Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition

Abstract

Individual risk of type 2 diabetes (T2D) is modified by perturbations to the mass, distribution and function of adipose tissue. To investigate the mechanisms underlying these associations, we explored the molecular, cellular and whole-body effects of T2D-associated alleles near KLF14. We show that KLF14 diabetes-risk alleles act in adipose tissue to reduce KLF14 expression and modulate, in trans, the expression of 385 genes. We demonstrate, in human cellular studies, that reduced KLF14 expression increases pre-adipocyte proliferation but disrupts lipogenesis, and in mice, that adipose tissue–specific deletion of Klf14 partially recapitulates the human phenotype of insulin resistance, dyslipidemia and T2D. We show that carriers of the KLF14 T2D risk allele shift body fat from gynoid stores to abdominal stores and display a marked increase in adipocyte cell size, and that these effects on fat distribution, and the T2D association, are female specific. The metabolic risk associated with variation at this imprinted locus depends on the sex both of the subject and of the parent from whom the risk allele derives.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A cell type–specific enhancer in the risk haplotype regulates KLF14 expression.
Fig. 2: KLF14 cis-eQTL is an adipose tissue–specific trans-regulator of a large network of genes.
Fig. 3: KLF14 expression is sex differentiated in biopsies and throughout adipocyte differentiation.
Fig. 4: Adipose tissue–specific knockout of Klf14 in the mouse.
Fig. 5: KLF14 expression affects the expression of genes encoding adipocyte-maturation markers and adipocyte function.
Fig. 6: Adipose tissue of homozygous carriers of the T2D risk allele contains fewer, larger mature adipocytes than does that of homozygous carriers of the non-risk allele.

Change history

  • 07 August 2018

    In the version of this article originally published, minus signs were missing from the three β-values for BMI given in Table 1. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    Kong, A. et al. Parental origin of sequence variants associated with complex diseases. Nature 462, 868–874 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 43, 561–564 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Dang, D. T., Pevsner, J. & Yang, V. W. The biology of the mammalian Kruppel-like family of transcription factors. Int. J. Biochem. Cell Biol. 32, 1103–1121 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Wu, Z. & Wang, S. Role of kruppel-like transcription factors in adipogenesis. Dev. Biol. 373, 235–243 (2013).

    CAS  PubMed  Google Scholar 

  6. 6.

    Parker-Katiraee, L. et al. Identification of the imprinted KLF14 transcription factor undergoing human-specific accelerated evolution. PLoS Genet. 3, e65 (2007).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Buil, A. et al. Gene–gene and gene–environment interactions detected by transcriptome sequence analysis in twins. Nat. Genet. 47, 88–91 (2015).

    CAS  PubMed  Google Scholar 

  8. 8.

    Greenawalt, D. M. et al. A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome Res. 21, 1008–1016 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Civelek, M. et al. Genetic regulation of adipose gene expression and cardio-metabolic traits. Am. J. Hum. Genet. 100, 428–443 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    CAS  PubMed  Google Scholar 

  11. 11.

    Keildson, S. et al. Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity. Diabetes 63, 1154–1165 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Innocenti, F. et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 7, e1002078 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    van de Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central  Google Scholar 

  15. 15.

    DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

    Google Scholar 

  16. 16.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  17. 17.

    Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Google Scholar 

  18. 18.

    Fairfax, B. P. et al. Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Naranbhai, V. et al. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6, 7545 (2015).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Rotival, M. et al. Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans. PLoS Genet. 7, e1002367 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    CAS  PubMed  Google Scholar 

  22. 22.

    Ronn, T. et al. Impact of age, BMI and HbA1c levels on the genome-wide DNA methylation and mRNA expression patterns in human adipose tissue and identification of epigenetic biomarkers in blood. Hum. Mol. Genet. 24, 3792–3813 (2015).

    PubMed  Google Scholar 

  23. 23.

    Najafabadi, H. S. et al. C2H2 zinc finger proteins greatly expand the human regulatory lexicon. Nat. Biotechnol. 33, 555–562 (2015).

    CAS  PubMed  Google Scholar 

  24. 24.

    Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Garvey, W. T. et al. Pretranslational suppression of a glucose transporter protein causes insulin resistance in adipocytes from patients with non-insulin-dependent diabetes mellitus and obesity. J. Clin. Invest. 87, 1072–1081 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Horikoshi, M. et al. Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet. 11, e1005230 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    Google Scholar 

  28. 28.

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Speliotes, E. K. et al. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet. 7, e1001324 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat. Genet. 43, 1131–1138 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Guo, Y. et al. Perhexiline activates KLF14 and reduces atherosclerosis by modulating ApoA-I production. J. Clin. Invest. 125, 3819–3830 (2015).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Arner, E. et al. Adipocyte turnover: relevance to human adipose tissue morphology. Diabetes 59, 105–109 (2010).

    CAS  PubMed  Google Scholar 

  37. 37.

    Hammarstedt, A., Graham, T. E. & Kahn, B. B. Adipose tissue dysregulation and reduced insulin sensitivity in non-obese individuals with enlarged abdominal adipose cells. Diabetol. Metab. Syndr. 4, 42 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Lonn, M., Mehlig, K., Bengtsson, C. & Lissner, L. Adipocyte size predicts incidence of type 2 diabetes in women. FASEB J. 24, 326–331 (2010).

    PubMed  Google Scholar 

  39. 39.

    Lundgren, M. et al. Fat cell enlargement is an independent marker of insulin resistance and ‘hyperleptinaemia’. Diabetologia 50, 625–633 (2007).

    CAS  PubMed  Google Scholar 

  40. 40.

    Weyer, C., Foley, J. E., Bogardus, C., Tataranni, P. A. & Pratley, R. E. Enlarged subcutaneous abdominal adipocyte size, but not obesity itself, predicts type II diabetes independent of insulin resistance. Diabetologia 43, 1498–1506 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Snijder, M. B. et al. Independent and opposite associations of waist and hip circumferences with diabetes, hypertension and dyslipidemia: the AusDiab Study. Int. J. Obes. Relat. Metab. Disord. 28, 402–409 (2004).

    CAS  PubMed  Google Scholar 

  42. 42.

    Yusuf, S. et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case–control study. Lancet 366, 1640–1649 (2005).

    PubMed  Google Scholar 

  43. 43.

    Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  Google Scholar 

  47. 47.

    Storey, J. D., Bass, A. J., Dabney, A. & Robinson, D. qvalue: Q-valueestimation for false discovery rate control. R package version 2.6.0 http://github.com/jdstorey/qvalue (2015).

  48. 48.

    Grundberg, E. et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am. J. Hum. Genet. 93, 876–890 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Tsaprouni, L. G. et al. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics 9, 1382–1396 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Pierce, B. L. et al. Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians. PLoS Genet. 10, e1004818 (2014).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Najafabadi, H. S., Albu, M. & Hughes, T. R. Identification of C2H2-ZF binding preferences from ChIP-seq data using RCADE. Bioinformatics 31, 2879–2881 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Janky, R. et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput. Biol. 10, e1003731 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Frith, M. C., Li, M. C. & Weng, Z. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 31, 3666–3668 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Parsons, M. J. et al. The regulatory factor ZFHX3 modifies circadian function in SCN via an AT motif-driven axis. Cell 162, 607–621 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Collins, J. M., Neville, M. J., Hoppa, M. B. & Frayn, K. N. De novo lipogenesis and stearoyl-CoA desaturase are coordinately regulated in the human adipocyte and protect against palmitate-induced cell injury. J. Biol. Chem. 285, 6044–6052 (2010).

    CAS  PubMed  Google Scholar 

  57. 57.

    Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 29, e45 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Neville, M. J., Collins, J. M., Gloyn, A. L., McCarthy, M. I. & Karpe, F. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity 19, 888–892 (2011).

    CAS  Google Scholar 

  59. 59.

    Lofgren, P., Hoffstedt, J., Naslund, E., Wiren, M. & Arner, P. Prospective and controlled studies of the actions of insulin and catecholamine in fat cells of obese women following weight reduction. Diabetologia 48, 2334–2342 (2005).

    CAS  PubMed  Google Scholar 

  60. 60.

    Marinou, K. et al. Structural and functional properties of deep abdominal subcutaneous adipose tissue explain its association with insulin resistance and cardiovascular risk in men. Diabetes Care 37, 821–829 (2014).

    PubMed  Google Scholar 

  61. 61.

    Dahlman, I. et al. Numerous genes in loci associated with body fat distribution are linked to adipose function. Diabetes 65, 433–437 (2016).

    CAS  PubMed  Google Scholar 

  62. 62.

    Hirsch, J. & Gallian, E. Methods for the determination of adipose cell size in man and animals. J. Lipid Res. 9, 110–119 (1968).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by MRC grant MR/J010642/1 to K.S.S., R.D.C., F.K. and M.I.M. K.S.S. is supported by an MRC New Investigator Award (MR/L01999X/1). M.I.M. is a Wellcome Senior Investigator and is supported by Wellcome (090532, 106130, 098381, 203141), NIDDK (U01-DK105535) and the MRC (MR/L020149/1). F.K. is supported by the British Heart Foundation (RG/17/1/32663). M.C. is supported by NIH Award R00 HL121172. A.L.G. is a Wellcome Senior Fellow in Basic Biomedical Science (095101/Z/10/Z and 200837/Z/16/Z). K.M. is supported by NIH award R01-DK099571, and A.J.L. is supported by NIH award NIH P01HL28481. R.D.C. is supported by MRC MC_U142661184. J.F. was a Marie Curie Fellow. M.L. is supported by Academy of Finland grants 77299 and 124243, the Finnish Heart Foundation, the Finnish Diabetes Foundation, and Commission of the European Community HEALTH-F2-2007-201681. A.V. and A.B. were supported by the European Union Framework Programme 7 grant EuroBATS (259749). Some computations were performed at the Vital-IT center for high-performance computing of the Swiss Institute of Bioinformatics (SIB; http://www.vital-it.ch/). The TwinsUK study was funded by Wellcome and European Community's Seventh Framework Programme (FP7/2007-2013). The TwinsUK study also receives support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas’ NHS Foundation Trust in partnership with King's College London. We acknowledge excellent technical support for animal husbandry (Mary Lyon Centre), genotyping, histology and pathology. We thank the volunteers from the Oxford Biobank, NIHR Oxford Biomedical Research Centre, for their participation. The Oxford Biobank (http://www.oxfordbiobank.org.uk) is also part of the NIHR National Bioresource, which supported the recalling process of the volunteers. GERA data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging (RC2 AG033067; C. Schaefer and N. Risch, principal investigators), awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation, the Wayne and Gladys Valley Foundation, and the Ellison Medical Foundation. This research utilized data from the UK Biobank Resource under application 9161.

Author information

Affiliations

Authors

Contributions

K.S.S., A.L.G., K.M., A.J.L., R.C., F.K. and M.I.M. conceived of and designed the project; M.T., M.C., J.S.E.-S.M., M.M.S., J.F.-T., C.A.G., L.Q., C.P., P.-C.T., A.N. and G.T. analyzed data; M.T., X.W., A.H., S.S., M.Y., N.C., A.R. and Q.D. performed experiments; A.M., M.H., M.J.N. and A.V. contributed experimental and technical support as well as discussions; A.B., A.P.M., J.T.B., U.T., K.S., M.L., I.D. and P.A. contributed data; K.S.S., M.T., M.C., J.S.E.-S.M., M.M.S., K.M., A.J.L., R.C., F.K. and M.I.M. wrote the manuscript; and all authors read and approved of the manuscript.

Corresponding authors

Correspondence to Kerrin S. Small or Mark I. McCarthy.

Ethics declarations

Competing interests

M.I.M. has received consulting and advisory board honoraria from Pfizer, Lilly and NovoNordisk; and G.T., U.T. and K.S. are employees of deCODE Genetics/Amgen.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Sex-stratified and parent of origin effects on the KLF14 trans- eQTL.

QQ-plots of association between rs473102 and all adipose probes in the deCODE dataset (female N=376 and male N=265). Top row displays results from standard genotypic tests, bottom row displays results for test of association to maternally inherited allele

Supplementary Figure 2 Geographic distribution of rs4731702 in the Human Genome Diversity Panel.

The ancestral T2D risk allele C is colored in orange, the derived allele T in blue. Figure generated from the HGDP Selection Browser at the Pritchard Lab (http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP/). rs4731702 does not display evidence of positive selection in the HGDP – with global FST in the 80th percentile genome-wide and non-significant iHS and XP-EHH scores in all populations71. Tests for recent positive selection at rs4731702 in samples from the United Kingdom using the Singleton Density Score (SDS) were not significant (P=0.20), but the trend was towards a recent increase of the non-risk allele T72. By contrast, the KLF14 transcript does exhibit evidence of intolerance to variation in the ExAC exome aggregation dataset73 (ExAC constraint scores: Missense z= 2.32; Synonymous z = 2.41) suggesting coding changes in KLF14 are under purifying selection

Supplementary Figure 3 Distinct 450K methylation probes at the KLF14 locus are associated to rs4731702 vs age.

Figure shows results for two 450K probes assayed in the TwinsUK adipose samples (N=603). cg02385110, which is ~3KB upstream of KLF14 is shown on the left, cg08097417 which is at the KLF14 transcription start site is shown on the right. The beta distributions (top row), association to rs4731702 (T2D risk allele homozygotes = 2) (middle row) and association to age (bottom row) differ between the two probes. cg02385110 has a higher mean beta value, is associated to rs4731702 (P=2.1x10-7), and is not associated to age (P=0.64). In contrast, cg08097417 is not associated to rs4731702 (P=0.99) but is highly associated to age (P=3.6x10-61). Associations were tested with linear mixed effect models adjusting for batch effects, BMI and famlly structure

Supplementary Figure 4 Clinical chemistry analysis of heterozygous Klf14tm1(KOMP)Vlcg knockout mice.

Clinical chemistry parameters were measured in male knockout C57BL/6N Klf14 mice and their wildtype controls. Since Klf14 is mono-allelically maternally expressed in mouse and human, we compared heterozygous mice that had inherited the deletion allele from their mother (heterozygous-MAT, expressing the deletion) with heterozygous mice that had inherited the deletion allele from their father (heterozygous-PAT, not expressing the deletion). We also compared the two groups to their own homozygous wildtype colonymate controls (wildtype-MAT and wildtype-PAT). This was because we used two separate crosses to produce the MAT and PAT carrier cohorts; one stock from heterozygous mothers and one from heterozygous fathers, each crossed to wildytpe C57BL/6N mice. Mice were fed a standard diet and then switched at 18-weeks of age to a 45kcal% high fat diet. Comparing across the timecourses for (a), HDL-C; (b), insulin; (c), glucose; (d), IPGTT by calculating area under the curve, base-lined to t=0 values (data not shown), and analysing with a 1-way ANOVA non-parametric Kruskal-Wallis test and Dunns multiple comparison test we found: that HDL-C (a) was lower in MAT compared to either PAT or wildtype-MAT (p= 0.0055 and 0.0086 respectively) and that wildtype-PAT compared to PAT was not significantly (p=0.56) different; that insulin (b) was lower in MAT compared to either PAT or wildtype-MAT (p= <0.0003 and 0.0020 respectively) and that wildtype-PAT compared to PAT was not significantly (p>0.99) different; that Glucose (c) was lower in MAT compared to PAT (p= <0.0001) and that wildtype–MAT and wildtype-PAT compared to MAT and PAT respectively were not significantly (p=0.79 and >0.99 respectively) different; and that for an IPGTT (d) none of the group comparisons were significantly different (p>0.99) to each other. Then to examine effects at specific times for (a) HDL-C, (b) insulin, (c) glucose and (d) IPGTT individual pairwise comparisons were made at each timepoint using a Mann-Whitney 2-tailed t-test and were significantly reduced in the MAT group compared to PAT group at 8, 22 and 27 weeks in (a,b,c) and at all timepoints in (d). The MAT groups were also significantly lower compared to wildtype-MAT for HDL-C and insulin at 22 and 27 weeks. Values are expressed as mean ± SD and in (a,b,c) wildtype-MAT n=16, wildtype-PAT n=9, heterozygous-MAT n=15, heterozygous-PAT n=16 and in (d) wildtype-MAT n =19, wildtype-PAT n=10, MAT n=16, PAT n=16. For (e), HDL-C; (f), LDL-C and (g), total cholesterol was measured in a 33-week blood sample collected under terminal anaesthetic and was significantly, using an unpaired 2-tailed t-test, reduced in heterozygous-MAT mice compared to PAT mice. Values are expressed as mean ± SD (PAT N =8, MAT N =8). Wildtype MAT grey, wildtype PAT black, MAT KO red, PAT KO blue lines and fill

Supplementary Figure 5 Clinical chemistry and histological analyses of global CRISPR-Cas9 KO mice.

Clinical chemistry parameters were measured in female and male CRISPR-Cas9 knockout (KO) C57BL/6J Klf14 mice and their wildtype (Wt) controls. Mice were fed a standard diet throughout their lifetimes. (a), HDL-C at 12 weeks of age was significantly reduced in female and reduced with borderline significance in male (unpaired two-tailed t-test). (b), triglycerides at 12 weeks of age were not significantly different in females or males (unpaired two-tailed t-test). Comparing across the IPGTT timecourses (c and d) by calculating area under the curve, base-lined to t=0 values, (data not shown) and analysing using a Mann-Whitney two-tailed t-test showed there was no significant difference between male or female KO and wildtype mice (p=0.23 and 0.93 respectively). Then in order to make comparisons at each timepoint for (c and d), individual pairwise Mann-Whitney 2-tailed t-tests were carried out, and as for AUC no differences were found in the female data, although in males glucose levels were nominally higher at 20, 60 and 120 minutes of the test. (e and f), ITT at 16 weeks was not significantly changed between wildtype and KO groups of either female (e) or male (f) mice, analysed by 2-way ANOVA with repeated measures and Bonferroni correction. Values are expressed as mean ± SD and in (a,b) female wildtype n=9, female KO n=9, male Wt n=9, male KO n=9 and in (c,d,ef) female wildtype n=9, female KO n=8, male Wt n=9, male KO N=9. Wildtype mice in blue lines and fill, KO mice in red lines and fill

Supplementary Figure 6 Gluteal adipose tissue of T2D risk allele homozygotes contains fewer, larger mature adipocytes compared to non-risk allele homozygotes.

Adipocyte cell area in histological sections of subcutaneous gluteal adipose biopsies from the Oxford BioBank. a, Median cell area in female (N=18, mean ± SEM) and male (N=18, mean ± SEM) gluteal biopsies stratified by genotype at rs4731702. b, Cumulative frequency distribution of adipocyte cell area in females (N=18). c, Cumulative frequency distribution of adipocyte cell area in males (N=18). Statistical significance was assessed using a Wilcoxon signed-rank two-sided test

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Note and Supplementary Tables

Life Sciences Reporting Summary

Supplementary Tables

Supplementary Tables 1, 5, 6, 8 and 9

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Small, K.S., Todorčević, M., Civelek, M. et al. Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat Genet 50, 572–580 (2018). https://doi.org/10.1038/s41588-018-0088-x

Download citation

Further reading