MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/m_MTBLS14236_LC-MS_positive_reverse-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/m_MTBLS14236_LC-MS_negative_reverse-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/a_MTBLS14236_LC-MS_positive_reverse-phase.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/a_MTBLS14236_LC-MS_negative_reverse-phase.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236/s_MTBLS14236.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14236Metabolite annotation was carried out by matching processed peaks against an in-house database constructed in our laboratory, combined with public spectral libraries and in silico prediction libraries.Finally, metabolites with an overall identification score > 0.5 and a coefficient of variation (CV) < 0.3 in QC samples were retained. Data acquired in positive and negative ionization modes were merged, keeping metabolites with the highest identification confidence level and minimum CV value. The final dataset was saved as all_sample_data.xlsxMetaboLightsPublicLiquid Chromatography MS - negative - reverse-phaseLiquid Chromatography MS - positive - reverse-phaseAll samples were for two LC/MS methods. One aliquot was analyzed using positive ion conditions and was eluted from T3 column (Waters ACQUITY Premier HSS T3 Column 1.8 µm, 2.1 mm all_fetch_status all_status eb_eye_copy_status eb_eye_entry_counts eb_eye_fetch_status eb_eye_metabolights_complete.xml eb_eye_metabolights_compounds.copy eb_eye_metabolights_compounds.xml eb_eye_metabolights_studies.copy e_fetch_status europe_PMC_metabolights_studies.copy europe_PMC_metabolights_studies.xml head.xml studies.copy study.xml tail.xml thomsonreuters_metabolights_studies.copy thomsonreuters_metabolights_studies.xml 100 mm) using 0.1 % formic acid in water as solvent A and 0.1 % formic acid in acetonitrile as solvent B in the following gradient: 5 to 20 % in 1 min, increased to 99 % in the following 2 mins and held for 1.5 min, then come back to 5 % mobile phase B witnin 0.1 min, held for 1.4 min. The analytical conditions were as follows, column temperature, 40 °C; flow rate, 0.4 mL/min; injection volume, 4 μL; Another aliquot was using negative ion conditions and was the same as the elution gradient of positive modeAn Integrated Multi-omics Analysis Reveals PDCoV Hijacks the PPAR Signaling Network to Orchestrate Metabolic-Immune Dysregulation and Intestinal Injury in Piglets.Xingchen GuoCollege of Animal Science/Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou UniversityWhole Organismmass spectrometry assayThe sample stored at -80 °C refrigerator was thawed on ice. The thawed sample was homogenized by a grinder (30 HZ) for 20 s. A 400 μL solution (Methanol : Water = 7:3, V/V) containing internal standard was added in to 20 mg grinded sample, and shaked at 1500 rpm for 5 min. After placing on ice for 15 min, the sample was centrifuged at 12000 rpm for 10 min (4 °C). A 300 μL of supernatant was collected and placed in -20 °C for 30 min. The sample was then centrifuged at 12000 rpm for 3 min (4°C). A 200 μL aliquots of supernatant were transferred for LC-MS analysis.Sus scrofahttps://www.ebi.ac.uk/metabolights/MTBLS14236yiyu zhang. College of Animal Science/Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region, Ministry of Education, Guizhou University. zyy8yyc@163.com.Xingchen Guo. guoxingchenn@163.com.Raw mass spectrometry data were converted to mzML format using ProteoWizard. Peak detection, peak alignment, and retention time correction were performed using the XCMS package. Features with missing values >50% across groups were filtered out. Missing values were imputed using a hybrid strategy: KNN imputation for variables with missing rates <50%, and **1/5 of the minimum observed value** for variables with missing rates >50%. Peak areas were subsequently normalized using support vector regression (SVR).Biological replicateguoxingchenn@163.comFifteen-day-old piglets were orally inoculated with PDCoV (USA/Nebraska145/2015 strain) or sterile saline (control). Jejunal tissues were collected at 3 days post-inoculationMetabolomicsThermo Scientific Vanquish UHPLC SystemMetabolomicsuntargeted analysisThermo Scientific Q Exactive HF-XjejunumSus scrofaviral infectious diseaseuntargeted metabolite profilingWhole OrganismThermo Scientific Vanquish UHPLC SystemMetabolomicsuntargeted analysisThermo Scientific Q Exactive HF-XjejunumSus scrofaviral infectious diseaseWhole Organismuntargeted metabolite profilingAll the methods alternated between full scan MS and data dependent MSn scans using dynamic ex-clusion. MS analyses were carried out using electrospray ionization in the positive ion mode and negative ion mode using full scan analysis over m/z 75-1000 at 35000 resolution. Additional MS settings are: ion spray voltage, 3.5 KV or 3.2 KV in positive or negative modes, respevtively; Sheath gas (Arb), 30; Aux gas, 5; Ion transfer tube temperature, 320 °C; Vaporizer temperature, 300 °C; Collision energy, 30,40,50 V; Signal Intensity Threshold, 1.00E+06 cps; Top N vs Top speed, 10; Exclusion duration, 3sfalseAn Integrated Multi-omics Analysis Reveals PDCoV Hijacks the PPAR Signaling Network to Orchestrate Metabolic-Immune Dysregulation and Intestinal Injury in PigletsBackground: Porcine deltacoronavirus (PDCoV) is an emerging enteric coronavirus that causes severe diarrhea and high mortality in neonatal piglets. The molecular mechanisms underlying PDCoV pathogenesis in the host intestine remain poorly understood. Objective: To characterize the host transcriptional, proteomic, and metabolomic responses to PDCoV infection in the primary target organ (jejunum) using a neonatal piglet model.2026-04-152026-04-08MTBLS14236