Evolution-guided engineering of small-molecule biosensors.

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.

Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast.

Whole-cell biocatalysts have proven a tractable path toward sustainable production of bulk and fine chemicals. Yet the screening of libraries of cellular designs to identify best-performing biocatalysts is most often a low-throughput endeavor. For this reason, the development of biosensors enabling real-time monitoring of production has attracted attention. Here we applied systematic engineering of multiple parameters to search for a general biosensor design in the budding yeast Saccharomyces cerevisiae based on small-molecule binding transcriptional activators from the prokaryote superfamily of LysR-type transcriptional regulators (LTTRs). We identified a design supporting LTTR-dependent activation of reporter gene expression in the presence of cognate small-molecule inducers. As proof of principle, we applied the biosensors for in vivo screening of cells producing naringenin or cis,cis-muconic acid at different levels, and found that reporter gene output correlated with production. The transplantation of prokaryotic transcriptional activators into the eukaryotic chassis illustrates the potential of a hitherto untapped biosensor resource useful for biotechnological applications.

How do yeast cells become tolerant to high ethanol concentrations?

The brewer's yeast Saccharomyces cerevisiae displays a much higher ethanol tolerance compared to most other organisms, and it is therefore commonly used for the industrial production of bioethanol and alcoholic beverages. However, the genetic determinants underlying this yeast's exceptional ethanol tolerance have proven difficult to elucidate. In this perspective, we discuss how different types of experiments have contributed to our understanding of the toxic effects of ethanol and the mechanisms and complex genetics underlying ethanol tolerance. In a second part, we summarize the different routes and challenges involved in obtaining superior industrial yeasts with improved ethanol tolerance.

Large-scale selection and breeding to generate industrial yeasts with superior aroma production.

The concentrations and relative ratios of various aroma compounds produced by fermenting yeast cells are essential for the sensory quality of many fermented foods, including beer, bread, wine, and sake. Since the production of these aroma-active compounds varies highly among different yeast strains, careful selection of variants with optimal aromatic profiles is of crucial importance for a high-quality end product. This study evaluates the production of different aroma-active compounds in 301 different Saccharomyces cerevisiae, Saccharomyces paradoxus, and Saccharomyces pastorianus yeast strains. Our results show that the production of key aroma compounds like isoamyl acetate and ethyl acetate varies by an order of magnitude between natural yeasts, with the concentrations of some compounds showing significant positive correlation, whereas others vary independently. Targeted hybridization of some of the best aroma-producing strains yielded 46 intraspecific hybrids, of which some show a distinct heterosis (hybrid vigor) effect and produce up to 45% more isoamyl acetate than the best parental strains while retaining their overall fermentation performance. Together, our results demonstrate the potential of large-scale outbreeding to obtain superior industrial yeasts that are directly applicable for commercial use.

Design, Engineering, and Characterization of Prokaryotic Ligand-Binding Transcriptional Activators as Biosensors in Yeast.

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.

An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast.

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.

Large-scale robot-assisted genome shuffling yields industrial Saccharomyces cerevisiae yeasts with increased ethanol tolerance.

BACKGROUND: During the final phases of bioethanol fermentation, yeast cells face high ethanol concentrations. This stress results in slower or arrested fermentations and limits ethanol production. Novel Saccharomyces cerevisiae strains with superior ethanol tolerance may therefore allow increased yield and efficiency. Genome shuffling has emerged as a powerful approach to rapidly enhance complex traits including ethanol tolerance, yet previous efforts have mostly relied on a mutagenized pool of a single strain, which can potentially limit the effectiveness. Here, we explore novel robot-assisted strategies that allow to shuffle the genomes of multiple parental yeasts on an unprecedented scale. RESULTS: Screening of 318 different yeasts for ethanol accumulation, sporulation efficiency, and genetic relatedness yielded eight heterothallic strains that served as parents for genome shuffling. In a first approach, the parental strains were subjected to multiple consecutive rounds of random genome shuffling with different selection methods, yielding several hybrids that showed increased ethanol tolerance. Interestingly, on average, hybrids from the first generation (F1) showed higher ethanol production than hybrids from the third generation (F3). In a second approach, we applied several successive rounds of robot-assisted targeted genome shuffling, yielding more than 3,000 targeted crosses. Hybrids selected for ethanol tolerance showed increased ethanol tolerance and production as compared to unselected hybrids, and F1 hybrids were on average superior to F3 hybrids. In total, 135 individual F1 and F3 hybrids were tested in small-scale very high gravity fermentations. Eight hybrids demonstrated superior fermentation performance over the commercial biofuel strain Ethanol Red, showing a 2 to 7% increase in maximal ethanol accumulation. In an 8-l pilot-scale test, the best-performing hybrid fermented medium containing 32% (w/v) glucose to dryness, yielding 18.7% (v/v) ethanol with a productivity of 0.90 g ethanol/l/h and a yield of 0.45 g ethanol/g glucose. CONCLUSIONS: We report the use of several different large-scale genome shuffling strategies to obtain novel hybrids with increased ethanol tolerance and fermentation capacity. Several of the novel hybrids show best-parent heterosis and outperform the commonly used bioethanol strain Ethanol Red, making them interesting candidate strains for industrial production.

Improving industrial yeast strains: exploiting natural and artificial diversity.

Yeasts have been used for thousands of years to make fermented foods and beverages, such as beer, wine, sake, and bread. However, the choice for a particular yeast strain or species for a specific industrial application is often based on historical, rather than scientific grounds. Moreover, new biotechnological yeast applications, such as the production of second-generation biofuels, confront yeast with environments and challenges that differ from those encountered in traditional food fermentations. Together, this implies that there are interesting opportunities to isolate or generate yeast variants that perform better than the currently used strains. Here, we discuss the different strategies of strain selection and improvement available for both conventional and nonconventional yeasts. Exploiting the existing natural diversity and using techniques such as mutagenesis, protoplast fusion, breeding, genome shuffling and directed evolution to generate artificial diversity, or the use of genetic modification strategies to alter traits in a more targeted way, have led to the selection of superior industrial yeasts. Furthermore, recent technological advances allowed the development of high-throughput techniques, such as 'global transcription machinery engineering' (gTME), to induce genetic variation, providing a new source of yeast genetic diversity.

An Oct4-centered protein interaction network in embryonic stem cells.

Transcription factors, such as Oct4, are critical for establishing and maintaining pluripotent cell identity. Whereas the genomic locations of several pluripotency transcription factors have been reported, the spectrum of their interaction partners is underexplored. Here, we use an improved affinity protocol to purify Oct4-interacting proteins from mouse embryonic stem cells (ESCs). Subsequent purification of Oct4 partners Sall4, Tcfcp2l1, Dax1, and Esrrb resulted in an Oct4 interactome of 166 proteins, including transcription factors and chromatin-modifying complexes with documented roles in self-renewal, but also many factors not previously associated with the ESC network. We find that Esrrb associated with the basal transcription machinery and also detect interactions between transcription factors and components of the TGF-beta, Notch, and Wnt signaling pathways. Acute depletion of Oct4 reduced binding of Tcfcp2l1, Dax1, and Esrrb to several target genes. In conclusion, our purification protocol allowed us to bring greater definition to the circuitry controlling pluripotent cell identity.

Jarid2 is a PRC2 component in embryonic stem cells required for multi-lineage differentiation and recruitment of PRC1 and RNA Polymerase II to developmental regulators.

Polycomb Repressor Complexes (PRCs) are important regulators of embryogenesis. In embryonic stem (ES) cells many genes that regulate subsequent stages in development are enriched at their promoters for PRC1, PRC2 and Ser 5-phosphorylated RNA Polymerase II (RNAP), and contain domains of 'bivalent' chromatin (enriched for H3K4me3; histone H3 di- or trimethylated at Lys 4 and H3K27me3; histone H3 trimethylated at Lys 27). Loss of individual PRC components in ES cells can lead to gene de-repression and to unscheduled differentiation. Here we show that Jarid2 is a novel subunit of PRC2 that is required for the co-recruitment of PRC1 and RNAP to genes that regulate development in ES cells. Jarid2-deficient ES cells showed reduced H3K4me2/me3 and H3K27me3 marking and PRC1/PRC2 recruitment, and did not efficiently establish Ser 5-phosporylated RNAP at target genes. ES cells lacking Jarid2, in contrast to previously characterized PRC1 and PRC2 mutants, did not inappropriately express PRC2 target genes. Instead, they show a severely compromised capacity for successful differentiation towards neural or mesodermal fates and failed to correctly initiate lineage-specific gene expression in vitro. Collectively, these data indicate that transcriptional priming of bivalent genes in pluripotent ES cells is Jarid2-dependent, and suggests that priming is critical for subsequent multi-lineage differentiation.