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Differential modulation of transcriptional activity of oestrogen receptors by direct protein-protein interactions with retinoid receptors.

ABSTRACT: Control of oestradiol-responsive gene regulation by oestrogen receptors (ERs) may involve complex cross-talk with retinoic acid receptors (RARs) and retinoid X receptors (RXRs). Recently, we have shown that ERalpha directly interacts with RARalpha and RXRalpha through their ligand binding domains (LBDs). In the present work, we extend these results by showing that ERbeta binds similarly to RARalpha and RXRalpha but not to the glucocorticoid receptor, as demonstrated by the yeast two-hybrid tests and glutathione S-transferase pull-down assays. These direct interactions were also demonstrated in gel-shift assays, in which the oestrogen response element (ERE) binding by ERalpha was enhanced by the RXRalpha LBD but was abolished by the RARalpha LBD. In addition, we showed that RARalpha and RXRalpha bound the ERE as efficiently as ERalpha, suggesting that competition for DNA binding may affect the transactivation function of the ER. In transient transfection experiments, co-expression of RARalpha or RXRalpha, along with ERalpha or ERbeta, revealed differential modulation of the ERE-dependent transactivation, which was distinct from the results when each receptor alone was co-transfected. Importantly, when the LBD of RARalpha was co-expressed with ERalpha, transactivation of ERalpha on the ERE was repressed as efficiently as when wild-type RARalpha was co-expressed. Furthermore, liganded RARalpha or unliganded RXRalpha enhanced the ERalpha transactivation, suggesting the formation of transcriptionally active heterodimer complexes between the ER and retinoid receptors. Taken together, these results suggest that direct protein-protein interactions may play major roles in the determination of the biological consequences of cross-talk between ERs and RARalpha or RXRalpha.

SUBMITTER: Song MR

PROVIDER: S-EPMC1219924 | biostudies-other | 1998 Dec

REPOSITORIES: biostudies-other

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Project description:BackgroundGenes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour.ResultsC3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis.ConclusionsOur approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.

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Differential modulation of transcriptional activity of oestrogen receptors by direct protein-protein interactions with retinoid receptors.

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