Jet lag, based on GABAergic network structure of the SCN (2/2)

Timely recovery from jet lag is increasingly gaining importance as inter-continnental air travel and nightshift work have become commonplace. Circadian behaviors including sleep-wake cycles are controlled by the suprachiasmatic nucleus (SCN), the master circadian clock in the anterior hypothalamus. The applicant and his international group of collaborators have previously found that the SCN utilizes its multi-oscillator network structure and also encodes the seasonal time through relative phases of two emergent clusters of circadian oscillators. Jet lag can be understood as a consequence of this network structure developed for seasonal homeostasis. Yet, the two-cluster structure has novel stability pockets that can create previously unforeseen circadian jet lag behaviors. These new insights enable specific model-based strategies to manipulate the behavior of the SCN and hence control the circadian behavior of the body. Exploiting the network structure and based on additional evidence from pilot data, the applicant proposes to discover methods to "hack" the hypothalamic network, which includes smart solutions to jet lag recovery. Although jet lag recovery is the oldest question in chronobiology, previous approaches have assumed that the SCN essentially acts as a single-oscillator. However, behaviors of the SCN can be best understood when the network heterogeneity is accounted for. Tonic GABA excitation in the dorsal region of the SCN is a major source of such heterogeneity that causes an intrinsic network adaptation time comparable to the recovery time of jet lag. Using unusual light-dark schedules and/or modulation of GABA excitation, the network adaptation time can be tweaked. This project will therefore consider both treatment paradigms. The light schedule-based approach is thoroughly based on mathematical techniques to find an optimal adaptation path for the SCN network, and the initial phase of the project will be devoted to clarifying this network structure. The drug-based approach employs a new transgenic mouse system lacking tonic GABA release (Best1 KO). The applicant plans to utilize this system to circumvent difficulties with interpreting pharmacological approaches often encountered with the GABA system. While uncovering the mystery of jet lag is the main goal of this project, the applicant expects that the refinement of the SCN network structure will be an achievement of its own and can serve as a future platform to understand other hypothalamic neural networks. Towards the final year of the project, practical solutions to jet lag will also be sought extensively through experimental verification of key strategies generated by massively parallel parameter search of the network model.