Systems Medicine and Metabolic Diseases
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Natal van Riel from Eindhoven University of Technology presented on systems biology approaches for understanding metabolic diseases. She discussed (1) using systems biology models and data integration to understand the complex networks underlying metabolic syndrome and related diseases, (2) applying these approaches to analyze the effects of therapies and interventions over time, and (3) opportunities for clinical application including analyzing changes from bariatric surgery, diet, and metabolic challenges.
![Preclinical study of pharmaceutical
intervention
• Longitudinal data: control, treated for 1, 2, 4, 7, 14, and 21 days
/ biomedical engineering PAGE 1319-8-2013
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Grefhorst et al. Atherosclerosis, 2012, 222: 382– 389](https://image.slidesharecdn.com/vanriel-final-130819064525-phpapp01/85/Systems-Medicine-and-Metabolic-Diseases-13-320.jpg)
Systems Medicine and Metabolic Diseases
- 1. BME Research Day 2013 May 28, 2013 Natal van Riel Eindhoven University of Technology, the Netherlands Dept. of Biomedical Engineering, Institute for Complex Molecular Systems n.a.w.v.riel@tue.nl Systems Biology and Metabolic Diseases
- 2. Systems Biology • Almost all parts of living systems are connected (networks) • Data-driven modeling and model-driven experiments • Understanding the dynamic functioning of these biomolecular interaction networks • Integration of different types of molecular data / biomedical engineering PAGE 219-8-2013 The Netherlands Platform for Systems Biology (SB@NL): 8 Dutch organizations, incl. Eindhoven-TU/e-BMT
- 3. What can Systems Biology do for Medicine? • More data on patients can be produced than ever before • But how efficiently can we use these data? • What: P4 medicine - Predictive, Preventive, Personalized, and Participatory • How: Systems Medicine / biomedical engineering PAGE 319-8-2013 Hood & Friend, Nat Rev Clin Oncol. 2011; 8(3):184-7
- 4. Systems Medicine • ‘Systems Medicine involves the implementation of systems biology approaches in medical concepts, research and practice, through iterative and reciprocal feedback between data-driven computational and mathematical models as well as model-driven translational and clinical investigations and practice’ / biomedical engineering PAGE 419-8-2013
- 5. Metabolic Syndrome (MetS) / biomedical engineering PAGE 519-8-2013 • Obesity: 33% of EU population by 2030 (200 million individuals) • A combination of related disease phenotypes, such as high cholesterol, disturbed sugar metabolism and insulin insensitivity • Comorbidities: type 2 diabetes, fatty liver, cardiovascular diseases • Costs of treating the comorbidities > 100 billion Euros per year beyond 2030 www.resolve-diabetes.org FP7 Health programme on Systems Medicine (Systems medicine: Applying systems biology approaches for understanding multifactorial human diseases and their co-morbidities).
- 6. Systems Medicine of MetS / biomedical engineering PAGE 619-8-2013 • Multi-factorial and progressive • Underlying molecular mechanisms are not understood • Intra- and intercellular networks • Current research typically focused on one component a/o comorbidity • Dyslipidemia and atherosclerosis • Dysglycemia and T2DM • Interrelationships are poorly understood • Patients receiving statins to lower cholesterol show a significant increase in incidence of T2DM (Sattar N et al., Lancet. 2010;375:735-42) • Identify network-based therapeutic targets aimed at restoring lipid homeostasis and glycemic control in patients with metabolic syndrome and associated comorbidities
- 7. Understanding effects of existing therapies and interventions • Clinical − lifestyle change − bariatric surgery / biomedical engineering PAGE 719-8-2013 Sjostrom L et al, N Engl J Med 2007; 357:741-52 • Preclinical − effect of diet and exercise − bariatric surgery − pharmaceutical intervention: statins, PPAR agonists, LXR agonist − genetic intervention: hepatic insulin signaling (FOXO1) Gastric bypass: • Currently, the most effective therapy for obesity • An immediate, weight- independent improvement of obesity-related comorbidities before significant body weight loss occurs.
- 8. The computational model • Lipid and lipoprotein metabolism • Coarse-grained when possible, detailed when necessary / biomedical engineering PAGE 819-8-2013 1.0: Tiemann et al. BMC Syst Biol. 2011; 5:174 2.0: Tiemann et al, 2013, submitted Lipoprotein Profiler: Sips et al, 2012
- 9. The computational method: ADAPT • From phenotype ‘snapshots’ to an integrated description of longitudinal changes in progressive diseases and therapeutic interventions • ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) • Linking phenotypes: modeling phenotype transitions with parameter transition trajectories / biomedical engineering PAGE 919-8-2013 ? ? ? Metabolic profiling (‘snapshots’) Longitudinal changes
- 10. The computational method: ADAPT • Data integration, analysis and prediction / biomedical engineering PAGE 1019-8-2013 Christian Tiemann, BME Research Day, 2012 Van Riel et al. (2013) Interface Focus, 3(2): 20120084 ( ) ( ( ), , ) d t t t dt x Nv x p ( ) ( ( ) ,( ), ) d t t t dt t x Nv x p 2 1 ( ) ( ) N i i d i i y d X p p ( ) ˆ( ) arg min ( ( )d t t X t p p p Progressive disease / Treatment intervention Patient phenotype data at different stages Monte Carlo sampling of data interpolants Estimation of parameter and flux trajectories Analysis A priori information Metabolic network topology & Reaction kinetics Differential Equation model with time-dependent parameters
- 11. Novel cholesterol lowering medication • Liver X Receptor (LXR, nuclear receptor), induce transcription of multiple genes modulating metabolism of fatty acids, triglycerides, and lipoproteins • LXR agonists stimulate cellular cholesterol efflux from pheripheral tissues (including macrophages) • LXR as target for anti- atherosclerotic therapy? • Applied model + ADAPT to identify and analyse effects of treatment with synthetic agonist T0901317 (beneficial effects and side-effects) / biomedical engineering PAGE 1119-8-2013 VLDL IDL LDL HDL chylomicron remnants FFA TG LXR LXR
- 12. Extensive phenotyping / biomedical engineering PAGE 1219-8-2013 1. Fluxes -VLDL-TG production -Hepatic HDL cholesterol uptake -Hepatic cholesterol synthesis -Biliary cholesterol excretion -Biliary bile acid excretion -Fecal cholesterol excretion -Fecal bile acid excretion -Transintestinal cholesterol excretion -Beta-oxidation (available but not included yet) -Hepatic FFA uptake (available but not included yet) -VLDL catabolism/clearance from the plasma 2. Metabolite concentrations -Hepatic FC -Hepatic CE -Hepatic TG -Plasma FFA -Plasma TG -Plasma total cholesterol -HDL cholesterol -Hepatic fractional DNL (de novo triglycerides) -Nascent VLDL particle diameter 3. Protein expression / enzyme activity -SRB1 protein expression 4. Transcriptomics -Hepatic gene expression levels of ldlr, vldlr, lrp1, cd36, mtp, apob, lpl, lcad, aox, hmgcoa, ucp2, gpat, fas, me1, srebp-1c, scd1, abcg1, abcg5, cyp7a1, sqs, hmgcoared, srebp-2, srb1 measuring modelling
- 13. Preclinical study of pharmaceutical intervention • Longitudinal data: control, treated for 1, 2, 4, 7, 14, and 21 days / biomedical engineering PAGE 1319-8-2013 0 10 20 0 100 200 Hepatic TG Time [days] [umol/g] 0 10 20 0 1 2 3 Hepatic CE Time [days] [umol/g] 0 10 20 0 2 4 6 Hepatic FC Time [days] [umol/g] 0 10 20 0 50 100 Hepatic TG Time [days] [umol] 0 10 20 0 0.5 1 1.5 Hepatic CE Time [days] [umol] 0 10 20 0 2 4 Hepatic FC Time [days] [umol] 0 10 20 0 1000 2000 3000 Plasma CE Time [days] [umol/L] 0 10 20 0 1000 2000 3000 HDL-CE Time [days] [umol/L] 0 10 20 0 500 1000 1500 Plasma TG Time [days] [umol/L] 0 10 20 6 8 10 12 VLDL clearance Time [days] [-] 0 10 20 100 200 300 400 ratio TG/CE Time [days] [-] 0 10 20 0 5 10 15 VLDL diameter Time [days] [nm] 0 10 20 0 1 2 3 VLDL-TG production Time [days] [umol/h] 0 10 20 1 2 3 Hepatic mass Time [days] [gram] 0 10 20 0 0.2 0.4 DNL Time [days] [-] Grefhorst et al. Atherosclerosis, 2012, 222: 382– 389
- 14. Propagation of Uncertainty Analyis • ADAPT accounts for uncertainty in the data and model • ADAPT accounts for different dynamic behavior / biomedical engineering PAGE 1419-8-2013 Gaussian distribution Sampling replicates from error model ( , )d dN Vanlier et al. Math Biosci. 2013 Mar 25 Vanlier et al. Bioinformatics. 2012, 28(8):1130-5
- 15. ADAPT of lipoprotein and lipid metabolism • Connecting the longitudinal data • Taking into account uncertainties / biomedical engineering PAGE 1519-8-2013 • Calculating unobserved quantities
- 16. Analysis: HDL cholesterol / biomedical engineering PAGE 1619-8-2013 Analysis: increased excretion of cholesterol Observation: increased concentration of HDL (the good cholesterol)
- 17. • SR-B1 • Protein expression/ activity: Experimental testing of model prediction • HDL excretion and uptake flux are increased • Transcription: / biomedical engineering PAGE 1719-8-2013 Transcription of cholesterol efflux transporters Grefhorst et al. Atherosclerosis, 2012; 222(2):382-9 Tiemann et al., submitted SR-B1 protein content is decreased in hepatic membranes Srb1 mRNA expression not changed model: decreased hepatic capacity to clear cholesterol
- 18. • Integration of different types of molecular data • Analysis of propagation of uncertainty • Treatment and Prevention • Multiple time-scale • Metabolic networks: metabolic diseases and diseases with changes in metabolism as hallmark / biomedical engineering PAGE 1819-8-2013 Metabolome Proteome Transcriptome Van Riel et al. (2013) Interface Focus, 3(2): 20120084
- 19. Clinical application • Interventions (longitudinal data): • bariatric surgery • diet-restriction (12 months) • hypercaloric diet and feeding/fasting cycles • Changes in post- prandial dynamics (metabolic flexibility) over time • Metabolic challenges (OGTT, OFTT) / biomedical engineering PAGE 1919-8-2013
- 20. / biomedical engineering PAGE 2019-8-2013 Peter Hilbers Christian Tiemann Joep Vanlier Ceylan Çölmekçi Öncü Anne Maas Joep Schmitz Fianne Sips Huili Yuan Niels Dekkers Marijke Dermois Robbin van den Eijnde Wouter Gevers Gijs Hendriks Tim Kuijpers Stefan Mariën Yvonne Rozendaal Roderick Snel Sjanneke Zwaan