MetaboLightsapplication/xmlftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/m_MTBLS13096_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/m_MTBLS13096_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/a_MTBLS13096_LC-MS_positive_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/s_MTBLS13096.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/a_MTBLS13096_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096/i_Investigation.txtprimary200OKftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13096<p>Statistical significance between two groups was assessed using paired or unpaired two-tailed t- tests, as appropriate. Unless otherwise specified, data are presented as mean ± standard error of the mean (SEM), and statistical significance was denoted as follows: *P &lt; 0.05, **P &lt; 0.01, and ***P &lt; 0.001. Spearman correlation analysis was employed to assess associations between two variables without assuming a normal distribution. Principal component analysis (PCA) and volcano plots were generated using the MetaboAnalyst 6.0 online platform (https://www.metaboanalyst.ca/) with the following parameters: Fold change (FC) threshold≥1.5, p-value ≤ 0.05. Pathway enrichment analysis of differential metabolites from untargeted urine metabolomics was performed using the Wukong platform (https://www.omicsolution.com/wkomics). Heatmaps were generated via the LC-Bio Cloud Platform (Hangzhou, China)</p>MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - reverse phase<p>Untargeted metabolomics was performed using a Vanquish UHPLC system coupled with a Q Exactive Plus mass spectrometer (Waters Co., USA), equipped with an ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.7 μm). The column temperature was set at 45°C, and the injection volume was 1 μL. The mobile phases were 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), with a flow rate of 0.4 mL/min. The gradient was programmed from 99% A to 0% A over 12-13 min, followed by isocratic elution at 100% B for 12-13 min.</p>Establishment of a Short-term Mouse Model of Non-alcoholic Fatty Liver Disease with Non-invasive Monitoring.SHANGHAI JIAOTONG UNIVERSITYLU YUurinemass spectrometry assay<p>Urine samples were collected approximately once per month throughout the experimental period, with the initial collection performed on day 5 following the commencement of the intervention. At each time point, urine was obtained over a 24 h period from at least six mice per group. Following collection, samples were immediately frozen and stored at −80°C until further analysis. For sample preparation, frozen urine samples were thawed on ice and aliquoted into 100 μL microcentrifuge tubes. An aliquot of 100 μL from each sample was transferred to a 4°C environment. Subsequently, 5 μL from each of these was combined and thoroughly mixed to prepare quality control (QC) samples. For metabolite extraction, 95 μL of each remaining urine sample and QC sample was mixed with an equal volume (95 μL) of extraction reagent consisting of methanol: acetonitrile (1:1, v/v) containing 2 μg/mL of chloropropionic acid. The mixtures were vortexed at 1500 rpm for 1 min and then incubated at −20°C for 2 h to promote protein precipitation. Samples were subsequently centrifuged at 12,000 rpm for 20 min at 4°C. Finally, 90 μL of the resulting supernatant was transferred into sample vials for instrumental analysis.</p>Mus musculushttps://www.ebi.ac.uk/metabolights/MTBLS13096Lu Yu. SHANGHAI JIAOTONG UNIVERSITY. yulu77@sjtu.edu.cn. 13786651139.<p>Data acquisition was performed using Xcalibur 3.0 software (Thermo Fisher Scientific). Raw data were processed using Progenesis QI v2.3 (Waters Co., USA) for peak alignment, peak picking, and deconvolution. Metabolite identification was conducted by matching experimental data against the Human Metabolome Database (HMDB, https://www.hmdb.ca/).</p>Foodyulu77@sjtu.edu.cn<p>Mice in the control (CON) group were fed a purified AIN-93M diet. In contrast, mice in the mWD group were provided with a high-fat, high-sugar diet (D09100310; 40% kcal from palm oil, 22% w/w fructose, 10% w/w sucrose, and 2% w/w cholesterol). In addition, the mWD group received HFCS (containing D-fructose (23.1 g/L) and D-glucose (18.9 g/L)) as their drinking source from 18:00-20:00.</p><p> Urine samples were collected approximately once per month throughout the experimental period, with the initial collection performed on day 5 following the commencement of the intervention. At each time point, urine was obtained over a 24 h period from at least six mice per group. Following collection, samples were immediately frozen and stored at −80°C until further analysis. For sample preparation, frozen urine samples were thawed on ice and aliquoted into 100 μL microcentrifuge tubes. An aliquot of 100 μL from each sample was transferred to a 4°C environment. Subsequently, 5 μL from each of these was combined and thoroughly mixed to prepare quality control (QC) samples. For metabolite extraction, 95 μL of each remaining urine sample and QC sample was mixed with an equal volume (95 μL) of extraction reagent consisting of methanol: acetonitrile (1:1, v/v) containing 2 μg/mL of chloropropionic acid. The mixtures were vortexed at 1500 rpm for 1 min and then incubated at −20°C for 2 h to promote protein precipitation. Samples were subsequently centrifuged at 12,000 rpm for 20 min at 4°C. Finally, 90 μL of the resulting supernatant was transferred into sample vials for instrumental analysis.</p>Metabolomicsglucose metabolism diseaseMetabolomicsmicro-computed tomographynon-alcoholic fatty liver diseaseBiomarkersDiet, Westernglucose metabolism diseaseMetabolomicsmicro-computed tomographynon-alcoholic fatty liver diseaseBiomarkersDiet, Western<p> Mass spectrometry was performed in both positive and negative heated electrospray ionization (H- ESI) modes using data-dependent acquisition (DDA): one full scan followed by 10 MS/MS scans. The scan range was m/z 67–1000. Full MS resolution was 70,000 with AGC 1e6 and IT 100 ms; MS/MS resolution was 17,500 with AGC 5e5 and IT 50 ms. Spray voltages were 3.2 kV (positive) and 2.8 kV (negative); capillary temperature was 320°C; S-lens RF level was 50 V</p>falseEstablishment of a Short-term Mouse Model of Non-alcoholic Fatty Liver Disease with Non-invasive Monitoring<p> Non-alcoholic fatty liver disease (NAFLD) and its progressive form, non-alcoholic steatohepatitis (NASH), are major chronic liver diseases associated with hepatocellular carcinoma. This study aimed to establish a short-term modified Western diet (mWD)-induced NAFLD model in middle-aged mice and explore non-invasive strategies for disease monitoring. Ten-month-old mice were fed a high-fat, high-fructose mWD for three months. Food and water intake, body weight, urine, and body composition were regularly monitored. At the endpoint, oral glucose tolerance tests (OGTT) and micro-computed tomography (Micro-CT) scans were performed. mWD feeding resulted in increased adiposity, impaired glucose metabolism, and significant metabolic reprogramming. Untargeted urinary metabolomics revealed progressive alterations in lipid and amino acid metabolism, identifying several metabolites as potential non- invasive biomarkers of disease progression. This study provides a rapid and practical mouse model of NAFLD and supports the use of urinary metabolomics and Micro-CT for non-invasive monitoring of disease dynamics.</p>2026-05-312025-10-06MTBLS13096