Yeast Transcriptome Research Articles

A Clickable APEX Probe for Proximity-Dependent Proteomic Profiling in Yeast

The engineered ascorbate peroxidase (APEX) is a powerful tool for the proximity-dependent labeling of proteins and RNAs in live cells. Although widely use in mammalian cells, APEX applications in microorganisms have been hampered by the poor labeling efficiency of its biotin-phenol (BP) substrate. In this study, we sought to address this challenge by designing and screening a panel of alkyne-functionalized substrates. Our best probe, Alk-Ph, substantially improves APEX-labeling efficiency in intact yeast cells, as it is more cell wall-permeant than BP. Through a combination of protein-centric and peptide-centric chemoproteomic experiments, we have identified 165 proteins with a specificity of 94% in the yeast mitochondrial matrix. In addition, we have demonstrated that Alk-Ph is useful for proximity-dependent RNA labeling in yeast, thus expanding the scope of APEX-seq. We envision that this improved APEX-labeling strategy would set the stage for the large-scale mapping of spatial proteome and transcriptome in yeast.

Suppression of Ribosomal Pausing by eIF5A Is Necessary to Maintain the Fidelity of Start Codon Selection

SUMMARYSequences within 5′ UTRs dictate the site and efficiency of translation initiation. In this study, an unbiased screen designed to interrogate the 5′ UTR-mediated regulation of the growth-promoting gene MYC unexpectedly revealed the ribosomal pause relief factor eIF5A as a regulator of translation initiation codon selection. Depletion of eIF5A enhances upstream translation within 5′ UTRs across yeast and human transcriptomes, including on the MYC transcript, where this results in increased production of an N-terminally extended protein. Furthermore, ribosome profiling experiments established that the function of eIF5A as a suppressor of ribosomal pausing at sites of suboptimal peptide bond formation is conserved in human cells. We present evidence that proximal ribosomal pausing on a transcript triggers enhanced use of upstream suboptimal or non-canonical initiation codons. Thus, we propose that eIF5A functions not only to maintain efficient translation elongation in eukaryotic cells but also to maintain the fidelity of translation initiation.

A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks.

Bayesian networks is a powerful method for identifying causal relationships among variables. However, as the network size increases, the time complexity of searching the optimal structure grows exponentially. We proposed a novel search algorithm - Fast and Furious Bayesian Network (FFBN). Compared to the existing greedy search algorithm, FFBN uses significantly fewer model configuration rules to determine the causal direction of edges when constructing the Bayesian network, which leads to greatly improved computational speed. We benchmarked the performance of FFBN by reconstructing gene regulatory networks (GRNs) from two DREAM5 challenge datasets: a synthetic dataset and a larger yeast transcriptome dataset. In both datasets, FFBN shows a much faster speed than the existing greedy search algorithm, while maintaining equally good or better performance in recall and precision. We then constructed three whole transcriptome GRNs for primary liver cancer (PL), primary colon cancer (PC) and colon to liver metastasis (CLM) expression data, which the existing greedy search algorithms failed. Three GRNs contain 12,099 common genes. Unprecedentedly, our newly developed FFBN algorithm is able to build up GRNs at a scale larger than 10,000 genes. Using FFBN, we discovered that CLM has its unique cancer molecular mechanisms and shares a certain degree of similarity with both PL and PC.

MCHM Acts as a Hydrotrope, Altering the Balance of Metals in Yeast

While drugs and other industrial chemicals are routinely studied to assess risks, many widely used chemicals have not been thoroughly evaluated. One such chemical, 4-methylcyclohexane methanol (MCHM), is an industrial coal-cleaning chemical that contaminated the drinking water supply in Charleston, WV, USA in 2014. While a wide range of ailments was reported following the spill, little is known about the molecular effects of MCHM exposure. We used the yeast model to explore the impacts of MCHM on cellular function. Exposure to MCHM dramatically altered the yeast transcriptome and the balance of metals in yeast. Underlying genetic variation in the response to MCHM, transcriptomics and, mutant analysis uncovered the role of the metal transporters, Arn2 and Yke4, to MCHM response. Expression of Arn2, which is involved in iron uptake, was lower in MCHM-tolerant yeast and loss of Arn2 further increased MCHM tolerance. Genetic variation within Yke4, an ER zinc transporter, also mediated response to MCHM, and loss of Yke4 decreased MCHM tolerance. The addition of zinc to MCHM-sensitive yeast rescued growth inhibition. In vitro assays demonstrated that MCHM acted as a hydrotrope and prevented protein interactions, while zinc induced the aggregation of proteins. We hypothesized that MCHM altered the structures of extracellular domains of proteins, and the addition of zinc stabilized the structure to maintain metal homeostasis in yeast exposed to MCHM.

PAT-Seq: A Method for Simultaneous Quantitation of Gene Expression, Poly(A)-Site Selection and Poly(A)-Length Distribution in Yeast Transcriptomes.

Next-generation sequencing (NGS) and its application to RNA (RNA-seq) has opened up multiple aspects of RNA processing to deep transcriptome-wide analysis at nucleotide resolution. This has been useful in delineating the transcribed areas of the genome, and in quantitation of RNA isoforms. Such isoforms can diversify the regulatory repertoire of mRNAs. For example, the 3'-end of mRNA can vary in two important ways, in the position chosen for cleavage and polyadenylation, and in the length of the poly(A)-tail. Accordingly, the step-up in resolution made possible by NGS has revealed an unexpectedly high level of alternative polyadenylation (APA). Moreover, it has massively simplified the transcriptome-wide detection of poly(A)-tail length changes. Here we present our approach to the study of 3'-end dynamics using a 3'-focused RNA-seq method called PAT-seq (for poly(A)-test sequencing). The approach records gene expression, APA, and poly(A)-tail changes between transcriptomes to reveal complex interplay between transcriptional and posttranscriptional control mechanisms.

Transcript Profiling Analysis Through Paired-End Ditag (PET) Approach Coupled with Deep Sequencing Reveals Transcriptome Complexity in Yeast.

The identification of structural and functional elements encoded in a genome is a challenging task. Although the transcriptome of budding yeast has been extensively analyzed, the boundaries and untranslated regions of yeast genes remain elusive. To address this least-explored field of yeast genomics, we performed a transcript profiling analysis through paired-end ditag (PET) approach coupled with deep sequencing. With 562,133 PET sequences we accurately defined the boundaries and untranslated regions of 3,409 ORFs, suggesting many yeast genes have multiple transcription start sites (TSSs). We also identified 85 previously uncharacterized transcripts either in intergenic regions or from the opposite strand of reported genomic features. Furthermore, our data revealed the extensive 3' end heterogeneity of yeast genes and identified a novel putative motif for polyadenylation. This study would serve as an invaluable resource for elucidating the regulation and evolution of yeast genes. Here we present a detailed protocol with minor modifications, which couldbe broadly applied to investigate transcripts from budding yeast to mammalian organisms.

Mitophagy in Yeast.

Mitochondria play a crucial role in energy production, general cell metabolism, cell signaling, and apoptosis. Mitochondria are also the main source of reactive oxygen species, especially in the case of their dysfunction. Therefore, damaged or even superfluous mitochondria not required for normal cell functioning represent risk factors and should be removed in order to maintain cell homeostasis. Mitochondria removal occurs via mitophagy, a type of selective autophagy (from Greek autos, self and phagein, to eat) that takes place in parallel with mitochondrial biogenesis and other processes. This review outlines general views on autophagy and mitophagy and summarizes information on the autophagy-related (Atg) proteins and their complexes involved in these processes. Yeast, especially Saccharomyces cerevisiae, is a convenient model system for studying molecular mechanisms of mitophagy because yeast genome, transcriptome, and proteome have been well characterized and because genetic manipulations with yeast are relatively simple and fast. Furthermore, yeast contain a number of orthologs of human proteins. Mitophagy in yeast is promoted by various factors, such as starvation, aging, oxidative stress, mitochondrial dysfunction, signaling proteins, and modification of mitochondrial proteins. In this review, we discuss molecular mechanisms underlying mitophagy and its regulation in yeast and present examples of relationships between mitophagy and ubiquitination-deubiquitination processes, as well as between mitophagy and other types of autophagy.

Overlapping open reading frames strongly reduce human and yeast STN1 gene expression and affect telomere function.

The levels of telomeric proteins, such as telomerase, can have profound effects on telomere function, cell division and human disease. Here we demonstrate how levels of Stn1, a component of the conserved telomere capping CST (Cdc13, Stn1, Ten1) complex, are tightly regulated by an upstream overlapping open reading frame (oORF). In budding yeast inactivation of the STN1 oORF leads to a 10-fold increase in Stn1 levels, reduced telomere length, suppression of cdc13-1 and enhancement of yku70Δ growth defects. The STN1 oORF impedes translation of the main ORF and reduces STN1 mRNA via the nonsense mediated mRNA decay (NMD) pathway. Interestingly, the homologs of the translation re-initiation factors, MCT-1Tma20/DENRTma22 also reduce Stn1 levels via the oORF. Human STN1 also contains oORFs, which reduce expression, demonstrating that oORFs are a conserved mechanism for reducing Stn1 levels. Bioinformatic analyses of the yeast and human transcriptomes show that oORFs are more underrepresented than upstream ORFs (uORFs) and associated with lower protein abundance. We propose that oORFs are an important mechanism to control expression of a subset of the proteome.

Translation elongation and mRNA stability are coupled through the ribosomal A-site.

Messenger RNA (mRNA) degradation plays a critical role in regulating transcript levels in eukaryotic cells. Previous work by us and others has shown that codon identity exerts a powerful influence on mRNA stability. In Saccharomyces cerevisiae, studies using a handful of reporter mRNAs show that optimal codons increase translation elongation rate, which in turn increases mRNA stability. However, a direct relationship between elongation rate and mRNA stability has not been established across the entire yeast transcriptome. In addition, there is evidence from work in higher eukaryotes that amino acid identity influences mRNA stability, raising the question as to whether the impact of translation elongation on mRNA decay is at the level of tRNA decoding, amino acid incorporation, or some combination of each. To address these questions, we performed ribosome profiling of wild-type yeast. In good agreement with other studies, our data showed faster codon-specific elongation over optimal codons and faster transcript-level elongation correlating with transcript optimality. At both the codon-level and transcript-level, faster elongation correlated with increased mRNA stability. These findings were reinforced by showing increased translation efficiency and kinetics for a panel of 11 HIS3 reporter mRNAs of increasing codon optimality. While we did observe that elongation measured by ribosome profiling is composed of both amino acid identity and synonymous codon effects, further analyses of these data establish that A-site tRNA decoding rather than other steps of translation elongation is driving mRNA decay in yeast.

Genome-wide search for candidate genes for yeast robustness improvement against formic acid reveals novel susceptibility (Trk1 and positive regulators) and resistance (Haa1-regulon) determinants

BackgroundFormic acid is an inhibitory compound present in lignocellulosic hydrolysates. Understanding the complex molecular mechanisms underlying Saccharomyces cerevisiae tolerance to this weak acid at the system level is instrumental to guide synthetic pathway engineering for robustness improvement of industrial strains envisaging their use in lignocellulosic biorefineries.ResultsThis study was performed to identify, at a genome-wide scale, genes whose expression confers protection or susceptibility to formic acid, based on the screening of a haploid deletion mutant collection to search for these phenotypes in the presence of 60, 70 and 80 mM of this acid, at pH 4.5. This chemogenomic analysis allowed the identification of 172 determinants of tolerance and 41 determinants of susceptibility to formic acid. Clustering of genes required for maximal tolerance to this weak acid, based on their biological function, indicates an enrichment of those involved in intracellular trafficking and protein synthesis, cell wall and cytoskeleton organization, carbohydrate metabolism, lipid, amino acid and vitamin metabolism, response to stress, chromatin remodelling, transcription and internal pH homeostasis. Among these genes is HAA1 encoding the main transcriptional regulator of yeast transcriptome reprograming in response to acetic acid and genes of the Haa1-regulon; all demonstrated determinants of acetic acid tolerance. Among the genes that when deleted lead to increased tolerance to formic acid, TRK1, encoding the high-affinity potassium transporter and a determinant of resistance to acetic acid, was surprisingly found. Consistently, genes encoding positive regulators of Trk1 activity were also identified as formic acid susceptibility determinants, while a negative regulator confers protection. At a saturating K+ concentration of 20 mM, the deletion mutant trk1Δ was found to exhibit a much higher tolerance compared with the parental strain. Given that trk1Δ accumulates lower levels of radiolabelled formic acid, compared to the parental strain, it is hypothesized that Trk1 facilitates formic acid uptake into the yeast cell.ConclusionsThe list of genes resulting from this study shows a few marked differences from the list of genes conferring protection to acetic acid and provides potentially valuable information to guide improvement programmes for the development of more robust strains against formic acid.

Populus simonii × Populus nigra WRKY70 is involved in salt stress and leaf blight disease responses.

WRKY transcription factors (TFs) are important regulators in the complex stress response signaling networks in plants, but the detailed mechanisms underlying these regulatory networks have not been fully characterized. In the present study, we identified a Group III WRKY gene (PsnWRKY70, Potri.016G137900) from Populussimonii × Populusnigra and explored its function under salt and pathogen stresses. The promoter sequence that is located 2471-bp upstream from the start codon (SC) of PsnWRKY70 contained many stress-responsive cis-elements. Yeast one-hybrid assay suggested the upstream regulators, PsnWRKY70, PsnNAM (Potri.009G141600), PsnMYB (Potri.006G000800) and PsnGT1 (Potri.010G055000), probably modulate the expression of the PsnWRKY70 gene by specifically binding to the W-box or GT1GMSCAM4 (GT1) element. Yeast two-hybrid assay and transcriptome analysis revealed that HP1 (Potri.004G092100), RRM (Potri.008G146700), Ulp1 (Potri.002G105700) and some mitogen-activated protein kinase cascade members probably interact with PsnWRKY70 TF to response to salt stress. Compared with non-transgenic (NT) plants, PsnWRKY70-overexpressing (OEX) plants exhibited improved leaf blight disease resistance, while PsnWRKY70-repressing (REX) plants displayed enhanced salt stress tolerance. PsnWRKY70, PsnNAM, PsnMYB and PsnGT1 exhibited similar expression patterns in NT under salt and leaf blight disease stresses. The differentially expressed genes (DEGs) from NT vs OEX1 and the DEGs from NT vs REX1 exhibited considerable diversification. Most of the DEGs between NT and OEX1 were involved in aromatic amino acid biosynthesis, secondary metabolism, programmed cell death, peroxisomes and disease resistance. Most of the DEGs between NT and REX1 were related to desiccation response, urea transmembrane transport, abscisic acid response, calcium ion transport and hydrogen peroxide transmembrane transport. Our findings not only revealed the salt stress response signal transduction pathway of PsnWRKY70, but also provided direct evidence for the opposite biological functions of PsnWRKY70 TF in response to salt stress and leaf blight disease in P. simonii × P. nigra.

The Natural Product Osthole Attenuates Yeast Growth by Extensively Suppressing the Gene Expressions of Mitochondrial Respiration Chain

The fast growing evidences have indicated that the natural product osthole is a promising drug candidate for fighting several serious human diseases, for example, cancer and inflammation. However, the mode-of-action (MoA) of osthole remains largely incomplete. In this study, we investigated the growth inhibition activity of osthole using fission yeast as a model, with the goal of understanding the osthole's mechanism of action, especially from the molecular level. Microarray analysis indicated that osthole has significant impacts on gene transcription levels (In total, 214 genes are up-regulated, and 97 genes are down-regulated). Gene set enrichment analysis (GSEA) indicated that 11 genes belong to the "Respiration module" category, especially including the components of complex III and V of mitochondrial respiration chain. Based on GSEA and network analysis, we also found that 54 up-regulated genes belong to the "Core Environmental Stress Responses" category, particularly including many transporter genes, which suggests that the rapidly activated nutrient exchange between cell and environment is part of the MoA of osthole. In summary, osthole can greatly impact on fission yeast transcriptome, and it primarily represses the expression levels of the genes in respiration chain, which next causes the inefficiency of ATP production and thus largely explains osthole's growth inhibition activity in Schizosaccharomyces pombe (S. pombe). The complexity of the osthole's MoA shown in previous studies and our current research demonstrates that the omics approach and bioinformatics tools should be applied together to acquire the complete landscape of osthole's growth inhibition activity.

A high-throughput approach to profile RNA structure.

Here we introduce the Computational Recognition of Secondary Structure (CROSS) method to calculate the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. We trained CROSS using data from high-throughput experiments such as Selective 2΄-Hydroxyl Acylation analyzed by Primer Extension (SHAPE; Mouse and HIV transcriptomes) and Parallel Analysis of RNA Structure (PARS; Human and Yeast transcriptomes) as well as high-quality NMR/X-ray structures (PDB database). The algorithm uses primary structure information alone to predict experimental structural profiles with >80% accuracy, showing high performances on large RNAs such as Xist (17 900 nucleotides; Area Under the ROC Curve AUC of 0.75 on dimethyl sulfate (DMS) experiments). We integrated CROSS in thermodynamics-based methods to predict secondary structure and observed an increase in their predictive power by up to 30%.

In Vivo Mapping of Eukaryotic RNA Interactomes Reveals Principles of Higher-Order Organization and Regulation

Identifying pairwise RNA-RNA interactions is key to understanding how RNAs fold and interact with other RNAs inside the cell. We present a high-throughput approach, sequencing of psoralen crosslinked, ligated, and selected hybrids (SPLASH), that maps pairwise RNA interactions invivo with high sensitivity and specificity, genome-wide. Applying SPLASH tohuman and yeast transcriptomes revealed the diversity and dynamics of thousands of long-range intra- and intermolecular RNA-RNA interactions. Our analysis highlighted key structural features of RNA classes, including the modular organization of mRNAs, its impact on translation and decay, and the enrichment of long-range interactions in noncoding RNAs. Additionally, intermolecular mRNA interactions were organized into network clusters and were remodeled during cellular differentiation. We also identified hundreds of known and new snoRNA-rRNA binding sites, expanding our knowledge of rRNA biogenesis. These results highlight the underexplored complexity of RNA interactomes and pave the way to better understanding how RNA organization impacts biology.

Enhancing Structural Annotation of Yeast Genomes with RNA-Seq Data.

The number of fully sequenced genomes of yeasts is dramatically increasing but both structural and functional annotation quality are usually neglected, as most frequently based on automatic annotation transfer tools from reference genomes. RNA sequencing technologies offer the possibility to better characterize yeast transcriptomes and to correct or improve the prediction of mRNA, ncRNA, or miscellaneous RNA. We describe a computational approach to enhance structural annotation of yeast genomes based on RNA-Seq data exploitation. The proposed pipeline is primarily based on read mapping with TopHat2. Mapping outputs are then used for various applications such as: (1) validation of exon-exon junctions of predicted transcripts, (2) definition of new transcribed features, (3) prediction of 3' UTR, and (4) identification of extra features absent from the genome assembly. We strongly encourage curators to proceed to a manual validation and editing of the reference genome. Releasing genomes with high-quality annotation is an important issue, as they will be considered as references for further predictions.

Outlier detection at the transcriptome-proteome interface.

In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation. Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence.

Transcriptome-wide mapping of pseudouridines: pseudouridine synthases modify specific mRNAs in S. cerevisiae.

We developed a novel technique, called pseudouridine site identification sequencing (PSI-seq), for the transcriptome-wide mapping of pseudouridylation sites with single-base resolution from cellular RNAs based on the induced termination of reverse transcription specifically at pseudouridines following CMCT treatment. PSI-seq analysis of RNA samples from S. cerevisiae correctly detected all of the 43 known pseudouridines in yeast 18S and 25S ribosomal RNA with high specificity. Moreover, application of PSI-seq to the yeast transcriptome revealed the presence of site-specific pseudouridylation within dozens of mRNAs, including RPL11a, TEF1, and other genes implicated in translation. To identify the mechanisms responsible for mRNA pseudouridylation, we genetically deleted candidate pseudouridine synthase (Pus) enzymes and reconstituted their activities in vitro. These experiments demonstrated that the Pus1 enzyme was necessary and sufficient for pseudouridylation of RPL11a mRNA, whereas Pus4 modified TEF1 mRNA, and Pus6 pseudouridylated KAR2 mRNA. Finally, we determined that modification of RPL11a at Ψ -68 was observed in RNA from the related yeast S. mikitae, and Ψ -239 in TEF1 mRNA was maintained in S. mikitae as well as S. pombe, indicating that these pseudouridylations are ancient, evolutionarily conserved RNA modifications. This work establishes that site-specific pseudouridylation of eukaryotic mRNAs is a genetically programmed RNA modification that naturally occurs in multiple yeast transcripts via distinct mechanisms, suggesting that mRNA pseudouridylation may provide an important novel regulatory function. The approach and strategies that we report here should be generally applicable to the discovery of pseudouridylation, or other RNA modifications, in diverse biological contexts.

Genome-wide profiling of untranslated regions by paired-end ditag sequencing reveals unexpected transcriptome complexity in yeast.

The identification of structural and functional elements encoded in a genome is a challenging task. Although the transcriptome of budding yeast has been extensively analyzed, the boundaries and untranslated regions of yeast genes remain elusive. To address this least-explored field of yeast genomics, we performed a transcript profiling analysis through paired-end ditag (PET) approach coupled with deep sequencing. With 562,133 PET sequences we accurately defined the boundaries and untranslated regions of 3,409 ORFs, suggesting many yeast genes have multiple transcription start sites (TSSs). We also identified 85 previously uncharacterized transcripts either in intergenic regions or from the opposite strand of reported genomic features. Furthermore, our data revealed the extensive 3' end heterogeneity of yeast genes and identified a novel putative motif for polyadenylation. Our results indicate the yeast transcriptome is more complex than expected. This study would serve as an invaluable resource for elucidating the regulation and evolution of yeast genes.

Transcriptome Maps of mRNP Biogenesis Factors Define Pre-mRNA Recognition

Biogenesis of eukaryotic messenger ribonucleoprotein complexes (mRNPs) involves the synthesis, splicing, and 3' processing of pre-mRNA, and the assembly of mature mRNPs for nuclear export. We mapped 23 mRNP biogenesis factors onto the yeast transcriptome, providing 10(4)-10(6) high-confidence RNA interaction sites per factor. The data reveal how mRNP biogenesis factors recognize pre-mRNA elements in vivo. They define conserved interactions between splicing factors and pre-mRNA introns, including the recognition of intron-exon junctions and the branchpoint. They also identify a unified arrangement of 3' processing factors at pre-mRNA polyadenylation (pA) sites in yeast and human, which results from an A-U sequence bias at pA sites. Global data analysis indicates that 3' processing factors have roles in splicing and RNA surveillance, and that they couple mRNP biogenesis events to restrict nuclear export to mature mRNPs.

Physiological and transcriptional responses of anaerobic chemostat cultures of Saccharomyces cerevisiae subjected to diurnal temperature cycles.

Diurnal temperature cycling is an intrinsic characteristic of many exposed microbial ecosystems. However, its influence on yeast physiology and the yeast transcriptome has not been studied in detail. In this study, 24-h sinusoidal temperature cycles, oscillating between 12°C and 30°C, were imposed on anaerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae. After three diurnal temperature cycles (DTC), concentrations of glucose and extracellular metabolites as well as CO2 production rates showed regular, reproducible circadian rhythms. DTC also led to waves of transcriptional activation and repression, which involved one-sixth of the yeast genome. A substantial fraction of these DTC-responsive genes appeared to respond primarily to changes in the glucose concentration. Elimination of known glucose-responsive genes revealed an overrepresentation of previously identified temperature-responsive genes as well as genes involved in the cell cycle and de novo purine biosynthesis. In-depth analysis demonstrated that DTC led to a partial synchronization of the cell cycle of the yeast populations in chemostat cultures, which was lost upon release from DTC. Comparison of DTC results with data from steady-state cultures showed that the 24-h DTC was sufficiently slow to allow S. cerevisiae chemostat cultures to acclimate their transcriptome and physiology at the DTC temperature maximum and to approach acclimation at the DTC temperature minimum. Furthermore, this comparison and literature data on growth rate-dependent cell cycle phase distribution indicated that cell cycle synchronization was most likely an effect of imposed fluctuations of the relative growth rate (μ/μmax) rather than a direct effect of temperature.