Abstract
Systemic insulin sensitivity shows a diurnal rhythm with a peak upon waking1,2. The molecular mechanism that underlies this temporal pattern is unclear. Here we show that the nuclear receptors REV-ERB-α and REV-ERB-β (referred to here as ‘REV-ERB’) in the GABAergic (γ-aminobutyric acid-producing) neurons in the suprachiasmatic nucleus (SCN) (SCNGABA neurons) control the diurnal rhythm of insulin-mediated suppression of hepatic glucose production in mice, without affecting diurnal eating or locomotor behaviours during regular light–dark cycles. REV-ERB regulates the rhythmic expression of genes that are involved in neurotransmission in the SCN, and modulates the oscillatory firing activity of SCNGABA neurons. Chemogenetic stimulation of SCNGABA neurons at waking leads to glucose intolerance, whereas restoration of the temporal pattern of either SCNGABA neuron firing or REV-ERB expression rescues the time-dependent glucose metabolic phenotype caused by REV-ERB depletion. In individuals with diabetes, an increased level of blood glucose after waking is a defining feature of the ‘extended dawn phenomenon’3,4. Patients with type 2 diabetes with the extended dawn phenomenon exhibit a differential temporal pattern of expression of REV-ERB genes compared to patients with type 2 diabetes who do not have the extended dawn phenomenon. These findings provide mechanistic insights into how the central circadian clock regulates the diurnal rhythm of hepatic insulin sensitivity, with implications for our understanding of the extended dawn phenomenon in type 2 diabetes.
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Data availability
The data that support the findings of this study are freely available from the corresponding authors upon request. RNA-seq data are available at the Gene Expression Omnibus (GEO) with the accession code GSE150840. Source data are provided with this paper.
Change history
15 June 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-03654-5
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Acknowledgements
We thank M. Lazar for the Nr1d1loxP and Nr1d2loxP mice; Q. Tong and Y. Xu for critical reading of the manuscript and technical guidance; H. Liu for technical consultation; X. Wang for statistics consultation; C. Yu and S. S. B. Lee for technical assistance; K. Oka and B. Arenkiel for viral vector production; and C. Ljungberg (U54HD083092 and 1S10OD016167) for some of the histology studies. The Mouse Metabolism and Phenotyping Core at Baylor College of Medicine was supported by R01DK114356 and UM1HG006348. The authors were supported by grants from the National Natural Science Foundation of China (81971458 and 31671222 to G.D.); the American Diabetes Association (ADA1-17-PDF-138 to Y. He, ADA1-19-PDF-012 to W.Z. and NIH P20 GM135002 to Y. He); the US Department of Agriculture (Cris51000-064-01S to Y.X.); the National Key R&D Program of China (2016YFC0901204 and 2018YFC1311801 to L.C. and 2017YFC1001300 to G.D.); and the NIH (R01DK111436, R01HL153320, RF1AG069966 and R01ES027544 to Z.S.). We also thank the John S. Dunn Foundation, the Mrs. Clifford Elder White Graham Endowed Research Fund, the Cardiovascular Research Institute at Baylor College of Medicine, the Dan L Duncan Comprehensive Cancer Center (P30CA125123), the Texas Medical Center Digestive Diseases Center (P30DK056338), the SPORE program in lymphoma at Baylor College of Medicine (P50 CA126752) and the Gulf Coast Center for Precision Environmental Health (P30ES030285).
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Contributions
Z.S. conceived the study. G.D. identified the mouse phenotype and oversaw the human study. X.L. performed gene expression analysis, chemogenetic studies, and stereotaxic injections in mice. X.H. recruited study participants and supervised the human study. W.Z. performed histological studies and ChIP. Y.G. coordinated the insulin clamp. W.L. made DNA constructs. S.Q. performed some of the mouse metabolic tests. J.S. performed gene expression analyses in human samples. J.W., F.L., J.W. and C.C. collected human blood samples, made clinical measurements and coordinated clinical studies. Y. He performed patch clamp recording. P.B. performed the initial mouse crossbreeding. G.D., X.L. and Y.G. maintained the mouse lines. P.S. performed CLAMS and insulin clamp analyses. G.D., X.L., W.Z., Y.G., J.S., J.W., Y. He, P.B., T.Y. and Z.S. analysed the data. K.Z., Y. Han and C.I.A. performed statistical analyses. Y.X., X.H. and Z.S. interpreted the data. L.C. and Z.S. obtained funding. G.D. and Z.S. wrote the manuscript with input from other authors.
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Extended data figures and tables
Extended Data Fig. 1 Behavioural characterization of knockout mice.
a, RNAscope analysis of Nr1d1 (REV-ERB-α) gene expression at ZT6–9 and ZT18–21 in three-month-old wild-type mice. Scale bars, 200 μm b, Representative wheel-running actogram for five-month-old mice in light–dark conditions (LD) or constant darkness (DD). c, Phase angle of the light entrainment in the last day of the light–dark conditions (n = 7 mice). d–i, Representative chi-square periodograms (d, e, h, i) and period length (f, g) for five-month-old mice in light–dark conditions or constant darkness (n = 7 mice). j, Average wheel-running activity in constant darkness after normalization to the intrinsic period (tau) (n = 7 mice). Data are mean ± s.e.m. *P <0.05 by two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 2 Metabolic characterization of knockout mice on a normal chow diet.
a, Daily food intake in three-month-old mice in home cages (n = 4 cages across 20 days). b, c, Food intake measured by the comprehensive laboratory animal monitoring system (CLAMS) in the 6 h (b) or 12 h (c) before GTT analyses at four months old (n = 5 mice). Box plot centre lines, box limits and whiskers represent the median, quartiles and minimum and maximum values, respectively. d, Blood glucose levels in four-month-old mice (n = 14 wild-type mice; n = 10 knockout mice). e, Serum insulin levels (n = 10 mice per group). f, Blood glucagon levels (n = 12 mice per group). g, Blood corticosterone levels (n = 11 mice per group). h, Blood GLP-1 levels (n = 12 wild-type mice; n = 10 knockout mice). i, Blood growth hormone (GH) levels (n = 11 wild-type mice; n = 12 knockout mice). j, k, GTTs in five-month-old mice at ZT6–8 (j) or ZT12–14 (k), with vgat-cre mice serving as the wild-type control (n = 7 mice). l, Body weight for clamp analyses at five months old (n = 4 mice). m, n, Blood glucose levels (m) and glucose infusion rate (n) during clamp analyses (n = 4 mice). o, Hyperinsulinaemia-mediated suppression of hepatic glucose production in the clamp analyses (n = 4 mice). Data are mean ± s.e.m. *P < 0.05 by two-way ANOVA or two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 3 Metabolic characterization of knockout mice on a high-fat diet.
a, Body weight on HFD. HFD started at 10 months (n = 12 mice). b, GTTs at ZT6–8 after two weeks on HFD (n = 12 mice). c, GTTs at ZT12–14 after three weeks on HFD (n = 12 mice). d, ITTs at ZT6–8 after four weeks on HFD (n = 12 mice). e, ITTs at ZT12–14 after five weeks on HFD (n = 12 mice). f, Injection of streptozotocin (STZ) six weeks after HFD. g, h, Body weight (g) and blood glucose levels (h) at ZT10 after STZ injection (n = 12 mice). i, Blood glucose levels at the indicated ZTs two weeks after STZ injection, n = 12 mice. Data are mean ± s.e.m. *P < 0.05 by two-way ANOVA or two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 4 Gene expression analysis of different brain regions.
a–e, RT–qPCR analysis of the indicated brain-region-specific marker genes (Vip (a), Pmch (b), Crf (c), Rfrp (also known as Npvf) (d) and Pomc1 (also known as Pomc) (e)) for brain regions isolated from both wild-type and knockout mice at ZT6 at the age of three months (n = 12 mice). Box plot centre lines, box limits and whiskers represent the median, quartiles and minimum and maximum values, respectively. f–i, RT–qPCR analysis comparing mRNA expression of Nr1d1 (f), Nr1d2 (g), Bmal1 (h) and Npas2 (i) in wild-type and knockout mice at ZT6 at the age of three months (n = 6 mice). Data are mean ± s.e.m. *P < 0.05 by two-way ANOVA or two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 5 Electrophysiological and molecular characterization of knockout mice.
a–c, Brain slice patch-clamp representative traces for spontaneous firing (a), mEPSCs (b) and mIPSCs (c) at ZT12–14. d–g, Temporal pattern of expression of Rgs16 (d), α7-Takusan (Gm10406) (e), Nr1d1 (f) and Nr1d2 (g) in the hypothalamus in light–dark conditions from CircaDB (http://circadb.hogeneschlab.org). h–k, RT–qPCR analysis of the mRNA levels of Nr1d1 (h), Nr1d2 (i), Bmal1 (j) and Npas2 (k) in the SCN of three-month-old mice (n = 6 mice). Primers for Nr1d1 and Nr1d2 did not span the floxed exons. l, RNAscope of Rgs16 at the SCN in wild-type and knockout mice at the indicated ZTs. Scale bars, 100 μm. m, Quantification of Rgs16 staining (n = 5 wild-type mice at ZT4; n = 3 knockout mice at ZT4; n = 4 wild-type or knockout mice at ZT16). n, In situ hybridization analysis of Takusan Gm3500 staining. Scale bars, 25 μm. o, Quantification of in situ hybridization analysis of Takusan Gm3500 (n = 4 wild-type mice at ZT4; n = 6 wild-type mice at ZT16; n = 3 knockout mice at ZT4 or ZT16). p, Genome browser views of transcription start sites (green arrows) and nearby AGGTCA elements (red arrows) for the indicated genes on GRCm38. q, ChIP–qPCR analysis of Nr1d1 in the hypothalamus of three-month-old wild-type mice at ZT9 and ZT21—the peak and the trough of REV-ERB-α expression, respectively (n = 4 samples). Hypothalami from five mice were pooled as one sample. The negative control primers target a gene desert region on chromosome 17. The primer sequences of ChIP–qPCR assays are in Supplementary Table 6. Data are mean ± s.e.m. *P < 0.05 by two-way ANOVA or two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 6 Metabolic characterization of mice overexpressing RGS16 or α7-Takusan in SCNGABA neurons.
a, Validation of the injection with GFP fluorescence signals. Scale bar, 200 μm. b–e, GTTs (b, c) and ITTs (d, e) at the indicated ZTs in four-month-old vgat-cre mice injected with AAV-FLEX vectors encoding GFP, RGS16 or α7-Takusan (n = 7 mice). f, Body weight of three-month-old vgat-cre mice at three weeks after AAV injection (n = 14 mice). Data are mean ± s.e.m. *P < 0.05 for RGS16 or α7-Takusan versus the GFP control by two-way ANOVA followed by Holm–Sidak’s test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 7 Rhythmicity of SCNGABA neuron firing in glucose metabolism.
a, Experimental design for chemogenetic activation of the SCNGABA neurons in wild-type mice with hM3Dq. b, Body weight of vgat-cre mice injected with AAV expressing hM3Dq or control mCherry (n = 11 mice). Mice were injected at the age of two months. c, Experimental design for chemogenetic repression of the SCNGABA neurons in wild-type and knockout mice with hM4Di. d, Body weight of wild-type and knockout mice injected with AAV expressing hM4Di (n = 12 wild-type mice; n = 14 knockout mice). Mice were injected at the age of two months. e, f, GTTs in wild-type or knockout mice injected with AAV expressing hM4Di at the indicated ZTs in the presence of saline (e) or CNO (f) (n = 12 wild-type mice; n = 14 knockout mice). Data are mean ± s.e.m. *P < 0.05 by two-sided t-test.
Extended Data Fig. 8 Rhythmicity of REV-ERB expression in SCNGABA neurons in glucose metabolism.
a, Experimental design for inducible re-expression of REV-ERB-α in the SCNGABA neurons of knockout mice. Mice were injected virus at the age of 2.5 months. b, Body weight at the time of euthanasia (n = 9 mice). c, d, GTTs in 4–4.5-month-old mice at ZT6–8 after injection of doxycycline at ZT0 (c) or ZT18 (d) (n = 9 mice). e, RT–qPCR analysis of the SCN from knockout mice with inducible re-expression of REV-ERB-α. Doxycycline was injected at ZT0 and the brain was collected at ZT12–14 (n = 4 mice). Data are mean ± s.e.m. *P < 0.05 by two-sided t-test. Statistical details are in Supplementary Table 1.
Extended Data Fig. 9 Assessment of CGM performance.
a, A representative comparison between fingertip glucometer reading and CGM reading for an individual at different times of the day. b, Pearson correlation coefficient between CGM and fingertip readings (n = 16 DP−; n = 11 DP+). c, MARD, the average of the absolute error between all CGM values and matched reference values (n = 16 DP−; n = 11 DP+). Data are mean ± s.e.m.
Supplementary information
Supplementary Tables
This file contains Supplementary Tables 1-6. Supplementary Table 1. Statistical tests. Statistical details were shown for results with significant differences, including the names of the statistical methods, p values, and sample sizes for those not included in the main figure legends due to limited space. Supplementary Table 2. Primer sequences for RT-qPCR and ChIP-qPCR. Nucleic acid sequences (from the 5’ end to the 3’ end) were shown for RT-qPCR primers, ChIP-qPCR primers, and the ISH probe. Supplementary Table 3. Inclusion and exclusion criteria for patient recruitment. Supplementary Table 4. Characteristics of human subjects. Supplementary Table 5. Cardiopulmonary Coupling-Polysomnography (CPC-PSG) of human subjects. Supplementary Table 6. Medication usage in human subjects.
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Ding, G., Li, X., Hou, X. et al. REV-ERB in GABAergic neurons controls diurnal hepatic insulin sensitivity. Nature 592, 763–767 (2021). https://doi.org/10.1038/s41586-021-03358-w
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DOI: https://doi.org/10.1038/s41586-021-03358-w
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