Identification of cytoplasmic capping targets reveals a role for cap homeostasis in translation and mRNA stability.

The notion that decapping leads irreversibly to messenger RNA (mRNA) decay was contradicted by the identification of capped transcripts missing portions of their 5' ends and a cytoplasmic complex that can restore the cap on uncapped mRNAs. In this study, we used accumulation of uncapped transcripts in cells inhibited for cytoplasmic capping to identify the targets of this pathway. Inhibition of cytoplasmic capping results in the destabilization of some transcripts and the redistribution of others from polysomes to nontranslating messenger ribonucleoproteins, where they accumulate in an uncapped state. Only a portion of the mRNA transcriptome is affected by cytoplasmic capping, and its targets encode proteins involved in nucleotide binding, RNA and protein localization, and the mitotic cell cycle. The 3' untranslated regions of recapping targets are enriched for AU-rich elements and microRNA binding sites, both of which function in cap-dependent mRNA silencing. These findings identify a cyclical process of decapping and recapping that we term cap homeostasis.

Using ChIPMotifs for de novo motif discovery of OCT4 and ZNF263 based on ChIP-based high-throughput experiments.

DNA motifs are short sequences varying from 6 to 25 bp and can be highly variable and degenerated. One major approach for predicting transcription factor (TF) binding is using position weight matrix (PWM) to represent information content of regulatory sites; however, when used as the sole means of identifying binding sites suffers from the limited amount of training data available and a high rate of false-positive predictions. ChIPMotifs program is a de novo motif finding tool developed for ChIP-based high-throughput data, and W-ChIPMotifs is a Web application tool for ChIPMotifs. It composes various ab initio motif discovery tools such as MEME, MaMF, Weeder and optimizes the significance of the detected motifs by using bootstrap re-sampling error estimation and a Fisher test. Using these techniques, we determined a PWM for OCT4 which is similar to canonical OCT4 consensus sequence. In a separate study, we also use de novo motif discovery to suggest that ZNF263 binds to a 24-nt site that differs from the motif predicted by the zinc finger code in several positions.

ChIP-seq defined genome-wide map of TGFß/SMAD4 targets: implications with clinical outcome of ovarian cancer.

Deregulation of the transforming growth factor-ß (TGFß) signaling pathway in epithelial ovarian cancer has been reported, but the precise mechanism underlying disrupted TGFß signaling in the disease remains unclear. We performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate genome-wide screening of TGFß-induced SMAD4 binding in epithelial ovarian cancer. Following TGFß stimulation of the A2780 epithelial ovarian cancer cell line, we identified 2,362 SMAD4 binding loci and 318 differentially expressed SMAD4 target genes. Comprehensive examination of SMAD4-bound loci, revealed four distinct binding patterns: 1) Basal; 2) Shift; 3) Stimulated Only; 4) Unstimulated Only. TGFß stimulated SMAD4-bound loci were primarily classified as either Stimulated only (74%) or Shift (25%), indicating that TGFß-stimulation alters SMAD4 binding patterns in epithelial ovarian cancer cells. Furthermore, based on gene regulatory network analysis, we determined that the TGFß-induced, SMAD4-dependent regulatory network was strikingly different in ovarian cancer compared to normal cells. Importantly, the TGFß/SMAD4 target genes identified in the A2780 epithelial ovarian cancer cell line were predictive of patient survival, based on in silico mining of publically available patient data bases. In conclusion, our data highlight the utility of next generation sequencing technology to identify genome-wide SMAD4 target genes in epithelial ovarian cancer and link aberrant TGFß/SMAD signaling to ovarian tumorigenesis. Furthermore, the identified SMAD4 binding loci, combined with gene expression profiling and in silico data mining of patient cohorts, may provide a powerful approach to determine potential gene signatures with biological and future translational research in ovarian and other cancers.

HRTBLDb: an informative data resource for hormone receptors target binding loci.

Three hormone receptors, the estrogen receptor (ER), the androgen receptor (AR) and glucocorticoid receptor (GR) play an important role in regulating the cellular differentiation tissue development of skin, bone, the brain and the endocrine system; therefore, there is a strong scientific need to identify and characterize hormone receptor transcriptional regulation. Given that the vast amount of regulatory data for hormone being produced by ChIP-based high-throughput experiments is widely scattered in disparate, poorly cross-indexed data stores, a flexible platform for organizing and relating these data would provide significant value. We created a data management system called the Hormone Receptor Target Binding Loci, HRTBLDb (http://motif.bmi.ohio-state.edu/hrtbldb), to address this problem. This database contains hormone receptor binding regions (binding loci) from in vivo ChIP-based high-throughput experiments as well as in silico, computationally predicted, binding motifs and cis-regulatory modules for the co-occurring transcription factor binding motifs, which are within a binding locus. It also contains individual binding sites whose regulatory action has been verified by in vitro experiments. The current version contains 44,673 binding elements with 114 hormone response elements which are verified by in vitro experiments; 75 binding motifs which occur with a hormone response element and whose co-regulatory action is verified by in vitro experiments; 18,472 binding loci from in vivo experiments; and 26,012 computationally predicted binding motifs.