Summary of Study ST002750
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 PR001713. The data can be accessed directly via it's Project DOI: 10.21228/M88X3T 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 | ST002750 |
| Study Title | A Comprehensive Metabolomics Profile for Newborns with Maple syrup urine disease |
| Study Type | Untargeted LCMS |
| Study Summary | Background: Maple syrup urine disease (MSUD) is a genetic inherited disorder caused by a defect in the branched-chain alpha-ketoacid dehydrogenase (BCKAD) complex function. This complex usually breaks down three amino acids: leucine, isoleucine, and valine. Therefore, abnormal activity in this process, can impact important bodily functions and lead to metabolic dysregulation related to the disease complications. A wide range of studied endogenous metabolites and dysregulated biomarkers and pathways provide a huge core support for the treatment and follow-up of newborn MSUD patients. Objectives: In this study, we aim to investigate MSUD’s distinctive profile in newborn MSUD patients using untargeted metabolomics to contribute to the growing knowledge surrounding MSUD and pathways involved for improving patient outcomes. Methods: In this study, untargeted metabolomics analyses via liquid chromatography–mass spectrometry was used to investigate metabolic changes in dry blood spot (DBS) of 22 MSUD newborns and 22 healthy newborns. Results: The metabolomics results revealed 1040 significantly dysregulated metabolites, where 303 and 737 were up- and down-regulated, respectively. 480 metabolites were annotated and 210 were identified as endogenous metabolites. The study identified potential biomarkers for MSUD such as L-Alloisoleucine and Methionine sulfoxide were upregulated in MSUD newborn compared to healthy newborns, while LysoPI was downregulated in MSUD newborns. In addition, the most affected pathways in MSUD Newborns were ascorbate and aldarate, Pentose and glucuronate interconversions and pyrimidine metabolism. Conclusion: Our results demonstrate metabolomics as a noninvasive strategy to understand the pathophysiology of the disease and is a promising tool to evaluate the potential biomarkers in the early diagnosis of newborn MSUD. Future studies are needed to correlate these dysregulated metabolites with defective mechanisms. |
| Institute | King Saud University |
| Department | Biochemistry |
| Laboratory | Biochemistry |
| Last Name | Alotaibi |
| First Name | Abeer |
| Address | 2808 |
| abeerotb12@gmail.com | |
| Phone | 966551933703 |
| Submit Date | 2023-05-29 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Waters) |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-07-18 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN004461 | AN004462 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Waters Acquity UPLC | Waters Acquity UPLC |
| Column | Waters XSelect CSH C18 (100 x 2.1mm 2.5um) | Waters XSelect CSH C18 (100 x 2.1mm 2.5um) |
| MS Type | ESI | ESI |
| MS instrument type | QTOF | QTOF |
| MS instrument name | Waters Xevo-G2-S | Waters Xevo-G2-S |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | Peak area | Peak area |
MS:
| MS ID: | MS004208 |
| Analysis ID: | AN004461 |
| Instrument Name: | Waters Xevo-G2-S |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | The DIA data were gathered with a Masslynx™ V4.1 Software (Waters Inc., Milford, MA, USA) in continuum mode. Quality control samples (QCs) were made with aliquots from all samples and introduced to the instrument after the randomization of each group, after 10 samples to validate the stability of the system (Aldubayan, Rodan, Berry, & Levy, 2017). Data and Statistical Analyses: The raw MS data were processed using a standard pipeline, beginning from an alignment depending on the mass to charge ratio (m/s) and the retention time (RT) of ion signals’, picking the best peak, followed by the filtering of signal depending on the quality of peak by utilizing the Progenesis QI (v.3.0) software (Waters Technologies, Milford, MA, USA). A multivariate statistics was applied by using MetaboAnalyst (v.5.0) (McGill University, Montreal, QB, Canada) (http://www.metaboanalyst.ca) (Pang et al., 2021). All the imported data-groups (compounds’ names also their raw abundances information) were Pareto scaled, log transformed and applied for creating partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models. The generated OPLS-DA model was measured through R2Y and Q2 values, that represents the fitness of the model and predictive ability, respectively (Worley & Powers, 2013). A univariate analysis was applied through Mass Profiler Professional (MPP) (v. 15.0) software (Agilent, Santa Clara, CA, USA). A volcano plot was applied to uncover significantly changed mass features based on a Moderated T-test, cut-off: no correction, p <0.05, FC 1.5. Heatmap analysis for altered features was performed using the Pearson distance measure according to the Pearson similarity test (Gu et al., 2020). |
| Ion Mode: | POSITIVE |
| MS ID: | MS004209 |
| Analysis ID: | AN004462 |
| Instrument Name: | Waters Xevo-G2-S |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Same as for POSITIVE mode |
| Ion Mode: | NEGATIVE |
