Several different neuronal populations are involved in regulating energy homeostasis. Among these, agouti-related protein (AgRP) neurons are thought to promote feeding and weight gain; however, the evidence supporting this view is incomplete. Using designer receptors exclusively activated by designer drugs (DREADD) technology to provide specific and reversible regulation of neuronal activity in mice, we have demonstrated that acute activation of AgRP neurons rapidly and dramatically induces feeding, reduces energy expenditure, and ultimately increases fat stores. All these effects returned to baseline after stimulation was withdrawn. In contrast, inhibiting AgRP neuronal activity in hungry mice reduced food intake. Together, these findings demonstrate that AgRP neuron activity is both necessary and sufficient for feeding. Of interest, activating AgRP neurons potently increased motivation for feeding and also drove intense food-seeking behavior, demonstrating that AgRP neurons engage brain sites controlling multiple levels of feeding behavior. Due to its ease of use and suitability for both acute and chronic regulation, DREADD technology is ideally suited for investigating the neural circuits hypothesized to regulate energy balance.
Michael J. Krashes, Shuichi Koda, ChianPing Ye, Sarah C. Rogan, Andrew C. Adams, Daniel S. Cusher, Eleftheria Maratos-Flier, Bryan L. Roth, Bradford B. Lowell
This article was first published March 1, 2011. Usage data is cumulative from October 2016 through October 2017.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.