Metabolite features detected in the GC–MS data were processed using Agilent Unknowns Analysis and MassHunter Quantitative Analysis software (Agilent Technologies, USA). Individual chromatographic peaks were detected and mass spectra were deconvoluted prior to metabolite annotation. The resulting extracted mass spectra were matched against the NIST 14.L mass spectral library for compound identification. Candidate annotations were evaluated based on spectral matching quality, chromatographic peak quality, and reproducibility across replicate samples. Low-quality features, duplicate annotations, and clearly non-endogenous compounds were excluded from the final metabolite dataset.
Metabolite annotations were reported according to the metabolite identification confidence levels recommended by the Metabolomics Standards Initiative (MSI). Because metabolite identities were assigned based on spectral library matching without confirmation using authentic reference standards analyzed under identical experimental conditions, the annotated compounds were classified as MSI Level 2, putatively annotated compounds.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Gas Chromatography MS - positive - low-polarity"],"chromatography_protocol":["Gas chromatography was performed using an Agilent 7890B gas chromatography system (Agilent Technologies, USA) equipped with an HP-5MS capillary column. Helium was used as the carrier gas at a constant flow rate of 1.0 mL/min. The injection volume was 1 μL with a split ratio of 50:1. The GC oven temperature was initially set at 60 °C and held for 4 min, followed by an increase to 300 °C at a rate of 8 °C/min and a final hold at 300 °C for 5 min. The injector temperature was maintained at 280 °C. No liquid mobile phase or solvent gradient was used because gas chromatography was employed for chromatographic separation.
"],"publication":["Multi-Organ Metabolomic and Ionomic Characterization of Chronic Alcohol Exposure-Induced Alcoholic Liver Disease."],"submitter_affiliation":["Peking University"],"submitter_name":["Duo Keai"],"organism_part":["kidney","cerebral cortex","hippocampus","spleen","liver","Lung","stomach","serum"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Serum samples (100 μL) were extracted with 350 μL methanol containing 100 μg/mL heptadecanoic acid (internal standard), vortexed, and centrifuged (14,000 rpm, 10 min, 4 °C). The supernatant was dried under nitrogen at 37 °C and derivatized with 80 μL methoxyamine hydrochloride (15 mg/mL in pyridine, 70 °C, 90 min), followed by 100 μL N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) containing 1% trimethylchlorosilane (TMCS) (70 °C, 60 min). After vortexing and centrifugation (14,000 rpm, 2 min, 4 °C), samples were filtered through a 0.22 μm membrane prior to GC–MS analysis. For tissue samples (cortex, hippocampus, liver, spleen, kidney, lung, and stomach), approximately 50 mg tissue was homogenized in 1 mL methanol containing 1 mg/mL heptadecanoic acid and centrifuged (14,000 rpm, 15 min, 4 °C). The supernatant was processed using the same derivatization procedure as serum samples. Quality control (QC) samples were prepared by pooling equal amounts of tissues from the control (CON) and alcoholic liver disease (ALD) groups.
"],"organism":["Mus musculus"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS15954"],"author":["Li Qiu. Department of Pediatrics, The Affiliated Tengzhou Central People's Hospital of Jining Medical University. sdtzqiuli@126.com.","Pei Jiang. Department of Pediatrics, The Affiliated Tengzhou Central People's Hospital of Jining Medical University. jiangpeicsu@sina.com.","Yanli Leng. Department of Pediatrics, The Affiliated Tengzhou Central People's Hospital of Jining Medical University. lengyanli9316@163.com."],"data_transformation_protocol":["Raw GC–MS data generated by the Agilent 7000C mass spectrometer were processed using Agilent Unknowns Analysis and MassHunter Quantitative Analysis software (Agilent Technologies, USA). The raw chromatographic and mass spectral data were subjected to peak detection and spectral deconvolution to generate individual metabolite features. Extracted mass spectra were matched against the NIST 14.L mass spectral library for metabolite annotation. The resulting peak areas were normalized to the total peak area to generate normalized metabolite abundance data for subsequent multivariate and statistical analyses.
For ICP–MS data, raw elemental signal data generated by the NexION 1000G ICP–MS platform (PerkinElmer, USA) were processed using the corresponding instrument software to obtain quantitative elemental concentration data. Internal standards (Sc, Ge, In, and Re) were used for signal correction. Missing values were subsequently imputed before multivariate statistical analysis in SIMCA 14.1. The resulting elemental concentration data were used for subsequent statistical and multivariate analyses.
"],"study_factor":["Group"],"submitter_email":["keaiduoduo998@126.com"],"sample_collection_protocol":["After model establishment, all mice were anesthetized by intraperitoneal injection of sodium pentobarbital. Blood samples were subsequently collected via the orbital venous plexus and centrifuged to obtain serum. Mice were then euthanized by cervical dislocation under anesthesia. Under cold conditions on ice, mice were rapidly dissected, and the cerebral cortex, hippocampus, lung, liver, spleen, kidney, and stomach tissues were sequentially collected. Tissue samples were immediately rinsed with phosphate-buffered saline (PBS) to remove residual blood. Tissue portions designated for histopathological analysis were fixed in 4% paraformaldehyde, whereas samples designated for GC–MS and ICP–MS analyses were immediately stored at 80 °C until analysis. Before sample preparation, the frozen samples were thawed once at 4 °C and kept on ice or at 4 °C during subsequent handling. No repeated freeze–thaw cycles were performed.
"],"omics_type":["Metabolomics"],"study_design":["Metabolomics","Mus musculus","gas chromatography-mass spectrometry","hippocampus","spleen","untargeted analysis","liver","Lung","Chronic-plus-binge ethanol feeding","inductively coupled plasma mass spectrometry","stomach","Multi-tissue analysis","Agilent 7000C MS","Kidney","Alcoholic liver disease","cerebral cortex","Agilent 7890B GC","nippocampus","serum"],"curator_keywords":["Metabolomics","Mus musculus","gas chromatography-mass spectrometry","hippocampus","liver","spleen","untargeted analysis","Lung","Chronic-plus-binge ethanol feeding","inductively coupled plasma mass spectrometry","stomach","Multi-tissue analysis","Agilent 7000C MS","Kidney","Alcoholic liver disease","cerebral cortex","Agilent 7890B GC","nippocampus","serum"],"mass_spectrometry_protocol":["Mass spectrometric detection was performed using an Agilent 7000C mass spectrometer (Agilent Technologies, USA) operated in electron ionization (EI) mode at an electron energy of 70 eV. The ion source temperature was maintained at 230 °C, and the transfer line temperature was maintained at 250 °C. Mass spectra were acquired in full-scan mode over an m/z range of 50–800 at an acquisition rate of 20 spectra/s. Because electron ionization was used, no ESI positive- or negative-ion mode was applied.
"],"metabolite_name":["Oleic Acid","Serine","Proline","Monopalmitin","5,8,11-Eicosatrienoic acid","Glucose","Glycine","Pyrimidine","5-Aminolevulinic acid","Stearic acid","Glyceric acid","2-Palmitoylglycerol","4-Aminobutanoic acid","Niacinamide","Myo-Inositol","Alanine","Phenylalanine","Threonine","Lactic Acid","Glycerol","2-Aminoethanol","Cholesterol","Linoleic acid","Urea","Myristic acid","Valine","Asparagine","Glycerol monostearate","Uracil","3-Hydroxybutyric acid"],"additional_accession":[]},"is_claimable":false,"name":"Multi-Organ Metabolomic and Ionomic Characterization of Chronic Alcohol Exposure-Induced Alcoholic Liver Disease","description":"Background: Alcoholic liver disease (ALD) is a prevalent chronic liver disorder worldwide. Chronic alcohol exposure not only damages the liver but also disrupts metabolic and elemental homeostasis in multiple organs. However, its systemic effects remain incompletely understood. This study used an integrated metabolomics–ionomics approach based on the chronic-plus-binge ethanol feeding (NIAAA) model to investigate multi-organ metabolic and ionic alterations induced by chronic alcohol exposure. Methods: An ALD mouse model was established using the NIAAA protocol. Liver, spleen, kidney, lung, stomach, serum, cerebral cortex, and hippocampus samples were analyzed by gas chromatography–mass spectrometry (GC–MS) and inductively coupled plasma mass spectrometry (ICP–MS). Differential metabolites, altered elements, and affected pathways were identified through statistical, pathway enrichment, and correlation analyses. Results: Chronic alcohol exposure induced oxidative stress, evidenced by increased malondialdehyde (MDA) and decreased reduced glutathione (GSH). A total of 48 differential metabolites were identified across eight tissues, involving 16 significantly altered pathways, including linoleic acid metabolism, amino acid metabolism, one-carbon metabolism, primary bile acid biosynthesis, galactose metabolism, and butanoate metabolism. Ionomic analysis revealed widespread elemental dyshomeostasis, particularly in the liver, cerebral cortex, and stomach, with significant alterations in Na, Mg, Fe, Cu, Zn, and Se. Extensive correlations were observed among elements and between elements and metabolites. Conclusions: Chronic alcohol exposure causes multi-organ metabolic disturbances and elemental dyshomeostasis, providing new insights into ALD pathogenesis and potential targets for diagnosis and treatment.","dates":{"publication":"2026-10-08","submission":"2026-10-08"},"accession":"MTBLS15954","cross_references":{"HMDB":["HMDB0010378","HMDB0000673","HMDB0000190","HMDB0000122","HMDB0001406","HMDB0000806","HMDB0000161","HMDB0000148","HMDB0003361","HMDB0000168","HMDB0011564","HMDB0011131","HMDB0000827","HMDB0000207","HMDB0000112","HMDB0000294","HMDB0000139","HMDB0000211","HMDB0000167","HMDB0000011","HMDB0000067","HMDB0011533","HMDB0000162","HMDB0000159","HMDB0000187","HMDB0000123","HMDB0000883","HMDB0000300","HMDB0000131","HMDB0001149"]}}