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A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
A systems biology approach for understanding skeletal muscle abnormalities in COPD.
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A systems biology approach for understanding skeletal muscle abnormalities in COPD.

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Presentation made during the EISBM workshop, 13-15 June 2012 by Nil Turan (Synergy-COPD).

Presentation made during the EISBM workshop, 13-15 June 2012 by Nil Turan (Synergy-COPD).

Published in: Health & Medicine, Technology
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  • 1. A systems biology approach forunderstanding skeletal muscle abnormalities in COPD Nil Turan University of Birmingham
  • 2. Muscle wasting in COPD patients• Many factors have been suggested to contribute to muscle wasting: – disuse – Systemic inflammation caused by the lungs – Tissue hypoxia – Oxidative stress• Proposed mechanism: Systemic inflammation NFKB activation Myod inactivation• However controversial role of systemic inflammation and also the NFKB hypothesis• Understanding the mechanisms behind muscle degeneration and limited exercise training response in COPD• Study the relationship between systemic inflammatory mediators, physiological outcome and muscle transcriptional state
  • 3. Experimental design and Aim• From 18 COPD patients and 12 aged-matched healthy individuals• Before and after 8weeks exercise training, measurements of: – Transcriptomics – physiological measurements – serum protein levels• model the relationship between key molecular and physiological variables in healthy and diseased individuals to derive a testable hypothesis on the disease mechanism• Using an unbiased approach based on network inference
  • 4. A systems biology approach for building networks from multi-level measurementsData integration * Building networks via• transcriptomics neighbourhood of hubs• physiological• serum protein levels ENO ARACNEHubs selection Measuring statistical • Genes receptors of dependencies between VEGFR cytokines and growth factors pairs of genes • genes in glycolysis/ gluconeogenesis • physiological • serum protein levels*Across healty and COPD patients before and after exercise training
  • 5. Network integrating multi-level measurements in the 8 weeks training study• plotted in Cytoscape using forced directed layoutSeparation between tissue remodelling sub-networks and energy sub-network
  • 6. Healthy and COPD networks separately constructed Uncoupling between expression of tissue remodelling and bioenergetics modules is a specific feature of skeletal muscles of COPD patients
  • 7. What about muscle from other diseases? Muscle dystrophy Network models representing diabetes and muscle dystrophy muscle biopsies. Diabetic MuscleNo separation between bioenergetics and tissue remodelling
  • 8. Mapping training response in healthy and COPDLack of response to exercise training in COPD patients
  • 9. Can IL-1 promote tissue remodelling pathway?Hypothesis IL-1 Tissue remodeling IL1 injection 24h extraction Transcription response glycolytic and Caudal oxidative muscle vein
  • 10. Effects of IL-1β in mouse glycolytic and oxidative muscles
  • 11. Testing the “overspill” hypothesis• Common view in the literature Systemic inflammation NFKB activation Myod inactivation• Observations in our data NFKB targets are not differentially regulated between healthy and diseased muscles activated in response to training in healthy but not in COPD The NFKB hypothesis is not supported in this data
  • 12. Expression of epigenetic histone modifiers discriminatesbetween healthy and diseased muscles and is correlated with peak oxygen consumption
  • 13. Genes represented in the neighborhood of VO2maxare transcriptionally regulated in a mouse model of hypoxia
  • 14. What did we learn from this study?• Failure to co-ordinately activate expression of several tissue remodelling and bioenergetic pathways is a specific landmark of COPD muscles• IL1 promotes tissue remodelling and mimics training response in healthy patients• No evidence for the role of systemic inflammation• Lack of activation of NFKB targets in COPD• Role of tissue hypoxia and chromatin modifiers
  • 15. Is metabolic unbalance a feature of muscle wasting in other clinical scenarios? Ageing• A network inference approach: – Network representing young muscle – Network representing old muscle
  • 16. Dataset Integration GSE9676 GSE9676 GSE21496 GSE1428 GSE1428 GSE19420 GSE9419 GSE14901 GSE1786 Plus 2 U133A COPD-healthyAge 18 30 40 50 60 70 80
  • 17. Network construction in ageing 3 3 31 1 1 4 5 4 5 4 5 6 6 6 2 2 2 7 7 7 Age-independent Elderly Young 3 1 4 5 6 2 7
  • 18. 31 GO.CC Extracellular matrix (32) GO.BP Proteolysis (12) GO.CC Collagen (11) GO.BP RNA processing (8) KEGG ECM-receptor interaction (13) KEGG Focal Adhesion (14) GO.BP Ossification GO.BP Response to Wounding (18) GO.BP Inflammatory response (11) 3 4 GO.BP Translation elongation (76) 1 GO.BP Ribosome Biogenesis (18) GO.BP Striated muscle contraction (15) 4 GO.BP Myofibril assembly (8) 5 GO.BP Translation initiation (8) GO.BP Actin-myosin filament sliding (5) 6 GO.BP Glycolysis (5) 2 5 GO.CC Mitocondrion (68) 7 GO.BP Electron transport chain (24) KEGG Oxidative phosphorylation (41) KEGG Cardiac muscle contraction (15) GO.BP Aerobic respiration (6) 2 6 GO.BP Energy derivation by oxidation of GO.CC Mitocondrion (52) organic compounds (8) GO.BP Generation of precursor GO.BP Glycogen metabolic processes (5) metabolites and energy (26) GO.BP Ubiquitin-dependent protein catabolism (10) KEGG Oxidative phosphorilation (12) KEGG Citrate cycle (TCA cycle) (13)
  • 19. Age specific and age independent sub-networks • Oxidative phosporylation sub-network is old specific 3 and mainly negative correlation with ribosomal subunits 1 4 5 • TCA cycle sub-network is young specific and positive 2 6 correlation with ribosomal subunit 7 Oxidative Phosphorylation Glycolysis / Gluconeogenesis TCA Cycle ENO3 Small and Large Ribosomal Subunits ALDOA Eukaryotic Initiation Factors EIF3K Contractile FibresGAPDH EIF2S2 What is the role of EIFs in controlling energy metabolsim?
  • 20. What might control energy metabolism? Linear Discriminant Analysis Classification accuracy: 82.7% No. Positive correlation Up regulated in elderly No. Negative correlation Down regulated in elderly * p < 0.05, ** p < 0.001, *** p < 0.0001EIF6 is only co-regulated with genes representing energy metabolism in elderly.Negative co-regulation
  • 21. Validation in soleus muscle of eIF6 +/- mutant mouseUp-regulated in eIF6 +/- Category Term Count Benjamini Key Genes CC Mitochondrion 244 1.4E-22 Cat KEGG TCA Cycle 16 1.4E-5 Cs, Sdha, Pck1, Sucla2 BP Protein serine/threonine kinase activity 67 6.3E-3 Mapks, Rps6ka2, Camk1 BP Apoptosis 71 2.1E-2 Trp53 BP Mitochondrial transport 15 6.6E-3 Mtx1/2, Timm KEGG Oxidative Phosphorylation 29 4.3E-3 COX2, Atp5d, Ndufaf4 CC Mitochondrial ribosome 11 8.2E-2 Mrps, Mrpl BP Lipid metabolism 26 3.0E-2 Hadh, Crat, Acads,Down-regulated in eIF6 +/- Category Term Count Benjamini BP Regulation of transcription, DNA-dependant 92 0.25 Notch1, Esr1, Hdac4, Igf1, Ppargc1b, Rb1 BP Cell migration 18 ns Prkca, Tgfbr1, BP Blood Vessel Remodelling 26 ns Itga4, Tgfb2 CC Contractile fibre 10 ns Drd2 Energy metabolism related genes are upregulated in EIF6 +/- mutant EIF6 plays a role in the regulation of bioenergetics pathways
  • 22. Overlap between genes co-regulated with protein carbonylation and changes in EIF6+/- mouse Oxidative Phosphorylation Ndufv1 Uqcrc1 NDUFV1 NDUFA11 SDHA UQCRC1Ndufv1 UQCRC2 15 14 Uqcrc1 CYC1 COX5B COX4I1 ATP5A1 ATP5G3 ATP5H carbonylation carbonylation r = -0.628, p = 0.0003 r= -0.752, p < 0.0001 p = 0.0000415 Correlated to Mouse EIF6+/- ** EIF6 vs SDHA SDHA vs Carbonylation carbonylation* TCA Cycle Carb EIF6 PDHB 5 OGDH SUCLG1 4 SDHA SDHA SDHA p = 0.0038679 r = -0.588, p = 0.0015 r = -0.548, p = 0.0041 ** FDR < 5%, mouse annotation *p < 0.005, uncorrected Significant overlap
  • 23. Overlap between hypoxia response and EIF6+/- mutant mice EIF6 expression in hypoxic mice Term Category Count FDR CC Mitochondrion 107 4.3E-29 KEGG TCA Cycle 13 6.2E-13 KEGG Oxidative phosphorylation 22 5.4E-10 KEGG Fatty acid metabolism 8 2.2E-3 KEGG Arginine and proline metabolism 7 2.0E-2Down in hypoxia Up in eIF6 +/- mice *** < 1% FDR 2847 398 1585 EIF6 expression in integrated dataset P = 2.996 x 10-12 Oxygen availability may be the link between EIF6 and energy metabolism p = 0.0003
  • 24. Conclusions•We have identified EIF6 as a potential regulator of energy metabolism in skeletal muscle•This regulation appears to be age dependant•Oxygen sensing pathways may provide the link between EIF6 and energy metabolism Future Work•ROS production in live muscles (eIF6 +/-)•C2C12 cell culture experiments (siRNA + over-expression)•Chip-on-chip experiments in yeast and muscle•Clear-native gel electrophoresis to look at mitochondrial complex function
  • 25. AcknowledgmentsUniversity of Birmingham Biomax IDIBAPSFrancesco Falciani Dieter Maier Josep RocaKim Clarke Susana KalkoAnna Stincone San Raffaele Marta CascanteStuart Egginton Stefano Biffo Diego A. RodriguezDan Tennant

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