Positive-feedback loops as a flexible biological module

An axon-T cell feedback loop enhances inflammation and axon degeneration

Inflammation is closely associated with many neurodegenerative disorders. Yet, whether inflammation causes, exacerbates, or responds to neurodegeneration has been challenging to define because the two processes are so closely linked. Here, we disentangle inflammation from the axon damage it causes by individually blocking cytotoxic T cell function and axon degeneration. We model inflammatory damage in mouse skin, a barrier tissue that, despite frequent inflammation, must maintain proper functioning of a dense array of axon terminals. We show that sympathetic axons modulate skin inflammation through release of norepinephrine, which suppresses activation of γδ T cells via the β2 adrenergic receptor. Strong inflammatory stimulation-modeled by application of the Toll-like receptor 7 agonist imiquimod-causes progressive γδ T cell-mediated, Sarm1-dependent loss of these immunosuppressive sympathetic axons. This removes a physiological brake on T cells, initiating a positive feedback loop of enhanced inflammation and further axon damage.

Reshaping the yeast galactose regulon via GPCR signaling cascade

Dynamically regulated systems are preferable to control metabolic pathways for an improved strain performance with better productivity. Here, we harnessed to the G protein-coupled receptor (GPCR) signaling pathway to reshape the yeast galactose regulon. The galactose-regulated (GAL) system was coupled with the GPCR signaling pathway for mating pheromone via a synthetic transcription factor. In this study, we refabricated the dynamic range, sensitivity, and response time of the GAL system to α factor by modulating the key components of the GPCR signaling cascade. A series of engineered yeasts with self-secretion of α factor were constructed to achieve quorum-sensing behaviors. In addition, we also repurposed the GAL system to make it responsive to heat shock. Taken together, our work showcases the great potential of synthetic biology in creating user-defined metabolic controls. We envision that the plasticity of our genetic design would be of significant interest for the future fabrication of novel gene expression systems.

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability

A ubiquitous feature of eukaryotic transcriptional regulation is cooperative self-assembly between transcription factors (TFs) and DNA cis-regulatory motifs. It is thought that this strategy enables specific regulatory connections to be formed in gene networks between otherwise weakly interacting, low-specificity molecular components. Here, using synthetic gene circuits constructed in yeast, we find that high regulatory specificity can emerge from cooperative, multivalent interactions among artificial zinc-finger-based TFs. We show that circuits "wired" using the strategy of cooperative TF assembly are effectively insulated from aberrant misregulation of the host cell genome. As we demonstrate in experiments and mathematical models, this mechanism is sufficient to rescue circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally inspired approach offers a simple, generalizable means for building high-fidelity, evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

Coupled mutual inhibition and mutual activation motifs as tools for cell-fate control

Multistability is central to biological systems as it plays a crucial role in adaptation, evolvability, and differentiation. The presence of positive feedback loops can enable multistability. The simplest of such feedback loops are a) a mutual inhibition loop (MI), b) a mutual activation loop (MA), and c) self-activation, all three of them known to give rise to bistability. However, the characteristic differences in the bistability exhibited by these motifs are relatively less understood. Here, we use dynamical simulations across a large ensemble of parameter sets and initial conditions to study the bistability characteristics of these motifs. Furthermore, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling amongst them. Our analysis revealed that MI-based architectures offer discrete and robust control over gene expression, multistability, and coordinated expression among multiple genes, as compared to MA-based architectures. We then devised a combination of MI and MA architectures to improve coordination and multistability. Such designs help improve our understanding of the control structures involved in robust cell-fate decisions and provide a way to achieve controlled decision-making in synthetic systems.

Coupling Cell Communication and Optogenetics: Implementation of a Light-Inducible Intercellular System in Yeast

Cell communication is a widespread mechanism in biology, allowing the transmission of information about environmental conditions. In order to understand how cell communication modulates relevant biological processes such as survival, division, differentiation, and apoptosis, different synthetic systems based on chemical induction have been successfully developed. In this work, we coupled cell communication and optogenetics in the budding yeast Saccharomyces cerevisiae. Our approach is based on two strains connected by the light-dependent production of α-factor pheromone in one cell type, which induces gene expression in the other type. After the individual characterization of the different variants of both strains, the optogenetic intercellular system was evaluated by combining the cells under contrasting illumination conditions. Using luciferase as a reporter gene, specific co-cultures at a 1:1 ratio displayed activation of the response upon constant blue light, which was not observed for the same cell mixtures grown in darkness. Then, the system was assessed at several dark/blue-light transitions, where the response level varies depending on the moment in which illumination was delivered. Furthermore, we observed that the amplitude of response can be tuned by modifying the initial ratio between both strains. Finally, the two-population system showed higher fold inductions in comparison with autonomous strains. Altogether, these results demonstrated that external light information is propagated through a diffusible signaling molecule to modulate gene expression in a synthetic system involving microbial cells, which will pave the road for studies allowing optogenetic control of population-level dynamics.

Natural promoters and promoter engineering strategies for metabolic regulation in Saccharomyces cerevisiae

Sharomyces cerevisiae is currently one of the most important foreign gene expression systems. S. cerevisiae is an excellent host for high-value metabolite cell factories due to its advantages of simplicity, safety, and nontoxicity. A promoter, as one of the basic elements of gene transcription, plays an important role in regulating gene expression and optimizing metabolic pathways. Promoters control the direction and intensity of transcription, and the application of promoters with different intensities and performances will largely determine the effect of gene expression and ultimately affect the experimental results. Due to its significant role, there have been many studies on promoters for decades. While some studies have explored and analyzed new promoters with different functions, more studies have focused on artificially modifying promoters to meet their own scientific needs. Thus, this article reviews current research on promoter engineering techniques and related natural promoters in S. cerevisiae. First, we introduce the basic structure of promoters and the classification of natural promoters. Then, the classification of various promoter strategies is reviewed. Finally, by grouping related articles together using various strategies, this review anticipates the future development direction of promoter engineering.

Time and age dependent regulation of neuroinflammation in a rat model of mesial temporal lobe epilepsy: Correlation with human data

Acute brain insults trigger diverse cellular and signaling responses and often precipitate epilepsy. The cellular, molecular and signaling events relevant to the emergence of the epileptic brain, however, remain poorly understood. These multiplex structural and functional alterations tend also to be opposing - some homeostatic and reparative while others disruptive; some associated with growth and proliferation while others, with cell death. To differentiate pathological from protective consequences, we compared seizure-induced changes in gene expression hours and days following kainic acid (KA)-induced status epilepticus (SE) in postnatal day (P) 30 and P15 rats by capitalizing on age-dependent differential physiologic responses to KA-SE; only mature rats, not immature rats, have been shown to develop spontaneous recurrent seizures after KA-SE. To correlate gene expression profiles in epileptic rats with epilepsy patients and demonstrate the clinical relevance of our findings, we performed gene analysis on four patient samples obtained from temporal lobectomy and compared to four control brains from NICHD Brain Bank. Pro-inflammatory gene expressions were at higher magnitudes and more sustained in P30. The inflammatory response was driven by the cytokines IL-1β, IL-6, and IL-18 in the acute period up to 72 h and by IL-18 in the subacute period through the 10-day time point. In addition, a panoply of other immune system genes was upregulated, including chemokines, glia markers and adhesion molecules. Genes associated with the mitogen activated protein kinase (MAPK) pathways comprised the largest functional group identified. Through the integration of multiple ontological databases, we analyzed genes belonging to 13 separate pathways linked to Classical MAPK ERK, as well as stress activated protein kinases (SAPKs) p38 and JNK. Interestingly, genes belonging to the Classical MAPK pathways were mostly transiently activated within the first 24 h, while genes in the SAPK pathways had divergent time courses of expression, showing sustained activation only in P30. Genes in P30 also had different regulatory functions than in P15: P30 animals showed marked increases in positive regulators of transcription, of signaling pathways as well as of MAPKKK cascades. Many of the same inflammation-related genes as in epileptic rats were significantly upregulated in human hippocampus, higher than in lateral temporal neocortex. They included glia-associated genes, cytokines, chemokines and adhesion molecules and MAPK pathway genes. Uniquely expressed in human hippocampus were adaptive immune system genes including immune receptors CDs and MHC II HLAs. In the brain, many immune molecules have additional roles in synaptic plasticity and the promotion of neurite outgrowth. We propose that persistent changes in inflammatory gene expression after SE leads not only to structural damage but also to aberrant synaptogenesis that may lead to epileptogenesis. Furthermore, the sustained pattern of inflammatory genes upregulated in the epileptic mature brain was distinct from that of the immature brain that show transient changes and are resistant to cell death and neuropathologic changes. Our data suggest that the epileptogenic process may be a result of failed cellular signaling mechanisms, where insults overwhelm the system beyond a homeostatic threshold.

A gene cluster with positive and negative elements controls bistability and hysteresis of the Crippled versus Normal growth in the fungus Podospora anserina

The Crippled Growth (CG) cell degeneration of the model ascomycete Podospora anserina (strain S) is controlled by a prion-like element and has been linked to the self-activation of the PaMpk1 MAP kinase cascade. Here, we report on the identification of the "86-11" locus containing twelve genes, ten of which are involved either in setting up the self-activation loop of CG or in inhibiting this loop, as demonstrated by targeted gene deletion. Interestingly, deletion of the whole locus results only in the elimination of CG and in no detectable additional physiological defect. Sequence comparison shows that these ten genes belong to four different families, each one endowed with a specific activity: two encode factors activating the loop, a third one encodes a factor crucial for inhibition of the loop and the fourth one participates in inhibiting the loop in a pathway parallel to the one controlled by the previously described PDC1 gene. Intriguingly, a very distant homologue of this "86-11" locus is present at the syntenic position in Podospora comata (strain T) that do not present Crippled Growth. Introgression of the P. comata strain T locus in P. anserina strain S and the P. anserina strain S in P. comata strain T showed that both drive CG in the P. anserina strain S genetic background, but not in the genetic background of strain P. comata T, indicating that genetic determinants outside the twelve-gene locus are responsible for lack of CG in P. comata strain T. Our data question the role of this twelve-gene locus in the physiology of P. anserina.

Roles of mTOR in thoracic aortopathy understood by complex intracellular signaling interactions

Thoracic aortopathy–aneurysm, dissection, and rupture–is increasingly responsible for significant morbidity and mortality. Advances in medical genetics and imaging have improved diagnosis and thus enabled earlier prophylactic surgical intervention in many cases. There remains a pressing need, however, to understand better the underlying molecular and cellular mechanisms with the hope of finding robust pharmacotherapies. Diverse studies in patients and mouse models of aortopathy have revealed critical changes in multiple smooth muscle cell signaling pathways that associate with disease, yet integrating information across studies and models has remained challenging. We present a new quantitative network model that includes many of the key smooth muscle cell signaling pathways and validate the model using a detailed data set that focuses on hyperactivation of the mechanistic target of rapamycin (mTOR) pathway and its inhibition using rapamycin. We show that the model can be parameterized to capture the primary experimental findings both qualitatively and quantitatively. We further show that simulating a population of cells by varying receptor reaction weights leads to distinct proteomic clusters within the population, and that these clusters emerge due to a bistable switch driven by positive feedback in the PI3K/AKT/mTOR signaling pathway.

Cell signaling drives changes across scales, from altered transcription at the single-cell level to tissue-level growth and remodeling. Studying complex interactions within cell signaling pathways can lead to a better understanding of the progression of disease. In particular, we are interested in how vascular cells can change their phenotype in a way that exacerbates aortopathy, namely, the development of aneurysms, dissections, and rupture. In this study we built a novel cell signaling network model of a vascular smooth muscle cell using archival data and used it to capture the effects of a genetic knock-out and subsequent pharmacologic rescue. We then used the model to simulate populations of smooth muscle cells and found that small perturbations to the strength of signaling can lead to distinct clusters of cells. With further analysis of the network substructures, we found that a positive feedback loop within the network was responsible for the distinct phenotypes we saw in our clusters of simulated cells. We believe that this work not only helps us to understand changes in smooth muscle cell phenotype but also opens the possibility to study other signaling perturbations associated with aortopathy.

Antagonism between killer yeast strains as an experimental model for biological nucleation dynamics

Antagonistic interactions are widespread in the microbial world and affect microbial evolutionary dynamics. Natural microbial communities often display spatial structure, which affects biological interactions, but much of what we know about microbial antagonism comes from laboratory studies of well-mixed communities. To overcome this limitation, we manipulated two killer strains of the budding yeast Saccharomyces cerevisiae, expressing different toxins, to independently control the rate at which they released their toxins. We developed mathematical models that predict the experimental dynamics of competition between toxin-producing strains in both well-mixed and spatially structured populations. In both situations, we experimentally verified theory’s prediction that a stronger antagonist can invade a weaker one only if the initial invading population exceeds a critical frequency or size. Finally, we found that toxin-resistant cells and weaker killers arose in spatially structured competitions between toxin-producing strains, suggesting that adaptive evolution can affect the outcome of microbial antagonism in spatial settings.

Synthetic biology applications of the yeast mating signal pathway

Cell fusion is a fundamental biological process that is involved in the development of most eukaryotic organisms. During the fusion process in Saccharomyces cerevisiae, cells respond to pheromones to trigger the MAPK (mitogen-activated protein kinase) cascade to initiate mating, followed by polarization, cell-wall remodeling, membrane fusion, and karyogamy. We highlight the applications of the yeast mating signal pathway in promoter engineering for tuning the expression of output genes, as well as in metabolic engineering for decoupling growth and metabolism, biosensors for sensitive detection and signal amplification, genetic circuits for programmable biological functionalities, and artificial consortia for cell-cell communication. Strategies such as exploiting rational engineering of modular circuits and optimizing the reproductive pathway to precisely maneuver physiological events have implications for scientific research and industrial development.

Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era

Simple Summary

Promoters are DNA sequences where the process of transcription starts. They can work constitutively or be controlled by environmental signals of different types. The quantity of proteins and RNA present in yeast genetic circuits highly depends on promoter strength. Hence, they have been deeply studied and modified over, at least, the last forty years, especially since the year 2000 when Synthetic Biology was born. Here, we present how promoter engineering changed over these four decades and discuss its possible future directions due to novel computational methods and technology.

Abstract

Synthetic gene circuits are made of DNA sequences, referred to as transcription units, that communicate by exchanging proteins or RNA molecules. Proteins are, mostly, transcription factors that bind promoter sequences to modulate the expression of other molecules. Promoters are, therefore, key components in genetic circuits. In this review, we focus our attention on the construction of artificial promoters for the yeast S. cerevisiae, a popular chassis for gene circuits. We describe the initial techniques and achievements in promoter engineering that predated the start of the Synthetic Biology epoch of about 20 years. We present the main applications of synthetic promoters built via different methods and discuss the latest innovations in the wet-lab engineering of novel promoter sequences.

Stability Analysis of a Signaling Circuit with Dual Species of GTPase Switches

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, function, vesicular transport between organelles, and signal transduction. These hydrolase enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we propose a model for a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. We develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through coupling and feedback loops. We provide explicit formulae for the steady states of the system and perform classical local stability analysis to systematically investigate the role of the different connections between the GTPase switches. Interestingly, a coupling of the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. Moreover, when a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the coupled system, two other locally stable states emerged. In both states the tGTPase is inactivated and active tGTPase concentrations are low. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states that can be parametrized by a range of inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady-state behaviors and shed light on how such coupling may impact signaling mechanisms in eukaryotic cells.

Basal leakage in oscillation: Coupled transcriptional and translational control using feed-forward loops

The circadian clock is a complex system that plays many important roles in most organisms. Previously, many mathematical models have been used to sharpen our understanding of the Arabidopsis clock, which brought to light the roles of each transcriptional and post-translational regulations. However, the presence of both regulations, instead of either transcription or post-translation, raised curiosity of whether the combination of these two regulations is important for the clock’s system. In this study, we built a series of simplified oscillators with different regulations to study the importance of post-translational regulation (specifically, 26S proteasome degradation) in the clock system. We found that a simple transcriptional-based oscillator can already generate sustained oscillation, but the oscillation can be easily destroyed in the presence of transcriptional leakage. Coupling post-translational control with transcriptional-based oscillator in a feed-forward loop will greatly improve the robustness of the oscillator in the presence of basal leakage. Using these general models, we were able to replicate the increased variability observed in the E3 ligase mutant for both plant and mammalian clocks. With this insight, we also predict a plausible regulator of several E3 ligase genes in the plant’s clock. Thus, our results provide insights into and the plausible importance in coupling transcription and post-translation controls in the clock system.

For circadian clocks, several current models had successfully captured the essential dynamic behavior of the clock system mainly with transcriptional regulation. Previous studies have shown that the 26S proteasome degradation controls are important in maintaining the stability of circadian rhythms. However, how the loss-of-function or over-expression mutant of this targeted degradations lead to unstable oscillation is still unclear. In this work, we investigate the importance of coupled transcriptional and post-translational feedback loop in the circadian oscillator. With general models our study indicate that the unstable behavior of degradation mutants could be caused by the increase in the basal level of the clock genes. We found that coupling a non-linear degradation control into this transcriptional based oscillator using feed-forward loop improves the robustness of the oscillator. Using this finding, we further predict some plausible regulators of Arabidopsis’s E3 ligase protein such as COP1 and SINAT5. Hence, our results provide insights on the importance of coupling transcription and post-translation controls in the clock system.

Understanding MAPK Signaling Pathways in Apoptosis

MAPK (mitogen-activated protein kinase) signaling pathways regulate a variety of biological processes through multiple cellular mechanisms. In most of these processes, such as apoptosis, MAPKs have a dual role since they can act as activators or inhibitors, depending on the cell type and the stimulus. In this review, we present the main pro- and anti-apoptotic mechanisms regulated by MAPKs, as well as the crosstalk observed between some MAPKs. We also describe the basic signaling properties of MAPKs (ultrasensitivity, hysteresis, digital response), and the presence of different positive feedback loops in apoptosis. We provide a simple guide to predict MAPKs’ behavior, based on the intensity and duration of the stimulus. Finally, we consider the role of MAPKs in osmostress-induced apoptosis by using Xenopus oocytes as a cell model. As we will see, apoptosis is plagued with multiple positive feedback loops. We hope this review will help to understand how MAPK signaling pathways engage irreversible cellular decisions.

Selecting for Altered Substrate Specificity Reveals the Evolutionary Flexibility of ATP-Binding Cassette Transporters

ATP-binding cassette (ABC) transporters are the largest family of ATP-hydrolyzing transporters, which import or export substrates across membranes, and have members in every sequenced genome. Structural studies and biochemistry highlight the contrast between the global structural similarity of homologous transporters and the enormous diversity of their substrates. How do ABC transporters evolve to carry such diverse molecules and what variations in their amino acid sequence alter their substrate selectivity? We mutagenized the transmembrane domains of a conserved fungal ABC transporter that exports a mating pheromone and selected for mutants that export a non-cognate pheromone. Mutations that alter export selectivity cover a region that is larger than expected for a localized substrate-binding site. Individual selected clones have multiple mutations, which have broadly additive contributions to specific transport activity. Our results suggest that multiple positions influence substrate selectivity, leading to alternative evolutionary paths toward selectivity for particular substrates and explaining the number and diversity of ABC transporters.

Srikant et al. find that mutations at many different positions in an ABC transporter of fungal mating pheromone have roughly additive effects on substrate recognition. This helps explain the evolvability of ABC transporters to transport a remarkable variety of substrates and their presence as the largest protein family across all domains of life.

Engineering a Model Cell for Rational Tuning of GPCR Signaling

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems.

Phenotypic instability in fungi

Fungi are prone to phenotypic instability, that is, the vegetative phase of these organisms, be they yeasts or molds, undergoes frequent switching between two or more behaviors, often with different morphologies, but also sometime having different physiologies without any obvious morphological outcome. In the context of industrial utilization of fungi, this can have a negative impact on the maintenance of strains and/or on their productivity. Instabilities have been shown to result from various mechanisms, either genetic or epigenetic. This chapter will review different types of instabilities and discuss some lesser-known ones, mostly in filamentous fungi, while it will direct readers to additional literature in the case of well-known phenomena such as the amyloid prions or fungal senescence. It will present in depth the "white/opaque" switch of Candida albicans and the "crippled growth" degeneration of the model fungus Podospora anserina. These are two of the most thoroughly studied epigenetic phenotypic switches. I will also discuss the "sectors" presented by many filamentous ascomycetes, for which a prion-based model exists but is not demonstrated. Finally, I will also describe intriguing examples of phenotypic instability for which an explanation has yet to be provided.

Pervasive Positive and Negative Feedback Regulation of Insulin-Like Signaling in Caenorhabditis elegans

The Caenorhabditis elegans insulin-like signaling network supports homeostasis and developmental plasticity. The genome encodes 40 insulin-like peptides and one known receptor. Feedback regulation has been reported, but the extent of feedback and its effect on signaling dynamics in response to changes in nutrient availability has not been determined. We measured messenger RNA expression for each insulin-like peptide, the receptor daf-2, components of the PI3K pathway, and its transcriptional effectors daf-16/FoxO and skn-1/Nrf at high temporal resolution during transition from a starved, quiescent state to a fed, growing state in wild type and mutants affecting daf-2/InsR and daf-16/FoxO. We also analyzed the effect of temperature on insulin-like gene expression. We found that most PI3K pathway components and insulin-like peptides are affected by signaling activity, revealing pervasive positive and negative feedback regulation at intra- and intercellular levels. Reporter gene analysis demonstrated that the daf-2/InsR agonist daf-28 positively regulates its own transcription and that the putative agonist ins-6 cross-regulates DAF-28 protein expression through feedback. Our results show that positive and negative feedback regulation of insulin-like signaling is widespread, giving rise to an organismal FoxO-to-FoxO signaling network that supports homeostasis during fluctuations in nutrient availability.

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.

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

Endogenous Molecular-Cellular Network Cancer Theory: A Systems Biology Approach

In light of ever apparent limitation of the current dominant cancer mutation theory, a quantitative hypothesis for cancer genesis and progression, endogenous molecular-cellular network hypothesis has been proposed from the systems biology perspective, now for more than 10 years. It was intended to include both the genetic and epigenetic causes to understand cancer. Its development enters the stage of meaningful interaction with experimental and clinical data and the limitation of the traditional cancer mutation theory becomes more evident. Under this endogenous network hypothesis, we established a core working network of hepatocellular carcinoma (HCC) according to the hypothesis and quantified the working network by a nonlinear dynamical system. We showed that the two stable states of the working network reproduce the main known features of normal liver and HCC at both the modular and molecular levels. Using endogenous network hypothesis and validated working network, we explored genetic mutation pattern in cancer and potential strategies to cure or relieve HCC from a totally new perspective. Patterns of genetic mutations have been traditionally analyzed by posteriori statistical association approaches in light of traditional cancer mutation theory. One may wonder the possibility of a priori determination of any mutation regularity. Here, we found that based on the endogenous network theory the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. Normal hepatocyte and cancerous hepatocyte stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable predictions on an accumulated and preferred mutation spectrum in normal tissue. The validation of predicted cancer state mutation patterns demonstrates the usefulness and potential of a causal dynamical framework to understand and predict genetic mutations in cancer. We also obtained the following implication related to HCC therapy, (1) specific positive feedback loops are responsible for the maintenance of normal liver and HCC; (2) inhibiting proliferation and inflammation-related positive feedback loops, and simultaneously inducing liver-specific positive feedback loop is predicated as the potential strategy to cure or relieve HCC; (3) the genesis and regression of HCC is asymmetric. In light of the characteristic property of the nonlinear dynamical system, we demonstrate that positive feedback loops must be existed as a simple and general molecular basis for the maintenance of phenotypes such as normal liver and HCC, and regulating the positive feedback loops directly or indirectly provides potential strategies to cure or relieve HCC.

Impact of Zygosity on Bimodal Phenotype Distributions

Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. However, the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedback. Here, we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells among donors infected with human cytomegalovirus with one or two copies of the allele. Crucially, the distribution of gene expression in many of the donors is bimodal, which indicates the presence of a positive feedback loop somewhere in the regulatory environment of the gene. Three separate gene-circuit models differing in the location of the positive feedback loop with respect to the gene can all reproduce the homozygous data. However, when the resulting fitted models are applied to the hemizygous donors, one model (the one with the positive feedback located at the level of gene transcription) is superior in describing the experimentally observed gene-expression profile. In that way, our work shows that zygosity can help us relate the structure and function of gene regulatory networks.

Measurement of bistability in a multidimensional parameter space

Bistability plays an important role to generate two stable states for alternative cell fates, or to promote cellular diversity and cell cycle oscillations. Positive feedback loops are necessary for the existence of bistability and ultrasensitive reactions in the loops broaden the parameter range of bistability. The broader parameter range a system's bistability covers, the more robust the two states are. It is challenging to determine the bistable range of a parameter because noise and transient processes induce transitions between the two states. We found that a threshold of transition rates coincides with the bistability boundaries determined by the open-loop approach. With this threshold, we estimated the boundaries for various synthetic single-gene positive feedback loops in yeast in a two dimensional parameter space: the inducer concentration and promoter dynamic range. While the bistable range of inducer concentration was influenced by many factors, the promoter dynamic range was more informative. The narrowest promoter dynamic range at which bistability can emerge revealed whether the full potential of an ultrasensitive reaction, such as dimerization, is exploited in the feedback loop. The convenient control of basal expression to adjust the promoter dynamic range permits a practical and reliable comparison of robustness of related positive feedback loops.

Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast

Biosensors are valuable and versatile tools in synthetic biology that are used to modulate gene expression in response to a wide range of stimuli. Ligand responsive transcription factors are a class of biosensor that can be used to couple intracellular metabolite concentration with gene expression to enable dynamic regulation and high-throughput metabolite producer screening. We have established the Saccharomyces cerevisiae WAR1 transcriptional regulator and PDR12 promoter as an organic acid biosensor that can be used to detect varying levels of para-hydroxybenzoic acid (PHBA) production from the shikimate pathway and output green fluorescent protein (GFP) expression in response. The dynamic range of GFP expression in response to PHBA was dramatically increased by engineering positive-feedback expression of the WAR1 transcriptional regulator from its target PDR12 promoter. In addition, the noise in GFP expression at the population-level was controlled by normalising GFP fluorescence to constitutively expressed mCherry fluorescence within each cell. These biosensor modifications increased the high-throughput screening efficiency of yeast cells engineered to produce PHBA by 5,000-fold, enabling accurate fluorescence activated cell sorting isolation of producer cells that were mixed at a ratio of 1 in 10,000 with non-producers. Positive-feedback, ratiometric transcriptional regulator expression is likely applicable to many other transcription-factor/promoter pairs used in synthetic biology and metabolic engineering for both dynamic regulation and high-throughput screening applications.

The Saccharomyces cerevisiae pheromone-response is a metabolically active stationary phase for bio-production

The growth characteristics and underlying metabolism of microbial production hosts are critical to the productivity of metabolically engineered pathways. Production in parallel with growth often leads to biomass/bio-product competition for carbon. The growth arrest phenotype associated with the Saccharomyces cerevisiae pheromone-response is potentially an attractive production phase because it offers the possibility of decoupling production from population growth. However, little is known about the metabolic phenotype associated with the pheromone-response, which has not been tested for suitability as a production phase. Analysis of extracellular metabolite fluxes, available transcriptomic data, and heterologous compound production (para-hydroxybenzoic acid) demonstrate that a highly active and distinct metabolism underlies the pheromone-response. These results indicate that the pheromone-response is a suitable production phase, and that it may be useful for informing synthetic biology design principles for engineering productive stationary phase phenotypes.

Engineering dynamical control of cell fate switching using synthetic phospho-regulons

Many cells can sense and respond to time-varying stimuli, selectively triggering changes in cell fate only in response to inputs of a particular duration or frequency. A common motif in dynamically controlled cells is a dual-timescale regulatory network: although long-term fate decisions are ultimately controlled by a slow-timescale switch (e.g., gene expression), input signals are first processed by a fast-timescale signaling layer, which is hypothesized to filter what dynamic information is efficiently relayed downstream. Directly testing the design principles of how dual-timescale circuits control dynamic sensing, however, has been challenging, because most synthetic biology methods have focused solely on rewiring transcriptional circuits, which operate at a single slow timescale. Here, we report the development of a modular approach for flexibly engineering phosphorylation circuits using designed phospho-regulon motifs. By then linking rapid phospho-feedback with slower downstream transcription-based bistable switches, we can construct synthetic dual-timescale circuits in yeast in which the triggering dynamics and the end-state properties of the ON state can be selectively tuned. These phospho-regulon tools thus open up the possibility to engineer cells with customized dynamical control.

Tools and applications in synthetic biology

Advances in synthetic biology have enabled the engineering of cells with genetic circuits in order to program cells with new biological behavior, dynamic gene expression, and logic control. This cellular engineering progression offers an array of living sensors that can discriminate between cell states, produce a regulated dose of therapeutic biomolecules, and function in various delivery platforms. In this review, we highlight and summarize the tools and applications in bacterial and mammalian synthetic biology. The examples detailed in this review provide insight to further understand genetic circuits, how they are used to program cells with novel functions, and current methods to reliably interface this technology in vivo; thus paving the way for the design of promising novel therapeutic applications.

Molecular recordings by directed CRISPR spacer acquisition

The ability to write a stable record of identified molecular events into a specific genomic locus would enable the examination of long cellular histories and have many applications, ranging from developmental biology to synthetic devices. We show that the type I-E CRISPR (clustered regularly interspaced short palindromic repeats)-Cas system of Escherichia coli can mediate acquisition of defined pieces of synthetic DNA. We harnessed this feature to generate records of specific DNA sequences into a population of bacterial genomes. We then applied directed evolution so as to alter the recognition of a protospacer adjacent motif by the Cas1-Cas2 complex, which enabled recording in two modes simultaneously. We used this system to reveal aspects of spacer acquisition, fundamental to the CRISPR-Cas adaptation process. These results lay the foundations of a multimodal intracellular recording device.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity.

Regulation of cell-to-cell variability in divergent gene expression

Cell-to-cell variability (noise) is an important feature of gene expression that impacts cell fitness and development. The regulatory mechanism of this variability is not fully understood. Here we investigate the effect on gene expression noise in divergent gene pairs (DGPs). We generated reporters driven by divergent promoters, rearranged their gene order, and probed their expressions using time-lapse fluorescence microscopy and single-molecule fluorescence in situ hybridization (smFISH). We show that two genes in a co-regulated DGP have higher expression covariance compared with the separate, tandem and convergent configurations, and this higher covariance is caused by more synchronized firing of the divergent transcriptions. For differentially regulated DGPs, the regulatory signal of one gene can stochastically ‘leak' to the other, causing increased gene expression noise. We propose that the DGPs' function in limiting or promoting gene expression noise may enhance or compromise cell fitness, providing an explanation for the conservation pattern of DGPs.

Gene expression noise affects cell fitness and development. Here, Yan et al. show that co-regulated divergent gene pairs (DGPs) suppress uncorrelated gene expression noise due to more synchronized transcription firing, and differentially regulated DGPs enhance gene expression noise due to transcription leakage.

A fungicide-responsive kinase as a tool for synthetic cell fate regulation

Engineered biological systems that precisely execute defined tasks have major potential for medicine and biotechnology. For instance, gene- or cell-based therapies targeting pathogenic cells may replace time- and resource-intensive drug development. Engineering signal transduction systems is a promising, yet presently underexplored approach. Here, we exploit a fungicide-responsive heterologous histidine kinase for pathway engineering and synthetic cell fate regulation in the budding yeast Saccharomyces cerevisiae. Rewiring the osmoregulatory Hog1 MAPK signalling system generates yeast cells programmed to execute three different tasks. First, a synthetic negative feedback loop implemented by employing the fungicide-responsive kinase and a fungicide-resistant derivative reshapes the Hog1 activation profile, demonstrating how signalling dynamics can be engineered. Second, combinatorial integration of different genetic parts including the histidine kinases, a pathway activator and chemically regulated promoters enables control of yeast growth and/or gene expression in a two-input Boolean logic manner. Finally, we implemented a genetic ‘suicide attack’ system, in which engineered cells eliminate target cells and themselves in a specific and controllable manner. Taken together, fungicide-responsive kinases can be applied in different constellations to engineer signalling behaviour. Sensitizing engineered cells to existing chemicals may be generally useful for future medical and biotechnological applications.

Transcriptional leakage versus noise: a simple mechanism of conversion between binary and graded response in autoregulated genes

We study the response of an autoregulated gene to a range of concentrations of signal molecules. We show that transcriptional leakage and noise due to translational bursting have the opposite effects. In a positively autoregulated gene, increasing the noise converts the response from graded to binary, while increasing the leakage converts the response from binary to graded. Our findings support the hypothesis that, being a common phenomenon, leaky expression may be a relatively easy way for evolutionary tuning of the type of gene response without changing the type of regulation from positive to negative.

Permanent genetic memory with >1-byte capacity

Genetic memory enables the recording of information in the DNA of living cells. Memory can record a transient environmental signal or cell state that is then recalled at a later time. Permanent memory is implemented using irreversible recombinases that invert the orientation of a unit of DNA, corresponding to the [0,1] state of a bit. To expand the memory capacity, we have applied bioinformatics to identify 34 phage integrases (and their cognate attB and attP recognition sites), from which we build 11 memory switches that are perfectly orthogonal to each other and the FimE and HbiF bacterial invertases. Using these switches, a memory array is constructed in Escherichia coli that can record 1.375 bytes of information. It is demonstrated that the recombinases can be layered and used to permanently record the transient state of a transcriptional logic gate.

Mean field analysis of a spatial stochastic model of a gene regulatory network

A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products. In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.

Quantitative implementation of the endogenous molecular-cellular network hypothesis in hepatocellular carcinoma

A quantitative hypothesis for cancer genesis and progression-the endogenous molecular-cellular network hypothesis, intended to include both genetic and epigenetic causes of cancer-has been proposed recently. Using this hypothesis, here we address the molecular basis for maintaining normal liver and hepatocellular carcinoma (HCC), and the potential strategy to cure or relieve HCC. First, we elaborate the basic assumptions of the hypothesis and establish a core working network of HCC according to the hypothesis. Second, we quantify the working network by a nonlinear dynamical system. We show that the working network reproduces the main known features of normal liver and HCC at both the modular and molecular levels. Lastly, the validated working network reveals that (i) specific positive feedback loops are responsible for the maintenance of normal liver and HCC; (ii) inhibiting proliferation and inflammation-related positive feedback loops and simultaneously inducing a liver-specific positive feedback loop is predicated as a potential strategy to cure or relieve HCC; and (iii) the genesis and regression of HCC are asymmetric. In light of the characteristic properties of the nonlinear dynamical system, we demonstrate that positive feedback loops must exist as a simple and general molecular basis for the maintenance of heritable phenotypes, such as normal liver and HCC, and regulating the positive feedback loops directly or indirectly provides potential strategies to cure or relieve HCC.

Rewiring yeast osmostress signalling through the MAPK network reveals essential and non-essential roles of Hog1 in osmoadaptation

Mitogen-activated protein kinases (MAPKs) have a number of targets which they regulate at transcriptional and post-translational levels to mediate specific responses. The yeast Hog1 MAPK is essential for cell survival under hyperosmotic conditions and it plays multiple roles in gene expression, metabolic regulation, signal fidelity and cell cycle regulation. Here we describe essential and non-essential roles of Hog1 using engineered yeast cells in which osmoadaptation was reconstituted in a Hog1-independent manner. We rewired Hog1-dependent osmotic stress-induced gene expression under the control of Fus3/Kss1 MAPKs, which are activated upon osmostress via crosstalk in hog1Δ cells. This approach revealed that osmotic up-regulation of only two Hog1-dependent glycerol biosynthesis genes, GPD1 and GPP2, is sufficient for successful osmoadaptation. Moreover, some of the previously described Hog1-dependent mechanisms appeared to be dispensable for osmoadaptation in the engineered cells. These results suggest that the number of essential MAPK functions may be significantly smaller than anticipated and that knockout approaches may lead to over-interpretation of phenotypic data.

Secreting and sensing the same molecule allows cells to achieve versatile social behaviors

Cells that secrete and sense the same signaling molecule are ubiquitous. To uncover the functional capabilities of the core "secrete-and-sense" circuit motif shared by these cells, we engineered yeast to secrete and sense the mating pheromone. Perturbing each circuit element revealed parameters that control the degree to which the cell communicated with itself versus with its neighbors. This tunable interplay of self-communication and neighbor communication enables cells to span a diverse repertoire of cellular behaviors. These include a cell being asocial by responding only to itself and social through quorum sensing, and an isogenic population of cells splitting into social and asocial subpopulations. A mathematical model explained these behaviors. The versatility of the secrete-and-sense circuit motif may explain its recurrence across species.

Genetically engineered transvestites reveal novel mating genes in budding yeast

Haploid budding yeast has two mating types, defined by the alleles of the MAT locus, MATa and MATα. Two haploid cells of opposite mating types mate by signaling to each other using reciprocal pheromones and receptors, polarizing and growing toward each other, and eventually fusing to form a single diploid cell. The pheromones and receptors are necessary and sufficient to define a mating type, but other mating-type-specific proteins make mating more efficient. We examined the role of these proteins by genetically engineering "transvestite" cells that swap the pheromone, pheromone receptor, and pheromone processing factors of one mating type for another. These cells mate with each other, but their mating is inefficient. By characterizing their mating defects and examining their transcriptomes, we found Afb1 (a-factor barrier), a novel MATα-specific protein that interferes with a-factor, the pheromone secreted by MATa cells. Strong pheromone secretion is essential for efficient mating, and the weak mating of transvestites can be improved by boosting their pheromone production. Synthetic biology can characterize the factors that control efficiency in biological processes. In yeast, selection for increased mating efficiency is likely to have continually boosted pheromone levels and the ability to discriminate between partners who make more and less pheromone. This discrimination comes at a cost: weak mating in situations where all potential partners make less pheromone.

Dynamically reshaping signaling networks to program cell fate via genetic controllers

Engineering of cell fate through synthetic gene circuits requires methods to precisely implement control around native decision-making pathways and offers the potential to direct cell processes. We demonstrate a class of genetic control systems, molecular network diverters, that interface with a native signaling pathway to route cells to divergent fates in response to environmental signals without modification of native genetic material. A method for identifying control points within natural networks is described that enables the construction of synthetic control systems that activate or attenuate native pathways to direct cell fate. We integrate opposing genetic programs by developing network architectures for reduced antagonism and demonstrate rational tuning of performance. Extension of these control strategies to mammalian systems should facilitate the engineering of complex cellular signaling systems.

Stochastic expression and epigenetic memory at the yeast HO promoter

Eukaryotic gene regulation usually involves sequence-specific transcription factors and sequence-nonspecific cofactors. A large effort has been made to understand how these factors affect the average gene expression level among a population. However, little is known about how they regulate gene expression in individual cells. In this work, we address this question by mutating multiple factors in the regulatory pathway of the yeast HO promoter (HOpr) and probing the corresponding promoter activity in single cells using time-lapse fluorescence microscopy. We show that the HOpr fires in an "on/off" fashion in WT cells as well as in different genetic backgrounds. Many chromatin-related cofactors that affect the average level of HO expression do not actually affect the firing amplitude of the HOpr; instead, they affect the firing frequency among individual cell cycles. With certain mutations, the bimodal expression exhibits short-term epigenetic memory across the mitotic boundary. This memory is propagated in "cis" and reflects enhanced activator binding after a previous "on" cycle. We present evidence that the memory results from slow turnover of the histone acetylation marks.

T-bet and GATA3 orchestrate Th1 and Th2 differentiation through lineage-specific targeting of distal regulatory elements

T-bet and GATA3 regulate the CD4+ T cell Th1/Th2 cell fate decision but little is known about the interplay between these factors outside of the murine Ifng and Il4/Il5/Il13 loci. Here we show that T-bet and GATA3 bind to multiple distal sites at immune regulatory genes in human effector T cells. These sites display markers of functional elements, act as enhancers in reporter assays and are associated with a requirement for T-bet and GATA3. Furthermore, we demonstrate that both factors bind distal sites at Tbx21 and that T-bet directly activates its own expression. We also show that in Th1 cells, GATA3 is distributed away from Th2 genes, instead occupying T-bet binding sites at Th1 genes, and that T-bet is sufficient to induce GATA3 binding at these sites. We propose these aspects of T-bet and GATA3 function are important for Th1/Th2 differentiation and for understanding transcription factor interactions in other T cell lineage decisions.

T-bet and GATA3 regulate differentiation of T cells into Th1 or Th2 cell fates, but little is known about their functional interaction outside of the IFNγ and Il4/Il5/Il13 loci. Kanhere et al. map these factors across the genome in human T cells, revealing unappreciated breadth of function and interplay between them.

Improved use of a public good selects for the evolution of undifferentiated multicellularity

We do not know how or why multicellularity evolved. We used the budding yeast, Saccharomyces cerevisiae, to ask whether nutrients that must be digested extracellularly select for the evolution of undifferentiated multicellularity. Because yeast use invertase to hydrolyze sucrose extracellularly and import the resulting monosaccharides, single cells cannot grow at low cell and sucrose concentrations. Three engineered strategies overcame this problem: forming multicellular clumps, importing sucrose before hydrolysis, and increasing invertase expression. We evolved populations in low sucrose to ask which strategy they would adopt. Of 12 successful clones, 11 formed multicellular clumps through incomplete cell separation, 10 increased invertase expression, none imported sucrose, and 11 increased hexose transporter expression, a strategy we had not engineered. Identifying causal mutations revealed genes and pathways, which frequently contributed to the evolved phenotype. Our study shows that combining rational design with experimental evolution can help evaluate hypotheses about evolutionary strategies.

DOI:http://dx.doi.org/10.7554/eLife.00367.001

Life first appeared on Earth more than 3 billion years ago in the form of single-celled microorganisms. The diverse array of complex life forms that we see today evolved from these humble beginnings, but it is not clear what triggered the evolution of multicellular organisms from single cells.

One of the simplest multicellular eukaryotes is the yeast, Saccharomyces cerevisiae—a fungus that has been used for centuries in baking and brewing and, more recently, as a model organism in molecular biology. Yeast cells feed on sugar (sucrose), but are unable to absorb it directly from their surroundings. Instead they secrete an enzyme called invertase, which breaks down the sucrose into simpler components that cells can take up with the help of sugar transporters.

However, single yeast cells living in a low-sucrose environment face a problem: most of the simple sugars that they produce diffuse out of reach. To overcome this difficulty, the cells could form multicellular clumps, which would enable each cell to consume the sugars that drift away from its neighbours. Alternatively, the cells could increase their production of invertase, or they could begin to take up sucrose directly.

Using genetic engineering, Koschwanez et al. produced three strains of yeast, each with one of these traits, and confirmed that all three strategies do indeed help fungi to grow in low sucrose. But could any of these traits evolve spontaneously? To test this possibility, Koschwanez et al. introduced wild-type yeast cells into a low-sucrose environment and studied any populations of cells that managed to survive. Of 12 that did, 11 had acquired the ability to form multicellular clumps, while 10 had increased their expression of invertase. Surprisingly, none had evolved the ability to import sucrose. However, 11 of the populations that survived also displayed an adaptation that the researchers had not predicted beforehand: they all expressed higher levels of the sugar transporters that take up sucrose breakdown products.

The work of Koschwanez et al. suggests that the benefits of being able to share invertase and, therefore, simple sugars, may have driven the evolution of multicellularity in ancient organisms. Moreover, their use of rational design (engineered mutations) combined with experimental evolution (allowing colonies to grow under selection pressure and studying the strategies that they adopt) offers a new approach to studying evolution in the lab.

DOI:http://dx.doi.org/10.7554/eLife.00367.002

Engineered quorum sensing using pheromone-mediated cell-to-cell communication in Saccharomyces cerevisiae

Population-density-dependent control of gene expression, or quorum sensing, is widespread in nature and is used to coordinate complex population-wide phenotypes through space and time. We have engineered quorum sensing in S. cerevisiae by rewiring the native pheromone communication system that is normally used by haploid cells to detect potential mating partners. In our system, populations consisting of only mating type "a" cells produce and respond to extracellular α-type pheromone by arresting growth and expressing GFP in a population-density-dependent manner. Positive feedback quorum sensing dynamics were tuned by varying α-pheromone production levels using different versions of the pheromone-responsive FUS1 promoter as well as different versions of pheromone genes (mfα1 or mfα2). In a second system, pheromone communication was rendered conditional upon the presence of aromatic amino acids in the growth medium by controlling α-pheromone expression with the aromatic amino acid responsive ARO9 promoter. In these circuits, pheromone communication and response could be fine-tuned according to aromatic amino acid type and concentration. The genetic control programs developed here are responsive to dynamic spatiotemporal and chemical cellular environments, resulting in up-regulation of gene expression. These programs could be used to control biochemical pathways for the production of fuels and chemicals that are toxic or place a heavy metabolic burden on cell growth.

Synthetic biology: lessons from engineering yeast MAPK signalling pathways

All living cells respond to external stimuli and execute specific physiological responses through signal transduction pathways. Understanding the mechanisms controlling signalling pathways is important for diagnosing and treating diseases and for reprogramming cells with desired functions. Although many of the signalling components in the budding yeast Saccharomyces cerevisiae have been identified by genetic studies, many features concerning the dynamic control of pathway activity, cross-talk, cell-to-cell variability or robustness against perturbation are still incompletely understood. Comparing the behaviour of engineered and natural signalling pathways offers insight complementary to that achievable with standard genetic and molecular studies. Here, we review studies that aim at a deeper understanding of signalling design principles and generation of novel signalling properties by engineering the yeast mitogen-activated protein kinase (MAPK) pathways. The underlying approaches can be applied to other organisms including mammalian cells and offer opportunities for building synthetic pathways and functionalities useful in medicine and biotechnology.