Summary of Study ST000923
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000639. The data can be accessed directly via it's Project DOI: 10.21228/M82T15 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
Study ID | ST000923 |
Study Title | Longitudinal Metabolomics of the Human Microbiome in Inflammatory Bowel Disease |
Study Summary | A number of factors contribute to the complex array of small molecules that occur in stool; including diet, gut flora, and gut function. Comprehensive profiling of the stool metabolome therefore can provide detailed phenotypic information on health status, metabolic interactions between the host and the microbiome, and interactions among gut microbes. Here, we applied metabolomics to characterize stool samples collected longitudinally from inflammatory bowel disease (IBD) patients and non-IBD controls who participated in the Integrative Human Microbiome Project (iHMP). A total of 546 stool samples were analyzed using a platform comprised of four complementary liquid chromatography tandem mass spectrometry (LC-MS) methods designed to measure polar metabolites and lipids. Each method used high resolution/accurate mass (HRAM) profiling to measure both metabolites of confirmed identity and yet to be identified metabolite peaks. 81,867 de-isotoped LC-MS peaks were measured, out of which 597 were annotated based on confirmation with authentic reference standards. Pooled stool extracts inserted and analyzed throughout the analysis queues to evaluate analytical reproducibility showed a median coefficient of variation of 5.1% among known metabolites and 24.2% across all 81,867 features. Owing to differences in water content and heterogeneity among stool samples, total median scaling was used to standardize the metabolomics data. In addition to being accessible at the Metabolomics Workbench repository, these metabolomics data will be incorporated into a multi’omic database (https://www.hmpdacc.org/ihmp/) that will enable the study of associations between the gut microbiome and IBD. |
Institute | Broad Institute of MIT and Harvard |
Last Name | Avila-Pacheco |
First Name | Julian |
Address | 415 Main Street |
jravilap@broadinstitute.org | |
Phone | 617-714-8264 |
Submit Date | 2017-11-14 |
Num Groups | 3 |
Total Subjects | 546 |
Num Males | 276 |
Num Females | 270 |
Raw Data Available | Yes |
Raw Data File Type(s) | raw(Thermo) |
Analysis Type Detail | LC-MS |
Release Date | 2018-02-07 |
Release Version | 1 |
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Sample Preparation:
Sampleprep ID: | SP000968 |
Sampleprep Summary: | Samples were thawed on ice and then centrifuged (4 ˚C, 5,000 x g) for 5 minutes. Ethanol was evaporated using a gentle stream of nitrogen gas using a nitrogen evaporator (TurboVap LV; Biotage, Charlotte, NC) and stored at -80 ˚C until all samples in the study had been dried. Aqueous homogenates were generated by sonicating each sample in 900 μl of H2O using an ultrasonic probe homogenizer (Branson Sonifier 250) set to a duty cycle of 25% and output control of 2 for 3 minutes. Samples were kept on ice during the homogenization process. The homogenate for each sample was aliquoted into two 10 μL and two 30 μL in 1.5mL centrifuge tubes for LC-MS sample preparation and 30 μL of homogenate from each sample were transferred into a 50 mL conical tube on ice to create a pooled reference sample. The pooled reference mixture was mixed by vortexing and then aliquoted (100 μL per aliquot) into 1.5 mL centrifuge tubes. Aliquots and reference sample aliquots were stored at -80 °C until LC-MS analyses were conducted. Pairs of pooled reference samples were inserted into the queue at intervals of approximately 20 samples in order to assess analytical variance and as a reference to standardize within and across batches by “nearest neighbor” scaling. |