Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P04155 (pS2)
 1,234 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Cross-talk between insulin-like growth factor 1 (IGF-1) and estrogen receptor alpha (ER) regulates gene expression in breast cancer cells, but the underlying mechanisms remain unclear. Here, we studied how 17-beta-estradiol (E2) and IGF-1 affect ER transcriptional machinery in MCF-7 cells. E2 treatment stimulated ER loading on the estrogen response element (ERE) in the pS2 promoter and on the AP-1 motif in the cyclin D1 promoter. On ERE, similar amounts of liganded ER were found at 1-24-h time points, whereas on AP-1, ER binding fluctuated over time. At 1 h, liganded ER was recruited to ERE together with histone acetyltransferases SRC-1 and p300, ubiquitin ligase E6-AP, histone methyltransferase Carm1 (Carm), and polymerase (pol) II. This coincided with increased histone H3 acetylation and up-regulation of pS2 mRNA levels. At the same time, E2 moderately increased cyclin D1 expression, which was associated with the recruitment of liganded ER, SRC-1, p300, ubiquitin ligase E6-AP (E6L), Mdm2, and pol II, but not other regulatory proteins, to AP-1. In contrast, at 1 h, IGF-1 increased the recruitment of the ER.SRC-1.p300.E6L.Mdm2.Carm.pol II complex on AP-1, but not on ERE, and induced cyclin D1, but not pS2, mRNA expression. Notably, ER knockdown reduced the association of ER, E6L, Mdm2, Carm, and pol II with AP-1 and resulted in down-regulation of cyclin D1 expression. IGF-1 potentiated the effects of E2 on ERE but not to AP-1 and increased E2-dependent pS2, but not cyclin D1, mRNA expression. In conclusion, E2 and IGF-1 differentially regulate ER transcription at ERE and AP-1 sites.
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PMID:Insulin-like growth factor 1 differentially regulates estrogen receptor-dependent transcription at estrogen response element and AP-1 sites in breast cancer cells. 1716 46

Organisms are equipped with regulatory systems that display a variety of dynamical behavior ranging from simple stable steady states, to switching and multistability, to oscillations. Earlier work has shown that oscillations in protein concentrations or gene expression levels are related to the presence of at least one negative feedback loop in the regulatory network. Here, we study the dynamics of a very general class of negative feedback loops. Our main result is that, when a single negative feedback loop dominates the dynamical behavior, the sequence of maxima and minima of the concentrations exhibit a pattern that uniquely identifies the interactions of the loop. This allows us to devise an algorithm to (i) test whether observed oscillating time series are consistent with a single underlying negative feedback loop, and if so, (ii) reconstruct the precise structure of the loop, i.e., the activating/repressing nature of each interaction. This method applies even when some variables are missing from the data set, or if the time series shows transients, like damped oscillations. We illustrate the relevance and the limits of validity of our method with three examples: p53-Mdm2 oscillations, circadian gene expression in cyanobacteria, and cyclic binding of cofactors at the estrogen-sensitive pS2 promoter.
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PMID:Oscillation patterns in negative feedback loops. 1741 33