Back

D-Serine agonism of GluN1-GluN3 NMDA receptors regulates the activity of enteric neurons and coordinates gut motility

Osorio, N.; Martineau, M.; Fortea, M.; Rouget, C.; Penalba, V.; Lee, C. J.; Boesmans, W.; Rolli-Derkinderen, M.; Patel, A. V.; Mondielli, G.; Conrod, S.; Labat-Gest, V.; Papin, A.; Sasabe, J.; Sweedler, J. V.; Vanden Berghe, P.; Delmas, P.; MOTHET, J.-P.

2023-04-19 neuroscience
10.1101/2023.04.19.537136 bioRxiv
Show abstract

The enteric nervous system (ENS) is a complex network of diverse molecularly defined classes of neurons embedded in the gastrointestinal wall and responsible for controlling the major functions of the gut. As in the central nervous system, the vast array of ENS neurons is interconnected by chemical synapses. Despite several studies reporting the expression of ionotropic glutamate receptors in the ENS, their roles in the gut remain elusive. Here, by using an array of immunohistochemistry, molecular profiling and functional assays, we uncover a new role for D-serine (D-Ser) and non-conventional GluN1-GluN3 N-methyl D-aspartate receptors (NMDARs) in regulating ENS functions. We demonstrate that D-Ser is produced by serine racemase (SR) expressed in enteric neurons. By using both in situ patch clamp recording and calcium imaging, we show that D-Ser alone acts as an excitatory neurotransmitter in the ENS independently of the conventional GluN1-GluN2 NMDARs. Instead, D-Ser directly gates the non-conventional GluN1-GluN3 NMDARs in enteric neurons from both mouse and guinea-pig. Pharmacological inhibition or potentiation of GluN1-GluN3 NMDARs had opposite effects on mouse colonic motor activities, while genetically driven loss of SR impairs gut transit and fluid content of pellet output. Our results demonstrate the existence of native GluN1-GluN3 NMDARs in enteric neurons and open new perspectives on the exploration of excitatory D-Ser receptors in gut function and diseases.

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.