Back

Elevated surface La promotes hyperfusion and contributes to impaired resorption in osteopetrosis.

WHITLOCK, J. M.; Leikina, E.; Wang, H.-Y.; Zhang, W.; Katz, G.; Reuven, N.; Elson, A.; Geiger, B.; Chernomordik, L. V.

2025-09-08 cell biology
10.1101/2025.09.07.674639 bioRxiv
Show abstract

The skeleton is a living, biological tissue responding to the biomechanical demands placed upon it throughout life. The individual bones creating this physiological system are each shaped by a multinucleated cell type - the osteoclast - that sculpts each bone in collaboration with local cellular partners, which offer chemical and even tactile feedback of many sorts. Unfortunately, the perturbation of osteoclast formation and function underpins a broad range of human skeletal pathologies, including osteopetrosis - a systemic pathology characterized by impaired osteoclast resorption leading to skeletal thickening, brittle bones, frailty, and lethality. Here, we describe a molecular dysfunction observed in murine and human models of two forms of osteoclast-rich, autosomal recessive osteopetrosis, and our approach for exploiting this molecular dysfunction to correct pathologic osteoclast hyperfusion and resorptive impairment. We find that La - a manager of osteoclast fusion and subsequent resorptive activity - is greatly elevated at the surface of osteoclasts upon loss of SNX10 or OSTM1. Using inhibitory antibodies, we suppress excessive La surface function in these mutant osteoclasts, impede osteopetrotic hyperfusion and restore osteoclast resorptive function. We share these observations as proofs-of-principle that osteoclast fusion represents a viable therapeutic target for addressing osteoclast dysfunction in diseases underpinned by excessive osteoclast multinucleation and perturbed resorptive function.

Matching journals

The top 6 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.