Discovery-driven research and bioinformatics in nuclear receptor and coregulator signaling

Overview of the structure-based non-genomic effects of the nuclear receptor RXRα

The nuclear receptor RXRα (retinoid X receptor-α) is a transcription factor that regulates the expression of multiple genes. Its non-genomic function is largely related to its structure, polymeric forms and modification. Previous research revealed that some non-genomic activity of RXRα occurs via formation of heterodimers with Nur77. RXRα-Nur77 heterodimers translocate from the nucleus to the mitochondria in response to certain apoptotic stimuli and this activity correlates with cell apoptosis. More recent studies revealed a significant role for truncated RXRα (tRXRα), which interacts with the p85α subunit of the PI3K/AKT signaling pathway, leading to enhanced activation of AKT and promoting cell growth in vitro and in animals. We recently reported on a series of NSAID sulindac analogs that can bind to tRXRα through a unique binding mechanism. We also identified one analog, K-80003, which can inhibit cancer cell growth by inducing tRXRα to form a tetramer, thus disrupting p85α-tRXRα interaction. This review analyzes the non-genomic effects of RXRα in normal and tumor cells, and discusses the functional differences based on RXRα protein structure (structure source: the RCSB Protein Data Bank).

30 YEARS OF THE MINERALOCORTICOID RECEPTOR: Coregulators as mediators of mineralocorticoid receptor signalling diversity

The cloning of the mineralocorticoid receptor (MR) 30 years ago was the start of a new era of research into the regulatory processes of MR signalling at target genes in the distal nephron, and subsequently in many other tissues. Nuclear receptor (NR) signalling is modified by interactions with coregulatory proteins that serve to enhance or inhibit the gene transcriptional responses. Over 400 coregulatory proteins have been described for the NR super family, many with functional roles in signalling, cellular function, physiology and pathophysiology. Relatively few coregulators have however been described for the MR although recent studies have demonstrated both ligand and/or tissue selectivity for MR-coregulator interactions. A full understanding of the cell, ligand and promoter-specific requirements for MR-coregulator signalling is an essential first step towards the design of small molecular inhibitors of these protein-protein interactions. Tissue-selective steroidal or non-steroidal modulators of the MR are also a desired therapeutic goal. Selectivity, as for other steroid hormone receptors, will probably depend on differential expression and recruitment of coregulatory proteins.

GEMIN4 functions as a coregulator of the mineralocorticoid receptor

The mineralocorticoid receptor (MR) is a member of the nuclear receptor superfamily. Pathological activation of the MR causes cardiac fibrosis and heart failure, but clinical use of MR antagonists is limited by the renal side effect of hyperkalemia. Coregulator proteins are known to be critical for nuclear receptor-mediated gene expression. Identification of coregulators, which mediate MR activity in a tissue-specific manner, may allow for the development of novel tissue-selective MR modulators that confer cardiac protection without adverse renal effects. Our earlier studies identified a consensus motif among MR-interacting peptides, MPxLxxLL. Gem (nuclear organelle)-associated protein 4 (GEMIN4) is one of the proteins that contain this motif. Transient transfection experiments in HEK293 and H9c2 cells demonstrated that GEMIN4 repressed agonist-induced MR transactivation in a cell-specific manner. Furthermore, overexpression of GEMIN4 significantly decreased, while knockdown of GEMIN4 increased, the mRNA expression of specific endogenous MR target genes. A physical interaction between GEMIN4 and MR is suggested by their nuclear co-localization upon agonist treatment. These findings indicate that GEMIN4 functions as a novel coregulator of the MR.

Exploring the effects of polymorphisms on cis-regulatory signal transduction response

cis-Regulatory sequences (CRSs) direct cell-specific and inducible gene expression in response to signal transduction networks, and it is becoming apparent that many cases of disease susceptibility and drug response stratification are due to polymorphisms that alter CRS responses in a context-dependent manner. In the current review, we describe successful methods for identifying CRSs and analyzing the effects of allelic variation on their responses to signal transduction. The technologies described build on the successes of ENCODE (ENCyclopedia Of DNA Elements) by exploring the effects of polymorphisms on CRS context dependency. This understanding is essential to uncover the genomic basis of disease susceptibility and will play a major role in delivering on the promise of personalized medicine.

Interactions of the mineralocorticoid receptor--within and without

The mineralocortoid receptor (MR) regulates salt homeostasis in the kidneys and plays a range of other roles in the heart, vasculature, brain and adipose tissue. It interacts with both mineralocorticoids and glucocorticoids to mediate transcription of target genes. The ability of the MR to exert tissue- and ligand-specific effects relies on its interactions with a range of binding partners, including the chaperone proteins, coregulators, other transcription factors, DNA and modifying proteins. Interactions within the domains of the MR also modulate the overall transcriptional complex. This review will discuss the current understanding of interactions involving the MR and highlight their relevance to ligand- or tissue-specificity as well as their suitability as therapeutic targets.

The transcriptional activity of co-activator AIB1 is regulated by the SUMO E3 ligase PIAS1

BACKGROUND INFORMATION

Amplified in breast cancer 1 (AIB1) is a transcriptional coactivator of nuclear receptors and other transcription factors. It is required for animal growth and reproductive development, and has also been implicated in breast carcinogenesis. Although AIB1 is known to be covalently modified by SUMO-1, which serves to regulate its stability and transcriptional activity, the exact SUMO E3 ligase involved in its sumoylation has not been determined. In order to resolve this question, we investigated the interaction between AIB1 and different members of PIAS proteins (all are SUMO E3 ligases) through immunoprecipiation.

RESULTS

Among the five different PIAS proteins, only PIAS1 co-immunoprecipitated with AIB1 in extract prepared from breast cancer cells (MCF-7). Over-expression of PIAS1 together with AIB1 in MCF-7 cells led to increased sumoylation of AIB1, resulting in repression of its transcriptional activity. In contrast, the PIAS1 mutant (C350S) lacking E3 ligase activity appeared to have no effect on the sumoylation of AIB1. Through sumoylation of AIB1, PIAS1 also promoted the stability of AIB1 and attenuated its interaction with estrogen receptor α (ERα), resulting in repression of the transactivation activity of ERα. In addition, MCF-7 cells co-transfected with wild-type PIAS1 and AIB1 showed about 40% reduction in cell growth, while cells co-transfected with wild-type PIAS1 and mutant AIB1 resistant to sumoylation showed about 34% increase in cell growth compared to cells transformed with wild-type AIB1 only.

CONCLUSIONS

Taken together, these results suggested that PIAS1 may play a crucial role in the regulation of AIB1 transcriptional activity through sumoylation.

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

BACKGROUND

Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study genome-wide binding sites of transcription factors. There is an increasing interest in understanding the mechanism of action of co-regulator proteins, which do not bind DNA directly, but exert their effects by binding to transcription factors such as the estrogen receptor (ER). However, due to the nature of detecting indirect protein-DNA interaction, ChIP-seq signals from co-regulators can be relatively weak and thus biologically meaningful interactions remain difficult to identify.

RESULTS

In this study, we investigated and compared different statistical and machine learning approaches including unsupervised, supervised, and semi-supervised classification (self-training) approaches to integrate multiple types of genomic and transcriptomic information derived from our experiments and public database to overcome difficulty of identifying functional DNA binding sites of the co-regulator SRC-1 in the context of estrogen response. Our results indicate that supervised learning with naïve Bayes algorithm significantly enhances peak calling of weak ChIP-seq signals and outperforms other machine learning algorithms. Our integrative approach revealed many potential ERα/SRC-1 DNA binding sites that would otherwise be missed by conventional peak calling algorithms with default settings.

CONCLUSIONS

Our results indicate that a supervised classification approach enables one to utilize limited amounts of prior knowledge together with multiple types of biological data to enhance the sensitivity and specificity of the identification of DNA binding sites from co-regulator proteins.