The Fission Yeast Homeodomain Protein Yox1p Binds to MBF and Confines MBF-Dependent Cell-Cycle Transcription to G1-S via Negative Feedback

The regulation of the G1- to S-phase transition is critical for cell-cycle progression. This transition is driven by a transient transcriptional wave regulated by transcription factor complexes termed MBF/SBF in yeast and E2F-DP in mammals. Here we apply genomic, genetic, and biochemical approaches to show that the Yox1p homeodomain protein of fission yeast plays a critical role in confining MBF-dependent transcription to the G1/S transition of the cell cycle. The yox1 gene is an MBF target, and Yox1p accumulates and preferentially binds to MBF-regulated promoters, via the MBF components Res2p and Nrm1p, when they are transcriptionally repressed during the cell cycle. Deletion of yox1 results in constitutively high transcription of MBF target genes and loss of their cell cycle–regulated expression, similar to deletion of nrm1. Genome-wide location analyses of Yox1p and the MBF component Cdc10p reveal dozens of genes whose promoters are bound by both factors, including their own genes and histone genes. In addition, Cdc10p shows promiscuous binding to other sites, most notably close to replication origins. This study establishes Yox1p as a new regulatory MBF component in fission yeast, which is transcriptionally induced by MBF and in turn inhibits MBF-dependent transcription. Yox1p may function together with Nrm1p to confine MBF-dependent transcription to the G1/S transition of the cell cycle via negative feedback. Compared to the orthologous budding yeast Yox1p, which indirectly functions in a negative feedback loop for cell-cycle transcription, similarities but also notable differences in the wiring of the regulatory circuits are evident

Comparative genetic, proteomic and phosphoproteomic analysis of C. <i>elegans </i>embryos with a focus on <i>ham</i>-1/STOX and <i>pig</i>-1/MELK in dopaminergic neuron development

Asymmetric cell divisions are required for cellular diversity and defects can lead to altered daughter cell fates and numbers. In a genetic screen for C. elegans mutants with defects in dopaminergic head neuron specification or differentiation, we isolated a new allele of the transcription factor HAM-1 [HSN (Hermaphrodite-Specific Neurons) Abnormal Migration]. Loss of both HAM-1 and its target, the kinase PIG-1 [PAR-1(I)-like Gene], leads to abnormal dopaminergic head neuron numbers. We identified discrete genetic relationships between ham-1, pig-1 and apoptosis pathway genes in dopaminergic head neurons. We used an unbiased, quantitative mass spectrometry-based proteomics approach to characterise direct and indirect protein targets and pathways that mediate the effects of PIG-1 kinase loss in C. elegans embryos. Proteins showing changes in either abundance, or phosphorylation levels, between wild-type and pig-1 mutant embryos are predominantly connected with processes including cell cycle, asymmetric cell division, apoptosis and actomyosin-regulation. Several of these proteins play important roles in C. elegans development. Our data provide an in-depth characterisation of the C. elegans wild-type embryo proteome and phosphoproteome and can be explored via the Encyclopedia of Proteome Dynamics (EPD) - an open access, searchable online database

Identification of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes

Systematic identification of yeast cell cycle transcription factors using multiple data sources

<p>Abstract</p> <p>Background</p> <p>Eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes.</p> <p>Results</p> <p>We developed a method to identify cell cycle TFs in yeast by integrating current ChIP-chip, mutant, transcription factor binding site (TFBS), and cell cycle gene expression data. We identified 17 cell cycle TFs, 12 of which are known cell cycle TFs, while the remaining five (Ash1, Rlm1, Ste12, Stp1, Tec1) are putative novel cell cycle TFs. For each cell cycle TF, we assigned specific cell cycle phases in which the TF functions and identified the time lag for the TF to exert regulatory effects on its target genes. We also identified 178 novel cell cycle-regulated genes, among which 59 have unknown functions, but they may now be annotated as cell cycle-regulated genes. Most of our predictions are supported by previous experimental or computational studies. Furthermore, a high confidence TF-gene regulatory matrix is derived as a byproduct of our method. Each TF-gene regulatory relationship in this matrix is supported by at least three data sources: gene expression, TFBS, and ChIP-chip or/and mutant data. We show that our method performs better than four existing methods for identifying yeast cell cycle TFs. Finally, an application of our method to different cell cycle gene expression datasets suggests that our method is robust.</p> <p>Conclusion</p> <p>Our method is effective for identifying yeast cell cycle TFs and cell cycle-regulated genes. Many of our predictions are validated by the literature. Our study shows that integrating multiple data sources is a powerful approach to studying complex biological systems.</p

A homeodomain transcription factor regulates the DNA replication checkpoint in yeast

Checkpoints monitor the successful completion of cell cycle processes, such as DNA replication, and also regulate the expression of cell cycle-dependent genes that are required for responses. In the model yeast Schizosaccharomyces pombe G1/S phase-specific gene expression is regulated by the MBF (also known as DSC1) transcription factor complex and is also activated by the mammalian ATM/ATR-related Rad3 DNA replication checkpoint. Here, we show that the Yox1 homeodomain transcription factor acts to co-ordinate the expression of MBF-regulated genes during the cell division cycle. Moreover, our data suggests that Yox1 is inactivated by the Rad3 DNA replication checkpoint via phosphorylation by the conserved Cds1 checkpoint kinase. Collectively, our data has implications for understanding the mechanisms underlying the coordination of cell cycle processes in eukaryotes