Sympathetic Neural Networks protecting against Obesity

Objective

The brain regulates various physiological functions, including body weight, through descending autonomic sympathetic neurons. While targeting this system can improve obesity, it often carries cardiovascular risks. These limitations arise from a limited understanding of sympathetic neural networks due to their complexity and anatomically inaccessible location. My laboratory has been developing innovative strategies, including a novel imaging technique with single-neuron resolution and advanced single-cell analysis algorithms. In mice, these methods have enabled my laboratory to identify sympathetic interneurons, offering a new perspective into autonomic neuroscience. These interneurons are melanocortinergic and reside in sympathetic ganglia that do not target the heart but a unique thermogenic fat cluster, which we hypothesize is analogous to one found in adult humans. SympNetO will investigate whether these sympathetic interneurons can provide a neurobiological basis for cardioprotective anti-obesity mechanisms that promote fat burning. My laboratory will expand its suite of unpublished techniques, integrating single-cell genomics data across humans and macaques, alongside intersectional mouse models. This approach will enable us to develop anatomical and molecular atlases and functionally characterize sympathetic neural networks conserved across species. In Aim 1, my laboratory will molecularly define sympathetic neural networks across ganglia. In Aim 2, we will manipulate these networks to differentiate those regulating energy expenditure from other physiological processes like heart rate and blood pressure. In Aim 3, we will determine how sympathetic circuits control previously unrecognized thermogenic fat pads to regulate energy expenditure. This research aims to bring sympathetic neuroscience into the era of neural networks, providing a platform that could identify new fat-burning medications that spare the cardiovascular system.