A diurnal serum lipid integrates hepatic lipogenesis and peripheral fatty acid use

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Abstract

Food intake increases the activity of hepatic de novo lipogenesis, which mediates the conversion of glucose to fats for storage or use. In mice, this program follows a circadian rhythm that peaks with nocturnal feeding1,2 and is repressed by Rev-erbα/β and an HDAC3-containing complex3,4,5 during the day. The transcriptional activators controlling rhythmic lipid synthesis in the dark cycle remain poorly defined. Disturbances in hepatic lipogenesis are also associated with systemic metabolic phenotypes6,7,8, suggesting that lipogenesis in the liver communicates with peripheral tissues to control energy substrate homeostasis. Here we identify a PPARδ-dependent de novo lipogenic pathway in the liver that modulates fat use by muscle via a circulating lipid. The nuclear receptor PPARδ controls diurnal expression of lipogenic genes in the dark/feeding cycle. Liver-specific PPARδ activation increases, whereas hepatocyte-Ppard deletion reduces, muscle fatty acid uptake. Unbiased metabolite profiling identifies phosphatidylcholine 18:0/18:1 (PC(18:0/18:1) as a serum lipid regulated by diurnal hepatic PPARδ activity. PC(18:0/18:1) reduces postprandial lipid levels and increases fatty acid use through muscle PPARα. High-fat feeding diminishes rhythmic production of PC(18:0/18:1), whereas PC(18:0/18:1) administration in db/db mice (also known as Lepr−/−) improves metabolic homeostasis. These findings reveal an integrated regulatory circuit coupling lipid synthesis in the liver to energy use in muscle by coordinating the activity of two closely related nuclear receptors. These data implicate alterations in diurnal hepatic PPARδ–PC(18:0/18:1) signalling in metabolic disorders, including obesity.

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Figure 1: Hepatic PPARδ and Acc1 are linked to muscle FA use.
Figure 2: Hepatic PPARδ controls liver lipogenic gene expression and muscle FA uptake.
Figure 3: PC(18:0/18:1) links hepatic PPARδ to serum lipid levels and muscle FA uptake.
Figure 4: PC(18:0/18:1) regulates muscle FA use through PPARα.

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Acknowledgements

We thank C. Newgard for providing shACC1 and shScramble adenovirus, U. Unluturk, X. Li and A. N. White for technical help and D. E. Cohen, S. Watkins and D. Jacobi for comments. This work is supported by the American Heart Association and the American Diabetes Association (C.-H.L.) and National Institutes of Health grants R01DK075046 (C.-H.L.), R01HL048743 (J.P.) and K08HL105678 (J.D.B.).

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Contributions

S.L., A.S., J.P. and C.-H.L. designed the research. S.L. performed most of the experiments with technical assistance from K.J.S., P.B., M.R.G. and L.D.; S.L., J.D.B., E.H., M.L. and A.S. developed and performed untargeted and targeted metabolite profiling. B.H. generated adGFP and adPPARδ virus. K.I. performed metabolic cage and lipid infusion experiments. G.S.H., J.P., A.S. and J.D.B. provided critical intellectual inputs and manuscript editing. S.L. and C.-H.L. analysed the data and wrote the paper.

Corresponding author

Correspondence to Chih-Hao Lee.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Analyses of liver lipid metabolites altered by PPARδ overexpression.

a, Metabolite set enrichment analysis (MSEA) of lipids from adGFP and adPPARδ liver lysates (n = 4). Metabolites were identified based on database search of matching mass-charge ratio and retention time. Identified metabolites and their relative quantity were used to calculate the enrichment and statistical significance. Top 30 perturbed enzyme or pathways were shown. List of metabolites recognized by the Metaboanalyst program and subsequently used for the MSEA analysis is shown in Extended Data Table 1. b, Correlation of hepatic PPARD and ACC1 expression in human liver. Human liver gene expression microarray data was downloaded from gene expression omnibus (GSE9588) and analysed using GraphPad Prism. *P < 0.05 (t-test).

Extended Data Figure 2 Molecular clock expression, food intake and glucose metabolism in wild-type and LPPARDKO mice.

a, Liver gene expression in wild-type and LPPARDKO mice (n = 4, each time point). White bar, light cycle starting at ZT 4; black bar, dark cycle. b, Ppard and Bmal1 expression in dexamethasone-synchronized primary hepatocytes (n = 3, each time point). Circadian time, hours after dexamethasone treatment. c, Gene expression in wild-type and LPPARDKO livers under daytime restricted feeding (n = 3, each time point). Red bar, time when food was available. d, Food intake in wild-type and LPPARDKO mice measured by metabolic cages (n = 8). e, glucose tolerance test and insulin tolerance test in wild-type (n = 6) and LPPARDKO (n = 7) mice. f, Comparison of liver and serum lipidomes. g, Column purification of serum lipids (See methods for detail). IPA, isopropyl alcohol; MeOH, methanol; HOAc, acetic acid. Data presented as mean ± s.e.m.

Extended Data Figure 3 Identification and characterization of PC(18:0/18:1), or SOPC.

a, Heat map of identified features in wild-type and LPPARDKO serum under daytime feeding (n = 3, each time point). White bar, light cycle starting at ZT 0; black bar, dark cycle; red bar, time when food was available. b, Dendrogram of serum samples under daytime restricted feeding. c, Principal component analysis (PCA) of positive mode features in wild-type, LPPARDKO, Scramble and LACC1KD serum under ad libitum feeding. Top, score plot of the first three PCs representing 53.2% of the total variation. Bottom, score plot of PC1 and PC3. Circle, 95% confidence interval. d, Loading plot of the PCA. The putative identities of 11 features identified in Fig. 3d are shown in red. Additional top features contributing to the segregation are highlighted in blue. e, Top panels, EIC of m/z = 788.6 in wild-type and LPPARDKO serum. Bottom panels, EIC of m/z = 788.6 in LACC1KD serum and adPPARδ livers. f, Normalized PC(36:1) intensity in wild-type and LPPARDKO mouse serum (n = 4) under ad libitum or daytime restricted feeding (DF). g, Top, multiple reaction monitoring (MRM) parameters for identification of acyl-chain composition of PC(36:1). Bottom left, co-elution of the PC (18:0/18:1) standard with m/z = 788.6. Bottom right, PC(36:1) acyl-chain composition determined by tandem mass spectrometry running in the MRM mode. h, Top panels, lipid levels in mice intraperitoneally injected with various doses of PC(18:0/18:1) (n = 4). Bottom, in vivo FA uptake in soleus muscle (left) and serum PC(36:1) enrichment (right) 4 h after PC(18:0/18:1) injection at 5mg kg−1 body weight. *P < 0.05 (t-test), data presented as mean ± s.e.m.

Extended Data Figure 4 Requirement of hepatic PPARδ and muscle PPARα for the inter-organ communication mediated by PC(18:0/18:1)/SOPC.

a, Cd36 gene expression in muscle of wild-type and LPPARDKO mice under daytime restricted feeding (n = 3, each time point). Red bar, time when food was available. #P < 0.05 (ANOVA). b, Effects of GW501516 on serum TG and muscle FA uptake in wild-type and LPPARDKO mice (n = 5). c, Cd36 and Fabp3 gene expression in C2C12 myotubes treated with vehicle or 25 μM PC(18:0/18:1) (n = 3). d, FA uptake in control or stable Cd36 knockdown C2C12 myotubes pretreated with indicated lipids. e, The mammalian one-hybrid assay (diagram shown on the top) to determine the transactivation activity of the PPAR ligand binding domain (LBD) (n = 3). Left panel, relative luciferase unit (RLU, presented as fold change) indicative of the reporter activity regulated by Gal4 DNA binding domain (DBD)-PPARαLBD fusion protein (Gal4-PPARαLBD) in 293 cells treated with indicated phospholipids at 100 μM. Right panel, RLU of Gal4-PPARδLBD and Gal4-PPARγLBD treated with 100 μM PC(18:0/18:1). f, Heat map showing serum phospholipid changes between ZT 20 and ZT 8 in 7-month-old male C57BL/6J mice on chow (n = 3) or high-fat diet (HFD for 4 months, n = 5) from targeted metabolomics. g, Serum PC(36:1) concentrations under chow or HFD. h, Blood glucose levels of ad libitum fed db/db mice measured between ZT 0 and ZT 3 before daily lipids injections (vehicle, n = 4; PC(18:0/18:1), n = 5). i, Model for the role of PPARδ–PC(18:0/18:1)–PPARα signalling in FA synthesis and use in the liver–muscle axis. j, Top panel, in vivo fatty acid uptake in soleus and gastrocnemius muscle 4 h after vehicle or 5 mg kg−1 PC(16:0/18:1) injection though the tail vein (n = 6); bottom panel, muscle Cd36 and Fabp3 gene expression after PC(16:0/18:1) injection (n = 4). k, Top panel, activities of a PPRE-containing luciferase reporter in PPARα-expressing C2C12 cells treated with vehicle, 50 μM PC(18:0/18:1) or PC(16:0/18:1) and 1 μM GW7647 (a PPARα synthetic ligand). Bottom panel, Cd36 expression in C2C12 myotubes. *P < 0.05, (t-test), data presented as mean ± s.e.m.

Extended Data Figure 5 Validation of metabolomics analyses.

a, The reproducibility of the untargeted metabolomics platform was validated from two separate runs of 6 serum samples. The Spearman’s rank correlations are between 0.9 and 0.94. The duplicate pair with the lowest correlation (Spearman’s r = 0.90) is shown. b, The raw intensity of samples was subject to normalization with median centering and inter-quartile range (IQR) scaling. The resulting data show equal distribution among different groups of samples. White bar represents samples obtained in the light cycle and black bar for those in the dark cycle.

Extended Data Figure 6 Flow chart of metabolomics data analysis showing the positive-mode metabolites.

See methods for detailed description.

Extended Data Table 1 List of metabolites recognized by Metaboanalyst program and subsequently used for MSEA analysis
Extended Data Table 2 Metabolic characteristics of mouse models
Extended Data Table 3 List of animal cohorts used for this study
Extended Data Table 4 List of primers used for qRT–PCR and oligonucleotides for short hairpin RNA constructs

Supplementary information

Supplementary Data

This file lists significantly changed metabolites in the three mouse models. (XLSX 237 kb)

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Liu, S., Brown, J., Stanya, K. et al. A diurnal serum lipid integrates hepatic lipogenesis and peripheral fatty acid use. Nature 502, 550–554 (2013). https://doi.org/10.1038/nature12710

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