Disrupted circadian oscillations in type 2 diabetes are linked to altered rhythmic mitochondrial metabolism in skeletal muscle

Brendan Gabriel, Ali Altıntaş, Jonathon A. B. Smith, Laura Sardon-Puig, Xiping Zhang, Astrid L. Basse, Rhianna C. Laker, Hui Gao, Zhengye Liu, Lucile Dollet, Jonas T. Treebak, Antonio Zorzano, Zhiguang Huo, Mikael Rydén, Johanna T. Lanner, Karyn A. Esser, Romain Barrès, Nicolas J. Pillon, Anna Krook, Juleen R. Zierath* (Corresponding Author)

*Corresponding author for this work

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Circadian rhythms are generated by an auto-regulatory feedback loop composed of transcriptional activators and repressors. Disruption of circadian rhythms contributes to Type 2 diabetes (T2D) pathogenesis. We elucidated whether altered circadian rhythmicity of clock genes is associated with metabolic dysfunction in T2D. Transcriptional cycling of core clock genes ARNTL, CLOCK, CRY1 and NR1D1 was altered in skeletal muscle from individuals with T2D and this was coupled with reduced number and amplitude of cycling genes and disturbed circadian oxygen consumption. Mitochondrial associated genes were enriched for differential circadian amplitudes in T2D, and positively correlated with insulin sensitivity. ChIP- sequencing identified CLOCK and BMAL1 binding to circadian mitochondrial genes associated with insulin sensitivity, implicating regulation by the core clock. Mitochondria disruption altered core-clock gene expression and free-radical production, phenomena that were restored by resveratrol treatment. We identify bi-directional communication between mitochondrial function and rhythmic gene expression, processes which are disturbed in diabetes.
Original languageEnglish
Article numbereabi9654
Number of pages19
JournalScience Advances
Issue number43
Early online date20 Oct 2021
Publication statusPublished - 22 Oct 2021

Bibliographical note

The authors are supported by grants from the AstraZeneca SciLifeLab Research Programme, Novo Nordisk Foundation (NNF14OC0011493, and NNF17OC0030088), Swedish Diabetes Foundation (DIA2018-357), Swedish Research Council (2015-00165 and 2018-02389), the Knut and Alice Wallenberg Foundation (2018-0094), the Strategic Research Programme in Diabetes at Karolinska Institutet (2009-1068), the Stockholm County Council (SLL20170159), and the Swedish Research Council for Sport Science (P2019-0140). B.M.G. was supported by fellowships from the Novo Nordisk Foundation (NNF19OC0055072), the Wenner-Gren Foundation, an Albert Renold Travel Fellowship from the European Foundation for the Study of Diabetes, and an Eric Reid Fund for Methodology from the Biochemical Society. N.J.P. and L.S.-P. were supported by an Individual Fellowship from the Marie Skłodowska-Curie Actions (European Commission: 704978 and 675610). X.Z. and K.A.E. were supported by NIH R01AR066082. N.J.P. was supported by grants from the Sigurd och Elsa Goljes Minne and Lars Hierta Memorial Foundations (Sweden). We acknowledge the Beta Cell in-vivo Imaging/Extracellular Flux Analysis core facility supported by the Strategic Research Program in Diabetes for the usage of the Seahorse flux analyzer. Additional support was received from the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (NNF18CC0034900). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen, partially funded by an unrestricted donation from the Novo Nordisk Foundation. We acknowledge the Single-Cell Omics platform at the Novo Nordisk Foundation Center for Basic Metabolic Research for technical and computational expertise and support. Schematics are created with BioRender.com.

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new unique reagents. Sequencing data have been archived in a publicly accessible database at Gene Expression Omnibus (http://ncbi.nlm.nih.gov/geo) with GEO accession numbers GSE182117, GSE182120, and GSE143334 for RNA sequencing, gene expression microarray, and ChIP sequencing, respectively.


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