Back

Mesoderm-specific transcript reduces ciliary sphingomyelin levels to promote tendon stem/progenitor cells osteochondrogenesis in traumatic heterotopic ossification

Lai, B.; Gao, Y.; Shen, Z.; Zhao, J.; Jiang, H.; Zhou, X.

2025-03-01 molecular biology
10.1101/2025.03.01.640937 bioRxiv
Show abstract

Traumatic heterotopic ossification (tHO) is a musculoskeletal disorder characterized by ectopic bone formation in soft tissues following trauma. However, the etiology and mechanisms of tHO remain unknown, and no treatments are highly effective for tHO. Here, by performing single-cell RNA sequencing (scRNA-seq) on two tHO mouse models, we found that ciliary Hedgehog (Hh) and PI3K-Akt signaling in tendon stem/progenitor cells (TSPCs) is upregulated during tHO development, leading to the activation of GLI family zinc finger 2 (GLI2) transcription factor, which promotes mesoderm-specific transcript (MEST) gene expression. Untargeted lipidomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) on detached cilia indicated that MEST could reduce ciliary sphingomyelin (SM) levels by inhibiting SM synthesis in TSPCs, creating a positive feedback loop that amplified ciliary Hh signaling, enhancing TSPCs osteochondrogenesis and driving tHO formation. Targeting ciliary genes intraflagellar transport 88 (IFT88) or ADP ribosylation factor like GTPase 3 (ARL3) restored normal TSPCs osteochondrogenesis in vitro and attenuated ectopic bone formation in vivo by suppressing Hh and PI3K-Akt signaling. AAV-mediated MEST inhibition or exogenous SM administration in vivo also alleviates tHO progression. These findings may provide novel insights into tHO pathogenesis and potential therapeutic strategies.

Published in Genes & Diseases · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.