H2B ubiquitylation is part of chromatin architecture that marks exon-intron structure in budding yeast.

BACKGROUND: The packaging of DNA into chromatin regulates transcription from initiation through 3' end processing. One aspect of transcription in which chromatin plays a poorly understood role is the co-transcriptional splicing of pre-mRNA. RESULTS: Here we provide evidence that H2B monoubiquitylation (H2BK123ub1) marks introns in Saccharomyces cerevisiae. A genome-wide map of H2BK123ub1 in this organism reveals that this modification is enriched in coding regions and that its levels peak at the transcribed regions of two characteristic subgroups of genes. First, long genes are more likely to have higher levels of H2BK123ub1, correlating with the postulated role of this modification in preventing cryptic transcription initiation in ORFs. Second, genes that are highly transcribed also have high levels of H2BK123ub1, including the ribosomal protein genes, which comprise the majority of intron-containing genes in yeast. H2BK123ub1 is also a feature of introns in the yeast genome, and the disruption of this modification alters the intragenic distribution of H3 trimethylation on lysine 36 (H3K36me3), which functionally correlates with alternative RNA splicing in humans. In addition, the deletion of genes encoding the U2 snRNP subunits, Lea1 or Msl1, in combination with an htb-K123R mutation, leads to synthetic lethality. CONCLUSION: These data suggest that H2BK123ub1 facilitates cross talk between chromatin and pre-mRNA splicing by modulating the distribution of intronic and exonic histone modifications.

Inferring coregulation of transcription factors and microRNAs in breast cancer.

Both transcription factors (TFs) and microRNAs (miRNAs) regulate gene expression. TFs activate or suppress the initiation of the transcription process and miRNAs regulate mRNAs post-transcriptionally, thus forming a temporally ordered regulatory event. Ectopic expression of key transcriptional regulators and/or miRNAs has been shown to be involved in various tumors. Therefore, uncovering the coregulation of TFs and miRNAs in human cancers may lead to the discovery of novel therapeutics. We introduced a two-stage learning fuzzy method to model TF-miRNA coregulation using both genomic data and verified regulatory relationships. In Stage 1, a learning (adaptive) fuzzy inference system (ANFIS) combines two sequence alignment features of TF and target by learning from verified TF-target pairs into a sequence matching score. Next, a non-learning FIS incorporates a sequence alignment score and a correlation score from paired TF-target gene expression to output a Stage 1 fuzzy score to infer whether a TF-target regulation exists. For significant TF-target pairs, in Stage 2, similar to Stage 1, we first infer whether a miRNA regulates each common target by an ANFIS, which incorporates their sequences and known miRNA-target relationships to output a sequence score. Next, an FIS incorporates the Stage 1 fuzzy score, Stage 2 sequence score and gene expression correlation score of a miRNA-target pair to determine whether TF-miRNA coregulation exists. We collected 54 (8) TF-miRNA-target triples validated in ER-positive (ER-negative) breast cancer cell lines in the same article, and they were used as positives. Negative examples were constructed for Stage 1 (Stage 2) by pairing TFs (miRNAs) with human housekeeping genes found in the literature; both positives and negatives were used to train ANFISs in the training step. This two-stage fizzy algorithm was applied to predict 54 (8) TF-miRNA coregulation triples in ER-positive (ER-negative) human breast cancer cell lines, and resulted in true-positive rates of 0.55 (0.74) and 0.57 (0.75) using 3-fold and n-fold cross validations (CVs), respectively. False-positive rate bound was 0.07 (0.13) for ER-positive (ER-negative) breast cancer using both 3-fold and n-fold CVs. Interestingly, among the 62 coregulatroy triples from ER-positive/negative breast cancer cells, about 72% have TF- and coregulatory miRNA expression simultaneously greater or less than the corresponding medians, while in the remaining 28% of TFs and their coregulatory miRNAs are conversely expressed. The proposed fuzzy algorithm performed well in identification of TF-miRNA coregulation triples in human breast cancer. After being trained by the corresponding verified coregulatory triples and genomic data, this algorithm can be applied to uncover novel coregulation in other cancers in the future.