Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Molecular and Genetic Crosstalks Between mTOR and ERRα are Key Determinants of Rapamycin-induced Non-Alcoholic Fatty Liver.

ABSTRACT: mTOR and ERRα are key regulators of common metabolic processes. However, the extent of functional overlap between these two factors has not been investigated. ChIP-sequencing analyses on mouse liver reveal mTOR recruitment to regulatory regions of genes on a genome-wide scale including enrichment at genes shared with ERRα that are involved in the TCA cycle and lipogenesis pathway. A total of 9469 and 23226 peaks were identified for mTOR and ERRα ChIP-seq datasets, respectively. mTOR and ERRα mouse liver ChIP-seq datasets obtained from pooling 8 individual ChIPs from a chromatin pool of 26 livers.

ORGANISM(S): Mus musculus

SUBMITTER: Vincent Giguère

PROVIDER: E-GEOD-43638 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Molecular and genetic crosstalks between mTOR and ERRα are key determinants of rapamycin-induced nonalcoholic fatty liver.

Chaveroux Cédric C Eichner Lillian J LJ Dufour Catherine R CR Shatnawi Aymen A Khoutorsky Arkady A Bourque Guillaume G Sonenberg Nahum N Giguère Vincent V

Cell metabolism 20130401 4

mTOR and ERRα are key regulators of common metabolic processes, including lipid homeostasis. However, it is currently unknown whether these factors cooperate in the control of metabolism. ChIP-sequencing analyses of mouse liver reveal that mTOR occupies regulatory regions of genes on a genome-wide scale including enrichment at genes shared with ERRα that are involved in the TCA cycle and lipid biosynthesis. Genetic ablation of ERRα and rapamycin treatment, alone or in combination, alter the expr ...[more]

PMID: 23562079

Similar Datasets

Project description:The mTOR (mammalian Target of Rapamycin) pathway is constitutively activated in Diffuse Large B-Cell Lymphoma (DLBCL). mTOR inhibition has been shown to have clinical activity in patients with DLBCL, although overall response rates remain low. We therefore evaluated differences in the transcriptome between DLBCL cell lines with differential sensitivity to the mTOR inhibitor Rapamycin, to (A) identify gene-expression patterns(GEP) capable of identifying sensitivity to Rapamycin, (B) understand the underlying mechanisms of resistance to Rapamycin in DLBCL and (C) identify bioactive molecules likely to synergize with mTOR inhibitors. Using Affymetrix HuGene ST 1.0 microarrays, we were able to identify a gene expression signature capable of accurately predicting sensitivity and resistance to Rapamycin in DLBCL cell lines. Pathway analysis identified the serine/threonine kinase Akt as central to the differentially-expressed gene network. Connectivity mapping of our datasets identified compounds targeting the AKT pathway with a high likelihood of reversing the GEP associated with resistance to Rapamycin. Specifically, we evaluated the HIV protease inhibitor (PI) Nelfinavir, which is known to have anti-cancer and Akt-inhibitory properties, as well as the small molecule Akt inhibitor MK-2206, for their potential to synergize with to Rapamycin in DLBCL. Nelfinavir and MK-2206 caused profound inhibition of cell viability in combination with Rapamycin in DLBCL cell lines. Low nanomolar concentrations of Rapamycin inhibited phosphorylation of Akt and also downstream targets of activated mTOR when used in combination with these Akt inhibitors. These findings have the potential to significantly improve patient selection for mTOR inhibitor therapy, and to improve rates and depths of response. More broadly, they support the use of global RNA expression and connectivity mapping to improve patient selection and identify synergistic drug combinations for cancer therapy. DLBCL cell lines were tested for Rapamycin sensitivity and classified as "sensitive" or "resistant." Genome-wide analysis of all cell lines were performed using the Affymetrix HuGene ST 1.0 Array Platform. Genes with differential expression between sensitive and resistant cell lines were analyzed using Statistical Analysis of Microarrays (SAM) software, and a signature of genes determnined. This signature was found to accurately predict sensitivity or resistance of other DLBCL cell lines, and to identify the protein kinase Akt as central to resistance.

Dataset Information

Molecular and Genetic Crosstalks Between mTOR and ERRα are Key Determinants of Rapamycin-induced Non-Alcoholic Fatty Liver.

Publications

Molecular and genetic crosstalks between mTOR and ERRα are key determinants of rapamycin-induced nonalcoholic fatty liver.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure