Tandem Analysis of Transcriptome and Proteome Changes after a Single Dose of Corticosteroid: A Systems Approach to Liver Function in Pharmacogenomics

Abstract

Corticosteroids (CS) such as methylprednisolone (MPL) affect almost all liver functions through multiple mechanisms of action, and long-term use results in dysregulation causing diverse side effects. The complexity of involved molecular mechanisms necessitates a systems approach. Integration of information from the transcriptomic and proteomic responses has potential to provide deeper insights into CS actions. The present report describes the tandem analysis of rich time-series transcriptomic and proteomic data in rat liver after a single dose of MPL. Hierarchical clustering of the common genes represented in both mRNA and protein datasets displayed two dominant patterns. One of these patterns exhibited complementary mRNA and protein expression profiles indicating that MPL affected the regulation of these genes at the transcriptional level. Some of the classic pharmacodynamic markers for CS actions, including tyrosine aminotransferase (TAT), were among this group, together with genes encoding urea cycle enzymes and ribosomal proteins. The other pattern was rather unexpected. For this group of genes, MPL had distinctly observable effects at the protein expression level, although a change in the reverse direction occurred at the transcriptional level. These genes were functionally associated with metabolic processes that might be essential to elucidate side effects of MPL on liver, most importantly including modulation of oxidative stress, fatty acid oxidation, and bile acid biosynthesis. Furthermore, profiling of gene and protein expression data was also done independently of one another by a two-way sequential approach. Prominent temporal shifts in expression and relevant cellular functions were described together with the assessment of changes in the complementary side.

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

“…In general, this observation is consistent with mRNA response profiles observed at the transcriptomic level in liver as well as in other tissues [13, 14, 95]. Proteins were annotated on the basis of temporal regulation by simply visually inspecting all 451 profiles for “up” and “down”-regulation based on deviation from baseline.…”

“…It is evident based on previous temporal cluster analyses of our genomic and proteomic studies that multiple patterns of changes in mRNA and protein expression occur in response to MPL dosing [13, 14, 27]. The direction of regulation of each altered protein is listed under “Regulation” in these tables.…”

“…The complexities in mRNA-protein correlation were highlighted in our previous report where tandem changes in MPL-induced mRNA and protein expression in liver were analyzed. The concordance between mRNA and protein dynamics were observed only for a small number of genes [14]. Since proteins better reflect drug-induced physiological changes, mining of functional information at the proteomic-level coupled with characterizing temporal responses of important proteins can provide mechanistic insight into the physiological and pharmacological effects of CS.…”

“…The development of a robust and reproducible ion-current-based quantitative nano-LC/MS method that enabled assessment of drug-induced dynamic proteomic changes in vivo , and its application in examining the temporal proteomic response of liver from these animals was reported [11]. In addition, tandem changes in the temporal responses of mRNA from a similar set of animals [12, 13] and protein expression from this animal set were also analyzed [14]. Since proteins are better predictors of phenotypic change as compared to mRNA [15, 16], a natural extension to our studies was performing a proteome-wide functional analysis to better understand the liver-specific effects of CS.…”

Quantitative Systems Pharmacology: A Framework for Context

“…Continued advances in high throughput – omics technologies facilitated the collection of information at the genomic, transcriptomic, metabolomic and proteomic levels, as well as regulatory and epigenomic levels. This wealth of information enabled us to further increase the complexity of our models by expanding the chain of events activated, or suppressed, as a result of a drug's action (Kamisoglu et al 2015). High-throughput analysis enabled us to decipher differences related to, for example, dosing (Nguyen et al 2010) or tissue-dependencies (Nguyen et al 2014).…”