Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes
Artikel i vetenskaplig tidskrift, 2017

Background: Skeletal muscle is one of the primary tissues involved in the development of type 2 diabetes (T2D). The close association between obesity and T2D makes it difficult to isolate specific effects attributed to the disease alone. Therefore, here we set out to identify and characterize intrinsic properties of myocytes, associated independently with T2D or obesity. Methods: We generated and analyzed RNA-seq data from primary differentiated myotubes from 24 human subjects, using a factorial design (healthy/T2D and non-obese/obese), to determine the influence of each specific factor on genome-wide transcription. This setup enabled us to identify intrinsic properties, originating from muscle precursor cells and retained in the corresponding myocytes. Bioinformatic and statistical methods, including differential expression analysis, gene-set analysis, and metabolic network analysis, were used to characterize the different myocytes. Results: We found that the transcriptional program associated with obesity alone was strikingly similar to that induced specifically by T2D. We identified a candidate epigenetic mechanism, H3K27me3 histone methylation, mediating these transcriptional signatures. T2D and obesity were independently associated with dysregulated myogenesis, down-regulated muscle function, and up-regulation of inflammation and extracellular matrix components. Metabolic network analysis identified that in T2D but not obesity a specific metabolite subnetwork involved in sphingolipid metabolism was transcriptionally regulated. Conclusions: Our findings identify inherent characteristics in myocytes, as a memory of the in vivo phenotype, without the influence from a diabetic or obese extracellular environment, highlighting their importance in the development of T2D.

Type 2 diabetes

Gene-set analysis

Gene expression

Obesity

RNA-seq

Metabolic network

Skeletal myocytes

Författare

Leif Wigge

Chalmers, Biologi och bioteknik, Systembiologi

T. I. Henriksen

Kobenhavns Universitet

C. Scheele

Kobenhavns Universitet

C. Broholm

Kobenhavns Universitet

M. Pedersen

Kobenhavns Universitet

M. Uhlen

The Royal Institute of Technology (KTH)

AlbaNova University Center

B. K. Pedersen

Kobenhavns Universitet

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Genome Medicine

1756-994X (ISSN)

Vol. 9 Article Number: 47- 47

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1186/s13073-017-0432-2

PubMed

28545587