In vivo multi-tissue corticosteroid microarray time series available online at Public Expression Profile Resource (PEPR)

Bayesian state space models for dynamic genetic network construction across multiple tissues

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Functional proteomic analysis of corticosteroid pharmacodynamics in rat liver: Relationship to hepatic stress, signaling, energy regulation, and drug metabolism

Corticosteroids (CS) are anti-inflammatory agents that cause extensive pharmacogenomic and proteomic changes in multiple tissues. An understanding of the proteome-wide effects of CS in liver and its relationships to altered hepatic and systemic physiology remains incomplete. Here, we report the application of a functional pharmacoproteomic approach to gain integrated insight into the complex nature of CS responses in liver in vivo. An in-depth functional analysis was performed using rich pharmacodynamic (temporal-based) proteomic data measured over 66h in rat liver following a single dose of methylprednisolone (MPL). Data mining identified 451 differentially regulated proteins. These proteins were analyzed on the basis of temporal regulation, cellular localization, and literature-mined functional information. Of the 451 proteins, 378 were clustered into six functional groups based on major clinically-relevant effects of CS in liver. MPL-responsive proteins were highly localized in the mitochondria (20%) and cytosol (24%). Interestingly, several proteins were related to hepatic stress and signaling processes, which appear to be involved in secondary signaling cascades and in protecting the liver from CS-induced oxidative damage. Consistent with known adverse metabolic effects of CS, several rate-controlling enzymes involved in amino acid metabolism, gluconeogenesis, and fatty-acid metabolism were altered by MPL. In addition, proteins involved in the metabolism of endogenous compounds, xenobiotics, and therapeutic drugs including cytochrome P450 and Phase-II enzymes were differentially regulated. Proteins related to the inflammatory acute-phase response were up-regulated in response to MPL. Functionally-similar proteins showed large diversity in their temporal profiles, indicating complex mechanisms of regulation by CS.

SIGNIFICANCE

Clinical use of corticosteroid (CS) therapy is frequent and chronic. However, current knowledge on the proteome-level effects of CS in liver and other tissues is sparse. While transcriptomic regulation following methylprednisolone (MPL) dosing has been temporally examined in rat liver, proteomic assessments are needed to better characterize the tissue-specific functional aspects of MPL actions. This study describes a functional pharmacoproteomic analysis of dynamic changes in MPL-regulated proteins in liver and provides biological insight into how steroid-induced perturbations on a molecular level may relate to both adverse and therapeutic responses presented clinically.

Glucocorticoid efficacy in asthma: is improved tissue remodeling upstream of anti-inflammation

Synthetic glucocorticoids (GCs), such as prednisone, are among the most widely prescribed drugs worldwide and are used to treat many acute and chronic inflammatory conditions. The current paradigm of GC efficacy is that they are potent anti-inflammatory agents. Decreased inflammation in many disorders is thought to lead to decreased pathological tissue remodeling. However, this model has never been validated. In particular, improvements in inflammation have not been shown to improve the rate of lung function decline in asthma. Herein, we present an alternative paradigm, where GC efficacy is mediated through more successful tissue remodeling, with reduction in inflammation secondary to successful regeneration.

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

Background

This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug.

Results and conclusion

Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

Pharmacodynamic/pharmacogenomic modeling of insulin resistance genes in rat muscle after methylprednisolone treatment: exploring regulatory signaling cascades

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.

Mathematical modeling of corticosteroid pharmacogenomics in rat muscle following acute and chronic methylprednisolone dosing

The pharmacogenomic effects of a corticosteroid (CS) were assessed in rat skeletal muscle using microarrays. Adrenalectomized (ADX) rats were treated with methylprednisolone (MPL) by either 50 mg/kg intravenous injection or 7-day 0.3 mg/kg/h infusion through subcutaneously implanted pumps. RNAs extracted from individual rat muscles were hybridized to Affymetrix Rat Genome Genechips. Data mining yielded 653 and 2316 CS-responsive probe sets following MPL bolus and infusion treatments. Of these, 196 genes were controlled by MPL under both dosing conditions. Cluster analysis revealed that 124 probe sets exhibited three typical expression dynamic profiles following acute dosing. Cluster A consisted of up-regulated probe sets which were grouped into five subclusters each exhibiting unique temporal patterns during the infusion. Cluster B comprised down-regulated probe sets which were divided into two subclusters with distinct dynamics during the infusion. Cluster C probe sets exhibited delayed down-regulation under both bolus and infusion conditions. Among those, 104 probe sets were further grouped into subclusters based on their profiles following chronic MPL dosing. Several mathematical models were proposed and adequately captured the temporal patterns for each subcluster. Multiple types of dosing regimens are needed to resolve common determinants of gene regulation as chronic exposure results in unexpected differences in gene expression compared to acute dosing. Pharmacokinetic/pharmacodynamic (PK/PD) modeling provides a quantitative tool for elucidating the complexities of CS pharmacogenomics in skeletal muscle.

Pharmacodynamic Modeling of Acute and Chronic Effects of Methylprednisolone on Hepatic Urea Cycle Genes in Rats

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 (RGU34A 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.

Transcriptional pathways associated with skeletal muscle disuse atrophy in humans

Disuse atrophy is a common clinical phenomenon that significantly impacts muscle function and activities of daily living. The purpose of this study was to implement genome-wide expression profiling to identify transcriptional pathways associated with muscle remodeling in a clinical model of disuse. Skeletal muscle biopsies were acquired from the medial gastrocnemius in patients with an ankle fracture and from healthy volunteers subjected to 4-11 days of cast immobilization. We identified 277 misregulated transcripts in immobilized muscles of patients, of which the majority were downregulated. The most broadly affected pathways were involved in energy metabolism, mitochondrial function, and cell cycle regulation. We also found decreased expression in genes encoding proteolytic proteins, calpain-3 and calpastatin, and members of the myostatin and IGF-I pathway. Only 26 genes showed increased expression in immobilized muscles, including apolipoprotein (APOD) and leptin receptor (LEPR). Upregulation of APOD (5.0-fold, P < 0.001) and LEPR (5.7-fold, P < 0.05) was confirmed by quantitative RT-PCR and immunohistochemistry. In addition, atrogin-1/MAFbx was found to be 2.4-fold upregulated (P < 0.005) by quantitative RT-PCR. Interestingly, 96% of the transcripts differentially regulated in immobilized limbs also showed the same trend of change in the contralateral legs of patients but not the contralateral legs of healthy volunteers. Information obtained in this study complements findings in animal models of disuse and provides important feedback for future clinical studies targeting the restoration of muscle function following limb disuse in humans.

Modeling of corticosteroid effects on hepatic low-density lipoprotein receptors and plasma lipid dynamics in rats

PURPOSE

This study examines methylprednisolone (MPL) effects on the dynamics of hepatic low-density lipoprotein receptor (LDLR) mRNA and plasma lipids associated with increased risks for atherosclerosis.

MATERIALS AND METHODS

Normal male Wistar rats were given 50 mg/kg MPL intramuscularly (IM) and sacrificed at various times. Measurements included plasma MPL and CST, hepatic glucocorticoid receptor (GR) mRNA, cytosolic GR density and hepatic LDLR mRNA, and plasma total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high density lipoprotein cholesterol (HDLC), and triglycerides (TG).

RESULTS

MPL showed bi-exponential disposition with two first-order absorption components. Hepatic GR and LDLR mRNA exhibited circadian patterns which were disrupted by MPL. Down-regulation in GR mRNA (40-50%) was followed by a delayed rebound phase. LDLR mRNA exhibited transient down-regulation (60-70%). Cytosolic GR density was significantly suppressed but returned to baseline by 72 h. Plasma TC and LDLC showed increases (55 and 142%) at 12 h. A mechanistic receptor/gene pharmacokinetic/pharmacodynamic model was developed to describe CS effects on hepatic LDLR mRNA and plasma cholesterols.

CONCLUSIONS

Our PK/PD model was able to satisfactorily capture the MPL effects on hepatic LDLR, its relationship to various plasma cholesterols, and builds the foundation to explore this area in the future.

Application of scaling factors in simultaneous modeling of microarray data from diverse chips

PURPOSE

Microarrays have been utilized in many biological, physiological and pharmacological studies as a high-throughput genomic technique. Several generations of Affymetrix GeneChip microarrays are widely used in gene expression studies. However, differences in intensities of signals for different probe sets that represent the same gene on various types of Affymetrix chips make comparison of datasets complicated.

MATERIALS AND METHODS

A power coefficient scaling factor was applied in the pharmacokinetic/pharmacodynamic (PK/PD) modeling to account for differences in probe set sensitivities (i.e., signal intensities). Microarray data from muscle and liver following methylprednisolone 50 mg/kg i.v. bolus and 0.3 mg/kg/h infusion regimens were taken as an exemplar.

RESULTS

The scaling factor applied to the pharmacodynamic output function was used to solve the problem of intensity differences between probe sets. This approach yielded consistent pharmacodynamic parameters for the applied models.

CONCLUSIONS

Modeling of pharmacodynamic/pharmacogenomic (PD/PG) data from diverse chips should be performed with caution due to differential probe set intensities. In such circumstances, a power scaling factor can be applied in the modeling.

Bayesian dynamic multivariate models for inferring gene interaction networks

Constructions of gene and protein dynamic network is a challenging and important problem in genomic research while estimating the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop Bayesian dynamic multivariate models to tackle this challenge for inferring the gene network profiles associated with diseases and treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian setting. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Bayesian approaches with various prior and hyper-prior models with MCMC algorithms are used to estimate the model parameters. We apply our models to multiple tissue polygenetic affymetrix data sets. Preliminary results show that the genomic dynamic behavior can be well captured by the proposed model.

Corticosteroid-regulated genes in rat kidney: mining time series array data

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.

Pharmacogenomic responses of rat liver to methylprednisolone: an approach to mining a rich microarray 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.

Exercise training increases electron and substrate shuttling proteins in muscle of overweight men and women with the metabolic syndrome

Aerobic conditioned muscle shows increased oxidative metabolism or glucose relative to untrained muscle at a given absolute exercise intensity. The studies of a targeted risk reduction intervention through defined exercise (STRRIDE) study is an aerobic exercise intervention in men and women with features of metabolic syndrome (Kraus WE, Torgan CE, Duscha BD, Norris J, Brown SA, Cobb FR, Bales CW, Annex BH, Samsa GP, Houmard JA, and Slentz CA, Med Sci Sports Exerc 33: 1774–1784, 2001), with four muscle biopsies taken during training and detraining time points. Here, we expanded a previous study (Hittel DS, Kraus WE, and Hoffman EP, J Physiol 548: 401–410, 2003) and used mRNA profiling to investigate gene transcripts associated with energy and substrate metabolism in STRRIDE participants. We found coordinate regulation of key metabolic enzymes with aerobic training in metabolic syndrome (aspartate aminotransferase 1, lactate dehydrogenase B, and pyruvate dehydrogenase-α1). All were also quickly downregulated by detraining, although the induction was not an acute response to activity. Protein and enzymatic assays were used to validate mRNA induction with aerobic training and loss with detraining (96 h to 2 wk) in 10 male and 10 female STRRIDE subjects. We propose that training coordinately increases the levels of aspartate aminotransferase 1, lactate dehydrogenase B, and pyruvate dehydrogenase-α1subunit, increasing glucose metabolism in muscle by liberating pyruvate for oxidative metabolism and, therefore, limiting lactate efflux. Serial measurement of fasting plasma lactate from 62 subjects from the same exercise group demonstrated a significant decrease of circulating lactate with training. We also found evidence for sex-specific molecular remodeling of muscle with ubiquinol-cytochrome c reductase core protein II, a component of mitochondrial respiratory complex III, which showed an increase after training that was specific to women. These biochemical adaptations complement existing molecular models for improved glucose tolerance with exercise intervention in prediabetic individuals.

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

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.