Shank3 Modulates Sleep and Expression of Circadian Transcription Factors
- Washington State University, United States
- National Research Council (CNR), Italy
- University of Padova, Italy
Abstract
Autism Spectrum Disorder (ASD) is the most prevalent neurodevelopmental disorder in the United States and often co-presents with sleep problems. Sleep problems in ASD predict the severity of ASD core diagnostic symptoms and have a considerable impact on the quality of life of caregivers. Little is known, however, about the underlying molecular mechanisms of sleep problems in ASD. We investigated the role of Shank3, a high confidence ASD gene candidate, in sleep architecture and regulation. We show that mice lacking exon 21 of Shank3 have problems falling asleep even when sleepy. Using RNA-seq we show that sleep deprivation increases the differences in prefrontal cortex gene expression between mutants and wild types, downregulating circadian transcription factors Per3, Bhlhe41, Hlf, Tef, and Nr1d1. Shank3 mutants also have trouble regulating wheel-running activity in constant darkness. Overall, our study shows that Shank3 is an important modulator of sleep and clock gene expression.
Data availability
Sequencing data have been deposited in GEO under accession code GSE113754. Source data files have been provided for Figures 1-4, Tables 1 and 2 and Supplementary Files 1 and 2. The R code used in this article is available on GitHub (github.com/drighelli/peixoto). The R code used for the statistical analysis of RNA-seq and circadian wheel running data is also available in Source Code file 1.
-
RNA-Seq analysis of Sleep deprivation in wildtype and Shank3 mutant miceNCBI Gene Expression Omnibus, GSE113754.
-
Gene expression linked to sleep homeostasis in murine cortexNCBI Gene Expression Omnibus, GSE78215.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (K01NS104172)
- Lucia Peixoto
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols of Washington State University. Protocol numbers: 04705-001 and 04704-001IACUC 6155-Peixoto BreedingIACUC 4705-Peixoto experimentalIACUC 4581-Frank experimental
Human subjects: PMSIR patient data was obtained de-identified under IRB exemption 15005-Peixoto
Copyright
© 2019, Ingiosi 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.
Metrics
-
- 6,598
- views
-
- 884
- downloads
-
- 68
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Shank3 Modulates Sleep and Expression of Circadian Transcription Factors
eLife 8:e42819.
https://doi.org/10.7554/eLife.42819
Further reading
-
- Neuroscience
Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain’s most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.
-
- Neuroscience
Moment-to-moment neural variability has been shown to scale positively with the complexity of stimulus input. However, the mechanisms underlying the ability to align variability to input complexity are unknown. Using a combination of behavioral methods, computational modeling, fMRI, MR spectroscopy, and pharmacological intervention, we investigated the role of aging and GABA in neural variability during visual processing. We replicated previous findings that participants expressed higher variability when viewing more complex visual stimuli. Additionally, we found that such variability modulation was associated with higher baseline visual GABA levels and was reduced in older adults. When pharmacologically increasing GABA activity, we found that participants with lower baseline GABA levels showed a drug-related increase in variability modulation while participants with higher baseline GABA showed no change or even a reduction, consistent with an inverted-U account. Finally, higher baseline GABA and variability modulation were jointly associated with better visual-discrimination performance. These results suggest that GABA plays an important role in how humans utilize neural variability to adapt to the complexity of the visual world.
