| Experiment Id | GSE190034 | Name | Characterisation of the epidermal circadian transcriptome of mice with tissue-specific circadian clock activity [DD] |
| Experiment Type | RNA-Seq | Study Type | WT vs. Mutant |
| Source | GEO | Curation Date | 2024-08-02 |
| description | To preserve a state of health, mammals employ an integrated network of molecular oscillators to drive daily rhythms of tissue-specific homeostatic processes. Importantly, the output, structure and coherence of this network is compromised by physiological ageing, disease and lifestyle changes. Yet, the key signalling nodes in this network, their underlying mechanisms of communication, and potential for therapeutic intervention, remain undefined. To dissect this system, we construct a minimal clock network consisting of two communicating nodes: the peripheral epidermal clock and central brain clock. We show that communication between the brain and epidermal clocks is sufficient for wiDD-type core clock activity and specific homeostatic processes, but inputs from other clock nodes are essential for full daily physiology. Unexpectedly, we find evidence that the epidermal clock selectively suppresses or interprets systemic signals to ensure coherence of specific homeostatic processes, identifying an unrecognised gatekeeper role for peripheral clocks. Together, we introduce a novel approach for dissecting a tissues daily physiology, and in turn identify key signalling nodes required to maintain epidermal homeostasis. The circadian transcriptome of the skin epidermis was profiled for mice in which circadian clock activity was present only in the skin epidermis, only in the brain, only in the skin and brain, or all tissues. After a 1 week exposure to constant darkness, RNA-seq of epidermis samples were taken at four hour intervals throughout the day (four biological replicates per timepoint), and subsequently the algorithms JTK_CYCLE and BIOCYCLE were used to generate a consensus circadian transcriptome for each condition. In addition, the algorithm DryR was used to identify groups of genes with specific rhythmic parameters either shared or differing between the different conditions. |
