IFT20 and WWTR1 govern bone homeostasis via synchronously regulating the expression and stability of TβRII in osteoblast lineage cells.
Yang LiShuting YangShuying YangPublished in: Research square (2024)
Balance of bone and marrow fat formation is critical for bone homeostasis. The imbalance of bone homeostasis will cause various bone diseases, such as osteoporosis. However, the precise mechanisms governing osteoporotic bone loss and marrow adipose tissue (MAT) accumulation remain poorly understood. By analysis of publicly available databases from bone samples of osteoporosis patients, we found that the expression of intraflagellar transport 20 (IFT20) and WW domain containing transcription regulator 1 (WWTR1) were significantly downregulated in osteoblast lineage cells. Additionally, we found that double deletions of IFT20 and WWTR1 in osteoblasts resulted in a significant accumulation of MAT and bone loss. Moreover, IFT20 and WWTR1 deficiency in osteoblasts exacerbated bone-fat imbalance in ovariectomy (OVX)- and high-fat-diet (HFD)-induced osteoporosis mouse models. Mechanistically, we found that deletions of IFT20 and WWTR1 in osteoblasts synergistically inhibited osteogenesis and promoted adipogenesis and osteoclastogenesis. We also found that IFT20 interacted with TGF-β receptor type II (TβRII) to enhance TβRII stability by blocking c-Cbl-mediated ubiquitination and degradation of TβRII. WWTR1 transcriptionally upregulated TβRII expression by directly binding its promoter. These findings indicate that targeting IFT20/WWTR1 may be a potential therapeutic strategy for the treatment of osteoporosis.
Keyphrases
- bone loss
- bone mineral density
- adipose tissue
- high fat diet
- postmenopausal women
- bone regeneration
- poor prognosis
- body composition
- insulin resistance
- transcription factor
- induced apoptosis
- ejection fraction
- soft tissue
- end stage renal disease
- binding protein
- dna methylation
- patient reported outcomes
- peritoneal dialysis
- cell death
- skeletal muscle
- dna binding
- big data
- high glucose
- transforming growth factor
- machine learning
- oxidative stress