Summary of Study ST001964
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 PR001095. The data can be accessed directly via it's Project DOI: 10.21228/M85976 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
Study ID | ST001964 |
Study Title | Quantitative genome-scale analysis of human liver reveals dysregulation of glycosphingolipid pathways in progressive nonalcoholic fatty liver disease |
Study Summary | Nonalcoholic fatty liver disease (NAFLD) is a well-defined chronic liver diseases closely related with metabolic disorders. The prevalence of NAFLD is rapidly increasing worldwide, while the pathology and the underlying mechanisms driving NAFLD are not fully understood. In NAFLD, a series of metabolic changes takes place in the liver. However, the alteration of the metabolic pathways in the human liver along the progression of NAFLD, i.e., the transition from nonalcoholic steatosis (NAFL) to steatohepatitis (NASH) through cirrhosis remains to be discovered. Here, we sought to examine the metabolic pathways of the human liver across the full histological spectrum of NAFLD. We analyzed the whole liver tissue transcriptomic (RNA-Seq) and serum metabolomics data obtained from a large, prospectively enrolled cohort of histologically characterized patients derived from the European NAFLD Registry (n=206), and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. The integrative approach employed in this study has enabled us to understand the regulation of the metabolic pathways of human liver in NAFL, and with progressive NASH-associated fibrosis (F0–F4). Our study identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids (GSLs), and their link with complex glycosaminoglycans (GAGs) in advanced fibrosis. The study provides insights into the underlying pathways of the progressive fibrosing steatohepatitis. Furthermore, by applying genome-scale metabolic modeling (GSMM), we were able to identify the metabolic differences among carriers of widely validated genetic variants associated with NAFLD / NASH disease severity in three genes (PNPLA3, TM6SF2 and HSD17B13). |
Institute | University of Turku |
Last Name | Sen |
First Name | Partho |
Address | Systems Medicine group, Turku Bioscience, University of Turku (UTU), Tykistökatu 6B, P.O. Box 123 FIN-20521 Turku, Finland |
partho.sen@utu.fi | |
Phone | Phone: +358 469608145 |
Submit Date | 2021-02-18 |
Raw Data Available | Yes |
Raw Data File Type(s) | mzML |
Analysis Type Detail | GC-MS |
Release Date | 2022-01-03 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
Project ID: | PR001095 |
Project DOI: | doi: 10.21228/M85976 |
Project Title: | Metabolomic signatures of NAFLD |
Project Summary: | Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is a progressive liver disease that is strongly associated with type 2 diabetes. Accurate, non-invasive diagnostic tests to delineate the different stages: degree of steatosis, grade of nonalcoholic steatohepatitis (NASH) and stage fibrosis represent an unmet medical need. In our previous studies, we successfully identified specific serum molecular lipid signatures which associate with the amount of liver fat as well as with NASH. Here we report underlying associations between clinical data, lipidomic profiles, metabolic profiles and clinical outcomes, including downstream identification of potential biomarkers for various stages of the disease. Method: We leverage several statistical and machine-learning approaches to analyse clinical, lipidomic and metabolomic profiles of individuals from the European Horizon 2020 project: Elucidating Pathways of Steatohepatitis (EPoS). We interrogate data on patients representing the full spectrum of NAFLD/NASH derived from the EPoS European NAFLD Registry (n = 627). We condense the EPoS lipidomic data into lipid clusters and subsequently apply non-rejection-rate-pruned partial correlation network techniques to facilitate network analysis between the datasets of lipidomic, metabolomic and clinical data. For biomarker identification, a random forest ensemble classification approach was used to both search for valid disease biomarkers and to compare classification performance of lipids, metabolites and clinical factors in combination. Results: We found that steatosis and fibrosis grades were strongly associated with (1) an increase of triglycerides with low carbon number and double bond count as well as (2) a decrease of specific phospholipids, including lysophosphatidylcholines. In addition to the network topology as a result itself, we also present lipid clusters (LCs) of interest to the derived network of proposed interactions in our NAFLD data from the EPoS cohort, along with preliminary metabolite and lipid biomarkers to classify NAFLD fibrosis. Conclusions: Our findings suggest that dysregulation of lipid metabolism in progressive stages of NAFLD is reflected in circulation and may thus hold diagnostic value as well as offer new insights about NAFLD pathogenesis. Using this cohort as a proof-of-concept, we demonstrate current progress in tuning the accuracy random forest approaches with a view to predicting various subtypes of NAFLD patient using a minimal set of lipidomic and metabolic markers. For the first time, a detailed network-based picture emerges between lipids, polar metabolites and clinical variables. Lipidomic / metabolomic markers may provide an alternative method of NAFLD patient classification and risk stratification to guide therapy. |
Institute: | Örebro University |
Last Name: | McGlinchey |
First Name: | Aidan |
Address: | School of Medical Sciences, Örebro, Örebro, 70281, Sweden |
Email: | aidan.mcglinchey@oru.se |
Phone: | +46736485638 |
Subject:
Subject ID: | SU002044 |
Subject Type: | Human |
Subject Species: | Homo sapiens |
Taxonomy ID: | 9606 |
Gender: | Male and female |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
mb_sample_id | local_sample_id | NAFLD.Status |
---|---|---|
SA184835 | 1022385877 | NAFL |
SA184836 | 1022386427 | NAFL |
SA184837 | 1022386463 | NAFL |
SA184838 | 1022385860 | NAFL |
SA184839 | 1022386190 | NAFL |
SA184840 | 1022386958 | NAFL |
SA184841 | 1022386202 | NAFL |
SA184842 | 1022385788 | NAFL |
SA184843 | 1022385839 | NAFL |
SA184844 | 1022385926 | NASH_F0/1 |
SA184845 | 1022386429 | NASH_F0/1 |
SA184846 | 1022386914 | NASH_F0/1 |
SA184847 | 1022386933 | NASH_F2 |
SA184848 | 1022386978 | NASH_F2 |
SA184849 | 1022386912 | NASH_F2 |
SA184850 | 1022386446 | NASH_F2 |
SA184851 | 1022386907 | NASH_F2 |
SA184852 | 1022386453 | NASH_F2 |
SA184853 | 1022386485 | NASH_F2 |
SA184854 | 1022386214 | NASH_F2 |
SA184855 | 1022385934 | NASH_F2 |
SA184856 | 1022385844 | NASH_F2 |
SA184857 | 1022385893 | NASH_F2 |
SA184858 | 1022386136 | NASH_F2 |
SA184859 | 1022384032 | NASH_F2 |
SA184860 | 1022386192 | NASH_F2 |
SA184861 | 1022386960 | NASH_F3 |
SA184862 | 1022386981 | NASH_F3 |
SA184863 | 1022385756 | NASH_F3 |
SA184864 | 1022385774 | NASH_F3 |
SA184865 | 1022385911 | NASH_F3 |
SA184866 | 1022386508 | NASH_F3 |
SA184867 | 1022385928 | NASH_F3 |
SA184868 | 1022386159 | NASH_F3 |
SA184869 | 1022386364 | NASH_F3 |
SA184870 | 1022386899 | NASH_F3 |
SA184871 | 1022385916 | NASH_F3 |
SA184872 | 1022385855 | NASH_F4 / cirrhosis |
SA184873 | 1022387276 | NASH_F4 / cirrhosis |
SA184874 | 1022387147 | NASH_F4 / cirrhosis |
SA184875 | 1022386957 | NASH_F4 / cirrhosis |
Showing results 1 to 41 of 41 |
Collection:
Collection ID: | CO002037 |
Collection Summary: | Serum samples were randomized and sample preparation was carried out as described previously (Castilloet al. 2011). In summary, 400 µL of MeOH containing ISTDs (heptadecanoic acid, deuterium-labeled DL-valine, deuterium-labeled succinic acid, and deuterium-labeled glutamic acid, c = 1 µg/mL) was added to 30 µl of the serum samples which were vortex mixed and incubated on ice for 30 min after which they were centrifuged (9400 × g, 3 min) and 350 µL of the supernatant was collected after centrifugation. The solvent was evaporated to dryness and 25 µL of MOX reagent was added and the sample was incubated for 60 min at 45 °C. 25 µL of MSTFA was added and after 60 min incubation at 45 °C 25 µL of the retention index standard mixture (n-alkanes, c=10 µg/mL) was added. The analyses were carried out on an Agilent 7890B GC coupled to 7200 QTOF MS. Injection volume was 1 µL with 100:1 cold solvent split on PTV at 70 °C, heating to 300 °C at 120 °C/minute. Column: Zebron ZB-SemiVolatiles. Length: 20m, I.D. 0.18mm, film thickness: 0.18 µm. With initial Helium flow 1.2 mL/min, increasing to 2.4 mL/min after 16 mins. Oven temperature program: 50 °C (5 min), then to 270°C at 20 °C/min and then to 300 °C at 40 °C/min (5 min). EI source: 250 °C, 70 eV electron energy, 35µA emission, solvent delay 3 min. Mass range 55 to 650 amu, acquisition rate 5 spectra/s, acquisition time 200 ms/spectrum. Quad at 150 °C, 1.5 mL/min N2 collision flow, aux-2 temperature: 280 °C. Calibration curves were constructed using alanine, citric acid, fumaric acid, glutamic acid, glycine, lactic acid, malic acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, linoleic acid, oleic acid, palmitic acid, stearic acid, cholesterol, fructose, glutamine, indole-3-propionic acid, isoleucine, leucine, proline, succinic acid, valine, asparagine, aspartic acid, arachidonic acid, glycerol-3-phosphate, lysine, methionine, ornithine, phenylalanine, serine and threonine purchased from Sigma-Aldrich (St. Louis, MO, USA) at concentration range of 0.1 to 80 µg/mL. An aliquot of each sample was collected and pooled and used as quality control samples, together with a NIST SRM 1950 serum sample and an in-house pooled serum sample. Relative standard deviations (% RSDs) of the metabolite concentrations in control serum samples showed % RSDs within accepted analytical limits at averages of 27.2% and 29.2% for in-house QC abd pooled QC samples. |
Sample Type: | Blood (serum) |
Treatment:
Treatment ID: | TR002056 |
Treatment Summary: | No treatment applied. |
Sample Preparation:
Sampleprep ID: | SP002050 |
Sampleprep Summary: | Serum samples were randomized and sample preparation was carried out as described previously (Castilloet al. 2011). In summary, 400 µL of MeOH containing ISTDs (heptadecanoic acid, deuterium-labeled DL-valine, deuterium-labeled succinic acid, and deuterium-labeled glutamic acid, c = 1 µg/mL) was added to 30 µl of the serum samples which were vortex mixed and incubated on ice for 30 min after which they were centrifuged (9400 × g, 3 min) and 350 µL of the supernatant was collected after centrifugation. The solvent was evaporated to dryness and 25 µL of MOX reagent was added and the sample was incubated for 60 min at 45 °C. 25 µL of MSTFA was added and after 60 min incubation at 45 °C 25 µL of the retention index standard mixture (n-alkanes, c=10 µg/mL) was added. The analyses were carried out on an Agilent 7890B GC coupled to 7200 QTOF MS. Injection volume was 1 µL with 100:1 cold solvent split on PTV at 70 °C, heating to 300 °C at 120 °C/minute. Column: Zebron ZB-SemiVolatiles. Length: 20m, I.D. 0.18mm, film thickness: 0.18 µm. With initial Helium flow 1.2 mL/min, increasing to 2.4 mL/min after 16 mins. Oven temperature program: 50 °C (5 min), then to 270°C at 20 °C/min and then to 300 °C at 40 °C/min (5 min). EI source: 250 °C, 70 eV electron energy, 35µA emission, solvent delay 3 min. Mass range 55 to 650 amu, acquisition rate 5 spectra/s, acquisition time 200 ms/spectrum. Quad at 150 °C, 1.5 mL/min N2 collision flow, aux-2 temperature: 280 °C. Calibration curves were constructed using alanine, citric acid, fumaric acid, glutamic acid, glycine, lactic acid, malic acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, linoleic acid, oleic acid, palmitic acid, stearic acid, cholesterol, fructose, glutamine, indole-3-propionic acid, isoleucine, leucine, proline, succinic acid, valine, asparagine, aspartic acid, arachidonic acid, glycerol-3-phosphate, lysine, methionine, ornithine, phenylalanine, serine and threonine purchased from Sigma-Aldrich (St. Louis, MO, USA) at concentration range of 0.1 to 80 µg/mL. An aliquot of each sample was collected and pooled and used as quality control samples, together with a NIST SRM 1950 serum sample and an in-house pooled serum sample. Relative standard deviations (% RSDs) of the metabolite concentrations in control serum samples showed % RSDs within accepted analytical limits at averages of 27.2% and 29.2% for in-house QC abd pooled QC samples. |
Processing Storage Conditions: | -20? |
Extract Storage: | -80? |
Combined analysis:
Analysis ID | AN003202 |
---|---|
Analysis type | MS |
Chromatography type | GC |
Chromatography system | Agilent 7890B |
Column | Zebron ZB-SemiVolatiles (20m x 0.18mm,0.18m) |
MS Type | EI |
MS instrument type | GC x GC-TOF |
MS instrument name | Agilent 7200 QTOF |
Ion Mode | UNSPECIFIED |
Units | log2 autoscaled abundance |
Chromatography:
Chromatography ID: | CH002367 |
Chromatography Comments: | Zebron ZB-SemiVolatiles (20m, I.D. 0.18mm, film thickness 0.18µm) |
Instrument Name: | Agilent 7890B |
Column Name: | Zebron ZB-SemiVolatiles (20m x 0.18mm,0.18m) |
Chromatography Type: | GC |
MS:
MS ID: | MS002980 |
Analysis ID: | AN003202 |
Instrument Name: | Agilent 7200 QTOF |
Instrument Type: | GC x GC-TOF |
MS Type: | EI |
MS Comments: | In summary, 400 µL of MeOH containing ISTDs (heptadecanoic acid, deuterium-labeled DL-valine, deuterium-labeled succinic acid, and deuterium-labeled glutamic acid, c = 1 µg/mL) was added to 30 µl of the serum samples which were vortex mixed and incubated on ice for 30 min after which they were centrifuged (9400 × g, 3 min) and 350 µL of the supernatant was collected after centrifugation. The solvent was evaporated to dryness and 25 µL of MOX reagent was added and the sample was incubated for 60 min at 45 °C. 25 µL of MSTFA was added and after 60 min incubation at 45 °C 25 µL of the retention index standard mixture (n-alkanes, c=10 µg/mL) was added. The analyses were carried out on an Agilent 7890B GC coupled to 7200 QTOF MS. Injection volume was 1 µL with 100:1 cold solvent split on PTV at 70 °C, heating to 300 °C at 120 °C/minute. Column: Zebron ZB-SemiVolatiles. Length: 20m, I.D. 0.18mm, film thickness: 0.18 µm. With initial Helium flow 1.2 mL/min, increasing to 2.4 mL/min after 16 mins. Oven temperature program: 50 °C (5 min), then to 270°C at 20 °C/min and then to 300 °C at 40 °C/min (5 min). EI source: 250 °C, 70 eV electron energy, 35µA emission, solvent delay 3 min. Mass range 55 to 650 amu, acquisition rate 5 spectra/s, acquisition time 200 ms/spectrum. Quad at 150 °C, 1.5 mL/min N2 collision flow, aux-2 temperature: 280 °C. Calibration curves were constructed using alanine, citric acid, fumaric acid, glutamic acid, glycine, lactic acid, malic acid, 2-hydroxybutyric acid, 3-hydroxybutyric acid, linoleic acid, oleic acid, palmitic acid, stearic acid, cholesterol, fructose, glutamine, indole-3-propionic acid, isoleucine, leucine, proline, succinic acid, valine, asparagine, aspartic acid, arachidonic acid, glycerol-3-phosphate, lysine, methionine, ornithine, phenylalanine, serine and threonine purchased from Sigma-Aldrich (St. Louis, MO, USA) at concentration range of 0.1 to 80 µg/mL. An aliquot of each sample was collected and pooled and used as quality control samples, together with a NIST SRM 1950 serum sample and an in-house pooled serum sample. Relative standard deviations (% RSDs) of the metabolite concentrations in control serum samples showed % RSDs within accepted analytical limits at averages of 27.2% and 29.2% for in-house QC abd pooled QC samples. Data values for final analysis were log2-transformed and scaled to zero mean and unit variance. |
Ion Mode: | UNSPECIFIED |