The metabolic background is a global player in Saccharomyces gene expression epistasis

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast

CO2 fixation plays a key role to make biobased production cost competitive. Here, we use 3-hydroxypropionic acid (3-HP) to showcase how CO2 fixation enables approaching theoretical-yield production. Using genome-scale metabolic models to calculate the production envelope, we demonstrate that the provision of bicarbonate, formed from CO2, restricts previous attempts for high yield production of 3-HP. We thus develop multiple strategies for bicarbonate uptake, including the identification of Sul1 as a potential bicarbonate transporter, domain swapping of malonyl-CoA reductase, identification of Esbp6 as a potential 3-HP exporter, and deletion of Uga1 to prevent 3-HP degradation. The combined rational engineering increases 3-HP production from 0.14 g/L to 11.25 g/L in shake flask using 20 g/L glucose, approaching the maximum theoretical yield with concurrent biomass formation. The engineered yeast forms the basis for commercialization of bio-acrylic acid, while our CO2 fixation strategies pave the way for CO2 being used as the sole carbon source.

Mind your marker: the effect of common auxotrophic markers on complex traits in yeast

Yeast cells are extensively used as a key model organism owing to their highly conserved genome, metabolic pathways, and cell biology processes. To assist in genetic engineering and analysis, laboratory yeast strains typically harbor auxotrophic selection markers. When uncompensated, auxotrophic markers cause significant phenotypic bias compared to prototrophic strains and have different combinatorial influences on the metabolic network. Here, we used BY4741, a laboratory strain commonly used as a "wild type" strain in yeast studies, to generate a set of revertant strains, containing all possible combinations of four common auxotrophic markers (leu2∆, ura3∆, his3∆1, met15∆). We examined the effect of the auxotrophic combinations on complex phenotypes such as resistance to rapamycin, acetic acid, and ethanol. Among the markers, we found that leucine auxotrophy most significantly affected the phenotype. We analyzed the phenotypic bias caused by auxotrophy at the genomic level using a prototrophic version of a genome-wide deletion library and a decreased mRNA perturbation (DAmP) library. Prototrophy was found to suppress rapamycin sensitivity in many mutants previously annotated for the phenotype, raising a possible need for reevaluation of the findings in a native metabolic context. These results reveal a significant phenotypic bias caused by common auxotrophic markers and support the use of prototrophic wild-type strains in yeast research.

2-Hydroxyglutarate modulates histone methylation at specific loci and alters gene expression via Rph1 inhibition

2-Hydroxyglutarate (2-HG) is an oncometabolite that accumulates in certain cancers. Gain-of-function mutations in isocitrate dehydrogenase lead to 2-HG accumulation at the expense of alpha-ketoglutarate. Elevated 2-HG levels inhibit histone and DNA demethylases, causing chromatin structure and gene regulation changes with tumorigenic consequences. We investigated the effects of elevated 2-HG levels in Saccharomyces cerevisiae, a yeast devoid of DNA methylation and heterochromatin-associated histone methylation. Our results demonstrate genetic background-dependent gene expression changes and altered H3K4 and H3K36 methylation at specific loci. Analysis of histone demethylase deletion strains indicated that 2-HG inhibits Rph1 sufficiently to induce extensive gene expression changes. Rph1 is the yeast homolog of human KDM4 demethylases and, among the yeast histone demethylases, was the most sensitive to the inhibitory effect of 2-HG in vitro. Interestingly, Rph1 deficiency favors gene repression and leads to further down-regulation of already silenced genes marked by low H3K4 and H3K36 trimethylation, but abundant in H3K36 dimethylation. Our results provide novel insights into the genome-wide effects of 2-HG and highlight Rph1 as its preferential demethylase target.

Cross-feeding promotes heterogeneity within yeast cell populations

Cellular heterogeneity in cell populations of isogenic origin is driven by intrinsic factors such as stochastic gene expression, as well as external factors like nutrient availability and interactions with neighbouring cells. Heterogeneity promotes population fitness and thus has important implications in antimicrobial and anticancer treatments, where stress tolerance plays a significant role. Here, we study plasmid retention dynamics within a population of plasmid-complemented ura3∆0 yeast cells, and show that the exchange of complementary metabolites between plasmid-carrying prototrophs and plasmid-free auxotrophs allows the latter to survive and proliferate in selective environments. This process also affects plasmid copy number in plasmid-carrying prototrophs, further promoting cellular functional heterogeneity. Finally, we show that targeted genetic engineering can be used to suppress cross-feeding and reduce the frequency of plasmid-free auxotrophs, or to exploit it for intentional population diversification and division of labour in co-culture systems.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Highlights

At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.

Controlling gene expression with deep generative design of regulatory DNA

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.

Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Here the authors present EspressionGAN, a generative adversarial network that uses genomic and transcriptomic data to generate regulatory sequences.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.

Growth-rate-dependent and nutrient-specific gene expression resource allocation in fission yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Here, we determined the importance of the growth rate in explaining relative changes in protein and mRNA levels in the simple eukaryote Schizosaccharomyces pombe grown on non-limiting nitrogen sources. Although expression of half of fission yeast genes was significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production apart from RNA polymerase II-dependent transcription. Negatively correlated genes belonged mainly to the environmental stress response programme. Critically, metabolic enzymes, which represent ∼55-70% of the proteome by mass, showed mostly condition-specific regulation. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate-dependent and nutrient-specific gene expression.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Purine Auxotrophic Starvation Evokes Phenotype Similar to Stationary Phase Cells in Budding Yeast

Purine auxotrophy is an abundant trait among eukaryotic parasites and a typical marker for many budding yeast strains. Supplementation with an additional purine source (such as adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We explored purine starvation effects in a model organism, a budding yeast Saccharomyces cerevisiae ade8 knockout, at the level of cellular morphology, central carbon metabolism, and global transcriptome. We observed that purine-starved cells stopped their cycle in G1/G0 state and accumulated trehalose, and the intracellular concentration of AXP decreased, but adenylate charge remained stable. Cells became tolerant to severe environmental stresses. Intracellular RNA concentration decreased, and massive downregulation of ribosomal biosynthesis genes occurred. We proved that the expression of new proteins during purine starvation is critical for cells to attain stress tolerance phenotype Msn2/4p targets are upregulated in purine-starved cells when compared to cells cultivated in purine-rich media. The overall transcriptomic response to purine starvation resembles that of stationary phase cells. Our results demonstrate that the induction of a strong stress resistance phenotype in budding yeast can be caused not only by natural starvation, but also starvation for metabolic intermediates, such as purines.

Expansion of the Yeast Modular Cloning Toolkit for CRISPR-Based Applications, Genomic Integrations and Combinatorial Libraries

Standardisation of genetic parts has become a topic of increasing interest over the last decades. The promise of simplifying molecular cloning procedures, while at the same time making them more predictable and reproducible has led to the design of several biological standards, one of which is modular cloning (MoClo). The Yeast MoClo toolkit provides a large library of characterised genetic parts combined with a comprehensive and flexible assembly strategy. Here we aimed to (1) simplify the adoption of the standard by providing a simple design tool for including new parts in the MoClo library, (2) characterise the toolkit further by demonstrating the impact of a BglII site in promoter parts on protein expression, and (3) expand the toolkit to enable efficient construction of gRNA arrays, marker-less integration cassettes and combinatorial libraries. These additions make the toolkit more applicable for common engineering tasks and will further promote its adoption in the yeast biological engineering community.

Massive QTL analysis identifies pleiotropic genetic determinants for stress resistance, aroma formation, and ethanol, glycerol and isobutanol production in Saccharomyces cerevisiae

BACKGROUND

The brewer's yeast Saccharomyces cerevisiae is exploited in several industrial processes, ranging from food and beverage fermentation to the production of biofuels, pharmaceuticals and complex chemicals. The large genetic and phenotypic diversity within this species offers a formidable natural resource to obtain superior strains, hybrids, and variants. However, most industrially relevant traits in S. cerevisiae strains are controlled by multiple genetic loci. Over the past years, several studies have identified some of these QTLs. However, because these studies only focus on a limited set of traits and often use different techniques and starting strains, a global view of industrially relevant QTLs is still missing.

RESULTS

Here, we combined the power of 1125 fully sequenced inbred segregants with high-throughput phenotyping methods to identify as many as 678 QTLs across 18 different traits relevant to industrial fermentation processes, including production of ethanol, glycerol, isobutanol, acetic acid, sulfur dioxide, flavor-active esters, as well as resistance to ethanol, acetic acid, sulfite and high osmolarity. We identified and confirmed several variants that are associated with multiple different traits, indicating that many QTLs are pleiotropic. Moreover, we show that both rare and common variants, as well as variants located in coding and non-coding regions all contribute to the phenotypic variation.

CONCLUSIONS

Our findings represent an important step in our understanding of the genetic underpinnings of industrially relevant yeast traits and open new routes to study complex genetics and genetic interactions as well as to engineer novel, superior industrial yeasts. Moreover, the major role of rare variants suggests that there is a plethora of different combinations of mutations that can be explored in genome editing.

The metabolic growth limitations of petite cells lacking the mitochondrial genome

Eukaryotic cells can survive the loss of their mitochondrial genome, but consequently suffer from severe growth defects. 'Petite yeasts', characterized by mitochondrial genome loss, are instrumental for studying mitochondrial function and physiology. However, the molecular cause of their reduced growth rate remains an open question. Here we show that petite cells suffer from an insufficient capacity to synthesize glutamate, glutamine, leucine and arginine, negatively impacting their growth. Using a combination of molecular genetics and omics approaches, we demonstrate the evolution of fast growth overcomes these amino acid deficiencies, by alleviating a perturbation in mitochondrial iron metabolism and by restoring a defect in the mitochondrial tricarboxylic acid cycle, caused by aconitase inhibition. Our results hence explain the slow growth of mitochondrial genome-deficient cells with a partial auxotrophy in four amino acids that results from distorted iron metabolism and an inhibited tricarboxylic acid cycle.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

An efficient transformation system for Trichoderma atroviride using the pyr4 gene as a selectable marker

The development of an efficient transformation system is essential to enrich the genetic understanding of Trichoderma atroviride. To acquire an additional homologous selectable marker, uracil auxotrophic mutants were generated. First, the pyr4 gene encoding OMP decarboxylase was replaced by the hph marker gene, encoding a hygromycin phosphotransferase. Then, uracil auxotrophs were employed to determine that 5 mM uracil restores their growth and conidia production, and 1 mg ml^-1 is the lethal dose of 5-fluoroorotic acid in T. atroviride. Subsequently, uracil auxotrophic strains, free of a drug-selectable marker, were selected by 5-fluoroorotic acid resistance. Two different deletions in pyr4 were mapped in four auxotrophs, encoding a protein with frameshifts at the 310 and 335 amino acids in their COOH-terminal. Six auxotrophs did not have changes in the pyr4 ORF even though a specific cassette to delete the pyr4 was used, suggesting that 5-FOA could have mutagenic activity. The Ura^-1 strain was selected as a genetic background to knock out the MAPKK Pbs2, MAPK Tmk3, and the blue light receptors Blr1/Blr2, using a short version of pyr4 as a homologous marker. The ∆tmk3 and ∆pbs2 mutants selected with pyr4 or hph marker were phenotypically identical, highly sensitive to different stressors, and affected in photoconidiation. The ∆blr1 and ∆blr2 mutants were not responsive to light, and complementation of uracil biosynthesis did not interfere in the expression of blu1, grg2, phr1, and env1 genes upregulated by blue light. Overall, uracil metabolism can be used as a tool for genetic manipulation in T. atroviride.

The role of microbiota in tissue repair and regeneration

A comprehensive understanding of the human body endogenous microbiota is essential for acquiring an insight into the involvement of microbiota in tissue healing and regeneration process in order to enable development of biomaterials with a better integration with human body environment. Biomaterials used for biomedical applications are normally germ-free, and the human body as the host of the biomaterials is not germ-free. The complexity and role of the body microbiota in tissue healing/regeneration have been underestimated historically. Traditionally, studies aiming at the development of novel biomaterials had focused on the effects of environment within the target tissue, neglecting the signals generated from the microbiota and their impact on tissue regeneration. The significance of the human body microbiota in relation to metabolism, immune system, and consequently tissue regeneration has been recently realised and is a growing research field. This review summarises recent findings on the role of microbiota and mechanisms involved in tissue healing and regeneration, in particular skin, liver, bone, and nervous system regrowth and regeneration highlighting the potential new roles of microbiota for development of a new generation of biomaterials.

A toolbox of stable integration vectors in the fission yeast Schizosaccharomyces pombe

Schizosaccharomyces pombe is a widely used model organism to study many aspects of eukaryotic cell physiology. Its popularity as an experimental system partially stems from the ease of genetic manipulations, where the innate homology-targeted repair is exploited to precisely edit the genome. While vectors to incorporate exogenous sequences into the chromosomes are available, most are poorly characterized. Here, we show that commonly used fission yeast vectors, which upon integration produce repetitive genomic regions, give rise to unstable genomic loci. We overcome this problem by designing a new series of stable integration vectors (SIVs) that target four different prototrophy genes. SIVs produce non-repetitive, stable genomic loci and integrate predominantly as single copy. Additionally, we develop a set of complementary auxotrophic alleles that preclude false-positive integration events. We expand the vector series to include antibiotic resistance markers, promoters, fluorescent tags and terminators, and build a highly modular toolbox to introduce heterologous sequences. Finally, as proof of concept, we generate a large set of ready-to-use, fluorescent probes to mark organelles and cellular processes with a wide range of applications in fission yeast research.This article has an associated First Person interview with the first author of the paper.

Yeast systems biology in understanding principles of physiology underlying complex human diseases

Complex human diseases commonly arise from deregulation of cell growth, metabolism, and/or gene expression. Yeast is a eukaryal model organism that is widely used to study these processes. Yeast systems biology benefits from the ability to exert fine experimental control over the cell growth rate and nutrient composition, which allows orthogonal experimental design and generation of multi-omics data at high resolution. This has led to several insights on the principles of cellular physiology, including many cellular processes associated with complex human diseases. Here we review these biological insights together with experimental and modeling approaches developed in yeast to study systems biology. The role of yeast systems biology to further advance systems and personalized therapies for complex diseases is discussed.

A novel ER membrane protein Ehg1/May24 plays a critical role in maintaining multiple nutrient permeases in yeast under high-pressure perturbation

Previously, we isolated 84 deletion mutants in Saccharomyces cerevisiae auxotrophic background that exhibited hypersensitive growth under high hydrostatic pressure and/or low temperature. Here, we observed that 24 deletion mutants were rescued by the introduction of four plasmids (LEU2, HIS3, LYS2, and URA3) together to grow at 25 MPa, thereby suggesting close links between the genes and nutrient uptake. Most of the highly ranked genes were poorly characterized, including MAY24/YPR153W. May24 appeared to be localized in the endoplasmic reticulum (ER) membrane. Therefore, we designated this gene as EHG (ER-associated high-pressure growth gene) 1. Deletion of EHG1 led to reduced nutrient transport rates and decreases in the nutrient permease levels at 25 MPa. These results suggest that Ehg1 is required for the stability and functionality of the permeases under high pressure. Ehg1 physically interacted with nutrient permeases Hip1, Bap2, and Fur4; however, alanine substitutions for Pro17, Phe19, and Pro20, which were highly conserved among Ehg1 homologues in various yeast species, eliminated interactions with the permeases as well as the high-pressure growth ability. By functioning as a novel chaperone that facilitated coping with high-pressure-induced perturbations, Ehg1 could exert a stabilizing effect on nutrient permeases when they are present in the ER.

Big data in yeast systems biology

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Antioxidant Vitamins and Ageing

The free radical theory of ageing (FRTA), presented by Denham Harman in 1950s, proposed that aerobic organisms age due to reactive oxygen species (ROS)/free radical induced damage that accumulates in cells over time. Since antioxidants can neutralize free radicals by electron donation, the most logical approach was to use them as supplements in order to prevent ageing. In this chapter, we will discuss the inability of antioxidant supplementation to improve health and longevity.Although many antioxidants are efficient free radical quenchers in vitro, their in vivo effects are less clear. Recent evidence from human trials implies that antioxidant supplements do not increase lifespan and can even increase the incidence of diseases. Synthetic antioxidants were unable to consistently prevent ROS-induced damage in vivo, possibly as dietary antioxidants may not act only as ROS scavengers. Antioxidants can have dichotomous roles on ROS production. They are easily oxidized and can act as oxidants to induce damage when present in large concentrations. In appropriate amounts, they can modulate cellular metabolism by induction of cell stress responses and/or activate cell damage repair and maintenance systems. Therefore, the antioxidants' beneficial role may be reversed/prevented by excessive amounts of antioxidant supplements. On the other hand, ROS are also involved in many important physiological processes in humans, such as induction of stress responses, pathogen defence, and systemic signalling. Thus, both "anti-oxidative or reductive stress" (the excess of antioxidants) as well as oxidative stress (the excess of ROS) can be damaging and contribute to the ageing processes.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

RMut: R package for a Boolean sensitivity analysis against various types of mutations

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.

IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

The Dynamical Emergence of Biology From Physics: Branching Causation via Biomolecules

Biology differs fundamentally from the physics that underlies it. This paper proposes that the essential difference is that while physics at its fundamental level is Hamiltonian, in biology, once life has come into existence, causation of a contextual branching nature occurs at every level of the hierarchy of emergence at each time. The key feature allowing this to happen is the way biomolecules such as voltage-gated ion channels can act to enable branching logic to arise from the underlying physics, despite that physics per se being of a deterministic nature. Much randomness occurs at the molecular level, which enables higher level functions to select lower level outcomes according to higher level needs. Intelligent causation occurs when organisms engage in deduction, enabling prediction and planning. This is possible because ion channels enable action potentials to propagate in axons. The further key feature is that such branching biological behavior acts down to cause the underlying physical interactions to also exhibit a contextual branching behavior.

Determination of the Global Pattern of Gene Expression in Yeast Cells by Intracellular Levels of Guanine Nucleotides

This paper investigates whether, independently of the supply of any specific nutrient, gene transcription responds to the energy status of the cell by monitoring ATP and GTP levels. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into the yeast Saccharomyces cerevisiae, and the effect of an increased demand for these purine nucleotides on gene transcription was analyzed. The resulting changes in transcription were most consistently associated with changes in GTP and GEC levels, although the reprogramming in gene expression during glucose repression is sensitive to adenine nucleotide levels. The results show that GTP levels play a central role in determining how genes act to respond to changes in energy supply and that any comprehensive understanding of the control of eukaryotic gene expression requires the elucidation of how changes in guanine nucleotide abundance are sensed and transduced to alter the global pattern of transcription.

Correlations between gene transcription and the abundance of high-energy purine nucleotides in Saccharomyces cerevisiae have often been noted. However, there has been no systematic investigation of this phenomenon in the absence of confounding factors such as nutrient status and growth rate, and there is little hard evidence for a causal relationship. Whether transcription is fundamentally responsive to prevailing cellular energetic conditions via sensing of intracellular purine nucleotides, independently of specific nutrition, remains an important question. The controlled nutritional environment of chemostat culture revealed a strong correlation between ATP and GTP abundance and the transcription of genes required for growth. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into S. cerevisiae, permitting analysis of the transcriptional effect of an increased demand for these nucleotides. During steady-state growth using the fermentable carbon source glucose, the futile consumption of ATP led to a decrease in intracellular ATP concentration but an increase in GTP and the guanylate energy charge (GEC). Expression of transcripts encoding proteins involved in ribosome biogenesis, and those controlled by promoters subject to SWI/SNF-dependent chromatin remodelling, was correlated with these nucleotide pool changes. Similar nucleotide abundance changes were observed using a nonfermentable carbon source, but an effect on the growth-associated transcriptional programme was absent. Induction of the GTP-cycling pathway had only marginal effects on nucleotide abundance and gene transcription. The transcriptional response of respiring cells to glucose was dampened in chemostats induced for ATP cycling, but not GTP cycling, and this was primarily associated with altered adenine nucleotide levels.

Self-Establishing Communities: A Yeast Model to Study the Physiological Impact of Metabolic Cooperation in Eukaryotic Cells

All biosynthetically active cells are able to export and import metabolites, the small molecule intermediaries of metabolism. In dense cell populations, this hallmark of cells results in the intercellular exchange of a wide spectrum of metabolites. Such metabolite exchange enables metabolic specialization of individual cells, leading to far reaching biological implications, as a consequence of the intrinsic connection between metabolism and cell physiology. In this chapter, we discuss methods on how to study metabolite exchange interactions by using self-establishing metabolically cooperating communities (SeMeCos) in the budding yeast Saccharomyces cerevisiae. SeMeCos exploit the stochastic segregation of episomes to progressively increase the number of essential metabolic interdependencies in a community that grows out from an initially prototrophic cell. By coupling genotype to metabotype, SeMeCos allow for the tracking of cells while they specialize metabolically and hence the opportunity to study their progressive change in physiology.

Methionine coordinates a hierarchically organized anabolic program enabling proliferation

Methionine availability during overall amino acid limitation metabolically reprograms cells to support proliferation, the underlying basis for which remains unclear. Here we construct the organization of this methionine-mediated anabolic program using yeast. Combining comparative transcriptome analysis and biochemical and metabolic flux-based approaches, we discover that methionine rewires overall metabolic outputs by increasing the activity of a key regulatory node. This comprises the pentose phosphate pathway (PPP) coupled with reductive biosynthesis, the glutamate dehydrogenase (GDH)-dependent synthesis of glutamate/glutamine, and pyridoxal-5-phosphate (PLP)-dependent transamination capacity. This PPP-GDH-PLP node provides the required cofactors and/or substrates for subsequent rate-limiting reactions in the synthesis of amino acids and therefore nucleotides. These rate-limiting steps in amino acid biosynthesis are also induced in a methionine-dependent manner. This thereby results in a biochemical cascade establishing a hierarchically organized anabolic program. For this methionine-mediated anabolic program to be sustained, cells co-opt a "starvation stress response" regulator, Gcn4p. Collectively, our data suggest a hierarchical metabolic framework explaining how methionine mediates an anabolic switch.

Changes of Cell Biochemical States Are Revealed in Protein Homomeric Complex Dynamics

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins, or other small molecules based on "homomer dynamics" protein-fragment complementation assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating the confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health span-affecting drugs, the immunosuppressant rapamycin and the type 2 diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency and includes a change in protein-bound iron levels. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatment of diabetes, cancers, and other chronic diseases of aging.

Comparative functional genomic screens of three yeast deletion collections reveal unexpected effects of genotype in response to diverse stress

The Yeast Knockout (YKO) collection has provided a wealth of functional annotations from genome-wide screens. An unintended consequence is that 76% of gene annotations derive from one genotype. The nutritional auxotrophies in the YKO, in particular, have phenotypic consequences. To address this issue, ‘prototrophic’ versions of the YKO collection have been constructed, either by introducing a plasmid carrying wild-type copies of the auxotrophic markers (Plasmid-Borne, PB_prot) or by backcrossing (Backcrossed, BC_prot) to a wild-type strain. To systematically assess the impact of the auxotrophies, genome-wide fitness profiles of prototrophic and auxotrophic collections were compared across diverse drug and environmental conditions in 250 experiments. Our quantitative profiles uncovered broad impacts of genotype on phenotype for three deletion collections, and revealed genotypic and strain-construction-specific phenotypes. The PB_prot collection exhibited fitness defects associated with plasmid maintenance, while BC_prot fitness profiles were compromised due to strain loss from nutrient selection steps during strain construction. The repaired prototrophic versions of the YKO collection did not restore wild-type behaviour nor did they clarify gaps in gene annotation resulting from the auxotrophic background. To remove marker bias and expand the experimental scope of deletion libraries, construction of a bona fide prototrophic collection from a wild-type strain will be required.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

Scale invariance in natural and artificial collective systems: a review

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties.

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5–8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.

Genetic engineering of host organisms for pharmaceutical synthesis

Pharmaceutical production hosts may be derived from almost any organism, from Chinese Hamster Ovary (CHO) cell lines to isolated actinomycetes. Each host can be improved, historically only through adaptive evolution. Recently, the maturation of organism engineering has expanded the available models, methods, and tools for altering host phenotypes. New tools like CRISPR-associated endonucleases promise to enable precise cellular reprogramming and to access previously intractable hosts. In this review, we discuss the most recent advances in engineering several types of pharmaceutical production hosts. These include model organisms, potential platform hosts with advantageous metabolism or physiology, specialized producers capable of unique biosynthesis, and CHO, the most widely used recombinant protein production host. To realize improved engineered hosts, an increasing number of approaches involving DNA sequencing and synthesis, host rewriting technologies, computational methods, and organism engineering strategies must be used. Integrative workflows that enable application of the right combination of methods to the right production host could enable economical production solutions for emerging human health treatments.

Natural Variation in SER1 and ENA6 Underlie Condition-Specific Growth Defects in Saccharomyces cerevisiae

Despite their ubiquitous use in laboratory strains, naturally occurring loss-of-function mutations in genes encoding core metabolic enzymes are relatively rare in wild isolates of Saccharomyces cerevisiae. Here, we identify a naturally occurring serine auxotrophy in a sake brewing strain from Japan. Through a cross with a honey wine (white tecc) brewing strain from Ethiopia, we map the minimal medium growth defect to SER1, which encodes 3-phosphoserine aminotransferase and is orthologous to the human disease gene, PSAT1. To investigate the impact of this polymorphism under conditions of abundant external nutrients, we examine growth in rich medium alone or with additional stresses, including the drugs caffeine and rapamycin and relatively high concentrations of copper, salt, and ethanol. Consistent with studies that found widespread effects of different auxotrophies on RNA expression patterns in rich media, we find that the SER1 loss-of-function allele dominates the quantitative trait locus (QTL) landscape under many of these conditions, with a notable exacerbation of the effect in the presence of rapamycin and caffeine. We also identify a major-effect QTL associated with growth on salt that maps to the gene encoding the sodium exporter, ENA6. We demonstrate that the salt phenotype is largely driven by variation in the ENA6 promoter, which harbors a deletion that removes binding sites for the Mig1 and Nrg1 transcriptional repressors. Thus, our results identify natural variation associated with both coding and regulatory regions of the genome that underlie strong growth phenotypes.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

Yeast Creates a Niche for Symbiotic Lactic Acid Bacteria through Nitrogen Overflow

Many microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, cross-feeding in a readily established community. In nitrogen-rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.

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Yeast overflows amino acids that enable survival of lactic acid bacteria (LAB)

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Overflow is in proportion to nitrogen excess and regulated via TORC1 pathway

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Phenotype supporting LAB growth is conserved across diverse yeast isolates

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Yeast-LAB mutualism readily emerges when lactose is the main C-source

Determinants and Regulation of Protein Turnover in Yeast

Protein turnover maintains the recycling needs of the proteome, and its malfunction has been linked to aging and age-related diseases. However, not all proteins turnover equally, and the factors that contribute to accelerate or slow down turnover are mostly unknown. We measured turnover rates for 3,160 proteins in exponentially growing yeast and analyzed their dependence on physical, functional, and genetic properties. We found that functional characteristics, including protein localization, complex membership, and connectivity, have greater effect on turnover than sequence elements. We also found that protein turnover and mRNA turnover are correlated. Analysis under nutrient perturbation and osmotic stress revealed that protein turnover highly depends on cellular state and is faster when proteins are being actively used. Finally, stress-induced changes in protein and transcript abundance correlated with changes in protein turnover. This study provides a resource of protein turnover rates and principles to understand the recycling needs of the proteome under basal conditions and perturbation.