Integrating multiple types of data to predict novel cell cycle-related genes

Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data

Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part. Results. LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.

Evolution of intraspecific transcriptomic landscapes in yeasts

Variations in gene expression have been widely explored in order to obtain an accurate overview of the changes in regulatory networks that underlie phenotypic diversity. Numerous studies have characterized differences in genomic expression between large numbers of individuals of model organisms such as Saccharomyces cerevisiae. To more broadly survey the evolution of the transcriptomic landscape across species, we measured whole-genome expression in a large collection of another yeast species: Lachancea kluyveri (formerly Saccharomyces kluyveri), using RNAseq. Interestingly, this species diverged from the S. cerevisiae lineage prior to its ancestral whole genome duplication. Moreover, L. kluyveri harbors a chromosome-scale compositional heterogeneity due to a 1-Mb ancestral introgressed region as well as a large set of unique unannotated genes. In this context, our comparative transcriptomic analysis clearly showed a link between gene evolutionary history and expression behavior. Indeed, genes that have been recently acquired or under function relaxation tend to be less transcribed show a higher intraspecific variation (plasticity) and are less involved in network (connectivity). Moreover, utilizing this approach in L. kluyveri also highlighted specific regulatory network signatures in aerobic respiration, amino-acid biosynthesis and glycosylation, presumably due to its different lifestyle. Our data set sheds an important light on the evolution of intraspecific transcriptomic variation across distant species.

Aim44p regulates phosphorylation of Hof1p to promote contractile ring closure during cytokinesis in budding yeast

Aim44p undergoes septin-dependent localization to the actomyosin ring and regulates contractile ring closure and the abundance, phosphorylation, and dynamics of Hof1p, a regulator of actomyosin ring closure. It also interacts directly with Hof1p. Thus Aim44p is a novel regulator of contractile ring closure in budding yeast.

Whereas actomyosin and septin ring organization and function in cytokinesis are thoroughly described, little is known regarding the mechanisms by which the actomyosin ring interacts with septins and associated proteins to coordinate cell division. Here we show that the protein product of YPL158C, Aim44p, undergoes septin-dependent recruitment to the site of cell division. Aim44p colocalizes with Myo1p, the type II myosin of the contractile ring, throughout most of the cell cycle. The Aim44p ring does not contract when the actomyosin ring closes. Instead, it forms a double ring that associates with septin rings on mother and daughter cells after cell separation. Deletion of AIM44 results in defects in contractile ring closure. Aim44p coimmunoprecipitates with Hof1p, a conserved F-BAR protein that binds both septins and type II myosins and promotes contractile ring closure. Deletion of AIM44 results in a delay in Hof1p phosphorylation and altered Hof1p localization. Finally, overexpression of Dbf2p, a kinase that phosphorylates Hof1p and is required for relocalization of Hof1p from septin rings to the contractile ring and for Hof1p-triggered contractile ring closure, rescues the cytokinesis defect observed in aim44∆ cells. Our studies reveal a novel role for Aim44p in regulating contractile ring closure through effects on Hof1p.

Identification of yeast cell cycle regulated genes based on genomic features

Background

Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic features – transcription factor binding and motif profiles.

Results

Through integrating gene-expression data with ChIP-chip binding and putative binding sites of transcription factors, our method shows high accuracy in discriminating yeast cell cycle regulated genes from non-cell cycle regulated ones. We predict 211 novel cell cycle regulated genes. Our model rediscovers the main cell cycle transcription factors and provides new insights into the regulatory mechanisms. The model also reveals a regulatory circuit mediated by a number of key cell cycle regulators.

Conclusions

Our model suggests that the periodical pattern of cell cycle genes is largely coded in their promoter regions, which can be captured by motif and transcription factor binding data. Cell cycle is controlled by a relatively small number of master transcription factors. The concept of genomic feature based method can be readily extended to human cell cycle process and other transcriptionally regulated processes, such as tissue-specific expression.