Summary of Study ST002722

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 PR001688. The data can be accessed directly via it's Project DOI: 10.21228/M8HH7C 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.

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Study IDST002722
Study TitleCirrhosis-related metabolomics study
Study SummaryThere are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. The aim of this study is to determine whether a stool genomic and metabolic signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to nonalcoholic fatty liver disease (NAFLD). Shotgun metagenomic sequencing was performed on fecal samples collected from a prospective cohort of adults with NAFLD-related cirrhosis. The signatures were further validated with a metabolomic study on serum samples. Finally, we developed a Random Forest machine learning algorithm to make predictions on hepatic decompensation and mortality in NAFLD-related cirrhosis. Here we uploaded the metabolomics study data from LC-MS/MS.
Institute
University of British Columbia
Last NameZhao
First NameTingting
Address2036 Main Mall, V6T 1Z1
Emailtingzhao@chem.ubc.ca
Phone6048221253
Submit Date2023-05-30
Raw Data AvailableYes
Raw Data File Type(s)mzXML
Analysis Type DetailLC-MS
Release Date2023-06-12
Release Version1
Tingting Zhao Tingting Zhao
https://dx.doi.org/10.21228/M8HH7C
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001688
Project DOI:doi: 10.21228/M8HH7C
Project Title:Gut genomic and metabolic signature predicts hepatic decompensation and mortality in NAFLD-related cirrhosis
Project Summary:There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. The aim of this study is to determine whether a stool genomic and metabolic signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to nonalcoholic fatty liver disease (NAFLD). Based on the severity of cirrhosis, cirrhosis patients can be categorized as compensated or decompensated. Shotgun metagenomic sequencing was performed on fecal samples collected from a prospective cohort of adults with NAFLD-related cirrhosis. The signatures were further validated with a metabolomic study on fecal and serum samples. Finally, we developed a Random Forest machine learning algorithm to make predictions on hepatic decompensation and mortality in NAFLD-related cirrhosis. Here we uploaded the metabolomics study data of serum samples in LC-MS/MS analysis.
Institute:University of British Columbia
Department:Chemistry
Last Name:Zhao
First Name:Tingting
Address:2036 Main Mall, Vancouver, V6T 1Z1
Email:tingzhao@chem.ubc.ca
Phone:6048221253
Publications:Gut metagenome-derived signature predicts hepatic decompensation and mortality in NAFLD-related cirrhosis. Aliment Pharmacol Ther. 2022;56:1475–1485. DOI: 10.1111/apt.17236

Subject:

Subject ID:SU002828
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Not applicable
Species Group:Mammals

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Phenotype
SA273838CIR52compensated
SA273839CIR51compensated
SA273840CIR5compensated
SA273841CIR55compensated
SA273842CIR59compensated
SA273843CIR63compensated
SA273844CIR60compensated
SA273845CIR23compensated
SA273846CIR41compensated
SA273847CIR19compensated
SA273848CIR16compensated
SA273849CIR7compensated
SA273850CIR6compensated
SA273851CIR22compensated
SA273852CIR21compensated
SA273853CIR18compensated
SA273854CIR61decompensated
SA273855CIR57decompensated
SA273856CIR20decompensated
SA273857CIR53decompensated
SA273858CIR32decompensated
SA273859CIR58decompensated
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Collection:

Collection ID:CO002821
Collection Summary:Participants with non-alcohol fatty liverdisease (NAFLD)-related cirrhosis were recruited in the outpatient setting between August 2014 and November 2017 at the University of California San Diego (UCSD) NAFLD Research Center. The participant met the criteria for NAFLD related cirrhosis if they had NAFLD and biopsy-proven cirrhosis or a diagnosis of cirrhosis from a board-certified gastroenterologist based on imaging and clinical parameters. All participants underwent a baseline standardized clinical research visit that included a detailed medical history and physical examination. The severity of cirrhosis at the baseline research visit was categorized as compensated or decompensated. Decompensation was defined by any of the following: ascites requiring intervention (diuretics, paracentesis and/or transjugular intrahepatic portosystemic shunt), upper gastrointestinal haemorrhage secondary to gastroesophageal varices or hepatic encephalopathy (grade ≥ 2 based on West Haven Criteria10). Serum samples were also collected from participants at baseline.
Sample Type:serum
Storage Conditions:-80℃

Treatment:

Treatment ID:TR002837
Treatment Summary:N/A The severity of cirrhosis at the baseline research visit was categorised as compensated or decompensated. Thus, the participants were divided into compensated and decompensated group.

Sample Preparation:

Sampleprep ID:SP002834
Sampleprep Summary:Dual extraction procedures of serum metabolome were as follows: A 50 μl serum sample was mixed with 300 μl ice-cold methanol in a 1.5 ml Eppendorf vial and vortex for 2 min. The solution was kept at −20°C for 4 h to precipitate proteins. After that, 1000 μl methyl tert-butyl ether was added to extract lipids. After 5 min shake, 350 μl H2O was added to induce the phase separation. The solution was vortex for 10 s and rested at room temperature for 10 min, followed by centrifugation at 14,000 rpm at 4°C for 15 min, for complete phase separation. The clear lower layer was separated into a new vial and dried in SpeedVac at 20°C for 4 h. The dried extract was then reconstituted in 150 μl acetonitrile and water (1:1, v:v) mixed solvent for LC–MS metabolomics analysis. The method blank was also prepared following the same protocol but without adding serum. A 5 μl aliquot from each individual sample was pooled together to make a quality control sample.

Combined analysis:

Analysis ID AN004413
Analysis type MS
Chromatography type HILIC
Chromatography system 1290 Infinity II UHPLC system (Agilent Technologies)
Column Merck SeQuant ZIC-pHILIC (150 x 2.1mm,5um)
MS Type ESI
MS instrument type QTOF
MS instrument name Bruker Impact II QqTOF
Ion Mode POSITIVE
Units Height

Chromatography:

Chromatography ID:CH003312
Chromatography Summary:SeQuant ZIC-pHILIC Column (5 μm, 2.1 mm × 150 mm)
Instrument Name:1290 Infinity II UHPLC system (Agilent Technologies)
Column Name:Merck SeQuant ZIC-pHILIC (150 x 2.1mm,5um)
Column Temperature:30°C
Flow Gradient:0 min, 95% B; 20 min, 5% B; 25 min, 5% B; 25.01 min, 95% B; 30 min, 95% B; 35 min, 95% B.
Flow Rate:0.15 ml/min
Solvent A:10 mM ammonium acetate at pH 4.8
Solvent B:pure ACN
Chromatography Type:HILIC

MS:

MS ID:MS004160
Analysis ID:AN004413
Instrument Name:Bruker Impact II QqTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:MS acquistion Comments: capillary voltage, 4.5 kV; nebulizer gas, 1.6 bar; dry gas, 7 L/min; dry gas temperature, 220 °C; mass scan range, 70–1000 (m/z); spectra rate, 8.00 Hz; and cycle time, 3.0 s; data-dependent acquisition (one MS1 scan is followed by multiple MS2 scan for the top-N abundant precursors) Data processing Comments: xcmsSet()function in XCMS R package.
Ion Mode:POSITIVE
Collision Energy:16~30 eV
Collision Gas:Nitrogen
Dry Gas Temp:220 °C
Fragmentation Method:Collision induced dissociation
Ionization:ESI
Ionization Potential:4.5 kV
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