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The Snail family of transcription factors are core inducers of epithelial-to-mesenchymal transition (EMT). Here we show that the F-box protein FBXO11 recognizes and promotes ubiquitin-mediated degradation of multiple Snail family members including Scratch. The association between FBXO11 and Snai1 in vitro is independent of Snai1 phosphorylation. Overexpression of FBXO11 in mesenchymal cells reduces Snail protein abundance and cellular invasiveness. Conversely, depletion of endogenous FBXO11 in epithelial cancer cells causes Snail protein accumulation, EMT, and tumor invasion, as well as loss of estrogen receptor expression in breast cancer cells. Expression of FBXO11 is downregulated by EMT-inducing signals TGFβ and nickel. In human cancer, high FBXO11 levels correlate with expression of epithelial markers and favorable prognosis. The results suggest that FBXO11 sustains the epithelial state and inhibits cancer progression. Inactivation of FBXO11 in mice leads to neonatal lethality, epidermal thickening, and increased Snail protein levels in epidermis, validating that FBXO11 is a physiological ubiquitin ligase of Snail. Moreover, in C. elegans, the FBXO11 mutant phenotype is attributed to the Snail factors as it is suppressed by inactivation/depletion of Snail homologs. Collectively, these findings suggest that the FBXO11-Snail regulatory axis is evolutionarily conserved and critically governs carcinoma progression and mammalian epidermal development.
Pubmed ID: 25827072
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Center that produces knockout mice and carries out high-throughput phenotyping of each line in order to determine function of every gene in mouse genome. These mice will be preserved in repositories and made available to scientific community representing valuable resource for basic scientific research as well as generating new models for human diseases.
View all literature mentionsProject exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsSoftware R package. Methods for Cluster analysis. Performs variety of types of cluster analysis and other types of processing on large microarray datasets.
View all literature mentionsCenter that acquires, maintains, and distributes genetic stocks and information about stocks of the small free-living nematode Caenorhabditis elegans for use by investigators initiating or continuing research on this genetic model organism. A searchable strain database, general information about C. elegans, and links to key Web sites of use to scientists, including WormBase, WormAtlas, and WormBook are available.
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
Email: info_at_ dknet.org
