Exploratory analysis of protein translation regulatory networks using hierarchical random graphs

Abstract Background Protein translation is a vital cellular process for any living organism. The availability of interaction databases provides an opportunity for researchers to exploit the immense amount of data in silico such as studying biological networks. There has been an extensive effort using computational methods in deciphering the transcriptional regulatory networks. However, research on translation regulatory networks has caught little attention in the bioinformatics and computational biology community. Results In this paper, we present an exploratory analysis of yeast protein translation regulatory networks using hierarchical random graphs. We derive a protein translation regulatory network from a protein-protein interaction dataset. Using a hierarchical random graph model, we show that the network exhibits well organized hierarchical structure. In addition, we apply this technique to predict missing links in the network. Conclusions The hierarchical random graph mode can be a potentially useful technique for inferring hierarchical structure from network data and predicting missing links in partly known networks. The results from the reconstructed protein translation regulatory networks have potential implications for better understanding mechanisms of translational control from a system’s perspective

Reduction of Orc6 Expression Sensitizes Human Colon Cancer Cells to 5-Fluorouracil and Cisplatin

Previous studies from our group have shown that the expression levels of Orc6 were highly elevated in colorectal cancer patient specimens and the induction of Orc6 was associated with 5-fluorouracil (5-FU) treatment. The goal of this study was to investigate the molecular and cellular impact of Orc6 in colon cancer. In this study, we use HCT116 (wt-p53) and HCT116 (null-p53) colon cancer cell lines as a model system to investigate the impact of Orc6 on cell proliferation, chemosensitivity and pathways involved with Orc6. We demonstrated that the down regulation of Orc6 sensitizes colon cancer cells to both 5-FU and cisplatin (cis-pt) treatment. Decreased Orc6 expression in HCT-116 (wt-p53) cells by RNA interference triggered cell cycle arrest at G1 phase. Prolonged inhibition of Orc6 expression resulted in multinucleated cells in HCT-116 (wt-p53) cell line. Western immunoblot analysis showed that down regulation of Orc6 induced p21 expression in HCT-116 (wt-p53) cells. The induction of p21 was mediated by increased level of phosphorylated p53 at ser-15. By contrast, there is no elevated expression of p21 in HCT-116 (null-p53) cells. Orc6 down regulation also increased the expression of DNA damaging repair protein GADD45β and reduced the expression level of JNK1. Orc6 may be a potential novel target for future anti cancer therapeutic development in colon cancer

Genetic Basis of Hidden Phenotypic Variation Revealed by Increased Translational Readthrough in Yeast

Eukaryotic release factors 1 and 3, encoded by SUP45 and SUP35, respectively, in Saccharomyces cerevisiae, are required for translation termination. Recent studies have shown that, besides these two key factors, several genetic and epigenetic mechanisms modulate the efficiency of translation termination. These mechanisms, through modifying translation termination fidelity, were shown to affect various cellular processes, such as mRNA degradation, and in some cases could confer a beneficial phenotype to the cell. The most studied example of such a mechanism is [PSI+], the prion conformation of Sup35p, which can have pleiotropic effects on growth that vary among different yeast strains. However, genetic loci underlying such readthrough-dependent, background-specific phenotypes have yet to be identified. Here, we used sup35C653R, a partial loss-of-function allele of the SUP35 previously shown to increase readthrough of stop codons and recapitulate some [PSI+]-dependent phenotypes, to study the genetic basis of phenotypes revealed by increased translational readthrough in two divergent yeast strains: BY4724 (a laboratory strain) and RM11_1a (a wine strain). We first identified growth conditions in which increased readthrough of stop codons by sup35C653R resulted in different growth responses between these two strains. We then used a recently developed linkage mapping technique, extreme QTL mapping (X-QTL), to identify readthrough-dependent loci for the observed growth differences. We further showed that variation in SKY1, an SR protein kinase, underlies a readthrough-dependent locus observed for growth on diamide and hydrogen peroxide. We found that the allelic state of SKY1 interacts with readthrough level and the genetic background to determine growth rate in these two conditions

Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex

Multiple Regulatory Mechanisms to Inhibit Untimely Initiation of DNA Replication Are Important for Stable Genome Maintenance

Genomic instability is a hallmark of human cancer cells. To prevent genomic instability, chromosomal DNA is faithfully duplicated in every cell division cycle, and eukaryotic cells have complex regulatory mechanisms to achieve this goal. Here, we show that untimely activation of replication origins during the G1 phase is genotoxic and induces genomic instability in the budding yeast Saccharomyces cerevisiae. Our data indicate that cells preserve a low level of the initiation factor Sld2 to prevent untimely initiation during the normal cell cycle in addition to controlling the phosphorylation of Sld2 and Sld3 by cyclin-dependent kinase. Although untimely activation of origin is inhibited on multiple levels, we show that deregulation of a single pathway can cause genomic instability, such as gross chromosome rearrangements (GCRs). Furthermore, simultaneous deregulation of multiple pathways causes an even more severe phenotype. These findings highlight the importance of having multiple inhibitory mechanisms to prevent the untimely initiation of chromosome replication to preserve stable genome maintenance over generations in eukaryotes

Mathematical Modelling of DNA Replication Reveals a Trade-off between Coherence of Origin Activation and Robustness against Rereplication

Eukaryotic genomes are duplicated from multiple replication origins exactly once per cell cycle. In Saccharomyces cerevisiae, a complex molecular network has been identified that governs the assembly of the replication machinery. Here we develop a mathematical model that links the dynamics of this network to its performance in terms of rate and coherence of origin activation events, number of activated origins, the resulting distribution of replicon sizes and robustness against DNA rereplication. To parameterize the model, we use measured protein expression data and systematically generate kinetic parameter sets by optimizing the coherence of origin firing. While randomly parameterized networks yield unrealistically slow kinetics of replication initiation, networks with optimized parameters account for the experimentally observed distribution of origin firing times. Efficient inhibition of DNA rereplication emerges as a constraint that limits the rate at which replication can be initiated. In addition to the separation between origin licensing and firing, a time delay between the activation of S phase cyclin-dependent kinase (S-Cdk) and the initiation of DNA replication is required for preventing rereplication. Our analysis suggests that distributive multisite phosphorylation of the S-Cdk targets Sld2 and Sld3 can generate both a robust time delay and contribute to switch-like, coherent activation of replication origins. The proposed catalytic function of the complex formed by Dpb11, Sld3 and Sld2 strongly enhances coherence and robustness of origin firing. The model rationalizes how experimentally observed inefficient replication from fewer origins is caused by premature activation of S-Cdk, while premature activity of the S-Cdk targets Sld2 and Sld3 results in DNA rereplication. Thus the model demonstrates how kinetic deregulation of the molecular network governing DNA replication may result in genomic instability

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p

Systematic Two-Hybrid and Comparative Proteomic Analyses Reveal Novel Yeast Pre-mRNA Splicing Factors Connected to Prp19

Prp19 is the founding member of the NineTeen Complex, or NTC, which is a spliceosomal subcomplex essential for spliceosome activation. To define Prp19 connectivity and dynamic protein interactions within the spliceosome, we systematically queried the Saccharomyces cerevisiae proteome for Prp19 WD40 domain interaction partners by two-hybrid analysis. We report that in addition to S. cerevisiae Cwc2, the splicing factor Prp17 binds directly to the Prp19 WD40 domain in a 1∶1 ratio. Prp17 binds simultaneously with Cwc2 indicating that it is part of the core NTC complex. We also find that the previously uncharacterized protein Urn1 (Dre4 in Schizosaccharomyces pombe) directly interacts with Prp19, and that Dre4 is conditionally required for pre-mRNA splicing in S. pombe. S. pombe Dre4 and S. cerevisiae Urn1 co-purify U2, U5, and U6 snRNAs and multiple splicing factors, and dre4Δ and urn1Δ strains display numerous negative genetic interactions with known splicing mutants. The S. pombe Prp19-containing Dre4 complex co-purifies three previously uncharacterized proteins that participate in pre-mRNA splicing, likely before spliceosome activation. Our multi-faceted approach has revealed new low abundance splicing factors connected to NTC function, provides evidence for distinct Prp19 containing complexes, and underscores the role of the Prp19 WD40 domain as a splicing scaffold