Familial Advanced Sleep Phase (FASP) is a heritable human sleep phenotype characterized by very early sleep and wake times. We identified a missense mutation in the human Cryptochrome 2 (CRY2) gene that co-segregates with FASP in one family. The mutation leads to replacement of an alanine residue at position 260 with a threonine (A260T). In mice, the CRY2 mutation causes a shortened circadian period and reduced phase-shift to early-night light pulse associated with phase-advanced behavioral rhythms in the light-dark cycle. The A260T mutation is located in the phosphate loop of the flavin adenine dinucleotide (FAD) binding domain of CRY2. The mutation alters the conformation of CRY2, increasing its accessibility and affinity for FBXL3 (an E3 ubiquitin ligase), thus promoting its degradation. These results demonstrate that CRY2 stability controlled by FBXL3 plays a key role in the regulation of human sleep wake behavior.
- Louis J Ptáček
- Ying-Hui Fu
- Arisa Hirano
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All experimental protocols (Protocol no. AN111686-02) were conducted according to US National Institutes of Health guidelines for animal research andwere approved by the Institutional Animal Care and Use Committee at the University of California, San Francisco.
Human subjects: All human subjects signed a consent form approved by the Institutional Review Boards at the University of Utah and the University of California, San Francisco (IRB# 10-03952). The consent form includes all confidentiality and ethic guidelines and also indicates not revealing subject information in the publication.
- Joseph G Gleeson, Howard Hughes Medical Institute, The Rockefeller University, United States
© 2016, Hirano et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Understanding neuronal representations of odor-evoked activities and their progressive transformation from the sensory level to higher brain centers features one of the major aims in olfactory neuroscience. Here, we investigated how odor information is transformed and represented in higher-order neurons of the lateral horn, one of the higher olfactory centers implicated in determining innate behavior, using Drosophila melanogaster. We focused on a subset of third-order glutamatergic lateral horn neurons (LHNs) and characterized their odor coding properties in relation to their presynaptic partner neurons, the projection neurons (PNs) by two-photon functional imaging. We show that odors evoke reproducible, stereotypic, and odor-specific response patterns in LHNs. Notably, odor-evoked responses in these neurons are valence-specific in a way that their response amplitude is positively correlated with innate odor preferences. We postulate that this valence-specific activity is the result of integrating inputs from multiple olfactory channels through second-order neurons. GRASP and micro-lesioning experiments provide evidence that glutamatergic LHNs obtain their major excitatory input from uniglomerular PNs, while they receive an odor-specific inhibition through inhibitory multiglomerular PNs. In summary, our study indicates that odor representations in glutamatergic LHNs encode hedonic valence and odor identity and primarily retain the odor coding properties of second-order neurons.
Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.