Multi-omics Analysis of Metabolic Characteristics and Validation of HBEGF-Mediated Regulatory Mechanisms of Metabolism during Catch-Up Growth in Small-for-Gestational-Age Infants

Small-for-gestational-age (SGA) is a globally recognized public health concern. Infants born SGA may experience metabolic disturbances. This study elucidated the key regulatory factors and mechanisms underlying catch-up growth (CUG) and metabolic homeostasis in SGA infants. High-throughput targeted metabolomics were employed to compare the serum profiles of SGA and appropriate-for-gestational-age (AGA) neonates, identifying key metabolic pathways and associated regulatory signaling pathways enriched with differentially expressed metabolites. Animal experiments were conducted, and liver tissue samples were collected from neonatal SGA and AGA rats. Subsequently, mRNA sequencing to identify and analyze differentially expressed genes. HBEGF—a gene critically involved in metabolic processes—was significantly upregulated in SGA rat liver tissues. HBEGF silencing can suppress CUG in SGA rats and influence carbohydrate and lipid metabolism. Furthermore, metabolomic profiling was performed to investigate the role of HBEGF in metabolic regulation during CUG in individuals with SGA. Finally, the impact of HBEGF on the AKT/GSK-3ß (protein kinase B/glycogen synthase kinase 3 beta) signaling pathway was explored in both in vivo and in vitro settings. This study confirms metabolic differences between SGA and AGA individuals are evident at early-life. HBEGF may be crucial in CUG progression in SGA by modulating metabolic processes via AKT/GSK-3ß signaling pathway. Overall design: The RNAs were isolated from rat liver tissues using TRIpure (BioTeke). RNA samples were evaluated using the A260/A280 absorbance ratio with a Nanodrop system, and only qualified samples were utilized to construct the library. Library quality was further evaluated using an Agilent 2100 Bioanalyzer system. Sequencing was performed on an Illumina NovaSeq 6000 platform. Q30 was chosen to filter the original data, and the clean reading was compared with the rat reference genome by HISAT2. Differentially expressed genes (DEGs) were identifined based on criteria of p < 0.05 and |log2FC| > 1. KEGG pathway enrichment analysis was conducted to investigate biological pathways significantly associated with the identified DEGs.