Proceedings of the National Academy of SciencesSystems BiologyPeer-reviewed journal article
Population level rhythms in human skin: implications for circadian medicine
Gang WuiD, Marc D. RubeniD, Robert E. SchmidtiD, Lauren J. FranceyiD, David F. SmithiD, Ron C. AnafiiD, Jacob HugheyiD, Ryan A. Tasseff, Joseph D. SherrilliD, John E. OblongiD, Kevin J. MillsiD, John B. HogeneschiD
Abstract
Skin is the largest organ in the body and serves important barrier, regulatory, and sensory functions. Like other tissues, skin is subject to temporal fluctuations in physiological responses under both homeostatic and stressed states. To gain insight into these fluctuations, we investigated the role of the circadian clock in the transcriptional regulation of epidermis using a hybrid experimental design, where a limited set of human subjects (n=20) were sampled throughout the 24 h cycle and a larger population (n=219) were sampled once. By looking at pairwise correlations of core clock genes in 298 skin samples, we found a robust circadian oscillator in skin at the population level. Encouraged by this, we used CYCLOPS to reconstruct the temporal order of all samples and identified hundreds of rhythmically-expressed genes at the population level in human skin. We compared these results with published time-series skin data from mouse and show strong concordance in circadian phase across species for both transcripts and pathways. Further, like blood, skin is readily accessible and a potential source of biomarkers. Using ZeitZeiger, we identified a biomarker set for human skin that is capable of reporting circadian phase to within 3 h from a single sample. In summary, we show rhythms in human skin that persist at the population scale and a path to develop robust single-sample circadian biomarkers.\n\nOne Sentence SummaryHuman epidermis shows strong circadian rhythms at the population scale and provides a better source for developing robust, single-sample circadian phase biomarkers than human blood.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: benchmarking
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 121
- FWCI
- 7.04
- Field percentile
- top 2%
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