Back

Mitochondria Organize Gap Junctions to Enable Neural Circuit Scaling

Qiu, S.; Jian, Y.; Yin, H.; Lin, J.; Xu, Y.; Yang, Y.; Huang, H.; Zhao, Z.; Wang, Y.; Yan, D.; Meng, L.

2026-03-05 neuroscience
10.64898/2026.03.04.709687 bioRxiv
Show abstract

Neural circuits must maintain function during dramatic growth-driven expansion. While chemical synapses can be independently reorganized in pre- and postsynaptic neurons, gap junctions physically couple partner cells, requiring coordinated spatial redistribution--a process whose mechanisms remain unknown. Using the C. elegans mechanosensory circuit, we show that gap junctions undergo stereotyped developmental reorganization, transitioning from clustered to dispersed configurations as partner neurites elongate. Forward genetic screening identified RIC-7, a mitochondrial adaptor protein, as essential for this redistribution. Loss of RIC-7 prevents gap junction dispersal, trapping partner neurites at clustered sites and severely impairing electrical transmission. Mechanistically, RIC-7 directs mitochondrial positioning to gap junctions, where mitochondria recruit the microtubule-organizing component PTRN-1 to locally enhance microtubule dynamics. Disrupting mitochondrial transport (miro-1;mtx-2 mutants) or PTRN-1 function phenocopies ric-7 defects, while metabolic dysfunction does not, establishing a non-metabolic role for mitochondria in organizing synaptic architecture. Remarkably, RIC-7 expression in one neuron alone suffices to restore gap junction and partner neurite distribution across both coupled cells. Structural modeling suggests mechanical adhesion between docked hemichannels enables this coordinated reorganization. These findings establish mitochondria as mobile organizers of synaptic spatial architecture, demonstrate that proper junction distribution is essential for electrical transmission efficacy, and reveal how cell-autonomous mechanisms can achieve bilateral structural coordination during circuit growth.

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.