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Updated: Jun 3, 2025

A Novel in vivo Gene Transfer Technique and in vitro Cell Based Assays for the Study of Bone Loss in Musculoskeletal Disorders
Published on: June 8, 2014
SNPs in GPCR Genes and Impaired Osteogenic Potency in Osteoporotic Patient Lines-Based Study
Julia Sopova1,2, Olga Krasnova1, Giomar Vasilieva3
1Institute of Cytology, Russian Academy of Sciences, St. Petersburg 194064, Russia.
Abstract:
G-protein-coupled receptors (GPCRs) have emerged as critical regulators of bone development and remodeling. In this study, we aimed to identify specific GPCR mutations in osteoporotic patients via next-generation sequencing (NGS). We performed NGS sequencing of six genomic DNA samples taken from osteoporotic patients and two genomic DNA samples from healthy donors. Next, we searched for single-nucleotide polymorphisms (SNPs) in GPCR genes that are associated with osteoporosis. For three osteoporotic patients and one healthy donor, bone biopsies were used to generate patient-specific mesenchymal stem cell (MSC) lines, and their ability to undergo osteodifferentiation was analyzed. We found that MSCs derived from osteoporotic patients have a different response to osteoinductive factors and impaired osteogenic differentiation using qPCR and histochemical staining assays. The NGS analysis revealed specific combinations of SNPs in GPCR genes in these patients, where SNPs in ADRB2 (rs1042713), GIPR (rs1800437), CNR2 (rs2501431, rs3003336), and WLS (rs3762371) were associated with impaired osteogenic differentiation capacity. By integrating NGS data with functional assessments of patient-specific cell lines, we linked GPCR mutations to impaired bone formation, providing a foundation for developing personalized therapeutic strategies. SNP analysis is recognized as a proactive approach to osteoporosis management, enabling earlier interventions and targeted preventive measures for individuals at risk. Furthermore, SNP analysis contributes to the development of robust, holistic risk prediction models that enhance the accuracy of risk assessments across the population. This integration of genetic data into public health strategies facilitates healthcare initiatives. This approach could guide treatment decisions tailored to the patient's genetic profile and provide a foundation for developing personalized therapeutic strategies.

