MetaboLights

application/xml

ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/m_MTBLS12824_LC-MS_negative_hilic_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/m_MTBLS12824_LC-MS_positive_hilic_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/m_MTBLS12824_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/m_MTBLS12824_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/m_MTBLS12824_LC-MS_negative_hilic_metabolite_profiling-1_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/a_MTBLS12824_LC-MS_positive_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/a_MTBLS12824_LC-MS_negative_hilic_metabolite_profiling-1.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/a_MTBLS12824_LC-MS_negative_hilic_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/a_MTBLS12824_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/s_MTBLS12824.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824/a_MTBLS12824_LC-MS_positive_hilic_metabolite_profiling.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS12824Metabolomics analysisData were analyzed using Bruker TASQ software version 2.1.22.3. All reported metabolite intensities were normalized to dry tissue weight, as well as to internal standards with comparable retention times and response in the MS. General repeatability of metabolite analysis was assessed for each metabolite using repeated measurements of a pooled quality control (QC) sample. Metabolite identification has been based on a combination of accurate mass, (relative) retention times and fragmentation spectra, compared to the analysis of a library of standards in separate experiments not described here. Lipidomics: bioinformatics for lipid identificationLipid identification was performed at the Core Facility Metabolomics at the Amsterdam UMC, essentially as described37. The raw LC/MS data were converted to mzXML format using MSConvert38. The dataset was processed using an in-house developed metabolomics pipeline written in the R programming language (http://www.r-project.org)39. In brief, it consisted of the following steps: (1) pre-processing using the R package XCMS40 with minor changes to some functions in order to better suit the Q Exactive data; notably, the definition of noise level in centWave was adjusted and the stepsize in fillPeaks (2) identification of metabolites using an in-house database of (phospho)lipids, with known internal standards indicating the position of most of the lipid clusters, matching m/z values within 3 ppm deviation, (3) isotope correction to obtain deconvoluted intensities for overlapping peak groups, (4) normalization on the intensity of the internal standard for lipid classes for which an internal standard was available (with normalization on the intensity of PE(14:0)2 for lipid classes for which no internal standard was present) and dry tissue weight. For quantifying abundances of lipid classes, the summed abundances of the individual lipid species from the relevant class were used. General repeatability of lipid analysis was assessed for each lipid using repeated measurements of a pooled quality control (QC) sample. Lipid class identification has been based on accurate mass, fragmentation analysis, relative retention times and ion mobility, compared to the analysis of relevant standards in separate experiments not described here. MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - HILICLiquid Chromatography MS - negative - HILICLiquid Chromatography MS - positive - reverse phaseMetabolomics analysisThe top layer, containing polar metabolites, was dried using a vacuum concentrator at 60°C. Dried samples were reconstituted in 100 µL 3:2 (v/v) MeOH:MilliQ. Metabolites were analyzed using a Waters Acquity ultra-high performance liquid chromatography system coupled to a Bruker Impact II™ Ultra-High Resolution Qq-Time-Of-Flight mass spectrometer. Samples were kept at 12°C during analysis and 5 µL of each sample was injected. Chromatographic separation was achieved using a Merck Millipore SeQuant ZIC-cHILIC column (PEEK 100 x 2.1 mm, 3 µm particle size). Column temperature was held at 30°C. Mobile phase consisted of (A) 1:9 (v/v) ACN:MilliQ and (B) 9:1 (v/v) ACN:MilliQ, both containing 5 mmol/L ammonium acetate. Using a flow rate of 0.25 mL/min, the LC gradient consisted of: 100% B for 0-2 min, reach 0% B at 28 min, 0% B for 28-30 min, reach 100% B at 31 min, 100% B for 31-32 min. Column re-equilibration is achieved at a flow rate of 0.4 mL/min at 100% B for 32-35 min.  Lipidomics analysisThe bottom layer of the extraction, containing lipids, was dried under a stream of nitrogen at 30°C and reconstituted in 100 µL 50:50 MeOH:CHCl3. The UPLC system consisted of an Ultimate 3000 binary HPLC pump, a vacuum degasser, a column temperature controller, and an auto sampler (Thermo Scientific, Waltham, MA, USA). Samples were run in normal and reverse phase and positive and negative ionization. For normal phase, 2μL of each sample was injected into the system. The normal phase system consisted of a Lichrospher Si 60, 2 x 250 mm silica 100 Å column, 5 µm particle diameter (Merck, Germany), the column temperature was maintained at 25°C. Lipids were separated using a linear gradient between solution B (CHCl3/MeOH, 97:3 v/v) and solution A (MeOH/MilliQ, 85:15, v/v). Solution A contained 0.0125% formic acid and 3.35 mmol/l ammonia per liter of eluent. Solution B contained 0.0125% formic acid per liter. The gradient (0.3 ml/min) was as follows: 0-1 min 10%A, 1–4 min 10%A–20%A, 4–12 min 20%A–85% A, 12–12.1 min, 85%A–100% A, 12.1–14.0 min 100% A, 14-14.1 min 100%A–10%A and 14.1–15 min equilibration with 10% A. All gradient steps were linear, and the total analysis time, including the equilibration, was 15 min. For reversed phase separation, 5 μL of each sample was injected onto a Waters HSS T3 column (150 x 2.1 mm, 1.8 μm particle size). Column temperature was held at 60°C. Mobile phase consisted of (A) 4:6 (v/v) MeOH:MilliQ and B 1:9 (v/v) MeOH:IPA, both containing 0.1% formic acid and 10 mmol/L ammonia. Using a flow rate of 0.4 mL/min, the LC gradient consisted of: Dwell at 100% A at 0 min, ramp to 80% A at 1 min, ramp to 0% A at 16 min, dwell at 0% A for 16-20 min, ramp to 100% A at 20.1 min, dwell at 100% A for 20.1-21 min. Delayed molecular aging, preservation of energy metabolism and enhanced exercise response in exercise-trained human muscle.Maria Magdalena TretowiczAmsterdam UMCmusclemass spectrometry assayMetabolite and Lipid extractionMetabolomics and lipidomics were performed at the Core Facility Metabolomics at the Amsterdam UMC, essentially as described13,36,37. In a 2 mL tube, the following amounts of internal standard dissolved in MilliQ were added to each sample of approximately 3-5 mg of freeze-dried muscle tissue: adenosine-15N5-monophosphate (5 nmol), adenosine-15N5-triphosphate (5 nmol), D4-alanine (0.5 nmol), D7-arginine (0.5 nmol), D3-aspartic acid (0.5 nmol), D3-carnitine (0.5 nmol), D4-citric acid (0.5 nmol), 13C1-citrulline (0.5 nmol), 13C6-fructose-1,6-diphosphate (1 nmol), guanosine-15N5-monophosphate (5 nmol), guanosine-15N5-triphosphate (5 nmol), 13C6-glucose (10 nmol), 13C6-glucose-6-phosphate (1 nmol), D3-glutamic acid (0.5 nmol), D5-glutamine (0.5 nmol), D5-glutathione (1 nmol), 13C6-isoleucine (0.5 nmol), D3-lactic acid (1 nmol), D3-leucine (0.5 nmol), D4-lysine (0.5 nmol), D3-methionine (0.5 nmol), D6-ornithine (0.5 nmol), D5-phenylalanine (0.5 nmol), D7-proline (0.5 nmol), 13C3-pyruvate (0.5 nmol), D3-serine (0.5 nmol), D6-succinic acid (0.5 nmol), D5-tryptophan (0.5 nmol), D4-tyrosine (0.5 nmol), D8-valine (0.5 nmol). For lipidomics, the following internal standards dissolved in 50:50 MeOH:CHCl3 were added: DG(14:0)2, TG(14:0)3, CE(16:0)-d7, PC(14:0)2, PS(14:0)2, PE(14:0)2, PA(14:0)2, ST(17:0), PI(8:0)2, LPE(14:0), LPC(14:0), LPA(14:0), SPH(d17:1), SM(12:0), SPH(d17:0), S1P(d17:1), S1P(d17:0), LacCer(d18:1/12:0), GlcCer(d18:1/12:0), Cer(d18:1/12:0), C1P(d18:1/12:0), Cer(d18:1/25:0). After adding the internal standard mixtures, a 5 mm stainless-steel bead and polar phase solvents (for a total of 500 µL MilliQ and 500 µL MeOH) were added and samples were homogenized using a TissueLyser II (Qiagen, Hilden, Germany) for 5 min at a frequency of 30 times/sec. Chloroform was added for a total of 1 mL to each sample before thorough mixing. Samples were then centrifuged for 10 minutes at 18.000g. The top and bottom layer were each transferred to a new 1.5 mL tube for separate processing. Homo sapiensMetabolomics analysisData were analyzed using Bruker TASQ software version 2.1.22.3. All reported metabolite intensities were normalized to dry tissue weight, as well as to internal standards with comparable retention times and response in the MS. General repeatability of metabolite analysis was assessed for each metabolite using repeated measurements of a pooled quality control (QC) sample.  Lipidomics: bioinformatics for lipid identificationLipid identification was performed at the Core Facility Metabolomics at the Amsterdam UMC, essentially as described37. The raw LC/MS data were converted to mzXML format using MSConvert38. The dataset was processed using an in-house developed metabolomics pipeline written in the R programming language (http://www.r-project.org)39. In brief, it consisted of the following steps: (1) pre-processing using the R package XCMS40 with minor changes to some functions in order to better suit the Q Exactive data; notably, the definition of noise level in centWave was adjusted and the stepsize in fillPeaks (2) identification of metabolites using an in-house database of (phospho)lipids, with known internal standards indicating the position of most of the lipid clusters, matching m/z values within 3 ppm deviation, (3) isotope correction to obtain deconvoluted intensities for overlapping peak groups, (4) normalization on the intensity of the internal standard for lipid classes for which an internal standard was available (with normalization on the intensity of PE(14:0)2 for lipid classes for which no internal standard was present) and dry tissue weight. For quantifying abundances of lipid classes, the summed abundances of the individual lipid species from the relevant class were used. General repeatability of lipid analysis was assessed for each lipid using repeated measurements of a pooled quality control (QC) sample. Lipid class identification has been based on accurate mass, fragmentation analysis, relative retention times and ion mobility, compared to the analysis of relevant standards in separate experiments not described here. Before exercise / after exercise exerciseAge groupm.m.tretowicz@amsterdamumc.nlhttps://www.ebi.ac.uk/metabolights/MTBLS12824Forty-seven participants, including 11 young and 36 older adults were recruited in the community of Maastricht and its surroundings through advertisements at Maastricht University, in local newspapers, supermarkets, and at sports clubs, to collect both –before and –after measures of an exercise intervention. The study protocol was approved by the institutional Medical Ethical Committee and conducted in agreement with the declaration of Helsinki. All participants provided their written informed consent, and the study was registered at clinicaltrials.gov with identifier NCT03666013. Physiological data from this clinical trial has been reported in our previous study as part of a different analysis, and the current study uses the same individuals for whom a pre and post-exercise biopsy was available – specifically, not all individuals from prior studies were eligible for RNA sequencing or other omics analyses. This was due to unavailable tissue at the time of transcriptomic / metabolomics / lipidomic profiling. In this regard, we note that the current study performed wholly new rounds of metabolomics and lipidomics (and RNAseq) on this cohort, and did not reuse data from our previous studies. Prior to inclusion, all subjects underwent a medical screening that included a physical examination by a physician and an assessment of physical function using the Short Physical Performance Battery (SPPB), comprised of a standing balance test, a 4-m walk test, and a chair-stand test. After the screening procedure, participants were assigned to the following study groups: Young individuals with normal physical activity (20 – 30 years), older adults with normal physical activity (65 – 80 years), physically trained older adults (65 – 80 years) and physically impaired older adults (65 – 80 years). Participants were considered normal, physically active if they completed no more than one structured exercise session per week. Participants were considered trained if they engaged in at least 3 structured exercise sessions of at least 1 hour each per week for an uninterrupted period of more than one year. Participants were classified as older adults with impaired physical function in case of an SPPB score of ≤ 9. The SPPB score was calculated according to the cut-off points determined by (Guralnik et al. 1994). Exercise interventionParticipants performed a 1-h submaximal exercise bout in the fasted state on an electronically braked cycle ergometer, at 50% of their Wmax as measured during a maximal aerobic cycling test8. Participants were instructed to pedal at a controlled cadence between 60 and 70 revolutions per minute. Muscle biopsy   At 9 AM, after an overnight fast from 10 PM the preceding evening, and immediately following the acute exercise bout, muscle biopsies were taken from the m. vastus lateralis under local anesthesia (1.0% lidocaine without epinephrine) according to the Bergström method. The muscle biopsies were immediately frozen in melting isopentane and stored at –80°C until further analysis.MetabolomicsMulti-omics studyExerciseAgingLifestyleMuscle TissueMulti-omics studyExerciseAgingLifestyleMuscle TissueMetabolomics analysisMetabolites were analyzed using a Waters Acquity ultra-high performance liquid chromatography system coupled to a Bruker Impact II™ Ultra-High Resolution Qq-Time-Of-Flight mass spectrometer.  MS data were acquired using negative and positive ionization in full scan mode over the range of m/z 50-1200.  Lipidomics analysisA Q Exactive Plus (Thermo Scientific) mass spectrometer was used in the negative and positive electrospray ionization mode. In both ionization modes, mass spectra of the lipid species were obtained by continuous scanning from m/z 150 to m/z 2000 with a resolution of 280.000. Nitrogen was used as the nebulizing gas. The spray voltage used was 2500 V (-) and 3500 V (+), and the capillary temperature was 256°C. S-lens RF level: 50, Auxiliary gas: 10, Auxiliary gas temperature 300°C, Sheath gas: 50, Sweep cone gas: 2.PE(P-41:1)FA(22:3)PE(P-39:5)LPE(P-19:1)PE(P-34:4)PE(P-40:4)PS(38:3)1-acyl LPE(17:0)PE(O-36:1)FA-OH(19:2)PS(44:3)2-acyl LPE(16:1)PE(P-35:0)FA-OH(20:1)LPE(P-20:0)PE(P-41:2)PE(P-39:4)FA(22:2)PE(P-40:5)PE(P-34:3)PS(38:2)FA(17:1)PS(42:8)1-acyl LPE(22:3)FA(17:0)PS(44:2)2-acyl LPE(16:0)PE(P-34:2)FA-OH(20:2)LPE(P-20:1)PS(34:1)FA(22:5)PE(P-40:2)LPE(P-20:2)PE(P-35:3)PS(40:1)PS(38:5)FA(17:2)PE(P-39:7)1-acyl LPE(24:0)PS(44:5)PE(P-30:1)FA-OH(21:0)PE(P-44:7)PE(P-40:3)PS(34:0)LPE(P-20:3)FA(22:4)PE(P-35:2)1-acyl LPE(18:0)FA(18:0)PS(38:4)PE(P-39:6)PE(O-36:0)PS(44:4)PE(P-30:0)1-acyl LPE(22:5)LPS(20:4)FA-OH(20:4)PE(P-35:1)FA-OH(21:1)PE(P-44:8)PE(P-41:5)PE(P-42:2)PE(P-36:2)PS(39:4)FA(18:1)2-acyl LPE(22:5)FA-OH(18:1)LPE(O-16:0)PE(O-32:0)1-acyl LPE(22:0)PS(40:3)LPS(22:5)PE(P-46:7)FA(22:6)2-acyl LPE(22:4)PE(P-41:6)1-acyl LPE(16:0)PE(P-35:4)PS(34:2)PE(P-42:3)PE(P-36:1)PS(40:2)FA(18:2)LPE(O-22:0)PS(38:6)PE(O-38:0)FA-OH(18:2)PE(P-36:0)PE(P-41:7)PE(P-46:8)PE(P-42:0)LPE(P-22:0)2-acyl LPE(18:1)PE(P-36:4)PE(P-40:6)PE(P-41:3)PE(P-37:1)FA(19:0)FA(18:3)1-acyl LPE(16:1)LPE(P-16:0)PS(39:6)FA-OH(19:0)FA-OH(18:3)PS(40:5)PE(P-46:5)LPE(P-22:1)LPI(20:4)PE(P-42:1)PE(P-40:7)PE(P-36:3)PE(P-41:4)PE(P-37:0)LPE(P-16:1)PS(41:1)FA(19:1)2-acyl LPE(22:6)PS(39:5)LPE(O-17:0)FA-OH(20:0)1-acyl LPE(22:1)PS(40:4)PE(P-40:8)PE(P-46:6)2-acyl LPE(18:3)PE(P-36:6)PE(P-37:3)PS(36:1)FA(20:1)PE(P-38:0)FA(19:2)FA-OH(17:0)PS(40:7)PE(P-32:1)PS(42:1)FA(14:0)PE(P-42:7)FA(24:6)FA(24:4)2-acyl LPE(18:2)PE(P-36:5)PE(P-37:2)PE(P-44:1)FA(20:0)LPE(P-17:0)FA-OH(17:1)PS(46:4)LPS(18:2)PE(P-32:0)PS(40:6)1-acyl LPE(20:1)PE(P-42:8)FA(24:5)FA-OH(22:4)PE(P-37:5)PE(P-42:4)PS(36:3)FA(20:3)PE(P-38:2)2-acyl LPE(20:1)LPE(P-17:1)PE(O-38:1)LPS(18:1)PS(41:6)PS(42:3)PE(P-33:0)LPE(O-18:0)FA-OH(22:5)PE(P-37:4)LPE(O-24:0)PS(36:2)PE(P-38:1)PE(P-42:5)FA(20:2)2-acyl LPE(24:4)FA-OH(18:0)LPS(18:0)PS(40:8)PE(P-32:2)FA(15:0)PS(42:2)LPE(O-18:1)LPS(22:6)PE(P-42:6)FA-OH(22:6)2-acyl LPE(20:3)LPE(P-24:0)FA(20:5)PE(P-38:4)PE(P-40:0)PS(32:1)PE(P-39:1)LPE(P-18:0)1-acyl LPE(18:1)PS(42:5)1-acyl LPE(24:2)LPS(20:3)PE(P-28:0)PE(P-33:2)FA-OH(21:2)PE(P-44:5)FA-OH(20:5)PE(P-37:6)PE(P-38:3)PE(P-40:1)LPE(P-24:1)PS(32:0)FA(20:4)LPE(P-18:1)2-acyl LPE(20:2)PE(P-39:0)PS(36:4)1-acyl LPE(24:1)FA-OH(16:0)PS(42:4)PE(P-33:1)PS(43:1)FA-OH(21:3)PE(P-45:3)FA-OH(22:0)PE(P-44:6)PE(P-39:3)PE(P-38:6)2-acyl LPE(20:5)LPE(P-18:2)FA(22:1)PS(38:1)PE(P-44:2)PS(37:4)LPI(18:1)PS(42:7)PE(O-34:0)FA-OH(16:1)FA(16:0)PS(44:1)1-acyl LPE(20:0)FA-OH(22:1)PE(O-40:0)PE(P-34:1)2-acyl LPE(20:4)PE(P-38:5)PE(P-39:2)LPE(P-19:0)PE(P-44:3)FA(16:2)1-acyl LPE(19:1)PE(O-34:1)PS(42:6)FA(16:1)FA-OH(22:2)PE(P-44:4)PE(P-34:0)PE(O-40:1)FA-OH(21:5)falseDelayed molecular aging, preservation of energy metabolism and enhanced exercise response in exercise-trained human muscleExercise is fundamental to healthy aging, yet the degree to which it mitigates age-related molecular changes and how varying physical fitness levels influence the molecular response to exercise with age remain unclear. To address this, we performed transcriptomics, lipidomics, and metabolomics on skeletal muscle of young and older adults with differing physical function, both before and after an acute bout of sub-maximal exercise. At baseline, older adults exhibited reduced expression of genes associated with cellular respiration and energy metabolism compared to young adults with comparable activity levels. Remarkably, in trained older adults, 50% of these age-related differences were absent, resulting in transcriptomic profiles for cellular respiration that closely aligned with those of young adults. Following acute exercise, trained older adults demonstrated molecular responses that more closely resembled those of younger individuals. While all participants displayed transcriptional immune and stress responses upon acute exercise, the magnitude of these responses in older adults correlated positively with their physical fitness. These findings underscore the capacity of sustained physical training to transform age-related molecular profiles, highlight a positive link between physical fitness level and exercise-induced inflammation in older adults, and provide a multi-omic molecular atlas for examining aging and fitness regulatory networks.2026-04-092025-08-06MTBLS12824CE(16:0) (IS)CE(16:0)CE(16:1)CE(17:0)CE(18:0)CE(18:1)CE(18:2)CE(18:3)CE(19:1)CE(19:2)CE(20:0)CE(20:1)CE(20:2)CE(20:3)CE(20:4)CE(20:5)CE(21:3)CE(22:0)CE(22:1)CE(22:2)CE(22:3)CE(22:4)CE(22:5)CE(22:6)CE(24:0)CE(24:1)CE(24:2)CE(24:4)CE(25:0)CE(26:0)CE(26:1)CL(56:0) (IS)CL(66:4)CL(66:5)CL(66:6)CL(68:2)CL(68:3)CL(68:4)CL(68:5)CL(68:6)CL(68:7)CL(69:6)CL(70:3)CL(70:4)CL(70:5)CL(70:6)CL(70:7)CL(70:8)CL(72:5)CL(72:6)CL(72:7)CL(72:8)CL(72:9)CL(73:7)CL(74:6)CL(74:7)CL(74:8)CL(74:9)CL(74:10)CL(74:11)CL(76:10)CL(76:11)CL(76:12)Cer(d43:1) (IS)Cer(d30:1) (IS)Cer(d32:1)Cer(d34:0)Cer(d34:1)Cer(d35:1)Cer(d36:0)Cer(d36:1)Cer(d36:2)Cer(d38:0)Cer(d38:1)Cer(d38:2)Cer(d39:1)Cer(d40:0)Cer(d40:1)Cer(d40:2)Cer(d40:3)Cer(d41:0)Cer(d41:1)Cer(d41:3)Cer(d42:0)Cer(d42:1)Cer(d42:2)Cer(d42:3)Cer(d42:4)Cer(d43:0)Cer(d43:2)Cer(d44:0)Cer(d44:1)Cer(d44:2)Cer(d44:3)Cer(d44:4)CL(72:10)C1P(d30:1) (IS)CDP-ethanolamineCitric acidCitric acid-D4CitrullineCitrulline-13C1CMPCoA-GlutathioneCoenzyme_ACreatineCreatine-PCreatinineCTPCystathionineCarnitineCarnitine (C10:0)Carnitine (C10:1)Carnitine (C12:0)Carnitine (C12:1)Carnitine (C14:0)Carnitine (C14:1)Carnitine (C16:0)Carnitine (C16:1)Carnitine (C18:1)Carnitine (C2:0)Carnitine (C3:0)Carnitine (C4:0)Carnitine (C6:0)Carnitine (C6:1)Carnitine (C8:0)Carnitine-D3CarnosineCDP-cholineCholine