Saccharomyces Cerevisiae Cell Cycle Research Articles

Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.

Timing of Chromosome DNA Integration throughout the Yeast Cell Cycle

The dynamic mechanism of cell uptake and genomic integration of exogenous linear DNA still has to be completely clarified, especially within each phase of the cell cycle. We present a study of integration events of double-stranded linear DNA molecules harboring at their ends sequence homologies to the host's genome, all throughout the cell cycle of the model organism Saccharomyces cerevisiae, comparing the efficiency of chromosomal integration of two types of DNA cassettes tailored for site-specific integration and bridge-induced translocation. Transformability increases in S phase regardless of the sequence homologies, while the efficiency of chromosomal integration during a specific cycle phase depends upon the genomic targets. Moreover, the frequency of a specific translocation between chromosomes XV and VIII strongly increased during DNA synthesis under the control of Pol32 polymerase. Finally, in the null POL32 double mutant, different pathways drove the integration in the various phases of the cell cycle and bridge-induced translocation was possible outside the S phase even without Pol32. The discovery of this cell-cycle dependent regulation of specific pathways of DNA integration, associated with an increase of ROS levels following translocation events, is a further demonstration of a sensing ability of the yeast cell in determining a cell-cycle-related choice of DNA repair pathways under stress.

How does the presence of mitochondrial DNA regulate the cell cycle in Saccharomyces cerevisiae?

How does the presence of mitochondrial DNA regulate the cell cycle in Saccharomyces cerevisiae?

Interactions, structural aspects and evolutionary perspectives of the yeast 'START'-regulatory network.

Molecular signal transduction networks, which conduct transcription at the G1 to S phase transition of the eukaryotic cell division cycle have been identified in diverse taxa from mammals to baker's yeast with analogous functional organization. However, regarding some network components, such as the transcriptional regulators STB1 and WHI5, only few orthologs exist, which are confined to individual Saccharomycotina species. While Whi5 has been characterized as yeast analog of human Rb protein, in the particular case of Stb1 (Sin three binding protein 1) identification of functional analogs emerges as difficult because to date its exact functionality still remains obscured. By aiming to resolve Stb1's enigmatic role this Perspective article especially surveys works covering relations between Cyclin/CDKs, the heteromeric transcription factor complexes SBF (Swi4/Swi6) and MBF (Mbp1/Swi6), as well as additional coregulators (Whi5, Sin3, Rpd3, Nrm1) which are collectively associated with the orderly transcription at 'Start' of the Saccharomyces cerevisiae cell cycle. In this context, interaction capacities of the Sin3-scaffold protein are widely surveyed because its four PAH domains (Paired Amphiphatic Helix) represent a 'recruitment-code' for gene-specific targeting of repressive histone deacetylase activity (Rpd3) via different transcription factors. Here, Stb1 plays a role in Sin3's action on transcription at the G1/S-boundary. Through bioinformatic analyses a potential Sin3-interaction domain (SID) was detected in Stb1, and beyond that, connections within the G1/S-regulatory network are discussed in structural and evolutionary context thereby providing conceptual perspectives.

A Novel Hybrid Approach for Multi-Objective Bi-Clustering in Microarray Data

Background: Today bi-clustering technique plays a vital role to analyze gene expression data in microarray technology. This technique performs clustering on both rows and columns of expression data simultaneously. It determines the expression level of genes set under the subset of several conditions or samples. Basically, obtained information is collected in the form of a sub matrix comprising of microarray data that satisfy coherent expression patterns of subsets of genes with respect to subsets of conditions. These sub matrices are represented as bi-clusters and overall process is called bi-clustering. In this paper, we proposed a new meta-heuristics hybrid ABC-MWOA-CC which is based on artificial bee colony (ABC), modified whale optimization algorithm (MWOA) and Cheng and Church (CC) algorithm to optimize the extracted bi-clusters. In order to validate this algorithm, we also delve into finding the statistical and biological relevancy of extracted genes with respect to various conditions. However, most of the bi-clustering techniques do not address the biological significance of genes belonging to extracted bi-clusters Objective: The major aim of the proposed work is to design and develop a novel hybrid multi-objective bi-clustering approach for in microarray data to produce desired number of valid bi-clusters. Further, these extracted bi-clusters are to be optimized to obtain optimal solution. Method: In the proposed approach, a hybrid multi-objective bi-clustering algorithm which is based on ABC along with MWOA is recommended to group the data into desired number of bi-clusters. Further, ABC with MWOA multi-objective optimization algorithm is applied in order to optimize the solutions using variety of the fitness functions. Results: In the analysis of the result, the multi-objective functions which are employed to judge the fitness calculation like Volume Mean (VM), Mean of Genes (GM), Mean of Conditions (CM) and Mean of MSR (MMSR) leads to improve the performance analysis of the CC bi-clustering algorithm on real life data set such as Yeast Saccharomyces cerevisiae cell cycle gene Expression datasets. Conclusion: The effectiveness of the ABC-MWOA-CC algorithm is comprehensively demonstrated by comparing it with well-known traditional ABC-CC, OPSM and CC algorithm in terms of VM, GM, CM and MMSR.

Deletion of the MBP1 Gene, Involved in the Cell Cycle, Affects Respiration and Pseudohyphal Differentiation in Saccharomyces cerevisiae.

ABSTRACTMbp1p is a component of MBF (MluI cell cycle box binding factor, Mbp1p-Swi6p) and is well known to regulate the G1–S transition of the cell cycle. However, few studies have provided clues regarding its role in fermentation. This work aimed to recognize the function of the MBP1 gene in ethanol fermentation in a wild-type industrial Saccharomyces cerevisiae strain. MBP1 deletion caused an obvious decrease in the final ethanol concentration under oxygen-limited (without agitation), but not under aerobic, conditions (130 rpm). Furthermore, the mbp1Δ strain showed 84% and 35% decreases in respiration intensity under aerobic and oxygen-limited conditions, respectively. These findings indicate that MBP1 plays an important role in responding to variations in oxygen content and is involved in the regulation of respiration and fermentation. Unexpectedly, mbp1Δ also showed pseudohyphal growth, in which cells elongated and remained connected in a multicellular arrangement on yeast extract-peptone-dextrose (YPD) plates. In addition, mbp1Δ showed an increase in cell volume, associated with a decrease in the fraction of budded cells. These results provide more detailed information about the function of MBP1 and suggest some clues to efficiently improve ethanol production by industrially engineered yeast strains.IMPORTANCE Saccharomyces cerevisiae is an especially favorable organism used for ethanol production. However, inhibitors and high osmolarity conferred by fermentation broth, and high concentrations of ethanol as fermentation runs to completion, affect cell growth and ethanol production. Therefore, yeast strains with high performance, such as rapid growth, high tolerance, and high ethanol productivity, are highly desirable. Great efforts have been made to improve their performance by evolutionary engineering, and industrial strains may be a better start than laboratory ones for industrial-scale ethanol production. The significance of our research is uncovering the function of MBP1 in ethanol fermentation in a wild-type industrial S. cerevisiae strain, which may provide clues to engineer better-performance yeast in producing ethanol. Furthermore, the results that lacking MBP1 caused pseudohyphal growth on YPD plates could shed light on the development of xylose-fermenting S. cerevisiae, as using xylose as the sole carbon source also caused pseudohyphal growth.

BIDEAL: A Toolbox for Bicluster Analysis—Generation, Visualization and Validation

This paper introduces a novel toolbox named BIDEAL for the generation of biclusters, their analysis, visualization, and validation. The objective is to facilitate researchers to use forefront biclustering algorithms embedded on a single platform. A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc. BIDEAL consists of seventeen biclustering algorithms, three biclusters visualization techniques, and six validation indices. The toolbox can analyze several types of data, including biological data through a graphical user interface. It also facilitates data preprocessing techniques i.e., binarization, discretization, normalization, elimination of null and missing values. The effectiveness of the developed toolbox has been presented through testing and validations on Saccharomyces cerevisiae cell cycle, leukemia cancer, mammary tissue profile, and ligand screen in B-cells datasets. The biclusters of these datasets have been generated using BIDEAL and evaluated in terms of coherency, differential co-expression ranking, and similarity measure. The visualization of generated biclusters has also been provided through a heat map and gene plot.

Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method

Abstract Over a decade ago, Lèbre (2009) proposed an inference method, G1DBN, to learn the structure of gene regulatory networks (GRNs) from high dimensional, sparse time-series gene expression data. Their approach is based on concept of low-order conditional independence graphs that they extend to dynamic Bayesian networks (DBNs). They present results to demonstrate that their method yields better structural accuracy compared to the related Lasso and Shrinkage methods, particularly where the data is sparse, that is, the number of time measurements n is much smaller than the number of genes p. This paper challenges these claims using a careful experimental analysis, to show that the GRNs reverse engineered from time-series data using the G1DBN approach are less accurate than claimed by Lèbre (2009). We also show that the Lasso method yields higher structural accuracy for graphs learned from the simulated data, compared to the G1DBN method, particularly when the data is sparse ( n < < p $n{< }{< }p$ ). The Lasso method is also better than G1DBN at identifying the transcription factors (TFs) involved in the cell cycle of Saccharomyces cerevisiae.

Multisite phosphorylation code of CDK.

The quantitative model of cyclin-dependent kinase (CDK) function states that cyclins temporally order cell cycle events at different CDK activity levels, or thresholds. The model lacks a mechanistic explanation, as it is not understood how different thresholds are encoded into substrates. We show that a multisite phosphorylation code governs the phosphorylation of CDK targets and that phosphorylation clusters act as timing tags that trigger specific events at different CDK thresholds. Using phospho-degradable CDK threshold sensors with rationally encoded phosphorylation patterns, we were able to predictably program thresholds over the entire range of the Saccharomyces cerevisiae cell cycle. We defined three levels of CDK multisite phosphorylation encoding: (i) Ser-Thr swapping in phosphorylation sites, (ii) patterning of phosphorylation sites, and (iii) cyclin-specific docking combined with modulation of CDK activity. Thus, CDK can signal via hundreds of differentially encoded targets at precise times to provide a temporally ordered phosphorylation pattern required for cell division.

Periodic expression of cell-cycle regulators: A laboratory experiment proposal for students in molecular and cell biology.

This article describes a laboratory exercise designed for undergraduate students in the subject of "Regulation of cell proliferation" which allows the students to carry out a research experiment in an important field such as cell cycle control, and to be introduced to a widely used technique in molecular biology laboratories such as the western blot. The cell cycle is regulated by the succession of cyclin-CDK kinase activities. Activation and inactivation of different cyclin-CDK complexes depend on the control of their positive and negative regulators, cyclins and CDK inhibitors (CKIs), respectively. In this experiment, fluctuations in the level of mitotic cyclin Clb2 and CDK inhibitor Sic1 throughout the cell cycle of Saccharomyces cerevisiae are analyzed, particularly in the context of the control of mitotic exit and Start, two of the most important cell cycle transitions. In order to do this, a cdc15 mutant strain is used to block cells in telophase and, upon release from this blocking, the variation in the levels of Clb2 and Sic1 proteins are analyzed by western blot. Progress along the cell cycle is also evaluated by microscopic analysis of cell morphology and nuclear staining. This practical illustrates the experimental basis of theoretical concepts worked in the classroom and it is a good framework for an in-depth discussion of these concepts based on experimental data analysis. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):527-535, 2018.

Genetic dissection of the signaling pathway required for the cell wall integrity checkpoint.

The cell wall integrity checkpoint monitors synthesis of cell wall materials during the Saccharomyces cerevisiae cell cycle. Upon perturbation of cell wall synthesis, the cell wall integrity checkpoint is activated, downregulating Clb2 transcription. Here, we identified genes involved in this checkpoint by genetic screening of deletion mutants. In addition to the previously identified dynactin complex, the Las17 complex, in particular the Bzz1 and Vrp1 components, plays a role in this checkpoint. We also revealed that the high osmolarity glycerol (HOG) and cell wall integrity mitogen-activated protein kinase (MAPK) signaling pathways are essential for checkpoint function. The defective checkpoint caused by the deficient dynactin and Las17 complexes was rescued by hyperactivation of the cell wall integrity MAPK pathway, but not by the activated form of Hog1, suggesting an order to these signaling pathways. Mutation of Fkh2, a transcription factor important for Clb2 expression, suppressed the checkpoint-defective phenotype of Las17, HOG MAPK and cell wall integrity MAPK mutations. These results provide genetic evidence that signaling from the cell surface regulates the downstream transcriptional machinery to activate the cell wall integrity checkpoint.

The Replication Kinase Cdc7 Marks Histones to Regulate Biosynthesis Genes

Post‐translational histone modifications (PTMs) play important roles in regulating various DNA‐templated cellular functions including transcription, DNA damage repair, DNA replication and telomeric silencing. Histone H3 Threonine 45 phosphorylation (H3T45ph) is a modification that is enriched during S‐phase of the cell cycle in Saccharomyces cerevisiae and is carried out by the kinase Cdc7‐Dbf4 (DDK) in vivo and in vitro. To understand the function of this mark we identified the genomic H3T45ph locations by performing chromatin immunoprecipitation followed by high throughput sequencing (ChIP‐Seq) of wild‐type budding yeast cell chromatin. Remarkably, this PTM is observed not only at origins of DNA replication, but is also uniquely distributed on the promoters of highly transcribed RNA polymerase I, II and III‐regulated genes dedicated to protein synthesis and glycolysis. Here we show that H3T45 phosphorylation, mediated by Cdc7‐Dbf4, is required for polymerase recruitment and full expression of ribosome protein (RP) and tRNA genes as well as for the regulation of cohesin loading. Collectively, we identify an unexpected gene regulatory function for the DNA‐replication kinase Cdc7‐Dbf4 and identify a novel histone modification regulating the protein biosynthesis gene machinery.Support or Funding InformationThis work is supported by NIH award R01 GM111911.

Analysis of Cell Cycle Progression in Saccharomyces cerevisiae Using the Budding Index and Tubulin Staining.

The budding index and the morphology of the spindle and the nucleus are excellent markers for the analysis of the progression through the different stages of the cell cycle in Saccharomyces cerevisiae. Here, we describe a protocol to evaluate the budding index in this model organism using phase contrast microscopy. We also describe an indirect immunofluorescence method designed for the visualization of microtubules and the nucleus in S. cerevisiae. Finally, we explain how both methodologies can be used in order to analyze cell cycle progression in the budding yeast.

Nucleosome architecture throughout the cell cycle.

Nucleosomes provide additional regulatory mechanisms to transcription and DNA replication by mediating the access of proteins to DNA. During the cell cycle chromatin undergoes several conformational changes, however the functional significance of these changes to cellular processes are largely unexplored. Here, we present the first comprehensive genome-wide study of nucleosome plasticity at single base-pair resolution along the cell cycle in Saccharomyces cerevisiae. We determined nucleosome organization with a specific focus on two regulatory regions: transcription start sites (TSSs) and replication origins (ORIs). During the cell cycle, nucleosomes around TSSs display rearrangements in a cyclic manner. In contrast to gap (G1 and G2) phases, nucleosomes have a fuzzier organization during S and M phases, Moreover, the choreography of nucleosome rearrangements correlate with changes in gene expression during the cell cycle, indicating a strong association between nucleosomes and cell cycle-dependent gene functionality. On the other hand, nucleosomes are more dynamic around ORIs along the cell cycle, albeit with tighter regulation in early firing origins, implying the functional role of nucleosomes on replication origins. Our study provides a dynamic picture of nucleosome organization throughout the cell cycle and highlights the subsequent impact on transcription and replication activity.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments.

Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments.

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach.

We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.

Mutations in Replicative Stress Response Pathways Are Associated with S Phase-specific Defects in Nucleotide Excision Repair

Nucleotide excision repair (NER) is a highly conserved pathway that removes helix-distorting DNA lesions induced by a plethora of mutagens, including UV light. Our laboratory previously demonstrated that human cells deficient in either ATM and Rad3-related (ATR) kinase or translesion DNA polymerase η (i.e. key proteins that promote the completion of DNA replication in response to UV-induced replicative stress) are characterized by profound inhibition of NER exclusively during S phase. Toward elucidating the mechanistic basis of this phenomenon, we developed a novel assay to quantify NER kinetics as a function of cell cycle in the model organism Saccharomyces cerevisiae. Using this assay, we demonstrate that in yeast, deficiency of the ATR homologue Mec1 or of any among several other proteins involved in the cellular response to replicative stress significantly abrogates NER uniquely during S phase. Moreover, initiation of DNA replication is required for manifestation of this defect, and S phase NER proficiency is correlated with the capacity of individual mutants to respond to replicative stress. Importantly, we demonstrate that partial depletion of Rfa1 recapitulates defective S phase-specific NER in wild type yeast; moreover, ectopic RPA1-3 overexpression rescues such deficiency in either ATR- or polymerase η-deficient human cells. Our results strongly suggest that reduction of NER capacity during periods of enhanced replicative stress, ostensibly caused by inordinate sequestration of RPA at stalled DNA replication forks, represents a conserved feature of the multifaceted eukaryotic DNA damage response.

A Framework for Mining Coherent Patterns Using Particle Swarm Optimization based Biclustering

High-throughput microarray technologies have enabled development of robust biclustering algorithms which are capable of discovering relevant local patterns in gene expression datasets wherein subset of genes shows coherent expression patterns under subset of experimental conditions. In this work, we have proposed an algorithm that combines biclustering technique with Particle Swarm Optimization (PSO) structure in order to extract significant biological relevant patterns from such dataset. This algorithm comprises of two phases for extracting biclusters, one is the seed finding phase and another is the seed growing phase. In the seed finding phase, gene clustering and condition clustering is done separately on the gene expression data matrix and the result obtained from both the clustering is combined to form small tightly bound submatrices and those submatrices are used as seeds for the algorithm, which are having the Mean Squared Residue (MSR) value less than the defined threshold value. In the seed growing phase, the number of genes and the number of conditions are added in these seeds to enlarge it by using the PSO structure. It is observed that by using our technique in Yeast Saccharomyces Cerevisiae cell cycle expression dataset, significant biclusters are obtained which are having large volume and less MSR value in comparison to other biclustering algorithms.

Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assembly.

Start sites of DNA replication are marked by the origin recognition complex (ORC), which coordinates Mcm2-7 helicase loading to form the prereplicative complex (pre-RC). Although pre-RC assembly is well characterized in vitro, the process is poorly understood within the local chromatin environment surrounding replication origins. To reveal how the chromatin architecture modulates origin selection and activation, we "footprinted" nucleosomes, transcription factors, and replication proteins at multiple points during the Saccharomyces cerevisiae cell cycle. Our nucleotide-resolution protein occupancy profiles resolved a precise ORC-dependent footprint at 269 origins in G2. A separate class of inefficient origins exhibited protein occupancy only in G1, suggesting that stable ORC chromatin association in G2 is a determinant of origin efficiency. G1 nucleosome remodeling concomitant with pre-RC assembly expanded the origin nucleosome-free region and enhanced activation efficiency. Finally, the local chromatin environment restricts the loading of the Mcm2-7 double hexamer either upstream of or downstream from the ARS consensus sequence (ACS).

A Simple FCM Method to Avoid Misinterpretation in Saccharomyces cerevisiae Cell Cycle Assessment between G0 and Sub-G1

Extensively developed for medical and clinical applications, flow cytometry is now being used for diverse applications in food microbiology. Most uses of flow cytometry for yeast cells are derived from methods for mammalian cells, but yeast cells can present specificities that must be taken into account for rigorous analysis of the data output to avoid any misinterpretation. We report an analysis of Saccharomyces cerevisiae cell cycle progression that highlights possible errors. The cell cycle was analyzed using an intercalating fluorochrome to assess cell DNA content. In analyses of yeast cultures, the presence of a sub-G1 peak in the fluorescent signal is often interpreted as a loss of DNA due to its fragmentation associated with apoptosis. However, the cell wall and its stucture may interfere with the fluorescent signal recorded. These observations indicate that misinterpretation of yeast DNA profiles is possible in analyses based on some of the most common probes: cells in G0 appeared to have a lower DNA content and may have been mistaken as a sub-G1 population. However, careful selection of the fluorochrome for DNA quantification allowed a direct discrimination between G0 and G1 yeast cell cycle steps, without additional labeling. We present and discuss results obtained with five current fluorochromes. These observations led us to recommend to use SYTOX Green for cycle analysis of living cells and SYBR Green I for the identification of the apoptosis sub-G1 population identification or the DNA ploidy application.