Serum Metabolomic Signatures of Hirschsprung's Disease Based on GC-MS and LC-MS

The mediating role of metabolites between gut microbiome and Hirschsprung disease: a bidirectional two-step Mendelian randomization study

Background

Gut microbiome (GM) was observed to be associated with the incidence of Hirschsprung disease (HD). However, the effect and mechanism of GM in HD is still unclear. To investigate the relationship between GM and HD and the effect of metabolites as mediators, a bidirectional two-step Mendelian randomization (MR) study was conducted.

Methods

The study selected instrument variables (IVs) from summary-level genome-wide association studies (GWAS). The MiBioGen consortium provided the GWAS data for GM, while the GWAS data for metabolites and HD were obtained from the GWAS Catalog consortium. Two-sample MR analyses were performed to estimate bidirectional correlations between IVs associated with GM and HD. Then, genetic variants related to 1,400 metabolite traits were selected for further mediation analyses using the Product method.

Results

This study found that seven genus bacteria had a significant causal relationship with the incidence of HD but not vice versa. 27 metabolite traits were significantly correlated with HD. After combining the significant results, three significant GM-metabolites-HD lines have been identified. In the Peptococcus-Stearoyl sphingomyelin (d18:1/18:0)-HD line, the Stearoyl sphingomyelin (d18:1/18:0) levels showed a mediation proportion of 14.5%, while in the Peptococcus-lysine-HD line, the lysine levels had a mediation proportion of 12.9%. Additionally, in the Roseburia-X-21733-HD line, the X-21733 levels played a mediation proportion of 23.5%.

Conclusion

Our MR study indicates a protective effect of Peptococcus on HD risk that is partially mediated through serum levels of stearoyl sphingomyelin (d18:1/18:0) and lysine, and a risk effect of Roseburia on HD that is partially mediated by X-21733 levels. These findings could serve as novel biomarkers and therapeutic targets for HD.

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

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Untargeted Maternal Plasma Metabolomics in Hirschsprung Disease: A Pilot Study

Background and Aims

Hirschsprung disease (HSCR) is a congenital disorder of unknown etiology affecting the enteric nervous system (ENS). Since the early gestational development of the ENS is dependent on the prenatal maternal metabolic environment, the objective of this pilot study was to explore the role of specific maternal plasma metabolites in the etiology of HSCR.

Methods

In this cross-sectional study, postnatal (as a surrogate for prenatal) plasma samples were obtained from mothers of children diagnosed with HSCR (n = 7) and age-matched mothers of normal children (n = 6). The plasma metabolome was analyzed by ultra-high-pressure liquid chromatography and mass spectrometry. Metabolites were identified by mzCloud using Compound Discoverer software. Using an untargeted metabolomics workflow, metabolites with case versus control group differences were identified.

Results

A total of 268 unique plasma metabolites were identified and annotated in maternal plasma. Of these, 57 were significantly different between case and control groups (P < 0.05, t-test). Using a false discovery rate corrected cutoff of 10% to adjust for multiple comparisons, 19 metabolites were significantly different in HSCR cases, including carnitines, medium-chain fatty acids, and glutamic acid. Pathways affected were for amino acid and lipid metabolism.

Conclusion

Disordered prenatal metabolic pathways may be involved in the etiopathogenesis of HSCR in the developing fetus. This is the first study to assess maternal plasma metabolomics in HSCR.