Multiomics profiling of DNA methylation, microRNA, and mRNA in skeletal muscle from monozygotic twin pairs discordant for type 2 diabetes identifies dysregulated genes controlling metabolism

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Abstract

Background

A large proportion of skeletal muscle insulin resistance in type 2 diabetes (T2D) is caused by environmental factors.

Methods

By applying multiomics mRNA, microRNA (miRNA), and DNA methylation platforms in biopsies from 20 monozygotic twin pairs discordant for T2D, we aimed to delineate the epigenetic and transcriptional machinery underlying non-genetic muscle insulin resistance in T2D.

Results

Using gene set enrichment analysis (GSEA), we found decreased mRNA expression of genes involved in extracellular matrix organization, branched-chain amino acid catabolism, metabolism of vitamins and cofactors, lipid metabolism, muscle contraction, signaling by receptor tyrosine kinases pathways, and translocation of glucose transporter 4 (GLUT4) to the plasma membrane in muscle from twins with T2D. Differential expression levels of one or more predicted target relevant miRNA(s) were identified for approximately 35% of the dysregulated GSEA pathways. These include miRNAs with a significant overrepresentation of targets involved in GLUT4 translocation (miR-4643 and miR-548z), signaling by receptor tyrosine kinases pathways (miR-607), and muscle contraction (miR-4658). Acquired DNA methylation changes in skeletal muscle were quantitatively small in twins with T2D compared with the co-twins without T2D. Key methylation and expression results were validated in muscle, myotubes, and/or myoblasts from unrelated subjects with T2D and controls. Finally, mimicking T2D-associated changes by overexpressing miR-548 and miR-607 in cultured myotubes decreased expression of target genes, GLUT4 and FGFR4, respectively, and impaired insulin-stimulated phosphorylation of Akt (Ser473) and TBC1D4.

Conclusions

Together, we show that T2D is associated with non- and epigenetically determined differential transcriptional regulation of pathways regulating skeletal muscle metabolism and contraction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-024-03789-y.

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Most cited references 56

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PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

Vamsi K Mootha, Cecilia M. Lindgren, Karl-Fredrik Eriksson … (2003)

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1alpha and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

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Adjusting batch effects in microarray expression data using empirical Bayes methods.

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Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.

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With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

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Author and article information

Contributors

Charlotte Ling: charlotte.ling@med.lu.se

Journal

Journal ID (nlm-ta): BMC Med

Journal ID (iso-abbrev): BMC Med

Title: BMC Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1741-7015

Publication date (Electronic): 2 December 2024

Publication date PMC-release: 2 December 2024

Publication date Collection: 2024

Volume: 22

Electronic Location Identifier: 572

Affiliations

[1 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Epigenetics and Diabetes Unit, Department of Clinical Sciences, , Lund University Diabetes Centre, Lund University, Scania University Hospital, ; Malmö, 205 02 Sweden

[2 ]Department of Cellular and Molecular Medicine, University of Copenhagen, ( https://ror.org/035b05819) Copenhagen, Denmark

[3 ]Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University Diabetes Center, Lund University, ( https://ror.org/012a77v79) Malmö, Sweden

[4 ]Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital, ( https://ror.org/04vgqjj36) Bruna Straket 16, Level 2/3, Gothenburg, 413 45 Sweden

[5 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Institute for Molecular Medicine Finland (FIMM), , Helsinki University, ; Helsinki, Finland

[6 ]GRID grid.419658.7, ISNI 0000 0004 0646 7285, Steno Diabetes Center Copenhagen, ; Herlev, Denmark

[7 ]Lund University Diabetes Centre, Lund University, ( https://ror.org/012a77v79) Malmö, 205 02 Sweden

[8 ]Department of Endocrinology, Scania University Hospital, ( https://ror.org/03g4sde39) Malmö, 205 02 Sweden

Article

Publisher ID: 3789

DOI: 10.1186/s12916-024-03789-y

PMC ID: 11613913

PubMed ID: 39623445

SO-VID: c6b6f28e-b5d4-421b-8aed-e95e7a875182

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 6 January 2024

Date accepted : 19 November 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;

Award ID: 2016-02486

Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;

Award ID: 2018-02567

Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;

Award ID: 2021-00628

Funded by: Lund University

Open Access :

Open access funding provided by Lund University.

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: type 2 diabetes (t2d),skeletal muscle,dna methylation,epigenetics,gene expression,microrna (mirna),discordant monozygotic twins

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ScienceOpen disciplines: Medicine

Keywords: type 2 diabetes (t2d), skeletal muscle, dna methylation, epigenetics, gene expression, microrna (mirna), discordant monozygotic twins

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Multiomics profiling of DNA methylation, microRNA, and mRNA in skeletal muscle from monozygotic twin pairs discordant for type 2 diabetes identifies dysregulated genes controlling metabolism

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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Most cited references 56

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.

Adjusting batch effects in microarray expression data using empirical Bayes methods.

Statistical significance for genomewide studies.

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Most referenced authors 1,902