Summary of Study ST003171
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 PR001971. The data can be accessed directly via it's Project DOI: 10.21228/M8Z72R 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 | ST003171 |
| Study Title | Untargeted Metabolomics for Exploring Metabolomic Profile of Maple Syrup Urine Disease Sick Patients |
| Study Type | Untargeted LCMS |
| Study Summary | Abstract non-newborn: Background: A malfunction in the activity of the branched-chain alpha-ketoacid dehydrogenase (BCKAD) complex results in maple syrup urine disease (MSUD), a genetically inherited illness. Three amino acids—leucine, isoleucine, and valine—are typically broken down by this complex. Abnormal activity in this process, therefore, can affect vital body systems and result in metabolic dysregulation associated with the consequences of the disease. The therapy and follow-up of ill MSUD patients are greatly aided by many researched endogenous metabolites as well as dysregulated biomarkers and pathways. Objectives: Our goal is to add to the increasing knowledge of information about sick MSUD with relation to MSUD newborns and the pathways that are involved in improving patient outcomes by utilizing untargeted metabolomics to examine the unique profile of MSUD in sick MSUD patients. Methods: This study evaluated the metabolic changes in the dry blood spot (DBS) of 14 sick MSUD patients and 14 healthy controls utilizing untargeted metabolomics studies performed with liquid chromatography–mass spectrometry. Findings: Based on metabolomics analysis,7754 metabolites were found to be highly dysregulated.Out of them,3716 were up-regulated and 4038 were down-regulated.1557 of the annotated metabolites were found to be endogenous metabolites. The research found possible biomarkers for MSUD, including Glutathioselenol and dUDP, which were elevated in sick MSUD relative to healthy controls and LysoPI downregulated in sick MSUD. Moreover, the Sphingolipid metabolism, selenocompound metabolism and porphyrin metabolism pathways were the most impacted in MSUD newborns.This study shows 92 endogenous metabolites between newborn MSUD and sick MSUD. In summary, our findings shows that metabolomics is a noninvasive approach to understanding the pathophysiology of the medical condition and a potentially useful technique for assessing novel biomarkers in the early detection of sick MSUD.Further research is required regarding the relationship of these dysregulated metabolites to compromised pathways. |
| Institute | King Saud University |
| Department | Biochemistry |
| Laboratory | Clinical Biochemistry |
| Last Name | AlOtaibi |
| First Name | Abeer |
| Address | 2808 |
| 441203289@student.ksu.edu.sa | |
| Phone | +966551933703 |
| Submit Date | 2023-12-09 |
| Num Groups | 2 |
| Total Subjects | 28 |
| Num Males | 7 |
| Num Females | 7 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Waters) |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-04-29 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN005204 | AN005205 |
|---|---|---|
| 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: | MS004937 |
| Analysis ID: | AN005204 |
| 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 |
| Analysis Protocol File: | Metabolomics_Pos_and_Neg.pdf |
| MS ID: | MS004938 |
| Analysis ID: | AN005205 |
| 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: | NEGATIVE |
| Analysis Protocol File: | Metabolomics_Pos_and_Neg.pdf |
