Mechanisms of cell cycle control revealed by a systematic and quantitative overexpression screen in S. cerevisiae

Cyclebase.org: version 2.0, an updated comprehensive, multi-species repository of cell cycle experiments and derived analysis results

Cell division involves a complex series of events orchestrated by thousands of molecules. To study this process, researchers have employed mRNA expression profiling of synchronously growing cell cultures progressing through the cell cycle. These experiments, which have been carried out in several organisms, are not easy to access, combine and evaluate. Complicating factors include variation in interdivision time between experiments and differences in relative duration of each cell-cycle phase across organisms. To address these problems, we created Cyclebase, an online resource of cell-cycle-related experiments. This database provides an easy-to-use web interface that facilitates visualization and download of genome-wide cell-cycle data and analysis results. Data from different experiments are normalized to a common timescale and are complimented with key cell-cycle information and derived analysis results. In Cyclebase version 2.0, we have updated the entire database to reflect changes to genome annotations, included information on cyclin-dependent kinase (CDK) substrates, predicted degradation signals and loss-of-function phenotypes from genome-wide screens. The web interface has been improved and provides a single, gene-centric graph summarizing the available cell-cycle experiments. Finally, key information and links to orthologous and paralogous genes are now included to further facilitate comparison of cell-cycle regulation across species. Cyclebase version 2.0 is available at http://www.cyclebase.org.

Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data

MOTIVATION

Array-based analysis of chromatin immunoprecipitation (ChIP-chip) data is a powerful technique for identifying DNA target regions of individual transcription factors. The identification of these target regions from comprehensive promoter array ChIP-chip data is challenging. Here, three approaches for the identification of transcription factor target genes from promoter array ChIP-chip data are presented. We compare (i) a standard log-fold-change analysis (LFC); (ii) a basic method based on a Hidden Markov Model (HMM); and (iii) a new extension of the HMM approach to an HMM with scaled transition matrices (SHMM) that incorporates information about the relative orientation of adjacent gene pairs on DNA.

RESULTS

All three methods are applied to different promoter array ChIP-chip datasets of the yeast Saccharomyces cerevisiae and the important model plant Arabidopsis thaliana to compare the prediction of transcription factor target genes. In the context of the yeast cell cycle, common target genes bound by the transcription factors ACE2 and SWI5, and ACE2 and FKH2 are identified and evaluated using the Saccharomyces Genome Database. Regarding A.thaliana, target genes of the seed-specific transcription factor ABI3 are predicted and evaluate based on publicly available gene expression profiles and transient assays performed in the wet laboratory experiments. The application of the novel SHMM to these two different promoter array ChIP-chip datasets leads to an improved identification of transcription factor target genes in comparison to the two standard approaches LFC and HMM.

AVAILABILITY

The software of LFC, HMM and SHMM, the ABI3 ChIP-chip dataset, and Supplementary Material can be downloaded from http://dig.ipk-gatersleben.de/SHMMs/ChIPchip/ChIPchip.html.

Intrinsic protein disorder and interaction promiscuity are widely associated with dosage sensitivity

Why are genes harmful when they are overexpressed? By testing possible causes of overexpression phenotypes in yeast, we identify intrinsic protein disorder as an important determinant of dosage sensitivity. Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased, and we demonstrate that this is the likely cause of pathology when genes are overexpressed. We validate our findings in two animals, Drosophila melanogaster and Caenorhabditis elegans. In mice and humans the same properties are strongly associated with dosage-sensitive oncogenes, such that mass-action-driven molecular interactions may be a frequent cause of cancer. Dosage-sensitive genes are tightly regulated at the transcriptional, RNA, and protein levels, which may serve to prevent harmful increases in protein concentration under physiological conditions. Mass-action-driven interaction promiscuity is a single theoretical framework that can be used to understand, predict, and possibly treat the effects of increased gene expression in evolution and disease.