Predictive analysis of network activation in response to lipid loading in the liver

Fat is an essential part of the human diet and dietary fatty acids are required for numerous functions in the cells and tissues of the human body. Both insufficient and excess intakes of dietary fat have important health implications particularly in aging. For example, the excess consumption of dietary fat, in particular saturated fat, is linked to the development of obesity, a fatty liver and consequently diabetes. The liver is the central metabolic organ of the body and processes fat both from the diet and from fatty acids in the blood coming from adipose and other tissues. Molecules in the liver cells called 'nuclear receptors' act as sensors to these fats, causing interactions with many other molecules in a complicated signalling network that is currently poorly understood. This research aims to generate a computer simulation model of the network of molecules, including nuclear receptors, that respond to increasing fat in the liver. The computer model then predicts which are the most important molecules that respond to fat levels in the liver. To do this, we first treat human liver cells in culture with different amounts of the most common saturated fat in the diet and in blood, palmitic acid, and measure changes in the liver molecules (gene transcripts and proteins). These changes are then inputted into the computer model. After the model predicts the key regulatory molecules we will test this prediction experimentally by blocking these molecules in our cells and measuring then how the cells respond to fat (e.g. do they take up more or less?). The goal of this predictive computer modelling is to identify molecules (genes and proteins) in the network that are critical to the liver's normal response to fat in the blood and from the diet. These molecules are quite possibly ones found disrupted in diseases such as obesity and diabetes and candidates for future research. In addition, this model will be also be used in future research to predict what other dietary components protect the liver from fat accumulation and contribute to healthy aging.

Dysregulation of nutrient metabolism and energy homeostasis leads to conditions such as obesity, fatty liver and type 2 diabetes that prevent healthy aging in the UK population. Nuclear receptors (NRs) are a large family of ligand-activated transcription factors that act as master transcriptional regulators in control of metabolic processes. The inherent complexities of NR signalling, particularly in response to their dietary ligands, are not fully understood, but it is clear that NRs do not act as linear signalling cascades, rather as highly connected signalling networks with cross-talk across multiple pathways and multiple feed-forward and feedback loops. This project will use a systems biology approach to characterise the molecular network involved in the response to lipid loading in human hepatocytes. We hypothesize that the human hepatocyte response to lipid exposure is regulated by the emergent network properties and total regulatory signal flow in the nuclear receptor network. Using our existing transcriptomic and proteomic datasets, molecular interaction databases (IPA, MetaCore, Reactome) and CellDesigner software we will reconstruct molecular interaction network diagrams of signal flow in response to lipid accumulation in hepatocytes. Monte Carlo sampling of the qualitative network behaviours will be run to predict key regulatory molecules involved in lipid loading in hepatocytes which will subsequently be artificially modulated using siRNA or cDNA transfection constructs as appropriate. After analysis of the mRNA and protein response to experimental network perturbation our predictive model will be refined. The predictive model developed by this project will be applicable to the interpretation of data on the effect of both genetic polymorphisms and dietary nutrients on liver homeostasis and healthy aging. Consequently it will be an important resource for future research and the development of predictive, preventive and personalised medicine.

Disruption of nutrient metabolism and energy homeostasis in the liver leads to conditions such as obesity, fatty liver and type 2 diabetes, all of which disrupt healthy aging in the UK population. In 2007 24% of adults and 17% of children in the UK were obese and the estimated costs of treating the consequences of obesity were £1 billion in 2002 and projected to be £5.3 billion by 2025 (NHS Information Centre, Lifestyle Statistics, 2009). The identification of rationalised strategies (dietary and pharmacological) to protect the liver from fat accumulation and contribute to healthy aging would have huge impact on the public health and well-being of the UK population, and in the longer term reduce the burden of the cost of UK healthcare leading to wealth creation. The potential impact of the proposed work is far reaching in terms of facilitating the design of novel strategies to improve liver metabolic health throughout aging and therefore is of tremendous interest to the Foods and Pharmaceutical Industries. This project aims to use a systems biology approach to characterise the molecular network involved in the response to lipid loading in human hepatocytes. Achieving the project aims will result in the reconstruction of the cellular signalling network orchestrating the response to dietary lipids in the liver, and contribute to a detailed understanding of the role of nuclear receptors in maintaining metabolic homeostasis. The completed model will have substantial predictive power for the identification of the critical regulatory molecules involved in the normal response to lipids in the liver. Identification of these molecules is essential to understanding the dysregulation events which can lead to obesity, fatty liver and diabetes. Furthermore, the predictive model developed by this project will be applicable to the interpretation of data on the effect of both genetic polymorphisms and other dietary nutrients on liver homeostasis and healthy aging. Consequently it will be an important resource for future scientific research advancement and the development of predictive, preventive and personalised medical strategies. Those who are likely to immediately benefit from the data, models and materials produced by the proposed research include academic researchers and scientists within the Foods and Pharmaceutical Industries. In addition, the findings will inform advice to the public concerning the importance of optimum nutrition for health and hence this research is of interest and relevance to clinicians, governing bodies and other agencies, such as the Food Standards Agency and the British Nutrition Foundation (BNF), who are responsible for making dietary recommendations and disseminating public health messages about nutrition. Other impacts of the project will include enhancement of the UK's research skills capacity, through knowledge transfer regarding computational modelling to the PI and through the training provided to the PDRA. As detailed in our impact plan, to achieve maximum impact we will widely disseminate our research findings through publication and presentation of our research at national and international scientific meetings. The predictive network model will be published in SBML format and made freely available for other researchers to use. We will actively engage the Foods and Pharmaceutical Industry as well as governing agencies through our established links and submission of review/overview articles to relevant publications such as the Nutrition Bulletin of the BNF. Lastly we will communicate our research to the public though press releases and public engagement activities including outreach programs to local schools.