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.
liver; muscle; glucocorticoids; corticosteroids; gene expression; gene regulation; promoter analysis
Glucocorticoids are important regulators of metabolism and immune function. Synthetic glucocorticoids are extensively used for immunosuppression/anti-inflammatory therapy. Since the glucocorticoid receptor (GR) is central to most hormone effects; its in vivo regulation will influence hormone/drug action. An alternative splice variant, GRβ, is present in humans and may function as a dominant negative regulator of GR transcriptional activity. Recently, a similar splice variant was reported in mouse, although the mechanism of alternative splicing differs from that in humans. We present evidence that a splice variant of GR with an alternative C-terminus also occurs in the rat by a mechanism of intron inclusion. A highly quantitative qRT-PCR assay for the simultaneous measurement of both splice variants in a single sample was developed in order to accurately measure their regulation. We used this assay to assess the tissue specific expression of both mRNAs, and demonstrate that GRα is predominant in all tissues. In addition, the regulation of both GRα and GRβ mRNA by various physiological factors in rat liver was assessed. GRα showed a robust circadian rhythm, which was entrained with the circadian oscillation of the endogenous hormone. Time series experiments showed that both corticosteroids and LPS but not insulin dosing resulted in the transient down-regulation of GRα mRNA. LPS treatment also resulted in down-regulation of GRβ expression. A modest up-regulation in GRβ expression was observed only in animals having chronically elevated plasma insulin concentrations. However the expression of GRβ was significantly lower than that of GRα in all cases.
glucocorticoids; glucocorticoid receptor; GRβ; qRTPCR
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.
The pharmacokinetics (PK) of salsalate (SS) and salicylic acid (SA) was assessed in normal Wistar and diabetic Goto-Kakizaki rats. Three PK studies were conducted: 1) PK of SA in normal rats after intravenous dosing of SA at 20, 40, 80 mg/kg. 2) PK of SS and SA in normal rats after oral dosing of SS at 28, 56, 112 mg/kg. 3) PK during 4 months feeding of SS-containing diet in both normal and diabetic rats. The disposition of SS and SA were simultaneously evaluated using a pharmacokinetic model comprised of several transit absorption steps and linear and nonlinear dual elimination pathways for SA. The results indicated that the nonlinear elimination pathway of SA only accounted for a small fraction of the total clearance (< 12%) at therapeutic concentrations. A flat profile of SA was observed after oral dosing SS, particularly at a high dose. The possible reasons for this flat profile were posed. During the SS-diet feeding, diabetic rats achieved lower blood concentrations of SA than normal rats with a higher apparent clearance (CL/F) possibly due to incomplete (47%) bioavailability. Such CL/F decreased with age in both diabetic and normal rats. The effect of diabetes on SA pharmacokinetics may necessitate increased dosing in future usage of SS in diabetes.
salsalate; salicylic acid; pharmacokinetics; diabetes
Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5′ GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.
data mining; gene arrays; glucocorticoids; pharmacogenomics; evolutionary conservation
The dynamics of aging and type 2 diabetes (T2D) disease progression were investigated in normal [Wistar-Kyoto (WKY)] and diabetic [Goto-Kakizaki (GK)] rats and a mechanistic disease progression model was developed for glucose, insulin, and glycosylated hemoglobin (HbA1c) changes over time. The study included 30 WKY and 30 GK rats. Plasma glucose and insulin, blood glucose and HbA1c concentrations and hematological measurements were taken at ages 4, 8, 12, 16 and 20 weeks. A mathematical model described the development of insulin resistance (IR) and β-cell function with age/growth and diabetes progression. The model utilized transit compartments and an indirect response model to quantitate biomarker changes over time. Glucose, insulin and HbA1c concentrations in WKY rats increased to a steady-state at 8 weeks due to developmental changes. Glucose concentrations at 4 weeks in GK rats were almost twice those of controls, and increased to a steady-state after 8 weeks. Insulin concentrations at 4 weeks in GK rats were similar to controls, and then hyperinsulinemia occurred until 12–16 weeks of age indicating IR. Subsequently, insulin concentrations in GK rats declined to slightly below WKY controls due to β-cell failure. HbA1c showed a delayed increase relative to glucose. Modeling of HbA1c was complicated by age-related changes in hematology in rats. The diabetes model quantitatively described the glucose/insulin inter-regulation and HbA1c production and reflected the underlying pathogenic factors of T2D—IR and β-cell dysfunction. The model could be extended to incorporate other biomarkers and effects of various anti-diabetic drugs.
Type 2 diabetes; Disease progression modeling; Insulin resistance; β-cell function
This comparison employs mathematical disease progression models to identify a rat model of arthritis with the least inter-animal variability and features lending to better study designs.
Arthritis was induced with either collagen (CIA) or mycobacterium (AIA) in either Lewis or Dark Agouti (DA) rats. Disease progression was monitored by paw edema and body weight. Models with production, loss, and feedback components were constructed and population analysis using NONMEM software was employed to identify inter-animal variability in the various disease progression parameters.
Onset time was the only parameter different within all four groups (DA–AIA 11.5 days, DA–CIA 16.5 days, Lewis–AIA 11.9 days, Lewis–CIA 13.9 days). The loss-of-edema rate constant was 20% slower in DA (0.362 h−1) than Lewis (0.466 h−1) rats. Most models exhibited peak paw edema 20 days post-induction. Edema in CIA returned to 150% of the initial value after the disease peaked. DA rats displayed more severe overall responses.
No statistical differences between groups were observed for inter-animal variation in disease onset, progression and severity parameters. Onset time varies and should be noted in the design of future studies. DA rats may offer a more dynamic range of edema response than Lewis rats.
arthritis; disease; model; progression; rat
Adrenal suppression and lymphocytopenia are commonly monitored pharmacological responses during systemic exposure to exogenously administered corticosteroids. The pharmacodynamics of plasma corticosterone (CS) and blood lymphocytes were investigated in 60 normal rats which received either 50 mg/kg methylprednisolone (MPL) or vehicle intramuscularly. Blood samples were collected between 0.5 and 96 h following treatment. Plasma CS displayed a transient suppression with re-establishment of a normal circadian rhythm 24 h following drug treatment. An indirect response model with suppression of production well captured plasma CS profiles. An early stress-induced rise in CS was also factored into the model. Blood lymphocyte numbers exhibited a sharp decline and then returned to a new circadian rhythm which was half of the original baseline level. An integrated pharmacodynamic (PD) model with inhibition of lymphocyte trafficking from tissue to blood by both MPL and CS and induction of cell apoptosis by MPL reasonably captured this lymphocytopenia. Rats and humans differ in lymphocyte responses with humans showing full recovery of baselines. Modeling provides a valuable tool in quantitative assessment of dual, complex drug responses.
pharmacokinetics; pharmacodynamics; hormones; mathematical model; pharmacokinetic/pharmacodynamic models; corticosteroid; lymphocyte; cell trafficking; indirect response model; circadian rhythm
Maintenance of energy metabolism and glucose homeostasis is achieved by the regulatory effects of many hormones and their interactions. Glucocorticoids produced from adrenal cortex and adiponectin produced by adipose tissue play important roles in the production, distribution, storage, and utilization of energy substrates. Glucocorticoids are involved in the activation of a number of catabolic processes by affecting the expression of a plethora of genes, while adiponectin acts primarily as an insulin sensitizer. Both are regulated by a number of physiological and pharmacological factors. Although the effects of glucocorticoids on adiponectin expression have been extensively studied in different in vitro, animal and clinical study settings, no consensus has been reached. This report reviews the primary literature concerning the effects of glucocorticoids on adiponectin expression and identifies potential reasons for the contradictory results between different studies. In addition, methods to gain better insights pertaining to the regulation of adiponectin expression are discussed.
Pyruvate dehydrogenase kinase 4 (PDK4) is a lipid status responsive gene involved in muscle fuel selection. Evidence is mounting in support of the therapeutic potential of PDK4 inhibitors to treat diabetes. Factors that regulate PDK4 mRNA expression include plasma corticosterone, insulin and free fatty acids. Our objective was to determine the impact of those plasma factors on PDK4 mRNA and to develop and validate a population mathematical model to differentiate aging, diet and disease effects on muscle PDK4 expression. The Goto-Kakizaki (GK) rat, a polygenic non-obese model of type 2 diabetes, was used as the diabetic animal model. We examined muscle PDK4 mRNA expression by real-time QRTPCR. Groups of GK rats along with controls fed with either a normal or high fat diet were sacrificed at 4, 8, 12, 16, and 20 weeks of age. Plasma corticosterone, insulin and free fatty acid were measured. The proposed mechanism-based model successfully described the age, disease and diet effects and the relative contribution of these plasma regulators on PDK4 mRNA expression. Muscle growth reduced the PDK4 mRNA production rate by 14% per gram increase. High fat diet increased the initial production rate constant in GK rats by 2.19-fold. The model indicated that corticosterone had a moderate effect and PDK4 was more sensitive to free fatty acid than insulin fluxes, which was in good agreement with the literature data.
population model; type 2 diabetes; disease progression; PDK4; Goto-Kakizaki rats
A population pharmacokinetic–pharmacodynamic–disease progression (PK/PD/DIS) model was developed to characterize the effects of anakinra in collagen-induced arthritic (CIA) rats and explore the role of interleukin-1β (IL-1β) in rheumatoid arthritis. The CIA rats received either vehicle, or anakinra at 100 mg/kg for about 33 h, 100 mg/kg for about 188 h, or 10 mg/kg for about 188 h by subcutaneous infusion. Plasma concentrations of anakinra were assayed by enzyme-linked immunosorbent assay. Swelling of rat hind paws was measured. Population PK/PD/DIS parameters were computed for the various groups using non-linear mixed-effects modeling software (NONMEM® Version VI). The final model was assessed using visual predictive checks and nonparameter stratified bootstrapping. A two-compartment PK model with two sequential absorption processes and linear elimination was used to capture PK profiles of anakinra. A transduction-based feedback model incorporating logistic growth rate captured disease progression and indirect response model I captured drug effects. The PK and paw swelling versus time profiles in CIA rats were fitted well. Anakinra has modest effects (Imax = 0.28) on paw edema in CIA rats. The profiles are well-described by our PK/PD/DIS model which provides a basis for future mechanism-based assessment of anakinra dynamics in rheumatoid arthritis.
Anakinra; Pharmacokinetics; Pharmacodynamics; Rheumatoid arthritis; Population model
Effects of high fat diet (HFD) on obesity and, subsequently, on diabetes are highly variable and modulated by genetics in both humans and rodents. In this report, we characterized the response of Goto-Kakizaki (GK) rats, a spontaneous polygenic model for lean diabetes and healthy Wistar-Kyoto (WKY) controls, to high fat feeding from weaning to 20 weeks of age. Animals fed either normal diet or HFD were sacrificed at 4, 8, 12, 16 and 20 weeks of age and a wide array of physiological measurements were made along with gene expression profiling using Affymetrix gene array chips. Mining of the microarray data identified differentially regulated genes (involved in inflammation, metabolism, transcription regulation, and signaling) in diabetic animals, as well as the response of both strains to HFD. Functional annotation suggested that HFD increased inflammatory differences between the two strains. Chronic inflammation driven by heightened innate immune response was identified to be present in GK animals regardless of diet. In addition, compensatory mechanisms by which WKY animals on HFD resisted the development of diabetes were identified, thus illustrating the complexity of diabetes disease progression.
diabetes; high fat diet; gene expression; microarray
The Goto-Kakizaki (GK) rat, a polygenic non-obese model of type 2 diabetes, is a useful surrogate for study of diabetes-related changes independent of obesity. GK rats and appropriate controls were killed at 4, 8, 12, 16 and 20 weeks post-weaning and differential muscle gene expression along with body and muscle weights, plasma hormones and lipids, and blood cell measurements were carried out. Gene expression analysis identified 204 genes showing 2-fold or greater differences between GK and controls in at least 3 ages. Array results suggested increased oxidative capacity in GK muscles, as well as differential gene expression related to insulin resistance, which was also indicated by HOMA-IR measurements. In addition, potential new biomarkers in muscle gene expression were identified that could be either a cause or consequence of T2DM. Furthermore, we demonstrate here the presence of chronic inflammation evident both systemically and in the musculature, despite the absence of obesity.
type 2 diabetes; skeletal muscle; inflammation; microarrays; gene expression
The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements.
An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses.
This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance.
The variable nature of the baseline is an important aspect to consider when calculating the AUC.
AUC; baseline; bioinformatics; microarrays; pharmacogenomics
Circadian rhythms (24 h cycles) are observed in virtually all aspects of mammalian function from expression of genes to complex physiological processes. The master clock is present in the suprachiasmatic nucleus (SCN) in the anterior part of the hypothalamus and controls peripheral clocks present in other parts of the body. Components of this core clock mechanism regulate the circadian rhythms in genome-wide mRNA expression, which in turn regulate various biological processes. Disruption of circadian rhythms can be either the cause or the effect of various disorders including metabolic syndrome, inflammatory diseases and cancer. Furthermore, circadian rhythms in gene expression regulate both the action and disposition of various drugs and affect therapeutic efficacy and toxicity based on dosing time. Understanding the regulation of circadian rhythms in gene expression plays an important role in both optimizing the dosing time for existing drugs and in development of new therapeutics targeting the molecular clock.
molecular clocks; metabolic disease; inflammation; cancer; drug targets; pharmacokinetics
Microarray experiments generate massive amounts of data, necessitating innovative algorithms to distinguish biologically relevant information from noise. Because the variability of gene expression data is an important factor in determining which genes are differentially expressed, analysis techniques that take into account repeated measurements are critically important. Additionally, the selection of informative genes is typically done by searching for the individual genes that vary the most across conditions. Yet because genes tend to act in groups rather than individually, it may be possible to glean more information from the data by searching specifically for concerted behavior in a set of genes. Applying a symbolic transformation to the gene expression data allows the detection overrepresented patterns in the data, in contrast to looking only for genes that exhibit maximal differential expression. These challenges are approached by introducing an algorithm based on a new symbolic representation that searches for concerted gene expression patterns; furthermore, the symbolic representation takes into account the variance in multiple replicates and can be applied to long time series data. The proposed algorithm's ability to discover biologically relevant signals in gene expression data is exhibited by applying it to three datasets that measure gene expression in the rat liver.
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.
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.
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.
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.
A mechanism-based model was developed to describe the time course of arthritis progression in the rat. Arthritis was induced in male Lewis rats with type II porcine collagen into the base of the tail. Disease progression was monitored by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST) concentrations, and TNF-α, IL-1β, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Bone mineral density was determined by PIXImus II dual energy x-ray densitometry. Plasma CST was assayed by HPLC. Cytokine and GR mRNA were determined by quantitative real-time polymerase chain reaction. Disease progression models were constructed from transduction and indirect response models and applied using S-ADAPT software. A delay in the onset of increased paw TNF-α and IL-6 mRNA concentrations was successfully characterized by simple transduction. This rise was closely followed by an up-regulation of GR mRNA and CST concentrations. Paw swelling and body weight responses peaked approximately 21 days post induction while bone mineral density changes were greatest at 23 days post induction. After peak response the time course in IL-1β, IL-6 mRNA, and paw edema slowly declined towards a disease steady-state. Model parameters indicate TNF-α and IL-1β mRNA most significantly induce paw edema while IL-6 mRNA exerted the most influence on BMD. The model for bone mineral density captures rates of turnover of cancellous and cortical bone and the fraction of each in the different regions analyzed. This small systems model integrates and quantitates multiple factors contributing to arthritis in rats.
A mechanism-based model for pharmacodynamic effects of dexamethasone (DEX) was incorporated into our model for arthritis disease progression in the rat to aid in identification of the primary factors responsible for edema and bone loss. Collagen-induced arthritis (CIA) was produced in male Lewis rats following injection of type II porcine collagen. DEX was given subcutaneously in single doses of 0.225 or 2.25 mg/kg or 7-day multiple doses of 0.045 or 0.225 mg/kg at 21 days post disease induction. Effects on disease progression were measured by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST), and TNF-α, IL-1β, IL-6, and GR mRNA expression in paw tissue. Lumbar and femur BMD was determined by PIXImus-II dual energy x-ray absorptiometry. Plasma CST was assayed by HPLC. Cytokine and GR mRNA were assayed by quantitative real-time PCR. Indirect response models, drug-interaction models, transduction processes, and the 5th-generation model of corticosteroid dynamics were integrated and applied using S-ADAPT software to describe how dexamethasone binding to GR can regulate diverse processes. Cytokine mRNA, GR mRNA, plasma CST, and paw edema were suppressed following DEX administration. TNF-α mRNA expression and BMD appeared to increase immediately after dosing but were ultimately reduced. Model parameters indicated that IL-6 and IL-1β were most sensitive to inhibition by DEX. TNF-α appeared to primarily influence edema while IL-6 contributed the most to bone loss. Lower doses of corticosteroids may be sufficient to suppress the cytokines most relevant to bone erosion.
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.
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.
corticosteroids; data mining; expression profiling; gene chips; methylprednisolone; microarrays; modeling; pharmacodynamics; skeletal muscle; time series
A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparations from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.
Data mining; gene arrays; glucocorticoids; mathematical modeling; pharmacogenomics