Summary of Study ST002243

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.

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	ST002243
Study Title	Lipidomics analysis of Friedreich's ataxia (FRDA) (part II)
Study Type	Untargeted and targeted (PRM) analysis
Study Summary	Friedreich’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 Name	Wang
First Name	Dezhen
Address	421 Curie Blvd, Philadelphia, PA, 19104, USA
Email	dezhen.wang@pennmedicine.upenn.edu
Phone	5312185610
Submit Date	2022-07-29
Raw Data Available	Yes
Raw Data File Type(s)	raw(Thermo)
Analysis Type Detail	LC-MS
Release Date	2022-08-22
Release Version	1

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Sample Preparation:

Sampleprep ID:	SP002335
Sampleprep Summary:	50 μL plasma, was mixed with 20 μL internal standard working solution (SPLASH® LIPIDOMIX® Mass Spec Standard + Cer/sph mixture I), extracted with 1000 μL butanol/methanol (1/1, v/v, 10 mM ammonium formate) for 10 min on a vortex mixture. Samples were pelleted by centrifugation at 4000 x g for 5 min at room temperature. The supernatant was moved to a clean glass tube, dried under nitrogen, and resuspended in 100 μL MTBE/methanol (1/3, v/v) with 10 mM ammonium formate. 5 μL of each sample was combined to make a pooled quality control (QC) sample and ran every ten samples in the long sequence to monitor retention time and signal intensity drift. The remaining samples (50 μL) were transferred into injection vials for lipidomic analysis.

Sampleprep ID:

SP002335

Sampleprep Summary:

50 μL plasma, was mixed with 20 μL internal standard working solution (SPLASH® LIPIDOMIX® Mass Spec Standard + Cer/sph mixture I), extracted with 1000 μL butanol/methanol (1/1, v/v, 10 mM ammonium formate) for 10 min on a vortex mixture. Samples were pelleted by centrifugation at 4000 x g for 5 min at room temperature. The supernatant was moved to a clean glass tube, dried under nitrogen, and resuspended in 100 μL MTBE/methanol (1/3, v/v) with 10 mM ammonium formate. 5 μL of each sample was combined to make a pooled quality control (QC) sample and ran every ten samples in the long sequence to monitor retention time and signal intensity drift. The remaining samples (50 μL) were transferred into injection vials for lipidomic analysis.