Receptor/gene/protein-mediated signaling connects methylprednisolone exposure to metabolic and immune-related pharmacodynamic actions in liver

Ayyar, Vivaswath S.; Sukumaran, Siddharth; DuBois, Debra C.; Almon, Richard R.; Qu, Jun; Jusko, William J.

doi:10.1007/s10928-018-9585-x

Cited by 7 publications

(8 citation statements)

References 73 publications

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“…An extensive comparative analysis of our -omics data sets to those reported by others is also challenging, as most have investigated transcriptomics or proteomics at single time points after dosing. Nonetheless, changes in Tat protein were validated with measurements of enzyme activity in the same animals (Ayyar et al, 2018), and the pathways perturbed within our transcriptomic and proteomic data sets are, in terms of function, in agreement with recognized adverse and therapeutic effects of CS (Ayyar et al, 2017).…”

Section: Discussionsupporting

confidence: 71%

“…Further integrated models incorporating RNA-protein, protein-protein, and protein-DNA interactions and their inter-regulation will provide additional insights into signaling networks at molecular, cellular, and systemic levels. This type of approach was adopted in a more focused modeling analysis that integrated selected signaling pathways with physiologic PD endpoints of MPL efficacy and toxicity (Ayyar et al, 2018). The present models serve to analyze the time course of CS-regulated transcriptomics and proteomics as a whole to provide hypotheses on how mRNA and protein turnover is controlled by direct and secondary factors.…”

Section: Discussionmentioning

confidence: 99%

“…The pharmacokinetics of MPL for both intramuscular and intravenous studies were simulated using previously estimated parameters (Ayyar et al, 2018). The simulated curves shown reflect biexponential disposition of MPL (Supplemental Fig.…”

Section: Pharmacokinetics and Receptor Dynamicsmentioning

confidence: 99%

“…Since hundreds of steroidtarget genes are regulated in an organ, the "horizontal" can be captured by studying large-scale gene expression changes within the tissue. The "vertical" is reflected by the intermediary mechanisms linking CS pharmacokinetics (PK) to resulting pharmacogenomic changes (Ramakrishnan et al, 2002b), and ultimately, to clinically relevant pharmacodynamic (PD) outcomes (Ayyar et al, 2018). We previously examined the entire temporal profiles of about 8000 genes in rat liver after a single 50-mg/kg dose of the synthetic CS methylprednisolone (MPL) (Almon et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Modeling Corticosteroid Pharmacogenomics and Proteomics in Rat Liver

Ayyar

Sukumaran

DuBois

et al. 2018

J Pharmacol Exp Ther

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Corticosteroids (CS) regulate the expression of numerous genes at the mRNA and protein levels. The time course of CS pharmacogenomics and proteomics were examined in livers obtained from adrenalectomized rats given a 50-mg/kg bolus dose of methylprednisolone. Microarrays and mass spectrometry-based proteomics were employed to quantify hepatic transcript and protein dynamics. One-hundred, sixty-three differentially expressed mRNA and their corresponding proteins (163 genes) were clustered into two dominant groups. The temporal profiles of most proteins were delayed compared with their mRNA, attributable to synthesis delays and slower degradation kinetics. On the basis of our fifth-generation model of CS, mathematical models were developed to simultaneously describe the emergent time patterns for an array of steroid-responsive mRNA and proteins. The majority of genes showed time-dependent increases in mRNA and protein expression before returning to baseline. A model assuming direct, steroid-mediated stimulation of mRNA synthesis was applied. Some mRNAs and their proteins displayed down-regulation following CS. A model assuming receptor-mediated inhibition of mRNA synthesis was used. More complex patterns were observed for other genes (e.g., biphasic behaviors and opposite directionality in mRNA and protein). Models assuming either stimulation or inhibition of mRNA synthesis coupled with dual secondarily induced regulatory mechanisms affecting mRNA or protein turnover were derived. These findings indicate that CS-regulated gene expression manifested at the mRNA and protein levels are controlled via mechanisms affecting key turnover processes. Our quantitative models of CS pharmacogenomics were expanded from mRNA to proteins and provide extended hypotheses for understanding the direct, secondary, and downstream mechanisms of CS actions.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Pharmacokinetics and Receptor Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Corticosteroid Pharmacogenomics and Proteomics in Rat Liver

Ayyar

Sukumaran

DuBois

et al. 2018

J Pharmacol Exp Ther

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“…Notably, by employing mathematical models, both ligand-dependent downregulation and circadian oscillation of GR receptor expression can be simulated and predicted [ 82 ]. Very recently, a multiscale pharmacodynamic model was developed to characterize the overall GR-mediated effects on transcriptome, proteome, and enzymatic activities after a single dose of methylprednisolone [ 83 ]. In the work, the authors also considered post-transcriptional processes in their mechanistic mathematical model that control some GR target gene expression.…”

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

Tebbens

Azar

Friedmann

et al. 2018

IJMS

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The pregnane X receptor (PXR) is a drug/xenobiotic-activated transcription factor of crucial importance for major cytochrome P450 xenobiotic-metabolizing enzymes (CYP) expression and regulation in the liver and the intestine. One of the major target genes regulated by PXR is the cytochrome P450 enzyme (CYP3A4), which is the most important human drug-metabolizing enzyme. In addition, PXR is supposed to be involved both in basal and/or inducible expression of many other CYPs, such as CYP2B6, CYP2C8, 2C9 and 2C19, CYP3A5, CYP3A7, and CYP2A6. Interestingly, the dynamics of PXR-mediated target genes regulation has not been systematically studied and we have only a few mechanistic mathematical and biologically based models describing gene expression dynamics after PXR activation in cellular models. Furthermore, few indirect mathematical PKPD models for prediction of CYP3A metabolic activity in vivo have been built based on compartmental models with respect to drug–drug interactions or hormonal crosstalk. Importantly, several negative feedback loops have been described in PXR regulation. Although current mathematical models propose these adaptive mechanisms, a comprehensive mathematical model based on sufficient experimental data is still missing. In the current review, we summarize and compare these models and address some issues that should be considered for the improvement of PXR-mediated gene regulation modelling as well as for our better understanding of the quantitative and spatial dynamics of CYPs expression.

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