Background: Several studies have pointed to the critical role of gut microbiota (GM) and their metabolites in Hirschsprung disease (HSCR) pathogenesis. However, the detailed causal relationship between GM and HSCR remains unknown.Methods: In this study, we used two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between GM and HSCR, based on the MiBioGen Consortium’s genome-wide association study (GWAS) and the GWAS Catalog’s HSCR data. Reverse MR analysis was performed subsequently, and the sensitivity analysis, Cochran’s Q-test, MR pleiotropy residual sum, outlier (MR-PRESSO), and the MR-Egger intercept were used to analyze heterogeneity or horizontal pleiotropy. 16S rDNA sequencing and targeted mass spectrometry were developed for initial validation.Results: In the forward MR analysis, inverse-variance weighted (IVW) estimates suggested that Eggerthella (OR: 2.66, 95%CI: 1.23–5.74, p = 0.01) was a risk factor for HSCR, while Peptococcus (OR: 0.37, 95%CI: 0.18–0.73, p = 0.004), Ruminococcus2 (OR: 0.32, 95%CI: 0.11–0.91, p = 0.03), Clostridiaceae1 (OR: 0.22, 95%CI: 0.06–0.78, p = 0.02), Mollicutes RF9 (OR: 0.27, 95%CI: 0.09–0.8, p = 0.02), Ruminococcaceae (OR: 0.16, 95%CI: 0.04–0.66, p = 0.01), and Paraprevotella (OR: 0.45, 95%CI: 0.21–0.98, p = 0.04) were protective factors for HSCR, which had no heterogeneity or horizontal pleiotropy. However, reverse MR analysis showed that HSCR (OR: 1.02, 95%CI: 1–1.03, p = 0.049) is the risk factor for Eggerthella. Furthermore, some of the above microbiota and short-chain fatty acids (SCFAs) were altered in HSCR, showing a correlation.Conclusion: Our analysis established the relationship between specific GM and HSCR, identifying specific bacteria as protective or risk factors. Significant microbiota and SCFAs were altered in HSCR, underlining the importance of further study and providing new insights into the pathogenesis and treatment.

Objective: Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and involves a complex regulatory mechanism. Transcriptional regulatory network (TRN) has been commonly reported in regulating gene expression and enteric nervous system development but remains to be investigated in HSCR. This study aimed to identify the potential TRN implicated in the pathogenesis and diagnosis of HSCR. Methods: Based on three microarray datasets from the Gene Expression Omnibus database, the multiMiR package was used to investigate the microRNA (miRNA)–target interactions, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF–miRNA–mRNA regulatory network and used cytoHubba to identify the key modules. Finally, the receiver operating characteristic (ROC) curve was determined and the integrated diagnostic models were established based on machine learning by the support vector machine method. Results: We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell–substrate adhesion, PI3K–Akt, Ras/mitogen-activated protein kinase and Rho/ROCK signaling. Moreover, 2 TFs (TP53 and TWIST1), 4 miRNAs (has-miR-107, has-miR-10b-5p, has-miR-659-3p, and has-miR-371a-5p), and 4 mRNAs (PIM3, CHUK, F2RL1, and CA1) were identified to construct the TF–miRNA–mRNA regulatory network. ROC analysis revealed a strong diagnostic value of the key TRN regulons (all area under the curve values were more than 0.8). Conclusion: This study suggests a potential role of the TF–miRNA–mRNA network that can help enrich the connotation of HSCR pathogenesis and diagnosis and provide new horizons for treatment.