Back

Targeted ablation and regeneration of enteric nervous system neurons in zebrafish

Shah, M. A.; Moran, K. K.; Rueckert, H.; Sharrock, A. V.; Ackerley, D. F.; Mumm, J. S.; Ganz, J.

2025-07-04 neuroscience
10.1101/2025.07.01.660701 bioRxiv
Show abstract

The enteric nervous system (ENS) is the intrinsic nervous system of the gut and regulates essential gut functions, including motility, digestion, and immune response, ensuring gut homeostasis. ENS dysfunction or loss is associated with gastrointestinal disorders such as Hirschsprung disease (HSCR). Currently, surgery is the only treatment for HSCR, but it often has lifelong, severe complications. Restoring missing ENS neurons by stimulating endogenous neuronal regeneration presents a promising therapeutic approach for ENS disease. To reveal the cellular-molecular mechanisms regulating neuronal regeneration we study a species capable of robust ENS restoration, the zebrafish. For this, we developed a chemogenetic ablation model in zebrafish using the Gal4/UAS NTR 2.0 system for targeted ENS neuron ablation. Spatially and temporally controlled neuronal death was confirmed by morphological changes, quantification of neuronal loss, and TUNEL assays. We observed an acute immune response that normalizes at 1 day of treatment. Quantification of regenerated neurons demonstrated complete restoration of ENS neuron numbers to control levels by 9 days post treatment, with recovery of gut motility. Among the regenerated neurons, nitrergic, cholinergic and VIPergic subtypes showed full recovery, whereas serotonergic neurons only displayed partial recovery, indicating subtype-specific differences in regenerative capacity and/or timing of cell replacement. Our study establishes a robust platform for dissecting the cellular-molecular mechanisms of ENS regeneration to develop potential treatment approaches for ENS-related diseases.

Published in Stem Cell Reports · training set

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.