p53 is one of the most important tumour suppressor proteins currently known. It is activated in response to DNA damage and this activation leads to proliferation arrest and cell death. The abundance and activity of p53 are tightly controlled and reductions in p53's activity can contribute to the development of cancer. Here, we show that Fam83F increases p53 protein levels by protein stabilisation. Fam83F interacts with p53 and decreases its ubiquitination and degradation. Overexpression of Fam83F also increases p53 activity in cell culture experiments and in zebrafish embryos. Downregulation of Fam83F increases cell proliferation in zebrafish xenografts. Fam83F expression is induced in response to DNA damage. Its downregulation decreases transcription of p53's target genes during the DNA damage response and increases cell proliferation and the colony forming ability of cells, identifying Fam83F as an important regulator of the DNA damage response. Fam83F also enhances migration of cells harbouring mutant p53 demonstrating that it can also activate mutant forms of p53.

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BackgroundThe p53 tumor suppressor protein is mainly regulated by alterations in the half-life of the protein, resulting in significant differences in p53 protein levels in cells. The major regulator of this process is Mdm2, which ubiquitinates p53 and targets it for proteasomal degradation. This process can be enhanced or reduced by proteins that associate with p53 or Mdm2 and several proteins have been identified with such an activity. Furthermore, additional ubiquitin ligases for p53 have been identified in recent years. Nevertheless, our understanding of how p53 abundance and Mdm2 activity are regulated remains incomplete. Here we describe a cell culture based overexpression screen to identify evolutionarily conserved regulators of the p53/Mdm2 circuit. The results from this large-scale screening method will contribute to a better understanding of the regulation of these important proteins.MethodsExpression screening was based on co-transfection of H1299 cells with pools of cDNA’s from a Medaka library together with p53, Mdm2 and, as internal control, Ror2. After cell lysis, SDS-PAGE/WB analysis was used to detect alterations in these proteins.ResultsMore than one hundred hits that altered the abundance of either p53, Mdm2, or both were identified in the primary screen. Subscreening of the library pools that were identified in the primary screen identified several potential novel regulators of p53 and/or Mdm2. We also tested whether the human orthologues of the Medaka genes regulate p53 and/or Mdm2 abundance. All human orthologues regulated p53 and/or Mdm2 abundance in the same manner as the proteins from Medaka, which underscores the suitability of this screening methodology for the identification of new modifiers of p53 and Mdm2.ConclusionsDespite enormous efforts in the last two decades, many unknown regulators for p53 and Mdm2 abundance are predicted to exist. This cross-species approach to identify evolutionarily conserved regulators demonstrates that our Medaka unigene cDNA library represents a powerful tool to screen for these novel regulators of the p53/Mdm2 pathway.Electronic supplementary materialThe online version of this article (doi:10.1186/s12896-015-0208-y) contains supplementary material, which is available to authorized users.

BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε´) and conductivity (σ). OBJECTIVE: To examine if the dielectric behavior of normal and malignant tissues at low frequencies (α dispersion) will lead to the threshold (separating) line between them and find the threshold values of capacitance and resistance. These data are used as input for deep learning neural networks, and the outcomes are normal or malignant. METHODS: ε´ and σ in the range of 50 Hz to 100 KHz for 15 human malignant tissues and their corresponding normal ones have been measured. The separating line equation between the two classes is found by mathematical calculations and verified via support vector machine (SVM). Normal range and the threshold value of both normal capacitance and resistance are calculated. RESULTS: Deep learning analysis has an accuracy of 91.7%, 85.7% sensitivity, and 100% specificity for instant and automatic prediction of the type of breast tissue, either normal or malignant. CONCLUSIONS: These data can be used in both cancer diagnosis and prognosis follow-up.