Integrative Analysis of Exosomal miR-452 and miR-4713 Downregulating NPY1R for the Prevention of Childhood Obesity

Abstract

Neuropeptides are associated with childhood obesity and exploring their regulatory mechanisms may reveal new insights for novel treatments. Childhood obesity data were downloaded from the GEO database and were used to screen for differentially expressed neuropeptides in patients with obesity. NPY1R expression was significantly upregulated in children with obesity compared to children without obesity (p < 0.05). The GEO database was used to filter differentially expressed miRNAs in patients with obesity. And hsa-mir-4713 and hsa-mir-452 were found significantly downregulated in adipose tissue. The GEO, TRRUST, and TFacts databases were used to screen all transcription factors for differentially expressed genes (DEGs). The potential regulatory networks between the differentially expressed miRNAs, TFs, and neuropeptides were mapped. In the constructed NPY1R regulatory network, the transcription factors TCF4, HEY1, and GATA3 are significantly associated with NPY1R. TCF4 and HEY1 were positively correlated with NPY1R, while GATA3 was negatively correlated with NPY1R. In the clinical peripheral blood samples, NPY1R, TCF4, and HEY1 were significantly more expressed in the obesity and the obesity with fracture group compared to the control group, while there was no statistically significant difference between the obesity group and the obesity with fracture group in terms of expression. The expression of GATA3, miR-452, and miR-4713 was also significantly lower in the obesity and the obesity with fracture groups when compared to the NC group. Therefore, NPY1R, TCF4, HEY1, GATA3, miR-452, and miR-4713 may be risk factors for fracture in obese children. The potential NPY1R regulatory function was exerted by two pathways: positive regulation caused by TCF4 and HEY1 acting on miR-4713 and negative regulation via GATA3 acting on miR-452. Potential NPY1R-related targets for the treatment of childhood obesity were provided in this study.

Figure 1

Characterization of NPY1R expression and function in children with obesity. The result of analysis of variance tests on the GSE9624 dataset (a); expression of NPY1R mRNA in the normal control, obesity, and obesity with fracture groups by qPCR (b); visualization of NPY1R-related GSEA (c, d).

Figure 2

Potential miRNAs and transcription factors regulating NPY1R. Venn diagram demonstrating common differential miRNAs in the GSE50574 and GSE68885 datasets (a); volcano map showing that hsa-mir-452 and mir-4713 were found to be upregulated in normal tissues relative to adipose tissue expression in GSE50574 and GSE68885 (b, c); transcription factors associated with differentially expressed genes (DEGs) in adipose tissue of obese patients revealed with Venn diagrams (d).

Figure 3

Construction of a potential regulatory network for NPY1R. Sankey diagram demonstrating a series of TFs-miRNAs-NPY1R regulatory networks (a); Sankey diagram showing potential TFs regulating NPY1R (b); graphs showing potential functionally relevant interaction factors and their scores, with all connecting lines indicating statistical significance (p < 0.05) (c).

Figure 4

Enrichment analysis of NPY1R. GO and KEGG enrichment analysis of NPY1R (a); the enrichment networks of NPY1R are also shown (b).

Figure 5

Screening for core transcription factors regulating NPY1R and expression analysis of clinical samples. Correlation analysis results on the identified regulators (a); Pearson's correlation (NPY1R, TCF4, HEY1, and GATA3) analysis between transcription factors (b); relative mRNA levels of TCF4, GATA3, HEY1, miR-452, and miR-4713 in clinical samples (c).

Figure 6

Mechanistic map of the TF-miRNA-NPY1R regulatory network demonstrates two signaling pathways associated with NPY1R in childhood obesity.