Gene expression in insulin resistance

Transcription

1 Gene expression in insulin resistance Name: Jules Jacobs Date: Supervisors: - Mirella Kalafati MSc - Dr. Lars Eijssen Department of Bioinformatics

2 BACKGROUND

3 Obesity Defined as: BMI > 30 kg/m 2 In 2030, 50% of population will be overweight/obese Energy intake > Energy expenditure Increased body fat mass Reference: (1)

4 Obesity and Insulin Resistance Obesity Lipotoxicity Inflammation Reactive Oxygen Species Insulin Resistance Reference: (2,3)

5 Type 2 Diabetes Mellitus Insulin Resistance β-cell dysfunction Type 2 DM Reference: (4)

6 Mechanism of insulin (1) Blood stream Ins Glucose β-cell Reference: (5)

7 Mechanism of insulin (2) Blood stream Ins Insulin Receptor IRS PI3K PDK Akt SOS RAS MEK ERK Reference: (6)

8 Insulin Resistance Blood stream Ins Insulin Receptor IRS PI3K PDK Akt SOS RAS MEK ERK Reference: (7)

9 Effects of insulin DNA transcription: - Activation of TF Energy Metabolism: - Glycogen synthesis Growth: - Cell proliferation Insulin Glucose uptake: - GLUT4 translocation Neurophysiology: - Regulation of satiety Vasoactivity: - Vascular recruitment Reference: (8, 9, 10, 11)

10 Insulin resistance and gene expression Insulin DNA transcription Resistance Energy Metabolism? Growth? Insulin? Glucose uptake?? Vasoactivity Neurophysiology Reference: (12)

11 Research Question How is gene expression affected in insulin resistance?

12 METHODS

13 Methods: Workflow Transcriptomics dataset QC & Pre-processing Network Analysis Pathway Analysis

14 Methods: Dataset Microarrays Retrieved from Gene Expression Omnibus; - Dataset by Wu et al. - GEO accession: GSE Retrieved on Affymetrix GeneChips Skeletal muscle biopsies 20 IS vs 20 IR arrays IS cut-off based on glucose uptake; - Glucose disposal rate > 13,9 mg/kg lean mass/min - Measured with hyperinsulinemic-euglycaemic clamp

15 Methods: Quality Control & Pre-processing Raw data Normalized data Gene statistics

16 Methods: Pathway Analysis Human pathway collection of WikiPathways Analyzed using PathVisio Significant pathways: - Z-score > 1,96 - P-value < 0,05 - At least 3 significant genes: - P-value < 0,05 - Absolute 2 LogFC > 1

17 Methods: Network Analysis Cytoscape Integration of selected pathways into network (done by Mirella Kalafati MSc) Visualizations based on: - Pathway - 2 LogFC & P-value Network extension with TF - CyTargetLinker plugin - Encode TF database (both proximal & distal) - Visualization based on TF-gene interaction

18 RESULTS

19 Results: QC & Pre-processing (1) QC & Pre-processing; outlier removal Figure 1. Relative Log Expression (RLE) before (left) and after (right) removal of the outlier (IR26). IR: Insulin resistant array, IS: Insulin sensitive array

20 Results: QC & Pre-processing (2) Insulin resistant array Insulin sensitive array Figure 2. Cluster dendrogram of normalized data. IR: insulin resistant array, IS: insulin sensitive array.

21 Results: Differential Expression genes were measured 286 genes were significant - (abs 2 LogFC> 1 & P-value < 0,05) Upregulation: 91% of sign. genes (n = 263)

22 Results: Pathway Analysis (1) Table 1. Significantly different pathways between IR and IS group Pathway Positive (r) Measured (n) Total % Z Score P-value Exercise-induced Circadian Regulation ,8 6,75 < 0,001 Glycogen Metabolism ,6 5,59 0,001 Parkin-Ubiquitin Proteasomal System pathway ,1 4,25 0,001 Mitochondrial LC-Fatty Acid Beta-Oxidation ,4 3,76 < 0,001 Translation Factors ,1 2,90 0,007 Transcription factor regulation in adipogenesis ,3 2,78 0,006 Proteasome Degradation ,7 2,78 0,010 TGF-beta Signaling Pathway ,4 2,52 0,011 EGF/EGFR Signaling Pathway ,8 2,32 0,017 Factors and pathways affecting insulin-like growth factor (IGF1)-Akt s ,5 2,32 0,031 T-Cell Receptor and Co-stimulatory Signaling ,5 2,32 0,032 mrna Processing ,0 2,19 0,025 Pathogenic Escherichia coli infection ,5 1,97 0,042 Fatty Acid Beta Oxidation ,7 1,96 0,037 Positive (r) indicates the number of genes that were significantly different (P < 0,05 & absolute logfc < 1), Measured (n) indicates the number of genes that were measured, Total indicates the total number of genes in the pathway and % indicates the percentage of Positive genes in relation to Measured genes. Reference: (13,14)

23 Results/Discussion: Fatty Acid β-oxidation Fatty Acid Beta-Oxidation Mitochondrial LC-Fatty Acid Beta-Oxidation Both & expression of FA-βOx genes Sign. FA-βOx gene expression Increased fat utilization Reference: (14)

24 Results/Discussion: Protein Degradation Parkin-Ubiquitin Proteasomal System Proteasome Degradation Sign. genes Differences in proteasome subunit expression - proteasome activity ER stress & UPR IR Reference: (13)

25 Results/Discussion: Adipogenesis & Glycogen Metabolism Transcription factor regulation in adipogenesis Glycogen Metabolism Direction adipogenesis/glycogen metabolism not clear; - Inhibition/stimulation? Literature: IR decreases adipogenesis & glycogen synthesis Reference: (15,16)

26 Results: Network Analysis (1)

27 Results: Network Extension

28 DISCUSSION

29 Discussion: Strengths & Limitations Strengths: - Pathway analysis put gene expression in context - Network analysis shows connections between genes and pathways - Network extension provides additional information based on existing data Limitations; - Lack of information about dataset and subjects - +/- 25% of microarray probes were not matched with gene - Statistical significance (LogFC & P-value) biological relevance

30 Conclusion How is gene expression affected in insulin resistance? IR increases overall gene expression; - T2DM Energy metabolism shifts; - Increased fat utilization - Changes in glycogen metabolism and adipogenesis Proteasome expression changes in IR; - Proteasome activity - Cell stress Pathways; - Minimally connected themselves - TF link pathways

31 Future Perspectives Ins-stimulated IR gene expression vs Insstimulated IS gene expression - both corrected for basal state expression Gene expression Biological relevance

32 Special thanks to: Mirella Kalafati MSc Dr. Lars Eijssen 1. Seidell, J. C. (2014). Worldwide Prevalence of Obesity in Adults. In Handbook of Obesity, vol. 1 (pp ). 2. van Herpen, N. A., & Schrauwen-Hinderling, V. B. (2008). Lipid accumulation in non-adipose tissue and lipotoxicity. Physiology and Behavior, 94(2), Gregor, M. F., & Hotamisligil, G. S. (2011). Inflammatory Mechanisms in Obesity. Annual Review of Immunology, 29(1), Mullur, R. (2015). Beta-cell failure as complication of diabetes. Rev Endocr Metab Disord, 9(4), MacDonald, P. E., Joseph, J. W., & Rorsman, P. (2005). Glucose-sensing mechanisms in pancreatic -cells. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1464), Hale, L. J., & Coward, R. J. M. (2013). Insulin signalling to the kidney in health and disease, 370, Samuel, V. T. and S. G. I. (2013). Integrating Mechanisms for Insulin Resistance; Common threads and missing links. Cell, 148(5), Dimitriadis, G., Mitrou, P., Lambadiari, V., Maratou, E., & Raptis, S. A. (2011). Insulin effects in muscle and adipose tissue. Diabetes Research and Clinical Practice, 93, S52 S Boer, M. P. D. E., Meijer, R. I., Newman, J., Stehouwer, C. D. A., Eringa, E. C., Smulders, Y. V. O. M., & E, E. H. S. (2014). Insulin-Induced Changes in Microvascular Vasomotion and Capillary Recruitment are Associated in Humans, Woods, S. C., Lutz, T. A., Geary, N., & Langhans, W. (2006). Pancreatic signals controlling food intake ; insulin, glucagon and amylin, (June), Laron, Z. (2017). Insulin a growth hormone. Archives of Physiology and Biochemistry IS, 3455(June) Konstantopoulos, N., Foletta, V. C., Segal, D. H., Shields, K. A., Sanigorski, A., Windmill, K., Walder, K. R. (2011). A gene expression signature for insulin resistance. Technology Development for Physiological Genomics, Iwawaki, T., & Oikawa, D. (2013). The role of the unfolded protein response in diabetes mellitus. Semin Immunopathol, Lopaschuk, G. D. (2016). Fatty Acid Oxidation and Its Relation with Insulin Resistance and Associated Disorders. Annals of Nutrition & Metabolism, 68(suppl 3), Litherland, G. J., Morris, N. J., Walker, M., & Yeaman, S. J. (2006). Role of Glycogen Content in Insulin Resistance in Human Muscle Cells. Journal of Cellular Physiology, (February), Gustafson, B., Hedjazifar, S., Gogg, S., Hammarstedt, A., & Smith, U. (2015). Insulin resistance and impaired adipogenesis. Trends in Endocrinology & Metabolism, 26(4),