VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC.

A Recurrent Silent Mutation Implicates fecA in Ethanol Tolerance by Escherichia coli.

BACKGROUND: An issue associated with efficient bioethanol production is the fact that the desired product is toxic to the biocatalyst. Among other effects, ethanol has previously been found to influence the membrane of E. coli in a dose-dependent manner and induce changes in the lipid composition of the plasma membrane. We describe here the characterization of a collection of ethanol-tolerant strains derived from the ethanologenic Escherichia coli strain FBR5. RESULTS: Membrane permeability assays indicate that many of the strains in the collection have alterations in membrane permeability and/or responsiveness of the membrane to environmental changes such as temperature shifts or ethanol exposure. However, analysis of the strains by gas chromatography and mass spectrometry revealed no qualitative changes in the acyl chain composition of membrane lipids in response to ethanol or temperature. To determine whether these strains contain any mutations that might contribute to ethanol tolerance or changes in membrane permeability, we sequenced the entire genome of each strain. Unexpectedly, none of the strains displayed mutations in genes known to control membrane lipid synthesis, and a few strains carried no mutations at all. Interestingly, we found that four independently-isolated strains acquired an identical C → A (V244 V) silent mutation in the ferric citrate transporter gene fecA. Further, we demonstrated that either a deletion of fecA or over-expression of fecA can confer increased ethanol survival, suggesting that any misregulation of fecA expression affects the cellular response to ethanol. CONCLUSIONS: The fact that no mutations were observed in several ethanol-tolerant strains suggested that epigenetic mechanisms play a role in E. coli ethanol tolerance and membrane permeability. Our data also represent the first direct phenotypic evidence that the fecA gene plays a role in ethanol tolerance. We propose that the recurring silent mutation may exert an effect on phenotype by altering RNA-mediated regulation of fecA expression.

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response.

Complex changes in gene expression typically mediate a large portion of a cellular response. Each gene may change expression with unique kinetics as the gene is regulated by the particular timing of one of many stimuli, signaling pathways or secondary effects. In order to capture the entire gene expression response to hypoxia in the yeast S. cerevisiae, RNA-seq analysis was used to monitor the mRNA levels of all genes at specific times after exposure to hypoxia. Hypoxia was established by growing cells in ~100% N2 gas. Importantly, unlike other hypoxic studies, ergosterol and unsaturated fatty acids were not added to the media because these metabolites affect gene expression. Time points were chosen in the range of 0 - 4 h after hypoxia because that period captures the major changes in gene expression. At each time point, mid-log hypoxic cells were quickly filtered and frozen, limiting exposure to O2 and concomitant changes in gene expression. Total RNA was extracted from cells and used to enrich for mRNA, which was then converted to cDNA. From this cDNA, multiplex libraries were created and eight or more samples were sequenced in one lane of a next-generation sequencer. A post-sequencing pipeline is described, which includes quality base trimming, read mapping and determining the number of reads per gene. DESeq2 within the R statistical environment was used to identify genes that change significantly at any one of the hypoxic time points. Analysis of three biological replicates revealed high reproducibility, genes of differing kinetics and a large number of expected O2-regulated genes. These methods can be used to study how the cells of various organisms respond to hypoxia over time and adapted to study gene expression during other cellular responses.

Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response.

Many cells experience hypoxia, or low oxygen, and respond by dramatically altering gene expression. In the yeast Saccharomyces cerevisiae, genes that respond are required for many oxygen-dependent cellular processes, such as respiration, biosynthesis, and redox regulation. To more fully characterize the global response to hypoxia, we exposed yeast to hypoxic conditions, extracted RNA at different times, and performed RNA sequencing (RNA-seq) analysis. Time-course statistical analysis revealed hundreds of genes that changed expression by up to 550-fold. The genes responded with varying kinetics suggesting that multiple regulatory pathways are involved. We identified most known oxygen-regulated genes and also uncovered new regulated genes. Reverse transcription-quantitative PCR (RT-qPCR) analysis confirmed that the lysine methyltransferase EFM6 and the recombinase DMC1, both conserved in humans, are indeed oxygen-responsive. Looking more broadly, oxygen-regulated genes participate in expected processes like respiration and lipid metabolism, but also in unexpected processes like amino acid and vitamin metabolism. Using principle component analysis, we discovered that the hypoxic response largely occurs during the first 2 hr and then a new steady-state expression state is achieved. Moreover, we show that the oxygen-dependent genes are not part of the previously described environmental stress response (ESR) consisting of genes that respond to diverse types of stress. While hypoxia appears to cause a transient stress, the hypoxic response is mostly characterized by a transition to a new state of gene expression. In summary, our results reveal that hypoxia causes widespread and complex changes in gene expression to prepare the cell to function with little or no oxygen.

Analysis of Polygenic Mutants Suggests a Role for Mediator in Regulating Transcriptional Activation Distance in Saccharomyces cerevisiae.

Studies of natural populations of many organisms have shown that traits are often complex, caused by contributions of mutations in multiple genes. In contrast, genetic studies in the laboratory primarily focus on studying the phenotypes caused by mutations in a single gene. However, the single mutation approach may be limited with respect to the breadth and degree of new phenotypes that can be found. We have taken the approach of isolating complex, or polygenic mutants in the lab to study the regulation of transcriptional activation distance in yeast. While most aspects of eukaryotic transcription are conserved from yeast to human, transcriptional activation distance is not. In Saccharomyces cerevisiae, the upstream activating sequence (UAS) is generally found within 450 base pairs of the transcription start site (TSS) and when the UAS is moved too far away, activation no longer occurs. In contrast, metazoan enhancers can activate from as far as several hundred kilobases from the TSS. Previously, we identified single mutations that allow transcription activation to occur at a greater-than-normal distance from the GAL1 UAS. As the single mutant phenotypes were weak, we have now isolated polygenic mutants that possess strong long-distance phenotypes. By identification of the causative mutations we have accounted for most of the heritability of the phenotype in each strain and have provided evidence that the Mediator coactivator complex plays both positive and negative roles in the regulation of transcription activation distance.

Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations.

The dynamics of adaptation determine which mutations fix in a population, and hence how reproducible evolution will be. This is central to understanding the spectra of mutations recovered in the evolution of antibiotic resistance, the response of pathogens to immune selection, and the dynamics of cancer progression. In laboratory evolution experiments, demonstrably beneficial mutations are found repeatedly, but are often accompanied by other mutations with no obvious benefit. Here we use whole-genome whole-population sequencing to examine the dynamics of genome sequence evolution at high temporal resolution in 40 replicate Saccharomyces cerevisiae populations growing in rich medium for 1,000 generations. We find pervasive genetic hitchhiking: multiple mutations arise and move synchronously through the population as mutational 'cohorts'. Multiple clonal cohorts are often present simultaneously, competing with each other in the same population. Our results show that patterns of sequence evolution are driven by a balance between these chance effects of hitchhiking and interference, which increase stochastic variation in evolutionary outcomes, and the deterministic action of selection on individual mutations, which favours parallel evolutionary solutions in replicate populations.

Phylogenetic portrait of the Saccharomyces cerevisiae functional genome.

The genome of budding yeast (Saccharomyces cerevisiae) contains approximately 5800 protein-encoding genes, the majority of which are associated with some known biological function. Yet the extent of amino acid sequence conservation of these genes over all phyla has only been partially examined. Here we provide a more comprehensive overview and visualization of the conservation of yeast genes and a means for browsing and exploring the data in detail, down to the individual yeast gene, at http://yeast-phylogroups.princeton.edu. We used data from the OrthoMCL database, which has defined orthologs from approximately 150 completely sequenced genomes, including diverse representatives of the archeal, bacterial, and eukaryotic domains. By clustering genes based on similar patterns of conservation, we organized and visualized all the protein-encoding genes in yeast as a single heat map. Most genes fall into one of eight major clusters, called "phylogroups." Gene ontology analysis of the phylogroups revealed that they were associated with specific, distinct trends in gene function, generalizations likely to be of interest to a wide range of biologists.

Fast-acting and nearly gratuitous induction of gene expression and protein depletion in Saccharomyces cerevisiae.

We describe the development and characterization of a system that allows the rapid and specific induction of individual genes in the yeast Saccharomyces cerevisiae without changes in nutrients or temperature. The system is based on the chimeric transcriptional activator Gal4dbd.ER.VP16 (GEV). Upon addition of the hormone ß-estradiol, cytoplasmic GEV localizes to the nucleus and binds to promoters containing Gal4p consensus binding sequences to activate transcription. With galactokinase Gal1p and transcriptional activator Gal4p absent, the system is fast-acting, resulting in readily detectable transcription within 5 min after addition of the inducer. ß-Estradiol is nearly a gratuitous inducer, as indicated by genome-wide profiling that shows unintended induction (by GEV) of only a few dozen genes. Response to inducer is graded: intermediate concentrations of inducer result in production of intermediate levels of product protein in all cells. We present data illustrating several applications of this system, including a modification of the regulated degron method, which allows rapid and specific degradation of a specific protein upon addition of ß-estradiol. These gene induction and protein degradation systems provide important tools for studying the dynamics and functional relationships of genes and their respective regulatory networks.

Coordinated regulation of sulfur and phospholipid metabolism reflects the importance of methylation in the growth of yeast.

A yeast strain lacking Met4p, the primary transcriptional regulator of the sulfur assimilation pathway, cannot synthesize methionine. This apparently simple auxotroph did not grow well in rich media containing excess methionine, forming small colonies on yeast extract/peptone/dextrose plates. Faster-growing large colonies were abundant when overnight cultures were plated, suggesting that spontaneous suppressors of the growth defect arise with high frequency. To identify the suppressor mutations, we used genome-wide single-nucleotide polymorphism and standard genetic analyses. The most common suppressors were loss-of-function mutations in OPI1, encoding a transcriptional repressor of phospholipid metabolism. Using a new system that allows rapid and specific degradation of Met4p, we could study the dynamic expression of all genes following loss of Met4p. Experiments using this system with and without Opi1p showed that Met4 activates and Opi1p represses genes that maintain levels of S-adenosylmethionine (SAM), the substrate for most methyltransferase reactions. Cells lacking Met4p grow normally when either SAM is added to the media or one of the SAM synthetase genes is overexpressed. SAM is used as a methyl donor in three Opi1p-regulated reactions to create the abundant membrane phospholipid, phosphatidylcholine. Our results show that rapidly growing cells require significant methylation, likely for the biosynthesis of phospholipids.

The Hog1 mitogen-activated protein kinase mediates a hypoxic response in Saccharomyces cerevisiae.

We have studied hypoxic induction of transcription by studying the seripauperin (PAU) genes of Saccharomyces cerevisiae. Previous studies showed that PAU induction requires the depletion of heme and is dependent upon the transcription factor Upc2. We have now identified additional factors required for PAU induction during hypoxia, including Hog1, a mitogen-activated protein kinase (MAPK) whose signaling pathway originates at the membrane. Our results have led to a model in which heme and ergosterol depletion alters membrane fluidity, thereby activating Hog1 for hypoxic induction. Hypoxic activation of Hog1 is distinct from its previously characterized response to osmotic stress, as the two conditions cause different transcriptional consequences. Furthermore, Hog1-dependent hypoxic activation is independent of the S. cerevisiae general stress response. In addition to Hog1, specific components of the SAGA coactivator complex, including Spt20 and Sgf73, are also required for PAU induction. Interestingly, the mammalian ortholog of Spt20, p38IP, has been previously shown to interact with the mammalian ortholog of Hog1, p38. Taken together, our results have uncovered a previously unknown hypoxic-response pathway that may be conserved throughout eukaryotes.

Heme levels switch the function of Hap1 of Saccharomyces cerevisiae between transcriptional activator and transcriptional repressor.

Changes in oxygen levels cause widespread changes in gene expression in organisms ranging from bacteria to humans. In Saccharomyces cerevisiae, this response is mediated in part by Hap1, originally identified as a heme-dependent transcriptional activator that functions during aerobic growth. We show here that Hap1 also plays a significant and direct role under hypoxic conditions, not as an activator, but as a repressor. The repressive activity of Hap1 controls several genes, including three ERG genes required for ergosterol biosynthesis. Chromatin immunoprecipitation experiments showed that Hap1 binds to the ERG gene promoters, while additional experiments showed that the corepressor Tup1/Ssn6 is recruited by Hap1 and is also required for repression. Furthermore, mutational analysis demonstrated that conserved Hap1 binding sites in the ERG5 5' regulatory region are required for repression. The switch of Hap1 from acting as a hypoxic repressor to an aerobic activator is determined by heme, which is synthesized only in the presence of oxygen. The ability of Hap1 to function as a ligand-dependent repressor and activator is a property shared with mammalian nuclear hormone receptors and likely allows greater transcriptional control by Hap1 in response to changing oxygen levels.

Apoptotic signaling in response to a single type of DNA lesion, O(6)-methylguanine.

Until now, it has been difficult to establish exactly how a specific DNA lesion signals apoptosis because each DNA damaging agent produces a collection of distinct DNA lesions and produces damage in RNA, protein, and lipids. We have developed a system in human cells that focuses on the response to a single type of DNA lesion, namely O(6)-methylguanine (O(6)MeG). We dissect the signaling pathways involved in O(6)MeG-induced apoptosis, a response dependent on the MutSalpha heterodimer that is normally involved in DNA mismatch repair. O(6)MeG triggers robust activation of caspases associated with both death receptor- and mitochondrial-mediated apoptosis. Despite this, O(6)MeG/MutSalpha-triggered apoptosis is only partly dependent on caspase activation; moreover, it is mediated solely by mitochondrial signaling and not at all by death receptor signaling. Finally, while Bcl-2 and Bcl-x(L), negative regulators of mitochondrial-regulated apoptosis, could effectively block O(6)MeG/MutSalpha-dependent apoptosis, they were unable to prevent the cells from ultimately dying.