pp 1–13 | Cite as

Genome-wide profiling of histone H3K27 acetylation featured fatty acid signalling in pancreatic beta cells in diet-induced obesity in mice

  • Takao Nammo
  • Haruhide Udagawa
  • Nobuaki Funahashi
  • Miho Kawaguchi
  • Takashi Uebanso
  • Masaki Hiramoto
  • Wataru Nishimura
  • Kazuki Yasuda



Epigenetic regulation of gene expression has been implicated in the pathogenesis of obesity and type 2 diabetes. However, detailed information, such as key transcription factors in pancreatic beta cells that mediate environmental effects, is not yet available.


To analyse genome-wide cis-regulatory profiles and transcriptome of pancreatic islets derived from a diet-induced obesity (DIO) mouse model, we conducted chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-Seq) of histone H3 lysine 27 acetylation (histone H3K27ac) and high-throughput RNA sequencing. Transcription factor-binding motifs enriched in differential H3K27ac regions were examined by de novo motif analysis. For the predicted transcription factors, loss of function experiments were performed by transfecting specific siRNA in INS-1, a rat beta cell line, with and without palmitate treatment. Epigenomic and transcriptional changes of possible target genes were evaluated by ChIP and quantitative RT-PCR.


After long-term feeding with a high-fat diet, C57BL/6J mice were obese and mildly glucose intolerant. Among 39,350 islet cis-regulatory regions, 13,369 and 4610 elements showed increase and decrease in ChIP-Seq signals, respectively, significantly associated with global change in gene expression. Remarkably, increased H3K27ac showed a distinctive genomic localisation, mainly in the proximal-promoter regions, revealing enriched elements for nuclear respiratory factor 1 (NRF1), GA repeat binding protein α (GABPA) and myocyte enhancer factor 2A (MEF2A) by de novo motif analysis, whereas decreased H3K27ac was enriched for v-maf musculoaponeurotic fibrosarcoma oncogene family protein K (MAFK), a known negative regulator of beta cells. By siRNA-mediated knockdown of NRF1, GABPA or MEF2A we found that INS-1 cells exhibited downregulation of fatty acid β-oxidation genes in parallel with decrease in the associated H3K27ac. Furthermore, in line with the epigenome in DIO mice, palmitate treatment caused increase in H3K27ac and induction of β-oxidation genes; these responses were blunted when NRF1, GABPA or MEF2A were suppressed.


These results suggest novel roles for DNA-binding proteins and fatty acid signalling in obesity-induced epigenomic regulation of beta cell function.

Data availability

The next-generation sequencing data in the present study were deposited at ArrayExpress.


Dataset name: ERR2538129 (Control), ERR2538130 (Diet-induced obesity)

Repository name and number: E-MTAB-6718 - RNA-Seq of pancreatic islets derived from mice fed a long-term high-fat diet against chow-fed controls.


Dataset name: ERR2538131 (Control), ERR2538132 (Diet-induced obesity)

Repository name and number: E-MTAB-6719 - H3K27ac ChIP-Seq of pancreatic islets derived from mice fed a long-term high-fat diet (HFD) against chow-fed controls.


Epigenetics Fatty acid oxidation Glucose intolerance High-fat diet Histone acetylation Insulin secretion Next-generation sequencing Obesity Pancreatic islets Transcriptome Type 2 diabetes 



Chromatin immunoprecipitation


Chromatin immunoprecipitation coupled with high-throughput sequencing


The Database for Annotation, Visualization and Integrated Discovery


Diet-induced obesity


Forkhead box A1


GA repeat binding protein α


Gene Expression Omnibus


Glucose-stimulated insulin secretion


Genome-wide association study (studies)


Histone H3 lysine 27 acetylation


High-fat diet


HNF1 homeobox A


Hypergeometric Optimization of Motif EnRichment


Intraperitoneal glucose tolerance test


Kyoto Encyclopedia of Genes and Genomes


v-maf musculoaponeurotic fibrosarcoma oncogene family protein K


Myocyte enhancer factor 2A


Nuclear respiratory factor 1


Quantitative PCR


Regulatory factor X, 7


High-throughput RNA sequencing


Spatial clustering for identification of ChIP-enriched regions


Transcription start site



We thank C. B. Wollheim (Lund University, Lund, Sweden; University of Geneva, Geneva, Switzerland) and N. Sekine (University of Geneva) for providing INS-1 cells. We appreciate the assistance given by D. Suzuki, K. Nagase, N. Ishibashi (Lab Managers), H. Shiina and T. Shibuya (Administrative Assistants) (Department of Metabolic Disorder, National Center for Global Health and Medicine). We would like to thank Editage ( for English language editing.

Contribution statement

TN and KY conceived this study. TN and HU performed the experiments. TN performed the computational analyses. TN and KY wrote the manuscript. TN, HU, NF, MK, TU, MH, WN and KY analysed the data, interpreted the results and contributed to discussions. The manuscript was critically reviewed, revised and given final approval by all co-authors. TN and KY are the guarantors of this work.


This work was supported by Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI), a grant from the National Center for Global Health and Medicine, the Japan Diabetes Foundation (to TN) and JSPS KAKENHI and a grant from the National Center for Global Health and Medicine (to KY). The study sponsors were not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; or the decision to submit the report for publication.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2018_4735_MOESM1_ESM.pdf (1007 kb)
ESM (PDF 0.98 mb)
125_2018_4735_MOESM2_ESM.xlsx (5.6 mb)
ESM Additional tables (XLSX 5749 kb)


  1. 1.
    Caspard H, Jabbour S, Hammar N, Fenici P, Sheehan JJ, Kosiborod M (2018) Recent trends in the prevalence of type 2 diabetes and the association with abdominal obesity lead to growing health disparities in the USA: an analysis of the NHANES surveys from 1999 to 2014. Diabetes Obes Metab 20(3):667–671. CrossRefPubMedGoogle Scholar
  2. 2.
    Flegal KM, Carroll MD, Ogden CL, Curtin LR (2010) Prevalence and trends in obesity among US adults, 1999-2008. JAMA 303(3):235–241. CrossRefGoogle Scholar
  3. 3.
    Visscher PM, Wray NR, Zhang Q et al (2017) 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101(1):5–22. CrossRefGoogle Scholar
  4. 4.
    Gaulton KJ, Nammo T, Pasquali L et al (2010) A map of open chromatin in human pancreatic islets. Nat Genet 42(3):255–259. CrossRefGoogle Scholar
  5. 5.
    Maurano MT, Humbert R, Rynes E et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337(6099):1190–1195. CrossRefGoogle Scholar
  6. 6.
    Farh KK, Marson A, Zhu J et al (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518(7539):337–343. CrossRefGoogle Scholar
  7. 7.
    Kahn SE, Hull RL, Utzschneider KM (2006) Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444(7121):840–846. CrossRefGoogle Scholar
  8. 8.
    Kulkarni RN, Stewart AF (2014) Summary of the Keystone islet workshop (April 2014): the increasing demand for human islet availability in diabetes research. Diabetes 63(12):3979–3981. CrossRefGoogle Scholar
  9. 9.
    Stitzel ML, Sethupathy P, Pearson DS et al (2010) Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab 12(5):443–455. CrossRefGoogle Scholar
  10. 10.
    Nica AC, Ongen H, Irminger JC et al (2013) Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res 23(9):1554–1562. CrossRefGoogle Scholar
  11. 11.
    Pasquali L, Gaulton KJ, Rodríguez-Seguí SA et al (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46(2):136–143. CrossRefGoogle Scholar
  12. 12.
    Rada-Iglesias A, Bajpai R, Swigut T, Brugmann SA, Flynn RA, Wysocka J (2011) A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470(7333):279–283. CrossRefGoogle Scholar
  13. 13.
    Cheng Y, Ma Z, Kim BH et al (2014) Principles of regulatory information conservation between mouse and human. Nature 515(7527):371–375. CrossRefGoogle Scholar
  14. 14.
    Gjoneska E, Pfenning AR, Mathys H et al (2015) Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518(7539):365–369. CrossRefGoogle Scholar
  15. 15.
    Carrer A, Parris JL, Trefely S et al (2017) Impact of a high-fat diet on tissue acyl-coA and histone acetylation levels. J Biol Chem 292(8):3312–3322. CrossRefGoogle Scholar
  16. 16.
    Malmgren S, Spégel P, Danielsson AP et al (2013) Coordinate changes in histone modifications, mRNA levels, and metabolite profiles in clonal INS-1 832/13 β-cells accompany functional adaptations to lipotoxicity. J Biol Chem 288(17):11973–11987. CrossRefGoogle Scholar
  17. 17.
    Siersbæk M, Varticovski L, Yang S et al (2017) High fat diet-induced changes of mouse hepatic transcription and enhancer activity can be reversed by subsequent weight loss. Sci Rep 7(1):40220.
  18. 18.
    Zaret KS (2018) Pioneering the chromatin landscape. Nat Genet 50(2):167–169. CrossRefGoogle Scholar
  19. 19.
    Nishimura W, Eto K, Miki A et al (2013) Quantitative assessment of Pdx1 promoter activity in vivo using a secreted luciferase reporter system. Endocrinology 154(11):4388–4395. CrossRefGoogle Scholar
  20. 20.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sun J, Nishiyama T, Shimizu K, Kadota K (2013) TCC: an R package for comparing tag count data with robust normalization strategies. BMC Bioinformatics 14(1):219. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 4(1):44–57. CrossRefGoogle Scholar
  23. 23.
    Robinson JT, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26. CrossRefGoogle Scholar
  24. 24.
    Zang C, Schones DE, Zeng C, Cui K, Zhao K, Peng W (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25(15):1952–1958. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Heinz S, Benner C, Spann N et al (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576–589. CrossRefGoogle Scholar
  26. 26.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 27(1):29–34. CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Clee SM, Attie AD (2007) The genetic landscape of type 2 diabetes in mice. Endocr Rev 28(1):48–83. CrossRefPubMedGoogle Scholar
  29. 29.
    Sharma RB, O’Donnell AC, Stamateris RE et al (2015) Insulin demand regulates β cell number via the unfolded protein response. J Clin Invest 125(10):3831–3846. CrossRefGoogle Scholar
  30. 30.
    Dekker J, Rippe K, Dekker M, Kleckner N (2002) Capturing chromosome conformation. Science 295(5558):1306–1311. CrossRefPubMedGoogle Scholar
  31. 31.
    McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28(5):495–501. CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Smith SB, Qu HQ, Taleb N et al (2010) Rfx6 directs islet formation and insulin production in mice and humans. Nature 463(7282):775–780. CrossRefGoogle Scholar
  33. 33.
    Gaulton KJ, Ferreira T, Lee Y et al (2015) Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 47(12):1415–1425. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Yamagata K, Oda N, Kaisaki PJ et al (1996) Mutations in the hepatocyte nuclear factor-1alpha gene in maturity-onset diabetes of the young (MODY3). Nature 384(6608):455–458. CrossRefGoogle Scholar
  35. 35.
    Busch AK, Gurisik E, Cordery DV et al (2005) Increased fatty acid desaturation and enhanced expression of stearoyl coenzyme A desaturase protects pancreatic β-cells from lipoapoptosis. Diabetes 54(10):2917–2924. CrossRefGoogle Scholar
  36. 36.
    Soni MS, Rabaglia ME, Bhatnagar S et al (2014) Downregulation of carnitine acyl-carnitine translocase by miRNAs 132 and 212 amplifies glucose-stimulated insulin secretion. Diabetes 63(11):3805–3814. CrossRefGoogle Scholar
  37. 37.
    Brun T, Scarcia P, Li N et al (2013) Changes in mitochondrial carriers exhibit stress-specific signatures in INS-1E β-cells exposed to glucose versus fatty acids. PLoS One 8(12):e82364. CrossRefGoogle Scholar
  38. 38.
    Lambert SA, Jolma A, Campitelli LF et al (2018) The human transcription factors. Cell 172(4):650–665. CrossRefGoogle Scholar
  39. 39.
    Scarpulla RC (2008) Transcriptional paradigms in mammalian mitochondrial biogenesis and function. Physiol Rev 88(2):611–638. CrossRefGoogle Scholar
  40. 40.
    Comuzzie AG, Cole SA, Laston SL et al (2012) Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One 7(12):e51954. CrossRefGoogle Scholar
  41. 41.
    Nomoto H, Kondo T, Miyoshi H et al (2015) Inhibition of small Maf function in pancreatic β-cells improves glucose tolerance through the enhancement of insulin gene transcription and insulin secretion. Endocrinology 156(10):3570–3580. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Metabolic Disorder, Diabetes Research Center, Research Institute, National Center for Global Health and MedicineTokyoJapan
  2. 2.Department of Preventive Environment and Nutrition, Institute of Biomedical SciencesTokushima University Graduate SchoolTokushimaJapan
  3. 3.Department of BiochemistryTokyo Medical UniversityTokyoJapan
  4. 4.Department of Molecular BiologyInternational University of Health and Welfare School of MedicineNaritaJapan
  5. 5.Division of Anatomy, Bio-imaging and Neuro-cell ScienceJichi Medical UniversityShimotsukeJapan

Personalised recommendations