Project description:This dataset is part of the manuscript titled "The metabolic regulator ERRalpha, a downstream target of HER2/IGF1, as a therapeutic target in breast cancer" (in review). The expression data obtained in human mammary epithelial cells were used to generate a list of ERRalpha-regulated genes that was later refined in clinical breast cancer datasets to generate a clinically relevant signature of ERalpha activity (referred to as Cluster 3 signature). Using this signature of the estrogen-related receptor alpha (ERRa) to profile more than eight-hundred breast tumors, we found that patients with tumors exhibiting higher ERRa activity were predicted to have shorter disease free survival. Further, the ability of an ERRa antagonist, XCT790, to inhibit breast cancer cell proliferation correlates with the cellâs intrinsic ERRa activity. These findings highlight the potential of using the ERRa signature and antagonists in targeted therapy for breast cancer. Using a chemical genomic approach we determined that activation of the HER2/IGF1 signaling pathways upregulates the expression of PGC-1b, an obligate cofactor for ERRa activity. Knockdown of PGC-1b in HER2 positive breast cancer cells impaired ERRa signaling and reduced cell proliferation, implicating a functional role of PGC1b/ERRa in the pathogenesis HER2 positive breast cancer. Primary human mammary epithelial cells were a gift from Dr. J. Marks (Duke University, Durham, NC) and cultured in MEBM (Cambrex, East Rutherford, NJ) with MEGM bullet kit and supplemented with 5mg/ml transferrin and 10-5M isoproterenol. To generate ERR-alpha signature, hMECs were serum starved for 36h followed by infection with MOI=150 of adenoviruses expressing two variants of PGC1alpha, a protein ligand for ERRalpha: PGC-1alpha2x9 or PGC-1alpha L2L3M. PGC-1-2x9 is specific to ERRalpha, while PGC-1-L2L3M lacks the NR box and does not interact with ERRalpha or other nuclear receptors. The generation and purification of variant PGC-1alpha viruses were described previously (Gaillard et al., Molecular Cell 24:5, 2006). Comparable expression levels of the two PGC-1alpha variants were verified by Western immonoblot analysis (data not shown). RNA was collect 16h after infection and purified using RNeasy mini kit (Qiagen, Valencia, CA). Ten independent biological replicates from each virus infection were collected.

Project description:Mitochondrial oxidative function is tightly controlled to maintain energy homeostasis in response to nutrient and hormonal signals. An important cellular component in the energy sensing response is the target of rapamycin (TOR) kinase pathway; however whether and how mTOR controls mitochondrial oxidative activity is unknown. Here, we show that mTOR kinase activity stimulates mitochondrial gene expression and oxidative function. In skeletal muscle cells and TSC2-/- MEFs, the mTOR inhibitor rapamycin largely decreased gene expression of mitochondrial transcriptional regulators such as PGC-1alpha and the transcription factors ERRalpha and NRFs. As a consequence, mitochondrial gene expression and oxygen consumption were reduced upon mTOR inhibition. Using computational genomics, we identified the transcription factor YY1 as a common target of mTOR and PGC-1alpha that controls mitochondrial gene expression. Inhibition of mTOR resulted in a failure of YY1 to interact and be coactivated by PGC-1alpha. Notably, knock-down of YY1 in skeletal muscle cells caused a significant decrease in mRNAs of mitochondrial regulators and mitochondrial genes that resulted in a decrease in respiration. Moreover, YY1 was required for rapamycin-dependent repression of mitochondrial genes. Thus, we have identified a novel mechanism in which a nutrient sensor (mTOR) balances energy metabolism via transcriptional control of mitochondrial oxidative function. These results have important implications for our understanding of how these pathways might be altered in metabolic diseases and cancer. Experiment Overall Design: Using Affymetrix MOE430 v2 gene chips, biological triplicates of each condition were analyzed: vehicle-treated, rapamycin-treated, gfp-infected, and pgc-1alpha-infected resulting in a total of 12 samples. Experiment Overall Design: Data were analyzed by RMA (with default settings) in BioConductor 1.2 -- one batch for the Rapamycin vs. Vehicle, and another batch for the PGC vs GFP.

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Homo sapiens

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