Impact of age on the circadian visual system and the sleep-wake cycle in mus musculus.
Ontology highlight
ABSTRACT:
SUBMITTER: Shuboni-Mulligan DD
PROVIDER: S-EPMC8096965 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Although the mammalian rest-activity cycle is controlled by a "master clock" in the suprachiasmatic nuclei (SCN) of the hypothalamus, it is unclear how firing of individual SCN neurons gates individual features of daily activity. Here, we demonstrate that a specific transcriptomically identified population of mouse VIP+ SCN neurons is active at the "wrong" time of the day -nighttime- when most SCN neurons are silent. Using chemogenetic and optogenetic strategies, we show that these neurons and their cellular clocks are necessary and sufficient to gate and time nighttime sleep, but have no effect upon daytime sleep. We propose mouse nighttime sleep, analogous to the human siesta, is a "hard-wired" property gated by specific neurons of the master clock to favor subsequent alertness prior to dawn (a circadian "wake maintenance zone"). Thus, the SCN is not simply a 24h metronome: specific populations sculpt critical features of the sleep-wake cycle.
2021-01-21 | GSE154038 | GEO
Project description:Although the mammalian rest-activity cycle is controlled by a "master clock" in the suprachiasmatic nucleus (SCN) of the hypothalamus, it is unclear how firing of individual SCN neurons gates individual features of daily activity. Here, we demonstrate that a specific transcriptomically identified population of mouse VIP+ SCN neurons is active at the "wrong" time of day-nighttime-when most SCN neurons are silent. Using chemogenetic and optogenetic strategies, we show that these neurons and their cellular clocks are necessary and sufficient to gate and time nighttime sleep but have no effect upon daytime sleep. We propose that mouse nighttime sleep, analogous to the human siesta, is a "hard-wired" property gated by specific neurons of the master clock to favor subsequent alertness prior to dawn (a circadian "wake maintenance zone"). Thus, the SCN is not simply a 24-h metronome: specific populations sculpt critical features of the sleep-wake cycle.
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Project description:Background and aims: Associated with numerous metabolic and behavioral abnormalities, obesity is classified by metrics reliant on body weight (such as body mass index). However, overnutrition is the common cause of obesity, and may independently contribute to these obesity-related abnormalities. Here, we use dietary challenges to parse apart the relative influence of diet and/or energy balance from body weight on various metabolic and behavioral outcomes.Materials and methods: Seventy male mice (mus musculus) were subjected to the diet switch feeding paradigm, generating groups with various body weights and energetic imbalances. Spontaneous activity patterns, blood metabolite levels, and unbiased gene expression of the nutrient-sensing ventral hypothalamus (using RNA-sequencing) were measured, and these metrics were compared using standardized multivariate linear regression models.Results: Spontaneous activity patterns were negatively related to body weight (p<0.0001) but not diet/energy balance (p = 0.63). Both body weight and diet/energy balance predicted circulating glucose and insulin levels, while body weight alone predicted plasma leptin levels. Regarding gene expression within the ventral hypothalamus, only two genes responded to diet/energy balance (neuropeptide y [npy] and agouti-related peptide [agrp]), while others were related only to body weight.Conclusions: Collectively, these results demonstrate that individual components of obesity-specifically obesogenic diets/energy imbalance and elevated body mass-can have independent effects on metabolic and behavioral outcomes. This work highlights the shortcomings of using body mass-based indices to assess metabolic health, and identifies novel associations between blood biomarkers, neural gene expression, and animal behavior following dietary challenges.
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Project description:BackgroundIndividual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumulated evidence should permit researchers to form fewer - and more accurate - hypotheses for further study through experimentation.ResultsHere a method previously used to predict Gene Ontology (GO) terms for Saccharomyces cerevisiae (Tian et al.: Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol 2008, 9(Suppl 1):S7) is applied to predict GO terms and phenotypes for 21,603 Mus musculus genes, using a diverse collection of integrated data sources (including expression, interaction, and sequence-based data). This combined 'guilt-by-profiling' and 'guilt-by-association' approach optimizes the combination of two inference methodologies. Predictions at all levels of confidence are evaluated by examining genes not used in training, and top predictions are examined manually using available literature and knowledge base resources.ConclusionWe assigned a confidence score to each gene/term combination. The results provided high prediction performance, with nearly every GO term achieving greater than 40% precision at 1% recall. Among the 36 novel predictions for GO terms and 40 for phenotypes that were studied manually, >80% and >40%, respectively, were identified as accurate. We also illustrate that a combination of 'guilt-by-profiling' and 'guilt-by-association' outperforms either approach alone in their application to M. musculus.
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