Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes

BackgroundSkeletal 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.

[1]  P. Arner,et al.  Different aetiologies of Type 2 (non-insulin-dependent) diabetes mellitus in obese and non-obese subjects , 1991, Diabetologia.

[2]  M. Blüher,et al.  Expression of anti-inflammatory macrophage genes within skeletal muscle correlates with insulin sensitivity in human obesity and type 2 diabetes , 2013, Diabetologia.

[3]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[4]  Jens Nielsen,et al.  Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks , 2008, BMC Systems Biology.

[5]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[6]  B. Pedersen,et al.  Deficient leukemia inhibitory factor signaling in muscle precursor cells from patients with type 2 diabetes. , 2012, American journal of physiology. Endocrinology and metabolism.

[7]  Avlant Nilsson,et al.  BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data , 2014, Nucleic Acids Res..

[8]  J. Zierath,et al.  Insulin signal transduction in human skeletal muscle: identifying the defects in Type II diabetes. , 2005, Biochemical Society transactions.

[9]  S. Shoelson,et al.  Type 2 diabetes as an inflammatory disease , 2011, Nature Reviews Immunology.

[10]  A. Mokdad,et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. , 2003, JAMA.

[11]  Y. Hannun,et al.  An overview of sphingolipid metabolism: from synthesis to breakdown. , 2010, Advances in experimental medicine and biology.

[12]  J. Bergström Percutaneous Needle Biopsy of Skeletal Muscle in Physiological and Clinical Research , 1975 .

[13]  P. Poulsen,et al.  The diabetic phenotype is conserved in myotubes established from diabetic subjects: evidence for primary defects in glucose transport and glycogen synthase activity. , 2002, Diabetes.

[14]  I. Nookaew,et al.  Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods , 2013, Nucleic acids research.

[15]  S. Summers,et al.  A role for sphingolipids in producing the common features of type 2 diabetes, metabolic syndrome X, and Cushing's syndrome. , 2005, Diabetes.

[16]  B. Pedersen,et al.  Satellite Cells Derived from Obese Humans with Type 2 Diabetes and Differentiated into Myocytes In Vitro Exhibit Abnormal Response to IL-6 , 2012, PloS one.

[17]  T. Scully Diabetes in numbers , 2012, Nature.

[18]  Howard Y. Chang,et al.  A histone H3 lysine 27 demethylase regulates animal posterior development , 2007, Nature.

[19]  S. Legrand-Poels,et al.  Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. , 2014, Diabetes research and clinical practice.

[20]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[21]  M. Patti,et al.  The emerging genetic architecture of type 2 diabetes. , 2008, Cell metabolism.

[22]  C. Palii,et al.  UTX mediates demethylation of H3K27me3 at muscle-specific genes during myogenesis , 2010, The EMBO journal.

[23]  B. Pedersen,et al.  Cognitive Functions in Middle Aged Individuals Are Related to Metabolic Disturbances and Aerobic Capacity: A Cross-Sectional Study , 2012, PloS one.

[24]  B. Pedersen,et al.  Elevated NF-κB Activation Is Conserved in Human Myocytes Cultured From Obese Type 2 Diabetic Patients and Attenuated by AMP-Activated Protein Kinase , 2011, Diabetes.

[25]  Megan F. Cole,et al.  Control of Developmental Regulators by Polycomb in Human Embryonic Stem Cells , 2006, Cell.

[26]  Y. Le Marchand-Brustel,et al.  Reduced activation of phosphatidylinositol-3 kinase and increased serine 636 phosphorylation of insulin receptor substrate-1 in primary culture of skeletal muscle cells from patients with type 2 diabetes. , 2003, Diabetes.

[27]  P. Holland,et al.  Synthesis and secretion of matrix‐degrading metalloproteases by human skeletal muscle satellite cells , 1995, Developmental dynamics : an official publication of the American Association of Anatomists.

[28]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[29]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[30]  S. Elbein,et al.  The Genetic Basis of Type 2 Diabetes. , 2006, Cellscience.

[31]  I. Kowalska,et al.  Relationship between insulin sensitivity and sphingomyelin signaling pathway in human skeletal muscle. , 2004, Diabetes.

[32]  S. Velleman,et al.  Review: The skeletal muscle extracellular matrix: Possible roles in the regulation of muscle development and growth , 2012, Canadian Journal of Animal Science.

[33]  P. Strålfors,et al.  Global differences in specific histone H3 methylation are associated with overweight and type 2 diabetes , 2013, Clinical Epigenetics.

[34]  D. Reinberg,et al.  Histone methyltransferase activity associated with a human multiprotein complex containing the Enhancer of Zeste protein. , 2002, Genes & development.

[35]  R. Henry,et al.  Multiple defects in muscle glycogen synthase activity contribute to reduced glycogen synthesis in non-insulin dependent diabetes mellitus. , 1991, The Journal of clinical investigation.

[36]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[37]  Gordon K. Smyth,et al.  Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..

[38]  Dustin E. Schones,et al.  High-Resolution Profiling of Histone Methylations in the Human Genome , 2007, Cell.

[39]  M. Uusitupa,et al.  Link between plasma ceramides, inflammation and insulin resistance: association with serum IL-6 concentration in patients with coronary heart disease , 2009, Diabetologia.

[40]  R. DeFronzo,et al.  Plasma Ceramides Are Elevated in Obese Subjects With Type 2 Diabetes and Correlate With the Severity of Insulin Resistance , 2009, Diabetes.

[41]  J. Sanes,et al.  Fibroblasts that proliferate near denervated synaptic sites in skeletal muscle synthesize the adhesive molecules tenascin(J1), N-CAM, fibronectin, and a heparan sulfate proteoglycan , 1989, The Journal of cell biology.

[42]  R. DeFronzo,et al.  Ceramide content is increased in skeletal muscle from obese insulin-resistant humans. , 2004, Diabetes.

[43]  D. Carey,et al.  Extracellular matrix is required for skeletal muscle differentiation but not myogenin expression , 1996, Journal of cellular biochemistry.

[44]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[45]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

[46]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[47]  Seng H. Cheng,et al.  Inhibiting Glycosphingolipid Synthesis Improves Glycemic Control and Insulin Sensitivity in Animal Models of Type 2 Diabetes , 2007, Diabetes.

[48]  M. Desai,et al.  Obesity is associated with macrophage accumulation in adipose tissue. , 2003, The Journal of clinical investigation.

[49]  Peter J. Chomentowski,et al.  Skeletal Muscle Triglycerides, Diacylglycerols, and Ceramides in Insulin Resistance , 2011, Diabetes.

[50]  J. Sowers,et al.  The metabolic syndrome: role of skeletal muscle metabolism. , 2006, Annals of medicine.

[51]  S. Rattigan Faculty Opinions recommendation of Skeletal muscle triglycerides, diacylglycerols, and ceramides in insulin resistance: another paradox in endurance-trained athletes? , 2011 .

[52]  M. Lorenzo,et al.  Ceramide mediates insulin resistance by tumor necrosis factor-alpha in brown adipocytes by maintaining Akt in an inactive dephosphorylated state. , 2001, Diabetes.

[53]  J. S. Rao,et al.  Extracellular-matrix synthesis by skeletal muscle in culture. Major secreted collagenous proteins of clonal myoblasts. , 1985, Biochemical Journal.

[54]  Paul Theodor Pyl,et al.  HTSeq – A Python framework to work with high-throughput sequencing data , 2014 .

[55]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[56]  Intawat Nookaew,et al.  Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes. , 2016, Cell reports.

[57]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[58]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[59]  M. Górska,et al.  Increased skeletal muscle ceramide level in men at risk of developing type 2 diabetes , 2007, Diabetologia.

[60]  Jens Nielsen,et al.  Kiwi: a tool for integration and visualization of network topology and gene-set analysis , 2014, BMC Bioinformatics.

[61]  Peter K. Davidsen,et al.  Dysregulation of a novel miR-23b/27b-p53 axis impairs muscle stem cell differentiation of humans with type 2 diabetes , 2017, Molecular metabolism.

[62]  R. DeFronzo,et al.  Skeletal Muscle Insulin Resistance Is the Primary Defect in Type 2 Diabetes , 2009, Diabetes Care.

[63]  R. Lieber,et al.  Structure and function of the skeletal muscle extracellular matrix , 2011, Muscle & nerve.

[64]  L. Cowart,et al.  Sphingolipids in obesity, type 2 diabetes, and metabolic disease. , 2013, Handbook of experimental pharmacology.

[65]  J. Zeitlinger,et al.  Polycomb complexes repress developmental regulators in murine embryonic stem cells , 2006, Nature.

[66]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.