Back

Wnt/β-Catenin coordinates muscle spindle development by regulating capsule differentiation, intrafusal fiber nuclei aggregation and proprioceptive nerve endings

Guo, Q.; Gatterer, A.; Rosner, A.; Krief, S.; Rotkopf, R.; Passini, F. S.; Wigoda, N.; Bornstein, B.; Zelzer, E.

2024-12-20 developmental biology
10.1101/2024.12.18.629133 bioRxiv
Show abstract

Proprioception is essential for the regulation of posture, movement, and musculoskeletal integrity. The muscle spindle, a mechanosensory organ composed of multiple specialized tissues, detects stretch and provides proprioceptive feedback. Despite its importance, the molecular mechanisms that orchestrate the development of the spindle components remain poorly understood. Here, we reveal the involvement of the Wnt/{beta}-catenin pathway in muscle spindle development. We show that Wnt ligands and their Frizzled receptors are expressed in the developing spindle and that {beta}-catenin is active throughout development in capsule cells and in bag2 intrafusal fibers. Embryonic deletion of {beta}-catenin from capsule and intrafusal fibers induced widespread transcriptomic changes, which led to significant malformations, including impaired capsule cell differentiation, disrupted nuclear organization in intrafusal fibers, and disorganized proprioceptive nerve endings. Postnatal deletion of {beta}-catenin from intrafusal fibers resulted in abnormal nerve endings and impaired proprioceptive function, indicating that the development of proprioceptive afferents is regulated by {beta}-catenin through a non-cell-autonomous mechanism acting in bag2 fibers. Collectively, our findings position the Wnt/{beta}-catenin pathway as a central regulator that acts through both cell-autonomous and non-cell-autonomous mechanisms to coordinate the development of the various spindle tissues into a functional organ.

Matching journals

The top 5 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.