Employing single-cell RNA sequencing coupled with an array of bioinformatics approaches to ascertain the shared genetic characteristics between osteoporosis and obesity
Year: 2024
Type: article
Abstract: Aims This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Methods Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes. Results WGCNA revealed critical gene modules for OB and OP, identifying the Toll-like receptor (TLR) signalling pathway as a common factor. TLR2 was the most significant gene, with a pronounced expression in macrophages. Elevated TLR2 expression correlated with increased adipose accumulation, inflammation, and osteoclast differentiation, linking it to OP development. Conclusion Our study underscores the pivotal role of TLR2 in connecting OP and OB. It highlights the influence of TLR2 in macrophages, driving both diseases through a pro-inflammatory mechanism. These insights propose TLR2 as a potential dual therapeutic target for treating OP and OB. Cite this article: Bone Joint Res 2024;13(10):573–587.
Source: Bone and Joint Research
Authors: Dingzhuo Liu, Fangming Cao, Dian Liu, Hao Li, Lin Tao, Yue Zhu
Institution: First Hospital of China Medical University
Language: English
Topic: Bone Metabolism and Diseases
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,594
OpenAlex ID: W4403455941