The acquired LC-MS untargeted data files were converted to *.mzXML format using the ProteWizard MSconvert tool (v 3.0.24074-2118430) and subsequently pre-processed with the open-source software MZmine (v 3.9.0) , including peak detection, retention time correction, peak matching, and peak filling. Peak detection was performed in three steps: (i) mass detection with noise value = 10000 and retention time range = 0.8 to 21 min; (ii) chromatogram builder with minimum time span = 0.2 min, minimum height = 20000, and m/z tolerance = 0.005 Da or 15 ppm; (iii) deconvolution with peak width = 0.05-1.0 min and noise = 10000. Retention time was corrected using m/z tolerance = 0.005 Da or 15 ppm, retention time tolerance = 1.0 min, and minimum standard intensity = 1.0E6. Peak matching among samples was performed with the Join aligner using m/z tolerance = 0.005 Da or 15 ppm (weight for m/z = 1), retention time tolerance = 0.2 min (weight for retention time = 0.9), and mobility weight = 1. Gap filling was applied using the peak finder method with retention time correction, with intensity tolerance = 30%, m/z tolerance = 0.005 Da or 15 ppm, and retention time tolerance = 0.3 min.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - negative - reverse-phase"],"chromatography_protocol":["The analyses were carried out using an Agilent 1290 Infinity LC system coupled to an Agilent 6550 iFunnel Accurate-Mass QTOF mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with a dual electrospray ionization source (ESI-Jet Stream Technology). The system allowed continuous mass correction through the simultaneous introduction of a reference mass solution, ensuring high mass accuracy throughout the analytical sequence. Data acquisition was performed using MassHunter Workstation software (version B.08.00, Agilent).
Chromatographic separation was performed on a Poroshell 120 EC reverse-phase column (3.0 × 100 mm, 2.7 µm; Agilent Technologies), maintained at 30 °C. The injection volume was 1 µL, and the chromatographic run was carried out at a flow rate of 0.5 mL/min. The mobile phases consisted of water acidified with 0.1% formic acid (solvent A) and acetonitrile acidified with 0.1% formic acid (solvent B). Chromatographic separation was achieved using the following gradient program: 0-3 min, 5-18% B; 3-10 min, 18-50% B; 10-13 min, 50-90% B; 13-14 min, 90-5% B; followed by re-equilibration at 5% B until 22 min.
The mass spectrometer was operated in negative ionization mode only, as this approach provides enhanced sensitivity for several metabolite classes expected to be modulated by berry-derived polyphenols, including phenolic acids, microbial-derived catabolites, organic acids, and other acidic polar metabolites. Therefore,the analytical workflow was optimized to maximize the detection of these biologically relevant compounds according to the objectives of the study. Nevertheless, the use of a single ionization mode may reduce the detection of metabolites preferentially ionized in positive mode, thereby limiting the overall metabolome coverage.
TOF spectra were acquired over a mass range of m/z 50-1100, at an acquisition rate of 1.5 spectra/s, corresponding to 666.7 ms per spectrum, with 5484 transients per spectrum. The operating parameters of the ion source were set as follows: gas temperature, 280 °C; drying gas flow, 9 L/min; nebulizer pressure, 45 psi; sheath gas temperature, 400 °C; sheath gas flow, 12 L/min; capillary voltage, 3500 V; nozzle voltage, 500 V; fragmentor voltage, 100 V; skimmer voltage, 65 V; and octopole radiofrequency voltage, 750 V. External calibration of the instrument was performed at the beginning of each batch using the manufacturer’s Tuning Mix. Internal mass calibration was applied during acquisition using reference ions atm/z 112.9855 and m/z 1033.9881 in negative polarity.
To monitor system performance and support analytical reliability, a quality control (QC) sample was included in the analytical batch. This QC injection was used to assess retention time consistency, signal response, and mass accuracy under the selected experimental conditions. In addition, methanol blank injections were introduced at different points in the sequence to monitor potential carry-over effects, background contamination, and signal memory from previously injected samples. The inclusion of multiple methanol blanks also allowed verification of the cleanliness of the chromatographic system and contributed to the reliability of the analytical data.
"],"publication":["Longitudinal Integration of Microbiota and Metabolomics Reveals (Poly)phenols‑Driven Gut Ecosystem Dynamics."],"submitter_affiliation":["NIMSB-UNL"],"submitter_name":["Catarina Pinto"],"organism_part":["Faecal sample","Quality control"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["For untargeted metabolomics, 20 mg of each sample were extracted with 1 mL of methanol/chloroform/water (30:10:60, v/v/v) to recover primarily polar metabolites. Samples were vortexed for 1 min and centrifuged at 14,000 × g for 10 min at 4°C. The resulting supernatants were collected and evaporated to dryness under vacuum using a SpeedVac concentrator. Dried extracts were then reconstituted in 200 µL of methanol containing 0.1% formic acid prior to LC-MS analysis.
"],"organism":["Mus musculus"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS14779"],"author":["Natasa Loncarevic. iNOVA4Health, NOVA Medical School | Faculdade de Ciências Médicas, NMS|FCM, Universidade Nova de Lisboa; Lisboa, Portugal..","Juan Carlos Espín. Laboratory of Food & Health, Group of Quality, Safety, and Bioactivity of Plant Foods, CEBAS-CSIC, Campus de Espinardo, 30100 Murcia, Spain..","Joana Séneca. Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna, Vienna, Austria.","Rafael Carecho. iNOVA4Health, NOVA Medical School | Faculdade de Ciências Médicas, NMS|FCM, Universidade Nova de Lisboa; Lisboa, Portugal..","Carlos Pita. NOVA Institute for Medical Systems Biology, Association, NOVA IMSB, 2770-186 Paço de Arcos, Portugal.","Catarina Pinto. NOVA Institute for Medical Systems Biology - NIMSB. catarina.jpinto@unl.pt.","María Ángeles Ávila-Gálvez. Laboratory of Food & Health, Group of Quality, Safety, and Bioactivity of Plant Foods, CEBAS-CSIC, Campus de Espinardo, 30100 Murcia, Spain..","Antonio González-Sarrías. Group of Quality, Safety, and Bioactivity of Plant Foods, CEBAS-CSIC, Campus de Espinardo, 30100 Murcia, Spain..","Cláudia Nunes dos Santos. NOVA Institute for Medical Systems Biology, Association, NOVA IMSB, 2770-186 Paço de Arcos, Portugal 2 iNOVA4Health, Programme in Translational Medicine. claudia.nunes.santos@unl.pt.","David Berry. Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna, Vienna, Austria."],"data_transformation_protocol":["The acquired LC-MS untargeted data files were converted to *.mzXML format using the ProteWizard MSconvert tool (v 3.0.24074-2118430) and subsequently pre-processed with the open-source software MZmine (v 3.9.0), including peak detection, retention time correction, peak matching, and peak filling. Peak detection was performed in three steps: (i) mass detection with noise value = 10000 and retention time range = 0.8 to 21 min; (ii) chromatogram builder with minimum time span = 0.2 min, minimum height = 20000, and m/z tolerance = 0.005 Da or 15 ppm; (iii) deconvolution with peak width = 0.05-1.0 min and noise = 10000. Retention time was corrected using m/z tolerance = 0.005 Da or 15 ppm, retention time tolerance = 1.0 min, and minimum standard intensity = 1.0E6. Peak matching among samples was performed with the Join aligner using m/z tolerance = 0.005 Da or 15 ppm (weight for m/z = 1), retention time tolerance = 0.2 min (weight for retention time = 0.9), and mobility weight = 1. Gap filling was applied using the peak finder method with retention time correction, with intensity tolerance = 30%, m/z tolerance = 0.005 Da or 15 ppm, and retention time tolerance = 0.3 min.
"],"study_factor":["Timepoint"],"submitter_email":["catarina.jpinto@unl.pt"],"sample_collection_protocol":["For untargeted metabolomics, 20 mg of each sample were extracted with 1 mL of methanol/chloroform/water (30:10:60, v/v/v) to recover primarily polar metabolites. Samples were vortexed for 1 min and centrifuged at 14,000 × g for 10 min at 4°C. The resulting supernatants were collected and evaporated to dryness under vacuum using a SpeedVac concentrator. Dried extracts were then reconstituted in 200 µL of methanol containing 0.1% formic acid prior to LC-MS analysis.
"],"omics_type":["Metabolomics"],"study_design":["Metabolomics","Mus musculus","untargeted analysis","Faecal sample","Diet-microbe-metabolite axis","Quality control","Agilent 1290 Infinity LC system","gut microbiota remodeling","untargeted metabolite profiling","Agilent 6550 iFunnel Accurate-Mass QTOF"],"curator_keywords":["Metabolomics","Mus musculus","untargeted analysis","Faecal sample","Diet-microbe-metabolite axis","Quality control","Agilent 1290 Infinity LC system","gut microbiota remodeling","untargeted metabolite profiling","Agilent 6550 iFunnel Accurate-Mass QTOF"],"mass_spectrometry_protocol":["The analyses were carried out using an Agilent 1290 Infinity LC system coupled to an Agilent 6550 iFunnel Accurate-Mass QTOF mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with a dual electrospray ionization source (ESI-Jet Stream Technology). The system allowed continuous mass correction through the simultaneous introduction of a reference mass solution, ensuring high mass accuracy throughout the analytical sequence. Data acquisition was performed using MassHunter Workstation software (version B.08.00, Agilent).
Chromatographic separation was performed on a Poroshell 120 EC reverse-phase column (3.0 × 100 mm, 2.7 µm; Agilent Technologies), maintained at 30 °C. The injection volume was 1 µL, and the chromatographic run was carried out at a flow rate of 0.5 mL/min. The mobile phases consisted of water acidified with 0.1% formic acid (solvent A) and acetonitrile acidified with 0.1% formic acid (solvent B). Chromatographic separation was achieved using the following gradient program: 0-3 min, 5-18% B; 3-10 min, 18-50% B; 10-13 min, 50-90% B; 13-14 min, 90-5% B; followed by re-equilibration at 5% B until 22 min.
The mass spectrometer was operated in negative ionization mode only, as this approach provides enhanced sensitivity for several metabolite classes expected to be modulated by berry-derived polyphenols, including phenolic acids, microbial-derived catabolites, organic acids, and other acidic polar metabolites. Therefore,the analytical workflow was optimized to maximize the detection of these biologically relevant compounds according to the objectives of the study. Nevertheless, the use of a single ionization mode may reduce the detection of metabolites preferentially ionized in positive mode, thereby limiting the overall metabolome coverage.
TOF spectra were acquired over a mass range of m/z 50-1100, at an acquisition rate of 1.5 spectra/s, corresponding to 666.7 ms per spectrum, with 5484 transients per spectrum. The operating parameters of the ion source were set as follows: gas temperature, 280 °C; drying gas flow, 9 L/min; nebulizer pressure, 45 psi; sheath gas temperature, 400 °C; sheath gas flow, 12 L/min; capillary voltage, 3500 V; nozzle voltage, 500 V; fragmentor voltage, 100 V; skimmer voltage, 65 V; and octopole radiofrequency voltage, 750 V. External calibration of the instrument was performed at the beginning of each batch using the manufacturer’s Tuning Mix. Internal mass calibration was applied during acquisition using reference ions atm/z 112.9855 and m/z 1033.9881 in negative polarity.
To monitor system performance and support analytical reliability, a quality control (QC) sample was included in the analytical batch. This QC injection was used to assess retention time consistency, signal response, and mass accuracy under the selected experimental conditions. In addition, methanol blank injections were introduced at different points in the sequence to monitor potential carry-over effects, background contamination, and signal memory from previously injected samples. The inclusion of multiple methanol blanks also allowed verification of the cleanliness of the chromatographic system and contributed to the reliability of the analytical data.
"],"additional_accession":[]},"is_claimable":false,"name":"Longitudinal Integration of Microbiota and Metabolomics Reveals Polyphenols-Driven Gut Ecosystem Dynamics","description":"The gut microbiota and its \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"theatre of activity\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\" (the collective pool of metabolites and signaling molecules) function as a plastic interface that responds to nutritional inputs. Dietary (poly)phenols are key bioactive components of berries and are increasingly recognized for their capacity to modulate gut microbiota composition and metabolic activity while being biotransformed by microbiota. However, the temporal dynamics of this interaction remain poorly resolved, as most studies rely on cross-sectional or endpoint-only designs. Here, we conducted a longitudinal multi-omic study to map the co-evolution of the fecal microbiota and the metabolome during a sustained consumption of a berry enriched diet. Using a murine model, fecal samples were collected at baseline, mid intervention (day 21) and endpoint (day 42). These samples were analyzed by 16S rRNA gene sequencing and untargeted metabolomics to distinguish transient perturbations from stabilized ecosystem reorganization. Two-stage ecosystem reconfiguration was observed concurrent with the intervention: an early, high-magnitude reconfiguration phase (D0 - D21), followed by a period of functional stabilization (D21 - D42). The initial phase was characterized by the enrichment of the tryptophan metabolic pathway and significant taxonomic shifts, including the proliferation of Lachnospiraceae and Oscillospiraceae alongside the depletion of Prevotellaceae and Akkermansiaceae. Conversely, the stabilization phase was defined by the emergence of tyrosine-derived aromatic signatures and the recovery of Muribaculaceae. Integrated Procrustes analysis confirmed that the strongest coordination between the microbiota and metabolome occurred during the first 21 days, suggesting that microbial composition and metabolic output co-evolve most dynamically during the initial exposure to berry (poly)phenols. We conclude that dietary exposure to berries is associated with a rapid, coordinated restructuring of the gut ecosystem that stabilizes over time, emphasizing that longitudinal multi-omic designs are essential to capture the transient metabolic nodes and stable functional configurations that define the diet-microbe-metabolite loop.","dates":{"publication":"2026-07-03","submission":"2026-06-17"},"accession":"MTBLS14779","cross_references":{}}