Insulin resistance is a pathological state where peripheral tissues, particularly skeletal muscle, fail to respond to circulating insulin (Gual et al. 2005
). One of the earliest abnormalities observed in skeletal muscle is the reduced insulin-induced uptake of glucose. The 50 mg/kg MPL i.v. injection in ADX rats leads to a transient increase in the plasma glucose concentration that begins about 2 h and lasts for 9 h (Almon et al. 2005b
). Plasma insulin concentrations are increased between 2 to 48 h after dosing, probably due to increased plasma glucose levels.
The development and progression of insulin resistance is not mediated by a single gene. Abnormalities in multiple molecules and tissues are involved in the pathogenesis of the disease. In such situations, research focused on single mediators may distort the true underlying molecular and cellular mechanisms and inadequately reveal overall regulatory factors and pathways. By analyzing joint temporal responses of many genes simultaneously, it should be possible to gain a better understanding of regulatory pathways and their contributions to this complex pathology. By using two different dosing regimens in the time series format, we not only obtained joint confirmation but also augmented the elucidation of underlying mechanisms of regulation. In the present report we describe the development of four models that encompass six genes associated with insulin resistance. These models include both direct effects of CS on the genes as well as indirect effects due to the effects of CS on other transcription factors such as MyoD and SREBP-1c.
The first model describes the regulation of IRS-1 which plays a central role in insulin signaling. IRS-1 is phosphorylated in response to insulin, insulin like growth factor-1, and cytokines and is preferentially involved in the metabolic actions of insulin (Gual et al. 2005
). A decline of IRS-1 amount or function has been linked to decreased glucose uptake in insulin resistant animal models and type II diabetic patients (Sesti et al. 2001
; Smith, 2002
Strong correlations between local and circulating proinflammatory cytokines and insulin resistance have been reported (Senn et al. 2002
). Among them, IL-6 has the strongest correlation with insulin resistance and type II diabetes. In liver, IL-6 elevates hepatic glucose output and increases blood glucose by interfering with insulin-induced kinase cascades such as tyrosine phosphorylation of IRS-1 (Senn et al. 2002
). Adipose tissue and muscle, two peripheral tissues that are responsible for glucose disposal, are important sites of IL-6 production (Senn et al. 2002
). Thus it is possible that IL-6 produced in these tissues may act at the local site and interfere with insulin-induced signaling. It has been found that IL-6 exerts long-term inhibitory effects on the transcription of IRS-1 (Rotter et al. 2003
). Although it is well accepted that CS also inhibit the expression of many cytokines including IL-6 in inflammation, it is not known if CS exhibits similar effects in healthy animals. In our studies, the expression of IL-6 mRNA in muscle did not show significant change. However, we observed a significant up-regulation of IL-6 receptor type 1 transcription. Thus it is reasonable to assume that increased expression of IL-6 receptor potentially enhances muscle sensitivity to IL-6 and augments the inhibitory effect of IL-6 on IRS-1 transcription.
The second model describes the regulation of two nuclear encoded mitochondrial genes that have been associated with insulin resistance, UCP3 and PDK4. Uncoupling proteins are a family of transporters that localize in the mitochondrial inner membrane and dissipate the transmembrane potential by transporting protons from the inter-membrane space back into the matrix (Garvey, 2003
). UCP3 exhibits limited tissue-specific expression confined to skeletal muscle in humans. Accumulating evidence indicates that in skeletal muscle, UCP3 contributes to lipid uptake by mitochondria rather than uncoupling oxidative phosphorylation (Garvey, 2003
). PDK4 is an enzyme that inactivates the mitochondrial pyruvate dehydrogenase complex. It thus reduces glucose utilization by skeletal muscle, preventing pyruvate (the product of glycolysis) from being used by the mitochondria (Sugden, 2003
). The PDK4 gene is preferentially expressed in skeletal muscle. Both genes are associated with enhanced fatty acid oxidation, thus probably are involved in the preferential fuel utilization of lipids instead of glucose in insulin resistance states. Enhanced expressions of both UCP3 and PDK4 in skeletal muscle have been associated with obesity and type II diabetes in humans (Bao et al. 1998
; Sugden and Holness, 2002
The bi-directional profiles of UCP3 and PDK4 mRNA expression following MPL treatment suggest that more than one mediator is involved in the regulatory pathways. The down-regulation of MyoD by CS that we observed in our studies along with literature searches and examination of the promoter regions of the two genes indicate that myogenic factor MyoD might be involved in a complex signaling network (Solanes et al. 2000
; Solanes et al. 2003
). MyoD belongs to the basic helix-loop-helix family of DNA-binding transcription factors. It is a master regulator of muscle lineage differentiation and is responsible for the preferential expression of skeletal muscle-specific genes (Solanes et al. 2000
). At least two phylogenetically conserved MyoD responsive sites were found in the promoter regions of both genes. MyoD is required not only for UCP3 promoter activity but also for its sensitivity to activation by other ligands (Solanes et al. 2000
; Solanes et al. 2003
). Additionally, the muscle-preferential expression of UCP3 and PDK4 also supports an essential role of MyoD in transcriptional regulation.
The third model describes the regulation of the expression of two genes involved in lipid metabolism in skeletal muscle. The first is FAT, also called CD36, which is a membrane protein that facilitates fatty acid uptake and use by skeletal muscle (Heron-Milhavet et al. 2004
). Muscle-specific over expression of FAT reverses insulin resistance and diabetes in animal models (Heron-Milhavet et al. 2004
). The second is GPAT which catalyzes the initial and committed step in the biosynthesis of triglycerides and phospholipids (Ericsson et al. 1997
). Expression of these genes indicates a shift of energy consumption in the skeletal muscle from glycolysis towards β-oxidation.
SREBP-1c is a transcription factor of the basic helix-loop-helix/leucine zipper family. It controls expression of genes which are related to adipogenesis and fatty acid metabolism as well as cholesterol metabolism (Nguyen et al. 2000
). SREBP-1c is transcriptionally induced by insulin and its enhanced expression mediates the transcriptional effects of insulin in skeletal muscle (Guillet-Deniau et al. 2002
). It has been observed that over-expression of SREBP-1c mimics the effects of insulin such as stimulation of glycolytic and lipogenic enzymes. It exerts a pivotal role in long-term muscle insulin sensitivity. The prolonged returning of FAT expression and the second trough of GPAT at 48 h raises the possibility that at least one intermediate biosignal is involved. Both genes are transcriptionally induced by SREBP-1c (Ericsson et al. 1997
; Ntambi, 1999
; Nguyen et al. 2000
; Almon et al. 2005b
). The addition of SREBP-1c as an intermediate driving force was able to capture the time profiles of FAT and GPAT following both acute and infusion dosing regimens.
ET-1 is a potent vasoconstrictor. It has been observed that many organs such as heart, lung and skeletal muscles synthesize ET-1, which may act locally to regulate blood flow (Sakurai et al. 1991
; Guo et al. 1998
). The ET-1 detected in this study might be muscle in origin, endothelial cell in origin, or both. Elevated muscle ET-1 may reduce blood flow to the musculature and thus reduce glucose disposal in skeletal muscle (Santure et al. 2002
). Reciprocal regulation of ET-1 and nitric oxide, a vasodilator have been demonstrated and summarized by Rossi et al. (Rossi et al. 2001
). The production of nitric oxide is activated by ET-1 while the elevated nitric oxide level is able to inhibit ET-1 synthesis (Namiki et al. 1992
; Takada et al. 1996
). This forms a negative feedback loop which may offer an oscillatory feature to ET-1 expression pattern once it is induced or inhibited by drug treatment. The fitted curve in our acute study exhibited a fluctuation around the baseline. Other studies demonstrate that ET-1 also exhibits similar time profiles following interleukin-1β and lipopolysaccharide treatments in human endothelial cells with an initial up-regulation and a later down-regulation at 24 h (Zhao et al. 2001
; Zhao et al. 2003
). Those results suggest that this oscillatory feature of ET-1 is not specific to CS treatment. ET-1 expression following drug infusion did not show significant change, possibly due to reduced probe set sensitivity in the chip used for the chronic study.
In the present report, most modeled time profiles feature long time delays between the intermediate biosignal and the regulated changes. One or more transit compartments were incorporated in the models to account for the delay. Following drug treatment, the resultant changes in message have to translate into protein changes before downstream regulatory steps can continue. Other steps such as translocation in or out of the nucleus or induction of other mediators might also be involved.
Time series studies of gene expression following CS treatment shows that many genes such as UCP3, PDK4, and ET-1 exhibit both induction and suppression depending on time. Evaluation of drug effects at a single time point may not reveal the overall effects of the drug. In such situations, time series design and mathematical modeling provide a useful approach to explore the diverse effects at different times and the actual or potential underlying mechanisms.
It is interesting to see that genes with common regulators such as UCP3 and PDK4 display comparable expression versus time patterns. This may suggest shared or similar signaling pathways associated with the regulation of these genes. However, genes regulated by common mediators may also exhibit distinct patterns, possibly due to diverse signaling pathways or differences in parameter values.
The sampling time points in the acute studies were selected based on prior knowledge of the CS-induced TAT expression profile following the same acute dosing regimen. The expression of TAT reaches a peak at 6 h following acute dosing and returns to baseline at 18 h (Sun et al. 1998
). Such a sampling strategy well characterized the earlier expression changes of most genes which are rapidly induced or repressed by steroids. However, the sparse sampling after 18 h leads to lack of information to characterize slower, more complex signaling events, especially for those genes that are controlled by multiple regulatory cascades. Two genes involved in fatty acid metabolism, glycerol-3-phosphate dehydrogenase (GPDH) and stearyl-CoA desaturase 2 (SCD2) also exhibited a second decline at 48 h following MPL (Almon et al. 2005b
). Both GPDH and SCD2 are activated by the master transcription factor SREBP-1c (Nguyen et al. 2000
). However, lack of information after 30 h excluded the possibility of using complex models to capture their profiles. The initial sampling time point in the chronic study (6 hours) was selected to allow the drug to reach equilibrium in circulation. This may have impaired our ability to capture initial dynamics. However, the responses to two dosing regimens together clearly augment each other.
A scaling factor was used in modeling of all genes when both acute and chronic data were simultaneously fitted. Two different chips U34A and 230A were utilized for acute and chronic studies. The probe sets representing the same gene on these two chips differ in their nucleotide length and sequences. This may lead to different hybridization efficiencies and therefore different relative sensitivities to gene expression changes. In the modeling, a power scaling factor was incorporated to account for these differences.
This study was limited by the availability of genes on the chips. The assay sensitivity and our data analysis methods also restricted the number of genes that were identified by this approach, especially for low abundance message or suppressed genes. Some important regulators and biomarkers may not be included. In addition to its transcriptional regulatory effects, CS also exerts effects by affecting protein synthesis or interaction with other proteins, which cannot be detected in the current study.
In the present study we used results from two gene array time series to develop dynamic models for the regulation of several genes associated with insulin resistance in skeletal muscle. In order to begin to understand complex polygenic phenomena such as insulin resistance, it necessary to develop quantitative, experimentally testable hypotheses such as the dynamic models presented in this report.