Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

Epigenotyping in Peripheral Blood Cell DNA and Breast Cancer Risk: A Proof of Principle Study

Background: Epigenetic changes are emerging as one of the most important events in carcinogenesis. Two alterations in the pattern of DNA methylation in breast cancer (BC) have been previously reported; active estrogen receptor-a (ER-a) is associated with decreased methylation of ER-a target (ERT) genes, and polycomb group target (PCGT) genes are more likely than other genes to have promoter DNA hypermethylation in cancer. However, whether DNA methylation in normal unrelated cells is associated with BC risk and whether these imprints can be related to factors which can be modified by the environment, is unclear.Methodology/Principal Findings: Using quantitative methylation analysis in a case-control study (n = 1,083) we found that DNA methylation of peripheral blood cell DNA provides good prediction of BC risk. We also report that invasive ductal and invasive lobular BC is characterized by two different sets of genes, the latter particular by genes involved in the differentiation of the mesenchyme (PITX2, TITF1, GDNF and MYOD1). Finally we demonstrate that only ERT genes predict ER positive BC; lack of peripheral blood cell DNA methylation of ZNF217 predicted BC independent of age and family history (odds ratio 1.49; 95% confidence interval 1.12-1.97; P = 0.006) and was associated with ER-a bioactivity in the corresponding serum.Conclusion/Significance: This first large-scale epigenotyping study demonstrates that DNA methylation may serve as a link between the environment and the genome. Factors that can be modulated by the environment (like estrogens) leave an imprint in the DNA of cells that are unrelated to the target organ and indicate the predisposition to develop a cancer. Further research will need to demonstrate whether DNA methylation profiles will be able to serve as a new tool to predict the risk of developing chronic diseases with sufficient accuracy to guide preventive measures

ZNF217, a candidate breast cancer oncogene amplified at 20q13, regulates expression of the ErbB3 receptor tyrosine kinase in breast cancer cells.

Understanding the mechanisms underlying ErbB3 overexpression in breast cancer will facilitate the rational design of therapies to disrupt ErbB2-ErbB3 oncogenic function. Although ErbB3 overexpression is frequently observed in breast cancer, the factors mediating its aberrant expression are poorly understood. In particular, the ErbB3 gene is not significantly amplified, raising the question as to how ErbB3 overexpression is achieved. In this study we showed that the ZNF217 transcription factor, amplified at 20q13 in ∼20% of breast tumors, regulates ErbB3 expression. Analysis of a panel of human breast cancer cell lines (n = 50) and primary human breast tumors (n = 15) showed a strong positive correlation between ZNF217 and ErbB3 expression. Ectopic expression of ZNF217 in human mammary epithelial cells induced ErbB3 expression, whereas ZNF217 silencing in breast cancer cells resulted in decreased ErbB3 expression. Although ZNF217 has previously been linked with transcriptional repression because of its close association with C-terminal-binding protein (CtBP)1/2 repressor complexes, our results show that ZNF217 also activates gene expression. We showed that ZNF217 recruitment to the ErbB3 promoter is CtBP1/2-independent and that ZNF217 and CtBP1/2 have opposite roles in regulating ErbB3 expression. In addition, we identify ErbB3 as one of the mechanisms by which ZNF217 augments PI-3K/Akt signaling