Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genetics and Epigenetics

Common and ethnic-specific derangements in skeletal muscle transcriptome associated with obesity

International Journal of Obesity volume 48, pages 330–338 (2024)Cite this article

Subjects

Abstract

Background

Obesity is a common disease with a higher prevalence among African Americans. Obesity alters cellular function in many tissues, including skeletal muscle, and is a risk factor for many life-threatening diseases, including cardiovascular disease and diabetes. The similarities and differences in molecular mechanisms that may explain ethnic disparities in obesity between African and European ancestry individuals have not been studied.

Methods

In this study, data from transcriptome-wide analyses on skeletal muscle tissues from well-powered human cohorts were used to compare genes and biological pathways affected by obesity in European and African ancestry populations. Data on obesity-induced differentially expressed transcripts and GWAS-identified SNPs were integrated to prioritize target genes for obesity-associated genetic variants.

Results

Linear regression analysis in the FUSION (European, N = 301) and AAGMEx (African American, N = 256) cohorts identified a total of 2569 body mass index (BMI)-associated transcripts (q < 0.05), of which 970 genes (at p < 0.05) are associated in both cohorts, and the majority showed the same direction of effect on BMI. Biological pathway analyses, including over-representation and gene-set enrichment analyses, identified enrichment of protein synthesis pathways (e.g., ribosomal function) and the ceramide signaling pathway in both cohorts among BMI-associated down- and up-regulated transcripts, respectively. A comparison using the IPA-tool suggested the activation of inflammation pathways only in Europeans with obesity. Interestingly, these analyses suggested repression of the mitochondrial oxidative phosphorylation pathway in Europeans but showed its activation in African Americans. Integration of SNP-to-Gene analyses-predicted target genes for obesity-associated genetic variants (GWAS-identified SNPs) and BMI-associated transcripts suggested that these SNPs might cause obesity by altering the expression of 316 critical target genes (e.g., GRB14) in the muscle.

Conclusions

This study provides a replication of obesity-associated transcripts and biological pathways in skeletal muscle across ethnicities, but also identifies obesity-associated processes unique in either African or European ancestry populations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Participant characteristics of FUSION and AAGMEx cohort.
Fig. 2: BMI-associated Transcripts in skeletal muscle.
Fig. 3: Gene Set Enrichment Analysis (GSEA) shows up- and down-regulated biological pathways in obesity.
Fig. 4: Ingenuity pathway Comparison analysis.

Similar content being viewed by others

Data availability

All transcriptomic and genomic datasets reported in this study are publicly available. The accession and/or web links for data sources are included in the method.

References

  1. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev. Endocrinol. 2019;15:288–98.

    Article  PubMed  Google Scholar 

  2. Heymsfield SB, Wadden TA. Mechanisms, pathophysiology, and management of obesity. N Eng J Med. 2017;376:254–66.

    Article  CAS  Google Scholar 

  3. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Eng J Med. 2017;377:13–27.

    Article  Google Scholar 

  4. Ghosh S, Bouchard C. Convergence between biological, behavioural and genetic determinants of obesity. Nat Rev Genet. 2017;18:731–48.

    Article  CAS  PubMed  Google Scholar 

  5. Acosta A, Camilleri M, Abu Dayyeh B, Calderon G, Gonzalez D, McRae A, et al. Selection of antiobesity medications based on phenotypes enhances weight loss: a pragmatic trial in an obesity clinic. Obesity. 2021;29:662–71.

    Article  PubMed  Google Scholar 

  6. Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23:120–33.

    Article  CAS  PubMed  Google Scholar 

  7. Lustig RH, Collier D, Kassotis C, Roepke TA, Kim MJ, Blanc E, et al. Obesity I: overview and molecular and biochemical mechanisms. Biochem Pharmacol. 2022;199:115012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Klein S, Gastaldelli A, Yki-Järvinen H, Scherer PE. Why does obesity cause diabetes? Cell Metabol. 2022;34:11–20.

    Article  CAS  Google Scholar 

  9. Müller TD, Blüher M, Tschöp MH, DiMarchi RD. Anti-obesity drug discovery: advances and challenges. Nat Rev Drug Discov. 2022;21:201–23.

    Article  PubMed  Google Scholar 

  10. 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 Rep Med. 2021;2:100226

    Article  PubMed  PubMed Central  Google Scholar 

  11. Das SK, Ma L, Sharma NK. Adipose tissue gene expression and metabolic health of obese adults. Int J Obes. 2015;39:869–73.

    Article  CAS  Google Scholar 

  12. Langefeld CD, Comeau ME, Sharma NK, Bowden DW, Freedman BI, Das SK. Transcriptional regulatory mechanisms in adipose and muscle tissue associated with composite glucometabolic phenotypes. Obesity. 2018;26:559–69.

    Article  CAS  PubMed  Google Scholar 

  13. CDC. Adult obesity prevalence maps. centers for disease control and prevention. national center for chronic disease prevention and health promotion, Division of Nutrition, Physical Activity, and Obesity. 27 September 2022. https://www.cdc.gov/obesity/data/prevalence-maps.html. In. USA: CDC, 2022.

  14. Scott LJ, Erdos MR, Huyghe JR, Welch RP, Beck AT, Wolford BN, et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat Commun. 2016;7:11764.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Taylor DL, Jackson AU, Narisu N, Hemani G, Erdos MR, Chines PS, et al. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. Proc Natl Acad Sci USA. 2019;116:10883–8.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sharma NK, Sajuthi SP, Chou JW, Calles-Escandon J, Demons J, Rogers S, et al. Tissue-specific and genetic regulation of insulin sensitivity-associated transcripts in African Americans. J Clin Endocrinol Metabol. 2016;101:1455–68.

    Article  CAS  Google Scholar 

  17. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Caraux G, Pinloche S. PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics. 2005;21:1280–1.

    Article  CAS  PubMed  Google Scholar 

  19. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50:W216–w21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gerstner N, Kehl T, Lenhof K, Müller A, Mayer C, Eckhart L, et al. GeneTrail 3: advanced high-throughput enrichment analysis. Nucleic Acids Res. 2020;48:W515–w20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gazal S, Weissbrod O, Hormozdiari F, Dey KK, Nasser J, Jagadeesh KA, et al. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity. Nature Genet. 2022;54:827–36.

    Article  CAS  PubMed  Google Scholar 

  22. Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023;51:D977–d85.

    Article  CAS  PubMed  Google Scholar 

  23. Jorquera G, Russell J, Monsalves-Álvarez M, Cruz G, Valladares-Ide D, Basualto-Alarcón C, et al. NLRP3 inflammasome: potential role in obesity related low-grade inflammation and insulin resistance in skeletal muscle. Int J Mol Sci. 2021;22:3254.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wu H, Ballantyne CM. Skeletal muscle inflammation and insulin resistance in obesity. J Clin Investig. 2017;127:43–54.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wu C, Xu G, Tsai SA, Freed WJ, Lee CT. Transcriptional profiles of type 2 diabetes in human skeletal muscle reveal insulin resistance, metabolic defects, apoptosis, and molecular signatures of immune activation in response to infections. Biochem Biophys Res Commun. 2017;482:282–8.

    Article  CAS  PubMed  Google Scholar 

  26. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–73.

    Article  CAS  PubMed  Google Scholar 

  27. Patti ME, Butte AJ, Crunkhorn S, Cusi K, Berria R, Kashyap S, et al. Coordinated reduction of genes of oxidative metabolism in humans with insulin resistance and diabetes: Potential role of PGC1 and NRF1. Proc Natl Acad Sci USA. 2003;100:8466–71.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jin W, Goldfine AB, Boes T, Henry RR, Ciaraldi TP, Kim EY, et al. Increased SRF transcriptional activity in human and mouse skeletal muscle is a signature of insulin resistance. J Clin Investig. 2011;121:918–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nair KS, Bigelow ML, Asmann YW, Chow LS, Coenen-Schimke JM, Klaus KA, et al. Asian Indians have enhanced skeletal muscle mitochondrial capacity to produce ATP in association with severe insulin resistance. Diabetes. 2008;57:1166–75.

    Article  CAS  PubMed  Google Scholar 

  30. Sales V, Patti ME. The ups and downs of insulin resistance and type 2 diabetes: lessons from genomic analyses in humans. Curr Cardiovasc Risk Rep. 2013;7:46–59.

    Article  PubMed  Google Scholar 

  31. Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51:679–89.

    Article  CAS  PubMed  Google Scholar 

  32. Park JJ, Berggren JR, Hulver MW, Houmard JA, Hoffman EP. GRB14, GPD1, and GDF8 as potential network collaborators in weight loss-induced improvements in insulin action in human skeletal muscle. Physiolo Genom. 2006;27:114–21.

    Article  CAS  Google Scholar 

  33. Glunk V, Laber S, Sinnott-Armstrong N, Sobreira DR, Strobel SM, Batista TM, et al. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat Metabol. 2023;5:861–79.

    Article  CAS  Google Scholar 

  34. Sun C, Förster F, Gutsmann B, Moulla Y, Stroh C, Dietrich A, et al. Metabolic effects of the waist-to-hip ratio associated locus GRB14/COBLL1 are related to GRB14 expression in adipose tissue. Int J Mol Sci. 2022;23:8558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Depetris RS, Hu J, Gimpelevich I, Holt LJ, Daly RJ, Hubbard SR. Structural basis for inhibition of the insulin receptor by the adaptor protein Grb14. Mol cell. 2005;20:325–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Taira J, Yoshida K, Takemoto M, Hanada K, Sakamoto H. Dephosphorylation of clustered phosphoserine residues in human Grb14 by protein phosphatase 1 and its effect on insulin receptor complex formation. J Peptide Sci. 2019;25:e3207.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was primarily supported by the National Institutes of Health (NIH) research grants R01 DK118243 to SKD. The authors thank all coinvestigators of the AAGMEx cohort at Wake Forest School of Medicine (WFSM). The authors also thank the FUSION study and other study investigators for publicly sharing of their data.

Author information

Authors and Affiliations

  1. The School of Biotechnology at Atkins, Atkins Academic and Technology High, Winston-Salem, NC, 27101, USA

    Sreejon S. Das

  2. Department of Internal Medicine, Section of Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA

    Swapan K. Das

Authors
  1. Sreejon S. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swapan K. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SKD and SSD perceived the concept and designed the study. SSD performed all analysis. SKD supervised and were involved in quality control of the analyses. SSD and SKD wrote the paper; all the authors participated in the discussion and interpretation of the results, reviewed and revised the paper.

Corresponding author

Correspondence to Swapan K. Das.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, S.S., Das, S.K. Common and ethnic-specific derangements in skeletal muscle transcriptome associated with obesity. Int J Obes 48, 330–338 (2024). https://doi.org/10.1038/s41366-023-01417-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-023-01417-y

Search

Advanced search

Quick links