The G(1) cyclin Cln3 promotes cell cycle entry via the transcription factor Swi6

In Saccharomyces cerevisiae (budding yeast), commitment to cell division in late G1 is promoted by the G1 cyclin Cln3 and its associated cyclin-dependent kinase, Cdc28. We show here that all known aspects of the function of Cln3 in G1 phase, including control of cell size, pheromone sensitivity, cell cycle progress, and transcription, require the protein Swi6. Swi6 is a component of two related transcription factors, SBF and MBF, which are known to regulate many genes at the G1-S transition. The Cln3-Cdc28 complex somehow activates SBF and MBF, but there was no evidence for direct phosphorylation of SBF/MBF by Cln3-Cdc28 or for a stable complex between SBF/MBF and Cln3-Cdc28. The activation also does not depend on the ability of Cln3 to activate transcription when artificially recruited directly to a promoter. The amino terminus and the leucine zipper of Swi6 are important for the ability of Swi6 to respond to Cln3 but are not essential for the basal transcriptional activity of Swi6. Cln3-Cdc28 may activate SBF and MBF indirectly, perhaps by phosphorylating some intermediary protein

Whi3 binds the mRNA of the G(1) cyclin CLN3 to modulate cell fate in budding yeast

Eukaryotic cells commit in G(1) to a new mitotic cycle or to diverse differentiation processes. Here we show that Whi3 is a negative regulator of Cln3, a G(1) cyclin that promotes transcription of many genes to trigger the G(1)/S transition in budding yeast. Whi3 contains an RNA-recognition motif that specifically binds the CLN3 mRNA, with no obvious effects on Cln3 levels, and localizes the CLN3 mRNA into discrete cytoplasmic foci. This is the first indication that G(1) events may be regulated by locally restricting the synthesis of a cyclin. Moreover, Whi3 is also required for restraining Cln3 function in meiosis, filamentation, and mating, thus playing a key role in cell fate determination in budding yeast

A developmentally regulated translational control pathway establishes the meiotic chromosome segregation pattern

Production of haploid gametes from diploid progenitor cells is mediated by a specialized cell division, meiosis, where two divisions, meiosis I and II, follow a single S phase. Errors in progression from meiosis I to meiosis II lead to aneuploid and polyploid gametes, but the regulatory mechanisms controlling this transition are poorly understood. Here, we demonstrate that the conserved kinase Ime2 regulates the timing and order of the meiotic divisions by controlling translation. Ime2 coordinates translational activation of a cluster of genes at the meiosis I–meiosis II transition, including the critical determinant of the meiotic chromosome segregation pattern CLB3. We further show that Ime2 mediates translational control through the meiosis-specific RNA-binding protein Rim4. Rim4 inhibits translation of CLB3 during meiosis I by interacting with the 5′ untranslated region (UTR) of CLB3. At the onset of meiosis II, Ime2 kinase activity rises and triggers a decrease in Rim4 protein levels, thereby alleviating translational repression. Our results elucidate a novel developmentally regulated translational control pathway that establishes the meiotic chromosome segregation pattern.American Cancer Society (Post-doctoral Fellowship)Virginia and D.K. Ludwig Fund for Cancer Research (Post-doctoral Fellowship)National Institutes of Health (U.S.) (Grant GM62207

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

EasyClone: method for iterative chromosomal integration of multiple genes in <em>Saccharomyces cerevisiae</em>

Development of strains for efficient production of chemicals and pharmaceuticals requires multiple rounds of genetic engineering. In this study, we describe construction and characterization of EasyClone vector set for baker's yeast Saccharomyces cerevisiae, which enables simultaneous expression of multiple genes with an option of recycling selection markers. The vectors combine the advantage of efficient uracil excision reaction-based cloning and Cre-LoxP-mediated marker recycling system. The episomal and integrative vector sets were tested by inserting genes encoding cyan, yellow, and red fluorescent proteins into separate vectors and analyzing for co-expression of proteins by flow cytometry. Cells expressing genes encoding for the three fluorescent proteins from three integrations exhibited a much higher level of simultaneous expression than cells producing fluorescent proteins encoded on episomal plasmids, where correspondingly 95% and 6% of the cells were within a fluorescence interval of Log(10) mean +/- 15% for all three colors. We demonstrate that selective markers can be simultaneously removed using Cre-mediated recombination and all the integrated heterologous genes remain in the chromosome and show unchanged expression levels. Hence, this system is suitable for metabolic engineering in yeast where multiple rounds of gene introduction and marker recycling can be carried out

Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

<p>Abstract</p> <p>Background</p> <p>Fission yeast <it>Schizosaccharomyces pombe </it>and budding yeast <it>Saccharomyces cerevisiae </it>are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.</p> <p>Results</p> <p>By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor <it>SPBC19G7.04</it>. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved <it>cis</it>-regulatory motifs. In particular, we show that <it>SPBC19G7.04 </it>has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor <it>fkh2</it>. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in <it>S. pombe</it>. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs.</p> <p>Conclusion</p> <p>Using a systems-level analysis of the phase-specific nature of the <it>S. pombe </it>cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.</p

Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets

BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment

MicroRNA-Integrated and Network-Embedded Gene Selection with Diffusion Distance

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways

Uncovering Genes with Divergent mRNA-Protein Dynamics in Streptomyces coelicolor

Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. Systems analyses of such processes incorporating large-scale transcriptome or proteome profiling can be quite revealing. Although consistency between mRNA and proteins is often implicitly assumed in many studies, examples of divergent trends are frequently observed. Here, we present a comparative transcriptome and proteome analysis of growth and stationary phase adaptation in Streptomyces coelicolor, taking the time-dynamics of process into consideration. These processes are of immense interest in microbiology as they pertain to the physiological transformations eliciting biosynthesis of many naturally occurring therapeutic agents. A shotgun proteomics approach based on mass spectrometric analysis of isobaric stable isotope labeled peptides (iTRAQ™) enabled identification and rapid quantification of approximately 14% of the theoretical proteome of S. coelicolor. Independent principal component analyses of this and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated. Despite this overall correlation, by employing a systematic concordance analysis, we estimated that over 30% of the analyzed genes likely exhibited significantly divergent patterns, of which nearly one-third displayed even opposing trends. Integrating this data with biological information, we discovered that certain groups of functionally related genes exhibit mRNA-protein discordance in a similar fashion. Our observations suggest that differences between mRNA and protein synthesis/degradation mechanisms are prominent in microbes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or sub-pathway level