YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis

YEASTRACT+ (http://yeastract-plus.org/) is a tool for the analysis, prediction and modelling of transcription regulatory data at the gene and genomic levels in yeasts. It incorporates three integrated databases: YEASTRACT (http://yeastract-plus.org/yeastract/), PathoYeastract (http://yeastract-plus.org/pathoyeastract/) and NCYeastract (http://yeastract-plus.org/ncyeastract/), focused on Saccharomyces cerevisiae, pathogenic yeasts of the Candida genus, and non-conventional yeasts of biotechnological relevance. In this release, YEASTRACT+ offers upgraded information on transcription regulation for the ten previously incorporated yeast species, while extending the database to another pathogenic yeast, Candida auris. Since the last release of YEASTRACT+ (January 2020), a fourth database has been integrated. CommunityYeastract (http://yeastract-plus.org/community/) offers a platform for the creation, use, and future update of YEASTRACT-like databases for any yeast of the users' choice. CommunityYeastract currently provides information for two Saccharomyces boulardii strains, Rhodotorula toruloides NP11 oleaginous yeast, and Schizosaccharomyces pombe 972h-. In addition, YEASTRACT+ portal currently gathers 304 547 documented regulatory associations between transcription factors (TF) and target genes and 480 DNA binding sites, considering 2771 TFs from 11 yeast species. A new set of tools, currently implemented for S. cerevisiae and C. albicans, is further offered, combining regulatory information with genome-scale metabolic models to provide predictions on the most promising transcription factors to be exploited in cell factory optimisation or to be used as novel drug targets. The expansion of these new tools to the remaining YEASTRACT+ species is ongoing.

Tec1, a member of the TEA transcription factors family, is involved in virulence and basidiocarp development in Ustilago maydis

The life cycle of Ustilago maydis involves alternation of a haploid saprophytic yeast-like stage and a dikaryotic hyphal virulent form. Under in vitro conditions, basidiocarps are formed. Analysis of the transcriptional network of basidiocarp formation revealed the possible involvement of a Tec transcription factor (Tec1, UMAG_02835) in the process. In some Ascomycota, Tec factors are involved in mycelial formation, pathogenesis, and interaction with other regulatory elements, but their role in Basidiomycota species is almost unknown. Accordingly, we proceeded to determine the role of this gene in U. maydis by its mutation. Tec1 was found to be a crucial factor for normal mating, basidiocarp development, and virulence, all of the functions related to the dikaryotic stage dependent of the b genes, whereas dimorphism and resistance to different stress conditions occurring in the haploid stage were not affected in tec1 mutants. The observation that mutants showed a low residual wild-type phenotype suggests the presence of a secondary mechanism that partially compensates the loss of Tec1.

Acid pH Strategy Adaptation through NRG1 in Ustilago maydis

The role of the Ustilago maydis putative homolog of the transcriptional repressor ScNRG1, previously described in Saccharomyces cerevisiae, Candida albicans and Cryptococcus neoformans, was analyzed by means of its mutation. In S. cerevisiae this gene regulates a set of stress-responsive genes, and in C. neoformans it is involved in pathogenesis. It was observed that the U. maydisNRG1 gene regulates several aspects of the cell response to acid pH, such as the production of mannosyl-erythritol lipids, inhibition of the expression of the siderophore cluster genes, filamentous growth, virulence and oxidative stress. A comparison of the gene expression pattern of the wild type strain versus the nrg1 mutant strain of the fungus, through RNA Seq analyses, showed that this transcriptional factor alters the expression of 368 genes when growing at acid pH (205 up-regulated, 163 down-regulated). The most relevant genes affected by NRG1 were those previously reported as the key ones for particular cellular stress responses, such as HOG1 for osmotic stress and RIM101 for alkaline pH. Four of the seven genes included WCO1 codifying PAS domain ( These has been shown as the key structural motif involved in protein-protein interactions of the circadian clock, and it is also a common motif found in signaling proteins, where it functions as a signaling sensor) domains sensors of blue light, two of the three previously reported to encode opsins, one vacuolar and non-pH-responsive, and another one whose role in the acid pH response was already known. It appears that all these light-reactive cell components are possibly involved in membrane potential equilibrium and as virulence sensors. Among previously described specific functions of this transcriptional regulator, it was found to be involved in glucose repression, metabolic adaptation to adverse conditions, cellular transport, cell rescue, defense and interaction with an acidic pH environment.

Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae

The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.

YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts

The YEASTRACT+ information system (http://YEASTRACT-PLUS.org/) is a wide-scope tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in yeasts of biotechnological or human health relevance. YEASTRACT+ is a new portal that integrates the previously existing YEASTRACT (http://www.yeastract.com/) and PathoYeastract (http://pathoyeastract.org/) databases and introduces the NCYeastract (Non-Conventional Yeastract) database (http://ncyeastract.org/), focused on the so-called non-conventional yeasts. The information in the YEASTRACT database, focused on Saccharomyces cerevisiae, was updated. PathoYeastract was extended to include two additional pathogenic yeast species: Candida parapsilosis and Candida tropicalis. Furthermore, the NCYeastract database was created, including five biotechnologically relevant yeast species: Zygosaccharomyces baillii, Kluyveromyces lactis, Kluyveromyces marxianus, Yarrowia lipolytica and Komagataella phaffii. The YEASTRACT+ portal gathers 289 706 unique documented regulatory associations between transcription factors (TF) and target genes and 420 DNA binding sites, considering 247 TFs from 10 yeast species. YEASTRACT+ continues to make available tools for the prediction of the TFs involved in the regulation of gene/genomic expression. In this release, these tools were upgraded to enable predictions based on orthologous regulatory associations described for other yeast species, including two new tools for cross-species transcription regulation comparison, based on multi-species promoter and TF regulatory network analyses.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT—www.yeastract.com) information system has been, for 11 years, a key tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in Saccharomyces cerevisiae. Since its last update in June 2017, YEASTRACT includes approximately 163000 regulatory associations between transcription factors (TF) and target genes in S. cerevisiae, based on more than 1600 bibliographic references; it also includes 247 specific DNA binding consensus recognized by 113 TFs. This release of the YEASTRACT database provides new visualization tools to visualize each regulatory network in an interactive fashion, enabling the user to select and observe subsets of the network such as: (i) considering only DNA binding evidence or both DNA binding and expression evidence; (ii) considering only either positive or negative regulatory associations; or (iii) considering only one set of related environmental conditions. A further tool to observe TF regulons is also offered, enabling a clear-cut understanding of the exact meaning of the available data. We believe that with this new version, YEASTRACT will improve its role as an open web resource instrumental for Yeast Biologists and Systems Biology researchers.

Decoupling the Variances of Heterosis and Inbreeding Effects Is Evidenced in Yeast's Life-History and Proteomic Traits

Heterosis (hybrid vigor) and inbreeding depression, commonly considered as corollary phenomena, could nevertheless be decoupled under certain assumptions according to theoretical population genetics works. To explore this issue on real data, we analyzed the components of genetic variation in a population derived from a half-diallel cross between strains from Saccharomyces cerevisiae and S. uvarum, two related yeast species involved in alcoholic fermentation. A large number of phenotypic traits, either molecular (coming from quantitative proteomics) or related to fermentation and life history, were measured during alcoholic fermentation. Because the parental strains were included in the design, we were able to distinguish between inbreeding effects, which measure phenotypic differences between inbred and hybrids, and heterosis, which measures phenotypic differences between a specific hybrid and the other hybrids sharing a common parent. The sources of phenotypic variation differed depending on the temperature, indicating the predominance of genotype-by-environment interactions. Decomposing the total genetic variance into variances of additive (intra- and interspecific) effects, of inbreeding effects, and of heterosis (intra- and interspecific) effects, we showed that the distribution of variance components defined clear-cut groups of proteins and traits. Moreover, it was possible to cluster fermentation and life-history traits into most proteomic groups. Within groups, we observed positive, negative, or null correlations between the variances of heterosis and inbreeding effects. To our knowledge, such a decoupling had never been experimentally demonstrated. This result suggests that, despite a common evolutionary history of individuals within a species, the different types of traits have been subject to different selective pressures.

Identifying novel genetic determinants for oxidative stress tolerance in Candida glabrata via adaptive laboratory evolution

Candida glabrata (C glabrata) is an important yeast of industrial and medical significance. Resistance to oxidative stress is an important trait affecting its robustness as a production host or virulence as a pathogenic agent, but current understanding of resistance mechanisms is still limited in this fungus. In this study, we rapidly evolved C glabrata population to adapt to oxidative challenge (from 80mM to 350mM of H₂ O₂ ) through short-term adaptive laboratory evolution. Adaptive mutants were isolated from evolved populations and subjected to phenotypic and omics analyses to identify potential mechanisms of tolerance to H₂ O₂ . Phenotypic characterizations revealed faster detoxification of H₂ O₂ and ability to initiate growth at a higher concentration of the oxidant in the isolated adaptive mutants compared with the wild type. Genome resequencing and genome-wide transcriptome analysis revealed multiple genetic determinants (eg, CAGL0E01243g, CAGL0F06831g, and CAGL0C00385g) that potentially contribute to enhanced H₂ O₂ resistance. Subsequent experimental verification confirmed that CgCth2 (CAGL0E01243g) and CgMga2 (CAGL0F06831g) are important in C glabrata tolerance to oxidative stress. Transcriptome profiling of adaptive mutants and bioinformatic analysis suggest that NADPH regeneration, modulation of membrane composition, cell wall remodeling, and/or global regulatory changes are involved in C glabrata tolerance to H₂ O₂ .

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Automated transition analysis of activated gene regulation during diauxic nutrient shift in Escherichia coli and adipocyte differentiation in mouse cells

Background

Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles.

Results

Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed.

Conclusions

Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine.

Eukaryotic and prokaryotic promoter databases as valuable tools in exploring the regulation of gene transcription: a comprehensive overview

The complete exploration of the regulation of gene expression remains one of the top-priority goals for researchers. As the regulation is mainly controlled at the level of transcription by promoters, study on promoters and findings are of great importance. This review summarizes forty selected databases that centralize experimental and theoretical knowledge regarding the organization of promoters, interacting transcription factors (TFs) and microRNAs (miRNAs) in many eukaryotic and prokaryotic species. The presented databases offer researchers valuable support in elucidating the regulation of gene transcription.

Three distinct mechanisms of long-distance modulation of gene expression in yeast

Recent Hi-C measurements have revealed numerous intra- and inter-chromosomal interactions in various eukaryotic cells. To what extent these interactions regulate gene expression is not clear. This question is particularly intriguing in budding yeast because it has extensive long-distance chromosomal interactions but few cases of gene regulation over-a-distance. Here, we developed a medium-throughput assay to screen for functional long-distance interactions that affect the average expression level of a reporter gene as well as its cell-to-cell variability (noise). We ectopically inserted an insulated MET3 promoter (MET3pr) flanked by ~1kb invariable sequences into thousands of genomic loci, allowing it to make contacts with different parts of the genome, and assayed the MET3pr activity in single cells. Changes of MET3pr activity in this case necessarily involve mechanisms that function over a distance. MET3pr has similar activities at most locations. However, at some locations, they deviate from the norm and exhibit three distinct patterns including low expression / high noise, low expression / low noise, and high expression / low noise. We provided evidence that these three patterns of MET3pr expression are caused by Sir2-mediated silencing, transcriptional interference, and 3D clustering. The clustering also occurs in the native genome and enhances the transcription of endogenous Met4-targeted genes. Overall, our results demonstrate that a small fraction of long-distance chromosomal interactions can affect gene expression in yeast.

Eukaryotic transcription occurs within the nucleus where DNA is packaged into high order chromosome structures. Some long-distance chromosomal interactions play an important role in gene regulation in higher eukaryotic species, such as mouse and human. In budding yeast, gene expression is traditionally thought to be regulated over short distances because the upstream regulatory sequences (URSs) are usually located close to the core promoters. However, recent chromosome conformation capture experiments have detected numerous long-distance chromosomal interactions in the yeast genome. The function of these interactions in gene regulation remains unclear. Here, we developed a new assay to screen for long-distance interactions that affect the activity of a reporter gene. We found three regulatory mechanisms that act from a distance: silencing, transcriptional interference, and 3D clustering, which alter expression level of the reporter gene as well as its cell-to-cell variability. Our results demonstrate that transcription in budding yeast, similar to transcription in higher eukaryotes, can be regulated over long distances. We anticipate our assay can be used as a general platform to screen for functional long-distance chromosomal interactions that affect gene expression.

Insight into the Kluyveromyces lactis Pdr1p regulon

The overexpression of efflux pumps is an important mechanism leading to the development of multidrug resistance phenomenon. The transcription factor KlPdr1p, belonging to the Zn₂Cys₆ family, is a central regulator of efflux pump expression in Kluyveromyces lactis. To better understand how KlPDR1-mediated drug resistance is achieved in K. lactis, we used DNA microarrays to identify genes whose expression was affected by deletion or overexpression of the KlPDR1 gene. Eighty-nine targets of the KlPDR1 were identified. From those the transcription of 16 genes was induced in the transformant overexpressing KlPDR1* and simultaneously repressed in the Klpdr1Δ deletion mutant. Almost all of these genes contain putative binding motifs for the AP-1-like transcription factors in their promoters. Furthermore, we studied the possible interplay between KlPdr1p and KlYap1p transcription factors. Our results show that KlYap1p does not significantly contribute to the regulation of KlPDR1 gene expression in the presence of azoles. However, KlPDR1 expression markedly increased in the presence of hydrogen peroxide and hinged upon the presence of KlYap1p. Our results show that although both KlPdr1p and KlYap1p transcription factors are involved in the control of K. lactis multidrug resistance, further studies will be needed to determine their interplay.

An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae

The accumulation of ethanol is one of the main environmental stresses that Saccharomyces cerevisiae cells are exposed to in industrial alcoholic beverage and bioethanol production processes. Despite the known impacts of ethanol, the molecular mechanisms underlying ethanol tolerance are still not fully understood. Novel gene targets leading to ethanol tolerance were previously identified via a network approach and the investigations of the deletions of these genes resulted in the improved ethanol tolerance of pmt7Δ/pmt7Δ and yhl042wΔ/yhl042wΔ strains. In the present study, an integrative system based approach was used to investigate the global transcriptional changes in these two ethanol tolerant strains in response to ethanol and hence to elucidate the mechanisms leading to the observed tolerant phenotypes. In addition to strain specific biological processes, a number of common and already reported biological processes were found to be affected in the reference and both ethanol tolerant strains. However, the integrative analysis of the transcriptome with the transcriptional regulatory network and the ethanol tolerance network revealed that each ethanol tolerant strain had a specific organization of the transcriptomic response. Transcription factors around which most important changes occur were determined and active subnetworks in response to ethanol and functional clusters were identified in all strains.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata. Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks.

Attenuation of transcriptional and signaling responses limits viability of ρ(0)Saccharomyces cerevisiae during periods of glucose deprivation

BACKGROUND

The maintenance of viability during periods when a glycolytic carbon source is limited (or absent) is a major obstacle for cells whose mitochondrial DNA (mtDNA) has been damaged or lost.

METHODS

We utilized genome wide transcriptional profiling and in gel mobility analyses to examine the transcriptional response and characterize defects in the phosphorylation dependent signaling events that occur during acute glucose starvation in ρ(0) cells that lack mtDNA. Genetic and pharmacological interventions were employed to clarify the contribution of nutrient responsive kinases to regulation of the transcription factors that displayed abnormal phosphoregulation in ρ(0) cells.

RESULTS

The transcriptional response to glucose deprivation is dampened but not blocked in ρ(0) cells. Genes regulated by the transcription factors Mig1, Msn2, Gat1, and Ume6 were noticeably affected and phosphorylation of these factors in response to nutrient depletion is abnormal in ρ(0) cells. Regulation of the nutrient responsive kinases PKA and Snf1 remains normal in ρ(0) cells. The phosphorylation defect results from ATP depletion and loss of the activity of kinases including GSK3β, Rim15, and Yak1. Interventions which rescue phosphoregulation of transcription factors bolster maintenance of viability in ρ(0) cells during subsequent glucose deprivation.

CONCLUSIONS

A subset of nutrient responsive kinases is especially sensitive to ATP levels and their misregulation may underlie regulatory defects presented by ρ(0) cells.

GENERAL SIGNIFICANCE

Abnormal regulation of mitochondrial function is implicated in numerous human disorders. This work illustrates that some signaling pathways are more sensitive than others to metabolic defects caused by mitochondrial dysfunction.

The WOPR Protein Ros1 Is a Master Regulator of Sporogenesis and Late Effector Gene Expression in the Maize Pathogen Ustilago maydis

The biotrophic basidiomycete fungus Ustilago maydis causes smut disease in maize. Hallmarks of the disease are large tumors that develop on all aerial parts of the host in which dark pigmented teliospores are formed. We have identified a member of the WOPR family of transcription factors, Ros1, as major regulator of spore formation in U. maydis. ros1 expression is induced only late during infection and hence Ros1 is neither involved in plant colonization of dikaryotic fungal hyphae nor in plant tumor formation. However, during late stages of infection Ros1 is essential for fungal karyogamy, massive proliferation of diploid fungal cells and spore formation. Premature expression of ros1 revealed that Ros1 counteracts the b-dependent filamentation program and induces morphological alterations resembling the early steps of sporogenesis. Transcriptional profiling and ChIP-seq analyses uncovered that Ros1 remodels expression of about 30% of all U. maydis genes with 40% of these being direct targets. In total the expression of 80 transcription factor genes is controlled by Ros1. Four of the upregulated transcription factor genes were deleted and two of the mutants were affected in spore development. A large number of b-dependent genes were differentially regulated by Ros1, suggesting substantial changes in this regulatory cascade that controls filamentation and pathogenic development. Interestingly, 128 genes encoding secreted effectors involved in the establishment of biotrophic development were downregulated by Ros1 while a set of 70 “late effectors” was upregulated. These results indicate that Ros1 is a master regulator of late development in U. maydis and show that the biotrophic interaction during sporogenesis involves a drastic shift in expression of the fungal effectome including the downregulation of effectors that are essential during early stages of infection.

The fungus Ustilago maydis is a pathogen of maize which induces tumor formation in the infected tissue. In these tumors huge amounts of fungal spores develop. As a biotrophic pathogen, U. maydis establishes itself in the plant with the help of a large number of secreted effector proteins. Many effector proteins are important for virulence because they counteract plant defense reactions. In this manuscript we have identified and characterized Ros1, a master regulator for the late stages of U. maydis development. This transcription factor is expressed late during infection and controls nuclear fusion, hyphal aggregation and late proliferation. ros1 mutants are still able to induce tumor formation but these are a dead end because they do not contain any spores. We show that Ros1 interferes with the early regulatory cascade controlled by a complex of two homeodomain proteins. In addition, Ros1 triggers a major switch in the effector repertoire, suggesting that different sets of effectors are needed for different stages of fungal development inside the plant.

Investigating the Combinatory Effects of Biological Networks on Gene Co-expression

Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharomyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related.

Microarray Analysis of Gene Expression in Saccharomyces cerevisiae kap108Δ Mutants upon Addition of Oxidative Stress

Protein transport between the nucleus and cytoplasm of eukaryotic cells is tightly regulated, providing a mechanism for controlling intracellular localization of proteins, and regulating gene expression. In this study, we have investigated the importance of nucleocytoplasmic transport mediated by the karyopherin Kap108 in regulating cellular responses to oxidative stress in Saccharomyces cerevisiae. We carried out microarray analyses on wild-type and kap108 mutant cells grown under normal conditions, shortly after introduction of oxidative stress, after 1 hr of oxidative stress, and 1 hr after oxidative stress was removed. We observe more than 500 genes that undergo a 40% or greater change in differential expression between wild-type and kap108Δ cells under at least one of these conditions. Genes undergoing changes in expression can be categorized in two general groups: 1) those that are differentially expressed between wild-type and kap108Δ cells, no matter the oxidative stress conditions; and 2) those that have patterns of response dependent upon both the absence of Kap108, and introduction or removal of oxidative stress. Gene ontology analysis reveals that, among the genes whose expression is reduced in the absence of Kap108 are those involved in stress response and intracellular transport, while those overexpressed are largely involved in mating and pheromone response. We also identified 25 clusters of genes that undergo similar patterns of change in gene expression when oxidative stresses are added and subsequently removed, including genes involved in stress response, oxidation–reduction processing, iron homeostasis, ascospore wall assembly, transmembrane transport, and cell fusion during mating. These data suggest that Kap108 is important for regulating expression of genes involved in a variety of specific cell functions.

Comprehensive Analysis of the SUL1 Promoter of Saccharomyces cerevisiae

In the yeast Saccharomyces cerevisiae, beneficial mutations selected during sulfate-limited growth are typically amplifications of the SUL1 gene, which encodes the high-affinity sulfate transporter, resulting in fitness increases of >35% . Cis-regulatory mutations have not been observed at this locus; however, it is not clear whether this absence is due to a low mutation rate such that these mutations do not arise, or they arise but have limited fitness effects relative to those of amplification. To address this question directly, we assayed the fitness effects of nearly all possible point mutations in a 493-base segment of the gene's promoter through mutagenesis and selection. While most mutations were either neutral or detrimental during sulfate-limited growth, eight mutations increased fitness >5% and as much as 9.4%. Combinations of these beneficial mutations increased fitness only up to 11%. Thus, in the case of SUL1, promoter mutations could not induce a fitness increase similar to that of gene amplification. Using these data, we identified functionally important regions of the SUL1 promoter and analyzed three sites that correspond to potential binding sites for the transcription factors Met32 and Cbf1 Mutations that create new Met32- or Cbf1-binding sites also increased fitness. Some mutations in the untranslated region of the SUL1 transcript decreased fitness, likely due to the formation of inhibitory upstream open reading frames. Our methodology-saturation mutagenesis, chemostat selection, and DNA sequencing to track variants-should be a broadly applicable approach.

Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis

Transcriptional regulation is one of the key steps in the control of gene expression, with huge impact on the survival, adaptation, and fitness of all organisms. However, it is becoming increasingly clear that transcriptional regulation is far more complex than initially foreseen. In model organisms such as the yeast Saccharomyces cerevisiae evidence has been piling up showing that the expression of each gene can be controlled by several transcription factors, in the close dependency of the environmental conditions. Furthermore, transcription factors work in intricate networks, being themselves regulated at the transcriptional, post-transcriptional, and post-translational levels, working in cooperation or antagonism in the promoters of their target genes.In this chapter, a step-by-step guide using the YEASTRACT database is provided, for the prediction and ranking of the transcription factors required for the regulation of the expression a single gene and of a genome-wide response. These analyses are illustrated with the regulation of the PDR18 gene and of the transcriptome-wide changes induced upon exposure to the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D), respectively. The newest potentialities of this information system are explored, and the various results obtained in the dependency of the querying criteria are discussed in terms of the knowledge gathered on the biological responses considered as case studies.

A Systems Approach to Elucidate Heterosis of Protein Abundances in Yeast

Heterosis is a universal phenomenon that has major implications in evolution and is of tremendous agro-economic value. To study the molecular manifestations of heterosis and to find factors that maximize its strength, we implemented a large-scale proteomic experiment in yeast. We analyzed the inheritance of 1,396 proteins in 55 inter- and intraspecific hybrids obtained from Saccharomyces cerevisiae and S. uvarum that were grown in grape juice at two temperatures. We showed that the proportion of heterotic proteins was highly variable depending on the parental strain and on the temperature considered. For intraspecific hybrids, this proportion was higher at nonoptimal temperature. Unexpectedly, heterosis for protein abundance was strongly biased toward positive values in interspecific hybrids but not in intraspecific hybrids. Computer modeling showed that this observation could be accounted for by assuming concave relationships between protein abundances and their controlling factors, in line with the metabolic model of heterosis. These results point to nonlinear processes that could play a central role in heterosis.

Cwc21p promotes the second step conformation of the spliceosome and modulates 3' splice site selection

Pre-mRNA splicing involves two transesterification steps catalyzed by the spliceosome. How RNA substrates are positioned in each step and the molecular rearrangements involved, remain obscure. Here, we show that mutations in PRP16, PRP8, SNU114 and the U5 snRNA that affect this process interact genetically with CWC21, that encodes the yeast orthologue of the human SR protein, SRm300/SRRM2. Our microarray analysis shows changes in 3′ splice site selection at elevated temperature in a subset of introns in cwc21Δ cells. Considering all the available data, we propose a role for Cwc21p positioning the 3′ splice site at the transition to the second step conformation of the spliceosome, mediated through its interactions with the U5 snRNP. This suggests a mechanism whereby SRm300/SRRM2, might influence splice site selection in human cells.

Quantitative proteomics identifies unanticipated regulators of nitrogen- and glucose starvation

The molecular mechanisms underlying how cells sense, respond, and adapt to alterations in nutrient availability have been studied extensively during the past years. While most of these studies have focused on the linear connections between signaling components, it is increasingly being recognized that signaling pathways are interlinked in molecular circuits and networks such that any metabolic perturbation will induce signaling-wide ripple effects. In the present study, we have used quantitative mass spectrometry (MS) to examine how the yeast Saccharomyces cerevisiae responds to nitrogen- or glucose starvation. We identify nearly 1400 phosphorylation sites of which more than 500 are regulated in a temporal manner in response to glucose- or nitrogen starvation. By bioinformatics and network analyses, we have identified the cyclin-dependent kinase (CDK) inhibitor Sic1, the Hsp90 co-chaperone Cdc37, and the Hsp90 isoform Hsp82 to putatively mediate some of the starvation responses. Consistently, quantitative expression analyses showed that Sic1, Cdc37, and Hsp82 are required for normal expression of nutrient-responsive genes. Collectively, we therefore propose that Sic1, Cdc37, and Hsp82 may orchestrate parts of the cellular starvation response by regulating transcription factor- and kinase activities.

Deconstructing the genetic basis of spent sulphite liquor tolerance using deep sequencing of genome-shuffled yeast

BACKGROUND

Identifying the genetic basis of complex microbial phenotypes is currently a major barrier to our understanding of multigenic traits and our ability to rationally design biocatalysts with highly specific attributes for the biotechnology industry. Here, we demonstrate that strain evolution by meiotic recombination-based genome shuffling coupled with deep sequencing can be used to deconstruct complex phenotypes and explore the nature of multigenic traits, while providing concrete targets for strain development.

RESULTS

We determined genomic variations found within Saccharomyces cerevisiae previously evolved in our laboratory by genome shuffling for tolerance to spent sulphite liquor. The representation of these variations was backtracked through parental mutant pools and cross-referenced with RNA-seq gene expression analysis to elucidate the importance of single mutations and key biological processes that play a role in our trait of interest. Our findings pinpoint novel genes and biological determinants of lignocellulosic hydrolysate inhibitor tolerance in yeast. These include the following: protein homeostasis constituents, including Ubp7p and Art5p, related to ubiquitin-mediated proteolysis; stress response transcriptional repressor, Nrg1p; and NADPH-dependent glutamate dehydrogenase, Gdh1p. Reverse engineering a prominent mutation in ubiquitin-specific protease gene UBP7 in a laboratory S. cerevisiae strain effectively increased spent sulphite liquor tolerance.

CONCLUSIONS

This study advances understanding of yeast tolerance mechanisms to inhibitory substrates and biocatalyst design for a biomass-to-biofuel/biochemical industry, while providing insights into the process of mutation accumulation that occurs during genome shuffling.

Nitric oxide-mediated antioxidative mechanism in yeast through the activation of the transcription factor Mac1

The budding yeast Saccharomyces cerevisiae possesses various defense mechanisms against environmental stresses that generate reactive oxygen species, leading to growth inhibition or cell death. Our recent study showed a novel antioxidative mechanism mediated by nitric oxide (NO) in yeast cells, but the mechanism underlying the oxidative stress tolerance remained unclear. We report here one of the downstream pathways of NO involved in stress-tolerance mechanism in yeast. Our microarray and real-time quantitative PCR analyses revealed that exogenous NO treatment induced the expression of genes responsible for copper metabolism under the control of the transcription factor Mac1, including the CTR1 gene encoding high-affinity copper transporter. Our ChIP analysis also demonstrated that exogenous NO enhances the binding of Mac1 to the promoter region of target genes. Interestingly, we found that NO produced under high-temperature stress conditions increased the transcription level of the CTR1 gene. Furthermore, NO produced during exposure to high temperature also increased intracellular copper content, the activity of Cu,Zn-superoxide dismutase Sod1, and cell viability after exposure to high-temperature in a manner dependent on Mac1. NO did not affect the expression of the MAC1 gene, indicating that NO activates Mac1 through its post-translational modification. Based on the results shown here, we propose a novel NO-mediated antioxidative mechanism that Mac1 activated by NO induces the CTR1 gene, leading to an increase in cellular copper level, and then Cu(I) activates Sod1. This is the first report to unveil the mechanism of NO-dependent antioxidative system in yeast.

De-novo learning of genome-scale regulatory networks in S. cerevisiae

De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.

Pulsatile dynamics in the yeast proteome

The activation of transcription factors in response to environmental conditions is fundamental to cellular regulation. Recent work has revealed that some transcription factors are activated in stochastic pulses of nuclear localization, rather than at a constant level, even in a constant environment [1-12]. In such cases, signals control the mean activity of the transcription factor by modulating the frequency, duration, or amplitude of these pulses. Although specific pulsatile transcription factors have been identified in diverse cell types, it has remained unclear how prevalent pulsing is within the cell, how variable pulsing behaviors are between genes, and whether pulsing is specific to transcriptional regulators or is employed more broadly. To address these issues, we performed a proteome-wide movie-based screen to systematically identify localization-based pulsing behaviors in Saccharomyces cerevisiae. The screen examined all genes in a previously developed fluorescent protein fusion library of 4,159 strains [13] in multiple media conditions. This approach revealed stochastic pulsing in ten proteins, all transcription factors. In each case, pulse dynamics were heterogeneous and unsynchronized among cells in clonal populations. Pulsing is the only dynamic localization behavior that we observed, and it tends to occur in pairs of paralogous and redundant proteins. Taken together, these results suggest that pulsatile dynamics play a pervasive role in yeast and may be similarly prevalent in other eukaryotic species.

Transcriptomic analysis of the role of Rim101/PacC in the adaptation of Ustilago maydis to an alkaline environment

Alkaline pH triggers an adaptation mechanism in fungi that is mediated by Rim101/PacCp, a zinc finger transcription factor. To identify the genes under its control in Ustilago maydis, we performed microarray analyses, comparing gene expression in a wild-type strain versus a rim101/pacC mutation strain of the fungus. In this study we obtained evidence of the large number of genes regulated mostly directly, but also indirectly (probably through regulation of other transcription factors), by Rim101/PacCp, including proteins involved in a large number of physiological activities of the fungus. Our analyses suggest that the response to alkaline conditions under the control of the Pal/Rim pathway involves changes in the cell wall and plasma membrane through alterations in their lipid, protein and polysaccharide composition, changes in cell polarity, actin cytoskeleton organization, and budding patterns. Also as expected, adaptation involves regulation by Rim101/PacC of genes involved in meiotic functions, such as recombination and segregation, and expression of genes involved in ion and nutrient transport, as well as general vacuole functions.

The regulation of coenzyme q biosynthesis in eukaryotic cells: all that yeast can tell us

Coenzyme Q (CoQ) is a mitochondrial lipid, which functions mainly as an electron carrier from complex I or II to complex III at the mitochondrial inner membrane, and also as antioxidant in cell membranes. CoQ is needed as electron acceptor in β-oxidation of fatty acids and pyridine nucleotide biosynthesis, and it is responsible for opening the mitochondrial permeability transition pore. The yeast model has been very useful to analyze the synthesis of CoQ, and therefore, most of the knowledge about its regulation was obtained from the Saccharomyces cerevisiae model. CoQ biosynthesis is regulated to support 2 processes: the bioenergetic metabolism and the antioxidant defense. Alterations of the carbon source in yeast, or in nutrient availability in yeasts or mammalian cells, upregulate genes encoding proteins involved in CoQ synthesis. Oxidative stress, generated by chemical or physical agents or by serum deprivation, modifies specifically the expression of some COQ genes by means of stress transcription factors such as Msn2/4p, Yap1p or Hsf1p. In general, the induction of COQ gene expression produced by metabolic changes or stress is modulated downstream by other regulatory mechanisms such as the protein import to mitochondria, the assembly of a multi-enzymatic complex composed by Coq proteins and also the existence of a phosphorylation cycle that regulates the last steps of CoQ biosynthesis. The CoQ biosynthetic complex assembly starts with the production of a nucleating lipid such as HHB by the action of the Coq2 protein. Then, the Coq4 protein recognizes the precursor HHB acting as the nucleus of the complex. The activity of Coq8p, probably as kinase, allows the formation of an initial pre-complex containing all Coq proteins with the exception of Coq7p. This pre-complex leads to the synthesis of 5-demethoxy-Q6 (DMQ6), the Coq7p substrate. When de novo CoQ biosynthesis is required, Coq7p becomes dephosphorylated by the action of Ptc7p increasing the synthesis rate of CoQ6. This critical model is needed for a better understanding of CoQ biosynthesis. Taking into account that patients with CoQ10 deficiency maintain to some extent the machinery to synthesize CoQ, new promising strategies for the treatment of CoQ10 deficiency will require a better understanding of the regulation of CoQ biosynthesis in the future.

State transitions in the TORC1 signaling pathway and information processing in Saccharomyces cerevisiae

TOR kinase complex I (TORC1) is a key regulator of cell growth and metabolism in all eukaryotes. Previous studies in yeast have shown that three GTPases-Gtr1, Gtr2, and Rho1-bind to TORC1 in nitrogen and amino acid starvation conditions to block phosphorylation of the S6 kinase Sch9 and activate protein phosphatase 2A (PP2A). This leads to downregulation of 450 Sch9-dependent protein and ribosome synthesis genes and upregulation of 100 PP2A-dependent nitrogen assimilation and amino acid synthesis genes. Here, using bandshift assays and microarray measurements, we show that the TORC1 pathway also populates three other stress/starvation states. First, in glucose starvation conditions, the AMP-activated protein kinase (AMPK/Snf1) and at least one other factor push the TORC1 pathway into an off state, in which Sch9-branch signaling and PP2A-branch signaling are both inhibited. Remarkably, the TORC1 pathway remains in the glucose starvation (PP2A inhibited) state even when cells are simultaneously starved for nitrogen and glucose. Second, in osmotic stress, the MAPK Hog1/p38 drives the TORC1 pathway into a different state, in which Sch9 signaling and PP2A-branch signaling are inhibited, but PP2A-branch signaling can still be activated by nitrogen starvation. Third, in oxidative stress and heat stress, TORC1-Sch9 signaling is blocked while weak PP2A-branch signaling occurs. Together, our data show that the TORC1 pathway acts as an information-processing hub, activating different genes in different conditions to ensure that available energy is allocated to drive growth, amino acid synthesis, or a stress response, depending on the needs of the cell.

Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on "hotspot" loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as "epi-hotspots," in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Network-dosage compensation topologies as recurrent network motifs in natural gene networks

Background

Global noise in gene expression and chromosome duplication during cell-cycle progression cause inevitable fluctuations in the effective number of copies of gene networks in cells. These indirect and direct alterations of network copy numbers have the potential to change the output or activity of a gene network. For networks whose specific activity levels are crucial for optimally maintaining cellular functions, cells need to implement mechanisms to robustly compensate the effects of network dosage fluctuations.

Results

Here, we determine the necessary conditions for generalized N-component gene networks to be network-dosage compensated and show that the compensation mechanism can robustly operate over large ranges of gene expression levels. Furthermore, we show that the conditions that are necessary for network-dosage compensation are also sufficient. Finally, using genome-wide protein-DNA and protein-protein interaction data, we search the yeast genome for the abundance of specific dosage-compensation motifs and show that a substantial percentage of the natural networks identified contain at least one dosage-compensation motif.

Conclusions

Our results strengthen the hypothesis that the special network topologies that are necessary for network-dosage compensation may be recurrent network motifs in eukaryotic genomes and therefore may be an important design principle in gene network assembly in cells.

Cell cycle-regulated transcription: effectively using a genomics toolbox

The cell cycle comprises a series of temporally ordered events that occur sequentially, including DNA replication, centrosome duplication, mitosis, and cytokinesis. What are the regulatory mechanisms that ensure proper timing and coordination of events during the cell cycle? Biochemical and genetic screens have identified a number of cell-cycle regulators, and it was recognized early on that many of the genes encoding cell-cycle regulators, including cyclins, were transcribed only in distinct phases of the cell cycle. Thus, "just in time" expression is likely an important part of the mechanism that maintains the proper temporal order of cell cycle events. New high-throughput technologies for measuring transcript levels have revealed that a large percentage of the Saccharomyces cerevisiae transcriptome (~20 %) is cell cycle regulated. Similarly, a substantial fraction of the mammalian transcriptome is cell cycle-regulated. Over the past 25 years, many studies have been undertaken to determine how gene expression is regulated during the cell cycle. In this review, we discuss contemporary models for the control of cell cycle-regulated transcription, and how this transcription program is coordinated with other cell cycle events in S. cerevisiae. In addition, we address the genomic approaches and analytical methods that enabled contemporary models of cell cycle transcription. Finally, we address current and future technologies that will aid in further understanding the role of periodic transcription during cell cycle progression.

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

RESULTS

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

CONCLUSIONS

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Molecular mechanisms of system responses to novel stimuli are predictable from public data

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ∼ 1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain-a common problem in laboratory animal and human studies.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

LoQAtE--Localization and Quantitation ATlas of the yeast proteomE. A new tool for multiparametric dissection of single-protein behavior in response to biological perturbations in yeast

Living organisms change their proteome dramatically to sustain a stable internal milieu in fluctuating environments. To study the dynamics of proteins during stress, we measured the localization and abundance of the Saccharomyces cerevisiae proteome under various growth conditions and genetic backgrounds using the GFP collection. We created a database (DB) called ‘LoQAtE’ (Localizaiton and Quantitation Atlas of the yeast proteomE), available online at http://www.weizmann.ac.il/molgen/loqate/, to provide easy access to these data. Using LoQAtE DB, users can get a profile of changes for proteins of interest as well as querying advanced intersections by either abundance changes, primary localization or localization shifts over the tested conditions. Currently, the DB hosts information on 5330 yeast proteins under three external perturbations (DTT, H₂O₂ and nitrogen starvation) and two genetic mutations [in the chaperonin containing TCP1 (CCT) complex and in the proteasome]. Additional conditions will be uploaded regularly. The data demonstrate hundreds of localization and abundance changes, many of which were not detected at the level of mRNA. LoQAtE is designed to allow easy navigation for non-experts in high-content microscopy and data are available for download. These data should open up new perspectives on the significant role of proteins while combating external and internal fluctuations.

Transcriptome response to alkane biofuels in Saccharomyces cerevisiae: identification of efflux pumps involved in alkane tolerance

BACKGROUND

Hydrocarbon alkanes have been recently considered as important next-generation biofuels because microbial production of alkane biofuels was demonstrated. However, the toxicity of alkanes to microbial hosts can possibly be a bottleneck for high productivity of alkane biofuels. To tackle this toxicity issue, it is essential to understand molecular mechanisms of interactions between alkanes and microbial hosts, and to harness these mechanisms to develop microbial host strains with improved tolerance against alkanes. In this study, we aimed to improve the tolerance of Saccharomyces cerevisiae, a model eukaryotic host of industrial significance, to alkane biofuels by exploiting cellular mechanisms underlying alkane response.

RESULTS

To this end, we first confirmed that nonane (C9), decane (C10), and undecane (C11) were significantly toxic and accumulated in S. cerevisiae. Transcriptome analyses suggested that C9 and C10 induced a range of cellular mechanisms such as efflux pumps, membrane modification, radical detoxification, and energy supply. Since efflux pumps could possibly aid in alkane secretion, thereby reducing the cytotoxicity, we formed the hypothesis that those induced efflux pumps could contribute to alkane export and tolerance. In support of this hypothesis, we demonstrated the roles of the efflux pumps Snq2p and Pdr5p in reducing intracellular levels of C10 and C11, as well as enhancing tolerance levels against C10 and C11. This result provided the evidence that Snq2p and Pdr5p were associated with alkane export and tolerance in S. cerevisiae.

CONCLUSIONS

Here, we investigated the cellular mechanisms of S. cerevisiae response to alkane biofuels at a systems level through transcriptome analyses. Based on these mechanisms, we identified efflux pumps involved in alkane export and tolerance in S. cerevisiae. We believe that the results here provide valuable insights into designing microbial engineering strategies to improve cellular tolerance for highly efficient alkane biofuel production.

Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests

MOTIVATION

Transcriptional regulatory network inference methods have been studied for years. Most of them rely on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment and equally well performing as existing methods.

RESULTS

We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard Escherichia coli and yeast reference datasets showed that this method performs equally well as the best existing methods. Analysis of the predicted interactions suggested that it works best to infer context-specific TF-target interactions which only co-express locally. We confirmed this hypothesis on a dataset of >1000 normal human tissue samples, where we found that our method predicts highly tissue-specific and functionally relevant interactions, whereas a global co-expression method only associates general TFs to non-specific biological processes.

AVAILABILITY

A software tool called TwixTrix is available from http://twixtrix.googlecode.com.

SUPPLEMENTARY INFORMATION

Supplementary Material is available from http://www.roslin.ed.ac.uk/tom-michoel/supplementary-data.

CONTACT

tom.michoel@roslin.ed.ac.uk.

Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci

Expression quantitative trait loci (eQTL) studies have generated large amounts of data in different organisms. The analyses of these data have led to many novel findings and biological insights on expression regulations. However, the role of epistasis in the joint regulation of multiple genes has not been explored. This is largely due to the computational complexity involved when multiple traits are simultaneously considered against multiple markers if an exhaustive search strategy is adopted. In this article, we propose a computationally feasible approach to identify pairs of chromosomal regions that interact to regulate co-expression patterns of pairs of genes. Our approach is built on a bivariate model whose covariance matrix depends on the joint genotypes at the candidate loci. We also propose a filtering process to reduce the computational burden. When we applied our method to a yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 225 and 224 modules, with each module consisting of two genes and two eQTLs where the two eQTLs epistatically regulate the co-expression patterns of the two genes. We found that many of these modules have biological interpretations. Under the glucose condition, ribosome biogenesis was co-regulated with the signaling and carbohydrate catabolic processes, whereas silencing and aging related genes were co-regulated under the ethanol condition with the eQTLs containing genes involved in oxidative stress response process.

eQTL studies collect both gene expression and genotype data, and they are highly informative as to how genes regulate expressions. Although much progress has been made in the analysis of such data, most studies have considered one marker at a time. As a result, those markers with weak marginal yet strong interactive effects may not be inferred from these single-marker-based analyses. In this article, using joint expression patterns between two genes (versus one gene) as the primary phenotype, we propose a novel statistical method to conduct an exhaustive search for joint marker analysis. When our method is applied to a well-studied dataset, we were able to identify many novel features that were overlooked by existing methods. Our general strategy has general applicability to other scientific problems.

Blessings in disguise: biological benefits of prion-like mechanisms

Prions and amyloids are often associated with disease, but related mechanisms provide beneficial functions in nature. Prion-like mechanisms (PriLiMs) are found from bacteria to humans, where they alter the biological and physical properties of prion-like proteins. We have proposed that prions can serve as heritable bet-hedging devices for diversifying microbial phenotypes. Other, more dynamic proteinaceous complexes may be governed by similar self-templating conformational switches. Additional PriLiMs continue to be identified and many share features of self-templating protein structure (including amyloids) and dependence on chaperone proteins. Here, we discuss several PriLiMs and their functions, intending to spur discussion and collaboration on the subject of beneficial prion-like behaviors.