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Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions.

ABSTRACT: Steroid receptors are pleiotropic transcription factors that coordinate adaptation to different physiological states. An important target organ is the brain, but even though their effects are well studied in specific regions, brain-wide steroid receptor targets and mediators remain largely unknown due to the complexity of the brain. Here, we tested the idea that novel aspects of steroid action can be identified through spatial correlation of steroid receptors with genome-wide mRNA expression across different regions in the mouse brain. First, we observed significant coexpression of six nuclear receptors (NRs) [androgen receptor (Ar), estrogen receptor alpha (Esr1), estrogen receptor beta (Esr2), glucocorticoid receptor (Gr), mineralocorticoid receptor (Mr), and progesterone receptor (Pgr)] with sets of steroid target genes that were identified in single brain regions. These coexpression relationships were also present in distinct other brain regions, suggestive of as yet unidentified coordinate regulation of brain regions by, for example, glucocorticoids and estrogens. Second, coexpression of a set of 62 known NR coregulators and the six steroid receptors in 12 nonoverlapping mouse brain regions revealed selective downstream pathways, such as Pak6 as a mediator for the effects of Ar and Gr on dopaminergic transmission. Third, Magel2 and Irs4 were identified and validated as strongly responsive targets to the estrogen diethylstilbestrol in the mouse hypothalamus. The brain- and genome-wide correlations of mRNA expression levels of six steroid receptors that we provide constitute a rich resource for further predictions and understanding of brain modulation by steroid hormones.

SUBMITTER: Mahfouz A

PROVIDER: S-EPMC4791033 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions.

Mahfouz Ahmed A Lelieveldt Boudewijn P F BP Grefhorst Aldo A van Weert Lisa T C M LT Mol Isabel M IM Sips Hetty C M HC van den Heuvel José K JK Datson Nicole A NA Visser Jenny A JA Reinders Marcel J T MJ Meijer Onno C OC

Proceedings of the National Academy of Sciences of the United States of America 20160125 10

Steroid receptors are pleiotropic transcription factors that coordinate adaptation to different physiological states. An important target organ is the brain, but even though their effects are well studied in specific regions, brain-wide steroid receptor targets and mediators remain largely unknown due to the complexity of the brain. Here, we tested the idea that novel aspects of steroid action can be identified through spatial correlation of steroid receptors with genome-wide mRNA expression acr ...[more]

PMID: 26811448

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Project description:BACKGROUND: Identifying the key transcription factors (TFs) controlling a biological process is the first step toward a better understanding of underpinning regulatory mechanisms. However, due to the involvement of a large number of genes and complex interactions in gene regulatory networks, identifying TFs involved in a biological process remains particularly difficult. The challenges include: (1) Most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation, making it difficult to recognize TFs for a biological process; (2) Transcription usually involves several hundred genes that generate a combination of intrinsic noise from upstream signaling networks and lead to fluctuations in transcription; (3) A TF can function in different cell types or developmental stages. Currently, the methods available for identifying TFs involved in biological processes are still very scarce, and the development of novel, more powerful methods is desperately needed. RESULTS: We developed a computational pipeline called TF-Cluster for identifying functionally coordinated TFs in two steps: (1) Construction of a shared coexpression connectivity matrix (SCCM), in which each entry represents the number of shared coexpressed genes between two TFs. This sparse and symmetric matrix embodies a new concept of coexpression networks in which genes are associated in the context of other shared coexpressed genes; (2) Decomposition of the SCCM using a novel heuristic algorithm termed "Triple-Link", which searches the highest connectivity in the SCCM, and then uses two connected TF as a primer for growing a TF cluster with a number of linking criteria. We applied TF-Cluster to microarray data from human stem cells and Arabidopsis roots, and then demonstrated that many of the resulting TF clusters contain functionally coordinated TFs that, based on existing literature, accurately represent a biological process of interest. CONCLUSIONS: TF-Cluster can be used to identify a set of TFs controlling a biological process of interest from gene expression data. Its high accuracy in recognizing true positive TFs involved in a biological process makes it extremely valuable in building core GRNs controlling a biological process. The pipeline implemented in Perl can be installed in various platforms.

Dataset Information

Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions.

Publications

Genome-wide coexpression of steroid receptors in the mouse brain: Identifying signaling pathways and functionally coordinated regions.

Similar Datasets

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