Robust Synthetic Circuits for Two-Dimensional Control of Gene Expression in Yeast

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Enhanced eMAGE applied to identify genetic factors of nuclear hormone receptor dysfunction via combinatorial gene editing

Technologies that generate precise combinatorial genome modifications are well suited to dissect the polygenic basis of complex phenotypes and engineer synthetic genomes. Genome modifications with engineered nucleases can lead to undesirable repair outcomes through imprecise homology-directed repair, requiring non-cleavable gene editing strategies. Eukaryotic multiplex genome engineering (eMAGE) generates precise combinatorial genome modifications in Saccharomyces cerevisiae without generating DNA breaks or using engineered nucleases. Here, we systematically optimize eMAGE to achieve 90% editing frequency, reduce workflow time, and extend editing distance to 20 kb. We further engineer an inducible dominant negative mismatch repair system, allowing for high-efficiency editing via eMAGE while suppressing the elevated background mutation rate 17-fold resulting from mismatch repair inactivation. We apply these advances to construct a library of cancer-associated mutations in the ligand-binding domains of human estrogen receptor alpha and progesterone receptor to understand their impact on ligand-independent autoactivation. We validate that this yeast model captures autoactivation mutations characterized in human breast cancer models and further leads to the discovery of several previously uncharacterized autoactivating mutations. This work demonstrates the development and optimization of a cleavage-free method of genome editing well suited for applications requiring efficient multiplex editing with minimal background mutations.

Depletion of cap-binding protein eIF4E dysregulates amino acid metabolic gene expression

Protein synthesis is metabolically costly and must be tightly coordinated with changing cellular needs and nutrient availability. The cap-binding protein eIF4E makes the earliest contact between mRNAs and the translation machinery, offering a key regulatory nexus. We acutely depleted this essential protein and found surprisingly modest effects on cell growth and recovery of protein synthesis. Paradoxically, impaired protein biosynthesis upregulated genes involved in the catabolism of aromatic amino acids simultaneously with the induction of the amino acid biosynthetic regulon driven by the integrated stress response factor GCN4. We further identified the translational control of Pho85 cyclin 5 (PCL5), a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. This regulation depended in part on a uniquely long poly(A) tract in the PCL5 5' UTR and poly(A) binding protein. Collectively, these results highlight how eIF4E connects protein synthesis to metabolic gene regulation, uncovering mechanisms controlling translation during environmental challenges.

Deep mutational scanning of Pneumocystis jirovecii dihydrofolate reductase reveals allosteric mechanism of resistance to an antifolate

Pneumocystis jirovecii is a fungal pathogen that causes pneumocystis pneumonia, a disease that mainly affects immunocompromised individuals. This fungus has historically been hard to study because of our inability to grow it in vitro. One of the main drug targets in P. jirovecii is its dihydrofolate reductase (PjDHFR). Here, by using functional complementation of the baker's yeast ortholog, we show that PjDHFR can be inhibited by the antifolate methotrexate in a dose-dependent manner. Using deep mutational scanning of PjDHFR, we identify mutations conferring resistance to methotrexate. Thirty-one sites spanning the protein have at least one mutation that leads to resistance, for a total of 355 high-confidence resistance mutations. Most resistance-inducing mutations are found inside the active site, and many are structurally equivalent to mutations known to lead to resistance to different antifolates in other organisms. Some sites show specific resistance mutations, where only a single substitution confers resistance, whereas others are more permissive, as several substitutions at these sites confer resistance. Surprisingly, one of the permissive sites (F199) is without direct contact to either ligand or cofactor, suggesting that it acts through an allosteric mechanism. Modeling changes in binding energy between F199 mutants and drug shows that most mutations destabilize interactions between the protein and the drug. This evidence points towards a more important role of this position in resistance than previously estimated and highlights potential unknown allosteric mechanisms of resistance to antifolate in DHFRs. Our results offer unprecedented resources for the interpretation of mutation effects in the main drug target of an uncultivable fungal pathogen.

A Standardized Set of MoClo-Compatible Inducible Promoter Systems for Tunable Gene Expression in Yeast

Small-molecule control of gene expression underlies the function of numerous engineered gene circuits that are capable of environmental sensing, computation, and memory. While many recently developed inducible promoters have been tailor-made for bacteria or mammalian cells, relatively few new systems have been built for Saccharomyces cerevisiae, limiting the scale of synthetic biology work that can be done in yeast. To address this, we created the yeast Tunable Expression Systems Toolkit (yTEST), which contains a set of five extensively characterized inducible promoter systems regulated by the small-molecules doxycycline (Dox), abscisic acid (ABA), danoprevir (DNV), 1-naphthaleneacetic acid (NAA), and 5-phenyl-indole-3-acetic acid (5-Ph-IAA). Assembly was made to be compatible with the modular cloning yeast toolkit (MoClo-YTK) to enhance the ease of use and provide a framework to benchmark and standardize each system. Using this approach, we built multiple systems with maximal expression levels greater than those of the strong constitutive TDH3 promoter. Furthermore, each of the five classes of systems could be induced at least 60-fold after a 6 h induction and the highest fold change observed was approximately 300. Thus, yTEST provides a reliable, diverse, and customizable set of inducible promoters to modulate gene expression in yeast for applications in synthetic biology, metabolic engineering, and basic research.

Dissection of the role of a Src homology 3 domain in the evolution of binding preference of paralogous proteins

Protein-protein interactions (PPIs) drive many cellular processes. Some interactions are directed by Src homology 3 (SH3) domains that bind proline-rich motifs on other proteins. The evolution of the binding specificity of SH3 domains is not completely understood, particularly following gene duplication. Paralogous genes accumulate mutations that can modify protein functions and, for SH3 domains, their binding preferences. Here, we examined how the binding of the SH3 domains of 2 paralogous yeast type I myosins, Myo3 and Myo5, evolved following duplication. We found that the paralogs have subtly different SH3-dependent interaction profiles. However, by swapping SH3 domains between the paralogs and characterizing the SH3 domains freed from their protein context, we find that very few of the differences in interactions, if any, depend on the SH3 domains themselves. We used ancestral sequence reconstruction to resurrect the preduplication SH3 domains and examined, moving back in time, how the binding preference changed. Although the most recent ancestor of the 2 domains had a very similar binding preference as the extant ones, older ancestral domains displayed a gradual loss of interaction with the modern interaction partners when inserted in the extant paralogs. Molecular docking and experimental characterization of the free ancestral domains showed that their affinity with the proline motifs is likely not the cause for this loss of binding. Taken together, our results suggest that a SH3 and its host protein could create intramolecular or allosteric interactions essential for the SH3-dependent PPIs, making domains not functionally equivalent even when they have the same binding specificity.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Biased placement of Mitochondria fission facilitates asymmetric inheritance of protein aggregates during yeast cell division

Mitochondria are essential and dynamic eukaryotic organelles that must be inherited during cell division. In yeast, mitochondria are inherited asymmetrically based on quality, which is thought to be vital for maintaining a rejuvenated cell population; however, the mechanisms underlying mitochondrial remodeling and segregation during this process are not understood. We used high spatiotemporal imaging to quantify the key aspects of mitochondrial dynamics, including motility, fission, and fusion characteristics, upon aggregation of misfolded proteins in the mitochondrial matrix. Using these measured parameters, we developed an agent-based stochastic model of dynamics of mitochondrial inheritance. Our model predicts that biased mitochondrial fission near the protein aggregates facilitates the clustering of protein aggregates in the mitochondrial matrix, and this process underlies asymmetric mitochondria inheritance. These predictions are supported by live-cell imaging experiments where mitochondrial fission was perturbed. Our findings therefore uncover an unexpected role of mitochondrial dynamics in asymmetric mitochondrial inheritance.

Surveying the global landscape of post-transcriptional regulators

Numerous proteins regulate gene expression by modulating mRNA translation and decay. To uncover the full scope of these post-transcriptional regulators, we conducted an unbiased survey that quantifies regulatory activity across the budding yeast proteome and delineates the protein domains responsible for these effects. Our approach couples a tethered function assay with quantitative single-cell fluorescence measurements to analyze ~50,000 protein fragments and determine their effects on a tethered mRNA. We characterize hundreds of strong regulators, which are enriched for canonical and unconventional mRNA-binding proteins. Regulatory activity typically maps outside the RNA-binding domains themselves, highlighting a modular architecture that separates mRNA targeting from post-transcriptional regulation. Activity often aligns with intrinsically disordered regions that can interact with other proteins, even in core mRNA translation and degradation factors. Our results thus reveal networks of interacting proteins that control mRNA fate and illuminate the molecular basis for post-transcriptional gene regulation.

Dysregulation of amino acid metabolism upon rapid depletion of cap-binding protein eIF4E

Protein synthesis is a crucial but metabolically costly biological process that must be tightly coordinated with cellular needs and nutrient availability. In response to environmental stress, translation initiation is modulated to control protein output while meeting new demands. The cap-binding protein eIF4E-the earliest contact between mRNAs and the translation machinery-serves as one point of control, but its contributions to mRNA-specific translation regulation remain poorly understood. To survey eIF4E-dependent translational control, we acutely depleted eIF4E and determined how this impacts protein synthesis. Despite its essentiality, eIF4E depletion had surprisingly modest effects on cell growth and protein synthesis. Analysis of transcript-level changes revealed that long-lived transcripts were downregulated, likely reflecting accelerated turnover. Paradoxically, eIF4E depletion led to simultaneous upregulation of genes involved in catabolism of aromatic amino acids, which arose as secondary effects of reduced protein biosynthesis on amino acid pools, and genes involved in the biosynthesis of amino acids. These futile cycles of amino acid synthesis and degradation were driven, in part, by translational activation of GCN4, a transcription factor typically induced by amino acid starvation. Furthermore, we identified a novel regulatory mechanism governing translation of PCL5, a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. This translational control was partial dependent on a uniquely long poly-(A) tract in the PCL5 5' UTR and on poly-(A) binding protein. Collectively, these results highlight how eIF4E connects translation to amino acid homeostasis and stress responses and uncovers new mechanisms underlying how cells tightly control protein synthesis during environmental challenges.

Recent advances in producing food additive L-malate: Chassis, substrate, pathway, fermentation regulation and application

In addition to being an important intermediate in the TCA cycle, L-malate is also widely used in the chemical and beverage industries. Due to the resulting high demand, numerous studies investigated chemical methods to synthesize L-malate from petrochemical resources, but such approaches are hampered by complex downstream processing and environmental pollution. Accordingly, there is an urgent need to develop microbial methods for environmentally-friendly and economical L-malate biosynthesis. The rapid progress and understanding of DNA manipulation, cell physiology, and cell metabolism can improve industrial L-malate biosynthesis by applying intelligent biochemical strategies and advanced synthetic biology tools. In this paper, we mainly focused on biotechnological approaches for enhancing L-malate synthesis, encompassing the microbial chassis, substrate utilization, synthesis pathway, fermentation regulation, and industrial application. This review emphasizes the application of novel metabolic engineering strategies and synthetic biology tools combined with a deep understanding of microbial physiology to improve industrial L-malate biosynthesis in the future.

A Toolkit for Precise, Multigene Control in Saccharomyces cerevisiae

Systems that allow researchers to precisely control the expression of genes are fundamental to biological research, biotechnology, and synthetic biology. However, few inducible gene expression systems exist that can enable simultaneous multigene control under common nutritionally favorable conditions in the important model organism and chassis Saccharomyces cerevisiae. Here we repurposed ligand binding domains from mammalian type I nuclear receptors to establish a family of up to five orthogonal synthetic gene expression systems in yeast. Our systems enable tight, independent, multigene control through addition of inert hormones and are capable of driving robust and rapid gene expression outputs, in some cases achieving up to 600-fold induction. As a proof of principle, we placed expression of four enzymes from the violacein biosynthetic pathway under independent expression control to selectively route pathway flux by addition of specific inducer combinations. Our results establish a modular, versatile, and potentially expandable toolkit for multidimensional control of gene expression in yeast that can be used to construct and control naturally occurring and synthetic gene networks.

A Modified Yeast Two-Hybrid Platform Enables Dynamic Control of Expression Intensities to Unmask Properties of Protein-Protein Interactions

The yeast two-hybrid (Y2H) assay is widely used for protein-protein interaction characterization due to its simplicity and accessibility. However, it may mask changes in affinity caused by mutations or ligand activation due to signal saturation. To overcome this drawback, we modified the Y2H system to have tunable protein expression by introducing a fluorescent reporter and a pair of synthetic inducible transcription factors to regulate the expression of interacting components. We found that the application of inducers allowed us to adjust the concentrations of interacting proteins to avoid saturation and observe interactions otherwise masked in the canonical Y2H assay, such as the abscisic acid-mediated increase in affinity of monomeric abscisic acid receptors to the coreceptor. When applied in future studies, our modified system may provide a more accurate characterization of protein-protein interactions.

Multiple molecular events underlie stochastic switching between 2 heritable cell states in fungi

Eukaryotic transcriptional networks are often large and contain several levels of feedback regulation. Many of these networks have the ability to generate and maintain several distinct transcriptional states across multiple cell divisions and to switch between them. In certain instances, switching between cell states is stochastic, occurring in a small subset of cells of an isogenic population in a seemingly homogenous environment. Given the scarcity and unpredictability of switching in these cases, investigating the determining molecular events is challenging. White-opaque switching in the fungal species Candida albicans is an example of stably inherited cell states that are determined by a complex transcriptional network and can serve as an experimentally accessible model system to study characteristics important for stochastic cell fate switching in eukaryotes. In standard lab media, genetically identical cells maintain their cellular identity (either "white" or "opaque") through thousands of cell divisions, and switching between the states is rare and stochastic. By isolating populations of white or opaque cells, previous studies have elucidated the many differences between the 2 stable cell states and identified a set of transcriptional regulators needed for cell type switching and maintenance of the 2 cell types. Yet, little is known about the molecular events that determine the rare, stochastic switching events that occur in single cells. We use microfluidics combined with fluorescent reporters to directly observe rare switching events between the white and opaque states. We investigate the stochastic nature of switching by beginning with white cells and monitoring the activation of Wor1, a master regulator and marker for the opaque state, in single cells and throughout cell pedigrees. Our results indicate that switching requires 2 stochastic steps; first an event occurs that predisposes a lineage of cells to switch. In the second step, some, but not all, of those predisposed cells rapidly express high levels of Wor1 and commit to the opaque state. To further understand the rapid rise in Wor1, we used a synthetic inducible system in Saccharomyces cerevisiae into which a controllable C. albicans Wor1 and a reporter for its transcriptional control region have been introduced. We document that Wor1 positive autoregulation is highly cooperative (Hill coefficient > 3), leading to rapid activation and producing an "all or none" rather than a graded response. Taken together, our results suggest that reaching a threshold level of a master regulator is sufficient to drive cell type switching in single cells and that an earlier molecular event increases the probability of reaching that threshold in certain small lineages of cells. Quantitative molecular analysis of the white-opaque circuit can serve as a model for the general understanding of complex circuits.

Plasmid and Sequencing Library Preparation for CRISPRi Barcoded Expression Reporter Sequencing (CiBER-seq) in Saccharomyces cerevisiae

Genetic networks regulate nearly all biological processes, including cellular differentiation, homeostasis, and immune responses. Determining the precise role of each gene within a regulatory network can explain its overall, integrated function, and pinpoint mechanisms underlying misregulation in disease states. Transcriptional reporter assays are a useful tool for dissecting these genetic networks, because they link a molecular process to a measurable readout, such as the expression of a fluorescent protein. Here, we introduce a new technique that uses expressed RNA barcodes as reporters, to measure transcriptional changes induced by CRISPRi-mediated genetic perturbation across a diverse, genome-wide library of guide RNAs. We describe an exemplary reporter based on the promoter that drives His4 expression in these guidelines, which can be used as a framework to interrogate other expression phenotypes. In this workflow, a library of plasmids is assembled, encoding a CRISPRi guide RNA (gRNA) along with one or more transcriptional reporters that drive expression of guide-specific nucleotide barcode sequences. For example, when interrogating regulation of the budding yeast HIS4 promoter normalized against a control housekeeping promoter that drives Pgk1 expression, this plasmid library contains a gRNA expression cassette, a HIS4 reporter driving expression of one gRNA-specific nucleotide barcode, and a PGK1 reporter driving expression of a second, gRNA-specific barcode. Long-read sequencing is used to determine which gRNA is associated with these nucleotide barcodes. The plasmid library is then transformed into yeast cells, where each cell receives one plasmid, and experiences a genetic perturbation driven by the guide on that plasmid. The expressed RNA barcodes are extracted in bulk and quantified using high-throughput sequencing, thereby measuring the effect of their corresponding gRNA on barcoded reporter expression. In the case of the HIS4 reporter described above, guides disrupting translation elongation will increase expression of the associated HIS4 barcode specifically, without changing expression of the PGK1 control barcode. It is further possible to quantify plasmid abundance by DNA sequencing, as an additional approach to normalize for differences in plasmid abundance within the population of cells. This protocol outlines the steps to prepare barcode reporter CRISPRi plasmid libraries, link guides to barcodes with long-read sequencing, and measure expression changes through barcode RNA and DNA sequencing. This method is ideal for probing transcriptional or post-transcriptional regulation, as it measures the effects of a genetic perturbation by directly quantifying reporter RNA abundance, rather than relying on indirect growth or fluorescence readouts. Graphic abstract.

Engineering eukaryote-like regulatory circuits to expand artificial control mechanisms for metabolic engineering in Saccharomyces cerevisiae

Temporal control of heterologous pathway expression is critical to achieve optimal efficiency in microbial metabolic engineering. The broadly-used GAL promoter system for engineered yeast (Saccharomyces cerevisiae) suffers from several drawbacks; specifically, unintended induction during laboratory development, and unintended repression in industrial production applications, which decreases overall production capacity. Eukaryotic synthetic circuits have not been well examined to address these problems. Here, we explore a modularised engineering method to deploy new genetic circuits applicable for expanding the control of GAL promoter-driven heterologous pathways in S. cerevisiae. Trans- and cis- modules, including eukaryotic trans-activating-and-repressing mechanisms, were characterised to provide new and better tools for circuit design. A eukaryote-like tetracycline-mediated circuit that delivers stringent repression was engineered to minimise metabolic burden during strain development and maintenance. This was combined with a novel 37 °C induction circuit to relief glucose-mediated repression on the GAL promoter during the bioprocess. This delivered a 44% increase in production of the terpenoid nerolidol, to 2.54 g L^-1 in flask cultivation. These negative/positive transcriptional regulatory circuits expand global strategies of metabolic control to facilitate laboratory maintenance and for industry applications.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

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.

Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits

MicroRNAs (miRNAs) have been shown to modulate gene expression noise, but less is known about how miRNAs with different properties may regulate noise differently. Here, we investigate the role of competing RNAs and the composition of miRNA response elements (MREs) in modulating noise. We find that weak competing RNAs could introduce lower noise than strong competing RNAs. In comparison with a single MRE, both repetitive and composite MREs can reduce the noise at low expression, but repetitive MREs can elevate the noise remarkably at high expression. We further observed the behavior of a synthetic cell-type classifier with miRNAs as inputs and find that miRNAs and MREs that could introduce higher noise tend to enhance cell state transition. These results provide a systematic and quantitative understanding of the function of miRNAs in controlling gene expression noise and the utilization of miRNAs to modulate the behavior of synthetic gene circuits.

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

In Situ Biomanufacturing of Small Molecules in the Mammalian Gut by Probiotic Saccharomyces boulardii

Saccharomyces boulardii is a probiotic yeast that exhibits rapid growth at 37 °C, is easy to transform, and can produce therapeutic proteins in the gut. To establish its ability to produce small molecules encoded by multigene pathways, we measured the amount and variance in protein expression enabled by promoters, terminators, selective markers, and copy number control elements. We next demonstrated efficient (>95%) CRISPR-mediated genome editing in this strain, allowing us to probe engineered gene expression across different genomic sites. We leveraged these strategies to assemble pathways enabling a wide range of vitamin precursor (β-carotene) and drug (violacein) titers. We found that S. boulardii colonizes germ-free mice stably for over 30 days and competes for niche space with commensal microbes, exhibiting short (1-2 day) gut residence times in conventional and antibiotic-treated mice. Using these tools, we enabled β-carotene synthesis (194 μg total) in the germ-free mouse gut over 14 days, estimating that the total mass of additional β-carotene recovered in feces was 56-fold higher than the β-carotene present in the initial probiotic dose. This work quantifies heterologous small molecule production titers by S. boulardii living in the mammalian gut and provides a set of tools for modulating these titers.

Dynamic post-transcriptional regulation by Mrn1 links cell wall homeostasis to mitochondrial structure and function

The RNA-binding protein Mrn1 in Saccharomyces cerevisiae targets over 300 messenger RNAs, including many involved in cell wall biogenesis. The impact of Mrn1 on these target transcripts is not known, however, nor is the cellular role for this regulation. We have shown that Mrn1 represses target mRNAs through the action of its disordered, asparagine-rich amino-terminus. Its endogenous targets include the paralogous SUN domain proteins Nca3 and Uth1, which affect mitochondrial and cell wall structure and function. While loss of MRN1 has no effect on fermentative growth, we found that mrn1Δ yeast adapt more quickly to respiratory conditions. These cells also have enlarged mitochondria in fermentative conditions, mediated in part by dysregulation of NCA3, and this may explain their faster switch to respiration. Our analyses indicated that Mrn1 acts as a hub for integrating cell wall integrity and mitochondrial biosynthesis in a carbon-source responsive manner.

APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia

The E4 allele of the apolipoprotein E gene (APOE) has been established as a genetic risk factor for many diseases including cardiovascular diseases and Alzheimer's disease (AD), yet its mechanism of action remains poorly understood. APOE is a lipid transport protein, and the dysregulation of lipids has recently emerged as a key feature of several neurodegenerative diseases including AD. However, it is unclear how APOE4 perturbs the intracellular lipid state. Here, we report that APOE4, but not APOE3, disrupted the cellular lipidomes of human induced pluripotent stem cell (iPSC)-derived astrocytes generated from fibroblasts of APOE4 or APOE3 carriers, and of yeast expressing human APOE isoforms. We combined lipidomics and unbiased genome-wide screens in yeast with functional and genetic characterization to demonstrate that human APOE4 induced altered lipid homeostasis. These changes resulted in increased unsaturation of fatty acids and accumulation of intracellular lipid droplets both in yeast and in APOE4-expressing human iPSC-derived astrocytes. We then identified genetic and chemical modulators of this lipid disruption. We showed that supplementation of the culture medium with choline (a soluble phospholipid precursor) restored the cellular lipidome to its basal state in APOE4-expressing human iPSC-derived astrocytes and in yeast expressing human APOE4 Our study illuminates key molecular disruptions in lipid metabolism that may contribute to the disease risk linked to the APOE4 genotype. Our study suggests that manipulating lipid metabolism could be a therapeutic approach to help alleviate the consequences of carrying the APOE4 allele.

Expression attenuation as a mechanism of robustness against gene duplication

Gene duplication is ubiquitous and a major driver of phenotypic diversity across the tree of life, but its immediate consequences are not fully understood. Deleterious effects would decrease the probability of retention of duplicates and prevent their contribution to long-term evolution. One possible detrimental effect of duplication is the perturbation of the stoichiometry of protein complexes. Here, we measured the fitness effects of the duplication of 899 essential genes in the budding yeast using high-resolution competition assays. At least 10% of genes caused a fitness disadvantage when duplicated. Intriguingly, the duplication of most protein complex subunits had small to nondetectable effects on fitness, with few exceptions. We selected four complexes with subunits that had an impact on fitness when duplicated and measured the impact of individual gene duplications on their protein-protein interactions. We found that very few duplications affect both fitness and interactions. Furthermore, large complexes such as the 26S proteasome are protected from gene duplication by attenuation of protein abundance. Regulatory mechanisms that maintain the stoichiometric balance of protein complexes may protect from the immediate effects of gene duplication. Our results show that a better understanding of protein regulation and assembly in complexes is required for the refinement of current models of gene duplication.

Orthogonal control of mean and variability of endogenous genes in a human cell line

Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.

CiBER-seq dissects genetic networks by quantitative CRISPRi profiling of expression phenotypes

To realize the promise of CRISPR-Cas9-based genetics, approaches are needed to quantify a specific, molecular phenotype across genome-wide libraries of genetic perturbations. We addressed this challenge by profiling transcriptional, translational, and posttranslational reporters using CRISPR interference (CRISPRi) with barcoded expression reporter sequencing (CiBER-seq). Our barcoding approach allowed us to connect an entire library of guides to their individual phenotypic consequences using pooled sequencing. CiBER-seq profiling fully recapitulated the integrated stress response (ISR) pathway in yeast. Genetic perturbations causing uncharged transfer RNA (tRNA) accumulation activated ISR reporter transcription. Notably, tRNA insufficiency also activated the reporter, independent of the uncharged tRNA sensor. By uncovering alternate triggers for ISR activation, we illustrate how precise, comprehensive CiBER-seq profiling provides a powerful and broadly applicable tool for dissecting genetic networks.

Multidimensional Characterization of Parts Enhances Modeling Accuracy in Genetic Circuits

Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. Using this multidimensional characterization, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.

PICALM Rescues Endocytic Defects Caused by the Alzheimer's Disease Risk Factor APOE4

The ε4 allele of apolipoprotein E (APOE4) is a genetic risk factor for many diseases, including late-onset Alzheimer's disease (AD). We investigate the cellular consequences of APOE4 in human iPSC-derived astrocytes, observing an endocytic defect in APOE4 astrocytes compared with their isogenic APOE3 counterparts. Given the evolutionarily conserved nature of endocytosis, we built a yeast model to identify genetic modifiers of the endocytic defect associated with APOE4. In yeast, only the expression of APOE4 results in dose-dependent defects in both endocytosis and growth. We discover that increasing expression of the early endocytic adaptor protein Yap1802p, a homolog of the human AD risk factor PICALM, rescues the APOE4-induced endocytic defect. In iPSC-derived human astrocytes, increasing expression of PICALM similarly reverses endocytic disruptions. Our work identifies a functional interaction between two AD genetic risk factors-APOE4 and PICALM-centered on the conserved biological process of endocytosis.

Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology

Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.

De novo design of protein logic gates

The design of modular protein logic for regulating protein function at the posttranscriptional level is a challenge for synthetic biology. Here, we describe the design of two-input AND, OR, NAND, NOR, XNOR, and NOT gates built from de novo-designed proteins. These gates regulate the association of arbitrary protein units ranging from split enzymes to transcriptional machinery in vitro, in yeast and in primary human T cells, where they control the expression of the TIM3 gene related to T cell exhaustion. Designed binding interaction cooperativity, confirmed by native mass spectrometry, makes the gates largely insensitive to stoichiometric imbalances in the inputs, and the modularity of the approach enables ready extension to three-input OR, AND, and disjunctive normal form gates. The modularity and cooperativity of the control elements, coupled with the ability to de novo design an essentially unlimited number of protein components, should enable the design of sophisticated posttranslational control logic over a wide range of biological functions.

Multiplexed Gene Expression Tuning with Orthogonal Synthetic Gene Circuits

Understanding the individual and joint contribution of multiple protein levels toward a phenotype requires precise and tunable multigene expression control. Here we introduce a pair of mammalian synthetic gene circuits that linearly and orthogonally control the expression of two reporter genes in mammalian cells with low variability in response to chemical inducers introduced into the growth medium. These gene expression systems can be used to simultaneously probe the individual and joint effects of two gene product concentrations on a cellular phenotype in basic research or biomedical applications.

Towards computer-aided design of cellular structure

Cells are complex machines with tremendous potential for applications in medicine and biotechnology. Although much effort has been devoted to engineering the metabolic, genetic, and signaling pathways of cells, methods for systematically engineering the physical structure of cells are less developed. Here we consider how coarse-grained models for cellular geometry at the organelle level can be used to build computer-aided design (CAD) tools for cellular structure.

Controlling cell-to-cell variability with synthetic gene circuits

Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.

De novo design of bioactive protein switches

Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix 'cage' with a single interface that can interact either intramolecularly with a terminal 'latch' helix or intermolecularly with a peptide 'key'. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage-key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering.

Modular and tunable biological feedback control using a de novo protein switch

De novo-designed proteins^1-3 hold great promise as building blocks for synthetic circuits, and can complement the use of engineered variants of natural proteins^4-7. One such designer protein-degronLOCKR, which is based on 'latching orthogonal cage-key proteins' (LOCKR) technology⁸-is a switch that degrades a protein of interest in vivo upon induction by a genetically encoded small peptide. Here we leverage the plug-and-play nature of degronLOCKR to implement feedback control of endogenous signalling pathways and synthetic gene circuits. We first generate synthetic negative and positive feedback in the yeast mating pathway by fusing degronLOCKR to endogenous signalling molecules, illustrating the ease with which this strategy can be used to rewire complex endogenous pathways. We next evaluate feedback control mediated by degronLOCKR on a synthetic gene circuit⁹, to quantify the feedback capabilities and operational range of the feedback control circuit. The designed nature of degronLOCKR proteins enables simple and rational modifications to tune feedback behaviour in both the synthetic circuit and the mating pathway. The ability to engineer feedback control into living cells represents an important milestone in achieving the full potential of synthetic biology^10,11,12. More broadly, this work demonstrates the large and untapped potential of de novo design of proteins for generating tools that implement complex synthetic functionalities in cells for biotechnological and therapeutic applications.

Role of network-mediated stochasticity in mammalian drug resistance

A major challenge in biology is that genetically identical cells in the same environment can display gene expression stochasticity (noise), which contributes to bet-hedging, drug tolerance, and cell-fate switching. The magnitude and timescales of stochastic fluctuations can depend on the gene regulatory network. Currently, it is unclear how gene expression noise of specific networks impacts the evolution of drug resistance in mammalian cells. Answering this question requires adjusting network noise independently from mean expression. Here, we develop positive and negative feedback-based synthetic gene circuits to decouple noise from the mean for Puromycin resistance gene expression in Chinese Hamster Ovary cells. In low Puromycin concentrations, the high-noise, positive-feedback network delays long-term adaptation, whereas it facilitates adaptation under high Puromycin concentration. Accordingly, the low-noise, negative-feedback circuit can maintain resistance by acquiring mutations while the positive-feedback circuit remains mutation-free and regains drug sensitivity. These findings may have profound implications for chemotherapeutic inefficiency and cancer relapse.

The role of gene expression noise in the evolution of drug resistance in mammalian cells is unclear. Here, by uncoupling noise from mean expression of a drug resistance gene in CHO cells the authors show that noisy expression aids adaptation to high drug levels, but delays it at low drug levels.

Synthetic transcriptional synergy

Synthetic transcription factors can mimic their naturally cooperative counterparts

Controlling CRISPR-Cas9 with ligand-activated and ligand-deactivated sgRNAs

The CRISPR-Cas9 system provides the ability to edit, repress, activate, or mark any gene (or DNA element) by pairing of a programmable single guide RNA (sgRNA) with a complementary sequence on the DNA target. Here we present a new method for small-molecule control of CRISPR-Cas9 function through insertion of RNA aptamers into the sgRNA. We show that CRISPR-Cas9-based gene repression (CRISPRi) can be either activated or deactivated in a dose-dependent fashion over a >10-fold dynamic range in response to two different small-molecule ligands. Since our system acts directly on each target-specific sgRNA, it enables new applications that require differential and opposing temporal control of multiple genes.

Lipidomic Analysis of α-Synuclein Neurotoxicity Identifies Stearoyl CoA Desaturase as a Target for Parkinson Treatment

In Parkinson's disease (PD), α-synuclein (αS) pathologically impacts the brain, a highly lipid-rich organ. We investigated how alterations in αS or lipid/fatty acid homeostasis affect each other. Lipidomic profiling of human αS-expressing yeast revealed increases in oleic acid (OA, 18:1), diglycerides, and triglycerides. These findings were recapitulated in rodent and human neuronal models of αS dyshomeostasis (overexpression; patient-derived triplication or E46K mutation; E46K mice). Preventing lipid droplet formation or augmenting OA increased αS yeast toxicity; suppressing the OA-generating enzyme stearoyl-CoA-desaturase (SCD) was protective. Genetic or pharmacological SCD inhibition ameliorated toxicity in αS-overexpressing rat neurons. In a C. elegans model, SCD knockout prevented αS-induced dopaminergic degeneration. Conversely, we observed detrimental effects of OA on αS homeostasis: in human neural cells, excess OA caused αS inclusion formation, which was reversed by SCD inhibition. Thus, monounsaturated fatty acid metabolism is pivotal for αS-induced neurotoxicity, and inhibiting SCD represents a novel PD therapeutic approach.

A System for Gene Expression Noise Control in Yeast

Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a "noise tuner" which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate, whereas the mean expression is determined by both the transcription rate and mRNA stability and can thus be decoupled from the noise. This noise tuner enables 2-fold changes in gene expression noise over a 5-fold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.

Perspective: Engineering noise in biological systems towards predictive stochastic design

Significant progress has been made towards engineering both single-cell and multi-cellular systems through a combination of synthetic and systems biology, nanobiotechnology, pharmaceutical science, and computational approaches. However, our ability to engineer systems that begin to approach the complexity of natural pathways is severely limited by important challenges, e.g. due to noise, or the fluctuations in gene expression and molecular species at multiple scales (e.g. both intra- and inter-cellular fluctuations). This barrier to engineering requires that biological noise be recognized as a design element with fundamentals that can be actively controlled. Here we highlight studies of an emerging discipline that collectively strives to engineer noise towards predictive stochastic design using interdisciplinary approaches at multiple-scales in diverse living systems.

Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity

Aggregates of human islet amyloid polypeptide (IAPP) in the pancreas of patients with type 2 diabetes (T2D) are thought to contribute to β cell dysfunction and death. To understand how IAPP harms cells and how this might be overcome, we created a yeast model of IAPP toxicity. Ste24, an evolutionarily conserved protease that was recently reported to degrade peptides stuck within the translocon between the cytoplasm and the endoplasmic reticulum, was the strongest suppressor of IAPP toxicity. By testing variants of the human homolog, ZMPSTE24, with varying activity levels, the rescue of IAPP toxicity proved to be directly proportional to the declogging efficiency. Clinically relevant ZMPSTE24 variants identified in the largest database of exomes sequences derived from T2D patients were characterized using the yeast model, revealing 14 partial loss-of-function variants, which were enriched among diabetes patients over 2-fold. Thus, clogging of the translocon by IAPP oligomers may contribute to β cell failure.

A Genetic Tool to Track Protein Aggregates and Control Prion Inheritance

Protein aggregation is a hallmark of many diseases but also underlies a wide range of positive cellular functions. This phenomenon has been difficult to study because of a lack of quantitative and high-throughput cellular tools. Here, we develop a synthetic genetic tool to sense and control protein aggregation. We apply the technology to yeast prions, developing sensors to track their aggregation states and employing prion fusions to encode synthetic memories in yeast cells. Utilizing high-throughput screens, we identify prion-curing mutants and engineer "anti-prion drives" that reverse the non-Mendelian inheritance pattern of prions and eliminate them from yeast populations. We extend our technology to yeast RNA-binding proteins (RBPs) by tracking their propensity to aggregate, searching for co-occurring aggregates, and uncovering a group of coalescing RBPs through screens enabled by our platform. Our work establishes a quantitative, high-throughput, and generalizable technology to study and control diverse protein aggregation processes in cells.