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1.  Variability in Zucker diabetic fatty rats: differences in disease progression in hyperglycemic and normoglycemic animals 
Both obesity and chronic inflammation are often associated with insulin resistance and type 2 diabetes. The Zucker diabetic fatty (ZDF) rat (fa/fa) is an obese animal model frequently used in type 2 diabetes research. The current study determines whether chronic administration (from 5 weeks of age through 24 weeks of age) of salsalate, a salicylate with anti-inflammatory properties, would be effective in mitigating diabetes disease progression in ZDF rats. Although a trend existed for lower blood glucose in the salsalate-treated group, significant differences were obscured by high animal-level variability. However, even in the non-drug-treated group, not all ZDF rats became diabetic as expected. Therefore, animals were parsed into two groups, regardless of drug treatment: normoglycemic ZDF rats, which maintained blood glucose profiles identical to nondiabetic Zucker lean rats (ZLRs), and hyperglycemic ZDF rats, which exhibited progressive elevation in blood glucose. To ascertain the differences between ZDF rats that became hyperglycemic and those that did not, relevant physiological indices and expression levels of adiponectin, tumor necrosis factor-α, interleukin-6, and glucocorticoid-induced leucine zipper messenger RNAs in adipose tissue were measured at sacrifice. Plasma C-reactive protein concentrations and expression levels of cytokine and glucocorticoid-induced leucine zipper messenger RNAs suggested more prevalent chronic inflammation in hyperglycemic animals. Early elevation of the insulin-sensitizing adipokine, adiponectin, was present in both ZDF groups, with the rate of its age-related decline faster in hyperglycemic animals. The most marked difference between the two groups of ZDF animals was in insulin output. Although the two ZDF populations had very similar elevated plasma insulin concentrations for the first 10 weeks, after that time, plasma insulin decreased markedly in the animals that became hyperglycemic, whereas it remained high in the normoglycemic ZDF rats. Thus, hyperglycemic ZDF animals exhibit both insulin resistance and progressive beta cell failure, whereas normoglycemic ZDF rats exhibit a lesser degree of insulin resistance that does not progress to beta cell failure. In these respects, the normoglycemic ZDF rats appear to revert back to a phenotype that strongly resembles that of nondiabetic Zucker fatty rats from which they were derived.
PMCID: PMC4234283  PMID: 25419150
type 2 diabetes; ZDF rats; animal models
2.  Diabetes disease progression in Goto-Kakizaki rats: effects of salsalate treatment 
This study investigates the antidiabetic effects of salsalate on disease progression of diabetes in non-obese diabetic Goto-Kakizaki (GK) rats, an experimental model of type 2 diabetes. Salsalate was formulated in rat chow (1,000 ppm) and used to feed rats from 5 to 21 weeks of age. At 5 weeks of age, GK and Wistar (WIS) control rats were subdivided into four groups, each composed of six rats: GK rats with standard diet (GK-C); GK rats with salsalate-containing diet (GK-S); WIS rats with standard diet (WIS-C); and WIS rats with salsalate-containing diet (WIS-S). The GK-C rats (167.2±11.6 mg/dL) showed higher blood glucose concentrations than WIS-C rats (133.7±4.9 mg/dL, P<0.001) at the beginning of the experiment, and had substantially elevated blood glucose from an age of 15 weeks until sacrifice at 21 weeks (341.0±133.6 mg/dL). The GK-S rats showed an almost flat profile of blood glucose from 4 weeks (165.1±11.0 mg/dL) until sacrifice at 21 weeks of age (203.7±22.2 mg/dL). While this difference in blood glucose between 4 and 21 weeks in GK-S animals was significant, blood glucose at 21 weeks was significantly lower in GK-S compared to GK-C animals. At sacrifice, salsalate decreased plasma insulin (GK-S =1.0±0.3; GK-C =2.0±0.3 ng/mL, P<0.001) and increased plasma adiponectin concentrations (GK-S =15.9±0.7; GK-C =9.7±2.0 μg/mL, P<0.001). Salsalate also lowered total cholesterol in GK-S rats (96.1±8.5 mg/dL) compared with GK-C rats (128.0±11.4 mg/dL, P<0.001). Inflammation-related genes (Ifit1 and Iigp1) exhibited much higher mRNA expression in GK-C rats than WIS-C rats in liver, adipose, and muscle tissues, while salsalate decreased the Ifit1 and Iigp1 mRNA only in adipose tissue. These results suggest that salsalate acts to both increase adiponectin and decrease adipose tissue-based inflammation while preventing type 2 diabetes disease progression in GK rats.
PMCID: PMC4128793  PMID: 25120374
type 2 diabetes; salicylates; inflammation; adiponectin
3.  Tissue-Specific Gene Expression and Regulation in Liver and Muscle Following Chronic Corticosteroid Administration 
Although corticosteroids (CSs) affect gene expression in multiple tissues, the array of genes that are regulated by these catabolic steroids is diverse, highly tissue specific, and depends on their functions in the tissue. Liver has many important functions in performing and regulating diverse metabolic processes. Muscle, in addition to its mechanical role, is critical in maintaining systemic energy homeostasis and accounts for about 80% of insulin-directed glucose disposal. Consequently, a better understanding of CS pharmacogenomic effects in these tissues would provide valuable information regarding the tissue-specificity of transcriptional dynamics, and would provide insights into the underlying molecular mechanisms of action for both beneficial and detrimental effects.
We performed an integrated analysis of transcriptional data from liver and muscle in response to methylprednisolone (MPL) infusion, which included clustering and functional annotation of clustered gene groups, promoter extraction and putative transcription factor (TF) identification, and finally, regulatory closeness (RC) identification.
This analysis allowed the identification of critical transcriptional responses and CS-responsive functions in liver and muscle during chronic MPL administration, the prediction of putative transcriptional regulators relevant to transcriptional responses of CS-affected genes which are also potential secondary bio-signals altering expression levels of target-genes, and the exploration of the tissue-specificity and biological significance of gene expression patterns, CS-responsive functions, and transcriptional regulation.
The analysis provided an integrated description of the genomic and functional effects of chronic MPL infusion in liver and muscle.
PMCID: PMC3956809  PMID: 24653645
liver; muscle; glucocorticoids; corticosteroids; gene expression; gene regulation; promoter analysis
4.  Circadian signatures in rat liver: from gene expression to pathways 
BMC Bioinformatics  2010;11:540.
Circadian rhythms are 24 hour oscillations in many behavioural, physiological, cellular and molecular processes that are controlled by an endogenous clock which is entrained to environmental factors including light, food and stress. Transcriptional analyses of circadian patterns demonstrate that genes showing circadian rhythms are part of a wide variety of biological pathways.
Pathway activity method can identify the significant pattern of the gene expression levels within a pathway. In this method, the overall gene expression levels are translated to a reduced form, pathway activity levels, via singular value decomposition (SVD). A given pathway represented by pathway activity levels can then be as analyzed using the same approaches used for analyzing gene expression levels. We propose to use pathway activity method across time to identify underlying circadian pattern of pathways.
We used synthetic data to demonstrate that pathway activity analysis can evaluate the underlying circadian pattern within a pathway even when circadian patterns cannot be captured by the individual gene expression levels. In addition, we illustrated that pathway activity formulation should be coupled with a significance analysis to distinguish biologically significant information from random deviations. Next, we performed pathway activity level analysis on a rich time series of transcriptional profiling in rat liver. The over-represented five specific patterns of pathway activity levels, which cannot be explained by random event, exhibited circadian rhythms. The identification of the circadian signatures at the pathway level identified 78 pathways related to energy metabolism, amino acid metabolism, lipid metabolism and DNA replication and protein synthesis, which are biologically relevant in rat liver. Further, we observed tight coordination between cholesterol biosynthesis and bile acid biosynthesis as well as between folate biosynthesis, one carbon pool by folate and purine-pyrimidine metabolism. These coupled pathways are parts of a sequential reaction series where the product of one pathway is the substrate of another pathway.
Rather than assessing the importance of a single gene beforehand and map these genes onto pathways, we instead examined the orchestrated change within a pathway. Pathway activity level analysis could reveal the underlying circadian dynamics in the microarray data with an unsupervised approach and biologically relevant results were obtained.
PMCID: PMC2990769  PMID: 21040584
5.  Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures 
BMC Bioinformatics  2010;11:515.
Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich in vivo datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.
The study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.
We have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich in vivo datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.
PMCID: PMC2973961  PMID: 20946642
6.  Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat liver 
BMC Bioinformatics  2010;11:279.
Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.
We propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze in vivo rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.
We have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.
PMCID: PMC2889936  PMID: 20500897
7.  The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series 
Pharmacogenomics  2004;5(5):525-552.
High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest.
PMCID: PMC2607486  PMID: 15212590
corticosteroids; data mining; expression profiling; gene chips; methylprednisolone; microarrays; modeling; pharmacodynamics; skeletal muscle; time series
8.  Temporal profiling of the transcriptional basis for the development of corticosteroid-induced insulin resistance in rat muscle 
The Journal of endocrinology  2005;184(1):219-232.
Elevated systemic levels of glucocorticoids are causally related to peripheral insulin resistance. The pharmacological use of synthetic glucocorticoids (corticosteroids) often results in insulin resistance/type II diabetes. Skeletal muscle is responsible for close to 80% of the insulin-induced systemic disposal of glucose and is a major target for glucocorticoid-induced insulin resistance. We used Affymetrix gene chips to profile the dynamic changes in mRNA expression in rat skeletal muscle in response to a single bolus dose of the synthetic glucocorticoid methyl-prednisolone. Temporal expression profiles (analyzed on individual chips) were obtained from tissues of 48 drug-treated animals encompassing 16 time points over 72 h following drug administration along with four vehicle-treated controls. Data mining identified 653 regulated probe sets out of 8799 present on the chip. Of these 653 probe sets we identified 29, which represented 22 gene transcripts, that were associated with the development of insulin resistance. These 29 probe sets were regulated in three fundamental temporal patterns. 16 probe sets coding for 12 different genes had a profile of enhanced expression. 10 probe sets coding for eight different genes showed decreased expression and three probe sets coding for two genes showed biphasic temporal signatures. These transcripts were grouped into four general functional categories: signal transduction, transcription regulation, carbohydrate/fat metabolism, and regulation of blood flow to the muscle. The results demonstrate the polygenic nature of transcriptional changes associated with insulin resistance that can provide a temporal scaffolding for translational and post-translational data as they become available.
PMCID: PMC2574435  PMID: 15642798

Results 1-8 (8)