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1.  Meta-Modeling of Methylprednisolone Effects on Glucose Regulation in Rats 
PLoS ONE  2013;8(12):e81679.
A retrospective meta-modeling analysis was performed to integrate previously reported data of glucocorticoid (GC) effects on glucose regulation following a single intramuscular dose (50 mg/kg), single intravenous doses (10, 50 mg/kg), and intravenous infusions (0.1, 0.2, 0.3 and 0.4 mg/kg/h) of methylprednisolone (MPL) in normal and adrenalectomized (ADX) male Wistar rats. A mechanistic pharmacodynamic (PD) model was developed based on the receptor/gene/protein-mediated GC effects on glucose regulation. Three major target organs (liver, white adipose tissue and skeletal muscle) together with some selected intermediate controlling factors were designated as important regulators involved in the pathogenesis of GC-induced glucose dysregulation. Assessed were dynamic changes of food intake and systemic factors (plasma glucose, insulin, free fatty acids (FFA) and leptin) and tissue-specific biomarkers (cAMP, phosphoenolpyruvate carboxykinase (PEPCK) mRNA and enzyme activity, leptin mRNA, interleukin 6 receptor type 1 (IL6R1) mRNA and Insulin receptor substrate-1 (IRS-1) mRNA) after acute and chronic dosing with MPL along with the GC receptor (GR) dynamics in each target organ. Upon binding to GR in liver, MPL dosing caused increased glucose production by stimulating hepatic cAMP and PEPCK activity. In adipose tissue, the rise in leptin mRNA and plasma leptin caused reduction of food intake, the exogenous source of glucose input. Down-regulation of IRS-1 mRNA expression in skeletal muscle inhibited the stimulatory effect of insulin on glucose utilization further contributing to hyperglycemia. The nuclear drug-receptor complex served as the driving force for stimulation or inhibition of downstream target gene expression within different tissues. Incorporating information such as receptor dynamics, as well as the gene and protein induction, allowed us to describe the receptor-mediated effects of MPL on glucose regulation in each important tissue. This advanced mechanistic model provides unique insights into the contributions of major tissues and quantitative hypotheses for the multi-factor control of a complex metabolic system.
doi:10.1371/journal.pone.0081679
PMCID: PMC3847111  PMID: 24312573
2.  Adipose Tissue Deficiency and Chronic Inflammation in Diabetic Goto-Kakizaki Rats 
PLoS ONE  2011;6(2):e17386.
Type 2 diabetes (T2DM) is a heterogeneous group of diseases that is progressive and involves multiple tissues. Goto-Kakizaki (GK) rats are a polygenic model with elevated blood glucose, peripheral insulin resistance, a non-obese phenotype, and exhibit many degenerative changes observed in human T2DM. As part of a systems analysis of disease progression in this animal model, this study characterized the contribution of adipose tissue to pathophysiology of the disease. We sacrificed subgroups of GK rats and appropriate controls at 4, 8, 12, 16 and 20 weeks of age and carried out a gene array analysis of white adipose tissue. We expanded our physiological analysis of the animals that accompanied our initial gene array study on the livers from these animals. The expanded analysis included adipose tissue weights, HbA1c, additional hormonal profiles, lipid profiles, differential blood cell counts, and food consumption. HbA1c progressively increased in the GK animals. Altered corticosterone, leptin, and adiponectin profiles were also documented in GK animals. Gene array analysis identified 412 genes that were differentially expressed in adipose tissue of GKs relative to controls. The GK animals exhibited an age-specific failure to accumulate body fat despite their relatively higher calorie consumption which was well supported by the altered expression of genes involved in adipogenesis and lipogenesis in the white adipose tissue of these animals, including Fasn, Acly, Kklf9, and Stat3. Systemic inflammation was reflected by chronically elevated white blood cell counts. Furthermore, chronic inflammation in adipose tissue was evident from the differential expression of genes involved in inflammatory responses and activation of natural immunity, including two interferon regulated genes, Ifit and Iipg, as well as MHC class II genes. This study demonstrates an age specific failure to accumulate adipose tissue in the GK rat and the presence of chronic inflammation in adipose tissue from these animals.
doi:10.1371/journal.pone.0017386
PMCID: PMC3045458  PMID: 21364767
3.  Identification of Global Transcriptional Dynamics 
PLoS ONE  2009;4(7):e5992.
Background
One of the challenges in exploiting high throughput measurement techniques such as microarrays is the conversion of the vast amounts of data obtained into relevant knowledge. Of particular importance is the identification of the intrinsic response of a transcriptional experiment and the characterization of the underlying dynamics.
Methodology and Findings
The proposed algorithm seeks to provide the researcher a summary as to various aspects relating to the dynamic progression of a biological system, rather than that of individual genes. The approach is based on the identification of smaller number of expression motifs that define the transcriptional state of the system which quantifies the deviation of the cellular response from a control state in the presence of an external perturbation. The approach is demonstrated with a number of data sets including a synthetic base case and four animal studies. The synthetic dataset will be used to establish the response of the algorithm on a “null” dataset, whereas the four different experimental datasets represent a spectrum of possible time course experiments in terms of the degree of perturbation associated with the experiment as well as representing a wide range of temporal sampling strategies. This wide range of experimental datasets will thus allow us to explore the performance of the proposed algorithm and determine its ability identify relevant information.
Conclusions and Significance
In this work, we present a computational approach which operates on high throughput temporal gene expression data to assess the information content of the experiment, identify dynamic markers of important processes associated with the experimental perturbation, and summarize in a concise manner the evolution of the system over time with respect to the experimental perturbation.
doi:10.1371/journal.pone.0005992
PMCID: PMC2705787  PMID: 19593450

Results 1-3 (3)