Summary of Study ST002332
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 PR001496. The data can be accessed directly via it's Project DOI: 10.21228/M8BX3F 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 | ST002332 |
| Study Title | Plasma metabolomic profiling of individuals with autism spectrum disorder and their family members. |
| Study Summary | Autism spectrum disorder (ASD) is a common neurodevelopmental condition affecting 2.3% of 8-year-old children and is attributable to polygenic risks in most cases. Gene discovery studies catalogued >1000 genes with de novo, rare and common genetic variants that are likely associated with ASD; however, the candidate genes are rarely translated to diagnostic and treatment biomarkers. As such no pharmacological treatment option is available for targeting core symptoms. Neural circuits involved in verbal/nonverbal communications and social interaction are likely changed, which may be caused by an excitatory-inhibitory (E-I) imbalance in individuals with ASD. To date, clinical trials targeting excitatory glutamatergic or inhibitory GABAergic receptors showed mixed results. These early clinical trials highlight the unmet need of biomarkers for target populations and outcome indicators. We investigated whether plasma biomarkers would be associated with genetic risk factors and core symptoms of ASD. Plasma samples were collected for metabolomics profiling from the Autism Genetics Resource Exchange (AGRE). Detailed phenotype information is available at NIMH Data Archive (Collection ID: 4214) and can be accessed using NDAR GUID for the individuals. |
| Institute | Boston Childrens Hospital |
| Department | Computational Health informatics Program |
| Laboratory | Kong Lab |
| Last Name | Kong |
| First Name | Sek Won |
| Address | 401 Park Drive, LM5528.4 |
| sekwon.kong@childrens.harvard.edu | |
| Phone | 6179192689 |
| Submit Date | 2022-10-14 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzXML |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-10-14 |
| Release Version | 1 |
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Treatment:
| Treatment ID: | TR002431 |
| Treatment Summary: | Plasma samples in 50μL aliquot were mixed with acetonitrile containing 14 stable isotope internal standards at a 2:1 ratio to precipitate proteins. Samples were then equilibrated on ice for 30 minutes and centrifuged for 10 minutes at 13,400 rpm at 4°C. The supernatant was transferred to autosampler vials and kept in a refrigerated autosampler until analysis. Each extract was analyzed in triplicate using a dual column chromatography scheme that includes hydrophilic interaction liquid chromatography (HILIC; XBridge BEH Amide XP HILIC column; Waters, Waltham, MA, 50x2.1mm, 2.5μm) and reversed phase liquid chromatography (RPLC; C18 column; Higgins Analytical, Mountain View, CA, 50x2.1mm, 2.6 μm). |
