MetaboLights

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LC-MS: Hydroquinone Spermidine Pyrophosphate D-fructose-1-phosphate Uric acid D-fructose-6-phosphate 2'-deoxyguanosine Hexitol Dihydroxyacetone phosphate D-ribulose 5-phosphate Quinic acid D-glyceraldehyde 3-phosphate Oxalic acid D-tagatose Glutathione Inosine-5'-monophosphate N-acetyl-d-hexosamine Citraconic acid O-phosphoserine L-cystathionine L-cysteine Stigmasterol D-xylulose Xanthine Ergosterol Uridine L-glutamine Tyramine Squalene Fumaric acid 1,2,4-benzenetriol Dimethyluric acid Leucyl-glycine Bicine Oleamide Glucose-6-phosphate 2-imidazolidinone D-fructose-1,6-bisphosphate Metharbital Diacetone alcohol 3,6-anhydro-d-galactose 2-aminoethyl methacrylate Butylboronic acid Erythrose D-ribose 5-phosphate Gluconic acid Catechin Methanephosphonothioic acid Diglycerol N-acetyl-d-glucosamine Diethylcarbamic acid Isoxanthopterin N-acetylaspartate Chlorogenic acid Nicotianamine 2-oxo-propanoic acid 2-diethylaminoethanol Lithocholic acid Sophorose Furoylglycine Butane-2,3-diol Malonic acid 1-butylamine Lidocaine Methionyl-Phenylalanine Creatine Dodecanedioylcarnitine L-Acetylcarnitine LysoPC(16:0) L-Palmitoylcarnitine Octadecanamide Phytosphingosine L-Lysine Hypoxanthine LysoPC(20:4(5Z,8Z,11Z,14Z)) Calcidiol D-Maltose Monoethylglycinexylidide Pipecolic acid 16-Hydroxy hexadecanoic acid Dihydrocarvone D-Gal alpha 1->6D-Gal alpha 1->6D-Glucose Ouabain beta-Phellandrene Linoleoyl ethanolamide Quinaldic acid Alpha-Linolenic acid Alpha-Lactose 3-Hydroxylidocaine Fucose 1-phosphate N-Oleoylethanolamine (E)-2-octenal 1-nitrosonaphthalene Monoethylhexyl phthalic acid gamma-Glutamylleucine N6-Acetyl-L-lysine 3beta,7alpha-Dihydroxy-5-cholestenoate 2-Nonenal 2-Heptanone 2-Phenylacetamide 3-Isopropylmalate Diphenylamine LysoPC(22:5(4Z,7Z,10Z,13Z,16Z)) Arachidonic acid 16(R)-HETE 4-Methylcatechol Myo-inositol 1-phosphate FAD Leucyl-Glutamate N-Desmethylcitalopram Sucrose Uridine diphosphate glucuronic acid LysoPC(20:2(11Z,14Z))MetaboLightsPublicGas Chromatography MS - positiveLiquid Chromatography MS - positive - reverse phaseGC-MS:The derived samples were analyzed on an Agilent 7890B gas chromatography system coupled to an Agilent 5977A MSD system (Agilent Technologies Inc., CA, USA). A DB-5MS fused-silica capillary column (30 m x 0.25 mm x 0.25 μm, Agilent J & W Scientific, Folsom, CA, USA) was utilized to separate the derivatives. Helium (> 99.999%) was used as the carrier gas at a constant flow rate of 1 mL/min through the column. The injector temperature was maintained at 260 °C. Injection volume was 1 μL by splitless mode. The initial oven temperature was 60 °C held at 60 °C for 0.5 min, ramped to 125 °C at a rate of 8 °C/min, to 210 °C at a rate of 5 °C/min, to 270 °C at a rate of 10 °C/min, to 305 °C at a rate of 20 °C/min, and finally held at 305 °C for 5 min. LC-MS:A Dionex Ultimate 3000 RS UHPLC fitted with Q-Exactive plus quadrupole-Orbitrap mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. An ACQUITY UPLC HSS T3 column (1.8 μm, 2.1 x 100 mm) was employed in both positive and negative modes. The binary gradient elution system consisted of (A) water (containing 0.1% formic acid, v/v) and (B) acetonitrile (containing 0.1% formic acid, v/v) and separation was achieved using the following gradient: 0 min, 5% B; 2 min, 5% B; 4 min, 30% B; 8 min, 50% B; 10 min, 80% B; 14 min, 100% B; 15 min, 100% B; 15.1 min, 5% and 16 min, 5% B. The flow rate was 0.35 mL/min and the column temperature was 45 °C. All samples were kept at 4 °C during analysis. The injection volume was 2 μL.Untargeted Metabolomics Analysis of Gingival Tissue in Patients with Severe Periodontitis. 10.1021/acs.jproteome.3c00105. PMID:38018860Chunbo TangNanjing medical universitygingival epitheliummass spectrometry assayGC-MS:The pre-processing operation for samples before grinding at 60 HZ for 2 min is the same as the operation for LC-MS. A total of 127 μL of chloroform was added to the samples, and the mixtures were vortexed, then the whole samples were extracted by ultrasonic for 10 min in ice-water bath, then placed at -20 °C for 30 min. Samples were centrifuged at 4 °C (13,000 rpm) for 10 min. Then 200 μL of supernatant in a glass vial was dried in a freeze concentration centrifugal dryer. QC sample was prepared by mixing aliquots of all the samples to be a pooled sample. An aliquot of the 200 μL supernatant was transferred to a glass sampling vial for vacuum-dry at room temperature. An 80 μL of 15 mg/mL methoxylamine hydrochloride in pyridine was subsequently added. The resultant mixture was vortexed vigorously for 2 min and incubated at 37 °C for 90 min. Then, 50 μL of BSTFA (with 1% TMCS), 20 μL n-hexane and 10 internal standards (C8/C9/C10/C12/C14/C16/C18/C20/C22/C24, all of which were chloroform configurations) were added into the mixture, which was vortexed vigorously for 2 min and then derivatized at 70 °C for 60 min. The samples were placed at ambient temperature for 30 min before GC-MS analysis. LC-MS:Briefly, 20 mg accurately weighed sample was transferred to a 1.5 mL Eppendorf tube with 20 μL of L-2-chlorophenylalanine (0.3 mg/mL) dissolved in methanol as internal standard and 800 μL mixture of methanol and water (1/4, vol/vol) were added to each sample. Two small steel balls were added to the tube. Samples were stored at -20 °C for 2 min and then grinded at 60 HZ for 2 min. The whole samples were extracted by ultrasonic for 10 min in ice-water bath and stored at -20 °C for 2 h. Samples were centrifuged at 4 °C (13,000 rpm) for 10 min. The supernatants (150 μL) were collected using crystal syringes from each tube, 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 aliquots of the all samples to be a pooled sample. And all extraction reagents were pre-cooled at -20 °C before use.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS8357Tang Chunbo. Stomatological Hospital of Chongqing Medical University. Department of Dental Implantology, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing 210029, China.. cbtang@njmu.edu.cn.GC-MS: LC-MS: The obtained GC/MS raw data in .D format were transferred to .abf format via software Analysis Base File Converter for quick retrieval of data. Then, data were imported into software MS-DIAL, which performs peak detection, peak identification, MS2Dec deconvolution, characterization, peak alignment, wave filtering, and missing value interpolation. Metabolite characterization is based on LUG database. A data matrix was derived. The three-dimensional matrix includes: sample information, the name of the peak of each substance, retention time, retention index, mass-to-charge ratio, and signal intensity. In each sample, all peak signal intensities were segmented and normalized according to the internal standards with RSD greater than 0.3 after screening. After the data was normalized, redundancy removal and peak merging were conducted to obtain the data matrix. 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) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were 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.TreatmentPeriodontitis diseaseczzlylotus@163.comDescribe the origin of samples, any relevant treatment, time points etc. and the collection and storage procedure.Metabolomicspooled quality control samplegingival tissueAgilent 5977A MSDuntargeted analysisThermo Scientific Dionex UltiMate 3000 RSLC SystemHomo sapiensbiomarkermetabolitesThermo Scientific Q Exactive Plusexperimental samplesevere periodontitismetabolic pathwayAgilent 7890B GCgingival epitheliumpooled quality control samplegingival tissueAgilent 5977A MSDuntargeted analysisThermo Scientific Dionex UltiMate 3000 RSLC SystemHomo sapiensbiomarkermetabolitesThermo Scientific Q Exactive Plusexperimental samplesevere periodontitismetabolic pathwayAgilent 7890B GCgingival epitheliumGC-MS:The derived samples were analyzed on an Agilent 7890B gas chromatography system coupled to an Agilent 5977A MSD system (Agilent Technologies Inc., CA, USA). The temperature of MS quadrupole and ion source (electron impact) was set to 150 and 230 °C, respectively. The collision energy was 70 eV. Mass spectrometric data was acquired in a full-scan mode (50-500 m/z), and the solvent delay time was set to 5 min. The QCs were injected at regular intervals (every 3 samples) throughout the analytical run to provide a set of data from which repeatability could be assessed. LC-MS:A Dionex Ultimate 3000 RS UHPLC fitted with Q-Exactive plus quadrupole-Orbitrap mass spectrometer equipped with heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. The mass range was from 100 to 1200 m/z. The resolution was set at 70,000 for the full MS scans and 17,500 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 (+) / 3000 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. The QCs were injected at regular intervals (every 3 samples) throughout the analytical run to provide a set of data from which repeatability can be assessed.The purpose of this study was to determine potential metabolic biomarkers and therapeutic drugs in the gingival tissue of individuals with periodontitis. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were used to analyze the gingival tissue samples from 20 patients with severe periodontitis and 20 healthy controls. Differential metabolites were identified using variable important in projection (VIP) values from the orthogonal partial least squares discrimination analysis (OPLS-DA) model and then verified for significance between groups using a two-tailed Student's t test. In total, 65 metabolites were enriched in 33 metabolic pathways, with 40 showing a significant increase and 25 expressing a significant decrease. In addition, it was found that patients with severe periodontitis have abnormalities in metabolic pathways, such as glucose metabolism, purine metabolism, amino acid metabolism, and so on. Furthermore, based on a multidimensional analysis, 12 different metabolites may be the potential biomarkers of severe periodontitis. The experiment's raw data have been uploaded to the MetaboLights database, and the project number is MTBLS8357. Moreover, osteogenesis differentiation characteristics were detected in the selected metabolites. The findings may provide a basis for the study of diagnostic biomarkers and therapeutic metabolites in severe periodontitis.Untargeted Metabolomics Analysis of Gingival Tissue in Patients with Severe Periodontitis.Chu Zhuangzhuang Z, Zhao Tong T, Zhang Zhewei Z, Chu Catherine Huihan CH, Cai Kunzhan K, Wu Jin J, Wu Wei W, Tang Chunbo CfalseUntargeted metabolomics analysis of gingival tissue in patients with severe periodontitisPeriodontitis, as a chronic inflammatory condition, its initiating factor is the plaque biofilm. The inflammation starts in the gingival tissue, if left untreated, may progress to severe periodontitis and cause bone and tooth loss. The purpose of this study was to determine the potential metabolic biomarkers and therapeutic drugs in the gingival tissue of patients with periodontitis in order to improve osteogenic differentiation in patients with severe periodontitis. The gingival tissue samples of 20 healthy controls and 20 patients with severe periodontitis were analyzed using ultra-performance liquid chromatography-mass spectrometry and ultra-performance gas chromatography-mass spectrometry in this study. Variable important in projection (VIP) values obtained from the Orthogonal partial least squares discrimination analysis (OPLS-DA) model were applied to identify differential metabolites. A two-tailed Student's t-test was further used to verify whether the differential metabolites between groups were significant, and the differential metabolites with VIP values > 1.0 and P values < 0.05 were selected for screening. In total, 65 metabolites were enriched in 33 metabolic pathways, 40 metabolites were increased significantly, and 25 metabolites expressed downregulation significantly. In addition, it was found that patients with severe periodontitis have abnormalities in metabolic pathways such as glucose metabolism, purine metabolism, amino acid metabolism, and so on. Furthermore, based on a multidimensional analysis, 12 different metabolites may be the potential biomarkers of severe periodontitis. Moreover, osteogenesis differentiation characteristics were detected in the selected metabolites. The findings may provide a basis for the study of diagnostic biomarkers and therapeutic metabolites to promote osteogenesis differentiation in severe periodontitis.2026-09-082026-08-06MTBLS835738018860