Summary of Study ST002244

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 PR001432. The data can be accessed directly via it's Project DOI: 10.21228/M8MQ5M This work is supported by NIH grant, U2C- DK119886.

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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 IDST002244
Study TitleMetabolomics analysis of Friedreich's ataxia (FRDA) (part I)
Study TypeUntargeted and targeted (PRM) analysis
Study SummaryFriedreich’s Ataxia (FRDA) is an autosomal neurodegenerative disease caused by the deficiency of protein frataxin. Frataxin functions in the assembly of iron-sulfur clusters that are important for iron homeostasis and metabolic functions. To identify metabolic features that can be used for potential biomarkers in FRDA plasma, we performed a targeted multi-omics (metabolomics, lipidomics, and proteomics) analysis using discovery-validation cohort design. Muti-omics analysis revealed that FRDA patients had dysregulated sphingolipid metabolism, phospholipid metabolism, citric acid cycle, amino acid metabolism, and apolipoprotein metabolism. Sphingolipid dysfunctions were revealed by decreased very long chain ceramides but unchanged long chain ceramides in FRDA plasma, which resulted in the increased ratio of long chain ceramides to very long chain ceramides. Decreased very long chain ceramides distinguished FRDA patients from healthy controls and showed good predictive capacities with AUC values from 0.75 to 0.85. Furthermore, by performing lipidomic and stable isotope tracing experiment in induced pluripotent stem cell differentiated cardiomyocytes (iPSC-CMs, we demonstrated that frataxin deficiency affected ceramide synthase (CerS2), and preferentially enriched long chain ceramides and depleted very long chain ceramides. Moreover, ceramide metabolism was differentially regulated in a tissue-specific manner. Finally, machine learning model increased the prediction of FRDA using the combination of three metabolites (AUC > 0.9). In conclusion, decreased very long chain ceramides are potential biomarkers and therapeutic target in FRDA patients.
Institute
University of Pennsylvania
Last NameWang
First NameDezhen
Address421 Curie Blvd, Philadelphia, PA, 19104, USA
Emaildezhen.wang@pennmedicine.upenn.edu
Phone5312185610
Submit Date2022-07-29
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2022-08-22
Release Version1
Dezhen Wang Dezhen Wang
https://dx.doi.org/10.21228/M8MQ5M
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Combined analysis:

Analysis ID AN003663 AN003664
Analysis type MS MS
Chromatography type HILIC HILIC
Chromatography system Thermo Dionex Ultimate 3000 Thermo Dionex Ultimate 3000
Column Ascentis Express HILIC HPLC (150 x 2.1mm,2.7um) Ascentis Express HILIC HPLC (150 x 2.1mm,2.7um)
MS Type ESI ESI
MS instrument type Orbitrap Orbitrap
MS instrument name Thermo Q Exactive HF hybrid Orbitrap Thermo Q Exactive HF hybrid Orbitrap
Ion Mode POSITIVE NEGATIVE
Units intensity intensity
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