Although loss of p53 function and activation of canonical Wnt signaling cascades are frequently coupled in cancer, the links between these two pathways remain unclear. We report here that p53 transactivates miRNA-34 (miR-34), which suppresses the transcriptional activity of β-catenin-T-cell factor/lymphoid enhancer factor (TCF/LEF) complexes by targeting the untranslated regions (UTRs) of a set of highly-conserved targets in a network of Wnt pathway-regulated genes. Loss of p53 function increases canonical Wnt signaling through miR-34-specific interactions with target UTRs, whereas miR-34 depletion relieves p53-mediated Wnt repression. Further, gene expression signatures reflecting the status of β-catenin-TCF/LEF transcriptional activity in breast cancer and pediatric neuroblastoma patients are closely associated with p53 and miR-34 functional status. Loss of p53 or miR-34 contributed to neoplastic progression by triggering the Wnt-dependent, tissue-invasive activity of colorectal cancer cells. Further, during development, miR-34 interactions with the β-catenin UTR determine Xenopus body axis polarity and Wnt-dependent gene patterning. These data provide insight into the mechanisms by which a p53-miR-34 network restrains canonical Wnt signaling cascades in developing organisms and human cancer.

Expression of the essential EMT inducer Snail1 is inhibited by miR-34 through a p53-dependent regulatory pathway.

The identification of genes to be deleted or amplified is an essential step in metabolic engineering for strain improvement toward the enhanced production of desired bioproducts. In the past, several methods based on flux analysis of genome-scale metabolic models have been developed for identifying gene targets for deletion. Genome-wide identification of gene targets for amplification, on the other hand, has been rather difficult. Here, we report a strategy called flux scanning based on enforced objective flux (FSEOF) to identify gene amplification targets. FSEOF scans all the metabolic fluxes in the metabolic model and selects fluxes that increase when the flux toward product formation is enforced as an additional constraint during flux analysis. This strategy was successfully employed for the identification of gene amplification targets for the enhanced production of the red-colored antioxidant lycopene. Additional metabolic engineering based on gene knockout simulation resulted in further synergistic enhancement of lycopene production. Thus, FSEOF can be used as a general strategy for selecting genome-wide gene amplification targets in silico.One of the ultimate objectives of industrial biotechnology is the improvement of the yields and productivities of bioproducts. Toward this end, metabolic engineering has been successfully employed in improving industrial strains and has recently become more powerful due to the integration of omics and computational methods at the systems level (19,27). Metabolic engineering improves the metabolic phenotype toward the overproduction of a desired product by the amplification and/or deletion of certain metabolic genes, together with the rewiring of regulatory circuits (10,14).Recent systemic approaches employing genome-scale metabolic models (7) have enabled researchers to identify gene deletion targets for improving microbial strains (1, 4, 30, 31) by utilizing various algorithms. For example, the nested optimization framework using mixed-integer linear programming (OptKnock) (4, 25) and a sequential gene deletion strategy based on the formalism of minimization of metabolic adjustment (MOMA) (1, 31) allowed successful identification of the genes to be deleted among a large number of gene candidates. Using this strategy in combination with regulatory engineering and omics analysis, systems-level metabolic engineering was performed for developing L-valine-and L-threonine-overproducing Escherichia coli strains (16, 24).However, it has been difficult to identify gene amplification targets by similar approaches. This is because predicting the metabolic phenotypes after gene deletion is much easier than predicting the metabolic phenotype after gene amplification; gene deletion causes the corresponding flux to become zero, while gene amplification does not necessarily increase the corresponding metabolic fluxes, due to complex regulation of the metabolic network. Additionally, it is even more difficult to predict how much the flux will increase upon gene amplification. A creative...

Purpose: Identification of novel biomarkers of cancer is important for improved diagnosis, prognosis, and therapeutic intervention. This study aimed to identify marker genes of colorectal cancer (CRC) by combining bioinformatics analysis of gene expression data and validation experiments using patient samples and to examine the potential connection between validated markers and the established oncogenes such as c-Myc and K-ras.Experimental Design: Publicly available data from GenBank and Oncomine were meta-analyzed leading to 34 candidate marker genes of CRC. Multiple case-matched normal and tumor tissues were examined by RT-PCR for differential expression, and 9 genes were validated as CRC biomarkers. Statistical analyses for correlation with major clinical parameters were carried out, and RNA interference was used to examine connection with major oncogenes.Results: We show with high confidence that 9 (ECT2, ETV4, DDX21, RAN, S100A11, RPS4X, HSPD1, CKS2, and C9orf140) of the 34 candidate genes are expressed at significantly elevated levels in CRC tissues compared to normal tissues. Furthermore, high-level expression of RPS4X was associated with nonmucinous cancer cell type and that of ECT2 with lack of lymphatic invasion while upregulation of CKS2 was correlated with early tumor stage and lack of family history of CRC. We also demonstrate that RPS4X and DDX21 are regulatory targets of c-Myc and ETV4 is downstream to K-ras signaling.Conclusions: We have identified multiple novel biomarkers of CRC. Further analyses of their function and connection to signaling pathways may reveal potential value of these biomarkers in diagnosis, prognosis, and treatment of CRC.