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The binary gradient elution system consisted of (A) water (containing 0.1% formic acid, v/v) and (B) acetonitrile and separation was achieved using the following gradient: 0.0 min, 5% B; 2.0 min, 5% B; 4.0 min, 30% B; 8.0 min, 50% B; 10.0 min, 80% B; 14.0 min, 100% B; 15.0 min, 100% B; 15.1 min, 5% and 16.0 min, 5% B. The flow rate was 0.35 mL/min and the column temperature was 45 °C. The injection volume was 5 μL.</p>"],"publication":["Metabolomics of marine bacterium Roseovarius nubinhibens ISM."],"submitter_name":["Ying Wei"],"submitter_affiliation":["Shandong University"],"organism_part":["Bacteria"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["<p>The 600 μL of ice-cold mixture of methanol and water (4/1, v/v) was added to samples and transferred to 1.5 mL Eppendorf tubes. Then, 200 μL chloroform was added and cells were disrupted by ultrasonic for 10 min in ice-water bath. The whole liquid was transferred to 1.5 mL Eppendorf tubes and was extracted by ultrasonic for 20 min in ice-water bath and then stored at -40 °C for 2 h. The extract was centrifuged at 4 °C (13,000 rpm) for 10 min and 400 μL of supernatants were added in a glass vial and dried. Next, 300 μL mixture of methanol and water (1/4, v/v) was added to each sample, and samples were vortexed for 30 s, extracted by ultrasonic for 3 min in ice-water bath and placed at -40 °C for 2 h. Samples were centrifuged at 4 °C (13,000 rpm) for 10 min and 150 μL of supernatants from each tube were filtered through 0.22 μm microfilters and transferred to LC vials. The vials were stored at -80 °C until LC-MS analysis. QC samples were prepared by mixing aliquot of all samples to be a pooled sample.</p>"],"organism":["Roseovarius nubinhibens ISM"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS12392"],"author":["Ying Wei. Shandong University. School of Environmental Science and Engineering, Shandong University, 72 Binhai Road, Qingdao 266237, China. weiying@mail.sdu.edu.cn. +86 15866232776."],"data_transformation_protocol":["<p>The original LC-MS data were processed by Progenesis QI v3.0 software for baseline filtering, peak identification, integral, retention time correction, peak alignment and normalization. Main parameters of 5 ppm precursor tolerance, 10 ppm product tolerance and 5% product ion threshold were applied. Compound identifications were based on precise mass-to-charge ratio (m/z), secondary fragments and isotopic distribution using The Human Metabolome Database (HMDB), Lipidmaps (V2.3), METLIN and LuMet-Animal self-built databases. The extracted data were then further processed by removing any peaks with a missing value (ion intensity = 0) in more than 50% in groups, by replacing zero value by half of the minimum value, and by screening according to the qualitative results of the compound. Compounds with resulting scores below 36 (out of 80) points were also deemed to be inaccurate and removed. A data matrix was combined from the positive and negative ion data. The matrix was imported in R to carry out Principle Component Analysis (PCA) to observe the overall distribution among the samples and the stability of the whole analysis process. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) was utilized to distinguish the metabolites that differ between groups. To prevent overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) were used to evaluate the quality of the model. Variable Importance of Projection (VIP) values obtained from the OPLS-DA model were used to rank the overall contribution of each variable to group discrimination. A two-tailed Student’s T-test was further used to verify whether the metabolites of difference between groups were significant. Differential metabolites were selected with VIP values greater than 1.0 and p-values less than 0.05. Differential metabolites were further used to for KEGG pathway (http://www.genome.jp/kegg) enrichment analysis.</p>"],"study_factor":["Treatment"],"submitter_email":["weiying@mail.sdu.edu.cn"],"sample_collection_protocol":["<p>The <em>Roseovarius nubinhibens</em> ISM strains WY10-pCtrl and WY10-pRn were cultivated in marine basal media with 4HB and acetate for 20 h and 36 h, respectively. Than, cells were harvested and washed twice with precooled phosphate buffered saline at 4 °C and were quickly frozen with liquid nitrogen.</p>"],"omics_type":["Metabolomics"],"study_design":["ultra-performance liquid chromatography-mass spectrometry","Roseovarius nubinhibens ISM","metabolomics","untargeted metabolites","marine bacteria"],"curator_keywords":["ultra-performance liquid chromatography-mass spectrometry","Roseovarius nubinhibens ISM","metabolomics","untargeted metabolites","marine bacteria"],"mass_spectrometry_protocol":["<p>The Q Exactive HF mass spectrometer (Thermo Fisher Scientific) equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. The mass range was from 70 to 1050 m/z. The resolution was set at 60000 for the full MS scans and 15000 for HCD MS/MS scans. The Collision energy was set at 10, 20 and 40 eV. The mass spectrometer operated as follows: spray voltage, 3800 V (+) and 3200 V (-); sheath gas flow rate, 35 arbitrary units; auxiliary gas flow rate, 8 arbitrary units; capillary temperature, 320 °C; Aux gas heater temperature, 350 °C; S-lens RF level, 50.</p>"],"additional_accession":[]},"is_claimable":false,"name":"Metabolomics of marine bacterium Roseovarius nubinhibens ISM","description":"<p>To analyze the metabolism of marine bacterium&nbsp;<em>Roseovarius nubinhibens</em>&nbsp;ISM, metabolomics of the control strain (Con) WY10-pCtrl (<em>pcaH</em>&nbsp;Gln40*,&nbsp;<em>pcaG</em>&nbsp;Gln3*) and the experimental strain (Exp) WY10-pRn (<em>pcaH</em>&nbsp;Gln40*,&nbsp;<em>pcaG</em>&nbsp;Gln3*,&nbsp;<em>pobA-Rn</em>) was performed. Through metabolomic analysis, 669 metabolites in the experimental strain showed significant changes compared with the control strain, where 406 metabolites were upregulated and 263 metabolites were downregulated. Metabolomics analysis revealed the distinct metabolic mechanism of the control and experimental strain.</p>","dates":{"publication":"2026-08-27","submission":"2025-04-16"},"accession":"MTBLS12392","cross_references":{}}