Genome Med. 2021 Aug 02;13(1):123. doi: 10.1186/s13073-021-00939-2.

Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes.

Genome medicine

David Z Pan, Zong Miao, Caroline Comenho, Sandhya Rajkumar, Amogha Koka, Seung Hyuk T Lee, Marcus Alvarez, Dorota Kaminska, Arthur Ko, Janet S Sinsheimer, Karen L Mohlke, Nicholas Mancuso, Linda Liliana Muñoz-Hernandez, Miguel Herrera-Hernandez, Maria Teresa Tusié-Luna, Carlos Aguilar-Salinas, Kirsi H Pietiläinen, Jussi Pihlajamäki, Markku Laakso, Kristina M Garske, Päivi Pajukanta

PMID: 34340684 PMCID: PMC8327600 DOI: 10.1186/s13073-021-00939-2

Abstract

BACKGROUND: Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.

METHODS: We searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function.

RESULTS: Here, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network.

CONCLUSIONS: Our study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity.

© 2021. The Author(s).

Keywords: Master transcription factor; Polygenic risk score (PRS); Trans regulation of genes; Transcriptional regulation of abdominal obesity; Type 2 diabetes (T2D); Waist-hip ratio adjusted for body mass index (WHRadjBMI)

References

1. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015–2016. [Internet]. Hyattsville: National Center for Health Statistics (US); 2017. 7 p. Report No. 288. [cited 2020 April 17]. Available from: https://www.cdc.gov/nchs/products/databriefs/db288.htm . - PubMed
2. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart Disease and Stroke Statistics-2017 Update: a report from the American Heart Association. Circulation. 2017;135(10):e146–603. https://doi.org/10.1161/CIR.0000000000000485 . - PubMed
3. Lighter J, Phillips M, Hochman S, Sterling S, Johnson D, Francois F, et al. Obesity in patients younger than 60 years is a risk factor for Covid-19 hospital admission. Clin Infect Dis. 2020;71(15):896–7. https://doi.org/10.1093/cid/ciaa415 . - PubMed
4. Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity. 2020;28(7):1195–9. https://doi.org/10.1002/oby.22831 . - PubMed
5. Rose KM, Newman B, Mayer-Davis EJ, Selby JV. Genetic and behavioral determinants of waist-hip ratio and waist circumference in women twins. Obes Res. 1998;6(6):383–92. https://doi.org/10.1002/j.1550-8528.1998.tb00369.x . - PubMed
6. Mills GW, Avery PJ, McCarthy MI, Hattersley AT, Levy JC, Hitman GA, et al. Heritability estimates for beta cell function and features of the insulin resistance syndrome in UK families with an increased susceptibility to Type 2 diabetes. Diabetologia. 2004;47(4):732–8. https://doi.org/10.1007/s00125-004-1338-2 . - PubMed
7. Souren NY, Paulussen ADC, Loos RJF, Gielen M, Beunen G, Fagard R, et al. Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: Heritabilities. Diabetologia. 2007;50(10):2107–16. https://doi.org/10.1007/s00125-007-0784-z . - PubMed
8. Shungin D, Winkler T, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–96. https://doi.org/10.1038/nature14132 . - PubMed
9. Ashwell M, Cole TJ, Dixon AK. Obesity: New insight into the anthropometric classification of fat distribution shown by computed tomography. Br Med J (Clin Res Ed). 1985;290:1692. - PubMed
10. Seidell JC, Björntorp P, Sjöström L, Sannerstedt R, Krotkiewski M, Kvist H. Regional distribution of muscle and fat mass in men--new insight into the risk of abdominal obesity using computed tomography. Int J Obes. 1989;13(3):289–303. - PubMed
11. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA. 2017;317(6):626–34. https://doi.org/10.1001/jama.2016.21042 . - PubMed
12. Meisinger C, Döring A, Thorand B, Heier M, Löwel H. Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? The MONICA/KORA Augsburg Cohort Study. Am J Clin Nutr. 2006;84(3):483–9. https://doi.org/10.1093/ajcn/84.3.483 . - PubMed
13. Rask-Andersen M, Karlsson T, Ek WE, Johansson Å. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nat Commun. 2019;10(1):339. https://doi.org/10.1038/s41467-018-08000-4 . - PubMed
14. Schorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, et al. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ. 2018;9:1–10. - PubMed
15. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-Analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019;28(1):166–74. https://doi.org/10.1093/hmg/ddy327 . - PubMed
16. Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Ssteinthorsdottir V, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010;42(11):949–60. https://doi.org/10.1038/ng.685 . - PubMed
17. Sassone-Corsi P, Borrelli E. Transcriptional regulation by trans-acting factors. Trends Genet. 1986;2:215–9. https://doi.org/10.1016/0168-9525(86)90233-7 . - PubMed
18. Borensztein P, Germain S, Fuchs S, Philippe J, Corvol P, Pinet F. cis-Regulatory elements and trans-acting factors directing basal and cAMP- stimulated human renin gene expression in chorionic cells. Circ Res. 1994;74(5):764–73. https://doi.org/10.1161/01.res.74.5.764 . - PubMed
19. Chen C, Meng Q, Xia Y, Ding C, Wang L, Dai R, et al. The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders. Sci Transl Med. 2018;10(472):eaat8178. https://doi.org/10.1126/scitranslmed.aat8178 . - PubMed
20. Stančáková A, Civelek M, Saleem NK, Soininen P, Kangas AJ, Cederberg H, et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 finnish men. Diabetes. 2012;61(7):1895–902. https://doi.org/10.2337/db11-1378 . - PubMed
21. Laakso M, Kuusisto J, Stančáková A, Kuulasmaa T, Pajukanta P, Lusis AJ, et al. The Metabolic Syndrome in Men study: a resource for studies of metabolic & cardiovascular diseases. J Lipid Res. 2017;58(3):481–93. https://doi.org/10.1194/jlr.O072629 . - PubMed
22. Pan DZ, Garske KM, Alvarez M, Bhagat YV, Boocock J, Nikkola E, et al. Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS. Nat Commun. 2018;9(1):1512. https://doi.org/10.1038/s41467-018-03554-9 . - PubMed
23. O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014;10(4):e1004234. https://doi.org/10.1371/journal.pgen.1004234 . - PubMed
24. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e1000529. https://doi.org/10.1371/journal.pgen.1000529 . - PubMed
25. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience. 2015;4:1–16. - PubMed
26. Raulerson CK, Ko A, Kidd JC, Currin KW, Brotman SM, Cannon ME, et al. Adipose tissue gene expression associations reveal hundreds of candidate genes for cardiometabolic traits. Am J Hum Genet. 2019;105(4):773–87. https://doi.org/10.1016/j.ajhg.2019.09.001 . - PubMed
27. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:1–10. - PubMed
28. Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177(3):587–96. https://doi.org/10.1016/j.cell.2019.03.028 . - PubMed
29. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z . - PubMed
30. Pihlajamäki J, Grönlund S, Simonen M, Käkelä P, Moilanen L, Pääkkönen M, et al. Cholesterol absorption decreases after Roux-en-Y gastric bypass but not after gastric banding. Metabolism. 2010;59(6):866–72. https://doi.org/10.1016/j.metabol.2009.10.004 . - PubMed
31. Männistö VT, Simonen M, Hyysalo J, Soininen P, Kangas AJ, Kaminska D, et al. Ketone body production is differentially altered in steatosis and non-alcoholic steatohepatitis in obese humans. Liver Int. 2015;35(7):1853–61. https://doi.org/10.1111/liv.12769 . - PubMed
32. Benhammou JN, Ko A, Alvarez M, Kaikkonen MU, Rankin C, Garske KM, et al. Novel lipid long intervening noncoding RNA, oligodendrocyte maturation-associated long intergenic noncoding RNA, regulates the liver steatosis gene stearoyl-coenzyme A desaturase as an enhancer RNA. Hepatol Commun. 2019;3(10):1356–72. https://doi.org/10.1002/hep4.1413 . - PubMed
33. van der Kolk BW, Muniandy M, Kaminska D, Alvarez M, Ko A, Miao Z, et al. Differential mitochondrial gene expression in adipose tissue following weight loss induced by diet or bariatric surgery. J Clin Endocrinol Metab. 2021;106(5):1312–24. https://doi.org/10.1210/clinem/dgab072 . - PubMed
34. Miao Z, Alvarez M, Ko A, Bhagat Y, Rahmani E, Jew B, et al. The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance. PLoS Genet. 2020;16:1–23. - PubMed
35. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From fastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinforma. 2013;43(1110):11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43 . - PubMed
36. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. https://doi.org/10.1038/nature15393 . - PubMed
37. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 . - PubMed
38. Rodríguez A, Gonzalez L, Ko A, Alvarez M, Miao Z, Bhagat Y, et al. Molecular characterization of the lipid genome-wide association study signal on chromosome 18q11.2 implicates HNF4A-mediated regulation of the TMEM241 gene. Arterioscler Thromb Vasc Biol. 2016;36:1350–5. - PubMed
39. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559. https://doi.org/10.1186/1471-2105-9-559 . - PubMed
40. Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLoS Comput Biol. 2011;7(1):e1001057. https://doi.org/10.1371/journal.pcbi.1001057 . - PubMed
41. The Genotype Tissue Expression Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues The Genotype Tissue Expression Consortium. Science. 2019;369(6509):1318–30. https://doi.org/10.1126/science.aaz1776 . - PubMed
42. Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–13. - PubMed
43. van der Kolk BW, Saari S, Lovric A, Arif M, Alvarez M, Ko A, et al. Molecular pathways behind acquired obesity: adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI. Cell Reports Med. 2021;2(4):100226. https://doi.org/10.1016/j.xcrm.2021.100226 . - PubMed
44. Jokinen R, Rinnankoski-Tuikka R, Kaye S, Saarinen L, Heinonen S, Myöhänen M, et al. Adipose tissue mitochondrial capacity associates with long-term weight loss success. Int J Obes. 2018;42(4):817–25. https://doi.org/10.1038/ijo.2017.299 . - PubMed
45. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):14049. https://doi.org/10.1038/ncomms14049 . - PubMed
46. Frankish A, Diekhans M, Ferreira A-M, Johnson R, Jungreis I, Loveland J, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019;47(D1):D766–73. https://doi.org/10.1093/nar/gky955 . - PubMed
47. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, et al. Comprehensive Integration of Single-Cell Data. Cell. 2019;177(7):1888–902. https://doi.org/10.1016/j.cell.2019.05.031 . - PubMed
48. Alvarez M, Rahmani E, Garske KM, Miao Z, Benhammou JN, Ye CJ, et al. Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM. Sci Rep. 2020;10(1):11019. https://doi.org/10.1038/s41598-020-67513-5 . - PubMed
49. Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14(10):955–8. https://doi.org/10.1038/nmeth.4407 . - PubMed
50. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–72. https://doi.org/10.1038/s41590-018-0276-y . - PubMed
51. Martens JHA, Stunnenberg HG. BLUEPRINT: Mapping human blood cell epigenomes. Haematologica. 2013;98(10):1487–9. https://doi.org/10.3324/haematol.2013.094243 . - PubMed
52. Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, Doyle F, et al. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. - PubMed
53. Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J, et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell. 2018;175(6):1701–15. https://doi.org/10.1016/j.cell.2018.10.022 . - PubMed
54. Loh P, Tucker G, Bulik-sullivan BK, Vilhjálmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Gen. 2015;47(3):284–90. https://doi.org/10.1038/ng.3190 . - PubMed
55. Bulik-Sullivan B, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. https://doi.org/10.1038/ng.3211 . - PubMed
56. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet. 2015;47(11):1228–35. https://doi.org/10.1038/ng.3404 . - PubMed
57. Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet. 2015;97(4):576–92. https://doi.org/10.1016/j.ajhg.2015.09.001 . - PubMed
58. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–52. https://doi.org/10.1038/ng.3506 . - PubMed
59. Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, et al. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019;51(4):675–82. https://doi.org/10.1038/s41588-019-0367-1 . - PubMed
60. Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51(4):592–9. https://doi.org/10.1038/s41588-019-0385-z . - PubMed
61. Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. https://doi.org/10.1093/bioinformatics/btt656 . - PubMed
62. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26:139–40. - PubMed
63. Law CW, Chen Y, Shi W, Smyth GK. Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:1–17. - PubMed
64. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. https://doi.org/10.1093/nar/gkv007 . - PubMed
65. Stegle O, Parts L, Piipari M, Winn J, Durbin R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat Protoc. 2012;7(3):500–7. https://doi.org/10.1038/nprot.2011.457 . - PubMed
66. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48(10):1284–7. https://doi.org/10.1038/ng.3656 . - PubMed
67. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353–8. https://doi.org/10.1093/bioinformatics/bts163 . - PubMed
68. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89. https://doi.org/10.1016/j.molcel.2010.05.004 . - PubMed
69. Fornes O, Castro-Mondragon JA, Khan A, Van Der Lee R, Zhang X, Richmond PA, et al. JASPAR 2020: Update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2020;48(D1):D87–92. https://doi.org/10.1093/nar/gkz1001 . - PubMed
70. Arribas J, Cajuso T, Rodio A, Marcos R, Leonardi A, Velázquez A. NF-κB mediates the expression of TBX15 in cancer cells. PLoS One. 2016;11:1–14. - PubMed
71. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. https://doi.org/10.1093/bioinformatics/bts034 . - PubMed
72. Leek JT. Svaseq: Removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 2014;42(21):e161. https://doi.org/10.1093/nar/gku864 . - PubMed
73. Phipson B, Lee S, Majewski IJ, Alexander WS, Smyth G. Robust hyperparameter estimation protects. Ann Appl Stat. 2016;10(2):946–63. https://doi.org/10.1214/16-AOAS920 . - PubMed
74. Winkler TW, Günther F, Höllerer S, Zimmermann M, Loos RJ, Kutalik Z, et al. A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications. Nat Commun. 2018;9(1):1946. https://doi.org/10.1038/s41467-018-04124-9 . - PubMed
75. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. https://doi.org/10.1038/s41467-017-01261-5 . - PubMed
76. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. Oxford University Press. 2019;47(W1):W199–205. https://doi.org/10.1093/nar/gkz401 . - PubMed
77. Sandelin A. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2004;32:91–4. - PubMed
78. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13(9):2129–41. https://doi.org/10.1101/gr.772403 . - PubMed
79. Sun W, Zhao X, Wang Z, Chu Y, Mao L, Lin S, et al. Tbx15 is required for adipocyte browning induced by adrenergic signaling pathway. Mol Metab. 2019;28:48–57. https://doi.org/10.1016/j.molmet.2019.07.004 . - PubMed
80. Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, et al. Genetics of gene expression and its effect on disease. Nature. 2008;452(7186):423–8. https://doi.org/10.1038/nature06758 . - PubMed
81. Gavin KM, Bessesen DH. Sex differences in adipose tissue function. Endocrinol Metab Clin North Am. 2020;49(2):215–28. https://doi.org/10.1016/j.ecl.2020.02.008 . - PubMed
82. Song QY, Meng XR, Hinney A, Song JY, Huang T, Ma J, et al. Waist-hip ratio related genetic loci are associated with risk of impaired fasting glucose in Chinese children: a case control study. Nutr Metab. 2018;15:1–9. - PubMed
83. Gao C, Langefeld CD, Ziegler JT, Taylor KD, Norris JM, Chen YDI, et al. Genome-wide study of subcutaneous and visceral adipose tissue reveals novel sex-specific adiposity loci in Mexican Americans. Obesity. 2018;26(1):202–12. https://doi.org/10.1002/oby.22074 . - PubMed
84. Gesta S, Bezy O, Mori MA, Macotela Y, Lee KY, Kahn CR. Mesodermal developmental gene Tbx15 impairs adipocyte differentiation and mitochondrial respiration. Proc Natl Acad Sci U S A. 2011;108(7):2771–6. https://doi.org/10.1073/pnas.1019704108 . - PubMed
85. Ejarque M, Ceperuelo-Mallafré V, Serena C, Maymo-Masip E, Duran X, Díaz-Ramos A, et al. Adipose tissue mitochondrial dysfunction in human obesity is linked to a specific DNA methylation signature in adipose-derived stem cells. Int J Obes. 2019;43(6):1256–68. https://doi.org/10.1038/s41366-018-0219-6 . - PubMed
86. Gburcik V, Cawthorn WP, Nedergaard J, Timmons JA, Cannon B. An essential role for tbx15 in the differentiation of brown and “brite” but not white adipocytes. Am J Physiol Endocrinol Metab. 2012;303:1053–60. - PubMed
87. Gesta S, Tseng YH, Kahn CR. Developmental origin of fat: tracking obesity to its source. Cell. 2007;131(2):242–56. https://doi.org/10.1016/j.cell.2007.10.004 . - PubMed
88. Fox CS, Liu Y, White CC, Feitosa M, Smith AV, Heard-Costa N, et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 2012;8(5):e1002695. https://doi.org/10.1371/journal.pgen.1002695 . - PubMed
89. Merlotti C, Ceriani V, Morabito A, Pontiroli AE. Subcutaneous fat loss is greater than visceral fat loss with diet and exercise, weight-loss promoting drugs and bariatric surgery: a critical review and meta-analysis. Int J Obes. Nature Publishing Group. 2017;41(5):672–82. https://doi.org/10.1038/ijo.2017.31 . - PubMed
90. Andersen E, Ingerslev LR, Fabre O, Donkin I, Altıntaş A, Versteyhe S, et al. Preadipocytes from obese humans with type 2 diabetes are epigenetically reprogrammed at genes controlling adipose tissue function. Int J Obes. 2019;43(2):306–18. https://doi.org/10.1038/s41366-018-0031-3 . - PubMed
91. Siddik MAB, Shin AC. Recent progress on branched-chain amino acids in obesity, diabetes, and beyond. Endocrinol Metab. 2019;34(3):234–46. https://doi.org/10.3803/EnM.2019.34.3.234 . - PubMed
92. Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, et al. Impact of regulatory variation from RNA to protein. Science 2015;347(6222):664–7. https://doi.org/10.1126/science.1260793 . - PubMed

Publication Types

• Journal Article
• Research Support, N.I.H., Intramural
• Research Support, N.I.H., Extramural
• Research Support, Non-U.S. Gov't

Grant support

• R01 DK093757/DK/NIDDK NIH HHS/United States
• 138006/Academy of Finland
• DK062370/DK/NIDDK NIH HHS/United States
• R56 DK062370/DK/NIDDK NIH HHS/United States
• F31 DK118865-02/DK/NIDDK NIH HHS/United States
• Gilliam Fellowhip/Howard Hughes Medical Institute (US)
• R01 DK072193/DK/NIDDK NIH HHS/United States
• P01 HL028481/HL/NHLBI NIH HHS/United States
• R01 DK062370/DK/NIDDK NIH HHS/United States
• Z01 HG000024/ImNIH/Intramural NIH HHS/United States
• F31 HL142180/HL/NHLBI NIH HHS/United States
• U01 DK062370/DK/NIDDK NIH HHS/United States
• F31 DK118865/DK/NIDDK NIH HHS/United States
• U01 DK105561/DK/NIDDK NIH HHS/United States
• T32 LM012424/LM/NLM NIH HHS/United States
• R01 HL095056/HL/NHLBI NIH HHS/United States