Obesity-associated variants within FTO form long-range functional connections with IRX3

Abstract

Genome-wide association studies (GWAS) have reproducibly associated variants within introns of FTO with increased risk for obesity and type 2 diabetes (T2D)1,2,3. Although the molecular mechanisms linking these noncoding variants with obesity are not immediately obvious, subsequent studies in mice demonstrated that FTO expression levels influence body mass and composition phenotypes4,5,6. However, no direct connection between the obesity-associated variants and FTO expression or function has been made7,8,9. Here we show that the obesity-associated noncoding sequences within FTO are functionally connected, at megabase distances, with the homeobox gene IRX3. The obesity-associated FTO region directly interacts with the promoters of IRX3 as well as FTO in the human, mouse and zebrafish genomes. Furthermore, long-range enhancers within this region recapitulate aspects of IRX3 expression, suggesting that the obesity-associated interval belongs to the regulatory landscape of IRX3. Consistent with this, obesity-associated single nucleotide polymorphisms are associated with expression of IRX3, but not FTO, in human brains. A direct link between IRX3 expression and regulation of body mass and composition is demonstrated by a reduction in body weight of 25 to 30% in Irx3-deficient mice, primarily through the loss of fat mass and increase in basal metabolic rate with browning of white adipose tissue. Finally, hypothalamic expression of a dominant-negative form of Irx3 reproduces the metabolic phenotypes of Irx3-deficient mice. Our data suggest that IRX3 is a functional long-range target of obesity-associated variants within FTO and represents a novel determinant of body mass and composition.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Long-range interactions in the IRX3-FTO locus.
Figure 2: BMI-associated SNPs are associated with expression of IRX3, but not FTO, in human brain.
Figure 3: Irx3-deficient mice are leaner and are protected against diet-induced obesity.
Figure 4: Hypothalamus-specific dominant-negative Irx3 mice recapitulate the metabolic phenotype of Irx3-deficient mice.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

Data were submitted to GEO under accession GSE52830.

References

  1. 1

    Dina, C. et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genet. 39, 724–726 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  3. 3

    Scuteri, A. et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115 (2007)

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Church, C. et al. Overexpression of Fto leads to increased food intake and results in obesity. Nature Genet. 42, 1086–1092 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Fischer, J. et al. Inactivation of the Fto gene protects from obesity. Nature 458, 894–898 (2009)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  6. 6

    Gao, X. et al. The fat mass and obesity associated gene FTO functions in the brain to regulate postnatal growth in mice. PLoS ONE 5, e14005 (2010)

    PubMed  PubMed Central  ADS  Google Scholar 

  7. 7

    Grunnet, L. G. et al. Regulation and function of FTO mRNA expression in human skeletal muscle and subcutaneous adipose tissue. Diabetes 58, 2402–2408 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Klöting, N. et al. Inverse relationship between obesity and FTO gene expression in visceral adipose tissue in humans. Diabetologia 51, 641–647 (2008)

    Google Scholar 

  9. 9

    Wåhlén, K., Sjolin, E. & Hoffstedt, J. The common rs9939609 gene variant of the fat mass- and obesity-associated gene FTO is related to fat cell lipolysis. J. Lipid Res. 49, 607–611 (2008)

    Google Scholar 

  10. 10

    McMurray, F. et al. Adult onset global loss of the fto gene alters body composition and metabolism in the mouse. PLoS Genet. 9, e1003166 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactions in human cells. Nature 503, 290–294 (2013)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Houweling, A. C. et al. Gene and cluster-specific expression of the Iroquois family members during mouse development. Mech. Dev. 107, 169–174 (2001)

    CAS  Google Scholar 

  13. 13

    van Tuyl, M. et al. Iroquois genes influence proximo-distal morphogenesis during rat lung development. Am. J. Physiol. Lung Cell. Mol. Physiol. 290, L777–L789 (2006)

    CAS  Google Scholar 

  14. 14

    Gerken, T. et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 318, 1469–1472 (2007)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  15. 15

    Qi, L. et al. Fat mass-and obesity-associated (FTO) gene variant is associated with obesity: longitudinal analyses in two cohort studies and functional test. Diabetes 57, 3145–3151 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Stratigopoulos, G. et al. Regulation of Fto/Ftm gene expression in mice and humans. Am. J. Physiol. Regul. Integr. Comp. Physiol. 294, R1185–R1196 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Ragvin, A. et al. Long-range gene regulation links genomic type 2 diabetes and obesity risk regions to HHEX, SOX4, and IRX3. Proc. Natl Acad. Sci. USA 107, 775–780 (2010)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  18. 18

    Visel, A. et al. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 35 (Database issue). D88–D92 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Bosse, A. et al. Identification of the vertebrate Iroquois homeobox gene family with overlapping expression during early development of the nervous system. Mech. Dev. 69, 169–181 (1997)

    CAS  Google Scholar 

  20. 20

    Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  21. 21

    Gamazon, E. R. et al. Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol. Psychiatry 18, 340–346 (2012)

    PubMed  PubMed Central  Google Scholar 

  22. 22

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genet. 42, 937–948 (2010)

    CAS  Google Scholar 

  23. 23

    Kong, D. et al. GABAergic RIP-Cre neurons in the arcuate nucleus selectively regulate energy expenditure. Cell 151, 645–657 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Shi, Y. C. et al. Arcuate NPY controls sympathetic output and BAT function via a relay of tyrosine hydroxylase neurons in the PVN. Cell Metab. 17, 236–248 (2013)

    CAS  Google Scholar 

  25. 25

    Mori, H. et al. Critical role for hypothalamic mTOR activity in energy balance. Cell Metab. 9, 362–374 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Dankel, S. N. et al. Switch from stress response to homeobox transcription factors in adipose tissue after profound fat loss. PLoS ONE 5, e11033 (2010)

    PubMed  PubMed Central  ADS  Google Scholar 

  27. 27

    Hagège, H. et al. Quantitative analysis of chromosome conformation capture assays (3C-qPCR). Nature Protocols 2, 1722–1733 (2007)

    Google Scholar 

  28. 28

    Rozen, S. & Skaletsky, H. Primer3 on the WWW for general users and for biologist programmers. Meth. Mol. Biol. 132, 365–386 (2000)

    CAS  Google Scholar 

  29. 29

    Dekker, J. et al. Capturing chromosome conformation. Science 295, 1306–1311 (2002)

    CAS  ADS  Google Scholar 

  30. 30

    Noordermeer, D. et al. The dynamic architecture of Hox gene clusters. Science 334, 222–225 (2011)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  31. 31

    Splinter, E. et al. Determining long-range chromatin interactions for selected genomic sites using 4C-seq technology: from fixation to computation. Methods 58, 221–230 (2012)

    CAS  Google Scholar 

  32. 32

    Stadhouders, R. et al. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nature Protocols 8, 509–524 (2013)

    CAS  Google Scholar 

  33. 33

    Denholtz, M. et al. Long-range chromatin contacts in embryonic stem cells reveal a role for pluripotency factors and polycomb proteins in genome organization. Cell Stem Cell 13, 602–616 (2013)

    CAS  Google Scholar 

  34. 34

    Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009)

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  35. 35

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    ENCODE. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011)

  38. 38

    Zhou, X. & Wang, T. Using the Wash U Epigenome Browser to examine genome-wide sequencing data. Curr. Protoc. Bioinformatics 40, 10.10.1–10.10.14 (2012)

    Google Scholar 

  39. 39

    Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–50 (2005)

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Smemo, S. et al. Regulatory variation in a TBX5 enhancer leads to isolated congenital heart disease. Hum. Mol. Genet. 21, 3255–3263 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Wilkinson, D. G. & Nieto, M. A. Detection of messenger RNA by in situ hybridization to tissue sections and whole mounts. Methods Enzymol. 225, 361–373 (1993)

    CAS  Google Scholar 

  43. 43

    Elbein, S. C. et al. Genetic risk factors for type 2 diabetes: a trans-regulatory genetic architecture? Am. J. Hum. Genet. 91, 466–477 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Zhang, S. S. et al. Iroquois homeobox gene 3 establishes fast conduction in the cardiac His-Purkinje network. Proc. Natl Acad. Sci. USA 108, 13576–13581 (2011)

    CAS  ADS  Google Scholar 

  45. 45

    Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Li, Z. J. et al. Kif7 regulates Gli2 through Sufu-dependent and -independent functions during skin development and tumorigenesis. Development 139, 4152–4161 (2012)

    CAS  Google Scholar 

  47. 47

    Eppig, J. T. et al. The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse. Nucleic Acids Res. 40 (Database issue). D881–D886 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Smith, C. L. & Eppig, J. T. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Biol. Med. 1 (3), 390–399 (2009)

    CAS  Google Scholar 

  49. 49

    Smith, R. N. et al. InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data. Bioinformatics 28, 3163–3165 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Dimitrieva, S. & Bucher, P. UCNEbase–a database of ultraconserved non-coding elements and genomic regulatory blocks Nucleic Acids Res. 41 (Database issue). D101–D109 (2013)

Download references

Acknowledgements

The authors thank F. Gage, C. Marchetto, B. Ren and F. Jin for their generosity in sharing reagents and data. This work was funded by grants from the National Institutes of Health (DK093972, HL119967, HL114010 and DK020595) to M.A.N. and (MH101820, MH090937 and DK20595) to N.J.C. J.L.G.-S. was funded by grants from the Spanish Ministerio de Economía y Competitividad (BFU2010-14839, CSD2007-00008) and the Andalusian Government (CVI-3488). C.-C.H. was supported by a grant from the Canadian Institute of Health Research. K.-H.K. is supported by a fellowship from the Heart and Stroke Foundation of Canada. S.S. is supported by an NIH postdoctoral training grant (T32HL007381)

Author information

Affiliations

Authors

Contributions

M.A.N., J.L.G.-S. and C.-C.H. designed the project. J.J.T. and C.G.-M. performed 4C-seq experiments, with analysis also aided by S.S. and N.J.S. N.J.S. performed the locus conservation and regulatory block analysis. I.A., F.L.C., D.R.S., N.F.W. and S.S. performed in vivo enhancer experiments. S.S. and M.S. performed in situ hybridizations and mouse knockout phenotype calculation. E.R.G. and N.J.C. performed eQTL analyses. K.-H.K. performed mouse metabolic experiments. J.H.L. and D.T. contributed to histological analysis and glucose homeostasis analysis. V.P. contributed to gene and protein expression analysis. J.E.S. contributed to metabolic cage analysis. H.K.S., D.R.S., M.M., S.N., N.A.V., R.D.A. and A.N. provided scientific discussion and technical support. S.S., C.C.H., J.L.G.-S. and M.A.N. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Chi-Chung Hui or Jose Luis Gomez-Skarmeta or Marcelo A. Nóbrega.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Long-range interactions in mouse and zebrafish.

4C-seq data for the Fto-Irx3 locus, visualized with the UCSC Genome Browser. a, Data (also shown in the circular plot in Fig. 1) generated using whole mouse embryos (E9.5), showing the frequency of interactions with the promoter of Irx3 (blue, top) or Fto (magenta, bottom). The background signal corrects for the strong correlation between (nonspecific) ligation events and the linear distance along the chromosome. Poisson statistical significance (−log(P value)) of the 4C-seq interactions over the background is plotted. Significant interactions (P < 0.01), ‘targets’, are displayed in black. b, As above for a but for adult mouse brain (8 weeks). c, As above for a but for whole zebrafish embryos (24 h post fertilization). In all, the region orthologous to the obesity association interval in the first intron of Fto is highlighted in pink.

Extended Data Figure 2 Long-range interactions at the FTO-IRX3 locus.

a, ENCODE data for ChIA-PET using RNA polymerase 2 (POL2) in MCF7 (human breast adenocarcinoma) cells shows interactions between IRX3 and the obesity association interval in the first intron of FTO. No interactions are observed between the FTO promoter and the association interval. These public data are available from and was visualized with the WashU EpiGenome Browser (http://epigenomegateway.wustl.edu/browser/). b, Hi-C data previously generated11 in human IMR-90 (fetal lung) cells. In the association interval, the IRX3 signal is stronger than the background (random) signal. However, the signal for FTO is not. c, 3C data generated with adult (8 weeks) mouse brain. Using bait (red circle) in the association interval (red rectangle), we observe more frequent interactions with the Irx3 promoter compared to control regions 1 and 2 that are 29 and 42 kb away, respectively, indicative of looping.

Extended Data Figure 3 Gene expression in mouse tissue.

a, FTO expression in lung and brain, shown by RNA in situ hybridization for mouse Fto mRNA, in newborn (P1) mouse. Lungs and heart (left, whole organs) were processed simultaneously and in the same well as brain (right, sagittal section) so that the relatively higher expression in brain can be observed. b, LacZ staining for β-galactosidase expression driven from the human FTO promoter. Top, the promoter–LacZ fusion is in the context of 162-kb of human genomic sequence carried in a BAC containing the first three exons of FTO, the entire obesity-associated interval and any enhancers present. The broad expression is consistent with previous reports in human and mouse (see main text for references). At bottom, the promoter–LacZ construct is isolated: only the 1,237 bp proximal to the transcriptional start site are included. Broad expression is recapitulated, indicating the robust transcriptional competency of the human FTO promoter. c, In contrast, the 2,820-bp proximal human IRX3 promoter is not sufficient to drive LacZ expression, which is consistent with an enhancer-dependent transcriptional control mechanism.

Extended Data Figure 4 IRX3 expression in human brain.

a, IRX3 expression in human tissues including brain. Expression data, measured on Affymetrix HG-U133 arrays, were obtained from the Body Atlas, Tissues (http://www.nextbio.com). The median expression across all 128 human tissues from 1,068 arrays is shown by the red line. b, IRX3 expression in 11 different regions of human brain. Data were retrieved from Human Brain Transcriptome data (http://www.molecularbrain.org). Amyg: amygdala; Caud nuc: caudate nucleus; Cere: cerebellum; Corp Call: corpus callosum; DRG: dorsal root ganglion; Frnt Cort: frontal cortex; Hippo: hippocampus; Hypo: hypothalamus; Pit: pituitary; Spine: spinal cord; Thal: thalamus.

Extended Data Figure 5 Linkage disequilibrium in FTO.

Linkage disequilibrium (LD) plot of (logarithm (base 10) of odds) (LOD) score from HapMap phase II European data set, visualized in the UCSC browser. LD blocks are outlined in black. Obesity-associated SNPs from the National Human Genome Research Institute (NHGRI) GWAS catalogue are shown above, in green, demonstrating why this LD block is considered to define the ‘association interval’.

Extended Data Figure 6 Irx3-knockout male mice are leaner with reduced adiposity.

a, Representative photograph of WT and Irx3 KO mice fed ND at 18 weeks of age. b, Representative anatomical views of WT and Irx3 KO mice fed ND. Yellow dotted lines depict subcutaneous IWAT (left) and visceral PWAT (right). c, Tissue weights as a percentage of body weight showed smaller fat pad sizes in Irx3 KO mice, compared to WT mice, in both ND and HFD conditions. (ND, WT/KO, n = 20/12; HFD, WT/KO, n = 8/5.) Data are mean ± s.e.m. (*P < 0.05 versus WT, ND; †P < 0.05 versus WT, HFD). d, Representative H&E sections of PWAT, IWAT and BAT from ND mice demonstrated smaller adipocyte size in Irx3 KO mice than control mice. e, Quantitative PCR of WT and Irx3 KO PWAT for the indicated marker genes: leptin (lep) and adiponectin (adipoq) are adipogenic markers, positively and negatively associated with adiposity, respectively; Mcp1 correlates positively with adiposity. (*P < 0.05 versus WT value.) (WT/KO, n = 10/7).

Extended Data Figure 7 Irx3-knockout female mice are leaner with reduced adiposity.

a, Body weight (BW) changes of WT and Irx3 KO female mice fed a normal diet (ND). (WT/KO: n = 15/14). b, BMI, calculated by BW/BL2 (BL, body length), is lower in Irx3 KO female mice. (WT/KO, n = 7/7). c, d, Body composition analysis showed reduced fat mass and to a lesser extent reduced lean mass in Irx3 KO female mice compared to WT mice, leading to decreased fat mass ratio (WT/KO, n = 9/8). e, Representative H&E-stained sections of mammary gland (MG) WAT and periovarian (PO) WAT revealed smaller adipocyte size in Irx3 KO female mice, compared to WT. f, MGWAT and BAT weights as a percentage of body weight (WT/KO, n = 4/5). Data are mean ± s.e.m. (*P < 0.05 versus WT value.)

Extended Data Figure 8 Higher energy expenditure of Irx3-knockout mice.

a, Energy expenditure over a 24-h period, corrected for lean mass (kcal kg−1 h−1), for 18-week-old WT and Irx3 KO mice fed with ND and HFD (ND WT/KO, n = 7/5; HFD WT/KO, n = 8/4). b, Locomotor activity of WT and Irx3 KO mice. c, Average amount of food intake over a 24-h period with or without normalization to lean mass. d, Average locomotor activity measured over 24 h. e, f, Elevated Ucp1 gene and protein expression in BAT (WT/KO, n = 7/6). Data are mean ± s.e.m. *P < 0.05 versus WT value.

Extended Data Figure 9 Hypothalamic-specific Irx3 dominant-negative mice are leaner with reduced adiposity.

a, Schematic diagram of generation of transgenic mice overexpressing dominant-negative Irx3 in the hypothalamus. b, Immunoblot analysis showed EnR-Irx3 expression in the hypothalamus of mutant mice without affecting endogenous Irx3 expression, compared to control mice. c, Tissue weights as a percentage of body weight showed that fat pad sizes are smaller in mutant mice, compared to control mice. d, Reduced leptin expression and increased adiponectin gene expression in PWAT of mutant mice (control/mutant, n = 5/7). Data are expressed as mean ± s.e.m. *P < 0.05 compared to control group.

Extended Data Figure 10 Higher energy expenditure of hypothalamic dominant-negative Irx3 mice.

a, Energy expenditure over a 24-h period, corrected for lean mass (kcal kg−1 h−1), for 18-week-old mice. b, Locomotor activity for mice in panel a. c, d, Average amount of food intake over a 24-h period with or without normalization to lean mass. e, Average locomotor activity measured over 24 h. f, g, Elevated gene and protein expression of Ucp1 in BAT of mutant mice (control/mutant, n = 5/7). Data are expressed as mean ± s.e.m. *P < 0.05 compared to control group.

Supplementary information

Supplementary Data

This file contains supplementary Tables 1-5. (XLS 6118 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Smemo, S., Tena, J., Kim, K. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014). https://doi.org/10.1038/nature13138

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing