ABSTRACT
The evolution of prostate cancer from an androgen-dependent state to one that is androgen-independent marks its lethal progression. The androgen receptor (AR) is essential in both, though its function in androgen-independent cancers is poorly understood. We have defined the direct AR-dependent target genes in both androgen-dependent and -independent cancer cells by generating AR-dependent gene expression profiles and AR cistromes. In contrast to what is found in androgen-dependent cells, AR selectively upregulates M-phase cell-cycle genes in androgen-independent cells, including UBE2C, a gene that inactivates the M-phase checkpoint. We find that epigenetic marks at the UBE2C enhancer, notably histone H3K4 methylation and FoxA1 transcription factor binding, are present in androgen-independent cells and direct AR-enhancer binding and UBE2C activation. Thus, the role of AR in androgen-independent cancer cells is not to direct the androgen-dependent gene expression program without androgen, but rather to execute a distinct program resulting in androgen-independent growth.
Subject(s)
Gene Expression Regulation, Neoplastic , Prostatic Neoplasms/metabolism , Receptors, Androgen/metabolism , Androgens/metabolism , Cell Division , Cell Line, Tumor , Hepatocyte Nuclear Factor 3-alpha/metabolism , Histones/metabolism , Humans , Male , Prostatic Neoplasms/genetics , Transcriptional Activation , Ubiquitin-Conjugating Enzymes/metabolismABSTRACT
Reversible protein phosphorylation on multiple sites is a key regulatory mechanism in most cellular processes. We consider here a kinase-phosphatase-substrate system with two sites, under mass-action kinetics, with no restrictions on the order of phosphorylation or dephosphorylation. We show that the concentrations of the four phosphoforms at steady state satisfy an algebraic formula-an invariant-that is independent of the other chemical species, such as free enzymes or enzyme-substrate complexes, and holds irrespective of the starting conditions and the total amounts of enzymes and substrate. Such invariants allow stringent quantitative predictions to be made without requiring any knowledge of site-specific parameter values. We introduce what we believe are novel methods from algebraic geometry-Gröbner bases, rational curves-to calculate invariants. These methods are particularly significant because they make it possible to treat parameters symbolically without having to specify their numerical values, and thereby allow us to sidestep the parameter problem. We anticipate that this approach will have much wider applications in biological modeling.
