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Genome-wide correlation analysis to identify amplitude regulators of circadian transcriptome output.

ABSTRACT: Cell-autonomous circadian system, consisting of core clock genes, generates near 24-h rhythms and regulates the downstream rhythmic gene expression. While it has become clear that the percentage of rhythmic genes varies among mouse tissues, it remains unclear how this variation can be generated, particularly when the clock machinery is nearly identical in all tissues. In this study, we sought to characterize circadian transcriptome datasets that are publicly available and identify the critical component(s) involved in creating this variation. We found that the relative amplitude of 13 genes and the average level of 197 genes correlated with the percentage of cycling genes. Of those, the correlation of Rorc in both relative amplitude and the average level was one of the strongest. In addition, the level of Per2AS, a novel non-coding transcript that is expressed at the Period 2 locus, was also linearly correlated, although with a much lesser degree compared to Rorc. Overall, our study provides insight into how the variation in the percentage of clock-controlled genes can be generated in mouse tissues and suggests that Rorc and potentially Per2AS are involved in regulating the amplitude of circadian transcriptome output.

SUBMITTER: Littleton ES

PROVIDER: S-EPMC7736363 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Genome-wide correlation analysis to identify amplitude regulators of circadian transcriptome output.

Littleton Evan S ES Childress Madison L ML Gosting Michaela L ML Jackson Ayana N AN Kojima Shihoko S

Scientific reports 20201214 1

Cell-autonomous circadian system, consisting of core clock genes, generates near 24-h rhythms and regulates the downstream rhythmic gene expression. While it has become clear that the percentage of rhythmic genes varies among mouse tissues, it remains unclear how this variation can be generated, particularly when the clock machinery is nearly identical in all tissues. In this study, we sought to characterize circadian transcriptome datasets that are publicly available and identify the critical c ...[more]

PMID: 33318596

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Project description:MotivationPersonalized medicine aims at providing patient-tailored therapeutics based on multi-type data toward improved treatment outcomes. Chronotherapy that consists in adapting drug administration to the patient's circadian rhythms may be improved by such approach. Recent clinical studies demonstrated large variability in patients' circadian coordination and optimal drug timing. Consequently, new eHealth platforms allow the monitoring of circadian biomarkers in individual patients through wearable technologies (rest-activity, body temperature), blood or salivary samples (melatonin, cortisol) and daily questionnaires (food intake, symptoms). A current clinical challenge involves designing a methodology predicting from circadian biomarkers the patient peripheral circadian clocks and associated optimal drug timing. The mammalian circadian timing system being largely conserved between mouse and humans yet with phase opposition, the study was developed using available mouse datasets.ResultsWe investigated at the molecular scale the influence of systemic regulators (e.g. temperature, hormones) on peripheral clocks, through a model learning approach involving systems biology models based on ordinary differential equations. Using as prior knowledge our existing circadian clock model, we derived an approximation for the action of systemic regulators on the expression of three core-clock genes: Bmal1, Per2 and Rev-Erbα. These time profiles were then fitted with a population of models, based on linear regression. Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. This agreed with biological knowledge on temperature-dependent control of Per2 transcription. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background.Availability and implementationhttps://gitlab.inria.fr/julmarti/model-learning-mb21eccb.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Genome-wide correlation analysis to identify amplitude regulators of circadian transcriptome output.

Publications

Genome-wide correlation analysis to identify amplitude regulators of circadian transcriptome output.

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