Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast

Single-cell heterogeneity in ribosome content and the consequences for the growth laws

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Transcriptomic balance and optimal growth are determined by cell size

Cell size and growth are intimately related across the evolutionary scale, but whether cell size is important to attain maximal growth or fitness is still an open question. We show that growth rate is a non-monotonic function of cell volume, with maximal values around the critical size of wild-type yeast cells. The transcriptome of yeast and mouse cells undergoes a relative inversion in response to cell size, which we associate theoretically and experimentally with the necessary genome-wide diversity in RNA polymerase II affinity for promoters. Although highly expressed genes impose strong negative effects on fitness when the DNA/mass ratio is reduced, transcriptomic alterations mimicking the relative inversion by cell size strongly restrain cell growth. In all, our data indicate that cells set the critical size to obtain a properly balanced transcriptome and, as a result, maximize growth and fitness during proliferation.

The fitness trade-off between growth and stress resistance determines the phenotypic landscape

BACKGROUND

A central challenge in biology is to discover a principle that determines individual phenotypic differences within a species. The growth rate is particularly important for a unicellular organism, and the growth rate under a certain condition is negatively associated with that of another condition, termed fitness trade-off. Therefore, there should exist a common molecular mechanism that regulates multiple growth rates under various conditions, but most studies so far have focused on discovering those genes associated with growth rates under a specific condition.

RESULTS

In this study, we found that there exists a recurrent gene expression signature whose expression levels are related to the fitness trade-off between growth preference and stress resistance across various yeast strains and multiple conditions. We further found that the genomic variation of stress-response, ribosomal, and cell cycle regulators are potential causal genes that determine the sensitivity between growth and survival. Intriguingly, we further observed that the same principle holds for human cells using anticancer drug sensitivities across multiple cancer cell lines.

CONCLUSIONS

Together, we suggest that the fitness trade-off is an evolutionary trait that determines individual growth phenotype within a species. By using this trait, we can possibly overcome anticancer drug resistance in cancer cells.

Influence of cell volume on the gene transcription rate

Cells vary in volume throughout their life cycle and in many other circumstances, while their genome remains identical. Hence, the RNA production factory must adapt to changing needs, while maintaining the same production lines. This paradox is resolved by different mechanisms in distinct cells and circumstances. RNA polymerases have evolved to cope with the particular circumstances of each case and the different characteristics of the several RNA molecule types, especially their stabilities. Here we review current knowledge on these issues. We focus on the yeast Saccharomyces cerevisiae, where many of the studies have been performed, although we compare and discuss the results obtained in other eukaryotes and propose several ideas and questions to be tested and solved in the future. TAKE AWAY.

Studying the Human Microbiota: Advances in Understanding the Fundamentals, Origin, and Evolution of Biological Timekeeping

The recently observed circadian oscillations of the intestinal microbiota underscore the profound nature of the human-microbiome relationship and its importance for health. Together with the discovery of circadian clocks in non-photosynthetic gut bacteria and circadian rhythms in anucleated cells, these findings have indicated the possibility that virtually all microorganisms may possess functional biological clocks. However, they have also raised many essential questions concerning the fundamentals of biological timekeeping, its evolution, and its origin. This narrative review provides a comprehensive overview of the recent literature in molecular chronobiology, aiming to bring together the latest evidence on the structure and mechanisms driving microbial biological clocks while pointing to potential applications of this knowledge in medicine. Moreover, it discusses the latest hypotheses regarding the evolution of timing mechanisms and describes the functions of peroxiredoxins in cells and their contribution to the cellular clockwork. The diversity of biological clocks among various human-associated microorganisms and the role of transcriptional and post-translational timekeeping mechanisms are also addressed. Finally, recent evidence on metabolic oscillators and host-microbiome communication is presented.

A Nonlinear Delay Model for Metabolic Oscillations in Yeast Cells

We introduce two time-delay models of metabolic oscillations in yeast cells. Our model tests a hypothesis that the oscillations occur as multiple pathways share a limited resource which we equate to the number of available ribosomes. We initially explore a single-protein model with a constraint equation governing the total resource available to the cell. The model is then extended to include three proteins that share a resource pool. Three approaches are considered at constant delay to numerically detect oscillations. First, we use a spectral element method to approximate the system as a discrete map and evaluate the stability of the linearized system about its equilibria by examining its eigenvalues. For the second method, we plot amplitudes of the simulation trajectories in 2D projections of the parameter space. We use a history function that is consistent with published experimental results to obtain metabolic oscillations. Finally, the spectral element method is used to convert the system to a boundary value problem whose solutions correspond to approximate periodic solutions of the system. Our results show that certain combinations of total resource available and the time delay, lead to oscillations. We observe that an oscillation region in the parameter space is between regions admitting steady states that correspond to zero and constant production. Similar behavior is found with the three-protein model where all proteins require the same production time. However, a shift in the protein production rates peaks occurs for low available resource suggesting that our model captures the shared resource pool dynamics.

Crucial aspects of metabolism and cell biology relating to industrial production and processing of Saccharomyces biomass

The multitude of applications to which Saccharomyces spp. are put makes these yeasts the most prolific of industrial microorganisms. This review considers biological aspects pertaining to the manufacture of industrial yeast biomass. It is proposed that the production of yeast biomass can be considered in two distinct but interdependent phases. Firstly, there is a cell replication phase that involves reproduction of cells by their transitions through multiple budding and metabolic cycles. Secondly, there needs to be a cell conditioning phase that enables the accrued biomass to withstand the physicochemical challenges associated with downstream processing and storage. The production of yeast biomass is not simply a case of providing sugar, nutrients, and other growth conditions to enable multiple budding cycles to occur. In the latter stages of culturing, it is important that all cells are induced to complete their current budding cycle and subsequently enter into a quiescent state engendering robustness. Both the cell replication and conditioning phases need to be optimized and considered in concert to ensure good biomass production economics, and optimum performance of industrial yeasts in food and fermentation applications. Key features of metabolism and cell biology affecting replication and conditioning of industrial Saccharomyces are presented. Alternatives for growth substrates are discussed, along with the challenges and prospects associated with defining the genetic bases of industrially important phenotypes, and the generation of new yeast strains."I must be cruel only to be kind: Thus bad begins, and worse remains behind." William Shakespeare: Hamlet, Act 3, Scene 4.

Advances in S. cerevisiae Engineering for Xylose Fermentation and Biofuel Production: Balancing Growth, Metabolism, and Defense

Genetically engineering microorganisms to produce chemicals has changed the industrialized world. The budding yeast Saccharomyces cerevisiae is frequently used in industry due to its genetic tractability and unique metabolic capabilities. S. cerevisiae has been engineered to produce novel compounds from diverse sugars found in lignocellulosic biomass, including pentose sugars, like xylose, not recognized by the organism. Engineering high flux toward novel compounds has proved to be more challenging than anticipated since simply introducing pathway components is often not enough. Several studies show that the rewiring of upstream signaling is required to direct products toward pathways of interest, but doing so can diminish stress tolerance, which is important in industrial conditions. As an example of these challenges, we reviewed S. cerevisiae engineering efforts, enabling anaerobic xylose fermentation as a model system and showcasing the regulatory interplay's controlling growth, metabolism, and stress defense. Enabling xylose fermentation in S. cerevisiae requires the introduction of several key metabolic enzymes but also regulatory rewiring of three signaling pathways at the intersection of the growth and stress defense responses: the RAS/PKA, Snf1, and high osmolarity glycerol (HOG) pathways. The current studies reviewed here suggest the modulation of global signaling pathways should be adopted into biorefinery microbial engineering pipelines to increase efficient product yields.

Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch

The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

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.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Deciphering the mechanism by which the yeast Phaffia rhodozyma responds adaptively to environmental, nutritional, and genetic cues

Phaffia rhodozyma is a basidiomycetous yeast that synthesizes astaxanthin (ASX), which is a powerful and highly valuable antioxidant carotenoid pigment. P. rhodozyma cells accrue ASX and gain an intense red-pink coloration when faced with stressful conditions such as nutrient limitations (e.g., nitrogen or copper), the presence of toxic substances (e.g., antimycin A), or are affected by mutations in the genes that are involved in nitrogen metabolism or respiration. Since cellular accrual of ASX occurs under a wide variety of conditions, this yeast represents a valuable model for studying the growth conditions that entail oxidative stress for yeast cells. Recently, we proposed that ASX synthesis can be largely induced by conditions that lead to reduction-oxidation (redox) imbalances, particularly the state of the NADH/NAD+ couple together with an oxidative environment. In this work, we review the multiple known conditions that elicit ASX synthesis expanding on the data that we formerly examined. When considered alongside the Mitchell's chemiosmotic hypothesis, the study served to rationalize the induction of ASX synthesis and other adaptive cellular processes under a much broader set of conditions. Our aim was to propose an underlying mechanism that explains how a broad range of divergent conditions converge to induce ASX synthesis in P. rhodozyma. The mechanism that links the induction of ASX synthesis with the occurrence of NADH/NAD+ imbalances may help in understanding how other organisms detect any of a broad array of stimuli or gene mutations, and then adaptively respond to activate numerous compensatory cellular processes.

A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences

Isogenic cells cultured together show heterogeneity in their proliferation rate. To determine the differences between fast and slow-proliferating cells, we developed a method to sort cells by proliferation rate, and performed RNA-seq on slow and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and mouse fibroblasts. We found that slowly proliferating mESCs have a more naïve pluripotent character. We identified an evolutionarily conserved proliferation-correlated transcriptomic signature that is common to all eukaryotes: fast cells have higher expression of genes for protein synthesis and protein degradation. This signature accurately predicted growth rate in yeast and cancer cells, and identified lineage-specific proliferation dynamics during development, using C. elegans scRNA-seq data. In contrast, sorting by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. The mitochondrial electron transport chain inhibitor antimycin affected slow and fast subpopulations differently. While a major transcriptional-signature associated with cell-to-cell heterogeneity in proliferation is conserved, the metabolic and energetic dependency of cell proliferation is cell-type specific.

By performing RNA sequencing on cells sorted by their proliferation rate, this study identifies a gene expression signature capable of predicting proliferation rates in diverse eukaryotic cell types and species. This signature, applied to single-cell RNA sequencing data from embryos of the roundworm C. elegans, reveals lineage-specific proliferation differences during development. In contrast to the universality of the proliferation signature, mitochondria and metabolism related genes show a high degree of cell-type specificity; mouse pluripotent stem cells (mESCs) and differentiated cells (fibroblasts) exhibit opposite relations between mitochondria state and proliferation. Furthermore, we identified a slow proliferating subpopulation of mESCs with higher expression of pluripotency genes. Finally, we show that fast and slow proliferating subpopulations are differentially sensitive to mitochondria inhibitory drugs in different cell types.

Highlights:

A FACS-based method to determine the transcriptomes of fast and slow proliferating subpopulations.

A universal proliferation-correlated transcriptional signature indicates high protein synthesis and degradation in fast proliferating cells across cell types and species.

Applied to scRNA-seq, the expression signature predicts the global proliferation slowdown during C. elegans development.

Mitochondria membrane potential predicts proliferation rate in a cell-type specific manner, with ETC complex III inhibitor having distinct effects on fibroblasts vs mESCs.

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies.

Analyzing Ribosome Remodeling in Health and Disease

Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.

Metabolic excretion associated with nutrient-growth dysregulation promotes the rapid evolution of an overt metabolic defect

In eukaryotes, conserved mechanisms ensure that cell growth is coordinated with nutrient availability. Overactive growth during nutrient limitation ("nutrient-growth dysregulation") can lead to rapid cell death. Here, we demonstrate that cells can adapt to nutrient-growth dysregulation by evolving major metabolic defects. Specifically, when yeast lysine-auxotrophic mutant lys- encountered lysine limitation, an evolutionarily novel stress, cells suffered nutrient-growth dysregulation. A subpopulation repeatedly evolved to lose the ability to synthesize organosulfurs (lys-orgS-). Organosulfurs, mainly reduced glutathione (GSH) and GSH conjugates, were released by lys- cells during lysine limitation when growth was dysregulated, but not during glucose limitation when growth was regulated. Limiting organosulfurs conferred a frequency-dependent fitness advantage to lys-orgS- by eliciting a proper slow growth program, including autophagy. Thus, nutrient-growth dysregulation is associated with rapid organosulfur release, which enables the selection of organosulfur auxotrophy to better tune cell growth to the metabolic environment. We speculate that evolutionarily novel stresses can trigger atypical release of certain metabolites, setting the stage for the evolution of new ecological interactions.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

tRNA wobble-uridine modifications as amino acid sensors and regulators of cellular metabolic state

Cells must appropriately sense available nutrients and accordingly regulate their metabolic outputs, to survive. This mini-review considers the idea that conserved chemical modifications of wobble (U34) position tRNA uridines enable cells to sense nutrients and regulate their metabolic state. tRNA wobble uridines are chemically modified at the 2- and 5- positions, with a thiol (s2), and (commonly) a methoxycarbonylmethyl (mcm5) modification, respectively. These modifications reflect sulfur amino acid (methionine and cysteine) availability. The loss of these modifications has minor translation defects. However, they result in striking phenotypes consistent with an altered metabolic state. Using yeast, we recently discovered that the s2 modification regulates overall carbon and nitrogen metabolism, dependent on methionine availability. The loss of this modification results in rewired carbon (glucose) metabolism. Cells have reduced carbon flux towards the pentose phosphate pathway and instead increased flux towards storage carbohydrates-primarily trehalose, along with reduced nucleotide synthesis, and perceived amino acid starvation signatures. Remarkably, this metabolic rewiring in the s2U mutants is caused by mechanisms leading to intracellular phosphate limitation. Thus this U34 tRNA modification responds to methionine availability and integratively regulates carbon and nitrogen homeostasis, wiring cells to a 'growth' state. We interpret the importance of U34 modifications in the context of metabolic sensing and anabolism, emphasizing their intimate coupling to methionine metabolism.

Widespread Cumulative Influence of Small Effect Size Mutations on Yeast Quantitative Traits

Quantitative traits are influenced by pathways that have traditionally been defined through genes that have a large loss- or gain-of-function effect. However, in theory, a large number of small effect size genes could cumulatively play a substantial role in pathway function. Here, we determine the number, strength, and identity of all non-essential test genes that affect two quantitative galactose-responsive traits in addition to re-analyzing two previously screened quantitative traits. We find that over a quarter of assayed genes have a detectable, quantitative effect on phenotype. Despite their ubiquity, these genes are enriched in core cellular processes in a trait-specific manner. In a simulated population with 50% frequency of all-or-none alleles, we show that small effect size alleles are capable of contributing more to trait variation than alleles in a canonical, large effect size pathway. In total, by demonstrating that the genes effecting quantitative traits can be highly distributed and interconnected, this work challenges the concept of pathways as modular and independent.

Global Transcriptional Programs in Archaea Share Features with the Eukaryotic Environmental Stress Response

The environmental stress response (ESR), a global transcriptional program originally identified in yeast, is characterized by a rapid and transient transcriptional response composed of large, oppositely regulated gene clusters. Genes induced during the ESR encode core components of stress tolerance, macromolecular repair, and maintenance of homeostasis. In this review, we investigate the possibility for conservation of the ESR across the eukaryotic and archaeal domains of life. We first re-analyze existing transcriptomics data sets to illustrate that a similar transcriptional response is identifiable in Halobacterium salinarum, an archaeal model organism. To substantiate the archaeal ESR, we calculated gene-by-gene correlations, gene function enrichment, and comparison of temporal dynamics. We note reported examples of variation in the ESR across fungi, then synthesize high-level trends present in expression data of other archaeal species. In particular, we emphasize the need for additional high-throughput time series expression data to further characterize stress-responsive transcriptional programs in the Archaea. Together, this review explores an open question regarding features of global transcriptional stress response programs shared across domains of life.

A tRNA modification balances carbon and nitrogen metabolism by regulating phosphate homeostasis

Cells must appropriately sense and integrate multiple metabolic resources to commit to proliferation. Here, we report that S. cerevisiae cells regulate carbon and nitrogen metabolic homeostasis through tRNA U34-thiolation. Despite amino acid sufficiency, tRNA-thiolation deficient cells appear amino acid starved. In these cells, carbon flux towards nucleotide synthesis decreases, and trehalose synthesis increases, resulting in a starvation-like metabolic signature. Thiolation mutants have only minor translation defects. However, in these cells phosphate homeostasis genes are strongly down-regulated, resulting in an effectively phosphate-limited state. Reduced phosphate enforces a metabolic switch, where glucose-6-phosphate is routed towards storage carbohydrates. Notably, trehalose synthesis, which releases phosphate and thereby restores phosphate availability, is central to this metabolic rewiring. Thus, cells use thiolated tRNAs to perceive amino acid sufficiency, balance carbon and amino acid metabolic flux and grow optimally, by controlling phosphate availability. These results further biochemically explain how phosphate availability determines a switch to a 'starvation-state'.

Competition in the chaperone-client network subordinates cell-cycle entry to growth and stress

The precise coordination of growth and proliferation has a universal prevalence in cell homeostasis. As a prominent property, cell size is modulated by the coordination between these processes in bacterial, yeast, and mammalian cells, but the underlying molecular mechanisms are largely unknown. Here, we show that multifunctional chaperone systems play a concerted and limiting role in cell-cycle entry, specifically driving nuclear accumulation of the G1 Cdk-cyclin complex. Based on these findings, we establish and test a molecular competition model that recapitulates cell-cycle-entry dependence on growth rate. As key predictions at a single-cell level, we show that availability of the Ydj1 chaperone and nuclear accumulation of the G1 cyclin Cln3 are inversely dependent on growth rate and readily respond to changes in protein synthesis and stress conditions that alter protein folding requirements. Thus, chaperone workload would subordinate Start to the biosynthetic machinery and dynamically adjust proliferation to the growth potential of the cell.

Ribosome Stoichiometry: From Form to Function

The existence of eukaryotic ribosomes with distinct ribosomal protein (RP) stoichiometry and regulatory roles in protein synthesis has been speculated for over 60 years. Recent advances in mass spectrometry (MS) and high-throughput analysis have begun to identify and characterize distinct ribosome stoichiometry in yeast and mammalian systems. In addition to RP stoichiometry, ribosomes host a vast array of protein modifications, effectively expanding the number of human RPs from 80 to many thousands of distinct proteoforms. Is it possible that these proteoforms combine to function as a 'ribosome code' to tune protein synthesis? We outline the specific benefits that translational regulation by specialized ribosomes can offer and discuss the means and methodologies available to correlate and characterize RP stoichiometry with function. We highlight previous research with a focus on formulating hypotheses that can guide future experiments and crack the ribosome code.

Heat Oscillations Driven by the Embryonic Cell Cycle Reveal the Energetic Costs of Signaling

All living systems function out of equilibrium and exchange energy in the form of heat with their environment. Thus, heat flow can inform on the energetic costs of cellular processes, which are largely unknown. Here, we have repurposed an isothermal calorimeter to measure heat flow between developing zebrafish embryos and the surrounding medium. Heat flow increased over time with cell number. Unexpectedly, a prominent oscillatory component of the heat flow, with periods matching the synchronous early reductive cleavage divisions, persisted even when DNA synthesis and mitosis were blocked by inhibitors. Instead, the heat flow oscillations were driven by the phosphorylation and dephosphorylation reactions catalyzed by the cell-cycle oscillator, the biochemical network controlling mitotic entry and exit. We propose that the high energetic cost of cell-cycle signaling reflects the significant thermodynamic burden of imposing accurate and robust timing on cell proliferation during development.

Metabolic rhythms: A framework for coordinating cellular function

Circadian clocks are widespread among eukaryotes and generally involve feedback loops coupled with metabolic processes and redox balance. The organising power of these oscillations has not only allowed organisms to anticipate day-night cycles, but also acts to temporally compartmentalise otherwise incompatible processes, enhance metabolic efficiency, make the system more robust to noise and propagate signals among cells. While daily rhythms and the function of the circadian transcription-translation loop have been the subject of extensive research over the past four decades, cycles of shorter period and respiratory oscillations, with which they are intertwined, have received less attention. Here, we describe features of yeast respiratory oscillations, which share many features with daily and 12 hr cellular oscillations in animal cells. This relatively simple system enables the analysis of dynamic rhythmic changes in metabolism, independent of cellular oscillations that are a product of the circadian transcription-translation feedback loop. Knowledge gained from studying ultradian oscillations in yeast will lead to a better understanding of the basic mechanistic principles and evolutionary origins of oscillatory behaviour among eukaryotes.

Predicting growth rate from gene expression

Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.

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.

The Plasmodium LAP complex affects crystalloid biogenesis and oocyst cell division

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Fusion of GFP to Plasmodium berghei LAP4 causes abnormal crystalloid formation.

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LAP4/GFP oocysts have reduced size.

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LAP4/GFP oocyst populations show earlier sporulation dynamics.

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LAP4/GFP sporozoites are not transmitted by mosquito bite.

Malaria parasite oocysts located on the mosquito midgut generate sporozoites by a process called sporogony. Plasmodium berghei parasites express six LCCL lectin domain adhesive-like proteins (LAPs), which operate as a complex and share a localisation in the crystalloid – an organelle found in the ookinete and young oocyst. Depletion of LAPs prevents crystalloid formation, increases oocyst growth, and blocks sporogony. Here, we describe a LAP4 mutant that has abnormal crystalloid biogenesis and produces oocysts that display reduced growth and premature sporogony. These findings provide evidence for a role of the LAP complex in regulating oocyst cell division via the crystalloid.

A minimal "push-pull" bistability model explains oscillations between quiescent and proliferative cell states

A minimal model for oscillating between quiescent and growth/proliferation states, dependent on the availability of a central metabolic resource, is presented. From the yeast metabolic cycles, metabolic oscillations in oxygen consumption are represented as transitions between quiescent and growth states. We consider metabolic resource availability, growth rates, and switching rates (between states) to model a relaxation oscillator explaining transitions between these states. This frustrated bistability model reveals a required communication between the metabolic resource that determines oscillations and the quiescent and growth state cells. Cells in each state reflect memory, or hysteresis of their current state, and “push–pull” cells from the other state. Finally, a parsimonious argument is made for a specific central metabolite as the controller of switching between quiescence and growth states. We discuss how an oscillator built around the availability of such a metabolic resource is sufficient to generally regulate oscillations between growth and quiescence through committed transitions.

Integrating impedance-based growth-rate monitoring into a microfluidic cell culture platform for live-cell microscopy

Growth rate is a widely studied parameter for various cell-based biological studies. Growth rates of cell populations can be monitored in chemostats and micro-chemostats, where nutrients are continuously replenished. Here, we present an integrated microfluidic platform that enables long-term culturing of non-adherent cells as well as parallel and mutually independent continuous monitoring of (i) growth rates of cells by means of impedance measurements and of (ii) specific other cellular events by means of high-resolution optical or fluorescence microscopy. Yeast colonies were grown in a monolayer under culturing pads, which enabled high-resolution microscopy, as all cells were in the same focal plane. Upon cell growth and division, cells leaving the culturing area passed over a pair of electrodes and were counted through impedance measurements. The impedance data could then be used to directly determine the growth rates of the cells in the culturing area. The integration of multiple culturing chambers with sensing electrodes enabled multiplexed long-term monitoring of growth rates of different yeast strains in parallel. As a demonstration, we modulated the growth rates of engineered yeast strains using calcium. The results indicated that impedance measurements provide a label-free readout method to continuously monitor the changes in the growth rates of the cells without compromising high-resolution optical imaging of single cells.

Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

Genetically identical cells growing in the same environment can vary in their cellular state and behavior. Such heterogeneity may explain why some cells in an isogenic population can survive sudden severe environmental stress whereas other cells succumb. Cell-to-cell variation in gene expression has been linked to variable stress survival, but how and why transcript levels vary across the transcriptome in single cells is only beginning to emerge. Here, we used single-cell RNA sequencing (scRNA-seq) to measure cell-to-cell heterogeneity in the transcriptome of budding yeast (Saccharomyces cerevisiae). We find surprising patterns of variation across known sets of transcription factor targets, indicating that cells vary in their transcriptome profile both before and after stress exposure. scRNA-seq analysis combined with live-cell imaging of transcription factor activation dynamics revealed some cells in which the stress response was coordinately activated and other cells in which the traditional response was decoupled, suggesting unrecognized regulatory nuances that expand our understanding of stress response and survival.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

The Yeast Cyclin-Dependent Kinase Routes Carbon Fluxes to Fuel Cell Cycle Progression

Cell division entails a sequence of processes whose specific demands for biosynthetic precursors and energy place dynamic requirements on metabolism. However, little is known about how metabolic fluxes are coordinated with the cell division cycle. Here, we examine budding yeast to show that more than half of all measured metabolites change significantly through the cell division cycle. Cell cycle-dependent changes in central carbon metabolism are controlled by the cyclin-dependent kinase (Cdk1), a major cell cycle regulator, and the metabolic regulator protein kinase A. At the G1/S transition, Cdk1 phosphorylates and activates the enzyme Nth1, which funnels the storage carbohydrate trehalose into central carbon metabolism. Trehalose utilization fuels anabolic processes required to reliably complete cell division. Thus, the cell cycle entrains carbon metabolism to fuel biosynthesis. Because the oscillation of Cdk activity is a conserved feature of the eukaryotic cell cycle, we anticipate its frequent use in dynamically regulating metabolism for efficient proliferation.

The molecular basis of metabolic cycles and their relationship to circadian rhythms

Metabolic cycles result from the partitioning of oxidative and reductive metabolism into rhythmic phases of gene expression and oscillating post-translational protein modifications. Relatively little is known about how these switches in gene expression are controlled, although recent studies have suggested that transcription itself may play a central role. This review explores the molecular basis of the metabolic and gene-expression oscillations in the yeast Saccharomyces cerevisiae, as well as how they relate to other biological time-keeping mechanisms, such as circadian rhythms.

Post-transcriptional regulation across human tissues

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.

The MADS-box XAANTAL1 increases proliferation at the Arabidopsis root stem-cell niche and participates in transition to differentiation by regulating cell-cycle components

Background Morphogenesis depends on the concerted modulation of cell proliferation and differentiation. Such modulation is dynamically adjusted in response to various external and internal signals via complex transcriptional regulatory networks that mediate between such signals and regulation of cell-cycle and cellular responses (proliferation, growth, differentiation). In plants, which are sessile, the proliferation/differentiation balance is plastically adjusted during their life cycle and transcriptional networks are important in this process. MADS-box genes are key developmental regulators in eukaryotes, but their role in cell proliferation and differentiation modulation in plants remains poorly studied. Methods We characterize the XAL1 loss-of-function xal1-2 allele and overexpression lines using quantitative cellular and cytometry analyses to explore its role in cell cycle, proliferation, stem-cell patterning and transition to differentiation. We used quantitative PCR and cellular markers to explore if XAL1 regulates cell-cycle components and PLETHORA1 (PLT1) gene expression, as well as confocal microscopy to analyse stem-cell niche organization. Key Results We previously showed that XAANTAL1 (XAL1/AGL12) is necessary for Arabidopsis root development as a promoter of cell proliferation in the root apical meristem. Here, we demonstrate that XAL1 positively regulates the expression of PLT1 and important components of the cell cycle: CYCD3;1, CYCA2;3, CYCB1;1, CDKB1;1 and CDT1a. In addition, we show that xal1-2 mutant plants have a premature transition to differentiation with root hairs appearing closer to the root tip, while endoreplication in these plants is partially compromised. Coincidently, the final size of cortex cells in the mutant is shorter than wild-type cells. Finally, XAL1 overexpression-lines corroborate that this transcription factor is able to promote cell proliferation at the stem-cell niche. Conclusion XAL1 seems to be an important component of the networks that modulate cell proliferation/differentiation transition and stem-cell proliferation during Arabidopsis root development; it also regulates several cell-cycle components.

Growth rate controls mRNA turnover in steady and non-steady states

Gene expression has been investigated in relation with growth rate in the yeast Saccharomyces cerevisiae, following different experimental strategies. The expression of some specific gene functional categories increases or decreases with growth rate. Our recently published results have unveiled that these changes in mRNA concentration with growth depend on the relative alteration of mRNA synthesis and decay, and that, in addition to this gene-specific transcriptomic signature of growth, global mRNA turnover increases with growth rate. We discuss here these results in relation with other previous and concurrent publications, and we add new evidence which indicates that growth rate controls mRNA turnover even under non-steady-state conditions.

A New Measure for Analyzing and Fusing Sequences of Objects

This work is related to the combinatorial data analysis problem of seriation used for data visualization and exploratory analysis. Seriation re-sequences the data, so that more similar samples or objects appear closer together, whereas dissimilar ones are further apart. Despite the large number of current algorithms to realize such re-sequencing, there has not been a systematic way for analyzing the resulting sequences, comparing them, or fusing them to obtain a single unifying one. We propose a new positional proximity measure that evaluates the similarity of two arbitrary sequences based on their agreement on pairwise positional information of the sequenced objects. Furthermore, we present various statistical properties of this measure as well as its normalized version modeled as an instance of the generalized correlation coefficient. Based on this measure, we define a new procedure for consensus seriation that fuses multiple arbitrary sequences based on a quadratic assignment problem formulation and an efficient way of approximating its solution. We also derive theoretical links with other permutation distance functions and present their associated combinatorial optimization forms for consensus tasks. The utility of the proposed contributions is demonstrated through the comparison and fusion of multiple seriation algorithms we have implemented, using many real-world datasets from different application domains.

The importance of controlling mRNA turnover during cell proliferation

Microbial gene expression depends not only on specific regulatory mechanisms, but also on cellular growth because important global parameters, such as abundance of mRNAs and ribosomes, could be growth rate dependent. Understanding these global effects is necessary to quantitatively judge gene regulation. In the last few years, transcriptomic works in budding yeast have shown that a large fraction of its genes is coordinately regulated with growth rate. As mRNA levels depend simultaneously on synthesis and degradation rates, those studies were unable to discriminate the respective roles of both arms of the equilibrium process. We recently analyzed 80 different genomic experiments and found a positive and parallel correlation between both RNA polymerase II transcription and mRNA degradation with growth rates. Thus, the total mRNA concentration remains roughly constant. Some gene groups, however, regulate their mRNA concentration by uncoupling mRNA stability from the transcription rate. Ribosome-related genes modulate their transcription rates to increase mRNA levels under fast growth. In contrast, mitochondria-related and stress-induced genes lower mRNA levels by reducing mRNA stability or the transcription rate, respectively. We critically review here these results and analyze them in relation to their possible extrapolation to other organisms and in relation to the new questions they open.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

Cell cycle progression is carefully coordinated with a cell's intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments.

Cell cycle Start is coupled to entry into the yeast metabolic cycle across diverse strains and growth rates

Cells have evolved oscillators with different frequencies to coordinate periodic processes. Here we studied the interaction of two oscillators, the cell division cycle (CDC) and the yeast metabolic cycle (YMC), in budding yeast. Previous work suggested that the CDC and YMC interact to separate high oxygen consumption (HOC) from DNA replication to prevent genetic damage. To test this hypothesis, we grew diverse strains in chemostat and measured DNA replication and oxygen consumption with high temporal resolution at different growth rates. Our data showed that HOC is not strictly separated from DNA replication; rather, cell cycle Start is coupled with the initiation of HOC and catabolism of storage carbohydrates. The logic of this YMC-CDC coupling may be to ensure that DNA replication and cell division occur only when sufficient cellular energy reserves have accumulated. Our results also uncovered a quantitative relationship between CDC period and YMC period across different strains. More generally, our approach shows how studies in genetically diverse strains efficiently identify robust phenotypes and steer the experimentalist away from strain-specific idiosyncrasies.

The cellular growth rate controls overall mRNA turnover, and modulates either transcription or degradation rates of particular gene regulons

We analyzed 80 different genomic experiments, and found a positive correlation between both RNA polymerase II transcription and mRNA degradation with growth rates in yeast. Thus, in spite of the marked variation in mRNA turnover, the total mRNA concentration remained approximately constant. Some genes, however, regulated their mRNA concentration by uncoupling mRNA stability from the transcription rate. Ribosome-related genes modulated their transcription rates to increase mRNA levels under fast growth. In contrast, mitochondria-related and stress-induced genes lowered mRNA levels by reducing mRNA stability or the transcription rate, respectively. We also detected these regulations within the heterogeneity of a wild-type cell population growing in optimal conditions. The transcriptomic analysis of sorted microcolonies confirmed that the growth rate dictates alternative expression programs by modulating transcription and mRNA decay.

The regulation of overall mRNA turnover keeps a constant ratio between mRNA decay and the dilution of [mRNA] caused by cellular growth. This regulation minimizes the indiscriminate transmission of mRNAs from mother to daughter cells, and favors the response capacity of the latter to physiological signals and environmental changes. We also conclude that, by uncoupling mRNA synthesis from decay, cells control the mRNA abundance of those gene regulons that characterize fast and slow growth.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

Differential Stoichiometry among Core Ribosomal Proteins

Understanding the regulation and structure of ribosomes is essential to understanding protein synthesis and its dysregulation in disease. While ribosomes are believed to have a fixed stoichiometry among their core ribosomal proteins (RPs), some experiments suggest a more variable composition. Testing such variability requires direct and precise quantification of RPs. We used mass spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast. Our data show that the stoichiometry among core RPs in wild-type yeast cells and ESC depends both on the growth conditions and on the number of ribosomes bound per mRNA. Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes. Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

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Wild-type yeast and mouse cells build ribosomes with different protein composition

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The stoichiometry among ribosomal proteins (RP) correlates to growth rate

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RP stoichiometry depends on the number of ribosomes bound per mRNA

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RP stoichiometry depends on the growth conditions

The dynamics of cellular energetics during continuous yeast culture

A plethora of data is accumulating from high throughput methods on metabolites, coenzymes, proteins, and nucleic acids and their interactions as well as the signalling and regulatory functions and pathways of the cellular network. The frozen moment viewed in a single discrete time sample requires frequent repetition and updating before any appreciation of the dynamics of component interaction becomes possible. Even then in a sample derived from a cell population, time-averaging of processes and events that occur in out-of-phase individuals blur the detailed complexity of single cell organization. Continuously-grown cultures of yeast can become spontaneously self-synchronized, thereby enabling resolution of far more detailed temporal structure. Continuous on-line monitoring by rapidly responding sensors (O2 electrode and membrane-inlet mass spectrometry for O2, CO2 and H2S; direct fluorimetry for NAD(P)H and flavins) gives dynamic information from time-scales of minutes to hours. Supplemented with capillary electophoresis and gas chromatography mass spectrometry and transcriptomics the predominantly oscillatory behaviour of network components becomes evident, with a 40 min cycle between a phase of increased respiration (oxidative phase) and decreased respiration (reductive phase). Highly pervasive, this ultradian clock provides a coordinating function that links mitochondrial energetics and redox balance to transcriptional regulation, mitochondrial structure and organelle remodelling, DNA duplication and cell division events. Ultimately, this leads to a global partitioning of anabolism and catabolism and the enzymes involved, mediated by a relatively simple ATP feedback loop on chromatin architecture.

SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data

BACKGROUND

Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations.

METHODS

We present in this paper a novel method, SW1PerS, for quantifying periodicity in time series in a shape-agnostic manner and with resistance to damping. The measurement is performed directly, without presupposing a particular pattern, by evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared to other algorithms using synthetic data and performance is quantified under varying noise models, noise levels, sampling densities, and signal shapes. Results on biological data are also analyzed and compared.

RESULTS

On the task of periodic/not-periodic classification, using synthetic data, SW1PerS outperforms all other algorithms in the low-noise regime. SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consistently classify damped signals as highly periodic. On biological data, and for several experiments, the lists of top 10% genes ranked with SW1PerS recover up to 67% of those generated with other popular algorithms. Moreover, the list of genes from data on the Yeast metabolic cycle which are highly-ranked only by SW1PerS, contains evidently non-cosine patterns (e.g. ECM33, CDC9, SAM1,2 and MSH6) with highly periodic expression profiles. In data from the Yeast cell cycle SW1PerS identifies genes not preferred by other algorithms, hence not previously reported as periodic, but found in other experiments such as the universal growth rate response of Slavov. These genes are BOP3, CDC10, YIL108W, YER034W, MLP1, PAC2 and RTT101.

CONCLUSIONS

In biological systems with low noise, i.e. where periodic signals with interesting shapes are more likely to occur, SW1PerS can be used as a powerful tool in exploratory analyses. Indeed, by having an initial set of periodic genes with a rich variety of signal types, pattern/shape information can be included in the study of systems and the generation of hypotheses regarding the structure of gene regulatory networks.

Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli

Ultimately, the genotype of a cell and its interaction with the environment determine the cell’s biochemical state. While the cell’s response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well.