Back

Novel fat-taste enhancers modulate functional connectivity of the reward system following sustained activation of gustatory pathways in mice

Cabeza, L.; Jantzen, L.; Houdayer, C.; Haffen, E.; Hichami, A.; Khan, N. A.; Van Waes, V.

2025-09-16 neuroscience
10.1101/2025.09.11.675492 bioRxiv
Show abstract

Humans and rodents exhibit an innate preference for dietary fat, and dysfunction of lingual fat-taste receptors CD36 and GPR120 in obesity suggests that impaired oro-sensory lipid detection may contribute to its development. Rodent models with reduced fat-taste sensitivity display increased fat intake, highlighting the role of taste perception in dietary regulation. The newly developed NKS-3 and NKS-5, high-affinity agonists for CD36 and CD36/GPR120 receptors, respectively, induce early fat satiation and reduce both fat-rich food intake and body weight gain in diet-induced obese mice. However, the neural mechanisms underlying these effects remain unclear. Here, we investigated the response of the reward system to a sustained activation of CD36 and GPR120 via fat-taste enhancers NKS-3 and NKS-5 compared to linoleic acid (LA). Male C57BL/6JRj mice (N=88) received oral administration of vehicle, 0.2% LA, 50 {micro}M NKS-3 or 75 {micro}M NKS-5 for 10 days. The immunohistochemical analysis of cerebral FosB neuronal expression revealed a reorganization of the functional network connectivity. Behavioral assessments over an additional 10-day period in a second cohort of animals detected no adverse motivational, compulsive-like, depressive or anxiogenic effect of the treatments. Our findings suggest that our fat-taste enhancers may offer a promising therapeutic strategy against obesity, leveraging the oral-gut-brain axis to regulate fat-rich food intake.

Matching journals

The top 9 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.