Dataset Information

Computational modelling of energy balance in individuals with Metabolic Syndrome.

ABSTRACT:

Background

A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations.

Results

We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment.

Conclusion

The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure - for instance by cold exposure to activate brown adipose tissue (BAT) - could resolve MetS symptoms.

SUBMITTER: Rozendaal YJW

PROVIDER: S-EPMC6390597 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational modelling of energy balance in individuals with Metabolic Syndrome.

Rozendaal Yvonne J W YJW Wang Yanan Y Hilbers Peter A J PAJ van Riel Natal A W NAW

BMC systems biology 20190226 1

<h4>Background</h4>A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expen ...[more]

PMID: 30808366

Similar Datasets

Project description:Although recent studies have shown that human genomes contain hundreds of loci that exhibit signatures of positive selection, variants that are associated with adaptation in energy-balance regulation remain elusive. We reasoned that the difficulty in identifying such variants could be due to heterogeneity in selection pressure and that an integrative approach that incorporated experiment-based evidence and population genetics-based statistical judgments would be needed to reveal important metabolic modifiers in humans.To identify common metabolic modifiers that underlie phenotypic variation in diabetes-associated or obesity-associated traits in humans, or both, we screened 207 candidate loci for regulatory single nucleotide polymorphisms (SNPs) that exhibited evidence of gene-environmental interactions.Three SNPs (rs3895874, rs3848460, and rs937301) at the 5' gene region of human GIP were identified as prime metabolic-modifier candidates at the enteroinsular axis. Functional studies have shown that GIP promoter reporters carrying derived alleles of these three SNPs (haplotype GIP(-1920A)) have significantly lower transcriptional activities than those with ancestral alleles at corresponding positions (haplotype GIP(-1920G)). Consistently, studies of pregnant women who have undergone a screening test for gestational diabetes have shown that patients with a homozygous GIP(-1920A/A) genotype have significantly lower serum concentrations of glucose-dependent insulinotropic polypeptide (GIP) than those carrying an ancestral GIP(-1920G) haplotype. After controlling for a GIPR variation, we showed that serum glucose concentrations of patients carrying GIP(-1920A/A) homozygotes are significantly higher than that of those carrying an ancestral GIP(-1920G) haplotype (odds ratio 3.53).Our proof-of-concept study indicates that common regulatory GIP variants impart a difference in GIP and glucose metabolism. The study also provides a rare example that identified the common variant-common phenotypic variation pattern based on evidence of moderate gene-environmental interactions.

Project description:BackgroundCells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals.MethodsTo understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+ -consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α.ResultsThe model simulates the interactions among key regulators such as AKT, mTORC1, AMPK, NAD+ , and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases.ConclusionsThis study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing-related diseases. Video Abstract.

Dataset Information

Computational modelling of energy balance in individuals with Metabolic Syndrome.

Background

Results

Conclusion

Publications

Computational modelling of energy balance in individuals with Metabolic Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets