Serum Metabolomic Signatures of Hirschsprung’s Disease Based on GC–MS and LC–MS

Abstract

Hirschsprung’s disease (HSCR) is a congenital digestive tract malformation characterized by the absence of intramural ganglion cells in the myenteric and submucosal plexuses along variable lengths of the gastrointestinal tract. Although the improvement of surgical methods has allowed great progress in the treatment of HSCR, its incidence and postoperative prognosis are still not ideal. The pathogenesis of HSCR remains unclear to date. In this study, metabolomic profiling of HSCR serum samples was performed by an integrated analysis of gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–high-resolution tandem mass spectrometry (LC–HRMS/MS) as well as multivariate statistical analyses. Based on the random forest algorithm and receiver operator characteristic analysis, 21 biomarkers related to HSCR were optimized. Several amino acid metabolism pathways were identified as important disordered pathways of HSCR, among which tryptophan metabolism was crucial. To our knowledge, this is the first serum metabolomics study focusing on HSCR, and it provides a new perspective for explaining the mechanism of HSCR.

Untargeted Maternal Plasma Metabolomics in Hirschsprung Disease: A Pilot Study

“…A recent study of the serum metabolome of HSCR patients[ 8 ] shows disordered tryptophan metabolism which is also reflected in the maternal metabolic profile in our study. The mothers with HSCR children had a change in the tryptophan–kynurenine pathway (unadjusted P < 0.03) which may point toward inflammation which can independently affect ENS structure and function.…”

“…Serum metabolomics were used to explore the biochemical profile of HSCR patients in a recently published study, which found that tryptophan metabolism was particularly disordered. [ 8 ] We hypothesized that circulating maternal metabolites could influence the disease risk of HSCR in the fetus and sought to compare the plasma metabolomic profile of mothers of children with HSCR and mothers of normal children. However, it is not feasible to evaluate the maternal plasma metabolome during the first trimester when HSCR develops as no prenatal diagnosis exists for HSCR.…”

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize

“…Generally speaking, the distinguishable factors for metabolomics data between any two groups (e.g., experiment vs. control) are a combination of variables rather than a single one. Therefore, several chemometric approaches, such as principal component analysis (PCA) [30][31][32], orthogonal partial least squares discriminant analysis (OPLS-DA) [30][31][32], random forest (RF) [33,34], and support vector machine (SVM) [33,35,36], have widely been used to deal with these metabolomics data. Among all these modeling methods, SVM is gaining popularity in a wide variety of metabolomics studies due to its prediction performance.…”