Gene expression in insulin resistance Name: Jules Jacobs Date: 4-7-2017 Supervisors: - Mirella Kalafati MSc - Dr. Lars Eijssen Department of Bioinformatics
BACKGROUND
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)
Obesity and Insulin Resistance Obesity Lipotoxicity Inflammation Reactive Oxygen Species Insulin Resistance Reference: (2,3)
Type 2 Diabetes Mellitus Insulin Resistance β-cell dysfunction Type 2 DM Reference: (4)
Mechanism of insulin (1) Blood stream Ins Glucose β-cell Reference: (5)
Mechanism of insulin (2) Blood stream Ins Insulin Receptor IRS PI3K PDK Akt SOS RAS MEK ERK Reference: (6)
Insulin Resistance Blood stream Ins Insulin Receptor IRS PI3K PDK Akt SOS RAS MEK ERK Reference: (7)
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)
Insulin resistance and gene expression Insulin DNA transcription Resistance Energy Metabolism? Growth? Insulin? Glucose uptake?? Vasoactivity Neurophysiology Reference: (12)
Research Question How is gene expression affected in insulin resistance?
METHODS
Methods: Workflow Transcriptomics dataset QC & Pre-processing Network Analysis Pathway Analysis
Methods: Dataset Microarrays Retrieved from Gene Expression Omnibus; - Dataset by Wu et al. - GEO accession: GSE22309 - Retrieved on 20-4-2017 - 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
Methods: Quality Control & Pre-processing Raw data Normalized data Gene statistics
Methods: Pathway Analysis Human pathway collection of WikiPathways Analyzed using PathVisio 3.2.2 Significant pathways: - Z-score > 1,96 - P-value < 0,05 - At least 3 significant genes: - P-value < 0,05 - Absolute 2 LogFC > 1
Methods: Network Analysis Cytoscape 3.5.1 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
RESULTS
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
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.
Results: Differential Expression 12.262 genes were measured 286 genes were significant - (abs 2 LogFC> 1 & P-value < 0,05) Upregulation: 91% of sign. genes (n = 263)
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 10 48 49 20,8 6,75 < 0,001 Glycogen Metabolism 7 34 45 20,6 5,59 0,001 Parkin-Ubiquitin Proteasomal System pathway 8 61 75 13,1 4,25 0,001 Mitochondrial LC-Fatty Acid Beta-Oxidation 3 14 22 21,4 3,76 < 0,001 Translation Factors 5 45 51 11,1 2,90 0,007 Transcription factor regulation in adipogenesis 3 21 24 14,3 2,78 0,006 Proteasome Degradation 6 62 67 9,7 2,78 0,010 TGF-beta Signaling Pathway 9 121 133 7,4 2,52 0,011 EGF/EGFR Signaling Pathway 10 148 163 6,8 2,32 0,017 Factors and pathways affecting insulin-like growth factor (IGF1)-Akt s 3 26 34 11,5 2,32 0,031 T-Cell Receptor and Co-stimulatory Signaling 3 26 45 11,5 2,32 0,032 mrna Processing 8 114 130 7,0 2,19 0,025 Pathogenic Escherichia coli infection 4 47 79 8,5 1,97 0,042 Fatty Acid Beta Oxidation 3 31 69 9,7 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)
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)
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)
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)
Results: Network Analysis (1)
Results: Network Extension
DISCUSSION
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
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
Future Perspectives Ins-stimulated IR gene expression vs Insstimulated IS gene expression - both corrected for basal state expression Gene expression Biological relevance
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