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Super-enhancer-driven metabolic reprogramming promotes cystogenesis in autosomal dominant polycystic kidney disease

Nature Metabolism volume 2, pages 717–731 (2020)Cite this article

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Abstract

Metabolic reprogramming is emerging as a key pathological contributor to the progression of autosomal dominant polycystic kidney disease (ADPKD), but the molecular mechanisms underlying dysregulated cellular metabolism in cystic cells remain elusive. Super-enhancers (SEs) are large clusters of transcriptional enhancers that drive robust expression of cell identity and disease genes. Here, we show that SEs undergo extensive remodelling during cystogenesis and that SE-associated transcripts are most enriched for metabolic processes in cystic cells. Inhibition of cyclin-dependent kinase 7 (CDK7), a transcriptional kinase required for assembly and maintenance of SEs, or AMP deaminase 3 (AMPD3), one of the SE-driven and CDK7-controlled metabolic target genes, delays cyst growth in ADPKD mouse models. In a cohort of people with ADPKD, CDK7 expression was frequently elevated, and its expression was correlated with AMPD3 expression and disease severity. Together, our findings elucidate a mechanism by which SE controls transcription of metabolic genes during cystogenesis, and identify SE-driven metabolic reprogramming as a promising therapeutic target for ADPKD treatment.

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Fig. 1: Characterization of the SE landscape of ADPKD cells.
Fig. 2: Control of expression of metabolic genes by cystic-cell-specific SEs.
Fig. 3: Expression and activity of CDK7 in ADPKD cells and kidney tissues.
Fig. 4: Effects of CDK7 inhibition on growth and survival of cystic cells in vitro.
Fig. 5: Effects of THZ1 treatment on cyst formation and growth in mouse models of early- and late-onset ADPKD.
Fig. 6: Identification of SE-driven and THZ1-rescued target genes in ADPKD cells.
Fig. 7: Regulation of AMP metabolism and AMPK activity by AMPD3 in ADPKD.
Fig. 8: Effects of AMPD3 inhibition on cystogenesis in the early-onset ADPKD model.

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Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The RNA-seq and ChIP–seq data can be found at the Gene Expression Omnibus database under accession number GSE141281. Source data are provided with this paper.

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Acknowledgements

This work was supported by grants (31571337 and 81770658 to Y.C. and 31700144 to Z.M.) from the National Natural Science Foundation of China, a grant (2017YFA0504102 to Y.C.) from the National Key Research and Development Program of China, a grant (to Y.C.) from the Excellent Talent Project of Tianjin Medical University, grants (19JCJQJC63800 to Y.C. and 17JCQNJC10600 to Z.M.) from Tianjin Municipal Science and Technology Commission and a grant (2019ZD027 to Y.C.) from Tianjin Municipal Education Commission.

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Authors and Affiliations

  1. 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China

    Zeyun Mi, Yandong Song, Xinyi Cao, Yi Lu, Zhiheng Liu, Xu Zhu, Meijuan Geng, Yongzhan Sun, Bingxue Lan, Chaoran He, Lirong Zhang & Yupeng Chen

  2. Department of Urology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China

    Hui Xiong

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Contributions

Z.M. performed mouse and biochemistry studies. Y. Song performed mouse and biochemistry studies. X.C. performed bioinformatics analysis. Y.L., Y. Sun and C.H. provided expertise on mouse studies. Z.L. provided expertise on bioinformatics analysis. X.Z. performed bioinformatics analysis. M.G. performed mouse studies. B.L. provided expertise on biochemistry studies. H.X. provided human specimens. L.Z. designed the project and analysed data. Y.C. conceived, designed and supervised the project, analysed data and wrote the manuscript.

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Correspondence to Yupeng Chen.

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Extended data

Extended Data Fig. 1 Enhancer characterization in PH and PN cells.

a, The heatmap of normalized H3K27ac ChIP-seq signal in PH and PN cells. The rows show 3 kb around the H3K27ac peak centre. b, Composite plots of normalized ChIP-seq signals for H3K27ac in PH and PN cells. The x-axis represents distance from the H3K27ac peaks and y-axis represents probability density. c, KEGG enrichment analysis of PN-lost SEs (n = 250 genes). Numbers beside bars indicate numbers of differently expressed genes in that KEGG category. P values obtained by hypergeometric test. d, Gene ontology enrichment analysis of the unchanged SEs (n = 347 genes). Numbers beside bars indicate numbers of differently expressed genes in that GO category. P values obtained by hypergeometric test.

Source data

Extended Data Fig. 2 Comparison of differential expressed genes in ADPKD cells, mouse and human kidney tissues.

Comparison of upregulated (a) and downregulated (c) genes in cells, mouse and human kidney tissues. Gene ontology enrichment analysis of the overlap upregulated genes (b) and downregulated genes (d). Numbers beside bars indicate numbers of differently expressed genes in that GO category. P values obtained by hypergeometric test. e, Scatter plot showing expression changes of SE-driven genes in cells and mouse kidney tissues. x-axis: log2 (fold change) from PN versus PH cells; y-axis: log2 (fold change) from ADPKD mouse kidney tissues versus normal kidney tissues. f, Heatmap demonstrating expression values of 29 candidate genes in cells and mouse kidney tissues. Rows show Z scores calculated for each group.

Source data

Extended Data Fig. 3 Effects of THZ1 treatment on cell proliferation, apoptosis and body weight in ADPKD mouse model.

a, Immunohistochemistry analysis of PCNA expression in P29 Pkd1−/− mice injected with THZ1 or DMSO, n = 3 biologically independent mice per group; Data are represented as means ± s.e.m. Unpaired two-sided Student’s t-test was used for statistical analysis, **P = 0.0051. b, Immunofluorescence analysis of TUNEL level from P29 Pkd1−/− mice injected with THZ1 or DMSO, n = 3 biologically independent mice per group; Data are represented as means ± s.e.m. Unpaired two-sided Student’s t-test was used for statistical analysis, *P = 0.0161. c, Body weight of mice from late-onset ADPKD mouse model. Scale bars, 10 µm (a) and 50 µm (b).

Source data

Extended Data Fig. 4 Expression of THZ1-rescued genes in ADPKD mouse kidneys.

qRT-PCR analysis of THZ1 downregulated (a) and upregulated (b) genes mRNA level in THZ1 treated mouse kidney tissues (n = 3 biologically independent mice per group). Data are represented as means ± s.e.m., analysed by unpaired two-sided Student’s t-test, Sh3rf2 *P = 0.0127, Adamts5 **P = 0.0053, Arhgef2 ****P < 0.0001, Sh3bp2 **P = 0.0096, Mdh1 **P = 0.0071, Fdxr **P = 0.0038, Csrp2 ***P = 0.0006, Aldh9a1 **P = 0.0098.

Source data

Extended Data Fig. 5 The ratio of AMP to ATP decreased in PN cells.

a, HPLC analysis of total AMP and ATP concentrations from PH and PN cell perchlorate extracts. b, The ratio of AMP to ATP in PH and PN cells (n = 3 biologically independent experiments). Data are represented as means ± s.e.m., analysed by unpaired two-sided Student’s t-test, **P = 0.0073.

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Mi, Z., Song, Y., Cao, X. et al. Super-enhancer-driven metabolic reprogramming promotes cystogenesis in autosomal dominant polycystic kidney disease. Nat Metab 2, 717–731 (2020). https://doi.org/10.1038/s42255-020-0227-4

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