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NFIA co-localizes with PPARγ and transcriptionally controls the brown fat gene program

Nature Cell Biology volume 19, pages 1081–1092 (2017)Cite this article

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Abstract

Brown fat dissipates energy as heat and protects against obesity. Here, we identified nuclear factor I-A (NFIA) as a transcriptional regulator of brown fat by a genome-wide open chromatin analysis of murine brown and white fat followed by motif analysis of brown-fat-specific open chromatin regions. NFIA and the master transcriptional regulator of adipogenesis, PPARγ, co-localize at the brown-fat-specific enhancers. Moreover, the binding of NFIA precedes and facilitates the binding of PPARγ, leading to increased chromatin accessibility and active transcription. Introduction of NFIA into myoblasts results in brown adipocyte differentiation. Conversely, the brown fat of NFIA-knockout mice displays impaired expression of the brown-fat-specific genes and reciprocal elevation of muscle genes. Finally, expression of NFIA and the brown-fat-specific genes is positively correlated in human brown fat. These results indicate that NFIA activates the cell-type-specific enhancers and facilitates the binding of PPARγ to control the brown fat gene program.

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Figure 1: The NFI-binding motif is highly enriched in brown-fat-specific open chromatin regions.
Figure 2: NFIA is capable of—and required for—driving brown adipocyte differentiation.
Figure 3: PRDM16 is dispensable for the effect of NFIA.
Figure 4: NFI binding is enriched at brown-fat-specific enhancers.
Figure 5: Co-localization of NFI and PPARγ at the brown-fat-specific enhancers.
Figure 6: Co-localization of NFIA facilitates PPARγ binding to the brown-fat-specific enhancers and drives active transcription.
Figure 7: Deficiency of NFIA causes an impaired brown fat gene signature and reciprocal elevation of skeletal muscle gene expression in vivo.
Figure 8: Expression of NFIA and the brown-fat-specific genes is positively correlated in perirenal brown fat of human patients with pheochromocytoma.

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Acknowledgements

We are grateful to S. Mandrup for her suggestion about our study. We thank K. Shiina and K. Tatsuno for their help in library preparation for high-throughput sequencing, S. Fukuda and T. Umehara for their help in computational analysis, K. Ueki, S. Kajimura and C. Villanueva for providing cells and plasmids, K. Nakashima for his suggestion regarding NFIA-KO mice, and T. Sugiyama, T. Kubota and N. Kubota for their help in animal experiments. We also thank T. Wada for his technical assistance. This work is funded by an AMED-CREST research grant from the Japan Agency for Medical Research and Development (AMED) to T.Y.; by a grant-in-aid for scientific research (B) from the Japan Society for the Promotion of Science (JSPS), grant number 25293209 to H.W.; by a grant-in-aid for JSPS fellows from JSPS, grant number 15J02835 to Y.Hiraike; and by a junior scientist development grant from the Japan Diabetes Society to Y.Hiraike. Y.Hiraike has been supported by a research fellowship from JSPS. The Centre of Inflammation and Metabolism (CIM) and the Centre for Physical Activity Research (CFAS), Department of Infectious Diseases, Rigshospitalet is supported by a grant from TrygFonden. CIM/CFAS is a member of DD2—the Danish Center for Strategic Research in Type 2 Diabetes (the Danish Council for Strategic Research, grant no. 09-067009 and 09-075724). T.J.L. has been supported by a research grant from the Danish Diabetes Academy supported by the Novo Nordisk Foundation. The Novo Nordisk Foundation Center for Basic Metabolic Research is supported by an unconditional grant from the Novo Nordisk Foundation to University of Copenhagen.

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Author notes

  1. Yuta Hiraike and Hironori Waki: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan

    Yuta Hiraike, Hironori Waki, Jing Yu, Masahiro Nakamura, Kana Miyake, Ken Suzuki, Hirofumi Kobayashi, Wei Sun, Tomohisa Aoyama, Yusuke Hirota, Toshimasa Yamauchi & Takashi Kadowaki

  2. Department of Molecular Science on Diabetes, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan

    Hironori Waki

  3. Department of Molecular and Internal Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan

    Gaku Nagano, Haruya Ohno, Kenji Oki & Masayasu Yoneda

  4. Genome Science Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan

    Ryo Nakaki, Shogo Yamamoto, Shuichi Tsutsumi & Hiroyuki Aburatani

  5. Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA

    Andrew P. White

  6. Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts 02215, USA

    Yu-Hua Tseng

  7. Translational Physiology Section, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, Maryland 20892, USA

    Aaron M. Cypess

  8. Department of Infectious Diseases, The Centre of Inflammation and Metabolism and the Centre for Physical Activity Research, Rigshospitalet, University of Copenhagen, DK-2100 Copenhagen, Denmark

    Therese J. Larsen, Naja Z. Jespersen & Camilla Scheele

  9. Danish Diabetes Academy, Odense University Hospital, DK-5000 Odense C, Denmark

    Therese J. Larsen

  10. Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology, University of Copenhagen, 2200 Copenhagen, Denmark

    Naja Z. Jespersen

  11. Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, 2200 Copenhagen, Denmark

    Camilla Scheele

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  1. Yuta Hiraike
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Contributions

Y.Hiraike and H.W. designed the study. Y.Hiraike., H.W., J.Y., K.M., H.K., W.S., Y.Hirota and T.A. performed experiments. M.N., R.N., K.S., S.Y., S.T., Y.Hiraike and H.W. performed computational analysis. G.N., H.O., K.O. and M.Y. performed analysis of human perirenal BAT. A.P.W., Y.-H.T. and A.M.C. performed analysis of human neck BAT and WAT. T.J.L., N.Z.J. and C.S. performed analysis of human brown and white adipocytes. Y.Hiraike and H.W. wrote the manuscript. H.A., T.Y. and T.K. supervised all aspects of this work.

Corresponding authors

Correspondence to Hironori Waki, Hiroyuki Aburatani, Toshimasa Yamauchi or Takashi Kadowaki.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 GO analysis of the in vivo FAIRE peaks, and changes in expression levels of Nfia after challenge.

(a) Top GO terms of genes near BAT-specific FAIRE peaks and iWAT- or eWAT- specific FAIRE peaks. (b) Top GO terms of genes near BAT FAIRE peaks and iWAT or eWAT FAIRE peaks. The genome-wide analyses were performed once based on the FAIRE-seq dataset. (c) RT-qPCR of Nfia and Ucp1 before and after mice are challenged at 4 °C for 4 hours. (mean ± S.E.M.; N = 5 mice for room temperature and 6 mice for cold challenge, respectively; ∗ p < 0.05, ∗∗ p < 0.01). The representative results of two independent experiments are shown. (d) RT-qPCR analysis of Nfia and Ucp1 of indicated tissues in mice treated with saline or β3 agonist CL316,243, 1 mg/kg body weight, intraperitoneal injection, for 7 days. (mean ± S.E.M.; N = 5 mice per group; ∗ p < 0.05, ∗∗ p < 0.01). The representative results of two independent experiments are shown. (e) Western blot analysis of NFIA and UCP1. β-actin was used as a loading control. The representative images of two independent experiments are shown.

Supplementary Figure 2 Gain- and loss-of-function experiments showed the effect of NFIA on the brown fat gene program.

(a) Hierarchal clustering analysis of genes up- or down-regulated by NFIA. For example, genes up-regulated NFIA (Cluster A) include Ucp1, Ppargc1a, and Adrb3—the vital components for the induction of thermogenesis in response to adrenergic stimulus, as well as Pparg—the master regulator of adipogenesis. In contrast, Cluster B represents genes down-regulated by NFIA, including critical myogenic genes such as Myog, Myh1, and Myl1. Note that the clusters of genes up-regulated by NFIA largely overlap with BAT-selective genes (p = 9.9 × 10−28), and the cluster of genes down-regulated by NFIA overlap with SKM-selective genes (p = 2.3 × 10−32). The definition of BAT- and SKM- selective genes (N = 254 and N = 312, respectively) are shown in the methods. The analyses were performed once based on the RNA-seq dataset. (b) Top GO terms of genes up- or down-regulated by introduction of NFIA into C2C12 myoblasts. The analyses were performed once based on the RNA-seq dataset. (c) Control and NFIA-expressing 3T3-F442A adipocytes were stained with Oil Red O seven days after inducing adipocyte differentiation. Scale bar, 50 μm. (d–f) Nfia (d), general adipocyte genes (e) and the brown-fat-specific genes (f) were quantified by RT-qPCR at the indicated time course (mean ± S.E.M.; N = 3 independent samples; ∗ p < 0.05, ∗∗ p < 0.01). (g) Immortalized, differentiated brown adipocytes were electroporated with a control siRNA or a siRNA for NFIA and stained with Oil Red O. Scale bar, 50 μm. (h–j) Nfia (h), Pparg (i), Cidea and Ucp1 (j) were quantified by RT-qPCR at the indicated time course (mean ± S.E.M.; N = 3 independent samples; ∗ p < 0.05, ∗∗ p < 0.01).

Supplementary Figure 3 ChIP-seq analysis of NFI, PPARγ and other transcription factors.

(a) Genomic location of PPARγ binding sites in brown adipocytes at day 0 and day 6 of differentiation. (b) Enriched known motifs within NFIA binding sites in NFIA-expressing myoblasts at day 7. (c) Enriched de novo motifs within NFIA binding sites in NFIA-expressing myoblasts at day 7. (d) Enriched known motifs within PPARγ binding sites in brown adipocytes at day 6. (e) Venn diagram showing the overlap of NFI binding sites in brown adipocytes and NFIA binding sites in NFIA-expressing C2C12 myoblasts within BAT FAIRE peaks. (f–j) Venn diagram showing the overlap of indicated transcription factors at day0 and day 6 of differentiation. (k) Venn diagram showing the overlap of NFI and PPARγ ChIP-seq peaks in 3T3-F442A white adipocytes at day 6. (l) Bar graph showing the number of co-localizing sites per gene within ±50 kb of BAT- and WAT-selective genes stratified by the fold changes of gene expression. (m) To the left, NF-1 motif density around the NFI binding sites near BAT genes, WAT genes and control genes (genes with invariant expression between BAT and WAT, N = 2000 genes). The definition of BAT- and WAT- selective genes (N = 549 and N = 849, respectively) are shown in the methods. To the right, distance from NFI binding sites to the nearest DR1 motif is shown for binding sites near BAT genes, WAT genes and control genes. Statistical significance was determined by Mann–Whitney U test. The analyses were performed once based on the ChIP-seq dataset.

Supplementary Figure 4 Co-localization of NFIA facilitates PPARγ binding even before differentiation.

(a,b) ChIP-qPCR analysis of NFIA (a) and PPARγ (b) in C2C12 cells with introduction of PPARγ alone or both PPARγ and NFIA, at day 0 of differentiation. Cidea 29k, Ppara 21k, Ppargc1a-97k, and Ucp1 9.5k are background sites. The representative result of two independent experiments is shown (N = 2 independent samples; mean ± S.E.M.). (c,d) ChIP-qPCR analysis of NFIA (c) and PPARγ (d) in C2C12 cells with or without introduction of NFIA, differentiation day 7. Cidea 29k, Ppara 21k, Ppargc1a-97k, and Ucp1 9.5k are background sites. The representative result of two independent experiments is shown (N = 2 independent samples; mean ± S.E.M.).

Supplementary Figure 5 Deficiency of NFIA results in decreased expression of the brown-fat-specific genes and reciprocal elevation of muscle genes.

(a) RT-qPCR analysis Nfia and Pparg in WT, NFIA +/−, and NFIA −/− BAT (mean ± S.E.M.; N = 11 mice for WT, 24 mice for NFIA +/−, and 15 mice for NFIA −/−, respectively; ∗ p < 0.05, ∗∗ p < 0.01). (b) ChIP-qPCR analysis of PPARγ in brown adipocytes with NFIA knockdown. Ucp1 9.5k is a background site. The representative result of three independent experiments is shown (N = 2 independent samples; mean ± S.E.M.). (c–f) RNA-seq analysis of representative BAT genes (c), common genes (d), mitochondrial genes (e) and SKM genes (f) (mean ± S.E.M.; N = 3 independent samples, ∗ p < 0.05, ∗∗ p < 0.01). (g) RT-qPCR analysis of Nfia and Ucp1 in C57BL/6J and db/db mice (mean ± S.E.M.; N = 5 mice per group; ∗ p < 0.05, ∗∗ p < 0.01). The representative result of two independent experiments is shown.

Supplementary Figure 6 PPARγ is indispensable, while PGC1α and Adrb3 are dispensable for the effect of NFIA.

(a) Control sh RNA or sh RNA for PPARγ was introduced into control or NFIA-expressing C2C12 myoblasts, and stained with Oil Red O seven days after inducing adipocyte differentiation. Scale bar, 50 μm. (b–d) Pparg and Nfia (b), general adipocyte marker Fabp4 (c) and the brown-fat-specific genes Cidea and Ucp1 (d) were quantified by RT-qPCR at the indicated time course (mean ± S.E.M.; N = 3 independent samples; ∗ p < 0.05, ∗∗ p < 0.01). (e) Control si RNA or si RNA for PGC1α was introduced into control or NFIA-expressing C2C12 myoblasts, and stained with Oil Red O seven days after inducing adipocyte differentiation. Scale bar, 50 μm. (f–h) Ppargc1a and Nfia (f), common adipocyte genes (g) and the brown-fat-specific genes (h) were quantified by RT-qPCR at the indicated time course (mean ± S.E.M.; N = 3 independent samples; ∗ p < 0.05, ∗∗ p < 0.01). The representative result of two independent experiments is shown. (i) Control si RNA or si RNA for Adrb3 was introduced into control or NFIA-expressing C2C12 myoblasts, and stained with Oil Red O seven days after inducing adipocyte differentiation. Scale bar, 50 μm. (j–l) Adrb3 and Nfia (j), common adipocyte genes (k) and the brown-fat-specific genes (l) were quantified by RT-qPCR at the indicated time course (mean ± S.E.M.; N = 3 independent samples; ∗ p < 0.05, ∗∗ p < 0.01). The representative result of two independent experiments is shown.

Supplementary Figure 7 Overlap of NFI binding sites and BAT or eWAT FAIRE.

(a) Venn diagram showing the overlap of NFI binding sites in brown adipocytes, FAIRE peaks in BAT and FAIRE peaks in eWAT. (b) Bar graph showing the percentage of NFI binding sites that overlap only with BAT FAIRE peaks or eWAT FAIRE peaks. The analyses were performed once based on the ChIP-seq and FAIRE-seq dataset.

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Hiraike, Y., Waki, H., Yu, J. et al. NFIA co-localizes with PPARγ and transcriptionally controls the brown fat gene program. Nat Cell Biol 19, 1081–1092 (2017). https://doi.org/10.1038/ncb3590

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  • DOI: https://doi.org/10.1038/ncb3590

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