Application of multivariate statistical procedures to identify transcription factors that correlate with MRP2, 3, and 4 mRNA in adult human livers

Abstract

1. Multidrug resistance-associated proteins 2–4 (MRP2–4) are membrane efflux transporters critical for the hepatic clearance of pharmaceuticals and endogenous chemicals. Little is known about the constitutive regulation of MRP2–4 mRNA in normal human liver.

2. The purpose of this study was to identify transcription factors whose expression significantly correlates with MRP2–4 mRNA in human liver specimens.

3. Ninety adult human livers were profiled for mRNA expression of MRP2–4 as well as aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR), pregnane X receptor (PXR), peroxisome proliferator-activated receptor alpha (PPARα) and gamma (γ), liver X receptor alpha (LXRα), farnesoid X receptor (FXR), glucocorticoid receptor (GR), retinoid X receptor alpha (RXRα), hepatocyte nuclear factor 1 alpha (HNF1α) and 4 alpha (4α), and nuclear factor E2-related factor 2 (Nrf2) transcription factors. Using linear regression and stepwise selection of partial R²-values, CAR, HNF1α, and PPARα mRNA exhibited the greatest correlation with MRP2, 3, and 4, respectively.

4. Interindividual variation in the expression of the identified transcription factors may account for the variability in constitutive mRNA levels of MRP2–4. The multivariate approach presented in this study should aid in predicting signalling pathways that participate either directly or indirectly in regulating hepatic drug disposition.

Keywords::

• Adenosine triphosphate-binding cassette subfamily C (ABCC)
• transporters
• constitutive androstane receptor (CAR)
• peroxisome proliferator-activated receptor alpha (PPARα)
• hepatocyte nuclear factor 1 alpha (HNF1α)

Acknowledgements

The authors thank Dr David Buckley for isolation of human RNA and gene analysis, Dr Jon Maher for analysis of nucleotide similarity between mouse and human MRP2–4 genes, and Dr Matthew Mayo for assistance with statistical analysis. This work was supported by NIH Grant Numbers ES-007079, ES-009716, and DK-080774.

Declaration of interest: The authors report no conflicts of interest.