Cell signaling can direct either binary or graded transcriptional responses

Gene loss and cis-regulatory novelty shaped core histone gene evolution in the apiculate yeast Hanseniaspora uvarum

Core histone genes display a remarkable diversity of cis-regulatory mechanisms despite their protein sequence conservation. However, the dynamics and significance of this regulatory turnover are not well understood. Here, we describe the evolutionary history of core histone gene regulation across 400 million years in budding yeasts. We find that canonical mode of core histone regulation-mediated by the trans-regulator Spt10-is ancient, likely emerging between 320 and 380 million years ago and is fixed in the majority of extant species. Unexpectedly, we uncovered the emergence of a novel core histone regulatory mode in the Hanseniaspora genus, from its fast-evolving lineage, which coincided with the loss of 1 copy of its paralogous core histone genes. We show that the ancestral Spt10 histone regulatory mode was replaced, via cis-regulatory changes in the histone control regions, by a derived Mcm1 histone regulatory mode and that this rewiring event occurred with no changes to the trans-regulator, Mcm1, itself. Finally, we studied the growth dynamics of the cell cycle and histone synthesis in genetically modified Hanseniaspora uvarum. We find that H. uvarum divides rapidly, with most cells completing a cell cycle within 60 minutes. Interestingly, we observed that the regulatory coupling between histone and DNA synthesis was lost in H. uvarum. Our results demonstrate that core histone gene regulation was fixed anciently in budding yeasts, however it has greatly diverged in the Hanseniaspora fast-evolving lineage.

Fluorescent Reporter Systems to Investigate Chromatin Effector Proteins in Living Cells

Epigenetic research faces the challenge of the high complexity and tight regulation in chromatin modification networks. Although many isolated mechanisms of chromatin-mediated gene regulation have been described, solid approaches for the comprehensive analysis of specific processes as parts of the bigger epigenome network are missing. In order to expand the toolbox of methods by a system that will help to capture and describe the complexity of transcriptional regulation, we describe here a robust protocol for the generation of stable reporter systems for transcriptional activity and summarize their applications. The system allows for the induced recruitment of a chromatin regulator to a fluorescent reporter gene, followed by the detection of transcriptional changes using flow cytometry. The reporter gene is integrated into an endogenous chromatin environment, thus enabling the detection of regulatory dependencies of the investigated chromatin regulator on endogenous cofactors. The system allows for an easy and dynamic readout at the single-cell level and the ability to compensate for cell-to-cell variances of transcription. The modular design of the system enables the simple adjustment of the method for the investigation of different chromatin regulators in a broad panel of cell lines. We also summarize applications of this technology to characterize the silencing velocity of different chromatin effectors, removal of activating histone modifications, analysis of stability and reversibility of epigenome modifications, the investigation of the effects of small molecule on chromatin effectors and of functional effector-coregulator relationships. The presented method allows to investigate the complexity of transcriptional regulation by epigenetic effector proteins in living cells.

Soft repression and chromatin modification by conserved transcriptional corepressors

Transcriptional regulation in eukaryotic cells involves the activity of multifarious DNA-binding transcription factors and recruited corepressor complexes. Together, these complexes interact with the core transcriptional machinery, chromatin, and nuclear environment to effect complex patterns of gene regulation. Much focus has been paid to the action of master regulatory switches that are key to developmental and environmental responses, as these genetic elements have important phenotypic effects. The regulation of widely-expressed metabolic control genes has been less well studied, particularly in cases in which physically-interacting repressors and corepressors have subtle influences on steady-state expression. This latter phenomenon, termed "soft repression" is a topic of increasing interest as genomic approaches provide ever more powerful tools to uncover the significance of this level of control. This review provides an oversight of classic and current approaches to the study of transcriptional repression in eukaryotic systems, with a specific focus on opportunities and challenges that lie ahead in the study of soft repression.

Divergence of alternative sugar preferences through modulation of the expression and activity of the Gal3 sensor in yeast

Optimized nutrient utilization is crucial for the progression of microorganisms in competing communities. Here we investigate how different budding yeast species and ecological isolates have established divergent preferences for two alternative sugar substrates: Glucose, which is fermented preferentially by yeast, and galactose, which is alternatively used upon induction of the relevant GAL metabolic genes. We quantified the dose-dependent induction of the GAL1 gene encoding the central galactokinase enzyme and found that a very large diversification exists between different yeast ecotypes and species. The sensitivity of GAL1 induction correlates with the growth performance of the respective yeasts with the alternative sugar. We further define some of the mechanisms, which have established different glucose/galactose consumption strategies in representative yeast strains by modulating the activity of the Gal3 inducer. (1) Optimal galactose consumers, such as Saccharomyces uvarum, contain a hyperactive GAL3 promoter, sustaining highly sensitive GAL1 expression, which is not further improved upon repetitive galactose encounters. (2) Desensitized galactose consumers, such as S. cerevisiae Y12, contain a less sensitive Gal3 sensor, causing a shift of the galactose response towards higher sugar concentrations even in galactose experienced cells. (3) Galactose insensitive sugar consumers, such as S. cerevisiae DBVPG6044, contain an interrupted GAL3 gene, causing extremely reluctant galactose consumption, which is, however, improved upon repeated galactose availability. In summary, different yeast strains and natural isolates have evolved galactose utilization strategies, which cover the whole range of possible sensitivities by modulating the expression and/or activity of the inducible galactose sensor Gal3.

Effective bet-hedging through growth rate dependent stability

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

Altered expression response upon repeated gene repression in single yeast cells

Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.

Cells have to continuously adjust to their environment and cope with changing temperature, stress conditions, or metabolic resources. Yeast cells exposed to repeated carbon source shifts have shown to be “primed” by their first exposure, exhibiting enhanced gene expression of specific genes later on. However, how cells respond to a repeated repressive stimulus, e.g., withdrawal of metabolic resources, has been so far much less studied.

For this, we investigated the expression kinetics of a carbon source-responsive gene across repeated repressions. We measured single-cell expression and used mathematical modeling to evaluate potential causes underlying an observed faster repression response upon a repeated stimulus. Specifically, we investigated whether i) an increased dilution due to e.g., proliferation, ii) an increased fraction of repressing cells, or iii) different kinetic parameters in the repeated repression cause the observed faster response in the second repression. Leveraging quantitative mathematical model comparison, we discovered that the faster response is mediated by a shortened estimated time between carbon source shift and protein production termination at the single-cell level.

Our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression.

A set of Saccharomyces cerevisiae integration vectors for fluorescent dye labeling of proteins

Protein fusions are frequently used for fluorescence imaging of individual molecules, both in vivo and in vitro. The SNAP, CLIP, HALO (aka HaloTag7), and DHFR protein tags can be linked to small molecule dyes that provide brightness and photo-stability superior to fluorescent proteins. To facilitate fluorescent dye tagging of proteins in the yeast Saccharomyces cerevisiae, we constructed a modular set of vectors with various combinations of labeling protein tags and selectable markers. These vectors can be used in combination to create strains where multiple proteins labeled with different colored dyes can be simultaneously observed.

Stochastic gene transcription with non-competitive transcription regulatory architecture

The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters and hence, enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to super-Poissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in the presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.

OLOGRAM-MODL: mining enriched n-wise combinations of genomic features with Monte Carlo and dictionary learning

Most epigenetic marks, such as Transcriptional Regulators or histone marks, are biological objects known to work together in n-wise complexes. A suitable way to infer such functional associations between them is to study the overlaps of the corresponding genomic regions. However, the problem of the statistical significance of n-wise overlaps of genomic features is seldom tackled, which prevent rigorous studies of n-wise interactions. We introduce OLOGRAM-MODL, which considers overlaps between n ≥ 2 sets of genomic regions, and computes their statistical mutual enrichment by Monte Carlo fitting of a Negative Binomial distribution, resulting in more resolutive P-values. An optional machine learning method is proposed to find complexes of interest, using a new itemset mining algorithm based on dictionary learning which is resistant to noise inherent to biological assays. The overall approach is implemented through an easy-to-use CLI interface for workflow integration, and a visual tree-based representation of the results suited for explicability. The viability of the method is experimentally studied using both artificial and biological data. This approach is accessible through the command line interface of the pygtftk toolkit, available on Bioconda and from https://github.com/dputhier/pygtftk

Variation in the modality of a yeast signaling pathway is mediated by a single regulator

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks and has been suggested to allow organisms to hedge their ‘bets’ in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and among natural isolates can be explained by changes in GAL3’s regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit

Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment.

An Improved CRISPR Interference Tool to Engineer Rhodococcus opacus

Rhodococcus opacus is a nonmodel bacterium that is well suited for valorizing lignin. Despite recent advances in our systems-level understanding of its versatile metabolism, studies of its gene functions at a single gene level are still lagging. Elucidating gene functions in nonmodel organisms is challenging due to limited genetic engineering tools that are convenient to use. To address this issue, we developed a simple gene repression system based on CRISPR interference (CRISPRi). This gene repression system uses a T7 RNA polymerase system to express a small guide RNA, demonstrating improved repression compared to the previously demonstrated CRISPRi system (i.e., the maximum repression efficiency improved from 58% to 85%). Additionally, our cloning strategy allows for building multiple CRISPRi plasmids in parallel without any PCR step, facilitating the engineering of this GC-rich organism. Using the improved CRISPRi system, we confirmed the annotated roles of four metabolic pathway genes, which had been identified by our previous transcriptomic analysis to be related to the consumption of benzoate, vanillate, catechol, and acetate. Furthermore, we showed our tool's utility by demonstrating the inducible accumulation of muconate that is a precursor of adipic acid, an important monomer for nylon production. While the maximum muconate yield obtained using our tool was 30% of the yield obtained using gene knockout, our tool showed its inducibility and partial repressibility. Our CRISPRi tool will be useful to facilitate functional studies of this nonmodel organism and engineer this promising microbial chassis for lignin valorization.

Variation, Variegation and Heritable Gene Repression in S. cerevisiae

Phenotypic heterogeneity provides growth advantages for a population upon changes of the environment. In S. cerevisiae, such heterogeneity has been observed as "on/off" states in the expression of individual genes in individual cells. These variations can persist for a limited or extended number of mitotic divisions. Such traits are known to be mediated by heritable chromatin structures, by the mitotic transmission of transcription factors involved in gene regulatory circuits or by the cytoplasmic partition of prions or other unstructured proteins. The significance of such epigenetic diversity is obvious, however, we have limited insight into the mechanisms that generate it. In this review, we summarize the current knowledge of epigenetically maintained heterogeneity of gene expression and point out similarities and converging points between different mechanisms. We discuss how the sharing of limiting repression or activation factors can contribute to cell-to-cell variations in gene expression and to the coordination between short- and long- term epigenetic strategies. Finally, we discuss the implications of such variations and strategies in adaptation and aging.

Learning causal networks using inducible transcription factors and transcriptome-wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

The past determines the future: sugar source history and transcriptional memory

Transcriptional reinduction memory is a phenomenon whereby cells “remember” their transcriptional response to a previous stimulus such that subsequent encounters with the same stimulus can result in altered gene expression kinetics. Chromatin structure is thought to play a role in certain transcriptional memory mechanisms, leading to questions as to whether and how memory can be actively maintained and inherited to progeny through cell division. Here we summarize efforts towards dissecting chromatin-based transcriptional memory inheritance of GAL genes in Saccharomyces cerevisiae. We focus on methods and analyses of GAL (as well as MAL and INO) memory in single cells and discuss the challenges in unraveling the underlying mechanisms in yeast and higher eukaryotes.

Heterogeneous Timing of Gene Induction as a Regulation Strategy

In response to environmental changes, cells often adapt by up-regulating genes to synthesize proteins that generate a benefit in the new environment. Several such cases of gene induction have been reported where the timing was heterogeneous, with some cells responding early and others responding late, although the microbial population was genetically homogeneous and the environment was well mixed. Here, we explore under which conditions heterogeneous timing of gene induction could be advantageous for the population as a whole. We base our study on a mathematical model that accounts for the cost of protein synthesis in terms of resources, which cells must provide immediately, whereas the associated benefit accumulates only slowly over the protein lifetime. Due to this delayed benefit, gene induction can be a risky investment, if resources are scarce and the environment fluctuates rapidly and unpredictably. Unprofitable gene induction then depletes the remaining limiting resource needed for maintenance of cell viability. We show that whenever gene induction is associated with a transient risk but beneficial in the long run, the stochastic timing of gene induction maximizes the reproductive success of a population. In particular, in an environment of stochastic periods of famine and feast, an optimum emerges from a trade-off between short-term growth, favoring rapid and homogeneous responses, and long-term survival, favoring a broadly heterogeneous response. Our analysis suggests that the optimal variability of induction times is just as large as the time required for the amortization of the initial investment into protein synthesis.

The Systems Biology of Single-Cell Aging

Aging is a leading cause of human morbidity and mortality, but efforts to slow or reverse its effects are hampered by an incomplete understanding of its multi-faceted origins. Systems biology, the use of quantitative and computational methods to understand complex biological systems, offers a toolkit well suited to elucidating the root cause of aging. We describe the known components of the aging network and outline innovative techniques that open new avenues of investigation to the aging research community. We propose integration of the systems biology and aging fields, identifying areas of complementarity based on existing and impending technological capabilities.

A GRX1 Promoter Variant Confers Constitutive Noisy Bimodal Expression That Increases Oxidative Stress Resistance in Yeast

Higher noise in the expression of stress-related genes was previously shown to confer better resistance in selective conditions. Thus, evolving the promoter of such genes toward higher transcriptional noise appears to be an attractive strategy to engineer microbial strains with enhanced stress resistance. Here we generated hundreds of promoter variants of the GRX1 gene involved in oxidative stress resistance in Saccharomyces cerevisiae and created a yeast library by replacing the native GRX1 promoter by these variants at the native locus. An outlier clone with very strong increase in noise (6-times) at the same mean expression level as the native strain was identified whereas the other noisiest clones were only 3-times increased. This variant provides constitutive bimodal expression and consists in 3 repeated but differently mutated copies of the GRX1 promoter. In spite of the multi-factorial oxidative stress-response in yeast, replacement of the native promoter by this variant is sufficient alone to confer strongly enhanced resistance to H₂O₂ and cumene hydroperoxide. New replacement of this variant by the native promoter in the resistant strain suppresses the resistance. This work shows that increasing noise of target genes in a relevant strategy to engineer microbial strains toward better stress resistance. Multiple promoter replacement could synergize the effect observed here with the sole GRX1 promoter replacement. Finally this work suggests that combining several mutated copies of the target promoter could allow enhancing transcriptional-mediated noise at higher levels than mutating a single copy by providing constitutive bimodal and highly heterogeneous expression distribution.

Bimodality of gene expression from yeast promoter can be instigated by DNA context, inducing conditions and strain background

Bimodality in gene expression is thought to provide a high phenotypic heterogeneity that can be favourable for adaptation or unfavourable notably in industrial processes that require stable and homogeneous properties. Whether this property is produced or suppressed in different conditions has been understudied. Here we identified tens of Saccharomyces cerevisiae genomic fragments conferring bimodal yEGFP expression on centromeric plasmid and studied some of these promoters in different DNA contexts, inducing conditions or strain backgrounds. First, we observed that the bimodal behaviour identified on plasmid is generally suppressed at the genomic level. Second, an inducible promoter such as the copper-regulated CUP1 promoter can produce bimodal expression in a time- and dose-dependent fashion. For a given copper sulphate concentration, a constant proportion of the subpopulation is induced and only the induction level of this subpopulation changed with induction duration, while for a same induction time, higher copper sulphate concentrations induced more cells at higher levels. Third, we showed that bimodality conferred by the CUP1 promoter in expression profile is strain background dependent, revealing epistasis in the generation of bimodality. The influence of these parameters on bimodality has to be taken into account when considering transgene expression for industrial microbial productions.

Chemically induced proximity in biology and medicine

Proximity, or the physical closeness of molecules, is a pervasive regulatory mechanism in biology. For example, most posttranslational modifications such as phosphorylation, methylation, and acetylation promote proximity of molecules to play deterministic roles in cellular processes. To understand the role of proximity in biologic mechanisms, chemical inducers of proximity (CIPs) were developed to synthetically model biologically regulated recruitment. Chemically induced proximity allows for precise temporal control of transcription, signaling cascades, chromatin regulation, protein folding, localization, and degradation, as well as a host of other biologic processes. A systematic analysis of CIPs in basic research, coupled with recent technological advances utilizing CRISPR, distinguishes roles of causality from coincidence and allows for mathematical modeling in synthetic biology. Recently, induced proximity has provided new avenues of gene therapy and emerging advances in cancer treatment.

Hill kinetics as a noise filter: the role of transcription factor autoregulation in gene cascades

An intuition based on deterministic models of chemical kinetics is that population heterogeneity of transcription factor levels in cells is transmitted unchanged downstream to the target genes. We use a stochastic model of a two-gene cascade with a self-regulating upstream gene to show that, counter to the intuition, there is no simple mapping (bimodal to bimodal, unimodal to unimodal) between the shapes of the distributions of transcription factor numbers and target protein numbers in cells. Due to the presence of the two regulations, the system contains two nonlinear transfer functions, defined by the Hill kinetics of transcription factor binding. The transfer function of the regulator can "interfere" with the transfer function of the target, converting the bimodal input into a unimodal output or vice versa. We show that this effect can be predicted by a geometric construction. As an example application of the method, we present a case study of a system of several downstream genes of different sensitivities, controlled by a common transcription factor which also regulates its own transcription. We show that a single regulator can induce qualitatively different patterns (binary or graded) of responses to a signal in different downstream genes, depending on whether the sensitivity regions of the transfer functions of the upstream and downstream genes overlap or not. Alternatively, the same model can be interpreted as describing a single downstream gene that has different sensitivities in different cell lines due to mutations. Our model shows, therefore, a possible kinetic mechanism by which different genes can interpret the same biological signal in a different manner.

Assigning function to natural allelic variation via dynamic modeling of gene network induction

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be “personalized” according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

Optimization-based synthesis of stochastic biocircuits with statistical specifications

Model-guided design has become a standard approach to engineering biomolecular circuits in synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell-cell heterogeneity causes unexpected deviation of biocircuit behaviours from model predictions and requires additional iterations of design-build-test cycles. To enhance the design process of stochastic biocircuits, this paper presents a computational framework to systematically specify the level of intrinsic noise using well-defined metrics of statistics and design highly heterogeneous biocircuits based on the specifications. Specifically, we use descriptive statistics of population distributions as an intuitive specification language of stochastic biocircuits and develop an optimization-based computational tool that explores parameter configurations satisfying design requirements. Sensitivity analysis methods are also performed to ensure the robustness of a biocircuit design against extrinsic perturbations. These design tools are formulated with convex optimization programs to enable rigorous and efficient quantification of the statistics. We demonstrate these features by designing a stochastic negative feedback biocircuit that satisfies multiple statistical constraints and perform an in-depth study of noise propagation and regulation in negative feedback pathways.

Genetic and Epigenetic Strategies Potentiate Gal4 Activation to Enhance Fitness in Recently Diverged Yeast Species

Certain genes show more rapid reactivation for several generations following repression, a conserved phenomenon called epigenetic transcriptional memory. Following previous growth in galactose, GAL gene transcriptional memory confers a strong fitness benefit in Saccharomyces cerevisiae adapting to growth in galactose for up to 8 generations. A genetic screen for mutants defective for GAL gene memory revealed new insights into the molecular mechanism, adaptive consequences, and evolutionary history of memory. A point mutation in the Gal1 co-activator that disrupts the interaction with the Gal80 inhibitor specifically and completely disrupted memory. This mutation confirms that cytoplasmically inherited Gal1 produced during previous growth in galactose directly interferes with Gal80 repression to promote faster induction of GAL genes. This mitotically heritable mode of regulation is recently evolved; in a diverged Saccharomyces species, GAL genes show constitutively faster activation due to genetically encoded basal expression of Gal1. Thus, recently diverged species utilize either epigenetic or genetic strategies to regulate the same molecular mechanism. The screen also revealed that the central domain of the Gal4 transcription factor both regulates the stochasticity of GAL gene expression and potentiates stronger GAL gene activation in the presence of Gal1. The central domain is critical for GAL gene transcriptional memory; Gal4 lacking the central domain fails to potentiate GAL gene expression and is unresponsive to previous Gal1 expression.

Asymmetry between Activation and Deactivation during a Transcriptional Pulse

Transcription in eukaryotic cells occurs in gene-specific bursts or pulses of activity. Recent studies identified a spectrum of transcriptionally active “on-states,” interspersed with periods of inactivity, but these “off-states” and the process of transcriptional deactivation are poorly understood. To examine what occurs during deactivation, we investigate the dynamics of switching between variable rates. We measured live single-cell expression of luciferase reporters from human growth hormone or human prolactin promoters in a pituitary cell line. Subsequently, we applied a statistical variable-rate model of transcription, validated by single-molecule FISH, to estimate switching between transcriptional rates. Under the assumption that transcription can switch to any rate at any time, we found that transcriptional activation occurs predominantly as a single switch, whereas deactivation occurs with graded, stepwise decreases in transcription rate. Experimentally altering cAMP signalling with forskolin or chromatin remodelling with histone deacetylase inhibitor modifies the duration of defined transcriptional states. Our findings reveal transcriptional activation and deactivation as mechanistically independent, asymmetrical processes.

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Gene transcription switches between variable rates

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Single-cell microscopy and mathematical modeling quantifies switch dynamics

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We observe an asymmetry in the activation/deactivation of transcriptional bursts

Epigenetic Transcriptional Memory of GAL Genes Depends on Growth in Glucose and the Tup1 Transcription Factor in Saccharomyces cerevisiae

Previously expressed inducible genes can remain poised for faster reactivation for multiple cell divisions, a conserved phenomenon called epigenetic transcriptional memory. The GAL genes in Saccharomyces cerevisiae show faster reactivation for up to seven generations after being repressed. During memory, previously produced Gal1 protein enhances the rate of reactivation of GAL1, GAL10, GAL2, and GAL7 These genes also interact with the nuclear pore complex (NPC) and localize to the nuclear periphery both when active and during memory. Peripheral localization of GAL1 during memory requires the Gal1 protein, a memory-specific cis-acting element in the promoter, and the NPC protein Nup100 However, unlike other examples of transcriptional memory, the interaction with NPC is not required for faster GAL gene reactivation. Rather, downstream of Gal1, the Tup1 transcription factor and growth in glucose promote GAL transcriptional memory. Cells only show signs of memory and only benefit from memory when growing in glucose. Tup1 promotes memory-specific chromatin changes at the GAL1 promoter: incorporation of histone variant H2A.Z and dimethylation of histone H3, lysine 4. Tup1 and H2A.Z function downstream of Gal1 to promote binding of a preinitiation form of RNA Polymerase II at the GAL1 promoter, poising the gene for faster reactivation. This mechanism allows cells to integrate a previous experience (growth in galactose, reflected by Gal1 levels) with current conditions (growth in glucose, potentially through Tup1 function) to overcome repression and to poise critical GAL genes for future reactivation.

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort

Motivation

High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required.

Results

We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS.

Availability and Implementation

Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

Dynamical Analysis of Drug Efficacy and Mechanism of Action Using GFP Reporters

To the development of effective cancer drug, it is necessary to, first, identify drugs and their possible combinations that could exert desired control over the type of cancer being considered; second, have a drug testing method that allows one to assess the variety of responses that can be provoked by drugs. To facilitate such an experiment-modeling-experiment cycle for drug development, a method based on the dynamical systems of pathways is presented. It involves a three-state experimental design: (1) formulate an oncologic pathway model of relevant cancer; (2) perturb the pathways with the drugs of known effects on components of the pathways of interest; and (3) measure process activity indicators at various points on cell populations. To evaluate the drug response in a high-throughput manner, a green fluorescent protein reporter-based technology has been developed. The authors apply the dynamical approach to several issues in the context of colon cancer cell lines.

Probabilistic adaptation in changing microbial environments

Microbes growing in animal host environments face fluctuations that have elements of both randomness and predictability. In the mammalian gut, fluctuations in nutrient levels and other physiological parameters are structured by the host's behavior, diet, health and microbiota composition. Microbial cells that can anticipate environmental fluctuations by exploiting this structure would likely gain a fitness advantage (by adapting their internal state in advance). We propose that the problem of adaptive growth in structured changing environments, such as the gut, can be viewed as probabilistic inference. We analyze environments that are "meta-changing": where there are changes in the way the environment fluctuates, governed by a mechanism unobservable to cells. We develop a dynamic Bayesian model of these environments and show that a real-time inference algorithm (particle filtering) for this model can be used as a microbial growth strategy implementable in molecular circuits. The growth strategy suggested by our model outperforms heuristic strategies, and points to a class of algorithms that could support real-time probabilistic inference in natural or synthetic cellular circuits.

An Orthogonal Permease-Inducer-Repressor Feedback Loop Shows Bistability

Feedback loops in biological networks, among others, enable differentiation and cell cycle progression, and increase robustness in signal transduction. In natural networks, feedback loops are often complex and intertwined, making it challenging to identify which loops are mainly responsible for an observed behavior. However, minimal synthetic replicas could allow for such identification. Here, we engineered a synthetic permease-inducer-repressor system in Saccharomyces cerevisiae to analyze if a transport-mediated positive feedback loop could be a core mechanism for the switch-like behavior in the regulation of metabolic gene networks such as the S. cerevisiae GAL system or the Escherichia coli lac operon. We characterized the synthetic circuit using deterministic and stochastic mathematical models. Similar to its natural counterparts, our synthetic system shows bistable and hysteretic behavior, and the inducer concentration range for bistability as well as the switching rates between the two stable states depend on the repressor concentration. Our results indicate that a generic permease-inducer-repressor circuit with a single feedback loop is sufficient to explain the experimentally observed bistable behavior of the natural systems. We anticipate that the approach of reimplementing natural systems with orthogonal parts to identify crucial network components is applicable to other natural systems such as signaling pathways.

Transcriptional rewiring over evolutionary timescales changes quantitative and qualitative properties of gene expression

Evolutionary changes in transcription networks are an important source of diversity across species, yet the quantitative consequences of network evolution have rarely been studied. Here we consider the transcriptional 'rewiring' of the three GAL genes that encode the enzymes needed for cells to convert galactose to glucose. In Saccharomyces cerevisiae, the transcriptional regulator Gal4 binds and activates these genes. In the human pathogen Candida albicans (which last shared a common ancestor with S. cerevisiae some 300 million years ago), we show that different regulators, Rtg1 and Rtg3, activate the three GAL genes. Using single-cell dynamics and RNA-sequencing, we demonstrate that although the overall logic of regulation is the same in both species-the GAL genes are induced by galactose-there are major differences in both the quantitative response of these genes to galactose and in the position of these genes in the overall transcription network structure of the two species.

The binary response of the GAL/MEL genetic switch of Saccharomyces cerevisiae is critically dependent on Gal80p-Gal4p interaction

Studies on the Saccharomyces cerevisiae GAL/MEL genetic switch have revealed that its bistability is dependent on ultrasensitivity that can be altered or abolished by disabling different combinations of nested feedback loops. In contrast, we have previously demonstrated that weakening of the interaction between Gal80p and Gal4p alone is sufficient to abolish the ultrasensitivity (Das Adhikari et al. 2014). Here, we demonstrate that altering the epistatic interaction between Gal80p and Gal4p also abolishes the bistability, and the switch response to galactose becomes graded instead of binary. However, the GAL/MEL switch of wild-type and epistatically altered strains responded in a graded fashion to melibiose. The properties of the epistatically altered strain resemble Kluyveromyces lactis, which separated from the Saccharomyces lineage 100 mya before whole-genome duplication (WGD). Based on the results reported here, we propose that epistatic interactions played a crucial role in the evolution of the fine regulation of S. cerevisiae GAL/MEL switch following WGD.

Frequency-dependent selection: a diversifying force in microbial populations

The benefits of “bet‐hedging” strategies have been assumed to be the main cause of phenotypic diversity in biological populations. However, in their recent work, Healey et al (2016) provide experimental support for negative frequency‐dependent selection (NFDS) as an alternative driving force of diversity. NFDS favors rare phenotypes over common ones, resulting in an evolutionarily stable mixture of phenotypes that is not necessarily optimal for population growth.

Interallelic interaction and gene regulation in budding yeast

InDrosophila, homologous chromosome pairing leads to "transvection," in which the enhancer of a gene can regulate the allelic transcription intrans.Interallelic interactions were also observed in vegetative diploid budding yeast, but their functional significance is unknown. Here, we show that aGAL1reporter can interact with its homologous allele and affect its expression. By ectopically inserting two allelic reporters, one driven by wild-typeGAL1promoter (WTGAL1pr) and the other by a mutant promoter with delayed response to galactose induction, we found that the two reporters physically associate, and the WTGAL1prtriggers synchronized firing of the defective promoter and accelerates its activation without affecting its steady-state expression level. This interaction and the transregulatory effect disappear when the same reporters are located at nonallelic sites. We further demonstrated that the activator Gal4 is essential for the interallelic interaction, and the transregulation requires fully activated WTGAL1prtranscription. The mechanism of this phenomenon was further discussed. Taken together, our data revealed the existence of interallelic gene regulation in yeast, which serves as a starting point for understanding long-distance gene regulation in this genetically tractable system.

Dynamics of epigenetic regulation at the single-cell level

Chromatin regulators play a major role in establishing and maintaining gene expression states. Yet how they control gene expression in single cells, quantitatively and over time, remains unclear. We used time-lapse microscopy to analyze the dynamic effects of four silencers associated with diverse modifications: DNA methylation, histone deacetylation, and histone methylation. For all regulators, silencing and reactivation occurred in all-or-none events, enabling the regulators to modulate the fraction of cells silenced rather than the amount of gene expression. These dynamics could be described by a three-state model involving stochastic transitions between active, reversibly silent, and irreversibly silent states. Through their individual transition rates, these regulators operate over different time scales and generate distinct types of epigenetic memory. Our results provide a framework for understanding and engineering mammalian chromatin regulation and epigenetic memory.

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition.

DOI:http://dx.doi.org/10.7554/eLife.07935.001

All organisms need to be able to react to the challenges thrown at them by their changing environment. Yeast, bacteria and other microbes have networks of genes that can give rise to many different traits and characteristics, which can also be referred to as phenotypes. A change in the environment can alter the activities' of the genes so that the microbes display a different phenotype. The point at which a small change in the environment can lead to a sudden switch in the phenotype is called a ‘critical transition’.

An individual microbe's history can influence the phenotype that it presents. However, it is not clear how microbes ‘remember’ their history, or how fluctuations in the environment might cause the microbe to lose the ability to store this memory and present a different phenotype instead. Here, Axelrod et al. studied phenotype memory in yeast cells grown in the laboratory. The experiments used cells that had been genetically modified to glow red in the presence of a molecule called anhydrotetracycline (or ATc) and to glow green in the absence of the molecule.

Axelrod et al. examined what effect altering the levels of this molecule would have on the phenotype produced by the cells. First, the cells were grown with no ATc present for several generations so that the cells glowed green. Next, Axelrod et al. added different amounts of ATc were added. For moderate levels of ATc the cells continued to glow green, illustrating that they ‘remembered’ their prior growth condition. However, cells exposed to higher levels of ATc lost this memory and changed color.

Next, Axelrod et al. carried out further experiments on cells exposed to ATc levels that were close to, or further away from the critical transition. At high levels of ATc (that is, close to the critical transition), many cells switched from green to red when exposed to high temperatures, salt and other changes in the environment. On the other hand, very few of the cells grown in low levels of ATc—and therefore further away from the critical transition—changed color in response to the same fluctuations in their environment.

These finding reveal that phenotype memory is less stable when yeast experience fluctuations in their environment close to a critical transition. Future work will seek to find out how salt or high temperatures can abolish phenotype memory.

DOI:http://dx.doi.org/10.7554/eLife.07935.002

Different Mechanisms Confer Gradual Control and Memory at Nutrient- and Stress-Regulated Genes in Yeast

Cells respond to environmental stimuli by fine-tuned regulation of gene expression. Here we investigated the dose-dependent modulation of gene expression at high temporal resolution in response to nutrient and stress signals in yeast. The GAL1 activity in cell populations is modulated in a well-defined range of galactose concentrations, correlating with a dynamic change of histone remodeling and RNA polymerase II (RNAPII) association. This behavior is the result of a heterogeneous induction delay caused by decreasing inducer concentrations across the population. Chromatin remodeling appears to be the basis for the dynamic GAL1 expression, because mutants with impaired histone dynamics show severely truncated dose-response profiles. In contrast, the GRE2 promoter operates like a rapid off/on switch in response to increasing osmotic stress, with almost constant expression rates and exclusively temporal regulation of histone remodeling and RNAPII occupancy. The Gal3 inducer and the Hog1 mitogen-activated protein (MAP) kinase seem to determine the different dose-response strategies at the two promoters. Accordingly, GAL1 becomes highly sensitive and dose independent if previously stimulated because of residual Gal3 levels, whereas GRE2 expression diminishes upon repeated stimulation due to acquired stress resistance. Our analysis reveals important differences in the way dynamic signals create dose-sensitive gene expression outputs.

Shifting sugars and shifting paradigms

No organism lives in a constant environment. Based on classical studies in molecular biology, many have viewed microbes as following strict rules for shifting their metabolic activities when prevailing conditions change. For example, students learn that the bacterium Escherichia coli makes proteins for digesting lactose only when lactose is available and glucose, a better sugar, is not. However, recent studies, including three PLOS Biology papers examining sugar utilization in the budding yeast Saccharomyces cerevisiae, show that considerable heterogeneity in response to complex environments exists within and between populations. These results join similar recent results in other organisms that suggest that microbial populations anticipate predictable environmental changes and hedge their bets against unpredictable ones. The classical view therefore represents but one special case in a range of evolutionary adaptations to environmental changes that all organisms face.

This Primer explores three recent PLOS Biology papers that increase our understanding of how microbes respond optimally to the changing availability of nutrients in their environment. Read the Research Articles.

The unforeseen challenge: from genotype-to-phenotype in cell populations

Biological cells present a paradox, in that they show simultaneous stability and flexibility, allowing them to adapt to new environments and to evolve over time. The emergence of stable cell states depends on genotype-to-phenotype associations, which essentially reflect the organization of gene regulatory modes. The view taken here is that cell-state organization is a dynamical process in which the molecular disorder manifests itself in a macroscopic order. The genome does not determine the ordered cell state; rather, it participates in this process by providing a set of constraints on the spectrum of regulatory modes, analogous to boundary conditions in physical dynamical systems. We have developed an experimental framework, in which cell populations are exposed to unforeseen challenges; novel perturbations they had not encountered before along their evolutionary history. This approach allows an unbiased view of cell dynamics, uncovering the potential of cells to evolve and develop adapted stable states. In the last decade, our experiments have revealed a coherent set of observations within this framework, painting a picture of the living cell that in many ways is not aligned with the conventional one. Of particular importance here, is our finding that adaptation of cell-state organization is essentially an efficient exploratory dynamical process rather than one founded on random mutations. Based on our framework, a set of concepts underlying cell-state organization-exploration evolving by global, non-specific, dynamics of gene activity-is presented here. These concepts have significant consequences for our understanding of the emergence and stabilization of a cell phenotype in diverse biological contexts. Their implications are discussed for three major areas of biological inquiry: evolution, cell differentiation and cancer. There is currently no unified theoretical framework encompassing the emergence of order, a stable state, in the living cell. Hopefully, the integrated picture described here will provide a modest contribution towards a physics theory of the cell.

Population diversification in a yeast metabolic program promotes anticipation of environmental shifts

Detailed study of the dynamic response of yeast to combinations of sugars reveals an anticipatory population diversification strategy that allows rapid adaptation to shifts in environmental carbon source availability.

To survive in resource-limited and dynamic environments, microbial populations implement a diverse repertoire of regulatory strategies. These strategies often rely on anticipating impending environmental shifts, enabling the population to be prepared for a future change in conditions. It has long been known that cells optimize nutritional value from mixtures of carbon sources, for example glucose and galactose, by sequential activation of regulatory programs that allow for metabolizing the preferred carbon source first before metabolizing the secondary carbon source. Using automated flow-cytometry, we mapped the dynamical behavior of populations simultaneously presented with a large panel of different glucose and galactose concentrations. We show that, counter to expectations, in populations presented with glucose and galactose simultaneously, the galactose regulatory pathway is activated in a fraction of the cell population hours before glucose is fully consumed. We demonstrate that the size of this fraction of cells is tuned by the concentration of the two sugars. This population diversification may constitute a tradeoff between the benefit of rapid galactose consumption once glucose is depleted and the cost of expressing the galactose pathway.

Delineating the strategies by which cells contend with combinatorial changing environments is crucial for understanding cellular regulatory organization. When presented with two carbon sources, microorganisms first consume the carbon substrate that supports the highest growth rate (e.g., glucose) and then switch to the secondary carbon source (e.g., galactose), a paradigm known as the Monod model. Sequential sugar utilization has been attributed to transcriptional repression of the secondary metabolic pathway, followed by activation of this pathway upon depletion of the preferred carbon source. In this work, we demonstrate that although Saccharomyces cerevisiae cells consume glucose before galactose, the galactose regulatory pathway is activated in a fraction of the cell population hours before glucose is fully consumed. This early activation reduces the time required for the population to transition between the two metabolic programs and provides a fitness advantage that might be crucial in competitive environments.

Natural variation in preparation for nutrient depletion reveals a cost-benefit tradeoff

Yeast can anticipate the depletion of a preferred nutrient by preemptively activating genes for alternative nutrients; the degree of this preparation varies across natural strains and is subject to a fitness tradeoff.

Maximizing growth and survival in the face of a complex, time-varying environment is a common problem for single-celled organisms in the wild. When offered two different sugars as carbon sources, microorganisms first consume the preferred sugar, then undergo a transient growth delay, the “diauxic lag,” while inducing genes to metabolize the less preferred sugar. This delay is commonly assumed to be an inevitable consequence of selection to maximize use of the preferred sugar. Contrary to this view, we found that many natural isolates of Saccharomyces cerevisiae display short or nonexistent diauxic lags when grown in mixtures of glucose (preferred) and galactose. These strains induce galactose utilization (GAL) genes hours before glucose exhaustion, thereby “preparing” for the transition from glucose to galactose metabolism. The extent of preparation varies across strains, and seems to be determined by the steady-state response of GAL genes to mixtures of glucose and galactose rather than by induction kinetics. Although early GAL gene induction gives strains a competitive advantage once glucose runs out, it comes at a cost while glucose is still present. Costs and benefits correlate with the degree of preparation: strains with higher expression of GAL genes prior to glucose exhaustion experience a larger upfront growth cost but also a shorter diauxic lag. Our results show that classical diauxic growth is only one extreme on a continuum of growth strategies constrained by a cost–benefit tradeoff. This type of continuum is likely to be common in nature, as similar tradeoffs can arise whenever cells evolve to use mixtures of nutrients.

When microorganisms encounter multiple sugars, they often consume a preferred sugar (such as glucose) before consuming alternative sugars (such as galactose). In experiments on laboratory strains of yeast, cells typically stop growing when the preferred sugar runs out, and start growing again only after taking time to turn on genes for alternative sugar utilization. This pause in growth, the “diauxic lag,” is a classic example of the ability of cells to make decisions based on environmental signals. Here we find, however, that when different natural yeast strains are grown in a mix of glucose and galactose, some strains do not exhibit a diauxic lag, or have a very short one. These “short lag” strains are able to turn on galactose utilization—or GAL—genes up to four hours before the glucose runs out, in effect preparing for the transition to galactose consumption. Although such preparation helps strains avoid the diauxic lag, it causes them to grow slower before glucose runs out, presumably because of the metabolic burden of expressing GAL genes. These observations suggest that microbes in nature may commonly face a tradeoff between growing efficiently on their preferred nutrient and being ready to consume alternative nutrients should the preferred nutrient run out.

Galactose metabolic genes in yeast respond to a ratio of galactose and glucose

Natural environments are filled with multiple, often competing, signals. In contrast, biological systems are often studied in "well-controlled" environments where only a single input is varied, potentially missing important interactions between signals. Catabolite repression of galactose by glucose is one of the best-studied eukaryotic signal integration systems. In this system, it is believed that galactose metabolic (GAL) genes are induced only when glucose levels drop below a threshold. In contrast, we show that GAL gene induction occurs at a constant external galactose:glucose ratio across a wide range of sugar concentrations. We systematically perturbed the components of the canonical galactose/glucose signaling pathways and found that these components do not account for ratio sensing. Instead we provide evidence that ratio sensing occurs upstream of the canonical signaling pathway and results from the competitive binding of the two sugars to hexose transporters. We show that a mutant that behaves as the classical model expects (i.e., cannot use galactose above a glucose threshold) has a fitness disadvantage compared with wild type. A number of common biological signaling motifs can give rise to ratio sensing, typically through negative interactions between opposing signaling molecules. We therefore suspect that this previously unidentified nutrient sensing paradigm may be common and overlooked in biology.

Mutually repressing repressor functions and multi-layered cellular heterogeneity regulate the bistable Salmonella fliC census

Bistable flagellar and virulence gene expression generates specialized Salmonella subpopulations with distinct functions. Repressing flagellar genes allows Salmonella to evade caspase-1 mediated host defenses and enhances systemic colonization. By definition, bistability arises when intermediate states of gene expression are rendered unstable by the underlying genetic circuitry. We demonstrate sustained bistable fliC expression in virulent Salmonella 14028 and document dynamic control of the distribution, or single-cell census, of flagellar gene expression by the mutually repressing repressors YdiV and FliZ. YdiV partitions cells into the fliC-OFF subpopulation, while FliZ partitions cells into the fliC-HIGH subpopulation at late time points during growth. Bistability of ΔfliZ populations and ydiV-independent FliZ control of flagellar gene expression provide evidence that the YdiV-FliZ mutually repressing repressor circuit is not required for bistability. Repression and activation by YdiV and FliZ (respectively) can shape the census of fliC expression independently, and bistability collapses into a predominantly intermediate population in the absence of both regulators. Metered expression of YdiV and FliZ reveals variable sensitivity to these regulators and defines conditions where expression of FliZ enhances fliC expression and where FliZ does not alter the fliC census. Thus, this evolved genetic circuitry coordinates multiple layers of regulatory heterogeneity into a binary response.

The yeast galactose network as a quantitative model for cellular memory

Recent experiments have revealed surprising behavior in the yeast galactose (GAL) pathway, one of the preeminent systems for studying gene regulation. Under certain circumstances, yeast cells display memory of their prior nutrient environments. We distinguish two kinds of cellular memory discovered by quantitative investigations of the GAL network and present a conceptual framework for interpreting new experiments and current ideas on GAL memory. Reinduction memory occurs when cells respond transcriptionally to one environment, shut down the response during several generations in a second environment, then respond faster and with less cell-to-cell variation when returned to the first environment. Persistent memory describes a long-term, arguably stable response in which cells adopt a bimodal or unimodal distribution of induction levels depending on their preceding environment. Deep knowledge of how the yeast GAL pathway responds to different sugar environments has enabled rapid progress in uncovering the mechanisms behind GAL memory, which include cytoplasmic inheritance of inducer proteins and positive feedback loops among regulatory genes. This network of genes, long used to study gene regulation, is now emerging as a model system for cellular memory.

Perturbation of the interaction between Gal4p and Gal80p of the Saccharomyces cerevisiae GAL switch results in altered responses to galactose and glucose

In S. cerevisiae, following the Whole Genome Duplication (WGD), GAL1-encoded galactokinase retained its signal transduction function but lost basal expression. On the other hand, its paralogue GAL3, lost kinase activity but retained its signalling function and basal expression, thus making it indispensable for the rapid induction of the S. cerevisiae GAL switch. However, a gal3Δ strain exhibits delayed growth kinetics due to the redundant signalling function of GAL1. The subfunctionalization between the paralogues GAL1 and GAL3 is due to expression divergence and is proposed to be due to the alteration in the Upstream Activating Sequences (UASG ). We demonstrate that the GAL switch becomes independent of GAL3 by altering the interaction between Gal4p and Gal80p without altering the configuration of UASG . In addition to the above, the altered switch of S. cerevisiae loses ultrasensitivity and stringent glucose repression. These changes caused an increase in fitness in the disaccharide melibiose at the expense of a decrease in fitness in galactose. The above altered features of the ScGAL switch are similar to the features of the GAL switch of K. lactis that diverged from S. cerevisiae before the WGD.

Modulating the frequency and bias of stochastic switching to control phenotypic variation

Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

Revisiting bistability in the lysis/lysogeny circuit of bacteriophage lambda

The lysis/lysogeny switch of bacteriophage lambda serves as a paradigm for binary cell fate decision, long-term maintenance of cellular state and stimulus-triggered switching between states. In the literature, the system is often referred to as “bistable.” However, it remains unclear whether this term provides an accurate description or is instead a misnomer. Here we address this question directly. We first quantify transcriptional regulation governing lysogenic maintenance using a single-cell fluorescence reporter. We then use the single-cell data to derive a stochastic theoretical model for the underlying regulatory network. We use the model to predict the steady states of the system and then validate these predictions experimentally. Specifically, a regime of bistability, and the resulting hysteretic behavior, are observed. Beyond the steady states, the theoretical model successfully predicts the kinetics of switching from lysogeny to lysis. Our results show how the physics-inspired concept of bistability can be reliably used to describe cellular phenotype, and how an experimentally-calibrated theoretical model can have accurate predictive power for cell-state switching.