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Tytuł:
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Model learning to identify systemic regulators of the peripheral circadian clock.
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Autorzy:
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Martinelli J; INSERM UMR-S 900, Institut Curie, MINES ParisTech CBIO, PSL Research University, 92210 Saint-Cloud, France.; Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France.
Dulong S; UPR 'Chronotherapy, Cancers and Transplantation', Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France.
Li XM; UPR 'Chronotherapy, Cancers and Transplantation', Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France.
Teboul M; Côte d'Azur University, CNRS, INSERM, iBV, Nice 06000, France.
Soliman S; Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France.
Lévi F; UPR 'Chronotherapy, Cancers and Transplantation', Paris-Saclay University, Faculty of Medicine Kremlin Bicêtre, Le Kremlin Bicêtre, 94270, France.; Hepato-Biliary Center, Paul-Brousse Hospital, Assistance Publique-Hôpitaux de Paris, Villejuif 94800, France.
Fages F; Lifeware Group, Inria Saclay Ile-de-France, Palaiseau 91120, France.
Ballesta A; INSERM UMR-S 900, Institut Curie, MINES ParisTech CBIO, PSL Research University, 92210 Saint-Cloud, France.
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Źródło:
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Bioinformatics (Oxford, England) [Bioinformatics] 2021 Jul 12; Vol. 37 (Suppl_1), pp. i401-i409.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: Oxford : Oxford University Press, c1998-
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MeSH Terms:
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Circadian Clocks*/genetics
Animals ; Circadian Rhythm ; Gene Expression Regulation ; Humans ; Mice
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References:
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Grant Information:
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INSERM; ANR-11-LABX-0028-01 French Plan Cancer; ANR-15-IDEX-01 UCAJEDI; Canceropôle Provence-Alpes-Cote d'Azur; French National Cancer Institute
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Entry Date(s):
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Date Created: 20210712 Date Completed: 20210715 Latest Revision: 20211102
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Update Code:
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20240105
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PubMed Central ID:
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PMC8557835
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DOI:
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10.1093/bioinformatics/btab297
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PMID:
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34252929
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Motivation: Personalized 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.
Results: We 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 Implementation: https://gitlab.inria.fr/julmarti/model-learning-mb21eccb.
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author(s) 2021. Published by Oxford University Press.)
