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
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.
Increased expression of inducible nitric oxide synthase (iNOS) resulting in nitric oxide elevation represents an important component of inflammatory responses. We assess the effects of methylprednisolone (MPL) on these processes during endotoxin-induced acute inflammation and provide a mechanism-based model to quantitatively describe them.
Male Lewis rats were dosed with lipopolysaccharide (50 μg/kg LPS) alone or with methylprednisolone (10 and 50 mg/kg) and sacrificed at different time points. Plasma MPL, lung iNOS mRNA expression, plasma nitric oxide (NO) and other physiological factors were measured. Sodium nitrate (750 μmole/kg) was given to a separate cohort of rats to assess NO disposition kinetics. PK-PD modeling was performed with ADAPT 5.
Disposition kinetics of plasma MPL and NO showed bi-exponential decline and were described by two-compartment models. LPS increased expression of iNOS mRNA in lung and increased plasma NO, while MPL dosing palliated this increase in a dose-dependent manner. These effects were well captured using tandem indirect response and precursor-pool models.
The model provides a quantitative assessment of the suppression of NO production by MPL and shows that the major effects are at the transcriptional level by reducing expression of iNOS mRNA.
corticosteroids; inflammation; iNOS; nitric oxide; PK-PD modeling
To develop a pharmacokinetic-pharmacodynamic disease progression (PK/PD/DIS) model to characterize the effect of etanercept in collagen-induced arthritis (CIA) rats on rheumatoid arthritis (RA) progression.
The CIA rats received either 5 mg/kg intravenous (IV), 1 mg/kg IV, or 5 mg/kg subcutaneous (SC) etanercept at day 21 post-disease induction. Effect on disease progression was measured by paw swelling. Plasma concentrations of etanercept were assayed by enzyme-linked immunosorbent assay (ELISA). PK profiles were fitted first; parameter estimates were applied to fit paw edema data for PD and DIS-related parameter estimation using ADAPT 5 software.
The model contained a two-compartment PK model with Michaelis-Menten elimination. For SC administration, two additional mathematical functions for absorption were added. The disease progression component was an indirect response model with a time-dependent change in paw edema production rate constant (kin) assumed to be inhibited by etanercept.
Etanercept has modest effects on paw swelling in CIA rats. The PK and PD profiles were well described by the developed PK/PD/DIS model, which may be used for other anti-cytokine biologic agents for RA.
arthritis; etanercept; model; pharmacodynamics; pharmacokinetics
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.
Chronopharmacology is an important but under-explored aspect of therapeutics. Rhythmic variations in biological processes can influence drug action, including pharmacodynamic responses, due to circadian variations in the availability or functioning of drug targets. We hypothesized that global gene expression analysis can be useful in the identification of circadian regulated genes involved in drug action. Circadian variations in gene expression in rat liver were explored using Affymetrix gene arrays. A rich time series involving animals analyzed at 18 time points within the 24 hour cycle was generated. Of the more than 15,000 probe sets on these arrays, 265 exhibited oscillations with a 24 hour frequency. Cluster analysis yielded 5 distinct circadian clusters, with approximately two-thirds of the transcripts reaching maximum expression during the animal’s dark/active period. Of the 265 probe sets, 107 of potential therapeutic importance were identified. The expression levels of clock genes were also investigated in this study. Five clock genes exhibited circadian variation in liver, and data suggest that these genes may also be regulated by corticosteroids.
Corticosteroids (CS) effects on insulin resistance related genes in rat skeletal muscle were studied. In our acute study, adrenalectomized (ADX) rats were given single doses of 50 mg/kg methylprednisolone (MPL) intravenously. In our chronic study, ADX rats were implanted with Alzet mini-pumps giving zero-order release rates of 0.3 mg/kg/h MPL and sacrificed at various times up to 7 days. Total RNA was extracted from gastrocnemius muscles and hybridized to Affymetrix GeneChips. Data mining and literature searches identified 6 insulin resistance related genes which exhibited complex regulatory pathways. Insulin receptor substrate-1 (IRS-1), uncoupling protein 3 (UCP3), pyruvate dehydrogenase kinase isoenzyme 4 (PDK4), fatty acid translocase (FAT) and glycerol-3-phosphate acyltransferase (GPAT) dynamic profiles were modeled with mutual effects by calculated nuclear drug-receptor complex (DR(N)) and transcription factors. The oscillatory feature of endothelin-1 (ET-1) expression was depicted by a negative feedback loop. These integrated models provide testable quantitative hypotheses for these regulatory cascades.
corticosteroid; glucocorticoid; microarrays; mathematical modeling; insulin resistance
Corticosteroids (CS) regulate many enzymes at both mRNA and protein levels. This study used microarrays to broadly assess regulation of various genes related to the greater urea cycle and employs pharmacokinetic/pharmacodynamic (PK/PD) modeling to quantitatively analyze and compare the temporal profiles of these genes during acute and chronic exposure to methylprednisolone (MPL). One group of adrenalectomized male Wistar rats received an intravenous bolus dose (50 mg/kg) of MPL, whereas a second group received MPL by a subcutaneous infusion (Alzet osmotic pumps) at a rate of 0.3 mg/kg/hr for seven days. The rats were sacrificed at various time points over 72 hours (acute) or 168 hours (chronic) and livers were harvested. Total RNA was extracted and Affymetrix® gene chips (RG_U34A for acute and RAE 230A for chronic) were used to identify genes regulated by CS. Besides five primary urea cycle enzymes, many other genes related to the urea cycle showed substantial changes in mRNA expression. Some genes that were simply up- or down-regulated after acute MPL showed complex biphasic patterns upon chronic infusion indicating involvement of secondary regulation. For the simplest patterns, indirect response models were used to describe the nuclear steroid-bound receptor mediated increase or decrease in gene transcription (e.g. tyrosine aminotransferase, glucocorticoid receptor). For the biphasic profiles, involvement of a secondary biosignal was assumed (e.g. ornithine decarboxylase, CCAAT/enhancer binding protein) and more complex models were derived. Microarrays were used successfully to explore CS effects on various urea cycle enzyme genes. PD models presented in this report describe testable hypotheses regarding molecular mechanisms and quantitatively characterize the direct or indirect regulation of various genes by CS.
urea cycle; corticosteroids; methylprednisolone; pharmacodynamics; genomics
Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp).
Influences of methylprednisolone (MPL) and food consumption on body weight (BW), and the effects of MPL on glycemic control including food consumption and the dynamic interactions among glucose, insulin, and free fatty acids (FFA) were evaluated in normal male Wistar rats. Six groups of animals received either saline or MPL via subcutaneous infusions at the rate of 0.03, 0.1, 0.2, 0.3 and 0.4 mg/kg/h for different treatment periods. BW and food consumption were measured twice a week. Plasma concentrations of MPL and corticosterone (CST) were determined at animal sacrifice. Plasma glucose, insulin, and FFA were measured at various times after infusion. Plasma MPL concentrations were simulated by a two-compartment model and used as the driving force in the pharmacodynamic (PD) analysis. All data were modeled using ADAPT 5. The MPL treatments caused reduction of food consumption and body weights in all dosing groups. The steroid also caused changes in plasma glucose, insulin, and FFA concentrations. Hyper-insulinemia was achieved rapidly at the first sampling time of 6 h; significant elevations of FFA were observed in all drug treatment groups; whereas only modest increases in plasma glucose were observed in the low dosing groups (0.03 and 0.1 mg/kg/h). Body weight changes were modeled by dual actions of MPL: inhibition of food consumption and stimulation of weight loss, with food consumption accounting for the input of energy for body weight. Dynamic models of glucose and insulin feedback interactions were extended to capture the major metabolic effects of FFA: stimulation of insulin secretion and inhibition of insulin-stimulated glucose utilization. These models of body weight and glucose regulation adequately captured the experimental data and reflect significant physiological interactions among glucose, insulin, and FFA. These mechanism-based PD models provide further insights into the multi-factor control of this essential metabolic system.
Glucocorticoids; Methylprednisolone; Pharmacodynamics; Food intake; Body weight; Glucose; Insulin; Free fatty acids