http://hdl.handle.net/2144/2425 2013-06-20T04:27:31Z http://hdl.handle.net/2144/3367 A Unique Family of Mrr-Like Modification-Dependent Restriction Endonucleases Zheng, Yu; Cohen-Karni, Devora; Xu, Derrick; Chin, Hang Gyeong; Wilson, Geoffrey; Pradhan, Sriharsa; Roberts, Richard J. Mrr superfamily of homologous genes in microbial genomes restricts modified DNA in vivo. However, their biochemical properties in vitro have remained obscure. Here, we report the experimental characterization of MspJI, a remote homolog of Escherichia coli's Mrr and show it is a DNA modification-dependent restriction endonuclease. Our results suggest MspJI recognizes mCNNR (R = G/A) sites and cleaves DNA at fixed distances (N12/N16) away from the modified cytosine at the 3′ side (or N9/N13 from R). Besides 5-methylcytosine, MspJI also recognizes 5-hydroxymethylcytosine but is blocked by 5-glucosylhydroxymethylcytosine. Several other close homologs of MspJI show similar modification-dependent endonuclease activity and display substrate preferences different from MspJI. A unique feature of these modification-dependent enzymes is that they are able to extract small DNA fragments containing modified sites on genomic DNA, for example ^∼32 bp around symmetrically methylated CG sites and ^∼31 bp around methylated CNG sites. The digested fragments can be directly selected for high-throughput sequencing to map the location of the modification on the genomic DNA. The MspJI enzyme family, with their different recognition specificities and cleavage properties, provides a basis on which many future methods can build to decode the epigenomes of different organisms. 2010-05-05T00:00:00Z http://hdl.handle.net/2144/3366 PC3 Prostate Tumor-Initiating Cells with Molecular Profile FAM65Bhigh/MFI2low/LEF1low Increase Tumor Angiogenesis Zhang, Kexiong; Waxman, David J BACKGROUND Cancer stem-like cells are proposed to sustain solid tumors by virtue of their capacity for self-renewal and differentiation to cells that comprise the bulk of the tumor, and have been identified for a variety of cancers based on characteristic clonal morphologies and patterns of marker gene expression. METHODS Single cell cloning and spheroid culture studies were used to identify a population of cancer stem-like cells in the androgen-independent human prostate cancer cell line PC3. RESULTS We demonstrate that, under standard culture conditions, ~10% of PC3 cells form holoclones with cancer stem cell characteristics. These holoclones display high self-renewal capability in spheroid formation assays under low attachment and serum-free culture conditions, retain their holoclone morphology when passaged at high cell density, exhibit moderate drug resistance, and show high tumorigenicity in scid immunodeficient mice. PC3 holoclones readily form spheres, and PC3-derived spheres yield a high percentage of holoclones, further supporting their cancer stem cell-like nature. We identified one gene, FAM65B, whose expression is consistently up regulated in PC3 holoclones compared to paraclones, the major cell morphology in the parental PC3 cell population, and two genes, MFI2 and LEF1, that are consistently down regulated. This molecular profile, FAM65Bhigh/MFI2low/LEF1low, also characterizes spheres generated from parental PC3 cells. The PC3 holoclones did not show significant enriched expression of the putative prostate cancer stem cell markers CD44 and integrin α2β1. PC3 tumors seeded with holoclones showed dramatic down regulation of FAM65B and dramatic up regulation of MFI2 and LEF1, and unexpectedly, a marked increase in tumor vascularity compared to parental PC3 tumors, suggesting a role of cancer stem cells in tumor angiogenesis. CONCLUSIONS These findings support the proposal that PC3 tumors are sustained by a small number of tumor-initiating cells with stem-like characteristics, including strong self-renewal and pro-angiogenic capability and marked by the expression pattern FAM65Bhigh/MFI2low/LEF1low. These markers may serve as targets for therapies designed to eliminate cancer stem cell populations associated with aggressive, androgen-independent prostate tumors such as PC3. 2010-12-29T00:00:00Z http://hdl.handle.net/2144/3151 Machine Learning for Regulatory Analysis and Transcription Factor Target Prediction in Yeast Holloway, Dustin T.; Kon, Mark; DeLisi, Charles High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps-the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data. ELECTRONIC SUPPLEMENTARY MATERIAL. Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11693-006-9003-3 and is accessible for authorized users. 2006-10-31T00:00:00Z http://hdl.handle.net/2144/3146 Classifying Transcription Factor Targets and Discovering Relevant Biological Features Holloway, Dustin T; Kon, Mark; DeLisi, Charles BACKGROUND. An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties. PRINCIPAL FINDINGS. (1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter. CONCLUSION. Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite. REVIEWERS. This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor. 2008-05-30T00:00:00Z