Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

Small-molecule binding allosteric transcription factors (aTFs) derived from bacteria enable real-time monitoring of metabolite abundances, high-throughput screening of genetic designs, and dynamic control of metabolism. Yet, engineering of reporter promoter designs of prokaryotic aTF biosensors in eukaryotic cells is complex. Here we investigate the impact of aTF binding site positions at single-nucleotide resolution in >300 reporter promoter designs in Saccharomyces cerevisiae. From this we identify biosensor output landscapes with transient and distinct aTF binding site position-effects for aTF repressors and activators, respectively. Next, we present positions for tunable reporter promoter outputs enabling metabolite-responsive designs for a total 2 of four repressor-type and three activator-type aTF biosensors with dynamic output ranges up to 8-and 26-fold, respectively. This study highlights aTF binding site positions in reporter promoters as key for successful biosensor engineering, and that repressor-type aTF biosensors allows for more flexibility in terms of choice of binding site positioning compared to activator-type aTF biosensors.

No abstract

SummaryDirected evolution is a powerful method to optimize proteins and metabolic reactions towards user‐defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence‐spaces is a labour‐intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high‐throughput using a transcription factor‐based biosensor. We demonstrate the strengths of this workflow by evolving a rate‐limiting enzymatic reaction of the biosynthetic pathway for cis,cis‐muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high‐throughput sorting of best‐performing variants using a transcription factor‐based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13‐fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

In cell factory development, screening procedures, often relying on low-throughput analytical methods, are lagging far behind diversity generation methods. This renders the identification and selection of the best cell factory designs tiresome and costly, conclusively hindering the manufacturing process. In the yeast Saccharomyces cerevisiae, implementation of allosterically regulated transcription factors from prokaryotes as metabolite biosensors has proven a valuable strategy to alleviate this screening bottleneck. Here, we present a protocol to select and incorporate prokaryotic transcriptional activators as metabolite biosensors in S. cerevisiae. As an example, we outline the engineering and characterization of the LysR-type transcriptional regulator (LTTR) family member BenM from Acetinobacter sp. ADP1 for monitoring accumulation of cis,cis-muconic acid, a bioplast precursor, in yeast by means of flow cytometry.

Activation of interferon genes constitutes an important anticancer pathway able to restrict proliferation of cancer cells. Here, we demonstrate that the H3K9me3 histone methyltransferase (HMT) suppressor of variegation 3–9 homolog 1 (SUV39H1) is required for the proliferation of acute myeloid leukemia (AML) and find that its loss leads to activation of the interferon pathway. Mechanistically, we show that this occurs via destabilization of a complex composed of SUV39H1 and the two H3K9me2 HMTs, G9A and GLP. Indeed, loss of H3K9me2 correlated with the activation of key interferon pathway genes, and interference with the activities of G9A/GLP largely phenocopied loss of SUV39H1. Last, we demonstrate that inhibition of G9A/GLP synergized with DNA demethylating agents and that SUV39H1 constitutes a potential biomarker for the response to hypomethylation treatment. Collectively, we uncovered a clinically relevant role for H3K9me2 in safeguarding cancer cells against activation of the interferon pathway.

Engineering living cells for production of chemicals, enzymes and therapeutics can burden cells due to use of limited native co-factor availability and/or expression burdens, totalling a fitness deficit compared to parental cells encoded through long evolutionary trajectories to maximise fitness. Ultimately, this discrepancy puts a selective pressure against fitness-burdened engineered cells under prolonged bioprocesses, and potentially leads to complete eradication of highperforming engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-β-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-βglucoside pathway intermediates in order to stabilize vanillin-β-glucoside production over ~55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-β-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and 92% higher vanillin-βglucoside production, including 5-fold increase in total vanillin-β-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-β-glucoside producing cells without control circuits.