Disruption of yeast forkhead-associated cell cycle transcription by oxidative stress

SIR telomere silencing depends on nuclear envelope lipids and modulates sensitivity to a lysolipid

The nuclear envelope (NE) is important in maintaining genome organization. The role of lipids in communication between the NE and telomere regulation was investigated, including how changes in lipid composition impact gene expression and overall nuclear architecture. Yeast was treated with the non-metabolizable lysophosphatidylcholine analog edelfosine, known to accumulate at the perinuclear ER. Edelfosine induced NE deformation and disrupted telomere clustering but not anchoring. Additionally, the association of Sir4 at telomeres decreased. RNA-seq analysis showed altered expression of Sir-dependent genes located at sub-telomeric (0-10 kb) regions, consistent with Sir4 dispersion. Transcriptomic analysis revealed that two lipid metabolic circuits were activated in response to edelfosine, one mediated by the membrane sensing transcription factors, Spt23/Mga2, and the other by a transcriptional repressor, Opi1. Activation of these transcriptional programs resulted in higher levels of unsaturated fatty acids and the formation of nuclear lipid droplets. Interestingly, cells lacking Sir proteins displayed resistance to unsaturated-fatty acids and edelfosine, and this phenotype was connected to Rap1.

Identification of ubiquitin Ser57 kinases regulating the oxidative stress response in yeast

Ubiquitination regulates many different cellular processes, including protein quality control, membrane trafficking, and stress responses. The diversity of ubiquitin functions in the cell is partly due to its ability to form chains with distinct linkages that can alter the fate of substrate proteins in unique ways. The complexity of the ubiquitin code is further enhanced by post-translational modifications on ubiquitin itself, the biological functions of which are not well understood. Here, we present genetic and biochemical evidence that serine 57 (Ser57) phosphorylation of ubiquitin functions in stress responses in Saccharomyces cerevisiae, including the oxidative stress response. We also identify and characterize the first known Ser57 ubiquitin kinases in yeast and human cells, and we report that two Ser57 ubiquitin kinases regulate the oxidative stress response in yeast. These studies implicate ubiquitin phosphorylation at the Ser57 position as an important modifier of ubiquitin function, particularly in response to proteotoxic stress.

Yeast Ppz1 protein phosphatase toxicity involves the alteration of multiple cellular targets

Control of the protein phosphorylation status is a major mechanism for regulation of cellular processes, and its alteration often lead to functional disorders. Ppz1, a protein phosphatase only found in fungi, is the most toxic protein when overexpressed in Saccharomyces cerevisiae. To investigate the molecular basis of this phenomenon, we carried out combined genome-wide transcriptomic and phosphoproteomic analyses. We have found that Ppz1 overexpression causes major changes in gene expression, affecting ~ 20% of the genome, together with oxidative stress and increase in total adenylate pools. Concurrently, we observe changes in the phosphorylation pattern of near 400 proteins (mainly dephosphorylated), including many proteins involved in mitotic cell cycle and bud emergence, rapid dephosphorylation of Snf1 and its downstream transcription factor Mig1, and phosphorylation of Hog1 and its downstream transcription factor Sko1. Deletion of HOG1 attenuates the growth defect of Ppz1-overexpressing cells, while that of SKO1 aggravates it. Our results demonstrate that Ppz1 overexpression has a widespread impact in the yeast cells and reveals new aspects of the regulation of the cell cycle.

Transcriptional and epigenetic control of regulated cell death in yeast

Unicellular organisms like yeast can undergo controlled demise in a manner that is partly reminiscent of mammalian cell death. This is true at the levels of both mechanistic and functional conservation. Yeast offers the combination of unparalleled genetic amenability and a comparatively simple biology to understand both the regulation and evolution of cell death. In this minireview, we address the capacity of the nucleus as a regulatory hub during yeast regulated cell death (RCD), which is becoming an increasingly central question in yeast RCD research. In particular, we explore and critically discuss the available data on stressors and signals that specifically impinge on the nucleus. Moreover, we also analyze the current knowledge on nuclear factors as well as on transcriptional control and epigenetic events that orchestrate yeast RCD. Altogether we conclude that the functional significance of the nucleus for yeast RCD in undisputable, but that further exploration beyond correlative work is necessary to disentangle the role of nuclear events in the regulatory network.

Rapid Nuclear Exclusion of Hcm1 in Aging Saccharomyces cerevisiae Leads to Vacuolar Alkalization and Replicative Senescence

The yeast, Saccharomyces cerevisiae, like other higher eukaryotes, undergo a finite number of cell divisions before exiting the cell cycle due to the effects of aging. Here, we show that yeast aging begins with the nuclear exclusion of Hcm1 in young cells, resulting in loss of acidic vacuoles. Autophagy is required for healthy aging in yeast, with proteins targeted for turnover by autophagy directed to the vacuole. Consistent with this, vacuolar acidity is necessary for vacuolar function and yeast longevity. Using yeast genetics and immunofluorescence microscopy, we confirm that vacuolar acidity plays a critical role in cell health and lifespan, and is potentially maintained by a series of Forkhead Box (Fox) transcription factors. An interconnected transcriptional network involving the Fox proteins (Fkh1, Fkh2 and Hcm1) are required for transcription of v-ATPase subunits and vacuolar acidity. As cells age, Hcm1 is rapidly excluded from the nucleus in young cells, blocking the expression of Hcm1 targets (Fkh1 and Fkh2), leading to loss of v-ATPase gene expression, reduced vacuolar acidification, increased α-syn-GFP vacuolar accumulation, and finally, diminished replicative lifespan (RLS). Loss of vacuolar acidity occurs about the same time as Hcm1 nuclear exclusion and is conserved; we have recently demonstrated that lysosomal alkalization similarly contributes to aging in C. elegans following a transition from progeny producing to post-reproductive life. Our data points to a molecular mechanism regulating vacuolar acidity that signals the end of RLS when acidification is lost.

Deciphering the Origin, Evolution, and Physiological Function of the Subtelomeric Aryl-Alcohol Dehydrogenase Gene Family in the Yeast Saccharomyces cerevisiae

Homology searches indicate that Saccharomyces cerevisiae strain BY4741 contains seven redundant genes that encode putative aryl-alcohol dehydrogenases (AAD). Yeast AAD genes are located in subtelomeric regions of different chromosomes, and their functional role(s) remain enigmatic. Here, we show that two of these genes, AAD4 and AAD14, encode functional enzymes that reduce aliphatic and aryl-aldehydes concomitant with the oxidation of cofactor NADPH, and that Aad4p and Aad14p exhibit different substrate preference patterns. Other yeast AAD genes are undergoing pseudogenization. The 5′ sequence of AAD15 has been deleted from the genome. Repair of an AAD3 missense mutation at the catalytically essential Tyr⁷³ residue did not result in a functional enzyme. However, ancestral-state reconstruction by fusing Aad6 with Aad16 and by N-terminal repair of Aad10 restores NADPH-dependent aryl-alcohol dehydrogenase activities. Phylogenetic analysis indicates that AAD genes are narrowly distributed in wood-saprophyte fungi and in yeast that occupy lignocellulosic niches. Because yeast AAD genes exhibit activity on veratraldehyde, cinnamaldehyde, and vanillin, they could serve to detoxify aryl-aldehydes released during lignin degradation. However, none of these compounds induce yeast AAD gene expression, and Aad activities do not relieve aryl-aldehyde growth inhibition. Our data suggest an ancestral role for AAD genes in lignin degradation that is degenerating as a result of yeast's domestication and use in brewing, baking, and other industrial applications.

IMPORTANCE Functional characterization of hypothetical genes remains one of the chief tasks of the postgenomic era. Although the first Saccharomyces cerevisiae genome sequence was published over 20 years ago, 22% of its estimated 6,603 open reading frames (ORFs) remain unverified. One outstanding example of this category of genes is the enigmatic seven-member AAD family. Here, we demonstrate that proteins encoded by two members of this family exhibit aliphatic and aryl-aldehyde reductase activity, and further that such activity can be recovered from pseudogenized AAD genes via ancestral-state reconstruction. The phylogeny of yeast AAD genes suggests that these proteins may have played an important ancestral role in detoxifying aromatic aldehydes in ligninolytic fungi. However, in yeast adapted to niches rich in sugars, AAD genes become subject to mutational erosion. Our findings shed new light on the selective pressures and molecular mechanisms by which genes undergo pseudogenization.

Mitotic degradation of yeast Fkh1 by the Anaphase Promoting Complex is required for normal longevity, genomic stability and stress resistance

The Saccharomyces cerevisiae Forkhead Box (Fox) orthologs, Forkheads (Fkh) 1 and 2, are conserved transcription factors required for stress response, cell cycle progression and longevity. These yeast proteins play a key role in mitotic progression through activation of the ubiquitin E3 ligase Anaphase Promoting Complex (APC) via transcriptional control. Here, we used genetic and molecular analyses to demonstrate that the APC E3 activity is necessary for mitotic Fkh1 protein degradation and subsequent cell cycle progression. We report that Fkh1 protein degradation occurs specifically during mitosis, requires APCCdc20 and proteasome activity, and that a stable Fkh1 mutant reduces normal chronological lifespan, increases genomic instability, and increases sensitivity to stress. Our data supports a model whereby cell cycle progression through mitosis and G1 requires the targeted degradation of Fkh1 by the APC. This is significant to many fields as these results impact our understanding of the mechanisms underpinning the control of aging and cancer.

Flavodoxin-Like Proteins Protect Candida albicans from Oxidative Stress and Promote Virulence

The fungal pathogen Candida albicans causes lethal systemic infections in humans. To better define how pathogens resist oxidative attack by the immune system, we examined a family of four Flavodoxin-Like Proteins (FLPs) in C. albicans. In agreement with previous studies showing that FLPs in bacteria and plants act as NAD(P)H quinone oxidoreductases, a C. albicans quadruple mutant lacking all four FLPs (pst1Δ, pst2Δ, pst3Δ, ycp4Δ) was more sensitive to benzoquinone. Interestingly, the quadruple mutant was also more sensitive to a variety of oxidants. Quinone reductase activity confers important antioxidant effects because resistance to oxidation was restored in the quadruple mutant by expressing either Escherichia coli wrbA or mammalian NQO1, two distinct types of quinone reductases. FLPs were detected at the plasma membrane in C. albicans, and the quadruple mutant was more sensitive to linolenic acid, a polyunsaturated fatty acid that can auto-oxidize and promote lipid peroxidation. These observations suggested that FLPs reduce ubiquinone (coenzyme Q), enabling it to serve as an antioxidant in the membrane. In support of this, a C. albicans coq3Δ mutant that fails to synthesize ubiquinone was also highly sensitive to oxidative stress. FLPs are critical for survival in the host, as the quadruple mutant was avirulent in a mouse model of systemic candidiasis under conditions where infection with wild type C. albicans was lethal. The quadruple mutant cells initially grew well in kidneys, the major site of C. albicans growth in mice, but then declined after the influx of neutrophils and by day 4 post-infection 33% of the mice cleared the infection. Thus, FLPs and ubiquinone are important new antioxidant mechanisms that are critical for fungal virulence. The potential of FLPs as novel targets for antifungal therapy is further underscored by their absence in mammalian cells.

Transcriptional control of fungal cell cycle and cellular events by Fkh2, a forkhead transcription factor in an insect pathogen

Transcriptional control of the cell cycle by forkhead (Fkh) transcription factors is likely associated with fungal adaptation to host and environment. Here we show that Fkh2, an ortholog of yeast Fkh1/2, orchestrates cell cycle and many cellular events of Beauveria bassiana, a filamentous fungal insect pathogen. Deletion of Fkh2 in B. bassiana resulted in dramatic down-regulation of the cyclin-B gene cluster and hence altered cell cycle (longer G₂/M and S, but shorter G₀/G₁, phases) in unicellular blastospores. Consequently, ΔFkh2 produced twice as many, but smaller, blastospores than wild-type under submerged conditions, and formed denser septa and shorter/broader cells in aberrantly branched hyphae. In these hyphae, clustered genes required for septation and conidiation were remarkedly up-regulated, followed by higher yield and slower germination of aerial conidia. Moreover, ΔFkh2 displayed attenuated virulence and decreased tolerance to chemical and environmental stresses, accompanied with altered transcripts and activities of phenotype-influencing proteins or enzymes. All the changes in ΔFkh2 were restored by Fkh2 complementation. All together, Fkh2-dependent transcriptional control is vital for the adaptation of B. bassiana to diverse habitats of host insects and hence contributes to its biological control potential against arthropod pests.

Segmentation of biological multivariate time-series data

Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.

A three step network based approach (TSNBA) to finding disease molecular signature and key regulators: a case study of IL-1 and TNF-alpha stimulated inflammation

A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.

Shrinkage regression-based methods for microarray missing value imputation

Background

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets.

Results

To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do.

Conclusions

Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.

Missing value imputation for microarray data: a comprehensive comparison study and a web tool

BACKGROUND

Microarray data are usually peppered with missing values due to various reasons. However, most of the downstream analyses for microarray data require complete datasets. Therefore, accurate algorithms for missing value estimation are needed for improving the performance of microarray data analyses. Although many algorithms have been developed, there are many debates on the selection of the optimal algorithm. The studies about the performance comparison of different algorithms are still incomprehensive, especially in the number of benchmark datasets used, the number of algorithms compared, the rounds of simulation conducted, and the performance measures used.

RESULTS

In this paper, we performed a comprehensive comparison by using (I) thirteen datasets, (II) nine algorithms, (III) 110 independent runs of simulation, and (IV) three types of measures to evaluate the performance of each imputation algorithm fairly. First, the effects of different types of microarray datasets on the performance of each imputation algorithm were evaluated. Second, we discussed whether the datasets from different species have different impact on the performance of different algorithms. To assess the performance of each algorithm fairly, all evaluations were performed using three types of measures. Our results indicate that the performance of an imputation algorithm mainly depends on the type of a dataset but not on the species where the samples come from. In addition to the statistical measure, two other measures with biological meanings are useful to reflect the impact of missing value imputation on the downstream data analyses. Our study suggests that local-least-squares-based methods are good choices to handle missing values for most of the microarray datasets.

CONCLUSIONS

In this work, we carried out a comprehensive comparison of the algorithms for microarray missing value imputation. Based on such a comprehensive comparison, researchers could choose the optimal algorithm for their datasets easily. Moreover, new imputation algorithms could be compared with the existing algorithms using this comparison strategy as a standard protocol. In addition, to assist researchers in dealing with missing values easily, we built a web-based and easy-to-use imputation tool, MissVIA (http://cosbi.ee.ncku.edu.tw/MissVIA), which supports many imputation algorithms. Once users upload a real microarray dataset and choose the imputation algorithms, MissVIA will determine the optimal algorithm for the users' data through a series of simulations, and then the imputed results can be downloaded for the downstream data analyses.

Fkh1 and Fkh2 associate with Sir2 to control CLB2 transcription under normal and oxidative stress conditions

The Forkhead (Fkh) box family of transcription factors is evolutionary conserved from yeast to higher eukaryotes and its members are involved in many physiological processes including metabolism, DNA repair, cell cycle, stress resistance, apoptosis, and aging. In budding yeast, four Fkh transcription factors were identified, namely Fkh1, Fkh2, Fhl1, and Hcm1, which are implicated in chromatin silencing, cell cycle regulation, and stress response. These factors impinge transcriptional regulation during cell cycle progression, and histone deacetylases (HDACs) play an essential role in this process, e.g., the nuclear localization of Hcm1 depends on Sir2 activity, whereas Sin3/Rpd3 silence cell cycle specific gene transcription in G2/M phase. However, a direct involvement of Sir2 in Fkh1/Fkh2-dependent regulation of target genes is at present unknown. Here, we show that Fkh1 and Fkh2 associate with Sir2 in G1 and M phase, and that Fkh1/Fkh2-mediated activation of reporter genes is antagonized by Sir2. We further report that Sir2 overexpression strongly affects cell growth in an Fkh1/Fkh2-dependent manner. In addition, Sir2 regulates the expression of the mitotic cyclin Clb2 through Fkh1/Fkh2-mediated binding to the CLB2 promoter in G1 and M phase. We finally demonstrate that Sir2 is also enriched at the CLB2 promoter under stress conditions, and that the nuclear localization of Sir2 is dependent on Fkh1 and Fkh2. Taken together, our results show a functional interplay between Fkh1/Fkh2 and Sir2 suggesting a novel mechanism of cell cycle repression. Thus, in budding yeast, not only the regulation of G2/M gene expression but also the protective response against stress could be directly coordinated by Fkh1 and Fkh2.

Network-based segmentation of biological multivariate time series

Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into [Formula: see text] segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.

Redox control of cell proliferation

Cell proliferation is regulated by multiple signaling pathways and stress surveillance systems to ensure cell division takes place with fidelity. In response to oxidative stress, cells arrest in the cell-cycle and aberrant redox control of proliferation underlies the pathogenesis of many diseases including cancer and neurodegenerative disorders. Redox sensing of cell-cycle regulation has recently been shown to involve reactive cysteine thiols that function as redox sensors in cell-cycle regulators. By modulating cell-cycle regulators these redox-active thiols ensure cell division is executed at the right redox environment. This review summarizes recent findings on regulation of cell division by the oxidation of cysteines in cell division regulators and the potential of targeting these critical cysteine residues for cancer therapy.

Bir1 deletion causes malfunction of the spindle assembly checkpoint and apoptosis in yeast

Cell division in yeast is a highly regulated and well studied event. Various checkpoints are placed throughout the cell cycle to ensure faithful segregation of sister chromatids. Unexpected events, such as DNA damage or oxidative stress, cause the activation of checkpoint(s) and cell cycle arrest. Malfunction of the checkpoints may induce cell death. We previously showed that under oxidative stress, the budding yeast cohesin Mcd1, a homolog of human Rad21, was cleaved by the caspase-like protease Esp1. The cleaved Mcd1 C-terminal fragment was then translocated to mitochondria, causing apoptotic cell death. In the present study, we demonstrated that Bir1 plays an important role in spindle assembly checkpoint and cell death. Similar to H₂O₂ treatment, deletion of BIR1 using a BIR1-degron strain caused degradation of the securin Pds1, which binds and inactivates Esp1 until metaphase-anaphase transition in a normal cell cycle. BIR1 deletion caused an increase level of ROS and mis-location of Bub1, a major protein for spindle assembly checkpoint. In wild type, Bub1 was located at the kinetochores, but was primarily in the cytoplasm in bir1 deletion strain. When BIR1 was deleted, addition of nocodazole was unable to retain the Bub1 localization on kinetochores, further suggesting that Bir1 is required to activate and maintain the spindle assembly checkpoint. Our study suggests that the BIR1 function in cell cycle regulation works in concert with its anti-apoptosis function.

Mechanistic insights into aging, cell-cycle progression, and stress response

The longevity of an organism depends on the health of its cells. Throughout life cells are exposed to numerous intrinsic and extrinsic stresses, such as free radicals, generated through mitochondrial electron transport, and ultraviolet irradiation. The cell has evolved numerous mechanisms to scavenge free radicals and repair damage induced by these insults. One mechanism employed by the yeast Saccharomycescerevisiae to combat stress utilizes the Anaphase Promoting Complex (APC), an essential multi-subunit ubiquitin-protein ligase structurally and functionally conserved from yeast to humans that controls progression through mitosis and G1. We have observed that yeast cells expressing compromised APC subunits are sensitive to multiple stresses and have shorter replicative and chronological lifespans. In a pathway that runs parallel to that regulated by the APC, members of the Forkhead box (Fox) transcription factor family also regulate stress responses. The yeast Fox orthologs Fkh1 and Fkh2 appear to drive the transcription of stress response factors and slow early G1 progression, while the APC seems to regulate chromatin structure, chromosome segregation, and resetting of the transcriptome in early G1. In contrast, under non-stress conditions, the Fkhs play a complex role in cell-cycle progression, partially through activation of the APC. Direct and indirect interactions between the APC and the yeast Fkhs appear to be pivotal for lifespan determination. Here we explore the potential for these interactions to be evolutionarily conserved as a mechanism to balance cell-cycle regulation with stress responses.

The yeast forkhead transcription factors fkh1 and fkh2 regulate lifespan and stress response together with the anaphase-promoting complex

Forkhead box O (FOXO) transcription factors have a conserved function in regulating metazoan lifespan. A key function in this process involves the regulation of the cell cycle and stress responses including free radical scavenging. We employed yeast chronological and replicative lifespan assays, as well as oxidative stress assays, to explore the potential evolutionary conservation of function between the FOXOs and the yeast forkhead box transcription factors FKH1 and FKH2. We report that the deletion of both FKH genes impedes normal lifespan and stress resistance, particularly in stationary phase cells, which are non-responsive to caloric restriction. Conversely, increased expression of the FKHs leads to extended lifespan and improved stress response. Here we show the Anaphase-Promoting Complex (APC) genetically interacts with the Fkh pathway, likely working in a linear pathway under normal conditions, as fkh1Δ fkh2Δ post-mitotic survival is epistatic to that observed in apc5(CA) mutants. However, under stress conditions, post-mitotic survival is dramatically impaired in apc5(CA) fkh1Δ fkh2Δ, while increased expression of either FKH rescues APC mutant growth defects. This study establishes the FKHs role as evolutionarily conserved regulators of lifespan in yeast and identifies the APC as a novel component of this mechanism under certain conditions, likely through combined regulation of stress response, genomic stability, and cell cycle regulation.

Oxidative stresses and ageing

Oxidative damage to cellular constituents has frequently been associated with aging in a wide range of organisms. The power of yeast genetics and biochemistry has provided the opportunity to analyse in some detail how reactive oxygen and nitrogen species arise in cells, how cells respond to the damage that these reactive species cause, and to begin to dissect how these species may be involved in the ageing process. This chapter reviews the major sources of reactive oxygen species that occur in yeast cells, the damage they cause and how cells sense and respond to this damage.

Deletion of a subgroup of ribosome-related genes minimizes hypoxia-induced changes and confers hypoxia tolerance

Hypoxia is a widely occurring condition experienced by diverse organisms under numerous physiological and disease conditions. To probe the molecular mechanisms underlying hypoxia responses and tolerance, we performed a genome-wide screen to identify mutants with enhanced hypoxia tolerance in the model eukaryote, the yeast Saccharomyces cerevisiae. Yeast provides an excellent model for genomic and proteomic studies of hypoxia. We identified five genes whose deletion significantly enhanced hypoxia tolerance. They are RAI1, NSR1, BUD21, RPL20A, and RSM22, all of which encode functions involved in ribosome biogenesis. Further analysis of the deletion mutants showed that they minimized hypoxia-induced changes in polyribosome profiles and protein synthesis. Strikingly, proteomic analysis by using the iTRAQ profiling technology showed that a substantially fewer number of proteins were changed in response to hypoxia in the deletion mutants, compared with the parent strain. Computational analysis of the iTRAQ data indicated that the activities of a group of regulators were regulated by hypoxia in the wild-type parent cells, but such regulation appeared to be diminished in the deletion strains. These results show that the deletion of one of the genes involved in ribosome biogenesis leads to the reversal of hypoxia-induced changes in gene expression and related regulators. They suggest that modifying ribosomal function is an effective mechanism to minimize hypoxia-induced specific protein changes and to confer hypoxia tolerance. These results may have broad implications in understanding hypoxia responses and tolerance in diverse eukaryotes ranging from yeast to humans.

Multiple pathways regulating the calorie restriction response in yeast

In yeast, SIR2 overexpression or calorie restriction (CR) results in life-span extension. It was previously suggested that CR activates Sir2 by reducing the levels of Sir2 inhibitors, NADH, or nicotinamide. Whereas NADH reduction is associated with an increase in respiration, nicotinamide clearance is induced by the upregulation of PNC1. Here, we show that, consistent with the hormesis hypothesis, PNC1 is part of a transcriptional stress response module consisting of 39 genes that increases under various stresses. Under high CR (0.1% glucose), Pnc1 becomes activated and its levels increase. However, low CR (0.5% glucose) increases yeast life span without PNC1 induction or activation of any transcriptional stress response. Instead, microarray analysis of low CR shows that the messenger RNA levels of iron transport genes increase, suggesting that this mode of CR is regulated by a shift toward respiration and lowering NADH levels. Thus, at least two pathways regulate the CR response in yeast.

Aging and the Mammalian regulatory triumvirate

A temporal framework linking circadian rhythms and clocks to aging rates identifies a specific window of target of rapamycin (TOR) signaling associated with growth hormone (GH) and insulin-like growth factor (IGF-1) (largely exclusive of insulin) in early sleep. IGF-1 signaling is released by growth hormone secretory peaks and downregulation of IGF-1 binding protein-1 resulting in activation of the mitogen activated protein kinase/extracellular signal response kinase (MAPK/ERK) and phosphoinositide 3-kinase-protein kinase B (PI3K-PKB/Akt) signaling pathways. Phosphorylation of Akt activates TOR which mediates the protein synthesis and growth functions of the GH axis. TOR activity is also associated with downregulated stress resistance, faster aging and reduced lifespan. IGF-1 signaling is terminated by falling GH and upregulation of IGF-1 binding proteins mediated by somatostatin and rising corticosteroids in later sleep. This suppresses PI3K-Akt signaling, thus activating the forkhead transcription factors (FOXOs) and stress-resistance pathways involved in promoting longevity. Thus, sleep appears to encompass both pathways currently identified as most relevant to aging and they toggle successively on the phosphorylation status of Akt. I propose a modified version of Pearl's rate of living theory emphasizing the hard-wired antagonism of growth (TOR) and stress resistance (FOXO). The sleep association of TOR and FOXO in temporally separated windows and their sequential temporal deployment may change much of the way we think about aging and how to manipulate it.

The forkhead transcription factor Hcm1 promotes mitochondrial biogenesis and stress resistance in yeast

In Saccharomyces cerevisiae, the forkhead transcription factor Hcm1 is involved in chromosome segregation, spindle pole dynamics, and budding. We found that Hcm1 interacts with the histone deacetylase Sir2 and shifts from cytoplasm to the nucleus in the G(1)/S phase or in response to oxidative stress stimuli. The nuclear localization of Hcm1 depends on the activity of Sir2 as revealed by activators and inhibitors of the sirtuins and the Δsir2 mutant. Hcm1-overexpressing cells display more mitochondria that can be attributed to increased amounts of Abf2, a protein involved in mitochondrial biogenesis. These cells also show higher rates of oxygen consumption and improved resistance to oxidative stress that would be explained by increased catalase and Sod2 activities and molecular chaperones such as Hsp26, Hsp30, and members of Hsp70 family. Microarray analyses also reveal increased expression of genes involved in mitochondrial energy pathways and those allowing the transition from the exponential to the stationary phase. Taken together, these results describe a new and relevant role of Hcm1 for mitochondrial functions, suggesting that this transcription factor would participate in the adaptation of cells from fermentative to respiratory metabolism.

Genome-wide analysis of the cis-regulatory modules of divergent gene pairs in yeast

In budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated.

Reverse engineering dynamic temporal models of biological processes and their relationships

Biological processes such as circadian rhythms, cell division, metabolism, and development occur as ordered sequences of events. The synchronization of these coordinated events is essential for proper cell function, and hence the determination of critical time points in biological processes is an important component of all biological investigations. In particular, such critical time points establish logical ordering constraints on subprocesses, impose prerequisites on temporal regulation and spatial compartmentalization, and situate dynamic reorganization of functional elements in preparation for subsequent stages. Thus, building temporal phenomenological representations of biological processes from genome-wide datasets is relevant in formulating biological hypotheses on: how processes are mechanistically regulated; how the regulations vary on an evolutionary scale, and how their inadvertent disregulation leads to a diseased state or fatality. This paper presents a general framework (GOALIE) to reconstruct temporal models of cellular processes from time-course gene expression data. We mathematically formulate the problem as one of optimally segmenting datasets into a succession of "informative" windows such that time points within a window expose concerted clusters of gene action whereas time points straddling window boundaries constitute points of significant restructuring. We illustrate here how GOALIE successfully brings out the interplay between multiple yeast processes, inferred from combined experimental datasets for the cell cycle and the metabolic cycle.

Preliminary exploration of time course DNA microarray data with correlation maps

In the analysis of data from high-throughput experiments, information regarding the underlying data structure provides the researcher with confidence in the appropriateness of various analysis methods. One extremely simple but powerful data visualization method is the correlation heat map, whereby correlations between experiments/conditions are calculated and represented using color. In this work, the use of correlation maps to shed light on transcription patterns from DNA microarray time course data prior to gene-level analysis is described. Using three different time course studies from the literature, it is shown how the patterns observed at the array level provide insights into the dynamics of the system under study and the experimental design.

Inference of large-scale gene regulatory networks using regression-based network approach

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crescentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.

POLAR MAPPER: a computational tool for integrated visualization of protein interaction networks and mRNA expression data

Polar Mapper is a computational application for exposing the architecture of protein interaction networks. It facilitates the system-level analysis of mRNA expression data in the context of the underlying protein interaction network. Preliminary analysis of a human protein interaction network and comparison of the yeast oxidative stress and heat shock gene expression responses are addressed as case studies.

Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis

BACKGROUND

Recent improvements in DNA microarray techniques have made a large variety of gene expression data available in public databases. This data can be used to evaluate the strength of gene coexpression by calculating the correlation of expression patterns among different genes between many experiments. However, gene expression levels differ significantly across various tissues in higher organisms, as well as in different cellular location in eukaryotes in different cell state. Thus the usual correlation measure can only evaluate the difference of tissues or cellular localizations, and cannot adequately elucidate the functional relationship from the coexpression of genes.

METHOD

We propose a new measure of coexpression by expanding the generally used correlation into a multidimensional one. We used principal component analyses to identify the major factors of gene expression correlation, and then re-calculate the correlation by subtracting the major components in order to remove biases cased by a few experiments. The repeated subtractions of the major components yielded a set of correlation values for each pair of genes. We observed the correlation changes when the first ten principal components were subtracted step-by-step in large-scale Arabidopsis expression data.

RESULTS

We found two extreme patterns of correlation changes, corresponding to stable and fragile coexpression. Our new indexes provided a good means to determine the functional relationships of the genes, by examining a few examples, and higher performance of Gene Ontology term prediction by using the support vector machine and the multidimensional correlation.

AVAILABILITY

The results are available from the expression detail pages in ATTED-II (http://atted.jp).

Genome-wide fitness and expression profiling implicate Mga2 in adaptation to hydrogen peroxide

Caloric restriction extends lifespan, an effect once thought to involve attenuation of reactive oxygen species (ROS) generated by aerobic metabolism. However, recent evidence suggests that caloric restriction may in fact raise ROS levels, which in turn provides protection from acute doses of oxidant through a process called adaptation. To shed light on the molecular mechanisms of adaptation, we designed a series of genome-wide deletion fitness and mRNA expression screens to identify genes involved in adaptation to hydrogen peroxide. Combined with known transcriptional interactions, the integrated data implicate Yap1 and Skn7 as central transcription factors of both the adaptive and acute oxidative responses. They also identify the transcription factors Mga2 and Rox1 as active exclusively in the adaptive response and show that Mga2 is essential for adaptation. These findings are striking because Mga2 and Rox1 have been thought to control the response to hypoxic, not oxidative, conditions. Expression profiling of mga2Δ and rox1Δ knockouts shows that these factors most strongly regulate targets in ergosterol, fatty-acid, and zinc metabolic pathways. Direct quantitation of ergosterol reveals that its basal concentration indeed depends on Mga2, but that Mga2 is not required for the decrease in ergosterol observed during adaptation.

Reactive oxygen species (ROS) damage a variety of structures within the cell, resulting in disease and aging. In a seemingly paradoxical effect termed adaptation, it is possible to prevent damage caused by ROS by pre-treating the cell with a small amount of oxidant. We studied this process in order to identify the mechanisms that provide this protection. Our study identified a number of genes and processes with previously unappreciated roles in adaptation. The mechanisms we identified are remarkable because they are distinct from those previously known to protect the cell from ROS. Although this study is conducted in yeast, the wide conservation of adaptation among many organisms suggests that the results from this study may be widely applicable.

Simultaneously segmenting multiple gene expression time courses by analyzing cluster dynamics

We present a new approach to segmenting multiple time series by analyzing the dynamics of cluster formation and rearrangement around putative segment boundaries. This approach finds application in distilling large numbers of gene expression profiles into temporal relationships underlying biological processes. By directly minimizing information-theoretic measures of segmentation quality derived from Kullback-Leibler (KL) divergences, our formulation reveals clusters of genes along with a segmentation such that clusters show concerted behavior within segments but exhibit significant regrouping across segmentation boundaries. The results of the segmentation algorithm can be summarized as Gantt charts revealing temporal dependencies in the ordering of key biological processes. Applications to the yeast metabolic cycle and the yeast cell cycle are described.

Cytotoxicity mechanism of two naphthoquinones (menadione and plumbagin) in Saccharomyces cerevisiae

Background

Quinones are compounds extensively used in studies of oxidative stress due to their role in plants as chemicals for defense. These compounds are of great interest for pharmacologists and scientists, in general, because several cancer chemotherapeutic agents contain the quinone nucleus. However, due to differences in structures and diverse pharmacological effects, the exact toxicity mechanisms exerted by quinones are far from elucidatation.

Methodology/Principal Findings

Using Saccharomyces cerevisiae, we evaluated the main mechanisms of toxicity of two naphthoquinones, menadione and plumbagin, by determining tolerance and oxidative stress biomarkers such as GSH and GSSG, lipid peroxidation levels, as well as aconitase activity. The importance of glutathione transferases (GST) in quinone detoxification was also addressed. The GSSG/GSH ratio showed that menadione seemed to exert its toxicity mainly through the generation of ROS while plumbagin acted as an electrophile reacting with GSH. However, the results showed that, even by different pathways, both drugs were capable of generating oxidative stress through their toxic effects. Our results showed that the control strain, BY4741, and the glutathione transferase deficient strains (gtt1Δ and gtt2Δ) were sensitive to both compounds. With respect to the role of GST isoforms in cellular protection against quinone toxicity, we observed that the Gtt2 deficient strain was unable to overcome lipid peroxidation, even after a plumbagin pre-treatment, indicating that this treatment did not improve tolerance when compared with the wild type strain. Cross-tolerance experiments confirmed distinct cytotoxicity mechanisms for these naphthoquinones since only a pre-treatment with menadione was able to induce acquisition of tolerance against stress with plumbagin.

Conclusions/Significance

These results suggest different responses to menadione and plumbagin which could be due to the fact that these compounds use different mechanisms to exert their toxicity. In addition, the Gtt2 isoform seemed to act as a general protective factor involved in quinone detoxification.

The effects of protein interactions, gene essentiality and regulatory regions on expression variation

BACKGROUND

Identifying factors affecting gene expression variation is a challenging problem in genetics. Previous studies have shown that the presence of TATA box, the number of cis-regulatory elements, gene essentiality, and protein interactions significantly affect gene expression variation. Nonetheless, the need to obtain a more complete understanding of such factors and how their interactions influence gene expression variation remains a challenge. The growth rates of yeast cells under several DNA-damaging conditions have been studied and a gene's toxicity degree is defined as the number of such conditions that the growth rate of the yeast deletion strain is significantly affected. Since toxicity degree reflects a gene's importance to cell survival under DNA-damaging conditions, we expect that it is negatively associated with gene expression variation. Mutations in both cis-regulatory elements and transcription factors (TF) regulating a gene affect the gene's expression and thus we study the relationship between gene expression variation and the number of TFs regulating a gene. Most importantly we study how these factors interact with each other influencing gene expression variation.

RESULTS

Using yeast as a model system, we evaluated the effects of four separate factors and their interactions on gene expression variation: protein interaction degree, toxicity degree, number of TFs, and the presence of TATA box. Results showed that 1) gene expression variation is negatively correlated with the protein interaction degree in the protein interaction network, 2) essential genes tend to have less expression variation than non-essential genes and gene expression variation decreases with toxicity degree, and 3) the number of TFs regulating a gene is the most important factor influencing gene expression variation (R2 = 8-14%). In addition, the number of TFs regulating a gene was found to be an important factor influencing gene expression variation for both TATA-containing and non-TATA-containing genes, but with different association strength. Moreover, gene expression variation was significantly negatively correlated with toxicity degree only for TATA-containing genes.

CONCLUSION

The finding that distinct mechanisms may influence gene expression variation in TATA-containing and non-TATA-containing genes, provides new insights into the mechanisms that underlie the evolution of gene expression.

Missing value imputation improves clustering and interpretation of gene expression microarray data

Background

Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used.

Results

We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods.

Conclusion

The results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can – up to a certain degree – be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).

A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies

We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a data tensor of genes x "x-settings," that is, different settings of the experimental variable x x "y-settings," which tabulates DNA microarray data from different studies, to a "core tensor" of "eigenarrays" x "x-eigengenes" x "y-eigengenes." Reformulating this multilinear HOSVD such that it decomposes the data tensor into a linear superposition of all outer products of an eigenarray, an x- and a y-eigengene, that is, rank-1 "subtensors," we define the significance of each subtensor in terms of the fraction of the overall information in the data tensor that it captures. We illustrate this HOSVD with an integration of genome-scale mRNA expression data from three yeast cell cycle time courses, two of which are under exposure to either hydrogen peroxide or menadione. We find that significant subtensors represent independent biological programs or experimental phenomena. The picture that emerges suggests that the conserved genes YKU70, MRE11, AIF1, and ZWF1, and the processes of retrotransposition, apoptosis, and the oxidative pentose phosphate pathway that these genes are involved in, may play significant, yet previously unrecognized, roles in the differential effects of hydrogen peroxide and menadione on cell cycle progression. A genome-scale correlation between DNA replication initiation and RNA transcription, which is equivalent to a recently discovered correlation and might be due to a previously unknown mechanism of regulation, is independently uncovered.

Effect of oxidative stress and involvement of poly(ADP-ribose) polymerase (PARP) in Dictyostelium discoideum development

Dictyostelium discoideum, a unicellular eukaryote, exhibits multicellularity upon nutrient starvation and is a good model system for developmental studies, and for the study of various signal transduction pathways. Reactive oxygen species at low doses act as signaling molecules; however, at high doses they are known to cause DNA damage that results in the activation of poly(ADP-ribose) polymerase (PARP). We have earlier reported the high resistance of the unicellular stage of D. discoideum to oxidative stress, and we now show the response of this organism to oxidative stress and the role of PARP during development. We used hydroxylamine (HA) to induce in situ generation of H(2)O(2) and monitored the effect of benzamide, a PARP inhibitor, on oxidative stress-induced changes in D. discoideum development. Interestingly, oxidative stress resulted in PARP activation within 5 min that was inhibited by benzamide. Oxidative stress-induced delay in developmental pattern was also partially restored by benzamide. We studied the long-term effects of PARP inhibition under oxidative stress, and our results demonstrated that spores formed under HA stress exhibited significant delay in germination in comparison to benzamide-pretreated HA-stressed cells. However, second-generation cells showed normal development, signifying that PARP inhibition has no deleterious effect on D. discoideum development under oxidative stress.

An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae

BACKGROUND

Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations.

METHODOLOGY/PRINCIPAL FINDINGS

We report a significantly improved version (v. 2) of a probabilistic functional gene network of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis.

CONCLUSIONS/SIGNIFICANCE

YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.

Computational identification of combinatorial regulation and transcription factor binding sites

A number of computational methods have been used to unravel the core mechanisms governing the regulation of gene expression, but these techniques examine only portions of the genetic regulatory mechanism. For example, some studies have failed to include the combined action of multiple transcription factors (TFs) or the importance of TF binding constraints (i.e., the binding position and orientation), while others have examined combinations of only 2 or 3 TFs. Thus, we sought to develop a new method for identifying regulatory modules in yeast, using an algorithm that includes all combinations of TFs plus a number of binding constraints when identifying target genes. We successfully developed a computational method for using microarray and TF-DNA interaction data to identify regulatory modules. All possible combinations of yeast TFs and various binding constraints were tested to identify regulatory modules. Within the identified modules, target genes were found to have common binding constraints such as fixed binding regions and orientations for each TF. Moreover, targets showed similar mRNA expression profiles and high functional coherence. Our novel approach, which accounts for both combined actions of TFs and their binding constraints, can be used to identify target genes and reliably predict regulatory modules over a broad range of functional categories. Complete results and additional information are available online at http://bisl. kaist.ac.kr/~dhlee/comModule/index.html.

Quantifying fitness distributions and phenotypic relationships in recombinant yeast populations

Studies of the role of sex in evolution typically involve a longitudinal comparison of a single ancestor to several intermediate descendants and to one terminally evolved descendant after many generations of adaptation under a given selective regime. Here we take a complementary, statistical approach to sex in evolution, by describing the distribution of phenotypic similarity in a population of yeast F1 meiotic recombinants. By applying graph theory to fitness measurements of thousands of Saccharomyces cerevisiae recombinants treated with 10 mechanistically distinct, growth-inhibitory small-molecule perturbagens (SMPs), we show that the network of phenotypic similarity among F1 recombinants exhibits a scale-free degree distribution. F1 recombinants are often phenotypically unique and sometimes exceptional, and their fitness strengths are unevenly distributed across the 10 compound treatments. By contrast, highly phenotypically similar F1 recombinants constitute failing hubs that display below-average fitness across all compound treatments and are candidate substrates for purifying selection. Comparison of the F1 generation with the parental strains reveals that (i) there is a specialist more fit in any given single condition than any of the parents but (ii) only rarely are there generalists that exhibit greater fitness than both parental strains across a majority of conditions. This analysis allows us to evaluate and to gain better theoretical understanding of the costs and benefits of sex in the F1 generation.

Effective similarity measures for expression profiles

It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners; they can participate in the same pathway without interacting directly; they can perform similar functions; or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/

Revealing Complex Traits with Small Molecules and Naturally Recombinant Yeast Strains

Here we demonstrate that natural variants of the yeast Saccharomyces cerevisiae are a model system for the systematic study of complex traits, specifically the response to small molecules. As a complement to artificial knockout collections of S. cerevisiae widely used to study individual gene function, we used 314- and 1932-member libraries of mutant strains generated by meiotic recombination to study the cumulative, quantitative effects of natural mutations on phenotypes induced by 23 small-molecule perturbagens (SMPs). This approach reveals synthetic lethality between SMPs, and genetic mapping studies confirm the involvement of multiple quantitative trait loci in the response to two SMPs that affect respiratory processes. The systematic combination of natural variants of yeast and small molecules that modulate evolutionarily conserved cellular processes can enable a better understanding of the general features of complex traits.

Profiling condition-specific, genome-wide regulation of mRNA stability in yeast

The steady-state abundance of an mRNA is determined by the balance between transcription and decay. Although regulation of transcription has been well studied both experimentally and computationally, regulation of transcript stability has received little attention. We developed an algorithm, MatrixREDUCE, that discovers the position-specific affinity matrices for unknown RNA-binding factors and infers their condition-specific activities, using only genomic sequence data and steady-state mRNA expression data as input. We identified and computationally characterized the binding sites for six mRNA stability regulators in Saccharomyces cerevisiae, which include two members of the Pumilio-homology domain (Puf) family of RNA-binding proteins, Puf3p and Puf4p. We provide computational and experimental evidence that regulation of mRNA stability by these factors is modulated in response to a variety of environmental stimuli.