{"database":"MetaboLights","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Tabular":["ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/m_MTBLS14967_LC-MS_positive_normal-phase_v2_maf.tsv","ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/m_MTBLS14967_LC-MS_negative_normal-phase_v2_maf.tsv"],"Txt":["ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/a_MTBLS14967_LC-MS_positive_normal-phase.txt","ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/s_MTBLS14967.txt","ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/i_Investigation.txt","ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967/a_MTBLS14967_LC-MS_negative_normal-phase.txt"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"ftp_download_link":["ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14967"],"metabolite_identification_protocol":["<p>Metabolite annotation was performed using Progenesis QI software by matching detected metabolic features against public metabolite databases, mainly the Human Metabolome Database (HMDB) and METLIN. Metabolite features were annotated based on accurate mass and available MS/MS information. Differential metabolites were identified using multivariate and univariate statistical criteria, with VIP &gt; 1 from the OPLS-DA model and p &lt; 0.05 from Student’s t-test. Identified differential metabolites were further mapped to biochemical pathways using the KEGG database for pathway enrichment and pathway analysis.</p>"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - positive - normal-phase","Liquid Chromatography MS - negative - normal-phase"],"chromatography_protocol":["<p>Chromatographic separation was performed using an UHPLC system coupled to a Q Exactive HF-X mass spectrometer. Samples were separated on an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, USA). The mobile phases consisted of solvent A, 0.1% formic acid in water:acetonitrile (2:98, v/v), and solvent B, 0.1% formic acid in acetonitrile. The flow rate was 0.40 mL/min, the column temperature was maintained at 40 °C, and the injection volume was 5 μL.</p>"],"publication":["Tire wear particles drive a systemic bioenergetic collapse in Eisenia fetida via coupled barrier erosion and metabolic-immune deadlock."],"submitter_affiliation":["Nankai University"],"submitter_name":["Shi Ruiying"],"organism_part":["gut"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["<p>For metabolite extraction, 100 mg of earthworm tissue was transferred into a 2 mL centrifuge tube with a 6 mm grinding bead. Metabolites were extracted using 800 μL of extraction solvent consisting of methanol:water (4:1, v/v) containing internal standards, including L-2-chlorophenylalanine at 0.02 mg/mL. Samples were homogenized using a Wonbio-96c frozen tissue grinder for 6 min at -10 °C and 50 Hz, followed by low-temperature ultrasonic extraction for 30 min at 5 °C and 40 kHz. The extracts were incubated at -20 °C for 30 min and then centrifuged at 13,000 g for 15 min at 4 °C. The supernatant was transferred to injection vials for LC-MS/MS analysis.</p>"],"organism":["Eisenia fetida"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS14967"],"author":["Ruiying Shi. ruiyingshi@yeah.net.","Ruiying Shi. Nankai University. ruiyingshi@yeah.net."],"data_transformation_protocol":["<p>Raw LC-MS/MS data were processed using Progenesis QI software (Waters Corporation, Milford, USA). A three-dimensional data matrix was exported in CSV format, including sample information, metabolite names, and mass spectral response intensities. Internal standard peaks and known false-positive peaks, including noise, column bleed, and derivatized reagent peaks, were removed. Features were de-redundant and peak-pooled. Metabolic features detected in at least 80% of samples within any group were retained. Missing values were filled using the minimum value in the data matrix. Peak intensities were normalized using sum normalization to reduce variation caused by sample preparation and instrument instability. Variables in quality control samples with a relative standard deviation greater than 30% were excluded, and the final data matrix was log10-transformed before downstream statistical analysis.</p>"],"study_factor":["Treatment"],"submitter_email":["ruiyingshi@yeah.net"],"sample_collection_protocol":["<p>Adult Eisenia fetida were used as the biological samples. After a 14-day acclimatization under controlled conditions, size-matched earthworms were exposed for 21 days in soil microcosms containing tire wear particles (TWPs). The metabolomics analysis focused on the control group (CK), the high-concentration small TWP treatment (HS, &lt;125 μm, 5% w/w), and the high-concentration large TWP treatment (HL, 250–500 μm, 5% w/w). After exposure, earthworms were depurated, surface-sterilized, and gut tissues were aseptically dissected. The collected tissues were immediately flash-frozen and maintained under frozen conditions until metabolite extraction.</p>"],"omics_type":["Metabolomics"],"study_design":["Multi-omics study","pooled quality control sample","Metabolomics","tandem mass spectrometry","untargeted analysis","gut","Waters ACQUITY UPLC system","experimental blank","Eisenia fetida","AB SCIEX TripleTOF 6600","untargeted metabolite profiling","experimental sample"],"curator_keywords":["Multi-omics study","pooled quality control sample","Metabolomics","tandem mass spectrometry","untargeted analysis","gut","Waters ACQUITY UPLC system","experimental blank","Eisenia fetida","AB SCIEX TripleTOF 6600","untargeted metabolite profiling","experimental sample"],"mass_spectrometry_protocol":["<p>Mass spectrometric analysis was performed on a Q Exactive HF-X mass spectrometer equipped with an electrospray ionization (ESI) source. Data were acquired in both positive and negative ionization modes. The source temperature was set to 400 °C. The sheath gas flow rate was 40 arb and the auxiliary gas flow rate was 10 arb. The ion-spray voltage floating was set to -2800 V in negative ion mode and 3500 V in positive ion mode. MS/MS fragmentation was performed using rolling normalized collision energies of 20, 40, and 60 V. Data acquisition was conducted in data-dependent acquisition (DDA) mode over a mass range of 70–1050 m/z.</p>"],"additional_accession":[]},"is_claimable":false,"name":"Tire wear particles drive a systemic bioenergetic collapse in Eisenia fetida via coupled barrier erosion and metabolic-immune deadlock","description":"<p>As global vehicular electrification accelerates, the exponential accumulation of tire wear particles (TWPs) poses a pervasive threat to edaphic ecosystems. To elucidate the systemic bioenergetic impact of TWP toxicity, we performed a non-targeted metabolomic profiling of the soil sentinel earthworm, Eisenia fetida. Earthworms were exposed to environmentally relevant ingestible sizes of TWPs at a high-concentration gradient (5% w/w) in a soil microcosm for 21 days. Post-exposure, gut tissues were aseptically dissected, and metabolites were extracted using a methanol:water (4:1 v/v) solvent system prior to analysis via UHPLC-Q Exactive HF-X MS/MS. The metabolomic landscape reveals a profound systemic metabolic reprogramming. Key findings include a severe carbohydrate assimilation blockade—evidenced by the abnormal intracellular accumulation of complex carbohydrates and energy intermediates—and a lipid-mediated inflammatory hyperactivation, specifically targeting arachidonic acid and sphingolipid pathways. This dataset provides the critical biochemical evidence for an unsustainable 'bioenergetic triage,' demonstrating how TWP exposure forces earthworms into a fatal metabolic-immune deadlock.</p>","dates":{"publication":"2026-07-07","submission":"2026-07-07"},"accession":"MTBLS14967","cross_references":{}}