The mammalian circadian system consists of a central clock in the brain that synchronizes clocks in the peripheral tissues. Although the hierarchy between central and peripheral clocks is established, little is known regarding the specificity and functional organization of peripheral clocks. Here, we employ altered feeding paradigms in conjunction with liver-clock mutant mice to map disparities and interactions between peripheral rhythms. We find that peripheral clocks largely differ in their responses to feeding time. Disruption of the liver-clock, despite its prominent role in nutrient processing, does not affect the rhythmicity of clocks in other peripheral tissues. Yet, unexpectedly, liver-clock disruption strongly modulates the transcriptional rhythmicity of peripheral tissues, primarily on daytime feeding. Concomitantly, liver-clock mutant mice exhibit impaired glucose and lipid homeostasis, which are aggravated by daytime feeding. Overall, our findings suggest that, upon nutrient challenge, the liver-clock buffers the effect of feeding-related signals on rhythmicity of peripheral tissues, irrespective of their clocks.
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The RNA-seq data are available from the GEO under accession no. GSE159135. Figures 1 and 3 and Extended Data Figs. 1 and 3 are associated with the statistical analysis available in Supplementary Data 1. In addition, the unprocessed blots of Fig. 1g are available as Source Data with this paper. Figures 2, 4, 5 and 6a and Extended Data Figs. 4 and 6 are associated with the RNA-seq data (raw and processed data accessible through the GEO) and with the statistical analysis available in Supplementary Data 2. Extended Data Figs. 2, 5 and 6c are associated with the complete results presented in Supplementary Data 3. Extended Data Fig. 4d,e is associated with the complete results presented in Supplementary Data 4. Extended Data Fig. 6d is associated with the complete results presented in Supplementary Data 5. All other data that support the findings of this study are available from the corresponding author upon request.
Schibler, U. et al. Clock-Talk: interactions between central and peripheral circadian oscillators in mammals. Cold Spring Harb. Symp. Quant. Biol. 80, 223–232 (2015).
Mohawk, J. A., Green, C. B. & Takahashi, J. S. Central and peripheral circadian clocks in mammals. Annu. Rev. Neurosci. 35, 445–462 (2012).
Albrecht, U. Timing to perfection: the biology of central and peripheral circadian clocks. Neuron 74, 246–260 (2012).
Dibner, C., Schibler, U. & Albrecht, U. The mammalian circadian timing system: organization and coordination of central and peripheral clocks. Annu. Rev. Physiol. 72, 517–549 (2010).
Reinke, H. & Asher, G. Crosstalk between metabolism and circadian clocks. Nat. Rev. Mol. Cell Biol. 20, 227–241 (2019).
Pickel, L. & Sung, H.-K. Feeding rhythms and the circadian regulation of metabolism. Front. Nutr. 7, 39 (2020).
Damiola, F. et al. Restricted feeding uncouples circadian oscillators in peripheral tissues from the central pacemaker in the suprachiasmatic nucleus. Genes Dev. 14, 2950–2961 (2000).
Mukherji, A., Kobiita, A. & Chambon, P. Shifting the feeding of mice to the rest phase creates metabolic alterations, which, on their own, shift the peripheral circadian clocks by 12 hours. Proc. Natl Acad. Sci. USA 112, E6683–E6690 (2015).
Mukherji, A. et al. Shifting eating to the circadian rest phase misaligns the peripheral clocks with the master SCN clock and leads to a metabolic syndrome. Proc. Natl Acad. Sci. USA 112, E6691–E6698 (2015).
Stokkan, K. A., Yamazaki, S., Tei, H., Sakaki, Y. & Menaker, M. Entrainment of the circadian clock in the liver by feeding. Science 291, 490–493 (2001).
Vollmers, C. et al. Time of feeding and the intrinsic circadian clock drive rhythms in hepatic gene expression. Proc. Natl Acad. Sci. USA 106, 21453–21458 (2009).
Yeung, J. & Naef, F. Rhythms of the genome: circadian dynamics from chromatin topology, tissue-specific gene expression, to behavior. Trends Genet. 34, 915–926 (2018).
Ruben, M. D. et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine. Sci. Transl. Med. 10, eaat8806 (2018).
Manella, G. et al. Hypoxia induces a time- and tissue-specific response that elicits intertissue circadian clock misalignment. Proc. Natl Acad. Sci. USA 117, 779–786 (2020).
Asher, G. et al. Poly(ADP-ribose) polymerase 1 participates in the phase entrainment of circadian clocks to feeding. Cell 142, 943–953 (2010).
Bray, M. S. et al. Quantitative analysis of light-phase restricted feeding reveals metabolic dyssynchrony in mice. Int. J. Obes. (Lond.) 37, 843–852 (2013).
Gilbert, M. R., Douris, N., Tongjai, S. & Green, C. B. Nocturnin expression is induced by fasting in the white adipose tissue of restricted fed mice. PLoS ONE 6, e17051 (2011).
Zvonic, S. et al. Characterization of peripheral circadian clocks in adipose tissues. Diabetes 55, 962–970 (2006).
Hughes, M. E., Hogenesch, J. B. & Kornacker, K. JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J. Biol. Rhythms 25, 372–380 (2010).
Greenwell, B. J. et al. Rhythmic food intake drives rhythmic gene expression more potently than the hepatic circadian clock in mice. Cell Rep. 27, 649–657.e5 (2019).
Kornmann, B., Schaad, O., Bujard, H., Takahashi, J. S. & Schibler, U. System-driven and oscillator-dependent circadian transcription in mice with a conditionally active liver clock. PLoS Biol. 5, e34 (2007).
Koronowski, K. B. et al. Defining the independence of the liver circadian clock. Cell 177, 1448–1462.e14 (2019).
Welz, P.-S. et al. BMAL1-driven tissue clocks respond independently to light to maintain homeostasis. Cell 178, 1029 (2019).
Reinke, H. & Asher, G. Circadian clock control of liver metabolic functions. Gastroenterology 150, 574–580 (2016).
Asher, G. & Sassone-Corsi, P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell 161, 84–92 (2015).
Lamia, K. A., Storch, K.-F. & Weitz, C. J. Physiological significance of a peripheral tissue circadian clock. Proc. Natl Acad. Sci. USA 105, 15172–15177 (2008).
Guan, D. et al. The hepatocyte clock and feeding control chronophysiology of multiple liver cell types. Science 369, 1388–1394 (2020).
Doi, R., Oishi, K. & Ishida, N. CLOCK regulates circadian rhythms of hepatic glycogen synthesis through transcriptional activation of Gys2. J. Biol. Chem. 285, 22114–22121 (2010).
Díaz-Muñoz, M. et al. Daytime food restriction alters liver glycogen, triacylglycerols, and cell size. A histochemical, morphometric, and ultrastructural study. Comp. Hepatol. 9, 5 (2010).
Sinturel, F. et al. Diurnal oscillations in liver mass and cell size accompany ribosome assembly cycles. Cell 169, 651–663.e14 (2017).
Wang, H. et al. Time-restricted feeding shifts the skin circadian clock and alters UVB-induced DNA damage. Cell Rep. 20, 1061–1072 (2017).
Minami, Y., Horikawa, K., Akiyama, M. & Shibata, S. Restricted feeding induces daily expression of clock genes and Pai-1 mRNA in the heart of Clock mutant mice. FEBS Lett. 526, 115–118 (2002).
Opperhuizen, A.-L. et al. Feeding during the resting phase causes profound changes in physiology and desynchronization between liver and muscle rhythms of rats. Eur. J. Neurosci. 44, 2795–2806 (2016).
Reznick, J. et al. Altered feeding differentially regulates circadian rhythms and energy metabolism in liver and muscle of rats. Biochim. Biophys. Acta 1832, 228–238 (2013).
Tahara, Y. et al. Entrainment of the mouse circadian clock by sub-acute physical and psychological stress. Sci. Rep. 5, 11417 (2015).
Woller, A. & Gonze, D. Modeling clock-related metabolic syndrome due to conflicting light and food cues. Sci. Rep. 8, 13641 (2018).
Bae, S.-A. & Androulakis, I. P. The synergistic role of light-feeding phase relations on entraining robust circadian rhythms in the periphery. Gene Regul. Syst. Bio. 11, 1177625017702393 (2017).
Adamovich, Y., Ladeuix, B., Golik, M., Koeners, M. P. & Asher, G. Rhythmic oxygen levels reset circadian clocks through HIF1α. Cell Metab. 25, 93–101 (2017).
Adamovich, Y. et al. Oxygen and carbon dioxide rhythms are circadian clock controlled and differentially directed by behavioral signals. Cell Metab. 29, 1092–1103.e3 (2019).
Buhr, E. D., Yoo, S.-H. & Takahashi, J. S. Temperature as a universal resetting cue for mammalian circadian oscillators. Science 330, 379–385 (2010).
Pezük, P., Mohawk, J. A., Wang, L. A. & Menaker, M. Glucocorticoids as entraining signals for peripheral circadian oscillators. Endocrinology 153, 4775–4783 (2012).
Crosby, P. et al. Insulin/IGF-1 drives PERIOD synthesis to entrain circadian rhythms with feeding time. Cell 177, 896–909.e20 (2019).
Hamada, T. et al. In vivo imaging of clock gene expression in multiple tissues of freely moving mice. Nat. Commun. 7, 11705 (2016).
Sinturel, F. et al. Circadian hepatocyte clocks keep synchrony in the absence of a master pacemaker in the suprachiasmatic nucleus or other extrahepatic clocks. Genes Dev. 35, 329–334 (2021).
Matsumura, T. et al. Liver-specific dysregulation of clock-controlled output signal impairs energy metabolism in liver and muscle. Biochem. Biophys. Res. Commun. 534, 415–421 (2021).
van den Berghe, G. The role of the liver in metabolic homeostasis: implications for inborn errors of metabolism. J. Inherit. Metab. Dis. 14, 407–420 (1991).
Gachon, F., Loizides-Mangold, U., Petrenko, V. & Dibner, C. Glucose homeostasis: regulation by peripheral circadian clocks in rodents and humans. Endocrinology 158, 1074–1084 (2017).
Gatfield, D. & Schibler, U. Circadian glucose homeostasis requires compensatory interference between brain and liver clocks. Proc. Natl Acad. Sci. USA 105, 14753–14754 (2008).
Kalsbeek, A., la Fleur, S. & Fliers, E. Circadian control of glucose metabolism. Mol. Metab. 3, 372–383 (2014).
Priest, C. & Tontonoz, P. Inter-organ cross-talk in metabolic syndrome. Nat. Metab. 1, 1177–1188 (2019).
Wu, G., Anafi, R. C., Hughes, M. E., Kornacker, K. & Hogenesch, J. B. MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics 32, 3351–3353 (2016).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Kohen, R. et al. UTAP: User-friendly Transcriptome Analysis Pipeline. BMC Bioinformatics 20, 154 (2019).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Strimmer, K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24, 1461–1462 (2008).
Lück, S., Thurley, K., Thaben, P. F. & Westermark, P. O. Rhythmic degradation explains and unifies circadian transcriptome and proteome data. Cell Rep. 9, 741–751 (2014).
Wang, X. et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 28, 2534–2536 (2012).
Singer, J. M. & Hughey, J. J. LimoRhyde: a flexible approach for differential analysis of rhythmic transcriptome data. J. Biol. Rhythms 34, 5–18 (2019).
Agostinelli, C. & Lund, U. R package ‘circular': Circular Statistics v.0.4-93) (2017); https://r-forge.r-project.org/projects/circular/
Jammalamadaka, S. R. & Sengupta, A. Topics in Circular Statistics (World Scientific, 2001).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
The Gene Ontology Consortium The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Zhang, R., Podtelezhnikov, A. A., Hogenesch, J. B. & Anafi, R. C. Discovering biology in periodic data through phase set enrichment analysis (PSEA). J. Biol. Rhythms 31, 244–257 (2016).
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Use R!) (Springer, 2016).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
We thank all the members of the Asher lab for their comments on the manuscript. We thank L. Flur and E. Elinav for their aid with plasma measurements and Y. Kuperman for her assistance with the metabolic cages. G.A. is supported by the European Research Council (ERC-2017 CIRCOMMUNICATION 770869), Abisch-Frenkel Foundation for the Promotion of Life Sciences, Adelis Foundation and Susan and Michael Stern. E.S. received a Martin Kushner Schnur and Armando and Maria Jinich postdoctoral fellowship for Mexican citizens.
The authors declare no competing interests.
Peer review information Nature Metabolism thanks Frederic Gachon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: George Caputa.
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Quantitative PCR results from mice (AlbCre) fed either ad libitum (AL) or exclusively during the light-phase (DF) for 30 days. Samples were collected at 2 h intervals for 24 h (n = 2 biologically independent mice per time point). Presented are representative clock genes from the (a) liver, (b) White Adipose Tissue (WAT), (c) lung, (d) quadriceps muscle, (e) kidney, and (f) heart. Dots mark individual measurements in each Zeitgeber Time (ZT). Significant rhythms according to JTK_CYCLE are denoted by cosine fit curve according to the captions, with a vertical line representing the phase; dashed curves represent non-significant (ns) rhythms. Note that the profiles for Arntl and Nr1d1 are re-plotted from Fig. 1a–f and are included here as well to enable side-by-side comparison with other clock genes in each tissue.
Extended Data Fig. 2 Daytime-restricted feeding affects the rhythmic transcriptome in a tissue-specific manner.
Gene Ontology (GO) Biological Processes (BP) enrichment analysis of rhythmic genes in mice fed either ad libitum (AL) or exclusively during the light-phase (DF) for 30 days in (a) liver, (b) WAT, and (c) lung (P < 0.05, over-representation test).
Quantitative PCR results of clock gene expression of liver-clock mutant mice (BLKO), fed either ad libitum (AL, upper panels) or exclusively during the light-phase (DF, lower panels) for 30 days. Samples were collected at 2 h intervals for 24 h (n = 2 biologically independent mice per time point). Presented are representative clock genes from the (a) liver, (b) WAT, (c) lung, (d) quadriceps muscle, (e) kidney, and (f) heart. Dots mark individual measurements in each Zeitgeber Time (ZT). Significant rhythms according to JTK_CYCLE are denoted by cosine fit curve according to the captions, with a vertical line representing the phase; dashed curves represent non-significant (ns) rhythms.
Extended Data Fig. 4 The liver-clock affects the composition of lung and WAT rhythmic transcriptome.
a, The transcriptomic data of either WAT or lung was subdivided into 15 subsets, based on their rhythmicity in each of the four conditions (AlbCre AL, AlbCre DF, BLKO AL, BLKO DF). For each condition a gene is consider rhythmic if overall Qmin < 0.05, and PHR < 0.05 in this condition (JTK_CYCLE and Harmonic Regression, see also Methods section). Depicted is a tabular summary of all the subsets and categories (L-Bmal1 = Liver-Bmal1). (b, c) Bar plot representation of the size of each subset in WAT (b) and lung (c), with coloration according to the categories as in (a). (d, e) Ingenuity Upstream Regulators analysis for each category in WAT (d) and lung (e) (presented are transcriptional regulators, P < 0.001, and at least 20 targets in the category).
Extended Data Fig. 5 Enrichment analysis for functional annotations of the different rhythmic categories.
Gene Ontology (GO) Biological Processes (BP) enrichment analysis of rhythmic genes which either loss their rhythmicity in BLKO (‘Loss’), or gained rhythmicity in BLKO (‘Gain’), or where not affected by BLKO (‘Retain’) in (a) WAT and (b) lung. (P < 0.05, over-representation test).
Extended Data Fig. 6 Impaired glucose homeostasis in liver-clock deficient mice in response to day feeding.
(a-c) Around-the-clock transcriptomic analysis of livers from either AlbCre control mice or liver-clock deficient mice (BLKO) fed either ad libitum (AL) or exclusively during the light-phase (DF) for 30 days. (a) Pie chart representing the distribution of genes between the different rhythmicity categories (see Fig. S4 for details). (b) Bar-plot representing the sizes of each rhythmicity subset (see Fig. S4 for details). (c) Gene Ontology (GO) Biological Processes (BP) enrichment analysis of rhythmic genes from the RRNN subset (P < 0.05, over-representation test). (d) Phase Set Enrichment Analysis of GO terms in WAT of AlbCre (left panel) or BLKO (right panel) mice. Only terms that are significantly phase-enriched in both ad libitum (AL) and day fed animals (DF) are presented (P < 0.05, Kuiper test), and the phase differences between the two regimens are depicted.
Extended Data Fig. 7 Behavioral and metabolic characterization of control and liver-clock deficient mice.
(a) Intra-Peritoneal Glucose Tolerance Test (IPGTT) analysis AlbCre or BLKO mice fed ad libitum (AL), performed at ZT10 after 2 h of food deprivation (Circles - individual mice, lines - mean levels; 2-way ANOVA with repeated measures design; AlbCre n = 9 mice, BLKO n = 8 mice). (b) IPGTT results from day-fed (DF) AlbCre or BLKO mice, performed at ZT22 (due to the daytime feeding, these mice were food deprived for 10 h), (Circles - individual mice, lines - mean levels; 2-way ANOVA with repeated measures design; n = 7 mice per condition). (c) Area Under the glucose Curve (AUC), as calculated for the data in (a-b), (two-sided Student’s t-test, n as in (a) and (b); boxplots: middle line= median, box= 25th to 75th Inter Quantile Range (IQR), whiskers= the largest/smallest value no greater/smaller than 1.5*IQR, outlier points= measurements outside this range). (d-g) Mice fed either ad libitum (AL) or exclusively during the light-phase (DF) for 4 weeks and were analyzed in metabolic cages for three consecutive days under the same feeding regimens. (d) Food consumption, (e) activity, (f) Respiratory Exchange Rate (RER), and (g) Energy Expenditure (EE) are presented. The data is presented as average of individual mice (n = 3 mice per genotype in AL, n = 4 mice per genotype in DF), with 1 h binning of the data (original measurements were taken every 15 minutes).
Rhythmicity analysis of qPCR data.
Summary tables of the statistical analyses.
GO term enrichment analyses.
Ingenuity upstream regulator analysis.
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Manella, G., Sabath, E., Aviram, R. et al. The liver-clock coordinates rhythmicity of peripheral tissues in response to feeding. Nat Metab 3, 829–842 (2021). https://doi.org/10.1038/s42255-021-00395-7
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