Project description:This project applies proteomic stable isotope probing (proteomic SIP) to evaluate detection and quantification of 13C‑labeled E. coli peptides within a complex mouse fecal microbiome background. Proteins were extracted, digested, and quantified before preparing spike‑in mixtures. Mouse fecal peptides (2 µg) were combined with E. coli peptides at 10:1 or 100:1 ratios using defined 13C labeling levels (2%, 5%, 50%), each in triplicate. Peptides were analyzed by nanoLC–MS/MS on an XSelect CSH C18 column coupled to an Orbitrap Fusion Tribrid mass spectrometer in data‑dependent acquisition mode. The dataset enables controlled evaluation of isotopic incorporation, quantitative behavior, and sensitivity of proteomic SIP in microbiome samples.
Project description:Metabolic dysfunction-associated steatohepatitis (MASH), affects nearly one-third of the global population with limited pharmacotherapy approved, underscoring the urgent need for new therapeutic strategies. N-acyl amino acids (NAAs), comprising amino acids linked to long-chain fatty acid acyl groups, are gaining interest, yet their metabolic regulation in MASH remains elusive. Metabolomic profiling in mice and humans with MASH revealed a marked depletion of NAAs, particularly C18:1-Leu, which inversely correlated with disease severity. The bidirectional PM20D1 expression was suppressed in the livers of human and mice with MASH, as well as in lipid-loaded primary mouse hepatocytes and hepatic cell lines. Stable isotope tracing studies in mice with and without MASH confirmed reduced biosynthesis of C18:1-Leu. Genetically, hepatocyte-specific overexpression of PM20D1 or pharmacological treatment with exogenous C18:1-Leu significantly attenuated or reversed established MASH. Mechanistically, C18:1-Leu bound and activated peroxisome proliferator-activated receptor alpha (PPARα), enhancing fatty acid β-oxidation and suppressing the NF-κB/CCL2 axis, thereby reducing hepatic macrophage infiltration, inflammation, and fibrosis. These therapeutic effects were abolished in hepatocyte-specific PPARα- and CCL2-deficient mice, identifying C18:1-Leu as a promising metabolic therapy for MASH.
Project description:This is an untargeted metabolomics dataset of the human gut metabolome derived from infant stool samples. Data acquisition was done using C18 negative and positive mode, as well as HILIC positive mode. In this case study there are 2 individuals, Baby 1 and Baby 2. Sample 1152 is pre-antibiotic sample of Baby 1, Sample 1162 is post-antibiotic without autologous fecal microbiota transplantation (aFMT). Sample 0036 is pre-antibiotic sample of Baby 2, and sample 0045 is post-antibiotic with post-aFMT treatment. The goal of this study was to determine if aFMT can restore the gut microbiota to the individuals pre-antibiotic state after exposure to antibiotics.
Project description:Fecal samples from 14 different species of felines were extracted using 50:50 MeOH: H2O and chromatographed using a Phenomenex polar C18 column. MS/MS data was acquired on Orbitrap in positive ionization mode on 12 min LC gradient.
Project description:NIST fecal reference material extracted with 50% MeOH and prepared in different dilutions and reconstitution solvents. The analysis was conducted using a Hypersil Gold (C18) in positive ionization mode on a Astral via different MS-modes (positive ionization mode).
Project description:This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.
Project description:Lean nonalcoholic fatty liver disease (NAFLD) is increasingly recognized as a distinct clinical phenotype with limited evidence for effective non-pharmacological interventions and unclear mechanistic pathways. Aerobic exercise is recommended for NAFLD management; however, its effects and the gut microbiota–associated mechanisms in lean NAFLD remain incompletely understood. This dataset was generated from a randomized controlled trial (ClinicalTrials.gov identifier: NCT04882644). Participants assigned to the aerobic exercise intervention group provided fecal samples at baseline and after the 3-month intervention. A total of 33 paired fecal samples were included in this dataset. Gut microbiota profiles were generated using shotgun metagenomic sequencing. The dataset includes processed and de-identified species-level relative abundance tables derived from fecal samples collected before and after the intervention. These data were used to characterize exercise-induced alterations in gut microbial composition and interindividual variability in microbiota responses to aerobic exercise in lean NAFLD. The data support integrative analyses with clinical phenotypes and circulating metabolomic profiles to explore gut microbiota–associated mechanisms underlying the metabolic benefits of aerobic exercise.
Project description:Human serum samples from Johns Hopkins University. LCMS run on 05-09-2023. Polar C18 column in positive mode. This project is about the chagas disease progression. Four groups in this dataset: infected-progressed, infected-non-progressed, uninfected-progressed, uninfected-non-progressed. We want to compare the progressed and non-progressed in infected groups to see the changes of metabolites related to the disease progression.