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SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies.

Naik A, Rozman D, Belič A - PLoS Comput. Biol. (2014)

Bottom Line: Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour.Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors.SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Sciences and Informatics, University of Ljubljana, Ljubljana, Slovenia; Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.

ABSTRACT
Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

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Peroxisome proliferator-activated receptor alpha (PPARα) activation in high fat diet- induced steatosis.Panels a–c illustrate the simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (increased triglyceride and cholesterol influx, from time = 0) and subsequent treatment with a PPARα agonist (from time = 3×105). a) Simulation of hepatic triglyceride (TGL), plasma high-density lipoprotein (HDLB) and serum fatty acids (FAB); b) Simulation of hepatic cholesterol (CholesterolL), plasma very low-density lipoprotein (VLDL), carnitine palmitoyltransferase 1 (CPT1) and active proliferator-activated receptor alpha (aPPARα); c) Simulation of low-density lipoprotein (LDLB).
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pcbi-1003993-g005: Peroxisome proliferator-activated receptor alpha (PPARα) activation in high fat diet- induced steatosis.Panels a–c illustrate the simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (increased triglyceride and cholesterol influx, from time = 0) and subsequent treatment with a PPARα agonist (from time = 3×105). a) Simulation of hepatic triglyceride (TGL), plasma high-density lipoprotein (HDLB) and serum fatty acids (FAB); b) Simulation of hepatic cholesterol (CholesterolL), plasma very low-density lipoprotein (VLDL), carnitine palmitoyltransferase 1 (CPT1) and active proliferator-activated receptor alpha (aPPARα); c) Simulation of low-density lipoprotein (LDLB).

Mentions: Fig. 5 panels a–c and Table 2 illustrate the SteatoNet simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (from time = 0 units) and subsequent treatment with a PPARα agonist (from time = 3×105 units). Simulation of a high fat diet by upregulating the triglyceride and cholesterol influx by 5-fold and 4-fold respectively, and downregulating the glucose influx by 2.5-fold results in hepatic triglycerides accumulation by 1.5-fold (Fig. 5a) and upregulation of plasma fatty acids (Fig. 5a), VLDL (Fig. 5b), hepatic cholesterol (Fig. 5b) and LDL (Fig. 5c). Under these conditions, treatment with fenofibrates i.e. PPARα activation (Fig. 5b, starting from time = 3×105 units) diminishes hepatic triglyceride accumulation (Fig. 5a), upregulates fatty acid oxidation illustrated by CPT-1 (Fig. 5b), lowers plasma fatty acids (Fig. 5a) and VLDL (Fig. 5b) in addition to decreasing hepatic cholesterol (Fig. 5b) and LDL-cholesterol (Fig. 5c) with no effects on plasma HDL (Fig. 5a). These simulation results are in accordance with biological observations on treatment with PPARα agonists. The transient spike in hepatic triglyceride concentration in Fig. 5a results from the rapid influx of lipids when simulating a high fat diet; however, the net increase in triglyceride concentration after reaching the steady state is 1.5-fold.


SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies.

Naik A, Rozman D, Belič A - PLoS Comput. Biol. (2014)

Peroxisome proliferator-activated receptor alpha (PPARα) activation in high fat diet- induced steatosis.Panels a–c illustrate the simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (increased triglyceride and cholesterol influx, from time = 0) and subsequent treatment with a PPARα agonist (from time = 3×105). a) Simulation of hepatic triglyceride (TGL), plasma high-density lipoprotein (HDLB) and serum fatty acids (FAB); b) Simulation of hepatic cholesterol (CholesterolL), plasma very low-density lipoprotein (VLDL), carnitine palmitoyltransferase 1 (CPT1) and active proliferator-activated receptor alpha (aPPARα); c) Simulation of low-density lipoprotein (LDLB).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263370&req=5

pcbi-1003993-g005: Peroxisome proliferator-activated receptor alpha (PPARα) activation in high fat diet- induced steatosis.Panels a–c illustrate the simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (increased triglyceride and cholesterol influx, from time = 0) and subsequent treatment with a PPARα agonist (from time = 3×105). a) Simulation of hepatic triglyceride (TGL), plasma high-density lipoprotein (HDLB) and serum fatty acids (FAB); b) Simulation of hepatic cholesterol (CholesterolL), plasma very low-density lipoprotein (VLDL), carnitine palmitoyltransferase 1 (CPT1) and active proliferator-activated receptor alpha (aPPARα); c) Simulation of low-density lipoprotein (LDLB).
Mentions: Fig. 5 panels a–c and Table 2 illustrate the SteatoNet simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (from time = 0 units) and subsequent treatment with a PPARα agonist (from time = 3×105 units). Simulation of a high fat diet by upregulating the triglyceride and cholesterol influx by 5-fold and 4-fold respectively, and downregulating the glucose influx by 2.5-fold results in hepatic triglycerides accumulation by 1.5-fold (Fig. 5a) and upregulation of plasma fatty acids (Fig. 5a), VLDL (Fig. 5b), hepatic cholesterol (Fig. 5b) and LDL (Fig. 5c). Under these conditions, treatment with fenofibrates i.e. PPARα activation (Fig. 5b, starting from time = 3×105 units) diminishes hepatic triglyceride accumulation (Fig. 5a), upregulates fatty acid oxidation illustrated by CPT-1 (Fig. 5b), lowers plasma fatty acids (Fig. 5a) and VLDL (Fig. 5b) in addition to decreasing hepatic cholesterol (Fig. 5b) and LDL-cholesterol (Fig. 5c) with no effects on plasma HDL (Fig. 5a). These simulation results are in accordance with biological observations on treatment with PPARα agonists. The transient spike in hepatic triglyceride concentration in Fig. 5a results from the rapid influx of lipids when simulating a high fat diet; however, the net increase in triglyceride concentration after reaching the steady state is 1.5-fold.

Bottom Line: Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour.Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors.SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Sciences and Informatics, University of Ljubljana, Ljubljana, Slovenia; Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.

ABSTRACT
Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Show MeSH
Related in: MedlinePlus