Back

Prefrontal cortex melanocortin 4 receptors (MC4R) mediate food intake behavior in mice

Ross, R. A.; Kim, A.; Das, P.; Li, Y.; Choi, Y. K.; Thompson, A. T.; Douglas, E.; Subramanian, S.; Ramos, K.; Callahan, K.; Bolshakov, V. Y.; Ressler, K. J.

2022-06-02 neuroscience
10.1101/2022.06.01.494383 bioRxiv
Show abstract

BackgroundMelanocortin 4 receptor (MC4R) activity in the hypothalamus is crucial for regulation of metabolism and food intake. The peptide ligands for the MC4R are associated with feeding, energy expenditure, and also with complex behaviors that orchestrate energy intake and expenditure, but the downstream neuroanatomical and neurochemical targets associated with these behaviors are elusive. In addition to strong expression in the hypothalamus, the MC4R is highly expressed in the medial prefrontal cortex, a region involved in executive function and decision-making. MethodsUsing viral techniques in genetically modified mice combined with molecular techniques, we identify and describe the neuronal dynamics, and define the effects on feeding behavior of a novel population of MC4R expressing neurons in the infralimbic region of the cortex. ResultsHere, we describe a novel population of MC4R-expressing neurons in the infralimbic (IL) region of the mouse prefrontal cortex that are glutamatergic, receive input from melanocortinergic neurons of the arcuate hypothalamus, and project to multiple regions that coordinate appetitive responses to food-related stimuli. The neurons are depolarized by application of MC4R-specific peptidergic agonist, THIQ. Deletion of MC4R from the IL neurons causes increased food intake and body weight gain and impaired executive function in simple food-related behavior tasks. ConclusionTogether, these data suggest that MC4R neurons of the IL play a critical role in the regulation of food intake.

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.