Hirschsprung disease (HSCR), one of several neurocristopathies in children, is characterized by nerve loss in the large intestine and is mainly treated by surgery, which causes severe complications. Enteric neural crest-derived cell (ENCC) transplantation is a potential therapeutic strategy; however, so far with poor efficacy. Here, we assessed whether and how fecal microbiota transplantation (FMT) could improve ENCC transplantation in a rat model of hypoganglionosis; a condition similar to HSCR, with less intestinal innervation. We found that the hypoganglionosis intestinal microenvironment negatively influenced the ENCC functional phenotype in vitro and in vivo. Combining 16S rDNA sequencing and targeted mass spectrometry revealed microbial dysbiosis and reduced short-chain fatty acid (SCFA) production in the hypoganglionic gut. FMT increased the abundance of Bacteroides and Clostridium, SCFA production, and improved outcomes following ENCC transplantation. SCFAs alone stimulated ENCC proliferation, migration, and supported ENCC transplantation. Transcriptomewide mRNA sequencing identified MAPK signaling as the top differentially regulated pathway in response to SCFA exposure, and inhibition of MEK1/2 signaling abrogated the SCFA-mediated effects on ENCC. This study demonstrates that FMT improves cell therapy for hypoganglionosis via short-chain fatty acid metabolisminduced MEK1/2 signaling.

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ObjectiveHirschsprung 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.MethodsBased 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.ResultsWe 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 (TP53andTWIST1), 4 miRNAs (has-miR-107,has-miR-10b-5p,has-miR-659-3p, andhas-miR-371a-5p), and 4 mRNAs (PIM3,CHUK,F2RL1, andCA1) 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).ConclusionThis 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.