Degradation of mRNA contributes to variation in transcript abundance. Studies of individual mRNAs have shown that both cis and trans factors affect mRNA degradation rates. However, the factors underlying transcriptome-wide variation in mRNA degradation rates are poorly understood. We investigated the contribution of different transcript properties to transcriptome-wide degradation rate variation in the budding yeast, Saccharomyces cerevisiae, using multiple regression analysis. We find that multiple transcript properties are significantly associated with variation in mRNA degradation rates, and that a model incorporating these properties explains ∼50% of the genome-wide variance. Predictors of mRNA degradation rates include transcript length, ribosome density, biased codon usage, and GC content of the third position in codons. To experimentally validate these factors, we studied individual transcripts expressed from identical promoters. We find that decreasing ribosome density by mutating the first translational start site of a transcript increases its degradation rate. Using coding sequence variants of green fluorescent protein (GFP) that differ only at synonymous sites, we show that increased GC content of the third position of codons results in decreased rates of mRNA degradation. Thus, in steady-state conditions, a large fraction of genome-wide variation in mRNA degradation rates is determined by inherent properties of transcripts, many of which are related to translation, rather than specific regulatory mechanisms.

A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.

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.

High concentrations of ammonium at physiological concentrations of potassium are toxic for the standard laboratory strain of Saccharomyces cerevisiae In the original description of this metabolic phenotype, we focused on the standard laboratory strains of Saccharomyces In this study, we screened a large collection of S. cerevisiae natural isolates and identified one strain that is resistant to high concentrations of ammonium. This strain, K12, was isolated in sake breweries. When the K12 strain was crossed to the standard laboratory strain (FY4), the resulting tetrads displayed 2:2 segregation of the resistance phenotype, suggesting a single gene trait. Using a bulk segregant analysis strategy, we mapped this trait to a 150-kb region on chromosome X containing the TRK1 gene. This gene encodes a transporter required for high-affinity potassium transport in S. cerevisiae Data from reciprocal hemizygosity experiments with TRK1 deletion strains in K12 and BY backgrounds, as well as analysis of the deletion of this gene in the K12 strain, demonstrate that the K12 allele of TRK1 is responsible for ammonium toxicity resistance. Furthermore, we determined the minimal amount of potassium required for both the K12 and laboratory strain needed for growth. These results demonstrate that the gene encoded by the K12 allele of TRK1 has a greater affinity for potassium than the standard allele of TRK1 found in Saccharomyces strains. We hypothesize that this greater-affinity allele of the potassium transporter reduces the flux of ammonium into the yeast cells under conditions of ammonium toxicity. These findings further refine our understanding of ammonium toxicity in yeast and provide an example of using natural variation to understand cellular processes.

In yeast, the pathways of sulfur assimilation are combinatorially controlled by five transcriptional regulators (three DNA-binding proteins [Met31p, Met32p, and Cbf1p], an activator [Met4p], and a cofactor [Met28p]) and a ubiquitin ligase subunit (Met30p). This regulatory system exerts combinatorial control not only over sulfur assimilation and methionine biosynthesis, but also on many other physiological functions in the cell. Recently we characterized a gene induction system that, upon the addition of an inducer, results in near-immediate transcription of a gene of interest under physiological conditions. We used this to perturb levels of single transcription factors during steady-state growth in chemostats, which facilitated distinction of direct from indirect effects of individual factors dynamically through quantification of the subsequent changes in genome-wide patterns of gene expression. We were able to show directly that Cbf1p acts sometimes as a repressor and sometimes as an activator. We also found circumstances in which Met31p/Met32p function as repressors, as well as those in which they function as activators. We elucidated and numerically modeled feedback relationships among the regulators, notably feedforward regulation of Met32p (but not Met31p) by Met4p that generates dynamic differences in abundance that can account for the differences in function of these two proteins despite their identical binding sites.

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) has created the Genome Snapshot (http://db.yeastgenome.org/cgi-bin/genomeSnapShot.pl). The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae. The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible through SGD's Advanced Search tool (http://db.yeastgenome.org/cgi-bin/search/featureSearch), and all the data presented on this page are available from the SGD ftp site (ftp://ftp.yeastgenome.org/yeast/).

Microbes tailor their growth rate to nutrient availability. Here, we measured, using liquid chromatography-mass spectrometry, >100 intracellular metabolites in steady-state cultures of Saccharomyces cerevisiae growing at five different rates and in each of five different limiting nutrients. In contrast to gene transcripts, where approximately 25% correlated with growth rate irrespective of the nature of the limiting nutrient, metabolite concentrations were highly sensitive to the limiting nutrient's identity. Nitrogen (ammonium) and carbon (glucose) limitation were characterized by low intracellular amino acid and high nucleotide levels, whereas phosphorus (phosphate) limitation resulted in the converse. Low adenylate energy charge was found selectively in phosphorus limitation, suggesting the energy charge may actually measure phosphorus availability. Particularly strong concentration responses occurred in metabolites closely linked to the limiting nutrient, e.g., glutamine in nitrogen limitation, ATP in phosphorus limitation, and pyruvate in carbon limitation. A simple but physically realistic model involving the availability of these metabolites was adequate to account for cellular growth rate. The complete data can be accessed at the interactive website http://growthrate.princeton.edu/metabolome.

Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.

Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g . TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

Macromolecular crowding is an important parameter that impacts multiple biological processes. Passive microrheology using single particle tracking is a powerful means of studying macromolecular crowding. Here we monitored the diffusivity of self-assembling fluorescent nanoparticles (μNS) in response to acute glucose starvation. mRNP diffusivity was reduced upon glucose starvation as previously reported. In contrast, we observed increased diffusivity of μNS particles. Our results suggest that, upon glucose starvation, mRNP granule diffusivity may be reduced due to changes in physical interactions, while global crowding in the cytoplasm may be reduced.

The leaf homogenate of Psychotria insularum is widely used in Samoan traditional medicine to treat inflammation associated with fever, body aches, swellings, wounds, elephantiasis, incontinence, skin infections, vomiting, respiratory infections, and abdominal distress. However, the bioactive components and underlying mechanisms of action are unknown. We used chemical genomic analyses in the model organism Saccharomyces cerevisiae (baker's yeast) to identify and characterize an iron homeostasis mechanism of action in the traditional medicine as an unfractionated entity to emulate its traditional use. Bioactivity-guided fractionation of the homogenate identified two flavonol glycosides, rutin and nicotiflorin, each binding iron in an ion-dependent molecular networking metabolomics analysis. Translating results to mammalian immune cells and traditional application, the iron chelator activity of the P. insularum homogenate or rutin decreased proinflammatory and enhanced anti-inflammatory cytokine responses in immune cells. Together, the synergistic power of combining traditional knowledge with chemical genomics, metabolomics, and bioassay-guided fractionation provided molecular insight into a relatively understudied Samoan traditional medicine and developed methodology to advance ethnobotany.

Copy-number variants (CNVs) are large-scale amplifications or deletions of DNA that can drive rapid adaptive evolution and result in large-scale changes in gene expression. Whereas alterations in the copy number of one or more genes within a CNV can confer a selective advantage, other genes within a CNV can decrease fitness when their dosage is changed. Dosage compensation - in which the gene expression output from multiple gene copies is less than expected - is one means by which an organism can mitigate the fitness costs of deleterious gene amplification. Previous research has shown evidence for dosage compensation at both the transcriptional level and at the level of protein expression; however, the extent of compensation differs substantially between genes, strains, and studies. Here, we investigated sources of dosage compensation at multiple levels of gene expression regulation by defining the transcriptome, translatome and proteome of experimentally evolved yeast (Saccharomyces cerevisiae) strains containing adaptive CNVs. We quantified the gene expression output at each step and found evidence of widespread dosage compensation at the protein abundance (~47%) level. By contrast we find only limited evidence for dosage compensation at the transcriptional (~8%) and translational (~3%) level. We also find substantial divergence in the expression of unamplified genes in evolved strains that could be due to either the presence of a CNV or adaptation to the environment. Detailed analysis of 82 amplified and 411 unamplified genes with significantly discrepant relationships between RNA and protein abundances identified enrichment for upstream open reading frames (uORFs). These uORFs are enriched for binding site motifs for SSD1, an RNA binding protein that has previously been associated with tolerance of aneuploidy. Our findings suggest that, in the presence of CNVs, SSD1 may act to alter the expression of specific genes by potentiating uORF mediated translational regulation.

The intracellular environment is packed with macromolecules of mesoscale size, and this crowded milieu significantly influences cell physiology. When exposed to stress, mRNAs released after translational arrest condense with RNA binding proteins, resulting in the formation of membraneless RNA protein (RNP) condensates known as processing bodies (P-bodies) and stress granules (SGs). However, the impact of the assembly of these condensates on the biophysical properties of the crowded cytoplasmic environment remains unclear. Here, we find that upon exposure to stress, polysome collapse and condensation of mRNAs increases mesoscale particle diffusivity in the cytoplasm. Increased mesoscale diffusivity is required for the efficient formation of Q-bodies, membraneless organelles that coordinate degradation of misfolded peptides that accumulate during stress. Additionally, we demonstrate that polysome collapse and stress granule formation has a similar effect in mammalian cells, fluidizing the cytoplasm at the mesoscale. We find that synthetic, light-induced RNA condensation is sufficient to fluidize the cytoplasm, demonstrating a causal effect of RNA condensation. Together, our work reveals a new functional role for stress-induced translation inhibition and formation of RNP condensates in modulating the physical properties of the cytoplasm to effectively respond to stressful conditions.

Genome rearrangements, especially amplifications and deletions, have regularly been observed as responses to sustained application of the same strong selective pressure in microbial populations growing in continuous culture. We studied eight strains of budding yeast (Saccharomyces cerevisiae) isolated after 100-500 generations of growth in glucose-limited chemostats. Changes in DNA copy number were assessed at single-gene resolution by using DNA microarray-based comparative genomic hybridization. Six of these evolved strains were aneuploid as the result of gross chromosomal rearrangements. Most of the aneuploid regions were the result of translocations, including three instances of a shared breakpoint on chromosome 14 immediately adjacent to CIT1, which encodes the citrate synthase that performs a key regulated step in the tricarboxylic acid cycle. Three strains had amplifications in a region of chromosome 4 that includes the high-affinity hexose transporters; one of these also had the aforementioned chromosome 14 break. Three strains had extensive overlapping deletions of the right arm of chromosome 15. Further analysis showed that each of these genome rearrangements was bounded by transposon-related sequences at the breakpoints. The observation of repeated, independent, but nevertheless very similar, chromosomal rearrangements in response to persistent selection of growing cells parallels the genome rearrangements that characteristically accompany tumor progression.

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 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.

High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.

Dynamic transcriptional regulation is critical for an organism's response to environmental signals and yet remains elusive to capture. Such transcriptional regulation is mediated by master transcription factors (TF) that control large gene regulatory networks. Recently, we described a dynamic mode of TF regulation named "hit-and-run". This model proposes that master TF can interact transiently with a set of targets, but the transcription of these transient targets continues after the TF dissociation from the target promoter. However, experimental evidence validating active transcription of the transient TF-targets is still lacking.

Fungal infections are an increasing clinical problem. Decreased treatment effectiveness is associated with biofilm formation and drug recalcitrance is thought to be biofilm specific. However, no systematic investigations have tested whether resistance mechanisms are shared between biofilm and planktonic populations. We performed multiplexed barcode sequencing (Bar-seq) screening of a pooled collection of gene-deletion mutants cultivated as biofilm and planktonic cells. Screening for resistance to the ergosterol-targeting fungicide amphotericin B (AmB) revealed that the two growth modes had significant overlap in AmB-persistent mutants. Mutants defective in sterol metabolism, ribosome biosynthesis, and the TORC1 and Ras pathways showed increased persistence when treated with AmB. The ras1, ras2 and tor1 mutants had a high-persister phenotype similar to wild-type biofilm and planktonic cells exposed to the TORC1 pathway inhibitor rapamycin. Inhibition of TORC1 with rapamycin also increased the proportion of persisters in Candida albicans and Candida glabrata. We propose that decreased TORC1-mediated induction of ribosome biosynthesis via Ras can lead to formation of AmB-persister cells regardless of whether the cells are in planktonic or biofilm growth mode. Identification of common pathways leading to growth mode-independent persister formation is important for developing novel strategies for treating fungal infections.