Pyruvate Dehydrogenase Complex (PDC) catalyses the irreversible oxidation of pyruvate to yield acetyl-CoA and CO2
, and therefore is a key enzyme controlling the rate of oxidative glycolysis and regulating the balance between oxidation of carbohydrate and lipid fuels. The activity of PDC is controlled through its inactivating phosphorylation by pyruvate dehydrogenase kinases (PDKs) and activating dephosphorylation by pyruvate dehydrogenase phosphatases. PDK4, one of four identified isoforms, is regulated by a number of hormonal and nutritional factors, and it has been suggested that this enzyme, through its inactivation of PDC, is a key molecular switch governing fuel utilization [1
Type 2 diabetes is associated with alterations in the balance between glucose and lipid pathways. Peripheral insulin resistance is a common feature of many type 2 diabetics. In addition, excessive hepatic glucose production, largely from gluconeogenesis, accounts for a significant proportion of hyperglycemia in some type 2 diabetics. Activation of PDC should act to mitigate diabetes by inhibiting gluconeogenesis and promoting glucose disposal in peripheral tissues [2
]. There is some evidence supporting this since dichloroacetate, an inhibitor of PDK, has been previously used for treatment of several metabolic and cardiovascular disorders. However, chronic use of dichloroacetate is restrained due to its toxicity on liver and kidney, therefore it is not suitable for diabetes treatment [3
Four distinct PDK isoenzymes have been identified [5
]. PDK2 is ubiquitously expressed, PDK1 is restricted to the heart and PDK3 is largely restricted to the testis with low abundance. PDK4 is of particular interest for diabetes because it is expressed at high levels in heart and skeletal muscle which are major sites of glucose disposal, and to a lesser extent in liver. PDK4 is rapidly up-regulated in response to starvation [7
] and is markedly up-regulated in skeletal muscle in animal models of type 1 diabetes [8
] and steroid-induced diabetic rats [9
Glucocorticoids are one hormonal factor which regulates PDK4 expression. PDK4 mRNA and protein are augmented in rats following dexamethasone treatment and are associated with decreased glucose oxidation rates [11
]. Glucocorticoids activate PDK4 expression through glucocorticoid receptor (GR) mediated glucocorticoid receptor element (GRE) binding pathway. The induction was suppressed after a GR antagonist RU486 treatment [9
]. Mutation of the glucocorticoid receptor element in the PDK4 promoter region also prevents PDK4 induction [12
An additional hormone controlling PDK4 expression is insulin. Evidence suggests that insulin suppresses PDK4 mRNA expression in skeletal muscle and its effect is no dependent on the insulin effect on plasma free fatty acid [13
]. The forkhead transcription factor forkhead box class O (FOXO) 1 has been implicated in the regulation of PDK4 expression by insulin [12
]. Insulin signaling via protein kinase B (PKB) phosphorylates and enhances the translocation of FOXO factors from the nucleus to the cytoplasm, resulting in inhibition of the expression of genes that are up-regulated by FOXO factors. The FOXO factors bind to insulin response elements (IREs) [14
] and their insulin directed removal from the nuclear compartment mediates the negative effects of insulin on gene expression [15
]. An IRE has been identified within the rat PDK4 promoter region [16
]. Insulin is no longer effective on human PDK4 after mutation of IREs [12
]. Therefore, one possible mechanism of insulin suppressing PDK4 expression in skeletal muscle is to promote translocation of FOXO1 from nucleus to cytoplasm therefore decreasing FOXO1 activity in the nucleus.
High fat diet induced diabetes is known to lead to an increase in the flux of free fatty acids (FFA) into a variety of tissues. Fatty acids are known to regulate expression of a number of important metabolic enzymes [17
]. One mechanism for this effect of fatty acids involves activation of peroxisome proliferator-activated receptors (PPARs) that control gene transcription in response to small lipophilic compounds [18
]. Activation of PPARα can promote PDK4 expression. WY-14,643, a synthetic PPARα ligand, induced PDK4 expression in heart muscle of wild-type mice but not in PPARα-null mice [19
]. These results suggest that increases in free fatty acids can directly affect the level of PDK4 via PPARα.
The development of type 2 diabetes involves many genetic and environmental factors, such as polygenic gene alterations and postnatal environmental factors (sedentary lifestyle and unhealthy diet). The Goto-Kakizaki (GK) rat represents an animal model of non-insulin dependent diabetes with glucose intolerance due to potential impaired insulin secretion, insulin resistance and abnormal glucose metabolism [20
]. In contrast to many other animal models of type 2 diabetes, GK rats don’t become obese [20
]. In the GK rat, moderate diabetes usually occurs between 3 and 4 weeks of age. Late complications include thickening of the glomerular and tubular basement membranes as well as glomerular hypertrophy. Therefore, this model provides a valuable tool for dissecting the pathogenesis of insulin resistance.
Modeling involves developing mathematical equations to describe quantitative relationships for individuals or populations. These equations can be used to predict the time course of disease and drug effects. The field of disease progression modeling has evolved greatly in the past few decades [22
]. However, disease progression models tend to have high unexplained between subject variability (IIV), which is consistent with the observation that IIV is greater for pharmacodynamic than pharmacokinetic evaluations [23
]. Nonlinear mixed effects models, also known as the hierarchical nonlinear models provide a tool for recognizing and estimating two distinct types of variability in the data: between-individual variability and within-individual variability. The relationship between the explanatory variable and the response variable is modeled as a single function but also allows the parameters to differ between individuals. NONMEM®
is a program that allows model building to be performed using a population approach. One important feature of population analysis is the ability to describe between-subject variability, and to account quantitatively for covariate influences, such as weight, which can explain some of this variability [24
]. Therefore the nonlinear mixed effects model is well suited for the analysis of biological data when observations are of the same biological category but the individual observations are different. The objective of this study was to investigate the dynamics of muscle type 2 diabetes (T2D) disease progression in normal-fed and high fat-fed Goto-Kakizaki (GK) rats and to develop a semi-mechanistic based population model for muscle PDK4 mRNA changes over time. The proposed model integrates information on the time course of the disease with effects of corticosterone, insulin, and free fatty acid on PDK4 mRNA expression into a single comprehensive, physiologically meaningful model structure.