Ciarán P. Fisher

Menopausal Status and Abdominal Obesity Are Significant Determinants of Hepatic Lipid Metabolism in Women

Background Android fat distribution (abdominal obesity) is associated with insulin resistance, hepatic steatosis, and greater secretion of large very low‐density lipoprotein (VLDL) particles in men. Since abdominal obesity is becoming increasingly prevalent in women, we aimed to investigate the relationship between android fat and hepatic lipid metabolism in pre‐ and postmenopausal women. Methods and Results We used a combination of stable isotope tracer techniques to investigate intrahepatic fatty acid synthesis and partitioning in 29 lean and 29 abdominally obese women (android fat/total fat 0.065 [0.02 to 0.08] and 0.095 [0.08 to 0.11], respectively). Thirty women were premenopausal aged 35 to 45 and they were matched for abdominal obesity with 28 postmenopausal women aged 55 to 65. As anticipated, abdominal obese women were more insulin resistant with enhanced hepatic secretion of large (404±30 versus 268±26 mg/kg lean mass, P<0.001) but not small VLDL (160±11 versus 142±13). However, postmenopausal status had a pronounced effect on the characteristics of small VLDL particles, which were considerably triglyceride‐enriched (production ratio of VLDL 2‐ triglyceride:apolipoprotein B 30±5.3 versus 19±1.6, P<0.05). In contrast to postmenopausal women, there was a tight control of hepatic fatty acid metabolism and triglyceride production in premenopausal women, whereby oxidation (r s=−0.49, P=0.006), de novo lipogenesis (r s=0.55, P=0.003), and desaturation (r s=0.48, P=0.012) were closely correlated with abdominal obesity‐driven large VLDL‐triglyceride secretion rate. Conclusions In women, abdominal obesity is a major driver of hepatic large VLDL particle secretion, whereas postmenopausal status was characterized by increased small VLDL particle size. These data provide a mechanistic basis for the hyperlipidemia observed in postmenopausal obesity.

QSSPN: dynamic simulation of molecular interaction networks describing gene regulation, signalling and whole-cell metabolism in human cells

Motivation: Dynamic simulation of genome-scale molecular interaction networks will enable the mechanistic prediction of genotype–phenotype relationships. Despite advances in quantitative biology, full parameterization of whole-cell models is not yet possible. Simulation methods capable of using available qualitative data are required to develop dynamic whole-cell models through an iterative process of modelling and experimental validation. Results: We formulate quasi-steady state Petri nets (QSSPN), a novel method integrating Petri nets and constraint-based analysis to predict the feasibility of qualitative dynamic behaviours in qualitative models of gene regulation, signalling and whole-cell metabolism. We present the first dynamic simulations including regulatory mechanisms and a genome-scale metabolic network in human cell, using bile acid homeostasis in human hepatocytes as a case study. QSSPN simulations reproduce experimentally determined qualitative dynamic behaviours and permit mechanistic analysis of genotype–phenotype relationships. Availability and implementation: The model and simulation software implemented in C++ are available in supplementary material and at http://sysbio3.fhms.surrey.ac.uk/qsspn/. Contact: a.kierzek@surrey.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.

Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models.

Probabilistic Orthology Analysis of the ATP-Binding Cassette Transporters: Implications for the Development of Multiple Drug Resistance Phenotype

Drug transporters are rapidly becoming recognized as central to determining a chemicals fate within the body. This action is a double-edged sword, protecting the body from toxicants, but also potentially leading to reduced clinical efficacy of drugs through multiple drug resistance phenotype. To examine the interrelationship of this superfamily, we have constructed phylogenetic trees over an extended evolutionary distance representing each of the seven subfamilies. In addition, using protein sequences from species important in the design and evaluation of novel chemicals, namely human, macaque, rat, mouse, and dog, we have undertaken probabilistic orthology analysis to examine speciation probabilities within this phylogeny. These data allow us to accurately predict orthologous sequences across these species, an important confirmatory step with implications for cross-species extrapolation of data during drug safety testing. Finally, we present the first complete phylogeny for subfamilies within humans constructed using the entire coding sequences, at both the DNA and protein levels. We demonstrate for the first time that genes associated with the multiple drug resistance phenotype cluster separately from other genes within the same subfamily, suggestive of a conserved, fundamental, difference in these proteins. Such work may help guide future studies on the mechanisms underlying multiple drug resistance as well as the development of novel therapeutic approaches to mitigate against its development.

Multi-scale, whole-system models of liver metabolic adaptation to fat and sugar in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a serious public health issue associated with high fat, high sugar diets. However, the molecular mechanisms mediating NAFLD pathogenesis are only partially understood. Here we adopt an iterative multi-scale, systems biology approach coupled to in vitro experimentation to investigate the roles of sugar and fat metabolism in NAFLD pathogenesis. The use of fructose as a sweetening agent is controversial; to explore this, we developed a predictive model of human monosaccharide transport, signalling and metabolism. The resulting quantitative model comprising a kinetic model describing monosaccharide transport and insulin signalling integrated with a hepatocyte-specific genome-scale metabolic network (GSMN). Differential kinetics for the utilisation of glucose and fructose were predicted, but the resultant triacylglycerol production was predicted to be similar for monosaccharides; these predictions were verified by in vitro data. The role of physiological adaptation to lipid overload was explored through the comprehensive reconstruction of the peroxisome proliferator activated receptor alpha (PPARα) regulome integrated with a hepatocyte-specific GSMN. The resulting qualitative model reproduced metabolic responses to increased fatty acid levels and mimicked lipid loading in vitro. The model predicted that activation of PPARα by lipids produces a biphasic response, which initially exacerbates steatosis. Our data support the evidence that it is the quantity of sugar rather than the type that is critical in driving the steatotic response. Furthermore, we predict PPARα-mediated adaptations to hepatic lipid overload, shedding light on potential challenges for the use of PPARα agonists to treat NAFLD.Fatty liver disease: Liver adaptation to sugars and fatsNovel computational modelling approaches predict the liver’s metabolic response to dietary fat and sugar. A multi-disciplinary team led by Dr. J. Bernadette Moore at the University of Leeds has used novel computational modelling and simulation approaches to understand the livers response to dietary sugars and fats, and the development of non-alcoholic fatty liver disease. Simulations of the team’s multi-scale models, along with their experimental findings, show it is the quantity, not type (e.g. fructose vs. glucose), of sugar that drives fat accumulation in liver cells. Furthermore, they model the behaviour of PPARα, a critical regulator of fat metabolism in the liver, providing cautionary insights into its potential as a therapeutic target. This work demonstrates the potential of systems approaches to elucidate mechanisms underpinning the role of diet in non-alcoholic fatty liver disease.

Correction to: Proteomic identification and characterization of hepatic glyoxalase 1 dysregulation in non-alcoholic fatty liver disease

Following publication of the original article [1]