Identification of a human nuclear receptor defines a new signaling pathway for CYP3A induction

Nuclear receptors regulate metabolic pathways in response to changes in the environment by appropriate alterations in gene expression of key metabolic enzymes. Here, a computational search approach based on iteratively built hidden Markov models of nuclear receptors was used to identify a human nuclear receptor, termed hPAR, that is expressed in liver and intestines. hPAR was found to be efficiently activated by pregnanes and by clinically used drugs including rifampicin, an antibiotic known to selectively induce human but not murine CYP3A expression. The CYP3A drug-metabolizing enzymes are expressed in gut and liver in response to environmental chemicals and clinically used drugs. Interestingly, hPAR is not activated by pregnenolone 16α-carbonitrile, which is a potent inducer of murine CYP3A genes and an activator of the mouse receptor PXR.1. Furthermore, hPAR was found to bind to and trans-activate through a conserved regulatory sequence present in human but not murine CYP3A genes. These results provide evidence that hPAR and PXR.1 may represent orthologous genes from different species that have evolved to regulate overlapping target genes in response to pharmacologically distinct CYP3A activators, and have potential implications for the in vitro identification of drug interactions important to humans.

Identification of protein–protein interfaces by decreased amide proton solvent accessibility

Matrix-assisted laser desorption ionization–time-of-flight mass spectrometry was used to identify peptic fragments from protein complexes that retained deuterium under hydrogen exchange conditions due to decreased solvent accessibility at the interface of the complex. Short deuteration times allowed preferential labeling of rapidly exchanging surface amides so that primarily solvent accessibility changes and not conformational changes were detected. A single mass spectrum of the peptic digest mixture was analyzed to determine the deuterium content of all proteolytic fragments of the protein. The protein–protein interface was reliably indicated by those peptides that retained more deuterons in the complex compared with control experiments in which only one protein was present. The method was used to identify the kinase inhibitor [PKI(5–24)] and ATP-binding sites in the cyclic-AMP-dependent protein kinase. Three overlapping peptides identified the ATP-binding site, three overlapping peptides identified the glycine-rich loop, and two peptides identified the PKI(5–24)-binding site. A complex of unknown structure also was analyzed, human α-thrombin bound to an 83-aa fragment of human thrombomodulin [TMEGF(4–5)]. Five peptides from thrombin showed significantly decreased solvent accessibility in the complex. Three peptides identified the anion-binding exosite I, confirming ligand competition experiments. Two peptides identified a new region of thrombin near the active site providing a potential mechanism of how thrombomodulin alters thrombin substrate specificity.

Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray HybridizationD⃞

We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: α factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle–regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu