Self-association of the Gal4 inhibitor protein Gal80 is impaired by Gal3: evidence for a new mechanism in the GAL gene switch

Transcription factor clusters enable target search but do not contribute to target gene activation

Many transcription factors (TFs) localize in nuclear clusters of locally increased concentrations, but how TF clustering is regulated and how it influences gene expression is not well understood. Here, we use quantitative microscopy in living cells to study the regulation and function of clustering of the budding yeast TF Gal4 in its endogenous context. Our results show that Gal4 forms clusters that overlap with the GAL loci. Cluster number, density and size are regulated in different growth conditions by the Gal4-inhibitor Gal80 and Gal4 concentration. Gal4 truncation mutants reveal that Gal4 clustering is facilitated by, but does not completely depend on DNA binding and intrinsically disordered regions. Moreover, we discover that clustering acts as a double-edged sword: self-interactions aid TF recruitment to target genes, but recruited Gal4 molecules that are not DNA-bound do not contribute to, and may even inhibit, transcription activation. We propose that cells need to balance the different effects of TF clustering on target search and transcription activation to facilitate proper gene expression.

De novo biosynthesis of bioactive isoflavonoids by engineered yeast cell factories

Isoflavonoids comprise a class of plant natural products with great nutraceutical, pharmaceutical and agricultural significance. Their low abundance in nature and structural complexity however hampers access to these phytochemicals through traditional crop-based manufacturing or chemical synthesis. Microbial bioproduction therefore represents an attractive alternative. Here, we engineer the metabolism of Saccharomyces cerevisiae to become a platform for efficient production of daidzein, a core chemical scaffold for isoflavonoid biosynthesis, and demonstrate its application towards producing bioactive glucosides from glucose, following the screening-reconstruction-application engineering framework. First, we rebuild daidzein biosynthesis in yeast and its production is then improved by 94-fold through screening biosynthetic enzymes, identifying rate-limiting steps, implementing dynamic control, engineering substrate trafficking and fine-tuning competing metabolic processes. The optimized strain produces up to 85.4 mg L^-1 of daidzein and introducing plant glycosyltransferases in this strain results in production of bioactive puerarin (72.8 mg L^-1) and daidzin (73.2 mg L^-1). Our work provides a promising step towards developing synthetic yeast cell factories for de novo biosynthesis of value-added isoflavonoids and the multi-phased framework may be extended to engineer pathways of complex natural products in other microbial hosts.

Decoupling transcription factor expression and activity enables dimmer switch gene regulation

Gene-regulatory networks achieve complex mappings of inputs to outputs through mechanisms that are poorly understood. We found that in the galactose-responsive pathway in Saccharomyces cerevisiae, the decision to activate the transcription of genes encoding pathway components is controlled independently from the expression level, resulting in behavior resembling that of a mechanical dimmer switch. This was not a direct result of chromatin regulation or combinatorial control at galactose-responsive promoters; rather, this behavior was achieved by hierarchical regulation of the expression and activity of a single transcription factor. Hierarchical regulation is ubiquitous, and thus dimmer switch regulation is likely a key feature of many biological systems. Dimmer switch gene regulation may allow cells to fine-tune their responses to multi-input environments on both physiological and evolutionary time scales.

Design and Characterization of Rapid Optogenetic Circuits for Dynamic Control in Yeast Metabolic Engineering

The use of optogenetics in metabolic engineering for light-controlled microbial chemical production raises the prospect of utilizing control and optimization techniques routinely deployed in traditional chemical manufacturing. However, such mechanisms require well-characterized, customizable tools that respond fast enough to be used as real-time inputs during fermentations. Here, we present OptoINVRT7, a new rapid optogenetic inverter circuit to control gene expression in Saccharomyces cerevisiae. The circuit induces gene expression in only 0.6 h after switching cells from light to darkness, which is at least 6 times faster than previous OptoINVRT optogenetic circuits used for chemical production. In addition, we introduce an engineered inducible GAL1 promoter (P_GAL1-S), which is stronger than any constitutive or inducible promoter commonly used in yeast. Combining OptoINVRT7 with P_GAL1-S achieves strong and light-tunable levels of gene expression with as much as 132.9 ± 22.6-fold induction in darkness. The high performance of this new optogenetic circuit in controlling metabolic enzymes boosts production of lactic acid and isobutanol by more than 50% and 15%, respectively. The strength and controllability of OptoINVRT7 and P_GAL1-S open the door to applying process control tools to engineered metabolisms to improve robustness and yields in microbial fermentations for chemical production.

Monitoring of switches in heterochromatin-induced silencing shows incomplete establishment and developmental instabilities

Position effect variegation (PEV) in Drosophila results from new juxtapositions of euchromatic and heterochromatic chromosomal regions, and manifests as striking bimodal patterns of gene expression. The semirandom patterns of PEV, reflecting clonal relationships between cells, have been interpreted as gene-expression states that are set in development and thereafter maintained without change through subsequent cell divisions. The rate of instability of PEV is almost entirely unexplored beyond the final expression of the modified gene; thus the origin of the expressivity and patterns of PEV remain unexplained. Many properties of PEV are not predicted from currently accepted biochemical and theoretical models. In this work we investigate the time at which expressivity of silencing is set, and find that it is determined before heterochromatin exists. We employ a mathematical simulation and a corroborating experimental approach to monitor switching (i.e., gains and losses of silencing) through development. In contrast to current views, we find that gene silencing is incompletely set early in embryogenesis, but nevertheless is repeatedly lost and gained in individual cells throughout development. Our data support an alternative to locus-specific "epigenetic" silencing at variegating gene promoters that more fully accounts for the final patterns of PEV.

Modeling transcriptional activation changes to Gal4 variants via structure-based computational mutagenesis

As a DNA binding transcriptional activator, Gal4 promotes the expression of genes responsible for galactose metabolism. The Gal4 protein from Saccharomyces cerevisiae (baker’s yeast) has become a model for studying eukaryotic transcriptional activation in general because its regulatory properties mirror those of several eukaryotic organisms, including mammals. Given the availability of a crystallographic structure for Gal4, here we implement an in silico mutagenesis technique that makes use of a four-body knowledge-based energy function, in order to empirically quantify the structural impacts associated with single residue substitutions on the Gal4 protein. These results were used to examine the structure-function relationship in Gal4 based on a recently published experimental mutagenesis study, whereby functional changes to a uniformly distributed set of 1,068 single residue Gal4 variants were obtained by measuring their transcriptional activation levels relative to wild-type. A significant correlation was observed between computed (scalar) structural effect data and measured activity values for this collection of single residue Gal4 variants. Additionally, attribute vectors quantifying position-specific environmental impacts were generated for each of the Gal4 variants via computational mutagenesis, and we implemented supervised classification and regression statistical machine learning algorithms to train predictive models of variant Gal4 activity based on these structural changes. All models performed well under cross-validation testing, with balanced accuracy reaching 91% among the classification models, and with the actual and predicted activity values displaying a correlation as high as r = 0.80 for the regression models. Reliable predictions of transcriptional activation levels for Gal4 variants that have yet to be studied can be instantly generated by submitting their respective structure-based feature vectors to the trained models for testing. Such a computational pre-screening of Gal4 variants may potentially reduce costs associated with running large-scale mutagenesis experiments.

Regulating the UAS/GAL4 system in adult Drosophila with Tet-off GAL80 transgenes

The UAS/GAL4 system is the most used method in Drosophila melanogaster for directing the expression of a gene of interest to a specific tissue. However, the ability to control the temporal activity of GAL4 with this system is very limited. This study constructed and characterized Tet-off GAL80 transgenes designed to allow temporal control of GAL4 activity in aging adult muscles. By placing GAL80 under the control of a Tet-off promoter, GAL4 activity is regulated by the presence or absence of tetracycline in the diet. Almost complete inhibition of the expression of UAS transgenes during the pre-adult stages of the life cycle is obtained by using four copies and two types of Tet-off GAL80 transgenes. Upon treatment of newly emerged adults with tetracycline, induction of GAL4 activity is observed but the level of induction is influenced by the concentration of the inducer, the age, the sex and the anatomical location of the expression. The inhibition of GAL4 activity and the maintenance of induced expression are altered in old animals. This study reveals that the repressive ability of GAL80 is affected by the age and sex of the animal which is a major limitation to regulate gene expression with GAL80 in aged Drosophila.

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.

Measurement of In Vivo Protein Binding Affinities in a Signaling Network with Mass Spectrometry

Protein interaction networks play a key role in signal processing. Despite the progress in identifying the interactions, the quantification of their strengths lags behind. Here we present an approach to quantify the in vivo binding of proteins to their binding partners in signaling-transcriptional networks, by the pairwise genetic isolation of each interaction and by varying the concentration of the interacting components over time. The absolute quantification of the protein concentrations was performed with targeted mass spectrometry. The strengths of the interactions, as defined by the apparent dissociation constants, ranged from subnanomolar to micromolar values in the yeast galactose signaling network. The weak homodimerization of the Gal4 activator amplifies the signal elicited by glucose. Furthermore, combining the binding constants in a feedback loop correctly predicted cellular memory, a characteristic network behavior. Thus, this genetic-proteomic binding assay can be used to faithfully quantify how strongly proteins interact with proteins, DNA and metabolites.

Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes

In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell's inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes' decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.

Analyzing negative feedback using a synthetic gene network expressed in the Drosophila melanogaster embryo

Background

A complex network of gene interactions controls gene regulation throughout development and the life of the organisms. Insights can be made into these processes by studying the functional interactions (or “motifs”) which make up these networks.

Results

We sought to understand the functionality of one of these network motifs, negative feedback, in a multi-cellular system. This was accomplished using a synthetic network expressed in the Drosophila melanogaster embryo using the yeast proteins Gal4 (a transcriptional activator) and Gal80 (an inhibitor of Gal4 activity). This network is able to produce an attenuation or shuttling phenotype depending on the Gal80/Gal4 ratio. This shuttling behavior was validated by expressing Gal3, which inhibits Gal80, to produce a localized increase in free Gal4 and therefore signaling. Mathematical modeling was used to demonstrate the capacity for negative feedback to produce these varying outputs.

Conclusions

The capacity of a network motif to exhibit different phenotypes due to minor changes to the network in multi-cellular systems was shown. This work demonstrates the importance of studying network motifs in multi-cellular systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0330-z) contains supplementary material, which is available to authorized users.

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.