Transcriptomic and proteomic analysis of osteogenesis imperfecta disease-specific induced pluripotent stem cells
Year: 2026
Type: article
Abstract: BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic bone disorder for which treatment options remain limited. Disease-specific induced pluripotent stem cells (iPSCs) offer a valuable model to investigate its underlying pathological mechanisms. METHODS: Peripheral blood cells from two OI patients were reprogrammed into iPSCs. Transcriptomic sequencing and proteomic analysis were employed to systematically identify differentially expressed genes (DEGs) and proteins (DEPs) between OI-iPSCs and normal iPSCs. Bioinformatics approaches were subsequently applied for enrichment analysis and protein-protein interaction network investigation. RESULTS: OI-iPSCs were successfully established. Integrated multi-omics analysis identified 671 common DEGs and 636 common DEPs. These molecules were significantly enriched in metabolic pathways, with the oxidative phosphorylation pathway being particularly prominent. Further cross-omics screening revealed 22 genes that were differentially expressed at both the mRNA and protein levels. Among these, the focal adhesion protein VCL emerged as a central hub within the protein-protein interaction network. CONCLUSION: Through multi-omics analysis, this study revealed widespread dysregulation of metabolic pathways in OI-iPSCs, suggesting that targeted interventions on specific metabolic pathways may represent a novel therapeutic strategy for OI. VCL emerged as a key candidate molecule, warranting further functional validation in subsequent studies. These findings provide new insights into the mechanisms of OI and potential avenues for therapeutic development.
Source: BMC Medical Genomics
Authors: Ruijun Guo, Lingyu Huang, Yu Sha, Chune Mo, Chunhong Li, Weiwei Gong, Xianliang Hou, Minglin Ou
Institution: Guilin Medical University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,190
OpenAlex ID: W7160779979
