, with Declustering potential (DP): ±60 V (both positive and negative modes), Collision Energy: 35±15 eV. IDA settings were as follows: Exclude isotopes within 4 Da, Candidate ions to monitor per cycle: 10. Data acquisition was segmented by mass range: 50-300, 290-600, 590-900, 890-1200, thereby expanding the acquisition rate of secondary spectra. Each segment was collected with four replicates per method. The acquired data were processed using in-house MetDDA and LipDDA methods for metabolite structural identification.L-LeucineCreatinineErgothioneineTrigonellineHippuric acidalpha-Linolenic acidThymineRibothymidinePhosphorylcholine1-Methylhistidine4-OxoretinolD-MannoseCytidineChenodeoxycholatePantothenateD-ProlineImidazolePC(16:0/16:0)L-CarnosineL-CarnitineDiethanolamine1-Stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC)L-AnserineGlycerophosphocholinefalseGut Microbiota Regulates Serum Metabolites in Mice with Nonalcoholic Fatty Liver Disease via Gut Metabolites: Mechanisms Involving Branched-Chain Amino Acids and Unsaturated Fatty AcidsIn recent years, nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease globally. Studies indicate that the gut-liver axis plays an important role in the occurrence and development of this disease. Our previous studies showed that the gut microbiota and gut metabolites in mice with NAFLD changed significantly, but it is unclear whether these changes influenced the disease process through serum metabolites. We conducted a non-targeted metabolome analysis on serum metabolites and systematically investigated the correlations between serum metabolites, gut microbiota, gut metabolites, and phenotypic index. Additionally, we traced the potential origins of serum metabolites and analyzed host-microbial interactions to elucidate the underlying mechanisms linking changes in serum metabolites with gut microbiota and gut metabolites. The findings suggest that the imbalance of gut pathogenic microbiota, specifically Blautia and Helicobacter, and beneficial microbiota such as Allobaculum, in mice with nonalcoholic fatty liver disease may be an important cause of gut metabolic disorders. This disorder results in a reduction of unsaturated fatty acid contend, particularly a decrease in Eicosapentaenoic Acid (EPA) and Docosahexaenoic Acid (DHA), and an accumulation of branched fatty acids in the serum. Consequently, there is a significant elevation in liver injury indices, potentially exacerbating the progression of nonalcoholic fatty liver disease and obesity in mice. These results suggest that serum metabolites are influenced by gut microbiota and their metabolites, and the variations in serum metabolites provide valuable insights into the relationship between gut microbiota and their metabolites in the context of nonalcoholic fatty liver disease.2025-07-022025-07-02MTBLS12668HMDB0002000HMDB0000619HMDB0001999HMDB0000826HMDB0000206HMDB0000673HMDB0003229HMDB0000795HMDB0002368HMDB0000407HMDB0000714/HMDB0011723/HMDB0062583HMDB0002068HMDB0000849HMDB0000283HMDB0003073HMDB0002183HMDB0001043/HMDB0060102HMDB0000624HMDB0000682HMDB0012138HMDB0000262HMDB0000462HMDB0001895HMDB0004089HMDB0000169HMDB0000210HMDB0000638HMDB0000126HMDB0002203HMDB0000701HMDB0001565HMDB0000387HMDB0000079HMDB0001882HMDB0000167HMDB0010325HMDB0006294HMDB0000883HMDB0062538HMDB0003345/HMDB0061922HMDB0000273HMDB0000207HMDB0000162HMDB0002994/HMDB0094689HMDB0003045HMDB0000875HMDB0012329HMDB0000884HMDB0000564/HMDB0007878/HMDB0007879/HMDB0007880/HMDB0007882/HMDB0007884/HMDB0007886/HMDB0007887/HMDB0007897/HMDB0007898/HMDB0007911/HMDB0007912/HMDB0007913/HMDB0007914/HMDB0007936/HMDB0007937/HMDB0007938/HMDB0007939/HMDB0007940/HMDB0007958/HMDB0007959/HMDB0007960/HMDB0007961/HMDB0007964/HMDB0007966/HMDB0007967/HMDB0007981/HMDB0007982/HMDB0007983/HMDB0007984/HMDB0007985/HMDB0008002/HMDB0008003/HMDB0008004/HMDB0008005/HMDB0008028/HMDB0008029/HMDB0008065/HMDB0008066/HMDB0008067/HMDB0008068/HMDB0008069/HMDB0008070/HMDB0008071/HMDB0008091/HMDB0008092/HMDB0008119/HMDB0008120/HMDB0008121/HMDB0008126/HMDB0008129/HMDB0008145/HMDB0008147/HMDB0008182/HMDB0008183/HMDB0008186/HMDB0008191/HMDB0008192/HMDB0008230/HMDB0008231/HMDB0008232/HMDB0008233/HMDB0008269/HMDB0008270/HMDB0008271/HMDB0008272/HMDB0008274/HMDB0008304/HMDB0008306/HMDB0008308/HMDB0008309/HMDB0008312/HMDB0008317/HMDB0008322/HMDB0008323/HMDB0008324/HMDB0008375/HMDB0008377/HMDB0008379/HMDB0008400/HMDB0008401/HMDB0008402/HMDB0008409/HMDB0008410/HMDB0008413/HMDB0008414/HMDB0008415/HMDB0008416/HMDB0008426/HMDB0008427/HMDB0008428/HMDB0008429/HMDB0008431/HMDB0008444/HMDB0008507/HMDB0008508/HMDB0008509/HMDB0008510/HMDB0008514/HMDB0008515/HMDB0008516/HMDB0008522/HMDB0008536/HMDB0008537/HMDB0008538/HMDB0008539/HMDB0008540/HMDB0008541/HMDB0008542/HMDB0008543/HMDB0008606/HMDB0008607/HMDB0008608/HMDB0008609/HMDB0008610/HMDB0008611/HMDB0008612/HMDB0008614/HMDB0008615/HMDB0008655/HMDB0008657/HMDB0008658/HMDB0008659/HMDB0008660/HMDB0008661/HMDB0008662/HMDB0008663/HMDB0008664/HMDB0008665/HMDB0008668/HMDB0008669/HMDB0008670/HMDB0008671/HMDB0008672/HMDB0008673/HMDB0008674/HMDB0008676/HMDB0008681/HMDB0008682/HMDB0008683/HMDB0008684/HMDB0008686/HMDB0008688/HMDB0008690/HMDB0008691/HMDB0008692/HMDB0008697/HMDB0008698/HMDB0008699/HMDB0008700/HMDB0008702/HMDB0008703/HMDB0008704/HMDB0008706/HMDB0008707/HMDB0008730/HMDB0008731/HMDB0008732/HMDB0008733/HMDB0008734/HMDB0008735/HMDB0008736/HMDB0008737/HMDB0008738/HMDB0008745/HMDB0008746/HMDB0008747/HMDB0008748/HMDB0008749/HMDB0008750/HMDB0008751/HMDB0008752/HMDB0008754/HMDB0008758/HMDB0008761/HMDB0008762/HMDB0008766/HMDB0008768/HMDB0008770/HMDB0008773/HMDB0008774/HMDB0008775/HMDB0008791/HMDB0008792/HMDB0008793/HMDB0008795/HMDB0008796/HMDB0008798/HMDB0008799/HMDB0008800HMDB0000518HMDB0000086HMDB0000062HMDB0000687HMDB0001388HMDB0000562HMDB0001525HMDB0004437HMDB0000001/HMDB0000479HMDB0003411HMDB0000089HMDB0000033HMDB0000194