Laboratory evolution reveals regulatory and metabolic trade-offs of glycerol utilization in Saccharomyces cerevisiae

Intein-mediated temperature control for complete biosynthesis of sanguinarine and its halogenated derivatives in yeast

While sanguinarine has gained recognition for antimicrobial and antineoplastic activities, its complex conjugated structure and low abundance in plants impede broad applications. Here, we demonstrate the complete biosynthesis of sanguinarine and halogenated derivatives using highly engineered yeast strains. To overcome sanguinarine cytotoxicity, we establish a splicing intein-mediated temperature-responsive gene expression system (SIMTeGES), a simple strategy that decouples cell growth from product synthesis without sacrificing protein activity. To debottleneck sanguinarine biosynthesis, we identify two reticuline oxidases and facilitated functional expression of flavoproteins and cytochrome P450 enzymes via protein molecular engineering. After comprehensive metabolic engineering, we report the production of sanguinarine at a titer of 448.64 mg L-1. Additionally, our engineered strain enables the biosynthesis of fluorinated sanguinarine, showcasing the biotransformation of halogenated derivatives through more than 15 biocatalytic steps. This work serves as a blueprint for utilizing yeast as a scalable platform for biomanufacturing diverse benzylisoquinoline alkaloids and derivatives.

Loss of function of Hog1 improves glycerol assimilation in Saccharomyces cerevisiae

We previously isolated a mutant of Saccharomyces cerevisiae strain 85_9 whose glycerol assimilation was improved through adaptive laboratory evolution. To investigate the mechanism for this improved glycerol assimilation, genome resequencing of the 85_9 strain was performed, and the mutations in the open reading frame of HOG1, SIR3, SSB2, and KGD2 genes were found. Among these, a frameshift mutation in the HOG1 open reading frame was responsible for the improved glycerol assimilation ability of the 85_9 strain. Moreover, the HOG1 gene disruption improved glycerol assimilation. As HOG1 encodes a mitogen-activated protein kinase (MAPK), which is responsible for the signal transduction cascade in response to osmotic stress, namely the high osmolarity glycerol (HOG) pathway, we investigated the effect of the disruption of PBS2 gene encoding MAPK kinase for Hog1 MAPK on glycerol assimilation, revealing that PBS2 disruption can increase glycerol assimilation. These results indicate that loss of function of Hog1 improves glycerol assimilation in S. cerevisiae. However, single disruption of the SSK2, SSK22 and STE11 genes encoding protein kinases responsible for Pbs2 phosphorylation in the HOG pathway did not increase glycerol assimilation, while their triple disruption partially improved glycerol assimilation in S. cerevisiae. In addition, the HOG1 frameshift mutation did not improve glycerol assimilation in the STL1-overexpressing RIM15 disruptant strain, which was previously constructed with high glycerol assimilation ability. Furthermore, the effectiveness of the HOG1 disruptant as a bioproduction host was validated, indicating that the HOG1 CYB2 double disruptant can produce L-lactic acid from glycerol.

Adaptive Laboratory Evolution Reveals the Selenium Efflux Process To Improve Selenium Tolerance Mediated by the Membrane Sulfite Pump in Saccharomyces cerevisiae

Selenium (Se) is a micronutrient in most eukaryotes, and Se-enriched yeast is the most common selenium supplement. However, selenium metabolism and transport in yeast have remained unclear, greatly hindering the application of this element. To explore the latent selenium transport and metabolism mechanisms, we performed adaptive laboratory evolution under the selective pressure of sodium selenite and successfully obtained selenium-tolerant yeast strains. Mutations in the sulfite transporter gene ssu1 and its transcription factor gene fzf1 were found to be responsible for the tolerance generated in the evolved strains, and the selenium efflux process mediated by ssu1 was identified in this study. Moreover, we found that selenite is a competitive substrate for sulfite during the efflux process mediated by ssu1, and the expression of ssu1 is induced by selenite rather than sulfite. Based on the deletion of ssu1, we increased the intracellular selenomethionine content in Se-enriched yeast. This work confirms the existence of the selenium efflux process, and our findings may benefit the optimization of Se-enriched yeast production in the future. IMPORTANCE Selenium is an essential micronutrient for mammals, and its deficiency severely threatens human health. Yeast is the model organism for studying the biological role of selenium, and Se-enriched yeast is the most popular selenium supplement to solve Se deficiency. The cognition of selenium accumulation in yeast always focuses on the reduction process. Little is known about selenium transport, especially selenium efflux, which may play a crucial part in selenium metabolism. The significance of our research is in determining the selenium efflux process in Saccharomyces cerevisiae, which will greatly enhance our knowledge of selenium tolerance and transport, facilitating the production of Se-enriched yeast. Moreover, our research further advances the understanding of the relationship between selenium and sulfur in transport.

Inducible Synthetic Growth Regulation Using the ClpXP Proteasome Enhances cis,cis-Muconic Acid and Glycolic Acid Yields in Saccharomyces cerevisiae

Engineered microbial cells can produce sustainable chemistry, but the production competes for resources with growth. Inducible synthetic control over the resource use would enable fast accumulation of sufficient biomass and then divert the resources to production. We developed inducible synthetic resource-use control overSaccharomyces cerevisiae by expressing a bacterial ClpXP proteasome from an inducible promoter. By individually targeting growth-essential metabolic enzymes Aro1, Hom3, and Acc1 to the ClpXP proteasome, cell growth could be efficiently repressed during cultivation. The ClpXP proteasome was specific to the target proteins, and there was no reduction in the targets when ClpXP was not induced. The inducible growth repression improved product yields from glucose (cis,cis-muconic acid) and per biomass (cis,cis-muconic acid and glycolic acid). The inducible ClpXP proteasome tackles uncertainties in strain optimization by enabling model-guided repression of competing, growth-essential, and metabolic enzymes. Most importantly, it allows improving production without compromising biomass accumulation when uninduced; therefore, it is expected to mitigate strain stability and low productivity challenges.

Effect of Short-Chain Fatty Acids on the Yield of 2,3-Butanediol by Saccharomyces cerevisiae W141: The Synergistic Effect of Acetic Acid and Dissolved Oxygen

As a platform chemical, 2,3-butanediol (2,3-BDO) has been widely used in various industrial fields. To improve the yield of 2,3-BDO produced by Saccharomyces cerevisiae W141, this paper explored the effects of exogenous short-chain fatty acids (SCFAs) as well as the synergistic effects of acetic acid and dissolved oxygen content on the yield of 2,3-BDO from the perspective of physiological metabolism. The results indicated that different SCFAs had different effects on the production of 2,3-BDO, and higher or lower concentrations of SCFAs were not conducive to the generation of 2,3-BDO. However, exogenically adding 1.0 g/L acetic acid significantly increased the yield of 2,3-BDO and the expression level of bdh1, a key gene in the synthesis of 2,3-BDO (p < 0.05). In addition, a dissolved oxygen concentration of 4.52 mg/L was proven to be the optimal condition for 2,3-BDO production. When the dissolved oxygen content and acetic acid concentration were 4.52 mg/L and 1.0 g/L, respectively, the maximum yield of 2,3-BDO was 3.25 ± 0.03 g/L, which was 66.59% higher than that produced by S. cerevisiae W141 alone. These results provide methodological guidance for the industrial production of 2,3-BDO by S. cerevisiae.

Contributions of Adaptive Laboratory Evolution towards the Enhancement of the Biotechnological Potential of Non-Conventional Yeast Species

Changes in biological properties over several generations, induced by controlling short-term evolutionary processes in the laboratory through selective pressure, and whole-genome re-sequencing, help determine the genetic basis of microorganism's adaptive laboratory evolution (ALE). Due to the versatility of this technique and the imminent urgency for alternatives to petroleum-based strategies, ALE has been actively conducted for several yeasts, primarily using the conventional species Saccharomyces cerevisiae, but also non-conventional yeasts. As a hot topic at the moment since genetically modified organisms are a debatable subject and a global consensus on their employment has not yet been attained, a panoply of new studies employing ALE approaches have emerged and many different applications have been exploited in this context. In the present review, we gathered, for the first time, relevant studies showing the ALE of non-conventional yeast species towards their biotechnological improvement, cataloging them according to the aim of the study, and comparing them considering the species used, the outcome of the experiment, and the employed methodology. This review sheds light on the applicability of ALE as a powerful tool to enhance species features and improve their performance in biotechnology, with emphasis on the non-conventional yeast species, as an alternative or in combination with genome editing approaches.

Adaptive Laboratory Evolution of Microorganisms: Methodology and Application for Bioproduction

Adaptive laboratory evolution (ALE) is a useful experimental methodology for fundamental scientific research and industrial applications to create microbial cell factories. By using ALE, cells are adapted to the environment that researchers set based on their objectives through the serial transfer of cell populations in batch cultivations or continuous cultures and the fitness of the cells (i.e., cell growth) under such an environment increases. Then, omics analyses of the evolved mutants, including genome sequencing, transcriptome, proteome and metabolome analyses, are performed. It is expected that researchers can understand the evolutionary adaptation processes, and for industrial applications, researchers can create useful microorganisms that exhibit increased carbon source availability, stress tolerance, and production of target compounds based on omics analysis data. In this review article, the methodologies for ALE in microorganisms are introduced. Moreover, the application of ALE for the creation of useful microorganisms as cell factories has also been introduced.

Predictive evolution of metabolic phenotypes using model-designed environments

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on the tricarboxylic acid (TCA) cycle for substrates and cofactors. Although a few TCA cycle enzymes have been reported in the nucleus, the corresponding pathways are considered to operate in mitochondria. Here, we show that a part of the TCA cycle is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Nuclear localization of the latter enzyme, which produces succinyl-CoA, changed from pluripotency to a differentiated state with accompanying changes in the nuclear protein succinylation. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus, warranting a revision of the canonical view on metabolic compartmentalization.

Adaptive Laboratory Evolution of Yeasts for Aroma Compound Production

Aroma compounds are important in the food and beverage industry, as they contribute to the quality of fermented products. Yeasts produce several aroma compounds during fermentation. In recent decades, production of many aroma compounds by yeasts obtained through adaptive laboratory evolution has become prevalent, due to consumer demand for yeast strains in the industry. This review presents general aspects of yeast, aroma production and adaptive laboratory evolution and focuses on the recent advances of yeast strains obtained by adaptive laboratory evolution to enhance the production of aroma compounds.

Rewiring the Metabolic Network to Increase Docosahexaenoic Acid Productivity in Crypthecodinium cohnii by Fermentation Supernatant-Based Adaptive Laboratory Evolution

Docosahexaenoic acid (DHA, 22:6n-3) plays significant roles in enhancing human health and preventing human diseases. The heterotrophic marine dinoflagellate Crypthecodinium cohnii is a good candidate to produce high-quality DHA. To overcome the inhibition caused by the fermentation supernatant in the late fermentation stage of DHA-producing C. cohnii, fermentation supernatant-based adaptive laboratory evolution (FS-ALE) was conducted. The cell growth and DHA productivity of the evolved strain (FS280) obtained after 280 adaptive cycles corresponding to 840 days of evolution were increased by 161.87 and 311.23%, respectively, at 72 h under stress conditions and increased by 19.87 and 51.79% without any stress compared with the starting strain, demonstrating the effectiveness of FS-ALE. In addition, a comparative proteomic analysis identified 11,106 proteins and 910 differentially expressed proteins, including six stress-responsive proteins, as well as the up- and downregulated pathways in FS280 that might contribute to its improved cell growth and DHA accumulation. Our study demonstrated that FS-ALE could be a valuable solution to relieve the inhibition of the fermentation supernatant at the late stage of normal fermentation of heterotrophic microalgae.

Microbial Cell Factory of Baccatin III Preparation in Escherichia coli by Increasing DBAT Thermostability and in vivo Acetyl-CoA Supply

Given the rapid development of genome mining in this decade, the substrate channel of paclitaxel might be identified in the near future. A robust microbial cell factory with gene dbat, encoding a key rate-limiting enzyme 10-deacetylbaccatin III-10-O-transferase (DBAT) in paclitaxel biosynthesis to synthesize the precursor baccatin III, will lay out a promising foundation for paclitaxel de novo synthesis. Here, we integrated gene dbat into the wild-type Escherichia coli BW25113 to construct strain BWD01. Yet, it was relatively unstable in baccatin III synthesis. Mutant gene dbat S189V with improved thermostability was screened out from a semi-rational mutation library of DBAT. When it was over-expressed in an engineered strain N05 with improved acetyl-CoA generation, combined with carbon source optimization of fermentation engineering, the production level of baccatin III was significantly increased. Using this combination, integrated strain N05S01 with mutant dbat S189V achieved a 10.50-fold increase in baccatin III production compared with original strain BWD01. Our findings suggest that the combination of protein engineering and metabolic engineering will become a promising strategy for paclitaxel production.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

Model-guided development of an evolutionarily stable yeast chassis

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Reprogramming of microRNA expression via E2F1 downregulation promotes Salmonella infection both in infected and bystander cells

Cells infected with pathogens can contribute to clearing infections by releasing signals that instruct neighbouring cells to mount a pro-inflammatory cytokine response, or by other mechanisms that reduce bystander cells’ susceptibility to infection. Here, we show the opposite effect: epithelial cells infected with Salmonella Typhimurium secrete host factors that facilitate the infection of bystander cells. We find that the endoplasmic reticulum stress response is activated in both infected and bystander cells, and this leads to activation of JNK pathway, downregulation of transcription factor E2F1, and consequent reprogramming of microRNA expression in a time-dependent manner. These changes are not elicited by infection with other bacterial pathogens, such as Shigella flexneri or Listeria monocytogenes. Remarkably, the protein HMGB1 present in the secretome of Salmonella-infected cells is responsible for the activation of the IRE1 branch of the endoplasmic reticulum stress response in non-infected, neighbouring cells. Furthermore, E2F1 downregulation and the associated microRNA alterations promote Salmonella replication within infected cells and prime bystander cells for more efficient infection.

Cells infected with pathogens can release signals that instruct neighbouring cells to mount an immune response or that reduce these cells’ susceptibility to infection. Here, Aguilar et al. show the opposite effect: cells infected with Salmonella Typhimurium secrete host factors that facilitate the infection of bystander cells by activating their ER-stress response.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

Engineered yeast genomes accurately assembled from pure and mixed samples

Yeast whole genome sequencing (WGS) lacks end-to-end workflows that identify genetic engineering. Here we present Prymetime, a tool that assembles yeast plasmids and chromosomes and annotates genetic engineering sequences. It is a hybrid workflow-it uses short and long reads as inputs to perform separate linear and circular assembly steps. This structure is necessary to accurately resolve genetic engineering sequences in plasmids and the genome. We show this by assembling diverse engineered yeasts, in some cases revealing unintended deletions and integrations. Furthermore, the resulting whole genomes are high quality, although the underlying assembly software does not consistently resolve highly repetitive genome features. Finally, we assemble plasmids and genome integrations from metagenomic sequencing, even with 1 engineered cell in 1000. This work is a blueprint for building WGS workflows and establishes WGS-based identification of yeast genetic engineering.

Innovative Tools and Strategies for Optimizing Yeast Cell Factories

Metabolic engineering (ME) aims to develop efficient microbial cell factories that can produce a wide variety of valuable compounds, ideally at the highest yield and from various feedstocks. We summarize recent developments in ME methods for tailoring different yeast cell factories (YCFs). In particular, we highlight the most timely and cutting-edge molecular tools and strategies for biosynthetic pathway optimization (including genome-editing tools), combinatorial transcriptional and post-transcriptional engineering (cis/trans regulators), dynamic control of metabolic fluxes (e.g., rewiring of primary metabolism), and spatial reconfiguration of metabolic pathways. Finally, we discuss challenges and perspectives for adaptive laboratory evolution (ALE) of yeast to advance ME of microbial cell factories.

Successful biosynthesis of natural antioxidant ergothioneine in Saccharomyces cerevisiae required only two genes from Grifola frondosa

Background

Ergothioneine (EGT) has a unique antioxidant ability and diverse beneficial effects on human health. But the content of EGT is very low in its natural producing organisms such as Mycobacterium smegmatis and mushrooms. Therefore, it is necessary to highly efficient heterologous production of EGT in food-grade yeasts such as Saccharomyces cerevisiae.

Results

Two EGT biosynthetic genes were cloned from the mushroom Grifola frondosa and successfully heterologously expressed in Saccharomyces cerevisiae EC1118 strain in this study. By optimization of the fermentation conditions of the engineered strain S. cerevisiae EC1118, the 11.80 mg/L of EGT production was obtained. With daily addition of 1% glycerol to the culture medium in the fermentation process, the EGT production of the engineered strain S. cerevisiae EC1118 can reach up to 20.61 mg/L.

Conclusion

A successful EGT de novo biosynthetic system of S. cerevisiae containing only two genes from mushroom Grifola frondosa was developed in this study. This system provides promising prospects for the large scales production of EGT for human health.

Myriocin-induced adaptive laboratory evolution of an industrial strain of Saccharomyces cerevisiae reveals its potential to remodel lipid composition and heat tolerance

The modification of lipid composition allows cells to adjust membrane biophysical properties in response to changes in environmental temperature. Here, we use adaptive laboratory evolution (ALE) in the presence of myriocin, a sphingolipid (SLs) biosynthesis inhibitor, to remodel the lipid profile of an industrial yeast strain (LH) of Saccharomyces cerevisiae. The approach enabled to obtain a heterogeneous population (LHev) of myriocin-tolerant evolved clones characterized by its growth capacity at high temperature. Myriocin exposure also caused tolerance to soraphen A, an inhibitor of the acetyl-CoA carboxylase Acc1, the rate-limiting enzyme in fatty acid de novo production, supporting a change in lipid metabolism during ALE. In line with this, characterization of two randomly selected clones, LH03 and LH09, showed the presence of lipids with increased saturation degree and reduced acyl length. In addition, the clone LH03, which displays the greater improvement in fitness at 40°C, exhibited higher SL content as compared with the parental strain. Analysis of the LH03 and LH09 genomes revealed a loss of chromosomes affecting genes that have a role in fatty acid synthesis and elongation. The link between ploidy level and growth at high temperature was further supported by the analysis of a fully isogenic set of yeast strains with ploidy between 1N and 4N which showed that the loss of genome content provides heat tolerance. Consistent with this, a thermotolerant evolved population (LH40°) generated from the parental LH strain by heat-driven ALE exhibited a reduction in the chromosome copy number. Thus, our results identify myriocin-driven evolution as a powerful approach to investigate the mechanisms of acquired thermotolerance and to generate improved strains.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Metabolic Engineering of Saccharomyces cerevisiae for Enhanced Dihydroartemisinic Acid Production

Direct bioproduction of DHAA (dihydroartemisinic acid) rather than AA (artemisinic acid), as suggested by previous work would decrease the cost of semi-biosynthesis artemisinin by eliminating the step of initial hydrogenation of AA. The major challenge in microbial production of DHAA is how to efficiently manipulate consecutive key enzymes ADH1 (artemisinic alcohol dehydrogenase), DBR2 [artemisinic aldehyde Δ11(13) reductase] and ALDH1 (aldehyde dehydrogenase) to redirect metabolic flux and elevate the ratio of DHAA to AA (artemisinic acid). Herein, DHAA biosynthesis was achieved in Saccharomyces cerevisiae by introducing a series of heterologous enzymes: ADS (amorpha-4,11-diene synthase), CYP71AV1 (amorphadiene oxidase), ADH1, DBR2 and ALDH1, obtaining initial DHAA/AA ratio at 2.53. The flux toward DHAA was enhanced by pairing fusion proteins DBR2-ADH1 and DBR2-ALDH1, leading to 1.75-fold increase in DHAA/AA ratio (to 6.97). Moreover, to promote the substrate preference of ALDH1 to dihydroartemisinic aldehyde (the intermediate for DHAA synthesis) over artemisinic aldehyde (the intermediate for AA synthesis), two rational engineering strategies, including downsizing the active pocket and enhancing the stability of enzyme/cofactor complex, were proposed to engineer ALDH1. It was found that the mutant H194R, which showed better stability of the enzyme/NAD+ complex, obtained the highest DHAA to AA ratio at 3.73 among all the mutations. Then the mutant H194R was incorporated into above rebuilt fusion proteins, resulting in the highest ratio of DHAA to AA (10.05). Subsequently, the highest DHAA reported titer of 1.70 g/L (DHAA/AA ratio of 9.84) was achieved through 5 L bioreactor fermentation. The study highlights the synergy of metabolic engineering and protein engineering in metabolic flux redirection to get the most efficient product to the chemical process, and simplified downstream conversion process.

Systems and synthetic biology tools for advanced bioproduction hosts

The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Glycerol as a substrate for Saccharomyces cerevisiae based bioprocesses - Knowledge gaps regarding the central carbon catabolism of this 'non-fermentable' carbon source

Glycerol is an interesting alternative carbon source in industrial bioprocesses due to its higher degree of reduction per carbon atom compared to sugars. During the last few years, significant progress has been made in improving the well-known industrial platform organism Saccharomyces cerevisiae with regard to its glycerol utilization capability, particularly in synthetic medium. This provided a basis for future metabolic engineering focusing on the production of valuable chemicals from glycerol. However, profound knowledge about the central carbon catabolism in synthetic glycerol medium is a prerequisite for such incentives. As a matter of fact, the current assumptions about the actual in vivo fluxes active on glycerol as the sole carbon source have mainly been based on omics data collected in complex media or were even deduced from studies with other non-fermentable carbon sources, such as ethanol or acetate. A number of uncertainties have been identified which particularly regard the role of the glyoxylate cycle, the subcellular localization of the respective enzymes, the contributions of mitochondrial transporters and the active anaplerotic reactions under these conditions. The review scrutinizes the current knowledge, highlights the necessity to collect novel experimental data using cells growing in synthetic glycerol medium and summarizes the current state of the art with regard to the production of valuable fermentation products from a carbon source that has been considered so far as 'non-fermentable' for the yeast S. cerevisiae.

Developments and opportunities in fungal strain engineering for the production of novel enzymes and enzyme cocktails for plant biomass degradation

Fungal strain engineering is commonly used in many areas of biotechnology, including the production of plant biomass degrading enzymes. Its aim varies from the production of specific enzymes to overall increased enzyme production levels and modification of the composition of the enzyme set that is produced by the fungus. Strain engineering involves a diverse range of methodologies, including classical mutagenesis, genetic engineering and genome editing. In this review, the main approaches for strain engineering of filamentous fungi in the field of plant biomass degradation will be discussed, including recent and not yet implemented methods, such as CRISPR/Cas9 genome editing and adaptive evolution.

Unraveling the Mechanisms for Low-Level Acetaldehyde Production during Alcoholic Fermentation in Saccharomyces pastorianus Lager Yeast

Acetaldehyde is produced by yeast during alcoholic fermentation, and its modification greatly affects beer flavor and quality. In the current study, we analyzed two yeast strains with a low level of acetaldehyde to reveal the potential mechanism underpinning the desirable low acetaldehyde production by these strains. We demonstrated that high alcohol dehydrogenase (ADH) activity and high NADH availability were the dominant factors for the low level of acetaldehyde in the fermentation liquor at the end of fermentation. High ADH activity resulted in reduced accumulation of acetaldehyde during the cell growth phase by increasing the flux to ethanol, whereas high NADH availability (in the cytosol or mitochondria) enhanced acetaldehyde reduction at the later phase of main fermentation. Furthermore, NADH availability is a more useful target trait than ADH activity for constructing yeast strains with a low level of acetaldehyde for industrial applications in terms of flavor contribution and unaltered fermentation period.

Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations

Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.

Transcriptional Profiling of Patient Isolates Identifies a Novel TOR/Starvation Regulatory Pathway in Cryptococcal Virulence

Human infection with Cryptococcus causes up to a quarter of a million AIDS-related deaths annually and is the most common cause of nonviral meningitis in the United States. As an opportunistic fungal pathogen, Cryptococcus neoformans is distinguished by its ability to adapt to diverse host environments, including plants, amoebae, and mammals. In the present study, comparative transcriptomics of the fungus within human cerebrospinal fluid identified expression profiles representative of low-nutrient adaptive responses. Transcriptomics of fungal isolates from a cohort of HIV/AIDS patients identified high expression levels of an alternative carbon nutrient transporter gene, STL1, to be associated with poor early fungicidal activity, an important clinical prognostic marker. Mouse modeling and pathway analysis demonstrated a role for STL1 in mammalian pathogenesis and revealed that STL1 expression is regulated by a novel multigene regulatory mechanism involving the CAC2 subunit of the chromatin assembly complex 1, CAF-1. In this pathway, the global regulator of virulence gene VAD1 was found to transcriptionally regulate a cryptococcal homolog of a cytosolic protein, Ecm15, in turn required for nuclear transport of the Cac2 protein. Derepression of STL1 by the CAC2-containing CAF-1 complex was mediated by Cac2 and modulated binding and suppression of the STL1 enhancer element. Derepression of STL1 resulted in enhanced survival and growth of the fungus in the presence of low-nutrient, alternative carbon sources, facilitating virulence in mice. This study underscores the utility of ex vivo expression profiling of fungal clinical isolates and provides fundamental genetic understanding of saprophyte adaption to the human host.IMPORTANCE Cryptococcus is a fungal pathogen that kills an estimated quarter of a million individuals yearly and is the most common cause of nonviral meningitis in the United States. The fungus is carried in about 10% of the adult population and, after reactivation, causes disease in a wide variety of immunosuppressed individuals, including the HIV infected and patients receiving transplant conditioning, cancer therapy, or corticosteroid therapy for autoimmune diseases. The fungus is widely carried in the soil but can also cause infections in plants and mammals. However, the mechanisms for this widespread ability to infect a variety of hosts are poorly understood. The present study identified adaptation to low nutrients as a key property that allows the fungus to inhabit these diverse environments. Further studies identified a nutrient transporter gene, STL1, to be upregulated under low nutrients and to be associated with early fungicidal activity, a marker of poor clinical outcome in a cohort of HIV/AIDS patients. Understanding molecular mechanisms involved in adaptation to the human host may help to design better methods of control and treatment of widely dispersed fungal pathogens such as Cryptococcus.

Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes

Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.